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

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/newsfeed/migrations/0004_configuration_total_votes.py

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ReEnTrust/mediationtool

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# Generated by Django 3.0.4 on 2021-01-08 14:43 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('newsfeed', '0003_auto_20210108_1426'), ] operations = [ migrations.AddField( model_name='configuration', name='total_votes', field=models.IntegerField(default=0), ), ]

[ "blegaste@ed.ac.uk" ]

blegaste@ed.ac.uk

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/pysonic.py

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saibotd/couchpytato

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refs/heads/master

2021-01-22T04:33:42.223304

2013-07-17T16:47:26

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import pySonic, time class Music: def __init__(self): self.music = pySonic.Source() self.playing = False self.paused = False def isplaying(self): return self.music.IsPlaying() def play(self, name=None): ok = True if self.paused: self.music.Play() self.playing = True self.paused = False elif name: try: self.music.Sound = pySonic.FileStream(name) except: ok = False if ok: self.music.Play() self.playing = True self.paused = False else: ok = False return ok def pause(self): if self.isplaying(): self.music.Pause() self.playing = False self.paused = True def time(self, what=0): if self.isplaying(): secs = int(self.music.CurrentTime) tim = time.localtime(secs) min = str(tim[4]) sec = str(tim[5]) if len(min) == 1: min = '0' + min if len(sec) == 1: sec = '0' + sec return min + ':' + sec else: return None def stop(self): if self.isplaying(): self.music.Stop() self.playing = 0

[ "tobi@saibotd.com" ]

tobi@saibotd.com

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/src/ryzom_django/management/commands/ryzom_bundle.py

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yourlabs/ryzom

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refs/heads/master

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2023-05-02T14:49:25

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import os from django.core.management.base import BaseCommand, CommandError from ryzom_django import bundle class Command(BaseCommand): help = 'Write JS & CSS bundles to ryzom_django/static/bundle.*' def handle(self, *args, **options): static_path = os.path.abspath( os.path.join( os.path.dirname(__file__), '..', '..', 'static', ) ) if not os.path.exists(static_path): os.makedirs(static_path) with open(f'{static_path}/bundle.js', 'w+') as f: f.write(bundle.js()) with open(f'{static_path}/bundle.css', 'w+') as f: f.write(bundle.css()) self.stdout.write(self.style.SUCCESS(f'Successfully wrote {static_path}/bundle.*')) self.stdout.write('Do not forget to collectstatic!')

[ "jpic@yourlabs.org" ]

jpic@yourlabs.org

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/BellmanFord.py

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ilius/Algorithms-Python

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refs/heads/master

2021-01-17T14:27:55.846495

2013-11-19T20:18:30

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import GraphLib import DirectedEdge, ShortestPath # negative cycle with open('tinyEWDnc.txt', 'r') as f: V = int(f.readline().strip()) E = int(f.readline().strip()) text = f.read() f.close() Gnc = GraphLib.EdgeWeightedDigraph(V) lines = text.split('\n') for line in lines[:-1]: # last line is empty l = line.split() v = int(l[0]) w = int(l[1]) weight = float(l[2]) Gnc.addEdge(DirectedEdge.DEdge(v, w, weight)) # negative weight with open('tinyEWDn.txt', 'r') as f: V = int(f.readline().strip()) E = int(f.readline().strip()) text = f.read() f.close() Gn = GraphLib.EdgeWeightedDigraph(V) lines = text.split('\n') for line in lines[:-1]: # last line is empty l = line.split() v = int(l[0]) w = int(l[1]) weight = float(l[2]) Gn.addEdge(DirectedEdge.DEdge(v, w, weight)) bn = ShortestPath.BellmanFord(Gn, 0) bn.hasNegativeCycle() bn.hasPathTo(6) bn.pathTo(6) bn.distTo(6)

[ "leslieklein@comcast.net" ]

leslieklein@comcast.net

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/intro/53_validTime.py

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big-ssk/CodeSignalPython

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refs/heads/master

2023-01-07T22:44:41.056283

2020-11-10T15:52:06

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def validTime(time): hours, minutes = time.split(':') return 0 <= int(hours) < 24 and 0 <= int(minutes) < 60

[ "noreply@github.com" ]

big-ssk.noreply@github.com

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/control/actuator_properties.py

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[]

no_license

gaffarelj/DSE-Mars-SRV

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refs/heads/master

2023-01-30T08:45:16.244930

2020-12-16T21:33:56

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import numpy as np from matplotlib import pyplot as plt #vehicle constants length_body = 14.01 length = 14.01 + 3.6 body_radius = 7.61 / 2 capsule_radius_bottom = 2.3 capsule_radius_top = 1.4 x_cg = 0. z_cg = 0. z_cg_full = 5.276 #m z_cg_empty = 9.238 #m z_cg_orbit = 9.007 z_cg_end_ascent = 6.120 x_body_side = body_radius x_capsule_bottomside = capsule_radius_bottom x_capsule_topside = capsule_radius_top y_bdoy_side = body_radius y_capsule_bottomside = capsule_radius_bottom y_capsule_topside = capsule_radius_top z_body_top = length_body z_capsule_bottom = z_body_top + 1.2 z_capsule_top = z_capsule_bottom + 2.7 Ix = 2875350.278 #kg/m^2 Iz = 306700.3372 #kg/m^2 Iy = 2875350.278 #kg/m^2 #RCS propellant properties Isp = 140 #bi liquid, LCH4 Isp_mono = 140 #mono liquid H2O2 g = 9.80665 #engines nx = 6 ny = 6 nz = 4 def thruster_arms(z_cg): #vehicle constants length_body = 14.01 length = 14.01 + 3.6 body_radius = 7.61 / 2 capsule_radius_bottom = 2.3 capsule_radius_top = 1.4 x_cg = 0. y_cg = 0. y_cg_full = 7.7085 #m y_cg_empty = 10.0344 #m y_cg_orbit = 6.62390 x_body_side = body_radius x_capsule_bottomside = capsule_radius_bottom x_capsule_topside = capsule_radius_top z_body_side = body_radius z_capsule_bottomside = capsule_radius_bottom z_capsule_topside = capsule_radius_top y_body_top = length_body y_capsule_bottom = y_body_top + 1.2 y_capsule_top = y_capsule_bottom + 2.7 lx_bottom = x_body_side lx_top = x_capsule_bottomside lz_bottom = z_body_side lz_top = z_capsule_bottomside ly_bottom = y_cg - 1. ly_top = y_cg_orbit * 2 - y_cg return lx_bottom, lx_top, ly_bottom, ly_top, lz_bottom, lz_top def RCS_torque_to_thrust(T,axis,cg,scenario): lx_bottom, lx_top, ly_bottom, ly_top, lz_bottom, lz_top = thruster_arms(cg) # n: number of thrusters to provide torque if axis == 'x' or axis == 'z': if scenario == 'normal': n_bottom = 2 n_top = 2 thrust = T / (ly_bottom * n_bottom + ly_top * n_top) elif scenario == 'error_bottom' or scenario == 'error_top': n = 1 thrust = T / (lz_bottom * n) elif scenario == 'failure': n_bottom = 1 n_top = 1 thrust = T / (lz_bottom * n_bottom + lz_top * n_top) elif axis == "y": if scenario == 'normal': n_bottom = 2 n_top = 2 thrust = T / (n_top * ly_top + n_bottom * ly_bottom) elif scenario == 'error_bottom': n = 1 thrust = T / (lx_bottom) elif scenario == 'error_top': n = 1 thrust = T / (lx_top) elif scenario == 'failure': n_bottom = 1 n_top = 1 thrust = T / (lz_bottom * n_bottom + lz_top * n_top) return thrust def RCS_displacement_to_thrust(F,axis,scenario): if axis == "x" or axis == 'z': if scenario == 'normal': n_bottom = 1 n_top = 1 n = n_bottom + n_top elif scenario == 'failure': n_bottom = 1 n_top = 1 n = n_bottom + n_top if axis == "y": if scenario == 'normal': n_bottom = 4 n_top = 0 n = n_bottom + n_top if scenario == 'failure': n_bottom = 2 n_top = 0 n = n_bottom + n_top f = F / n return f def RCS_thrust_to_torque(f,axis,cg): lx_bottom, lx_top, ly_bottom, ly_top, lz_bottom, lz_top = thruster_arms(cg) if axis == 'x' or axis == 'z': n_bottom = 2 n_top = 2 T = f * (lz_bottom * n_bottom + lz_top * n_top) elif axis == 'y': #bottom RCS n_bottom = 2 n_top = 2 T = f * (n_top * ly_top + n_bottom * ly_bottom) return T def slew(thrust,tburn,slew_angle,I): torque = RCS_thrust_to_torque(thrust,'z','normal') spin_acc = torque / I spin_rate = spin_acc * tburn slew_time = slew_angle / spin_rate return slew_time,torque def thrust_error(f,cg,angle): lx_bottom, lx_top, ly_bottom, ly_top, lz_bottom, lz_top = thruster_arms(cg) T_error_x = np.sin(angle*np.pi/180) * ly_bottom * f T_error_z = T_error_x T_error_y = np.sin(angle*np.pi/180) * lx_bottom * f return T_error_x, T_error_y, T_error_z def RCSpropellant(f,t,Isp): g = 9.80665 impulse = f * t Mp = impulse / (Isp * g) return Mp

[ "wiegerhelsdingen@gmail.com" ]

wiegerhelsdingen@gmail.com

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/wechat_stat.py

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xushubo/learn-python

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2021-01-19T17:00:05.247958

2017-09-03T03:22:28

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import itchat from echarts import Echart, Legend, Pie itchat.login() #登录 friends = itchat.get_friends(update=True)[0:] #获取好友列表 male = female = other = 0 #初始化计数器，男、女、不填的 for i in friends[1:]: #遍历好友列表，列表第一个是自己，所以从1开始计算 1表示男性，2女性 sex = i['Sex'] if sex == 1: male +=1 elif sex == 2: female += 1 else: other += 1 total = len(friends[1:]) print('wechat好友总数：%d' % total) print('男性好友： %.2f%%' % (float(male)/total*100)) print('女性好友： %.2f%%' % (float(female)/total*100)) print('其他： %.2f%%' % (float(other)/total*100)) ''' chart = Echart('%s的微信好友性别比例' % (friends[0]['NickName']), 'from WeChat') chart.use(Pie('WeChat', [{'value': male, 'name': '男性 %.2f%%' % (float(male) / total * 100)}, {'value': female, 'name': '女性 %.2f%%' % (float(female) / total * 100)}, {'value': other, 'name': '其他 %.2f%%' % (float(other) / total * 100)}], radius=["50%", "70%"])) chart.use(Legend(['male', 'female', 'other'])) del chart.json['xAxis'] del chart.json['yAxis'] chart.plot() '''

[ "tmac523@163.com" ]

tmac523@163.com

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/telegram_unvoicer_bot/telegram/const.py

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[ "Apache-2.0" ]

permissive

nabokihms/telegram_unvoicer_bot

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refs/heads/master

2023-02-22T19:25:22.964360

2022-10-14T10:35:22

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from os import environ from typing import FrozenSet TELEGRAM_BOT_API_KEY: str = environ['TELEGRAM_API_KEY'] TELEGRAM_BOT_API_URL_PREFIX: str = 'https://api.telegram.org' TELEGRAM_BOT_API_URL: str = \ f'{TELEGRAM_BOT_API_URL_PREFIX}/bot{TELEGRAM_BOT_API_KEY}/' TELEGRAM_BOT_FILE_PATH_API_URL: str = \ f'{TELEGRAM_BOT_API_URL_PREFIX}/file/bot{TELEGRAM_BOT_API_KEY}/' TELEGRAM_MESSAGE_AUDIO_KEYS: FrozenSet[str] = frozenset( ('voice', 'audio', 'document') )

[ "noreply@github.com" ]

nabokihms.noreply@github.com

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/scripts/delta_scan-join_five.py

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[]

no_license

RoslawSzaybo/bosons_on_lattice

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refs/heads/master

2020-03-25T11:39:41.477564

2018-11-29T18:26:23

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Aug 24 12:24:12 2018 """ import numpy as np import pickle import sys def delta(pkl): return pkl[1]['delta'] zero = 1e-14 def the_same(a,b): delta_a = delta(a) delta_b = delta(b) diff = abs(delta_b-delta_a) return (diff < zero) def repetitions(c): repeted_idxes = [] for idx, elem in enumerate(c): if idx == 0: continue if the_same(c[idx-1], elem): repeted_idxes += [idx] return repeted_idxes def clean_repetitions(c): to_del = repetitions(c) print("Initial number of data points\t\t {dp}".format(dp=len(c))) print("Number of elements to be removed\t {td}".format(td=len(to_del))) for idx in reversed(to_del): c.pop(idx) print("Final number of data points\t\t {dp}".format(dp=len(c))) return c def main(): if len(sys.argv) != 6: print("it joins four!!!") print("it joins four!!!") print("it joins four!!!") print("I need name of a file as a command line argument! like this:") print("$ python join.py A.pkl B.pkl C.pkl D.pkl E.pkl output.pkl") sys.exit() in_a = sys.argv[1] in_b = sys.argv[2] in_c = sys.argv[3] in_d = sys.argv[4] out = sys.argv[5] print("="*80) print("Join") print("="*80) print("Reading files in progress") with open(in_a, 'rb') as g: a = pickle.load(g) with open(in_b, 'rb') as g: b = pickle.load(g) with open(in_c, 'rb') as g: x = pickle.load(g) with open(in_d, 'rb') as g: y = pickle.load(g) print("Files are open.") c = sorted(a+b+x+y, key = delta) print("Data in now united to a one, sorted file.") d = clean_repetitions(c) print("All repetitions are removed.") with open(out, 'wb') as g: pickle.dump(d,g) print("Data is saved to {}".format(out)) return 0 if __name__ == '__main__': main()

[ "pawel.wojcik5@gmail.com" ]

pawel.wojcik5@gmail.com

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/Assignment4/data_loader.py

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[]

no_license

FreddieSun/CS520_IntroToAI

d39a7d842108c189655b500365f4a480632bd6ee

f53a0fbb7a4431a7667c4894d6f36d324c1b79b4

refs/heads/master

2021-10-09T02:57:17.464117

2018-12-18T07:19:23

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import scipy.misc from glob import glob import numpy as np class DataLoader(): def __init__(self, img_res=(128, 128)): self.img_res = img_res def load_train_batch(self, batch_size=1, is_testing=False): path = glob('./datasets/train/*') self.n_batches = int(len(path) / batch_size) for i in range(self.n_batches): batch = path[i*batch_size:(i+1)*batch_size] imgs_A, imgs_B = [], [] for img in batch: img = self.imread(img) h, w, _ = img.shape half_w = int(w/2) img_A = img[:, :half_w, :] img_B = img[:, half_w:, :] img_A = scipy.misc.imresize(img_A, self.img_res) img_B = scipy.misc.imresize(img_B, self.img_res) if not is_testing and np.random.random() > 0.5: img_A = np.fliplr(img_A) img_B = np.fliplr(img_B) imgs_A.append(img_A) imgs_B.append(img_B) imgs_A = np.array(imgs_A)/127.5 - 1. imgs_B = np.array(imgs_B)/127.5 - 1. yield imgs_A, imgs_B def load_test_batch(self, batch_size): path = glob('./datasets/test/*' ) self.n_batches = int(len(path) / batch_size) for i in range(self.n_batches): batch = path[i*batch_size:(i+1)*batch_size] imgs_A, imgs_B = [], [] for img in batch: img = self.imread(img) h, w, _ = img.shape half_w = int(w/2) img_A = img[:, :half_w, :] img_B = img[:, half_w:, :] img_A = scipy.misc.imresize(img_A, self.img_res) img_B = scipy.misc.imresize(img_B, self.img_res) imgs_A.append(img_A) imgs_B.append(img_B) imgs_A = np.array(imgs_A)/127.5 - 1. imgs_B = np.array(imgs_B)/127.5 - 1. yield imgs_A, imgs_B def load_self_test_batch(self, batch_size): path = glob('./self_test/*') self.n_batches = int(len(path) / batch_size) for i in range(self.n_batches): batch = path[i * batch_size:(i + 1) * batch_size] imgs_A, imgs_B = [], [] for img in batch: img = self.imread(img) h, w, _ = img.shape img_A = img[:, :w, :] img_A = scipy.misc.imresize(img_A, self.img_res) imgs_A.append(img_A) imgs_A = np.array(imgs_A) / 127.5 - 1. yield imgs_A def imread(self, path): return scipy.misc.imread(path, mode='RGB').astype(np.float)

[ "xl422@scarletmail.rutgers.edu" ]

xl422@scarletmail.rutgers.edu

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/env.py

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liuzhonghaolpp/H-DQN

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import numpy as np import torch from gym import spaces, core from gym.envs.classic_control import rendering import entity from utils import uav_mobility import configparser import time config = configparser.RawConfigParser() config.read('./paramaters.ini') # 参数 MAX_AoI = 100 DURATION = int(config.get('simulation parameters', 'duration')) IoT_COMMUNICATION_RANGE = int(config.get('simulation parameters', 'iot_communication_range')) BS_COMMUNICATION_RAGE = int(config.get('simulation parameters', 'BS_communication_rage')) class MyEnv(core.Env): def __init__(self): self.action_space = spaces.Box(low=-1, high=1, shape=(2,)) self.observation_space = spaces.Box(shape=(14, )) self.num_sensors = 6 self.sensors = [] self.side_length = 1000 # 目标区域的边长 self.time = 0 self.get_reward = False self.reward = 0 self.viewer = None for i in range(self.num_sensors): sensor = entity.Sensor(i) self.sensors.append(sensor) self.uav = entity.UAV() self.BS = entity.BS() def reset(self): # 初始化sensor的位置(非随机初始化) self.sensors[0].pos = np.array([250, 550]) self.sensors[1].pos = np.array([550, 150]) self.sensors[2].pos = np.array([750, 250]) self.sensors[3].pos = np.array([350, 850]) self.sensors[4].pos = np.array([550, 750]) self.sensors[5].pos = np.array([750, 950]) # 初始化UAV的位置和所携带数据AoI self.uav.pos = np.array([500, 500]) self.uav.aoi = np.array([MAX_AoI, MAX_AoI, MAX_AoI, MAX_AoI, MAX_AoI, MAX_AoI]) # 初始化BS的AoI self.BS.pos = np.array([500, 500]) self.BS.aoi = np.array([MAX_AoI, MAX_AoI, MAX_AoI, MAX_AoI, MAX_AoI, MAX_AoI]) # 初始化响应参数 self.time = 0 self.get_reward = False self.reward = 0 obs = self._get_observation() return obs def step(self, action): self.time += 1 self.get_reward = False self.reward = 0 self.uav.action = action # 更新UAV的位置 uav_mobility.get_next_pos(self.uav) # 查看UAV是否在sensor的通信范围内，且未采集该设备数据，然后更新uav_aoi self._update_uav_aoi() # 查看UAV是否在BS通信范围内，且携带有未上传的数据，然后更新bs_aoi self._update_bs_aoi() done = self._get_done() reward = self._get_reward() obs = self._get_observation() info = {} return obs, reward, done, info def render(self, mode='human'): screen_width = 500 # 按比例缩小一下，1:2的比例 screen_height = 500 # 如果没有viewer，创建viewer和uav、landmarks if self.viewer is None: self.viewer = rendering.Viewer(screen_height, screen_width) self.viewer.set_bounds(0, 500, 0, 500) self.viewer.geoms.clear() for sensor in self.sensors: geom = rendering.make_circle(sensor.size) geom.set_color(1, 0, 0) geom_form = rendering.Transform(translation=(sensor.pos[0]/2, sensor.pos[1]/2)) geom.add_attr(geom_form) self.viewer.add_geom(geom) geom = rendering.make_circle(self.BS.size) geom.set_color(0, 0, 1) geom_form = rendering.Transform(translation=(self.BS.pos[0]/2, self.BS.pos[1]/2)) geom.add_attr(geom_form) self.viewer.add_geom(geom) geom = rendering.make_circle(self.uav.size) geom.set_color(0, 1, 0) geom_form = rendering.Transform(translation=(self.uav.pos[0]/2, self.uav.pos[1]/2)) geom.add_attr(geom_form) self.viewer.add_geom(geom) return self.viewer.render(return_rgb_array=mode == 'rgb_array') def _get_observation(self): obs_uav = np.concatenate((self.uav.pos, self.uav.aoi), axis=0) obs_bs = self.BS.aoi obs = np.concatenate((obs_uav, obs_bs), axis=0) return obs def _get_done(self): done = False if self.uav.pos[0] < 0 or self.uav.pos[0] > 1000 or self.uav.pos[1] < 0 or self.uav.pos[1] > 1: done = True return done def _get_reward(self): if self.get_reward: return self.reward else: return 0 def _update_uav_aoi(self): for i, sensor in enumerate(self.sensors): distance = np.sqrt(np.sum((sensor.pos - self.uav.pos)**2)) if distance <= IoT_COMMUNICATION_RANGE: self.uav.aoi[i] = 1 else: self.uav.aoi[i] += 1 def _update_bs_aoi(self): distance = np.sqrt(np.sum((self.uav.pos - self.BS.pos)**2)) if distance <= BS_COMMUNICATION_RAGE: self.get_reward = True for i in range(len(self.BS.aoi)): if self.BS.aoi[i] > self.uav.aoi[i]: self.reward += self.BS.aoi[i] - self.uav.aoi[i] self.BS.aoi[i] = min(self.BS.aoi[i], self.uav.aoi[i]) else: for i in range(len(self.BS.aoi)): self.BS.aoi[i] += 1 if __name__ == '__main__': env = MyEnv() obs = env.reset() env.render() while True: action = env.action_space.sample() obs, reward, done, _ = env.step(action) print(obs) print(reward) print('---------------') env.render() time.sleep(4) # 可以在未全部采集完时完成交付，也可以在全部采集完成后只进行一次交付。 # 动态环境，QoS函数的参数是动态变化的，即用户对应用的延时要求是动态变化的。

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/vurapy/config/production_env.py

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crisboleda/vurapy

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from .base import * from decouple import config # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = config('SECRET_KEY') # SECURITY WARNING: don't run with debug turned on in production! DEBUG = config('DEBUG', default=False, cast=bool) ALLOWED_HOSTS = config('ALLOWED_HOSTS', default=['*'], cast=list) # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/'

[ "cristhian.2002.0@gmail.com" ]

cristhian.2002.0@gmail.com

b9dac58212d011f1c76f030f0631c55f20b3f02f

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/face_ID_net/face_1024s/face_1024_vals.py

2b929cc2b67254c5a37f697a6093fc0d6f3d68f1

[]

no_license

wosxcc/bot

e93b92fbca79a915feb186160f3f72c99218ffcb

c097f5455bc6264c9f778fb72900475963836153

refs/heads/master

2021-06-12T12:43:47.314071

2018-12-14T08:51:43

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import os import cv2 as cv import numpy as np import random import tensorflow as tf from face_ID_net.face_1024s.ID_pb_net1024s import face_net os.environ["CUDA_VISIBLE_DEVICES"] = "1" IMG_H=64 IMG_W =64 N_CLASSES =1024 learning_rate =0.001 def face_val(image_arr,run_train): print('搞毛线啊') log_dir = './face72/face_big1024/' with tf.Graph().as_default(): graph = face_net(1, IMG_H,IMG_W, N_CLASSES,learning_rate,2,run_train) saver = tf.train.Saver() with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state(log_dir) if ckpt and ckpt.model_checkpoint_path: global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] saver.restore(sess, ckpt.model_checkpoint_path) else: print('没有保存的模型') if run_train ==True: pos_d,neg_d = sess.run([graph['d_pos'],graph['d_neg']],feed_dict={graph['x']: np.reshape(image_arr, (3, 64, 64, 3))}) return pos_d, neg_d elif run_train ==False: print('下面出错了',len(image_arr),image_arr[0].shape) anchor_data = sess.run(graph['anchor_out'],feed_dict={graph['x']: np.reshape(image_arr, ( 1, 64, 64, 3))}) print('上面出错了') return anchor_data pacth = 'E:/faceID' for i in range(10): file = random.sample(os.listdir(pacth),1)[0] while(1): negative_file= random.sample(os.listdir(pacth),1)[0] if negative_file!=file: break print(file,negative_file) anchor_img = random.sample(os.listdir(pacth+'/'+file),1)[0] while(1): positive_img = random.sample(os.listdir(pacth+'/'+file),1)[0] if anchor_img!=positive_img: break negative_img = random.sample(os.listdir(pacth+'/'+negative_file),1)[0] img_anchor=cv.imread(pacth+'/'+file+'/'+anchor_img) img_positive=cv.imread(pacth+'/'+file+'/'+positive_img) img_negative=cv.imread(pacth+'/'+negative_file+'/'+negative_img) sh_anchor=cv.resize(img_anchor,(240,240),interpolation=cv.INTER_CUBIC) sh_positive=cv.resize(img_positive,(240,240),interpolation=cv.INTER_CUBIC) sh_negative=cv.resize(img_negative,(240,240),interpolation=cv.INTER_CUBIC) image_data=[] image_data.append(cv.resize(img_anchor,(64,64),interpolation=cv.INTER_CUBIC)) image_data.append(cv.resize(img_negative,(64,64),interpolation=cv.INTER_CUBIC)) image_data.append(cv.resize(img_positive,(64,64),interpolation=cv.INTER_CUBIC)) image_data =np.array(image_data,dtype='float32') image_data =(image_data-128.0)/256.0 anchor_score = face_val(image_data[0],False) print(anchor_score) pos_d,neg_d =face_val(image_data,True) print(pos_d,neg_d) cv.imshow('anchor', sh_anchor) cv.imshow('positive', sh_positive) cv.imshow('negative', sh_negative) cv.waitKey() cv.destroyAllWindows()

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821022156@qq.com

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/hpc_bin/wrf_postproc

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[]

no_license

dbreusch/pythonExamples

99c24dc1d28b8c3be3b4fadd30a05d8ae317b0c0

7b8c06208fefcadf918cb9517ac313535c4df010

refs/heads/master

2022-10-13T12:21:52.182752

2020-06-14T23:10:50

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#!/usr/bin/env python # wrf_postproc # run wrf_postproc.ncl on a specific file # output goes to a directory matching yy/mm/dd of input file # 03/30/17, dbr, removed hardcoded dependence on home directory name import pdb, sys, os nargin = len(sys.argv) if nargin < 2: print "Syntax: wrf_postproc input_fn ensemble expname model branch" print " input_fn = name of wrfout file to be processed" print " ens = ensemble" print " expname = region name (gis, ant)" print " model = model name (erai, cesmle)" print " branch = time period (e.g., historical)" sys.exit() # get command line args input_fn = sys.argv[1] enss = sys.argv[2] ens = int( enss ) expname = sys.argv[3] model = sys.argv[4] branch = sys.argv[5] # get home directory home = os.path.expanduser("~") # ncl command ncl_cmd = home+"/wrf/ncl/wrfout_postproc.ncl" # split filename and gather yy/mm/dd info dir_tmp = os.path.dirname( input_fn ) file_in = os.path.basename( input_fn ) file_yy = file_in[ 11:15 ] file_mm = file_in[ 16:18 ] file_dd = file_in[ 19:21 ] # convert input dir_in to working dir_in a = dir_tmp.split('/') if len(a) < 3: print "Input file name too short, missing parent directories" sys.exit() dir1 = a[-2] dir2 = a[-1] if ens > 0: base = home+"/scratch/wrf/"+expname+"_"+model+"/"+branch+"/wrf/run/"+enss+"/" else: base = home+"/scratch/wrf/"+expname+"_"+model+"/"+branch+"/wrf/run/" dir_in = base+dir1+"/"+dir2+"/" # output names if ens > 0: dir_out = home+"/scratch/wrf/%s_%s/%s/wrf/postproc/%s/%s/%s%s%s/" % (expname, model, branch, enss, file_yy, file_yy, file_mm, file_dd) else: dir_out = home+"/scratch/wrf/%s_%s/%s/wrf/postproc/%s/%s%s%s/" % (expname, model, branch, file_yy, file_yy, file_mm, file_dd) file_out = file_in+".nc" ofn = dir_out+file_out if os.path.exists( ofn ): os.unlink( ofn ) if not os.path.exists( dir_out ): try: os.makedirs( dir_out ) except OSError: pass # print "Caught makedir of existing dir "+dir_out arg1 = "'dir_in=\""+dir_in+"\"'" arg2 = "'file_in=\""+file_in+"\"'" arg3 = "'dir_out=\""+dir_out+"\"'" arg4 = "'file_out=\""+file_out+"\"'" cmd = "ncl "+arg1+" "+arg2+" "+arg3+" "+arg4+" "+ncl_cmd #pdb.set_trace() os.system( cmd )

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dbreusch@me.com

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/run.py

6b7ebbdfe73be630d7affd8741dcdd2f91792392

[]

no_license

JonatanMariscal/P2_E1

a809dfaf11e2dedf008b1e0264e596d4786a4f4d

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refs/heads/master

2022-06-17T02:14:45.405225

2020-05-10T19:47:10

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import tkinter as tk import matplotlib.pyplot as plt from matplotlib.backends.backend_tkagg import ( FigureCanvasTkAgg, NavigationToolbar2Tk) import SimpleITK as sitk def resample_image(image, reference): pixel_spacing = image.GetSpacing() new_spacing = [old_sz * old_spc / new_sz for old_sz, old_spc, new_sz in zip(image.GetSize(), pixel_spacing, reference.GetSize())] image_resampled = sitk.Resample(image, reference.GetSize(), sitk.Transform(), sitk.sitkNearestNeighbor, image.GetOrigin(), new_spacing, image.GetDirection(), 0.0, image.GetPixelIDValue()) return image_resampled # Register two images with same shape. def register_images(image, reference): initial_transform = sitk.CenteredTransformInitializer(sitk.Cast(reference, image.GetPixelID()), image, sitk.Euler3DTransform(), sitk.CenteredTransformInitializerFilter.GEOMETRY) registration_method = sitk.ImageRegistrationMethod() registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=250) registration_method.SetMetricSamplingStrategy(registration_method.RANDOM) registration_method.SetMetricSamplingPercentage(0.01) registration_method.SetInterpolator(sitk.sitkNearestNeighbor) registration_method.SetOptimizerAsGradientDescent(learningRate=3.0, numberOfIterations=10000, convergenceMinimumValue=1e-6, convergenceWindowSize=10) registration_method.SetOptimizerScalesFromPhysicalShift() registration_method.SetInitialTransform(initial_transform, inPlace=False) final_transform = registration_method.Execute(sitk.Cast(reference, sitk.sitkFloat32), sitk.Cast(image, sitk.sitkFloat32)) register = sitk.ResampleImageFilter() register.SetReferenceImage(reference) register.SetInterpolator(sitk.sitkNearestNeighbor) register.SetTransform(final_transform) ds_register = register.Execute(image) return ds_register def main(): def mode_selector(): status = selector.get() if not status: frame_alpha.tkraise() selector.set(True) else: frame.tkraise() selector.set(False) def update_slice(self): pos = slice_selector.get() alpha = alpha_selector.get() status = selector.get() if not status: axs[0].imshow(ds_array[pos,:,:], cmap=plt.cm.get_cmap(colormap.get())) axs[1].imshow(phantom_array[pos,:,:], cmap=plt.cm.get_cmap(colormap.get())) fig.canvas.draw_idle() else: ax.imshow(ds_array[pos, :, :], cmap=plt.cm.get_cmap(colormap.get())) ax.imshow(phantom_array[pos, :, :], cmap=plt.cm.get_cmap("prism"), alpha=alpha/100) fig2.canvas.draw_idle() slice_pos = "Nº Slice: " + str(pos) label_slice.config(text=slice_pos) #Reading RM_Brain_3D-SPGR DICOM path_dcm = "data/RM_Brain_3D-SPGR" reader = sitk.ImageSeriesReader() dicom_names = reader.GetGDCMSeriesFileNames(path_dcm) reader.SetFileNames(dicom_names) ds = reader.Execute() #Reading phantom DICOM ds_phantom = sitk.ReadImage('data/icbm_avg_152_t1_tal_nlin_symmetric_VI.dcm') phantom_array = sitk.GetArrayFromImage(ds_phantom) # z, y, x #Reading atlas DICOM ds_atlas = sitk.ReadImage('data/AAL3_1mm.dcm') # Resample Brain DICOM and atlas DICOM to phantom shape ds_resample = resample_image(ds, ds_phantom) ds_atlas_resample = resample_image(ds_atlas, ds_phantom) # Register Brain DICOM and atlas DICOM with phantom ds_atlas_register = register_images(ds_atlas_resample, ds_phantom) atlas_array = sitk.GetArrayFromImage(ds_atlas_register) # z, y, x ds_register = register_images(ds_resample, ds_phantom) ds_array = sitk.GetArrayFromImage(ds_register) # z, y, x # Creating window and frames root = tk.Tk() root.title("DICOM Image Display") top_frame = tk.Frame() # frame with buttons and sliders frame = tk.Frame() #frame with synchron visualizator frame_alpha = tk.Frame() #frame with alpha visualizator top_frame.grid(row = 0, column = 0, sticky = tk.W, columnspan=6) frame.grid(row = 1,sticky="nsew", column = 0, columnspan=6) frame_alpha.grid(row = 1,sticky="nsew", column = 0, columnspan=6) frame.tkraise() selector = tk.BooleanVar() # Displaying images on synchron visualizator fig, axs = plt.subplots(1,2, figsize=(15, 6), dpi=100, sharex=True, sharey=True) axs = axs.ravel() colormap = tk.StringVar() colormap.set("bone") axs[0].imshow(ds_array[0,:,:], cmap=plt.cm.get_cmap(colormap.get())) axs[1].imshow(phantom_array[0,:,:], cmap=plt.cm.get_cmap(colormap.get())) canvas = FigureCanvasTkAgg(fig, master=frame) canvas.draw() canvas.get_tk_widget().pack(side=tk.TOP, expand=1) toolbar = NavigationToolbar2Tk(canvas, frame) toolbar.update() canvas.get_tk_widget().pack(side=tk.TOP, expand=1) # Displaying images on alpha visualizator fig2, ax = plt.subplots(1, figsize=(15, 6), dpi=100, sharex=True, sharey=True) alpha = 0 ax.imshow(ds_array[0, :, :], cmap=plt.cm.get_cmap(colormap.get())) ax.imshow(phantom_array[0, :, :], cmap=plt.cm.get_cmap("prism"), alpha=alpha/100) canvas_alpha = FigureCanvasTkAgg(fig2, master=frame_alpha) canvas_alpha.draw() canvas_alpha.get_tk_widget().pack(side=tk.TOP, expand=1) toolbar_alpha = NavigationToolbar2Tk(canvas_alpha, frame_alpha) toolbar_alpha.update() canvas_alpha.get_tk_widget().pack(side=tk.TOP, expand=1) # Selecting slices pos = 0 slice_selector = tk.Scale(top_frame, label="Slice selector", from_=0, to=ds_array.shape[0] - 1, orient=tk.HORIZONTAL, length=400, command=update_slice, tickinterval=20) slice_selector.pack(side=tk.LEFT, anchor=tk.NW) # Showing actual number of slice label_slice = tk.Label(top_frame) label_slice.pack(side=tk.TOP, anchor=tk.NW, before=slice_selector) slice_pos = "Nº Slice: " + str(pos) label_slice.config(text=slice_pos) # Change between synchron and alhpa visualization b = tk.Button(top_frame, text="Mode selector", command=mode_selector, width=10) b.pack(side=tk.TOP) # Selecting which percentage of alpha use for alpha visualization alpha_selector = tk.Scale(top_frame, label="alpha value", from_=0, to=100, orient=tk.HORIZONTAL, length=400, command=update_slice, tickinterval=5) alpha_selector.pack(side=tk.TOP) root.mainloop() if __name__ == '__main__': main()

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JonatanMariscal.noreply@github.com

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/train.py

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no_license

Tyler-Shamsuddoha/python-image-classifier-keras

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refs/heads/master

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2021-07-07T20:20:21

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from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras import backend as K from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D from tensorflow.keras.preprocessing.image import img_to_array, ImageDataGenerator from imutils import paths from tensorflow.keras.losses import categorical_crossentropy #cosine_proximity, from tensorflow.keras.optimizers import Nadam, Adam from tensorflow.keras.utils import plot_model from sklearn.neural_network import MLPClassifier from tensorflow.keras.callbacks import Callback from tensorflow.keras.applications import InceptionResNetV2 from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping import cv2 import numpy as np import tensorflow as tf import random import os import matplotlib.pyplot as plt import sklearn # Set random seeds to ensure the reproducible results SEED = 309 np.random.seed(SEED) random.seed(SEED) tf.random.set_seed(SEED) IMG_HEIGHT = 64 IMG_WIDTH = 64 INPUT_SHAPE = (64, 64) # conv_base = InceptionResNetV2(weights = 'imagenet', include_top = False, input_shape = (IMG_HEIGHT,IMG_WIDTH,3)) train_dir = './Train_data' test_dir = './test_data' validation_images_dir = './Validation_Sets' batch_size = 32 epoch = 100 def construct_model(): """ Construct the CNN model. *** Please add your model implementation here, and don't forget compile the model E.g., model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) NOTE, You must include 'accuracy' in as one of your metrics, which will be used for marking later. *** :return: model: the initial CNN model """ model = Sequential([ Conv2D(16, kernel_size=3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)), MaxPooling2D(2, 2), Conv2D(32, kernel_size=3, padding='same', activation='relu'), MaxPooling2D(2, 2), Conv2D(64, 3, padding='same', activation='relu'), MaxPooling2D(2, 2), Conv2D(128, 3, padding='same', activation='relu'), Flatten(), Dense(64, 'relu'), Dropout(0.5), Dense(3, activation='softmax') ]) model.compile(loss=categorical_crossentropy, optimizer= Nadam(), metrics=['accuracy']) return model # ************************************* # Building Model Using Transfer Learning # model = Sequential([ # conv_base, # Flatten(), # Dense(512, 'relu'), # Dense(3, activation='softmax') # ]) # model.compile(loss=categorical_crossentropy, optimizer= Nadam(), metrics=['accuracy']) # return model def train_model(model): """ Train the CNN model *** Please add your training implementation here, including pre-processing and training *** :param model: the initial CNN model :return:model: the trained CNN model """ print("Loading Images...") validation_image_generator = ImageDataGenerator(rescale=1./255, rotation_range=40) val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size, directory=validation_images_dir, target_size=INPUT_SHAPE, class_mode='categorical') print("Loaded Validation Set Images Successfully\n") train_image_generator = ImageDataGenerator(rescale=1./255, zoom_range=0.2, rotation_range=40) train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size, directory=train_dir, shuffle=True, target_size=INPUT_SHAPE, class_mode='categorical') print("Loaded Training Images Successfully\n") print("Starting training....\n") model = construct_model() filepath = 'model/newmodel111.h5' model_checkpoint = ModelCheckpoint(filepath, monitor='val_loss', save_best_only= True, verbose=1, mode = 'min') # early_stop = EarlyStopping(filepath, monitor='val_acc', mode='max', patience=5) history = model.fit_generator( train_data_gen, steps_per_epoch= 3748/batch_size, epochs= epoch, validation_data= val_data_gen, validation_steps= 562/batch_size, callbacks = [model_checkpoint], ) visualise_results(history) return model def save_model(model): """ Save the keras model for later evaluation :param model: the trained CNN model :return: """ # *** # Please remove the comment to enable model save. # However, it will overwrite the baseline model we provided. # *** model.save("model/new_model.h5") print("Model Saved Successfully.") def visualise_results(history): plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.title('Model accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') plt.show() # Plot training & validation loss values plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') plt.show() if __name__ == '__main__': model = train_model(train_dir) save_model(model)

[ "tylershamsuddoha@gmail.com" ]

tylershamsuddoha@gmail.com

1bc7d1b8360e79b34496216b5177441ca38e9763

22a8a20397148dd4aef2fbd529121df8b91c918f

/sklearn/iris_classification.py

c1cf8b64148764486906cd0a30d043cd5a016d06

[]

no_license

greenchapter/playground

87313d6926e21ee79c45114b0ff922948e35c601

033f300ccb8ef55dcbebb8197d5d1a8709fb258e

refs/heads/main

2022-06-01T08:57:16.896340

2022-05-13T18:09:45

96,816,180

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null

2022-05-13T18:09:46

2017-07-10T19:59:37

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# # Beispiel der Klassifikations von Iris-Blumen # import pandas as pd from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.metrics import classification_report, confusion_matrix import matplotlib.pyplot as plt # Lade den Iris-Datenset data_train = pd.read_csv('./iris.csv') # Die 3 zu erkennenden Klassifikationsklassen werden zu numerischen Werten 0, 1 bzw. 2 umgewandelt. data_train.loc[data_train['species']=='Iris-setosa', 'species']=0 data_train.loc[data_train['species']=='Iris-versicolor', 'species']=1 data_train.loc[data_train['species']=='Iris-virginica', 'species']=2 data_train = data_train.apply(pd.to_numeric) # Der eingelesene Datenset wird als Matrix dargestellt data_train_array = data_train.to_numpy() # Das Datenset wird in zwei separate Kategorie gespaltet: Testdaten und Trainingsdaten. # 80% der Daten werden zum Trainieren und 20% zum Testen des Modells verwendet. # Da es sich bei der Eingabe um einen Vektor handelt, werden wird den Großbuchstaben X benutzen; # Für die Ausgabe hingegen handelt es sich um ein einzelner Werte, # daher die Bezeichung mit dem Kleinbuchstaben y X_train, X_test, y_train, y_test = train_test_split(data_train_array[:,:4], data_train_array[:,4], test_size=0.2) # VERSION 1 # Ein neuronales Netz zur Klassifikation (MultiLayerPerceptron) wird mit folgenden Eigenschaften gebildet: # einem Input-Layer mit 4 Neuronen, die die Merkmale der Iris-Planze repräsentieren; # einem Hidden-Layer mit 10 Neuronen # eime Output-Layer mit 4 Neuronen, die die zu erkennenden Klassen repräsentieren. # Dabei wird als Aktivierungsfunktion relu und als Optimierer adam verwenden. mlp = MLPClassifier(hidden_layer_sizes=(10,),activation='relu', solver='adam', max_iter=350, batch_size=10, verbose=True) # VERSION 2 # In der zweiten Variante werden 2 Hidden-Layers mit jeweils 5 bzw. 3 Neuronen verwendet # Dabei wird als Aktivierungsfunktion tanh und als Optimierer adam verwenden. # mlp = MLPClassifier(hidden_layer_sizes=(5,3),activation='tanh', solver='adam', max_iter=500, batch_size=10, verbose=True) # Das neuronale Netz wird mit den Trainingsdaten traniert mlp.fit(X_train, y_train) # Das Ergebnis des Training wird ausgegeben print("Trainingsergebnis: %5.3f" % mlp.score(X_train, y_train)) # Das Modell wird mit den Testdatensdaten evaluiert predictions = mlp.predict(X_test) # und die Konfusionsmatrix ausgegeben print(confusion_matrix(y_test,predictions)) # Aus der Konfusionsmatrix werden precison, recall und f1-score berechnet und ausgebenen print(classification_report(y_test,predictions)) # Das Modell wird getest und das Ergebnis ausgegeben print("Testergebnis: %5.3f" % mlp.score(X_test,y_test)) # Folgendes gibt die Werte der Gewichte pro Layer aus print("WEIGHTS:", mlp.coefs_) print("BIASES:", mlp.intercepts_) # Das Modell wird beispielsweise zur Vorhersage auf folgenden Werten # aus dem Testset angewandt mit den Merkmalen [sepal-length, sepal-width, # petal-length, petal-width] print(mlp.predict([[5.1,3.5,1.4,0.2], [5.9,3.,5.1,1.8], [4.9,3.,1.4,0.2], [5.8,2.7,4.1,1.]])) # Die Loss-Kurve wird visualisiert und in der Datei Plot_of_loss_values.png im PNG-Format gespeichert. loss_values = mlp.loss_curve_ plt.plot(loss_values) plt.savefig("./Plot_of_loss_values.png") plt.show()

[ "noreply@github.com" ]

greenchapter.noreply@github.com

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ea3272d707f3a6e5d097301d300a0ea97ddd82b5

/psm/oop1/oop1_2/info_hiding_property.py

b12f77b06d357a78fd4c81646ba553fa9c6dce8c

[]

no_license

gambler1541/BootCamp

d05850f256ed7a8baa02545551176959a66a9bb3

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refs/heads/master

2020-04-07T07:21:51.363439

2019-06-08T11:10:27

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2018-11-19T06:38:36

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class Account: def __init__(self, name, money): self.user = name # 인스턴스 멤버 선언이 아니라 setter 메서드를 호출 self.balance = money @property def balance(self): return self._balance @balance.setter def balance(self, money): if money < 0: return # 실제 인스턴스 멤버 선언이 일어나는 부분 self._balance = money if __name__ == '__main__': my_acnt = Account('greg', 5000) # setter 함수를 통해 변경을 시도하므로 _balance 메버의 값은 음수로 변경되지 않음 # 음수로 변경되지 않았으므로 실행 결과는 5000이 나옴 my_acnt.balance =- 3000 # getter 함수인 balance() 메서드를 호출해 _balance apaqjdp wjqrms, print(my_acnt.balance)

[ "sungmin3231@gmail.com" ]

sungmin3231@gmail.com

7a5460a7b68fee36094e0189412d4ec7e108cebe

c11666437e45f6b771f4004e919dccfdab6c4640

/pet_cats.py

cfadb02fc56200f91fb9545b7a676ebdd094354a

[]

no_license

mesare11/pet_market

30ba4b06161550e2abdd1e2fac073d9b1c906c91

c8e4006e578898c381be6f76cf6e05af39b94862

refs/heads/master

2021-01-02T19:02:34.933362

2020-07-23T12:16:01

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from pet_goods import pet_goods from pet_animals import pet_animals class pet_cats(pet_animals): def __init__(self, name, price, kind_pet, age, cats_breed): pet_animals.__init__(self, name, price, kind_pet, age) self.cats_breed = cats_breed def display_cats(self): print(self.name, self.price, self.kind_pet, self.age, self.cats_breed) def add_cat(): goods_array=[] name=input("Please input name goods: ") price=int(input("Please input price goods: ")) kind_pet=input("Please input kind of pet: ") age=input("Please input age pet: ") cats_breed=input("Please input cats breed: ") goods_array.append(name) goods_array.append(price) goods_array.append(kind_pet) goods_array.append(age) goods_array.append(cats_breed)

[ "mr.mesare@gmail.com" ]

mr.mesare@gmail.com

a291c7bfaadb64ce0f0f8fe7ff044a54344a7ba5

77c8d29caad199fb239133e6267d1b75bd2dfe48

/packages/pyright-internal/typeshed-fallback/stdlib/builtins.pyi

78b29c5aa7e794980ce9212e92a26b6fbb0c2072

[ "MIT", "LicenseRef-scancode-generic-cla", "Apache-2.0" ]

permissive

simpoir/pyright

9c80e596f99dfd1341a55373a96d8795cb72fb56

320a0a2fd31e4ffc69d4bd96d7202bbe8d8cb410

refs/heads/master

2023-04-18T06:42:16.194352

2021-04-29T15:20:19

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import sys import types from _typeshed import ( AnyPath, OpenBinaryMode, OpenBinaryModeReading, OpenBinaryModeUpdating, OpenBinaryModeWriting, OpenTextMode, ReadableBuffer, SupportsDivMod, SupportsKeysAndGetItem, SupportsLessThan, SupportsLessThanT, SupportsRDivMod, SupportsWrite, ) from ast import AST, mod from io import BufferedRandom, BufferedReader, BufferedWriter, FileIO, TextIOWrapper from types import CodeType, TracebackType from typing import ( IO, AbstractSet, Any, BinaryIO, ByteString, Callable, Container, Dict, FrozenSet, Generic, ItemsView, Iterable, Iterator, KeysView, List, Mapping, MutableMapping, MutableSequence, MutableSet, NoReturn, Optional, Protocol, Reversible, Sequence, Set, Sized, SupportsAbs, SupportsBytes, SupportsComplex, SupportsFloat, SupportsInt, SupportsRound, Tuple, Type, TypeVar, Union, ValuesView, overload, runtime_checkable, ) from typing_extensions import Literal if sys.version_info >= (3, 9): from types import GenericAlias class _SupportsIndex(Protocol): def __index__(self) -> int: ... class _SupportsTrunc(Protocol): def __trunc__(self) -> int: ... _T = TypeVar("_T") _T_co = TypeVar("_T_co", covariant=True) _T_contra = TypeVar("_T_contra", contravariant=True) _KT = TypeVar("_KT") _VT = TypeVar("_VT") _S = TypeVar("_S") _T1 = TypeVar("_T1") _T2 = TypeVar("_T2") _T3 = TypeVar("_T3") _T4 = TypeVar("_T4") _T5 = TypeVar("_T5") _TT = TypeVar("_TT", bound="type") _TBE = TypeVar("_TBE", bound="BaseException") class object: __doc__: Optional[str] __dict__: Dict[str, Any] __slots__: Union[str, Iterable[str]] __module__: str __annotations__: Dict[str, Any] @property def __class__(self: _T) -> Type[_T]: ... # Ignore errors about type mismatch between property getter and setter @__class__.setter def __class__(self, __type: Type[object]) -> None: ... # type: ignore # noqa: F811 def __init__(self) -> None: ... def __new__(cls) -> Any: ... def __setattr__(self, name: str, value: Any) -> None: ... def __eq__(self, o: object) -> bool: ... def __ne__(self, o: object) -> bool: ... def __str__(self) -> str: ... def __repr__(self) -> str: ... def __hash__(self) -> int: ... def __format__(self, format_spec: str) -> str: ... def __getattribute__(self, name: str) -> Any: ... def __delattr__(self, name: str) -> None: ... def __sizeof__(self) -> int: ... def __reduce__(self) -> Union[str, Tuple[Any, ...]]: ... def __reduce_ex__(self, protocol: int) -> Union[str, Tuple[Any, ...]]: ... def __dir__(self) -> Iterable[str]: ... def __init_subclass__(cls) -> None: ... class staticmethod(object): # Special, only valid as a decorator. __func__: Callable[..., Any] __isabstractmethod__: bool def __init__(self, f: Callable[..., Any]) -> None: ... def __new__(cls: Type[_T], *args: Any, **kwargs: Any) -> _T: ... def __get__(self, obj: _T, type: Optional[Type[_T]] = ...) -> Callable[..., Any]: ... class classmethod(object): # Special, only valid as a decorator. __func__: Callable[..., Any] __isabstractmethod__: bool def __init__(self, f: Callable[..., Any]) -> None: ... def __new__(cls: Type[_T], *args: Any, **kwargs: Any) -> _T: ... def __get__(self, obj: _T, type: Optional[Type[_T]] = ...) -> Callable[..., Any]: ... class type(object): __base__: type __bases__: Tuple[type, ...] __basicsize__: int __dict__: Dict[str, Any] __dictoffset__: int __flags__: int __itemsize__: int __module__: str __mro__: Tuple[type, ...] __name__: str __qualname__: str __text_signature__: Optional[str] __weakrefoffset__: int @overload def __init__(self, o: object) -> None: ... @overload def __init__(self, name: str, bases: Tuple[type, ...], dict: Dict[str, Any], **kwds: Any) -> None: ... @overload def __new__(cls, o: object) -> type: ... @overload def __new__(cls, name: str, bases: Tuple[type, ...], namespace: Dict[str, Any], **kwds: Any) -> type: ... def __call__(self, *args: Any, **kwds: Any) -> Any: ... def __subclasses__(self: _TT) -> List[_TT]: ... # Note: the documentation doesnt specify what the return type is, the standard # implementation seems to be returning a list. def mro(self) -> List[type]: ... def __instancecheck__(self, instance: Any) -> bool: ... def __subclasscheck__(self, subclass: type) -> bool: ... @classmethod def __prepare__(metacls, __name: str, __bases: Tuple[type, ...], **kwds: Any) -> Mapping[str, Any]: ... if sys.version_info >= (3, 10): def __or__(self, t: Any) -> types.Union: ... class super(object): @overload def __init__(self, t: Any, obj: Any) -> None: ... @overload def __init__(self, t: Any) -> None: ... @overload def __init__(self) -> None: ... class int: @overload def __new__(cls: Type[_T], x: Union[str, bytes, SupportsInt, _SupportsIndex, _SupportsTrunc] = ...) -> _T: ... @overload def __new__(cls: Type[_T], x: Union[str, bytes, bytearray], base: int) -> _T: ... if sys.version_info >= (3, 8): def as_integer_ratio(self) -> Tuple[int, Literal[1]]: ... @property def real(self) -> int: ... @property def imag(self) -> int: ... @property def numerator(self) -> int: ... @property def denominator(self) -> int: ... def conjugate(self) -> int: ... def bit_length(self) -> int: ... def to_bytes(self, length: int, byteorder: str, *, signed: bool = ...) -> bytes: ... @classmethod def from_bytes( cls, bytes: Union[Iterable[int], SupportsBytes], byteorder: str, *, signed: bool = ... ) -> int: ... # TODO buffer object argument def __add__(self, x: int) -> int: ... def __sub__(self, x: int) -> int: ... def __mul__(self, x: int) -> int: ... def __floordiv__(self, x: int) -> int: ... def __truediv__(self, x: int) -> float: ... def __mod__(self, x: int) -> int: ... def __divmod__(self, x: int) -> Tuple[int, int]: ... def __radd__(self, x: int) -> int: ... def __rsub__(self, x: int) -> int: ... def __rmul__(self, x: int) -> int: ... def __rfloordiv__(self, x: int) -> int: ... def __rtruediv__(self, x: int) -> float: ... def __rmod__(self, x: int) -> int: ... def __rdivmod__(self, x: int) -> Tuple[int, int]: ... @overload def __pow__(self, __x: Literal[2], __modulo: Optional[int] = ...) -> int: ... @overload def __pow__(self, __x: int, __modulo: Optional[int] = ...) -> Any: ... # Return type can be int or float, depending on x. def __rpow__(self, x: int, mod: Optional[int] = ...) -> Any: ... def __and__(self, n: int) -> int: ... def __or__(self, n: int) -> int: ... def __xor__(self, n: int) -> int: ... def __lshift__(self, n: int) -> int: ... def __rshift__(self, n: int) -> int: ... def __rand__(self, n: int) -> int: ... def __ror__(self, n: int) -> int: ... def __rxor__(self, n: int) -> int: ... def __rlshift__(self, n: int) -> int: ... def __rrshift__(self, n: int) -> int: ... def __neg__(self) -> int: ... def __pos__(self) -> int: ... def __invert__(self) -> int: ... def __trunc__(self) -> int: ... def __ceil__(self) -> int: ... def __floor__(self) -> int: ... def __round__(self, ndigits: Optional[int] = ...) -> int: ... def __getnewargs__(self) -> Tuple[int]: ... def __eq__(self, x: object) -> bool: ... def __ne__(self, x: object) -> bool: ... def __lt__(self, x: int) -> bool: ... def __le__(self, x: int) -> bool: ... def __gt__(self, x: int) -> bool: ... def __ge__(self, x: int) -> bool: ... def __str__(self) -> str: ... def __float__(self) -> float: ... def __int__(self) -> int: ... def __abs__(self) -> int: ... def __hash__(self) -> int: ... def __bool__(self) -> bool: ... def __index__(self) -> int: ... class float: def __new__(cls: Type[_T], x: Union[SupportsFloat, _SupportsIndex, str, bytes, bytearray] = ...) -> _T: ... def as_integer_ratio(self) -> Tuple[int, int]: ... def hex(self) -> str: ... def is_integer(self) -> bool: ... @classmethod def fromhex(cls, __s: str) -> float: ... @property def real(self) -> float: ... @property def imag(self) -> float: ... def conjugate(self) -> float: ... def __add__(self, x: float) -> float: ... def __sub__(self, x: float) -> float: ... def __mul__(self, x: float) -> float: ... def __floordiv__(self, x: float) -> float: ... def __truediv__(self, x: float) -> float: ... def __mod__(self, x: float) -> float: ... def __divmod__(self, x: float) -> Tuple[float, float]: ... def __pow__( self, x: float, mod: None = ... ) -> float: ... # In Python 3, returns complex if self is negative and x is not whole def __radd__(self, x: float) -> float: ... def __rsub__(self, x: float) -> float: ... def __rmul__(self, x: float) -> float: ... def __rfloordiv__(self, x: float) -> float: ... def __rtruediv__(self, x: float) -> float: ... def __rmod__(self, x: float) -> float: ... def __rdivmod__(self, x: float) -> Tuple[float, float]: ... def __rpow__(self, x: float, mod: None = ...) -> float: ... def __getnewargs__(self) -> Tuple[float]: ... def __trunc__(self) -> int: ... if sys.version_info >= (3, 9): def __ceil__(self) -> int: ... def __floor__(self) -> int: ... @overload def __round__(self, ndigits: None = ...) -> int: ... @overload def __round__(self, ndigits: int) -> float: ... def __eq__(self, x: object) -> bool: ... def __ne__(self, x: object) -> bool: ... def __lt__(self, x: float) -> bool: ... def __le__(self, x: float) -> bool: ... def __gt__(self, x: float) -> bool: ... def __ge__(self, x: float) -> bool: ... def __neg__(self) -> float: ... def __pos__(self) -> float: ... def __str__(self) -> str: ... def __int__(self) -> int: ... def __float__(self) -> float: ... def __abs__(self) -> float: ... def __hash__(self) -> int: ... def __bool__(self) -> bool: ... class complex: @overload def __new__(cls: Type[_T], real: float = ..., imag: float = ...) -> _T: ... @overload def __new__(cls: Type[_T], real: Union[str, SupportsComplex, _SupportsIndex]) -> _T: ... @property def real(self) -> float: ... @property def imag(self) -> float: ... def conjugate(self) -> complex: ... def __add__(self, x: complex) -> complex: ... def __sub__(self, x: complex) -> complex: ... def __mul__(self, x: complex) -> complex: ... def __pow__(self, x: complex, mod: None = ...) -> complex: ... def __truediv__(self, x: complex) -> complex: ... def __radd__(self, x: complex) -> complex: ... def __rsub__(self, x: complex) -> complex: ... def __rmul__(self, x: complex) -> complex: ... def __rpow__(self, x: complex, mod: None = ...) -> complex: ... def __rtruediv__(self, x: complex) -> complex: ... def __eq__(self, x: object) -> bool: ... def __ne__(self, x: object) -> bool: ... def __neg__(self) -> complex: ... def __pos__(self) -> complex: ... def __str__(self) -> str: ... def __abs__(self) -> float: ... def __hash__(self) -> int: ... def __bool__(self) -> bool: ... class _FormatMapMapping(Protocol): def __getitem__(self, __key: str) -> Any: ... class str(Sequence[str]): @overload def __new__(cls: Type[_T], o: object = ...) -> _T: ... @overload def __new__(cls: Type[_T], o: bytes, encoding: str = ..., errors: str = ...) -> _T: ... def capitalize(self) -> str: ... def casefold(self) -> str: ... def center(self, __width: int, __fillchar: str = ...) -> str: ... def count(self, x: str, __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def encode(self, encoding: str = ..., errors: str = ...) -> bytes: ... def endswith( self, __suffix: Union[str, Tuple[str, ...]], __start: Optional[int] = ..., __end: Optional[int] = ... ) -> bool: ... def expandtabs(self, tabsize: int = ...) -> str: ... def find(self, __sub: str, __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def format(self, *args: object, **kwargs: object) -> str: ... def format_map(self, map: _FormatMapMapping) -> str: ... def index(self, __sub: str, __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def isalnum(self) -> bool: ... def isalpha(self) -> bool: ... if sys.version_info >= (3, 7): def isascii(self) -> bool: ... def isdecimal(self) -> bool: ... def isdigit(self) -> bool: ... def isidentifier(self) -> bool: ... def islower(self) -> bool: ... def isnumeric(self) -> bool: ... def isprintable(self) -> bool: ... def isspace(self) -> bool: ... def istitle(self) -> bool: ... def isupper(self) -> bool: ... def join(self, __iterable: Iterable[str]) -> str: ... def ljust(self, __width: int, __fillchar: str = ...) -> str: ... def lower(self) -> str: ... def lstrip(self, __chars: Optional[str] = ...) -> str: ... def partition(self, __sep: str) -> Tuple[str, str, str]: ... def replace(self, __old: str, __new: str, __count: int = ...) -> str: ... if sys.version_info >= (3, 9): def removeprefix(self, __prefix: str) -> str: ... def removesuffix(self, __suffix: str) -> str: ... def rfind(self, __sub: str, __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def rindex(self, __sub: str, __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def rjust(self, __width: int, __fillchar: str = ...) -> str: ... def rpartition(self, __sep: str) -> Tuple[str, str, str]: ... def rsplit(self, sep: Optional[str] = ..., maxsplit: int = ...) -> List[str]: ... def rstrip(self, __chars: Optional[str] = ...) -> str: ... def split(self, sep: Optional[str] = ..., maxsplit: int = ...) -> List[str]: ... def splitlines(self, keepends: bool = ...) -> List[str]: ... def startswith( self, __prefix: Union[str, Tuple[str, ...]], __start: Optional[int] = ..., __end: Optional[int] = ... ) -> bool: ... def strip(self, __chars: Optional[str] = ...) -> str: ... def swapcase(self) -> str: ... def title(self) -> str: ... def translate(self, __table: Union[Mapping[int, Union[int, str, None]], Sequence[Union[int, str, None]]]) -> str: ... def upper(self) -> str: ... def zfill(self, __width: int) -> str: ... @staticmethod @overload def maketrans(__x: Union[Dict[int, _T], Dict[str, _T], Dict[Union[str, int], _T]]) -> Dict[int, _T]: ... @staticmethod @overload def maketrans(__x: str, __y: str, __z: Optional[str] = ...) -> Dict[int, Union[int, None]]: ... def __add__(self, s: str) -> str: ... # Incompatible with Sequence.__contains__ def __contains__(self, o: str) -> bool: ... # type: ignore def __eq__(self, x: object) -> bool: ... def __ge__(self, x: str) -> bool: ... def __getitem__(self, i: Union[int, slice]) -> str: ... def __gt__(self, x: str) -> bool: ... def __hash__(self) -> int: ... def __iter__(self) -> Iterator[str]: ... def __le__(self, x: str) -> bool: ... def __len__(self) -> int: ... def __lt__(self, x: str) -> bool: ... def __mod__(self, x: Any) -> str: ... def __mul__(self, n: int) -> str: ... def __ne__(self, x: object) -> bool: ... def __repr__(self) -> str: ... def __rmul__(self, n: int) -> str: ... def __str__(self) -> str: ... def __getnewargs__(self) -> Tuple[str]: ... class bytes(ByteString): @overload def __new__(cls: Type[_T], ints: Iterable[int]) -> _T: ... @overload def __new__(cls: Type[_T], string: str, encoding: str, errors: str = ...) -> _T: ... @overload def __new__(cls: Type[_T], length: int) -> _T: ... @overload def __new__(cls: Type[_T]) -> _T: ... @overload def __new__(cls: Type[_T], o: SupportsBytes) -> _T: ... def capitalize(self) -> bytes: ... def center(self, __width: int, __fillchar: bytes = ...) -> bytes: ... def count(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def decode(self, encoding: str = ..., errors: str = ...) -> str: ... def endswith( self, __suffix: Union[bytes, Tuple[bytes, ...]], __start: Optional[int] = ..., __end: Optional[int] = ... ) -> bool: ... def expandtabs(self, tabsize: int = ...) -> bytes: ... def find(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... if sys.version_info >= (3, 8): def hex(self, sep: Union[str, bytes] = ..., bytes_per_sep: int = ...) -> str: ... else: def hex(self) -> str: ... def index(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def isalnum(self) -> bool: ... def isalpha(self) -> bool: ... if sys.version_info >= (3, 7): def isascii(self) -> bool: ... def isdigit(self) -> bool: ... def islower(self) -> bool: ... def isspace(self) -> bool: ... def istitle(self) -> bool: ... def isupper(self) -> bool: ... def join(self, __iterable_of_bytes: Iterable[Union[ByteString, memoryview]]) -> bytes: ... def ljust(self, __width: int, __fillchar: bytes = ...) -> bytes: ... def lower(self) -> bytes: ... def lstrip(self, __bytes: Optional[bytes] = ...) -> bytes: ... def partition(self, __sep: bytes) -> Tuple[bytes, bytes, bytes]: ... def replace(self, __old: bytes, __new: bytes, __count: int = ...) -> bytes: ... if sys.version_info >= (3, 9): def removeprefix(self, __prefix: bytes) -> bytes: ... def removesuffix(self, __suffix: bytes) -> bytes: ... def rfind(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def rindex(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def rjust(self, __width: int, __fillchar: bytes = ...) -> bytes: ... def rpartition(self, __sep: bytes) -> Tuple[bytes, bytes, bytes]: ... def rsplit(self, sep: Optional[bytes] = ..., maxsplit: int = ...) -> List[bytes]: ... def rstrip(self, __bytes: Optional[bytes] = ...) -> bytes: ... def split(self, sep: Optional[bytes] = ..., maxsplit: int = ...) -> List[bytes]: ... def splitlines(self, keepends: bool = ...) -> List[bytes]: ... def startswith( self, __prefix: Union[bytes, Tuple[bytes, ...]], __start: Optional[int] = ..., __end: Optional[int] = ... ) -> bool: ... def strip(self, __bytes: Optional[bytes] = ...) -> bytes: ... def swapcase(self) -> bytes: ... def title(self) -> bytes: ... def translate(self, __table: Optional[bytes], delete: bytes = ...) -> bytes: ... def upper(self) -> bytes: ... def zfill(self, __width: int) -> bytes: ... @classmethod def fromhex(cls, __s: str) -> bytes: ... @classmethod def maketrans(cls, frm: bytes, to: bytes) -> bytes: ... def __len__(self) -> int: ... def __iter__(self) -> Iterator[int]: ... def __str__(self) -> str: ... def __repr__(self) -> str: ... def __int__(self) -> int: ... def __float__(self) -> float: ... def __hash__(self) -> int: ... @overload def __getitem__(self, i: int) -> int: ... @overload def __getitem__(self, s: slice) -> bytes: ... def __add__(self, s: bytes) -> bytes: ... def __mul__(self, n: int) -> bytes: ... def __rmul__(self, n: int) -> bytes: ... def __mod__(self, value: Any) -> bytes: ... # Incompatible with Sequence.__contains__ def __contains__(self, o: Union[int, bytes]) -> bool: ... # type: ignore def __eq__(self, x: object) -> bool: ... def __ne__(self, x: object) -> bool: ... def __lt__(self, x: bytes) -> bool: ... def __le__(self, x: bytes) -> bool: ... def __gt__(self, x: bytes) -> bool: ... def __ge__(self, x: bytes) -> bool: ... def __getnewargs__(self) -> Tuple[bytes]: ... class bytearray(MutableSequence[int], ByteString): @overload def __init__(self) -> None: ... @overload def __init__(self, ints: Iterable[int]) -> None: ... @overload def __init__(self, string: str, encoding: str, errors: str = ...) -> None: ... @overload def __init__(self, length: int) -> None: ... def capitalize(self) -> bytearray: ... def center(self, __width: int, __fillchar: bytes = ...) -> bytearray: ... def count(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def copy(self) -> bytearray: ... def decode(self, encoding: str = ..., errors: str = ...) -> str: ... def endswith( self, __suffix: Union[bytes, Tuple[bytes, ...]], __start: Optional[int] = ..., __end: Optional[int] = ... ) -> bool: ... def expandtabs(self, tabsize: int = ...) -> bytearray: ... def find(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... if sys.version_info >= (3, 8): def hex(self, sep: Union[str, bytes] = ..., bytes_per_sep: int = ...) -> str: ... else: def hex(self) -> str: ... def index(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def insert(self, __index: int, __item: int) -> None: ... def isalnum(self) -> bool: ... def isalpha(self) -> bool: ... if sys.version_info >= (3, 7): def isascii(self) -> bool: ... def isdigit(self) -> bool: ... def islower(self) -> bool: ... def isspace(self) -> bool: ... def istitle(self) -> bool: ... def isupper(self) -> bool: ... def join(self, __iterable_of_bytes: Iterable[Union[ByteString, memoryview]]) -> bytearray: ... def ljust(self, __width: int, __fillchar: bytes = ...) -> bytearray: ... def lower(self) -> bytearray: ... def lstrip(self, __bytes: Optional[bytes] = ...) -> bytearray: ... def partition(self, __sep: bytes) -> Tuple[bytearray, bytearray, bytearray]: ... if sys.version_info >= (3, 9): def removeprefix(self, __prefix: bytes) -> bytearray: ... def removesuffix(self, __suffix: bytes) -> bytearray: ... def replace(self, __old: bytes, __new: bytes, __count: int = ...) -> bytearray: ... def rfind(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def rindex(self, __sub: Union[bytes, int], __start: Optional[int] = ..., __end: Optional[int] = ...) -> int: ... def rjust(self, __width: int, __fillchar: bytes = ...) -> bytearray: ... def rpartition(self, __sep: bytes) -> Tuple[bytearray, bytearray, bytearray]: ... def rsplit(self, sep: Optional[bytes] = ..., maxsplit: int = ...) -> List[bytearray]: ... def rstrip(self, __bytes: Optional[bytes] = ...) -> bytearray: ... def split(self, sep: Optional[bytes] = ..., maxsplit: int = ...) -> List[bytearray]: ... def splitlines(self, keepends: bool = ...) -> List[bytearray]: ... def startswith( self, __prefix: Union[bytes, Tuple[bytes, ...]], __start: Optional[int] = ..., __end: Optional[int] = ... ) -> bool: ... def strip(self, __bytes: Optional[bytes] = ...) -> bytearray: ... def swapcase(self) -> bytearray: ... def title(self) -> bytearray: ... def translate(self, __table: Optional[bytes], delete: bytes = ...) -> bytearray: ... def upper(self) -> bytearray: ... def zfill(self, __width: int) -> bytearray: ... @classmethod def fromhex(cls, __string: str) -> bytearray: ... @classmethod def maketrans(cls, __frm: bytes, __to: bytes) -> bytes: ... def __len__(self) -> int: ... def __iter__(self) -> Iterator[int]: ... def __str__(self) -> str: ... def __repr__(self) -> str: ... def __int__(self) -> int: ... def __float__(self) -> float: ... __hash__: None # type: ignore @overload def __getitem__(self, i: int) -> int: ... @overload def __getitem__(self, s: slice) -> bytearray: ... @overload def __setitem__(self, i: int, x: int) -> None: ... @overload def __setitem__(self, s: slice, x: Union[Iterable[int], bytes]) -> None: ... def __delitem__(self, i: Union[int, slice]) -> None: ... def __add__(self, s: bytes) -> bytearray: ... def __iadd__(self, s: Iterable[int]) -> bytearray: ... def __mul__(self, n: int) -> bytearray: ... def __rmul__(self, n: int) -> bytearray: ... def __imul__(self, n: int) -> bytearray: ... def __mod__(self, value: Any) -> bytes: ... # Incompatible with Sequence.__contains__ def __contains__(self, o: Union[int, bytes]) -> bool: ... # type: ignore def __eq__(self, x: object) -> bool: ... def __ne__(self, x: object) -> bool: ... def __lt__(self, x: bytes) -> bool: ... def __le__(self, x: bytes) -> bool: ... def __gt__(self, x: bytes) -> bool: ... def __ge__(self, x: bytes) -> bool: ... class memoryview(Sized, Container[int]): format: str itemsize: int shape: Optional[Tuple[int, ...]] strides: Optional[Tuple[int, ...]] suboffsets: Optional[Tuple[int, ...]] readonly: bool ndim: int obj: Union[bytes, bytearray] c_contiguous: bool f_contiguous: bool contiguous: bool nbytes: int def __init__(self, obj: ReadableBuffer) -> None: ... def __enter__(self) -> memoryview: ... def __exit__( self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType] ) -> None: ... def cast(self, format: str, shape: Union[List[int], Tuple[int]] = ...) -> memoryview: ... @overload def __getitem__(self, i: int) -> int: ... @overload def __getitem__(self, s: slice) -> memoryview: ... def __contains__(self, x: object) -> bool: ... def __iter__(self) -> Iterator[int]: ... def __len__(self) -> int: ... @overload def __setitem__(self, s: slice, o: bytes) -> None: ... @overload def __setitem__(self, i: int, o: int) -> None: ... if sys.version_info >= (3, 8): def tobytes(self, order: Optional[Literal["C", "F", "A"]] = ...) -> bytes: ... else: def tobytes(self) -> bytes: ... def tolist(self) -> List[int]: ... if sys.version_info >= (3, 8): def toreadonly(self) -> memoryview: ... def release(self) -> None: ... if sys.version_info >= (3, 8): def hex(self, sep: Union[str, bytes] = ..., bytes_per_sep: int = ...) -> str: ... else: def hex(self) -> str: ... class bool(int): def __new__(cls: Type[_T], __o: object = ...) -> _T: ... @overload def __and__(self, x: bool) -> bool: ... @overload def __and__(self, x: int) -> int: ... @overload def __or__(self, x: bool) -> bool: ... @overload def __or__(self, x: int) -> int: ... @overload def __xor__(self, x: bool) -> bool: ... @overload def __xor__(self, x: int) -> int: ... @overload def __rand__(self, x: bool) -> bool: ... @overload def __rand__(self, x: int) -> int: ... @overload def __ror__(self, x: bool) -> bool: ... @overload def __ror__(self, x: int) -> int: ... @overload def __rxor__(self, x: bool) -> bool: ... @overload def __rxor__(self, x: int) -> int: ... def __getnewargs__(self) -> Tuple[int]: ... class slice(object): start: Any step: Any stop: Any @overload def __init__(self, stop: Any) -> None: ... @overload def __init__(self, start: Any, stop: Any, step: Any = ...) -> None: ... __hash__: None # type: ignore def indices(self, len: int) -> Tuple[int, int, int]: ... class tuple(Sequence[_T_co], Generic[_T_co]): def __new__(cls: Type[_T], iterable: Iterable[_T_co] = ...) -> _T: ... def __len__(self) -> int: ... def __contains__(self, x: object) -> bool: ... @overload def __getitem__(self, x: int) -> _T_co: ... @overload def __getitem__(self, x: slice) -> Tuple[_T_co, ...]: ... def __iter__(self) -> Iterator[_T_co]: ... def __lt__(self, x: Tuple[_T_co, ...]) -> bool: ... def __le__(self, x: Tuple[_T_co, ...]) -> bool: ... def __gt__(self, x: Tuple[_T_co, ...]) -> bool: ... def __ge__(self, x: Tuple[_T_co, ...]) -> bool: ... @overload def __add__(self, x: Tuple[_T_co, ...]) -> Tuple[_T_co, ...]: ... @overload def __add__(self, x: Tuple[Any, ...]) -> Tuple[Any, ...]: ... def __mul__(self, n: int) -> Tuple[_T_co, ...]: ... def __rmul__(self, n: int) -> Tuple[_T_co, ...]: ... def count(self, __value: Any) -> int: ... def index(self, __value: Any, __start: int = ..., __stop: int = ...) -> int: ... if sys.version_info >= (3, 9): def __class_getitem__(cls, item: Any) -> GenericAlias: ... class function: # TODO not defined in builtins! __name__: str __module__: str __code__: CodeType __qualname__: str __annotations__: Dict[str, Any] class list(MutableSequence[_T], Generic[_T]): @overload def __init__(self) -> None: ... @overload def __init__(self, iterable: Iterable[_T]) -> None: ... def clear(self) -> None: ... def copy(self) -> List[_T]: ... def append(self, __object: _T) -> None: ... def extend(self, __iterable: Iterable[_T]) -> None: ... def pop(self, __index: int = ...) -> _T: ... def index(self, __value: _T, __start: int = ..., __stop: int = ...) -> int: ... def count(self, __value: _T) -> int: ... def insert(self, __index: int, __object: _T) -> None: ... def remove(self, __value: _T) -> None: ... def reverse(self) -> None: ... @overload def sort(self: List[SupportsLessThanT], *, key: None = ..., reverse: bool = ...) -> None: ... @overload def sort(self, *, key: Callable[[_T], SupportsLessThan], reverse: bool = ...) -> None: ... def __len__(self) -> int: ... def __iter__(self) -> Iterator[_T]: ... def __str__(self) -> str: ... __hash__: None # type: ignore @overload def __getitem__(self, i: _SupportsIndex) -> _T: ... @overload def __getitem__(self, s: slice) -> List[_T]: ... @overload def __setitem__(self, i: _SupportsIndex, o: _T) -> None: ... @overload def __setitem__(self, s: slice, o: Iterable[_T]) -> None: ... def __delitem__(self, i: Union[_SupportsIndex, slice]) -> None: ... def __add__(self, x: List[_T]) -> List[_T]: ... def __iadd__(self: _S, x: Iterable[_T]) -> _S: ... def __mul__(self, n: int) -> List[_T]: ... def __rmul__(self, n: int) -> List[_T]: ... def __imul__(self: _S, n: int) -> _S: ... def __contains__(self, o: object) -> bool: ... def __reversed__(self) -> Iterator[_T]: ... def __gt__(self, x: List[_T]) -> bool: ... def __ge__(self, x: List[_T]) -> bool: ... def __lt__(self, x: List[_T]) -> bool: ... def __le__(self, x: List[_T]) -> bool: ... if sys.version_info >= (3, 9): def __class_getitem__(cls, item: Any) -> GenericAlias: ... class dict(MutableMapping[_KT, _VT], Generic[_KT, _VT]): @overload def __init__(self: Dict[_KT, _VT]) -> None: ... @overload def __init__(self: Dict[str, _VT], **kwargs: _VT) -> None: ... @overload def __init__(self, map: SupportsKeysAndGetItem[_KT, _VT], **kwargs: _VT) -> None: ... @overload def __init__(self, iterable: Iterable[Tuple[_KT, _VT]], **kwargs: _VT) -> None: ... def __new__(cls: Type[_T1], *args: Any, **kwargs: Any) -> _T1: ... def clear(self) -> None: ... def copy(self) -> Dict[_KT, _VT]: ... def popitem(self) -> Tuple[_KT, _VT]: ... def setdefault(self, __key: _KT, __default: _VT = ...) -> _VT: ... @overload def update(self, __m: Mapping[_KT, _VT], **kwargs: _VT) -> None: ... @overload def update(self, __m: Iterable[Tuple[_KT, _VT]], **kwargs: _VT) -> None: ... @overload def update(self, **kwargs: _VT) -> None: ... def keys(self) -> KeysView[_KT]: ... def values(self) -> ValuesView[_VT]: ... def items(self) -> ItemsView[_KT, _VT]: ... @classmethod @overload def fromkeys(cls, __iterable: Iterable[_T]) -> Dict[_T, Any]: ... @classmethod @overload def fromkeys(cls, __iterable: Iterable[_T], __value: _S) -> Dict[_T, _S]: ... def __len__(self) -> int: ... def __getitem__(self, k: _KT) -> _VT: ... def __setitem__(self, k: _KT, v: _VT) -> None: ... def __delitem__(self, v: _KT) -> None: ... def __iter__(self) -> Iterator[_KT]: ... if sys.version_info >= (3, 8): def __reversed__(self) -> Iterator[_KT]: ... def __str__(self) -> str: ... __hash__: None # type: ignore if sys.version_info >= (3, 9): def __class_getitem__(cls, item: Any) -> GenericAlias: ... def __or__(self, __value: Mapping[_KT, _VT]) -> Dict[_KT, _VT]: ... def __ior__(self, __value: Mapping[_KT, _VT]) -> Dict[_KT, _VT]: ... class set(MutableSet[_T], Generic[_T]): def __init__(self, iterable: Iterable[_T] = ...) -> None: ... def add(self, element: _T) -> None: ... def clear(self) -> None: ... def copy(self) -> Set[_T]: ... def difference(self, *s: Iterable[Any]) -> Set[_T]: ... def difference_update(self, *s: Iterable[Any]) -> None: ... def discard(self, element: _T) -> None: ... def intersection(self, *s: Iterable[Any]) -> Set[_T]: ... def intersection_update(self, *s: Iterable[Any]) -> None: ... def isdisjoint(self, s: Iterable[Any]) -> bool: ... def issubset(self, s: Iterable[Any]) -> bool: ... def issuperset(self, s: Iterable[Any]) -> bool: ... def pop(self) -> _T: ... def remove(self, element: _T) -> None: ... def symmetric_difference(self, s: Iterable[_T]) -> Set[_T]: ... def symmetric_difference_update(self, s: Iterable[_T]) -> None: ... def union(self, *s: Iterable[_T]) -> Set[_T]: ... def update(self, *s: Iterable[_T]) -> None: ... def __len__(self) -> int: ... def __contains__(self, o: object) -> bool: ... def __iter__(self) -> Iterator[_T]: ... def __str__(self) -> str: ... def __and__(self, s: AbstractSet[object]) -> Set[_T]: ... def __iand__(self, s: AbstractSet[object]) -> Set[_T]: ... def __or__(self, s: AbstractSet[_S]) -> Set[Union[_T, _S]]: ... def __ior__(self, s: AbstractSet[_S]) -> Set[Union[_T, _S]]: ... def __sub__(self, s: AbstractSet[Optional[_T]]) -> Set[_T]: ... def __isub__(self, s: AbstractSet[Optional[_T]]) -> Set[_T]: ... def __xor__(self, s: AbstractSet[_S]) -> Set[Union[_T, _S]]: ... def __ixor__(self, s: AbstractSet[_S]) -> Set[Union[_T, _S]]: ... def __le__(self, s: AbstractSet[object]) -> bool: ... def __lt__(self, s: AbstractSet[object]) -> bool: ... def __ge__(self, s: AbstractSet[object]) -> bool: ... def __gt__(self, s: AbstractSet[object]) -> bool: ... __hash__: None # type: ignore if sys.version_info >= (3, 9): def __class_getitem__(cls, item: Any) -> GenericAlias: ... class frozenset(AbstractSet[_T_co], Generic[_T_co]): def __init__(self, iterable: Iterable[_T_co] = ...) -> None: ... def copy(self) -> FrozenSet[_T_co]: ... def difference(self, *s: Iterable[object]) -> FrozenSet[_T_co]: ... def intersection(self, *s: Iterable[object]) -> FrozenSet[_T_co]: ... def isdisjoint(self, s: Iterable[_T_co]) -> bool: ... def issubset(self, s: Iterable[object]) -> bool: ... def issuperset(self, s: Iterable[object]) -> bool: ... def symmetric_difference(self, s: Iterable[_T_co]) -> FrozenSet[_T_co]: ... def union(self, *s: Iterable[_T_co]) -> FrozenSet[_T_co]: ... def __len__(self) -> int: ... def __contains__(self, o: object) -> bool: ... def __iter__(self) -> Iterator[_T_co]: ... def __str__(self) -> str: ... def __and__(self, s: AbstractSet[_T_co]) -> FrozenSet[_T_co]: ... def __or__(self, s: AbstractSet[_S]) -> FrozenSet[Union[_T_co, _S]]: ... def __sub__(self, s: AbstractSet[_T_co]) -> FrozenSet[_T_co]: ... def __xor__(self, s: AbstractSet[_S]) -> FrozenSet[Union[_T_co, _S]]: ... def __le__(self, s: AbstractSet[object]) -> bool: ... def __lt__(self, s: AbstractSet[object]) -> bool: ... def __ge__(self, s: AbstractSet[object]) -> bool: ... def __gt__(self, s: AbstractSet[object]) -> bool: ... if sys.version_info >= (3, 9): def __class_getitem__(cls, item: Any) -> GenericAlias: ... class enumerate(Iterator[Tuple[int, _T]], Generic[_T]): def __init__(self, iterable: Iterable[_T], start: int = ...) -> None: ... def __iter__(self) -> Iterator[Tuple[int, _T]]: ... def __next__(self) -> Tuple[int, _T]: ... if sys.version_info >= (3, 9): def __class_getitem__(cls, item: Any) -> GenericAlias: ... class range(Sequence[int]): start: int stop: int step: int @overload def __init__(self, stop: _SupportsIndex) -> None: ... @overload def __init__(self, start: _SupportsIndex, stop: _SupportsIndex, step: _SupportsIndex = ...) -> None: ... def count(self, value: int) -> int: ... def index(self, value: int) -> int: ... # type: ignore def __len__(self) -> int: ... def __contains__(self, o: object) -> bool: ... def __iter__(self) -> Iterator[int]: ... @overload def __getitem__(self, i: _SupportsIndex) -> int: ... @overload def __getitem__(self, s: slice) -> range: ... def __repr__(self) -> str: ... def __reversed__(self) -> Iterator[int]: ... class property(object): def __init__( self, fget: Optional[Callable[[Any], Any]] = ..., fset: Optional[Callable[[Any, Any], None]] = ..., fdel: Optional[Callable[[Any], None]] = ..., doc: Optional[str] = ..., ) -> None: ... def getter(self, fget: Callable[[Any], Any]) -> property: ... def setter(self, fset: Callable[[Any, Any], None]) -> property: ... def deleter(self, fdel: Callable[[Any], None]) -> property: ... def __get__(self, obj: Any, type: Optional[type] = ...) -> Any: ... def __set__(self, obj: Any, value: Any) -> None: ... def __delete__(self, obj: Any) -> None: ... def fget(self) -> Any: ... def fset(self, value: Any) -> None: ... def fdel(self) -> None: ... class _NotImplementedType(Any): # type: ignore # A little weird, but typing the __call__ as NotImplemented makes the error message # for NotImplemented() much better __call__: NotImplemented # type: ignore NotImplemented: _NotImplementedType def abs(__x: SupportsAbs[_T]) -> _T: ... def all(__iterable: Iterable[object]) -> bool: ... def any(__iterable: Iterable[object]) -> bool: ... def ascii(__obj: object) -> str: ... def bin(__number: Union[int, _SupportsIndex]) -> str: ... if sys.version_info >= (3, 7): def breakpoint(*args: Any, **kws: Any) -> None: ... def callable(__obj: object) -> bool: ... def chr(__i: int) -> str: ... # This class is to be exported as PathLike from os, # but we define it here as _PathLike to avoid import cycle issues. # See https://github.com/python/typeshed/pull/991#issuecomment-288160993 _AnyStr_co = TypeVar("_AnyStr_co", str, bytes, covariant=True) @runtime_checkable class _PathLike(Protocol[_AnyStr_co]): def __fspath__(self) -> _AnyStr_co: ... if sys.version_info >= (3, 8): def compile( source: Union[str, bytes, mod, AST], filename: Union[str, bytes, _PathLike[Any]], mode: str, flags: int = ..., dont_inherit: int = ..., optimize: int = ..., *, _feature_version: int = ..., ) -> Any: ... else: def compile( source: Union[str, bytes, mod, AST], filename: Union[str, bytes, _PathLike[Any]], mode: str, flags: int = ..., dont_inherit: int = ..., optimize: int = ..., ) -> Any: ... def copyright() -> None: ... def credits() -> None: ... def delattr(__obj: Any, __name: str) -> None: ... def dir(__o: object = ...) -> List[str]: ... @overload def divmod(__x: SupportsDivMod[_T_contra, _T_co], __y: _T_contra) -> _T_co: ... @overload def divmod(__x: _T_contra, __y: SupportsRDivMod[_T_contra, _T_co]) -> _T_co: ... def eval( __source: Union[str, bytes, CodeType], __globals: Optional[Dict[str, Any]] = ..., __locals: Optional[Mapping[str, Any]] = ... ) -> Any: ... def exec( __source: Union[str, bytes, CodeType], __globals: Optional[Dict[str, Any]] = ..., __locals: Optional[Mapping[str, Any]] = ..., ) -> Any: ... def exit(code: object = ...) -> NoReturn: ... @overload def filter(__function: None, __iterable: Iterable[Optional[_T]]) -> Iterator[_T]: ... @overload def filter(__function: Callable[[_T], Any], __iterable: Iterable[_T]) -> Iterator[_T]: ... def format(__value: object, __format_spec: str = ...) -> str: ... # TODO unicode def getattr(__o: Any, name: str, __default: Any = ...) -> Any: ... def globals() -> Dict[str, Any]: ... def hasattr(__obj: Any, __name: str) -> bool: ... def hash(__obj: object) -> int: ... def help(*args: Any, **kwds: Any) -> None: ... def hex(__number: Union[int, _SupportsIndex]) -> str: ... def id(__obj: object) -> int: ... def input(__prompt: Any = ...) -> str: ... @overload def iter(__iterable: Iterable[_T]) -> Iterator[_T]: ... @overload def iter(__function: Callable[[], Optional[_T]], __sentinel: None) -> Iterator[_T]: ... @overload def iter(__function: Callable[[], _T], __sentinel: Any) -> Iterator[_T]: ... def isinstance(__obj: object, __class_or_tuple: Union[type, Tuple[Union[type, Tuple[Any, ...]], ...]]) -> bool: ... def issubclass(__cls: type, __class_or_tuple: Union[type, Tuple[Union[type, Tuple[Any, ...]], ...]]) -> bool: ... def len(__obj: Sized) -> int: ... def license() -> None: ... def locals() -> Dict[str, Any]: ... @overload def map(__func: Callable[[_T1], _S], __iter1: Iterable[_T1]) -> Iterator[_S]: ... @overload def map(__func: Callable[[_T1, _T2], _S], __iter1: Iterable[_T1], __iter2: Iterable[_T2]) -> Iterator[_S]: ... @overload def map( __func: Callable[[_T1, _T2, _T3], _S], __iter1: Iterable[_T1], __iter2: Iterable[_T2], __iter3: Iterable[_T3] ) -> Iterator[_S]: ... @overload def map( __func: Callable[[_T1, _T2, _T3, _T4], _S], __iter1: Iterable[_T1], __iter2: Iterable[_T2], __iter3: Iterable[_T3], __iter4: Iterable[_T4], ) -> Iterator[_S]: ... @overload def map( __func: Callable[[_T1, _T2, _T3, _T4, _T5], _S], __iter1: Iterable[_T1], __iter2: Iterable[_T2], __iter3: Iterable[_T3], __iter4: Iterable[_T4], __iter5: Iterable[_T5], ) -> Iterator[_S]: ... @overload def map( __func: Callable[..., _S], __iter1: Iterable[Any], __iter2: Iterable[Any], __iter3: Iterable[Any], __iter4: Iterable[Any], __iter5: Iterable[Any], __iter6: Iterable[Any], *iterables: Iterable[Any], ) -> Iterator[_S]: ... @overload def max( __arg1: SupportsLessThanT, __arg2: SupportsLessThanT, *_args: SupportsLessThanT, key: None = ... ) -> SupportsLessThanT: ... @overload def max(__arg1: _T, __arg2: _T, *_args: _T, key: Callable[[_T], SupportsLessThanT]) -> _T: ... @overload def max(__iterable: Iterable[SupportsLessThanT], *, key: None = ...) -> SupportsLessThanT: ... @overload def max(__iterable: Iterable[_T], *, key: Callable[[_T], SupportsLessThanT]) -> _T: ... @overload def max(__iterable: Iterable[SupportsLessThanT], *, key: None = ..., default: _T) -> Union[SupportsLessThanT, _T]: ... @overload def max(__iterable: Iterable[_T1], *, key: Callable[[_T1], SupportsLessThanT], default: _T2) -> Union[_T1, _T2]: ... @overload def min( __arg1: SupportsLessThanT, __arg2: SupportsLessThanT, *_args: SupportsLessThanT, key: None = ... ) -> SupportsLessThanT: ... @overload def min(__arg1: _T, __arg2: _T, *_args: _T, key: Callable[[_T], SupportsLessThanT]) -> _T: ... @overload def min(__iterable: Iterable[SupportsLessThanT], *, key: None = ...) -> SupportsLessThanT: ... @overload def min(__iterable: Iterable[_T], *, key: Callable[[_T], SupportsLessThanT]) -> _T: ... @overload def min(__iterable: Iterable[SupportsLessThanT], *, key: None = ..., default: _T) -> Union[SupportsLessThanT, _T]: ... @overload def min(__iterable: Iterable[_T1], *, key: Callable[[_T1], SupportsLessThanT], default: _T2) -> Union[_T1, _T2]: ... @overload def next(__i: Iterator[_T]) -> _T: ... @overload def next(__i: Iterator[_T], default: _VT) -> Union[_T, _VT]: ... def oct(__number: Union[int, _SupportsIndex]) -> str: ... _OpenFile = Union[AnyPath, int] _Opener = Callable[[str, int], int] # Text mode: always returns a TextIOWrapper @overload def open( file: _OpenFile, mode: OpenTextMode = ..., buffering: int = ..., encoding: Optional[str] = ..., errors: Optional[str] = ..., newline: Optional[str] = ..., closefd: bool = ..., opener: Optional[_Opener] = ..., ) -> TextIOWrapper: ... # Unbuffered binary mode: returns a FileIO @overload def open( file: _OpenFile, mode: OpenBinaryMode, buffering: Literal[0], encoding: None = ..., errors: None = ..., newline: None = ..., closefd: bool = ..., opener: Optional[_Opener] = ..., ) -> FileIO: ... # Buffering is on: return BufferedRandom, BufferedReader, or BufferedWriter @overload def open( file: _OpenFile, mode: OpenBinaryModeUpdating, buffering: Literal[-1, 1] = ..., encoding: None = ..., errors: None = ..., newline: None = ..., closefd: bool = ..., opener: Optional[_Opener] = ..., ) -> BufferedRandom: ... @overload def open( file: _OpenFile, mode: OpenBinaryModeWriting, buffering: Literal[-1, 1] = ..., encoding: None = ..., errors: None = ..., newline: None = ..., closefd: bool = ..., opener: Optional[_Opener] = ..., ) -> BufferedWriter: ... @overload def open( file: _OpenFile, mode: OpenBinaryModeReading, buffering: Literal[-1, 1] = ..., encoding: None = ..., errors: None = ..., newline: None = ..., closefd: bool = ..., opener: Optional[_Opener] = ..., ) -> BufferedReader: ... # Buffering cannot be determined: fall back to BinaryIO @overload def open( file: _OpenFile, mode: OpenBinaryMode, buffering: int, encoding: None = ..., errors: None = ..., newline: None = ..., closefd: bool = ..., opener: Optional[_Opener] = ..., ) -> BinaryIO: ... # Fallback if mode is not specified @overload def open( file: _OpenFile, mode: str, buffering: int = ..., encoding: Optional[str] = ..., errors: Optional[str] = ..., newline: Optional[str] = ..., closefd: bool = ..., opener: Optional[_Opener] = ..., ) -> IO[Any]: ... def ord(__c: Union[str, bytes]) -> int: ... def print( *values: object, sep: Optional[str] = ..., end: Optional[str] = ..., file: Optional[SupportsWrite[str]] = ..., flush: bool = ..., ) -> None: ... _E = TypeVar("_E", contravariant=True) _M = TypeVar("_M", contravariant=True) class _SupportsPow2(Protocol[_E, _T_co]): def __pow__(self, __other: _E) -> _T_co: ... class _SupportsPow3(Protocol[_E, _M, _T_co]): def __pow__(self, __other: _E, __modulo: _M) -> _T_co: ... if sys.version_info >= (3, 8): @overload def pow(base: int, exp: int, mod: None = ...) -> Any: ... # returns int or float depending on whether exp is non-negative @overload def pow(base: int, exp: int, mod: int) -> int: ... @overload def pow(base: float, exp: float, mod: None = ...) -> float: ... @overload def pow(base: _SupportsPow2[_E, _T_co], exp: _E) -> _T_co: ... @overload def pow(base: _SupportsPow3[_E, _M, _T_co], exp: _E, mod: _M) -> _T_co: ... else: @overload def pow( __base: int, __exp: int, __mod: None = ... ) -> Any: ... # returns int or float depending on whether exp is non-negative @overload def pow(__base: int, __exp: int, __mod: int) -> int: ... @overload def pow(__base: float, __exp: float, __mod: None = ...) -> float: ... @overload def pow(__base: _SupportsPow2[_E, _T_co], __exp: _E) -> _T_co: ... @overload def pow(__base: _SupportsPow3[_E, _M, _T_co], __exp: _E, __mod: _M) -> _T_co: ... def quit(code: object = ...) -> NoReturn: ... @overload def reversed(__sequence: Sequence[_T]) -> Iterator[_T]: ... @overload def reversed(__sequence: Reversible[_T]) -> Iterator[_T]: ... def repr(__obj: object) -> str: ... @overload def round(number: SupportsRound[Any]) -> int: ... @overload def round(number: SupportsRound[Any], ndigits: None) -> int: ... @overload def round(number: SupportsRound[_T], ndigits: int) -> _T: ... def setattr(__obj: Any, __name: str, __value: Any) -> None: ... @overload def sorted(__iterable: Iterable[SupportsLessThanT], *, key: None = ..., reverse: bool = ...) -> List[SupportsLessThanT]: ... @overload def sorted(__iterable: Iterable[_T], *, key: Callable[[_T], SupportsLessThan], reverse: bool = ...) -> List[_T]: ... if sys.version_info >= (3, 8): @overload def sum(__iterable: Iterable[_T]) -> Union[_T, int]: ... @overload def sum(__iterable: Iterable[_T], start: _S) -> Union[_T, _S]: ... else: @overload def sum(__iterable: Iterable[_T]) -> Union[_T, int]: ... @overload def sum(__iterable: Iterable[_T], __start: _S) -> Union[_T, _S]: ... def vars(__object: Any = ...) -> Dict[str, Any]: ... @overload def zip(__iter1: Iterable[_T1]) -> Iterator[Tuple[_T1]]: ... @overload def zip(__iter1: Iterable[_T1], __iter2: Iterable[_T2]) -> Iterator[Tuple[_T1, _T2]]: ... @overload def zip(__iter1: Iterable[_T1], __iter2: Iterable[_T2], __iter3: Iterable[_T3]) -> Iterator[Tuple[_T1, _T2, _T3]]: ... @overload def zip( __iter1: Iterable[_T1], __iter2: Iterable[_T2], __iter3: Iterable[_T3], __iter4: Iterable[_T4] ) -> Iterator[Tuple[_T1, _T2, _T3, _T4]]: ... @overload def zip( __iter1: Iterable[_T1], __iter2: Iterable[_T2], __iter3: Iterable[_T3], __iter4: Iterable[_T4], __iter5: Iterable[_T5] ) -> Iterator[Tuple[_T1, _T2, _T3, _T4, _T5]]: ... @overload def zip( __iter1: Iterable[Any], __iter2: Iterable[Any], __iter3: Iterable[Any], __iter4: Iterable[Any], __iter5: Iterable[Any], __iter6: Iterable[Any], *iterables: Iterable[Any], ) -> Iterator[Tuple[Any, ...]]: ... def __import__( name: str, globals: Optional[Mapping[str, Any]] = ..., locals: Optional[Mapping[str, Any]] = ..., fromlist: Sequence[str] = ..., level: int = ..., ) -> Any: ... # Actually the type of Ellipsis is <type 'ellipsis'>, but since it's # not exposed anywhere under that name, we make it private here. class ellipsis: ... Ellipsis: ellipsis class BaseException(object): args: Tuple[Any, ...] __cause__: Optional[BaseException] __context__: Optional[BaseException] __suppress_context__: bool __traceback__: Optional[TracebackType] def __init__(self, *args: object) -> None: ... def __str__(self) -> str: ... def __repr__(self) -> str: ... def with_traceback(self: _TBE, tb: Optional[TracebackType]) -> _TBE: ... class GeneratorExit(BaseException): ... class KeyboardInterrupt(BaseException): ... class SystemExit(BaseException): code: int class Exception(BaseException): ... class StopIteration(Exception): value: Any _StandardError = Exception class OSError(Exception): errno: int strerror: str # filename, filename2 are actually Union[str, bytes, None] filename: Any filename2: Any if sys.platform == "win32": winerror: int EnvironmentError = OSError IOError = OSError if sys.platform == "win32": WindowsError = OSError class ArithmeticError(_StandardError): ... class AssertionError(_StandardError): ... class AttributeError(_StandardError): ... class BufferError(_StandardError): ... class EOFError(_StandardError): ... class ImportError(_StandardError): def __init__(self, *args: object, name: Optional[str] = ..., path: Optional[str] = ...) -> None: ... name: Optional[str] path: Optional[str] msg: str # undocumented class LookupError(_StandardError): ... class MemoryError(_StandardError): ... class NameError(_StandardError): ... class ReferenceError(_StandardError): ... class RuntimeError(_StandardError): ... class StopAsyncIteration(Exception): value: Any class SyntaxError(_StandardError): msg: str lineno: Optional[int] offset: Optional[int] text: Optional[str] filename: Optional[str] class SystemError(_StandardError): ... class TypeError(_StandardError): ... class ValueError(_StandardError): ... class FloatingPointError(ArithmeticError): ... class OverflowError(ArithmeticError): ... class ZeroDivisionError(ArithmeticError): ... class ModuleNotFoundError(ImportError): ... class IndexError(LookupError): ... class KeyError(LookupError): ... class UnboundLocalError(NameError): ... class BlockingIOError(OSError): characters_written: int class ChildProcessError(OSError): ... class ConnectionError(OSError): ... class BrokenPipeError(ConnectionError): ... class ConnectionAbortedError(ConnectionError): ... class ConnectionRefusedError(ConnectionError): ... class ConnectionResetError(ConnectionError): ... class FileExistsError(OSError): ... class FileNotFoundError(OSError): ... class InterruptedError(OSError): ... class IsADirectoryError(OSError): ... class NotADirectoryError(OSError): ... class PermissionError(OSError): ... class ProcessLookupError(OSError): ... class TimeoutError(OSError): ... class NotImplementedError(RuntimeError): ... class RecursionError(RuntimeError): ... class IndentationError(SyntaxError): ... class TabError(IndentationError): ... class UnicodeError(ValueError): ... class UnicodeDecodeError(UnicodeError): encoding: str object: bytes start: int end: int reason: str def __init__(self, __encoding: str, __object: bytes, __start: int, __end: int, __reason: str) -> None: ... class UnicodeEncodeError(UnicodeError): encoding: str object: str start: int end: int reason: str def __init__(self, __encoding: str, __object: str, __start: int, __end: int, __reason: str) -> None: ... class UnicodeTranslateError(UnicodeError): ... class Warning(Exception): ... class UserWarning(Warning): ... class DeprecationWarning(Warning): ... class SyntaxWarning(Warning): ... class RuntimeWarning(Warning): ... class FutureWarning(Warning): ... class PendingDeprecationWarning(Warning): ... class ImportWarning(Warning): ... class UnicodeWarning(Warning): ... class BytesWarning(Warning): ... class ResourceWarning(Warning): ...

[ "erictr@microsoft.com" ]

erictr@microsoft.com

f532fa34ab974fc95dff6d166910eebb381c45a5

68a155e62686542dc8e7e8069382085c88612280

/application/routes.py

eab784019f5a8d46370a9c487ff0b720756d01de

[]

no_license

MohamedEmad1998/Movie-Recommendation-AI

2f4de729452734e221625effa8809a07256cb8d8

f6384117c6be758e4043ec6cb39d679bef627b7e

refs/heads/main

2023-02-19T11:40:48.749679

2021-01-20T21:04:09

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from application import app import bot from flask import render_template ,request @app.route('/') @app.route('/home') @app.route('/index') def hello_world(): return render_template("index2.html") @app.route('/get') def form_post(): message = request.args.get('msg') response = bot.chat(message) return str(response) # if request.method == 'POST' : # message = request.form['usermsg'] # update_html.update(message,'user') # render_template('index2.html') # reply=bot.chat(message) # update_html.update(reply,'bot') # return render_template('index2.html')

[ "noreply@github.com" ]

MohamedEmad1998.noreply@github.com

8aa9836ef60eecec8665ea91de8d724b9abc0328

f1c3614d6ef3874e816a2616ea0ae83704b052da

/tests/selenium_page.py

58331945adeee298ace5dec647cb39d512dfa78d

[]

no_license

margaritaumaniuc/presto

a46ed88c433a68c762a28e80e413bb1b61d46705

f95e85c7abb6da3919ed4fbfc96827f4bba473e2

refs/heads/master

2020-08-04T20:55:34.755285

2019-10-11T06:06:22

212,275,851

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from selenium.webdriver.common.keys import Keys from selenium.common.exceptions import StaleElementReferenceException, ElementNotVisibleException from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.support.wait import WebDriverWait from tests.settings import WDW_TIME class BasePage(object): def __init__(self, driver): self.driver = driver def find_by_locator(self, locator, wait=WDW_TIME): while self.driver.execute_script('return document.readyState') == 'complete': return WebDriverWait(self.driver, wait, ignored_exceptions=[StaleElementReferenceException]). \ until(EC.presence_of_element_located(locator)) def find_by_locator_and_clickability(self, locator, wait=WDW_TIME): while self.driver.execute_script('return document.readyState') == 'complete': return WebDriverWait(self.driver, wait, ignored_exceptions=[StaleElementReferenceException]). \ until(EC.element_to_be_clickable(locator)) def find_all_elements(self, locator, wait=WDW_TIME): items = WebDriverWait(self.driver, wait, ignored_exceptions=[StaleElementReferenceException, ElementNotVisibleException]). \ until(EC.presence_of_all_elements_located(locator)) return items def fill_element(self, locator, value): element = self.find_by_locator(locator) element.send_keys(value) def press_enter(self, locator): element = self.find_by_locator(locator) element.send_keys(Keys.ENTER)

[ "margarita.u@sparkcentral.com" ]

margarita.u@sparkcentral.com

97fb86a6fc77d10f0b1704b970c7eb502694ccf4

693d005c1d1b1dc39d970bb9683282a5eede389f

/fourth_project/manage.py

9e85fa14b8338c2a7e0f3e9e88dc04cd538a69e3

[]

no_license

FranklinMonro/UDEMY---Full-Stack-Django

db3d3646e973dec7d74dbb1b50c54d68c7f1a366

21ac64652342b9d7e97c34b7d4b8d10247431317

refs/heads/master

2020-04-19T03:59:26.269493

2019-05-14T15:06:41

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#!/usr/bin/env python import os import sys if __name__ == '__main__': os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'fourth_project.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv)

[ "noreply@github.com" ]

FranklinMonro.noreply@github.com

3b9fd7f7cb708040b5bbad3d59b9b3d09d01e4bd

e960a8d11fd8bf86742a82d879ec765e0c801bf6

/GeneratePredictionT.py

84b77119a8dc9062b6a76af69cea3b825ad2a7a3

[]

no_license

limcrong/trafficmanagement

91801a2bce86135325f031fff51ae575255e1118

c76303020300df265a0c0a05dd1d0c7e679d3a31

refs/heads/master

2020-06-05T01:30:53.748716

2019-06-19T02:40:57

192,266,519

1

0

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#!/usr/bin/env python # coding: utf-8 # In[2]: import pandas as pd import xgboost as xgb from xgboost import XGBClassifier from xgboost import XGBRegressor import numpy as np from sklearn.preprocessing import MinMaxScaler from sklearn.externals import joblib from sklearn.metrics import mean_squared_error from math import sqrt import pickle import ConvertScript # In[4]: # load xgb models model1 = pickle.load(open("model/final1.model.dat", "rb")) model2 = pickle.load(open("model/final2.model.dat", "rb")) model3 = pickle.load(open("model/final3.model.dat", "rb")) model4 = pickle.load(open("model/final4.model.dat", "rb")) model5 = pickle.load(open("model/final5.model.dat", "rb")) model6 = pickle.load(open("model/final6.model.dat", "rb")) #load scaler scaler = joblib.load('scaler.pkl') # In[5]: def increaseT(d,hr,m): if(hr > 22.5) & (m > 35): return d+1,0,0 if(m>35): return d,hr+1,0 return d,hr,m+15 def getLastT(df): lastday = df.iloc[-1,:]['day'] lasthr = df.iloc[-1,:]['hour'] lastmin = df.iloc[-1,:]['minute'] print("Last time stamp is: {} day {} hour {} min".format(lastday,lasthr,lastmin)) return (lastday,lasthr,lastmin) def findAndReturnNextT(df): d,hr,m = getLastT(df) return increaseT(d,hr,m) def applyScaling(dfx): dff = dfx.copy(deep=True) dff.drop('geohash6',axis=1,inplace=True) dff = dff.astype(np.float32) dff = dff.fillna(0) scaledx = scaler.transform(dff) print(scaledx.shape) return scaledx col2 = ['day', 'long', 'lat', 'min', 'max', 'zone', 'dist_to_high_demand5', 'dist_to_7', 'hour', 'minute', 'demand_s', 'mean', 'ma7', 'ma14', 'ma21', 'ma50', 'ma100', 'std', 'zoneAverage', 'geoEma7', 'geoEma14', 'zoneEma14', 'dayOfWeek', 'peak', 'totalDist', 'sin_hour', 'cos_hour', 'demand_s_2', 'demand_s_3', 'demand_s_4', 'demand_s_5', 'demand_s_6', 'demand_s_7', 'geoEma7_2', 'x', 'y', 'z', 'geo4ZoneEma7', 'geo5ZoneEma7', 'high_demand_perc', 'geoEma7_var', 'ma100_med', 'demand_last_week', 'demand'] # In[6]: def generatePred(df): #Create next timestep T dfnextT = pd.DataFrame() static = pd.read_hdf('staticValues.h5') d,hr,m = findAndReturnNextT(df) print("Next time stamp is: {} day {} hour {} min".format(d,hr,m)) dfnextT['geohash6'] = static['geohash6'] dfnextT['day'] = d dfnextT['hour'] = hr dfnextT['minute'] = m dfn = pd.concat([df,dfnextT]) dfn= dfn[df.columns] print("Created next timestep..") #Generate Features print("Running feature generation script..") dfcon = ConvertScript.convertDf(dfn) lastday,lasthr,lastmin = getLastT(dfcon) dfcon = dfcon.loc[(dfcon.day == lastday)&(dfcon.hour == lasthr)&(dfcon.minute == lastmin)] print("Generated features..") #Scale features scaled = applyScaling(dfcon) x_test = scaled[:, :-1] print("Scaled features..") # Predict demand y_pred = (model1.predict(x_test) + model2.predict(x_test)+model3.predict(x_test)+ model4.predict(x_test) + model5.predict(x_test) + model6.predict(x_test))/6 print("Predicted demand..") print("Reconstructed original..") #Construct original withPred = np.concatenate([x_test,y_pred.reshape(y_pred.shape[0], 1)],axis=1) newDf = pd.DataFrame(scaler.inverse_transform(withPred)) newDf.columns = col2 df_static = static[['geohash6','lat','long']] df_merge = pd.merge(newDf,df_static, how='left', left_on=['lat','long'],right_on = ['lat','long']) df_merge = df_merge[df.columns] df_merge.head() return df_merge

[ "rongronggg@gmail.com" ]

rongronggg@gmail.com

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/MotionSensor.py

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majorpeter/puha-manager

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2020-03-19T04:35:44.466718

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import logging from datetime import datetime class MotionSensor: def __init__(self, node): self.node = node self.node.PulseCount.subscribe_to_changes(self.on_pulse_count_changed) self.last_movement_time = datetime.now() # let's assume there is movement on startup to avoid checks later def on_pulse_count_changed(self, name, value): logging.debug('movement detected! (%s)' % value) self.last_movement_time = datetime.now() def get_time_since_last_movement(self): return datetime.now() - self.last_movement_time

[ "majorpeter29@gmail.com" ]

majorpeter29@gmail.com

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[]

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toantt28/django-bypass-gcaptcha

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refs/heads/master

2022-12-28T11:14:45.468781

2020-10-14T07:12:38

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from http.server import BaseHTTPRequestHandler, HTTPServer from urllib.parse import urlparse import json #system libraries import os import random import time #selenium libraries from seleniumwire import webdriver from selenium.webdriver.common.keys import Keys from webdriver_manager.chrome import ChromeDriverManager #recaptcha libraries import speech_recognition as sr import urllib import pydub from django.contrib.auth.models import User, Group from rest_framework import viewsets from rest_framework.response import Response from rest_framework.views import APIView def delay(): print("[INFO] delay") time.sleep(random.randint(3, 5)) # Create your views here. class UserViewSet(viewsets.ModelViewSet): """ API endpoint that allows users to be viewed or edited. """ queryset = User.objects.all().order_by('-date_joined') class Test(APIView): def get(self, request, *args, **kwargs): # create chrome driver options = webdriver.ChromeOptions() options.add_argument('headless') options.add_argument('window-size=1920x1080') options.add_argument("disable-gpu") driver = webdriver.Chrome(ChromeDriverManager().install(), chrome_options=options) delay() driver.get( "https://www.google.com/search?q=hello&oq=hello&aqs=chrome..69i57j69i59j69i60.821j0j1&sourceid=chrome&ie=UTF-8" ) # go to website driver.get("https://www.gstatic.com/cloud-site-ux/vision/vision.min.html") delay() shadow_section = driver.execute_script('''return document.querySelector("vs-app").shadowRoot''') element = shadow_section.find_element_by_tag_name('input') driver.execute_script("document.getElementById('input').removeAttribute('hidden')") randNum = random.randint(1, 100) randImg = '{}.png'.format(randNum) element.send_keys( os.path.join( os.getcwd(), 'gcaptcha', 'rest', 'images', randImg ) ) delay() # switch to recaptcha frame frames = driver.find_elements_by_tag_name("iframe") driver.switch_to.frame(frames[0]) delay() # click on checkbox to activate recaptcha driver.find_element_by_class_name("recaptcha-checkbox-border").click() # switch to recaptcha audio control frame driver.switch_to.default_content() frames = driver.find_elements_by_tag_name("iframe") driver.switch_to.frame(frames[len(frames) - 1]) delay() # click on audio challenge driver.find_element_by_id("recaptcha-audio-button").click() # switch to recaptcha audio challenge frame driver.switch_to.default_content() frames = driver.find_elements_by_tag_name("iframe") driver.switch_to.frame(frames[-1]) delay() flag = True while flag: try: # click on the play button button_div = driver.find_element_by_class_name('rc-audiochallenge-play-button') button = button_div.find_element_by_class_name('rc-button-default') button.click() delay() # get the mp3 audio file src = driver.find_element_by_id("audio-source").get_attribute("src") print("[INFO] Audio src: %s" % src) # download the mp3 audio file from the source urllib.request.urlretrieve(src, os.getcwd() + "\\sample.mp3") sound = pydub.AudioSegment.from_mp3(os.getcwd() + "\\sample.mp3") sound.export(os.getcwd() + "\\sample.wav", format="wav") sample_audio = sr.AudioFile(os.getcwd() + "\\sample.wav") r = sr.Recognizer() with sample_audio as source: audio = r.record(source) # translate audio to text with google voice recognition key = r.recognize_google(audio) print("[INFO] Recaptcha Passcode: %s" % key) time.sleep(1) # key in results and submit driver.find_element_by_id("audio-response").send_keys(key.lower()) time.sleep(2) driver.find_element_by_id("audio-response").send_keys(Keys.ENTER) delay() except Exception as e: # pass print('[Exception]', e) driver.save_screenshot(os.path.join( os.getcwd(), 'gcaptcha', 'rest', 'screenshots', 'error.png' )) flag = False driver.switch_to.default_content() delay() # HERE IS success image token = "Google mark as spam. Please try again later." for request in driver.requests: if 'https://cxl-services.appspot.com/proxy' in request.url: key = 'token=' queryString = request.querystring index = queryString.index(key) token = queryString[index + len(key): len(queryString)] print(token) driver.close() return Response({ "token": token })

[ "toan.tt@neo-lab.vn" ]

toan.tt@neo-lab.vn

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basharE/pythonFirstProject

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import requests from db_connector import get_user from selenium import webdriver user_id = 1001 requests.post('http://localhost:5000/users/' + str(user_id), json={"user_name": "user name " + str(user_id)}) res = requests.get('http://localhost:5000/users/' + str(user_id)) assert res assert get_user(str(user_id))[0][0] == "user name " + str(user_id) driver = webdriver.Chrome(executable_path="/Users/basharegbariya/Downloads/chromedriver") driver.get("http://127.0.0.1:5001/users/get_user_data/"+str(user_id)) assert driver.find_element_by_id("user").text == 'user name ' + str(user_id) driver.quit()

[ "bashar.egbariya@peer39.com" ]

bashar.egbariya@peer39.com

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saraattia412/python_projects

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refs/heads/master

2023-08-28T04:01:02.293203

2021-09-25T18:30:10

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#sum and average any number print('welcome in sum_average_game ^-^') count=int(input('how many number :')) current_count=0 sum=0 while current_count<count : number=float(input('enter number:')) sum += number current_count += 1 print('sum=',sum) print('average=',sum/count)

[ "saraattia412@gmail.com" ]

saraattia412@gmail.com

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sravanre/python

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2021-07-03T18:11:01.094397

2020-09-18T10:04:28

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import csv from openpyxl import Workbook wb = Workbook() ws = wb.active #ws = wb['Sheet1'] f = open("marks.csv","r") for lines in csv.reader(f): ws.append(lines) f.close() wb.save('marks1.xlsx') wb.close() ##r=1 ##for lines in csv.reader(f): ## for c,val in enumerate(lines): ## ws.cell(row=r,column=c+1, value=val) ## r+=1

[ "noreply@github.com" ]

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/xai/brain/wordbase/otherforms/_zoologists.py

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cash2one/xai

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2021-01-19T12:33:54.964379

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#calss header class _ZOOLOGISTS(): def __init__(self,): self.name = "ZOOLOGISTS" self.definitions = zoologist self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.basic = ['zoologist']

[ "xingwang1991@gmail.com" ]

xingwang1991@gmail.com

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/django_tutorials/Django1.8Pro/mypro/mypro/settings.py

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Jadatravu/Tutorials

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refs/heads/master

2021-01-17T13:11:21.119328

2019-11-01T08:49:53

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""" Django settings for mypro project. Generated by 'django-admin startproject' using Django 1.8. For more information on this file, see https://docs.djangoproject.com/en/1.8/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.8/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '*+kw(pk_khm##%c#bxck0x6vzf7#2b%rdcs2mmkgndp6_+9#6i' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = ( 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'myapp', 'mapp', ) MIDDLEWARE_CLASSES = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'django.middleware.security.SecurityMiddleware', ) ROOT_URLCONF = 'mypro.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['/home/ubuntu/developer/18Jul16/mypro/templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mypro.wsgi.application' # Database # https://docs.djangoproject.com/en/1.8/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR,'db.sqlite3'), } } # Internationalization # https://docs.djangoproject.com/en/1.8/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.8/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT ='/home/ubuntu/developer/18Jul16/mypro/staticfiles/dfiles' STATICFILES_DIRS= ( ('/home/ubuntu/developer/18Jul16/mypro/staticfiles/sfiles'), ) STATICFILES_DIRS = ( os.path.join(BASE_DIR, "static"), '/var/www/static/', ) LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'formatters':{ 'simple':{ 'format':'%(levelname)s %(message)s' } }, 'handlers': { 'file':{ 'level':'DEBUG', 'class':'logging.FileHandler', 'filename':'/home/ubuntu/developer/18Jul16/mypro/logapp.log', 'formatter':'simple' }, }, 'loggers': { 'myapp.views':{ 'handlers': ['file'], 'level': 'DEBUG', }, } }

[ "ubuntu@ip-172-31-35-183.us-west-2.compute.internal" ]

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''' epoch = 1 forward and backward pass of ALL training samples batch_size = number of training samples in one forward& backward pass number of iterations = number of passes, each pass using [batch_size] number of samples e.g. 100 [samples, batch_size=20 --> 100/20 = 5 iterations for 1 epoch ''' import torch import torchvision from torch.utils.data import Dataset, DataLoader import numpy as np import math class WineDataset(Dataset): def __init__(self): # data loading xy = np.loadtxt('../../data/wine.csv', delimiter=",", dtype=np.float32, skiprows=1) self.x = torch.from_numpy(xy[:, 1:]) self.y = torch.from_numpy(xy[:, [0]]) self.n_samples = xy.shape[0] def __getitem__(self, index): return self.x[index], self.y[index] def __len__(self): # len(dataset) return self.n_samples dataset = WineDataset() dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=2) # dataiter = iter(dataloader) # data = dataiter.next() # features, labels = data # print(features, labels) # training loop num_epochs = 2 total_samples = len(dataset) n_iterations = math.ceil(total_samples/4) print(total_samples, n_iterations) for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(dataloader): #forward backward, update if (i+1) % 5 == 0: print(f'epoch {epoch+1}/{num_epochs}, step {i+1}/{n_iterations}, inputs {inputs.shape}')

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christopher@mminderlein.de

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/data_control.py

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2015-10-30T21:01:05

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# -*- coding: utf-8 -*- #-----------------------------------------------------------------------------# #!!!! # # # #please note the code is not up to date. It may not work actually due to # #change on the web site host from where data is extracted. # # # #!!!! # #-----------------------------------------------------------------------------# from tool_kit import path_log, create_path, dispatch, curl, oldnew_ipadress from bs4 import BeautifulSoup import time import pandas as pd #-----------------------------------------------------------------------------# # get urls from real estate announces # #-----------------------------------------------------------------------------# def function_2(path_log, file_name="backup_file1.txt") : create_path() url = ["http://www.seloger.com/immobilier/tout/immo-paris-%seme-75/" % (str(i)) for i in range(1,21)] url_liste = dispatch(path_log + "dispatch1.txt", url) backup_file2 = open(path_log + "backup_file2.txt", "w") for url in url_liste : pool = curl(url) # oldnew_ipadress(path_log) for c in pool : data = c.body.getvalue() soup1 = BeautifulSoup(data) s1 = soup1.findAll('div', {'class' : 'content_infos othergroupsite'}) s1 = s1[0].findAll('li') print "len(s1) : ", len(s1) print "\n" som_nbr_annonce = 0 som_list = [] for i in range(len(s1)) : url = s1[i].findAll('a')[0]['href'] len_url = len(url.split("/")) len_departement = len(url.split("/")[len_url-4].split("-")) departement = url.split("/")[len_url-4].split("-")[len_departement-1] type_bien1 = url.split("/")[len_url-3].replace("bien-", "") nbr_annonce = s1[i].findAll('b')[0].string if nbr_annonce != None : pass else : nbr_annonce = 0 som_nbr_annonce = float(som_nbr_annonce) + float(nbr_annonce) som_list.append(float(som_nbr_annonce)) nbr_piece = s1[i].findAll('a')[0]['title'].replace("Immobilier ", "").replace(type_bien1, "").strip().split(" ")[2] if nbr_piece == "studio" : nbr_piece = '1' else : pass type_transaction = s1[i].findAll('a')[0]['title'].replace("Immobilier ", "").replace(type_bien1, "").strip().split(" ")[0] print i, str(som_nbr_annonce), departement, str(nbr_annonce), type_transaction, type_bien1, nbr_piece, url backup_file2.write(departement + ";" + str(nbr_annonce)+ ";" + type_transaction + ";" + type_bien1 + ";" + nbr_piece + ";" + url + ";") backup_file2.write("\n") backup_file2.close() print "\n" #-----------------------------------------------------------------------------# # Get number of page and urls to get through # #-----------------------------------------------------------------------------# def function_3(path_log) : backup_file = open(path_log + "backup_file2.txt", "r").readlines() print "len(backup_file) : ", len(backup_file) print "\n" urls_parcours = open(path_log + "urls_parcours.txt", "w") urls_list = [] for i in range(len(backup_file)) : url = backup_file[i].split(";")[5] nbr = float(backup_file[i].split(";")[1]) nbr_page_init = nbr/10 partie_entiere = int(str(nbr_page_init).split(".")[0]) apres_dec = int(str(nbr_page_init).split(".")[1]) if apres_dec == 0 : nbr_page = partie_entiere elif apres_dec > 0 : nbr_page = partie_entiere + 1 else : print "Probleme nbr_page" print "nbr : ", nbr print "url : ", url print nbr, nbr_page_init, "nous donne :", nbr_page, "page(s)", "\n" if nbr_page == 1 or nbr_page == 0 : if nbr_page == 0 : print "Attention prise en charge du cas '0' page releve : ", "\n" else : b = url urls_list.append(b) urls_parcours.write(b + ";" + "\n") print b elif nbr_page == 2 : b = url c = url + "?ANNONCEpg=2" urls_list.append(b) urls_list.append(c) urls_parcours.write(b + ";" + "\n") urls_parcours.write(c + ";" + "\n") print c print b elif nbr_page > 2 : for j in range(2, nbr_page) : b = url + "?ANNONCEpg=%s" %(str(j)) urls_list.append(b) urls_parcours.write(b + ";" + "\n") print b else : print "Problem nbr_page re construction" print "len(urls_list) : ", len(urls_list) #-----------------------------------------------------------------------------# # get urls from real estate announces for each link # #-----------------------------------------------------------------------------# def function_4(path_log, file_name="urls_parcours.txt") : # d = str(time.strftime('%d-%m-%y_%Hh%Mmin%Ssec',time.localtime())) d2 = str(time.strftime('%d/%m/%y %H:%M:%S',time.localtime())) d3 = str(time.strftime('%d-%m-%y',time.localtime())) backup_file1 = open(path_log + file_name, "r").readlines() url = [] for i in range(len(backup_file1)) : a = backup_file1[i].split(";")[0].strip() url.append(a) url_liste = dispatch(path_log + "dispatch1.txt", url) url_done = open(path_log + "url_done.txt", "w") path_logout = "log/" compteur = 0 for url in url_liste : compteur += 1 print compteur, "/", len(url_liste) for i in range(len(url)) : url_done.write(url[i] + "\n") pool = curl(url) # oldnew_ipadress(path_log) compteur1 = 0 for c in pool : compteur1 += 1 print compteur1, "/", len(pool) data = c.body.getvalue() soup1 = BeautifulSoup(data) d = str(time.strftime('%d-%m-%y_%Hh%Mmin%Ssec',time.localtime())) l0, l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14, l15, l16, l17 = [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [] dico = {'TYPE_TRANSACTION' : l0, 'NOMBRE_PHOTOS' : l1 , 'NOMBRE_PIECE' : l2, 'NOMBRE_M2' : l3, 'ETAGE' : l4, 'BALCON' : l5, 'CUISINE' : l6, 'AUTRE' : l7, 'CHAMBRE(S)' : l8, 'MEUBLE' : l9, 'TYPE_CHAUFFAGE' : l10, 'LOCALISATION' : l11, 'PROXIMITE' : l12, 'PRIX' : l13, 'CHARGE' : l14, 'NOM_AGENCE' : l15, 'URL' : l16, 'EXTRACTION_DATE' : l17} #-----------------------------------------------------------------# #HERE LOOKING FOR WORDS LOCATIONS / VENTES / INVESTISSEMENT / VIAGER : s0 = soup1.findAll('div', {'class' : 'main'}) for i in range(len(s0)) : if s0[i].findAll('span', {'class' : 'title_recherche'}) == [] : transaction_type = "NA" else : transaction_type = s0[i].findAll('span', {'class' : 'title_recherche'}) transaction_type = transaction_type[0].text if "locations" in transaction_type : transaction_type = "LOCATION" elif "ventes" in transaction_type : transaction_type = "ACHAT" elif "investissement" in transaction_type : transaction_type = "INVESTISSEMENT" elif "viager" in transaction_type : transaction_type = "VIAGER" else : pass #-----------------------------------------------------------------# #We are looking for the photo number in html page then add var TRANSACTION_TYPE s1 = soup1.findAll('div', {'class' : 'annonce__visuel__pictogrammes'}) for i in range(len(s1)) : if s1[i].findAll('a', {'class' : 'annonce__visuel__picto picto__photo'}) == [] : nbr_photo = 0 else : nbr_photo = s1[i].findAll('a', {'class' : 'annonce__visuel__picto picto__photo'}) nbr_photo = nbr_photo[0]['title'] nbr_photo = nbr_photo.replace(" photos", "") nbr_photo = int(nbr_photo) l1.append(nbr_photo) l0.append(transaction_type) #-----------------------------------------------------------------# s2 = soup1.findAll('div', {'class' : 'annonce__detail'}) for i in range(len(s2)) : details1 = s2[i].findAll('span', {'class' : 'annone__detail__param'})[0].text details1 = details1.replace("\xe8", "e") details1 = details1.replace("m\xb2", "m2") details1 = details1.replace("\xe9", "e") details1 = details1.split(",") nbr_piece = "NA" nbr_m2 = "NA" etage = "NA" balcon = "NA" cuisine = "NA" autre = "NA" chambre = "NA" meuble = "NA" chauffage = "NA" for j in details1 : if "Piece" in j : if nbr_piece == "NA" : nbr_piece = j.replace(" Piece", "").replace("s", "").strip() else : pass if "m2" in j : if nbr_m2 == "NA" : nbr_m2 = j.replace(" m2", "").strip() else : pass if "Etage" in j : if etage == "NA" : etage = j.replace(" Etage", "").strip() else : pass if "Balcon" in j : if balcon == "NA" : balcon = j.replace(" Balcon", "").strip() balcon = j.replace("s", "").strip() else : pass if "cuisine" in j : if cuisine == "NA" : cuisine = j.replace(" cuisine", "").strip() else : pass if "Chambre" in j : if chambre == "NA" : chambre = j.replace(" Chambre", "") chambre = chambre.replace("s", "").strip() else : pass if "Meuble" in j : if meuble == "NA" : meuble = "YES" else : pass if "chauffage" in j : if chauffage == "NA" : chauffage = j.replace("chauffage ", "") chauffage = j.replace(" radiateur", "") else : pass if "Piece" not in j and "m2" not in j and "Etage" not in j \ and "Balcon" not in j and "cuisine" not in j and "Chambre" not in j \ and "Meuble" not in j and "chauffage" not in j : autre = j.strip() else : pass l2.append(nbr_piece) l3.append(nbr_m2) l4.append(etage) l5.append(balcon) l6.append(cuisine) l7.append(autre) l8.append(chambre) l9.append(meuble) l10.append(chauffage) #-----------------------------------------------------------------# #LOCATION : s3 = soup1.findAll('span', {'class' : 'annone__detail__localisation'}) for i in range(len(s3)) : details2 = s3[i].findAll('span', {'class' : 'annone__detail__param'})[0].text details2 = details2.replace(" (Paris)", "") details2 = details2.replace(" ()", "") l11.append(details2) #-----------------------------------------------------------------# #NEAR LOCATION : s4 = soup1.findAll('div', {'class' : 'annonce__detail'}) for i in range(len(s4)) : details3 = s4[i].findAll('span', {'class' : 'annone__detail__proximite'}) if details3 != [] : details3 = details3[0].text details3 = details3.replace("É", "E") details3 = details3.replace("é", "e") details3 = details3.replace("ê", "e") details3 = details3.replace("ë", "e") details3 = details3.replace("â", "a") details3 = details3.replace("ô", "o") details3 = details3.replace("&quot", "") details3 = details3.replace("Î", "") details3 = details3.replace("ç", "c") details3 = details3.replace("M°", "Metro ") details3 = details3.replace("Metro ", "") details3 = details3.replace("Metro", "") details3 = details3.replace("'", "'") details3 = details3.replace("&", "et") details3 = details3.replace("è", "e") details3 = details3.replace("/", ",") details3 = details3.replace(": ", "") details3 = details3.replace("metro", "") details3 = details3.replace("à", "a") details3 = details3.replace("î", "i") details3 = details3.replace("ï", "i") details3 = details3.replace("Centre ville,", "") details3 = details3.replace("ecole,", "") details3 = details3.replace("commerces,", "") details3 = details3.replace("bus,", "") details3 = details3.replace("*", "") else : details3 = "NA" proximite = details3 l12.append(proximite) #-----------------------------------------------------------------# #PRICE AND DETAILS OF ADDITIVE PRICE CHARGES : s5 = soup1.findAll('div', {'class' : 'annonce__agence'}) for i in range(len(s5)) : details4 = s5[i].findAll('span', {'class' : 'annonce__agence__prix annonce__nologo'}) details5 = s5[i].findAll('span', {'class' : 'annonce__agence__prix '}) if details4 != [] : details4 = details4[0].text details4 = details4.replace("\xa0", "") details4 = details4.replace("\x80", "") details4 = details4.split(" ") else : details4 = 0 if details5 != [] : details5 = details5[0].text details5 = details5.replace("\xa0", "") details5 = details5.replace("\x80", "") details5 = details5.split(" ") else : details5 = 0 if details4 == 0 : detailsx = details5 elif details5 == 0 : detailsx = details4 try : l13.append(float(detailsx[0].replace(",", ".").replace("Â", ""))) except : l13.append(str(detailsx[0])) if "FAI" in detailsx[1] : new = detailsx[1].replace("FAI", "") try : l14.append(float(new)) except : l14.append(new) elif "+" in detailsx[1] : new = detailsx[1].replace("+", "") l14.append(new) else : l14.append(detailsx[1].strip()) #-----------------------------------------------------------------# #REAL ESTATE AGENCY NAMES : s6 = soup1.findAll('div', {'class' : 'annonce__agence'}) for i in range(len(s6)) : details6 = s6[i].findAll('span', {'class' : 'annone__detail__nom'}) if details6 != [] : details6 = details6[0].text else : details6 = "NA" l15.append(details6) #-----------------------------------------------------------------# #GET THE URL VALUE : s7 = soup1.findAll('div', {'class' : 'annonce__detail'}) for i in range(len(s7)) : url_cible = s7[i].findAll('a', {'class' : 'annone__detail__title annonce__link'}) url_cible = url_cible[0]['href'] url_cible = url_cible.split("?")[0] l16.append(url_cible) #-----------------------------------# #DATE : l17.append(d2) #-----------------------------------------------------------------# #WRITE DATA IN FILE : if dico['CUISINE'] == [] : pass else : try : df = pd.DataFrame(dico) df.to_csv(path_logout + 'seloger_%s.txt' %(d3), mode="a", header=False) print compteur, df print "\n" except : print "ValueError : ", ValueError print "dico : ", dico log_dico = open(path_log + "log_dico.txt", "a") for i in dico : print "len(dico[i]) : ", str(len(dico[i])), str(i), str(dico[i]) log_dico.write(str(len(dico[i])) + ";" + str(i) + ";" + str(dico[i])) log_dico.close() print "\n"

[ "mic100@hotmail.fr" ]

mic100@hotmail.fr

b2ecd23d0488f2b3bea55b15c9d3b6bc34e739ab

98e7edf7a3f834580ba6a3c1058506e5a773d1bb

/fileHandling.py

b4d3e6ae4d6684d77027c28ed20cabc3eedba404

[]

no_license

gauravhansda/InterviewQuestions

ef95363440d9eb0057ab0551f155793ea3e5e3b0

5134c59d3d543982a30465d7d4eaa1c8902185e2

refs/heads/master

2021-01-01T17:07:11.537838

2017-07-22T02:31:23

98,001,531

0

null

UTF-8

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false

478

py

class FILEHANDLING(): def __init__(self): self.file_name = "file.txt" # Remove blank lines from the file def remBlanks(self): with open(self.file_name, 'r') as inp: lines = inp.readlines() print lines with open("out.txt", 'w') as out: for line in lines: if not line.isspace(): out.writelines(line) if __name__ == '__main__': fh = FILEHANDLING() fh.remBlanks()

[ "GauravHansda@Gauravs-MacBook-Pro.local" ]

GauravHansda@Gauravs-MacBook-Pro.local

267e66a7d5ee24cddd6245f2bb5aaf4c8f0bee87

4d051a2875532ee9825559261927218bbac3dbf4

/src/arm/joint/__init__.py

c5d36d433fbcca594e664ed8910fe826eec87b91

[]

no_license

swol-kat/Arm_Code

4554b9f79460929515dc4e5c0dc7f0b7b23985da

389a8fe4875c6ab90e2ec79dedf445088b21c855

refs/heads/master

2023-04-11T11:06:38.759482

2021-04-23T00:28:15

335,827,731

0

null

UTF-8

Python

false

64

py

from .joint import Joint from .virtual_joint import VirtualJoint

[ "arjungandhi06@gmail.com" ]

arjungandhi06@gmail.com

c148d463b278c19a66dbc6bfe9f7997cdb5d3cb7

e517fcf60b982bb843ae846fa881102d504c368c

/poncho/postprocess/prepare_data.py

e6bf2e27d2a127b733c008f9ca472b820e8fb482

[ "MIT" ]

permissive

waduhek/poncho

b79aa9087faf9809872eeb6a36a21de3e34bb5d8

47a8f2600d1afcfb8f9fa76ff0c68eb9f132f56c

refs/heads/master

2020-04-17T15:14:31.865951

2019-04-19T09:24:54

166,690,766

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1

MIT

2019-04-19T06:35:26

2019-01-20T17:45:39

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Python

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py

import os import time from datetime import datetime import pandas as pd from poncho.utils.get_base_dir import get_base_dir def main(unique_years): (BASE_DIR) = get_base_dir() # Log file log = open(os.path.join(BASE_DIR, 'data', 'logs', 'train_{}.txt'.format(str(time.time()).split('.')[0])), mode='a') log.write('Beginning to create training dataset. Time: {}\n\n'.format(str(datetime.now()))) print('Beginning to create training dataset. Time: {}\n'.format(str(datetime.now()))) for year in unique_years: log.write('Converting data of {} into train dataset\n. Time: {}'.format(year, str(datetime.now()))) print('Converting data of {} into train dataset. Time: {}'.format(year, str(datetime.now()))) # Open required CSV file df = pd.read_csv(os.path.join(BASE_DIR, 'data', 'prepared', 'prepared_{}.csv'.format(year))).dropna() # Convert the comments and replies to a Pandas DataFrame object comment = pd.DataFrame(df['comment']) reply = pd.DataFrame(df['reply']) # Write the comments and replies to separate files in the directory of 'nmt-chatbot' comment.to_csv( os.path.join(os.path.dirname(BASE_DIR), 'nmt-chatbot', 'new_data', 'train.from'), mode='w', index=False, header=None ) reply.to_csv( os.path.join(os.path.dirname(BASE_DIR), 'nmt-chatbot', 'new_data', 'train.to'), mode='w', index=False, header=None ) log.write('Finishing up... Time: {}\n'.format(str(datetime.now()))) log.write('==========================================================================================\n\n') print('Finishing up... Time: {}'.format(str(datetime.now()))) print('==========================================================================================') log.close()

[ "ryannor56a@gmail.com" ]

ryannor56a@gmail.com

9ec2452996d72632a51586c21518412a127f1081

95a9ee042c6d0aa9cc5ee44c6b9317a10e7b02ee

/day07/python/day07.py

4b491ffdeb3195bf4f11b03bf979dea285e470e7

[]

no_license

ochronus/advent-of-code-2019

4e273a236a801cffc347c419d408ebd717366f7f

ba47c63913c6b85953d04eaad15f8fac56ed1c5b

refs/heads/master

2020-09-21T13:32:51.679255

2019-12-13T06:05:08

224,802,962

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UTF-8

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py

from itertools import permutations from queue import SimpleQueue from computer import Computer, HaltException def part1(program): phase_settings = permutations(range(5)) highest_output_signal = 0 for phase_setting in phase_settings: amplifiers = [] for i in range(5): amplifiers.append(Computer(program)) amplifiers[i].give_signal(phase_setting[i]) signal = 0 for i in range(5): amplifiers[i].give_signal(signal) signal = amplifiers[i].run() highest_output_signal = max(highest_output_signal, signal) return(highest_output_signal) def part2(program): phase_settings = permutations(range(5, 10)) highest_output_signal = 0 for phase_setting in phase_settings: amplifiers = [] for i in range(5): amplifiers.append(Computer(program)) amplifiers[i].give_signal(phase_setting[i]) signal = 0 should_halt = False while not should_halt: for i in range(5): amplifiers[i].give_signal(signal) try: signal = amplifiers[i].run() except HaltException: should_halt = True highest_output_signal = max(highest_output_signal, signal) return(highest_output_signal) with open("../input.txt") as f: acs = list([int(i) for i in f.read().split(",")]) print(part1(acs)) print(part2(acs))

[ "ochronus@ochronus.com" ]

ochronus@ochronus.com

382ed93decb7215d582077e08f363fa17473c08f

dbb0a4d452ac0faf00411a09b7e32f13ffdb31e8

/tests/ssh_test.py

e2dda308c8bbe2ee15c26bb156ddb008f3404399

[]

no_license

koverman47/EGEN_310

3ef66b7fb773b4e5fb833c250c87c7cf4fc84d49

f69e292baa48bca441dd0f7d9ba7789db417d42a

refs/heads/master

2020-04-18T00:39:47.999960

2019-04-24T20:14:44

167,086,003

0

null

UTF-8

Python

false

1,130

py

#!/usr/bin/env python3 import paramiko import sys import tty import termios # RPi Zero w SSH Credentials host = "zeropythirty" ip = "10.152.247.52" user = "pi" passw = "pi" # Establish SSH tunnel try: ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) ssh.connect(hostname=ip, username=user, password=passw) except BadHostKeyException: print("Host could not be found.") sys.exit(0) except AuthenticationException: print("Could not authenticate host.") sys.exit(0) except SSHException: print("Unknown SSH error.") sys.exit(0) # Give stdin to this script tty.setcbreak(sys.stdin) try: while True: result = None # Read and pass key over SSH tunnel key = ord(sys.stdin.read(1)) result = ssh.exec_command(key) except KeyboardInterrupt: pass except SSHException: if result: print(result) finally: # Return stdin to ECHO fd = sys.stdin.fileno() old = termios.tcgetattr(fd) old[3] = old[3] | termios.ECHO termios.tcsetattr(fd, termios.TCSADRAIN, old) # Close SSH tunnel ssh.close()

[ "knoverman@comcast.net" ]

knoverman@comcast.net

f32e1d9a57e44c6badf99051fc94750d8a7f23e3

0c4d481936c5cde601e3b9d6c7e83b8e70ba9fae

/.history/train_search_param_20201009092152.py

12cf377b195daa81cd3800b44ababebd327c74c8

[]

no_license

CGCL-codes/PDAS

7d98afaf60abe7ce3075e80125d98e51d92f42f0

578011f738f2418fe5be2dd38eb819566e7fc3bb

refs/heads/master

2023-05-08T03:26:56.450175

2021-05-27T02:50:38

371,215,287

1

2

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py

import os import sys import time import glob import math import random import logging import numpy as np import argparse import torch import torch.nn as nn import torch.nn.functional as F import torchvision.datasets as datasets import torch.backends.cudnn as cudnn import torchvision.transforms as transforms #from resnet_change2 import * from resnet_new_change import * from prune_params import ResNet20_Channel_Prune from net_measure import measure_model, measure_param from utils import AverageMeter, accuracy, count_parameters_in_MB, save_checkpoint from architect import Architect parser = argparse.ArgumentParser(description='Cifar10 Train Search') parser.add_argument('--data', type=str, default='/home/cyh/workspace/cifar10', help='location of the data corpus') parser.add_argument('--batch_size', type=int, default=256, help='batch size') parser.add_argument('--learning_rate', type=float, default=0.1, help='init learning rate') parser.add_argument('--learning_rate_min', type=float, default=0.001, help='min learning rate(0.0)') parser.add_argument('--momentum', type=float, default=0.9, help='momentum') parser.add_argument('--weight_decay', type=float, default=3e-4, help='weight decay') parser.add_argument('--report_freq', type=int, default=50, help='report frequency') #parser.add_argument('--gpu', type=int, default=0, help='gpu device id') parser.add_argument('--epochs', type=int, default=120, help='num of training epochs') parser.add_argument('--save', type=str, default='./checkpoint/', help='folder to save checkpoints and logs') parser.add_argument('--seed', type=int, default=1, help='random seed') parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping') parser.add_argument('--train_portion', type=float, default=0.5, help='portion of training data') parser.add_argument('--unrolled', action='store_true', default=False, help='use one-step unrolled validation loss') parser.add_argument('--arch_learning_rate', type=float, default=6e-4, help='learning rate for arch encoding') parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding') parser.add_argument('--change', action='store_true', default=False, help='change prune ratio during searching') parser.add_argument('--gpu', type=int, default=0, help='gpu device id') args = parser.parse_args() log = open(os.path.join(args.save, 'channel-search-resnet20-0915.txt'),'w') prune_index = ResNet20_Channel_Prune.index prune_ratio = ResNet20_Channel_Prune.prune_ratio #max2_ratio = torch.zeros(len(prune_index), 3) min_ratio = torch.zeros(len(prune_index), 3) min_ratio[:, 2] = -1 channel16 = list(range(2, 17, 2)) channel32 = list(range(2, 33, 2)) channel64 = list(range(2, 65, 2)) def main(): if not torch.cuda.is_available(): print('no gpu device available!!!') sys.exit(1) np.random.seed(args.seed) torch.cuda.set_device(args.gpu) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) cudnn.benchmark = True cudnn.enabled = True print_log('=> parameters: {}'.format(args), log) best_acc = 0 best_epoch = 0 criterion = nn.CrossEntropyLoss().cuda() count_ops, count_params, conv_list = measure_model(depth=20) print('=> count_ops: {}, count_params: {}'.format(count_ops, count_params)) model = resnet(depth=20).cuda() #model = torch.nn.DataParallel(model).cuda() optimizer = torch.optim.SGD(model.parameters(), args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) '''arch_optimizer = torch.optim.Adam(model.module.arch_parameters(), lr=args.arch_learning_rate, betas=(0.5, 0.999), weight_decay=args.arch_weight_decay)''' train_transform = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465),(0.2023, 0.1994, 0.2010)) ]) valid_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ]) train_data = datasets.CIFAR10(root=args.data, train=True, download=True, transform=train_transform) num_train = len(train_data) indices = list(range(num_train)) split = int(np.floor(args.train_portion * num_train)) train_queue = torch.utils.data.DataLoader( train_data, batch_size=args.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]), pin_memory=True, num_workers=2) valid_queue = torch.utils.data.DataLoader( train_data, batch_size=args.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]), pin_memory=True, num_workers=2) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, float(args.epochs), eta_min=args.learning_rate_min) architect = Architect(model, criterion, count_params, args) print_log('==> arch parameters: {}'.format(model.arch_parameters()), log) print_log('==> arch parameters ratio: {}'.format(F.softmax(model.arch_params, dim=-1)), log) for epoch in range(args.epochs): #scheduler.step() lr = scheduler.get_last_lr()[0] print_log('=> epoch {}, lr {}'.format(epoch, lr), log) if args.change and epoch >= 15 and epoch <= args.epochs-5: arch_weights = F.softmax(model.arch_params, dim=-1) _, index = arch_weights.topk(4, 1, True, True) for j in range(len(prune_index)): new_index = prune_ratio[j][index[j][3].item()] old_index = min_ratio[j][1].item() '''new_ratio = prune_ratio[j][index[j][4].item()] old_ratio = min_ratio[j][1].item()''' count = min_ratio[j][0].item() if abs(new_index-old_index) < 1e-6: if count >= 19: max_ratio = prune_ratio[j][index[j][0].item()] if j < 7: a = random.randint(max(max_ratio-3, 0), min(max_ratio+3, len(channel16)-1)) elif j < 13: a = random.randint(max(max_ratio-5, 0), min(max_ratio+5, len(channel32)-1)) else: a = random.randint(max(max_ratio-11, 0), min(max_ratio+11, len(channel64)-1)) if abs(new_index - min_ratio[j][2].item()) < 1e-6: prune_ratio[j][index[j][2].item()] = a else: prune_ratio[j][index[j][3].item()] = a min_ratio[j][0] = 0 ratios = 1e-3 * torch.randn(1, 4) with torch.no_grad(): for k in range(4): model.module.arch_params[j][k] = ratios[0][k].item() else: min_ratio[j][0] += 1 else: min_ratio[j][0] = 0 min_ratio[j][1] = new_ratio train_acc, train_loss = train(train_queue, valid_queue, model, architect, criterion, optimizer, lr, epoch, count_params, count_ops, conv_list) scheduler.step() print_log('=> train acc: {}'.format(train_acc), log) print_log('=> min ratio: {}'.format(min_ratio), log) print_log('=> arch parameters ratio: {}'.format(F.softmax(model.arch_params, dim=-1)),log) print_log('=> prune ratio: {}'.format(prune_ratio), log) if args.epochs - epoch <= 1: valid_acc, valid_loss = infer(valid_queue, model, criterion) print_log('valid_acc: {}'.format(valid_acc), log) arch_weights = F.softmax(model.arch_params, dim=-1) _, index = arch_weights.topk(1, 1, True, True) max_cfg = [] #mix_cfg = [] for j in range(len(prune_index)): if j < 7: channel = channel16[prune_ratio[j][index[j][0].item()]] max_cfg.append(channel) elif j < 13: channel = channel32[prune_ratio[j][index[j][0].item()]] max_cfg.append(channel) else: channel = channel64[prune_ratio[j][index[j][0].item()]] max_cfg.append(channel) '''channel = max(int(round(cfg[j] * (1 - prune_ratio[j][index[j][0].item()]) / 2) * 2), 2) max_cfg.append(channel) mix_prune_ratio = 0 for k in range(5): mix_prune_ratio += prune_ratio[j][k] * arch_weights[j][k].item() #mix_channel += max(int(round(cfg[j] * (1 - prune_ratio[j][k]) * arch_weights[j][k].item() / 2) * 2), 2) mix_channel = max(int(round(cfg[j] * (1 - mix_prune_ratio) / 2) * 2), 2) mix_cfg.append(mix_channel)''' print_log('==> max cfg: {}'.format(max_cfg), log) #print_log('==> mix cfg: {}'.format(mix_cfg), log) print_log("==> arch parameters： {}".format(model.arch_parameters()), log) #print_log('==> best acc: {}, best epoch: {}'.format(best_acc, best_epoch), log) def train(train_queue, valid_queue, model, architect, criterion, optimizer, lr, epoch, count_params, count_ops, conv_list): losses = AverageMeter() basic_losses = AverageMeter() param_losses = AverageMeter() top1 = AverageMeter() top5 = AverageMeter() model.train() for index, (inputs, targets) in enumerate(train_queue): inputs, targets = inputs.cuda(), targets.cuda(non_blocking=True) try: input_search, target_search = next(valid_queue_iter) except: valid_queue_iter = iter(valid_queue) input_search, target_search = next(valid_queue_iter) #input_search, target_search = next(iter(valid_queue)) input_search, target_search = input_search.cuda(), target_search.cuda(non_blocking=True) if epoch >= 15: architect.step(inputs, targets, input_search, target_search, lr, optimizer, unrolled=args.unrolled) optimizer.zero_grad() logits = model(inputs) loss = criterion(logits, targets) loss.backward() nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) optimizer.step() prec1, prec5 = accuracy(logits.data, targets.data, topk=(1, 5)) losses.update(loss.item(), inputs.size(0)) #basic_losses.update(basic_loss.item(), inputs.size(0)) #param_losses.update(param_loss.item(), inputs.size(0)) top1.update(prec1.item(), inputs.size(0)) top5.update(prec5.item(), inputs.size(0)) else: arch_optimizer.zero_grad() output_search = model(input_search) arch_loss = criterion(output_search, target_search) arch_loss.backward() arch_optimizer.step() optimizer.zero_grad() logits = model(inputs) basic_loss = criterion(logits, targets) total_params = count_model_params(model) if total_params > (1 + 0.05) * (0.5 * count_params): param_loss = 2 * math.log(total_params / (0.5 * count_params)) elif total_params < (1 - 0.05) * (0.5 * count_params): param_loss = -2 * math.log(total_params / (0.5 * count_params)) else: param_loss = 0 #param_loss = 0.11 * (math.log(total_params) ** 0.9) #param_loss = 0.086 * (math.log(total_params)) #flops = count_model_flops(model, count_ops, conv_list) #print('=> flops: {}'.format(flops)) #flop_loss = 0.083*(math.log(flops)**0.9) #flop_loss = 0.084 * (math.log(flops) ** 0.9) #flop_loss = 0.06 * math.log(flops) #print('=> flop loss: {}'.format(flop_loss)) #loss = basic_loss * param_loss loss = basic_loss + param_loss loss.backward() nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) optimizer.step() prec1, prec5 = accuracy(logits.data, targets.data, topk=(1, 5)) losses.update(loss.item(), inputs.size(0)) #basic_losses.update(basic_loss.item(), inputs.size(0)) #param_losses.update(param_loss.item(), inputs.size(0)) top1.update(prec1.item(), inputs.size(0)) top5.update(prec5.item(), inputs.size(0)) if index % args.report_freq == 0: print_log('=> time: {}, train index: {}, loss: {}, top1: {}, top5: {}'.format(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), index, losses.avg, top1.avg, top5.avg), log) return top1.avg, losses.avg def infer(valid_queue, model, criterion): losses = AverageMeter() top1 = AverageMeter() top5 = AverageMeter() model.eval() with torch.no_grad(): for index, (inputs, targets) in enumerate(valid_queue): inputs, targets = inputs.cuda(), targets.cuda() logits = model(inputs) loss = criterion(logits, targets) prec1, prec5 = accuracy(logits.data, targets.data, topk=(1, 5)) losses.update(loss.item(), inputs.size(0)) top1.update(prec1.item(), inputs.size(0)) top5.update(prec5.item(), inputs.size(0)) if index % args.report_freq == 0: print_log('=> time: {}, valid index: {}, loss: {}, top1: {}, top5: {}'.format(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), index, losses.avg, top1.avg, top5.avg), log) return top1.avg, losses.avg def count_model_params(model): arch_weights = F.softmax(model.module.arch_params, dim=-1) _, index = arch_weights.topk(1, 1, True, True) cfg = [] for k, m in enumerate(model.module.modules()): if k in prune_index: index_p = prune_index.index(k) if index_p < 7: channel = channel16[prune_ratio[index_p][index[index_p][0].item()]] cfg.append(channel) elif index_p < 13: channel = channel32[prune_ratio[index_p][index[index_p][0].item()]] cfg.append(channel) else: channel = channel64[prune_ratio[index_p][index[index_p][0].item()]] cfg.append(channel) '''pr = prune_ratio[index_p][index[index_p][0].item()] oC = max(int(round((m.weight.data.shape[0] * (1 - pr)) / 2) * 2), 2) cfg.append(oC)''' total = measure_param(depth=20, cfg=cfg) return total '''total = sum(p.numel() for p in model.parameters()) arch_weights = F.softmax(model.module.arch_params, dim=-1) _, index = arch_weights.topk(1, 1, True, True) for k, m in enumerate(model.module.modules()): if k in prune_index: index_p = prune_index.index(k) if index_p == 0 : pr = prune_ratio[index_p][index[index_p][0].item()] oC = m.weight.data.shape[0] - int(round((m.weight.data.shape[0] * (1 - pr)) / 2) * 2) total -= oC * m.weight.data.shape[1] * m.weight.data.shape[2] * m.weight.data.shape[3] #total -= int(m.weight.data.numel() * (1 - prune_ratio[index_p][index[index_p][0].item()])) else: pr0 = prune_ratio[index_p-1][index[index_p-1][0].item()] pr1 = prune_ratio[index_p][index[index_p][0].item()] iC = m.weight.data.shape[1] - int(round((m.weight.data.shape[1] * (1 - pr0)) / 2) * 2) oC = m.weight.data.shape[0] - int(round((m.weight.data.shape[0] * (1 - pr1)) / 2) * 2) total -= oC * iC * m.weight.data.shape[2] * m.weight.data.shape[3] #total -= int(m.weight.data.numel() * (1 - prune_ratio[index_p][index[index_p][0].item()])) return total''' def count_model_flops(model, total_flops, conv_list): arch_weights = F.softmax(model.module.arch_params, dim=-1) _, index = arch_weights.topk(1, 1, True, True) total = total_flops #print(total) #print('=> prune index: {}'.format(prune_index)) for k, m in enumerate(model.module.modules()): if k in prune_index: if k == 1: pr = prune_ratio[0][index[0][0].item()] total -= int(conv_list[0] // 2 * pr) #print('=> total: {}'.format(total)) elif k == 6: pr0 = 1 - prune_ratio[0][index[0][0].item()] pr1 = 1 - prune_ratio[1][index[1][0].item()] total -= int(conv_list[1] // 2 * (1 - pr0 * pr1)) #print('=> total: {}'.format(total)) else: index_p = prune_index.index(k) pr = prune_ratio[index_p][index[index_p][0].item()] total -= int(conv_list[2*index_p-1] // 2 * pr) #print('=> total: {}'.format(total)) elif k-3 in prune_index and k-3 != 1: index_p = prune_index.index(k-3) pr = prune_ratio[index_p][index[index_p][0].item()] total -= int(conv_list[2*index_p] // 2 * pr) #print('=> total: {}'.format(total)) return total def print_log(print_string, log): print("{}".format(print_string)) log.write('{}\n'.format(print_string)) log.flush() if __name__ == '__main__': main()

[ "1300302496@qq.com" ]

1300302496@qq.com

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0ceabf1234cc921e1cd81c4290168b2f770d1aa1

/home/migrations/0004_auto_20200521_1808.py

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[]

no_license

deekhari00716/Doctor-s-webstite

f4cdf915fbda676a17efd8db94712dbe0456d0b4

78f5e671277f041d21f362f267d5b9c893caf50c

refs/heads/master

2022-08-18T16:31:28.691028

2020-05-23T13:02:47

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# Generated by Django 3.0.3 on 2020-05-21 18:08 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('home', '0003_project_date'), ] operations = [ migrations.AlterField( model_name='project', name='image', field=models.ImageField(height_field=330, upload_to='home/images', width_field=185), ), ]

[ "deekhsri00716@gmail.com" ]

deekhsri00716@gmail.com

5a8680c24a91be453228f2818013da9f4dc66067

05dc7b4b44200cc022b09f773dd868049ecfb3e1

/rss/admin.py

c0a8c6aaa5087612094e649d116e9e1783bec26f

[]

no_license

micah66/rssFeed

e7cea9a3f68713585990aaa039ff80c289d01e75

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refs/heads/master

2020-03-21T12:44:29.659306

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py

from django.contrib import admin from .models import Headlines # Register your models here. admin.site.register(Headlines)

[ "micahgordon66@gmail.com" ]

micahgordon66@gmail.com

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f876ed037442b60e964bb53e4a0cc7e14818a746

/rnn.py

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[]

no_license

shksa/cs231n-Assignment3

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refs/heads/master

2021-01-20T12:42:09.528428

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import numpy as np from cs231n.layers import * from cs231n.rnn_layers import * class CaptioningRNN(object): """ A CaptioningRNN produces captions from image features using a recurrent neural network. The RNN receives input vectors of size D, has a vocab size of V, works on sequences of length T, has an RNN hidden dimension of H, uses word vectors of dimension W, and operates on minibatches of size N. Note that we don't use any regularization for the CaptioningRNN. """ def __init__(self, word_to_idx, input_dim=512, wordvec_dim=128, hidden_dim=128, cell_type='rnn', dtype=np.float32): """ Construct a new CaptioningRNN instance. Inputs: - word_to_idx: A dictionary giving the vocabulary. It contains V entries, and maps each string to a unique integer in the range [0, V). - input_dim: Dimension D of input image feature vectors. - wordvec_dim: Dimension W of word vectors. - hidden_dim: Dimension H for the hidden state of the RNN. - cell_type: What type of RNN to use; either 'rnn' or 'lstm'. - dtype: numpy datatype to use; use float32 for training and float64 for numeric gradient checking. """ if cell_type not in {'rnn', 'lstm'}: raise ValueError('Invalid cell_type "%s"' % cell_type) self.cell_type = cell_type self.dtype = dtype self.word_to_idx = word_to_idx self.idx_to_word = {i: w for w, i in word_to_idx.items()} self.params = {} vocab_size = len(word_to_idx) self._null = word_to_idx['<NULL>'] self._start = word_to_idx.get('<START>', None) self._end = word_to_idx.get('<END>', None) # Initialize word vectors self.params['W_embed'] = np.random.randn(vocab_size, wordvec_dim) self.params['W_embed'] /= 100 # Initialize CNN -> hidden state projection parameters self.params['W_proj'] = np.random.randn(input_dim, hidden_dim) self.params['W_proj'] /= np.sqrt(input_dim) self.params['b_proj'] = np.zeros(hidden_dim) # Initialize parameters for the RNN dim_mul = {'lstm': 4, 'rnn': 1}[cell_type] self.params['Wx'] = np.random.randn(wordvec_dim, dim_mul * hidden_dim) self.params['Wx'] /= np.sqrt(wordvec_dim) self.params['Wh'] = np.random.randn(hidden_dim, dim_mul * hidden_dim) self.params['Wh'] /= np.sqrt(hidden_dim) self.params['b'] = np.zeros(dim_mul * hidden_dim) # Initialize output to vocab weights self.params['W_vocab'] = np.random.randn(hidden_dim, vocab_size) self.params['W_vocab'] /= np.sqrt(hidden_dim) self.params['b_vocab'] = np.zeros(vocab_size) # Cast parameters to correct dtype for k, v in self.params.items(): self.params[k] = v.astype(self.dtype) def loss(self, features, captions): """ Compute training-time loss for the RNN. We input image features and ground-truth captions for those images, and use an RNN (or LSTM) to compute loss and gradients on all parameters. Inputs: - features: Input image features, of shape (N, D) - captions: Ground-truth captions; an integer array of shape (N, T) where each element is in the range 0 <= y[i, t] < V Returns a tuple of: - loss: Scalar loss - grads: Dictionary of gradients parallel to self.params """ # Cut captions into two pieces: captions_in has everything but the last word # and will be input to the RNN; captions_out has everything but the first # word and this is what we will expect the RNN to generate. These are offset # by one relative to each other because the RNN should produce word (t+1) # after receiving word t. The first element of captions_in will be the START # token, and the first element of captions_out will be the first word. captions_in = captions[:, :-1] captions_out = captions[:, 1:] # You'll need this mask = (captions_out != self._null) # Weight and bias for the affine transform from image features to initial # hidden state W_proj, b_proj = self.params['W_proj'], self.params['b_proj'] # Word embedding matrix W_embed = self.params['W_embed'] # Input-to-hidden, hidden-to-hidden, and biases for the RNN Wx, Wh, b = self.params['Wx'], self.params['Wh'], self.params['b'] # Weight and bias for the hidden-to-vocab transformation. W_vocab, b_vocab = self.params['W_vocab'], self.params['b_vocab'] loss, grads = 0.0, {} ############################################################################ # TODO: Implement the forward and backward passes for the CaptioningRNN. # # In the forward pass you will need to do the following: # # (1) Use an affine transformation to compute the initial hidden state # # from the image features. This should produce an array of shape (N, H)# # (2) Use a word embedding layer to transform the words in captions_in # # from indices to vectors, giving an array of shape (N, T, W). # # (3) Use either a vanilla RNN or LSTM (depending on self.cell_type) to # # process the sequence of input word vectors and produce hidden state # # vectors for all timesteps, producing an array of shape (N, T, H). # # (4) Use a (temporal) affine transformation to compute scores over the # # vocabulary at every timestep using the hidden states, giving an # # array of shape (N, T, V). # # (5) Use (temporal) softmax to compute loss using captions_out, ignoring # # the points where the output word is <NULL> using the mask above. # # # # In the backward pass you will need to compute the gradient of the loss # # with respect to all model parameters. Use the loss and grads variables # # defined above to store loss and gradients; grads[k] should give the # # gradients for self.params[k]. # ############################################################################ if self.cell_type == 'rnn': ############################# Forward Pass ################################# # (1) Image features projection onto RNN by affine transformation with W_proj weights array. IFP_to_RNN, cache_for_feature_projection = affine_forward(features, W_proj, b_proj) # (2) Convert word indices to word vectors of the captions_in matrix Embedded_captions_in, cache_for_word_embedding = word_embedding_forward(captions_in, W_embed) # (3) Run RNN for the "T" length of sequence over the minibatch N. h0 = IFP_to_RNN HdSV_ForAllTimeSteps_OverMiniBatch, cache_for_rnn_1 = rnn_forward(Embedded_captions_in, h0, Wx, Wh, b) # (4) Compute scores over the words in vocabulary for all time stpes over the mini-batch N. Scores_ForAllTimeSteps_OverMiniBatch, cache_for_rnn_2 = temporal_affine_forward(HdSV_ForAllTimeSteps_OverMiniBatch, W_vocab, b_vocab) # (5) Compute loss using ground-truth captions_out matrix. loss, d_Loss__d_Scores_ForAllTimeSteps_OverMiniBatch = temporal_softmax_loss(Scores_ForAllTimeSteps_OverMiniBatch, captions_out, mask) ############################# Backward Pass ################################ # (4) Backprop into temporal_affine_forward function. d_HdSV_ForAllTimeSteps_OverMiniBatch, d_W_vocab, d_b_vocab = temporal_affine_backward(d_Loss__d_Scores_ForAllTimeSteps_OverMiniBatch, cache_for_rnn_2) # (3) Backprop into rnn_forward function. d_Embedded_captions_in, d_h0, d_Wx, d_Wh, d_b = rnn_backward(d_HdSV_ForAllTimeSteps_OverMiniBatch, cache_for_rnn_1) # (2) Backprop into word_embedding_forward function. d_W_embed = word_embedding_backward(d_Embedded_captions_in, cache_for_word_embedding) # (1) Backprop into Image features - affine tranformation function d_IFP_to_RNN = d_h0 d_features, d_W_proj, d_b_proj = affine_backward(d_IFP_to_RNN, cache_for_feature_projection) elif self.cell_type == 'lstm': # (1) Image features projection onto RNN by affine transformation with W_proj weights array. IFP_to_RNN, cache_for_feature_projection = affine_forward(features, W_proj, b_proj) # (2) Convert word indices to word vectors of the captions_in matrix Embedded_captions_in, cache_for_word_embedding = word_embedding_forward(captions_in, W_embed) # (3) Run RNN for the "T" length of sequence over the minibatch N. h0 = IFP_to_RNN HdSV_ForAllTimeSteps_OverMiniBatch, cache_for_rnn_1 = lstm_forward(Embedded_captions_in, h0, Wx, Wh, b) # (4) Compute scores over the words in vocabulary for all time stpes over the mini-batch N. Scores_ForAllTimeSteps_OverMiniBatch, cache_for_rnn_2 = temporal_affine_forward(HdSV_ForAllTimeSteps_OverMiniBatch, W_vocab, b_vocab) # (5) Compute loss using ground-truth captions_out matrix. loss, d_Loss__d_Scores_ForAllTimeSteps_OverMiniBatch = temporal_softmax_loss(Scores_ForAllTimeSteps_OverMiniBatch, captions_out, mask) ############################# Backward Pass ################################ # (4) Backprop into temporal_affine_forward function. d_HdSV_ForAllTimeSteps_OverMiniBatch, d_W_vocab, d_b_vocab = temporal_affine_backward(d_Loss__d_Scores_ForAllTimeSteps_OverMiniBatch, cache_for_rnn_2) # (3) Backprop into rnn_forward function. d_Embedded_captions_in, d_h0, d_Wx, d_Wh, d_b = lstm_backward(d_HdSV_ForAllTimeSteps_OverMiniBatch, cache_for_rnn_1) # (2) Backprop into word_embedding_forward function. d_W_embed = word_embedding_backward(d_Embedded_captions_in, cache_for_word_embedding) # (1) Backprop into Image features - affine tranformation function d_IFP_to_RNN = d_h0 d_features, d_W_proj, d_b_proj = affine_backward(d_IFP_to_RNN, cache_for_feature_projection) grads.update({ 'W_proj': d_W_proj, 'b_proj': d_b_proj, 'W_embed': d_W_embed, 'Wx': d_Wx, 'Wh': d_Wh, 'b': d_b, 'W_vocab': d_W_vocab, 'b_vocab': d_b_vocab }) ############################################################################ # END OF YOUR CODE # ############################################################################ return loss, grads def sample(self, features, max_length=30): """ Run a test-time forward pass for the model, sampling captions for input feature vectors. At each timestep, we embed the current word, pass it and the previous hidden state to the RNN to get the next hidden state, use the hidden state to get scores for all vocab words, and choose the word with the highest score as the next word. The initial hidden state is computed by applying an affine transform to the input image features, and the initial word is the <START> token. For LSTMs you will also have to keep track of the cell state; in that case the initial cell state should be zero. Inputs: - features: Array of input image features of shape (N, D). - max_length: Maximum length T of generated captions. Returns: - captions: Array of shape (N, max_length) giving sampled captions, where each element is an integer in the range [0, V). The first element of captions should be the first sampled word, not the <START> token. """ N = features.shape[0] captions = self._null * np.ones((N, max_length), dtype=np.int32) # Unpack parameters W_proj, b_proj = self.params['W_proj'], self.params['b_proj'] W_embed = self.params['W_embed'] Wx, Wh, b = self.params['Wx'], self.params['Wh'], self.params['b'] W_vocab, b_vocab = self.params['W_vocab'], self.params['b_vocab'] ########################################################################### # TODO: Implement test-time sampling for the model. You will need to # # initialize the hidden state of the RNN by applying the learned affine # # transform to the input image features. The first word that you feed to # # the RNN should be the <START> token; its value is stored in the # # variable self._start. At each timestep you will need to do to: # # (1) Embed the previous word using the learned word embeddings # # (2) Make an RNN step using the previous hidden state and the embedded # # current word to get the next hidden state. # # (3) Apply the learned affine transformation to the next hidden state to # # get scores for all words in the vocabulary # # (4) Select the word with the highest score as the next word, writing it # # to the appropriate slot in the captions variable # # # # For simplicity, you do not need to stop generating after an <END> token # # is sampled, but you can if you want to. # # # # HINT: You will not be able to use the rnn_forward or lstm_forward # # functions; you'll need to call rnn_step_forward or lstm_step_forward in # # a loop. # ########################################################################### # Initial hidden state for the RNN h0, cache = affine_forward(features, W_proj, b_proj) # First word to the RNN should be the special <START> token V, wordVec_dim = W_embed.shape Start_Vector_Batch = np.zeros((N, wordVec_dim)) Start_Vector = W_embed[self._start] Start_Vector_Batch[np.arange(N)] = Start_Vector Hidden_States = {} Hidden_States[0] = h0 H = h0.shape[1] c0 = np.zeros((N, H)) Cell_States = {} Cell_States[0] = c0 Scores = {} if self.cell_type == 'rnn': for t in range(1, max_length+1): if t == 1: x = Start_Vector_Batch else: word_indices = np.argmax(Scores[t-1], axis=1) captions[range(N), t-1] = word_indices x = W_embed[word_indices] prev_h = Hidden_States[t-1] Hidden_States[t], cache = rnn_step_forward(x, prev_h, Wx, Wh, b) Scores[t], cache = affine_forward(Hidden_States[t], W_vocab, b_vocab) elif self.cell_type == 'lstm': for t in range(1, max_length+1): if t == 1: x = Start_Vector_Batch else: word_indices = np.argmax(Scores[t-1], axis=1) captions[range(N), t-1] = word_indices x = W_embed[word_indices] prev_h = Hidden_States[t-1] prev_c = Cell_States[t-1] Hidden_States[t], Cell_States[t], cache = lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b) Scores[t], cache = affine_forward(Hidden_States[t], W_vocab, b_vocab) ############################################################################ # END OF YOUR CODE # ############################################################################ return captions

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/Deck_of_cards.py

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PrzemyslawMisiura/Deck_of_cards

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2020-07-17T23:32:25.635935

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# Specifications # Card: # Each instance of Card should have a suit ("Hearts", "Diamonds", "Clubs", or "Spades"). # Each instance of Card should have a value ("A", "2", "3", "4", "5", "6", "7", "8", "9", "10", "J", "Q", "K"). # Card's __repr__ method should return the card's value and suit (e.g. "A # of Clubs", "J of Diamonds", etc.) class Card: def __init__(self, value, suit): self.value = value self.suit = suit def __repr__(self): return "{} of {}".format(self.value, self.suit) # Deck: # Each instance of Deck should have a cards attribute with all 52 possible # instances of Card. # Deck should have an instance method called count which returns a count # of how many cards remain in the deck. # Deck's __repr__ method should return information on how many cards are # in the deck (e.g. "Deck of 52 cards", "Deck of 12 cards", etc.) # Deck should have an instance method called _deal which accepts a number # and removes at most that many cards from the deck (it may need to remove # fewer if you request more cards than are currently in the deck!). If # there are no cards left, this method should raise a ValueError with the # message "All cards have been dealt". # Deck should have an instance method called shuffle which will shuffle a # full deck of cards. If there are cards missing from the deck, this # method should raise a ValueError with the message "Only full decks can # be shuffled". Shuffle should return the shuffled deck. # Deck should have an instance method called deal_card which uses the # _deal method to deal a single card from the deck and return that single # card. # Deck should have an instance method called deal_hand which accepts a # number and uses the _deal method to deal a list of cards from the deck # and return that list of cards. from random import shuffle class Deck: def __init__(self): suits = ["Hearts", "Diamonds", "Clubs", "Spades"] values = [ "A", "2", "3", "4", "5", "6", "7", "8", "9", "10", "J", "Q", "K"] self.cards = [Card(v, s) for v in values for s in suits] print(self.cards) def count(self): return len(self.cards) def __repr__(self): return "Deck of {} cards".format(self.count()) def _deal(self, num): count = self.count() actual = min([count, num]) if count == 0: raise ValueError("All cards have been dealt") cards = self.cards[-actual:] self.cards = self.cards[:-actual] return cards def deal_card(self): return self._deal(1)[0] def deal_hand(self,n): return self._deal(n) def shuffle(self): if self.count() < 52: raise ValueError("Only full decks can be shuffled") print(shuffle(self.cards))

[ "przem.misiura@gmail.com" ]

przem.misiura@gmail.com

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/Even Matrix.py

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no_license

22Rahul22/Codechef

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2022-11-29T21:51:09.578798

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t = int(input()) for _ in range(t): n = int(input()) arr = [[0 for i in range(n)] for j in range(n)] sr = 0 er = n sc = 0 ec = n z = 0 num = 1 if n % 2 == 0: x = n // 2 else: x = 1 + (n // 2) while z != x: j = sc while j < ec: arr[sr][j] = num num += 1 j += 1 sr += 1 i = sr while i < er: arr[i][ec - 1] = num num += 1 i += 1 ec -= 1 j = ec - 1 while j >= sc: arr[er - 1][j] = num num += 1 j -= 1 er -= 1 i = er - 1 while i >= sr: arr[i][sc] = num num += 1 i -= 1 sc += 1 z += 1 for i in range(n): for j in range(n): print(arr[i][j], end=" ") print()

[ "rahulbhl22@gmail.com" ]

rahulbhl22@gmail.com

a26d87e83ed554ff4bfb8c5fe46b37fc647dc7a7

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/SSModel/InternalSSClassifiers/BERTModel.py

cf423b5df71bfe2f145b1cfe985d4fe5c78849ff

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chris414862/LiSSA

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2023-09-05T03:16:22.935872

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import torch import torch.nn as nn from transformers import BertModel, BertTokenizer from SSModel.InternalSSVectorizers.BERTVectorizer import BERTVectorizer from SSModel.ModelInterface import Model from SSModel.VectroizerInterface import Vectorizer from transformers import AdamW import pandas as pd from utils.AnnUtils import get_df_from_csv from tqdm import tqdm import re from SSModel.InternalSSVectorizers.BoWVectorizer import BoWVectorizer class BERTModel(Model): def __init__(self, model_class = None, tokenizer_class=None, pretrained_weights=None, num_man_feats=None , trainable_bert_layers:tuple=None): self.internal_model = self.BERTInternal( model_class, pretrained_weights, 768, 3, num_man_feats) self.vectorizer = BERTVectorizer(tokenizer_class, pretrained_weights) self.class_labels = None self.model_type = 'BERT' def my_filter(x): mo = re.search(r"encoder\.layer\.(\d+)\.", x[0]) if mo is None: return True try: layer_number = int(mo.group(1)) except ValueError as e: print("Namespace conflict:", x[0], "\n'encoder.layer' should be reserved for bert layer.") raise e if trainable_bert_layers[0] <= layer_number+1 <= trainable_bert_layers[1]: return True else: return False if trainable_bert_layers is not None: training_params = [p for p in filter(my_filter, self.internal_model.named_parameters())] else: training_params = self.internal_model.named_parameters() self.optimizer = AdamW(training_params) self.loss_function = nn.CrossEntropyLoss() def get_class_labels(self) -> list: return self.class_labels def get_model_type(self) -> str: return self.model_type def get_internal_model(self): return self.internal_model def get_weights(self): return self.internal_model.get_weights() def train(self, X:pd.Series, y:pd.Series, batch_size=2, epochs =1, man_feats=None): model = self.get_internal_model() model.train() self.class_labels:list = y.unique().tolist() num_entries = y.shape[0] for epoch in range(epochs): X = X.sample(frac=1.0) y = y[X.index] y = self.vectorizer.transform_labels(y,labels= self.class_labels) with tqdm(total=num_entries) as epoch_pbar: epoch_pbar.set_description(f'Epoch {epoch}') accum_loss = 0 for idx, i in enumerate(range(0,len(X), batch_size)): batch_X, batch_y = X[i:i+batch_size], y[i:i+batch_size] batch_man_feats = man_feats[i:i+batch_size] batch_X = self.vectorizer.transform_methods(batch_X) self.optimizer.zero_grad() predictions : torch.Tensor = model.forward(batch_X, batch_man_feats) loss = self.loss_function(predictions, batch_y) loss.backward() self.optimizer.step() # Add loss to accumulated loss accum_loss += loss # Update progress bar description avg_loss = accum_loss / (idx + 1) desc = f'Epoch {epoch} - avg_loss {avg_loss:.4f} - curr_loss {loss:.4f}' epoch_pbar.set_description(desc) epoch_pbar.update(batch_size) def get_vectorizer(self) -> Vectorizer: raise NotImplementedError() def predict(self, X): self.vectorizer.transform_methods(X) class BERTInternal(nn.Module): def __init__(self, model_class, pretrained_weights, embed_dimensions, num_classes, num_man_feats): super(BERTModel.BERTInternal, self).__init__() self.L1 = model_class.from_pretrained(pretrained_weights) self.L2 = self.CustomAttentionLayer(embed_dimensions, num_classes) self.final = nn.Linear(embed_dimensions+num_man_feats, num_classes, bias=False) self.final_bias = nn.Linear(num_classes, 1, bias=False) self.softmax = nn.Softmax(dim=1) def forward(self, encoded_input, man_feats:pd.DataFrame): input_ids= torch.tensor(encoded_input['input_ids'], dtype=torch.long) token_type_ids = torch.tensor(encoded_input['token_type_ids'], dtype=torch.long) attention_mask = torch.tensor(encoded_input['attention_mask'], dtype=torch.long) # Size of model output ==> (batch_size, seq_len, embed_dimensions) model_output, _ = self.L1(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) # S1 size ==> (batch_size, num_classes, embed_dimensions) # Each vector in the class dimension represents the document's feature with respect to that class S1, word_attention_weights = self.L2.forward(model_output) # FINAL LAYER DIMENSION TRACKING # output = softmax(sum(haddarmad(S1, W_c), dim=-1)+b_c) ==> # X = haddarmad(S1,W_c): # (batch_size, num_classes, embed_dims + num_manual_features) \hadamard (batch_size, num_classes, embed_dims + num_manual_features) # ==> (batch_size, num_classes, embed_dims + num_manual_features) # X = Sum(X, dim=-1): # \sigma (batch_size, num_classes, embed_dims + num_manual_features) ==> (batch_size, num_classes) # X = X + b_c: # (batch_size, num_classes) + (1, num_classes) ==> (batch_size, num_classes) # softmax(X): # \softmax (batch_size, num_classes) ==> (batch_size, num_classes) man_feats_tens = torch.tensor(man_feats.to_numpy(dtype=int), dtype=torch.float32).unsqueeze(dim=1) # Manual features are repeated for every class man_feats_tens = man_feats_tens.repeat(1,S1.size()[1],1) inter = torch.cat((S1,man_feats_tens), dim=-1) # Using the Hadamard product and summation ensures there's no interaction between the document's # different class representations. This makes analysis more straight forward output = self.softmax(torch.sum(torch.mul(inter, self.final.weight), 2, keepdim=False)+self.final_bias.weight) return output def get_weights(self): return None class CustomAttentionLayer(nn.Module): def __init__(self, dimensions, num_classes): super(BERTModel.BERTInternal.CustomAttentionLayer, self).__init__() self.linear_in = nn.Linear(dimensions, dimensions) self.tanh = nn.Tanh() self.queries = nn.Linear(dimensions, num_classes) self.softmax = nn.Softmax(dim=2) def forward(self, X:torch.Tensor): # X.size() == (batch_size, seq_length, embed_dimensions) # U = tanh(X*W_w) ==> (batch_size, seq_length, embed_dimensions)*(embed_dimensions, embed_dimensions) --> # (batch_size, seq_length, embed_dimensions) U = self.tanh(self.linear_in(X)) # A = softmax(X*Q +b_q) ==> (batch_size, seq_length, embed_dimensions)*(embed_dimensions, num_classes/queries) --> # (batch_size, seq_length, num_classes/queries) attention_weights = self.softmax(self.queries(U)) # S = A^T*X +b_a (batch_size, num_classes/queries, seq_length)*(batch_size, seq_length, embed_dimensions) --> # (batch_size, num_classes/queries, embed_dimension) S = torch.bmm(attention_weights.transpose(1, 2), X) return S, attention_weights if __name__ == "__main__": model_class, tokenizer_class, pretrained_weights = BertModel, BertTokenizer, 'bert-base-cased' tokenizer = tokenizer_class.from_pretrained(pretrained_weights) # model = model_class.from_pretrained(pretrained_weights) class_descrip_file = '../../Inputs/class_descriptions_android.csv' package_descrip_file = '../../Inputs/package_descriptions_android.csv' ignore_if_next_contains = [r'^javax?\..*', r'^com\..*', r'^dalvic\..*', r'^junit\..*', r'^j\..*', r'^junit\..*'] package_descrip_cols = ['QualPackageName', 'NumMethods', 'Description'] class_descrip_cols = ['QualClassName', 'NumMethods', 'Description'] cols_4_class_sig = (0, 2) cols_4_package_sig = (0, 1) create_cache = False cache_name = 'bert_debug_cache.pickle' if create_cache: df = pd.read_pickle('../../Inputs/Caches/cache2.pickle') class_descrips = get_df_from_csv(class_descrip_file, aggregate_cols=cols_4_class_sig, col_names=class_descrip_cols , ignore_if_next_contains=ignore_if_next_contains, index_col=class_descrip_cols[0]) package_descrips = get_df_from_csv(package_descrip_file, aggregate_cols=cols_4_package_sig, col_names=package_descrip_cols , ignore_if_next_contains=ignore_if_next_contains, index_col=package_descrip_cols[0], add_period=True) cols_to_embed = ['Description', "ClassDescription", "PackageDescription"] df["PackageDescription"] = '' df['ClassDescription'] = '' df_qualified_classname = df['QualifiedPackage'].str.cat( df['Classname'].copy(), sep='.') # print(df_qualified_classname) for package in package_descrips.index.tolist(): df.loc[df['QualifiedPackage']== package, 'PackageDescription'] = package_descrips.loc[package, 'Description'] for classname in class_descrips.index.tolist(): df.loc[df_qualified_classname== classname, 'ClassDescription'] = class_descrips.loc[classname, 'Description'] def concat_str_cols(X:pd.DataFrame, columns:list=None): combined_data = pd.Series(index=X.index, dtype='object') combined_data = combined_data.fillna('') for col in columns: combined_data= combined_data.str.cat(X[col].copy().fillna(''), sep=' ') return combined_data s = concat_str_cols(df, cols_to_embed) df2 = pd.DataFrame(index=s.index) df2['X'] = s.copy() df2['y'] = df['Source/Sink'].copy() bow = BoWVectorizer() mf_cols = bow.find_man_feat_cols(df) df2[mf_cols] = df[mf_cols].copy() df2.to_pickle(cache_name) df = df2 else: print('reading cache') df = pd.read_pickle(cache_name) bow = BoWVectorizer() mf_cols = bow.find_man_feat_cols(df) bm = BERTModel(model_class, tokenizer_class, pretrained_weights, len(mf_cols), trainable_bert_layers=(7,12)) bow = BoWVectorizer() mf_cols = bow.find_man_feat_cols(df) bm.train(df['X'],df['y'], man_feats = df[mf_cols]) # for little_s, enc in zip(s[:10],t['input_ids']): # print(re.sub(r"\n", '',little_s)) # print(enc) # print(len([e for e in enc if e != 0])) # text = df['Description'].to_list() # print(text[0]) # encs = tokenizer.batch_encode_plus(text[:2],add_special_tokens=True, max_length=512, pad_to_max_length=True, return_token_type_ids=True) # doc_lens = [] # input_ids = torch.tensor(encs['input_ids'] , dtype=torch.long) # print(input_ids.size()) # token_type_ids = torch.tensor(encs['token_type_ids'], dtype=torch.long) # attention_mask = torch.tensor(encs['attention_mask'], dtype=torch.long) # # model = model_class.from_pretrained(pretrained_weights) # # last_hidden_state, pooler_output = model(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) # # print(last_hidden_state.size()) # custom_bert = BERTModel(pretrained_weights, 768, 512, 3) # custom_bert.forward(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)

[ "chris414862@gmail.com" ]

chris414862@gmail.com

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/demo/backtest_f/main.py

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permissive

lgh0504/snake

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refs/heads/master

2023-03-18T18:13:53.237850

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# coding: utf-8 from datetime import datetime import pandas_datareader as pdr from f.portfolios import MarketOnClosePortfolio from f.strategy.ma_cross import MovingAverageCrossStrategy # from f.strategy.random_forecast import RandomForecastingStrategy def run_backtest(symbol, date_range=(datetime(2016, 8, 29), datetime.now())): # get data from yahoo bars = pdr.get_data_yahoo(symbol, start=date_range[0], end=date_range[1]) print 'stock bars: ', bars.head(10) # create strategy class and get signals strategy_inst = MovingAverageCrossStrategy(symbol, bars) signals = strategy_inst.generate_signals() print 'signals', signals.head() # create a portfolio portfolio_inst = MarketOnClosePortfolio( symbol, bars, signals, initial_capital=100000.0, shares_per_position=1000 ) returns = portfolio_inst.backtest_portfolio() print 'head returns:', returns.head(10) print 'tail returns:', returns.tail(10) return returns if __name__ == '__main__': run_backtest( # symbol='000333.SZ', # symbol='000034.SZ', symbol='600016.SH', )

[ "lbj.world@gmail.com" ]

lbj.world@gmail.com

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/addon/addon_dependencies/mod/46461-46487M/space_userpref.py

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2012-08-20T16:05:05

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40,958

py

# ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### # <pep8 compliant> lnmod = ('46461-46487M',(0, 72)) import bpy from bpy.types import Header, Menu, Panel import os import addon_utils def ui_items_general(col, context): """ General UI Theme Settings (User Interface) """ row = col.row() subsplit = row.split(percentage=0.95) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(context, "outline") colsub.row().prop(context, "item", slider=True) colsub.row().prop(context, "inner", slider=True) colsub.row().prop(context, "inner_sel", slider=True) subsplit = row.split(percentage=0.85) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(context, "text") colsub.row().prop(context, "text_sel") colsub.prop(context, "show_shaded") subsub = colsub.column(align=True) subsub.active = context.show_shaded subsub.prop(context, "shadetop") subsub.prop(context, "shadedown") col.separator() def opengl_lamp_buttons(column, lamp): split = column.split(percentage=0.1) split.prop(lamp, "use", text="", icon='OUTLINER_OB_LAMP' if lamp.use else 'LAMP_DATA') col = split.column() col.active = lamp.use row = col.row() row.label(text="Diffuse:") row.prop(lamp, "diffuse_color", text="") row = col.row() row.label(text="Specular:") row.prop(lamp, "specular_color", text="") col = split.column() col.active = lamp.use col.prop(lamp, "direction", text="") class USERPREF_HT_header(Header): bl_space_type = 'USER_PREFERENCES' def draw(self, context): layout = self.layout layout.template_header(menus=False) userpref = context.user_preferences layout.operator_context = 'EXEC_AREA' layout.operator("wm.save_homefile", text="Save As Default") layout.operator_context = 'INVOKE_DEFAULT' if userpref.active_section == 'INPUT': layout.operator("wm.keyconfig_import") layout.operator("wm.keyconfig_export") elif userpref.active_section == 'ADDONS': layout.operator("wm.addon_install") layout.menu("USERPREF_MT_addons_dev_guides") elif userpref.active_section == 'THEMES': layout.operator("ui.reset_default_theme") class USERPREF_PT_tabs(Panel): bl_label = "" bl_space_type = 'USER_PREFERENCES' bl_region_type = 'WINDOW' bl_options = {'HIDE_HEADER'} def draw(self, context): layout = self.layout userpref = context.user_preferences layout.prop(userpref, "active_section", expand=True) class USERPREF_MT_interaction_presets(Menu): bl_label = "Presets" preset_subdir = "interaction" preset_operator = "script.execute_preset" draw = Menu.draw_preset class USERPREF_MT_appconfigs(Menu): bl_label = "AppPresets" preset_subdir = "keyconfig" preset_operator = "wm.appconfig_activate" def draw(self, context): self.layout.operator("wm.appconfig_default", text="Blender (default)") # now draw the presets Menu.draw_preset(self, context) class USERPREF_MT_splash(Menu): bl_label = "Splash" def draw(self, context): layout = self.layout split = layout.split() row = split.row() row.label("") row = split.row() row.label("Interaction:") # XXX, no redraws # text = bpy.path.display_name(context.window_manager.keyconfigs.active.name) # if not text: # text = "Blender (default)" row.menu("USERPREF_MT_appconfigs", text="Preset") class USERPREF_PT_interface(Panel): bl_space_type = 'USER_PREFERENCES' bl_label = "Interface" bl_region_type = 'WINDOW' bl_options = {'HIDE_HEADER'} @classmethod def poll(cls, context): userpref = context.user_preferences return (userpref.active_section == 'INTERFACE') def draw(self, context): layout = self.layout userpref = context.user_preferences view = userpref.view row = layout.row() col = row.column() col.label(text="Display:") col.prop(view, "show_tooltips") col.prop(view, "show_tooltips_python") col.prop(view, "show_object_info", text="Object Info") col.prop(view, "show_large_cursors") col.prop(view, "show_view_name", text="View Name") col.prop(view, "show_playback_fps", text="Playback FPS") col.prop(view, "use_global_scene") col.prop(view, "object_origin_size") col.separator() col.separator() col.separator() col.prop(view, "show_mini_axis", text="Display Mini Axis") sub = col.column() sub.active = view.show_mini_axis sub.prop(view, "mini_axis_size", text="Size") sub.prop(view, "mini_axis_brightness", text="Brightness") col.separator() row.separator() row.separator() col = row.column() col.label(text="View Manipulation:") col.prop(view, "use_mouse_auto_depth") col.prop(view, "use_zoom_to_mouse") col.prop(view, "use_rotate_around_active") col.prop(view, "use_global_pivot") col.prop(view, "use_camera_lock_parent") col.separator() col.prop(view, "use_auto_perspective") col.prop(view, "smooth_view") col.prop(view, "rotation_angle") col.separator() col.separator() col.label(text="2D Viewports:") col.prop(view, "view2d_grid_spacing_min", text="Minimum Grid Spacing") col.prop(view, "timecode_style") row.separator() row.separator() col = row.column() #Toolbox doesn't exist yet #col.label(text="Toolbox:") #col.prop(view, "show_column_layout") #col.label(text="Open Toolbox Delay:") #col.prop(view, "open_left_mouse_delay", text="Hold LMB") #col.prop(view, "open_right_mouse_delay", text="Hold RMB") col.prop(view, "show_manipulator") sub = col.column() sub.active = view.show_manipulator sub.prop(view, "manipulator_size", text="Size") sub.prop(view, "manipulator_handle_size", text="Handle Size") sub.prop(view, "manipulator_hotspot", text="Hotspot") col.separator() col.separator() col.separator() col.label(text="Menus:") col.prop(view, "use_mouse_over_open") col.label(text="Menu Open Delay:") col.prop(view, "open_toplevel_delay", text="Top Level") col.prop(view, "open_sublevel_delay", text="Sub Level") col.separator() col.prop(view, "show_splash") if os.name == 'nt': col.prop(view, "quit_dialog") class USERPREF_PT_edit(Panel): bl_space_type = 'USER_PREFERENCES' bl_label = "Edit" bl_region_type = 'WINDOW' bl_options = {'HIDE_HEADER'} @classmethod def poll(cls, context): userpref = context.user_preferences return (userpref.active_section == 'EDITING') def draw(self, context): layout = self.layout userpref = context.user_preferences edit = userpref.edit row = layout.row() col = row.column() col.label(text="Link Materials To:") col.prop(edit, "material_link", text="") col.separator() col.separator() col.separator() col.label(text="New Objects:") col.prop(edit, "use_enter_edit_mode") col.label(text="Align To:") col.prop(edit, "object_align", text="") col.separator() col.separator() col.separator() col.label(text="Undo:") col.prop(edit, "use_global_undo") col.prop(edit, "undo_steps", text="Steps") col.prop(edit, "undo_memory_limit", text="Memory Limit") row.separator() row.separator() col = row.column() col.label(text="Grease Pencil:") col.prop(edit, "grease_pencil_manhattan_distance", text="Manhattan Distance") col.prop(edit, "grease_pencil_euclidean_distance", text="Euclidean Distance") #~ col.prop(edit, "use_grease_pencil_simplify_stroke", text="Simplify Stroke") col.prop(edit, "grease_pencil_eraser_radius", text="Eraser Radius") col.prop(edit, "use_grease_pencil_smooth_stroke", text="Smooth Stroke") col.separator() col.separator() col.separator() col.label(text="Playback:") col.prop(edit, "use_negative_frames") col.separator() col.separator() col.separator() col.label(text="Animation Editors:") col.prop(edit, "fcurve_unselected_alpha", text="F-Curve Visibility") row.separator() row.separator() col = row.column() col.label(text="Keyframing:") col.prop(edit, "use_visual_keying") col.prop(edit, "use_keyframe_insert_needed", text="Only Insert Needed") col.separator() col.prop(edit, "use_auto_keying", text="Auto Keyframing:") sub = col.column() #~ sub.active = edit.use_keyframe_insert_auto # incorrect, time-line can enable sub.prop(edit, "use_keyframe_insert_available", text="Only Insert Available") col.separator() col.label(text="New F-Curve Defaults:") col.prop(edit, "keyframe_new_interpolation_type", text="Interpolation") col.prop(edit, "keyframe_new_handle_type", text="Handles") col.prop(edit, "use_insertkey_xyz_to_rgb", text="XYZ to RGB") col.separator() col.separator() col.separator() col.label(text="Transform:") col.prop(edit, "use_drag_immediately") row.separator() row.separator() col = row.column() col.prop(edit, "sculpt_paint_overlay_color", text="Sculpt Overlay Color") col.separator() col.separator() col.separator() col.label(text="Duplicate Data:") col.prop(edit, "use_duplicate_mesh", text="Mesh") col.prop(edit, "use_duplicate_surface", text="Surface") col.prop(edit, "use_duplicate_curve", text="Curve") col.prop(edit, "use_duplicate_text", text="Text") col.prop(edit, "use_duplicate_metaball", text="Metaball") col.prop(edit, "use_duplicate_armature", text="Armature") col.prop(edit, "use_duplicate_lamp", text="Lamp") col.prop(edit, "use_duplicate_material", text="Material") col.prop(edit, "use_duplicate_texture", text="Texture") #col.prop(edit, "use_duplicate_fcurve", text="F-Curve") col.prop(edit, "use_duplicate_action", text="Action") col.prop(edit, "use_duplicate_particle", text="Particle") class USERPREF_PT_system(Panel): bl_space_type = 'USER_PREFERENCES' bl_label = "System" bl_region_type = 'WINDOW' bl_options = {'HIDE_HEADER'} @classmethod def poll(cls, context): userpref = context.user_preferences return (userpref.active_section == 'SYSTEM') def draw(self, context): layout = self.layout userpref = context.user_preferences system = userpref.system split = layout.split() # 1. Column column = split.column() colsplit = column.split(percentage=0.85) col = colsplit.column() col.label(text="General:") col.prop(system, "dpi") col.prop(system, "frame_server_port") col.prop(system, "scrollback", text="Console Scrollback") col.separator() col.separator() col.label(text="Sound:") col.row().prop(system, "audio_device", expand=True) sub = col.column() sub.active = system.audio_device != 'NONE' #sub.prop(system, "use_preview_images") sub.prop(system, "audio_channels", text="Channels") sub.prop(system, "audio_mixing_buffer", text="Mixing Buffer") sub.prop(system, "audio_sample_rate", text="Sample Rate") sub.prop(system, "audio_sample_format", text="Sample Format") col.separator() col.separator() col.label(text="Screencast:") col.prop(system, "screencast_fps") col.prop(system, "screencast_wait_time") col.separator() col.separator() if hasattr(system, 'compute_device'): col.label(text="Compute Device:") col.row().prop(system, "compute_device_type", expand=True) sub = col.row() sub.active = system.compute_device_type != 'CPU' sub.prop(system, "compute_device", text="") # 2. Column column = split.column() colsplit = column.split(percentage=0.85) col = colsplit.column() col.label(text="OpenGL:") col.prop(system, "gl_clip_alpha", slider=True) col.prop(system, "use_mipmaps") col.prop(system, "use_16bit_textures") col.label(text="Anisotropic Filtering") col.prop(system, "anisotropic_filter", text="") col.prop(system, "use_vertex_buffer_objects") # Anti-aliasing is disabled as it breaks border/lasso select #~ col.prop(system, "use_antialiasing") col.label(text="Window Draw Method:") col.prop(system, "window_draw_method", text="") col.label(text="Text Draw Options:") col.prop(system, "use_text_antialiasing") col.label(text="Textures:") col.prop(system, "gl_texture_limit", text="Limit Size") col.prop(system, "texture_time_out", text="Time Out") col.prop(system, "texture_collection_rate", text="Collection Rate") col.separator() col.separator() col.separator() col.label(text="Sequencer:") col.prop(system, "prefetch_frames") col.prop(system, "memory_cache_limit") # 3. Column column = split.column() column.label(text="Solid OpenGL lights:") split = column.split(percentage=0.1) split.label() split.label(text="Colors:") split.label(text="Direction:") lamp = system.solid_lights[0] opengl_lamp_buttons(column, lamp) lamp = system.solid_lights[1] opengl_lamp_buttons(column, lamp) lamp = system.solid_lights[2] opengl_lamp_buttons(column, lamp) column.separator() column.label(text="Color Picker Type:") column.row().prop(system, "color_picker_type", text="") column.separator() column.prop(system, "use_weight_color_range", text="Custom Weight Paint Range") sub = column.column() sub.active = system.use_weight_color_range sub.template_color_ramp(system, "weight_color_range", expand=True) column.separator() column.prop(system, "use_international_fonts") if system.use_international_fonts: column.prop(system, "language") row = column.row() row.label(text="Translate:") row.prop(system, "use_translate_interface", text="Interface") row.prop(system, "use_translate_tooltips", text="Tooltips") class USERPREF_MT_interface_theme_presets(Menu): bl_label = "Presets" preset_subdir = "interface_theme" preset_operator = "script.execute_preset" preset_type = 'XML' preset_xml_map = (("user_preferences.themes[0]", "Theme"), ) draw = Menu.draw_preset class USERPREF_PT_theme(Panel): bl_space_type = 'USER_PREFERENCES' bl_label = "Themes" bl_region_type = 'WINDOW' bl_options = {'HIDE_HEADER'} @staticmethod def _theme_generic(split, themedata): col = split.column() def theme_generic_recurse(data): col.label(data.rna_type.name) row = col.row() subsplit = row.split(percentage=0.95) padding1 = subsplit.split(percentage=0.15) padding1.column() subsplit = row.split(percentage=0.85) padding2 = subsplit.split(percentage=0.15) padding2.column() colsub_pair = padding1.column(), padding2.column() props_type = {} for i, prop in enumerate(data.rna_type.properties): if prop.identifier == "rna_type": continue props_type.setdefault((prop.type, prop.subtype), []).append(prop) for props_type, props_ls in sorted(props_type.items()): if props_type[0] == 'POINTER': for i, prop in enumerate(props_ls): theme_generic_recurse(getattr(data, prop.identifier)) else: for i, prop in enumerate(props_ls): colsub_pair[i % 2].row().prop(data, prop.identifier) theme_generic_recurse(themedata) @classmethod def poll(cls, context): userpref = context.user_preferences return (userpref.active_section == 'THEMES') def draw(self, context): layout = self.layout theme = context.user_preferences.themes[0] split_themes = layout.split(percentage=0.2) sub = split_themes.column() sub.label(text="Presets:") subrow = sub.row(align=True) subrow.menu("USERPREF_MT_interface_theme_presets", text=USERPREF_MT_interface_theme_presets.bl_label) subrow.operator("wm.interface_theme_preset_add", text="", icon='ZOOMIN') subrow.operator("wm.interface_theme_preset_add", text="", icon='ZOOMOUT').remove_active = True sub.separator() sub.prop(theme, "theme_area", expand=True) split = layout.split(percentage=0.4) layout.separator() layout.separator() split = split_themes.split() if theme.theme_area == 'USER_INTERFACE': col = split.column() ui = theme.user_interface.wcol_regular col.label(text="Regular:") ui_items_general(col, ui) ui = theme.user_interface.wcol_tool col.label(text="Tool:") ui_items_general(col, ui) ui = theme.user_interface.wcol_radio col.label(text="Radio Buttons:") ui_items_general(col, ui) ui = theme.user_interface.wcol_text col.label(text="Text:") ui_items_general(col, ui) ui = theme.user_interface.wcol_option col.label(text="Option:") ui_items_general(col, ui) ui = theme.user_interface.wcol_toggle col.label(text="Toggle:") ui_items_general(col, ui) ui = theme.user_interface.wcol_num col.label(text="Number Field:") ui_items_general(col, ui) ui = theme.user_interface.wcol_numslider col.label(text="Value Slider:") ui_items_general(col, ui) ui = theme.user_interface.wcol_box col.label(text="Box:") ui_items_general(col, ui) ui = theme.user_interface.wcol_menu col.label(text="Menu:") ui_items_general(col, ui) ui = theme.user_interface.wcol_pulldown col.label(text="Pulldown:") ui_items_general(col, ui) ui = theme.user_interface.wcol_menu_back col.label(text="Menu Back:") ui_items_general(col, ui) ui = theme.user_interface.wcol_tooltip col.label(text="Tooltip:") ui_items_general(col, ui) ui = theme.user_interface.wcol_menu_item col.label(text="Menu Item:") ui_items_general(col, ui) ui = theme.user_interface.wcol_scroll col.label(text="Scroll Bar:") ui_items_general(col, ui) ui = theme.user_interface.wcol_progress col.label(text="Progress Bar:") ui_items_general(col, ui) ui = theme.user_interface.wcol_list_item col.label(text="List Item:") ui_items_general(col, ui) ui = theme.user_interface.wcol_state col.label(text="State:") row = col.row() subsplit = row.split(percentage=0.95) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(ui, "inner_anim") colsub.row().prop(ui, "inner_anim_sel") colsub.row().prop(ui, "inner_driven") colsub.row().prop(ui, "inner_driven_sel") subsplit = row.split(percentage=0.85) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(ui, "inner_key") colsub.row().prop(ui, "inner_key_sel") colsub.row().prop(ui, "blend") col.separator() col.separator() ui = theme.user_interface col.label("Icons:") row = col.row() subsplit = row.split(percentage=0.95) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(ui, "icon_file") subsplit = row.split(percentage=0.85) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(ui, "icon_alpha") col.separator() col.separator() ui = theme.user_interface.panel col.label("Panels:") row = col.row() subsplit = row.split(percentage=0.95) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() rowsub = colsub.row() rowsub.prop(ui, "show_header") rowsub.label() subsplit = row.split(percentage=0.85) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(ui, "header") layout.separator() layout.separator() elif theme.theme_area == 'BONE_COLOR_SETS': col = split.column() for i, ui in enumerate(theme.bone_color_sets): col.label(text="Color Set" + " %d:" % (i + 1)) # i starts from 0 row = col.row() subsplit = row.split(percentage=0.95) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(ui, "normal") colsub.row().prop(ui, "select") colsub.row().prop(ui, "active") subsplit = row.split(percentage=0.85) padding = subsplit.split(percentage=0.15) colsub = padding.column() colsub = padding.column() colsub.row().prop(ui, "show_colored_constraints") else: self._theme_generic(split, getattr(theme, theme.theme_area.lower())) class USERPREF_PT_file(Panel): bl_space_type = 'USER_PREFERENCES' bl_label = "Files" bl_region_type = 'WINDOW' bl_options = {'HIDE_HEADER'} @classmethod def poll(cls, context): userpref = context.user_preferences return (userpref.active_section == 'FILES') def draw(self, context): layout = self.layout userpref = context.user_preferences paths = userpref.filepaths system = userpref.system split = layout.split(percentage=0.7) col = split.column() col.label(text="File Paths:") colsplit = col.split(percentage=0.95) col1 = colsplit.split(percentage=0.3) sub = col1.column() sub.label(text="Fonts:") sub.label(text="Textures:") sub.label(text="Texture Plugins:") sub.label(text="Sequence Plugins:") sub.label(text="Render Output:") sub.label(text="Scripts:") sub.label(text="Sounds:") sub.label(text="Temp:") sub.label(text="Image Editor:") sub.label(text="Animation Player:") sub = col1.column() sub.prop(paths, "font_directory", text="") sub.prop(paths, "texture_directory", text="") sub.prop(paths, "texture_plugin_directory", text="") sub.prop(paths, "sequence_plugin_directory", text="") sub.prop(paths, "render_output_directory", text="") sub.prop(paths, "script_directory", text="") sub.prop(paths, "sound_directory", text="") sub.prop(paths, "temporary_directory", text="") sub.prop(paths, "image_editor", text="") subsplit = sub.split(percentage=0.3) subsplit.prop(paths, "animation_player_preset", text="") subsplit.prop(paths, "animation_player", text="") col.separator() col.separator() colsplit = col.split(percentage=0.95) sub = colsplit.column() sub.label(text="Author:") sub.prop(system, "author", text="") col = split.column() col.label(text="Save & Load:") col.prop(paths, "use_relative_paths") col.prop(paths, "use_file_compression") col.prop(paths, "use_load_ui") col.prop(paths, "use_filter_files") col.prop(paths, "show_hidden_files_datablocks") col.prop(paths, "hide_recent_locations") col.prop(paths, "show_thumbnails") col.separator() col.separator() col.prop(paths, "save_version") col.prop(paths, "recent_files") col.prop(paths, "use_save_preview_images") col.label(text="Auto Save:") col.prop(paths, "use_auto_save_temporary_files") sub = col.column() sub.active = paths.use_auto_save_temporary_files sub.prop(paths, "auto_save_time", text="Timer (mins)") col.separator() col.label(text="Scripts:") col.prop(system, "use_scripts_auto_execute") col.prop(system, "use_tabs_as_spaces") from bl_ui.space_userpref_keymap import InputKeyMapPanel class USERPREF_MT_ndof_settings(Menu): # accessed from the window key-bindings in C (only) bl_label = "3D Mouse Settings" def draw(self, context): layout = self.layout input_prefs = context.user_preferences.inputs layout.separator() layout.prop(input_prefs, "ndof_sensitivity") if context.space_data.type == 'VIEW_3D': layout.separator() layout.prop(input_prefs, "ndof_show_guide") layout.separator() layout.label(text="Orbit options") if input_prefs.view_rotate_method == 'TRACKBALL': layout.prop(input_prefs, "ndof_roll_invert_axis") layout.prop(input_prefs, "ndof_tilt_invert_axis") layout.prop(input_prefs, "ndof_rotate_invert_axis") layout.prop(input_prefs, "ndof_zoom_invert") layout.separator() layout.label(text="Pan options") layout.prop(input_prefs, "ndof_panx_invert_axis") layout.prop(input_prefs, "ndof_pany_invert_axis") layout.prop(input_prefs, "ndof_panz_invert_axis") layout.label(text="Zoom options") layout.prop(input_prefs, "ndof_zoom_updown") layout.separator() layout.label(text="Fly options") layout.prop(input_prefs, "ndof_fly_helicopter", icon='NDOF_FLY') layout.prop(input_prefs, "ndof_lock_horizon", icon='NDOF_DOM') class USERPREF_PT_input(Panel, InputKeyMapPanel): bl_space_type = 'USER_PREFERENCES' bl_label = "Input" @classmethod def poll(cls, context): userpref = context.user_preferences return (userpref.active_section == 'INPUT') def draw_input_prefs(self, inputs, layout): # General settings row = layout.row() col = row.column() sub = col.column() sub.label(text="Presets:") subrow = sub.row(align=True) subrow.menu("USERPREF_MT_interaction_presets", text=bpy.types.USERPREF_MT_interaction_presets.bl_label) subrow.operator("wm.interaction_preset_add", text="", icon='ZOOMIN') subrow.operator("wm.interaction_preset_add", text="", icon='ZOOMOUT').remove_active = True sub.separator() sub.label(text="Mouse:") sub1 = sub.column() sub1.active = (inputs.select_mouse == 'RIGHT') sub1.prop(inputs, "use_mouse_emulate_3_button") sub.prop(inputs, "use_mouse_continuous") sub.prop(inputs, "drag_threshold") sub.prop(inputs, "tweak_threshold") sub.label(text="Select With:") sub.row().prop(inputs, "select_mouse", expand=True) sub = col.column() sub.label(text="Double Click:") sub.prop(inputs, "mouse_double_click_time", text="Speed") sub.separator() sub.prop(inputs, "use_emulate_numpad") sub.separator() sub.label(text="Orbit Style:") sub.row().prop(inputs, "view_rotate_method", expand=True) sub.label(text="Zoom Style:") sub.row().prop(inputs, "view_zoom_method", text="") if inputs.view_zoom_method in {'DOLLY', 'CONTINUE'}: sub.row().prop(inputs, "view_zoom_axis", expand=True) sub.prop(inputs, "invert_mouse_zoom") #sub.prop(inputs, "use_mouse_mmb_paste") #col.separator() sub = col.column() sub.label(text="Mouse Wheel:") sub.prop(inputs, "invert_zoom_wheel", text="Invert Wheel Zoom Direction") #sub.prop(view, "wheel_scroll_lines", text="Scroll Lines") col.separator() sub = col.column() sub.label(text="NDOF Device:") sub.prop(inputs, "ndof_sensitivity", text="NDOF Sensitivity") row.separator() def draw(self, context): layout = self.layout #import time #start = time.time() userpref = context.user_preferences inputs = userpref.inputs split = layout.split(percentage=0.25) # Input settings self.draw_input_prefs(inputs, split) # Keymap Settings self.draw_keymaps(context, split) #print("runtime", time.time() - start) class USERPREF_MT_addons_dev_guides(Menu): bl_label = "Development Guides" # menu to open web-pages with addons development guides def draw(self, context): layout = self.layout layout.operator("wm.url_open", text="API Concepts", icon='URL').url = "http://wiki.blender.org/index.php/Dev:2.5/Py/API/Intro" layout.operator("wm.url_open", text="Addon Guidelines", icon='URL').url = "http://wiki.blender.org/index.php/Dev:2.5/Py/Scripts/Guidelines/Addons" layout.operator("wm.url_open", text="How to share your addon", icon='URL').url = "http://wiki.blender.org/index.php/Dev:Py/Sharing" class USERPREF_PT_addons(Panel): bl_space_type = 'USER_PREFERENCES' bl_label = "Addons" bl_region_type = 'WINDOW' bl_options = {'HIDE_HEADER'} _support_icon_mapping = { 'OFFICIAL': 'FILE_BLEND', 'COMMUNITY': 'POSE_DATA', 'TESTING': 'MOD_EXPLODE', } @classmethod def poll(cls, context): userpref = context.user_preferences return (userpref.active_section == 'ADDONS') @staticmethod def is_user_addon(mod, user_addon_paths): if not user_addon_paths: user_script_path = bpy.utils.user_script_path() if user_script_path is not None: user_addon_paths.append(os.path.join(user_script_path, "addons")) user_addon_paths.append(os.path.join(bpy.utils.resource_path('USER'), "scripts", "addons")) for path in user_addon_paths: if bpy.path.is_subdir(mod.__file__, path): return True return False @staticmethod def draw_error(layout, message): lines = message.split("\n") box = layout.box() rowsub = box.row() rowsub.label(lines[0]) rowsub.label(icon='ERROR') for l in lines[1:]: box.label(l) def draw(self, context): layout = self.layout userpref = context.user_preferences used_ext = {ext.module for ext in userpref.addons} # collect the categories that can be filtered on addons = [(mod, addon_utils.module_bl_info(mod)) for mod in addon_utils.modules(addon_utils.addons_fake_modules)] split = layout.split(percentage=0.2) col = split.column() col.prop(context.window_manager, "addon_search", text="", icon='VIEWZOOM') col.label(text="Supported Level") col.prop(context.window_manager, "addon_support", expand=True) col.label(text="Categories") col.prop(context.window_manager, "addon_filter", expand=True) col = split.column() # set in addon_utils.modules(...) if addon_utils.error_duplicates: self.draw_error(col, "Multiple addons using the same name found!\n" "likely a problem with the script search path.\n" "(see console for details)", ) if addon_utils.error_encoding: self.draw_error(col, "One or more addons do not have UTF-8 encoding\n" "(see console for details)", ) filter = context.window_manager.addon_filter search = context.window_manager.addon_search.lower() support = context.window_manager.addon_support # initialized on demand user_addon_paths = [] for mod, info in addons: module_name = mod.__name__ is_enabled = module_name in used_ext if info["support"] not in support: continue # check if addon should be visible with current filters if ((filter == "All") or (filter == info["category"]) or (filter == "Enabled" and is_enabled) or (filter == "Disabled" and not is_enabled)): if search and search not in info["name"].lower(): if info["author"]: if search not in info["author"].lower(): continue else: continue # Addon UI Code box = col.column().box() colsub = box.column() row = colsub.row() row.operator("wm.addon_expand", icon='TRIA_DOWN' if info["show_expanded"] else 'TRIA_RIGHT', emboss=False).module = module_name rowsub = row.row() rowsub.active = is_enabled rowsub.label(text='%s: %s' % (info['category'], info["name"])) if info["warning"]: rowsub.label(icon='ERROR') # icon showing dependencies (child or parent). disable_check = module_name if info["dependencies"] : rowsub.label(icon='LINKED') # icon showing support level. rowsub.label(icon=self._support_icon_mapping.get(info["support"], 'QUESTION')) if info["childs"] and is_enabled : row.label(icon='LINKED') elif is_enabled: row.operator("wm.addon_disable", icon='CHECKBOX_HLT', text="", emboss=False).module = module_name else: row.operator("wm.addon_enable", icon='CHECKBOX_DEHLT', text="", emboss=False).module = module_name # Expanded UI (only if additional info is available) if info["show_expanded"]: if info["description"]: split = colsub.row().split(percentage=0.15) split.label(text="Description:") split.label(text=info["description"]) if info["location"]: split = colsub.row().split(percentage=0.15) split.label(text="Location:") split.label(text=info["location"]) if mod: split = colsub.row().split(percentage=0.15) split.label(text="File:") split.label(text=mod.__file__) if info["author"]: split = colsub.row().split(percentage=0.15) split.label(text="Author:") split.label(text=info["author"]) if info["version"]: split = colsub.row().split(percentage=0.15) split.label(text="Version:") split.label(text='.'.join(str(x) for x in info["version"])) if info["dependencies"]: split = colsub.row().split(percentage=0.15) split.label(text='Dependencies:') parent_list, msg = addon_utils.parent_list(info["dependencies"]) if parent_list : txt = '' for n,v in parent_list : txt += '%s v%s, '%(n,'.'.join(str(x) for x in v) ) else : txt = msg split.label(text=txt[:-2]) if info["childs"] : split = colsub.row().split(percentage=0.15) split.label(text='In use by:') txt = '' for n in info["childs"] : txt += '%s, '%(n) split.label(text=txt[:-2]) if info["warning"]: split = colsub.row().split(percentage=0.15) split.label(text="Warning:") split.label(text=' ' + info["warning"], icon='ERROR') user_addon = USERPREF_PT_addons.is_user_addon(mod, user_addon_paths) tot_row = bool(info["wiki_url"]) + bool(info["tracker_url"]) + bool(user_addon) if tot_row: split = colsub.row().split(percentage=0.15) split.label(text="Internet:") if info["wiki_url"]: split.operator("wm.url_open", text="Link to the Wiki", icon='HELP').url = info["wiki_url"] if info["tracker_url"]: split.operator("wm.url_open", text="Report a Bug", icon='URL').url = info["tracker_url"] if user_addon: split.operator("wm.addon_remove", text="Remove", icon='CANCEL').module = mod.__name__ for i in range(4 - tot_row): split.separator() # Append missing scripts # First collect scripts that are used but have no script file. module_names = {mod.__name__ for mod, info in addons} missing_modules = {ext for ext in used_ext if ext not in module_names} if missing_modules and filter in {"All", "Enabled"}: col.column().separator() col.column().label(text="Missing script files") module_names = {mod.__name__ for mod, info in addons} for module_name in sorted(missing_modules): is_enabled = module_name in used_ext # Addon UI Code box = col.column().box() colsub = box.column() row = colsub.row() row.label(text=module_name, icon='ERROR') if is_enabled: row.operator("wm.addon_disable", icon='CHECKBOX_HLT', text="", emboss=False).module = module_name if __name__ == "__main__": # only for live edit. bpy.utils.register_module(__name__)

[ "jerome.le.chat@free.fr" ]

jerome.le.chat@free.fr

29459d2f2495bd6eabb00953ccd6e2064a3749f5

d82a8844c7d46c752e567cca41a8ae1c15c975f7

/API/urls.py

aaae4d1d1c0b11959a544fed6876085e896c1700

[]

no_license

golammahmud/job_evaluations_project

f1be9f8f8b27c0f9db6539294ccff25254ff08f3

fe362f2d6bc57e1d550c39263312ef046eb7754c

refs/heads/master

2023-08-04T10:20:59.442703

2021-09-27T02:31:03

410,347,863

0

null

UTF-8

Python

false

679

py

from django.contrib import admin from django.urls import path,include from rest_framework import routers from .views import UserInputView,UserBasedInputView from rest_framework_simplejwt.views import TokenObtainPairView,TokenRefreshView router=routers.DefaultRouter() router.register('all-userinputs',UserInputView) router.register('user-based-inputs',UserBasedInputView) urlpatterns = [ path('', include(router.urls)), path('api-auth/',include('rest_framework.urls')), path('get_token/', TokenObtainPairView.as_view(), name='token_obtain_pair'), #get token path('token_refresh/', TokenRefreshView.as_view(), name='token_refresh'),# get refresh token ]

[ "golam.mahmud99@gmail.com" ]

golam.mahmud99@gmail.com

bec4636056c0dc596b344cbdca2e4857ec559ff4

41299f375dbd79fc6e1163333de11a27623ab7fd

/server/dbpedia/__init__.py

cc74d451e46371ba2a1e49114ba091f920692a32

[]

no_license

MatrixReloaded/ArtViz

b18315562f30e2f0388d824ee9fcdf02fcca3591

b479079287a4e3f82fb1e6f9b1b223ef977af73e

refs/heads/master

2021-01-14T09:45:47.122465

2015-10-03T17:02:31

2015-10-03T17:11:50

null

0

null

UTF-8

Python

false

97

py

""" DBpedia quepy. """ from basic import * from people import * from BasicQuestions import *

[ "oana.balaceanu@ymail.com" ]

oana.balaceanu@ymail.com

0873be18a9c6f1322e01559626604b34c5ef88c1

fea6ceb798d612368a27888e6490b4f91c04384f

/continue.py

bdfc1b2cc6ab4f708e4b8266f81d187ed8caf26f

[]

no_license

neogeolee/PythonWorkspace

b4a8d8cf0ef451bf3bc0e00ccaecaf48253bc0b8

f8b9aff2ce821142990acac1cd2406bbe140ab4b

refs/heads/master

2022-12-17T11:30:29.974230

2020-09-22T16:28:58

297,708,060

0

null

UTF-8

Python

false

309

py

absent = [2, 5] # 결석 no_book = [7] # 책 안들고 왔음 for student in range(1, 11): if student in absent: continue elif student in no_book: print('오늘 수업여기까지 {0} 개새끼야 따라와'.format(student)) break print('{0}, 책읽어라'.format(student))

[ "neogeolee@nate.com" ]

neogeolee@nate.com

f7863d8927d006aaf6bb1f2450af7fe6550ab070

e34d4bf879910b8f41068c1efb90915897e53d53

/system_design_ladder/GeoHashII.py

b15bec1dd5ca21a631b684b5a96092a0772cec5f

[]

no_license

ZhouningMan/LeetCodePython

6cfc30f0b76f6162502410fef5639fde4801bd74

cad9585c440efb329c9321648f94c58ded198438

refs/heads/master

2020-12-10T03:53:48.824344

2020-01-13T02:29:02

233,494,907

0

null

UTF-8

Python

false

1,179

py

class GeoHash: BASE32 = "0123456789bcdefghjkmnpqrstuvwxyz" """ @param: geohash: geohash a base32 string @return: latitude and longitude a location coordinate pair """ def decode(self, geohash): binary = self._to_bin(geohash) lon_bin = [binary[i] for i in range(0, len(binary), 2)] lat_bin = [binary[i] for i in range(1, len(binary), 2)] longitude = self._bin_to_val(-180, 180, lon_bin) latitude = self._bin_to_val(-90, 90, lat_bin) return latitude, longitude def _to_bin(self, geohash): binary = '' for c in geohash: idx = GeoHash.BASE32.index(c) b = '' for i in range(5): b = str(idx % 2) + b idx = idx // 2 binary += b return binary def _bin_to_val(self, low, high, binary): for b in binary: mid = (high + low) / 2 if b == '1': # our value is higher low = mid else: # our value is lower high = mid return (low + high) / 2 if __name__ == '__main__': geoHash = GeoHash() geoHash.decode("wx4g0s")

[ "linfenglee321@gmail.com" ]

linfenglee321@gmail.com

5e6f288758fa99fdf7f7a34041decfca9b7f7f42

dac906538145808a71e94e030d63c6f20753977a

/webapp/models.py

1816f0d14f43481ce923fe42af4e351a6fad5b98

[]

no_license

nikofil/NMProject

3a0846b72cf66afb140feff9674d053e144f8087

150971c67e509b2bdc874b85c1f6abda4e27c793

refs/heads/master

2020-12-31T05:24:39.311930

2015-07-01T10:16:26

null

0

null

UTF-8

Python

false

3,832

py

from django.db import models def toint(x): try: return int(x) except ValueError: return -1 def tofloat(x): try: return float(x) except ValueError: return -1.0 class BaseStation(models.Model): # describes a base station record rid = models.IntegerField(default=-1) email = models.CharField(max_length=100) operator = models.CharField(max_length=100) mcc = models.IntegerField(default=-1) mnc = models.IntegerField(default=-1) cid = models.IntegerField(default=-1) lac = models.IntegerField(default=-1) latitude = models.FloatField(default=-1.0) longitude = models.FloatField(default=-1.0) timestamp = models.DateTimeField() def __str__(self): return str(self.rid) + " " + self.email def setdata(self, data): self.rid = toint(data[0]) self.email = data[1] self.operator = data[2] self.mcc = toint(data[3]) self.mnc = toint(data[4]) self.cid = toint(data[5]) self.lac = toint(data[6]) self.latitude = tofloat(data[7]) self.longitude = tofloat(data[8]) self.timestamp = data[9] + "+03:00" class BatteryStatus(models.Model): # describes a battery status record rid = models.IntegerField(default=-1) email = models.CharField(max_length=100) level = models.IntegerField(default=-1) plugged = models.IntegerField(default=-1) temperature = models.IntegerField(default=-1) voltage = models.IntegerField(default=-1) timestamp = models.DateTimeField() def __str__(self): return str(self.rid) + " " + self.email def setdata(self, data): self.rid = toint(data[0]) self.email = data[1] self.level = toint(data[2]) self.plugged = toint(data[3]) self.temperature = toint(data[4]) self.voltage = toint(data[5]) self.timestamp = data[6] + "+03:00" class GPSStatus(models.Model): #describes a GPS position record rid = models.IntegerField(default=-1) email = models.CharField(max_length=100) latitude = models.FloatField(default=-1.0) longitude = models.FloatField(default=-1.0) timestamp = models.DateTimeField() def __str__(self): return str(self.rid) + " " + self.email def setdata(self, data): self.rid = toint(data[0]) self.email = data[1] self.latitude = tofloat(data[2]) self.longitude = tofloat(data[3]) self.timestamp = data[4] + "+03:00" class WifiPos(models.Model): #descibes a wifi position (average of positions from wifi statuses) ssid = models.CharField(max_length=100) bssid = models.CharField(max_length=100) latitude = models.FloatField(default=-1.0) longitude = models.FloatField(default=-1.0) def __str__(self): return self.ssid + " - " + self.bssid class WifiStatus(models.Model): #descibes a wifi status record rid = models.IntegerField(default=-1) email = models.CharField(max_length=100) ssid = models.CharField(max_length=100) bssid = models.CharField(max_length=100) level = models.IntegerField(default=-1) frequency = models.IntegerField(default=-1) latitude = models.FloatField(default=-1.0) longitude = models.FloatField(default=-1.0) timestamp = models.DateTimeField() realpos = models.ForeignKey(WifiPos, null=True, blank=True) def __str__(self): return str(self.rid) + " " + self.email def setdata(self, data): self.rid = toint(data[0]) self.email = data[1] self.ssid = data[2] self.bssid = data[3] self.level = toint(data[4]) self.frequency = toint(data[5]) self.latitude = tofloat(data[6]) self.longitude = tofloat(data[7]) self.timestamp = data[8] + "+03:00"

[ "aesmade@gmail.com" ]

aesmade@gmail.com

8dbfd84f504b14bdf50f58fb927e9dff65dae76d

4714f19916c27a49d3a29f9bd96bdf92ca5affea

/extensions/sympyex/__init__.py

223f96e875e112503e4003a6481b283447616b09

[]

no_license

Sroka/jupyter_maths

803c6834eff5186f8262cbc5246c9aca80dbec41

81c385cceae192c23f3c33ccb203708b7fe349d6

refs/heads/master

2020-03-22T08:11:20.482729

2019-03-03T10:19:31

139,750,821

0

null

UTF-8

Python

false

709

py

from sympy import Expr, Equality, latex from IPython.display import display, Latex def expressionEqualityExtension(self: Expr, function): return Equality(self, function, evaluate=False) def latexify(self: Expr, mode='plain'): return latex(self, mode=mode) def latexify_inline(self: Expr): return latex(self, mode='inline') def latexify_equation(self: Expr): return latex(self, mode='equation') def display_latex(latex: str): return display(Latex(latex)) Expr.eq = expressionEqualityExtension Expr.latex = latexify Expr.latex_inline = latexify_inline Expr.latex_equation = latexify_equation del expressionEqualityExtension del latexify del latexify_inline del latexify_equation

[ "srokowski.maciej@gmail.com" ]

srokowski.maciej@gmail.com

1c2f7f5a73f04021dcfaf564920e8a0ffd7f0374

93c43eb538a14261f1af29e91d1770a99184eb82

/adding milk routes v2.py

8b5e441034fca3677075ea48e0b054e984fc133c

[]

no_license

VS-DavidSouth/FMD_Truck_python

e61bc0c2b278ee52a8848b5eed457ce7c1c775b0

6d241f454379936b7192d2422db19bc507d09e1c

refs/heads/master

2020-03-27T05:41:15.030656

2018-08-24T21:03:56

146,039,869

0

null

UTF-8

Python

false

3,383

py

#adding milk routes v2.py #Created by David South -- Last updated: 6/1/17 #Adapted from "adding routes v3" for the Feed Truck Model #Purpose: to create a Feature Class for each region with a specific number #of blank rows with specific fields that will be used to make routes in the VRP #(Vehicle Routing Problem) in the Network Analyst addon in ArcMap. #Note: this can be run in the Python IDE or in the arcpy window. ######################SETUP################### import arcpy, os from arcpy import env #set workplace environment ws = r'F:\FMD_Project\Davids_work\Milk_Truck_Model\Python' env.workspace = ws env.overwriteOutput = True ######################PARAMETERS############## #max capacity, in gallons per delivery truck capacities = 6500 #location of routes folder routes_folder = r'F:\FMD_Project\Davids_work\Milk_Truck_Model\Routes' #location of the creameries folder creameries_folder = r'F:\FMD_Project\Davids_work\Milk_Truck_Model\Creameries' #location of the creameries file creameries = r'F:\FMD_Project\Davids_work\Milk_Truck_Model\Creameries\creameries_v4_3.shp' #define fields to search in the creameries file fields1 = ['FID', 'Trucks_per'] #define fields for the routes file (EStartTime and LStartTime fields are for #determining which day the route will run, Earliest and Latest start time) fields2 = ['Name', 'StartDepot', 'EndDepot','EStartTime', 'LStartTime', \ 'Capacities'] #these fields were not used but can be added in: #'MaxOrder' #name of the new file newFile = 'MTM_routes.shp' #MTM means Milk Truck Model newFileFull = os.path.join(routes_folder, newFile) #define days of the week that the routes can run on. Note there is one extra #day, this is to incorporate a 24 hour period for each day. date = ['1994-08-22', '1994-08-23', '1994-08-24'] #define days of the week DotW = ['Mon', 'Tues'] ####################ACTUAL CODE################# #count how many Depots there are in the region creameries_C = arcpy.GetCount_management(in_rows= creameries) creameries_Count = int(creameries_C.getOutput(0)) #define blank lists trucks_per_creamery = [] #make a search cursor to save the info from the creameries and save it to a list with arcpy.da.SearchCursor(creameries, fields1) as cursor1: for row_1 in cursor1: trucks_per_creamery += [row_1[1]] #create a new completely blank shapefile for the routes arcpy.CreateFeatureclass_management(out_path= \ routes_folder, out_name= newFile, geometry_type="POLYLINE", template= "", \ has_m="DISABLED", has_z="DISABLED") #add new fields for num1 in range(0, len(fields2)): arcpy.AddField_management(newFileFull, fields2[num1], "TEXT", "", "", 15) #define a cursor cursor2 = arcpy.da.InsertCursor(newFileFull, fields2) ##add in a buncha blank rows for the routes## #make two copies of the routes, one for each 24 hour period for s in range (0, len(date)-1): #do this for each for each creamery for q in range (0, creameries_Count): #do this for for each route for p in range (0, trucks_per_creamery[q]): #fill the fields from fields2 with the following info cursor2.insertRow(['Route_' + str(q)+ '_' + str(p) + DotW[s], \ str(q), str(q), date[s], date[s+1], str(capacities)]) #get outta here cursor. You ain't gotta go home, but you can't stay here. del cursor2 print "Completed."

[ "12001003523326@FEDIDCARD.GOV" ]

12001003523326@FEDIDCARD.GOV

30afeecf7a442f626392bcc9b54728254bb8a8be

60d5ea4f007d49768d250ef394003f554003e4d0

/python/Linked List/142.Linked List Cycle II.py

dec51f534aabccb931d8e8932d39d11aac643c6f

[]

no_license

EvanJamesMG/Leetcode

dd7771beb119ea1250dbb3b147a09053298cd63b

fa638c7fda3802e9f4e0751a2c4c084edf09a441

refs/heads/master

2021-01-10T17:11:10.896393

2017-12-01T16:04:44

46,968,756

5

1

null

UTF-8

Python

false

1,911

py

# coding=utf-8 ''' Given a linked list, return the node where the cycle begins. If there is no cycle, return null. Note: Do not modify the linked list. ''' # Definition for singly-linked list. class ListNode(object): def __init__(self, x): self.val = x self.next = None ''' 使用快慢指针。若链表存在环，两指针必在环中相遇，此时将慢指针移回头结点，两指针以相同的速度移动，在环开始的节点处再次相遇。图中(http://www.cnblogs.com/zuoyuan/p/3701877.html)，head到环路起点的距离为K，起点到fast和slow的相遇点的距离为M，环路周长为L。假设，在fast和slow相遇时，fast走过了Lfast，slow走过了Lslow。根据题意：　　　　　Lslow=K+M；Lfast=K+M+n*L（n为正整数）；Lfast=2*Lslow 　　　　可以推出：Lslow=n*L；K=n*L-M 　　　　　则当slow重新回到head，而fast还在相遇点，slow和fast都向前走，且每次走一个节点。　　　　则slow从head走到起点走了K，而fast从相遇点出发也走了K，而fast向前走了距离K后到了哪里呢？由于K=（n-1）*L+（L-M），所以fast转了n-1圈，再走L-M，也到了起点。这样起点就找到了。 ''' class Solution(object): def detectCycle(self, head): """ :type head: ListNode :rtype: ListNode """ if head == None or head.next == None: return None slow = fast = head while fast and fast.next: slow = slow.next fast = fast.next.next if fast == slow: break if slow == fast: slow = head while slow != fast: slow = slow.next fast = fast.next return slow return None # if __name__ == "__main__": # # result = Solution().numTrees(3) # print result

[ "Evan123mg@gmail.com" ]

Evan123mg@gmail.com

1b464ae08fac263159c6d7d58623f8f8b5db8153

2e3f7b74e5e14eb9b12d316f609c3f057de1e845

/ka_kun_blog/forms.py

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katsuhikonakano/blog

edf3de81b1cfe45d2739fa55375bafbf326a0263

2c46d7e8b7044b1ab05ba9c9e595f9d4861b2674

refs/heads/master

2021-03-08T08:46:11.347696

2020-07-27T03:36:39

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from django import forms from .models import Post from django.forms import TextInput, Textarea, FileInput, Select, SelectMultiple from django.contrib.auth.forms import AuthenticationForm class PostForm(forms.ModelForm): class Meta: model = Post fields = ["title", "content", "thumnail", "image", "category", "tag"] widgets = { 'title': TextInput(attrs={ 'class': 'form-control', 'placeholder': 'タイトル'}), 'content': Textarea(attrs={ 'class': 'form-control', 'placeholder': '本文', 'cols': 80, 'rows': 15}), 'thumnail': FileInput(attrs={ 'class': 'form-control-file'}), 'image': FileInput(attrs={ 'class': 'form-control-file'}), 'category': Select(attrs={ 'class': 'form-control'}), 'tag': SelectMultiple(attrs={ 'class': 'form-control' }) } class LoginForm(AuthenticationForm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) for fields in self.fields.values(): fields.widget.attrs['class'] = 'form-control' fields.widget.attrs['placeholder']= fields.label

[ "katsuhiko1211@gmail.com" ]

katsuhiko1211@gmail.com

ade97c5dd853443919aed5d2d9e2775c257ab39f

b7c788979e8f0f7cb9283874e46f9ec12d13819c

/app/Member.py

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[ "MIT" ]

permissive

luwoldy/masonite-wcv

457ba95c7722f1434e35b31973f5b89102ce5434

c520f8b30c1775b3e337c3fa63ae8f08c1b0adf4

refs/heads/master

2023-01-09T05:34:49.048144

2019-12-20T02:55:54

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2023-01-04T11:19:06

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"""Member Model.""" from config.database import Model class Member(Model): """Member Model.""" pass

[ "leul.woldeab@gmail.com" ]

leul.woldeab@gmail.com

c4cf2cde8e5feb6718ee02fd9cd86914090cb1ec

55d9fd08d587c7a724a41c447bf7f57252788f5b

/lfp_causal/meso/stat_easy_vs_hard.py

3f46205f09f1dcec82319ae802294193264b9147

[]

no_license

StanSStanman/lfp_causal

1f400b5d5da285eacc273e04ecd2fcc4ee274bc6

832c22dd16aab5650355b58b96dbd3743805f640

refs/heads/master

2022-05-10T03:03:28.256695

2022-04-21T14:38:01

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import os import os.path as op import xarray as xr import numpy as np import pandas as pd from frites.dataset import DatasetEphy from frites.workflow import WfMi from itertools import product from research.get_dirs import get_dirs from lfp_causal.IO import read_session from lfp_causal.compute_bad_epochs import get_ch_bad_epo, get_log_bad_epo from lfp_causal.compute_power import normalize_power # os.system("taskset -p 0xff %d" % os.getpid()) from lfp_causal.profiling import (RepeatedTimer, memory_usage, cpu_usage) import time import json def prepare_data(powers, regressors, l_bad, e_bad, conditions, reg_name, reg_val, times=None, freqs=None, avg_freq=False, norm=None, bline=None, fbl=None): if times is not None: if isinstance(times, tuple): tmin, tmax = times elif isinstance(times, (list, np.ndarray)): tmin, tmax = times[0], times[-1] else: raise ValueError('times must be NoneType ' 'tuple of values (tmin, tmax),' 'list or numpy array') if freqs is not None: if isinstance(freqs, tuple): fmin, fmax = freqs else: raise ValueError('freqs must be NoneType ' 'or tuple of values (fmin, fmax)') all_pow = [] for p in powers: # for p, r, lb, eb in zip(powers, regresors, l_bad, e_bad): print('Opening', p) pow = xr.open_dataset(p) if fbl is not None: _fbl = p.split('/')[:-1] + [fbl] _fbl = '/' + op.join(*_fbl) if norm is not None: pow = normalize_power(pow, norm, bline, _fbl) if isinstance(times, (tuple, list, np.ndarray)): pow = pow.loc[dict(times=slice(tmin, tmax))] all_times = pow.times.values if isinstance(freqs, tuple): pow = pow.loc[dict(freqs=slice(fmin, fmax))] all_freqs = pow.freqs.values pow = pow.to_array().values.transpose(1, 0, 2, 3) # pow = pow[nans, :, :, :] if avg_freq: pow = pow.mean(2) all_pow.append(pow) all_reg = {} for rn in reg_name: # for rv in reg_val: _reg = [] for r, lb, eb, cn, _idx in zip(regressors, l_bad, e_bad, conditions, range(len(regressors))): xls = pd.read_excel(r, index_col=0) reg = xls[rn].values if len(lb) != 0: reg = np.delete(reg, lb) if len(eb) != 0: reg = np.delete(reg, eb) # I actually don't remember why they should be usefull, consider to eliminate them # all_pow[_idx] = np.delete(all_pow[_idx], np.where(reg != reg_val), # axis=0) # reg = np.delete(reg, np.where(reg != reg_val)) if cn == 'easy': reg = np.full_like(reg, 0) elif cn == 'hard': reg = np.full_like(reg, 1) _reg.append(reg) all_reg['{0}_{1}'.format(rn, reg_val)] = _reg return all_pow, all_reg, all_times, all_freqs def compute_stats_meso(fname_pow, fname_reg, rois, log_bads, bad_epo, conditions, regressor, reg_vals, mi_type, inference, times=None, freqs=None, avg_freq=True, norm=None): power, regr, \ times, freqs = prepare_data(fname_pow, fname_reg, log_bads, bad_epo, conditions=conditions, reg_name=regressor, reg_val=reg_vals, times=times, freqs=freqs, avg_freq=avg_freq, norm=norm, bline=(-.55, -0.05), fbl='cue_on_pow_beta_mt.nc') ########################################################################### for i, p in enumerate(power): power[i] = p[:25, :, :] for k in regr.keys(): for i, r in enumerate(regr[k]): regr[k][i] = r[:25] # for k in cond.keys(): # if cond[k] is not None: # for i, c in enumerate(cond[k]): # cond[k][i] = c[:25] ########################################################################### mi_results = {} pv_results = {} conj_ss_results = {} conj_results = {} for _r, _mt, _inf in zip(regr, mi_type, inference): if _mt == 'cc': regr[_r] = [r.astype('float32') for r in regr[_r]] elif _mt == 'cd': regr[_r] = [r.astype('int32') for r in regr[_r]] elif _mt == 'ccd': regr[_r] = [r.astype('float32') for r in regr[_r]] cond[_r] = [c.astype('int32') for c in cond[_r]] ds_ephy = DatasetEphy(x=power.copy(), y=regr[_r], roi=rois, z=None, times=times) wf = WfMi(mi_type=_mt, inference=_inf, kernel=np.hanning(20)) mi, pval = wf.fit(ds_ephy, n_perm=1000, n_jobs=-1) mi['times'] = times pval['times'] = times if _inf == 'rfx': conj_ss, conj = wf.conjunction_analysis(ds_ephy) if not avg_freq: mi.assign_coords({'freqs': freqs}) pval.assign_coords({'freqs': freqs}) mi_results[_r] = mi pv_results[_r] = pval if _inf == 'rfx': conj_ss_results[_r] = conj_ss conj_results[_r] = conj ds_mi = xr.Dataset(mi_results) ds_pv = xr.Dataset(pv_results) if len(conj_ss_results) == len(conj_results) == 0: return ds_mi, ds_pv else: ds_conj_ss = xr.Dataset(conj_ss_results) ds_conj = xr.Dataset(conj_results) return ds_mi, ds_pv, ds_conj_ss, ds_conj if __name__ == '__main__': from lfp_causal import MCH, PRJ dirs = get_dirs(MCH, PRJ) monkeys = ['teddy'] conditions = ['easy', 'hard'] event = 'trig_off' norm = 'fbline_relchange' n_power = '{0}_pow_beta_mt.nc'.format(event) times = [(-1.5, 1.3)] # freqs = [(5, 120)] # freqs = [(8, 15), (15, 30), (25, 45), (40, 70), (60, 120)] freqs = [(8, 12), (15, 35), (40, 65), (70, 120)] freqs = [(15, 35)] avg_frq = True overwrite = True # regressors = ['Correct', 'Reward', # 'is_R|C', 'is_nR|C', 'is_R|nC', 'is_nR|nC', # 'RnR|C', 'RnR|nC', # '#R', '#nR', '#R|C', '#nR|C', '#R|nC', '#nR|nC', # 'learn_5t', 'learn_2t', 'early_late_cons', # 'P(R|C)', 'P(R|nC)', 'P(R|Cho)', 'P(R|A)', # 'dP', 'log_dP', 'delta_dP', # 'surprise', 'surprise_bayes', 'rpe', # 'q_pcorr', 'q_pincorr', 'q_dP', # 'q_entropy', 'q_rpe', 'q_absrpe', # 'q_shann_surp', 'q_bayes_surp'] # conditionals = [None, None, # None, None, None, None, # None, None, # None, None, None, None, None, None, # None, None, None, # None, None, None, None, # None, None, None, # None, None, None, # None, None, None, # None, None, None, # None, None] # conditionals = ['Condition' for r in regressors] # mi_type = ['cd', 'cd', # 'cd', 'cd', 'cd', 'cd', # 'cd', 'cd', # 'cc', 'cc', 'cc', 'cc', 'cc', 'cc', # 'cd', 'cd', 'cd', # 'cc', 'cc', 'cc', 'cc', # 'cc', 'cc', 'cc', # 'cc', 'cc', 'cc', # 'cc', 'cc', 'cc', # 'cc', 'cc', 'cc', # 'cc', 'cc'] # mi_type = ['ccd' for r in regressors] regressors = ['Condition'] regressors = ['q_rpe'] reg_vals = 0 conditionals = [None] mi_type = ['cc'] inference = ['ffx' for r in regressors] fn_pow_list = [] fn_reg_list = [] rois = [] log_bads = [] bad_epo = [] conds = [] rej_files = [] rej_files += ['1204', '1217', '1231', '0944', # Bad sessions '0845', '0847', '0939', '0946', '0963', '1036', '1231', '1233', '1234', '1514', '1699', '0940', '0944', '0964', '0967', '0969', '0970', '0971', '0977', '0985', '1037', '1280'] rej_files += ['0210', '0219', '0221', '0225', '0226', '0227', '0230', '0252', '0268', '0276', '0277', '0279', '0281', '0282', '0283', '0285', '0288', '0290', '0323', '0362', '0365', '0393', '0415', '0447', '0449', '0450', '0456', '0541', '0573', '0622', '0628', '0631', '0643', '0648', '0653', '0660', '0688', '0689', '0690', '0692', '0697', '0706', '0710', '0717', '0718', '0719', '0713', '0726', '0732', '0220', '0223', '0271', '0273', '0278', '0280', '0284', '0289', '0296', '0303', '0363', '0416', '0438', '0448', '0521', '0618', '0656', '0691', '0693', '0698', '0705', '0707', '0711', '0712', '0716', '0720', '0731'] for monkey in monkeys: for condition in conditions: epo_dir = dirs['epo'].format(monkey, condition) power_dir = dirs['pow'].format(monkey, condition) regr_dir = dirs['reg'].format(monkey, condition) fname_info = op.join(dirs['ep_cnds'].format(monkey, condition), 'files_info.xlsx') for d in os.listdir(power_dir): if d in rej_files: continue if op.isdir(op.join(power_dir, d)): fname_power = op.join(power_dir, d, n_power) fname_regr = op.join(regr_dir, '{0}.xlsx'.format(d)) fname_epo = op.join(epo_dir, '{0}_{1}_epo.fif'.format(d, event)) fn_pow_list.append(fname_power) fn_reg_list.append(fname_regr) # rois.append(read_session(fname_info, d)['sector'].values) rois.append(np.array(['striatum'])) lb = get_log_bad_epo(fname_epo) log_bads.append(lb) be = get_ch_bad_epo(monkey, condition, d, fname_info=fname_info) bad_epo.append(be) conds.append(condition) mi_results = {} pv_results = {} for t, f in product(times, freqs): ds_mi, ds_pv = compute_stats_meso(fn_pow_list, fn_reg_list, rois, log_bads, bad_epo, conds, regressors, reg_vals, mi_type, inference, t, f, avg_frq, norm) mk = 'teddy' cd = 'easy_hard_rpe_one_roi' if avg_frq: save_dir = op.join(dirs['st_prj'], mk, cd, event, norm, '{0}_{1}_mt'.format(f[0], f[1])) elif not avg_frq: save_dir = op.join(dirs['st_prj'], mk, cd, event, norm, '{0}_{1}_tf_mt'.format(f[0], f[1])) os.makedirs(save_dir, exist_ok=True) fname_mi = op.join(save_dir, 'mi_results.nc'.format(f[0], f[1])) fname_pv = op.join(save_dir, 'pv_results.nc'.format(f[0], f[1])) if not overwrite and op.exists(fname_mi): mi = xr.load_dataset(fname_mi) pv = xr.load_dataset(fname_pv) ds_mi['times'] = mi['times'] ds_pv['times'] = pv['times'] ds_mi = mi.update(ds_mi) ds_pv = pv.update(ds_pv) ds_mi.to_netcdf(fname_mi) ds_pv.to_netcdf(fname_pv)

[ "ruggero.basanisi@gmail.com" ]

ruggero.basanisi@gmail.com

a8f1f9f4f7be1763c648b87bb38cc25de8b350de

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/entregable/script2.py

096bd5a6367f2b9e6bbe68b8be07dccec8ddcfce

[]

no_license

turtlean/sistemas-tp1

dc2fda54adb331a46962a59adc5867cc071f6e70

e0769d4234fb412cd9ca8c860f5bdf969155cf28

refs/heads/master

2021-05-27T06:29:31.538873

2013-11-19T01:30:58

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#! usr/bin/python from matplotlib.pyplot import * import sys import time import os import math import matplotlib.pyplot as plt #./simusched <lote.tsk> <num_cores> <costo_cs> <costo_mi> <sched> [<params_sched>] os.system("rm -rf experimento") os.system("mkdir experimento") #for plotting ywt=[] #waiting times yta=[] #ta times x=[] #quantums desviosWt=[] desviosTa=[] #random tasks wtProm=[] taProm=[] wtMin = 1000000 taMin = 1000000 fo = open("wtta.out", "a") #"wtta.out" sched = str(sys.argv[1]) #scheduler lote = str(sys.argv[2]) #lote de tareas qm = int(sys.argv[3]) #quantumLimite cores = str(sys.argv[4]) #coresLimite for k in range(1,int(cores)+1): for i in range (1,qm): j = str(i) print j for s in range (0,40): if (sched=="SchedLottery"): os.system("./simusched "+lote+" "+str(k)+" 0 0 "+sched+" "+j+" "+j+" > test") if (sched=="SchedRR" or sched=="SchedRR2"): string = "./simusched "+lote+" "+str(k)+" 0 0 "+sched+" " for h in range(0,k): string = string+j+" " string = string+"> test" os.system(string) #os.system("python graphsched.py test") os.system("./wtta < test > tmp") tmp1 = open("tmp",'r').read() tmp = open("tmp",'r').readlines() wt = tmp[0].split() wt = float(wt[2]) ta = tmp[1].split() ta = float(ta[2]) wtProm.append(wt) taProm.append(ta) wt = sum(wtProm) / float(len(wtProm)) ta = sum(taProm) / float(len(taProm)) desvioWt = [(z-wt)*(z-wt) for z in wtProm] desvioWt = math.sqrt(sum(desvioWt) / (float(len(desvioWt))-1)) desviosWt.append(desvioWt) desvioTa = [(b-ta)*(b-ta) for b in taProm] desvioTa = math.sqrt(sum(desvioTa) / (float(len(desvioTa))-1)) desviosTa.append(desvioTa) wtProm=[] taProm=[] ywt.append(wt) yta.append(ta) x.append(i) if (taMin > ta): taMin = ta quantum1 = i if (wtMin > wt): wtMin = wt quantum2 = i fo.write("Quantum: "+j+"\n") fo.write("Waiting time: "+str(wt)+"\n") fo.write("Turnaround time: "+str(ta)+"\n") #nombre_test = "test_"+j #os.system("mv test.png experimento/"+nombre_test+".png") #os.system("mv test experimento/"+nombre_test+".out") #os.system("rm test") fo.write("\n") fo.write("Quantum con menor waiting time: "+str(quantum2)+"("+str(wtMin)+")\n") fo.write("Quantum con menor turnaround time: "+str(quantum1)+"("+str(taMin)+")") #os.system("mv wtta.out experimento/") #Graficador plt.figure(k) plt.xlabel("Quantum") plt.ylabel("Waiting time") plt.errorbar(x, ywt, yerr=desviosWt, fmt='.', color='black') plt.axis([0,qm,0,max(ywt)+2]) savefig("experimento/cores_"+str(k)+"_wt.jpg") plt.figure(k+1) plt.xlabel("Quantum") plt.ylabel("Turnaround time") plt.errorbar(x, yta, yerr=desviosTa, fmt='.', color='black') plt.axis([0,qm,taMin-60,max(yta)+60]) savefig("experimento/cores_"+str(k)+"_ta.jpg") wtMin = 1000000 taMin = 1000000 ywt=[] #waiting times yta=[] #ta times x=[] #quantums desviosWt=[] desviosTa=[] os.system("rm tmp") os.system("rm test") os.system("mv wtta.out experimento/")

[ "martin@martin-pc.(none)" ]

martin@martin-pc.(none)

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/trigger_lambda_on_s3_put.py

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[]

no_license

smusongwe/s3

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refs/heads/main

2023-01-19T03:25:22.171853

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import json import urllib.parse import boto3 print('Loading function') s3 = boto3.client('s3') def lambda_handler(event, context): #1 - Get the bucket name bucket = event['Records'][0]['s3']['bucket']['name'] #2 - Get the file/key name key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'], encoding='utf-8') try: #3 - Fetch the file from S3 response = s3.get_object(Bucket=bucket, Key=key) #4 - Deserialize the file's content text = response["Body"].read().decode() data = json.loads(text) #5 - Print the content print(data) #6 - Parse and print the transactions transactions = data['transactions'] for record in transactions: print(record['transactionType']) return 'Success!' except Exception as e: print(e) raise e

[ "noreply@github.com" ]

smusongwe.noreply@github.com

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/src/meron_background.py

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[]

no_license

haugstve/use-watson-ml-python

e9f750a1daa85ba9f83b83d9690d95d1beeb3c9c

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refs/heads/master

2020-08-10T14:21:14.586297

2019-10-11T11:34:25

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import schedule import time import random import json from think_meron import aim_token, evaluate_meron_model from path import processed_data_path, root def job(): data_path = processed_data_path / 'payloads_meron.json' with open(data_path, 'r') as f: payloads = json.load(f) token = aim_token() selection = random.randint(0, len(payloads)) response = evaluate_meron_model(token, payloads[selection]) log_path = root / 'response.log' with open(log_path, 'a+') as f: f.write(f'status: {response.status_code}' f', content: {response.content}\n') schedule.every().minute.at(":00").do(job) while True: schedule.run_pending() time.sleep(1)

[ "daniel.haugstvedt@gmail.com" ]

daniel.haugstvedt@gmail.com

280a05ec34f5669a8ef15a8fd75fa5be7a8a3981

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/PDF_Sales/Old_files/PDF_IND_Dates_Old/PDF_IND_Dates_2.py

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[]

no_license

GolovanPriherel/Python_Projects

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refs/heads/main

2023-08-28T10:59:35.320556

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UTF-8

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5,909

py

import re, fitz, pytesseract, urllib.request # ---- Работа с PDF from PIL import Image # Шаблоны поиска patternsIND = [' IND ([0-9,]+)', ' IND Number.([0-9,]+)', ' IND No. ([0-9,]+)'] patternsINDDate = ['IND\D{4}(\d{1,2}.\d{1,2}.\d{1,4})', 'IND \d{5,6}..(\d{1,2}.\D+.\d{1,4})', 'IND\D{4}(\d{1,2}.\D+.\d{1,4})', 'IND \d{5,6}..(\d{1,2}.\d{1,2}.\d{1,4})', 'IND.[0-9,]+.Review #01 dated\s\s(\w+.\d+..\d+)', 'was originally submitted'] # Путь для подключения tesseract pytesseract.pytesseract.tesseract_cmd = 'C:\\Program Files\\Tesseract-OCR\\tesseract.exe' # Открывает по страницам ПДФ (для текста) def Extract_PDF_Text(pdf_file): pdf_document = pdf_file doc = fitz.open(pdf_document) for pages in range(doc.pageCount): page = doc.loadPage(pages) page_text = page.getText("text") yield page_text # print(page_text) # Находит в тексте из ПДФ-а номер и дату def extract_text(pdf_file, pattern1, pattern2): Date, IND = [], [] for page in Extract_PDF_Text(pdf_file): for patt in pattern1: result_IND = re.findall(patt, page) for sub in range(len(result_IND)): IND.append(result_IND[sub].replace('\n', '')) for patt in pattern2: result_IND_Date = re.findall(patt, page) for sub in range(len(result_IND_Date)): Date.append(result_IND_Date[sub].replace('\n', '')) if IND: IND = (max(set(IND), key=IND.count)) if Date: Date = (max(set(Date), key=Date.count)) elif IND and Date: IND = (max(set(IND), key=IND.count)) Date = (max(set(Date), key=Date.count)) # print('Текст победил') return IND, Date # Получает картинки из ПДФ-а def extract_png(PDF): ind_num, ind_date = '','' doc = fitz.open(PDF) for i in range(0, len(doc)): for img in doc.getPageImageList(i): xref = img[0] pix = fitz.Pixmap(doc, xref) if pix.n < 5: # this is GRAY or RGB pix.writePNG("Text1.png") else: # CMYK: convert to RGB first pix1 = fitz.Pixmap(fitz.csRGB, pix) pix1.writePNG("Text1.png") pix1 = None pix = None text = Find_IND_Date_Tess("Text1.png") IND_Num, IND_Date = extract_text_png(text, patternsIND, patternsINDDate) # print('---', i) if IND_Num and IND_Date: return IND_Num, IND_Date elif IND_Num: ind_num = IND_Num elif IND_Date: ind_date = IND_Date return ind_num, ind_date # Распознавание текста в картинках def Find_IND_Date_Tess(picture): img = Image.open(f'{picture}') file_name = img.filename file_name = file_name.split(".")[0] custom_config = r'--oem 3 --psm 6' text = pytesseract.image_to_string(img, config=custom_config) return text # Находит из картинок номер и дату def extract_text_png(text, pattern1, pattern2): IND, Date = [], [] for patt in pattern1: result_IND = re.findall(patt, text) for sub in range(len(result_IND)): IND.append(result_IND[sub].replace('\n', '')) for patt in pattern2: result_IND_Date = re.findall(patt, text) for sub in range(len(result_IND_Date)): Date.append(result_IND_Date[sub].replace('\n', '')) if IND: IND = (max(set(IND), key=IND.count)) elif Date: Date = (max(set(Date), key=Date.count)) elif IND and Date: IND = (max(set(IND), key=IND.count)) Date = (max(set(Date), key=Date.count)) # print('Изображения победили') return IND, Date # Вызов всех ф-ий для парсера def Find_IND_Date (url): # Просто впиши ссылку на ПДФ и всё urllib.request.urlretrieve(url, "../IND1.pdf") # Скачиваем ПДФ IND_Num, IND_Date = extract_text("../IND1.pdf", patternsIND, patternsINDDate) # Зырим текст TrueNum, TrueDate = None, None if IND_Num and IND_Date: # Проверям хватит ли только текста для проверки return IND_Num, IND_Date elif IND_Num: TrueNum = IND_Num elif IND_Date: TrueDate = IND_Date else: IND_Num1, IND_Date1 = extract_png("IND1.pdf") # Проверка с помощью распознавания if IND_Num1: TrueNum = IND_Num1 elif IND_Date1: TrueDate = IND_Date1 # Доделать распознавание приколсов return TrueNum, TrueDate # Проверенные ссылки на пдф # url = 'https://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/202971Orig1s000PharmR.pdf' # с инд (5 цифр) и датой (через пробел) # url = 'https://www.accessdata.fda.gov/drugsatfda_docs/nda/2021/212887Orig1s000,212888Orig1s000Approv.pdf' # с инд (6) # url = 'https://www.accessdata.fda.gov/drugsatfda_docs/nda/2012/202428Orig1s000PharmR.pdf' # с инд (6) # url = 'https://www.accessdata.fda.gov/drugsatfda_docs/nda/2002/21-456_Aciphex_Medr_P1.pdf' # Картинка с датой IND, но без номера url = 'https://www.accessdata.fda.gov/drugsatfda_docs/nda/2010/022518Orig1s000PharmR.pdf' # Куча разных номеров инд с датой IND_Num, IND_Date = Find_IND_Date(url) print(IND_Num, '|', IND_Date)

[ "noreply@github.com" ]

GolovanPriherel.noreply@github.com

ef2e1856553562a52e2e69951f8546c91328ccd8

5c41073897f791bc6915c874a9304d7d7f6c13db

/gravitate/domain/bookings/view_mediator.py

853566c6ab0c9429b78407c69be23fce3ed1e7bc

[]

no_license

billyrrr/gravitate-backend

5c8ce8b95607b06bd2d850b085e9129d9dc8632b

f1e98f0002046cb4c932f9f1badbdf2eb8af92d1

refs/heads/master

2023-05-11T10:58:07.871694

2020-05-07T23:04:50

156,035,235

0

null

2020-01-19T02:32:39

2018-11-04T00:19:16

Python

UTF-8

Python

false

4,130

py

import time from flask_boiler.utils import snapshot_to_obj from flask_boiler.view.query_delta import ViewMediatorDeltaDAV, ProtocolBase from google.cloud.firestore import DocumentSnapshot from gravitate import CTX from gravitate.domain.user import User from . import RiderBooking, BookingStoreBpss, RiderTarget, RiderBookingView, \ RiderBookingForm, RiderBookingReadModel from google.cloud.firestore import Query from ..location.models import Sublocation class UserBookingMediator(ViewMediatorDeltaDAV): """ Forwards a rider booking to a user subcollection """ class Protocol(ProtocolBase): @staticmethod def on_create(snapshot, mediator): obj = RiderBookingReadModel.new(snapshot=snapshot) mediator.notify(obj=obj) @staticmethod def on_update(snapshot, mediator): obj: RiderBooking = snapshot_to_obj(snapshot) if obj.status == "removed": RiderBookingReadModel.remove_one(obj=obj) model_cls = RiderBooking class UserBookingEditMediator(ViewMediatorDeltaDAV): """ Forwards a rider booking to a user subcollection """ def notify(self, obj): obj.propagate_change() class Protocol(ProtocolBase): @staticmethod def on_create(snapshot, mediator): assert isinstance(snapshot, DocumentSnapshot) path = snapshot.reference booking_id = path.id user_id = path.parent.parent.id d = snapshot.to_dict() obj = RiderBookingForm.from_dict(doc_id=booking_id, d=dict(**d, user_id=user_id)) mediator.notify(obj=obj) snapshot.reference.delete() class BookingTargetMediator(ViewMediatorDeltaDAV): """ Generate booking target from a rider booking newly added or edited. """ model_cls = RiderBooking def notify(self, obj): obj.save() class Protocol(ProtocolBase): @staticmethod def on_create(snapshot, mediator): obj: RiderBooking = snapshot_to_obj(snapshot=snapshot) for from_sublocation_ref in obj.from_location.sublocations: from_sublocation = Sublocation.get(doc_ref=from_sublocation_ref) for to_sublocation_ref in obj.to_location.sublocations: to_sublocation = Sublocation.get( doc_ref=to_sublocation_ref) d = dict( r_ref=obj.doc_ref, from_lat=from_sublocation.coordinates[ "latitude"], from_lng=from_sublocation.coordinates[ "longitude"], from_id=from_sublocation.doc_id, to_lat=to_sublocation.coordinates["latitude"], to_lng=to_sublocation.coordinates["longitude"], to_id=to_sublocation.doc_id, user_id=obj.user_id ) ts = dict( earliest_arrival=obj.earliest_arrival, latest_arrival=obj.latest_arrival, earliest_departure=obj.earliest_departure, latest_departure=obj.latest_departure, ) ts = {k: v for k, v in ts.items() if v is not None} target = RiderTarget.new( **d, **ts ) mediator.notify(obj=target) @staticmethod def on_delete(snapshot, mediator): obj: RiderBooking = snapshot_to_obj(snapshot=snapshot) if obj.status in {"matched", }: """ Delete targets for matched rider bookings """ booking_ref = obj.doc_ref for target in RiderTarget.where(r_ref=booking_ref): target.delete() def _get_query(self): query = Query(parent=self.model_cls._get_collection()) return query

[ "billrao@me.com" ]

billrao@me.com

6ffd2e78972b8fd0b9f8b3259fae0c6b13ecaf63

0f3c7d268349336160d592eaa3acf56a1bb12cc2

/reports/ISOPE_outline/create_latex_report.py

42c30523f9cc8383a9d80aa0358fe68d4ba36499

[ "MIT" ]

permissive

ramisetti/Prediction-of-roll-motion-using-fully-nonlinear-potential-flow-and-Ikedas-method

ac0f7eac820e5f381d1073bd52556ac313026727

cce8abde16a15a2ae45008e48b1bba9f4aeaaad4

refs/heads/main

2023-05-19T05:54:41.448268

2021-06-11T09:48:15

null

0

null

UTF-8

Python

false

917

py

import os.path from src.notebook_to_latex import convert_notebook_to_latex import reports notebook_path = os.path.join(reports.path,'ISOPE_outline','01.1.outline.ipynb') build_directory = os.path.join(reports.path,'ISOPE') if not os.path.exists(build_directory): os.mkdir(build_directory) skip_figures=True convert_notebook_to_latex(notebook_path=notebook_path, build_directory=build_directory, save_main=False, skip_figures=skip_figures) if not skip_figures: ## Special treatment import joblib import graphviz from sklearn import tree clf = joblib.load('models/C_r_tree.pkl') dot_data = tree.export_graphviz(clf, out_file=None, feature_names=[r'sigma', r'a_1', r'a_3'], rounded=True, special_characters=True) graph = graphviz.Source(dot_data) graph.render("reports/ISOPE/figures/decision_tree")

[ "marale@kth.se" ]

marale@kth.se

ea147069bc358894d57aadf2f0981d4f2f8e0902

8d6418d8f813961318a962638467cdea5adec882

/test.py

4e4d2e798b46c69735d1b4597e4283d944ae03bd

[]

no_license

demakaituan/code

6d7045d7a085ff843b636ecb3e88d9959f68d32e

6578bc0dbd7018449e9f8c83f4fc5304126036a6

refs/heads/master

2021-01-21T14:04:26.549322

2016-05-30T08:54:51

54,120,862

0

null

UTF-8

Python

false

2,303

py

import unittest import seuif97 class Region1_Test(unittest.TestCase): def setUp(self): self.tab5 = [[3, 300, 0.100215168e-2, 0.115331273e3, 0.112324818e3, 0.392294792, 4.17301218, 0.150773921e4], [80, 300, 0.971180894e-3, 0.184142828e3, 0.106448356e3, 0.368563852, 4.01008987, 0.163469054e4], [3, 500, 0.120241800e-2, 0.975542239e3, 0.971934985e3, 2.58041912, 4.65580682, 0.124071337e4]] def test_specific_volume(self): places = 11 for item in self.tab5: self.assertAlmostEqual(seuif97.specific_volume(item[0], item[1]), item[2], places) def test_specific_enthalpy(self): places = 6 for item in self.tab5: self.assertAlmostEqual(seuif97.specific_enthalpy(item[0], item[1]), item[3], places) def test_specific_internal_energy(self): places = 6 for item in self.tab5: self.assertAlmostEqual(seuif97.specific_internal_energy( item[0], item[1]), item[4], places) def test_specific_entropy(self): places = 8 for item in self.tab5: self.assertAlmostEqual(seuif97.specific_entropy(item[0], item[1]), item[5], places) def test_specific_isobaric_heat_capacity(self): places = 8 for item in self.tab5: self.assertAlmostEqual(seuif97.specific_isobaric_heat_capacity( item[0], item[1]), item[6], places) def test_speed_of_sound(self): places = 5 for item in self.tab5: self.assertAlmostEqual(seuif97.speed_of_sound(item[0], item[1]), item[7], places) class additional_Test(unittest.TestCase): def setUp(self): self.tab6 = [[0.001, 0, 9.800980612e-4], [90, 0, 91.92954727], [1500, 3.4, 58.68294423]] def test_backward_equations(self): places = 8 for item in self.tab6: self.assertAlmostEqual(seuif97.backward_equations(item[0], item[1]), item[2], places) def suite_test(): suite = unittest.TestSuite() suite.addTest(unittest.makeSuite(Region1_Test)) suite.addTest(unittest.makeSuite(additional_Test)) return suite if __name__ == '__main__': unittest.main(defaultTest = 'suite_test')

[ "599146992@qq.com" ]

599146992@qq.com

fe329d17aaaa67a21a1b92f02c926bb41085e379

28df0dfb458a3c0f37d5a597307017717f9b1cc3

/auth_utils.py

4880fdd53294b5dadd961c64186bd5575b88e666

[]

no_license

dagou1992/flask

5533aa7c48a98c561b4235cbfcc6675ebfd81471

67afd661e0d14a62c55789b844ae11e790ca531d

refs/heads/master

2020-03-24T00:02:55.456559

2018-07-25T08:12:49

142,269,991

0

null

UTF-8

Python

false

732

py

# coding: utf-8 from flask_login import LoginManager, UserMixin from flask_login import login_user from app import json_response_error login_manager = LoginManager() def init_auth(app): login_manager.init_app(app) class User(UserMixin): def __init__(self, user): self.info = user def get_id(self): """登录成功后，就会调用get_id()获取到id存入session中""" return self.info @login_manager.user_loader def load_user(user_id): return User(user_id) # 在session中记录user已经登录 def register_login(user): login_user(User(user)) @login_manager.unauthorized_handler def unauthorized_handler(): return json_response_error(401, "用户未登录。"), 401

[ "jbq19920903@163.com" ]

jbq19920903@163.com

3393ba44a415f8540c1fe8973955eef2de703ff1

c211c25b0dde4435f09357880ac0db97c05fd9a7

/Spider.py

9575f749de3597014c98b646d7e53ddc7af3777c

[]

no_license

Monday666/maoyanSpider

2abfd912a56afeef9563b9f6f4e18b3377fc4c7f

68036975face7256d4b6286a325a28174943465c

refs/heads/master

2020-03-31T07:10:07.798737

2018-10-08T02:58:28

152,010,437

1

0

null

UTF-8

Python

false

5,377

py

import requests from bs4 import BeautifulSoup import re import csv def Gethtml(url): """ 使用浏览器模拟的方式获取网页源码 :param url: 网页URL地址 :return:html """ # 将cookies字符串组装为字典 cookies_str = '__mta=208903469.1537876239606.1537966087518.1537966271718.11; uuid_n_v=v1; uuid=3851AE40C0B911E895764F985E386DE202DFFDFED118403EB9BA5E7A9C9D6698; _lxsdk_cuid=16610912361c8-07a50566ed1d0e-8383268-1fa400-16610912362c8; _lxsdk=3851AE40C0B911E895764F985E386DE202DFFDFED118403EB9BA5E7A9C9D6698; _csrf=33645a5e9922420ef22609cd9965dd58afac2d82a9caca9afd817c97d4a41563; _lx_utm=utm_source%3Dmeituanweb; __mta=208903469.1537876239606.1537964122287.1537964124676.6; _lxsdk_s=16615ccbec7-4dc-ef6-e2a%7C%7C20' cookies_dict = {} for cookie in cookies_str.split(";"): k, v = cookie.split("=", 1) cookies_dict[k.strip()] = v.strip() # 其他请求头参数 headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36' } # 访问页面 page = requests.get(url=url, cookies=cookies_dict, headers=headers) # 返回页面HTML return page.text def FindMovie(sp): """ 截取各部电影的所在标签返回列表，每个值是一部电影所在整个标签里的内容 """ movie = sp.find_all('div', attrs={'class': re.compile(r"show-list(\s\w+)?")}) return movie def FindPage(sp): """ 寻找并截取每个日期的界面 :param sp: :return: """ page = sp.find_all('div', attrs={'class': re.compile(r"plist-container(\s\w+)?")}) return page def FindName(sp): """ 找到电影名 :param sp:soup :return:str name """ name = sp.find('h3', class_='movie-name') return name.text def FindDate(sp): """ 找到日期 :param sp: :return: """ lsdate = sp.find_all('span', attrs={'class': re.compile(r"date-item(\s\w+)?")}) data = [] for l in lsdate: data.append(l.text) return data def FindTime(sp): """ 找到时间 :param sp: :return: """ time = [] page = FindPage(sp) for i in range(len(page)): lstime = page[i].find_all('span', attrs={'class': 'begin-time'}) timei = [] if lstime == []: timei.append("无场次") else: for l in lstime: timei.append(l.text) time.append(timei) return time def FindPrice(sp): """ 找到价格 :param sp: :return: """ lsprice = sp.find('span', class_='value text-ellipsis', text=re.compile(r"(.\d+.\d.)张")) return lsprice.text def FindPeople(sp): """ 找到人数，返回已售票，和未售票 :param sp: :return: """ Npeople = sp.find_all('span', class_='seat selectable') Hpeople = sp.find_all('span', class_='seat sold') return Npeople, Hpeople def ReturnPrice(sp): """ 到价格界面找到价格并返回价格值 :param sp: :return: """ page = FindPage(sp) server = "http://maoyan.com" price = [] for i in range(len(page)): pricei = [] Url = [] a = page[i].find_all('a', attrs={'class': 'buy-btn normal'}) if a == []: pricei.append('无') else: for each in a: Url.append(server + each.get('href')) for j in Url: pricei.append(FindPrice(BeautifulSoup(Gethtml(j), 'html.parser'))) price.append(pricei) return price def ReturnPN(sp): """ 到人数界面找到人数并返回已售票之和未售票值 :param sp: :return: """ peopleN = [] page = FindPage(sp) server = "http://maoyan.com" for i in range(len(page)): Url = [] peopleNi = [] a = page[i].find_all('a', attrs={'class': 'buy-btn normal'}) if a == []: peopleNi.append('无') else: for each in a: Url.append(server + each.get('href')) for j in Url: people = FindPeople(BeautifulSoup(Gethtml(j), 'html.parser')) Npeople, Hpeople = people peopleNi.append("已售出：" + str(len(Hpeople)) + "剩余票数：" + str(len(Npeople))) peopleN.append(peopleNi) return peopleN # 获取网页源码 URL = "http://maoyan.com/cinema/2714?poi=2367020" sp1 = BeautifulSoup(Gethtml(URL), 'html.parser') movie = FindMovie(sp1) name = [] data = [] time = [] price = [] peopleN = [] # 获取数据 for i in range(len(movie)): name.append(FindName(movie[i])) data.append(FindDate(movie[i])) time.append(FindTime(movie[i])) price.append(ReturnPrice(movie[i])) peopleN.append(ReturnPN(movie[i])) # 整合数据 info = [] for i in range(len(movie)): for j in range(len(data[i])): for k in range(len(time[i][j])): infok = [name[i], data[i][j], time[i][j][k], price[i][j][k], peopleN[i][j][k]] info.append(infok) #存储数据 with open('myinfo.csv', 'w', newline='') as csvfile: csvwriter = csv.writer(csvfile) csvwriter.writerow(['电影名', '日期', '时间', '票价', '余票']) for i in range(len(info)): csvwriter.writerow(info[i])

[ "[18361233771@163.com]" ]

[18361233771@163.com]

baba79af33bbf688b0cc90d14d78060c6b946973

3a771b72dae1aae406b94726bcbcf73915577b18

/q56.py

e7701a5af9e0d8749ed043cc4977a73042423870

[]

no_license

SHANK885/Python-Basic-Programs

4fcb29280412baa63ffd33efba56d9f59770c9dc

157f0f871b31c4523b6873ce5dfe0d6e26a6dc61

refs/heads/master

2021-07-18T18:24:10.455282

2018-11-19T07:02:27

138,009,231

0

null

UTF-8

Python

false

51

py

unicodeString =u"Heello world" print(unicodeString)

[ "shashankshekhar885@gmail.com" ]

shashankshekhar885@gmail.com

41255f5976ab13155649263d540c618488794b94

08a329d07172a384be41eb58a0586032b18787d2

/if1.py

c5867a5c4dee5850f5bb2049812193dbe20c31e6

[]

no_license

atsuhisa-i/Python_study1

9bc39d058fe8bdd00adb35324758ad8fa08f4ca1

439a654f09e81208658355d99c8ce1c3cd4bcc4e

refs/heads/main

2023-04-06T12:44:12.099067

2021-04-14T13:24:56

348,309,405

0

null

2021-04-14T13:24:57

2021-03-16T10:45:48

Python

UTF-8

Python

false

69

py

number = '123456' if number == '123456': print('1st Prize:Money')

[ "atsuhisa.1124@gmail.com" ]

atsuhisa.1124@gmail.com

6932b26cbfad7fb87ae3a6ac07694d091c54d719

e35594083b7dfc15a8e790efa26fc36ac264ccce

/pages/base_page.py

0583a1adc7f65adf2a1972a4236615998f514a99

[]

no_license

quiprodest/testauto-study-project

4f10bc8552fe8b8ca354bc856bab2ddcf76982e5

b338ea8d83dfa046c38507e2524e47431d172b26

refs/heads/master

2021-06-19T16:46:40.660719

2019-07-30T12:43:27

199,621,325

0

null

2021-04-20T18:26:23

2019-07-30T09:35:41

Python

UTF-8

Python

false

496

py

from selenium.common.exceptions import NoSuchElementException class BasePage(object): def __init__(self, browser, url): # , timeout=10 self.browser = browser self.url = url # self.browser.implicitly_wait(timeout) def open(self): self.browser.get(self.url) def is_element_present(self, how, what): try: self.browser.find_element(how, what) except (NoSuchElementException): return False return True

[ "qui_p@tutanota.com" ]

qui_p@tutanota.com

6f54f5939a8fda03c24dfa9d9dbe33c08f498424

096ccaca86872b03a137edf58221413073d770cb

/helpers/24_web_apis_sources.py

0a219f85661a944bd17fb1db67075e5cf05ea372

[]

no_license

DH-heima/webscrapping

f142962b50deed2628052dd7a48098a4afbcbada

1dc8f81f45db0d4366391c3052c5ab36f4d4bc5d

refs/heads/master

2022-02-02T23:26:22.520064

2019-06-13T13:38:10

null

0

null

UTF-8

Python

false

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import os # from ers import shops, COLLECTION_DATE, web_apis_traffic_sources_csv, web_apis_traffic_sources_aggregates_csv import os.path as op import numpy as np import pandas as pd BASE_DIR = "/code/mhers" WAVE_NUMBER = 8 shops = pd.read_excel(op.join(BASE_DIR, "ressources/ERS-referential-shops.xlsx"), index_col=None) COLLECTION_DATE = "2018-06-10" web_apis_traffic_sources_csv = os.path.join(BASE_DIR,'data/w_{}/final_csvs'.format(WAVE_NUMBER), 'shopgrid_details - web_apis_traffic_sources_w{}.csv'.format(WAVE_NUMBER)) web_apis_traffic_sources_aggregates_csv = os.path.join(BASE_DIR,'data/w_{}/final_csvs'.format(WAVE_NUMBER), 'shopgrid_summary - web_apis_traffic_sources_w{}.csv'.format(WAVE_NUMBER)) # ##################################################################################################################### # web_apis_demographics_csv # ##################################################################################################################### # This generates the dummy data and shouldn't be in production mask = pd.DataFrame({'to_delete': [1]}) df = pd.DataFrame() for c, row in shops.iterrows(): tmp = pd.DataFrame(mask.copy()) for k in ['shop_id', 'continent', 'country', 'region', 'segment']: tmp[k] = row[k] df = df.append(tmp) df.drop(columns=['to_delete'], inplace=True) # TODO : delete the random data creation and fetch the data in the proper dataset df['direct'] = np.random.random(size=(df.shape[0], 1)) * 0.3 df['email'] = np.random.random(size=(df.shape[0], 1)) * 0.2 df['referrals'] = np.random.random(size=(df.shape[0], 1)) * 0.2 df['social'] = np.random.random(size=(df.shape[0], 1)) * 0.1 df['paid_search'] = np.random.random(size=(df.shape[0], 1)) * 0.1 df['display_ads'] = np.random.random(size=(df.shape[0], 1)) * 0.1 df['organic_search'] = 1 - df['direct'] - df['email'] - df['referrals'] - df['social'] - df['paid_search'] - df['display_ads'] # Todo : Time Span is the time over which the aggregates are calculated df['time_span'] = "Apr. 2016 - Aug. 2018" # Collection date print('WARNING : PLEASE ENSURE THE COLLECTION_DATE is accurate :', COLLECTION_DATE) df['collection_date'] = COLLECTION_DATE final_cols = ['collection_date', 'time_span', 'continent', 'country', 'region', 'segment', 'shop_id', 'direct', 'email', 'referrals', 'social', 'paid_search', 'display_ads', 'organic_search'] df = df[final_cols] df.to_csv(web_apis_traffic_sources_csv, sep=';', index=False, encoding='utf-8') print("File web_apis_traffic_sources_csv stored at : ", web_apis_traffic_sources_csv) # ##################################################################################################################### # web_apis_demographics_aggregates_csv # ##################################################################################################################### df['region'].fillna("", inplace=True) # Aggregating res = [] agregation_levels_list = [ ['continent', 'country', 'region', 'segment'], ['continent', 'country', 'segment'], ['continent', 'segment'], ['segment'], ['continent', 'country', 'region'], ['continent', 'country'], ['continent'], ['collection_date'] ] # All agregations for agg_level in agregation_levels_list: dfG2 = df.groupby(agg_level, as_index=False) dfG2 = dfG2.agg({ 'direct': {'direct': 'mean'}, 'email': {'email': 'mean'}, 'referrals': {'referrals': 'mean'}, 'social': {'social': 'mean'}, 'paid_search': {'paid_search': 'mean'}, 'display_ads': {'display_ads': 'mean'}, 'organic_search': {'organic_search': 'mean'}, }).reset_index() dfG2.columns = dfG2.columns.droplevel(1) dfG2 = pd.DataFrame(dfG2) print(agg_level, 'adding', dfG2.shape) res.append(dfG2) # Aggregate on all-levels all_dfs = pd.concat(res, axis=0, ignore_index=True) # Collection date print('WARNING : PLEASE ENSURE THE COLLECTION_DATE is accurate :', COLLECTION_DATE) all_dfs['collection_date'] = COLLECTION_DATE # Todo : Time Span is the time over which the aggregates are calculated all_dfs['time_span'] = "Apr. 2016 - Aug. 2018" final_cols = ['collection_date', 'time_span', 'continent', 'country', 'region', 'segment', 'direct', 'display_ads', 'email', 'organic_search', 'paid_search', 'referrals', 'social'] all_dfs = all_dfs[final_cols] all_dfs.to_csv(web_apis_traffic_sources_aggregates_csv, sep=';', index=None, encoding='utf-8') print("File web_apis_traffic_sources_aggregates_csv stored at : ", web_apis_traffic_sources_aggregates_csv, " -")

[ "pierre.chevalier@epitech.eu" ]

pierre.chevalier@epitech.eu

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2021-01-01T06:34:58.613504

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def main(): sayi = "731671765313306249192251196744265747423553491949349698352031277450632623957831801698480186947885184385861560789112949495459501737958331952853208805511125406987471585238630507156932909632952274430435576689664895044524452316173185640309871112172238311362229893423380308135336276614282806444486645238749303589072962904915604407723907138105158593079608667017242712188399879790879227492190169972088809377665727333001053367881220235421809751254540594752243525849077116705560136048395864467063244157221553975369781797784617406495514929086256932197846862248283972241375657056057490261407972968652414535100474821663704844031998900088952434506585412275886668811642717147992444292823086346567481391912316282458617866458359124566529476545682848912883142607690042242190226710556263211111093705442175069416589604080719840385096245544436298123098787992724428490918884580156166097919133875499200524063689912560717606058861164671094050775410022569831552000559357297257163626956188267042825248360082325753" #Son 13 hane sığmadı. enBuyuk = 0 for i in range(0, len(sayi) - 13): sayilar = [] carpim = 1 for j in range(0, 13): carpim *= int(sayi[j + i]) sayilar.append(int(sayi[j + i])) if carpim > enBuyuk: enBuyuk = carpim print(sayilar) print(enBuyuk) if __name__ == "__main__": main()

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/application.py

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Mohamed24444/HW-todo-api

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import helper from flask import Flask, request, Response import json application = app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello World!' @app.route('/item/new', methods=['POST']) def add_item(): # Get item from the POST body req_data = request.get_json() item = req_data['item'] # Add item to the list res_data = helper.add_to_list(item) # Return error if item not added if res_data is None: response = Response("{'error': 'Item not added - " + item + "'}", status=400 , mimetype='application/json') return response # Return response response = Response(json.dumps(res_data), mimetype='application/json') return response @app.route('/items/all') def get_all_items(): # Get items from the helper res_data = helper.get_all_items() # Return response response = Response(json.dumps(res_data), mimetype='application/json') return response @app.route('/item/status', methods=['GET']) def get_item(): # Get parameter from the URL item_name = request.args.get('name') # Get items from the helper status = helper.get_item(item_name) # Return 404 if item not found if status is None: response = Response("{'error': 'Item Not Found - %s'}" % item_name, status=404 , mimetype='application/json') return response # Return status res_data = { 'status': status } response = Response(json.dumps(res_data), status=200, mimetype='application/json') return response @app.route('/item/update', methods=['PUT']) def update_status(): # Get item from the POST body req_data = request.get_json() item = req_data['item'] status = req_data['status'] # Update item in the list res_data = helper.update_status(item, status) # Return error if the status could not be updated if res_data is None: response = Response("{'error': 'Error updating item - '" + item + ", " + status + "}", status=400 , mimetype='application/json') return response # Return response response = Response(json.dumps(res_data), mimetype='application/json') return response @app.route('/item/remove', methods=['DELETE']) def delete_item(): # Get item from the POST body req_data = request.get_json() item = req_data['item'] # Delete item from the list res_data = helper.delete_item(item) # Return error if the item could not be deleted if res_data is None: response = Response("{'error': 'Error deleting item - '" + item + "}", status=400 , mimetype='application/json') return response # Return response response = Response(json.dumps(res_data), mimetype='application/json') return response @app.route('/items/delall', methods=['DELETE']) def del_all(): # Get items from the helper res_data = helper.del_all_items() # Return response response = Response(json.dumps(res_data), mimetype='application/json') return response

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Mohamed24444.noreply@github.com

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/CodersLab/MSP/buffor_23.10.2017/workspace/SCIAGA/D8_MySql_laczenie_relacje/__init__.py

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AleksandraSandor/Learning-Python

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# =============================================================================== # MySql laczenie relacje # =============================================================================== from mysql.connector import connect cnx = connect(user="root", password="1", host="localhost") cur = cnx.cursor() sql = "use test_domowy;" cur.execute(sql) # cur = cnx.cursor(dictionary=False) # sql = """create table customers(customer_id int not null auto_increment, # name varchar(255) not null, # primary key(customer_id));""" # cur.execute(sql) # # sql = """create table addresses(customer_id int not null, # street varchar(255), # primary key(customer_id), # foreign key(customer_id) references customers(customer_id) on delete cascade);""" #<--------- jeden do jedn # cur.execute(sql) # # sql = """create table orders(order_id int not null auto_increment, # customer_id int not null, # order_details varchar(255), # primary key(order_id), # foreign key(customer_id) REFERENCES customers(customer_id));""" #<--------- jeden do wielu # cur.execute(sql) # # # sql = """create table items(item_id int not null auto_increment, # description varchar(255), # primary key(item_id));""" # cur.execute(sql) # # sql = """create table items_orders(id int auto_increment, # item_id int not null, # order_id int not null, # primary key(id), # foreign key(order_id) REFERENCES orders(order_id), #<--------- dodatkowa tableawiele do wielu # FOREIGN KEY(item_id) REFERENCES items(item_id))""" # cur.execute(sql) # sql = """insert into items_orders(order_id, item_id) VALUES (1,1), (2,1), (2,2);""" # cur.execute(sql) # sql = """insert into customers(name) VALUES ("Januszaaa"), ("Kubaaaa"), ("Wojtekkkk");""" # cur.execute(sql) # sql = """insert into addresses(customer_id, street) VALUES (1, "ul. jeden"), (2, "ulica dwa");""" # cur.execute(sql) # sql = """insert into orders(customer_id, order_details) VALUES (3, "zam1"), (3, "zam2"), (1, "zam3");""" # cur.execute(sql) # sql = """insert into items(description) VALUES ("itm1"), ("itm2"), ("itm3");""" # cur.execute(sql); # sql = """select * from customers join addresses on customers.customer_id=addresses.customer_id # where customers.customer_id=2;""" # cur.execute(sql) # for row in cur: # print(row) # sql = """select * from customers join orders on customers.customer_id=orders.customer_id # where customers.customer_id=3;""" # cur.execute(sql) # for row in cur: # print(row) # sql = """select * from orders join items_orders on orders.order_id=items_orders.order_id;""" # cur.execute(sql) # for row in cur: # print(row) # insert into addresses(customer_id, street) values (6,"ul. zaciszna"); #<---6 dopisze adres jeśli jest ID u customers # delete from customers where customer_id = 6; # <--- skasowało również adres # =============================================================================== # Formatowanie zapytania # =============================================================================== a = "user_name" b = "user_id" i = 1 ii = 6 sql = """"select {}, {} from user where user_id = %s or user_id = %s""".format(b, a) #/todo dlaczegoto nie działą 1 cur.execute(sql,(i, ii)) for row in cur: print(row) cnx.commit() cur.close() cnx.close()

[ "wojciech.gaudnik@gmail.com" ]

wojciech.gaudnik@gmail.com

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from setuptools import setup setup( name='project', packages=['project'], include_package_data=True, install_requires=[ 'flask', ], )

[ "49805944+hammadyasir@users.noreply.github.com" ]

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/MockGvh/agentThread.py

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jzw2/KoordLanguage

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import random from abc import ABC, abstractmethod class Pos(): pass class AgentThread(ABC): def __init__(self, config): self._pid = config.pid self._num_agents = config.num_agents self._pos = (random.randrange(1, 10), random.randrange(1, 10)) self.shared_vars = config.shared_vars @abstractmethod def loop_body(self): pass @abstractmethod def initialize_vars(self): pass def pos3d(self, x, y, z): pass def write_to_shared(self, var_name, index, value): self.shared_vars[var_name][index] = value def read_from_shared(self, var_name, index): return self.shared_vars[var_name][index] def read_from_sensor(self, var_name): if var_name == "Motion.position": return self._pos def write_to_actuator(self, var_name, value): if var_name == "Motion.target": self._pos = value def create_ar_var(self, name, type, initial_value=None): if name not in self.shared_vars: self.shared_vars[name] = [initial_value] * self._num_agents def create_aw_var(self, name, type, initial_value=None): if name not in self.shared_vars: self.shared_vars[name] = [initial_value] * self._num_agents def log(self, message): pass def pid(self): return self._pid def num_agents(self): return self._num_agents def start(self): pass def run(self): for i in range(10): self.loop_body() def midpoint(self, x, y): a, b = x c, d = y return (a + c) / 2, (b + d) / 2

[ "hc825b@gmail.com" ]

hc825b@gmail.com

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/frontstage_api/resources/register/confirm_organisation_survey.py

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refs/heads/master

2018-07-15T00:35:22.130352

2018-06-01T14:09:13

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import logging from flask import jsonify, make_response, request from flask_restplus import Resource, fields from structlog import wrap_logger from frontstage_api import auth, register_api from frontstage_api.controllers import case_controller, collection_exercise_controller, iac_controller, party_controller, survey_controller logger = wrap_logger(logging.getLogger(__name__)) enrolment_details = register_api.model('EnrolmentDetails', { 'enrolment_code': fields.String(required=True), }) @register_api.route('/confirm-organisation-survey') class ConfirmOrganisationSurvey(Resource): @staticmethod @auth.login_required @register_api.expect(enrolment_details, validate=True) def post(): logger.info('Attempting to retrieve organisation and survey data') enrolment_code = request.get_json().get('enrolment_code') # Verify enrolment code is active iac = iac_controller.get_iac_from_enrolment(enrolment_code) if not iac['active']: return make_response(jsonify(iac), 401) # Get organisation name case = case_controller.get_case_by_enrolment_code(enrolment_code) business_party_id = case['caseGroup']['partyId'] organisation_name = party_controller.get_party_by_business_id(business_party_id).get('name') # Get survey name collection_exercise_id = case['caseGroup']['collectionExerciseId'] collection_exercise = collection_exercise_controller.get_collection_exercise(collection_exercise_id) survey_id = collection_exercise['surveyId'] survey_name = survey_controller.get_survey(survey_id).get('longName') response_json = { "organisation_name": organisation_name, "survey_name": survey_name } logger.info('Successfully retrieved organisation and survey data') return make_response(jsonify(response_json), 200)

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"""Developer portal smoke tests""" import pytest from testsuite import settings from testsuite.ui.views.admin.audience import BaseAudienceView from testsuite.ui.views.devel import BaseDevelView, AccessView, LandingView @pytest.fixture(scope="module") def provider_account(provider_account): """Fixture returns Provider account. If `site_access_code` was changed in tests, it is restored to its original value""" access_code = provider_account['site_access_code'] yield provider_account provider_account.update(dict(site_access_code=access_code)) # pylint: disable=unused-argument @pytest.mark.smoke def test_devel_from_admin(login, navigator, browser): """Tests if developer portal is accessible via navigation menu (Developer portal > Visit Portal)""" audience = navigator.navigate(BaseAudienceView) with browser.new_tab(audience.visit_portal): assert settings["threescale"]["devel"]["url"] in browser.url view = LandingView(browser) view.post_navigate() assert view.is_displayed # pylint: disable=unused-argument @pytest.mark.smoke def test_devel_login(devel_login, browser): """Tests simple developer portal login""" assert BaseDevelView(browser).is_displayed @pytest.mark.smoke def test_empty_access_code(browser, provider_account): """Test developer portal accessibility when `site_access_code` is empty""" browser.selenium.delete_all_cookies() browser.url = settings["threescale"]["devel"]["url"] assert AccessView(browser).is_displayed provider_account.update(dict(site_access_code="")) browser.selenium.refresh() assert LandingView(browser).is_displayed

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jsmolar@redhat.com

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#!/usr/bin/env python3 """A library of helpful functions. Notably to set up output-folders safely, with time-stamped copies of the source code included. holzfigure 2019 """ # import os # import csv import time import math import shutil # import argparse import logging import logging.handlers from pathlib import Path from datetime import datetime # , timedelta import tkinter from tkinter import Tk, filedialog # import numpy as np import matplotlib from matplotlib import pyplot as plt # NOTE: module "imp" is deprecated.. import warnings warnings.filterwarnings('error', category=DeprecationWarning) # # GLOBALS POSTFIX_DIR = "out" TIME_FMT = "%y%m%d-%H%M%S-utc" DAY_FMT = "%y%m%d-utc" # Logging LOG_MAX_BYTES = 20000000 # ~ 20 MB LOG_BACKUP_COUNT = 50 # Plotting DEF_EXT = "png" DEF_WF = 3.0 COLORMAP = plt.cm.viridis # colormap = plt.cm.viridis # colormap = plt.cm.jet # colormap = plt.cm.gist_ncar # colormap = plt.cm.Set1 def now_str(pattern=TIME_FMT): """Return a formatted timestring for the current time.""" # return time.strftime(pattern, time.gmtime()) return datetime.utcnow().strftime(pattern) def parse_subtree(filedir, pattern): """Parse a subtree (including subfolders) for the pattern. from: https://stackoverflow.com/questions/2186525/ use-a-glob-to-find-files-recursively-in-python + sorting [requires 'import fnmatch'] Deprecated since using pathlib! (v180817) """ # matches = [] # for root, dirnames, filenames in os.walk(filedir): # for filename in fnmatch.filter(filenames, pattern): # matches.append(os.path.join(root, filename)) # return sorted(matches) filedir = Path(filedir).resolve() return sorted(filedir.rglob(pattern)) def safename(s, s_type="file"): """Append stuff to a file or folder if it already exists. Check whether a given file or folder 's' exists, return a non-existing filename. s ........ (full) filename or directory s_type ... 'file' or 'f' for files, 'directory' or 'dir' or 'd' for folders Returns a file- or pathname that is supposedly safe to save without overwriting data. """ # Ensure s is a Path object p = Path(s) low_type = str.lower(s_type) if low_type == "file" or low_type == "f": # if os.path.isfile(ss if p.is_file(): stem = p.stem suffix = p.suffix counter = 0 while p.is_file(): # p = p.with_name(f"{stem}-{counter:02d}{suffix}") p = p.with_name("{}-{:02d}{}".format(stem, counter, suffix)) counter += 1 elif low_type == "directory" or low_type == "dir" or low_type == "d": if p.is_dir(): stem = p.stem counter = 0 while p.is_dir(): # s = s_base + "-{:02d}".format(counter) # p = p.with_name(f"{stem}-{counter:02d}") p = p.with_name("{}-{:02d}".format(stem, counter)) counter += 1 return p def safesavefig(path, ext=".png", close=True, verbose=False): """Safely save a figure from pyplot. adapted from: http://www.jesshamrick.com/2012/09/03/saving-figures-from-pyplot/ # plt.gcf().canvas.get_supported_filetypes() # plt.gcf().canvas.get_supported_filetypes_grouped() filetypes = { 'ps': 'Postscript', 'eps': 'Encapsulated Postscript', 'pdf': 'Portable Document Format', 'pgf': 'PGF code for LaTeX', 'png': 'Portable Network Graphics', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics', 'jpg': 'Joint Photographic Experts Group', 'jpeg': 'Joint Photographic Experts Group', 'tif': 'Tagged Image File Format', 'tiff': 'Tagged Image File Format' } 180817 Added a '.' to the default extension to be compatible with path.suffix """ valid_extensions = plt.gcf().canvas.get_supported_filetypes() fallback_ext = ".png" # Ensure path is a pathlib.Path object path = Path(path) # Parse path components directory = path.parent stem = path.stem suffix = path.suffix # Check whether path already has an extension if suffix: if suffix in valid_extensions: if suffix != ext: logging.debug(f"Overwriting kwarg ext '{ext}' " + f"with suffix '{suffix}' from {path}!") ext = suffix else: logging.debug(f"Overwriting file suffix '{suffix}' " f"with kwarg ext '{ext}'!") # Ensure extension is correct ext = ext.lower() if not ext.startswith("."): logging.debug(f"Adding '.' to {ext}") ext = f".{ext}" if ext.split(".")[-1] not in valid_extensions: logging.warning(f"Invalid extension '{ext}', " + f"replacing with '{fallback_ext}'") ext = fallback_ext # Generate filename # filename = "%s.%s" % (os.path.split(path)[1], ext) filename = stem + ext # Ensure valid directory if not directory: directory = Path.cwd() directory = directory.resolve() if not directory.is_dir(): directory.mkdir(parents=True) # Finalize full filename # savepath = os.path.join(directory, filename) savepath = directory / filename savepath = safename(savepath, 'file') # Save figure to file # TODO: Remove str() once matplotlib is updated?? plt.savefig(str(savepath)) if verbose: logging.info(f"Saved figure to {savepath}") if close: plt.close() # if verbose: # logging.debug("Done") return savepath def latexify(fig_width=None, fig_height=None, columns=1): """Set up matplotlib's RC params for LaTeX plotting. Call this before plotting a figure. Parameters ---------- fig_width : float, optional, inches fig_height : float, optional, inches columns : {1, 2} Code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples Width and max height in inches for IEEE journals taken from https://www.computer.org/cms/Computer.org/Journal%20templates/ transactions_art_guide.pdf from https://nipunbatra.github.io/blog/2014/latexify.html (v180817: updated this link) """ assert(columns in [1, 2]) if fig_width is None: fig_width = 3.39 if columns == 1 else 6.9 # width in inches if fig_height is None: golden_mean = (math.sqrt(5) - 1.0) / 2.0 # aesthetic ratio fig_height = fig_width * golden_mean # height in inches MAX_HEIGHT_INCHES = 8.0 if fig_height > MAX_HEIGHT_INCHES: print("WARNING: fig_height too large:" + fig_height + "so will reduce to" + MAX_HEIGHT_INCHES + "inches.") fig_height = MAX_HEIGHT_INCHES params = {'backend': 'ps', 'text.latex.preamble': ['\\usepackage{gensymb}'], 'axes.labelsize': 6, # fontsize for x and y labels (was 10) 'axes.titlesize': 6, 'font.size': 6, # 'text.fontsize': 8, # was 10 'legend.fontsize': 6, # was 10 'xtick.labelsize': 6, 'ytick.labelsize': 6, 'text.usetex': True, 'figure.figsize': [fig_width, fig_height], 'font.family': 'serif' } matplotlib.rcParams.update(params) def format_axes(ax): """Format axes.""" spine_color = 'gray' for spine in ['top', 'right']: ax.spines[spine].set_visible(False) for spine in ['left', 'bottom']: ax.spines[spine].set_color(spine_color) ax.spines[spine].set_linewidth(0.5) ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') for axis in [ax.xaxis, ax.yaxis]: axis.set_tick_params(direction='out', color=spine_color) return ax # # The following functions require numpy: # def euclid(p1, p2): # return np.sqrt((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2) # # def euclid_array(p1s, p2s): # """The inputs "p1s" and "p2s" are 2-column arrays # of XY-coordinates. # """ # return np.sqrt((p2s[:, 0] - p1s[:, 0])**2 + # (p2s[:, 1] - p1s[:, 1])**2) def setup_environment( thisfile, dir_targ=None, level=1, new_dir=True, prefix_file=None, postfix_dir=None, daystamp=False, day_fmt=DAY_FMT, dependencies=None, ): """Create an output directory safely. No overwriting of existing files and folders. in (optional): dir_targ .... str full path to a directory. def="" level .... int (-1, [0], 1) are the choices. [-1: dir-out is sibling to the given directory 0: dir_out is the given directory CAUTION: will only be this directory if new_dir=False and postfix=None! DEFAULT: 1: dir_out is child of the given directory] new_dir .... bool if True, create a new directory, even if one already exists if False, write into an existing directory with the given name thisfile .... bool if True, get full path to current file [if i call "os.path.basename(__file__)" here, will i get the path to the calling code, or to this file 'holzhelp.py'?] prefix_file .... str prefix file with this (and '_') postfix_dir .... str append to name of output-folder. dependencies ... list paths to other files to copy to dir_out out: dir_out .... str full path to created output directory """ # Set up directories # ================== # # if interactive: # dir_targ = filedialog.askdirectory(initialdir=DIR_INI) # thisfile = os.path.basename(__file__) # thisname = os.path.splitext(os.path.split(thisfile)[1])[0] thisfile = Path(thisfile).resolve() thisname = thisfile.stem if prefix_file: # thisname = f"{prefix_file}_{thisname}" thisname = "{}_{}".format(prefix_file, thisname) if not dir_targ: # dir_targ = os.path.join(os.getcwd(), postfix) # dir_targ = os.getcwd() # dir_targ = Path.cwd() / f"{thisname}_{postfix_dir}" dir_targ = Path.cwd() else: dir_targ = Path(dir_targ) # determine level to place directory if level < 0: # basedir, lastdir = os.path.split(dir_targ) # os.path.join(basedir, thisname) # dir_out = dir_targ.with_name(f"{dir_targ.stem}_{thisname}") dir_out = dir_targ.with_name("{}_{}".format( dir_targ.stem, thisname)) elif level == 0: # NOTE: only stays if new_dir=False and postfix=None! dir_out = dir_targ elif level > 0: # dir_out = os.path.join(dir_targ, thisname) dir_out = dir_targ / thisname if postfix_dir: # dir_out += "_" + postfix_dir # dir_out = dir_out.with_name(f"{dir_out.stem}_{postfix_dir}") dir_out = dir_out.with_name("{}_{}".format( dir_out.stem, postfix_dir)) if daystamp: # dir_out += now_str("_%y%m%d-utc") # dir_out = dir_out.with_name(f"{dir_out.stem}_{now_str(day_fmt)}") dir_out = dir_out.with_name("{}_{}".format( dir_out.stem, now_str(day_fmt))) if new_dir: dir_out = safename(dir_out, 'directory') if not dir_out.is_dir(): # os.mkdir(dir_out) dir_out.mkdir(parents=True) # logging.info("created output directory at '{}'".format(dir_out)) # logwarn = [] # else: # logwarn = ("output directoy already exists, " + # "error in function safename()") # copy files to output-directory src_out = dir_out / "src" if not src_out.is_dir(): src_out.mkdir() print(f"Created folder '{src_out}'") if not dependencies: dependencies = [] dependencies.append(thisfile) for filename in dependencies: # path, fname = os.path.split(filename) # name, ext = os.path.splitext(fname) # path = filename.parent filename = Path(filename).resolve() name = filename.stem suffix = filename.suffix if prefix_file: # name = f"{prefix_file}_{name}" name = "{}_{}".format(prefix_file, name) # thatfile = os.path.join( # dir_out, name + now_str() + ext) # thatfile = dir_out / f"{name}_{now_str()}{suffix}" thatfile = src_out / "{}_{}{}".format(name, now_str(), suffix) thatfile = safename(thatfile, 'file') # TODO: Replace this with a proper pathlib method once? # And remove the 'str()' once Raspbian is n Python 3.6.. shutil.copy2(str(filename), str(thatfile)) # this_split = os.path.splitext(thisfile) # thatfile = os.path.join( # dir_out, this_split[0] + now_str() + this_split[1]) # thatfile = safename(thatfile, 'file') # shutil.copy2(thisfile, thatfile) return dir_out, thisname # , logwarn def setup_logging( thisname, args, dir_log=None, max_bytes=LOG_MAX_BYTES, backup_count=LOG_BACKUP_COUNT, ): """Set up the logging module to log to a file. Rotate logfiles if they are bigger than LOG_MAX_BYTES. https://docs.python.org/3/howto/logging-cookbook.html """ err_msg = [] if dir_log is None: # dir_log = os.path.join(os.getcwd(), "DIR_LOG") dir_log = Path.cwd() / "log" dir_log = safename(dir_log, 'dir') if not dir_log.is_dir(): try: dir_log.mkdir(parents=False) except Exception as err: # err_msg.append( # f"Failed to create directory {dir_log}\n" + # f"Error: {err}\n" + # "Now creating full path...") err_msg.append(( "Failed to create directory {}\n" + "Error: {}\n" + "Now creating full path...").format(dir_log, err)) dir_log.mkdir(parents=True) # log_path = os.path.join(LOC_PATH, "logs") # thisfile = os.path.basename(__file__) # logfile = safename(os.path.join( # dir_log, "{}_{}.log".format(thisname, now_str())), 'file') logfile = safename( # (dir_log / f"{thisname}_{now_str()}.log"), 'file') (dir_log / "{}_{}.log".format(thisname, now_str())), 'file') # logfile = safename(logfile, 'file') # logname = thisfile[0:-3] + '.log' # + now_str() + '.log' if args.debug: loglevel = logging.DEBUG else: loglevel = logging.INFO # logging.basicConfig( # level=loglevel, # format=("%(asctime)s %(levelname)-8s " + # "%(funcName)-12s: %(message)s"), # datefmt='%y-%m-%d %H:%M:%S UTC', # filename=logfile, # filemode='a') # # logging.basicConfig(filename=logfile, level=logging.INFO) # logging.debug("logging to file {}".format(logfile)) # Set level logging.getLogger('').setLevel(loglevel) # All times in UTC logging.Formatter.converter = time.gmtime # format=('%(asctime)s %(name)-12s %(levelname)-8s %(message)s', # Rotating logs # https://docs.python.org/2/howto/ # logging-cookbook.html#using-file-rotation # Add the log message handler to the logger # TODO: Remove the "str()" once RPIs have Python3.6 rotater = logging.handlers.RotatingFileHandler( str(logfile), mode='a', maxBytes=max_bytes, backupCount=backup_count) # encoding=None, # delay=0) # rotater.setLevel(loglevel) rotate_formatter = logging.Formatter( fmt="%(asctime)s %(levelname)-8s %(funcName)-12s: %(message)s", datefmt='%y-%m-%d %H:%M:%S UTC') rotater.setFormatter(rotate_formatter) logging.getLogger('').addHandler(rotater) # if not cron: # Define a Handler which writes INFO messages or # higher to the sys.stderr console = logging.StreamHandler() # console.setLevel(loglevel) # (logging.INFO) # Set a format which is simpler for console use # formatter = logging.Formatter( # '%(name)-12s: %(levelname)-8s %(message)s') console_formatter = logging.Formatter( "%(levelname)-8s: %(message)s") # Tell the handler to use this format console.setFormatter(console_formatter) # Add the handler to the root logger logging.getLogger('').addHandler(console) if len(err_msg) > 0: for msg in err_msg: logging.warning(msg) logging.debug("Logging to screen and to {}".format(logfile)) # return dir_log def select_files(title="select file(s)", dir_ini=None, filetypes=[("all files", "*")], more=False): """Interactively pick a file (actually its path-string). If 'more=True', a tuple of files will be returned. see: http://infohost.nmt.edu/tcc/help/pubs/tkinter/web/tkFileDialog.html http://www.programcreek.com/python/example/4281/ tkFileDialog.askopenfilename http://effbot.org/tkinterbook/tkinter-file-dialogs.htm Not mentioned in the above refs is ".askopenfilenames()", which takes the same options but returns a tuple of selected files. >> dir(filedialog) ['ACTIVE', 'ALL', 'ANCHOR', 'ARC', 'BASELINE', 'BEVEL', 'BOTH', 'BOTTOM', 'BROWSE', 'BUTT', 'BaseWidget', 'BitmapImage', 'BooleanVar', 'Button', 'CASCADE', 'CENTER', 'CHAR', 'CHECKBUTTON', 'CHORD', 'COMMAND', 'CURRENT', 'CallWrapper', 'Canvas', 'Checkbutton', 'DISABLED', 'DOTBOX', 'Dialog', 'Directory', 'DoubleVar', 'E', 'END', 'EW', 'EXCEPTION', 'EXTENDED', 'Entry', 'Event', 'EventType', 'FALSE', 'FIRST', 'FLAT', 'FileDialog', 'Frame', 'GROOVE', 'Grid', 'HIDDEN', 'HORIZONTAL', 'INSERT', 'INSIDE', 'Image', 'IntVar', 'LAST', 'LEFT', 'Label', 'LabelFrame', 'Listbox', 'LoadFileDialog', 'MITER', 'MOVETO', 'MULTIPLE', 'Menu', 'Menubutton', 'Message', 'Misc', 'N', 'NE', 'NO', 'NONE', 'NORMAL', 'NS', 'NSEW, 'NUMERIC', 'NW', 'NoDefaultRoot', 'OFF', 'ON', 'OUTSIDE', 'Open', 'OptionMenu', 'PAGES', 'PIESLICE', 'PROJECTING', 'Pack', 'PanedWindow', 'PhotoImage', 'Place', 'RADIOBUTTON', 'RAISED', 'READABLE', 'RIDGE', 'RIGHT', 'ROUND', 'Radiobutton', 'S', 'SCROLL', 'SE', 'SEL', 'SEL_FIRST', 'SEL_LAST', 'SEPARATOR', 'SINGLE', 'SOLID', 'SUNKEN', 'SW', 'SaveAs', 'SaveFileDialog', 'Scale', 'Scrollbar', 'Spinbox', 'StringVar', 'TOP', 'TRUE', 'Tcl', 'TclError', 'TclVersion', 'Text', 'Tk', 'TkVersion', 'Toplevel', 'UNDERLINE', 'UNITS', 'VERTICAL', 'Variable', 'W', 'WORD', 'WRITABLE', 'Widget', 'Wm', 'X', 'XView', 'Y', 'YES', 'YView', '_Dialog', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'askdirectory', 'askopenfile', 'askopenfilename', 'askopenfilenames', 'askopenfiles', 'asksaveasfile', 'asksaveasfilename', 'commondialog', 'constants', 'dialogstates', 'enum', 'fnmatch', 'getboolean', 'getdouble', 'getint', 'image_names', 'image_types', 'mainloop', 'os', 're', 'sys', 'test', 'wantobjects'] """ # Resolve initial directory if not dir_ini: # or not dir_ini.is_dir(): dir_ini = Path.cwd() else: dir_ini = Path(dir_ini).resolve() # Include this to make the crappy empty window go away root = Tk() root.withdraw() print(f"tkinter version: {tkinter.TkVersion}") # Set options opts = {} opts["parent"] = root opts["title"] = title opts["initialdir"] = dir_ini opts['filetypes'] = filetypes # Check whether single file or tuple of files is requested if more: # tuple of full filenames (paths) # ffn_return = tkFileDialog.askopenfilenames(**opts) ffn_return = filedialog.askopenfilenames(**opts) if len(ffn_return) > 0: ffn_return = [Path(ffn) for ffn in ffn_return] else: # String of full filename (path) # ffn_return = tkFileDialog.askopenfilename(**opts) ffn_return = filedialog.askopenfilename(**opts) if ffn_return: ffn_return = Path(ffn_return) # If cancelled, return None if not ffn_return: return None # Return full filename(s) return ffn_return def select_directory(title="select directory", dir_ini=None): """Interactively retrieve the path to a directory.""" # include this to make the crappy empty window go away root = Tk() root.withdraw() print(f"tkinter version: {tkinter.TkVersion}") # open directory dialog # dir_select = tkFileDialog.askdirectory( dir_select = filedialog.askdirectory( parent=root, title=title, initialdir=dir_ini) # check cancel or false directoy if not dir_select: print("Cancelled by user, returning 'None'") return None else: dir_select = Path(dir_select) if not dir_select.is_dir(): print(f"Directory '{dir_select}' doesn't exist, returning 'None'") return None # return full path of selected diretory return dir_select def main(): """Mock main-function. Write test cases here. """ # setup environment # thisfile = os.path.basename(__file__) # dir_out = setup_environment(thisfile, postfix=postfix) pass if __name__ == "__main__": main()

[ "daniel.hofstadler@uni-graz.at" ]

daniel.hofstadler@uni-graz.at

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/class_factorial.py

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[]

no_license

Me-Pri/Python-programs

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refs/heads/main

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2020-12-17T06:38:54

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class factorial: no=0; fact=1; def input(self): self.no=int(input("Enter the no of terms: ")) def factor(self): for i in range(1,self.no+1): self.fact=self.fact*i print("The factorial of {0} is {1}".format(self.no,self.fact)) fac=factorial(); fac.input(); fac.factor();

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Me-Pri.noreply@github.com

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/lesson_1/example_4.py

609ef451896008c74c8f80517aaf02d220e649b0

[]

no_license

Evgeniy-Nagornyy/Python_1

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2022-10-22T09:23:52.062377

2020-06-10T13:27:23

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line_numbers = int(input('Введите положительное число - ')) check_number = line_numbers % 10 # Проверяемое число на максимум (очень не хватает цикла do while) check_line_number = str(check_number) # проверочное число для выхода из цикла max_number = 0 # Максимальное число i = 1 # счетчик для передвижения if line_numbers == 0: # вдруг введут 0 print(max_number) else: while int(check_line_number) <= line_numbers: check_number = ((line_numbers % (10 ** i)) // (10 ** (i - 1))) # переключение на следующую цифру if check_number == 9: # проверяем, есть ли смысл проверять дальше max_number = check_number break elif check_number > max_number: # проверка числа max_number = check_number check_line_number = str(check_number) + check_line_number # формируем число для проверки выхода из цикла i += 1 print(f'Самая большая цифра в числе - {max_number}') # второй вариант решения ("запрещенный") # line_numbers = input('Введите положительное число - ') # print(f'Самая большая цифра в числе - {max([int(line_numbers[i]) for i in range(len(line_numbers))])}')

[ "Evgeniy_kott@mail.ru" ]

Evgeniy_kott@mail.ru

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7573bbf969a0b90ba9015a35b0ab59c29db4688f

/architectures/cnn_utils.py

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no_license

Huetous/pytoss

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2023-05-05T21:26:13.495008

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import torch.nn as nn def init_cnn(module): if getattr(module, "bias", None) is not None: nn.init.constant_(module.bias, 0) if isinstance(module, (nn.Conv2d, nn.Linear)): nn.init.kaiming_normal_(module.weight) for m in module.children(): init_cnn(m) def conv(n_in, nf, ks=3, stride=1, bias=False): return nn.Conv2d(n_in, nf, kernel_size=ks, stride=stride, padding=ks // 2, bias=bias) def conv_layer(n_in, n_out, kernel_size=3, stride=1, bn=True, zero_bn=False, act=True): layers = [conv(n_in, n_out, kernel_size, stride)] if bn: bn = nn.BatchNorm2d(n_out) nn.init.constant_(bn.weight, 0. if zero_bn else 1.) layers.append(bn) if act: layers.append(nn.ReLU()) return nn.Sequential(*layers)

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daddudota22@mail.ru

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/web_indexer_starter.py

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[]

no_license

undefinedobjects/web_indexer.py

6a41508fe050b58858b5e9645e41c7adde1f7f96

c031a29fd8ba7930b7be36bdcf1b9a3d1dc5c1a7

refs/heads/master

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import os for number in range(90,100): start = "172." + str(number) + ".0.0" end = "172." + str((number + 1)) + ".0.0" os.system("start web_indexer.py -s " + start + " -e " + end)

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undefinedobjects.noreply@github.com

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/FireModules/Websurfing/google_search_deleting_files.py

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[ "MIT" ]

permissive

alex14324/DumpsterFire

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refs/heads/master

2020-08-07T05:33:15.058377

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#!/usr/bin/python # # Filename: # # Version: 1.0.0 # # Author: Joe Gervais (TryCatchHCF) # # Summary: # # Part of the DumpsterFire Toolset. See documentation at https://github.com/TryCatchHCF/DumpsterFire # # # Description: # # # Example: # # import urllib, time, random from FireModules.fire_module_base_class import * class google_search_deleting_files( FireModule ): def __init__(self): self.commentsStr = "Websurfing/google_search_deleting_files" def __init__(self, moofStr): self.moofStr = moofStr self.commentsStr = "Websurfing/google_search_deleting_files" return; def Description( self ): self.Description = "Performs Google search on securely deleting files" return self.Description def Configure( self ): return def GetParameters( self ): return "" def SetParameters( self, parametersStr ): print parametersStr return def ActivateLogging( self, logFlag ): print self.commentsStr + ": Setting Logging flag!" print logFlag return def Ignite( self ): print self.commentsStr + ": Opening URL session for Google search on securely deleting files" self.webSession = urllib.urlopen( 'https://www.google.com/search?q=securely+deleting+files&oq=securely+deleting+files' ) trash = self.webSession.read() return

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from django.test import TestCase from django.core.urlresolvers import resolve from lists.views import home_page # Create your tests here. class HomePageTest(TestCase): def test_root_url_resolves_to_home_page_view(self): found = resolve('/') self.assertEqual(found.func, home_page)

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# Generated by Django 3.0.2 on 2020-03-28 02:57 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('shop', '0029_remove_product_kurs_d'), ] operations = [ migrations.DeleteModel( name='Kurs_d', ), ]

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# coding: utf-8 """ SevOne API Documentation Supported endpoints by the new RESTful API # noqa: E501 OpenAPI spec version: 2.1.18, Hash: db562e6 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.models.flow_falcon_visualization import FlowFalconVisualization # noqa: E501 from swagger_client.rest import ApiException class TestFlowFalconVisualization(unittest.TestCase): """FlowFalconVisualization unit test stubs""" def setUp(self): pass def tearDown(self): pass def testFlowFalconVisualization(self): """Test FlowFalconVisualization""" # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.flow_falcon_visualization.FlowFalconVisualization() # noqa: E501 pass if __name__ == '__main__': unittest.main()

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from test1 import age class Person: p_age = age def __init__(self, name): self.name = name

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# Generated by Django 2.2.5 on 2019-12-27 05:25 from django.db import migrations, models import taskmanager_app.models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('email', models.EmailField(blank=True, max_length=50)), ('phone', models.CharField(blank=True, max_length=100)), ], ), migrations.CreateModel( name='Project', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100)), ('description', models.TextField(blank=True)), ('file', models.FilePathField(path=taskmanager_app.models.images_path)), ('end_date', models.DateField()), ('author', models.ForeignKey(on_delete='models.CASCADE', to='taskmanager_app.User')), ], ), ]

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# coding: utf-8 # # Linear Regression # # In this tutorial we will implement a linear regression model. We will also implement a function that splits the available data into a training and a testting part. # # ## Problem Setting # # We will use the Boston Housing Dataset. This dataset contains information collected by the U.S Census Service concerning housing in the city of Boston in the state of Massachusetts in 1978. Our goal is to predict the median value of the houses in a particular town in the city of Boston given its attributes. Check the file ’housing.names’ for more information on the attributes. # In[ ]: import urllib import pandas as pd import numpy as np # for auto-reloading external modules # see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython get_ipython().magic(u'load_ext autoreload') get_ipython().magic(u'autoreload 2') from sklearn.datasets import load_boston boston=load_boston() testfile = urllib.URLopener() testfile.retrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.names", "housing.names") df=pd.DataFrame(boston.data) df.columns=['crime_rate','res_land_zoned','industry','charles_river','nox','avg_num_rooms','prop_bf_1940','dst_emply_center','rd_highway_idx','tax_rate','stdnt_tchr_ratio','prop_blacks','low_status_pct'] X=boston.data y=boston.target # In[ ]: df.head(10) # ### Exercise 1 # # Write the *split_train_test(X,y,split,seed)*, given an instance matrix $X \in \mathbb{R}^{N \times D}$, labels $y \in Y^N$, a split ratio in $[0, 1]$ and a random seed $\in \mathbb{Z}$. Split the dataset in $(split×100)\%$ of the instances for training our model and the rest for testing, i.e. # # $$ \left|X_{\text{train}}\right| = \lceil \text{split} \cdot N \rceil, \qquad |X_{\text{train}}| + |X_{\text{test}}| = N. $$ # Make sure you use the given random number generator seed so we all get the same results. The function is supposed to return: # # - X_train, y_train: the training instances and labels; # - X_test, y_test: the test instances and labels, # # in the same order as was mentioned. # # Hint: It may be helpful to use shuffling functionality (e.g. np.random.shuffle). # In[ ]: def split_train_test(X,y,split,seed): ################## #INSERT CODE HERE# ################## return None # X_train, y_train, X_test, y_test # ### Exercise 2 # # Write the function *train_linear_reg(X_train,y_train,lmbd)*. # Implement the ridge regression model (slide 24). The function should output the learned weight vector $\theta \in \mathbb{R}^D$ or $\mathbb{R}^{D+1}$ (depending on whether you are adding *bias*). # In[ ]: def train_linear_reg(X, y, lmbd): ################## #INSERT CODE HERE# ################## return None # theta # ### Exercise 3 # # Write the function *predict(X,theta)* which predicts housing values vector pred for a dataset X and a previously trained parameter vector $\theta$. # In[ ]: def predict(X, theta): ################## #INSERT CODE HERE# ################## return None # y_pred # ### Exercise 4 # # Write the function *mean_abs_loss(y_true,y_pred)* which computes the mean of the absolute differences between our prediction vector $y\_pred$ and the real housing values $y\_true$. # In[ ]: def mean_abs_loss(y_true,y_pred): ################## #INSERT CODE HERE# ################## return 0 # ### Exercise 5 # # Evaluate your solutions by running the following code. # # Moreover, answer the following questions: What is the most important feature in your model? Are there features that are not so important? What happens if you remove them? Are there outliers with a high absolute loss? # In[ ]: seed = 3 lmbd=1 split=0.7 X_train,y_train,X_test,y_test=split_train_test(X,y,split,seed) theta=train_linear_reg(X_train,y_train,lmbd) y_pred=predict(X_test,theta) mae=mean_abs_loss(y_test,y_pred) print 'The mean absolute loss is {loss:0.3f}'.format(loss=mae*1000)

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#!/usr/bin/env python ####################################################################### # This file is part of Fusuma website management system. # # Copyright (c) hylom <hylomm at gmail.com>, 2008. # # This file is released under the GPL. # # $Id: DataMan.py,v 1.1.1.1 2008/11/27 17:15:36 hylom Exp $ ####################################################################### """ This is Fusuma's article data management module. """ __revision__ = "$Revision: 1.1.1.1 $" import datetime import sys import os VERSION = "0.0.1" VERSION_DATE = VERSION + " 09/26/2008" VERSION_SPLIT = tuple(VERSION.split('.')) class DataMan(object): """ This is Fusuma's story data management class. """ def __init__(self, document_root): """ Initialize Data Manager. @param document_root: document root directory @type document_root: string """ self._document_root = document_root def new_story(self): """ create new story. """ story = Story() story.output_dir = _get_output_path # Story.property()[""] = def _get_output_path(self): """ return directory which story is saved. default is: <document_root>/yyyy/mm/dd/ """ dt = datetime.datetime.today(); current_locale = locale.getlocale( locale.LC_CTYPE ) locale.setlocale( locale.LC_CTYPE, "" ) path = dt_expire.strftime( "%Y/%m/%d" ) locale.setlocale( locale.LC_CTYPE, current_locale ) return path; class Story: """ DataMan's Story object. """ def __init__(self): """ Initialize Story. """ # store session object # TODO: This session-data-file-name-rule may be insecure! self._output_file = "" self._output_dir = "" self._story = "" self._property = {} def save(self): """ Save story to file. """ ## accessor def property(self): return self._property def output_file(self): return self._output_file def set_output_file(self, str): self._output_file = str def output_dir(self): return self._output_dir def set_output_dir(self, str): self._output_dir = str def story(self): return self._story def set_story(self, str): self._story = str

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#smallest multiple #2520 is the smallest number that can be divided by each of the numbers from#1 to 10 without any remainder. # #What is the smallest positive number that is evenly divisible by all of the#numbers from 1 to 20? from math import log def primelist(n): l = [0] * (n + 1) p = 2 while p <= n: q = 2 while q <= n // p: l[p * q] = 1 q = q + 1 p = p + 1 l[0], l[1], l[2], l[3] = 1, 1, 0, 0 return l n = 20 l = primelist(n) prod = 1 for i in range(len(l)): if l[i] == 0: power = int(log(n, i)) prod = prod * (i ** power) print prod

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import os import json import argparse import numpy as np import onnx import caffe2.python.onnx.frontend from caffe2.proto import caffe2_pb2 from .base_convert import BaseConverter INIT_NET = 'init_net.pb' PREDICT_NET = 'predict_net.pb' DEL_ATTR = 'ws_nbytes_limit' MODEL_NAME = 'model.onnx' def del_attr(netdef): for op in netdef.op: for i, attr in enumerate(op.arg): if attr.name == DEL_ATTR: op.arg.pop(i) def np2onnx(s): def _modified2np(_s): if _s == 'float32': return 'float' if _s == 'float64': return 'double' return _s s = _modified2np(s) return onnx.TensorProto.DataType.Value(s.upper()) class Caffe2ToONNX(BaseConverter): def _load_model(self): self.init_net = self._find_with_name(INIT_NET) self.predict_net = self._find_with_name(PREDICT_NET) def _parse_input(self): value_info = {} for input in self.input_value: value_info[input['name']] = (np2onnx(input['dType']), tuple(input['size'])) return value_info def _convert(self): self._load_model() value_info = self._parse_input() predict_net = caffe2_pb2.NetDef() with open(self.predict_net, 'rb') as f: predict_net.ParseFromString(f.read()) init_net = caffe2_pb2.NetDef() with open(self.init_net, 'rb') as f: init_net.ParseFromString(f.read()) del_attr(predict_net) out_path = os.path.join(self.output_dir, 'model', MODEL_NAME) onnx_model = caffe2.python.onnx.frontend.caffe2_net_to_onnx_model( predict_net, init_net, value_info, ) onnx.save(onnx_model, out_path) if __name__ == '__main__': convert = Caffe2ToONNX() convert.convert()

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import numpy as np import matplotlib.pylab as plt from io import BytesIO from io import StringIO from zipfile import ZipFile from scipy.interpolate import interp2d from scipy.interpolate import interp1d site_classes = { 'A/B':{'Vs30':1500, 'name':'AB'}, 'AB':{'Vs30':1500, 'name':'AB'}, 'B' :{'Vs30':1080, 'name':'B'}, 'B/C':{'Vs30':760 , 'name':'BC'}, 'BC':{'Vs30':760 , 'name':'BC'}, 'C' :{'Vs30':530 , 'name':'C'}, 'C/D':{'Vs30':365 , 'name':'CD'}, 'CD':{'Vs30':365 , 'name':'CD'}, 'D' :{'Vs30':260 , 'name':'D'}, 'D/E':{'Vs30':185 , 'name':'DE'}, 'DE':{'Vs30':185 , 'name':'DE'}, 'E' :{'Vs30':150 , 'name':'E'}, } main_zip_file_address = r'C:\AlborzFiles\MyDesktop\Literature\USGS-Hazard-Map\0p01 Degree WUS Basin Map Data.zip' def _get_hazard_curve(site_class='B', ordinate='PGA'): with ZipFile(main_zip_file_address, 'r') as USGS_zip_file: lower_zip_name = fr'0p01 Degree WUS Basin Map Data/2018_nshm_{site_classes[site_class]["name"]}_vs30_{str(site_classes[site_class]["Vs30"])}_0p01_degree_seattle_basin_maps.zip' lower_zip = BytesIO(USGS_zip_file.read(lower_zip_name)) with ZipFile(lower_zip) as lower_zip_file: csv_address = fr'2018_nshm_{site_classes[site_class]["name"]}_vs30_{str(site_classes[site_class]["Vs30"])}_0p01_degree_seattle_basin_maps/{ordinate}/curves.csv' with lower_zip_file.open(csv_address, 'r') as curve_file: top_row = curve_file.readline().decode('utf-8').rstrip().split(',')[3:] hazard_x = np.array([float(x) for x in top_row]) phantom_file = StringIO(curve_file.read().decode('utf-8')) data = np.loadtxt(phantom_file, delimiter=',', usecols=tuple(range(1,23))) lon = data[:,0] lat = data[:,1] hazard_y = data[:,2:] del data return (lat, lon, hazard_x, hazard_y) def get_USGS_hazard_2018(lat, lon, site_class='B', return_period=2475): x_vals = np.array([0.0,0.01,0.02,0.03,0.05,0.075,0.1,0.15,0.2,0.25,0.3,0.4,0.5,0.75,1.0,1.5,2.0,3.0,4.0,5.0,7.5,10.0]) y_vals = np.zeros(x_vals.shape) for ii, x in enumerate(x_vals): ordinate_text = '' if x == 0: ordinate_text = 'PGA' else: ordinate_text = 'SA' + str(x).replace('.','P') lat_list, lon_list, hazard_x, hazard_y = _get_hazard_curve(site_class, ordinate_text) loc_to_del = np.where(np.abs(lat_list - lat) > 0.02) lat_list = np.delete(lat_list,loc_to_del) lon_list = np.delete(lon_list,loc_to_del) hazard_y = np.delete(hazard_y,loc_to_del, 0) loc_to_del = np.where(np.abs(lon_list - lon) > 0.02) lat_list = np.delete(lat_list,loc_to_del) lon_list = np.delete(lon_list,loc_to_del) hazard_y = np.delete(hazard_y,loc_to_del, 0) cur_loc_hazard = np.zeros(hazard_x.shape) for jj, _ in enumerate(hazard_x): z = hazard_y[:,jj] f = interp2d(lat_list, lon_list, z, kind='linear') cur_loc_hazard[jj] = f(lat, lon) y_vals[ii] = np.exp(interp1d(np.log(cur_loc_hazard), np.log(hazard_x), kind='linear')(np.log(1.0/return_period))) return x_vals, y_vals def main(): print('150 year return period:') x_vals, y_vals = get_USGS_hazard_2018(lat=47.572260,lon = -122.347509, site_class='E', return_period=150) for x, y in zip(x_vals, y_vals): print(f'{x} {y}') print('2500 year return period:') x_vals, y_vals = get_USGS_hazard_2018(lat=47.572260,lon = -122.347509, site_class='E', return_period=2500) for x, y in zip(x_vals, y_vals): print(f'{x} {y}') if __name__ == "__main__": main()

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from copy import copy, deepcopy import math import torch from torch import nn import torch.nn.functional as F import unittest from time import time import numpy as np import random import silu_cuda try: import apex.amp as amp from apex.amp import half_function except (ModuleNotFoundError, ImportError) as e: amp = None from ..optimized.compat import half_function try: from torch.cuda.amp import custom_fwd, custom_bwd except (ModuleNotFoundError, ImportError) as e: from ..optimized.compat import custom_fwd, custom_bwd try: import fused_mlp_relu except (ModuleNotFoundError, ImportError) as e: fused_mlp_relu = None try: import fused_mlp_agelu except (ModuleNotFoundError, ImportError) as e: fused_mlp_agelu = None try: import fused_mlp_gelu except (ModuleNotFoundError, ImportError) as e: fused_mlp_gelu = None # # class MlpReluFunction(torch.autograd.Function): # @staticmethod # @custom_fwd(cast_inputs=torch.float16) # def forward(ctx, activation, *args): # output = fused_mlp.forward(args) # ctx.save_for_backward(*args) # ctx.outputs = output # return output[0] # # @staticmethod # @custom_bwd # def backward(ctx, grad_o): # grads = fused_mlp.backward(grad_o, ctx.outputs, ctx.saved_tensors) # del ctx.outputs # return (None, *grads) # # class MlpReluFunction(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch.float16) def forward(ctx, p, *args): output = fused_mlp_relu.forward(p, args) ctx.save_for_backward(*args) ctx.outputs = output dropout_mask = output[-1] ctx.p = p return output[0], dropout_mask @staticmethod @custom_bwd def backward(ctx, *grad_o): p = ctx.p grads = fused_mlp_relu.backward(p, grad_o[0], ctx.outputs, ctx.saved_tensors) del ctx.outputs return (None, *grads) class MlpSiluFunction(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch.float16) def forward(ctx, p, *args): outputs = fused_mlp_silu.forward(p, args) ctx.save_for_backward(*args) ctx.outputs = outputs dropout_mask = outputs[-1] ctx.p = p return outputs[0], dropout_mask @staticmethod @custom_bwd def backward(ctx, *grad_o): p = ctx.p grads = fused_mlp_silu.backward(p, grad_o[0], ctx.outputs, ctx.saved_tensors) del ctx.outputs return (None, *grads) class MlpAGeLUFunction(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch.float16) def forward(ctx, p, *args): outputs = fused_mlp_agelu.forward(p, args) ctx.save_for_backward(*args) ctx.outputs = outputs dropout_mask = outputs[-1] ctx.p = p return outputs[0], dropout_mask @staticmethod @custom_bwd def backward(ctx, *grad_o): p = ctx.p grads = fused_mlp_agelu.backward(p, grad_o[0], ctx.outputs, ctx.saved_tensors) del ctx.outputs return (None, *grads) class MlpGeLUFunction(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch.float16) def forward(ctx, p, *args): outputs = fused_mlp_gelu.forward(p, args) ctx.save_for_backward(*args) ctx.outputs = outputs dropout_mask = outputs[-1] ctx.p = p return outputs[0], dropout_mask @staticmethod @custom_bwd def backward(ctx, *grad_o): p = ctx.p grads = fused_mlp_gelu.backward(p, grad_o[0], ctx.outputs, ctx.saved_tensors) del ctx.outputs return (None, *grads) if fused_mlp_agelu: mlp_agelu_function = half_function(MlpAGeLUFunction.apply) else: mlp_agelu_function = None if fused_mlp_gelu: mlp_gelu_function = half_function(MlpGeLUFunction.apply) else: mlp_gelu_function = None class SwishFunction(torch.autograd.Function): @staticmethod @custom_fwd def forward(ctx, inp): ctx.save_for_backward(inp) return silu_cuda.forward(inp) @staticmethod @custom_bwd def backward(ctx, grad_out): inp, = ctx.saved_tensors if not ctx.needs_input_grad[0]: return (None,) return silu_cuda.backward(inp, grad_out) def fast_silu(input): return SwishFunction.apply(input) class FastSiLU(torch.nn.Module): def forward(self, input): return fast_silu(input) class AGELUFunction(torch.autograd.Function): @staticmethod @custom_fwd def forward(ctx, x): ctx.save_for_backward(x) SQRT_M2_PI = 0.7978845608 COEFF = 0.044715 return 0.5 * x * (1.0 + torch.tanh(SQRT_M2_PI * (x + COEFF * torch.pow(x, 3)))) @staticmethod @custom_bwd def backward(ctx, grad_out): x, = ctx.saved_tensors SQRT_M2_PI = 0.7978845608 COEFF = 0.044715 BACKCOEFF = 0.1070322243 tanh_outf = torch.tanh(SQRT_M2_PI * (x + COEFF * torch.pow(x, 3))) retf = 0.5 * x * (1.0 - torch.pow(tanh_outf, 2)) * (SQRT_M2_PI + BACKCOEFF * torch.pow(x, 2)) + 0.5 * ( 1.0 + tanh_outf) return grad_out * retf class AGELU(torch.nn.Module): def forward(self, input): return AGELUFunction.apply(input) def agelu(x): SQRT_M2_PI = 0.7978845608 COEFF = 0.044715 BACKCOEFF = SQRT_M2_PI * COEFF * 3 return 0.5 * x * (1.0 + torch.tanh(SQRT_M2_PI * (x + COEFF * torch.pow(x, 3)))) def agelu_backward(x, dy): SQRT_M2_PI = 0.7978845608 COEFF = 0.044715 BACKCOEFF = 0.1070322243 tanh_outf = torch.tanh(SQRT_M2_PI * (x + COEFF * torch.pow(x, 3))) retf = 0.5 * x * (1.0 - torch.pow(tanh_outf, 2)) * (SQRT_M2_PI + BACKCOEFF * torch.pow(x, 2)) + 0.5 * ( 1.0 + tanh_outf) return dy * retf if __name__ == '__main__': class MLP(torch.nn.Module): """Launch MLP in C++ Args: mlp_sizes (list of int): MLP sizes. Example: [1024,1024,1024] will create 2 MLP layers with shape 1024x1024 bias (bool): Default True: relu (bool): Default True """ def __init__(self, mlp_sizes, activation='gelu', dropout=0.25): super(MLP, self).__init__() self.num_layers = len(mlp_sizes) - 1 self.mlp_sizes = copy(mlp_sizes) self.dropout = dropout if activation is 'relu': self.activation = 1 elif activation is 'sigmoid': self.activation = 2 elif activation is 'gelu': self.activation = 3 else: raise TypeError("activation must be relu or none.") self.weights = [] self.biases = [] for i in range(self.num_layers): w = torch.nn.Parameter(torch.empty(mlp_sizes[i + 1], mlp_sizes[i])) self.weights.append(w) name = 'weight_{}'.format(i) setattr(self, name, w) b = torch.nn.Parameter(torch.empty(mlp_sizes[i + 1])) self.biases.append(b) name = 'bias_{}'.format(i) setattr(self, name, b) self.reset_parameters() def reset_parameters(self): for weight in self.weights: dimsum = weight.size(0) + weight.size(1) std = math.sqrt(2. / float(dimsum)) nn.init.normal_(weight, 0., std) for bias in self.biases: std = math.sqrt(1. / float(bias.size(0))) nn.init.normal_(bias, 0., std) def forward(self, input, mask=None, ref=False): if ref: return self.forward_ref(input, mask) # return mlp_relu_function(self.dropout, input, *self.weights, *self.biases) # return mlp_agelu_function(self.dropout, input, *self.weights, *self.biases) return mlp_gelu_function(self.dropout, input, *self.weights, *self.biases) def forward_ref(self, input, mask): i = 0 output = input for l in range(self.num_layers): output = F.linear(output, self.weights[l], self.biases[l]) dropout_mask = mask[i:i + output.numel()] pinv = 1 / (1 - self.dropout) if l < self.num_layers - 1: # print(mask.size()) # output = fast_silu(output) * dropout_mask.view(output.size(0), -1) * pinv # output = GELUFunction.apply(output) * dropout_mask.view(output.size(0), -1) * pinv output = F.gelu(output) * dropout_mask.view(output.size(0), -1) * pinv i += output.numel() return output def extra_repr(self): # TODO add dropout probability s = F"MLP sizes: {self.mlp_sizes}, activation={self.activation}" return s batch_size = 24568 mlp_sizes = [1024, 4096, 1024] # mlp_sizes = [4, 7, 4] num_iters = 10 class TestMLP(unittest.TestCase): def test_creation(self): MLP(mlp_sizes) def test_numeric(self): mlp = MLP(mlp_sizes, activation='relu').cuda() print(mlp) ref_mlp = deepcopy(mlp) for _ in range(1): bsz = random.randint(2850, batch_size // 8) * 8 test_input = torch.empty(bsz, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_() ref_input = test_input.clone().detach().requires_grad_() mlp_out, dropout_mask = mlp(test_input) ref_out = ref_mlp.forward(ref_input, dropout_mask, ref=True) print(dropout_mask.sum() / dropout_mask.numel()) np.testing.assert_allclose( mlp_out.detach().cpu().numpy(), ref_out.detach().cpu().numpy(), atol=1e-5, rtol=1e-4) # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out mlp_out.mean().mul(10.).backward() ref_out.mean().mul(10.).backward() np.testing.assert_allclose( test_input.grad.detach().cpu().numpy(), ref_input.grad.detach().cpu().numpy(), atol=1e-7, rtol=1e-5) np.testing.assert_allclose( mlp.biases[0].grad.detach().cpu().numpy(), ref_mlp.biases[0].grad.detach().cpu().numpy(), atol=1e-7, rtol=1e-5) def test_with_bias(self): for use_activation in ['relu']: mlp = MLP(mlp_sizes, activation=use_activation).cuda() ref_mlp = deepcopy(mlp) test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_() ref_input = test_input.clone().detach().requires_grad_() mlp_out, dropout_mask = mlp(test_input) ref_out = ref_mlp(ref_input, dropout_mask, ref=True) np.testing.assert_allclose( mlp_out.detach().cpu().numpy(), ref_out.detach().cpu().numpy(), atol=1e-7, rtol=1e-5) # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out mlp_out.mean().mul(10.).backward() ref_out.mean().mul(10.).backward() np.testing.assert_allclose( test_input.grad.detach().cpu().numpy(), ref_input.grad.detach().cpu().numpy(), atol=1e-5, rtol=1e-4) for l in range(mlp.num_layers): np.testing.assert_allclose( mlp.weights[l].grad.detach().cpu().numpy(), ref_mlp.weights[l].grad.detach().cpu().numpy(), atol=1e-7, rtol=1e-5) np.testing.assert_allclose( mlp.biases[l].grad.detach().cpu().numpy(), ref_mlp.biases[l].grad.detach().cpu().numpy(), atol=1e-7, rtol=1e-5) def test_no_grad(self): mlp = MLP(mlp_sizes).cuda() ref_mlp = deepcopy(mlp) test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.) ref_input = test_input.clone().detach() mlp_out, dropout_mask = mlp(test_input) ref_out = ref_mlp(ref_input, dropout_mask, ref=True) np.testing.assert_allclose( mlp_out.detach().cpu().numpy(), ref_out.detach().cpu().numpy(), atol=1e-7, rtol=1e-5) def test_performance_half(self): mlp = MLP(mlp_sizes).cuda().half() mlp_layers = [] for i in range(mlp.num_layers): linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1]) mlp.weights[i].data.copy_(linear.weight) mlp.biases[i].data.copy_(linear.bias) mlp_layers.append(linear) if i < mlp.num_layers - 1: # mlp_layers.append(nn.ReLU(inplace=True)) mlp_layers.append(torch.nn.GELU()) mlp_layers.append(nn.Dropout(0.25)) ref_mlp = nn.Sequential(*mlp_layers).cuda().half() test_input = torch.empty( batch_size, mlp_sizes[0], device="cuda", dtype=torch.half).fill_(10.).requires_grad_() ref_input = torch.empty( batch_size, mlp_sizes[0], device="cuda", dtype=torch.half).fill_(10.).requires_grad_() # Warm up GPU for _ in range(100): ref_out = ref_mlp(ref_input) ref_loss = ref_out.mean() ref_mlp.zero_grad() ref_loss.backward() mlp_out, _ = mlp(test_input) test_loss = mlp_out.mean() mlp.zero_grad() test_loss.backward() torch.cuda.profiler.start() torch.cuda.synchronize() start_time = time() for _ in range(num_iters): ref_out = ref_mlp(ref_input) ref_loss = ref_out.mean() ref_mlp.zero_grad() ref_loss.backward() torch.cuda.synchronize() stop_time = time() print(F"\nPytorch MLP time {(stop_time - start_time) * 1000. / num_iters:.4f} ms") torch.cuda.synchronize() start_time = time() for _ in range(num_iters): mlp_out, _ = mlp(test_input) test_loss = mlp_out.mean() mlp.zero_grad() test_loss.backward() torch.cuda.synchronize() stop_time = time() print(F"C++ MLP time {(stop_time - start_time) * 1000. / num_iters:.4f} ms") torch.cuda.profiler.stop() unittest.main() # test = TestMLP() # test.test_creation() # test.test_performance_half() # test.test_with_bias()

[ "quanpn90@gmail.com" ]

quanpn90@gmail.com

6dd47cf9abf6588f76b33b1300c80b06fe34f86b

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/src/easy/test_build_array_from_permutation.py

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[]

no_license

Takuma-Ikeda/other-LeetCode

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refs/heads/master

2023-04-14T06:09:35.341039

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import unittest from answer.build_array_from_permutation import Solution class TestSolution(unittest.TestCase): def setUp(self): self.nums = [ [0, 2, 1, 5, 3, 4], [5, 0, 1, 2, 3, 4], ] self.answers = [ [0, 1, 2, 4, 5, 3], [4, 5, 0, 1, 2, 3], ] def solution(self, i): s = Solution() result = s.buildArray(self.nums[i]) self.assertEqual(self.answers[i], result) def test_solution0(self): self.solution(0) def test_solution1(self): self.solution(1) if __name__ == "__main__": unittest.main()

[ "el.programdear@gmail.com" ]

el.programdear@gmail.com

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ee3ba2af93581aaca5a1393f3eb22fa794be2a12

/app/main/form.py

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no_license

wbchief/myflask

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refs/heads/master

2020-03-30T23:48:10.771252

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from flask_pagedown.fields import PageDownField from flask_wtf import FlaskForm from wtforms import StringField, TextAreaField, SubmitField, BooleanField, SelectField from wtforms.validators import Length, DataRequired, Email, ValidationError from app.models import User, Role class EditProfileForm(FlaskForm): ''' 编辑资料表单 ''' name = StringField('真实姓名', validators=[Length(0, 64)]) location = StringField('地址', validators=[Length(0, 64)]) about_me = TextAreaField('自我介绍') submit = SubmitField('确认') class EditProfileAdminForm(FlaskForm): ''' 管理员编辑资料表单 ''' email = StringField('邮箱',validators=[DataRequired(), Length(1, 64), Email()]) username = StringField('用户名', validators=[DataRequired(), Length(1, 64)]) confirmed = BooleanField('是否确认') role = SelectField('Role', coerce=int) name = StringField('真实姓名', validators=[Length(0, 64)]) location = StringField('地址', validators=[Length(0, 64)]) about_me = TextAreaField('自我介绍') submit = SubmitField('确认') def __init__(self, user, *args, **kwargs): super(EditProfileAdminForm, self).__init__(*args, **kwargs) # selectField 必须在choice属性中设置各选项 self.role.choices = [(role.id, role.name) for role in Role.query.order_by(Role.name).all()] self.user = user def validate_email(self, field): ''' 验证邮箱 :param field: 邮箱 :return: ''' if field.data != self.user.email and User.query.filter_by(email=field.data).first(): raise ValidationError('邮箱已经被注册') def validate_username(self, field): ''' 验证用户名 :param field: 用户名 :return: ''' if field.data != self.user.username and User.query.filter_by(username=field.data).first(): raise ValidationError('用户名已经存在') class PostForm(FlaskForm): body = PageDownField('想写点什么', validators=[DataRequired()]) submit = SubmitField('提交') class CommentForm(FlaskForm): body = StringField('', validators=[DataRequired()]) submit = SubmitField('提交')

[ "712640388@qq.com" ]

712640388@qq.com

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/goldcoin/full_node/weight_proof.py

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chia-os/goldcoin-blockchain

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refs/heads/main

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import asyncio import dataclasses import logging import math import random from concurrent.futures.process import ProcessPoolExecutor from typing import Dict, List, Optional, Tuple from goldcoin.consensus.block_header_validation import validate_finished_header_block from goldcoin.consensus.block_record import BlockRecord from goldcoin.consensus.blockchain_interface import BlockchainInterface from goldcoin.consensus.constants import ConsensusConstants from goldcoin.consensus.deficit import calculate_deficit from goldcoin.consensus.full_block_to_block_record import header_block_to_sub_block_record from goldcoin.consensus.pot_iterations import ( calculate_ip_iters, calculate_iterations_quality, calculate_sp_iters, is_overflow_block, ) from goldcoin.consensus.vdf_info_computation import get_signage_point_vdf_info from goldcoin.types.blockchain_format.classgroup import ClassgroupElement from goldcoin.types.blockchain_format.sized_bytes import bytes32 from goldcoin.types.blockchain_format.slots import ChallengeChainSubSlot, RewardChainSubSlot from goldcoin.types.blockchain_format.sub_epoch_summary import SubEpochSummary from goldcoin.types.blockchain_format.vdf import VDFInfo from goldcoin.types.end_of_slot_bundle import EndOfSubSlotBundle from goldcoin.types.header_block import HeaderBlock from goldcoin.types.weight_proof import ( SubEpochChallengeSegment, SubEpochData, SubSlotData, WeightProof, SubEpochSegments, RecentChainData, ) from goldcoin.util.block_cache import BlockCache from goldcoin.util.hash import std_hash from goldcoin.util.ints import uint8, uint32, uint64, uint128 from goldcoin.util.streamable import dataclass_from_dict, recurse_jsonify log = logging.getLogger(__name__) class WeightProofHandler: LAMBDA_L = 100 C = 0.5 MAX_SAMPLES = 20 def __init__( self, constants: ConsensusConstants, blockchain: BlockchainInterface, ): self.tip: Optional[bytes32] = None self.proof: Optional[WeightProof] = None self.constants = constants self.blockchain = blockchain self.lock = asyncio.Lock() async def get_proof_of_weight(self, tip: bytes32) -> Optional[WeightProof]: tip_rec = self.blockchain.try_block_record(tip) if tip_rec is None: log.error("unknown tip") return None if tip_rec.height < self.constants.WEIGHT_PROOF_RECENT_BLOCKS: log.debug("chain to short for weight proof") return None async with self.lock: if self.proof is not None: if self.proof.recent_chain_data[-1].header_hash == tip: return self.proof wp = await self._create_proof_of_weight(tip) if wp is None: return None self.proof = wp self.tip = tip return wp def get_sub_epoch_data(self, tip_height: uint32, summary_heights: List[uint32]) -> List[SubEpochData]: sub_epoch_data: List[SubEpochData] = [] for sub_epoch_n, ses_height in enumerate(summary_heights): if ses_height > tip_height: break ses = self.blockchain.get_ses(ses_height) log.debug(f"handle sub epoch summary {sub_epoch_n} at height: {ses_height} ses {ses}") sub_epoch_data.append(_create_sub_epoch_data(ses)) return sub_epoch_data async def _create_proof_of_weight(self, tip: bytes32) -> Optional[WeightProof]: """ Creates a weight proof object """ assert self.blockchain is not None sub_epoch_segments: List[SubEpochChallengeSegment] = [] tip_rec = self.blockchain.try_block_record(tip) if tip_rec is None: log.error("failed not tip in cache") return None log.info(f"create weight proof peak {tip} {tip_rec.height}") recent_chain = await self._get_recent_chain(tip_rec.height) if recent_chain is None: return None summary_heights = self.blockchain.get_ses_heights() prev_ses_block = await self.blockchain.get_block_record_from_db(self.blockchain.height_to_hash(uint32(0))) if prev_ses_block is None: return None sub_epoch_data = self.get_sub_epoch_data(tip_rec.height, summary_heights) # use second to last ses as seed seed = self.get_seed_for_proof(summary_heights, tip_rec.height) rng = random.Random(seed) weight_to_check = _get_weights_for_sampling(rng, tip_rec.weight, recent_chain) sample_n = 0 ses_blocks = await self.blockchain.get_block_records_at(summary_heights) if ses_blocks is None: return None for sub_epoch_n, ses_height in enumerate(summary_heights): if ses_height > tip_rec.height: break # if we have enough sub_epoch samples, dont sample if sample_n >= self.MAX_SAMPLES: log.debug("reached sampled sub epoch cap") break # sample sub epoch # next sub block ses_block = ses_blocks[sub_epoch_n] if ses_block is None or ses_block.sub_epoch_summary_included is None: log.error("error while building proof") return None if _sample_sub_epoch(prev_ses_block.weight, ses_block.weight, weight_to_check): # type: ignore sample_n += 1 segments = await self.blockchain.get_sub_epoch_challenge_segments(ses_block.header_hash) if segments is None: segments = await self.__create_sub_epoch_segments(ses_block, prev_ses_block, uint32(sub_epoch_n)) if segments is None: log.error( f"failed while building segments for sub epoch {sub_epoch_n}, ses height {ses_height} " ) return None await self.blockchain.persist_sub_epoch_challenge_segments(ses_block.header_hash, segments) log.debug(f"sub epoch {sub_epoch_n} has {len(segments)} segments") sub_epoch_segments.extend(segments) prev_ses_block = ses_block log.debug(f"sub_epochs: {len(sub_epoch_data)}") return WeightProof(sub_epoch_data, sub_epoch_segments, recent_chain) def get_seed_for_proof(self, summary_heights: List[uint32], tip_height) -> bytes32: count = 0 ses = None for sub_epoch_n, ses_height in enumerate(reversed(summary_heights)): if ses_height <= tip_height: count += 1 if count == 2: ses = self.blockchain.get_ses(ses_height) break assert ses is not None seed = ses.get_hash() return seed async def _get_recent_chain(self, tip_height: uint32) -> Optional[List[HeaderBlock]]: recent_chain: List[HeaderBlock] = [] ses_heights = self.blockchain.get_ses_heights() min_height = 0 count_ses = 0 for ses_height in reversed(ses_heights): if ses_height <= tip_height: count_ses += 1 if count_ses == 2: min_height = ses_height - 1 break log.debug(f"start {min_height} end {tip_height}") headers = await self.blockchain.get_header_blocks_in_range(min_height, tip_height, tx_filter=False) blocks = await self.blockchain.get_block_records_in_range(min_height, tip_height) ses_count = 0 curr_height = tip_height blocks_n = 0 while ses_count < 2: if curr_height == 0: break # add to needed reward chain recent blocks header_block = headers[self.blockchain.height_to_hash(curr_height)] block_rec = blocks[header_block.header_hash] if header_block is None: log.error("creating recent chain failed") return None recent_chain.insert(0, header_block) if block_rec.sub_epoch_summary_included: ses_count += 1 curr_height = uint32(curr_height - 1) # type: ignore blocks_n += 1 header_block = headers[self.blockchain.height_to_hash(curr_height)] recent_chain.insert(0, header_block) log.info( f"recent chain, " f"start: {recent_chain[0].reward_chain_block.height} " f"end: {recent_chain[-1].reward_chain_block.height} " ) return recent_chain async def create_prev_sub_epoch_segments(self): log.debug("create prev sub_epoch_segments") heights = self.blockchain.get_ses_heights() if len(heights) < 3: return None count = len(heights) - 2 ses_sub_block = self.blockchain.height_to_block_record(heights[-2]) prev_ses_sub_block = self.blockchain.height_to_block_record(heights[-3]) assert prev_ses_sub_block.sub_epoch_summary_included is not None segments = await self.__create_sub_epoch_segments(ses_sub_block, prev_ses_sub_block, uint32(count)) assert segments is not None await self.blockchain.persist_sub_epoch_challenge_segments(ses_sub_block.header_hash, segments) log.debug("sub_epoch_segments done") return None async def create_sub_epoch_segments(self): log.debug("check segments in db") """ Creates a weight proof object """ assert self.blockchain is not None peak_height = self.blockchain.get_peak_height() if peak_height is None: log.error("no peak yet") return None summary_heights = self.blockchain.get_ses_heights() prev_ses_block = await self.blockchain.get_block_record_from_db(self.blockchain.height_to_hash(uint32(0))) if prev_ses_block is None: return None ses_blocks = await self.blockchain.get_block_records_at(summary_heights) if ses_blocks is None: return None for sub_epoch_n, ses_height in enumerate(summary_heights): log.debug(f"check db for sub epoch {sub_epoch_n}") if ses_height > peak_height: break ses_block = ses_blocks[sub_epoch_n] if ses_block is None or ses_block.sub_epoch_summary_included is None: log.error("error while building proof") return None await self.__create_persist_segment(prev_ses_block, ses_block, ses_height, sub_epoch_n) prev_ses_block = ses_block await asyncio.sleep(2) log.debug("done checking segments") return None async def __create_persist_segment(self, prev_ses_block, ses_block, ses_height, sub_epoch_n): segments = await self.blockchain.get_sub_epoch_challenge_segments(ses_block.header_hash) if segments is None: segments = await self.__create_sub_epoch_segments(ses_block, prev_ses_block, uint32(sub_epoch_n)) if segments is None: log.error(f"failed while building segments for sub epoch {sub_epoch_n}, ses height {ses_height} ") return None await self.blockchain.persist_sub_epoch_challenge_segments(ses_block.header_hash, segments) async def __create_sub_epoch_segments( self, ses_block: BlockRecord, se_start: BlockRecord, sub_epoch_n: uint32 ) -> Optional[List[SubEpochChallengeSegment]]: segments: List[SubEpochChallengeSegment] = [] start_height = await self.get_prev_two_slots_height(se_start) blocks = await self.blockchain.get_block_records_in_range( start_height, ses_block.height + self.constants.MAX_SUB_SLOT_BLOCKS ) header_blocks = await self.blockchain.get_header_blocks_in_range( start_height, ses_block.height + self.constants.MAX_SUB_SLOT_BLOCKS, tx_filter=False ) curr: Optional[HeaderBlock] = header_blocks[se_start.header_hash] height = se_start.height assert curr is not None first = True idx = 0 while curr.height < ses_block.height: if blocks[curr.header_hash].is_challenge_block(self.constants): log.debug(f"challenge segment {idx}, starts at {curr.height} ") seg, height = await self._create_challenge_segment(curr, sub_epoch_n, header_blocks, blocks, first) if seg is None: log.error(f"failed creating segment {curr.header_hash} ") return None segments.append(seg) idx += 1 first = False else: height = height + uint32(1) # type: ignore curr = header_blocks[self.blockchain.height_to_hash(height)] if curr is None: return None log.debug(f"next sub epoch starts at {height}") return segments async def get_prev_two_slots_height(self, se_start: BlockRecord) -> uint32: # find prev 2 slots height slot = 0 batch_size = 50 curr_rec = se_start blocks = await self.blockchain.get_block_records_in_range(curr_rec.height - batch_size, curr_rec.height) end = curr_rec.height while slot < 2 and curr_rec.height > 0: if curr_rec.first_in_sub_slot: slot += 1 if end - curr_rec.height == batch_size - 1: blocks = await self.blockchain.get_block_records_in_range(curr_rec.height - batch_size, curr_rec.height) end = curr_rec.height curr_rec = blocks[self.blockchain.height_to_hash(uint32(curr_rec.height - 1))] return curr_rec.height async def _create_challenge_segment( self, header_block: HeaderBlock, sub_epoch_n: uint32, header_blocks: Dict[bytes32, HeaderBlock], blocks: Dict[bytes32, BlockRecord], first_segment_in_sub_epoch: bool, ) -> Tuple[Optional[SubEpochChallengeSegment], uint32]: assert self.blockchain is not None sub_slots: List[SubSlotData] = [] log.debug(f"create challenge segment block {header_block.header_hash} block height {header_block.height} ") # VDFs from sub slots before challenge block first_sub_slots, first_rc_end_of_slot_vdf = await self.__first_sub_slot_vdfs( header_block, header_blocks, blocks, first_segment_in_sub_epoch ) if first_sub_slots is None: log.error("failed building first sub slots") return None, uint32(0) sub_slots.extend(first_sub_slots) ssd = await _challenge_block_vdfs( self.constants, header_block, blocks[header_block.header_hash], blocks, ) sub_slots.append(ssd) # # VDFs from slot after challenge block to end of slot log.debug(f"create slot end vdf for block {header_block.header_hash} height {header_block.height} ") challenge_slot_end_sub_slots, end_height = await self.__slot_end_vdf( uint32(header_block.height + 1), header_blocks, blocks ) if challenge_slot_end_sub_slots is None: log.error("failed building slot end ") return None, uint32(0) sub_slots.extend(challenge_slot_end_sub_slots) if first_segment_in_sub_epoch and sub_epoch_n != 0: return ( SubEpochChallengeSegment(sub_epoch_n, sub_slots, first_rc_end_of_slot_vdf), end_height, ) return SubEpochChallengeSegment(sub_epoch_n, sub_slots, None), end_height # returns a challenge chain vdf from slot start to signage point async def __first_sub_slot_vdfs( self, header_block: HeaderBlock, header_blocks: Dict[bytes32, HeaderBlock], blocks: Dict[bytes32, BlockRecord], first_in_sub_epoch: bool, ) -> Tuple[Optional[List[SubSlotData]], Optional[VDFInfo]]: # combine cc vdfs of all reward blocks from the start of the sub slot to end header_block_sub_rec = blocks[header_block.header_hash] # find slot start curr_sub_rec = header_block_sub_rec first_rc_end_of_slot_vdf = None if first_in_sub_epoch and curr_sub_rec.height > 0: while not curr_sub_rec.sub_epoch_summary_included: curr_sub_rec = blocks[curr_sub_rec.prev_hash] first_rc_end_of_slot_vdf = self.first_rc_end_of_slot_vdf(header_block, blocks, header_blocks) else: if header_block_sub_rec.overflow and header_block_sub_rec.first_in_sub_slot: sub_slots_num = 2 while sub_slots_num > 0 and curr_sub_rec.height > 0: if curr_sub_rec.first_in_sub_slot: assert curr_sub_rec.finished_challenge_slot_hashes is not None sub_slots_num -= len(curr_sub_rec.finished_challenge_slot_hashes) curr_sub_rec = blocks[curr_sub_rec.prev_hash] else: while not curr_sub_rec.first_in_sub_slot and curr_sub_rec.height > 0: curr_sub_rec = blocks[curr_sub_rec.prev_hash] curr = header_blocks[curr_sub_rec.header_hash] sub_slots_data: List[SubSlotData] = [] tmp_sub_slots_data: List[SubSlotData] = [] while curr.height < header_block.height: if curr is None: log.error("failed fetching block") return None, None if curr.first_in_sub_slot: # if not blue boxed if not blue_boxed_end_of_slot(curr.finished_sub_slots[0]): sub_slots_data.extend(tmp_sub_slots_data) for idx, sub_slot in enumerate(curr.finished_sub_slots): curr_icc_info = None if sub_slot.infused_challenge_chain is not None: curr_icc_info = sub_slot.infused_challenge_chain.infused_challenge_chain_end_of_slot_vdf sub_slots_data.append(handle_finished_slots(sub_slot, curr_icc_info)) tmp_sub_slots_data = [] ssd = SubSlotData( None, None, None, None, None, curr.reward_chain_block.signage_point_index, None, None, None, None, curr.reward_chain_block.challenge_chain_ip_vdf, curr.reward_chain_block.infused_challenge_chain_ip_vdf, curr.total_iters, ) tmp_sub_slots_data.append(ssd) curr = header_blocks[self.blockchain.height_to_hash(uint32(curr.height + 1))] if len(tmp_sub_slots_data) > 0: sub_slots_data.extend(tmp_sub_slots_data) for idx, sub_slot in enumerate(header_block.finished_sub_slots): curr_icc_info = None if sub_slot.infused_challenge_chain is not None: curr_icc_info = sub_slot.infused_challenge_chain.infused_challenge_chain_end_of_slot_vdf sub_slots_data.append(handle_finished_slots(sub_slot, curr_icc_info)) return sub_slots_data, first_rc_end_of_slot_vdf def first_rc_end_of_slot_vdf( self, header_block, blocks: Dict[bytes32, BlockRecord], header_blocks: Dict[bytes32, HeaderBlock], ) -> Optional[VDFInfo]: curr = blocks[header_block.header_hash] while curr.height > 0 and not curr.sub_epoch_summary_included: curr = blocks[curr.prev_hash] return header_blocks[curr.header_hash].finished_sub_slots[-1].reward_chain.end_of_slot_vdf async def __slot_end_vdf( self, start_height: uint32, header_blocks: Dict[bytes32, HeaderBlock], blocks: Dict[bytes32, BlockRecord] ) -> Tuple[Optional[List[SubSlotData]], uint32]: # gets all vdfs first sub slot after challenge block to last sub slot log.debug(f"slot end vdf start height {start_height}") curr = header_blocks[self.blockchain.height_to_hash(start_height)] curr_header_hash = curr.header_hash sub_slots_data: List[SubSlotData] = [] tmp_sub_slots_data: List[SubSlotData] = [] while not blocks[curr_header_hash].is_challenge_block(self.constants): if curr.first_in_sub_slot: sub_slots_data.extend(tmp_sub_slots_data) curr_prev_header_hash = curr.prev_header_hash # add collected vdfs for idx, sub_slot in enumerate(curr.finished_sub_slots): prev_rec = blocks[curr_prev_header_hash] eos_vdf_iters = prev_rec.sub_slot_iters if idx == 0: eos_vdf_iters = uint64(prev_rec.sub_slot_iters - prev_rec.ip_iters(self.constants)) sub_slots_data.append(handle_end_of_slot(sub_slot, eos_vdf_iters)) tmp_sub_slots_data = [] tmp_sub_slots_data.append(self.handle_block_vdfs(curr, blocks)) curr = header_blocks[self.blockchain.height_to_hash(uint32(curr.height + 1))] curr_header_hash = curr.header_hash if len(tmp_sub_slots_data) > 0: sub_slots_data.extend(tmp_sub_slots_data) log.debug(f"slot end vdf end height {curr.height} slots {len(sub_slots_data)} ") return sub_slots_data, curr.height def handle_block_vdfs(self, curr: HeaderBlock, blocks: Dict[bytes32, BlockRecord]): cc_sp_proof = None icc_ip_proof = None cc_sp_info = None icc_ip_info = None block_record = blocks[curr.header_hash] if curr.infused_challenge_chain_ip_proof is not None: assert curr.reward_chain_block.infused_challenge_chain_ip_vdf icc_ip_proof = curr.infused_challenge_chain_ip_proof icc_ip_info = curr.reward_chain_block.infused_challenge_chain_ip_vdf if curr.challenge_chain_sp_proof is not None: assert curr.reward_chain_block.challenge_chain_sp_vdf cc_sp_vdf_info = curr.reward_chain_block.challenge_chain_sp_vdf if not curr.challenge_chain_sp_proof.normalized_to_identity: (_, _, _, _, cc_vdf_iters, _,) = get_signage_point_vdf_info( self.constants, curr.finished_sub_slots, block_record.overflow, None if curr.height == 0 else blocks[curr.prev_header_hash], BlockCache(blocks), block_record.sp_total_iters(self.constants), block_record.sp_iters(self.constants), ) cc_sp_vdf_info = VDFInfo( curr.reward_chain_block.challenge_chain_sp_vdf.challenge, cc_vdf_iters, curr.reward_chain_block.challenge_chain_sp_vdf.output, ) cc_sp_proof = curr.challenge_chain_sp_proof cc_sp_info = cc_sp_vdf_info return SubSlotData( None, cc_sp_proof, curr.challenge_chain_ip_proof, icc_ip_proof, cc_sp_info, curr.reward_chain_block.signage_point_index, None, None, None, None, curr.reward_chain_block.challenge_chain_ip_vdf, icc_ip_info, curr.total_iters, ) def validate_weight_proof_single_proc(self, weight_proof: WeightProof) -> Tuple[bool, uint32]: assert self.blockchain is not None assert len(weight_proof.sub_epochs) > 0 if len(weight_proof.sub_epochs) == 0: return False, uint32(0) peak_height = weight_proof.recent_chain_data[-1].reward_chain_block.height log.info(f"validate weight proof peak height {peak_height}") summaries, sub_epoch_weight_list = _validate_sub_epoch_summaries(self.constants, weight_proof) if summaries is None: log.warning("weight proof failed sub epoch data validation") return False, uint32(0) constants, summary_bytes, wp_segment_bytes, wp_recent_chain_bytes = vars_to_bytes( self.constants, summaries, weight_proof ) log.info("validate sub epoch challenge segments") seed = summaries[-2].get_hash() rng = random.Random(seed) if not validate_sub_epoch_sampling(rng, sub_epoch_weight_list, weight_proof): log.error("failed weight proof sub epoch sample validation") return False, uint32(0) if not _validate_sub_epoch_segments(constants, rng, wp_segment_bytes, summary_bytes): return False, uint32(0) log.info("validate weight proof recent blocks") if not _validate_recent_blocks(constants, wp_recent_chain_bytes, summary_bytes): return False, uint32(0) return True, self.get_fork_point(summaries) def get_fork_point_no_validations(self, weight_proof: WeightProof) -> Tuple[bool, uint32]: log.debug("get fork point skip validations") assert self.blockchain is not None assert len(weight_proof.sub_epochs) > 0 if len(weight_proof.sub_epochs) == 0: return False, uint32(0) summaries, sub_epoch_weight_list = _validate_sub_epoch_summaries(self.constants, weight_proof) if summaries is None: log.warning("weight proof failed to validate sub epoch summaries") return False, uint32(0) return True, self.get_fork_point(summaries) async def validate_weight_proof(self, weight_proof: WeightProof) -> Tuple[bool, uint32, List[SubEpochSummary]]: assert self.blockchain is not None assert len(weight_proof.sub_epochs) > 0 if len(weight_proof.sub_epochs) == 0: return False, uint32(0), [] peak_height = weight_proof.recent_chain_data[-1].reward_chain_block.height log.info(f"validate weight proof peak height {peak_height}") summaries, sub_epoch_weight_list = _validate_sub_epoch_summaries(self.constants, weight_proof) if summaries is None: log.error("weight proof failed sub epoch data validation") return False, uint32(0), [] seed = summaries[-2].get_hash() rng = random.Random(seed) if not validate_sub_epoch_sampling(rng, sub_epoch_weight_list, weight_proof): log.error("failed weight proof sub epoch sample validation") return False, uint32(0), [] executor = ProcessPoolExecutor(1) constants, summary_bytes, wp_segment_bytes, wp_recent_chain_bytes = vars_to_bytes( self.constants, summaries, weight_proof ) segment_validation_task = asyncio.get_running_loop().run_in_executor( executor, _validate_sub_epoch_segments, constants, rng, wp_segment_bytes, summary_bytes ) recent_blocks_validation_task = asyncio.get_running_loop().run_in_executor( executor, _validate_recent_blocks, constants, wp_recent_chain_bytes, summary_bytes ) valid_segment_task = segment_validation_task valid_recent_blocks_task = recent_blocks_validation_task valid_recent_blocks = await valid_recent_blocks_task if not valid_recent_blocks: log.error("failed validating weight proof recent blocks") return False, uint32(0), [] valid_segments = await valid_segment_task if not valid_segments: log.error("failed validating weight proof sub epoch segments") return False, uint32(0), [] return True, self.get_fork_point(summaries), summaries def get_fork_point(self, received_summaries: List[SubEpochSummary]) -> uint32: # iterate through sub epoch summaries to find fork point fork_point_index = 0 ses_heights = self.blockchain.get_ses_heights() for idx, summary_height in enumerate(ses_heights): log.debug(f"check summary {idx} height {summary_height}") local_ses = self.blockchain.get_ses(summary_height) if idx == len(received_summaries) - 1: # end of wp summaries, local chain is longer or equal to wp chain break if local_ses is None or local_ses.get_hash() != received_summaries[idx].get_hash(): break fork_point_index = idx if fork_point_index > 2: # Two summeries can have different blocks and still be identical # This gets resolved after one full sub epoch height = ses_heights[fork_point_index - 2] else: height = uint32(0) return height def _get_weights_for_sampling( rng: random.Random, total_weight: uint128, recent_chain: List[HeaderBlock] ) -> Optional[List[uint128]]: weight_to_check = [] last_l_weight = recent_chain[-1].reward_chain_block.weight - recent_chain[0].reward_chain_block.weight delta = last_l_weight / total_weight prob_of_adv_succeeding = 1 - math.log(WeightProofHandler.C, delta) if prob_of_adv_succeeding <= 0: return None queries = -WeightProofHandler.LAMBDA_L * math.log(2, prob_of_adv_succeeding) for i in range(int(queries) + 1): u = rng.random() q = 1 - delta ** u # todo check division and type conversions weight = q * float(total_weight) weight_to_check.append(uint128(int(weight))) weight_to_check.sort() return weight_to_check def _sample_sub_epoch( start_of_epoch_weight: uint128, end_of_epoch_weight: uint128, weight_to_check: List[uint128], ) -> bool: """ weight_to_check: List[uint128] is expected to be sorted """ if weight_to_check is None: return True if weight_to_check[-1] < start_of_epoch_weight: return False if weight_to_check[0] > end_of_epoch_weight: return False choose = False for weight in weight_to_check: if weight > end_of_epoch_weight: return False if start_of_epoch_weight < weight < end_of_epoch_weight: log.debug(f"start weight: {start_of_epoch_weight}") log.debug(f"weight to check {weight}") log.debug(f"end weight: {end_of_epoch_weight}") choose = True break return choose # wp creation methods def _create_sub_epoch_data( sub_epoch_summary: SubEpochSummary, ) -> SubEpochData: reward_chain_hash: bytes32 = sub_epoch_summary.reward_chain_hash # Number of subblocks overflow in previous slot previous_sub_epoch_overflows: uint8 = sub_epoch_summary.num_blocks_overflow # total in sub epoch - expected # New work difficulty and iterations per sub-slot sub_slot_iters: Optional[uint64] = sub_epoch_summary.new_sub_slot_iters new_difficulty: Optional[uint64] = sub_epoch_summary.new_difficulty return SubEpochData(reward_chain_hash, previous_sub_epoch_overflows, sub_slot_iters, new_difficulty) async def _challenge_block_vdfs( constants: ConsensusConstants, header_block: HeaderBlock, block_rec: BlockRecord, sub_blocks: Dict[bytes32, BlockRecord], ): (_, _, _, _, cc_vdf_iters, _,) = get_signage_point_vdf_info( constants, header_block.finished_sub_slots, block_rec.overflow, None if header_block.height == 0 else sub_blocks[header_block.prev_header_hash], BlockCache(sub_blocks), block_rec.sp_total_iters(constants), block_rec.sp_iters(constants), ) cc_sp_info = None if header_block.reward_chain_block.challenge_chain_sp_vdf: cc_sp_info = header_block.reward_chain_block.challenge_chain_sp_vdf assert header_block.challenge_chain_sp_proof if not header_block.challenge_chain_sp_proof.normalized_to_identity: cc_sp_info = VDFInfo( header_block.reward_chain_block.challenge_chain_sp_vdf.challenge, cc_vdf_iters, header_block.reward_chain_block.challenge_chain_sp_vdf.output, ) ssd = SubSlotData( header_block.reward_chain_block.proof_of_space, header_block.challenge_chain_sp_proof, header_block.challenge_chain_ip_proof, None, cc_sp_info, header_block.reward_chain_block.signage_point_index, None, None, None, None, header_block.reward_chain_block.challenge_chain_ip_vdf, header_block.reward_chain_block.infused_challenge_chain_ip_vdf, block_rec.total_iters, ) return ssd def handle_finished_slots(end_of_slot: EndOfSubSlotBundle, icc_end_of_slot_info): return SubSlotData( None, None, None, None, None, None, None if end_of_slot.proofs.challenge_chain_slot_proof is None else end_of_slot.proofs.challenge_chain_slot_proof, None if end_of_slot.proofs.infused_challenge_chain_slot_proof is None else end_of_slot.proofs.infused_challenge_chain_slot_proof, end_of_slot.challenge_chain.challenge_chain_end_of_slot_vdf, icc_end_of_slot_info, None, None, None, ) def handle_end_of_slot( sub_slot: EndOfSubSlotBundle, eos_vdf_iters: uint64, ): assert sub_slot.infused_challenge_chain assert sub_slot.proofs.infused_challenge_chain_slot_proof if sub_slot.proofs.infused_challenge_chain_slot_proof.normalized_to_identity: icc_info = sub_slot.infused_challenge_chain.infused_challenge_chain_end_of_slot_vdf else: icc_info = VDFInfo( sub_slot.infused_challenge_chain.infused_challenge_chain_end_of_slot_vdf.challenge, eos_vdf_iters, sub_slot.infused_challenge_chain.infused_challenge_chain_end_of_slot_vdf.output, ) if sub_slot.proofs.challenge_chain_slot_proof.normalized_to_identity: cc_info = sub_slot.challenge_chain.challenge_chain_end_of_slot_vdf else: cc_info = VDFInfo( sub_slot.challenge_chain.challenge_chain_end_of_slot_vdf.challenge, eos_vdf_iters, sub_slot.challenge_chain.challenge_chain_end_of_slot_vdf.output, ) assert sub_slot.proofs.infused_challenge_chain_slot_proof is not None return SubSlotData( None, None, None, None, None, None, sub_slot.proofs.challenge_chain_slot_proof, sub_slot.proofs.infused_challenge_chain_slot_proof, cc_info, icc_info, None, None, None, ) def compress_segments(full_segment_index, segments: List[SubEpochChallengeSegment]) -> List[SubEpochChallengeSegment]: compressed_segments = [] compressed_segments.append(segments[0]) for idx, segment in enumerate(segments[1:]): if idx != full_segment_index: # remove all redundant values segment = compress_segment(segment) compressed_segments.append(segment) return compressed_segments def compress_segment(segment: SubEpochChallengeSegment) -> SubEpochChallengeSegment: # find challenge slot comp_seg = SubEpochChallengeSegment(segment.sub_epoch_n, [], segment.rc_slot_end_info) for slot in segment.sub_slots: comp_seg.sub_slots.append(slot) if slot.is_challenge(): break return segment # wp validation methods def _validate_sub_epoch_summaries( constants: ConsensusConstants, weight_proof: WeightProof, ) -> Tuple[Optional[List[SubEpochSummary]], Optional[List[uint128]]]: last_ses_hash, last_ses_sub_height = _get_last_ses_hash(constants, weight_proof.recent_chain_data) if last_ses_hash is None: log.warning("could not find last ses block") return None, None summaries, total, sub_epoch_weight_list = _map_sub_epoch_summaries( constants.SUB_EPOCH_BLOCKS, constants.GENESIS_CHALLENGE, weight_proof.sub_epochs, constants.DIFFICULTY_STARTING, ) log.info(f"validating {len(summaries)} sub epochs") # validate weight if not _validate_summaries_weight(constants, total, summaries, weight_proof): log.error("failed validating weight") return None, None last_ses = summaries[-1] log.debug(f"last ses sub height {last_ses_sub_height}") # validate last ses_hash if last_ses.get_hash() != last_ses_hash: log.error(f"failed to validate ses hashes block height {last_ses_sub_height}") return None, None return summaries, sub_epoch_weight_list def _map_sub_epoch_summaries( sub_blocks_for_se: uint32, ses_hash: bytes32, sub_epoch_data: List[SubEpochData], curr_difficulty: uint64, ) -> Tuple[List[SubEpochSummary], uint128, List[uint128]]: total_weight: uint128 = uint128(0) summaries: List[SubEpochSummary] = [] sub_epoch_weight_list: List[uint128] = [] for idx, data in enumerate(sub_epoch_data): ses = SubEpochSummary( ses_hash, data.reward_chain_hash, data.num_blocks_overflow, data.new_difficulty, data.new_sub_slot_iters, ) if idx < len(sub_epoch_data) - 1: delta = 0 if idx > 0: delta = sub_epoch_data[idx].num_blocks_overflow log.debug(f"sub epoch {idx} start weight is {total_weight+curr_difficulty} ") sub_epoch_weight_list.append(uint128(total_weight + curr_difficulty)) total_weight = total_weight + uint128( # type: ignore curr_difficulty * (sub_blocks_for_se + sub_epoch_data[idx + 1].num_blocks_overflow - delta) ) # if new epoch update diff and iters if data.new_difficulty is not None: curr_difficulty = data.new_difficulty # add to dict summaries.append(ses) ses_hash = std_hash(ses) # add last sub epoch weight sub_epoch_weight_list.append(uint128(total_weight + curr_difficulty)) return summaries, total_weight, sub_epoch_weight_list def _validate_summaries_weight(constants: ConsensusConstants, sub_epoch_data_weight, summaries, weight_proof) -> bool: num_over = summaries[-1].num_blocks_overflow ses_end_height = (len(summaries) - 1) * constants.SUB_EPOCH_BLOCKS + num_over - 1 curr = None for block in weight_proof.recent_chain_data: if block.reward_chain_block.height == ses_end_height: curr = block if curr is None: return False return curr.reward_chain_block.weight == sub_epoch_data_weight def _validate_sub_epoch_segments( constants_dict: Dict, rng: random.Random, weight_proof_bytes: bytes, summaries_bytes: List[bytes], ): constants, summaries = bytes_to_vars(constants_dict, summaries_bytes) sub_epoch_segments: SubEpochSegments = SubEpochSegments.from_bytes(weight_proof_bytes) rc_sub_slot_hash = constants.GENESIS_CHALLENGE total_blocks, total_ip_iters = 0, 0 total_slot_iters, total_slots = 0, 0 total_ip_iters = 0 prev_ses: Optional[SubEpochSummary] = None segments_by_sub_epoch = map_segments_by_sub_epoch(sub_epoch_segments.challenge_segments) curr_ssi = constants.SUB_SLOT_ITERS_STARTING for sub_epoch_n, segments in segments_by_sub_epoch.items(): prev_ssi = curr_ssi curr_difficulty, curr_ssi = _get_curr_diff_ssi(constants, sub_epoch_n, summaries) log.debug(f"validate sub epoch {sub_epoch_n}") # recreate RewardChainSubSlot for next ses rc_hash sampled_seg_index = rng.choice(range(len(segments))) if sub_epoch_n > 0: rc_sub_slot = __get_rc_sub_slot(constants, segments[0], summaries, curr_ssi) prev_ses = summaries[sub_epoch_n - 1] rc_sub_slot_hash = rc_sub_slot.get_hash() if not summaries[sub_epoch_n].reward_chain_hash == rc_sub_slot_hash: log.error(f"failed reward_chain_hash validation sub_epoch {sub_epoch_n}") return False for idx, segment in enumerate(segments): valid_segment, ip_iters, slot_iters, slots = _validate_segment( constants, segment, curr_ssi, prev_ssi, curr_difficulty, prev_ses, idx == 0, sampled_seg_index == idx ) if not valid_segment: log.error(f"failed to validate sub_epoch {segment.sub_epoch_n} segment {idx} slots") return False prev_ses = None total_blocks += 1 total_slot_iters += slot_iters total_slots += slots total_ip_iters += ip_iters return True def _validate_segment( constants: ConsensusConstants, segment: SubEpochChallengeSegment, curr_ssi: uint64, prev_ssi: uint64, curr_difficulty: uint64, ses: Optional[SubEpochSummary], first_segment_in_se: bool, sampled: bool, ) -> Tuple[bool, int, int, int]: ip_iters, slot_iters, slots = 0, 0, 0 after_challenge = False for idx, sub_slot_data in enumerate(segment.sub_slots): if sampled and sub_slot_data.is_challenge(): after_challenge = True required_iters = __validate_pospace(constants, segment, idx, curr_difficulty, ses, first_segment_in_se) if required_iters is None: return False, uint64(0), uint64(0), uint64(0) assert sub_slot_data.signage_point_index is not None ip_iters = ip_iters + calculate_ip_iters( # type: ignore constants, curr_ssi, sub_slot_data.signage_point_index, required_iters ) if not _validate_challenge_block_vdfs(constants, idx, segment.sub_slots, curr_ssi): log.error(f"failed to validate challenge slot {idx} vdfs") return False, uint64(0), uint64(0), uint64(0) elif sampled and after_challenge: if not _validate_sub_slot_data(constants, idx, segment.sub_slots, curr_ssi): log.error(f"failed to validate sub slot data {idx} vdfs") return False, uint64(0), uint64(0), uint64(0) slot_iters = slot_iters + curr_ssi # type: ignore slots = slots + uint64(1) # type: ignore return True, ip_iters, slot_iters, slots def _validate_challenge_block_vdfs( constants: ConsensusConstants, sub_slot_idx: int, sub_slots: List[SubSlotData], ssi: uint64, ) -> bool: sub_slot_data = sub_slots[sub_slot_idx] if sub_slot_data.cc_signage_point is not None and sub_slot_data.cc_sp_vdf_info: assert sub_slot_data.signage_point_index sp_input = ClassgroupElement.get_default_element() if not sub_slot_data.cc_signage_point.normalized_to_identity and sub_slot_idx >= 1: is_overflow = is_overflow_block(constants, sub_slot_data.signage_point_index) prev_ssd = sub_slots[sub_slot_idx - 1] sp_input = sub_slot_data_vdf_input( constants, sub_slot_data, sub_slot_idx, sub_slots, is_overflow, prev_ssd.is_end_of_slot(), ssi ) if not sub_slot_data.cc_signage_point.is_valid(constants, sp_input, sub_slot_data.cc_sp_vdf_info): log.error(f"failed to validate challenge chain signage point 2 {sub_slot_data.cc_sp_vdf_info}") return False assert sub_slot_data.cc_infusion_point assert sub_slot_data.cc_ip_vdf_info ip_input = ClassgroupElement.get_default_element() cc_ip_vdf_info = sub_slot_data.cc_ip_vdf_info if not sub_slot_data.cc_infusion_point.normalized_to_identity and sub_slot_idx >= 1: prev_ssd = sub_slots[sub_slot_idx - 1] if prev_ssd.cc_slot_end is None: assert prev_ssd.cc_ip_vdf_info assert prev_ssd.total_iters assert sub_slot_data.total_iters ip_input = prev_ssd.cc_ip_vdf_info.output ip_vdf_iters = uint64(sub_slot_data.total_iters - prev_ssd.total_iters) cc_ip_vdf_info = VDFInfo( sub_slot_data.cc_ip_vdf_info.challenge, ip_vdf_iters, sub_slot_data.cc_ip_vdf_info.output ) if not sub_slot_data.cc_infusion_point.is_valid(constants, ip_input, cc_ip_vdf_info): log.error(f"failed to validate challenge chain infusion point {sub_slot_data.cc_ip_vdf_info}") return False return True def _validate_sub_slot_data( constants: ConsensusConstants, sub_slot_idx: int, sub_slots: List[SubSlotData], ssi: uint64, ) -> bool: sub_slot_data = sub_slots[sub_slot_idx] assert sub_slot_idx > 0 prev_ssd = sub_slots[sub_slot_idx - 1] if sub_slot_data.is_end_of_slot(): if sub_slot_data.icc_slot_end is not None: input = ClassgroupElement.get_default_element() if not sub_slot_data.icc_slot_end.normalized_to_identity and prev_ssd.icc_ip_vdf_info is not None: assert prev_ssd.icc_ip_vdf_info input = prev_ssd.icc_ip_vdf_info.output assert sub_slot_data.icc_slot_end_info if not sub_slot_data.icc_slot_end.is_valid(constants, input, sub_slot_data.icc_slot_end_info, None): log.error(f"failed icc slot end validation {sub_slot_data.icc_slot_end_info} ") return False assert sub_slot_data.cc_slot_end_info assert sub_slot_data.cc_slot_end input = ClassgroupElement.get_default_element() if (not prev_ssd.is_end_of_slot()) and (not sub_slot_data.cc_slot_end.normalized_to_identity): assert prev_ssd.cc_ip_vdf_info input = prev_ssd.cc_ip_vdf_info.output if not sub_slot_data.cc_slot_end.is_valid(constants, input, sub_slot_data.cc_slot_end_info): log.error(f"failed cc slot end validation {sub_slot_data.cc_slot_end_info}") return False else: # find end of slot idx = sub_slot_idx while idx < len(sub_slots) - 1: curr_slot = sub_slots[idx] if curr_slot.is_end_of_slot(): # dont validate intermediate vdfs if slot is blue boxed assert curr_slot.cc_slot_end if curr_slot.cc_slot_end.normalized_to_identity is True: log.debug(f"skip intermediate vdfs slot {sub_slot_idx}") return True else: break idx += 1 if sub_slot_data.icc_infusion_point is not None and sub_slot_data.icc_ip_vdf_info is not None: input = ClassgroupElement.get_default_element() if not prev_ssd.is_challenge() and prev_ssd.icc_ip_vdf_info is not None: input = prev_ssd.icc_ip_vdf_info.output if not sub_slot_data.icc_infusion_point.is_valid(constants, input, sub_slot_data.icc_ip_vdf_info, None): log.error(f"failed icc infusion point vdf validation {sub_slot_data.icc_slot_end_info} ") return False assert sub_slot_data.signage_point_index is not None if sub_slot_data.cc_signage_point: assert sub_slot_data.cc_sp_vdf_info input = ClassgroupElement.get_default_element() if not sub_slot_data.cc_signage_point.normalized_to_identity: is_overflow = is_overflow_block(constants, sub_slot_data.signage_point_index) input = sub_slot_data_vdf_input( constants, sub_slot_data, sub_slot_idx, sub_slots, is_overflow, prev_ssd.is_end_of_slot(), ssi ) if not sub_slot_data.cc_signage_point.is_valid(constants, input, sub_slot_data.cc_sp_vdf_info): log.error(f"failed cc signage point vdf validation {sub_slot_data.cc_sp_vdf_info}") return False input = ClassgroupElement.get_default_element() assert sub_slot_data.cc_ip_vdf_info assert sub_slot_data.cc_infusion_point cc_ip_vdf_info = sub_slot_data.cc_ip_vdf_info if not sub_slot_data.cc_infusion_point.normalized_to_identity and prev_ssd.cc_slot_end is None: assert prev_ssd.cc_ip_vdf_info input = prev_ssd.cc_ip_vdf_info.output assert sub_slot_data.total_iters assert prev_ssd.total_iters ip_vdf_iters = uint64(sub_slot_data.total_iters - prev_ssd.total_iters) cc_ip_vdf_info = VDFInfo( sub_slot_data.cc_ip_vdf_info.challenge, ip_vdf_iters, sub_slot_data.cc_ip_vdf_info.output ) if not sub_slot_data.cc_infusion_point.is_valid(constants, input, cc_ip_vdf_info): log.error(f"failed cc infusion point vdf validation {sub_slot_data.cc_slot_end_info}") return False return True def sub_slot_data_vdf_input( constants: ConsensusConstants, sub_slot_data: SubSlotData, sub_slot_idx: int, sub_slots: List[SubSlotData], is_overflow: bool, new_sub_slot: bool, ssi: uint64, ) -> ClassgroupElement: cc_input = ClassgroupElement.get_default_element() sp_total_iters = get_sp_total_iters(constants, is_overflow, ssi, sub_slot_data) ssd: Optional[SubSlotData] = None if is_overflow and new_sub_slot: if sub_slot_idx >= 2: if sub_slots[sub_slot_idx - 2].cc_slot_end_info is None: for ssd_idx in reversed(range(0, sub_slot_idx - 1)): ssd = sub_slots[ssd_idx] if ssd.cc_slot_end_info is not None: ssd = sub_slots[ssd_idx + 1] break if not (ssd.total_iters > sp_total_iters): break if ssd and ssd.cc_ip_vdf_info is not None: if ssd.total_iters < sp_total_iters: cc_input = ssd.cc_ip_vdf_info.output return cc_input elif not is_overflow and not new_sub_slot: for ssd_idx in reversed(range(0, sub_slot_idx)): ssd = sub_slots[ssd_idx] if ssd.cc_slot_end_info is not None: ssd = sub_slots[ssd_idx + 1] break if not (ssd.total_iters > sp_total_iters): break assert ssd is not None if ssd.cc_ip_vdf_info is not None: if ssd.total_iters < sp_total_iters: cc_input = ssd.cc_ip_vdf_info.output return cc_input elif not new_sub_slot and is_overflow: slots_seen = 0 for ssd_idx in reversed(range(0, sub_slot_idx)): ssd = sub_slots[ssd_idx] if ssd.cc_slot_end_info is not None: slots_seen += 1 if slots_seen == 2: return ClassgroupElement.get_default_element() if ssd.cc_slot_end_info is None and not (ssd.total_iters > sp_total_iters): break assert ssd is not None if ssd.cc_ip_vdf_info is not None: if ssd.total_iters < sp_total_iters: cc_input = ssd.cc_ip_vdf_info.output return cc_input def _validate_recent_blocks(constants_dict: Dict, recent_chain_bytes: bytes, summaries_bytes: List[bytes]) -> bool: constants, summaries = bytes_to_vars(constants_dict, summaries_bytes) recent_chain: RecentChainData = RecentChainData.from_bytes(recent_chain_bytes) sub_blocks = BlockCache({}) first_ses_idx = _get_ses_idx(recent_chain.recent_chain_data) ses_idx = len(summaries) - len(first_ses_idx) ssi: uint64 = constants.SUB_SLOT_ITERS_STARTING diff: Optional[uint64] = constants.DIFFICULTY_STARTING last_blocks_to_validate = 100 # todo remove cap after benchmarks for summary in summaries[:ses_idx]: if summary.new_sub_slot_iters is not None: ssi = summary.new_sub_slot_iters if summary.new_difficulty is not None: diff = summary.new_difficulty ses_blocks, sub_slots, transaction_blocks = 0, 0, 0 challenge, prev_challenge = None, None tip_height = recent_chain.recent_chain_data[-1].height prev_block_record = None deficit = uint8(0) for idx, block in enumerate(recent_chain.recent_chain_data): required_iters = uint64(0) overflow = False ses = False height = block.height for sub_slot in block.finished_sub_slots: prev_challenge = challenge challenge = sub_slot.challenge_chain.get_hash() deficit = sub_slot.reward_chain.deficit if sub_slot.challenge_chain.subepoch_summary_hash is not None: ses = True assert summaries[ses_idx].get_hash() == sub_slot.challenge_chain.subepoch_summary_hash ses_idx += 1 if sub_slot.challenge_chain.new_sub_slot_iters is not None: ssi = sub_slot.challenge_chain.new_sub_slot_iters if sub_slot.challenge_chain.new_difficulty is not None: diff = sub_slot.challenge_chain.new_difficulty if (challenge is not None) and (prev_challenge is not None): overflow = is_overflow_block(constants, block.reward_chain_block.signage_point_index) deficit = get_deficit(constants, deficit, prev_block_record, overflow, len(block.finished_sub_slots)) log.debug(f"wp, validate block {block.height}") if sub_slots > 2 and transaction_blocks > 11 and (tip_height - block.height < last_blocks_to_validate): required_iters, error = validate_finished_header_block( constants, sub_blocks, block, False, diff, ssi, ses_blocks > 2 ) if error is not None: log.error(f"block {block.header_hash} failed validation {error}") return False else: required_iters = _validate_pospace_recent_chain( constants, block, challenge, diff, overflow, prev_challenge ) if required_iters is None: return False curr_block_ses = None if not ses else summaries[ses_idx - 1] block_record = header_block_to_sub_block_record( constants, required_iters, block, ssi, overflow, deficit, height, curr_block_ses ) log.debug(f"add block {block_record.height} to tmp sub blocks") sub_blocks.add_block_record(block_record) if block.first_in_sub_slot: sub_slots += 1 if block.is_transaction_block: transaction_blocks += 1 if ses: ses_blocks += 1 prev_block_record = block_record return True def _validate_pospace_recent_chain( constants: ConsensusConstants, block: HeaderBlock, challenge: bytes32, diff: uint64, overflow: bool, prev_challenge: bytes32, ): if block.reward_chain_block.challenge_chain_sp_vdf is None: # Edge case of first sp (start of slot), where sp_iters == 0 cc_sp_hash: bytes32 = challenge else: cc_sp_hash = block.reward_chain_block.challenge_chain_sp_vdf.output.get_hash() assert cc_sp_hash is not None q_str = block.reward_chain_block.proof_of_space.verify_and_get_quality_string( constants, challenge if not overflow else prev_challenge, cc_sp_hash, ) if q_str is None: log.error(f"could not verify proof of space block {block.height} {overflow}") return None required_iters = calculate_iterations_quality( constants.DIFFICULTY_CONSTANT_FACTOR, q_str, block.reward_chain_block.proof_of_space.size, diff, cc_sp_hash, ) return required_iters def __validate_pospace( constants: ConsensusConstants, segment: SubEpochChallengeSegment, idx: int, curr_diff: uint64, ses: Optional[SubEpochSummary], first_in_sub_epoch: bool, ) -> Optional[uint64]: if first_in_sub_epoch and segment.sub_epoch_n == 0 and idx == 0: cc_sub_slot_hash = constants.GENESIS_CHALLENGE else: cc_sub_slot_hash = __get_cc_sub_slot(segment.sub_slots, idx, ses).get_hash() sub_slot_data: SubSlotData = segment.sub_slots[idx] if sub_slot_data.signage_point_index and is_overflow_block(constants, sub_slot_data.signage_point_index): curr_slot = segment.sub_slots[idx - 1] assert curr_slot.cc_slot_end_info challenge = curr_slot.cc_slot_end_info.challenge else: challenge = cc_sub_slot_hash if sub_slot_data.cc_sp_vdf_info is None: cc_sp_hash = cc_sub_slot_hash else: cc_sp_hash = sub_slot_data.cc_sp_vdf_info.output.get_hash() # validate proof of space assert sub_slot_data.proof_of_space is not None q_str = sub_slot_data.proof_of_space.verify_and_get_quality_string( constants, challenge, cc_sp_hash, ) if q_str is None: log.error("could not verify proof of space") return None return calculate_iterations_quality( constants.DIFFICULTY_CONSTANT_FACTOR, q_str, sub_slot_data.proof_of_space.size, curr_diff, cc_sp_hash, ) def __get_rc_sub_slot( constants: ConsensusConstants, segment: SubEpochChallengeSegment, summaries: List[SubEpochSummary], curr_ssi: uint64, ) -> RewardChainSubSlot: ses = summaries[uint32(segment.sub_epoch_n - 1)] # find first challenge in sub epoch first_idx = None first = None for idx, curr in enumerate(segment.sub_slots): if curr.cc_slot_end is None: first_idx = idx first = curr break assert first_idx idx = first_idx slots = segment.sub_slots # number of slots to look for slots_n = 1 assert first assert first.signage_point_index is not None if is_overflow_block(constants, first.signage_point_index): if idx >= 2 and slots[idx - 2].cc_slot_end is None: slots_n = 2 new_diff = None if ses is None else ses.new_difficulty new_ssi = None if ses is None else ses.new_sub_slot_iters ses_hash = None if ses is None else ses.get_hash() overflow = is_overflow_block(constants, first.signage_point_index) if overflow: if idx >= 2 and slots[idx - 2].cc_slot_end is not None and slots[idx - 1].cc_slot_end is not None: ses_hash = None new_ssi = None new_diff = None sub_slot = slots[idx] while True: if sub_slot.cc_slot_end: slots_n -= 1 if slots_n == 0: break idx -= 1 sub_slot = slots[idx] icc_sub_slot_hash: Optional[bytes32] = None assert sub_slot is not None assert sub_slot.cc_slot_end_info is not None assert segment.rc_slot_end_info is not None if idx != 0: cc_vdf_info = VDFInfo(sub_slot.cc_slot_end_info.challenge, curr_ssi, sub_slot.cc_slot_end_info.output) if sub_slot.icc_slot_end_info is not None: icc_slot_end_info = VDFInfo( sub_slot.icc_slot_end_info.challenge, curr_ssi, sub_slot.icc_slot_end_info.output ) icc_sub_slot_hash = icc_slot_end_info.get_hash() else: cc_vdf_info = sub_slot.cc_slot_end_info if sub_slot.icc_slot_end_info is not None: icc_sub_slot_hash = sub_slot.icc_slot_end_info.get_hash() cc_sub_slot = ChallengeChainSubSlot( cc_vdf_info, icc_sub_slot_hash, ses_hash, new_ssi, new_diff, ) rc_sub_slot = RewardChainSubSlot( segment.rc_slot_end_info, cc_sub_slot.get_hash(), icc_sub_slot_hash, constants.MIN_BLOCKS_PER_CHALLENGE_BLOCK, ) return rc_sub_slot def __get_cc_sub_slot(sub_slots: List[SubSlotData], idx, ses: Optional[SubEpochSummary]) -> ChallengeChainSubSlot: sub_slot: Optional[SubSlotData] = None for i in reversed(range(0, idx)): sub_slot = sub_slots[i] if sub_slot.cc_slot_end_info is not None: break assert sub_slot is not None assert sub_slot.cc_slot_end_info is not None icc_vdf = sub_slot.icc_slot_end_info icc_vdf_hash: Optional[bytes32] = None if icc_vdf is not None: icc_vdf_hash = icc_vdf.get_hash() cc_sub_slot = ChallengeChainSubSlot( sub_slot.cc_slot_end_info, icc_vdf_hash, None if ses is None else ses.get_hash(), None if ses is None else ses.new_sub_slot_iters, None if ses is None else ses.new_difficulty, ) return cc_sub_slot def _get_curr_diff_ssi(constants: ConsensusConstants, idx, summaries): curr_difficulty = constants.DIFFICULTY_STARTING curr_ssi = constants.SUB_SLOT_ITERS_STARTING for ses in reversed(summaries[0:idx]): if ses.new_sub_slot_iters is not None: curr_ssi = ses.new_sub_slot_iters curr_difficulty = ses.new_difficulty break return curr_difficulty, curr_ssi def vars_to_bytes(constants, summaries, weight_proof): constants_dict = recurse_jsonify(dataclasses.asdict(constants)) wp_recent_chain_bytes = bytes(RecentChainData(weight_proof.recent_chain_data)) wp_segment_bytes = bytes(SubEpochSegments(weight_proof.sub_epoch_segments)) summary_bytes = [] for summary in summaries: summary_bytes.append(bytes(summary)) return constants_dict, summary_bytes, wp_segment_bytes, wp_recent_chain_bytes def bytes_to_vars(constants_dict, summaries_bytes): summaries = [] for summary in summaries_bytes: summaries.append(SubEpochSummary.from_bytes(summary)) constants: ConsensusConstants = dataclass_from_dict(ConsensusConstants, constants_dict) return constants, summaries def _get_last_ses_hash( constants: ConsensusConstants, recent_reward_chain: List[HeaderBlock] ) -> Tuple[Optional[bytes32], uint32]: for idx, block in enumerate(reversed(recent_reward_chain)): if (block.reward_chain_block.height % constants.SUB_EPOCH_BLOCKS) == 0: idx = len(recent_reward_chain) - 1 - idx # reverse # find first block after sub slot end while idx < len(recent_reward_chain): curr = recent_reward_chain[idx] if len(curr.finished_sub_slots) > 0: for slot in curr.finished_sub_slots: if slot.challenge_chain.subepoch_summary_hash is not None: return ( slot.challenge_chain.subepoch_summary_hash, curr.reward_chain_block.height, ) idx += 1 return None, uint32(0) def _get_ses_idx(recent_reward_chain: List[HeaderBlock]) -> List[int]: idxs: List[int] = [] for idx, curr in enumerate(recent_reward_chain): if len(curr.finished_sub_slots) > 0: for slot in curr.finished_sub_slots: if slot.challenge_chain.subepoch_summary_hash is not None: idxs.append(idx) return idxs def get_deficit( constants: ConsensusConstants, curr_deficit: uint8, prev_block: BlockRecord, overflow: bool, num_finished_sub_slots: int, ) -> uint8: if prev_block is None: if curr_deficit >= 1 and not (overflow and curr_deficit == constants.MIN_BLOCKS_PER_CHALLENGE_BLOCK): curr_deficit -= 1 return curr_deficit return calculate_deficit(constants, uint32(prev_block.height + 1), prev_block, overflow, num_finished_sub_slots) def get_sp_total_iters(constants: ConsensusConstants, is_overflow: bool, ssi: uint64, sub_slot_data: SubSlotData): assert sub_slot_data.cc_ip_vdf_info is not None assert sub_slot_data.total_iters is not None assert sub_slot_data.signage_point_index is not None sp_iters: uint64 = calculate_sp_iters(constants, ssi, sub_slot_data.signage_point_index) ip_iters: uint64 = sub_slot_data.cc_ip_vdf_info.number_of_iterations sp_sub_slot_total_iters = uint128(sub_slot_data.total_iters - ip_iters) if is_overflow: sp_sub_slot_total_iters = uint128(sp_sub_slot_total_iters - ssi) return sp_sub_slot_total_iters + sp_iters def blue_boxed_end_of_slot(sub_slot: EndOfSubSlotBundle): if sub_slot.proofs.challenge_chain_slot_proof.normalized_to_identity: if sub_slot.proofs.infused_challenge_chain_slot_proof is not None: if sub_slot.proofs.infused_challenge_chain_slot_proof.normalized_to_identity: return True else: return True return False def validate_sub_epoch_sampling(rng, sub_epoch_weight_list, weight_proof): tip = weight_proof.recent_chain_data[-1] weight_to_check = _get_weights_for_sampling(rng, tip.weight, weight_proof.recent_chain_data) sampled_sub_epochs: dict[int, bool] = {} for idx in range(1, len(sub_epoch_weight_list)): if _sample_sub_epoch(sub_epoch_weight_list[idx - 1], sub_epoch_weight_list[idx], weight_to_check): sampled_sub_epochs[idx - 1] = True if len(sampled_sub_epochs) == WeightProofHandler.MAX_SAMPLES: break curr_sub_epoch_n = -1 for sub_epoch_segment in weight_proof.sub_epoch_segments: if curr_sub_epoch_n < sub_epoch_segment.sub_epoch_n: if sub_epoch_segment.sub_epoch_n in sampled_sub_epochs: del sampled_sub_epochs[sub_epoch_segment.sub_epoch_n] curr_sub_epoch_n = sub_epoch_segment.sub_epoch_n if len(sampled_sub_epochs) > 0: return False return True def map_segments_by_sub_epoch(sub_epoch_segments) -> Dict[int, List[SubEpochChallengeSegment]]: segments: Dict[int, List[SubEpochChallengeSegment]] = {} curr_sub_epoch_n = -1 for idx, segment in enumerate(sub_epoch_segments): if curr_sub_epoch_n < segment.sub_epoch_n: curr_sub_epoch_n = segment.sub_epoch_n segments[curr_sub_epoch_n] = [] segments[curr_sub_epoch_n].append(segment) return segments def validate_total_iters( segment: SubEpochChallengeSegment, sub_slot_data_idx, expected_sub_slot_iters: uint64, finished_sub_slots_since_prev: int, prev_b: SubSlotData, prev_sub_slot_data_iters, genesis, ) -> bool: sub_slot_data = segment.sub_slots[sub_slot_data_idx] if genesis: total_iters: uint128 = uint128(expected_sub_slot_iters * finished_sub_slots_since_prev) elif segment.sub_slots[sub_slot_data_idx - 1].is_end_of_slot(): assert prev_b.total_iters assert prev_b.cc_ip_vdf_info total_iters = prev_b.total_iters # Add the rest of the slot of prev_b total_iters = uint128(total_iters + prev_sub_slot_data_iters - prev_b.cc_ip_vdf_info.number_of_iterations) # Add other empty slots total_iters = uint128(total_iters + (expected_sub_slot_iters * (finished_sub_slots_since_prev - 1))) else: # Slot iters is guaranteed to be the same for header_block and prev_b # This takes the beginning of the slot, and adds ip_iters assert prev_b.cc_ip_vdf_info assert prev_b.total_iters total_iters = uint128(prev_b.total_iters - prev_b.cc_ip_vdf_info.number_of_iterations) total_iters = uint128(total_iters + sub_slot_data.cc_ip_vdf_info.number_of_iterations) return total_iters == sub_slot_data.total_iters

[ "faurepierre78@yahoo.com" ]

faurepierre78@yahoo.com

393fe84cca759b9236a600d0c71f10ffda0a904a

fc78f7cfa4e63e5d6df787d4bd5ba58d50ac2895

/manage.py

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[]

no_license

Erecik1/boostingweb

d608c109d61d4e2f0badd9af5477e10f4b780291

e0032c039b1a527dccc76a4b1cf5e04355001aa3

refs/heads/master

2023-08-01T03:43:19.992022

2021-09-22T22:03:19

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#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): """Run administrative tasks.""" os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'tipsy.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "eret112@gmail.com" ]

eret112@gmail.com

81aee84f897fcf6c6ae6f9a8c473d7c9123dda6d

e6db96ae32326be9b448f4c3bff158a295036571

/tensorFlowStudy/classification_test.py

95c54c9b0eba777168868053a5ce2ffcab877578

[]

no_license

houweitao/TensorFlow

f2e65285f01b52df282bdecc2e01e2e30e3b9b43

d8a70592dde70e37f47aae2649b3003b746188f7

refs/heads/master

2021-01-12T14:59:27.935750

2017-01-13T07:48:34

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py

# -*- coding: utf-8 -*- # _author_ = 'hou' # _project_: classification_test # _date_ = 16/10/23 下午4:15 # https://www.youtube.com/watch?v=aNjdw9w_Qyc&index=17&list=PLXO45tsB95cKI5AIlf5TxxFPzb-0zeVZ8 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import add_layer as layer # data http://yann.lecun.com/exdb/mnist/ mnist = input_data.read_data_sets('MNIST_data', one_hot=True) # define placeholder xs = tf.placeholder(tf.float32, [None, 784]) # xs = tf.placeholder(tf.float(32), [None, 784]) # 28*28个像素点 ys = tf.placeholder(tf.float32, [None, 10]) # 10个输出 # add output layer predication = layer.add_layer(xs, 784, 10, activation_function=tf.nn.softmax) # another # b = tf.Variable(tf.zeros([10])) # W = tf.Variable(tf.zeros([784,10])) # predication= tf.nn.softmax(tf.matmul(xs,W) + b); # loss # neg? cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(predication), reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy) sess = tf.Session() sess.run(tf.initialize_all_variables()) def compute_accuracy(v_xs, v_ys): global predication y_pre = sess.run(predication, feed_dict={xs: v_xs}) correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys}) return result # def compute_accuracy1(v_xs, v_ys): # global predication # y_pre = sess.run(predication, feed_dict={xs: v_xs}) # correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1)) # accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys}) # return result for step in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) # 每次取100 sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys}) if step % 50 == 0: print compute_accuracy(mnist.test.images, mnist.test.labels)

[ "hou103880@163.com" ]

hou103880@163.com