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

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/setup.py

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aliabd/GEM-metrics

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refs/heads/main

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0

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#!/usr/bin/env python3 from setuptools import setup from setuptools import find_packages install_requires = [] dependency_links = [] for package in [l.strip() for l in open('requirements.txt').readlines()]: if package.startswith('git+'): pk_name = package.split('/')[-1][:-4] install_requires.append(f'{pk_name} @ {package}') else: install_requires.append(package) setup( name='gem_metrics', version='0.1dev', description='GEM Challenge metrics', author='Ondrej Dusek, Aman Madaan, Emiel van Miltenburg, Sebastian Gehrmann, Nishant Subramani, Dhruv Kumar, Miruna Clinciu', author_email='odusek@ufal.mff.cuni.cz', url='https://github.com/GEM-benchmark/GEM-metrics', download_url='https://github.com/GEM-benchmark/GEM-metrics.git', license='MIT License', install_requires=install_requires, dependency_links=dependency_links, packages=find_packages(), entry_points = { 'console_scripts': ['gem_metrics=gem_metrics:main'] } )

[ "odusek@ufal.mff.cuni.cz" ]

odusek@ufal.mff.cuni.cz

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/inventoryanalytics/simulation/stochastic/des.py

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emilio-garcia-ie/inventoryanalytics

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refs/heads/master

2023-03-17T02:56:46.309779

2020-12-27T00:27:37

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import matplotlib.pyplot as plt import numpy as np import pandas as pd from queue import PriorityQueue from collections import defaultdict def plot_inventory(values, label): # data df=pd.DataFrame({'x': np.array(values)[:,0], 'fx': np.array(values)[:,1]}) # plot plt.xticks(range(len(values)), range(1,len(values)+1)) plt.xlabel("t") plt.ylabel("items") plt.plot( 'x', 'fx', data=df, linestyle='-', marker='', label=label) ######################### ## DES ## ######################### class EventWrapper(): def __init__(self, event): self.event = event def __lt__(self, other): return self.event.priority < other.event.priority class DES(): def __init__(self, end): self.events, self.end, self.time = PriorityQueue() , end, 0 def start(self): while True: event = self.events.get() self.time = event[0] if self.time <= self.end: event[1].event.end() else: break def schedule(self, event: EventWrapper, time_lag: int): self.events.put((self.time + time_lag, event)) ########################## ## WAREHOUSE ## ########################## class Warehouse: def __init__(self, inventory_level, fixed_ordering_cost, holding_cost, penalty_cost): self.i, self.K, self.h, self.p = inventory_level, fixed_ordering_cost, holding_cost, penalty_cost self.o = 0 # outstanding_orders self.period_costs = defaultdict(int) # a dictionary recording cost in each period def receive_order(self, Q, time): self.review_inventory(time) self.i, self.o = self.i + Q, self.o - Q self.review_inventory(time) def order(self, Q, time): self.review_inventory(time) self.period_costs[time] += self.K # incur ordering cost and store it in a dictionary self.o += Q self.review_inventory(time) def on_hand_inventory(self): return max(0,self.i) def backorders(self): return max(0,-self.i) def issue(self, demand, time): self.review_inventory(time) self.i = self.i-demand def inventory_position(self): return self.o+self.i def review_inventory(self, time): try: self.levels.append([time, self.i]) self.on_hand.append([time, self.on_hand_inventory()]) self.positions.append([time, self.inventory_position()]) except AttributeError: self.levels, self.on_hand = [[0, self.i]], [[0, self.on_hand_inventory()]] self.positions = [[0, self.inventory_position()]] def incur_end_of_period_costs(self, time): # incur holding and penalty costs self._incur_holding_cost(time) self._incur_penalty_cost(time) def _incur_holding_cost(self, time): # incur holding cost and store it in a dictionary self.period_costs[time] += self.on_hand_inventory()*self.h def _incur_penalty_cost(self, time): # incur penalty cost and store it in a dictionary self.period_costs[time] += self.backorders()*self.p ########################## ## EVENTS ## ########################## class CustomerDemand: def __init__(self, des: DES, demand_rate: float, warehouse: Warehouse): self.d = demand_rate # the demand rate per period self.w = warehouse # the warehouse self.des = des # the Discrete Event Simulation engine self.priority = 2 # denotes a low priority def end(self): self.w.issue(1, self.des.time) self.des.schedule(EventWrapper(self), np.random.exponential(1/self.d)) # schedule another demand class EndOfPeriod: def __init__(self, des: DES, warehouse: Warehouse): self.w = warehouse # the warehouse self.des = des # the Discrete Event Simulation engine self.priority = 0 # denotes a low priority def end(self): self.w.incur_end_of_period_costs(self.des.time-1) self.des.schedule(EventWrapper(EndOfPeriod(self.des, self.w)), 1) class Order: def __init__(self, des: DES, Q: float, warehouse: Warehouse, lead_time: float): self.Q = Q # the order quantity self.w = warehouse # the warehouse self.des = des # the Discrete Event Simulation engine self.lead_time = lead_time self.priority = 1 # denotes a medium priority def end(self): self.w.order(self.Q, self.des.time) self.des.schedule(EventWrapper(ReceiveOrder(self.des, self.Q, self.w)), self.lead_time) class ReceiveOrder: def __init__(self, des: DES, Q: float, warehouse: Warehouse): self.Q = Q # the order quantity self.w = warehouse # the warehouse self.des = des # the Discrete Event Simulation engine self.priority = 1 # denotes a medium priority def end(self): self.w.receive_order(self.Q, self.des.time) np.random.seed(1234) instance = {"inventory_level": 10, "fixed_ordering_cost": 100, "holding_cost": 1, "penalty_cost": 5} w = Warehouse(**instance) N = 20 # planning horizon length des = DES(N) d = CustomerDemand(des, 10, w) des.schedule(EventWrapper(d), 0) # schedule a demand immediately lead_time = 1 o = Order(des, 50, w, lead_time) for t in range(0,20,5): des.schedule(EventWrapper(o), t) # schedule orders des.schedule(EventWrapper(EndOfPeriod(des, w)), 1) # schedule EndOfPeriod at the end of the first period des.start() print("Period costs: "+str([w.period_costs[e] for e in w.period_costs])) print("Average cost per period: "+ '%.2f' % (sum([w.period_costs[e] for e in w.period_costs])/len(w.period_costs))) plot_inventory(w.positions, "inventory position") plot_inventory(w.levels, "inventory level") plt.legend(loc="lower right") plt.show()

[ "robros@gmail.com" ]

robros@gmail.com

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/data_analysis/draw-policy-graph.py

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msgpo/trade-dst

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refs/heads/master

2022-05-31T01:38:03.614691

2020-04-27T17:17:18

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#!/usr/bin/env python3 import json import sys from collections import Counter from utils.fix_label import fix_general_label_error EXPERIMENT_DOMAINS = ["none", "hotel", "train", "restaurant", "attraction", "taxi"] DOMAIN_INDICES = dict() for domain in EXPERIMENT_DOMAINS: DOMAIN_INDICES[domain] = len(DOMAIN_INDICES) def get_slot_information(): ontology = json.load(open("data/multi-woz/MULTIWOZ2.1/ontology.json", 'r')) ontology_domains = dict([(k, v) for k, v in ontology.items() if k.split("-")[0] in EXPERIMENT_DOMAINS]) SLOTS = [k.replace(" ","").lower() if ("book" not in k) else k.lower() for k in ontology_domains.keys()] return SLOTS ALL_SLOTS = get_slot_information() def fix_none_typo(value): if value in ("not men", "not", "not mentioned", "", "not mendtioned", "fun", "art"): return 'none' else: return value def get_node_key_slot_names(label_dict): slots = [] domains = set() for slot_key, slot_value in label_dict.items(): slot_value = fix_none_typo(slot_value) if slot_value == 'none': continue domains.add(slot_key.split('-')[0]) slots.append(slot_key.replace(' ', '-')) if len(slots) == 0: return 'none', 'none' return ','.join(domains), ','.join(slots) def get_node_key_slot_names_delta(turn_label): slots = [] domains = set() for slot_key, slot_value in turn_label: slot_value = fix_none_typo(slot_value) if slot_value == 'none': continue domains.add(slot_key.split('-')[0]) slots.append(slot_key.replace(' ', '-')) if len(slots) == 0: return 'none', 'none' return ','.join(domains), ','.join(slots) def get_node_key_slot_counts(label_dict): slots = Counter() for slot_key, slot_value in label_dict.items(): slot_value = fix_none_typo(slot_value) if slot_value == 'none': continue slot_domain = slot_key.split('-')[0] slots[slot_domain] += 1 if len(slots) == 0: return 'none' return ','.join(domain + '=' + str(count) for domain, count in slots.items()) def get_node_key_slot_domains(label_dict): slots = set() for slot_key, slot_value in label_dict.items(): slot_value = fix_none_typo(slot_value) if slot_value == 'none': continue slot_domain = slot_key.split('-')[0] slots.add(slot_domain) if len(slots) == 0: return 'none' return ','.join(slots) def get_node_key_slot_counts_delta(turn_label): slots = Counter() for slot_key, slot_value in turn_label: slot_value = fix_none_typo(slot_value) if slot_value == 'none': continue slot_domain = slot_key.split('-')[0] slots[slot_domain] += 1 if len(slots) == 0: return 'none' return ','.join(domain + '=' + str(count) for domain, count in slots.items()) def get_node_key_domains_delta(turn_label): slots = set() for slot_key, slot_value in turn_label: slot_value = fix_none_typo(slot_value) if slot_value == 'none': continue slot_domain = slot_key.split('-')[0] slots.add(slot_domain) if len(slots) == 0: return 'none' return ','.join(slots) def load_data(): filename = 'data/train_dials.json' if len(sys.argv) >= 2: filename = sys.argv[1] with open(filename) as fp: data = json.load(fp) nodes = Counter() edges = Counter() for dialogue in data: prev_node_key = 'none' for turn in dialogue['dialogue']: label_dict = fix_general_label_error(turn['belief_state'], False, ALL_SLOTS) #domains, node_key = get_node_key_slot_names(label_dict) domains, node_key = get_node_key_slot_names_delta(turn['turn_label']) if domains not in ('none', 'restaurant'): continue #node_key = get_node_key_domains_delta(turn['turn_label']) #node_key = get_node_key_slot_domains(label_dict) nodes[node_key] += 1 edges[(prev_node_key, node_key)] += 1 if prev_node_key == 'train' and node_key == 'attraction': print(json.dumps(dialogue, indent=2), file=sys.stderr) prev_node_key = node_key return nodes, edges def main(): nodes, edges = load_data() print('strict digraph states {') node_num = dict() for i, (node, node_count) in enumerate(nodes.most_common()): #if ',' in node: # continue node_num[node] = i node_label = node.replace('restaurant-', '') #node_count = nodes[node] print(f's{i} [label="{node_label}"]; # {node_count}') for (_from, _to), count in edges.items(): #if ',' in _from or ',' in _to: # continue print(f's{node_num[_from]} -> s{node_num[_to]} [label="{count}"];') print('}') if __name__ == '__main__': main()

[ "gcampagn@cs.stanford.edu" ]

gcampagn@cs.stanford.edu

4217ffcfb6d14595b20ca22faca926672b8147a9

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/recursionDigitSum.py

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vivek28111992/python-for-algo-and-ds

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refs/heads/master

2021-09-05T11:25:37.981814

2018-01-26T22:15:00

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# recursion digit sum def sum_func(n): # Base Case if len(str(n)) == 1: return n else: return n%10 + sum_func(n//10) print(sum_func(1235))

[ "pawarvivek29@gmail.com" ]

pawarvivek29@gmail.com

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KongDeyu1532/Helmet_Face_Detection

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refs/heads/master

2023-04-30T01:09:44.470828

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# encoding: utf-8 # author: KongDeyu import argparse import time import mxnet as mx from gluoncv import model_zoo, utils, data import telegram import cv2 import dlib from PyQt5.QtCore import QTimer, QThread, pyqtSignal, QRegExp, Qt from PyQt5.QtGui import QImage, QPixmap, QIcon, QTextCursor, QRegExpValidator from PyQt5.QtWidgets import QDialog, QApplication, QMainWindow, QMessageBox from PyQt5.uic import loadUi import os import webbrowser import logging import logging.config import sqlite3 import sys import threading import queue import multiprocessing import winsound from configparser import ConfigParser from datetime import datetime # 找不到已训练的人脸数据文件 class TrainingDataNotFoundError(FileNotFoundError): pass # 找不到数据库文件 class DatabaseNotFoundError(FileNotFoundError): pass ImgQueue= multiprocessing.Queue() ResultQueue = multiprocessing.Queue() class CoreUI(QMainWindow): database = './FaceBase.db' trainingData = './recognizer/trainingData.yml' cap = cv2.VideoCapture() captureQueue = queue.Queue() # 图像队列 alarmQueue = queue.LifoQueue() # 报警队列，后进先出 logQueue = multiprocessing.Queue() # 日志队列 receiveLogSignal = pyqtSignal(str) # LOG信号 def __init__(self): super(CoreUI, self).__init__() loadUi('./ui/Core.ui', self) self.setWindowIcon(QIcon('./icons/icon.png')) self.setFixedSize(1161, 623) # 图像捕获 self.isExternalCameraUsed = False self.useExternalCameraCheckBox.stateChanged.connect( lambda: self.useExternalCamera(self.useExternalCameraCheckBox)) # 初始化通信队列 # ImgQueue= multiprocessing.Queue # ResultQueue = multiprocessing.Queue self.faceProcessingThread = FaceProcessingThread() self.faceProcessingThread1 = FaceProcessingThread2() self.startWebcamButton.clicked.connect(self.startWebcam) # 数据库 self.initDbButton.setIcon(QIcon('./icons/warning.png')) self.initDbButton.clicked.connect(self.initDb) self.timer = QTimer(self) # 初始化一个定时器 self.timer.timeout.connect(self.updateFrame) # 功能开关 self.faceTrackerCheckBox.stateChanged.connect( lambda: self.faceProcessingThread.enableFaceTracker(self)) self.faceTrackerCheckBox.stateChanged.connect( lambda: self.faceProcessingThread1.enableFaceTracker(self)) self.faceRecognizerCheckBox.stateChanged.connect( lambda: self.faceProcessingThread.enableFaceRecognizer(self)) self.panalarmCheckBox.stateChanged.connect(lambda: self.faceProcessingThread.enablePanalarm(self)) # 直方图均衡化 self.equalizeHistCheckBox.stateChanged.connect( lambda: self.faceProcessingThread.enableEqualizeHist(self)) # 调试模式 self.debugCheckBox.stateChanged.connect(lambda: self.faceProcessingThread.enableDebug(self)) self.confidenceThresholdSlider.valueChanged.connect( lambda: self.faceProcessingThread.setConfidenceThreshold(self)) self.autoAlarmThresholdSlider.valueChanged.connect( lambda: self.faceProcessingThread.setAutoAlarmThreshold(self)) # 报警系统 self.alarmSignalThreshold = 10 self.panalarmThread = threading.Thread(target=self.recieveAlarm, daemon=True) self.isBellEnabled = True self.bellCheckBox.stateChanged.connect(lambda: self.enableBell(self.bellCheckBox)) self.isTelegramBotPushEnabled = False self.telegramBotPushCheckBox.stateChanged.connect( lambda: self.enableTelegramBotPush(self.telegramBotPushCheckBox)) self.telegramBotSettingsButton.clicked.connect(self.telegramBotSettings) # 帮助与支持 self.viewGithubRepoButton.clicked.connect( lambda: webbrowser.open('https://github.com/winterssy/face_recognition_py')) self.contactDeveloperButton.clicked.connect(lambda: webbrowser.open('https://t.me/winterssy')) # 日志系统 self.receiveLogSignal.connect(lambda log: self.logOutput(log)) self.logOutputThread = threading.Thread(target=self.receiveLog, daemon=True) self.logOutputThread.start() # 检查数据库状态 def initDb(self): try: if not os.path.isfile(self.database): raise DatabaseNotFoundError if not os.path.isfile(self.trainingData): raise TrainingDataNotFoundError conn = sqlite3.connect(self.database) cursor = conn.cursor() cursor.execute('SELECT Count(*) FROM users') result = cursor.fetchone() dbUserCount = result[0] except DatabaseNotFoundError: logging.error('系统找不到数据库文件{}'.format(self.database)) self.initDbButton.setIcon(QIcon('./icons/error.png')) self.logQueue.put('Error：未发现数据库文件，你可能未进行人脸采集') except TrainingDataNotFoundError: logging.error('系统找不到已训练的人脸数据{}'.format(self.trainingData)) self.initDbButton.setIcon(QIcon('./icons/error.png')) self.logQueue.put('Error：未发现已训练的人脸数据文件，请完成训练后继续') except Exception as e: logging.error('读取数据库异常，无法完成数据库初始化') self.initDbButton.setIcon(QIcon('./icons/error.png')) self.logQueue.put('Error：读取数据库异常，初始化数据库失败') else: cursor.close() conn.close() if not dbUserCount > 0: logging.warning('数据库为空') self.logQueue.put('warning：数据库为空，人脸识别功能不可用') self.initDbButton.setIcon(QIcon('./icons/warning.png')) else: self.logQueue.put('Success：数据库状态正常，发现用户数：{}'.format(dbUserCount)) self.initDbButton.setIcon(QIcon('./icons/success.png')) self.initDbButton.setEnabled(False) self.faceRecognizerCheckBox.setToolTip('须先开启人脸跟踪') self.faceRecognizerCheckBox.setEnabled(True) # 是否使用外接摄像头 def useExternalCamera(self, useExternalCameraCheckBox): if useExternalCameraCheckBox.isChecked(): self.isExternalCameraUsed = True else: self.isExternalCameraUsed = False # 打开/关闭摄像头 def startWebcam(self): if not self.cap.isOpened(): if self.isExternalCameraUsed: camID = 1 else: camID = 0 self.cap.open(camID) self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640) self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) ret, frame = self.cap.read() if not ret: logging.error('无法调用电脑摄像头{}'.format(camID)) self.logQueue.put('Error：初始化摄像头失败') self.cap.release() self.startWebcamButton.setIcon(QIcon('./icons/error.png')) else: # self.myprocess = multiprocessing.Process(target=self.MyCore, args=()) # self.myprocess.start() self.faceProcessingThread.start() # 启动OpenCV图像处理线程 self.faceProcessingThread1.start() # 头盔 self.timer.start(5) # 启动定时器 self.panalarmThread.start() # 启动报警系统线程 self.startWebcamButton.setIcon(QIcon('./icons/success.png')) self.startWebcamButton.setText('关闭摄像头') else: text = '如果关闭摄像头，须重启程序才能再次打开。' informativeText = '<b>是否继续？</b>' ret = CoreUI.callDialog(QMessageBox.Warning, text, informativeText, QMessageBox.Yes | QMessageBox.No, QMessageBox.No) if ret == QMessageBox.Yes: self.faceProcessingThread.stop() self.faceProcessingThread1.stop() # self.myprocess.stop() if self.cap.isOpened(): if self.timer.isActive(): self.timer.stop() self.cap.release() self.realTimeCaptureLabel.clear() self.realTimeCaptureLabel.setText('<font color=red>摄像头未开启</font>') self.startWebcamButton.setText('摄像头已关闭') self.startWebcamButton.setEnabled(False) self.startWebcamButton.setIcon(QIcon()) # 定时器，实时更新画面 def updateFrame(self): if self.cap.isOpened(): # ret, frame = self.cap.read() # if ret: # self.showImg(frame, self.realTimeCaptureLabel) if not self.captureQueue.empty(): captureData = self.captureQueue.get() realTimeFrame = captureData.get('realTimeFrame') self.displayImage(realTimeFrame, self.realTimeCaptureLabel) # 显示图片 def displayImage(self, img, qlabel): # BGR -> RGB img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # default：The image is stored using 8-bit indexes into a colormap， for example：a gray image qformat = QImage.Format_Indexed8 if len(img.shape) == 3: # rows[0], cols[1], channels[2] if img.shape[2] == 4: # The image is stored using a 32-bit byte-ordered RGBA format (8-8-8-8) # A: alpha channel，不透明度参数。如果一个像素的alpha通道数值为0%，那它就是完全透明的 qformat = QImage.Format_RGBA8888 else: qformat = QImage.Format_RGB888 # img.shape[1]：图像宽度width，img.shape[0]：图像高度height，img.shape[2]：图像通道数 # QImage.__init__ (self, bytes data, int width, int height, int bytesPerLine, Format format) # 从内存缓冲流获取img数据构造QImage类 # img.strides[0]：每行的字节数（width*3）,rgb为3，rgba为4 # strides[0]为最外层(即一个二维数组所占的字节长度)，strides[1]为次外层（即一维数组所占字节长度），strides[2]为最内层（即一个元素所占字节长度） # 从里往外看，strides[2]为1个字节长度（uint8），strides[1]为3*1个字节长度（3即rgb 3个通道） # strides[0]为width*3个字节长度，width代表一行有几个像素 outImage = QImage(img, img.shape[1], img.shape[0], img.strides[0], qformat) qlabel.setPixmap(QPixmap.fromImage(outImage)) qlabel.setScaledContents(True) # 图片自适应大小 # 报警系统：是否允许设备响铃 def enableBell(self, bellCheckBox): if bellCheckBox.isChecked(): self.isBellEnabled = True self.statusBar().showMessage('设备发声：开启') else: if self.isTelegramBotPushEnabled: self.isBellEnabled = False self.statusBar().showMessage('设备发声：关闭') else: self.logQueue.put('Error：操作失败，至少选择一种报警方式') self.bellCheckBox.setCheckState(Qt.Unchecked) self.bellCheckBox.setChecked(True) # print('isBellEnabled：', self.isBellEnabled) # 报警系统：是否允许TelegramBot推送 def enableTelegramBotPush(self, telegramBotPushCheckBox): if telegramBotPushCheckBox.isChecked(): self.isTelegramBotPushEnabled = True self.statusBar().showMessage('TelegramBot推送：开启') else: if self.isBellEnabled: self.isTelegramBotPushEnabled = False self.statusBar().showMessage('TelegramBot推送：关闭') else: self.logQueue.put('Error：操作失败，至少选择一种报警方式') self.telegramBotPushCheckBox.setCheckState(Qt.Unchecked) self.telegramBotPushCheckBox.setChecked(True) # print('isTelegramBotPushEnabled：', self.isTelegramBotPushEnabled) # TelegramBot设置 def telegramBotSettings(self): cfg = ConfigParser() cfg.read('./config/telegramBot.cfg', encoding='utf-8-sig') read_only = cfg.getboolean('telegramBot', 'read_only') # read_only = False if read_only: text = '基于安全考虑，系统拒绝了本次请求。' informativeText = '<b>请联系设备管理员。</b>' CoreUI.callDialog(QMessageBox.Critical, text, informativeText, QMessageBox.Ok) else: token = cfg.get('telegramBot', 'token') chat_id = cfg.get('telegramBot', 'chat_id') proxy_url = cfg.get('telegramBot', 'proxy_url') message = cfg.get('telegramBot', 'message') self.telegramBotDialog = TelegramBotDialog() self.telegramBotDialog.tokenLineEdit.setText(token) self.telegramBotDialog.telegramIDLineEdit.setText(chat_id) self.telegramBotDialog.socksLineEdit.setText(proxy_url) self.telegramBotDialog.messagePlainTextEdit.setPlainText(message) self.telegramBotDialog.exec() # 设备响铃进程 @staticmethod def bellProcess(queue): logQueue = queue logQueue.put('Info：设备正在响铃...') winsound.PlaySound('./alarm.wav', winsound.SND_FILENAME) # TelegramBot推送进程 @staticmethod def telegramBotPushProcess(queue, img=None): logQueue = queue cfg = ConfigParser() try: cfg.read('./config/telegramBot.cfg', encoding='utf-8-sig') # 读取TelegramBot配置 token = cfg.get('telegramBot', 'token') chat_id = cfg.getint('telegramBot', 'chat_id') proxy_url = cfg.get('telegramBot', 'proxy_url') message = cfg.get('telegramBot', 'message') # 是否使用代理 if proxy_url: proxy = telegram.utils.request.Request(proxy_url=proxy_url) bot = telegram.Bot(token=token, request=proxy) else: bot = telegram.Bot(token=token) bot.send_message(chat_id=chat_id, text=message) # 发送疑似陌生人脸截屏到Telegram if img: bot.send_photo(chat_id=chat_id, photo=open(img, 'rb'), timeout=10) except Exception as e: logQueue.put('Error：TelegramBot推送失败') else: logQueue.put('Success：TelegramBot推送成功') # 报警系统服务常驻，接收并处理报警信号 def recieveAlarm(self): while True: jobs = [] # print(self.alarmQueue.qsize()) if self.alarmQueue.qsize() > self.alarmSignalThreshold: # 若报警信号触发超出既定计数，进行报警 if not os.path.isdir('./unknown'): os.makedirs('./unknown') lastAlarmSignal = self.alarmQueue.get() timestamp = lastAlarmSignal.get('timestamp') img = lastAlarmSignal.get('img') # 疑似陌生人脸，截屏存档 cv2.imwrite('./unknown/{}.jpg'.format(timestamp), img) logging.info('报警信号触发超出预设计数，自动报警系统已被激活') self.logQueue.put('Info：报警信号触发超出预设计数，自动报警系统已被激活') # 是否进行响铃 if self.isBellEnabled: p1 = multiprocessing.Process(target=CoreUI.bellProcess, args=(self.logQueue,)) p1.start() jobs.append(p1) # 是否进行TelegramBot推送 if self.isTelegramBotPushEnabled: if os.path.isfile('./unknown/{}.jpg'.format(timestamp)): img = './unknown/{}.jpg'.format(timestamp) else: img = None p2 = multiprocessing.Process(target=CoreUI.telegramBotPushProcess, args=(self.logQueue, img)) p2.start() jobs.append(p2) # 等待本轮报警结束 for p in jobs: p.join() # 重置报警信号 with self.alarmQueue.mutex: self.alarmQueue.queue.clear() else: continue # 系统日志服务常驻，接收并处理系统日志 def receiveLog(self): while True: data = self.logQueue.get() if data: self.receiveLogSignal.emit(data) else: continue # LOG输出 def logOutput(self, log): # 获取当前系统时间 time = datetime.now().strftime('[%Y/%m/%d %H:%M:%S]') log = time + ' ' + log + '\n' self.logTextEdit.moveCursor(QTextCursor.End) self.logTextEdit.insertPlainText(log) self.logTextEdit.ensureCursorVisible() # 自动滚屏 # 系统对话框 @staticmethod def callDialog(icon, text, informativeText, standardButtons, defaultButton=None): msg = QMessageBox() msg.setWindowIcon(QIcon('./icons/icon.png')) msg.setWindowTitle('OpenCV Face Recognition System - Core') msg.setIcon(icon) msg.setText(text) msg.setInformativeText(informativeText) msg.setStandardButtons(standardButtons) if defaultButton: msg.setDefaultButton(defaultButton) return msg.exec() # 窗口关闭事件，关闭OpenCV线程、定时器、摄像头 def closeEvent(self, event): if self.faceProcessingThread.isRunning: self.faceProcessingThread.stop() if self.faceProcessingThread1.isRunning: self.faceProcessingThread1.stop() # self.faceProcessingThread1.stop() if self.timer.isActive(): self.timer.stop() if self.cap.isOpened(): self.cap.release() event.accept() # TelegramBot设置对话框 class TelegramBotDialog(QDialog): def __init__(self): super(TelegramBotDialog, self).__init__() loadUi('./ui/TelegramBotDialog.ui', self) self.setWindowIcon(QIcon('./icons/icon.png')) self.setFixedSize(550, 358) chat_id_regx = QRegExp('^\d+$') chat_id_validator = QRegExpValidator(chat_id_regx, self.telegramIDLineEdit) self.telegramIDLineEdit.setValidator(chat_id_validator) self.okButton.clicked.connect(self.telegramBotSettings) def telegramBotSettings(self): # 获取用户输入 token = self.tokenLineEdit.text().strip() chat_id = self.telegramIDLineEdit.text().strip() proxy_url = self.socksLineEdit.text().strip() message = self.messagePlainTextEdit.toPlainText().strip() # 校验并处理用户输入 if not (token and chat_id and message): self.okButton.setIcon(QIcon('./icons/error.png')) CoreUI.logQueue.put('Error：API Token、Telegram ID和消息内容为必填项') else: ret = self.telegramBotTest(token, proxy_url) if ret: cfg_file = './config/telegramBot.cfg' cfg = ConfigParser() cfg.read(cfg_file, encoding='utf-8-sig') cfg.set('telegramBot', 'token', token) cfg.set('telegramBot', 'chat_id', chat_id) cfg.set('telegramBot', 'proxy_url', proxy_url) cfg.set('telegramBot', 'message', message) try: with open(cfg_file, 'w', encoding='utf-8') as file: cfg.write(file) except: logging.error('写入telegramBot配置文件发生异常') CoreUI.logQueue.put('Error：写入配置文件时发生异常，更新失败') else: CoreUI.logQueue.put('Success：测试通过，系统已更新TelegramBot配置') self.close() else: CoreUI.logQueue.put('Error：测试失败，无法更新TelegramBot配置') # TelegramBot 测试 def telegramBotTest(self, token, proxy_url): try: # 是否使用代理 if proxy_url: proxy = telegram.utils.request.Request(proxy_url=proxy_url) bot = telegram.Bot(token=token, request=proxy) else: bot = telegram.Bot(token=token) bot.get_me() except Exception as e: return False else: return True class Img: def __init__(self,name,img): self.name = name self.img = img class Result: def __init__(self,classname): self.classname = classname class FaceProcessingThread2(QThread): def __init__(self): super(FaceProcessingThread2, self).__init__() self.isRunning = True # self.imgqueue = imgqueue # self.resultqueue = resultqueue def run(self): def parse_args(): parser = argparse.ArgumentParser(description='Train YOLO networks with random input shape.') parser.add_argument('--network', type=str, default='yolo3_mobilenet0.25_voc', # use yolo3_darknet53_voc, yolo3_mobilenet1.0_voc, yolo3_mobilenet0.25_voc help="Base network name which serves as feature extraction base.") parser.add_argument('--short', type=int, default=416, help='Input data shape for evaluation, use 320, 416, 512, 608, ' 'larger size for dense object and big size input') parser.add_argument('--threshold', type=float, default=0.4, help='confidence threshold for object detection') parser.add_argument('--gpu', action='store_false', help='use gpu or cpu.') args = parser.parse_args() return args args = parse_args() ctx = mx.cpu() net = model_zoo.get_model(args.network, pretrained=False) classes = ['hat', 'person'] for param in net.collect_params().values(): if param._data is not None: continue param.initialize() net.reset_class(classes) net.collect_params().reset_ctx(ctx) if args.network == 'yolo3_darknet53_voc': net.load_parameters('darknet.params', ctx=ctx) print('use darknet to extract feature') elif args.network == 'yolo3_mobilenet1.0_voc': net.load_parameters('mobilenet1.0.params', ctx=ctx) print('use mobile1.0 to extract feature') else: net.load_parameters('mobile0.25.params', ctx=ctx) print('use mobile0.25 to extract feature') while self.isRunning: imgResult = ImgQueue.get() frame = imgResult.img start = time.clock() x = mx.nd.array(frame) x, orig_img = data.transforms.presets.yolo.transform_test(x) x = x.as_in_context(ctx) box_ids, scores, bboxes = net(x) ax = utils.viz.cv_plot_bbox(orig_img, bboxes[0], scores[0], box_ids[0], class_names=net.classes, thresh=args.threshold) print(ax) ResultQueue.put(Result(ax)) elapsed = (time.clock() - start) print("耗时:", elapsed) def stop(self): self.isRunning = False self.quit() self.wait() # OpenCV线程 class FaceProcessingThread(QThread): def __init__(self): super(FaceProcessingThread, self).__init__() self.isRunning = True self.isFaceTrackerEnabled = True self.isFaceRecognizerEnabled = False self.isPanalarmEnabled = True self.isDebugMode = False self.confidenceThreshold = 50 self.autoAlarmThreshold = 65 self.isEqualizeHistEnabled = False # 是否开启人脸跟踪 def enableFaceTracker(self, coreUI): if coreUI.faceTrackerCheckBox.isChecked(): self.isFaceTrackerEnabled = True coreUI.statusBar().showMessage('人脸跟踪：开启') else: self.isFaceTrackerEnabled = False coreUI.statusBar().showMessage('人脸跟踪：关闭') # 是否开启人脸识别 def enableFaceRecognizer(self, coreUI): if coreUI.faceRecognizerCheckBox.isChecked(): if self.isFaceTrackerEnabled: self.isFaceRecognizerEnabled = True coreUI.statusBar().showMessage('人脸识别：开启') else: CoreUI.logQueue.put('Error：操作失败，请先开启人脸跟踪') coreUI.faceRecognizerCheckBox.setCheckState(Qt.Unchecked) coreUI.faceRecognizerCheckBox.setChecked(False) else: self.isFaceRecognizerEnabled = False coreUI.statusBar().showMessage('人脸识别：关闭') # 是否开启报警系统 def enablePanalarm(self, coreUI): if coreUI.panalarmCheckBox.isChecked(): self.isPanalarmEnabled = True coreUI.statusBar().showMessage('报警系统：开启') else: self.isPanalarmEnabled = False coreUI.statusBar().showMessage('报警系统：关闭') # 是否开启调试模式 def enableDebug(self, coreUI): if coreUI.debugCheckBox.isChecked(): self.isDebugMode = True coreUI.statusBar().showMessage('调试模式：开启') else: self.isDebugMode = False coreUI.statusBar().showMessage('调试模式：关闭') # 设置置信度阈值 def setConfidenceThreshold(self, coreUI): if self.isDebugMode: self.confidenceThreshold = coreUI.confidenceThresholdSlider.value() coreUI.statusBar().showMessage('置信度阈值：{}'.format(self.confidenceThreshold)) # 设置自动报警阈值 def setAutoAlarmThreshold(self, coreUI): if self.isDebugMode: self.autoAlarmThreshold = coreUI.autoAlarmThresholdSlider.value() coreUI.statusBar().showMessage('自动报警阈值：{}'.format(self.autoAlarmThreshold)) # 直方图均衡化 def enableEqualizeHist(self, coreUI): if coreUI.equalizeHistCheckBox.isChecked(): self.isEqualizeHistEnabled = True coreUI.statusBar().showMessage('直方图均衡化：开启') else: self.isEqualizeHistEnabled = False coreUI.statusBar().showMessage('直方图均衡化：关闭') def run(self): faceCascade = cv2.CascadeClassifier('./haarcascades/haarcascade_frontalface_default.xml') # 帧数、人脸ID初始化 frameCounter = 0 currentFaceID = 0 # 人脸跟踪器字典初始化 faceTrackers = {} isTrainingDataLoaded = False isDbConnected = False while self.isRunning: if CoreUI.cap.isOpened(): ret, frame = CoreUI.cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 是否执行直方图均衡化 if self.isEqualizeHistEnabled: gray = cv2.equalizeHist(gray) faces = faceCascade.detectMultiScale(gray, 1.3, 5, minSize=(90, 90)) # 预加载数据文件 if not isTrainingDataLoaded and os.path.isfile(CoreUI.trainingData): recognizer = cv2.face.LBPHFaceRecognizer_create() recognizer.read(CoreUI.trainingData) isTrainingDataLoaded = True if not isDbConnected and os.path.isfile(CoreUI.database): conn = sqlite3.connect(CoreUI.database) cursor = conn.cursor() isDbConnected = True captureData = {} realTimeFrame = frame.copy() alarmSignal = {} if self.isFaceTrackerEnabled: # 要删除的人脸跟踪器列表初始化 fidsToDelete = [] for fid in faceTrackers.keys(): # 实时跟踪 trackingQuality = faceTrackers[fid].update(realTimeFrame) # 如果跟踪质量过低，删除该人脸跟踪器 if trackingQuality < 7: fidsToDelete.append(fid) # 删除跟踪质量过低的人脸跟踪器 for fid in fidsToDelete: faceTrackers.pop(fid, None) for (_x, _y, _w, _h) in faces: isKnown = False if self.isFaceRecognizerEnabled: # cv2.rectangle(realTimeFrame, (_x, _y), (_x + _w+50, _y + _h+50), (232, 138, 30), 2) cut_img = realTimeFrame[_y:_y+_h,_x:_x+_w] face_id, confidence = recognizer.predict(gray[_y:_y + _h, _x:_x + _w]) logging.debug('face_id：{}，confidence：{}'.format(face_id, confidence)) if self.isDebugMode: CoreUI.logQueue.put('Debug -> face_id：{}，confidence：{}'.format(face_id, confidence)) # 从数据库中获取识别人脸的身份信息 try: cursor.execute("SELECT * FROM users WHERE face_id=?", (face_id,)) result = cursor.fetchall() # print(result) if result: en_name = result[0][3] ImgQueue.put(Img(en_name,cut_img)) else: raise Exception except Exception as e: logging.error('读取数据库异常，系统无法获取Face ID为{}的身份信息'.format(face_id)) CoreUI.logQueue.put('Error：读取数据库异常，系统无法获取Face ID为{}的身份信息'.format(face_id)) en_name = '' # 若置信度评分小于置信度阈值，认为是可靠识别 #帧数自增： frameCounter += 1 if confidence < self.confidenceThreshold: isKnown = True cv2.putText(realTimeFrame, en_name, (_x - 5, _y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 97, 255), 2) if frameCounter == 10: classnameresult = ResultQueue.get() classname = classnameresult.classname cv2.putText(realTimeFrame, classname, (_x + 150, _y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1,(0, 97, 255), 2) if classname == "hat": cv2.rectangle(realTimeFrame, (_x, _y), (_x + _w + 50, _y + _h + 50), (232, 138, 30), 2) if self.isPanalarmEnabled: alarmSignal['timestamp'] = datetime.now().strftime('%Y-%m-%d-%H-%M-%S') alarmSignal['img'] = realTimeFrame CoreUI.alarmQueue.put(alarmSignal) logging.info('系统发出了报警信号') in_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S') out_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S') try: if int(datetime.now().strftime('%H%M%S')) < 155900 and list(cursor.execute( "select count(*) from workers where name='{0}'".format(en_name)))[0][ 0] == 0: cursor.execute('INSERT INTO workers (name,in_time,status) VALUES (?, ?)', (en_name, in_time,classname)) if int(datetime.now().strftime('%H%M%S')) > 160200 and list(cursor.execute( "select count(*) from workers where name='{0}'".format(en_name)))[0][ 0] == 1: cursor.execute( "update workers set out_time = '{0}' where name = '{1}'".format(out_time, en_name)) finally: conn.commit() else: # 若置信度评分大于置信度阈值，该人脸可能是陌生人 cv2.putText(realTimeFrame, 'unknown', (_x - 5, _y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) # 若置信度评分超出自动报警阈值，触发报警信号 if confidence > self.autoAlarmThreshold: # 检测报警系统是否开启 if self.isPanalarmEnabled: alarmSignal['timestamp'] = datetime.now().strftime('%Y-%m-%d-%H-%M-%S') alarmSignal['img'] = realTimeFrame CoreUI.alarmQueue.put(alarmSignal) logging.info('系统发出了报警信号') # 每读取10帧，检测跟踪器的人脸是否还在当前画面内 if frameCounter % 10 == 0: frameCounter=0 # 这里必须转换成int类型，因为OpenCV人脸检测返回的是numpy.int32类型， # 而dlib人脸跟踪器要求的是int类型 x = int(_x) y = int(_y) w = int(_w) h = int(_h) # 计算中心点 x_bar = x + 0.5 * w y_bar = y + 0.5 * h # matchedFid表征当前检测到的人脸是否已被跟踪 matchedFid = None for fid in faceTrackers.keys(): # 获取人脸跟踪器的位置 # tracked_position 是 dlib.drectangle 类型，用来表征图像的矩形区域，坐标是浮点数 tracked_position = faceTrackers[fid].get_position() # 浮点数取整 t_x = int(tracked_position.left()) t_y = int(tracked_position.top()) t_w = int(tracked_position.width()) t_h = int(tracked_position.height()) # 计算人脸跟踪器的中心点 t_x_bar = t_x + 0.5 * t_w t_y_bar = t_y + 0.5 * t_h # 如果当前检测到的人脸中心点落在人脸跟踪器内，且人脸跟踪器的中心点也落在当前检测到的人脸内 # 说明当前人脸已被跟踪 if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))): matchedFid = fid # 如果当前检测到的人脸是陌生人脸且未被跟踪 if not isKnown and matchedFid is None: # 创建一个人脸跟踪器 tracker = dlib.correlation_tracker() # 锁定跟踪范围 tracker.start_track(realTimeFrame, dlib.rectangle(x - 5, y - 10, x + w + 5, y + h + 10)) # 将该人脸跟踪器分配给当前检测到的人脸 faceTrackers[currentFaceID] = tracker # 人脸ID自增 currentFaceID += 1 # 使用当前的人脸跟踪器，更新画面，输出跟踪结果 for fid in faceTrackers.keys(): tracked_position = faceTrackers[fid].get_position() t_x = int(tracked_position.left()) t_y = int(tracked_position.top()) t_w = int(tracked_position.width()) t_h = int(tracked_position.height()) # 在跟踪帧中圈出人脸 cv2.rectangle(realTimeFrame, (t_x, t_y), (t_x + t_w, t_y + t_h), (0, 0, 255), 2) cv2.putText(realTimeFrame, 'tracking...', (15, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2) captureData['originFrame'] = frame captureData['realTimeFrame'] = realTimeFrame CoreUI.captureQueue.put(captureData) else: continue # 停止OpenCV线程 def stop(self): self.isRunning = False self.quit() self.wait() if __name__ == '__main__': logging.config.fileConfig('./config/logging.cfg') app = QApplication(sys.argv) window = CoreUI() window.show() sys.exit(app.exec())

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py

# multi_threaded server import socketserver import threading HOST = '' PORT = 9009 lock = threading.Lock() # syncronized 동기화 진행하는 스레드 생성 class UserManager: # 사용자관리 메세지 전송을 담당하는 클래스 def __init__(self): self.users = {} # 사용자의 등록 정보를 담을 사전 {사용자 이름:(소켓,주소),...} def addUser(self, username, conn, addr): # 사용자 ID를 self.users에 추가하는 함수 if username in self.users: # 이미 등록된 사용자라면 conn.send('이미 등록된 아두이노입니다.\n'.encode()) return None # 새로운 사용자를 등록함 lock.acquire() # 스레드 동기화를 막기위한 락 self.users[username] = (conn, addr) lock.release() # 업데이트 후 락 해제 #self.sendMessageToAll('아두이노[%s] 연결됨.' %username) print('+++ 현재 연결된 아두이노 수: [%d]' %len(self.users)) return username def removeUser(self, username): #사용자를 제거하는 함수 if username not in self.users: return lock.acquire() del self.users[username] lock.release() #self.sendMessageToAll('아두이노 [%s] 종료' %username) print('--- 현재 연결된 아두이노 수 : [%d]' %len(self.users)) def messageHandler(self, username, msg): # 전송한 msg를 처리하는 부분 if msg[0] != '/': # 보낸 메세지의 첫문자가 '/'가 아니면 #self.sendMessageToAll('[%s] %s' %(username, msg)) print('[%s] %s' %(username, msg)) return if msg.strip() == '/quit': # 보낸 메세지가 'quit'이면 self.removeUser(username) return -1 def sendMessageToAll(self, msg): for conn, addr in self.users.values(): conn.send(msg.encode()) class MyTcpHandler(socketserver.BaseRequestHandler): userman = UserManager() def handle(self): # 클라이언트가 접속시 클라이언트 주소 출력 print('[%s] 연결됨' %self.client_address[0]) try: username = self.registerUsername() msg = self.request.recv(1024) while msg: print(msg.decode()) if self.userman.messageHandler(username, msg.decode()) == -1: self.request.close() break msg = self.request.recv(1024) except Exception as e: print(e) print('[%s] 접속종료' %self.client_address[0]) self.userman.removeUser(username) def registerUsername(self): while True: self.request.send('Enter your Arduino ID:'.encode()) username = self.request.recv(1024) username = username.decode().strip() if self.userman.addUser(username, self.request, self.client_address): return username class ChatingServer(socketserver.ThreadingMixIn, socketserver.TCPServer): pass def runServer(): print('+++ 서버 시작.') print('+++ 서버를 끝내려면 Ctrl-C를 누르세요.') try: server = ChatingServer((HOST, PORT), MyTcpHandler) server.serve_forever() except KeyboardInterrupt: print('--- 서버를 종료합니다.') server.shutdown() server.server_close() runServer()

[ "gygacpu@naver.com" ]

gygacpu@naver.com

32156869f1eead248fef32104f8ef2a838ccc3c7

58c0604f0ddd38a0cb7a8b8b9fa7c70abdc974b9

/setup.py

e012c83b78a10d467aca9d7e2f127db0069537ee

[ "MIT" ]

permissive

yohann84L/plot_metric

fffd5cdb8ec11074f0440b1f9f2aa10183cd7924

52cae945276b808f829471bd28537d3e7718317c

refs/heads/master

2023-07-20T02:34:02.330775

2022-08-22T08:55:50

195,234,816

58

9

MIT

2023-09-08T10:02:26

2019-07-04T12:09:45

Python

UTF-8

Python

false

940

py

import setuptools with open("README.rst", "r") as fh: long_description = fh.read() setuptools.setup( name='plot_metric', version='0.0.6', scripts=['plot_metric_package'], install_requires=[ "scipy>=1.1.0", "matplotlib>=3.0.2", "colorlover>=0.3.0", "pandas>=0.23.4", "seaborn>=0.9.0", "numpy>=1.15.4", "scikit_learn>=0.21.2", ], author="Yohann Lereclus", author_email="lereclus84L@gmail.com", description="A package with tools for plotting metrics", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/yohann84L/plot_metric/", packages=setuptools.find_packages(), py_modules=['plot_metric/functions'], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], )

[ "lereclus84L@gmail.com" ]

lereclus84L@gmail.com

7d039a870ec27bb80f11107da20eb106f895ae64

49ca62eb4bdbe24aa09eaf51a42a5f5abe9c25a6

/noway/__init__.py

31a973a69d4347d35c52bb14253d0179f8d63d3e

[]

no_license

emergencybutter/noway

6c2d49ba2192f338baf37ad993b50f14f0c14f3f

1ad0a16229b3c29de451a5505a29cf8ccaf35a7d

refs/heads/master

2020-04-05T18:03:16.311851

2018-11-11T22:14:26

157,087,009

0

null

UTF-8

Python

false

21

py

from .noway import *

[ "arnaud.cornet@gmail.com" ]

arnaud.cornet@gmail.com

4bb90a6ca68e445eccebebed337a3cd0bead0c5d

8d44932bdd08424eed23e886525ca507e4267351

/back/api/urls.py

4cbd49cb0a38817f96b192032a63e7db79f88f77

[]

no_license

Almanova/WebDevelopment-Project

df6ddf74d279744314f11555f71f992c48a44fab

80e0e0acb929b922597f9447c7543d7c10fff5e2

refs/heads/master

2023-05-13T07:24:18.052668

2020-04-26T17:04:13

253,298,794

0

2

null

2023-05-10T07:00:54

2020-04-05T18:01:49

TypeScript

UTF-8

Python

false

833

py

from django.urls import path from api.views import views_cbv, views_fbv, views_auth from rest_framework_jwt.views import obtain_jwt_token urlpatterns = [ path('login/', obtain_jwt_token), path('sections/', views_cbv.SectionsListAPIView.as_view()), path('sections/<int:section_id>/topics/', views_fbv.topics_list), path('sections/<int:section_id>/', views_fbv.section_details), path('topics/<int:topic_id>/subtopics/', views_cbv.SubtopicsListAPIView.as_view()), path('topics/<int:topic_id>/edit/', views_cbv.TopicDetailsAPIView.as_view()), path('subtopics/<int:subtopic_id>/edit/', views_fbv.subtopic_details), path('signup/', views_auth.sign_up), path('users/<str:username>/', views_auth.get_user), path('subtopics/', views_fbv.subtopics_list), path('manager/', views_fbv.topics_count) ]

[ "almanovamadina@yahoo.com" ]

almanovamadina@yahoo.com

25ae3799ea54378963bca701e93f18238e04cdca

ee162f79b7913c4434666f4b71e275e7310f7fb4

/efarmer/advertisements/admin.py

c2b95db5a508407ea41c7903de0429e25a223803

[]

no_license

oskarsakol/eFarmer

a18c72723920b8497bbad29d9c1513cc3f353397

83943152471e35e07907a4fc5b2d469d1abbc2ba

refs/heads/master

2020-09-30T19:44:54.250216

2020-02-19T10:23:39

227,359,460

4

0

null

2020-01-25T16:34:49

2019-12-11T12:20:39

Python

UTF-8

Python

false

104

py

from django.contrib import admin from .models import Advertisement admin.site.register(Advertisement)

[ "sakoloskar@gmail.com" ]

sakoloskar@gmail.com

fcfa2095ffb3cfdbd3bdfa217f2ca38005c23e47

650c5dcf150820ac14d6fac3e10234a27a7827cf

/main/migrations/0063_alter_heroes_group.py

0a2358ced03c0e5f21871616d605b8583a552ca9

[]

no_license

Nurik110/django-site

f5713e7f4e9f635b7b81591b7a04ae3754693d0e

746cd4eae523b93920505357305ccfecc51567e7

refs/heads/main

2023-06-15T04:34:33.719427

2021-06-28T11:26:21

377,363,863

1

0

null

UTF-8

Python

false

582

py

# Generated by Django 3.2.3 on 2021-06-25 17:45 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('main', '0062_auto_20210625_2312'), ] operations = [ migrations.AlterField( model_name='heroes', name='group', field=models.CharField(choices=[('fighter', 'fighter'), ('shooter', 'shooter'), ('support', 'support'), ('assassin', 'assassin'), ('magician', 'magician'), ('tank', 'tank')], default='fighter', max_length=20, verbose_name='Группа'), ), ]

[ "Naltynbek_1998@mail.ru" ]

Naltynbek_1998@mail.ru

afd1dacedf308d638fdc5ded844094d8eccf879c

6df76f8a6fcdf444c3863e3788a2f4b2c539c22c

/django code/p69/manage.py

79b1d93f10fd7ae7bfe5d80fbfe7380a839f69cc

[]

no_license

basantbhandari/DjangoProjectsAsDocs

068e4a704fade4a97e6c40353edb0a4299bd9678

594dbb560391eaf94bb6db6dc07702d127010b88

refs/heads/master

2022-12-18T22:33:23.902228

2020-09-22T13:11:01

297,651,728

1

0

null

UTF-8

Python

false

623

py

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'p69.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()

[ "36443209+basantbhandari@users.noreply.github.com" ]

36443209+basantbhandari@users.noreply.github.com

2c1fdd4d765900888e77a4416496947d10c2917e

c61773a6ae76ae258589784ee02135c91d31b5bb

/cars/rental/migrations/0005_auto_20191127_1504.py

ea502731cf548175f627e39d144bbccf9c55ff1b

[]

no_license

burbaljaka/cars_rental

437e53d3f72d2bf6c0201a46f83de6415ffd8e27

c83ffd60d9a48bc96b10fc1964c5ef0402a03bd5

refs/heads/master

2020-09-16T13:54:21.923668

2019-12-04T15:00:03

223,789,750

0

null

UTF-8

Python

false

1,018

py

# Generated by Django 2.2.7 on 2019-11-27 15:04 import datetime from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('rental', '0004_auto_20191127_1459'), ] operations = [ migrations.AlterField( model_name='car', name='car_adding_date', field=models.DateField(default=datetime.datetime(2019, 11, 27, 15, 4, 29, 824286)), ), migrations.RemoveField( model_name='loan', name='loan_car', ), migrations.AddField( model_name='loan', name='loan_car', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='rental.Car'), ), migrations.AlterField( model_name='loan', name='loan_date_of_loan', field=models.DateField(default=datetime.datetime(2019, 11, 27, 15, 4, 29, 825285)), ), ]

[ "kapitonov.timur@gmail.com" ]

kapitonov.timur@gmail.com

a4136c31c7cf706db3e0de2db9535461a060f5fe

9945f91a1a677d8e8175dc33ec2a791bcdf7bc48

/ImbalancedClassificationMammographyMicrocalcification/mammographyLoadSplitEvaluateModel.py

7b930a7d28aad25f55dbca31abf7419a5407341f

[]

no_license

AWhelan33/DataScience

0db84b2ae9194fb7efdbe1252ee507e30b811b14

439a244c6652bcea75d290cad8ca3c9e270c8f19

refs/heads/master

2021-01-09T16:25:35.338245

2020-03-28T18:18:22

242,371,293

1

0

null

UTF-8

Python

false

1,367

py

#test harness and baseline model evaluation from collections import Counter from numpy import mean from numpy import std from pandas import read_csv from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import cross_val_score from sklearn.model_selection import RepeatedStratifiedKFold from sklearn.dummy import DummyClassifier #load the dataset def load_dataset(full_path): #load the dataset as a numpy array data = read_csv(full_path, header=None) #retrieve numpy array data = data.values #split into input and output elements X, y = data[:, :-1], data[:, -1] #label and encode the target variable to have the classes 0 and 1 y = LabelEncoder().fit_transform(y) return X, y #evalute a model def evaluate_model(X, y, model): #define evaluation procedure cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) #evaluate model scores = cross_val_score(model, X, y, scoring='roc_auc', cv=cv, n_jobs=-1) return scores #define the location of the dataset full_path = 'mammography.csv' #load the dataset X, y = load_dataset(full_path) #summarize the loaded dataset print(X.shape, y.shape, Counter(y)) #define the reference model model = DummyClassifier(strategy='stratified') #evaluate the model scores = evaluate_model(X, y, model) #summarize performance print('Mean ROC AUC: %.3f (%.3f)' %(mean(scores), std(scores)))

[ "amiiwhelan@gmail.com" ]

amiiwhelan@gmail.com

0cd9ff06bd48a6f67a5f13c9351fd7e7da1819c6

52e677932d7263d4ef6ea47f0a5f64829def290c

/customimg.py

658013d2c91b125c74e85ef2c1824bb98cb7f88c

[]

no_license

ianmah/instasched.py

6a2cea4cf9151e4c46eeee016378fe40b397e308

0aff3622e16a015356db19563dbe4bca8c0ff600

refs/heads/master

2020-05-01T15:01:24.343790

2019-03-26T06:08:14

177,535,826

2

0

null

UTF-8

Python

false

1,263

py

from PIL import Image import math import io from io import BytesIO import requests class Img: __image = '' def __init__(self, img): if img[:4] == 'http': imgGet = requests.get(img) self.__image = Image.open(BytesIO(imgGet.content)) else: self.__image = Image.open(img) self.__image.load() def crop(self): height = self.__image.height width = self.__image.width mod_ratio = height/width-1.25 if mod_ratio > 0: newheight = height-math.floor(height*mod_ratio) hmid = height/2 bottom = hmid+newheight/2 top = hmid-newheight/2 area = (0, top, width, bottom) self.__image = self.__image.crop(area) def resize(self): height = self.__image.height width = self.__image.width if height > 1080: self.__image.thumbnail((1080, 1080), Image.ANTIALIAS) def getImg(self): self.crop() self.resize() return self.__image def size(self): return self.__image.size def getByteArr(self): imgByteArr = io.BytesIO() self.__image.save(imgByteArr, format='JPEG') return imgByteArr.getvalue()

[ "ianmmah@gmail.com" ]

ianmmah@gmail.com

f866ff506707811bdf06b64d8846de512b2c0264

d63899ba9ce7f06841ce47b181bea34e1442f595

/learn-python/learn_python-ex10.py

a698f2bf074981cb6485934ba490563cd9e76d1b

[]

no_license

tillyoswellwheeler/module-02_python-learning

ba419a0428cc0e6e3e3d8951a79bfba9c78891ae

a27a49d050d6526df3db93992f03b7157d8de5e1

refs/heads/master

2022-12-25T04:32:59.468417

2021-09-04T14:10:30

169,213,767

0

null

2021-09-04T14:11:10

2019-02-05T09:05:14

CSS

UTF-8

Python

false

359

py

# -*- coding: utf-8 -*- """ Created on Thu Nov 29 18:56:00 2018 @author: 612383362 """ tabby_cat = "/tI'm tabbed in." persian_cat = "I'm split\non a line" backslash_cat = "I'm \\ a \\ cat." #fat_cat = """" #I'll do a list: #\t* Cat food #\t* Fishies #\t* Catnip\n\t* Grass #"""" print(tabby_cat) print(persian_cat) print(backslash_cat) #print(fat_cat)

[ "tilly.oswellwheeler@hotmail.com" ]

tilly.oswellwheeler@hotmail.com

d77fcded26296c4d07bfe63f33e2846953955337

7860ed6d27512c4601400f89c70c6ccbf654ff99

/claritick/qbuilder/management/commands/temps_rendu.py

7ba912aab3c9b74f998eb649683160d7549ccc36

[]

no_license

zehome/claritick

a7e4ed39e535163bc54e58e9611b84122de298c6

69290d639df55aba6f17526c97868c2238cd962f

refs/heads/master

2020-06-03T05:03:22.433046

2014-02-07T23:20:37

5,178,975

0

1

null

2022-10-26T08:16:16

2012-07-25T12:49:27

JavaScript

UTF-8

Python

false

3,901

py

#!/usr/bin/env python # -*- coding: utf-8 -*- from django.core.management.base import BaseCommand from django.db import connection from django.conf import settings from optparse import make_option import logging import time import datetime import traceback from tat.models import MemorisationTempsRendu, ModeleEvenement logger = logging.getLogger("qbuilder.temps_rendu") class Command(BaseCommand): option_list = BaseCommand.option_list + ( make_option("--start", dest="start_date_string", help=u"Calculer les temps de rendu à partir de cette date (YYYY-mm-dd)", ), make_option("--end", dest="end_date_string", help=u"Calculer les temps de rendu jusqu'à cette date (YYYY-mm-dd)", ), ) def handle(self, *args, **kwargs): allowed_hour = getattr(settings, 'QB_TEMPS_RENDU_HOUR', 1) actual_hour = datetime.datetime.now().hour if actual_hour != allowed_hour: logger.warning(u"Only allowed to run at %s and it is %s", allowed_hour, actual_hour) return start_date = datetime.date.today() - datetime.timedelta(days = 1) end_date = datetime.date.today() start_date_string = kwargs.pop('start_date_string') end_date_string = kwargs.pop('end_date_string') if start_date_string: start_date = datetime.datetime.strptime(start_date_string,'%Y-%m-%d') if end_date_string: end_date = datetime.datetime.strptime(end_date_string,'%Y-%m-%d') SQL = """ SELECT series.date, mca3.vstats_tat.start_evt, mca3.vstats_tat.end_evt, mca3.vstats_tat.start, mca3.vstats_tat."end", AVG(mca3.vstats_tat.temps_reel), MIN(mca3.vstats_tat.temps_reel), MAX(mca3.vstats_tat.temps_reel), COUNT(mca3.vstats_tat.temps_reel) FROM mca3.vstats_tat, (select start, "end" from mca3.delai) as const, (select generate_series(%s, %s, '1 day'::interval) as date) as series WHERE mca3.vstats_tat.end_date > series.date AND mca3.vstats_tat.end_date < series.date + '1 day'::interval AND mca3.vstats_tat.start = const.start AND mca3.vstats_tat."end" = const."end" AND mca3.vstats_tat.temps_reel > '0'::interval AND mca3.vstats_tat.start != mca3.vstats_tat."end" GROUP BY series.date, mca3.vstats_tat.start_evt, mca3.vstats_tat.end_evt, mca3.vstats_tat.start, mca3.vstats_tat."end" """ logger.info("Calcul des temps de rendu (%s - %s)", start_date, end_date,) modeles = ModeleEvenement.objects.all() modeles = dict([(modele.pk, modele) for modele in modeles]) now = time.time() cursor = connection.cursor() try: cursor.execute(SQL, [start_date, end_date,]) except: logger.error("Erreur d'exécution : %s" % (traceback.format_exc(),)) else: logger.info("Temps d'exécution : %s" % (time.time() - now,)) for dataline in cursor.fetchall(): stev = modeles[dataline[3]] edev = modeles[dataline[4]] MemorisationTempsRendu.objects.create( date = dataline[0], start_evt = stev.evenement, end_evt = edev.evenement, start = stev, end = edev, avg_temps_rendu = dataline[5], min_temps_rendu = dataline[6], max_temps_rendu = dataline[7], compte = dataline[8] )

[ "gl@clarisys.fr" ]

gl@clarisys.fr

524fcd5377baf5b11c259061610ad7632b9376d3

14e941b0d3b3b754c1b1476981b1f7a6a00804c8

/3raEntrega/Prototipo 6/GenGraphic1.py

18a60988623a9fd3ae215d9658476aad33d47e14

[]

no_license

JoshuaMeza/CodePain_PE

ca8b4eea8ef0e4d39b1b8d2d540860267bb21189

b30cf36286b8709c20577ead8b5fb36f7a14a1db

refs/heads/master

2022-10-15T00:21:49.819120

2020-06-09T23:17:01

256,617,299

0

1

null

2020-04-20T21:58:51

2020-04-17T21:52:22

C

UTF-8

Python

false

1,246

py

""" Author Joshua Meza, Jonathan Gómez, and Irving Poot Date 20/05/2020 Version 1.0.0 Program who generates the confirmed cases vs actual deaths graphic. """ from matplotlib import pyplot def genGraphic1(casesAmount,deathsAmount): """ Function that generates the first graphic Args: casesAmount (int): Total amount of confirmed cases deathsAmount (int): Total amount of deathsths parts (list): List of the two titles of the graphic slices (list): Parts of the graphic colors (list): Colors that the graphic will use values (list): Values with which size is defined Returns: Nothing """ if casesAmount[0]!=0 and deathsAmount[0]!=0: parts = ('Confirmed Cases', 'Deaths') slices = (casesAmount[0], deathsAmount[0]) colors = ('green', 'red') values = (0.1, 0) pyplot.rcParams['toolbar'] = 'None' _, _, text = pyplot.pie(slices, colors = colors, labels = parts, autopct='%1.1f%%', explode=values, startangle = 90) for tex in text: tex.set_color('white') pyplot.axis('equal') pyplot.title('Graph of the data collected from the country') pyplot.show()

[ "56287951+JoshuaMeza@users.noreply.github.com" ]

56287951+JoshuaMeza@users.noreply.github.com

20ca5110f2ffd78b936237416ee7819f2352fa64

869d300c764911d468f5b1e5a98e5d07a27535ab

/python-basics/com/learn/David.py

77f3346b022c12ee9af0b39c59d63b8154c782ed

[]

no_license

thananauto/python-test-frameworks

df5962996cd9c4cded9355fef6cb2a099c69e3b1

abaf9d11d8c65f2cd9f916b241898ad11e26bf43

refs/heads/master

2022-12-15T18:14:57.745930

2019-12-16T13:51:04

228,375,228

0

null

2022-09-16T18:15:48

2019-12-16T11:47:11

Python

UTF-8

Python

false

257

py

from com.learn.Employee import Employee from com.learn.Cars import Cars class David(Employee, Cars): def __init__(self, name , salary): print('Calling the constrcutor') def displayChildmethod(self): print('Print calling the child methods')

[ "r.thananjayan@superp.nl" ]

r.thananjayan@superp.nl

c5e0bd194a89e25927fb4e9f61e1b29307bb8af1

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/venv/lib/python3.7/site-packages/Satchmo-0.9.3-py3.7.egg/satchmo_ext/productratings/listeners.py

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siddhant3030/djangoecommerce

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2019-09-28T10:30:02

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"""Utility functions used by signals to attach Ratings to Comments""" import logging from django.contrib.sites.models import Site from django.utils.encoding import smart_str from django.conf import settings try: from django.core.urlresolvers import reverse except ImportError: from django.urls import reverse try: from django.contrib.comments.models import Comment except ImportError: from django_comments.models import Comment from livesettings.functions import config_value from product.models import Product from satchmo_utils import url_join from .models import ProductRating log = logging.getLogger('productratings') def save_rating(comment=None, request=None, **kwargs): """Create a rating and save with the comment""" # should always be true if request.method != "POST": return data = request.POST.copy() if 'rating' not in data: return raw = data['rating'] try: rating = int(raw) except ValueError: log.error('Could not parse rating from posted rating: %s', raw) return if comment.content_type.app_label == "product" and comment.content_type.model == "product": ProductRating.objects.update_or_create(comment=comment, defaults = {'rating': rating}) else: log.debug('Not saving rating for comment on a %s object', comment.content_type.model) def one_rating_per_product(comment=None, request=None, **kwargs): site = Site.objects.get_current() product_ratings = ProductRating.objects.rated_products() product_ratings = product_ratings.filter(comment__object_pk=comment.object_pk, comment__site=site, comment__user=request.user).exclude(comment__pk=comment.pk).distinct() for product_rating in product_ratings: product_rating.comment.delete() def check_with_akismet(comment=None, request=None, **kwargs): if config_value("PRODUCT", "AKISMET_ENABLE"): akismet_key = config_value("PRODUCT", "AKISMET_KEY") if akismet_key: site = Site.objects.get_current() shop = reverse('satchmo_shop_home') from akismet import Akismet akismet = Akismet( key=akismet_key, blog_url='http://%s' % url_join(site.domain, shop)) if akismet.verify_key(): akismet_data = { 'comment_type': 'comment', 'referrer': request.META.get('HTTP_REFERER', ""), 'user_ip': comment.ip_address, 'user_agent': '' } if akismet.comment_check(smart_str(comment.comment), data=akismet_data, build_data=True): comment.is_public=False comment.save() log.info("Akismet marked comment #%i as spam", comment.id) else: log.debug("Akismet accepted comment #%i", comment.id) else: log.warn("Akismet key '%s' not accepted by akismet service.", akismet_key) else: log.info("Akismet enabled, but no key found. Please put in your admin settings.")

[ "ssiddhant3030@gmail.com" ]

ssiddhant3030@gmail.com

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/test/infrastructuration/print_components/formatted_text.py

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Zhouhao12345/redcli

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refs/heads/master

2021-10-06T13:03:57.752655

2021-09-28T06:51:04

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import unittest from pygments.token import Token from redcli.infrastructuration.print_components.constant import PrintType from redcli.infrastructuration.print_components.base import ( BasePrintComponents, FormattedTextFactory, ) class FormattedText(unittest.TestCase): def test_1_formatted_plain_text(self): type_ = PrintType.PLAIN_TEXT formatted_cls: BasePrintComponents = FormattedTextFactory().produce(type_) ins = formatted_cls() ins.type_context(context="Hello World") ins.to_print_formatted_text() def test_2_formatted_ansi_text(self): type_ = PrintType.FORMATTED_TEXT_ANSI formatted_cls: BasePrintComponents = FormattedTextFactory().produce(type_) ins = formatted_cls() ins.type_context(context="\x1b[31mhello \x1b[32mworld") ins.to_print_formatted_text() def test_3_formatted_html_text(self): type_ = PrintType.FORMATTED_TEXT_HTML formatted_cls: BasePrintComponents = FormattedTextFactory().produce(type_) ins = formatted_cls() ins.type_context(context="<a url='www.baidu.com'>hello world</a>") style_dict = { "a": "#44ff00 italic" } ins.wrapper_style(style_dict=style_dict) ins.to_print_formatted_text() def test_4_formatted_token_text(self): type_ = PrintType.FORMATTED_TEXT_TOKEN_TEXT formatted_cls: BasePrintComponents = FormattedTextFactory().produce(type_) ins = formatted_cls() contexts = ("hello", "world") ins.type_context(context=contexts) ins.wrapper_style(style_dict=(Token.Keyword, Token.Punctuation)) ins.to_print_formatted_text() def test_5_formatted_style_text(self): type_ = PrintType.FORMATTED_TEXT_STYLE_TEXT formatted_cls: BasePrintComponents = FormattedTextFactory().produce(type_) ins = formatted_cls() contexts = [ ("class:a", "hello"), ("class:b", "world"), ] ins.type_context(context=contexts) style_dict = { "a": "#ff0066", "b": "#ff0066", } ins.wrapper_style(style_dict=style_dict) ins.to_print_formatted_text()

[ "alex.zhou@gllue.com" ]

alex.zhou@gllue.com

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/jupyterhub/files/jupyterhub_config.py

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nathanhilbert/ScienceAnsible

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refs/heads/master

2021-01-17T18:30:50.311788

2016-10-20T13:58:44

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# Configuration file for jupyterhub. #------------------------------------------------------------------------------ # Configurable configuration #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # LoggingConfigurable configuration #------------------------------------------------------------------------------ # A parent class for Configurables that log. # # Subclasses have a log trait, and the default behavior is to get the logger # from the currently running Application. #------------------------------------------------------------------------------ # SingletonConfigurable configuration #------------------------------------------------------------------------------ # A configurable that only allows one instance. # # This class is for classes that should only have one instance of itself or # *any* subclass. To create and retrieve such a class use the # :meth:`SingletonConfigurable.instance` method. #------------------------------------------------------------------------------ # Application configuration #------------------------------------------------------------------------------ # This is an application. # The date format used by logging formatters for %(asctime)s # c.Application.log_datefmt = '%Y-%m-%d %H:%M:%S' # The Logging format template # c.Application.log_format = '[%(name)s]%(highlevel)s %(message)s' # Set the log level by value or name. # c.Application.log_level = 30 #------------------------------------------------------------------------------ # JupyterHub configuration #------------------------------------------------------------------------------ # An Application for starting a Multi-User Jupyter Notebook server. # Grant admin users permission to access single-user servers. # # Users should be properly informed if this is enabled. # c.JupyterHub.admin_access = False # DEPRECATED, use Authenticator.admin_users instead. # c.JupyterHub.admin_users = set() # Answer yes to any questions (e.g. confirm overwrite) # c.JupyterHub.answer_yes = False # Dict of token:username to be loaded into the database. # # Allows ahead-of-time generation of API tokens for use by services. # c.JupyterHub.api_tokens = {} # Class for authenticating users. # # This should be a class with the following form: # # - constructor takes one kwarg: `config`, the IPython config object. # # - is a tornado.gen.coroutine # - returns username on success, None on failure # - takes two arguments: (handler, data), # where `handler` is the calling web.RequestHandler, # and `data` is the POST form data from the login page. # c.JupyterHub.authenticator_class = 'jupyterhub.auth.PAMAuthenticator' # The base URL of the entire application # c.JupyterHub.base_url = '/' # Whether to shutdown the proxy when the Hub shuts down. # # Disable if you want to be able to teardown the Hub while leaving the proxy # running. # # Only valid if the proxy was starting by the Hub process. # # If both this and cleanup_servers are False, sending SIGINT to the Hub will # only shutdown the Hub, leaving everything else running. # # The Hub should be able to resume from database state. # c.JupyterHub.cleanup_proxy = True # Whether to shutdown single-user servers when the Hub shuts down. # # Disable if you want to be able to teardown the Hub while leaving the single- # user servers running. # # If both this and cleanup_proxy are False, sending SIGINT to the Hub will only # shutdown the Hub, leaving everything else running. # # The Hub should be able to resume from database state. # c.JupyterHub.cleanup_servers = True # The config file to load # c.JupyterHub.config_file = 'jupyterhub_config.py' # Confirm that JupyterHub should be run without SSL. This is **NOT RECOMMENDED** # unless SSL termination is being handled by another layer. # c.JupyterHub.confirm_no_ssl = True # Number of days for a login cookie to be valid. Default is two weeks. # c.JupyterHub.cookie_max_age_days = 14 # The cookie secret to use to encrypt cookies. # # Loaded from the JPY_COOKIE_SECRET env variable by default. # c.JupyterHub.cookie_secret = b'' # File in which to store the cookie secret. # c.JupyterHub.cookie_secret_file = 'jupyterhub_cookie_secret' # The location of jupyterhub data files (e.g. /usr/local/share/jupyter/hub) # c.JupyterHub.data_files_path = '/usr/local/share/jupyter/hub' # Include any kwargs to pass to the database connection. See # sqlalchemy.create_engine for details. # c.JupyterHub.db_kwargs = {} # url for the database. e.g. `sqlite:///jupyterhub.sqlite` # c.JupyterHub.db_url = 'sqlite:///jupyterhub.sqlite' # log all database transactions. This has A LOT of output # c.JupyterHub.debug_db = False # show debug output in configurable-http-proxy # c.JupyterHub.debug_proxy = False # Send JupyterHub's logs to this file. # # This will *only* include the logs of the Hub itself, not the logs of the proxy # or any single-user servers. # c.JupyterHub.extra_log_file = '' # Extra log handlers to set on JupyterHub logger # c.JupyterHub.extra_log_handlers = [] # Generate default config file # c.JupyterHub.generate_config = False # The ip for this process # c.JupyterHub.hub_ip = '127.0.0.1' # The port for this process # c.JupyterHub.hub_port = 8081 # The prefix for the hub server. Must not be '/' # c.JupyterHub.hub_prefix = '/hub/' # The public facing ip of the whole application (the proxy) c.JupyterHub.ip = '0.0.0.0' # Supply extra arguments that will be passed to Jinja environment. # c.JupyterHub.jinja_environment_options = {} # Interval (in seconds) at which to update last-activity timestamps. # c.JupyterHub.last_activity_interval = 300 # Specify path to a logo image to override the Jupyter logo in the banner. # c.JupyterHub.logo_file = '' # File to write PID Useful for daemonizing jupyterhub. # c.JupyterHub.pid_file = '' # The public facing port of the proxy # c.JupyterHub.port = 8000 # The ip for the proxy API handlers # c.JupyterHub.proxy_api_ip = '127.0.0.1' # The port for the proxy API handlers # c.JupyterHub.proxy_api_port = 0 # The Proxy Auth token. # # Loaded from the CONFIGPROXY_AUTH_TOKEN env variable by default. # c.JupyterHub.proxy_auth_token = '' # Interval (in seconds) at which to check if the proxy is running. # c.JupyterHub.proxy_check_interval = 30 # The command to start the http proxy. # # Only override if configurable-http-proxy is not on your PATH # c.JupyterHub.proxy_cmd = ['configurable-http-proxy'] # Purge and reset the database. # c.JupyterHub.reset_db = False # The class to use for spawning single-user servers. # # Should be a subclass of Spawner. # c.JupyterHub.spawner_class = 'jupyterhub.spawner.LocalProcessSpawner' # Path to SSL certificate file for the public facing interface of the proxy # # Use with ssl_key # c.JupyterHub.ssl_cert = '' # Path to SSL key file for the public facing interface of the proxy # # Use with ssl_cert # c.JupyterHub.ssl_key = '' # Host to send statds metrics to # c.JupyterHub.statsd_host = '' # Port on which to send statsd metrics about the hub # c.JupyterHub.statsd_port = 8125 # Prefix to use for all metrics sent by jupyterhub to statsd # c.JupyterHub.statsd_prefix = 'jupyterhub' # Run single-user servers on subdomains of this host. # # This should be the full https://hub.domain.tld[:port] # # Provides additional cross-site protections for javascript served by single- # user servers. # # Requires <username>.hub.domain.tld to resolve to the same host as # hub.domain.tld. # # In general, this is most easily achieved with wildcard DNS. # # When using SSL (i.e. always) this also requires a wildcard SSL certificate. # c.JupyterHub.subdomain_host = '' # Paths to search for jinja templates. # c.JupyterHub.template_paths = [] # Extra settings overrides to pass to the tornado application. # c.JupyterHub.tornado_settings = {} #------------------------------------------------------------------------------ # Spawner configuration #------------------------------------------------------------------------------ # Base class for spawning single-user notebook servers. # # Subclass this, and override the following methods: # # - load_state - get_state - start - stop - poll # Extra arguments to be passed to the single-user server # c.Spawner.args = [] # The command used for starting notebooks. # c.Spawner.cmd = ['jupyterhub-singleuser'] # Enable debug-logging of the single-user server # c.Spawner.debug = False # The default URL for the single-user server. # # Can be used in conjunction with --notebook-dir=/ to enable full filesystem # traversal, while preserving user's homedir as landing page for notebook # # `%U` will be expanded to the user's username # c.Spawner.default_url = '' # Disable per-user configuration of single-user servers. # # This prevents any config in users' $HOME directories from having an effect on # their server. # c.Spawner.disable_user_config = False # Whitelist of environment variables for the subprocess to inherit # c.Spawner.env_keep = ['PATH', 'PYTHONPATH', 'CONDA_ROOT', 'CONDA_DEFAULT_ENV', 'VIRTUAL_ENV', 'LANG', 'LC_ALL'] # Environment variables to load for the Spawner. # # Value could be a string or a callable. If it is a callable, it will be called # with one parameter, which will be the instance of the spawner in use. It # should quickly (without doing much blocking operations) return a string that # will be used as the value for the environment variable. # c.Spawner.environment = {} # Timeout (in seconds) before giving up on a spawned HTTP server # # Once a server has successfully been spawned, this is the amount of time we # wait before assuming that the server is unable to accept connections. # c.Spawner.http_timeout = 30 # The IP address (or hostname) the single-user server should listen on # c.Spawner.ip = '127.0.0.1' # The notebook directory for the single-user server # # `~` will be expanded to the user's home directory `%U` will be expanded to the # user's username # c.Spawner.notebook_dir = '/vagrant/shares/homeshare' # An HTML form for options a user can specify on launching their server. The # surrounding `<form>` element and the submit button are already provided. # # For example: # # Set your key: # <input name="key" val="default_key"></input> # <br> # Choose a letter: # <select name="letter" multiple="true"> # <option value="A">The letter A</option> # <option value="B">The letter B</option> # </select> # c.Spawner.options_form = '' # Interval (in seconds) on which to poll the spawner. # c.Spawner.poll_interval = 30 # Timeout (in seconds) before giving up on the spawner. # # This is the timeout for start to return, not the timeout for the server to # respond. Callers of spawner.start will assume that startup has failed if it # takes longer than this. start should return when the server process is started # and its location is known. # c.Spawner.start_timeout = 60 #------------------------------------------------------------------------------ # LocalProcessSpawner configuration #------------------------------------------------------------------------------ # A Spawner that just uses Popen to start local processes as users. # # Requires users to exist on the local system. # # This is the default spawner for JupyterHub. # Seconds to wait for process to halt after SIGINT before proceeding to SIGTERM # c.LocalProcessSpawner.INTERRUPT_TIMEOUT = 10 # Seconds to wait for process to halt after SIGKILL before giving up # c.LocalProcessSpawner.KILL_TIMEOUT = 5 # Seconds to wait for process to halt after SIGTERM before proceeding to SIGKILL # c.LocalProcessSpawner.TERM_TIMEOUT = 5 #------------------------------------------------------------------------------ # Authenticator configuration #------------------------------------------------------------------------------ # A class for authentication. # # The primary API is one method, `authenticate`, a tornado coroutine for # authenticating users. # set of usernames of admin users # # If unspecified, only the user that launches the server will be admin. # c.Authenticator.admin_users = set() # Dictionary mapping authenticator usernames to JupyterHub users. # # Can be used to map OAuth service names to local users, for instance. # # Used in normalize_username. # c.Authenticator.username_map = {} # Regular expression pattern for validating usernames. # # If not defined: allow any username. # c.Authenticator.username_pattern = '' # Username whitelist. # # Use this to restrict which users can login. If empty, allow any user to # attempt login. # c.Authenticator.whitelist = set() #------------------------------------------------------------------------------ # LocalAuthenticator configuration #------------------------------------------------------------------------------ # Base class for Authenticators that work with local Linux/UNIX users # # Checks for local users, and can attempt to create them if they exist. # The command to use for creating users as a list of strings. # # For each element in the list, the string USERNAME will be replaced with the # user's username. The username will also be appended as the final argument. # # For Linux, the default value is: # # ['adduser', '-q', '--gecos', '""', '--disabled-password'] # # To specify a custom home directory, set this to: # # ['adduser', '-q', '--gecos', '""', '--home', '/customhome/USERNAME', # '--disabled-password'] # # This will run the command: # # adduser -q --gecos "" --home /customhome/river --disabled-password river # # when the user 'river' is created. # c.LocalAuthenticator.add_user_cmd = [] # If a user is added that doesn't exist on the system, should I try to create # the system user? # c.LocalAuthenticator.create_system_users = False # Automatically whitelist anyone in this group. # c.LocalAuthenticator.group_whitelist = set() #------------------------------------------------------------------------------ # PAMAuthenticator configuration #------------------------------------------------------------------------------ # Authenticate local Linux/UNIX users with PAM # The encoding to use for PAM # c.PAMAuthenticator.encoding = 'utf8' # Whether to open PAM sessions when spawners are started. # # This may trigger things like mounting shared filsystems, loading credentials, # etc. depending on system configuration, but it does not always work. # # It can be disabled with:: # # c.PAMAuthenticator.open_sessions = False # c.PAMAuthenticator.open_sessions = True # The PAM service to use for authentication. # c.PAMAuthenticator.service = 'login' c.Authenticator.admin_users = {'jupyter'} c.LocalAuthenticator.create_system_users = True

[ "nathanhilbert@gmail.com" ]

nathanhilbert@gmail.com

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/tests.py

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[]

no_license

NilVidalRafols/iGNNspector

0a608ff2aa6a003a6ae22105e286db6023841bcb

3d8742e20a5df0606fe201440fe4c382350f68a6

refs/heads/main

2023-06-05T09:51:00.840402

2021-06-30T03:43:57

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2

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import sys from ignnspector import Graph # from tests import custom_studies_test # from tests import time_test from tests import pyg_builder_test as b b.main()

[ "NilVidalRafols@github.com" ]

NilVidalRafols@github.com

edf4eb25316ca438a6a3f65240a0e6680a163702

9958136f29a0c80c7ab64370b987e3ecb4bc178b

/other_tasks/GümüşBar.py

915c159143efc8a313511dd5f3abb8652e1bfd33

[]

no_license

yasinalp/MMOPytautoGUI

9c8f06bbb707d8c43b2469ea46f85c7dedca324c

ec0c321ba6bbbe77420b7612c5166f0cab508cd1

refs/heads/master

2022-10-22T07:00:05.216298

2020-06-09T13:01:55

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1,041

py

import pyautogui import time rgb = pyautogui.pixelMatchesColor(31, 615, (0, 55, 132), tolerance=5) while rgb == True: pyautogui.hotkey('ctrl', 'win', '1') time.sleep(1) rgb = pyautogui.pixelMatchesColor(31, 615, (0, 55, 132), tolerance=5) time.sleep(1) pyautogui.moveTo(1250, 400) # Çanta2 time.sleep(0.1) pyautogui.click(1250, 400) time.sleep(0.1) pyautogui.moveTo(1210, 400) # Çanta1 time.sleep(0.1) pyautogui.click(1210, 400) gumusbar = pyautogui.locateCenterOnScreen('C:/Python34/Ticaret/100Mbar.png',region=(1130, 385, 236, 360)) pyautogui.moveTo(gumusbar) time.sleep(0.1) pyautogui.click(gumusbar) gumusbar = pyautogui.locateCenterOnScreen('C:/Python34/Ticaret/100Mbar.png',region=(1130, 385, 236, 360)) while gumusbar != None: gbx, gby = gumusbar pyautogui.moveTo(gumusbar) time.sleep(0.1) pyautogui.click(gbx, gby) time.sleep(0.2) gumusbar = pyautogui.locateCenterOnScreen('C:/Python34/Ticaret/100Mbar.png',region=(1130, 385, 236, 360)) pyautogui.click(gbx, gby)

[ "noreply@github.com" ]

yasinalp.noreply@github.com

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/member/migrations_old/0007_auto__add_field_person_other_club__add_field_person_nfb_membership__ch.py

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cschaffner/crunchsite

36c97a22a27c85a5e9450a90c57c5099f00db5bd

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refs/heads/master

2021-01-01T18:06:27.873728

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# -*- coding: utf-8 -*- from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding field 'Person.other_club' db.add_column(u'member_person', 'other_club', self.gf('django.db.models.fields.CharField')(max_length=100, null=True, blank=True), keep_default=False) # Adding field 'Person.nfb_membership' db.add_column(u'member_person', 'nfb_membership', self.gf('django.db.models.fields.NullBooleanField')(default=None, null=True, blank=True), keep_default=False) # Changing field 'Person.phone' db.alter_column(u'member_person', 'phone', self.gf('django.db.models.fields.CharField')(max_length=40, null=True)) def backwards(self, orm): # Deleting field 'Person.other_club' db.delete_column(u'member_person', 'other_club') # Deleting field 'Person.nfb_membership' db.delete_column(u'member_person', 'nfb_membership') # Changing field 'Person.phone' db.alter_column(u'member_person', 'phone', self.gf('django.db.models.fields.CharField')(max_length=20, null=True)) models = { u'auth.group': { 'Meta': {'object_name': 'Group'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, u'auth.permission': { 'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, u'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Group']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Permission']"}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'cms.placeholder': { 'Meta': {'object_name': 'Placeholder'}, 'default_width': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'slot': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}) }, u'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, u'member.memberjob': { 'Meta': {'object_name': 'MemberJob'}, 'end_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'job': ('django.db.models.fields.CharField', [], {'default': "'1PL'", 'max_length': '3'}), 'person': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'jobs'", 'to': u"orm['member.Person']"}), 'start_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}) }, u'member.person': { 'Meta': {'ordering': "['last_name']", 'object_name': 'Person'}, 'account_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'birthday': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), 'citizenship': ('django_countries.fields.CountryField', [], {'max_length': '2', 'null': 'True', 'blank': 'True'}), 'city': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.Placeholder']", 'null': 'True'}), 'discount': ('django.db.models.fields.CharField', [], {'default': "'1NO'", 'max_length': '3'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'gender': ('django.db.models.fields.CharField', [], {'default': "'1M'", 'max_length': '2'}), 'house_number': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'blank': 'True'}), 'house_number_extension': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}), 'iban': ('django_iban.fields.IBANField', [], {'max_length': '34', 'null': 'True', 'blank': 'True'}), 'iban_authorisation': ('django.db.models.fields.CharField', [], {'default': "'1OK'", 'max_length': '3'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'nfb_membership': ('django.db.models.fields.NullBooleanField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}), 'other_club': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'phone': ('django.db.models.fields.CharField', [], {'max_length': '40', 'null': 'True', 'blank': 'True'}), 'playing_level': ('django.db.models.fields.CharField', [], {'default': "'1DN'", 'max_length': '3'}), 'preposition': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}), 'street': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'user': ('django.db.models.fields.related.OneToOneField', [], {'blank': 'True', 'related_name': "'profile'", 'unique': 'True', 'null': 'True', 'to': u"orm['auth.User']"}), 'zip_code': ('django.db.models.fields.CharField', [], {'max_length': '7', 'null': 'True', 'blank': 'True'}) } } complete_apps = ['member']

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import dash import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output from app import app column1 = dbc.Col( [ dcc.Markdown( """ ## Process This web app is an assignment from Lambda School's Data Science program. The assignment asked to predict something given a dataset. In my case, I chose to predict video game sales. ### The Dataset In order to predict anything in data science, you need features - that is, ways we can influence the prediction. For instance, game genre is a feature because that could possibly impact game sales. As such, I needed a decent dataset with enough features. I found the needed dataset from this [Reddit post](https://old.reddit.com/r/datasets/comments/bco2rd/video_games_sales_2019_dataset/), which linked to it on Kaggle, a dataset-sharing website. The dataset contained over 50,000 video games, with game sales and relevant characteristics like genre, ESRB Rating, and critic scores. ### Cleaning the Dataset I loaded the dataset with Pandas, a python library that handles datasets. Here's a sample output of the dataset: """ ), html.Img(src='assets/InitialDataset.png', className='img-fluid'), dcc.Markdown( """ As you can see, there's a lot of data here, and some are irrelevant and/or need cleaning. For instance, `basename` is pretty much the same as `Name`. And while we have game sales, some are expressed in the column `Total_Shipped`, while others divide sales into various regions (`NA_Sales`, `EU_Sales`, `JP_Sales`). With some python magic, I removed completely irrelevant columns and merged total game sales in a single column. I also created a new column that said if the game sold over 100,000 copies and another column that averaged the critic and user scores (a form of feature engineering). In addition, I had to remove ~30,000 games from the list because they didn't have any game sales listed. ### Regression vs Classification You may be wondering, why predict if a game will sell over 100,000 copies instead of specifically saying how many copies the game will sell? To explain, have a look at this histogram: """ ), html.Img(src='assets/SalesHistogram.png', className='img-fluid'), dcc.Markdown( """ This dataset has many outliers. Many games don't sell 100,000 copies, and there are a few that sell tens of millions of copies. If I tried predicting exact game sales with mathematical models, it's very likely some predictions will shoot way higher than they should due to the extreme outliers. That's why I chose to go for a yes or no question (will the game sell 100k+). That way, I won't ever need to consider exact game sales in the model, and the results are more focused and less out of hand. In data science, this kind of question is a classification, while the former would be a regression. ### Making the Predictions With the dataset cleaned, it was time to decide which features to use for the prediction. Usually in data science, the more features, the better. But since this is a web app, I needed to condense the features so the user wouldn't get overwhelmed. For instance, instead of having dropdowns for the critic, user, and vgchartz scores, there's just one dropdown for the averaged score. In the end, I chose the seven features you see in the predictions page. As for the prediction model, I chose the random forest classifier because it led to the highest accuracy score. I explain how accuracy scores work in the Insights page. """ ), ], ) layout = dbc.Row([column1])

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TimTree@users.noreply.github.com

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2019-07-08T12:03:25

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def swap(a, i, j): a[i], a[j] = a[j], a[i] def select_sort(a): for i in range(len(a)): j = i + a[i:].index(min(a[i:])) swap(a, i, j) def sort_method(a): select_sort(a) return a

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2020-04-07T23:48:59.119376

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def computeHCF(x, y): if x > y: smaller = y else: smaller = x for i in range(1, smaller+1): if((x % i == 0) and (y % i == 0)): hcf = i return hcf num1 = int(input("Enter first number: ")) num2 = int(input("Enter second number: ")) print("The H.C.F. of", num1,"and", num2,"is", computeHCF(num1, num2))

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ffkirill/vertigo_billing

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from bootstrap.forms import BootstrapModelForm, BootstrapMixin class VertigoModelForm(BootstrapModelForm): def __init__(self, *args, **kwargs): super(VertigoModelForm, self).__init__(*args, **kwargs) for field in self.fields.values(): field.widget.attrs['class'] = 'form-control' class VertigoBootstrapMixin(BootstrapMixin): def __init__(self, *args, **kwargs): super(VertigoBootstrapMixin, self).__init__(*args, **kwargs) for field in self.fields.values(): field.widget.attrs['class'] = 'form-control'

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ff.kirill@gmail.com

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import numpy as np from norfair.camera_motion import TranslationTransformation from norfair.utils import warn_once class FixedCamera: """ Class used to stabilize video based on the camera motion. Starts with a larger frame, where the original frame is drawn on top of a black background. As the camera moves, the smaller frame moves in the opposite direction, stabilizing the objects in it. Useful for debugging or demoing the camera motion. ![Example GIF](../../videos/camera_stabilization.gif) !!! Warning This only works with [`TranslationTransformation`][norfair.camera_motion.TranslationTransformation], using [`HomographyTransformation`][norfair.camera_motion.HomographyTransformation] will result in unexpected behaviour. !!! Warning If using other drawers, always apply this one last. Using other drawers on the scaled up frame will not work as expected. !!! Note Sometimes the camera moves so far from the original point that the result won't fit in the scaled-up frame. In this case, a warning will be logged and the frames will be cropped to avoid errors. Parameters ---------- scale : float, optional The resulting video will have a resolution of `scale * (H, W)` where HxW is the resolution of the original video. Use a bigger scale if the camera is moving too much. attenuation : float, optional Controls how fast the older frames fade to black. Examples -------- >>> # setup >>> tracker = Tracker("frobenious", 100) >>> motion_estimator = MotionEstimator() >>> video = Video(input_path="video.mp4") >>> fixed_camera = FixedCamera() >>> # process video >>> for frame in video: >>> coord_transformations = motion_estimator.update(frame) >>> detections = get_detections(frame) >>> tracked_objects = tracker.update(detections, coord_transformations) >>> draw_tracked_objects(frame, tracked_objects) # fixed_camera should always be the last drawer >>> bigger_frame = fixed_camera.adjust_frame(frame, coord_transformations) >>> video.write(bigger_frame) """ def __init__(self, scale: float = 2, attenuation: float = 0.05): self.scale = scale self._background = None self._attenuation_factor = 1 - attenuation def adjust_frame( self, frame: np.ndarray, coord_transformation: TranslationTransformation ) -> np.ndarray: """ Render scaled up frame. Parameters ---------- frame : np.ndarray The OpenCV frame. coord_transformation : TranslationTransformation The coordinate transformation as returned by the [`MotionEstimator`][norfair.camera_motion.MotionEstimator] Returns ------- np.ndarray The new bigger frame with the original frame drawn on it. """ # initialize background if necessary if self._background is None: original_size = ( frame.shape[1], frame.shape[0], ) # OpenCV format is (width, height) scaled_size = tuple( (np.array(original_size) * np.array(self.scale)).round().astype(int) ) self._background = np.zeros( [scaled_size[1], scaled_size[0], frame.shape[-1]], frame.dtype, ) else: self._background = (self._background * self._attenuation_factor).astype( frame.dtype ) # top_left is the anchor coordinate from where we start drawing the fame on top of the background # aim to draw it in the center of the background but transformations will move this point top_left = ( np.array(self._background.shape[:2]) // 2 - np.array(frame.shape[:2]) // 2 ) top_left = ( coord_transformation.rel_to_abs(top_left[::-1]).round().astype(int)[::-1] ) # box of the background that will be updated and the limits of it background_y0, background_y1 = (top_left[0], top_left[0] + frame.shape[0]) background_x0, background_x1 = (top_left[1], top_left[1] + frame.shape[1]) background_size_y, background_size_x = self._background.shape[:2] # define box of the frame that will be used # if the scale is not enough to support the movement, warn the user but keep drawing # cropping the frame so that the operation doesn't fail frame_y0, frame_y1, frame_x0, frame_x1 = (0, frame.shape[0], 0, frame.shape[1]) if ( background_y0 < 0 or background_x0 < 0 or background_y1 > background_size_y or background_x1 > background_size_x ): warn_once( "moving_camera_scale is not enough to cover the range of camera movement, frame will be cropped" ) # crop left or top of the frame if necessary frame_y0 = max(-background_y0, 0) frame_x0 = max(-background_x0, 0) # crop right or bottom of the frame if necessary frame_y1 = max( min(background_size_y - background_y0, background_y1 - background_y0), 0 ) frame_x1 = max( min(background_size_x - background_x0, background_x1 - background_x0), 0 ) # handle cases where the limits of the background become negative which numpy will interpret incorrectly background_y0 = max(background_y0, 0) background_x0 = max(background_x0, 0) background_y1 = max(background_y1, 0) background_x1 = max(background_x1, 0) self._background[ background_y0:background_y1, background_x0:background_x1, : ] = frame[frame_y0:frame_y1, frame_x0:frame_x1, :] return self._background

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javier.berneche@gmail.com

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2015-01-26T03:41:46

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# -*- coding: utf-8 -*- import flask from flask.ext.wtf import Form from wtforms import TextField, HiddenField, IntegerField, BooleanField, validators class GreaterThan(object): """ Compares the value of two fields the value of self is to be greater than the supplied field. :param fieldname: The name of the other field to compare to. :param message: Error message to raise in case of a validation error. Can be interpolated with `%(other_label)s` and `%(other_name)s` to provide a more helpful error. """ def __init__(self, fieldname, message=None): self.fieldname = fieldname self.message = message def __call__(self, form, field): try: other = form[self.fieldname] except KeyError: raise validators.ValidationError(field.gettext(u"Invalid field name '%s'.") % self.fieldname) if field.data != '' and field.data < other.data: d = { 'other_label': hasattr(other, 'label') and other.label.text or self.fieldname, 'other_name': self.fieldname } if self.message is None: self.message = field.gettext(u'Field must be greater than %(other_name)s.') raise validators.ValidationError(self.message % d) class QuestionForm(Form): qtype = HiddenField('qtype', default='yesno') created_by = HiddenField('created_by') name = TextField('name') text = TextField('text') min_value = IntegerField('min_value', validators=[validators.Optional()], default=0) max_value = IntegerField('max_value', validators=[validators.Optional(), GreaterThan('min_value')], default=5) is_public = BooleanField('is_public', default=True) unlimited_number = BooleanField('unlimited_number', default=True)

[ "ihodes@mac.com" ]

ihodes@mac.com

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/Programas Python/Password(4).py

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[]

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EduardoMSA/Proyectos_ISC_ITESM

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refs/heads/master

2020-05-25T17:03:12.950148

2019-05-22T01:05:00

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# coding: utf-8 # In[13]: def Password(s): for i in range(len(s)-1,0,-1): if (s[i-1] != s[len(s)-1]) or (s[0]!=s[len(s)-i]): continue prefix = s[0:i] suffix = s[len(s)-i:len(s)] if prefix!=suffix: continue obelix = prefix in s[1:len(s)-1] if not obelix: continue return prefix return "Just a legend" s = input() print(Password(s))

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EduardoMSA.noreply@github.com

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[]

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JiajunChen123/IPO_under_review_crawler

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2023-02-26T08:23:09.622725

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import requests from bs4 import BeautifulSoup import time import csv import pandas as pd import numpy as np # login = {'user':'13710149700', # 'password':'123456'} # 使用的网站是企查查 # requests.post('https://www.qichamao.com',data=login,headers=afterLogin_headers) afterLogin_headers = {'Cookie':'qznewsite.uid=y4eseo3a1q4xbrwimor3o5tm; qz.newsite=6C61702DD95709F9EE190BD7CCB7B62C97136BAC307B6F0B818EC0A943307DAB61627F0AC6CD818268C10D121B37F840C1EF255513480EC3012A7707443FE523DD7FF79A7F3058E5E7FB5CF3FE3544235D5313C4816B54C0CDB254F24D8ED5235B722BCBB23BE62B19A2370E7F0951CD92A731FE66C208D1BE78AA64758629806772055F7210C67D442DE7ABBE138EF387E6258291F8FBF85DFF6C785E362E2903705A0963369284E8652A61531293304D67EBB8D28775FBC7D7EBF16AC3CCA96F5A5D17; Hm_lvt_55ad112b0079dd9ab00429af7113d5e3=1611805092,1612262918; Hm_lpvt_55ad112b0079dd9ab00429af7113d5e3=1612262927', 'Referer':'https://www.qichamao.com/', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36'} def get_compInfo(comp): r = requests.get('https://www.qichamao.com/search/all/{}'.format(comp),headers=afterLogin_headers) r.raise_for_status() r.encoding = 'utf-8' #linux utf-8 soup = BeautifulSoup(r.text,features="html.parser") url = 'http://www.qichamao.com' + soup.find(attrs={'class':'listsec_con'}).a['href'] # soup.find(attrs={'class':'listsec_con'}) time.sleep(5) rs = requests.get(url,headers=afterLogin_headers) rs.encoding='utf-8' soup2 = BeautifulSoup(rs.text,'html.parser') info = soup2.find(attrs={'class':'qd-table-body li-half f14'}).findAll('div') info = [i.get_text().strip() for i in info] compinfo = {'法定代表人':info[0], '纳税人识别号':info[1], '名称':info[2], '机构代码':info[3], '注册号':info[4], '注册资本':info[5], '统一社会信用代码':info[6], '登记机关':info[7], '经营状态':info[8], '成立日期':info[9], '企业类型':info[10], '经营期限':info[11], '所属地区':info[12], '核准时间':info[13], '企业地址':info[14], '经营范围':info[15]} return compinfo if __name__ == '__main__': import pickle with open('C:/Users/chen/Desktop/IPO_info/zb_zxb_stocksInfo.pkl', 'rb') as file: all_data = pickle.load(file) try: for i, (k, v) in enumerate(all_data.items()): if v['统一社会信用代码'] == '': compinfo = get_compInfo(v['机构名称']) v['统一社会信用代码'] = compinfo['统一社会信用代码'] v[i]['经营范围'] = compinfo['经营范围'] else: continue except: with open('C:/Users/chen/Desktop/IPO_info/zb_zxb_stocksInfo.pkl', 'rb') as file: pickle.dump(file,f, pickle.HIGHEST_PROTOCOL) # your stuff # df = pd.read_excel('C:/Users/chen/Desktop/IPO_info/P020210122657813200711.xls',skipfooter=1,skiprows=2,index_col='序号',keep_default_na=False,encoding='utf-8',sheet_name=0) # comp1 = df[' 企业名称'].values # df2 = pd.read_excel('C:/Users/chen/Desktop/IPO_info/P020210122657813200711.xls',skipfooter=1,skiprows=2,index_col='序号',keep_default_na=False,encoding='utf-8',sheet_name=1) # comp2 = df2[' 企业名称'].values # compList =np.append(comp1,comp2) # # for i in compList: # # compinfo = get_compInfo(i) # # csv_columns = ['法定代表人','纳税人识别号','名称','机构代码','注册号','注册资本','统一社会信用代码','登记机关',\ # # '经营状态','成立日期','企业类型','经营期限','所属地区','核准时间','企业地址','经营范围'] # # csv_file = "credit.csv" # # try: # # with open(csv_file, 'a+') as csvfile: # # writer = csv.DictWriter(csvfile, fieldnames=csv_columns) # # writer.writeheader() # # writer.writerow(compinfo) # # except IOError: # # print("I/O error") # try: # with open('C:/Users/chen/Desktop/IPO_info/csrc_dict.pkl', 'rb') as file: # csrc_dict = pickle.load(file) # except: # csrc_dict = {} # count = 0 # for i in compList: # count +=1 # i = i.replace(r'*','') # if i in data: # if i in csrc_dict and i['统一社会信用代码'] != '': # continue # try: # compinfo = get_compInfo(i) # data[i]['统一社会信用代码'] = compinfo['统一社会信用代码'] # data[i]['经营范围'] = compinfo['经营范围'] # csrc_dict.update(data[i]) # except: # print('cannot use anymore') # else: # print('cannot found value: ',i) # if count % 20 == 0: # time.sleep(60) # with open('C:/Users/chen/Desktop/IPO_info/csrc.pkl', 'rb') as file: # pickle.dump(csrc_dict, file, pickle.HIGHEST_PROTOCOL)

[ "chenjiajun.jason@outlook.com" ]

chenjiajun.jason@outlook.com

ccd8e606272604a00cf077ced256354b41d45c2b

350ea74735002ddeb22b6c8a6fa0dc7628bc2451

/engineer/unittests/config_tests.py

f2c87e033b667113f7c6cec2a6d953a9411bd9dc

[ "MIT", "BSD-3-Clause", "Apache-2.0" ]

permissive

pridkett/engineer

576b205c534ed0a9c81b44b3436f6610d870ccdc

2d0227f65fbd977cb84f138c043cdbf8f6ab5351

refs/heads/master

2021-01-18T05:42:25.439204

2012-12-06T20:47:51

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# coding=utf-8 import logging import os from path import path from testfixtures import LogCapture from engineer.log import bootstrap from engineer.plugins import load_plugins from engineer.unittests import CopyDataTestCase, SettingsTestCase __author__ = 'Tyler Butler <tyler@tylerbutler.com>' test_data_root = path(__file__).dirname() / 'test_data' simple_site = test_data_root / 'simple_site' class BaseTestCase(CopyDataTestCase): def setUp(self): bootstrap() #bootstrap logging infrastructure load_plugins() #load plugins self.source_path = simple_site os.chdir(self.copied_data_path) class TestConfig(BaseTestCase): def test_config_yaml(self): from engineer.conf import settings settings.reload('config.yaml') self.assertEqual(settings.SITE_TITLE, 'Test Config') self.assertEqual(settings.HOME_URL, '/') def test_global_settings(self): """All EngineerConfiguration instances share state""" from engineer.conf import settings as s1 from engineer.conf import EngineerConfiguration s2 = EngineerConfiguration() self.assertEqual(s1.SITE_TITLE, s2.SITE_TITLE) def test_manual_config_yaml(self): """Creating an EngineerConfiguration manually also shares state with configs created other ways""" from engineer.conf import settings as s1 from engineer.conf import EngineerConfiguration os.chdir(test_data_root) s2 = EngineerConfiguration('configs/config2.yaml') self.assertEqual(s1.SITE_TITLE, s2.SITE_TITLE) def test_config_inheritance(self): from engineer.conf import settings settings.reload('inheritance.yaml') self.assertEqual(settings.SITE_TITLE, 'Inheritance Test') self.assertEqual(settings.HOME_URL, '/') def test_config_inheritance_dicts(self): from engineer.conf import settings settings.reload('inheritance_dicts.yaml') expected = { 'key1': 'value1new', 'key2': 'value2', 'key3': 'value3' } self.assertEqual(settings.test_dict, expected) def test_deprecated_settings(self): from engineer.conf import settings with LogCapture('engineer.conf', level=logging.WARNING) as log_output: settings.reload('deprecated_settings.yaml') log_output.check( ('engineer.conf', 'CONSOLE', "Loading configuration from %s\deprecated_settings.yaml." % self.copied_data_path), ('engineer.conf', 'WARNING', "The 'NORMALIZE_INPUT_FILES' setting was deprecated in version 0.4: This " "setting is now ignored."), ('engineer.conf', 'WARNING', "The 'NORMALIZE_INPUT_FILE_MASK' setting was deprecated in version 0.4: " "This setting is now ignored.") )

[ "tyler@tylerbutler.com" ]

tyler@tylerbutler.com

de001f929d93ab043a3ecef62cd39654249ae9ba

f8a4fe5da0db0f857f70565930b439ea372ac945

/pbb/views/__init__.py

3104ccf92b8307f8b15679cfa6f241ef09f9488b

[]

no_license

aagusti/opensipkd-pbb-old

27dadf7526277662ae54179806f32d1e2b0926b5

333a0dc9dc58d0c0666386d7cbfe7a9a8a0e5096

refs/heads/master

2021-05-01T21:30:45.407063

2015-12-13T16:27:41

null

0

null

UTF-8

Python

false

5,236

py

from datetime import datetime from pyramid.view import view_config from pyramid.httpexceptions import ( HTTPFound, HTTPForbidden, ) from pyramid.security import ( remember, forget, authenticated_userid, ) import transaction import colander from deform import ( Form, ValidationFailure, widget, ) from ..tools import create_now from ..models import ( DBSession, User, ) ######## # Home # ######## @view_config(route_name='home', renderer='templates/home.pt', permission='view') def view_home(request): return dict(project='Opensipkd PBB') @view_config(route_name='home-auth', renderer='templates/home.pt', permission='view') def view_homeauth(request): return dict(project='Opensipkd PBB') ######### # Login # ######### class Login(colander.Schema): username = colander.SchemaNode(colander.String(), oid='username') password = colander.SchemaNode(colander.String(), widget=widget.PasswordWidget(), oid='password') # http://deformdemo.repoze.org/interfield/ def login_validator(form, value): user = form.user if not user: raise colander.Invalid(form, 'Login failed') if not user.user_password: raise colander.Invalid(form, 'Login failed') if not user.check_password(value['password']): raise colander.Invalid(form, 'Login failed') def get_login_headers(request, user): headers = remember(request, user.email) user.last_login_date = create_now() DBSession.add(user) DBSession.flush() transaction.commit() return headers @view_config(context=HTTPForbidden, renderer='templates/login.pt') @view_config(route_name='login', renderer='templates/login.pt') def view_login(request): if authenticated_userid(request): return HTTPFound(location=request.route_url('home')) schema = Login(validator=login_validator) form = Form(schema, buttons=('login',)) if 'login' in request.POST: controls = request.POST.items() identity = request.POST.get('username') user = schema.user = User.get_by_identity(identity) try: c = form.validate(controls) except ValidationFailure, e: return dict(form=form) #request.session['login failed'] = e.render() return HTTPFound(location=request.route_url('login')) headers = get_login_headers(request, user) return HTTPFound(location=request.route_url('home'), headers=headers) elif 'login failed' in request.session: r = dict(form=request.session['login failed']) del request.session['login failed'] return r return dict(form=form) #return dict(form=form.render()) @view_config(route_name='logout') def view_logout(request): headers = forget(request) return HTTPFound(location = request.route_url('home'), headers = headers) ################### # Change password # ################### class Password(colander.Schema): old_password = colander.SchemaNode(colander.String(), title="Kata Sandi Lama", widget=widget.PasswordWidget()) new_password = colander.SchemaNode(colander.String(), title="Kata Sandi Baru", widget=widget.PasswordWidget()) retype_password = colander.SchemaNode(colander.String(), title="Ketik Ulang Kata Sandi", widget=widget.PasswordWidget()) def password_validator(form, value): if not form.request.user.check_password(value['old_password']): raise colander.Invalid(form, 'Invalid old password.') if value['new_password'] != value['retype_password']: raise colander.Invalid(form, 'Retype mismatch.') @view_config(route_name='password', renderer='templates/password.pt', permission='view') def view_password(request): schema = Password(validator=password_validator) form = Form(schema, buttons=('simpan','batal')) if request.POST: if 'simpan' in request.POST: schema.request = request controls = request.POST.items() try: c = form.validate(controls) except ValidationFailure, e: request.session['invalid password'] = e.render() return HTTPFound(location=request.route_url('password')) user = request.user user.password = c['new_password'] DBSession.add(user) DBSession.flush() transaction.commit() #request.session.flash('Your password has been changed.') request.session.flash('Password telah berhasil dirubah.') return HTTPFound(location=request.route_url('reklame')) elif 'invalid password' in request.session: r = dict(form=request.session['invalid password']) del request.session['invalid password'] return r return dict(form=form.render())

[ "aa.gustiana@gmail.com" ]

aa.gustiana@gmail.com

3c9f7c4c6c272f5e17c9724a399749a113eaf730

c9b7782e6464d7d26e46825232daa51f41e2cd7b

/lista.py

f5cca0d01718a3f20a98e4c03f6f5e7999b5abd9

[]

no_license

A01377832/Mision_06

ea4a4d067350748340c5bb2432d3d6e972cb90e9

9021e0bf5c762250f7231e323ecf408c1e0743d7

refs/heads/master

2022-09-02T23:14:40.294809

2020-05-29T20:56:32

null

0

null

UTF-8

Python

false

4,147

py

#Autor: Ana Fernanda Martínez #Ejercicio con listas #Ejercicio 1 def recortarLista(lista): if len(lista)<=2: return [] nuevaLista= list(lista) último = nuevaLista [len(nuevaLista)-1] nuevaLista.remove(último) #Elimina el último dato primero = nuevaLista [0] nuevaLista.remove (primero) return nuevaLista #Ejercicio 2 def estanOrdenados (lista): #Copia nuevaLista = list(lista) nuevaLista.sort() #Ordenas la lista if nuevaLista == lista: return True return False #Ejercicio 3 def sonAnagramas(cadena1, cadena2): cadena1 = cadena1.upper() cadena2 = cadena2.upper() lista1 = list(cadena1) lista2 = list(cadena2) lista1.sort() if lista1==lista2: return True else: return False #Ejercicio 4 def hayDuplicados(lista): for dato in lista: if lista.count(dato)>=2: return True #termina y me da un resultado return False #Ejercicio 5 def borrarDuplicados(lista): while hayDuplicados(lista) == True: #Eliminar duplicados for k in range(len(lista)): dato =lista[k] veces = lista.count(dato) for n in range (veces-1): #Borra tantas veces menos 1 , como aparesca el valor en la lista owo lista.remove(dato) if veces>=2: break #Función principal def main (): #Ejercicio 1 print("Ejercicio 1: ") lista = [1,2,3,4,5] nuevaLista = recortarLista(lista) print("La lista", lista, "recortada es: ", nuevaLista) lista1_2 = [1,2] nuevaLista = recortarLista(lista1_2) print("La lista", lista1_2, "recortada queda así: ", nuevaLista) print("_____________________________") #Ejercicio 2 print("Ejercicio 2: ") lista2_1 = [1,2,3,4,5,6,7] print ("La secuencia", lista2_1) orden = estanOrdenados(lista2_1) if orden == True: print ("está ordenada") else: print ("no está ordenada") lista2_2 = [7,5,4,2] print ("La secuencia", lista2_2) orden = estanOrdenados(lista2_2) if orden == True: print ("está ordenada") else: print ("no está ordenada") print("_____________________________") #Ejercicio 3 print("Ejercicio 3: ") a = "roma" b = "amor" print(a, "y", b) if sonAnagramas (a, b) == True: print ("sí son anagramas") else: print ("no son anagramas") b = "anime" c= "calaca" print(b, "y", c) if sonAnagramas (b, c) == True: print ("sí son anagramas") else: print ("no son anagramas") print("_____________________________") #Ejercicio 4 print("Ejercicio 4: ") lista4_1 = [3,2,5,67,8,9,40] if hayDuplicados(lista4_1) == False: print("En la lista:",lista4_1 , "no tiene duplicados") else: print("En la lista",lista4_1 , "hay duplicados") lista4_2 = [2,4,55,60,55,1] if hayDuplicados(lista4_2) == False: print("En la lista", lista4_2, "no tiene duplicados") else: print("En la lista", lista4_2, "tiene duplicados") print("_____________________________") #Ejercicio 5 lista5 = [1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,] print("La lista original es: ", lista5) borrarDuplicados(lista5) print("Si los duplicados son eliminados es: ", lista5) lista5_2= [2,4,5,7,9,2] print("La lista original es: ", lista5_2) borrarDuplicados(lista5_2) print("Si los duplicados son eliminados es: ", lista5_2) print("_____________________________") main()

[ "noreply@github.com" ]

A01377832.noreply@github.com

d5b42b93b8afa5614f86641bad57d66c9d3db2c9

ed8e842c9813ccaf9eeef9b7446294ff2ac0716a

/cadpy/timetable/migrations/0005_auto_20200910_1915.py

870699b069c57d19712b453273f706bea95f4319

[]

no_license

shanesoysa/CAD

89353e1dfa6a0b1f4074f23bc3c57cb41cfd28ec

af32baa5f4b15ce990a97bc40ee561bd7c7ff40a

refs/heads/master

2023-04-14T05:27:49.879339

2020-10-12T18:29:26

286,243,466

2

0

null

2023-04-10T07:44:58

2020-08-09T13:34:20

HTML

UTF-8

Python

false

410

py

# Generated by Django 3.1 on 2020-09-10 13:45 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('timetable', '0004_auto_20200909_1511'), ] operations = [ migrations.AlterField( model_name='programme', name='programme_abbv', field=models.CharField(max_length=20, unique=True), ), ]

[ "rehani44perera@gmail.com" ]

rehani44perera@gmail.com

8920cfdf3f6ac9451b85fb30a81bb9da93c0f5fb

0022232ab0dc5382d596581357ffaaad16b526cc

/infra/backup.py

a2c6a52beba1706b7c0f4e36e40a923d567a1c30

[]

no_license

dr-natetorious/aws-emr-hive

1806231f9c2877629b361a3a38615c0c46d4878b

7ec8483e8fb270c1f7fe034780b55e4cd37485d8

refs/heads/master

2023-04-19T04:09:36.162220

2021-04-29T22:11:02

362,522,415

0

null

UTF-8

Python

false

1,777

py

from typing import List from infra.landing_zone import ILandingZone from aws_cdk import ( core, aws_backup as backup, aws_iam as iam, aws_kms as kms, aws_sns as sns, ) class BackupStrategyConstruct(core.Construct): def __init__(self, scope:core.Construct, id:str, landing_zone:ILandingZone, **kwargs): """ Configure Dns Resolver """ super().__init__(scope,id, **kwargs) region = core.Stack.of(self).region self.encryption_key = kms.Key(self,'EncryptionKey', description='Encryption Key for BackupStrategy') self.topic = sns.Topic(self,'Topic') self.role = iam.Role(self,'Role', description='Account Backup Role', assumed_by= iam.ServicePrincipal(service='backup')) self.vault = backup.BackupVault(self,'Vault', encryption_key=self.encryption_key, notification_topic= self.topic, backup_vault_name='{}-Backup-Vault'.format(landing_zone.zone_name), access_policy= iam.PolicyDocument( statements=[ iam.PolicyStatement( effect= iam.Effect.ALLOW, resources=["*"], actions=['backup:CopyIntoBackupVault'], principals= [ iam.ArnPrincipal(arn = self.role.role_arn) ]) ])) self.default_plan = backup.BackupPlan(self,'DefaultPlan', backup_vault= self.vault, backup_plan_name='Default Plan {} in {}'.format(landing_zone.zone_name, region), backup_plan_rules=[ backup.BackupPlanRule.daily(), backup.BackupPlanRule.weekly(), ]) self.default_plan.add_selection('SelectionPolicy', allow_restores=True, role=self.role, resources=[ backup.BackupResource.from_tag("landing_zone", landing_zone.zone_name), ])

[ "nate@bachmeier" ]

nate@bachmeier

2f7ac23001956b4c1523aab7ac6226d2da155d0f

db1aabc54998f99b9d77aafad167265c92394593

/hw13_train.py

a925acab2587f2d697d507b845c554d074c471de

[]

no_license

Stanwang1210/ML_HW13

02c252deff002f9272c3f088940c614f4b6d88be

0f3863781f0c5449116868ea2aef191d7a7576c8

refs/heads/master

2022-11-06T15:08:22.125551

2020-06-30T06:55:14

273,843,180

1

0

null

UTF-8

Python

false

146,116

py

# -*- coding: utf-8 -*- """ Created on Tue Jun 30 14:15:50 2020 @author: 王式珩 """ import sys workspace_dir = sys.argv[1]#'HW13_Data' model_path = sys.argv[2] """接著我們把 dataset 的檔案解壓縮因為檔案非常大，要等一下子，可以先執行解壓縮，同時看一下 model 的部分程式 """ #!tar -zxvf "{workspace_dir}/Omniglot.tar.gz" -C "{workspace_dir}/" #這行會印出解壓縮的所有檔案，因為很煩所以我註解掉了 """我們看一下 Omniglot 的 dataset 長什麼樣子""" from PIL import Image #from IPython.display import display #for i in range(10, 20): # im = Image.open("Omniglot/images_background/Japanese_(hiragana).0/character13/0500_" + str (i) + ".png") # display(im) """## **Step 2: 建立模型** 以下我們就要開始建立核心的 MAML 模型首先我們將需要的套件引入 """ # Import modules we need import os import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import DataLoader, Dataset import torchvision.transforms as transforms import glob from tqdm import tqdm import numpy as np from collections import OrderedDict """``` # This is formatted as code ``` 接著我們要建立一個 nn.Module，作為 omniglot 的分類器 (Classifier) 我們使用的是 CNN-based 的分類器。以下是 MAML 的演算法: ![image.png](data:image/png;base64,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) 由於在第10行，我們是要對原本的參數 θ 微分，並非 inner-loop (Line5~8) 的 θ' 微分，因此在 inner-loop，我們需要用 functional forward 的方式算出 input image 的 output logits，而不是直接用 nn.module 裡面的 forward（直接對 θ 微分）。在下面我們分別定義了 functional forward 以及 forward 函數。 """ def ConvBlock(in_ch, out_ch): return nn.Sequential(nn.Conv2d(in_ch, out_ch, 3, padding = 1), nn.BatchNorm2d(out_ch), nn.ReLU(), nn.MaxPool2d(kernel_size = 2, stride = 2)) # 原作者在 paper 裡是說她在 omniglot 用的是 strided convolution # 不過這裡我改成 max pool (mini imagenet 才是 max pool) # 這並不是你們在 report 第三題要找的 tip def ConvBlockFunction(x, w, b, w_bn, b_bn): x = F.conv2d(x, w, b, padding = 1) x = F.batch_norm(x, running_mean = None, running_var = None, weight = w_bn, bias = b_bn, training = True) x = F.relu(x) x = F.max_pool2d(x, kernel_size = 2, stride = 2) return x class Classifier(nn.Module): def __init__(self, in_ch, k_way): super(Classifier, self).__init__() self.conv1 = ConvBlock(in_ch, 64) self.conv2 = ConvBlock(64, 64) self.conv3 = ConvBlock(64, 64) self.conv4 = ConvBlock(64, 64) self.logits = nn.Linear(64, k_way) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.conv4(x) x = nn.Flatten(x) x = self.logits(x) return x def functional_forward(self, x, params): ''' Arguments: x: input images [batch, 1, 28, 28] params: 模型的參數，也就是 convolution 的 weight 跟 bias，以及 batchnormalization 的 weight 跟 bias 這是一個 OrderedDict ''' for block in [1, 2, 3, 4]: x = ConvBlockFunction(x, params[f'conv{block}.0.weight'], params[f'conv{block}.0.bias'], params.get(f'conv{block}.1.weight'), params.get(f'conv{block}.1.bias')) x = x.view(x.shape[0], -1) x = F.linear(x, params['logits.weight'] , params['logits.bias']) return x """這個函數是用來產生 label 的。在 n_way, k_shot 的 few-shot classification 問題中，每個 task 會有 n_way 個類別，每個類別k_shot張圖片。這是產生一個 n_way, k_shot 分類問題的 label 的函數""" def create_label(n_way, k_shot): return torch.arange(n_way).repeat_interleave(k_shot).long() # 我們試著產生 5 way 2 shot 的 label 看看 create_label(5, 2) """接下來這裡是 MAML 的核心。演算法就跟原文完全一樣，這個函數做的事情就是用 "一個 meta-batch的 data" 更新參數。這裡助教實作的是二階MAML(inner_train_step = 1)，對應老師投影片 meta learning p.13~p.18。如果要找一階的數學推導，在老師投影片 p.25。 (http://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2019/Lecture/Meta1%20(v6).pdf) 以下詳細解釋： """ def MAML(model, optimizer, x, n_way, k_shot, q_query, loss_fn, epcoh, inner_train_step = 1, inner_lr = 0.4, train = True): """ Args: x is the input omniglot images for a meta_step, shape = [batch_size, n_way * (k_shot + q_query), 1, 28, 28] n_way: 每個分類的 task 要有幾個 class k_shot: 每個類別在 training 的時候會有多少張照片 q_query: 在 testing 時，每個類別會用多少張照片 update """ criterion = loss_fn task_loss = [] # 這裡面之後會放入每個 task 的 loss task_acc = [] # 這裡面之後會放入每個 task 的 loss for meta_batch in x: train_set = meta_batch[:n_way*k_shot] # train_set 是我們拿來 update inner loop 參數的 data val_set = meta_batch[n_way*k_shot:] # val_set 是我們拿來 update outer loop 參數的 data fast_weights = OrderedDict(model.named_parameters()) # 在 inner loop update 參數時，我們不能動到實際參數，因此用 fast_weights 來儲存新的參數 θ' for inner_step in range(inner_train_step): # 這個 for loop 是 Algorithm2 的 line 7~8 # 實際上我們 inner loop 只有 update 一次 gradients，不過某些 task 可能會需要多次 update inner loop 的 θ'， # 所以我們還是用 for loop 來寫 train_label = create_label(n_way, k_shot).cuda() logits = model.functional_forward(train_set, fast_weights) loss = criterion(logits, train_label) grads = torch.autograd.grad(loss, fast_weights.values(), create_graph = True) # 這裡是要計算出 loss 對 θ 的微分 (∇loss) fast_weights = OrderedDict((name, param - inner_lr * grad) for ((name, param), grad) in zip(fast_weights.items(), grads)) # 這裡是用剛剛算出的 ∇loss 來 update θ 變成 θ' val_label = create_label(n_way, q_query).cuda() logits = model.functional_forward(val_set, fast_weights) # 這裡用 val_set 和 θ' 算 logit loss = criterion(logits, val_label) # 這裡用 val_set 和 θ' 算 loss task_loss.append(loss) # 把這個 task 的 loss 丟進 task_loss 裡面 acc = np.asarray([torch.argmax(logits, -1).cpu().numpy() == val_label.cpu().numpy()]).mean() # 算 accuracy task_acc.append(acc) model.train() if epoch % 2 == 0: optimizer.zero_grad() meta_batch_loss = torch.stack(task_loss).mean() # 我們要用一整個 batch 的 loss 來 update θ (不是 θ') if train: meta_batch_loss.backward() if epoch % 2 == 0: optimizer.step() task_acc = np.mean(task_acc) return meta_batch_loss, task_acc """定義 dataset。這個 dataset 會回傳某個 character 的 image，總共會有 k_shot+q_query 張，所以回傳的 tensor 大小是 [k_shot+q_query, 1, 28, 28]""" class Omniglot(Dataset): def __init__(self, data_dir, k_way, q_query): self.file_list = [f for f in glob.glob(data_dir + "**/character*", recursive=True)] self.transform = transforms.Compose([transforms.ToTensor()]) self.n = k_way + q_query def __getitem__(self, idx): sample = np.arange(20) np.random.shuffle(sample) # 這裡是為了等一下要 random sample 出我們要的 character img_path = self.file_list[idx] img_list = [f for f in glob.glob(img_path + "**/*.png", recursive=True)] img_list.sort() imgs = [self.transform(Image.open(img_file)) for img_file in img_list] imgs = torch.stack(imgs)[sample[:self.n]] # 每個 character，取出 k_way + q_query 個 return imgs def __len__(self): return len(self.file_list) """## **Step 3: 開始訓練** 定義 hyperparameter """ n_way = 5 k_shot = 1 q_query = 1 inner_train_step = 1 inner_lr = 0.4 meta_lr = 0.001 meta_batch_size = 32 max_epoch = 100 eval_batches = test_batches = 20 train_data_path = os.path.join(workspace_dir,'Omniglot/images_background/') test_data_path = os.path.join(workspace_dir,'Omniglot/images_evaluation/') """初始化 dataloader""" #dataset = Omniglot(train_data_path, k_shot, q_query) train_set, val_set = torch.utils.data.random_split(Omniglot(train_data_path, k_shot, q_query), [3200,656]) train_loader = DataLoader(train_set, batch_size = n_way, # 這裡的 batch size 並不是 meta batch size, 而是一個 task裡面會有多少不同的 # characters，也就是 few-shot classifiecation 的 n_way num_workers = 8, shuffle = True, drop_last = True) val_loader = DataLoader(val_set, batch_size = n_way, num_workers = 8, shuffle = True, drop_last = True) test_loader = DataLoader(Omniglot(test_data_path, k_shot, q_query), batch_size = n_way, num_workers = 8, shuffle = True, drop_last = True) train_iter = iter(train_loader) val_iter = iter(val_loader) test_iter = iter(test_loader) """初始化 model 和 optimizer""" meta_model = Classifier(1, n_way).cuda() optimizer = torch.optim.Adam(meta_model.parameters(), lr = meta_lr) loss_fn = nn.CrossEntropyLoss().cuda() """這是一個用來抓一個 meta-batch 的 data 出來的 function""" def get_meta_batch(meta_batch_size, k_shot, q_query, data_loader, iterator): data = [] for _ in range(meta_batch_size): try: task_data = iterator.next() # 一筆 task_data 就是一個 task 裡面的 data，大小是 [n_way, k_shot+q_query, 1, 28, 28] except StopIteration: iterator = iter(data_loader) task_data = iterator.next() train_data = task_data[:, :k_shot].reshape(-1, 1, 28, 28) val_data = task_data[:, k_shot:].reshape(-1, 1, 28, 28) task_data = torch.cat((train_data, val_data), 0) data.append(task_data) return torch.stack(data).cuda(), iterator """開始 train!!!""" for epoch in range(max_epoch): print("Epoch %d" %(epoch)) train_meta_loss = [] train_acc = [] for step in tqdm(range(len(train_loader) // (meta_batch_size))): # 這裡的 step 是一次 meta-gradinet update step x, train_iter = get_meta_batch(meta_batch_size, k_shot, q_query, train_loader, train_iter) meta_loss, acc = MAML(meta_model, optimizer, x, n_way, k_shot, q_query, loss_fn, epcoh = epoch) train_meta_loss.append(meta_loss.item()) train_acc.append(acc) print(" Loss : ", np.mean(train_meta_loss)) print(" Accuracy: ", np.mean(train_acc)) # 每個 epoch 結束後，看看 validation accuracy 如何 # 助教並沒有做 early stopping，同學如果覺得有需要是可以做的 val_acc = [] for eval_step in tqdm(range(len(val_loader) // (eval_batches))): x, val_iter = get_meta_batch(eval_batches, k_shot, q_query, val_loader, val_iter) _, acc = MAML(meta_model, optimizer, x, n_way, k_shot, q_query, loss_fn, epcoh = epoch, inner_train_step = 3, train = False) # testing時，我們更新三次 inner-step val_acc.append(acc) print(" Validation accuracy: ", np.mean(val_acc)) torch.save(meta_model.state_dict(), os.path.join(model_path, 'model_b07701209.bin'))

[ "ch995308@gmail.com" ]

ch995308@gmail.com

b6aae62636e47dfa3e6947450b42fa9406b95b58

818d3556aaf830f7a0711dea79c44f22a5d6a69e

/catalog/admin.py

0f7b15d1b76d9b889477e8d80eda953e19129a17

[]

no_license

kelyip99/django_local_library

f3b1580535f811494e4332e5bf6edfa1302f985e

1f3fbcc247ba606f1fa872b2d3ceb353e4d5aa59

refs/heads/master

2020-05-30T11:12:11.991642

2019-06-01T06:27:38

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from django.contrib import admin # Register your models here. from catalog.models import Author, Genre, Book, BookInstance #admin.site.register(Book) #admin.site.register(Author) admin.site.register(Genre) #admin.site.register(BookInstance) # Define the admin class @admin.register(Author) class AuthorAdmin(admin.ModelAdmin): list_display = ('last_name', 'first_name', 'date_of_birth', 'date_of_death') fields = ['first_name', 'last_name', ('date_of_birth', 'date_of_death')] class BooksInstanceInline(admin.TabularInline): model = BookInstance # Register the Admin classes for Book using the decorator @admin.register(Book) class BookAdmin(admin.ModelAdmin): list_display = ('title', 'author', 'display_genre') inlines = [BooksInstanceInline] # Register the Admin classes for BookInstance using the decorator @admin.register(BookInstance) class BookInstanceAdmin(admin.ModelAdmin): list_filter = ('status', 'due_back') fieldsets = ( (None, { 'fields': ('book', 'imprint', 'id') }), ('Availability', { 'fields': ('status', 'due_back') }), )

[ "kelyip@gmail.com" ]

kelyip@gmail.com

320d64cb2e3c2d21af72fd2be18bd590d13d625b

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/file_instance.py

c2dc80e103466be521531bfa102e04b16fa701d9

[]

no_license

jordsti/sufs

11f8c8f5f714761f3884675b8f63c88229cc25cb

b1838972e08777678587c2c717db11eb023f00ea

refs/heads/master

2016-09-06T21:12:35.329764

2014-07-21T22:02:45

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__author__ = 'JordSti' import network import block class file_block(block.block): def __init__(self, parent_hash, block_id, data=None): block.block.__init__(self, parent_hash, block_id) self.data = data self.length = len(data) class file_instance: (DefaultBlockSize) = 1024 def __init__(self, file_entry, block_size=DefaultBlockSize): self.entry = file_entry self.block_size = block_size self.__blocks = [] self.length = 0 self.hash = self.entry.get_hash() #todo need to verify that hash maybe. ? self.__load_blocks() def generate_file_info_packet(self): p = network.packet() p.header = network.packet_header() p.header.packet_type = network.packet_header.FileInformation p.header.fields['name'] = self.entry.name p.header.fields['hash'] = self.hash p.header.fields['length'] = self.length b_str = "" for b in self.each_blocks_length(): b_str += "%d," % b b_str = b_str.rstrip(',') p.header.fields['blocks'] = b_str return p def blocks_count(self): return len(self.__blocks) def each_blocks_length(self): lengths = [] for b in self.__blocks: lengths.append(b.length) return lengths def get_block(self, b_i): return self.__blocks[b_i] def get_block_packet(self, b_i): b = self.__blocks[b_i] p = network.packet() p.header = network.packet_header() p.header.packet_type = network.packet_header.FileBlock p.header.length = b.length p.header.fields['block_id'] = b.block_id p.header.fields['parent_hash'] = self.hash p.bytes = b.data #print p.to_string() return p def __load_blocks(self): fp = open(self.entry.get_fullpath(), 'rb') chunk = fp.read(self.block_size) self.length = 0 b_i = 0 while len(chunk) == self.block_size: self.length += self.block_size block = file_block(self.hash, b_i, chunk) self.__blocks.append(block) chunk = fp.read(self.block_size) b_i += 1 if len(chunk) > 0: block = file_block(self.hash, b_i, chunk) self.length += len(chunk) self.__blocks.append(block) fp.close()

[ "jord52@gmail.com" ]

jord52@gmail.com

273b210bcebf54dd3ed4b1884abd6bb9070894cb

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/pyshadowsocks/protocol/shadowsocks/client.py

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[ "MIT" ]

permissive

FTwOoO/pyShadowsocks

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refs/heads/master

2021-01-18T00:00:28.969382

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#!/usr/bin/env python # -*- coding: utf-8 -*- # # Author: booopooob@gmail.com # # Info: asyncio - Stream<https://docs.python.org/3/library/asyncio-stream.html#asyncio-tcp-echo-server-streams> # - Transport<https://docs.python.org/3/library/asyncio-protocol.html> # from protocol.COMMON.common_client_relay_protocol import CommonClientRelayProtocol from protocol.shadowsocks.encoder import ShadowsocksEncryptionWrapperEncoder class ShadowsocksClientRelayProtocol(CommonClientRelayProtocol): def create_encoder(self): return ShadowsocksEncryptionWrapperEncoder( encrypt_method=self.config.cipher_method, password=self.config.password, encript_mode=True) def create_decoder(self): return ShadowsocksEncryptionWrapperEncoder( encrypt_method=self.config.cipher_method, password=self.config.password, encript_mode=False)

[ "booopooob@gmail.com" ]

booopooob@gmail.com

a7cbe793db0ef9035c8f238811ce432679915a9c

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/multipage_backup/app_pages/app3.py

1cffbceaab49f46c3814e92f1b8c79e4fb9dac48

[]

no_license

kestefon/dev

a18c33e18a8ee8ffe41349b3d8441b28fead9b64

06f5045aa051e01eae1d794a3292c5e1d2292e42

refs/heads/master

2020-04-07T04:08:18.964640

2019-03-14T04:28:28

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import dash import dash_core_components as dcc import dash_html_components as html layout = html.Div([ html.H3(children="App 3"), dcc.Link('Go to App 1', href='/page-1'), dcc.Link('Go to App 2', href='/page-2'), dcc.Link('Go to App 3', href='/page-3') ])

[ "kestefon@gmail.com" ]

kestefon@gmail.com

55ac6d7265c63689a96ee072219e7ee700d94fa1

eae704ccddad3e7774b8de47e6620aa55706be97

/Capture.py

56ea8be4a12733ccf1bdd3f8384d5d7b559e5203

[]

no_license

mixify/Ptolemy

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refs/heads/master

2020-04-27T17:36:40.288571

2019-06-18T12:31:13

174,528,644

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2019-03-08T11:48:06

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import numpy as np from PIL import ImageGrab import cv2 import time def process_img(image): original_image = image ##convert to gray processed_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) ##edge detection processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300) return processed_img def main(): last_time = time.time() while (True): screen = np.array(ImageGrab.grab()) ##print('look took {} seconds'.format(time.time()-last_time)) last_time = time.time() new_screen = process_img(screen) cv2.imshow('window', new_screen) ## ##cv2.imshow('window', cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)) if cv2.waitKey(25) & 0xFF == ord('q'): cv2.destroyAllWindows() break main()

[ "ohsg0315@naver.com" ]

ohsg0315@naver.com

52b20659db76ef82f748aba5040175eb212060a5

57094f0d09fd3e74eeb511e94400c3ec97051ad3

/Quax_dev_archive/integrals_dev/tei_trials/teis_trial2/custom_boys/primitive_trial2.py

b6f5e70300c1b4aa596fff3f12bbfd0543cd8f18

[]

no_license

adabbott/Research_Notes

cccba246e81065dc4a663703fe225fc1ebbf806b

644394edff99dc6542e8ae6bd0ce8bcf158cff69

refs/heads/master

2023-05-12T20:26:58.938617

2021-06-02T17:15:35

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import jax import jax.numpy as np import numpy as onp from functools import partial from jax.config import config; config.update("jax_enable_x64", True) _float = {onp.floating, jax.dtypes.bfloat16} def boys(x): return boys_p.bind(x) boys_p = jax.core.Primitive('boys') # evalutation rule of primitive def boys_eval(x): x = x + 1e-12 return 0.88622692545275798 * jax.lax.rsqrt(x) * jax.lax.erf(jax.lax.sqrt(x)) #def boys_jvp_rule(g, x): # tmp = boys(x) # result = jax.lax.select(x < 1e-8, (-0.3333333333333333333) + + (2 * x * 0.1) + -(3 * x**2 * 0.023809523809523808) + (4 * x**3 * 0.004629629629629629), # jax.lax.div(-jax.lax.sub(tmp, jax.lax.exp(-x)), jax.lax.mul(jax.lax._const(x,2), x))) # return result #def boys_jvp_rule(g, x): # tmp = boys(x) # result = jax.lax.select(x < 1e-8, (-0.3333333333333333333) + (2 * x * 0.1) + -(3 * x**2 * 0.023809523809523808) + (4 * x**3 * 0.004629629629629629), # jax.lax.div(-jax.lax.sub(tmp, jax.lax.exp(-x)), jax.lax.mul(jax.lax._const(x,2), x))) # return result def boys_jvp_rule(g, ans, x): result = jax.lax.select(x < 1e-8, (-0.3333333333333333333) + (2 * x * 0.1) + -(3 * x**2 * 0.023809523809523808) + (4 * x**3 * 0.004629629629629629), jax.lax.div(-jax.lax.sub(ans, jax.lax.exp(-x)), jax.lax.mul(jax.lax._const(x,2), x))) return result def f_vjp(x): return boys(x), lambda g: (2 * g * x,) jax.lax.lax.standard_unop(_float, 'boys') boys_p.def_impl(boys_eval) #jax.interpreters.ad.defjvp(boys_p, boys_jvp_rule) # okay, defjvp2 assumes 3 arguments: tangent, result of original function, function argument jax.interpreters.ad.defjvp2(boys_p, boys_jvp_rule) jax.interpreters.ad.defvjp(boys_p, f_vjp) print(boys(0.5)) print(jax.jacfwd(boys)(0.5)) print(jax.jacrev(boys)(0.5))

[ "adabbott@uga.edu" ]

adabbott@uga.edu

c236f001913a5f71151ca7fa4dfda4d570104b86

42b71380ef5ea0fe904127bef483d7854facbd68

/blog/models.py

0eb6108265978aa2533da7c273d1edf8c25e73a8

[]

no_license

akash2415/my-first-blog

86247b78ca35e1d1fe9eb859a73e9becdfcac3bb

f653dbaa8018f5c0175e43138f7251e33e1ab526

refs/heads/master

2020-03-14T03:57:51.272942

2018-05-30T17:00:31

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from django.db import models from django.utils import timezone class Post(models.Model): author = models.ForeignKey('auth.User', on_delete=models.CASCADE) title = models.CharField(max_length=200) text = models.TextField() created_date = models.DateTimeField(default=timezone.now) published_date = models.DateTimeField(blank = True, null=True) def publish(self): self.published_date = timezone.now() self.save() def __str__(self): return self.title

[ "akash2415@gmail.com" ]

akash2415@gmail.com

a460fa5c367496aefb344eb50f9975890e0e03f5

c1eb833c4164b6d411cecc2d18edb959971b1395

/apps/operations/migrations/0001_initial.py

b93de47e36fbf0bc576c69924e7fada22253fd8c

[]

no_license

Ylrving/Django_sxonline

90c848fdf36534509d7f75cd4bd5d4ee5587a95d

5f360ae2db59960e21793fa4a79e2a63892c038d

refs/heads/master

2020-05-23T22:53:08.894486

2019-05-16T09:00:04

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UTF-8

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# -*- coding: utf-8 -*- # Generated by Django 1.9 on 2019-05-08 14:07 from __future__ import unicode_literals import datetime from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ('course', '0001_initial'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='CourseComments', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('comments', models.CharField(max_length=200, verbose_name='评论')), ('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')), ('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='course.Course', verbose_name='课程')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')), ], options={ 'verbose_name': '课程评论', 'verbose_name_plural': '课程评论', }, ), migrations.CreateModel( name='UserAsk', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=20, verbose_name='姓名')), ('mobile', models.CharField(max_length=11, verbose_name='手机')), ('course_name', models.CharField(max_length=50, verbose_name='课程名')), ('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')), ], options={ 'verbose_name': '用户咨询', 'verbose_name_plural': '用户咨询', }, ), migrations.CreateModel( name='UserCourse', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')), ('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='course.Course', verbose_name='课程')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')), ], options={ 'verbose_name': '用户课程', 'verbose_name_plural': '用户课程', }, ), migrations.CreateModel( name='UserFavorite', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('fav_id', models.IntegerField(default=0, verbose_name='数据id')), ('fav_type', models.IntegerField(choices=[(1, '课程'), (2, '课程机构'), (3, '讲师')], default=1, verbose_name='收藏类型')), ('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')), ], options={ 'verbose_name': '用户收藏', 'verbose_name_plural': '用户收藏', }, ), migrations.CreateModel( name='UserMessage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('user', models.IntegerField(default=0, verbose_name='接受用户')), ('message', models.CharField(max_length=500, verbose_name='消息内容')), ('has_read', models.BooleanField(default=False, verbose_name='是否已读')), ('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')), ], options={ 'verbose_name': '用户消息', 'verbose_name_plural': '用户消息', }, ), ]

[ "670823709@qq.com" ]

670823709@qq.com

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/random_problems/delete_str_repeats.py

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[]

no_license

razzlepdx/practice-algorithms

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ef8c5c5ea2340d698aafcd6f42b6153019428e24

refs/heads/master

2020-03-06T20:45:39.684838

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''' QUESTION: Given a string as your input, delete any reoccurring character, and return the new string. ''' # strategy 1 - iterate over string, utilize dictionary def delete_repeat_chars(phrase): """ Given a string, returns a new string that contains only the first occurance of each letter. """ final_str = '' letters = {} for char in phrase: if letters.get(char): # if already in dict, move on to next letter continue # otherwise, add char to dictionary and append to final_str letters[char] = True final_str += char return final_str print(delete_repeat_chars("aabbcc")) #------------------------------------------- # optimization - use set instead of dictionary, since value is not pertinent def del_repeat_chars(phrase): """ Given a string, returns a new string that contains only the first occurance of each letter. """ final_str = '' letters = set() for char in phrase: if char not in letters: letters.add(char) final_str += char return final_str print(del_repeat_chars("aabbcc")) #------------------------------------------ # strategy 2 - split string, setify, and rejoin to return new string def del_repeat_letters(phrase): """ Given a string, returns a new strings that contains only one occurance of each letter. """ final = set(list(phrase)) return "".join(final) print(del_repeat_letters("aabbccdd")) # cons: does not maintain order, no memory benefit

[ "rwilson.or@gmail.com" ]

rwilson.or@gmail.com

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/Python/3sum.py

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gitPratikSingh/LeetCode-Solutions

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refs/heads/master

2021-01-25T11:14:25.071566

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class Solution: def threeSum(self, nums): """ :type nums: List[int] :rtype: List[List[int]] """ nums.sort() res = [] for i in range(len(nums)): if i>0 and nums[i]==nums[i-1]: continue j=i+1 k=len(nums)-1 while j<k: vsum = nums[i] + nums[j] + nums[k] if vsum == 0: res.append([nums[i], nums[j], nums[k]]) numj = nums[j] while j<k and numj == nums[j]: j += 1 numk = nums[k] while k>j and numk == nums[k]: k -= 1 elif vsum>0: k -= 1 else: j += 1 return res

[ "noreply@github.com" ]

gitPratikSingh.noreply@github.com

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/openai/mybalance.py

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[]

no_license

flaska/tensorflow

9a2704334a08b97aaddaaece9b28356f79800f43

0606858d3e9a0bfc1c2e46da19645709ccd8a023

refs/heads/master

2021-09-07T22:08:38.786701

2018-03-01T21:24:24

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import gym import random import numpy import tflearn from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.estimator import regression from statistics import mean, median from collections import Counter LR = 1e-3 env = gym.make('CartPole-v0') env.reset(); goal_steps = 500 score_requirement = 50 initial_games = 10000 def random_games(): for episode in range(5): env.reset(); for t in range(goal_steps): env.render() # performance killer action = env.action_space.sample() # takes random action observation, reward, done, info = env.step(action) print(observation) if (done): break #random_games(); def initial_population(): training_data = [] for _ in range(initial_games): score = 0 game_memory = [] for _ in range(goal_steps) action = random.randrange(0,2) observation, reward, done, info = env.step(action) game_memory.append([observation, action]) score += reward if done: break if score >= score_requirement training_data.append([data[0], output]) env.reset() scores.append(score) training_data_save = np.array(training_data) np.save('saved.npy', training_data_save) print('Average accepted score', mean(accepted_scores)) print('Median accepted score', median(accepted_scores)) print(Counter(accepted_scores))

[ "jakub.flaska@nih.gov" ]

jakub.flaska@nih.gov

4c8cb1e79b3b034781b6d1a4bfdeb60f2c91a057

63432112bb1f72465e04771dbdabb050a44ce025

/eventanalyzer/management/commands/reportsexecute.py

ce93a426931724b598b16ca398b7d2523dbc3333

[]

no_license

MichalMaM/Django-event-analyzer

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e2181b0dbbb16c6a4748c94bbc5197cc62aca2ea

refs/heads/master

2021-01-01T19:23:54.018055

2011-04-20T09:13:39

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py

""" This file execute reports that are saved in db """ from django.core.management.base import BaseCommand, CommandError from eventanalyzer.jobs import create_reports class Command(BaseCommand): args = '<>' help = 'execute saved periodic reports' def handle(self, *args, **options): """ execute saved periodic reports """ if not create_reports(): raise CommandError('error - in execute priodic reports') print'execute reports successfull'

[ "michal.dub@centrumholdings.com" ]

michal.dub@centrumholdings.com

ef48691497b882b63085622f491b55a4f665df1c

c6f06ee1506c064c12ba55d7a90f4f148fd5127a

/flask-api/app.py

574718689dc4432b1e048d4e67dd954c77db1ef2

[]

no_license

ashleylobo/Restaurant-Recommendation

2fc6f0168c8c038a4759d0895674e3bbe2cb3cdf

6e1251515e1fe98121070b77e650fa068a056b0e

refs/heads/master

2022-12-10T21:48:23.125071

2019-04-21T17:53:55

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2022-12-08T04:56:41

2019-04-02T15:18:03

Jupyter Notebook

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import flask import os from flask import jsonify, request from flask import flash, redirect, url_for, session from joblib import load from flask_cors import CORS, cross_origin import requests, json import pandas as pd import requests predictions = pd.read_csv("predictionApi.csv",index_col=0) prof = pd.read_csv("profileApi.csv") hotelData = pd.read_csv("hotels-dataset.csv") hotelData.set_index('id', inplace=True) prof.set_index('userID', inplace=True) app = flask.Flask(__name__) app.config["DEBUG"] = True app.secret_key = 'super secret key' cors = CORS(app, resources={r"/*": {"origins": "*"}}) @app.route('/test', methods=['GET','POST']) def test(): print(prof.head()) data = [ 1 , 2 , "Buckle My Shoe" , 3 , 4 , "Shut the Door" ] return jsonify( data ) @app.route('/predict', methods=['GET']) def predict(): # print( json.dumps( request.json['data'] ) ) try : print("hi") user = request.args.get('user') myProfile = { "U1001" : { "name" : "Riya Patil", "password" : "123" }, "U1002" : { "name" : "Prachiti Patil", "password" : "123" }, "U1003" : { "name" : "Amey Patil", "password" : "123" }, "U1004" : { "name" : "Priyanka Patil", "password" : "123" } } userData = ( prof.loc[user , :].to_json() ) userData = json.loads(userData.replace("\'", '"')) hotelList = predictions[user] # print(hotelList[:10]) arr = ( hotelList.sort_values(ascending=False)[:10].index ) # print(arr) hotels = hotelData.loc[ arr , : ].to_dict('records') # hotels = json.loads(hotels.replace("\'", '"')) print(hotels) # hot = {} # i = 0 # for row in hotels: # hot[i] = row # i += 1 # # print(hot) return jsonify( { "userData" : userData, "profile" : myProfile[user],"hotels" : hotels , "status" : True } ) except Exception as e: return jsonify( { "result" : "error" , "status" : False } ) @app.route('/', methods=['GET']) def home(): print("loaded") return "Welcome to My API" if __name__ == '__main__': app.run()

[ "yenwiikae@gmail.com" ]

yenwiikae@gmail.com

3a31fc2dd4edca530b34791eb9d6e5597e8e55b0

9ae6ce54bf9a2a86201961fdbd5e7b0ec913ff56

/google/ads/googleads/v9/services/services/ad_group_feed_service/transports/__init__.py

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[ "Apache-2.0" ]

permissive

GerhardusM/google-ads-python

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refs/heads/master

2022-07-06T19:05:50.932553

2022-06-17T20:41:17

207,535,443

0

Apache-2.0

2019-09-10T10:58:55

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# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from collections import OrderedDict from typing import Dict, Type from .base import AdGroupFeedServiceTransport from .grpc import AdGroupFeedServiceGrpcTransport # Compile a registry of transports. _transport_registry = ( OrderedDict() ) # type: Dict[str, Type[AdGroupFeedServiceTransport]] _transport_registry["grpc"] = AdGroupFeedServiceGrpcTransport __all__ = ( "AdGroupFeedServiceTransport", "AdGroupFeedServiceGrpcTransport", )

[ "noreply@github.com" ]

GerhardusM.noreply@github.com

932ec679b52ef439e98647e518026b2e061da703

c13e453358cc87050fd196319576fbc392fbeee3

/Generator/genProgramming/p10.py

125b038e6a3c2850db9e16eb8de2b75cb30d797f

[]

no_license

ChrisJaunes/generating_test_paper

4eabcc60a28318ec24901e220a268e0dccaf69ce

e29eb725331cea807b3aeef908ab9c9e552b0ec4

refs/heads/main

2023-04-22T18:44:27.629498

2021-05-11T08:47:56

362,047,858

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null

UTF-8

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import random import string from sample import ProgrammingProblemDesc, ProgrammingProblemSTD def generatorProblemDesc() -> ProgrammingProblemDesc: return ProgrammingProblemDesc("将数字用字符代替", """ 给你一个下标从 0开始的字符串 s，它的偶数下标处为小写英文字母，奇数下标处为数字。定义一个函数shift(c, x)，其中c是一个字符且x是一个数字，函数返回字母表中c后面第 x个字符。比方说，shift('a', 5) = 'f'和shift('x', 0) = 'x'。对于每个奇数下标i，你需要将数字s[i] 用shift(s[i-1], s[i])替换。请你替换所有数字以后，将字符串 s返回。题目保证shift(s[i-1], s[i])不会超过 'z'。示例 1：输入：a1c1e1 输出：abcdef 解释：数字被替换结果如下： - s[1] -> shift('a',1) = 'b' - s[3] -> shift('c',1) = 'd' - s[5] -> shift('e',1) = 'f' 示例 2：输入：a1b2c3d4e 输出：abbdcfdhe 解释：数字被替换结果如下： - s[1] -> shift('a',1) = 'b' - s[3] -> shift('b',2) = 'd' - s[5] -> shift('c',3) = 'f' - s[7] -> shift('d',4) = 'h' 提示： 1 <= s.length <= 100 s只包含小写英文字母和数字。对所有奇数下标处的i，满足shift(s[i-1], s[i]) <= 'z'。 """) def generatorProblemSTD() -> ProgrammingProblemSTD: return ProgrammingProblemSTD("Java", """ import java.io.*; import java.util.*; public class Solution { static public String replaceDigits(String s) { char[] a=s.toCharArray(); char temp='a'; for(int i=0;i<a.length;i++){ if(a[i] <48 || a[i] >57 ){ temp=a[i]; }else{ a[i] = (char)(temp + Integer.parseInt(a[i]+"")); } } return String.valueOf(a); } public static void main(String[] args) { Scanner in = new Scanner(System.in); String s = in.next(); System.out.println(replaceDigits(s)); } } """) def generatorProblemTestSingle(f, seed: int = 2021): random.seed(seed) n = random.randint(1, 100) mix_string = [] for i in range(n): if i % 2: mix_string.append(random.sample('0123456789', 1)[0]) else: mix_string.append(random.choice(string.ascii_lowercase)) print("".join(temp for temp in mix_string), file=f)

[ "201930343469@mail.scut.edu.cn" ]

201930343469@mail.scut.edu.cn

9d7abb05c706a4078cc1f7b3d0acded44fe1087d

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/python/week 1/3day/exp/ex1.py

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[]

no_license

esthergoldman/DI_Bootcamp

54fe2e140858600dcf33a46a954ed9221f8524b7

2f02ceb349e8239bb46ffcccfd4a15b1213e3656

refs/heads/master

2023-04-09T14:12:36.747406

2021-04-17T07:50:55

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keys = ['Ten', 'Twenty', 'Thirty'] values = [10, 20, 30] zip_list = zip(keys,values) print(zip_list) num_dict = {} for info in zip_list: #print(info) num_dict[info[0]] = info[1] print(num_dict)

[ "esthergoldman@gmail.com" ]

esthergoldman@gmail.com

4be43c66932644e65085748949e25ae90c6a63b2

8ce09769f4d9858cb02f5263a75d12ea11b68c65

/venv/bin/distro

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[]

no_license

rabbanimd/ipFinder-webapp-Flask-RESTapi

50c16ae321cdb3a358b6734d0cb83df3aceba880

247e0e38e729b8be6648a8d34d147dbf449df2ff

refs/heads/master

2023-01-14T06:11:11.039045

2020-11-15T13:08:32

312,975,537

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#!/home/c0rt3s/PycharmProjects/flaskProject/venv/bin/python # -*- coding: utf-8 -*- import re import sys from distro import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "c0rt3s@k4l1.k4l1" ]

c0rt3s@k4l1.k4l1

2f64e51170ad09aae09260381e2fcf5f84d80675

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/src/cormorant/models/cormorant_lep.py

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[]

no_license

drorlab/cormorant

e6a196320dd8ca124283aa602442d639b4a4682c

3920a8f453c9c71c09b20c92c99fd0e9eec265ce

refs/heads/master

2023-05-08T03:04:59.271118

2021-05-15T08:02:18

265,646,271

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2020-05-20T17:59:50

2020-05-20T17:59:49

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UTF-8

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import torch import torch.nn as nn import logging from cormorant.cg_lib import CGModule, SphericalHarmonicsRel from cormorant.models.cormorant_cg import CormorantCG from cormorant.nn import RadialFilters from cormorant.nn import InputLinear, InputMPNN from cormorant.nn import OutputLinear, OutputPMLP, OutputSoftmax, GetScalarsAtom from cormorant.nn import NoLayer class CormorantLEP(CGModule): """ Basic Cormorant Network used to train on BDBBind. Parameters ---------- maxl : :obj:`int` of :obj:`list` of :obj:`int` Maximum weight in the output of CG products. (Expanded to list of length :obj:`num_cg_levels`) max_sh : :obj:`int` of :obj:`list` of :obj:`int` Maximum weight in the output of the spherical harmonics (Expanded to list of length :obj:`num_cg_levels`) num_cg_levels : :obj:`int` Number of cg levels to use. num_channels : :obj:`int` of :obj:`list` of :obj:`int` Number of channels that the output of each CG are mixed to (Expanded to list of length :obj:`num_cg_levels`) num_species : :obj:`int` Number of species of atoms included in the input dataset. device : :obj:`torch.device` Device to initialize the level to dtype : :obj:`torch.dtype` Data type to initialize the level to level to cg_dict : :obj:`nn.cg_lib.CGDict` """ def __init__(self, maxl, max_sh, num_cg_levels, num_channels, num_species, cutoff_type, hard_cut_rad, soft_cut_rad, soft_cut_width, weight_init, level_gain, charge_power, basis_set, charge_scale, gaussian_mask, #top, input, num_mpnn_layers, activation='leakyrelu', num_classes=2, cgprod_bounded=False, cg_agg_normalization='none', cg_pow_normalization='none', device=None, dtype=None, cg_dict=None): logging.info('Initializing network!') level_gain = expand_var_list(level_gain, num_cg_levels) hard_cut_rad = expand_var_list(hard_cut_rad, num_cg_levels) soft_cut_rad = expand_var_list(soft_cut_rad, num_cg_levels) soft_cut_width = expand_var_list(soft_cut_width, num_cg_levels) maxl = expand_var_list(maxl, num_cg_levels) max_sh = expand_var_list(max_sh, num_cg_levels) num_channels = expand_var_list(num_channels, num_cg_levels+1) logging.info('hard_cut_rad: {}'.format(hard_cut_rad)) logging.info('soft_cut_rad: {}'.format(soft_cut_rad)) logging.info('soft_cut_width: {}'.format(soft_cut_width)) logging.info('maxl: {}'.format(maxl)) logging.info('max_sh: {}'.format(max_sh)) logging.info('num_channels: {}'.format(num_channels)) super().__init__(maxl=max(maxl+max_sh), device=device, dtype=dtype, cg_dict=cg_dict) device, dtype, cg_dict = self.device, self.dtype, self.cg_dict self.num_cg_levels = num_cg_levels self.num_channels = num_channels self.charge_power = charge_power self.charge_scale = charge_scale self.num_species = num_species # Set up spherical harmonics self.sph_harms = SphericalHarmonicsRel(max(max_sh), conj=True, device=device, dtype=dtype, cg_dict=cg_dict) # Set up position functions, now independent of spherical harmonics self.rad_funcs = RadialFilters(max_sh, basis_set, num_channels, num_cg_levels, device=self.device, dtype=self.dtype) tau_pos = self.rad_funcs.tau num_scalars_in = self.num_species * (self.charge_power + 1) num_scalars_out = num_channels[0] self.input_func_atom = InputLinear(num_scalars_in, num_scalars_out, device=self.device, dtype=self.dtype) self.input_func_edge = NoLayer() tau_in_atom = self.input_func_atom.tau tau_in_edge = self.input_func_edge.tau self.cormorant_cg = CormorantCG(maxl, max_sh, tau_in_atom, tau_in_edge, tau_pos, num_cg_levels, num_channels, level_gain, weight_init, cutoff_type, hard_cut_rad, soft_cut_rad, soft_cut_width, cat=True, gaussian_mask=False, cgprod_bounded=cgprod_bounded, cg_agg_normalization=cg_agg_normalization, cg_pow_normalization=cg_pow_normalization, device=self.device, dtype=self.dtype, cg_dict=self.cg_dict) tau_cg_levels_atom = self.cormorant_cg.tau_levels_atom tau_cg_levels_edge = self.cormorant_cg.tau_levels_edge self.get_scalars_atom = GetScalarsAtom(tau_cg_levels_atom, device=self.device, dtype=self.dtype) self.get_scalars_edge = NoLayer() num_scalars_atom = self.get_scalars_atom.num_scalars num_scalars_edge = self.get_scalars_edge.num_scalars self.output_layer_atom = OutputSoftmax(num_scalars_atom, num_classes, bias=True, device=self.device, dtype=self.dtype) self.output_layer_edge = NoLayer() logging.info('Model initialized. Number of parameters: {}'.format( sum([p.nelement() for p in self.parameters()]))) def forward_once(self, data): """ Runs a single forward pass of the network. Parameters ---------- data : :obj:`dict` Dictionary of data to pass to the network. covariance_test : :obj:`bool`, optional If true, returns all of the atom-level representations twice. Returns ------- prediction : :obj:`torch.Tensor` The output of the layer """ # Get and prepare the data atom_scalars, atom_mask, edge_scalars, edge_mask, atom_positions = self.prepare_input(data) # Calculate spherical harmonics and radial functions spherical_harmonics, norms = self.sph_harms(atom_positions, atom_positions) rad_func_levels = self.rad_funcs(norms, edge_mask * (norms > 0)) # Prepare the input reps for both the atom and edge network atom_reps_in = self.input_func_atom(atom_scalars, atom_mask, edge_scalars, edge_mask, norms) edge_net_in = self.input_func_edge(atom_scalars, atom_mask, edge_scalars, edge_mask, norms) # Clebsch-Gordan layers central to the network atoms_all, edges_all = self.cormorant_cg(atom_reps_in, atom_mask, edge_net_in, edge_mask, rad_func_levels, norms, spherical_harmonics) # Construct scalars for network output atom_scalars = self.get_scalars_atom(atoms_all) edge_scalars = self.get_scalars_edge(edges_all) # Prediction in this case will depend only on the atom_scalars. Can make # it more general here. prediction = self.output_layer_atom(atom_scalars, atom_mask) return prediction, atoms_all, edges_all def forward(self, data, covariance_test=False): """ Runs a single forward pass of the network. Parameters ---------- data : :obj:`dict` Dictionary of data to pass to the network. covariance_test : :obj:`bool`, optional If true, returns all of the atom-level representations twice. Returns ------- prediction : :obj:`torch.Tensor` The output of the network """ data1 = {} data2 = {} data1['label'] = data['label'] data2['label'] = data['label'] data1['charges'] = data['charges1'] data2['charges'] = data['charges2'] data1['positions'] = data['positions1'] data2['positions'] = data['positions2'] data1['one_hot'] = data['one_hot1'] data2['one_hot'] = data['one_hot2'] data1['atom_mask'] = data['atom_mask1'] data2['atom_mask'] = data['atom_mask2'] data1['edge_mask'] = data['edge_mask1'] data2['edge_mask'] = data['edge_mask2'] prediction1, atoms_all1, edges_all1 = self.forward_once(data1) prediction2, atoms_all2, edges_all2 = self.forward_once(data2) prediction = (prediction2 - prediction1)**2 # Covariance test if covariance_test: return prediction, atoms_all1, edges_all1 else: return prediction def prepare_input(self, data): """ Extracts input from data class Parameters ---------- data : ????? Information on the state of the system. Returns ------- atom_scalars : :obj:`torch.Tensor` Tensor of scalars for each atom. atom_mask : :obj:`torch.Tensor` Mask used for batching data. atom_positions: :obj:`torch.Tensor` Positions of the atoms edge_mask: :obj:`torch.Tensor` Mask used for batching data. """ charge_power, charge_scale, device, dtype = self.charge_power, self.charge_scale, self.device, self.dtype atom_positions = data['positions'].to(device, dtype) one_hot = data['one_hot'].to(device, dtype) charges = data['charges'].to(device, dtype) atom_mask = data['atom_mask'].to(device) edge_mask = data['edge_mask'].to(device) charge_tensor = (charges.unsqueeze(-1)/charge_scale).pow(torch.arange(charge_power+1., device=device, dtype=dtype)) charge_tensor = charge_tensor.view(charges.shape + (1, charge_power+1)) atom_scalars = (one_hot.unsqueeze(-1) * charge_tensor).view(charges.shape[:2] + (-1,)) edge_scalars = torch.tensor([]) return atom_scalars, atom_mask, edge_scalars, edge_mask, atom_positions def expand_var_list(var, num_cg_levels): if type(var) is list: var_list = var + (num_cg_levels-len(var))*[var[-1]] elif type(var) in [float, int]: var_list = [var] * num_cg_levels else: raise ValueError('Incorrect type {}'.format(type(var))) return var_list

[ "martinvoegele1989@gmail.com" ]

martinvoegele1989@gmail.com

54ef8914727cd09544ff6c9b95288d9a954859c4

c19ba27d1a4aa2615e831e72f84dab05d37210c9

/single_byte_xor_cipher.py

07c55b9fcfc1c19557fdb37eee039e915b35b3c7

[]

no_license

starVader/cryptography

5c1b2164b16078b4405943101c12d70a3ae0cfda

b64ab8e290c108e2256ba0909bdc87524065b43f

refs/heads/master

2020-09-05T03:05:25.701440

2019-11-06T11:03:29

219,964,020

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py

from fixed_xor import calculate_xor def single_byte_xor_cipher(output): alphabets = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] if __name__ == '__main__': hex_enc_xored_str = "1b37373331363f78151b7f2b783431333d78397828372d363c78373e783a393b3736" single_byte_xor_cipher(hex_enc_xored_str)

[ "rakesh@coffeebeans.io" ]

rakesh@coffeebeans.io

6ff1199e076538633df47e9a7c40ce99750acf2d

f12907ab992b85a5e7e19953fbe1dab2305c8d2d

/CosasDeCasa/UserModeLoader/generator_data_file.py

3f4db3fabc0be714b5f7d61b030e32457fa3275c

[]

no_license

Fare9/SomeVirusesTechniques

c460364c6b612d1c64fb88363da06ba48d312736

ca452c14de71ea11828aa7171562982a9dfff5aa

refs/heads/master

2020-05-30T17:46:33.427451

2019-06-23T11:54:09

189,882,986

6

0

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Python

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py

#!python3 #-*- coding: utf-8 -*- __author__ = "Fare9" __credits__ = ["Fare9"] __license__ = "GPL" __version__ = "1.0.0" __maintainer__ = "Fare9" __email__ = "farenain9@gmail.com" __status__ = "Production" import os # standard library import sys import random import string file_name = "data.cpp" file_to_generate = ''' #include "common.h" std::string key = "%s" uint64_t file_size = 0x%X; uint8_t encrypted_file[] = {%s}; ''' def randomString(stringLength=10): """Generate a random string of fixed length """ letters = string.ascii_letters + string.hexdigits return ''.join(random.choice(letters) for i in range(stringLength)) def crypt(key, data): S = list(range(256)) j = 0 for i in list(range(256)): j = (j + S[i] + ord(key[i % len(key)])) % 256 S[i], S[j] = S[j], S[i] j = 0 y = 0 out = [] for byte in data: j = (j + 1) % 256 y = (y + S[j]) % 256 S[j], S[y] = S[y], S[j] if sys.version_info.major == 2: out.append(unichr(ord(byte) ^ S[(S[j] + S[y]) % 256])) if sys.version_info.major == 3: out.append(byte ^ S[(S[j] + S[y]) % 256]) print("Real data = ") for a in data[0:10]: sys.stdout.write('%X ' % a) print ("") print ("Encrypted data = ") for a in out[0:10]: sys.stdout.write('%X ' % a) print("") print ("Key = %s" % key) return out def read_file_and_get_data(file_to_open=""): ''' Method to read the file and generate the data to generate the file ''' exists = os.path.isfile(file_to_open) file_size = 0 file_content = "" key = "" if exists: if (file_to_open.endswith('.dll')): file_ = open(file_to_open,'rb') data = file_.read() file_size = len(data) file_.close() counter = 0 key = randomString(15) data = crypt(key,data) for c in data: file_content += '0x%X,' % c counter += 1 if counter == 10: file_content += '\n' counter = 0 if file_content[-1] == ',': file_content = file_content[0:-1] else: print ('File must be .dll file') else: print ("File '%s' does not exists..." % (file_to_open)) return file_size, file_content, key def main(): if len(sys.argv) != 2: print ("USAGE: generator_data_file.py <dll_file_to_generate_data>") sys.exit(-1) file_size, file_content, key = read_file_and_get_data(str(sys.argv[1])) if file_size == 0 or file_content == "": print ("Error generating data file") sys.exit(-1) data_file_content = file_to_generate % (key, file_size, file_content) opened_file = open(file_name, 'w') opened_file.write(data_file_content) opened_file.close() if __name__ == '__main__': main()

[ "eduardo.blazquez@edu.uah.es" ]

eduardo.blazquez@edu.uah.es

788d31e1c22321163e5c3a2f40bc386cceba1c6c

0e6bdf6801934f7e5374c47159e8c7f5925cb95d

/src/utils/logger.py

139853abd0d6815d7af2a67907f3b7b6d1f7411f

[]

no_license

HawChang/learn_tf2

5c8029cea339410f44e5f6c7b51b113e9ce326c1

21de6d6659281542264c20dd1a58a845214fdb3d

refs/heads/master

2021-03-26T13:07:14.187899

2020-03-31T03:43:46

247,706,742

0

null

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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2020/3/16 3:49 PM # @Author : ZhangHao # @File : logger.py # @Desc : import logging """ Logger的级别： 1. DEBUG 2. INFO 3. WARNING 4. ERROR 5. CRITICAL """ class Logger(object): _is_init = False def __init__(self): if not self._is_init: logging.basicConfig( # filename="log/run.log", level=logging.DEBUG, format="[%(asctime)s][%(filename)s:%(funcName)s:%(lineno)s][%(levelname)s]:%(message)s", datefmt='%Y-%m-%d %H:%M:%S') # ch = logging.StreamHandler() self.logger = logging.getLogger() # self.logger.addHandler(ch) self._is_init = True def get_logger(self): return self.logger if __name__ == "__main__": pass

[ "changhaw@126.com" ]

changhaw@126.com

96ec42e448b7eeb8921d53eb0ed2c1f012f9e714

f0d0ea29240c53b6ce1c4b06095b528ece02fdd7

/apps/config/migrations/0009_mail.py

f05d496f1a8ec6c6047e85a0dbb57b16caf33448

[]

no_license

zhifuliu/dianjing

477529ccd6159329e1bc121aeb2ff328ee499f4a

7b3f6d58f5bc0738651d8d72c9a24df4ade0ed36

refs/heads/master

2020-03-21T09:10:28.343268

2017-03-24T03:06:24

null

0

null

UTF-8

Python

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3,492

py

# -*- coding: utf-8 -*- # Generated by Django 1.10.2 on 2016-10-21 09:17 from __future__ import unicode_literals import django.core.validators from django.db import migrations, models import re class Migration(migrations.Migration): dependencies = [ ('config', '0008_auto_20161020_1321'), ] operations = [ migrations.CreateModel( name='Mail', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=255, verbose_name=b'\xe6\xa0\x87\xe9\xa2\x98')), ('content', models.TextField(verbose_name=b'\xe5\x86\x85\xe5\xae\xb9')), ('items', models.TextField(blank=True, verbose_name=b'\xe9\x99\x84\xe4\xbb\xb6')), ('send_at', models.DateTimeField(db_index=True, verbose_name=b'\xe5\x8f\x91\xe9\x80\x81\xe6\x97\xb6\xe9\x97\xb4')), ('condition_type', models.IntegerField(choices=[(1, b'\xe5\x85\xa8\xe9\x83\xa8\xe6\x9c\x8d\xe5\x8a\xa1\xe5\x99\xa8'), (2, b'\xe6\x8c\x87\xe5\xae\x9a\xe6\x9c\x8d\xe5\x8a\xa1\xe5\x99\xa8'), (3, b'\xe6\x8e\x92\xe9\x99\xa4\xe6\x8c\x87\xe5\xae\x9a\xe6\x9c\x8d\xe5\x8a\xa1\xe5\x99\xa8'), (11, b'\xe6\x8c\x87\xe5\xae\x9a\xe8\xa7\x92\xe8\x89\xb2ID')], verbose_name=b'\xe5\x8f\x91\xe9\x80\x81\xe6\x9d\xa1\xe4\xbb\xb6')), ('condition_value', models.CharField(blank=True, max_length=255, null=True, validators=[django.core.validators.RegexValidator(re.compile('^\\d+(?:\\,\\d+)*\\Z'), code='invalid', message='Enter only digits separated by commas.')], verbose_name=b'\xe6\x9d\xa1\xe4\xbb\xb6\xe5\x80\xbcID\xe5\x88\x97\xe8\xa1\xa8')), ('condition_club_level', models.PositiveIntegerField(blank=True, null=True, verbose_name=b'\xe4\xbf\xb1\xe4\xb9\x90\xe9\x83\xa8\xe7\xad\x89\xe7\xba\xa7\xe5\xa4\xa7\xe4\xba\x8e\xe7\xad\x89\xe4\xba\x8e')), ('condition_vip_level', models.PositiveIntegerField(blank=True, null=True, verbose_name=b'VIP\xe7\xad\x89\xe7\xba\xa7\xe5\xa4\xa7\xe4\xba\x8e\xe7\xad\x89\xe4\xba\x8e')), ('condition_login_at_1', models.DateTimeField(blank=True, null=True, verbose_name=b'\xe7\x99\xbb\xe9\x99\x86\xe6\x97\xb6\xe9\x97\xb4\xe5\xa4\xa7\xe4\xba\x8e\xe7\xad\x89\xe4\xba\x8e')), ('condition_login_at_2', models.DateTimeField(blank=True, null=True, verbose_name=b'\xe7\x99\xbb\xe9\x99\x86\xe6\x97\xb6\xe9\x97\xb4\xe5\xb0\x8f\xe4\xba\x8e\xe7\xad\x89\xe4\xba\x8e')), ('condition_exclude_chars', models.CharField(blank=True, max_length=255, null=True, validators=[django.core.validators.RegexValidator(re.compile('^\\d+(?:\\,\\d+)*\\Z'), code='invalid', message='Enter only digits separated by commas.')], verbose_name=b'\xe6\x8e\x92\xe9\x99\xa4\xe8\xa7\x92\xe8\x89\xb2ID\xe5\x88\x97\xe8\xa1\xa8')), ('create_at', models.DateTimeField(auto_now_add=True, verbose_name=b'\xe5\x88\x9b\xe5\xbb\xba\xe6\x97\xb6\xe9\x97\xb4')), ('status', models.IntegerField(choices=[(0, b'\xe7\xad\x89\xe5\xbe\x85'), (1, b'\xe6\xad\xa3\xe5\x9c\xa8\xe5\x8f\x91\xe9\x80\x81'), (2, b'\xe5\xae\x8c\xe6\x88\x90'), (3, b'\xe5\xa4\xb1\xe8\xb4\xa5')], db_index=True, default=0, verbose_name=b'\xe7\x8a\xb6\xe6\x80\x81')), ], options={ 'db_table': 'mail', 'verbose_name': '\u90ae\u4ef6', 'verbose_name_plural': '\u90ae\u4ef6', }, ), ]

[ "yueyoum@gmail.com" ]

yueyoum@gmail.com

78eedfc25bf4d66206ca15b411bd9d49ddb21226

233f2321abd301b52ed5a22ae191ae82ce71e4e4

/app/__init__.py

5406bc31951e69021e61f49c8c276f8d08455cdf

[]

no_license

kishoresvk21/tech_support

f7ee1d23eb80eae2e5215c9c122e4f5b07394509

3577bc00c79ce4388809801be6cbce9ca60373d8

refs/heads/main

2023-08-27T03:02:22.675670

2021-11-02T04:40:56

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from flask_sqlalchemy import SQLAlchemy from flask import Flask from flask_restplus import Api from flask_cors import CORS from flask_migrate import Migrate app = Flask(__name__) CORS(app) cors = CORS(app, resources={r"": {"origins": ""}, }) app.config['SECRET_KEY'] = 'rmijlkqqqawtre@1((11' app.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://root:Root#123@localhost/tech_support_database' app.config['SQLALCHEMY_TRACK_MODIFICATIONS']=False db = SQLAlchemy(app) migrate=Migrate(app,db) api = Api(app) #USER APIs from app.user.users.views import Login,Register,UpdatePassword,ForgotPassword,Logout,GetProfile,UserProfile api.add_resource(Login, "/login") api.add_resource(Register,"/register") api.add_resource(Logout,"/logout") api.add_resource(UpdatePassword,"/changepassword") #profile/changepassword api.add_resource(ForgotPassword,"/forgotpassword") api.add_resource(GetProfile,"/getprofile/user/<int:user_id>") api.add_resource(UserProfile,"/editprofile") api.add_resource(UserProfile,"/editprofile") from app.user.queries.views import QueriesClass,GetQueryByUserId,GetQueryByTechnology,GetQueryByTitle api.add_resource(QueriesClass,"/query") api.add_resource(GetQueryByUserId,"/getqueries/user/<int:user_id>") api.add_resource(GetQueryByTechnology,"/getqueries/technology/<int:tech_id>") api.add_resource(GetQueryByTitle,"/getqueries/title/<string:title>") from app.user.user_comments.views import GetCommentByQuery,GetCommentsByUserId,CommentCRUD api.add_resource(CommentCRUD,"/comment") api.add_resource(GetCommentsByUserId,"/getcomments/user/<int:user_id>") api.add_resource(GetCommentByQuery,"/getcomments/query") from app.user.technologies.views import TechFilter api.add_resource(TechFilter,"/filter") from app.user.likes_dislikes.views import Likes,DisLikes api.add_resource(Likes,"/comment/like") api.add_resource(DisLikes,"/comment/dislike") #ADMIN APIs from app.admin.users.views import Login,ForgotPassword,GetAllUsers,GetProfile,UserDelete,UserSearch api.add_resource(Login,"/admin/login") api.add_resource(ForgotPassword,"/admin/forgotpassword") api.add_resource(GetAllUsers,"/admin/getallusers") api.add_resource(GetProfile,"/admin/getuserprofile/<int:user_id>") api.add_resource(UserDelete,"/admin/deleteusers") api.add_resource(UserSearch,"/admin/usersearch") from app.admin.dashboards.views import FilterRecord,TopUsersList api.add_resource(FilterRecord,"/admin/datefilter") #,methods="[PUT]" #/<string:from_date>/<string:to_date>/<string:record_selection> # api.add_resource(TopUsers,"/admin/topusers/<int:users_limit>") api.add_resource(TopUsersList,"/admin/topusers") from app.admin.comments.views import CommentClass,GetCommentsByUserId,GetCommentByQuery api.add_resource(CommentClass,"/admin/comment") #delete edit comments api.add_resource(GetCommentByQuery,"/admin/comment/query") api.add_resource(GetCommentsByUserId,"/admin/getcomments/user") from app.admin.queries.views import QueriesClass,GetQueryByUserId,GetQueryByTechnology,GetQueryByTitle,Unanswered api.add_resource(GetQueryByUserId,"/admin/getqueries/user/<int:user_id>") api.add_resource(QueriesClass,"/admin/query") #edit delete api.add_resource(Unanswered,"/admin/query/unanswered") #edit delete from app.admin.technologies.views import TechnologiesCRUD,TechFilter,AdminTechClass api.add_resource(TechFilter,"/admin/gettechnologies") api.add_resource(AdminTechClass,"/admin/technologies") from app.admin.admin_users.views import AdminUserDetails,EditProfile,RolesClass,ChangePassword,AdminUsersEditDel,GetAllAdminUsers api.add_resource(AdminUserDetails, "/admin/adminuserdetails") api.add_resource(ChangePassword, "/admin/changepassword") api.add_resource(EditProfile, "/admin/editadminuserdetails") api.add_resource(RolesClass, "/admin/roles") api.add_resource(AdminUsersEditDel, "/admin/users") api.add_resource(GetAllAdminUsers,"/admin/getalladminusers") from app.utils.file_upload import upload api.add_resource(upload,"/file") # api.add_resource(, "/admin/users") # api.add_resource(TopTenUsers,"/toptenusers") # methods="[GET]" # from app.admin.technologies import # from app.admin.users.views import # from app.admin.queries.views import # from app.admin.comments.views import # from app.admin.likes_dislikes import # from app. # api.add_resource(CommentClass,"/comment") # api.add_resource(TechFilter,"/filter") # api.add_resource(UserProfile,"/profile") # api.add_resource(GetProfile,"/getprofile/<int:user_id>") # api.add_resource(GetCommentByQuery,"/getcomments/query/<int:query_id>") # # api.add_resource(AdminTechClass,"/admin/technologies") #admin/addtechnologies # api.add_resource(UserStatus,"/userstatuschange") #admin/userroles

[ "svkrishnakishore2000@gmail.com" ]

svkrishnakishore2000@gmail.com

fd5655dff832d4c4084d5d15f8af917e6b2e04f0

e5a25acd14fd7e080ffb255cb2bcbfa921c06806

/users/management/commands/utils/init_drp.py

c2d679d6e9f44c22178042d24c1dd7765ac46bc3

[ "MIT" ]

permissive

GolamMullick/HR_PROJECT

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refs/heads/master

2021-06-21T00:16:28.132340

2019-11-24T08:56:59

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2021-06-10T22:18:24

2019-11-24T08:52:54

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py

from users.models import DepartmentRoleModelPermission def init_drp(license): DepartmentRoleModelPermission.load_on_migrate(license) print("department role model permission worked!!")

[ "fahadmullick89@gmail.com" ]

fahadmullick89@gmail.com

47108a87a1e8c3ab03cd93554fe0455da8e314e3

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/src/python/probdist/_SquareMatrix.py

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[ "MIT" ]

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from _ProbDistExt import * class SquareMatrix(SquareMatrixBase): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Encapsulates a square matrix of floating point values (underlying C++ implementation stores these as doubles). """ def __init__(self, dimension, value): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Create a square matrix of size dimension containing value in every cell. """ SquareMatrixBase.__init__(self, dimension, value) def duplicate(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns a copy of this matrix. """ return SquareMatrixBase.duplicate(self) def identity(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Converts existing matrix to an identity matrix (1s on diagonal, 0s everywhere else). Dimension of the matrix is not changed. """ SquareMatrixBase.identity(self) def trace(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns the sum of the elements on the main diagonal. """ return SquareMatrixBase.trace(self) def inverse(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns a SquareMatrix that is the inverse of this matrix. """ return SquareMatrixBase.inverse(self) def pow(self, p): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns a SquareMatrix that is raised to the (postive) power p. """ return SquareMatrixBase.pow(self, p) def getDimension(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns an integer representing the number of rows of the matrix. The number of columns is the same value because this is a square matrix. """ return SquareMatrixBase.getDimension(self) def getElement(self, i, j): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns (i,j)th element of the square matrix. """ return SquareMatrixBase.getElement(self, i, j) def setElement(self, i, j, v): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Sets (i,j)th element of the square matrix to value v. """ SquareMatrixBase.setElement(self, i, j, v) def getMatrix(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns square matrix in the form of a two-dimensional list. """ dim = self.getDimension() v = SquareMatrixBase.getMatrix(self) m = [] k = 0 for i in range(dim): tmp = [] for j in range(dim): tmp.append(v[k]) k += 1 m.append(tmp) return m def setMatrixFromFlattenedList(self, dim, v): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Replaces existing or creates a new square matrix using the supplied unidimensional list or tuple v. The supplied list v is expected to have length equal to the square of dim, the number of elements in a single row or column of the matrix. """ SquareMatrixBase.setMatrix(self, dim, v) def setMatrix(self, m): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Replaces existing or creates a new square matrix using the supplied two-dimensional list or tuple m. """ dim = len(m[0]) v = [] for row in m: for col in row: v.append(col) SquareMatrixBase.setMatrix(self, dim, v) def __repr__(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Represents matrix as string. """ s = SquareMatrixBase.__repr__(self) return s def leftMultiplyMatrix(self, matrixOnLeft): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns a SquareMatrix that equals the product of supplied matrixOnLeft with this matrix (on right). """ return SquareMatrixBase.leftMultiplyMatrix(self, matrixOnLeft) def rightMultiplyMatrix(self, matrixOnRight): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns a SquareMatrix that equals the product of this matrix (on left) with supplied matrixOnRight. """ return SquareMatrixBase.rightMultiplyMatrix(self, matrixOnRight) def leftMultiplyVector(self, vectorOnLeft): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns a SquareMatrix that equals the product of supplied (transposed) vectorOnLeft with this matrix (on right). """ return SquareMatrixBase.leftMultiplyVector(self, vectorOnLeft) def rightMultiplyVector(self, vectorOnRight): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns a SquareMatrix that equals the product of this matrix (on left) with supplied vectorOnRight. """ return SquareMatrixBase.rightMultiplyVector(self, vectorOnRight) def logAbsDet(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns natural logarithm of the absolute value of the determinant of this square matrix. """ return SquareMatrixBase.logAbsDet(self) def CholeskyDecomposition(self): #---+----|----+----|----+----|----+----|----+----|----+----|----+----| """ Returns Cholesky decomposition of this square matrix as a lower triangular matrix. Note: if this matrix is not symmetric and positive definite, result will be None. """ return SquareMatrixBase.CholeskyDecomposition(self)

[ "paul.lewis@uconn.edu" ]

paul.lewis@uconn.edu

bd8ef05ba2857891d6e8b09adf4efd715afddbbd

e7e42ae069a5d3165fbe6acc86f4a0cd4b709194

/djsite/settings.py

f7ba93283b05bd36a83ff1b736bef4bf2638f220

[]

no_license

Kearenus/testblog

c1c59732f999af64c079447dc02982d0f36d361d

75f8bd2d4faefd13ee97f319a0ef7f19e5e5843a

refs/heads/master

2022-12-18T01:56:35.635762

2020-09-14T13:01:49

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""" Django settings for djsite project. Generated by 'django-admin startproject' using Django 3.1.1. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'y1fgb)n!1gy4u0e8i#=r4(prvtj0apbvb#(xz72@*zw+1#^3i8' # 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', 'core', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'djsite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], '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 = 'djsite.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': 'test1dj', 'USER' : 'postgres', 'PASSWORD' : '123456', 'HOST' : 'localhost', 'PORT' : '5432', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'ru-rus' TIME_ZONE = 'Europe/Moscow' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/'

[ "Kearenus@gmail.com" ]

Kearenus@gmail.com

774e1042c1b495b81805a042253a3386768be94b

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/depot_tools/recipe_modules/gclient/api.py

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[ "BSD-3-Clause", "MIT" ]

permissive

bopopescu/MQUIC

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refs/heads/master

2022-11-22T07:41:11.374401

2016-04-08T22:27:32

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# Copyright 2013 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. from recipe_engine import recipe_api class RevisionResolver(object): """Resolves the revision based on build properties.""" def resolve(self, properties): # pragma: no cover raise NotImplementedError() class RevisionFallbackChain(RevisionResolver): """Specify that a given project's sync revision follows the fallback chain.""" def __init__(self, default=None): self._default = default def resolve(self, properties): """Resolve the revision via the revision fallback chain. If the given revision was set using the revision_fallback_chain() function, this function will follow the chain, looking at relevant build properties until it finds one set or reaches the end of the chain and returns the default. If the given revision was not set using revision_fallback_chain(), this function just returns it as-is. """ return (properties.get('parent_got_revision') or properties.get('orig_revision') or properties.get('revision') or self._default) class ProjectRevisionResolver(RevisionResolver): """Revision resolver that takes into account the project.""" def __init__(self, project, parent_got_revision=None): self.project = project self.parent_got_revision = parent_got_revision or 'parent_got_revision' # TODO(phajdan.jr): Move to proper repo and add coverage. def resolve(self, properties): # pragma: no cover """Resolve the revision if project matches, otherwise default to HEAD.""" if properties.get('project') == self.project: return (properties.get(self.parent_got_revision) or properties.get('revision') or 'HEAD') return (properties.get(self.parent_got_revision) or 'HEAD') def jsonish_to_python(spec, is_top=False): ret = '' if is_top: # We're the 'top' level, so treat this dict as a suite. ret = '\n'.join( '%s = %s' % (k, jsonish_to_python(spec[k])) for k in sorted(spec) ) else: if isinstance(spec, dict): ret += '{' ret += ', '.join( "%s: %s" % (repr(str(k)), jsonish_to_python(spec[k])) for k in sorted(spec) ) ret += '}' elif isinstance(spec, list): ret += '[' ret += ', '.join(jsonish_to_python(x) for x in spec) ret += ']' elif isinstance(spec, basestring): ret = repr(str(spec)) else: ret = repr(spec) return ret class GclientApi(recipe_api.RecipeApi): # Singleton object to indicate to checkout() that we should run a revert if # we detect that we're on the tryserver. RevertOnTryserver = object() def __init__(self, **kwargs): super(GclientApi, self).__init__(**kwargs) self.USE_MIRROR = None self._spec_alias = None def __call__(self, name, cmd, infra_step=True, **kwargs): """Wrapper for easy calling of gclient steps.""" assert isinstance(cmd, (list, tuple)) prefix = 'gclient ' if self.spec_alias: prefix = ('[spec: %s] ' % self.spec_alias) + prefix kwargs.setdefault('env', {}) kwargs['env'].setdefault('PATH', '%(PATH)s') kwargs['env']['PATH'] = self.m.path.pathsep.join([ kwargs['env']['PATH'], str(self._module.PACKAGE_DIRECTORY)]) return self.m.python(prefix + name, self.package_resource('gclient.py'), cmd, infra_step=infra_step, **kwargs) @property def use_mirror(self): """Indicates if gclient will use mirrors in its configuration.""" if self.USE_MIRROR is None: self.USE_MIRROR = self.m.properties.get('use_mirror', True) return self.USE_MIRROR @use_mirror.setter def use_mirror(self, val): # pragma: no cover self.USE_MIRROR = val @property def spec_alias(self): """Optional name for the current spec for step naming.""" return self._spec_alias @spec_alias.setter def spec_alias(self, name): self._spec_alias = name @spec_alias.deleter def spec_alias(self): self._spec_alias = None def get_config_defaults(self): ret = { 'USE_MIRROR': self.use_mirror } ret['CACHE_DIR'] = self.m.path['root'].join('git_cache') return ret def resolve_revision(self, revision): if hasattr(revision, 'resolve'): return revision.resolve(self.m.properties) return revision def sync(self, cfg, with_branch_heads=False, **kwargs): revisions = [] for i, s in enumerate(cfg.solutions): if s.safesync_url: # prefer safesync_url in gclient mode continue if i == 0 and s.revision is None: s.revision = RevisionFallbackChain() if s.revision is not None and s.revision != '': fixed_revision = self.resolve_revision(s.revision) if fixed_revision: revisions.extend(['--revision', '%s@%s' % (s.name, fixed_revision)]) for name, revision in sorted(cfg.revisions.items()): fixed_revision = self.resolve_revision(revision) if fixed_revision: revisions.extend(['--revision', '%s@%s' % (name, fixed_revision)]) test_data_paths = set(cfg.got_revision_mapping.keys() + [s.name for s in cfg.solutions]) step_test_data = lambda: ( self.test_api.output_json(test_data_paths, cfg.GIT_MODE)) try: if not cfg.GIT_MODE: args = ['sync', '--nohooks', '--force', '--verbose'] if cfg.delete_unversioned_trees: args.append('--delete_unversioned_trees') if with_branch_heads: args.append('--with_branch_heads') self('sync', args + revisions + ['--output-json', self.m.json.output()], step_test_data=step_test_data, **kwargs) else: # clean() isn't used because the gclient sync flags passed in checkout() # do much the same thing, and they're more correct than doing a separate # 'gclient revert' because it makes sure the other args are correct when # a repo was deleted and needs to be re-cloned (notably # --with_branch_heads), whereas 'revert' uses default args for clone # operations. # # TODO(mmoss): To be like current official builders, this step could # just delete the whole <slave_name>/build/ directory and start each # build from scratch. That might be the least bad solution, at least # until we have a reliable gclient method to produce a pristine working # dir for git-based builds (e.g. maybe some combination of 'git # reset/clean -fx' and removing the 'out' directory). j = '-j2' if self.m.platform.is_win else '-j8' args = ['sync', '--verbose', '--with_branch_heads', '--nohooks', j, '--reset', '--force', '--upstream', '--no-nag-max'] if cfg.delete_unversioned_trees: args.append('--delete_unversioned_trees') self('sync', args + revisions + ['--output-json', self.m.json.output()], step_test_data=step_test_data, **kwargs) finally: result = self.m.step.active_result data = result.json.output for path, info in data['solutions'].iteritems(): # gclient json paths always end with a slash path = path.rstrip('/') if path in cfg.got_revision_mapping: propname = cfg.got_revision_mapping[path] result.presentation.properties[propname] = info['revision'] return result def inject_parent_got_revision(self, gclient_config=None, override=False): """Match gclient config to build revisions obtained from build_properties. Args: gclient_config (gclient config object) - The config to manipulate. A value of None manipulates the module's built-in config (self.c). override (bool) - If True, will forcibly set revision and custom_vars even if the config already contains values for them. """ cfg = gclient_config or self.c for prop, custom_var in cfg.parent_got_revision_mapping.iteritems(): val = str(self.m.properties.get(prop, '')) # TODO(infra): Fix coverage. if val: # pragma: no cover # Special case for 'src', inject into solutions[0] if custom_var is None: # This is not covered because we are deprecating this feature and # it is no longer used by the public recipes. if cfg.solutions[0].revision is None or override: # pragma: no cover cfg.solutions[0].revision = val else: if custom_var not in cfg.solutions[0].custom_vars or override: cfg.solutions[0].custom_vars[custom_var] = val def checkout(self, gclient_config=None, revert=RevertOnTryserver, inject_parent_got_revision=True, with_branch_heads=False, **kwargs): """Return a step generator function for gclient checkouts.""" cfg = gclient_config or self.c assert cfg.complete() if revert is self.RevertOnTryserver: revert = self.m.tryserver.is_tryserver if inject_parent_got_revision: self.inject_parent_got_revision(cfg, override=True) spec_string = jsonish_to_python(cfg.as_jsonish(), True) self('setup', ['config', '--spec', spec_string], **kwargs) sync_step = None try: if not cfg.GIT_MODE: try: if revert: self.revert(**kwargs) finally: sync_step = self.sync(cfg, with_branch_heads=with_branch_heads, **kwargs) else: sync_step = self.sync(cfg, with_branch_heads=with_branch_heads, **kwargs) cfg_cmds = [ ('user.name', 'local_bot'), ('user.email', 'local_bot@example.com'), ] for var, val in cfg_cmds: name = 'recurse (git config %s)' % var self(name, ['recurse', 'git', 'config', var, val], **kwargs) finally: cwd = kwargs.get('cwd', self.m.path['slave_build']) if 'checkout' not in self.m.path: self.m.path['checkout'] = cwd.join( *cfg.solutions[0].name.split(self.m.path.sep)) return sync_step def revert(self, **kwargs): """Return a gclient_safe_revert step.""" # Not directly calling gclient, so don't use self(). alias = self.spec_alias prefix = '%sgclient ' % (('[spec: %s] ' % alias) if alias else '') return self.m.python(prefix + 'revert', self.m.path['build'].join('scripts', 'slave', 'gclient_safe_revert.py'), ['.', self.m.path['depot_tools'].join('gclient', platform_ext={'win': '.bat'})], infra_step=True, **kwargs ) def runhooks(self, args=None, name='runhooks', **kwargs): args = args or [] assert isinstance(args, (list, tuple)) return self( name, ['runhooks'] + list(args), infra_step=False, **kwargs) @property def is_blink_mode(self): """ Indicates wether the caller is to use the Blink config rather than the Chromium config. This may happen for one of two reasons: 1. The builder is configured to always use TOT Blink. (factory property top_of_tree_blink=True) 2. A try job comes in that applies to the Blink tree. (patch_project is blink) """ return ( self.m.properties.get('top_of_tree_blink') or self.m.properties.get('patch_project') == 'blink') def break_locks(self): """Remove all index.lock files. If a previous run of git crashed, bot was reset, etc... we might end up with leftover index.lock files. """ self.m.python.inline( 'cleanup index.lock', """ import os, sys build_path = sys.argv[1] if os.path.exists(build_path): for (path, dir, files) in os.walk(build_path): for cur_file in files: if cur_file.endswith('index.lock'): path_to_file = os.path.join(path, cur_file) print 'deleting %s' % path_to_file os.remove(path_to_file) """, args=[self.m.path['slave_build']], infra_step=True, )

[ "alyssar@google.com" ]

alyssar@google.com

e0e86e42242d9e8b93db20b6f8b31985d4cae909

f38e78214992de722a6ec2012e844bce7b3c59ed

/lib/clckwrkbdgr/oldrogue/__main__.py

5d5846a28afc44aecd837e2b664887d748df9f23

[ "MIT" ]

permissive

clckwrkbdgr/dotfiles

20fb86f54d93ae4936c334898c3d7b1b3820fb06

a7e880e189bfa4793f30ff928b049e4a182a38cd

refs/heads/master

2023-08-31T13:13:47.533868

2023-08-30T18:32:00

20,396,084

2

MIT

2022-10-01T16:35:31

2014-06-02T07:26:38

Python

UTF-8

Python

false

27,391

py

import os, sys import curses, curses.ascii import json, itertools, copy, functools import logging import inspect from operator import itemgetter from collections import namedtuple import six if six.PY2: import itertools filter = itertools.ifilter import vintage from clckwrkbdgr import xdg from clckwrkbdgr.utils import get_type_by_name from clckwrkbdgr.math import Point, Matrix from clckwrkbdgr.fs import SerializedEntity import clckwrkbdgr.math from clckwrkbdgr.collections import dotdict, AutoRegistry import clckwrkbdgr.collections import clckwrkbdgr.text from clckwrkbdgr import tui import clckwrkbdgr.logging trace = logging.getLogger('rogue') from clckwrkbdgr.events import Events, MessageEvent from . import game from .game import Version, Item, Consumable, Wearable, Monster, Room, Tunnel, GridRoomMap, GridRoomMap as Map, Furniture, LevelPassage, GodMode, Dungeon, Event from . import pcg class MakeEntity: """ Creates builders for bare-properties-based classes to create subclass in one line. """ def __init__(self, base_classes, *properties): """ Properties are either list of strings, or a single strings with space-separated identifiers. """ self.base_classes = base_classes if clckwrkbdgr.utils.is_collection(base_classes) else (base_classes,) self.properties = properties if len(self.properties) == 1 and ' ' in self.properties[0]: self.properties = self.properties[0].split() def __call__(self, class_name, *values): """ Creates class object and puts it into global namespace. Values should match properties given at init. """ assert len(self.properties) == len(values), len(values) entity_class = type(class_name, self.base_classes, dict(zip(self.properties, values))) globals()[class_name] = entity_class return entity_class class EntityClassDistribution: def __init__(self, prob): self.prob = prob self.classes = [] def __lshift__(self, entity_class): self.classes.append(entity_class) def __iter__(self): return iter(self.classes) def get_distribution(self, param): if callable(self.prob): value = self.prob(param) else: value = self.prob return [(value, entity_class) for entity_class in self.classes] class StairsUp(LevelPassage): _sprite = '<' _name = 'stairs up' _id = 'enter' _can_go_up = True class DungeonGates(LevelPassage): _sprite = '<' _name = 'exit from the dungeon' _id = 'enter' _can_go_up = True def use(self, who): if who.has_item(McGuffin): raise GameCompleted() raise Furniture.Locked(McGuffin) class StairsDown(LevelPassage): _sprite = '>' _name = 'stairs down' _id = 'exit' _can_go_down = True class McGuffin(Item): _sprite = "*" _name = "mcguffin" class HealingPotion(Item, Consumable): _sprite = "!" _name = "potion" def consume_by(self, who): who.heal(10) Events().trigger(DrinksHealingPotion(Who=who.name.title())) return True make_weapon = MakeEntity(Item, '_sprite _name _attack') make_weapon('Dagger', '(', 'dagger', 1) make_weapon('Sword', '(', 'sword', 2) make_weapon('Axe', '(', 'axe', 4) make_armor = MakeEntity((Item, Wearable), '_sprite _name _protection') make_armor('Rags', "[", "rags", 1) make_armor('Leather', "[", "leather", 2) make_armor('ChainMail', "[", "chain mail", 3) make_armor('PlateArmor', "[", "plate armor", 4) class Rogue(Monster): _hostile_to = [Monster] _sprite = "@" _name = "rogue" _max_hp = 25 _attack = 1 _max_inventory = 26 class RealMonster(Monster): _hostile_to = [Rogue] animal_drops = [ (70, None), (20, HealingPotion), (5, Dagger), (5, Rags), ] monster_drops = [ (78, None), (3, HealingPotion), (3, Dagger), (3, Sword), (3, Axe), (3, Rags), (3, Leather), (3, ChainMail), (1, PlateArmor), ] thug_drops = [ (10, None), (20, HealingPotion), (30, Dagger), (10, Sword), (30, Leather), ] warrior_drops = [ (40, None), (30, HealingPotion), (10, Dagger), (5, Sword), (10, Leather), (5, ChainMail), ] super_warrior_drops = [ (80, None), (5, HealingPotion), (5, Axe), (10, Leather), ] easy_monsters = EntityClassDistribution(1) norm_monsters = EntityClassDistribution(lambda depth: max(0, (depth-2))) hard_monsters = EntityClassDistribution(lambda depth: max(0, (depth-7)//2)) make_monster = MakeEntity((RealMonster), '_sprite _name _max_hp _attack _drops') easy_monsters << make_monster('Ant', 'a', 'ant', 5, 1, animal_drops) easy_monsters << make_monster('Bat', 'b', 'bat', 5, 1, animal_drops) easy_monsters << make_monster('Cockroach', 'c', 'cockroach', 5, 1, animal_drops) easy_monsters << make_monster('Dog', 'd', 'dog', 7, 1, animal_drops) norm_monsters << make_monster('Elf', 'e', 'elf', 10, 2, warrior_drops) easy_monsters << make_monster('Frog', 'f', 'frog', 5, 1, animal_drops) norm_monsters << make_monster('Goblin', "g", "goblin", 10, 2, warrior_drops*2) norm_monsters << make_monster('Harpy', 'h', 'harpy', 10, 2, monster_drops) norm_monsters << make_monster('Imp', 'i', 'imp', 10, 3, monster_drops) easy_monsters << make_monster('Jelly', 'j', 'jelly', 5, 2, animal_drops) norm_monsters << make_monster('Kobold', 'k', 'kobold', 10, 2, warrior_drops) easy_monsters << make_monster('Lizard', 'l', 'lizard', 5, 1, animal_drops) easy_monsters << make_monster('Mummy', 'm', 'mummy', 10, 2, monster_drops) norm_monsters << make_monster('Narc', 'n', 'narc', 10, 2, thug_drops) norm_monsters << make_monster('Orc', 'o', 'orc', 15, 3, warrior_drops*2) easy_monsters << make_monster('Pigrat', 'p', 'pigrat', 10, 2, animal_drops) easy_monsters << make_monster('Quokka', 'q', 'quokka', 5, 1, animal_drops) easy_monsters << make_monster('Rat', "r", "rat", 5, 1, animal_drops) norm_monsters << make_monster('Skeleton', 's', 'skeleton', 20, 2, monster_drops) norm_monsters << make_monster('Thug', 't', 'thug', 15, 3, thug_drops*2) norm_monsters << make_monster('Unicorn', 'u', 'unicorn', 15, 3, monster_drops) norm_monsters << make_monster('Vampire', 'v', 'vampire', 20, 2, monster_drops) easy_monsters << make_monster('Worm', 'w', 'worm', 5, 2, animal_drops) hard_monsters << make_monster('Exterminator', 'x', 'exterminator', 20, 3, super_warrior_drops) norm_monsters << make_monster('Yak', 'y', 'yak', 10, 2, animal_drops) easy_monsters << make_monster('Zombie', 'z', 'zombie', 5, 2, thug_drops) hard_monsters << make_monster('Angel', 'A', 'angel', 30, 5, super_warrior_drops) norm_monsters << make_monster('Beholder', 'B', 'beholder', 20, 2, warrior_drops) hard_monsters << make_monster('Cyborg', 'C', 'cyborg', 20, 5, super_warrior_drops*3) hard_monsters << make_monster('Dragon', 'D', 'dragon', 40, 5, monster_drops*3) norm_monsters << make_monster('Elemental', 'E', 'elemental', 10, 2, []) hard_monsters << make_monster('Floater', 'F', 'floater', 40, 1, animal_drops) hard_monsters << make_monster('Gargoyle', 'G', 'gargoyle', 30, 3, monster_drops) hard_monsters << make_monster('Hydra', 'H', 'hydra', 30, 2, monster_drops) norm_monsters << make_monster('Ichthyander', 'I', 'ichthyander', 20, 2, thug_drops) hard_monsters << make_monster('Juggernaut', 'J', 'juggernaut', 40, 4, monster_drops) hard_monsters << make_monster('Kraken', 'K', 'kraken', 30, 3, monster_drops) norm_monsters << make_monster('Lich', 'L', 'lich', 20, 2, monster_drops) norm_monsters << make_monster('Minotaur', 'M', 'minotaur', 20, 2, warrior_drops*2) norm_monsters << make_monster('Necromancer', 'N', 'necromancer', 20, 2, warrior_drops) hard_monsters << make_monster('Ogre', 'O', 'ogre', 30, 5, super_warrior_drops) hard_monsters << make_monster('Phoenix', 'P', 'phoenix', 20, 3, monster_drops) norm_monsters << make_monster('QueenBee', 'Q', 'queen bee', 20, 2, animal_drops) hard_monsters << make_monster('Revenant', 'R', 'revenant', 20, 3, super_warrior_drops) norm_monsters << make_monster('Snake', 'S', 'snake', 10, 2, animal_drops) hard_monsters << make_monster('Troll', "T", "troll", 25, 5, super_warrior_drops) norm_monsters << make_monster('Unseen', 'U', 'unseen', 10, 2, thug_drops) norm_monsters << make_monster('Viper', 'V', 'viper', 10, 2, animal_drops) hard_monsters << make_monster('Wizard', 'W', 'wizard', 40, 5, thug_drops*2) hard_monsters << make_monster('Xenomorph', 'X', 'xenomorph', 30, 3, animal_drops) norm_monsters << make_monster('Yeti', 'Y', 'yeti', 10, 2, animal_drops) norm_monsters << make_monster('Zealot', 'Z', 'zealot', 10, 2, thug_drops) class GodModeSwitched(MessageEvent): _message = "God {name} -> {state}" class NeedMcGuffin(MessageEvent): _message = "You cannot escape the dungeon without mcguffin!" class GoingUp(MessageEvent): _message = "Going up..." class GoingDown(MessageEvent): _message = "Going down..." class CannotGoBelow(MessageEvent): _message = "No place down below." class CannotDig(MessageEvent): _message = "Cannot dig through the ground." class CannotReachCeiling(MessageEvent): _message = "Cannot reach the ceiling." class NoSuchItem(MessageEvent): _message = "No such item '{char}'." class InventoryFull(MessageEvent): _message = "Inventory is full! Cannot pick up {item}" class GrabbedItem(MessageEvent): _message = "Grabbed {item}." class NothingToPickUp(MessageEvent): _message = "There is nothing here to pick up." class InventoryEmpty(MessageEvent): _message = "Inventory is empty." class ItemDropped(MessageEvent): _message = "Dropped {item}." class DropsItem(MessageEvent): _message = "{Who} drops {item}." class CannotConsume(MessageEvent): _message = "Cannot consume {item}." class ItemConsumed(MessageEvent): _message = "Consumed {item}." class DrinksHealingPotion(MessageEvent): _message = "{Who} heals itself." class NothingToUnwield(MessageEvent): _message = "Nothing is wielded already." class Unwielding(MessageEvent): _message = "Unwielding {item}." class Wielding(MessageEvent): _message = "Wielding {item}." class CannotWear(MessageEvent): _message = "Cannot wear {item}." class NothingToTakeOff(MessageEvent): _message = "Nothing is worn already." class TakingOff(MessageEvent): _message = "Taking off {item}." class Wearing(MessageEvent): _message = "Wearing {item}." class Attacking(MessageEvent): _message = "{Who} hit {whom} for {damage} hp." class IsDead(MessageEvent): _message = "{Who} is dead." class BumpsIntoWall(MessageEvent): _message = "{Who} bumps into wall." class BumpsIntoOther(MessageEvent): _message = "{Who} bumps into {whom}." class WelcomeBack(MessageEvent): _message = "Welcome back, {who}!" Event.register('WelcomeBack', 'who') class RogueDungeonGenerator(pcg.Generator): MAX_LEVELS = 26 def build_level(self, level_id): if level_id < 0 or level_id >= self.MAX_LEVELS: raise KeyError("Invalid level ID: {0} (supports only [0; {1}))".format(level_id, self.MAX_LEVELS)) depth = level_id is_bottom = depth >= (self.MAX_LEVELS - 1) result = self.original_rogue_dungeon( map_size=(78, 21), grid_size=(3, 3), room_class=Room, tunnel_class=Tunnel, item_distribution = [ (50, HealingPotion), (depth, Dagger), (depth // 2, Sword), (max(0, (depth-5) // 3), Axe), (depth, Rags), (depth // 2, Leather), (max(0, (depth-5) // 3), ChainMail), (max(0, (depth-10) // 3), PlateArmor), ], item_amount=(2, 4), monster_distribution = list(itertools.chain( easy_monsters.get_distribution(depth), norm_monsters.get_distribution(depth), hard_monsters.get_distribution(depth), )), monster_amount=5, prev_level_id=level_id - 1 if level_id > 0 else None, next_level_id=level_id + 1 if not is_bottom else None, enter_object_type=StairsUp if level_id > 0 else DungeonGates, exit_object_type=StairsDown, enter_connected_id='exit', exit_connected_id='enter', item_instead_of_exit=McGuffin if is_bottom else None, ) result.level_id = level_id return GridRoomMap(**vars(result)) class ExitWithoutSave(tui.app.AppExit): def __init__(self): super(ExitWithoutSave, self).__init__(False) class SaveAndExit(tui.app.AppExit): def __init__(self): super(SaveAndExit, self).__init__(True) class GameCompleted(Exception): pass def to_main_screen(mode): return MessageView(StatusLine(MainGame, mode.data), mode.data) class MessageView(tui.widgets.MessageLineOverlay): def get_new_messages(self): process_game_events(self.data, self.data.history) del self.data.history[:] events = Events() while events.listen(): trace.debug("Message posted: {0}: {1}".format(repr(events.current), str(events.current))) yield events.current def force_ellipsis(self): return not self.data.rogue.is_alive() StatusSection = tui.widgets.StatusLine.LabeledSection class StatusLine(tui.widgets.StatusLine): CORNER = "[?]" SECTIONS = [ StatusSection('Lvl', 2, lambda dungeon: 1+dungeon.current_level_id), StatusSection("HP", 6, lambda dungeon: "{0}/{1}".format(dungeon.rogue.hp, dungeon.rogue.max_hp)), StatusSection("Items", 2, lambda dungeon:( None if not dungeon.rogue.inventory else ( ''.join(item.sprite for item in dungeon.rogue.inventory) if len(dungeon.rogue.inventory) <= 2 else len(dungeon.rogue.inventory) ))), StatusSection("Wld", 7, lambda dungeon: dungeon.rogue.wielding.name if dungeon.rogue.wielding else None), StatusSection("Wear", 7, lambda dungeon: dungeon.rogue.wearing.name if dungeon.rogue.wearing else None), StatusSection("Here", 1, lambda dungeon: getattr(next(dungeon.current_level.items_at(dungeon.rogue.pos), next(dungeon.current_level.objects_at(dungeon.rogue.pos), None)), 'sprite', None)), ] Controls = AutoRegistry() class MainGame(tui.app.MVC): _full_redraw = True def _view(self, window): stdscr, dungeon = window, self.data trace.debug(list(dungeon.current_level.rooms.keys())) for room in dungeon.current_level.rooms.values(): if not dungeon.is_remembered(room): continue stdscr.addstr(1 + room.top, room.left, "+") stdscr.addstr(1 + room.bottom, room.left, "+") stdscr.addstr(1 + room.top, room.right, "+") stdscr.addstr(1 + room.bottom, room.right, "+") for x in range(room.left+1, room.right): stdscr.addstr(1 + room.top, x, "-") stdscr.addstr(1 + room.bottom, x, "-") for y in range(room.top+1, room.bottom): stdscr.addstr(1 + y, room.left, "|") stdscr.addstr(1 + y, room.right, "|") if dungeon.is_visible(room): for y in range(room.top+1, room.bottom): for x in range(room.left+1, room.right): stdscr.addstr(1 + y, x, ".") else: for y in range(room.top+1, room.bottom): for x in range(room.left+1, room.right): stdscr.addstr(1 + y, x, " ") for tunnel in dungeon.current_level.tunnels: for cell in tunnel.iter_points(): if dungeon.is_visible(tunnel, cell): stdscr.addstr(1 + cell.y, cell.x, "#") if dungeon.is_visible(tunnel, tunnel.start): stdscr.addstr(1 + tunnel.start.y, tunnel.start.x, "+") if dungeon.is_visible(tunnel, tunnel.stop): stdscr.addstr(1 + tunnel.stop.y, tunnel.stop.x, "+") for pos, obj in dungeon.current_level.objects: if dungeon.is_remembered(pos) or dungeon.is_visible(pos): stdscr.addstr(1 + pos.y, pos.x, obj.sprite) for pos, item in dungeon.current_level.items: if dungeon.is_remembered(pos) or dungeon.is_visible(pos): stdscr.addstr(1 + pos.y, pos.x, item.sprite) for monster in dungeon.current_level.monsters: if dungeon.is_visible(monster.pos): stdscr.addstr(1 + monster.pos.y, monster.pos.x, monster.sprite) stdscr.addstr(1 + dungeon.rogue.pos.y, dungeon.rogue.pos.x, dungeon.rogue.sprite) def _control(self, ch): self.step_is_over = False try: new_mode = Controls[str(ch)](self) if new_mode: return new_mode if not self.step_is_over: return return self.process_others() except KeyError: trace.debug("Unknown key: {0}".format(ch)) pass @Controls('Q') def quit(self): """ Abandon game. """ return SuicideAttempt(to_main_screen(self), self.data) @Controls('S') def save_and_exit(self): """ Save & exit. """ raise SaveAndExit() @Controls('~') def god_mode(self): return GodModeAction @Controls('?') def show_help(self): """ Show help message. """ return HelpScreen @Controls('>') def descend(self): """ Go down. """ dungeon = self.data stairs_here = next(filter(lambda obj: isinstance(obj, LevelPassage) and obj.can_go_down, dungeon.current_level.objects_at(dungeon.rogue.pos)), None) if stairs_here: dungeon.use_stairs(stairs_here) Events().trigger(GoingDown()) return to_main_screen(self) else: Events().trigger(CannotDig()) @Controls('<') def ascend(self): """ Go up. """ dungeon = self.data stairs_here = next(filter(lambda obj: isinstance(obj, LevelPassage) and obj.can_go_up, dungeon.current_level.objects_at(dungeon.rogue.pos)), None) if stairs_here: try: dungeon.use_stairs(stairs_here) Events().trigger(GoingUp()) return to_main_screen(self) except Furniture.Locked: Events().trigger(NeedMcGuffin()) except GameCompleted: return Greetings else: Events().trigger(CannotReachCeiling()) @Controls('g') def grab(self): """ Grab item. """ dungeon = self.data item_here = next( (index for index, (pos, item) in enumerate(reversed(dungeon.current_level.items)) if pos == dungeon.rogue.pos), None) trace.debug("Items: {0}".format(dungeon.current_level.items)) trace.debug("Rogue: {0}".format(dungeon.rogue.pos)) trace.debug("Items here: {0}".format([(index, pos, item) for index, (pos, item) in enumerate(reversed(dungeon.current_level.items)) if pos == dungeon.rogue.pos])) trace.debug("Item here: {0}".format(item_here)) if item_here is not None: item_here = len(dungeon.current_level.items) - 1 - item_here # Index is from reversed list. trace.debug("Unreversed item here: {0}".format(item_here)) _, item = dungeon.current_level.items[item_here] self.data.history += dungeon.current_level.grab_item(dungeon.rogue, item) self.step_is_over = True else: Events().trigger(NothingToPickUp()) @Controls('d') def drop(self): """ Drop item. """ dungeon = self.data if not dungeon.rogue.inventory: Events().trigger(InventoryEmpty()) else: return QuickDropItem(to_main_screen(self), self.data) @Controls('e') def eat(self): """ Consume item. """ dungeon = self.data if not dungeon.rogue.inventory: Events().trigger(InventoryEmpty()) else: return QuickConsumeItem(to_main_screen(self), self.data) @Controls('w') def wield(self): """ Wield item. """ dungeon = self.data if not dungeon.rogue.inventory: Events().trigger(InventoryEmpty()) else: return QuickWieldItem(to_main_screen(self), self.data) @Controls('U') def unwield(self): """ Unwield item. """ dungeon = self.data if not dungeon.rogue.wielding: Events().trigger(NothingToUnwield()) else: self.data.history += dungeon.rogue.wield(None) @Controls('W') def wear(self): """ Wear item. """ dungeon = self.data if not dungeon.rogue.inventory: Events().trigger(InventoryEmpty()) else: return QuickWearItem(to_main_screen(self), self.data) @Controls('T') def take_off(self): """ Take item off. """ dungeon = self.data if not dungeon.rogue.wearing: Events().trigger(NothingToTakeOff()) else: self.data.history += dungeon.rogue.wear(None) @Controls('i') def inventory(self): """ Toggle inventory. """ return Inventory @Controls('.') def wait(self): """ Wait. """ self.step_is_over = True @Controls('h') def move_west(self): """ Move around. """ self.move_by(Point(-1, 0)) @Controls('j') def move_south(self): """ Move around. """ self.move_by(Point( 0, +1)) @Controls('k') def move_north(self): """ Move around. """ self.move_by(Point( 0, -1)) @Controls('l') def move_east(self): """ Move around. """ self.move_by(Point(+1, 0)) @Controls('y') def move_north_west(self): """ Move around. """ self.move_by(Point(-1, -1)) @Controls('u') def move_north_east(self): """ Move around. """ self.move_by(Point(+1, -1)) @Controls('b') def move_south_west(self): """ Move around. """ self.move_by(Point(-1, +1)) @Controls('n') def move_south_east(self): """ Move around. """ self.move_by(Point(+1, +1)) def move_by(self, shift): dungeon = self.data self.data.history += dungeon.move_monster(dungeon.rogue, dungeon.rogue.pos + shift) dungeon.current_level.visit(dungeon.rogue.pos) self.step_is_over = True def process_others(self): dungeon = self.data for monster in dungeon.current_level.monsters: if not dungeon.current_room: continue sees_rogue = dungeon.current_room.contains(monster.pos) if not sees_rogue: continue shift = Point( clckwrkbdgr.math.sign(dungeon.rogue.pos.x - monster.pos.x), clckwrkbdgr.math.sign(dungeon.rogue.pos.y - monster.pos.y), ) new_pos = monster.pos + shift self.data.history += dungeon.move_monster(monster, new_pos, with_tunnels=False) if not dungeon.rogue.is_alive(): return MessageView(Grave, self.data) def process_game_events(dungeon, events): for event in events: if isinstance(event, Event.BumpIntoTerrain): if event.who != dungeon.rogue: Events().trigger(BumpsIntoWall(Who=event.who.name.title())) elif isinstance(event, Event.BumpIntoMonster): Events().trigger(BumpsIntoOther(Who=event.who.name.title(), whom=event.whom.name)) elif isinstance(event, Event.AttackMonster): Events().trigger(Attacking(Who=event.who.name.title(), whom=event.whom.name, damage=event.damage)) elif isinstance(event, Event.MonsterDied): Events().trigger(IsDead(Who=event.who.name.title())) elif isinstance(event, Event.MonsterDroppedItem): Events().trigger(DropsItem(Who=event.who.name.title(), item=event.item.name)) elif isinstance(event, Event.MonsterConsumedItem): Events().trigger(ItemConsumed(item=event.item.name)) elif isinstance(event, Event.Unwielding): Events().trigger(Unwielding(item=event.item.name)) elif isinstance(event, Event.TakingOff): Events().trigger(TakingOff(item=event.item.name)) elif isinstance(event, Event.Wielding): Events().trigger(Wielding(item=event.item.name)) elif isinstance(event, Event.Wearing): Events().trigger(Wearing(item=event.item.name)) elif isinstance(event, Event.NotWearable): Events().trigger(CannotWear(item=event.item.name)) elif isinstance(event, Event.NotConsumable): Events().trigger(CannotConsume(item=event.item.name)) elif isinstance(event, Event.WelcomeBack): trace.debug(event) Events().trigger(WelcomeBack(who=event.who.name)) elif isinstance(event, Event.InventoryFull): Events().trigger(InventoryFull(item=event.item.name)) elif isinstance(event, Event.GrabbedItem): Events().trigger(GrabbedItem(who=event.who.name, item=event.item.name)) class GodModeAction(tui.widgets.Menu): KEYS_TO_CLOSE = [curses.ascii.ESC, ord('~')] def items(self): return [ tui.widgets.Menu.Item('v', 'see all: {0}'.format('ON' if self.data.god.vision else 'off'), 'vision'), ] def on_close(self): return to_main_screen(self) def on_item(self, item): new_state = not getattr(self.data.god, item.data) setattr(self.data.god, item.data, new_state) Events().trigger(GodModeSwitched(name=item.text, state='ON' if new_state else 'off')) return to_main_screen(self) class ConsumeItem: def prompt(self): return "Which item to consume?" def item_action(self, index): item = self.data.rogue.inventory[index] self.data.history += self.data.rogue.consume(item) class DropItem: def prompt(self): return "Which item to drop?" def item_action(self, index): item = self.data.rogue.inventory[index] self.data.history += self.data.current_level.drop_item(self.data.rogue, item) class WieldItem: def prompt(self): return "Which item to wield?" def item_action(self, index): item = self.data.rogue.inventory[index] self.data.history += self.data.rogue.wield(item) class WearItem: def prompt(self): return "Which item to wear?" def item_action(self, index): item = self.data.rogue.inventory[index] self.data.history += self.data.rogue.wear(item) class QuickItemSelection(tui.widgets.Prompt): def extended_mode(self): raise NotImplementedError def choices(self): return [chr(ord('a') + i) for i in range(len(self.data.rogue.inventory))] + ['*'] def on_choice(self, key): if key == '*': return self.extended_mode() index = key.value - ord('a') self.item_action(index) return self.actual_mode class QuickConsumeItem(ConsumeItem, QuickItemSelection): def extended_mode(self): return ConsumeFromInventory class QuickDropItem(DropItem, QuickItemSelection): def extended_mode(self): return DropFromInventory class QuickWearItem(WearItem, QuickItemSelection): def extended_mode(self): return WearFromInventory class QuickWieldItem(WieldItem, QuickItemSelection): def extended_mode(self): return WieldFromInventory class Inventory(tui.widgets.Menu): COLUMNS = 2 KEYS_TO_CLOSE = ['i', curses.ascii.ESC] def on_close(self): return to_main_screen(self) def prompt(self): if not self.data.rogue.inventory: return "(empty)" return "" def items(self): for index, item in enumerate(self.data.rogue.inventory): line = "{0} {1}".format(item.sprite, item.name) if self.data.rogue.wielding == item: line += " (wielding)" if self.data.rogue.wearing == item: line += " (wearing)" key = ord('a') + index yield tui.widgets.Menu.Item(key, line, key) def on_item(self, item): if hasattr(self, 'item_action'): self.item_action(item.data - ord('a')) return to_main_screen(self) return None class ConsumeFromInventory(ConsumeItem, Inventory): pass class DropFromInventory(DropItem, Inventory): pass class WieldFromInventory(WieldItem, Inventory): pass class WearFromInventory(WearItem, Inventory): pass class HelpScreen(tui.widgets.TextScreen): _full_redraw = True LINES = ["{0} - {1}".format(''.join(map(itemgetter(1), keys)), text) for text, keys in itertools.groupby(sorted([ (inspect.getdoc(value), key) for key, value in Controls.items() if value.__doc__ ]), key=itemgetter(0))] def on_close(self): return to_main_screen(self) class SuicideAttempt(tui.widgets.Confirmation): MESSAGE = "Do you really want to quit without saving?" def on_yes(self): raise ExitWithoutSave() class Grave(tui.widgets.TextScreen): LINES = [ "You failed to reach mcguffin!" ] RETURN_VALUE = ExitWithoutSave class Greetings(tui.widgets.TextScreen): LINES = [ "Mcguffin is successfully retrieved!" ] RETURN_VALUE = ExitWithoutSave class Game(tui.app.App): pass def main(stdscr): curses.curs_set(0) with SerializedEntity(xdg.save_data_path('dotrogue')/'rogue.sav', Version._top(), entity_name='dungeon', unlink=True, readable=True) as savefile: if savefile.entity: dungeon = savefile.entity dungeon.generator = RogueDungeonGenerator() dungeon.history.append(Event.WelcomeBack(dungeon.rogue)) else: dungeon = Dungeon(RogueDungeonGenerator(), Rogue) dungeon.go_to_level(0) dungeon.rogue.inventory.append(Dagger()) savefile.reset(dungeon) game = Game(stdscr) return_code = game.run(to_main_screen(dotdict(data=dungeon))) if return_code is False: savefile.reset() import click @click.command() @click.option('--debug', is_flag=True) def cli(debug=False): clckwrkbdgr.logging.init('rogue', debug=debug, filename=xdg.save_state_path('dotrogue')/'rogue.log', stream=None, ) curses.wrapper(main) if __name__ == '__main__': cli()

[ "umi0451@gmail.com" ]

umi0451@gmail.com

7e6ae3866209330b045a79d660c0ab9423b0337b

b559fb774f770a1d7bf594e58bad582e5bc5a145

/partlist/migrations/0001_initial.py

6de925cdb4bfa4d404ade61195f927ee8f256027

[]

no_license

sktometometo/Test_django_samplesite

2da194e91e66c4482a0eaf19d337e4ef41aad273

6077d4c80fa8129f259b10554285a637711af3e6

refs/heads/master

2022-11-16T02:11:12.165776

2020-07-22T07:01:14

279,798,307

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# Generated by Django 3.0.8 on 2020-07-15 05:17 import datetime from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Part', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('part_name', models.CharField(max_length=200, verbose_name='部品名')), ('part_number', models.CharField(max_length=20, unique=True, verbose_name='部品番号')), ('part_amount', models.FloatField(default=0, verbose_name='数量')), ('part_unit', models.CharField(max_length=200, verbose_name='単位')), ('part_place', models.CharField(max_length=200, verbose_name='保管場所')), ('part_supplier', models.URLField(blank=True, verbose_name='調達先')), ('part_remark', models.TextField(blank=True, verbose_name='備考')), ], options={ 'db_table': 'partlist', }, ), migrations.CreateModel( name='Transaction', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('transaction_date', models.DateTimeField(default=datetime.datetime.now, verbose_name='日付')), ('transaction_diff', models.FloatField(default=0, verbose_name='変更量')), ('transaction_remark', models.TextField(blank=True, verbose_name='備考')), ('transaction_part', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='partlist.Part', verbose_name='部品名')), ('transaction_user', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to=settings.AUTH_USER_MODEL, verbose_name='担当者')), ], options={ 'db_table': 'transactions', }, ), ]

[ "sktometometo@gmail.com" ]

sktometometo@gmail.com

615bb0ef4d02e7266ab2d4ff06b73adeb290dc2f

445b16e1754234ed9afea078872545f2fee32b1e

/chasha.py

fde8781a6cccbc9ab1252f4f133b0f6d6d2e93dd

[]

no_license

dlwngh1113/2DGP

ee7013ea0a22b87f15098d0c1f0f20c1e846024b

07566128bdee8af8027ae6298a96be381fdc73a3

refs/heads/master

2020-08-03T16:27:26.336450

2019-12-10T13:10:45

null

0

null

UTF-8

Python

false

3,010

py

from pico2d import * import random import game_framework from BehaviorTree import BehaviorTree, SelectorNode, SequenceNode, LeafNode PIXEL_PER_METER = (10.0 / 0.3) # 10 pixel 30 cm RUN_SPEED_KMPH = 25.0 # Km / Hour RUN_SPEED_MPM = (RUN_SPEED_KMPH * 1000.0 / 60.0) RUN_SPEED_MPS = (RUN_SPEED_MPM / 60.0) RUN_SPEED_PPS = (RUN_SPEED_MPS * PIXEL_PER_METER) # Boy Action Speed TIME_PER_ACTION = 0.5 ACTION_PER_TIME = 1.0 / TIME_PER_ACTION FRAMES_PER_ACTION = 4 class Chasha: image = None def __init__(self, level=None, x=None, y=None): if self.image is None: self.image = load_image('image_resources\\chasha.png') self.font = load_font('gothic.ttf', 12) if x is None and y is None: x, y = 250, 600 self.x, self.y = x, y if level is None: level = 3 self.level = level self.charWidth = self.level * 33 self.charHeight = self.level * 32 self.money = self.level * 1087 self.atk = level * 43 self.timer = 1.0 self.speed = 0 self.dir = random.random() * 2 * math.pi self.life = self.level * int(game_framework.player.atk * 5.5) self.frame = 0 self.build_behavior_tree() def calculate_current_position(self): self.frame = (self.frame + FRAMES_PER_ACTION * ACTION_PER_TIME * game_framework.frame_time) % FRAMES_PER_ACTION self.x += self.speed * math.cos(self.dir) * game_framework.frame_time self.y += self.speed * math.sin(self.dir) * game_framework.frame_time self.x = clamp(33, self.x, 500 - 33) self.y = clamp(32, self.y, 800 - 64) def draw(self): if 0 < self.dir < math.pi: self.image.clip_draw(int(self.frame) * 33, 0, 33, 32, self.x, self.y, self.charWidth, self.charHeight) else: self.image.clip_draw(int(self.frame) * 33, 4 * 32, 33, 32, self.x, self.y, self.charWidth, self.charHeight) self.font.draw(self.x + self.charWidth / 2, self.y + self.charHeight / 2, str(self.life), (255,0,0)) def update(self): self.bt.run() pass def add_event(self, event): pass def handle_event(self, event): pass def build_behavior_tree(self): wander_node = LeafNode("WanderNode", self.wander) self.bt = BehaviorTree(wander_node) pass def wander(self): # fill here self.speed = RUN_SPEED_PPS self.calculate_current_position() self.timer -= game_framework.frame_time / 2 if self.timer < 0: self.timer = 1.0 self.dir = random.random() * 2 * math.pi self.x = clamp(33, self.x, 500 - 33) self.y = clamp(32, self.y, 800 - 64) return BehaviorTree.SUCCESS pass def get_bb(self): return self.x - self.charWidth / 2, self.y - self.charHeight / 2, self.x + self.charWidth / 2 - 10, self.y + self.charHeight / 2

[ "dlwngh1113@naver.com" ]

dlwngh1113@naver.com

f3d42a10d50a212195491831b823aee6a236d05b

f18ee8805c738b2cd22634ea728a6a35c0153eee

/result_generator/result_feature_db/index.py

dfab4c112be45ea8fed8554ee4fb67d1f1cc85ed

[ "MIT" ]

permissive

shijack/feature_extract

e9a115085e1c82dd9a2782e1464e90f18f273885

2c45750ea42a30a1f0b5cbe305edc4c8ab0461d7

refs/heads/master

2020-04-04T19:15:26.941570

2018-11-05T10:34:49

156,198,465

1

0

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# -*- coding: utf-8 -*- # Author: shijack import sys import time sys.path.append('../../') import os from nets import resnet_v2 from net_model.extract_cnn_vgg16 import VGG16_MODIFIED import h5py import numpy as np import tensorflow as tf from keras.preprocessing import image os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "0" from util import utils from net_model.extract_cnn_densenet_keras import DenseNETMAX from z_extend_rmac.rmac import rmac from z_extend_rmac.get_regions import rmac_regions, get_size_vgg_feat_map def feature_generator_densenet(file_img, file_feature_output): tmp_img_list = [] img_list = [] with open(file_img, 'r') as f: tmp_img_list = f.readlines() for item_img in tmp_img_list: img_list.append(item_img.split(' ')[0]) print "--------------------------------------------------" print " feature extraction starts" print "--------------------------------------------------" feats = [] names = [] start_time = time.time() model = DenseNETMAX() for i, img_path in enumerate(img_list): norm_feat = model.extract_feat(img_path) # dct_feat = np.multiply(np.array(DCT_binaray(img_path)),np.full((1,256),0.01)) # dct_feat = get_dct_feature(img_path) # dct_feat = DCT_binaray(img_path) # final_feat = np.append(dct_feat,norm_feat) img_name = img_path # norm_feat = np.hstack((norm_feat,np.zeros([32,],dtype=np.float32))) feats.append(norm_feat) names.append(img_name) print "extracting feature from image No. %d , %d images in total" % ((i + 1), len(img_list)) end_time = time.time() print ("final_feature extract time:", (end_time - start_time)) feats = np.array(feats) # directory for storing extracted features output = file_feature_output print "--------------------------------------------------" print " writing feature extraction results ..." print "--------------------------------------------------" h5f = h5py.File(output, 'w') h5f.create_dataset('dataset_1', data=feats) h5f.create_dataset('dataset_2', data=names) h5f.close() def feature_generator_rmac_vgg16(dir_img, file_feature_output, is_split_dir=False): ''' 按照文件夹目录，每个目录生成一个文件夹所有图片特征的集合.bow文件，format：每行一个图片的特征。 :param dir_img: :param file_feature_output: :param is_split_dir: :return: ''' path = dir_img print "--------------------------------------------------" print " feature extraction starts" print "--------------------------------------------------" if is_split_dir: model = rmac.rmac(20) for child_dirs in utils.get_dirs_child(path): img_list = utils.get_all_files_suffix(child_dirs) start_time = time.time() feats = [] names = [] for i, img_path in enumerate(img_list): img = image.load_img(img_path) # Resize scale = utils.IMG_SIZE / max(img.size) new_size = (int(np.ceil(scale * img.size[0])), int(np.ceil(scale * img.size[1]))) # print('Original size: %s, Resized image: %s' % (str(img.size), str(new_size))) img = img.resize(new_size) # Mean substraction x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = utils.preprocess_image(x) # Load RMAC model Wmap, Hmap = get_size_vgg_feat_map(x.shape[2], x.shape[1]) regions = rmac_regions(Wmap, Hmap, 3) # Compute RMAC vector # print('Extracting RMAC from image...') # print (len(regions)) norm_feat = model.predict([x, np.expand_dims(regions, axis=0)]) norm_feat = norm_feat.reshape((-1,)) img_name = os.path.split(img_path)[1] final_feat = np.hstack((norm_feat.reshape((-1,)), np.zeros([288, ], dtype=np.float32))) feats.append(final_feat) names.append(img_name) print "extracting feature from image No. %d , %d images in total" % ((i + 1), len(img_list)) feats = np.array(feats) print "--------------------------------------------------" print " writing feature extraction results ..." print "--------------------------------------------------" feats_6 = feats.astype('float32') np.savetxt(child_dirs + "/" + child_dirs.split("/")[-1] + '.bow', feats_6, fmt='%f') end_time = time.time() print ('the total time cnsumed is %d\n', (end_time - start_time)) else: feats = [] names = [] start_time = time.time() model = rmac.rmac(20) img_list = utils.get_all_files_suffix(dir_img) for i, img_path in enumerate(img_list): img = image.load_img(img_path) # Resize scale = utils.IMG_SIZE / max(img.size) new_size = (int(np.ceil(scale * img.size[0])), int(np.ceil(scale * img.size[1]))) # print('Original size: %s, Resized image: %s' % (str(img.size), str(new_size))) img = img.resize(new_size) # Mean substraction x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = utils.preprocess_image(x) # Load RMAC model Wmap, Hmap = get_size_vgg_feat_map(x.shape[2], x.shape[1]) regions = rmac_regions(Wmap, Hmap, 3) # Compute RMAC vector # print('Extracting RMAC from image...') # print (len(regions)) norm_feat = model.predict([x, np.expand_dims(regions, axis=0)]) norm_feat = norm_feat.reshape((-1,)) img_name = os.path.split(img_path)[1] final_feat = np.hstack((norm_feat.reshape((-1,)), np.zeros([288, ], dtype=np.float32))) feats.append(final_feat) names.append(img_name) print "extracting feature from image No. %d , %d images in total" % ((i + 1), len(img_list)) end_time = time.time() print ("final_feature extract time:", (end_time - start_time)) feats = np.array(feats) print "--------------------------------------------------" print " writing feature extraction results ..." print "--------------------------------------------------" # directory for storing extracted features output = file_feature_output h5f = h5py.File(output, 'w') h5f.create_dataset('dataset_1', data=feats) h5f.create_dataset('dataset_2', data=names) h5f.close() def feature_generator_vae(file_img, file_meta_graph, file_ckpt, file_feature_output): print os.path.abspath(file_meta_graph) print file_ckpt tmp_img_list = [] img_list = [] with open(file_img, 'r') as f: tmp_img_list = f.readlines() for item_img in tmp_img_list: img_list.append(item_img.split(' ')[0]) print "--------------------------------------------------" print " feature extraction starts" print "--------------------------------------------------" feats = [] names = [] start_time = time.time() with tf.Session() as sess: saver = tf.train.import_meta_graph(file_meta_graph) saver.restore(sess, file_ckpt) graph = tf.get_default_graph() x_input = graph.get_tensor_by_name('encoder/input_img:0') latent_feature = graph.get_tensor_by_name('variance/latent_feature:0') for i, img_path in enumerate(img_list): img = utils.img_process(img_path) norm_feat = sess.run(latent_feature, feed_dict={x_input: img}) img_name = img_path # norm_feat = np.hstack((norm_feat,np.zeros([160,],dtype=np.float32))) feats.append(norm_feat.flatten()) names.append(img_name) print "extracting feature from image No. %d , %d images in total" % ((i + 1), len(img_list)) end_time = time.time() print ("final_feature extract time:", (end_time - start_time)) feats = np.array(feats) print "--------------------------------------------------" print " writing feature extraction results ..." print "--------------------------------------------------" # directory for storing extracted features output = file_feature_output h5f = h5py.File(output, 'w') h5f.create_dataset('dataset_1', data=feats) h5f.create_dataset('dataset_2', data=names) h5f.close() def feature_generator_basenet(file_img, checkpoints_dir, file_feature_output): tmp_img_list = [] img_list = [] with open(file_img, 'r') as f: tmp_img_list = f.readlines() for item_img in tmp_img_list: img_list.append(item_img.split(' ')[0]) print "--------------------------------------------------" print " feature extraction starts" print "--------------------------------------------------" feats = [] names = [] from tensorflow.contrib import slim x_input = tf.placeholder(tf.float32, shape=[None, 224, 224, 3], name='input_img') # latent_mean, latent_stddev = encoder(x_input, train_logical=True, latent_dim=LATENT_DIM) # latent_mean, latent_stddev = encoder_vgg16(x_input, latent_dim=LATENT_DIM) # latent_mean, latent_stddev = encoder_vgg19(x_input, latent_dim=LATENT_DIM) # latent_mean, latent_stddev = encoder_inceptionv1(x_input, latent_dim=LATENT_DIM) # latent_mean, latent_stddev = encoder_inceptionv4(x_input, latent_dim=LATENT_DIM) # latent_mean, latent_stddev = encoder_inception_resnetv2(x_input, latent_dim=LATENT_DIM) # latent_mean, latent_stddev = encoder_resnetv2_152(x_input, latent_dim=LATENT_DIM)#参数过多，训练很慢 with slim.arg_scope(resnet_v2.resnet_arg_scope()): logits, _ = resnet_v2.resnet_v2_101(x_input, num_classes=None, is_training=False) init_fn = slim.assign_from_checkpoint_fn( os.path.join(checkpoints_dir, 'resnet_v2_101.ckpt'), slim.get_model_variables('resnet_v2_101')) start_time = time.time() with tf.Session() as sess: init_fn(sess) latent_feature = logits for i, img_path in enumerate(img_list): img = utils.img_process_vgg_tf(img_path) norm_feat = sess.run(latent_feature, feed_dict={x_input: img}) img_name = img_path # norm_feat = np.hstack((norm_feat,np.zeros([160,],dtype=np.float32))) feats.append(norm_feat.flatten()) names.append(img_name) print "extracting feature from image No. %d , %d images in total" % ((i + 1), len(img_list)) end_time = time.time() print ("final_feature extract time:", (end_time - start_time)) feats = np.array(feats) print "--------------------------------------------------" print " writing feature extraction results ..." print "--------------------------------------------------" # directory for storing extracted features output = file_feature_output h5f = h5py.File(output, 'w') h5f.create_dataset('dataset_1', data=feats) h5f.create_dataset('dataset_2', data=names) h5f.close() def feature_generator_basenet_vgg(file_img, file_feature_output): tmp_img_list = [] img_list = [] with open(file_img, 'r') as f: tmp_img_list = f.readlines() for item_img in tmp_img_list: img_list.append(item_img.split(' ')[0]) print "--------------------------------------------------" print " feature extraction starts" print "--------------------------------------------------" feats = [] names = [] # model = DenseNETMAX() model = VGG16_MODIFIED() start_time = time.time() for i, img_path in enumerate(img_list): norm_feat = model.extract_feat(img_path) # dct_feat = DCT_binaray(img_path) # final_feat = np.append(dct_feat, norm_feat) img_name = img_path # norm_feat = np.hstack((norm_feat,np.zeros([160,],dtype=np.float32))) feats.append(norm_feat.flatten()) names.append(img_name) print "extracting feature from image No. %d , %d images in total" % ((i + 1), len(img_list)) end_time = time.time() print ("final_feature extract time:", (end_time - start_time)) feats = np.array(feats) print "--------------------------------------------------" print " writing feature extraction results ..." print "--------------------------------------------------" # directory for storing extracted features h5f = h5py.File(file_feature_output, 'w') h5f.create_dataset('dataset_1', data=feats) h5f.create_dataset('dataset_2', data=names) h5f.close() if __name__ == "__main__": args = {'index_basenet': './result_generator/features/feature_densenet169_trans_imgs_basenet.h5', 'index': '../features/feature_vae_resnetv2_101_trans_imgs_136000_basenet.h5', 'database': '/data/datasets/trans_imgs'} # feature_generator_densenet(dir_img=args["database"], file_feature_output=args["index"]) # feature_generator_rmac_vgg16(dir_img=args["database"], file_feature_output=args["index"]) # file_ckpt = '/shihuijie/project/densenet/model_new/model_vae_resnetv2_101/vae-136000' # feature_generator_vae(file_img='/shihuijie/project/vae/data/image_list.txt', # file_meta_graph=file_ckpt + '.meta', # file_ckpt=file_ckpt, # file_feature_output=args["index"]) feature_generator_basenet(file_img='/shihuijie/project/vae/data/image_list.txt', checkpoints_dir='/shihuijie/project/vae/checkpoints/resnet_v2_101/', file_feature_output=args["index_basenet"]) # feature_generator_basenet_vgg(file_img='/shihuijie/project/vae/data/image_list.txt', # file_feature_output=args["index_basenet"]) # feature_generator_densenet(file_img='/shihuijie/project/vae-system/data/image_list.txt', # file_feature_output=args["index_basenet"])

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import reactivex as rx import reactivex.operators as ops from reactivex.subject import Subject def wrap_items(i): return i.pipe(ops.map(lambda j: 'obs {}: {}'.format(i, j))) numbers = rx.from_([1, 2, 3, 4, 5, 6]) numbers.pipe( ops.group_by(lambda i: i % 2 == 0), ops.flat_map(wrap_items), ).subscribe( on_next=lambda i: print("on_next {}".format(i)), on_error=lambda e: print("on_error: {}".format(e)), on_completed=lambda: print("on_completed") )

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""" WSGI config for favorite_book project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.10/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "favorite_book.settings") application = get_wsgi_application()

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# -*- coding: utf-8 -*- """Cisco DNA Center retrievesPreviousPathtrace data model. Copyright (c) 2019-2021 Cisco Systems. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from __future__ import ( absolute_import, division, print_function, unicode_literals, ) import fastjsonschema import json from dnacentersdk.exceptions import MalformedRequest from builtins import * class JSONSchemaValidatorEd5Cbafc332A5Efa97547736Ba8B6044(object): """retrievesPreviousPathtrace request schema definition.""" def __init__(self): super(JSONSchemaValidatorEd5Cbafc332A5Efa97547736Ba8B6044, self).__init__() self._validator = fastjsonschema.compile(json.loads( '''{ "$schema": "http://json-schema.org/draft-04/schema#", "properties": { "response": { "properties": { "detailedStatus": { "properties": { "aclTraceCalculation": { "type": "string" }, "aclTraceCalculationFailureReason": { "type": "string" } }, "type": "object" }, "lastUpdate": { "type": "string" }, "networkElements": { "items": { "properties": { "accuracyList": { "items": { "properties": { "percent": { "type": "integer" }, "reason": { "type": "string" } }, "type": "object" }, "type": "array" }, "detailedStatus": { "properties": { "aclTraceCalculation": { "type": "string" }, "aclTraceCalculationFailureReason": { "type": "string" } }, "type": "object" }, "deviceStatistics": { "properties": { "cpuStatistics": { "properties": { "fiveMinUsageInPercentage": { "type": "number" }, "fiveSecsUsageInPercentage": { "type": "number" }, "oneMinUsageInPercentage": { "type": "number" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "memoryStatistics": { "properties": { "memoryUsage": { "type": "integer" }, "refreshedAt": { "type": "integer" }, "totalMemory": { "type": "integer" } }, "type": "object" } }, "type": "object" }, "deviceStatsCollection": { "type": "string" }, "deviceStatsCollectionFailureReason": { "type": "string" }, "egressPhysicalInterface": { "properties": { "aclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "interfaceStatistics": { "properties": { "adminStatus": { "type": "string" }, "inputPackets": { "type": "integer" }, "inputQueueCount": { "type": "integer" }, "inputQueueDrops": { "type": "integer" }, "inputQueueFlushes": { "type": "integer" }, "inputQueueMaxDepth": { "type": "integer" }, "inputRatebps": { "type": "integer" }, "operationalStatus": { "type": "string" }, "outputDrop": { "type": "integer" }, "outputPackets": { "type": "integer" }, "outputQueueCount": { "type": "integer" }, "outputQueueDepth": { "type": "integer" }, "outputRatebps": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "interfaceStatsCollection": { "type": "string" }, "interfaceStatsCollectionFailureReason": { "type": "string" }, "name": { "type": "string" }, "pathOverlayInfo": { "items": { "properties": { "controlPlane": { "type": "string" }, "dataPacketEncapsulation": { "type": "string" }, "destIp": { "type": "string" }, "destPort": { "type": "string" }, "protocol": { "type": "string" }, "sourceIp": { "type": "string" }, "sourcePort": { "type": "string" }, "vxlanInfo": { "properties": { "dscp": { "type": "string" }, "vnid": { "type": "string" } }, "type": "object" } }, "type": "object" }, "type": "array" }, "qosStatistics": { "items": { "properties": { "classMapName": { "type": "string" }, "dropRate": { "type": "integer" }, "numBytes": { "type": "integer" }, "numPackets": { "type": "integer" }, "offeredRate": { "type": "integer" }, "queueBandwidthbps": { "type": "string" }, "queueDepth": { "type": "integer" }, "queueNoBufferDrops": { "type": "integer" }, "queueTotalDrops": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "type": "array" }, "qosStatsCollection": { "type": "string" }, "qosStatsCollectionFailureReason": { "type": "string" }, "usedVlan": { "type": "string" }, "vrfName": { "type": "string" } }, "type": "object" }, "egressVirtualInterface": { "properties": { "aclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "interfaceStatistics": { "properties": { "adminStatus": { "type": "string" }, "inputPackets": { "type": "integer" }, "inputQueueCount": { "type": "integer" }, "inputQueueDrops": { "type": "integer" }, "inputQueueFlushes": { "type": "integer" }, "inputQueueMaxDepth": { "type": "integer" }, "inputRatebps": { "type": "integer" }, "operationalStatus": { "type": "string" }, "outputDrop": { "type": "integer" }, "outputPackets": { "type": "integer" }, "outputQueueCount": { "type": "integer" }, "outputQueueDepth": { "type": "integer" }, "outputRatebps": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "interfaceStatsCollection": { "type": "string" }, "interfaceStatsCollectionFailureReason": { "type": "string" }, "name": { "type": "string" }, "pathOverlayInfo": { "items": { "properties": { "controlPlane": { "type": "string" }, "dataPacketEncapsulation": { "type": "string" }, "destIp": { "type": "string" }, "destPort": { "type": "string" }, "protocol": { "type": "string" }, "sourceIp": { "type": "string" }, "sourcePort": { "type": "string" }, "vxlanInfo": { "properties": { "dscp": { "type": "string" }, "vnid": { "type": "string" } }, "type": "object" } }, "type": "object" }, "type": "array" }, "qosStatistics": { "items": { "properties": { "classMapName": { "type": "string" }, "dropRate": { "type": "integer" }, "numBytes": { "type": "integer" }, "numPackets": { "type": "integer" }, "offeredRate": { "type": "integer" }, "queueBandwidthbps": { "type": "string" }, "queueDepth": { "type": "integer" }, "queueNoBufferDrops": { "type": "integer" }, "queueTotalDrops": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "type": "array" }, "qosStatsCollection": { "type": "string" }, "qosStatsCollectionFailureReason": { "type": "string" }, "usedVlan": { "type": "string" }, "vrfName": { "type": "string" } }, "type": "object" }, "flexConnect": { "properties": { "authentication": { "enum": [ "LOCAL", "CENTRAL" ], "type": "string" }, "dataSwitching": { "enum": [ "LOCAL", "CENTRAL" ], "type": "string" }, "egressAclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "ingressAclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "wirelessLanControllerId": { "type": "string" }, "wirelessLanControllerName": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "ingressPhysicalInterface": { "properties": { "aclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "interfaceStatistics": { "properties": { "adminStatus": { "type": "string" }, "inputPackets": { "type": "integer" }, "inputQueueCount": { "type": "integer" }, "inputQueueDrops": { "type": "integer" }, "inputQueueFlushes": { "type": "integer" }, "inputQueueMaxDepth": { "type": "integer" }, "inputRatebps": { "type": "integer" }, "operationalStatus": { "type": "string" }, "outputDrop": { "type": "integer" }, "outputPackets": { "type": "integer" }, "outputQueueCount": { "type": "integer" }, "outputQueueDepth": { "type": "integer" }, "outputRatebps": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "interfaceStatsCollection": { "type": "string" }, "interfaceStatsCollectionFailureReason": { "type": "string" }, "name": { "type": "string" }, "pathOverlayInfo": { "items": { "properties": { "controlPlane": { "type": "string" }, "dataPacketEncapsulation": { "type": "string" }, "destIp": { "type": "string" }, "destPort": { "type": "string" }, "protocol": { "type": "string" }, "sourceIp": { "type": "string" }, "sourcePort": { "type": "string" }, "vxlanInfo": { "properties": { "dscp": { "type": "string" }, "vnid": { "type": "string" } }, "type": "object" } }, "type": "object" }, "type": "array" }, "qosStatistics": { "items": { "properties": { "classMapName": { "type": "string" }, "dropRate": { "type": "integer" }, "numBytes": { "type": "integer" }, "numPackets": { "type": "integer" }, "offeredRate": { "type": "integer" }, "queueBandwidthbps": { "type": "string" }, "queueDepth": { "type": "integer" }, "queueNoBufferDrops": { "type": "integer" }, "queueTotalDrops": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "type": "array" }, "qosStatsCollection": { "type": "string" }, "qosStatsCollectionFailureReason": { "type": "string" }, "usedVlan": { "type": "string" }, "vrfName": { "type": "string" } }, "type": "object" }, "ingressVirtualInterface": { "properties": { "aclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "interfaceStatistics": { "properties": { "adminStatus": { "type": "string" }, "inputPackets": { "type": "integer" }, "inputQueueCount": { "type": "integer" }, "inputQueueDrops": { "type": "integer" }, "inputQueueFlushes": { "type": "integer" }, "inputQueueMaxDepth": { "type": "integer" 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"classMapName": { "type": "string" }, "dropRate": { "type": "integer" }, "numBytes": { "type": "integer" }, "numPackets": { "type": "integer" }, "offeredRate": { "type": "integer" }, "queueBandwidthbps": { "type": "string" }, "queueDepth": { "type": "integer" }, "queueNoBufferDrops": { "type": "integer" }, "queueTotalDrops": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "type": "array" }, "qosStatsCollection": { "type": "string" }, "qosStatsCollectionFailureReason": { "type": "string" }, "usedVlan": { "type": "string" }, "vrfName": { "type": "string" } }, "type": "object" }, "ip": { "type": "string" }, "linkInformationSource": { "type": "string" }, "name": { "type": "string" }, "perfMonCollection": { "type": "string" }, "perfMonCollectionFailureReason": { "type": "string" }, "perfMonStatistics": { "items": { "properties": { "byteRate": { "type": "integer" }, "destIpAddress": { "type": "string" }, "destPort": { "type": "string" }, "inputInterface": { "type": "string" }, "ipv4DSCP": { "type": "string" }, "ipv4TTL": { "type": "integer" }, "outputInterface": { "type": "string" }, "packetBytes": { "type": "integer" }, "packetCount": { "type": "integer" }, "packetLoss": { "type": "integer" }, "packetLossPercentage": { "type": "number" }, "protocol": { "type": "string" }, "refreshedAt": { "type": "integer" }, "rtpJitterMax": { "type": "integer" }, "rtpJitterMean": { "type": "integer" }, "rtpJitterMin": { "type": "integer" }, "sourceIpAddress": { "type": "string" }, "sourcePort": { "type": "string" } }, "type": "object" }, "type": "array" }, "role": { "type": "string" }, "ssid": { "type": "string" }, "tunnels": { "items": { "type": "string" }, "type": "array" }, "type": { "type": "string" }, "wlanId": { "type": "string" } }, "type": "object" }, "type": "array" }, "networkElementsInfo": { "items": { "properties": { "accuracyList": { "items": { "properties": { "percent": { "type": "integer" }, "reason": { "type": "string" } }, "type": "object" }, "type": "array" }, "detailedStatus": { "properties": { "aclTraceCalculation": { "type": "string" }, "aclTraceCalculationFailureReason": { "type": "string" } }, "type": "object" }, "deviceStatistics": { "properties": { "cpuStatistics": { "properties": { "fiveMinUsageInPercentage": { "type": "number" }, "fiveSecsUsageInPercentage": { "type": "number" }, "oneMinUsageInPercentage": { "type": "number" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "memoryStatistics": { "properties": { "memoryUsage": { "type": "integer" }, "refreshedAt": { "type": "integer" }, "totalMemory": { "type": "integer" } }, "type": "object" } }, "type": "object" }, "deviceStatsCollection": { "type": "string" }, "deviceStatsCollectionFailureReason": { "type": "string" }, "egressInterface": { "properties": { "physicalInterface": { "properties": { "aclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "interfaceStatistics": { "properties": { "adminStatus": { "type": "string" }, "inputPackets": { "type": "integer" }, "inputQueueCount": { "type": "integer" }, "inputQueueDrops": { "type": "integer" }, "inputQueueFlushes": { "type": "integer" }, "inputQueueMaxDepth": { "type": "integer" }, "inputRatebps": { "type": "integer" }, "operationalStatus": { "type": "string" }, "outputDrop": { "type": "integer" }, "outputPackets": { "type": "integer" }, "outputQueueCount": { "type": "integer" }, "outputQueueDepth": { "type": "integer" }, "outputRatebps": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "interfaceStatsCollection": { "type": "string" }, "interfaceStatsCollectionFailureReason": { "type": "string" }, "name": { "type": "string" }, "pathOverlayInfo": { "items": { "properties": { "controlPlane": { "type": "string" }, "dataPacketEncapsulation": { "type": "string" }, "destIp": { "type": "string" }, "destPort": { "type": "string" }, "protocol": { "type": "string" }, "sourceIp": { "type": "string" }, "sourcePort": { "type": "string" }, "vxlanInfo": { "properties": { "dscp": { "type": "string" }, "vnid": { "type": "string" } }, "type": "object" } }, "type": "object" }, "type": "array" }, "qosStatistics": { "items": { "properties": { "classMapName": { "type": "string" }, "dropRate": { "type": "integer" }, "numBytes": { "type": "integer" }, "numPackets": { "type": "integer" }, "offeredRate": { "type": "integer" }, "queueBandwidthbps": { "type": "string" }, "queueDepth": { "type": "integer" }, "queueNoBufferDrops": { "type": "integer" }, "queueTotalDrops": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "type": "array" }, "qosStatsCollection": { "type": "string" }, "qosStatsCollectionFailureReason": { "type": "string" }, "usedVlan": { "type": "string" }, "vrfName": { "type": "string" } }, "type": "object" }, "virtualInterface": { "items": { "properties": { "aclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "interfaceStatistics": { "properties": { "adminStatus": { "type": "string" }, "inputPackets": { "type": "integer" }, "inputQueueCount": { "type": "integer" }, "inputQueueDrops": { "type": "integer" }, "inputQueueFlushes": { "type": "integer" }, "inputQueueMaxDepth": { "type": "integer" }, "inputRatebps": { "type": "integer" }, "operationalStatus": { "type": "string" }, "outputDrop": { "type": "integer" }, "outputPackets": { "type": "integer" }, "outputQueueCount": { "type": "integer" }, "outputQueueDepth": { "type": "integer" }, "outputRatebps": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "interfaceStatsCollection": { "type": "string" }, "interfaceStatsCollectionFailureReason": { "type": "string" }, "name": { "type": "string" }, "pathOverlayInfo": { "items": { "properties": { "controlPlane": { "type": "string" }, "dataPacketEncapsulation": { "type": "string" }, "destIp": { "type": "string" }, "destPort": { "type": "string" }, "protocol": { "type": "string" }, "sourceIp": { "type": "string" }, "sourcePort": { "type": "string" }, "vxlanInfo": { "properties": { "dscp": { "type": "string" }, "vnid": { "type": "string" } }, "type": "object" } }, "type": "object" }, "type": "array" }, "qosStatistics": { "items": { "properties": { "classMapName": { "type": "string" }, "dropRate": { "type": "integer" }, "numBytes": { "type": "integer" }, "numPackets": { "type": "integer" }, "offeredRate": { "type": "integer" }, "queueBandwidthbps": { "type": "string" }, "queueDepth": { "type": "integer" }, "queueNoBufferDrops": { "type": "integer" }, "queueTotalDrops": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "type": "array" }, "qosStatsCollection": { "type": "string" }, "qosStatsCollectionFailureReason": { "type": "string" }, "usedVlan": { "type": "string" }, "vrfName": { "type": "string" } }, "type": "object" }, "type": "array" } }, "type": "object" }, "flexConnect": { "properties": { "authentication": { "enum": [ "LOCAL", "CENTRAL" ], "type": "string" }, "dataSwitching": { "enum": [ "LOCAL", "CENTRAL" ], "type": "string" }, "egressAclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "ingressAclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "wirelessLanControllerId": { "type": "string" }, "wirelessLanControllerName": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "ingressInterface": { "properties": { "physicalInterface": { "properties": { "aclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "interfaceStatistics": { "properties": { "adminStatus": { "type": "string" }, "inputPackets": { "type": "integer" }, "inputQueueCount": { "type": "integer" }, "inputQueueDrops": { "type": "integer" }, "inputQueueFlushes": { "type": "integer" }, "inputQueueMaxDepth": { "type": "integer" }, "inputRatebps": { "type": "integer" }, "operationalStatus": { "type": "string" }, "outputDrop": { "type": "integer" }, "outputPackets": { "type": "integer" }, "outputQueueCount": { "type": "integer" }, "outputQueueDepth": { "type": "integer" }, "outputRatebps": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "interfaceStatsCollection": { "type": "string" }, "interfaceStatsCollectionFailureReason": { "type": "string" }, "name": { "type": "string" }, "pathOverlayInfo": { "items": { "properties": { "controlPlane": { "type": "string" }, "dataPacketEncapsulation": { "type": "string" }, "destIp": { "type": "string" }, "destPort": { "type": "string" }, "protocol": { "type": "string" }, "sourceIp": { "type": "string" }, "sourcePort": { "type": "string" }, "vxlanInfo": { "properties": { "dscp": { "type": "string" }, "vnid": { "type": "string" } }, "type": "object" } }, "type": "object" }, "type": "array" }, "qosStatistics": { "items": { "properties": { "classMapName": { "type": "string" }, "dropRate": { "type": "integer" }, "numBytes": { "type": "integer" }, "numPackets": { "type": "integer" }, "offeredRate": { "type": "integer" }, "queueBandwidthbps": { "type": "string" }, "queueDepth": { "type": "integer" }, "queueNoBufferDrops": { "type": "integer" }, "queueTotalDrops": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "type": "array" }, "qosStatsCollection": { "type": "string" }, "qosStatsCollectionFailureReason": { "type": "string" }, "usedVlan": { "type": "string" }, "vrfName": { "type": "string" } }, "type": "object" }, "virtualInterface": { "items": { "properties": { "aclAnalysis": { "properties": { "aclName": { "type": "string" }, "matchingAces": { "items": { "properties": { "ace": { "type": "string" }, "matchingPorts": { "items": { "properties": { "ports": { "items": { "properties": { "destPorts": { "items": { "type": "string" }, "type": "array" }, "sourcePorts": { "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "protocol": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "type": "array" }, "result": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "interfaceStatistics": { "properties": { "adminStatus": { "type": "string" }, "inputPackets": { "type": "integer" }, "inputQueueCount": { "type": "integer" }, "inputQueueDrops": { "type": "integer" }, "inputQueueFlushes": { "type": "integer" }, "inputQueueMaxDepth": { "type": "integer" }, "inputRatebps": { "type": "integer" }, "operationalStatus": { "type": "string" }, "outputDrop": { "type": "integer" }, "outputPackets": { "type": "integer" }, "outputQueueCount": { "type": "integer" }, "outputQueueDepth": { "type": "integer" }, "outputRatebps": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "interfaceStatsCollection": { "type": "string" }, "interfaceStatsCollectionFailureReason": { "type": "string" }, "name": { "type": "string" }, "pathOverlayInfo": { "items": { "properties": { "controlPlane": { "type": "string" }, "dataPacketEncapsulation": { "type": "string" }, "destIp": { "type": "string" }, "destPort": { "type": "string" }, "protocol": { "type": "string" }, "sourceIp": { "type": "string" }, "sourcePort": { "type": "string" }, "vxlanInfo": { "properties": { "dscp": { "type": "string" }, "vnid": { "type": "string" } }, "type": "object" } }, "type": "object" }, "type": "array" }, "qosStatistics": { "items": { "properties": { "classMapName": { "type": "string" }, "dropRate": { "type": "integer" }, "numBytes": { "type": "integer" }, "numPackets": { "type": "integer" }, "offeredRate": { "type": "integer" }, "queueBandwidthbps": { "type": "string" }, "queueDepth": { "type": "integer" }, "queueNoBufferDrops": { "type": "integer" }, "queueTotalDrops": { "type": "integer" }, "refreshedAt": { "type": "integer" } }, "type": "object" }, "type": "array" }, "qosStatsCollection": { "type": "string" }, "qosStatsCollectionFailureReason": { "type": "string" }, "usedVlan": { "type": "string" }, "vrfName": { "type": "string" } }, "type": "object" }, "type": "array" } }, "type": "object" }, "ip": { "type": "string" }, "linkInformationSource": { "type": "string" }, "name": { "type": "string" }, "perfMonCollection": { "type": "string" }, "perfMonCollectionFailureReason": { "type": "string" }, "perfMonitorStatistics": { "items": { "properties": { "byteRate": { "type": "integer" }, "destIpAddress": { "type": "string" }, "destPort": { "type": "string" }, "inputInterface": { "type": "string" }, "ipv4DSCP": { "type": "string" }, "ipv4TTL": { "type": "integer" }, "outputInterface": { "type": "string" }, "packetBytes": { "type": "integer" }, "packetCount": { "type": "integer" }, "packetLoss": { "type": "integer" }, "packetLossPercentage": { "type": "number" }, "protocol": { "type": "string" }, "refreshedAt": { "type": "integer" }, "rtpJitterMax": { "type": "integer" }, "rtpJitterMean": { "type": "integer" }, "rtpJitterMin": { "type": "integer" }, "sourceIpAddress": { "type": "string" }, "sourcePort": { "type": "string" } }, "type": "object" }, "type": "array" }, "role": { "type": "string" }, "ssid": { "type": "string" }, "tunnels": { "items": { "type": "string" }, "type": "array" }, "type": { "type": "string" }, "wlanId": { "type": "string" } }, "type": "object" }, "type": "array" }, "properties": { "items": { "type": "string" }, "type": "array" }, "request": { "properties": { "controlPath": { "type": "boolean" }, "createTime": { "type": "integer" }, "destIP": { "type": "string" }, "destPort": { "type": "string" }, "failureReason": { "type": "string" }, "id": { "type": "string" }, "inclusions": { "items": { "type": "string" }, "type": "array" }, "lastUpdateTime": { "type": "integer" }, "periodicRefresh": { "type": "boolean" }, "protocol": { "type": "string" }, "sourceIP": { "type": "string" }, "sourcePort": { "type": "string" }, "status": { "type": "string" } }, "type": "object" } }, "type": "object" }, "version": { "type": "string" } }, "type": "object" }'''.replace("\n" + ' ' * 16, '') )) def validate(self, request): try: self._validator(request) except fastjsonschema.exceptions.JsonSchemaException as e: raise MalformedRequest( '{} is invalid. Reason: {}'.format(request, e.message) )

[ "wastorga@altus.co.cr" ]

wastorga@altus.co.cr

9518b3790b2967f59fe55686f22287926c8fd7fe

599709e7687a78f92b268315590d6ad750ce97d6

/src_py/l2func.py

490832659c4a2d9e01839b63a45ed3f2d32af2da

[]

no_license

ReiMatsuzaki/cbasis2

b99d096150d87f9301ed0e34f7be5f0203e4a81e

86f21146fab6fc6f750d02fb2200ea94616ca896

refs/heads/master

2021-01-19T23:15:32.864686

2017-04-27T07:29:26

88,953,186

0

null

UTF-8

Python

false

190

py

from l2func_bind import * from linspace import * from set_l2func import * from hatom import * from basis_set import * from l2func_io import * from lindep import * from d_basis import *

[ "matsuzaki.rei@sepia.chem.keio.ac.jp" ]

matsuzaki.rei@sepia.chem.keio.ac.jp

57ea1c2505e1c09b048701ba91772ab40663dfce

c234f93c1812d8c5cf07b6f91574d8b0818989ae

/restoran/restconf/main.py

1dcb9111bf692418a2c32f74294e814054beb199

[]

no_license

Alymbekov/RESTORAN

6a8cd6117eee40be82dee737ccbddc51f34fbf8e

fdd82aaa80ad70bf1a9645bd3e5d00675948ebe7

refs/heads/master

2020-05-06T12:33:31.206625

2019-05-20T14:17:07

180,128,403

2

0

null

2019-05-20T14:17:08

2019-04-08T10:46:57

Python

UTF-8

Python

false

1,371

py

import datetime from django.conf import settings REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': ( # 'rest_framework.authentication.BasicAuthentication', 'rest_framework_jwt.authentication.JSONWebTokenAuthentication', 'rest_framework.authentication.SessionAuthentication', ), 'DEFAULT_PERMISSION_CLASSES': ( 'rest_framework.permissions.IsAuthenticatedOrReadOnly', ), 'DEFAULT_PAGINATION_CLASS': 'restoran.restconf.pagination.CustomPagination', 'DEFAULT_FILTER_BACKENDS': ( 'rest_framework.filters.SearchFilter', 'rest_framework.filters.OrderingFilter', ), 'SEARCH_PARAM': 'q', 'ORDERING_PARAM': 'ordering' } JWT_AUTH = { 'JWT_ENCODE_HANDLER': 'rest_framework_jwt.utils.jwt_encode_handler', 'JWT_DECODE_HANDLER': 'rest_framework_jwt.utils.jwt_decode_handler', 'JWT_PAYLOAD_HANDLER': 'rest_framework_jwt.utils.jwt_payload_handler', 'JWT_PAYLOAD_GET_USER_ID_HANDLER': 'rest_framework_jwt.utils.jwt_get_user_id_from_payload_handler', 'JWT_RESPONSE_PAYLOAD_HANDLER': # 'rest_framework_jwt.utils.jwt_response_payload_handler', 'users.utils.jwt_response_payload_handler', 'JWT_ALLOW_REFRESH': True, 'JWT_REFRESH_EXPIRATION_DELTA': datetime.timedelta(days=7), 'JWT_AUTH_HEADER_PREFIX': 'JWT', 'JWT_AUTH_COOKIE': None, }

[ "maxim.makarov.1997@mail.ru" ]

maxim.makarov.1997@mail.ru

41eedf17f955552608d5964a14ccb3227ffbbd8c

99287c727e2249336d6c27025920df620d7b124c

/streams/consumers.py

7b8d6a6ac941a9bc6fda109bc50daa6acf6a6215

[]

no_license

dadoeyad/event-stream

30809bdfec1958754dc10050bf4330c8e37a9a03

6c6aa6536fbd8e57b4dfbc6b519f8da2d418ae64

refs/heads/master

2021-01-12T09:54:28.396750

2016-12-17T15:45:00

76,292,264

0

null

UTF-8

Python

false

1,591

py

import logging from django.conf import settings from channels import Group from channels.sessions import channel_session from .tweets import tweets from slack import slack log = logging.getLogger(__name__) @channel_session def ws_connect(message): try: prefix, label = message['path'].decode('ascii').strip('/').split('/') if prefix != 'streams': log.debug('invalid ws path=%s', message['path']) return except ValueError: log.debug('invalid ws path=%s', message['path']) return log.debug('stream connect to label=%s', label) Group(label).add(message.reply_channel) message.channel_session['label'] = label if label == 'tweets': tweets.listener.set_group() tweets.filter(track=settings.LISTENER_WORDS, async=True) elif label == 'slack': slack.set_group() slack.start() else: log.warning('unknown label=%s', label) return @channel_session def ws_disconnect(message): try: prefix, label = message['path'].decode('ascii').strip('/').split('/') if prefix != 'streams': log.debug('invalid ws path=%s', message['path']) return except ValueError: log.debug('invalid ws path=%s', message['path']) return log.debug('stream disconnect to label=%s', label) Group(label).discard(message.reply_channel) if label == 'tweets': tweets.disconnect() elif label == 'slack': slack.disconnect() else: log.warning('unknown label=%s', label) return

[ "dado_eyad@Eyads-MacBook-Pro.local" ]

dado_eyad@Eyads-MacBook-Pro.local

e3474b32d77a685b3c23b5eca37bf340c0143dd5

44ee7102af2f141a51fb1086b0bb9f97fa214859

/p20.py

38517fbd6b150cdf1a3ec74a78aecc7d603e91b7

[]

no_license

ramyaramy/ramya

3ced7c19e543559d33525a09ba5e8453d4a6c2f1

5cc45ea822c1238299ad796cad5d0918c8cc5564

refs/heads/master

2021-05-10T12:09:00.570336

2018-02-21T17:11:11

118,432,451

0

1

null

UTF-8

Python

false

181

py

def main(): str=input() list=[] for i in str: x=ord(i)+3 y=chr(n) list.append(y) print(''.join(list)) if __name__ == '__main__': main()

[ "noreply@github.com" ]

ramyaramy.noreply@github.com

b062a23c7a8e0e59d13d4a6a55b09da524ed6b52

6262c5dc440d9b2e595d0a6d567d5e78d45b5ca6

/Coursera/compute_pay_func.py

8063ecd9b1ba66172bb1f637ae296309746018c2

[]

no_license

hasija-bhawna/Python

d19da940078afe44c40dbe1f1b35942e758f5681

eb207f7147c75ec3593b4365c6c2604e5bfef860

refs/heads/master

2022-11-18T08:13:16.340242

2020-07-14T00:48:06

279,179,489

0

null

UTF-8

Python

false

411

py

#compute pay with a function h = input('Enter the number of hours:') hours = float(h) r = input('Enter the rate per hour:') rate = float(r) def computepay(hours, rate): if hours <= 40: pay = hours*rate else: extra_hours = (hours-40) new_rate = (1.5*rate) pay = (40*rate) + (new_rate*extra_hours) return pay Pay = computepay(hours,rate) print('The pay is:', Pay)

[ "hasija.bhawna@gmail.com" ]

hasija.bhawna@gmail.com

d8a1362bd606a719b92a372f935715deb8496ba1

7e04c5851097c9f18a56b758022c16a8be3853d0

/conTable.py

c3309d23df63f261a5b17602c32d3fccd4eb34da

[]

no_license

fareise/segment-cluster

5faaa9e54ae96401539ff6968f82b10286c01907

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refs/heads/master

2021-01-23T07:44:14.478443

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def makeTable(files): skip = 0 rollSstick = ['_ROLL_CAPT_SSTICK', '_ROLL_CAPT_SSTICK-1', '_ROLL_CAPT_SSTICK-2', '_ROLL_CAPT_SSTICK-3'] sstick = ['_SSTICK_CAPT', '_SSTICK_CAPT-1','_SSTICK_CAPT-2','_SSTICK_CAPT-3'] tableFactor = pd.DataFrame(columns=['FILNAME','LAST','OPERNUM','RANGENUM','VAR','GAP','NI']) for i, fileName in enumerate(files): try: df = pd.read_csv(wd+fileName) df = df.fillna(method='pad') df = df.loc[(df['_ALTITUDE'] < 1000) & (df['_ALTITUDE'] >100), :] rollSum = pd.Series(df[rollSstick].values.ravel()) sstickSum = pd.Series(df[sstick].values.ravel()) factors = segmentOne(rollSum, sstickSum) #其他参数 height = df['_ALT_RADIO'][(factors['start']+factors['end'])/2] wind = df['_WIND_SPD'][factors['start']:factors['end']] windir = df['_WINDIR'][factors['start']:factors['end']] windMean = sum(wind)/(factors['end']-factors['start']) windirMean = sum(windir)/(factors['end']-factors['start']) windVar = calVar(wind) windirVar = calVar(windir) for m in range(0, len(factors["last"])): newFactor = pd.DataFrame({ "FILNAME": fileName, "LAST": factors["last"][m], "OPERNUM": factors["operNum"][m], "RANGENUM": factors["rangeNum"][m], "VAR": factors["var"][m], "GAP": factors["gap"][m], "NI": factors["ni"][m], "HEIGHT": height, "WINDMEAN": windMean, "WINDVAR": windVar, "WINDIRMEAN": windirMean, "WINDIRVAR": windir}, index=[i]) tableFactor = tableFactor.append(newFactor) except: skip = skip + 1 continue return tableFactor, skip

[ "noreply@github.com" ]

fareise.noreply@github.com

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/train_coarse_type_lstm_glove.py

d221d47017e9dc9a1e4537975b228cf87977b12f

[]

no_license

SearchGuru/DNN4QueryType

738882bbd402075edda355b7eb135c0dd60ed7bf

2940131767e910b772bb9ed7350afb5d121d62f0

refs/heads/master

2021-01-10T15:36:06.223671

2016-04-02T23:06:20

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from __future__ import print_function import numpy as np np.random.seed(1337) # for reproducibility from keras.preprocessing import sequence from keras.utils import np_utils from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.layers.embeddings import Embedding from keras.layers.recurrent import LSTM from six.moves import cPickle import deepctxt_util from deepctxt_util import DCTokenizer maxlen = 25 # cut texts after this number of words (among top max_features most common words) batch_size = 100 epoch = 3 tokenizer = DCTokenizer() print('Loading tokenizer') tokenizer.load('./glove.6B.100d.txt') #tokenizer.load('./glove.42B.300d.txt') print('Done') max_features = tokenizer.n_symbols vocab_dim = tokenizer.vocab_dim print('Loading data... (Train)') (X1, y_train) = deepctxt_util.load_raw_data_x_y(path='./raw_data/Train_CoarseType.tsv') print('Done') print('Loading data... (Test)') (X2, y_test) = deepctxt_util.load_raw_data_x_y(path='./raw_data/Test_CoarseType.tsv') print('Done') print('Converting data... (Train)') X_train = tokenizer.texts_to_sequences(X1, maxlen) print('Done') print('Converting data... (Test)') X_test = tokenizer.texts_to_sequences(X2, maxlen) print('Done') print(len(X_train), 'y_train sequences') print(len(X_test), 'y_test sequences') nb_classes = np.max(y_train)+1 Y_train = np_utils.to_categorical(y_train, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes) print('Y_train shape:', Y_train.shape) print('Y_test shape:', Y_test.shape) print("Pad sequences (samples x time)") X_train = sequence.pad_sequences(X_train, maxlen=maxlen) X_test = sequence.pad_sequences(X_test, maxlen=maxlen) print('X_train shape:', X_train.shape) print('X_test shape:', X_test.shape) print('Build model...') model = Sequential() model.add(Embedding(input_dim=max_features, output_dim=vocab_dim, input_length=maxlen, weights=[tokenizer.embedding_weights])) model.add(LSTM(128)) # try using a GRU instead, for fun model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) # try using different optimizers and different optimizer configs model.compile(loss='categorical_crossentropy', optimizer='adam') print("Train...") model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=epoch, validation_data=(X_test, Y_test), show_accuracy=True) score, acc = model.evaluate(X_test, Y_test, batch_size=100, show_accuracy=True) print('Test score:', score) print('Test accuracy:', acc) json_model_string = model.to_json() with open("./coarse_type_model_lstm_glove_"+str(batch_size)+"b.json", "w") as f: f.write(json_model_string) model.save_weights("./coarse_type_model_lstm_glove_" + str(batch_size) + "b.h5")

[ "cguihong@hotmail.com" ]

cguihong@hotmail.com

67744552c910cdc284977b1a4a0740afe3f8ebb5

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/This is a coding test with python/page 367.py

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[]

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Areum0921/Abox

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f4739c0c0835054afeca82484769e71fb8de47c8

refs/heads/master

2021-12-13T11:16:33.583366

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#bisect 이용 import bisect answer = -1 N,x = map(int,input().split(" ")) a = list(map(int,input().split(" "))) left_a = bisect.bisect_left(a,x) # 첫번째 a 인덱스 right_a = bisect.bisect_right(a,x) # 마지막 a 다음 인덱스 print(left_a,right_a) if right_a - left_a>0: answer = right_a - left_a print(answer)

[ "a90907@gmail.com" ]

a90907@gmail.com

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/Code/utils.py

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sprihap/Learning-generative-principles-of-a-symbol-system

b4ae2bfaa67bbf3739c2d02a11ff359f651c669d

7fedfcf0684874d197f439ad97d61339fc6bf54a

refs/heads/master

2023-05-28T01:14:20.219417

2021-06-17T19:26:21

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""" This script is from: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning/blob/master/utils.py """ import os import numpy as np import h5py import json import torch from scipy.misc import imread, imresize from tqdm import tqdm from collections import Counter from random import seed, choice, sample def create_input_files(dataset, karpathy_json_path, image_folder, captions_per_image, min_word_freq, output_folder, max_len=100): """ Creates input files for training, validation, and test data. :param dataset: name of dataset, one of 'coco', 'flickr8k', 'flickr30k' :param karpathy_json_path: path of Karpathy JSON file with splits and captions :param image_folder: folder with downloaded images :param captions_per_image: number of captions to sample per image :param min_word_freq: words occuring less frequently than this threshold are binned as <unk>s :param output_folder: folder to save files :param max_len: don't sample captions longer than this length """ assert dataset in {'coco', 'flickr8k', 'flickr30k'} # Read Karpathy JSON with open(karpathy_json_path, 'r') as j: data = json.load(j) # Read image paths and captions for each image train_image_paths = [] train_image_captions = [] val_image_paths = [] val_image_captions = [] test_image_paths = [] test_image_captions = [] word_freq = Counter() for img in data['images']: captions = [] for c in img['sentences']: # Update word frequency word_freq.update(c['tokens']) if len(c['tokens']) <= max_len: captions.append(c['tokens']) if len(captions) == 0: continue path = os.path.join(image_folder, img['filepath'], img['filename']) if dataset == 'coco' else os.path.join( image_folder, img['filename']) if img['split'] in {'train', 'restval'}: train_image_paths.append(path) train_image_captions.append(captions) elif img['split'] in {'val'}: val_image_paths.append(path) val_image_captions.append(captions) elif img['split'] in {'test'}: test_image_paths.append(path) test_image_captions.append(captions) # Sanity check assert len(train_image_paths) == len(train_image_captions) assert len(val_image_paths) == len(val_image_captions) assert len(test_image_paths) == len(test_image_captions) # Create word map #words = [w for w in word_freq.keys() if word_freq[w] > min_word_freq] # All the number words should be included even if they are not in training set words = ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten', 'eleven', 'twelve', 'thirteen', 'fourteen', 'fifteen', 'sixteen', 'seventeen', 'eighteen', 'nineteen', 'twenty', 'thirty', 'forty', 'fifty', 'sixty', 'seventy', 'eighty', 'ninety', 'hundred', 'thousand'] word_map = {k: v + 1 for v, k in enumerate(words)} word_map['<unk>'] = len(word_map) + 1 word_map['<start>'] = len(word_map) + 1 word_map['<end>'] = len(word_map) + 1 word_map['<pad>'] = 0 # Create a base/root name for all output files base_filename = dataset + '_' + str(captions_per_image) + '_cap_per_img_' + str(min_word_freq) + '_min_word_freq' # Save word map to a JSON with open(os.path.join(output_folder, 'WORDMAP_' + base_filename + '.json'), 'w') as j: json.dump(word_map, j) # Sample captions for each image, save images to HDF5 file, and captions and their lengths to JSON files seed(123) for impaths, imcaps, split in [(train_image_paths, train_image_captions, 'TRAIN'), (val_image_paths, val_image_captions, 'VAL'), (test_image_paths, test_image_captions, 'TEST')]: with h5py.File(os.path.join(output_folder, split + '_IMAGES_' + base_filename + '.hdf5'), 'a') as h: # Make a note of the number of captions we are sampling per image h.attrs['captions_per_image'] = captions_per_image # Create dataset inside HDF5 file to store images images = h.create_dataset('images', (len(impaths), 3, 256, 256), dtype='uint8') print("\nReading %s images and captions, storing to file...\n" % split) enc_captions = [] caplens = [] for i, path in enumerate(tqdm(impaths)): # Sample captions if len(imcaps[i]) < captions_per_image: captions = imcaps[i] + [choice(imcaps[i]) for _ in range(captions_per_image - len(imcaps[i]))] else: captions = sample(imcaps[i], k=captions_per_image) # Sanity check assert len(captions) == captions_per_image # Read images img = imread(impaths[i]) if len(img.shape) == 2: img = img[:, :, np.newaxis] img = np.concatenate([img, img, img], axis=2) img = imresize(img, (256, 256)) img = img.transpose(2, 0, 1) assert img.shape == (3, 256, 256) assert np.max(img) <= 255 # Save image to HDF5 file images[i] = img for j, c in enumerate(captions): # Encode captions enc_c = [word_map['<start>']] + [word_map.get(word, word_map['<unk>']) for word in c] + [ word_map['<end>']] + [word_map['<pad>']] * (max_len - len(c)) # Find caption lengths c_len = len(c) + 2 enc_captions.append(enc_c) caplens.append(c_len) # Sanity check assert images.shape[0] * captions_per_image == len(enc_captions) == len(caplens) # Save encoded captions and their lengths to JSON files with open(os.path.join(output_folder, split + '_CAPTIONS_' + base_filename + '.json'), 'w') as j: json.dump(enc_captions, j) with open(os.path.join(output_folder, split + '_CAPLENS_' + base_filename + '.json'), 'w') as j: json.dump(caplens, j) def init_embedding(embeddings): """ Fills embedding tensor with values from the uniform distribution. :param embeddings: embedding tensor """ bias = np.sqrt(3.0 / embeddings.size(1)) torch.nn.init.uniform_(embeddings, -bias, bias) def load_embeddings(emb_file, word_map): """ Creates an embedding tensor for the specified word map, for loading into the model. :param emb_file: file containing embeddings (stored in GloVe format) :param word_map: word map :return: embeddings in the same order as the words in the word map, dimension of embeddings """ # Find embedding dimension with open(emb_file, 'r') as f: emb_dim = len(f.readline().split(' ')) - 1 vocab = set(word_map.keys()) # Create tensor to hold embeddings, initialize embeddings = torch.FloatTensor(len(vocab), emb_dim) init_embedding(embeddings) # Read embedding file print("\nLoading embeddings...") for line in open(emb_file, 'r'): line = line.split(' ') emb_word = line[0] embedding = list(map(lambda t: float(t), filter(lambda n: n and not n.isspace(), line[1:]))) # Ignore word if not in train_vocab if emb_word not in vocab: continue embeddings[word_map[emb_word]] = torch.FloatTensor(embedding) return embeddings, emb_dim def clip_gradient(optimizer, grad_clip): """ Clips gradients computed during backpropagation to avoid explosion of gradients. :param optimizer: optimizer with the gradients to be clipped :param grad_clip: clip value """ for group in optimizer.param_groups: for param in group['params']: if param.grad is not None: param.grad.data.clamp_(-grad_clip, grad_clip) def save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, bleu4, is_best): """ Saves model checkpoint. :param data_name: base name of processed dataset :param epoch: epoch number :param epochs_since_improvement: number of epochs since last improvement in BLEU-4 score :param encoder: encoder model :param decoder: decoder model :param encoder_optimizer: optimizer to update encoder's weights, if fine-tuning :param decoder_optimizer: optimizer to update decoder's weights :param bleu4: validation BLEU-4 score for this epoch :param is_best: is this checkpoint the best so far? """ state = {'epoch': epoch, 'epochs_since_improvement': epochs_since_improvement, 'bleu-4': bleu4, 'encoder': encoder, 'decoder': decoder, 'encoder_optimizer': encoder_optimizer, 'decoder_optimizer': decoder_optimizer} filename = 'checkpoint_' + data_name + '.pth.tar' torch.save(state, filename) # If this checkpoint is the best so far, store a copy so it doesn't get overwritten by a worse checkpoint if is_best: torch.save(state, 'BEST_' + filename) class AverageMeter(object): """ Keeps track of most recent, average, sum, and count of a metric. """ def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def adjust_learning_rate(optimizer, shrink_factor): """ Shrinks learning rate by a specified factor. :param optimizer: optimizer whose learning rate must be shrunk. :param shrink_factor: factor in interval (0, 1) to multiply learning rate with. """ print("\nDECAYING learning rate.") for param_group in optimizer.param_groups: param_group['lr'] = param_group['lr'] * shrink_factor print("The new learning rate is %f\n" % (optimizer.param_groups[0]['lr'],)) def accuracy(scores, targets, k): """ Computes top-k accuracy, from predicted and true labels. :param scores: scores from the model :param targets: true labels :param k: k in top-k accuracy :return: top-k accuracy """ batch_size = targets.size(0) _, ind = scores.topk(k, 1, True, True) correct = ind.eq(targets.view(-1, 1).expand_as(ind)) correct_total = correct.view(-1).float().sum() # 0D tensor return correct_total.item() * (100.0 / batch_size)

[ "ziyxiang@electrode.sice.indiana.edu" ]

ziyxiang@electrode.sice.indiana.edu

970ac043cb0d2a47e40e680f4553f16167d33ddf

02d0714edfef5a2d3630d7659c553c157e291e52

/tempest/api/volume/admin/test_volumes_actions.py

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[ "Apache-2.0" ]

permissive

atulbangar09/tempest

f07dced592481a7ec71a9c7469b7d50d30cdc171

9f5644ce2784cd882e86ac89236f8f8f828d7c43

refs/heads/master

2023-02-06T10:26:40.112917

2020-01-21T16:17:16

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Apache-2.0

2020-01-17T09:52:53

2020-01-17T09:52:52

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# Copyright 2013 Huawei Technologies Co.,LTD # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from tempest.api.volume import base from tempest.common.utils import data_utils as utils from tempest import test class VolumesActionsV2Test(base.BaseVolumeAdminTest): @classmethod def setup_clients(cls): super(VolumesActionsV2Test, cls).setup_clients() cls.client = cls.volumes_client @classmethod def resource_setup(cls): super(VolumesActionsV2Test, cls).resource_setup() # Create a test shared volume for tests vol_name = utils.rand_name(cls.__name__ + '-Volume') cls.name_field = cls.special_fields['name_field'] params = {cls.name_field: vol_name} cls.volume = cls.client.create_volume(**params)['volume'] cls.client.wait_for_volume_status(cls.volume['id'], 'available') @classmethod def resource_cleanup(cls): # Delete the test volume cls.client.delete_volume(cls.volume['id']) cls.client.wait_for_resource_deletion(cls.volume['id']) super(VolumesActionsV2Test, cls).resource_cleanup() def _reset_volume_status(self, volume_id, status): # Reset the volume status body = self.admin_volume_client.reset_volume_status(volume_id, status) return body def tearDown(self): # Set volume's status to available after test self._reset_volume_status(self.volume['id'], 'available') super(VolumesActionsV2Test, self).tearDown() def _create_temp_volume(self): # Create a temp volume for force delete tests vol_name = utils.rand_name('Volume') params = {self.name_field: vol_name} temp_volume = self.client.create_volume(**params)['volume'] self.client.wait_for_volume_status(temp_volume['id'], 'available') return temp_volume def _create_reset_and_force_delete_temp_volume(self, status=None): # Create volume, reset volume status, and force delete temp volume temp_volume = self._create_temp_volume() if status: self._reset_volume_status(temp_volume['id'], status) self.admin_volume_client.force_delete_volume(temp_volume['id']) self.client.wait_for_resource_deletion(temp_volume['id']) @test.idempotent_id('d063f96e-a2e0-4f34-8b8a-395c42de1845') def test_volume_reset_status(self): # test volume reset status : available->error->available self._reset_volume_status(self.volume['id'], 'error') volume_get = self.admin_volume_client.show_volume( self.volume['id'])['volume'] self.assertEqual('error', volume_get['status']) @test.idempotent_id('21737d5a-92f2-46d7-b009-a0cc0ee7a570') def test_volume_force_delete_when_volume_is_creating(self): # test force delete when status of volume is creating self._create_reset_and_force_delete_temp_volume('creating') @test.idempotent_id('db8d607a-aa2e-4beb-b51d-d4005c232011') def test_volume_force_delete_when_volume_is_attaching(self): # test force delete when status of volume is attaching self._create_reset_and_force_delete_temp_volume('attaching') @test.idempotent_id('3e33a8a8-afd4-4d64-a86b-c27a185c5a4a') def test_volume_force_delete_when_volume_is_error(self): # test force delete when status of volume is error self._create_reset_and_force_delete_temp_volume('error') class VolumesActionsV1Test(VolumesActionsV2Test): _api_version = 1

[ "jignasha.vithalani@triliodata.com" ]

jignasha.vithalani@triliodata.com

bd6f314634efe8002e6d96e077d9e37cf04f2978

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/srcb/.idea/gen_loan_acct.py

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no_license

ccf738/ccfCodes

295238991a497f9a6f38b89bf76f8f39a3a965b1

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refs/heads/master

2021-01-01T06:44:38.630535

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# -*- coding:utf-8 -*- __author__ = 'qctest' import os import shutil import stat import excel import time import win32com.client import sqlite3 year = 2002 acct_seq = 0 num_seq = 0 id_seq = 0 def set_acct_no(file_name, branch_no, row_no): global acct_seq acct_seq += 1 acct_no = "68" + str(branch_no) + "709" + str(acct_seq).rjust(7,'0') file_name.set_value(row_no, 2, acct_no) def set_acct_seq(file_name, row_no): global year, num_seq num_seq += 1 if num_seq == 10000: num_seq = 1 year += 1 loan_seq = str(year) + "9" + str(num_seq).rjust(4, '0') file_name.set_value(row_no, 4, loan_seq) def set_id_no(file_name, branch_no,row_no, id_type, name_len): global id_seq id_seq += 1 file_name.set_value(row_no, 20, '01') if name_len <= 4: id_type = "11" id_no = "zhdk" + str(branch_no) + str(id_seq).rjust(6,'0') else: id_type = "44" id_no = "zhkr" + str(id_seq).rjust(6, '0') file_name.set_value(row_no, 19, id_no) file_name.set_value(row_no, 18, id_type) rep_loan_file = excel.Excel(unicode(r'D:\陈超峰\陈超峰\数据\20141203接收的20141124补录\1124补录发何东杰\置换贷款汇总表.xlsx', "utf-8"), "zhdk") for i in xrange(rep_loan_file.used_range(), 2, -1): row = rep_loan_file.get_row_data(i) if row[2] is None: continue try: branch_no = int(row[0]) except: branch_no = '85611001' id_type = row[18] set_acct_no(rep_loan_file,branch_no,i) set_acct_seq(rep_loan_file,i) if row[18] is None or row[19] is None: set_id_no(rep_loan_file,branch_no,i,id_type, len(row[2])) rep_loan_file.quit()

[ "ccf738@sina.com" ]

ccf738@sina.com

b2af9ea8f603ad82066c9c195b15839ee775b30e

02c9c208d20a04b50b9a88483ca87dd0922fb106

/vision/faceDetection.py

b57050df2c1b6926133ef263d54490bac1a03f5f

[]

no_license

shubhamagarwal92/deepLearning

ca80aa6f310d0fff0c3f3172bcbeb156336ef949

a9a73544a5b5e5d4a10cfcaa8ff4d43fb251a27c

refs/heads/master

2020-04-18T12:25:28.431481

2016-09-29T16:30:22

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# Face detection in images using haar classifier from opencv # Download first haarcascade_frontalface_alt.xml to get the code working import os import cv2 from PIL import Image cascadeClassifier = '/path-to-dir/haarcascade_frontalface_alt.xml' rootDir = "/path-to-dir/" classDirPath = rootDir + "binaryClasses/" faceFileDir = rootDir + 'faces/' classDirNames = next(os.walk(classDirPath))[1] for classDir in classDirNames: imageDirPath = classDirPath+classDir+'/' imageNames = next(os.walk(imageDirPath))[2] classFaceFilePath = faceFileDir + classDir+'/' print(classDir) for imageName in imageNames: imageFile = imageDirPath+imageName img = cv2.imread(imageFile) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faceCascade = cv2.CascadeClassifier(cascadeClassifier) faces = faceCascade.detectMultiScale(gray, 1.3, 5) if(len(faces)==1): for (x,y,w,h) in faces: cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) sub_face = img[y:y+h, x:x+w] face_file_name = classFaceFilePath + imageName cv2.imwrite(face_file_name, sub_face)

[ "shubhamagarwal92@gmail.com" ]

shubhamagarwal92@gmail.com

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import urllib import urllib.request try: site = urllib.request.urlopen('http://www.pudim.com.br') except urllib.error.URLError: print('O site não está funcionando.') else: print('Consegui acessar o site.')

[ "luanpetruitis@hotmail.com" ]

luanpetruitis@hotmail.com

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/watch/views.py

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[]

no_license

grim-GO/worldIT1

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from django.shortcuts import render def watch(request): return render(request, 'watch.html')

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/atcoder/ABC/194_c.py

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recuraki/PythonJunkTest

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import sys from io import StringIO import unittest import logging logging.basicConfig(level=logging.DEBUG) def resolve(): def do(): import collections n = int(input()) dat = list(map(int, input().split())) d = collections.defaultdict(int) for i in range(n): d[dat[i]] += 1 keys = list(d.keys()) keys.sort() #print(keys) res = 0 l = len(keys) for i in range(l): numi = keys[i] counti = d[numi] for j in range(i+1, l): numj = keys[j] countj = d[numj] #print(numi, counti, numj, countj) x = (numi - numj) ** 2 res += x * (counti * countj) print(res) do() class TestClass(unittest.TestCase): def assertIO(self, input, output): stdout, stdin = sys.stdout, sys.stdin sys.stdout, sys.stdin = StringIO(), StringIO(input) resolve() sys.stdout.seek(0) out = sys.stdout.read()[:-1] sys.stdout, sys.stdin = stdout, stdin self.assertEqual(out, output) def test_input_1(self): print("test_input_1") input = """3 2 8 4""" output = """56""" self.assertIO(input, output) def test_input_2(self): print("test_input_2") input = """5 -5 8 9 -4 -3""" output = """950""" self.assertIO(input, output) if __name__ == "__main__": unittest.main()

[ "kanai@wide.ad.jp" ]

kanai@wide.ad.jp

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/tests/test_fingerprint.py

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fastavro/fastavro

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import pytest from fastavro.schema import ( FINGERPRINT_ALGORITHMS, fingerprint, to_parsing_canonical_form, ) @pytest.mark.parametrize( "fingerprint", ["CRC-64-AVRO", "SHA-256", "MD5", "sha256", "md5"] ) def test_required_fingerprints(fingerprint): assert fingerprint in FINGERPRINT_ALGORITHMS def test_unknown_algorithm(): unknown_algorithm = "UNKNOWN" assert unknown_algorithm not in FINGERPRINT_ALGORITHMS with pytest.raises(ValueError, match="Unknown schema fingerprint algorithm"): fingerprint("string", unknown_algorithm) @pytest.mark.parametrize( "original_schema,algorithm,expected_fingerprint", [ ("int", "CRC-64-AVRO", "8f5c393f1ad57572"), ("int", "md5", "ef524ea1b91e73173d938ade36c1db32"), ( "int", "sha256", "3f2b87a9fe7cc9b13835598c3981cd45e3e355309e5090aa0933d7becb6fba45", ), ( {"type": "int"}, "CRC-64-AVRO", "8f5c393f1ad57572", ), ( {"type": "int"}, "md5", "ef524ea1b91e73173d938ade36c1db32", ), ( {"type": "int"}, "sha256", "3f2b87a9fe7cc9b13835598c3981cd45e3e355309e5090aa0933d7becb6fba45", ), ( "float", "CRC-64-AVRO", "90d7a83ecb027c4d", ), ( "float", "md5", "50a6b9db85da367a6d2df400a41758a6", ), ( "float", "sha256", "1e71f9ec051d663f56b0d8e1fc84d71aa56ccfe9fa93aa20d10547a7abeb5cc0", ), ( {"type": "float"}, "CRC-64-AVRO", "90d7a83ecb027c4d", ), ( {"type": "float"}, "md5", "50a6b9db85da367a6d2df400a41758a6", ), ( {"type": "float"}, "sha256", "1e71f9ec051d663f56b0d8e1fc84d71aa56ccfe9fa93aa20d10547a7abeb5cc0", ), ( "long", "CRC-64-AVRO", "b71df49344e154d0", ), ( "long", "md5", "e1dd9a1ef98b451b53690370b393966b", ), ( "long", "sha256", "c32c497df6730c97fa07362aa5023f37d49a027ec452360778114cf427965add", ), ( {"type": "long"}, "CRC-64-AVRO", "b71df49344e154d0", ), ( {"type": "long"}, "md5", "e1dd9a1ef98b451b53690370b393966b", ), ( {"type": "long"}, "sha256", "c32c497df6730c97fa07362aa5023f37d49a027ec452360778114cf427965add", ), ( "double", "CRC-64-AVRO", "7e95ab32c035758e", ), ( "double", "md5", "bfc71a62f38b99d6a93690deeb4b3af6", ), ( "double", "sha256", "730a9a8c611681d7eef442e03c16c70d13bca3eb8b977bb403eaff52176af254", ), ( {"type": "double"}, "CRC-64-AVRO", "7e95ab32c035758e", ), ( {"type": "double"}, "md5", "bfc71a62f38b99d6a93690deeb4b3af6", ), ( {"type": "double"}, "sha256", "730a9a8c611681d7eef442e03c16c70d13bca3eb8b977bb403eaff52176af254", ), ( "bytes", "CRC-64-AVRO", "651920c3da16c04f", ), ( "bytes", "md5", "b462f06cb909be57c85008867784cde6", ), ( "bytes", "sha256", "9ae507a9dd39ee5b7c7e285da2c0846521c8ae8d80feeae5504e0c981d53f5fa", ), ( {"type": "bytes"}, "CRC-64-AVRO", "651920c3da16c04f", ), ( {"type": "bytes"}, "md5", "b462f06cb909be57c85008867784cde6", ), ( {"type": "bytes"}, "sha256", "9ae507a9dd39ee5b7c7e285da2c0846521c8ae8d80feeae5504e0c981d53f5fa", ), ( "string", "CRC-64-AVRO", "c70345637248018f", ), ( "string", "md5", "095d71cf12556b9d5e330ad575b3df5d", ), ( "string", "sha256", "e9e5c1c9e4f6277339d1bcde0733a59bd42f8731f449da6dc13010a916930d48", ), ( {"type": "string"}, "CRC-64-AVRO", "c70345637248018f", ), ( {"type": "string"}, "md5", "095d71cf12556b9d5e330ad575b3df5d", ), ( {"type": "string"}, "sha256", "e9e5c1c9e4f6277339d1bcde0733a59bd42f8731f449da6dc13010a916930d48", ), ( "boolean", "CRC-64-AVRO", "64f7d4a478fc429f", ), ( "boolean", "md5", "01f692b30d4a1c8a3e600b1440637f8f", ), ( "boolean", "sha256", "a5b031ab62bc416d720c0410d802ea46b910c4fbe85c50a946ccc658b74e677e", ), ( {"type": "boolean"}, "CRC-64-AVRO", "64f7d4a478fc429f", ), ( {"type": "boolean"}, "md5", "01f692b30d4a1c8a3e600b1440637f8f", ), ( {"type": "boolean"}, "sha256", "a5b031ab62bc416d720c0410d802ea46b910c4fbe85c50a946ccc658b74e677e", ), ( "null", "CRC-64-AVRO", "8a8f25cce724dd63", ), ( "null", "md5", "9b41ef67651c18488a8b08bb67c75699", ), ( "null", "sha256", "f072cbec3bf8841871d4284230c5e983dc211a56837aed862487148f947d1a1f", ), ( {"type": "null"}, "CRC-64-AVRO", "8a8f25cce724dd63", ), ( {"type": "null"}, "md5", "9b41ef67651c18488a8b08bb67c75699", ), ( {"type": "null"}, "sha256", "f072cbec3bf8841871d4284230c5e983dc211a56837aed862487148f947d1a1f", ), ( {"type": "fixed", "name": "Test", "size": 1}, "CRC-64-AVRO", "6869897b4049355b", ), ( {"type": "fixed", "name": "Test", "size": 1}, "md5", "db01bc515fcfcd2d4be82ed385288261", ), ( {"type": "fixed", "name": "Test", "size": 1}, "sha256", "f527116a6f44455697e935afc31dc60ad0f95caf35e1d9c9db62edb3ffeb9170", ), ( { "type": "fixed", "name": "MyFixed", "namespace": "org.apache.hadoop.avro", "size": 1, }, "CRC-64-AVRO", "fadbd138e85bdf45", ), ( { "type": "fixed", "name": "MyFixed", "namespace": "org.apache.hadoop.avro", "size": 1, }, "md5", "d74b3726484422711c465d49e857b1ba", ), ( { "type": "fixed", "name": "MyFixed", "namespace": "org.apache.hadoop.avro", "size": 1, }, "sha256", "28e493a44771cecc5deca4bd938cdc3d5a24cfe1f3760bc938fa1057df6334fc", ), ( {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, "CRC-64-AVRO", "03a2f2c2e27f7a16", ), ( {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, "md5", "d883f2a9b16ed085fcc5e4ca6c8f6ed1", ), ( {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, "sha256", "9b51286144f87ce5aebdc61ca834379effa5a41ce6ac0938630ff246297caca8", ), ( {"type": "array", "items": "long"}, "CRC-64-AVRO", "715e2ea28bc91654", ), ( {"type": "array", "items": "long"}, "md5", "c1c387e8d6a58f0df749b698991b1f43", ), ( {"type": "array", "items": "long"}, "sha256", "f78e954167feb23dcb1ce01e8463cebf3408e0a4259e16f24bd38f6d0f1d578b", ), ( { "type": "array", "items": {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, }, "CRC-64-AVRO", "10d9ade1fa3a0387", ), ( { "type": "array", "items": {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, }, "md5", "cfc7b861c7cfef082a6ef082948893fa", ), ( { "type": "array", "items": {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, }, "sha256", "0d8edd49d7f7e9553668f133577bc99f842852b55d9f84f1f7511e4961aa685c", ), ( {"type": "map", "values": "long"}, "CRC-64-AVRO", "6f74f4e409b1334e", ), ( {"type": "map", "values": "long"}, "md5", "32b3f1a3177a0e73017920f00448b56e", ), ( {"type": "map", "values": "long"}, "sha256", "b8fad07d458971a07692206b8a7cf626c86c62fe6bcff7c1b11bc7295de34853", ), ( { "type": "map", "values": {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, }, "CRC-64-AVRO", "df2ab0626f6b812d", ), ( { "type": "map", "values": {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, }, "md5", "c588da6ba99701c41e73fd30d23f994e", ), ( { "type": "map", "values": {"type": "enum", "name": "Test", "symbols": ["A", "B"]}, }, "sha256", "3886747ed1669a8af476b549e97b34222afb2fed5f18bb27c6f367ea0351a576", ), ( ["string", "null", "long"], "CRC-64-AVRO", "65a5be410d687566", ), ( ["string", "null", "long"], "md5", "b11cf95f0a55dd55f9ee515a37bf937a", ), ( ["string", "null", "long"], "sha256", "ed8d254116441bb35e237ad0563cf5432b8c975334bd222c1ee84609435d95bb", ), ( { "type": "record", "name": "Test", "fields": [{"name": "f", "type": "long"}], }, "CRC-64-AVRO", "ed94e5f5e6eb588e", ), ( { "type": 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"record", "name": "Cons", "fields": [ {"name": "car", "type": "Lisp"}, {"name": "cdr", "type": "Lisp"}, ], }, ], }, ], }, "sha256", "e5ce4f4a15ce19fa1047cfe16a3b0e13a755db40f00f23284fdd376fc1c7dd21", ), ( { "type": "record", "name": "HandshakeRequest", "namespace": "org.apache.avro.ipc", "fields": [ { "name": "clientHash", "type": {"type": "fixed", "name": "MD5", "size": 16}, }, {"name": "clientProtocol", "type": ["null", "string"]}, {"name": "serverHash", "type": "MD5"}, { "name": "meta", "type": ["null", {"type": "map", "values": "bytes"}], }, ], }, "CRC-64-AVRO", "b96ad79e5a7c5757", ), ( { "type": "record", "name": "HandshakeRequest", "namespace": "org.apache.avro.ipc", "fields": [ { "name": "clientHash", "type": {"type": "fixed", "name": "MD5", "size": 16}, }, {"name": "clientProtocol", "type": ["null", "string"]}, {"name": "serverHash", "type": "MD5"}, { "name": "meta", "type": ["null", {"type": "map", "values": "bytes"}], }, ], }, "md5", "4c822af2e17eecd92422827eede97f5b", ), ( { "type": "record", "name": "HandshakeRequest", "namespace": "org.apache.avro.ipc", "fields": [ { "name": "clientHash", "type": {"type": "fixed", "name": "MD5", "size": 16}, }, {"name": "clientProtocol", "type": ["null", "string"]}, {"name": "serverHash", "type": "MD5"}, { "name": "meta", "type": ["null", {"type": "map", "values": "bytes"}], }, ], }, "sha256", "2b2f7a9b22991fe0df9134cb6b5ff7355343e797aaea337e0150e20f3a35800e", ), ( { "type": "record", "name": "HandshakeResponse", "namespace": "org.apache.avro.ipc", "fields": [ { "name": "match", "type": { "type": "enum", "name": "HandshakeMatch", "symbols": ["BOTH", "CLIENT", "NONE"], }, }, {"name": "serverProtocol", "type": ["null", "string"]}, { "name": "serverHash", "type": ["null", {"name": "MD5", "size": 16, "type": "fixed"}], }, { "name": "meta", "type": ["null", {"type": "map", "values": "bytes"}], }, ], }, "CRC-64-AVRO", "00feee01de4ea50e", ), ( { "type": "record", "name": "HandshakeResponse", "namespace": "org.apache.avro.ipc", "fields": [ { "name": "match", "type": { "type": "enum", "name": "HandshakeMatch", "symbols": ["BOTH", "CLIENT", "NONE"], }, }, {"name": "serverProtocol", "type": ["null", "string"]}, { "name": "serverHash", "type": ["null", {"name": "MD5", "size": 16, "type": "fixed"}], }, { "name": "meta", "type": ["null", {"type": "map", "values": "bytes"}], }, ], }, "md5", "afe529d01132daab7f4e2a6663e7a2f5", ), ( { "type": "record", "name": "HandshakeResponse", "namespace": "org.apache.avro.ipc", "fields": [ { "name": "match", "type": { "type": "enum", "name": "HandshakeMatch", "symbols": ["BOTH", "CLIENT", "NONE"], }, }, {"name": "serverProtocol", "type": ["null", "string"]}, { "name": "serverHash", "type": ["null", {"name": "MD5", "size": 16, "type": "fixed"}], }, { "name": "meta", "type": ["null", {"type": "map", "values": "bytes"}], }, ], }, "sha256", "a303cbbfe13958f880605d70c521a4b7be34d9265ac5a848f25916a67b11d889", ), ( { "type": "record", "name": "Interop", "namespace": "org.apache.avro", "fields": [ {"name": "intField", "type": "int"}, {"name": "longField", "type": "long"}, {"name": "stringField", "type": "string"}, {"name": "boolField", "type": "boolean"}, {"name": "floatField", "type": "float"}, {"name": "doubleField", "type": "double"}, {"name": "bytesField", "type": "bytes"}, {"name": "nullField", "type": "null"}, { "name": "arrayField", "type": {"type": "array", "items": "double"}, }, { "name": "mapField", "type": { "type": "map", "values": { "name": "Foo", "type": "record", "fields": [{"name": "label", "type": "string"}], }, }, }, { "name": "unionField", "type": [ "boolean", "double", {"type": "array", "items": "bytes"}, ], }, { "name": "enumField", "type": { "type": "enum", "name": "Kind", "symbols": ["A", "B", "C"], }, }, { "name": "fixedField", "type": {"type": "fixed", "name": "MD5", "size": 16}, }, { "name": "recordField", "type": { "type": "record", "name": "Node", "fields": [ {"name": "label", "type": "string"}, { "name": "children", "type": {"type": "array", "items": "Node"}, }, ], }, }, ], }, "CRC-64-AVRO", "e82c0a93a6a0b5a4", ), ( { "type": "record", "name": "Interop", "namespace": "org.apache.avro", "fields": [ {"name": "intField", "type": "int"}, {"name": "longField", "type": "long"}, {"name": "stringField", "type": "string"}, {"name": "boolField", "type": "boolean"}, {"name": "floatField", "type": "float"}, {"name": "doubleField", "type": "double"}, {"name": "bytesField", "type": "bytes"}, {"name": "nullField", "type": "null"}, { "name": "arrayField", "type": {"type": "array", "items": "double"}, }, { "name": "mapField", "type": { "type": "map", "values": { "name": "Foo", "type": "record", "fields": [{"name": "label", "type": "string"}], }, }, }, { "name": "unionField", "type": [ "boolean", "double", {"type": "array", "items": "bytes"}, ], }, { "name": "enumField", "type": { "type": "enum", "name": "Kind", "symbols": ["A", "B", "C"], }, }, { "name": "fixedField", "type": {"type": "fixed", "name": "MD5", "size": 16}, }, { "name": "recordField", "type": { "type": "record", "name": "Node", "fields": [ {"name": "label", "type": "string"}, { "name": "children", "type": {"type": "array", "items": "Node"}, }, ], }, }, ], }, "md5", "994fea1a1be7ff8603cbe40c3bc7e4ca", ), ( { "type": "record", "name": "Interop", "namespace": "org.apache.avro", "fields": [ {"name": "intField", "type": "int"}, {"name": "longField", "type": "long"}, {"name": "stringField", "type": "string"}, {"name": "boolField", "type": "boolean"}, {"name": "floatField", "type": "float"}, {"name": "doubleField", "type": "double"}, {"name": "bytesField", "type": "bytes"}, {"name": "nullField", "type": "null"}, { "name": "arrayField", "type": {"type": "array", "items": "double"}, }, { "name": "mapField", "type": { "type": "map", "values": { "name": "Foo", "type": "record", "fields": [{"name": "label", "type": "string"}], }, }, }, { "name": "unionField", "type": [ "boolean", "double", {"type": "array", "items": "bytes"}, ], }, { "name": "enumField", "type": { "type": "enum", "name": "Kind", "symbols": ["A", "B", "C"], }, }, { "name": "fixedField", "type": {"type": "fixed", "name": "MD5", "size": 16}, }, { "name": "recordField", "type": { "type": "record", "name": "Node", "fields": [ {"name": "label", "type": "string"}, { "name": "children", "type": {"type": "array", "items": "Node"}, }, ], }, }, ], }, "sha256", "cccfd6e3f917cf53b0f90c206342e6703b0d905071f724a1c1f85b731c74058d", ), ( { "type": "record", "name": "ipAddr", "fields": [ { "name": "addr", "type": [ {"name": "IPv6", "type": "fixed", "size": 16}, {"name": "IPv4", "type": "fixed", "size": 4}, ], } ], }, "CRC-64-AVRO", "8d961b4e298a1844", ), ( { "type": "record", "name": "ipAddr", "fields": [ { "name": "addr", "type": [ {"name": "IPv6", "type": "fixed", "size": 16}, {"name": "IPv4", "type": "fixed", "size": 4}, ], } ], }, "md5", "45d85c69b353a99b93d7c4f2fcf0c30d", ), ( { "type": "record", "name": "ipAddr", "fields": [ { "name": "addr", "type": [ {"name": "IPv6", "type": "fixed", "size": 16}, {"name": "IPv4", "type": "fixed", "size": 4}, ], } ], }, "sha256", "6f6fc8f685a4f07d99734946565d63108806d55a8620febea047cf52cb0ac181", ), ( { "type": "record", "name": "TestDoc", "doc": "Doc string", "fields": [{"name": "name", "type": "string", "doc": "Doc String"}], }, "CRC-64-AVRO", "0e6660f02bcdc109", ), ( { "type": "record", "name": "TestDoc", "doc": "Doc string", "fields": [{"name": "name", "type": "string", "doc": "Doc String"}], }, "md5", "f2da75f5131f5ab80629538287b8beb2", ), ( { "type": "record", "name": "TestDoc", "doc": "Doc string", "fields": [{"name": "name", "type": "string", "doc": "Doc String"}], }, "sha256", "0b3644f7aa5ca2fc4bad93ca2d3609c12aa9dbda9c15e68b34c120beff08e7b9", ), ( { "type": "enum", "name": "Test", "symbols": ["A", "B"], "doc": "Doc String", }, "CRC-64-AVRO", "03a2f2c2e27f7a16", ), ( { "type": "enum", "name": "Test", "symbols": ["A", "B"], "doc": "Doc String", }, "md5", "d883f2a9b16ed085fcc5e4ca6c8f6ed1", ), ( { "type": "enum", "name": "Test", "symbols": ["A", "B"], "doc": "Doc String", }, "sha256", "9b51286144f87ce5aebdc61ca834379effa5a41ce6ac0938630ff246297caca8", ), ( {"type": "int"}, "MD5", # JAVA Name "ef524ea1b91e73173d938ade36c1db32", ), ( {"type": "int"}, "SHA-256", # JAVA Name "3f2b87a9fe7cc9b13835598c3981cd45e3e355309e5090aa0933d7becb6fba45", ), ], ) def test_random_cases(original_schema, algorithm, expected_fingerprint): # All of these random test cases came from the test cases here: # https://github.com/apache/avro/blob/0552c674637dd15b8751ed5181387cdbd81480d5/lang/py3/avro/tests/test_normalization.py canonical_form = to_parsing_canonical_form(original_schema) assert fingerprint(canonical_form, algorithm) == expected_fingerprint

[ "scottabelden+github.sb@gmail.com" ]

scottabelden+github.sb@gmail.com

4e1b78b9c648e807f68585113cc426c3bdc0cfd1

fc8fbda7dba622a2242a6783919c36e5e0fd6cc8

/final/run.py

e031666b5f15c90a865bdcb96ea4a098df9b62b3

[]

no_license

canibal/Course6

c110fed13338bca9509948b82a1d4dd0e7200e00

005d8eb8265f8f735d991c39af19f88d441f303b

refs/heads/master

2022-05-06T14:09:01.587671

2020-04-20T08:29:39

255,115,793

0

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UTF-8

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py

#! /usr/bin/env python3 import os import requests data_dir = ('supplier-data/descriptions/') file_list = os.listdir(data_dir) def post_request(p): response = requests.post("http://35.223.215.137/fruits/", json=p) code = response.status_code body = response.text print("The request returned code {}.".format(code), body) def create_dicts(files): description_d = {} fd = [] for f in files: description = os.path.join(data_dir, f) with open(description, 'r') as file: key_list = ['name', 'weight', 'description', 'image_name'] val_list = [] fil = file.readlines() for line in fil: if 'lbs' in line: line = line.split(' ') line = int(line[0]) val_list.append(line) else: val_list.append(line.strip()) val_list.append(os.path.splitext(f)[0] + '.jpeg') print(val_list) z = zip(key_list, val_list) f_d = dict(z) post_request(f_d) #feedback_d = dict(list(enumerate(fd))) #return feedback_d if __name__=="__main__": create_dicts(file_list)

[ "canaan@thetomato.co" ]

canaan@thetomato.co

70f0f5a1e3be3fd611edb474c21c64ad9da1b84b

0b358a0d64eb03655c030b36c0ae87880b153951

/mmcv-1.4.7/tests/test_load_model_zoo.py

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permissive

jshilong/DDQ

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refs/heads/main

2023-06-03T15:02:09.949907

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2022-06-02T05:01:53

2022-06-02T03:10:25

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UTF-8

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py

# Copyright (c) OpenMMLab. All rights reserved. import os import os.path as osp from unittest.mock import patch import pytest import mmcv from mmcv.runner.checkpoint import (DEFAULT_CACHE_DIR, ENV_MMCV_HOME, ENV_XDG_CACHE_HOME, _get_mmcv_home, _load_checkpoint, get_deprecated_model_names, get_external_models) from mmcv.utils import TORCH_VERSION @patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')]) def test_set_mmcv_home(): os.environ.pop(ENV_MMCV_HOME, None) mmcv_home = osp.join(osp.dirname(__file__), 'data/model_zoo/mmcv_home/') os.environ[ENV_MMCV_HOME] = mmcv_home assert _get_mmcv_home() == mmcv_home @patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')]) def test_default_mmcv_home(): os.environ.pop(ENV_MMCV_HOME, None) os.environ.pop(ENV_XDG_CACHE_HOME, None) assert _get_mmcv_home() == os.path.expanduser( os.path.join(DEFAULT_CACHE_DIR, 'mmcv')) model_urls = get_external_models() assert model_urls == mmcv.load( osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')) @patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')]) def test_get_external_models(): os.environ.pop(ENV_MMCV_HOME, None) mmcv_home = osp.join(osp.dirname(__file__), 'data/model_zoo/mmcv_home/') os.environ[ENV_MMCV_HOME] = mmcv_home ext_urls = get_external_models() assert ext_urls == { 'train': 'https://localhost/train.pth', 'test': 'test.pth', 'val': 'val.pth', 'train_empty': 'train.pth' } @patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')]) def test_get_deprecated_models(): os.environ.pop(ENV_MMCV_HOME, None) mmcv_home = osp.join(osp.dirname(__file__), 'data/model_zoo/mmcv_home/') os.environ[ENV_MMCV_HOME] = mmcv_home dep_urls = get_deprecated_model_names() assert dep_urls == { 'train_old': 'train', 'test_old': 'test', } def load_from_http(url, map_location=None): return 'url:' + url def load_url(url, map_location=None, model_dir=None): return load_from_http(url) def load(filepath, map_location=None): return 'local:' + filepath @patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')]) @patch('mmcv.runner.checkpoint.load_from_http', load_from_http) @patch('mmcv.runner.checkpoint.load_url', load_url) @patch('torch.load', load) def test_load_external_url(): # test modelzoo:// url = _load_checkpoint('modelzoo://resnet50') if TORCH_VERSION < '1.9.0': assert url == ('url:https://download.pytorch.org/models/resnet50-19c8e' '357.pth') else: # filename of checkpoint is renamed in torch1.9.0 assert url == ('url:https://download.pytorch.org/models/resnet50-0676b' 'a61.pth') # test torchvision:// url = _load_checkpoint('torchvision://resnet50') if TORCH_VERSION < '1.9.0': assert url == ('url:https://download.pytorch.org/models/resnet50-19c8e' '357.pth') else: # filename of checkpoint is renamed in torch1.9.0 assert url == ('url:https://download.pytorch.org/models/resnet50-0676b' 'a61.pth') # test open-mmlab:// with default MMCV_HOME os.environ.pop(ENV_MMCV_HOME, None) os.environ.pop(ENV_XDG_CACHE_HOME, None) url = _load_checkpoint('open-mmlab://train') assert url == 'url:https://localhost/train.pth' # test open-mmlab:// with deprecated model name os.environ.pop(ENV_MMCV_HOME, None) os.environ.pop(ENV_XDG_CACHE_HOME, None) with pytest.warns( Warning, match='open-mmlab://train_old is deprecated in favor of ' 'open-mmlab://train'): url = _load_checkpoint('open-mmlab://train_old') assert url == 'url:https://localhost/train.pth' # test openmmlab:// with deprecated model name os.environ.pop(ENV_MMCV_HOME, None) os.environ.pop(ENV_XDG_CACHE_HOME, None) with pytest.warns( Warning, match='openmmlab://train_old is deprecated in favor of ' 'openmmlab://train'): url = _load_checkpoint('openmmlab://train_old') assert url == 'url:https://localhost/train.pth' # test open-mmlab:// with user-defined MMCV_HOME os.environ.pop(ENV_MMCV_HOME, None) mmcv_home = osp.join(osp.dirname(__file__), 'data/model_zoo/mmcv_home') os.environ[ENV_MMCV_HOME] = mmcv_home url = _load_checkpoint('open-mmlab://train') assert url == 'url:https://localhost/train.pth' with pytest.raises(FileNotFoundError, match='train.pth can not be found.'): _load_checkpoint('open-mmlab://train_empty') url = _load_checkpoint('open-mmlab://test') assert url == f'local:{osp.join(_get_mmcv_home(), "test.pth")}' url = _load_checkpoint('open-mmlab://val') assert url == f'local:{osp.join(_get_mmcv_home(), "val.pth")}' # test http:// https:// url = _load_checkpoint('http://localhost/train.pth') assert url == 'url:http://localhost/train.pth' # test local file with pytest.raises(FileNotFoundError, match='train.pth can not be found.'): _load_checkpoint('train.pth') url = _load_checkpoint(osp.join(_get_mmcv_home(), 'test.pth')) assert url == f'local:{osp.join(_get_mmcv_home(), "test.pth")}'

[ "2392587229zsl@gmail.com" ]

2392587229zsl@gmail.com

7db54fa4b19419ad6f5a61d90b947002b137d91a

42ab657221e7a7f7e3e185f7ea0f04821477e3d9

/240/searchMatrix.py

1db4b305730129e847603143bd03441397c815d6

[]

no_license

Sevendeadlys/leetcode

2238f86e3b6c687c48e2d612658730826d4be983

e030f32b26daffbb57ca9bc13c2e8d3ea1c1c1eb

refs/heads/master

2021-01-10T15:35:52.912711

2016-02-25T03:14:02

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0

null

UTF-8

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false

1,036

py

class Solution1(object): def searchMatrix(self, matrix, target): """ :type matrix: List[List[int]] :type target: int :rtype: bool """ if not matrix or not matrix[0]: return False m = len(matrix) n = len(matrix[0]) i = 0 while i < m: """ Binary search every list """ if target in matrix[i]: return True i += 1 return False class Solution(object): def searchMatrix(self, matrix, target): """ :type matrix: List[List[int]] :type target: int :rtype: bool """ if not matrix or not matrix[0]: return False m = len(matrix) n = len(matrix[0]) r = 0 c = n - 1 while r < m and c >= 0: if target == matrix[r][c]: return True elif target > matrix[r][c]: r += 1 else : c -= 1 return False

[ "yi_nan@615-PC76.careri.com" ]

yi_nan@615-PC76.careri.com

089a617446ed4d8811d8d82c91893eefc52314f3

44a071e30cf5ab17e6519e4d3edb2a6cb207ecef

/피보나치 함수/main.py

34a67a6319b18460ed6173b7ffccdba8366933ce

[]

no_license

isp5708/Algorithm_python_bj_2cotae

f56e957677b0f2a6756172c21583a0be00c6b2cd

56e630dc36b976177fd0c2d0d4396e1a1ba0ad33

refs/heads/master

2023-01-30T23:07:18.594616

2020-12-11T02:31:06

320,441,238

0

null

UTF-8

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false

308

py

t= int(input()) array=[] for i in range(t): array.append(int(input())) n=max(array) dp0=[0]*(n+1) dp1=[0]*(n+1) dp0[0],dp1[0]=1,0 dp0[1],dp1[1]=0,1 for i in range(2,n+1): dp0[i],dp1[i]=dp0[i-2]+dp0[i-1],dp1[i-2]+dp1[i-1] for i in range(t): print(str(dp0[array[i]])+' '+str(dp1[array[i]]))

[ "dlwnsdud3737@naver.com" ]

dlwnsdud3737@naver.com

fc32e950371b1885f07097d5fa5b19c2fe75426e

4bc24011c65cb5194eb94abfd8d394a6b0dc6a50

/packages/OpenWeatherMap/nodes/OpenWeatherMap___BreakTemp0/OpenWeatherMap___BreakTemp0.py

be0fa2f76d4d13639e7780853aa71a56c8e552b8

[ "MIT" ]

permissive

ManojKumarTiwari/Ryven

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2b8ef0bdcf05a458a6cf8791cbc2fda6870932f8

refs/heads/master

2022-11-12T00:23:45.303378

2020-07-08T09:32:10

null

0

null

UTF-8

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py

from custom_src.NodeInstance import NodeInstance from custom_src.Node import Node from custom_src.retain import m # API METHODS # self.main_widget <- access to main widget # self.update_shape() <- recomputes the whole shape and content positions # Ports # self.input(index) <- access to input data # self.set_output_val(self, index, val) <- set output data port value # self.exec_output(index) <- executes an execution output # self.create_new_input(type_, label, append=True, widget_type='', widget_name='', widget_pos='under', pos=-1) # self.delete_input(index or input) # self.create_new_output(type_, label, append=True, pos=-1) # self.delete_output(index or output) # Logging # mylog = self.new_log('Example Log') # mylog.log('I\'m alive!!') # self.log_message('hello global!', 'global') # self.log_message('that\'s not good', 'error') # ------------------------------------------------------------------------------ from pyowm.utils.measurables import kelvin_to_celsius, kelvin_to_fahrenheit class BreakTemp_NodeInstance(NodeInstance): def __init__(self, parent_node: Node, flow, configuration=None): super(BreakTemp_NodeInstance, self).__init__(parent_node, flow, configuration) # self.special_actions['action name'] = self.actionmethod ... # ... self.initialized() # don't call self.update_event() directly, use self.update() instead def update_event(self, input_called=-1): temp_dict = self.input(0) if self.input(1) != 'kelvin': for key in list(temp_dict.keys()): item = temp_dict[key] if item is not None: if self.input(1) == 'celsius': temp_dict[key] = kelvin_to_celsius(item) elif self.input(1) == 'fahrenheit': temp_dict[key] = kelvin_to_fahrenheit(item) # temp_dict = kelvin_dict_to(temp_dict, self.input(1)) doesn't work with NoneType values -.- which happen to persist temp = temp_dict['temp'] temp_kf = temp_dict['temp_kf'] temp_max = temp_dict['temp_max'] temp_min = temp_dict['temp_min'] feels_like = temp_dict['feels_like'] self.set_output_val(0, temp) self.set_output_val(1, temp_kf) self.set_output_val(2, temp_min) self.set_output_val(3, temp_max) self.set_output_val(4, feels_like) def get_data(self): data = {} # ... return data def set_data(self, data): pass # ... # optional - important for threading - stop everything here def removing(self): pass

[ "leon.thomm@gmx.de" ]

leon.thomm@gmx.de

749f5c88ee3cfd6b790f1c722c970946167645ae

235267656f98a7583b39f0550d147aca17880c75

/machine_learning_basics/layers/dense.py

67419dd58659f63f5b3cb623d8d6d625fe764cb0

[]

no_license

calvinfeng/machine-learning-notebook

5cd2695947cd6058916f39a51431b162bc6a4e32

7da789ef34d5e5bcf9033cfbe0ff5df607b2437a

refs/heads/master

2023-08-16T10:49:28.114020

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null

2023-08-14T22:59:02

2018-04-01T23:22:12

Jupyter Notebook

UTF-8

Python

false

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py

import numpy as np class Dense: def __init__(self): self.x = None self.w = None self.b = None def __call__(self, x, w, b): """Perform forward propagation Args: x (np.ndarray): Input w (np.ndarray): Kernel weights b (np.ndarray): Biases Returns: np.ndarray: Output """ self.x = x self.w = w self.b = b return x @ w + b def gradients(self, grad_out): """Perform back propagation and return gradients with respect to upstream loss function. Args: grad_out (np.ndarray): Gradient of loss with respect to output. Returns: np.ndarray: Gradient of loss with respect to x np.ndarray: Gradient of loss with respect to w np.ndarray: Gradient of loss with respect to b """ if self.x is None: raise ValueError("layer must be forward propagated first") grad_x = grad_out @ self.w.T grad_w = self.x.T @ grad_out grad_b = np.sum(grad_out, axis=0) return grad_x, grad_w, grad_b

[ "calvin.j.feng@gmail.com" ]

calvin.j.feng@gmail.com

93d424fffa96884d8805000d5236dce8511322b6

0c477e8196d94216bbea8b260579c7e84aba4363

/gui/TopBarUI.py

2e88f9ab74500348bab56745b4c1c8bbed38afbb

[]

no_license

industry4-0/1UP

caa00eed15f6fbccbadea62596519291738232d2

7903fbca7a478c9618a776bdcc29ee95be5c1f94

refs/heads/master

2020-09-22T02:10:50.516217

2019-12-01T13:57:32

225,013,860

0

1

null

UTF-8

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false

1,061

py

import datetime from gui.core.UINode import UINode from enums.UIState import UIState from utils.commons import DEFAULT_FONT, FONT_AWESOME_FONT_FILE, ICONS, right_text, make_font class TopBarUI(UINode): def __init__(self): super().__init__(UIState.ActivityList) self._battery_level = 100 self.fontawesome_font = make_font(FONT_AWESOME_FONT_FILE, 48) self.keys = list(ICONS.keys()) self.index = 0 self.iterations = 0 def _getTime(self): return datetime.datetime.now().strftime("%d/%m/%y %H:%M %S") def render(self, engine): engine.text((0, 0), self._getTime(), font=DEFAULT_FONT, fill="white") right_text(engine, 0, 128, 0, text="{}%".format(self._battery_level)) self.iterations += 1 if (self.iterations > 20): self.iterations = 0 self.index += 1 if (self.index >= len(self.keys)): self.index = 0 engine.text((40, 40), ICONS[self.keys[self.index]], font=self.fontawesome_font, fill="yellow")

[ "noreply@github.com" ]

industry4-0.noreply@github.com

dde66bb3f35b3fa9cd30f1ad61a5d6953a0be77b

b706b62f91bcf1010d865d0eaa9adecd86dc0b67

/login_registration_2.py

a7da7cfea642344fbbe2d50b68ef85783f15c6a0

[]

no_license

IvanPliska/Book-store-testing

d4ed0896fb885df4456b701d5aa8d4788915df5d

7d4a96b653b53673c1df574219ea6525e0026ec8

refs/heads/master

2023-08-30T00:32:34.609825

2021-11-16T17:28:15

428,748,032

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null

UTF-8

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false

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import time from selenium import webdriver from selenium.webdriver.support.select import Select driver = webdriver.Chrome() driver.maximize_window() driver.get("http://practice.automationtesting.in/") time.sleep(3) My_Account = driver.find_element_by_link_text("My Account").click() time.sleep(3) Email_address = driver.find_element_by_id("username").send_keys("plis_in@mail.ru") time.sleep(3) Password_in = driver.find_element_by_id("password").send_keys("123Qaz456!@#$<>?") time.sleep(3) Remember_me = driver.find_element_by_id("rememberme").click() time.sleep(3) Register = driver.find_element_by_xpath("//input[@value='Login']").click() time.sleep(2) Logout = driver.find_element_by_link_text("Logout") if Logout is not None: print("Присутствует элемент Logout!") else: print("Отсутствует элемент Logout!") time.sleep(2) driver.quit()

[ "plis_in@mail.ru" ]

plis_in@mail.ru

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abb51df4a0bac65428be0bbe79aabcf7cb3676a6

/Invaders.py

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[]

no_license

Sinaeskandari/Invaders

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74de652d952a8feccc0ae6d02ea87435241c97de

refs/heads/master

2020-08-06T19:51:19.622445

2019-10-06T08:20:39

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# Sina Eskandari # Student number = 97521054 # for more information read 'readme.txt' import pygame import sys import random from pygame.locals import * # This part is for initializing the pygame pygame.init() # Variables for window windowWidth = 680 windowHeight = 680 window = pygame.display.set_mode((windowWidth, windowHeight)) pygame.display.set_caption('Game') # Used to manage how fast the screen updates clock = pygame.time.Clock() FPS = 60 # Choosing a font for displaying texts font_obj = pygame.font.Font('freesansbold.ttf', 20) class Ship(pygame.sprite.Sprite): '''With This class we can initialize our player(It's a space ship) ''' global windowWidth, windowHeight def __init__(self): '''Create the player''' # Call the parent class (Sprite) constructor super().__init__() # Create the image of the ship self.image = pygame.image.load('ship.png') # Download from here : http://s9.picofile.com/file/8350448434/ship.png # Fetch the rectangle object that has the dimensions of the ship self.rect = self.image.get_rect() self.shipSize = self.image.get_rect().size # Some black part bellow of the ship self.distanceFromBottom = 45 # Define location of ship self.X = (windowWidth - self.shipSize[0]) / 2 self.Y = windowHeight - self.distanceFromBottom # Define velocity of ship self.velShip = 5 def update(self): '''Update the location of ship''' # Moving right if keys[K_RIGHT]: self.X += self.velShip if self.X + self.shipSize[0] > windowWidth: self.X = windowWidth - self.shipSize[0] # Moving left if keys[K_LEFT]: self.X -= self.velShip if self.X < 0: self.X = 0 class Invader(pygame.sprite.Sprite): '''This class is for enemy soldiers('Invaders')''' def __init__(self): '''Create Invaders''' # Call the parent class (Sprite) constructor super().__init__() # Create the image of the invader self.image = pygame.image.load('invader1.png') # Download from : http://s8.picofile.com/file/8350448284/invader1.png # Fetch the rectangle object that has the dimensions of the invader self.rect = self.image.get_rect() # Bellow part is not necessary.It's just for reducing the typing self.size = self.image.get_rect().size class Bullet(pygame.sprite.Sprite): '''Class for the bullets that our spaceship shoots''' def __init__(self): '''Create the bullets''' # Call the parent class (Sprite) constructor super().__init__() # Create the image of the bullet and fill it with red self.image = pygame.Surface([2, 10]) self.image.fill((255, 0, 0)) # Fetch the rectangle object that has the dimensions of the bullet self.rect = self.image.get_rect() def update(self): '''Updating the location of bullets''' # With this method our bullets will rise up self.rect.y -= 5 class Obstacle(pygame.sprite.Sprite): '''This class is for making some obstacles for protecting our spaceship''' def __init__(self): '''Create the obstacles''' # Call the parent class (Sprite) constructor super().__init__() # Create the image of the obstacle self.image = pygame.image.load('obstacle1.png') # Download from: http://s8.picofile.com/file/8350438384/obstacle1.png # Fetch the rectangle object that has the dimensions of the obstacle self.rect = self.image.get_rect() class InvBullet(pygame.sprite.Sprite): '''This class is for making invaders bullets so they can attack our spaceship''' def __init__(self): '''Create the bullets''' # Call the parent class (Sprite) constructor super().__init__() # Create the image of the bullet and fill it with blue self.image = pygame.Surface([2, 10]) self.image.fill((0, 0, 255)) # Fetch the rectangle object that has the dimensions of the obstacle self.rect = self.image.get_rect() def update(self): '''Updating th location of bullets''' # With this method bullets can descend and hurt our spaceship self.rect.y += 5 class BossFight(pygame.sprite.Sprite): '''This class is for making 'THE BOSSFIGHT' the boss fight shoots our space ship and if the spaceship kills its invaders he will die and we win the game ''' def __init__(self): '''Creating the bossfight''' super().__init__() # Create the image of the bossfight self.image = pygame.image.load('bossfight.png') # Download from here : http://s9.picofile.com/file/8350449134/bossfight.png # Fetch the rectangle object that has the dimensions of the obstacle self.rect = self.image.get_rect() # Define a object of BossFight class boss = BossFight() # Define a object of Ship class ship = Ship() # Make some sprite groups for drawing and colliding the shipGroup = pygame.sprite.Group() inv_list = pygame.sprite.Group() all_sprites_list = pygame.sprite.Group() bullet_list = pygame.sprite.Group() obstacle_list = pygame.sprite.Group() inv_bullet_list = pygame.sprite.Group() # Add ship and boss to sprite groups all_sprites_list.add(ship) all_sprites_list.add(boss) shipGroup.add(ship) # This is for making invaders and adding them to sprite groups for i in range(17): for j in range(10): invader = Invader() invader.rect.x = 50 + (35 * i) invader.rect.y = 90 + (27 * j) inv_list.add(invader) all_sprites_list.add(invader) # This is for making obstacles and adding them to sprite groups for i in range(6): obstacle = Obstacle() obstacle.rect.y = 515 obstacle.rect.x = 40 + (110 * i) obstacle_list.add(obstacle) all_sprites_list.add(obstacle) def collide(): '''This function checks if enemy's bullets hits the obstacles don't pass from them''' for iBullet in inv_bullet_list: for obs in obstacle_list: if pygame.sprite.collide_rect(iBullet, obs): all_sprites_list.remove(iBullet) inv_bullet_list.remove(iBullet) def game_over(): '''This function quits the game''' pygame.quit() sys.exit() # User Score and health score = 0 health = 100 while True: # Fill our surface with black window.fill((0, 0, 0)) # A variable for checking when a key got pressed # Actually this for pressing and holding a key because with handling the events if we hold a key the function just calls one time keys = pygame.key.get_pressed() # Handling the events in game for event in pygame.event.get(): if event.type == QUIT: game_over() if event.type == KEYDOWN: if event.key == K_ESCAPE: game_over() # When we press the space;Makes a bullet and shoots it if event.type == KEYDOWN: if event.key == K_SPACE: bullet = Bullet() bullet.rect.x = ship.rect.x + (ship.shipSize[0]) / 2 bullet.rect.y = ship.rect.y all_sprites_list.add(bullet) bullet_list.add(bullet) # Win or lose the game if (health <= 0) or (score >= 170): game_over() # Check if the bullet hits any invader, the invader will be killed for bullet in bullet_list: hit_list = pygame.sprite.spritecollide(bullet, inv_list, True) # This is for removing the bullet when hits the invader for i in hit_list: bullet_list.remove(bullet) all_sprites_list.remove(bullet) # If the bullets leave the window gets destroyed if bullet.rect.y < -10: bullet_list.remove(bullet) # We get a score if we kill a invader for i in hit_list: score += 1 # Locating the bossfight with rectangular method boss.rect.x = 50 boss.rect.y = 0 # Making the enemy's bullets inv_bullet = InvBullet() inv_bullet.rect.x = random.randrange(boss.rect.x, boss.rect.x + boss.rect.size[0]) inv_bullet.rect.y = boss.rect.size[1] inv_bullet_list.add(inv_bullet) all_sprites_list.add(inv_bullet) for invbullet in inv_bullet_list: # If the bullets leave the window gets destroyed if invbullet.rect.y > 710: inv_bullet_list.remove(invbullet) # If the bullet hits us, our health get reduced and also the bullet get destroyed if pygame.sprite.collide_rect(invbullet, ship): inv_bullet_list.remove(invbullet) all_sprites_list.remove(invbullet) health -= 2 # Printing the score and health score_text = font_obj.render('Score=' + str(score), True, (255, 255, 255)) score_text_rect = score_text.get_rect() score_text_rect.x = 0 score_text_rect.y = 0 health_text = font_obj.render('Health=' + str(health), True, (255, 255, 255)) health_text_rect = health_text.get_rect() health_text_rect.top = 0 health_text_rect.right = windowWidth window.blit(health_text, health_text_rect) window.blit(score_text, score_text_rect) # Call 'update' method for all sprites all_sprites_list.update() # Locating our spaceship ship.rect.x = ship.X ship.rect.y = ship.Y # drawing all of sprites all_sprites_list.draw(window) collide() clock.tick(FPS) pygame.display.update()

[ "sinaeskandari007@gmail.com" ]

sinaeskandari007@gmail.com

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/glancesync/glance/sync/clients.py

cadc8f4acf63c25e2f1cd27c2d81506c7f9bc455

[ "Apache-2.0" ]

permissive

luqitao/tricircle

ca612a4d4fc0287af3857091f2bfbf0c90dbe7bd

d5acf44b5f097e1fe1e94e220138e11c7fdd25fe

refs/heads/master

2021-01-16T23:06:35.882882

2015-04-16T09:30:57

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# Copyright (c) 2014 OpenStack Foundation. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # # @author: Jia Dong, HuaWei from oslo.config import cfg from keystoneclient.v2_0 import client as ksclient import glance.openstack.common.log as logging from glanceclient.v2 import client as gclient2 LOG = logging.getLogger(__name__) CONF = cfg.CONF class Clients(object): def __init__(self, auth_token=None, tenant_id=None): self._keystone = None self._glance = None self._cxt_token = auth_token self._tenant_id = tenant_id self._ks_conf = cfg.CONF.keystone_authtoken @property def auth_token(self, token=None): return token or self.keystone().auth_token @property def ks_url(self): protocol = self._ks_conf.auth_protocol or 'http' auth_host = self._ks_conf.auth_host or '127.0.0.1' auth_port = self._ks_conf.auth_port or '35357' return protocol + '://' + auth_host + ':' + str(auth_port) + '/v2.0/' def url_for(self, **kwargs): return self.keystone().service_catalog.url_for(**kwargs) def get_urls(self, **kwargs): return self.keystone().service_catalog.get_urls(**kwargs) def keystone(self): if self._keystone: return self._keystone if self._cxt_token and self._tenant_id: creds = {'token': self._cxt_token, 'auth_url': self.ks_url, 'project_id': self._tenant_id } else: creds = {'username': self._ks_conf.admin_user, 'password': self._ks_conf.admin_password, 'auth_url': self.ks_url, 'project_name': self._ks_conf.admin_tenant_name} try: self._keystone = ksclient.Client(**creds) except Exception as e: LOG.error(_('create keystone client error: reason: %s') % (e)) return None return self._keystone def glance(self, auth_token=None, url=None): gclient = gclient2 if gclient is None: return None if self._glance: return self._glance args = { 'token': auth_token or self.auth_token, 'endpoint': url or self.url_for(service_type='image') } self._glance = gclient.Client(**args) return self._glance

[ "joehuang@huawei.com" ]

joehuang@huawei.com

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/src/python/py27hash/key.py

453720684b1f2a86cde296f3657b2f6953976a0f

[ "MIT" ]

permissive

Amli/py27hash

57e0c56bcdb43d6d9490e0d5e93afae9ebc7660d

02ebc8a97f4eb6c9838288ec875873971097a456

refs/heads/master

2023-01-04T03:13:08.855753

2020-11-03T00:46:42

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""" Compatibility methods to support Python 2.7 style key iteration in Python 3.X+ This is designed for compatibility not performance. """ import ctypes # pylint: disable = E0401 from .hash import Hash class Keys(object): """ Compatibility class to support Python 2.7 style iteration in Python 3.X+ Logic ported from the 2.7 Python branch: cpython/Objects/dictobject.c Logic ported from the 2.7 Python branch: cpython/Objects/setobject.c """ # Min dict size MINSIZE = 8 # Hash collisions PERTURB_SHIFT = 5 def __init__(self): """ Initializes a keys object. """ self.keylist = [] self.keysort = None # Python 2 dict default size self.mask = Keys.MINSIZE - 1 def __setstate__(self, state): """ Overrides default pickling object to force re-adding all keys and match Python 2.7 deserialization logic. Args: state: input state """ self.__dict__ = state keys = self.keys() # Clear keys and re-add to match deserialization logic self.__init__() for k in keys: self.add(k) def __iter__(self): """ Default iterator. Returns: iterator """ return iter(self.keys()) def keys(self): """ Returns keys ordered using Python 2.7's iteration algorithm. Method: static PyDictEntry *lookdict(PyDictObject *mp, PyObject *key, register long hash) Returns: list of keys """ if not self.keysort: keys = [] hids = set() for k in self.keylist: # C API uses unsigned values h = ctypes.c_size_t(Hash.hash(k)).value i = h & self.mask hid = i perturb = h while hid in hids: i = (i << 2) + i + perturb + 1 hid = i & self.mask perturb >>= Keys.PERTURB_SHIFT keys.append((hid, k)) hids.add(hid) # Cache result - performance - clear if more keys added self.keysort = [v for (k, v) in sorted(keys, key=lambda x: x[0])] return self.keysort def add(self, key): """ Called each time a new item is inserted. Tracks via insertion order and will maintain the same order as a dict in Python 2.7. Method: static int dict_set_item_by_hash_or_entry(register PyObject *op, PyObject *key, long hash, PyDictEntry *ep, PyObject *value) Args: key: key to add """ # Add key to list. If this is a replace/update then size won't change. if key and key not in self.keylist: # Append key to list self.keylist.append(key) # Clear cached keys self.keysort = None # Resize dict if 2/3 capacity if len(self.keylist) * 3 >= ((self.mask + 1) * 2): # Reset key list to simulate the dict resize + copy operation self.keylist = self.keys() self.keysort = None self.setMask() def remove(self, key): """ Remove a key from the backing list. Args: key: key to remove """ if key in self.keylist: # Remove key from list self.keylist.remove(key) # Clear cached keys self.keysort = None def merge(self, d): """ Merges keys from an existing iterable into this key list. Method: int PyDict_Merge(PyObject *a, PyObject *b, int override) Args: d: input dict """ # PyDict_Merge initial merge size is double the size of the current + incoming dict self.setMask((len(self.keylist) + len(d)) * 2) # Copy actual keys for k in d: self.add(k) def copy(self): """ Makes a copy of self. Method: PyObject *PyDict_Copy(PyObject *o) Returns: copy of self """ # Copy creates a new object and merges keys in new = Keys() new.merge(self.keys()) return new def pop(self): """ Pops the top element from the sorted keys if it exists. Returns None otherwise. Method: static PyObject *dict_popitem(PyDictObject *mp) Return: top element or None if Keys is empty """ if self.keylist: # Pop the top element value = self.keys()[0] self.remove(value) return value return None def setMask(self, request=None): """ Key based on the total size of this dict. Matches ma_mask in Python 2.7's dict. Method: static int dictresize(PyDictObject *mp, Py_ssize_t minused) """ if not request: length = len(self.keylist) # Python 2 dict increases by a factor of 4 for small dicts, 2 for larger ones request = length * (2 if length > 50000 else 4) newsize = Keys.MINSIZE while newsize <= request: newsize <<= 1 self.mask = newsize - 1

[ "561939+davidmezzetti@users.noreply.github.com" ]

561939+davidmezzetti@users.noreply.github.com