Datasets:
tim-lawson
/
the-stack-v2-dedup-python-starcoder2-3b

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#!/usr/bin/env python # @HEADER # ************************************************************************ # # TriBITS: Tribal Build, Integrate, and Test System # Copyright 2013 Sandia Corporation # # Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation, # the U.S. Government retains certain rights in this software. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # 3. Neither the name of the Corporation nor the names of the # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # ************************************************************************ # @HEADER # # Usage: mockprogram.py [any arguments] # # Mock program that takes input arguments and produces stdout by reading from # a file .mockprogram_inout.txt in the current directory or the file specified # by the env var MOCKPROGRAM_INOUT_FILE_OVERRIDE (which can be in any # directory). This script is used to take the place of real commands during a # test that involves calling commands on the commandline. # # The file .mockprogram_inout.txt (or pointed to by # MOCKPROGRAM_INOUT_FILE_OVERRIDE) is of the form: # # MOCK_PROGRAM_INPUT: <args_1> # MOCK_PROGRAM_RETURN: <rtn> # MOCK_PROGRAM_OUTPUT: <outline_1_line_1> # <outline_1_line_2> # ... # MOCK_PROGRAM_INPUT: <args_2> # # The program reads in the blocks starting at the time and removes the block # from the file after it runs. After all of the blocks are read in, if run # again it will error out with error code 2. # # This program can be used, for example, to simulate git command. For # example, a couple of git commits might be simulated like: # # MOCK_PROGRAM_INPUT: log -1 # MOCK_PROGRAM_RETURN: 0 # MOCK_PROGRAM_OUTPUT: This is the summary line # # The is the body of the commit msg # MOCK_PROGRAM_INPUT: diff --name-only HEAD --not @{u} # MOCK_PROGRAM_RETURN: 0 # MOCK_PROGRAM_OUTPUT: file_name_1.txt # file_name_2.txt # file_name_3.txt # import sys import os inputArgs = " ".join(sys.argv[1:]) # print("inputArgs = '" + inputArgs + "'" if os.environ.get("MOCKPROGRAM_INOUT_FILE_OVERRIDE"): mockProgramInOutFilePath = os.environ.get("MOCKPROGRAM_INOUT_FILE_OVERRIDE") else: mockProgramInOutFilePath = ".mockprogram_inout.txt" if not os.path.exists(mockProgramInOutFilePath): print("Error: " + mockProgramInOutFilePath + " is missing!") sys.exit(1) mockprogramInout = open(mockProgramInOutFilePath, "r").read() mockprogramInoutArray = mockprogramInout.splitlines() if len(mockprogramInoutArray) and mockprogramInoutArray[-1] == "": mockprogramInoutArray = mockprogramInoutArray[:-1] if len(mockprogramInoutArray) < 3: print( "Error: " + mockProgramInOutFilePath + " has less than three lines:\n" "-------------\n" + mockprogramInout + "-------------" ) sys.exit(2) # Assert input expectedInputLine = mockprogramInoutArray[0] if expectedInputLine.find("MOCK_PROGRAM_INPUT:") != 0: print("Error, first line = '" + expectedInputLine + "', does not match ^MOCK_PROGRAM_INPUT:") sys.exit(3) expectedInput = expectedInputLine.replace("MOCK_PROGRAM_INPUT:", "").strip() if inputArgs != expectedInput: print("Error, input args='" + inputArgs + "' does not match expected='" + expectedInput + "'") sys.exit(4) # Get return code returnCodeLine = mockprogramInoutArray[1] if returnCodeLine.find("MOCK_PROGRAM_RETURN:") != 0: print("Error, second line = '" + returnCodeLine + "', does not match ^MOCK_PROGRAM_RETURN:") sys.exit(5) returnCode = returnCodeLine.replace("MOCK_PROGRAM_RETURN:", "").strip() # Get output (can be multi-line) outputLine = mockprogramInoutArray[2] if outputLine.find("MOCK_PROGRAM_OUTPUT:") != 0: print("Error, third line = '" + outputLine + "', does not match ^MOCK_PROGRAM_OUTPUT:") sys.exit(6) outputStr = outputLine.replace("MOCK_PROGRAM_OUTPUT: ", "") numLinesOuput = 1 if len(mockprogramInoutArray) > 3: for line in mockprogramInoutArray[3:]: if line.find("MOCK_PROGRAM_INPUT:") == 0: break outputStr = outputStr + "\n" + line numLinesOuput = numLinesOuput + 1 print(outputStr) # Write the remaining lines back into the file lineLineIndex = 2 + numLinesOuput if len(mockprogramInoutArray) > lineLineIndex: open(mockProgramInOutFilePath, "w").write(("\n".join(mockprogramInoutArray[lineLineIndex:])) + "\n") else: open(mockProgramInOutFilePath, "w").write("") # Return exit code sys.exit(int(returnCode))
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4fa9d16f979acf3edce05a209e1c6636e50fc315
from jox_api import label_image, Mysql, Utils from jox_config import api_base_url import json class Menu: def __init__(self): self.mysqlClass = Mysql.MySQL() self.timeClass = Utils.Time() def get_menu(self, type, openid): try: if type == "mine": self.sql = "SELECT * FROM get_menu WHERE openid='%s' order by watch DESC " % (openid) self.resql = self.mysqlClass.select_data(self.sql) if self.resql["state"] != "E": return {"code": 1007, "menu_list": self.resql["alldata"]} else: return {"code": -1} elif type == "main": self.sql = "SELECT * FROM get_menu order by watch DESC" self.resql = self.mysqlClass.select_data(self.sql) if self.resql["state"] != "E": return {"code": 1007, "top_list": self.resql["alldata"][0:3], "menu_list": self.resql["alldata"][3:-1]} else: return {"code": -1} except Exception as e: print(str(e)) return {"code": -1} def get_menu_info(self, menu_id): try: self.sql = "SELECT * FROM menu WHERE id=%s" % (menu_id) self.resql = self.mysqlClass.select_data(self.sql) if self.resql["state"] != "E": self.menu = self.resql["alldata"][0] self.sql3 = "UPDATE menu SET watch=%s where id=%s" % (self.menu["watch"] + 1, menu_id) self.resql3 = self.mysqlClass.insert_data(self.sql3) print(self.resql3) self.sql2 = "SELECT * FROM menu_step WHERE menu_id=%s order by num ASC " % (menu_id) self.resql2 = self.mysqlClass.select_data(self.sql2) self.step_list = [] if self.resql2["state"] != "E": for ai_menu_log in self.resql2["alldata"]: self.step_list.append(ai_menu_log) self.menu["menu_step"] = self.step_list return {"code": 1008, "menu_info": self.menu} else: return {"code": -1} except Exception as e: print(str(e)) return {"code": -1} def add_menu(self, data, openid): try: self.create_time = self.timeClass.get_time() self.sql = """ INSERT INTO menu (openid,title,photo,material,accessories,ingredient,create_date) VALUES (\'%s\',\'%s\',\'%s\',\'%s\',\'%s\',\'%s\',\'%s\') """ % (openid, data.title, data.photo, data.material, data.accessories, data.ingredient, self.create_time) self.resql = self.mysqlClass.add_insert(self.sql, "") self.conn = self.resql["conn"] self.cur = self.resql["cur"] self.menu_id = self.cur.lastrowid steps = json.loads(data.steps) for step in steps: print(step["num"]) self.sql2 = """ INSERT INTO menu_step (menu_id,num,content,image,create_date) VALUES (%s,%d,\'%s\',\'%s\',\'%s\') """ % (self.menu_id, step["num"], step["content"], step["image"], self.create_time) self.resql2 = self.mysqlClass.add_insert(self.sql2, self.conn) self.conn = self.resql2["conn"] self.resql = self.mysqlClass.commit_inserst(self.conn) if self.resql["state"] != "E": return {"code": 1010} else: return {"code": -1} except Exception as e: print(str(e)) return {"code": -1}
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e5e012e40a71dee9f4dbd9913590aef125b758df
from classes.Board import Board class Visualiser: coordinate_map = ("a", "b", "c", "d", "e", "f", "g", "h") __dimensions = 8 def __init__(self): self.map = [] self.__build_map() def __build_map(self): """ Creates the array of the battlefield. Should never be used for logical operations :return: """ columns = [] for i in range(self.__dimensions): columns.append([]) for i in range(self.__dimensions): self.map.append(columns) def print(self, board: Board): """ Print the entire battlefield :param board: The current board in play :return: void """ # Render first horizontal alphabetical x-axis markers row = [" "] for x_marker in self.coordinate_map: row.append(" " + x_marker) print("".join(row)) # Render the rest of the cheese board for y, y_row in enumerate(self.map): # Render left side row numbers row = [str((8 - y)) + " "] # Render battlefield for x, square in enumerate(y_row): # Check with Board if there is a piece on this coordinate anybody = board.who_is_in(*[x, y]) # Anybody out there? if anybody is not None: # Oh hai row.append(anybody.name) else: # Print a simple dot row.append(" .") # Print the entire row print("".join(row)) @staticmethod def to_algebraic(x, y): return Visualiser.coordinate_map[x] + str(abs((y - Visualiser.__dimensions)))
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27e9e63338d422b5fca6f7a67fa3d255602a3358
from abstract_class_V import V import torch import torch.nn as nn class V_test_abstract(V): def __init__(self): super(V_test_abstract, self).__init__() def V_setup(self, y, X, nu): self.explicit_gradient = False self.need_higherorderderiv = True self.dim = X.shape[1] self.beta = nn.Parameter(torch.zeros(self.dim * 5 + 4), requires_grad=True) self.y = y self.X = X self.nu = nu # beta[:dim] = z # beta[(dim):(2dim)] = r1_local # beta[(2dim):(3dim)] = r2_local # beta[(3dim):(4dim)] = r1_local_plus # beta[(4dim):(5dim)] = r2_local_plus # beta[5dim] = r1_global # beta[5dim+1] = r2_global # beta[5dim+2] = sigma # beta[5dim+3] = w0 return () def forward(self): z = self.beta[: self.dim] r1_local = self.beta[(self.dim) : (2 * self.dim)] r2_local = self.beta[(2 * self.dim) : (3 * self.dim)] r1_local_plus = self.beta[(3 * self.dim) : (4 * self.dim)] r2_local_plus = self.beta[(4 * self.dim) : (5 * self.dim)] r1_global = self.beta[5 * self.dim] r2_global = self.beta[5 * self.dim + 1] sigma = self.beta[5 * self.dim + 2] w0 = self.beta[5 * self.dim + 3] tau = r1_global * torch.sqrt(r2_global) lamb = r1_local * torch.sqrt(r2_local) lambda_plus = r1_local_plus * torch.sqrt(r2_local_plus) w = z * lamb * lambda_plus * tau outy = (self.y - (w0 + self.X.mv(w))) * (self.y - (w0 + self.X.mv(w))) / (sigma * sigma) * 0.5 outz = torch.dot(z, z) * 0.5 outr1_local = torch.dot(r1_local, r1_local) outr2_local = ((0.5 * self.nu + 1) * torch.log(r2_local) + 0.5 * self.nu / r2_local).sum() outr1_global = r1_global * r1_global * 0.5 outr2_global = 1.5 * torch.log(r2_global) + 0.5 / r2_global outw0 = w0 * w0 / (25.0) out = outy + outz + outr1_local + outr2_local + outr1_global + outr2_global + outw0 return out def load_explcit_gradient(self): return ()
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d0a3f332e04627eb275168972bd92cd1ea9b9447
from board.ttt import TTT from mctsai.mcts import MCTS import unittest # skip = [0, 1] skip = [0] class TestTTT(unittest.TestCase): def test_mcts(self): if 0 in skip: print("Skipping ai self-play") return ttt = TTT() for i in range(1000): mcts = MCTS(ttt) state = mcts.root.state while not mcts.board.ending_state(state): move = mcts.search() print(move) state = mcts.board.get_state(state, move) mcts.board.print(state) mcts.make_move(move) self.assertEqual(mcts.board.ending_state(state), -1) def test_play_mcts(self): if 1 in skip: print("Skipping human-ai play") return ttt = TTT() mcts = MCTS(ttt) state = mcts.root.state my_player = 2 while not mcts.board.ending_state(state): mcts.board.print(state) move = mcts.search() print(move) if state[1] == my_player: move = input("Make move!\n") move = (int(move[0]), int(move[1])) mcts.make_move(move) state = mcts.root.state mcts.board.print(state) # state = mcts.board.get_state(state, move) # mcts = MCTS(ttt) # mcts.root.state = state # mcts.root.remaining_moves = mcts.board.get_legal_moves(mcts.root.state) def test_positions(self): # simple block move_sequence = [(1, 1), (2, 0), (0, 1)] # self.from_position(move_sequence, (2, 1), "Simple block 1") # simple block 2 move_sequence = [(1, 1), (2, 2), (2, 1)] # self.from_position(move_sequence, (0, 1), "Simple block 2") # simple win 1 move_sequence = [(1, 1), (2, 2), (2, 0), (0, 2), (1, 2), (2, 1)] # self.from_position(move_sequence, (1, 0), "Simple win") def from_position(self, move_sequence, expected_move, name): ttt = TTT() mcts = MCTS(ttt, searchtime=30) mcts.board.print(mcts.root.state) for move in move_sequence: mcts.search() mcts.make_move(move) mcts.board.print(mcts.root.state) move = mcts.search() print("Testing {} block (that was lost before) on the following board".format(name)) self.assertEqual(move, expected_move) def test_trick_win(self): pass # ttt = TTT() # state = ttt.get_initial_state() # state = ttt.get_state(state, (1, 1)) # state = ttt.get_state(state, (2, 2)) # state = ttt.get_state(state, (2, 0)) # print("Testing trick win on the following board") # ttt.print(state) # for _ in range(100): # mcts = MCTS(ttt) # mcts.set_root_state(state) # move = mcts.search() # self.assertEqual(move, (0, 2)) def test_defend_trick_win(self): pass
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f85a703b47d981397ed6048e941030a3fbee7b6d
# -*- coding: utf-8 -*- # Generated by Django 1.9.8 on 2018-04-27 08:05 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("talk", "0023_auto_20180207_1121"), ] operations = [ migrations.AddField( model_name="talkmedia", name="codelink", field=models.CharField(blank=True, max_length=255, verbose_name="Source code"), ), ]
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f8d815bcdc74452b66a1b3b33bf0fbe976e728c8
# This is a sample Python script. # Press Shift+F10 to execute it or replace it with your code. # Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings. import tensorflow as tf from tensorflow.keras import datasets, layers, models import matplotlib.pyplot as plt import numpy as np from PIL import Image import glob def next_batch(num, data, labels): """ Return a total of `num` random samples and labels. """ idx = np.arange(0, len(data)) np.random.shuffle(idx) idx = idx[:num] data_shuffle = [data[i] for i in idx] labels_shuffle = [labels[i] for i in idx] return np.asarray(data_shuffle), np.asarray(labels_shuffle) def dataX(features, set): data_x = np.array([]) count = 0 for filepath in glob.iglob(set): globpath = filepath + "\*.jpg" for filepath in glob.iglob(r"" + globpath): count = count + 1 img = Image.open(filepath).convert("L") # convert image to 8-bit grayscale data = list(img.getdata()) x = np.array(data) data_x = np.append(data_x, x) data_x = data_x.reshape(count, features) # return data_x return data_x.astype(int) def dataY(categories, set): data_y = np.array([]) count = 0 for filepath in glob.iglob(set): path = filepath.split("\\") globpath = filepath + "\*.jpg" for filepath in glob.iglob(r"" + globpath): count = count + 1 y = np.array([]) t = [int(path[2])] y = np.append(y, t) data_y = np.append(data_y, y) # data_y = data_y.reshape(count, categories) # return data_y return data_y.astype(int) def model(): print(" model") batch_size = 50 features = 32 * 32 categories = 5 filter1 = 32 filter2 = 64 train_path = r"dataset\train\[0-4]" test_path = r"dataset\test\[0-4]" validation_path = r"dataset\validation\[0-4]" data_x = dataX(features, train_path) print("datax: ", data_x) data_y = dataY(categories, train_path) print("datay: ", data_y) data_x_test = dataX(features, test_path) data_y_test = dataY(categories, test_path) data_x_validation = dataX(features, validation_path) data_y_validation = dataY(categories, validation_path) x_image = tf.reshape(data_x, [-1, 32, 32, 1]) x_image_test = tf.reshape(data_x_test, [-1, 32, 32, 1]) x_image_validation = tf.reshape(data_x_validation, [-1, 32, 32, 1]) model = models.Sequential() model.add(layers.Conv2D(filter1, (5, 5), activation="relu", padding="SAME", input_shape=(32, 32, 1))) model.add(layers.MaxPooling2D((2, 2), padding="SAME", strides=(2, 2))) model.add(layers.Conv2D(filter2, (5, 5), activation="relu", padding="SAME", input_shape=(filter1, 16, 16, 1))) model.add(layers.MaxPooling2D((2, 2), padding="SAME", strides=(2, 2))) model.add(layers.Flatten()) model.add(layers.Dense(filter2, activation="relu")) model.add(layers.Dropout(0.5)) model.add(layers.Dense(categories)) model.summary() model.compile(optimizer="adam", loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=["accuracy"]) history = model.fit(x=x_image, y=data_y, epochs=10, validation_data=(x_image_validation, data_y_validation)) # Show results in graph view plt.plot(history.history["accuracy"], label="accuracy") plt.plot(history.history["val_accuracy"], label="val_accuracy") plt.xlabel("Epoch") plt.ylabel("Accuracy") plt.ylim([0.5, 1]) plt.legend(loc="lower right") # plt.show() test_loss, test_acc = model.evaluate(x_image_test, data_y_test, verbose=2) plt.show() if __name__ == "__main__": model()
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# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'src/ui_LibraryTab.ui' # # Created: Tue Jun 9 21:46:41 2015 # by: PyQt5 UI code generator 5.4 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_Tab(object): def setupUi(self, Tab): Tab.setObjectName("Tab") Tab.resize(762, 523) self.verticalLayout = QtWidgets.QVBoxLayout(Tab) self.verticalLayout.setObjectName("verticalLayout") self.hLayout = QtWidgets.QHBoxLayout() self.hLayout.setObjectName("hLayout") self.btn_enterPassword = QtWidgets.QPushButton(Tab) self.btn_enterPassword.setObjectName("btn_enterPassword") self.hLayout.addWidget(self.btn_enterPassword) spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.hLayout.addItem(spacerItem) self.verticalLayout.addLayout(self.hLayout) self.scrollArea = QtWidgets.QScrollArea(Tab) self.scrollArea.setWidgetResizable(True) self.scrollArea.setObjectName("scrollArea") self.scrollAreaWidgetContents = QtWidgets.QWidget() self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 736, 455)) self.scrollAreaWidgetContents.setObjectName("scrollAreaWidgetContents") self.gridLayout = QtWidgets.QGridLayout(self.scrollAreaWidgetContents) self.gridLayout.setObjectName("gridLayout") self.scrollArea.setWidget(self.scrollAreaWidgetContents) self.verticalLayout.addWidget(self.scrollArea) self.retranslateUi(Tab) QtCore.QMetaObject.connectSlotsByName(Tab) def retranslateUi(self, Tab): _translate = QtCore.QCoreApplication.translate Tab.setWindowTitle(_translate("Tab", "Form")) self.btn_enterPassword.setText(_translate("Tab", "Enter Password"))
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DATABASE_NAME = "user_db"
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# -*- coding=utf-8 -*- from mako.template import Template from xblock.fragment import Fragment from .lookup import TemplateLookup # xblock_ifmo.lookup from .utils import deep_update class FragmentMakoChain(Fragment): """ Класс, позволяющий последовательно оборачивать экземпляры Fragment друг в друга. Для того, чтобы цепочка отработала, шаблон должен наследоваться от шаблона ifmo_xblock_base и определять блок block_body. Порядок оборачивания не определён. """ base = None context = {} _content = None lookup_dirs = None def __init__(self, content=None, base=None, lookup_dirs=None): """ Класс, позволяющий последовательно оборачивать экземпляры Fragment друг в друга. :param content: Содержимое фрагмента :param base: Базовый фрагмент, тот, в который будет обёрнут этот фрагмент; должен быть экземпляром FragmentMakoChain или None :param lookup_dirs: Директории поиска шаблонов :return: """ assert isinstance(base, FragmentMakoChain) or base is None super(FragmentMakoChain, self).__init__(content=content) self.base = base self.lookup_dirs = lookup_dirs def body_html(self): template = self.build_chain() return template.render(**self.context.get("render_context", {})) def add_context(self, new_context): deep_update(self.context, new_context) def build_chain(self): """ Строит цепочку шаблонов. В цепочке каждый шаблон наследуется от одного и того же ifmo_xblock_base, поэтому порядок оборачивания не определён (точнее, его вычисляет метод super()). Поскольку при рендере шаблона используется исключительно lookup от шаблона, над которым он вызван, а не собственный Lookup для каждого из шаблона в коллекции, необходимо добавить в коллекцию все пути и шаблоны, использующиеся в шаблоне выше по цепочке. Более того, необходимо изменить имена шаблонов (ifmo_xblock_base) на уникальные. :param lookup: экземпляр TemplateLookup, в который будут записываться новые пути и шаблоны, использующиеся как родительские :return: tuple(template, lookup, base_template_id) - template -- шаблон, который должен будет стать родителем - lookup -- изменённый lookup """ def _build_chain(self, lookup=None): old_base_name = "ifmo_xblock_base" new_base_name = None if self.base is not None: import uuid new_base_name = "{name}_{rnd}".format(name=old_base_name, rnd=str(uuid.uuid4())) if hasattr(self.base, "build_chain"): base_template, base_lookup = _build_chain(self.base, lookup) lookup.put_template(new_base_name, base_template) else: lookup.put_string(new_base_name, self.base.body_html()) lookup.append_dirs(self.base.lookup_dirs) return Template( text=self._content.replace(old_base_name, new_base_name) if new_base_name else self._content, lookup=lookup ), lookup lookup = TemplateLookup(directories=self.lookup_dirs) template, _ = _build_chain(self, lookup) return template @property def resources(self): seen = set() parent_res = self.base.resources if self.base else [] return [x for x in parent_res + self._resources if x not in seen and not seen.add(x)] @property def content(self): return self.body_html() @content.setter def content(self, value): self._content = value
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8cc0393082448bb8f61068b5c96e89ef3aee77ed
# coding: utf-8 import logging from threading import Thread from ldap import SCOPE_BASE from seafevents.ldap_syncer.ldap_conn import LdapConn from seafevents.ldap_syncer.utils import bytes2str, add_group_uuid_pair from seaserv import get_group_dn_pairs logger = logging.getLogger(__name__) def migrate_dn_pairs(settings): grp_dn_pairs = get_group_dn_pairs() if grp_dn_pairs is None: logger.warning("get group dn pairs from db failed when migrate dn pairs.") return grp_dn_pairs.reverse() for grp_dn_pair in grp_dn_pairs: for config in settings.ldap_configs: search_filter = "(objectClass=*)" ldap_conn = LdapConn(config.host, config.user_dn, config.passwd, config.follow_referrals) ldap_conn.create_conn() if not ldap_conn.conn: logger.warning("connect ldap server [%s] failed." % config.user_dn) return if config.use_page_result: results = ldap_conn.paged_search(grp_dn_pair.dn, SCOPE_BASE, search_filter, [config.group_uuid_attr]) else: results = ldap_conn.search(grp_dn_pair.dn, SCOPE_BASE, search_filter, [config.group_uuid_attr]) ldap_conn.unbind_conn() results = bytes2str(results) if not results: continue else: uuid = results[0][1][config.group_uuid_attr][0] add_group_uuid_pair(grp_dn_pair.group_id, uuid) class LdapSync(Thread): def __init__(self, settings): Thread.__init__(self) self.settings = settings def run(self): if self.settings.enable_group_sync: migrate_dn_pairs(settings=self.settings) self.start_sync() self.show_sync_result() def show_sync_result(self): pass def start_sync(self): data_ldap = self.get_data_from_ldap() if data_ldap is None: return data_db = self.get_data_from_db() if data_db is None: return self.sync_data(data_db, data_ldap) def get_data_from_db(self): return None def get_data_from_ldap(self): ret = {} for config in self.settings.ldap_configs: cur_ret = self.get_data_from_ldap_by_server(config) # If get data from one server failed, then the result is failed if cur_ret is None: return None for key in cur_ret.keys(): if key not in ret: ret[key] = cur_ret[key] ret[key].config = config return ret def get_data_from_ldap_by_server(self, config): return None def sync_data(self, data_db, data_ldap): pass
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eb853e430b996a81dc2ef20c320979a3e04d956a
#!/usr/bin/env python3 # -*- coding: UTF-8 -*- import hashlib import re from datetime import datetime import gevent import requests import scrapy from gevent.pool import Pool from lxml import etree from scrapy.http import HtmlResponse from sqlalchemy import create_engine, func from sqlalchemy.orm import sessionmaker from ..items import Album, AlbumImageRelationItem, AlbumItem, AlbumImageItem from ..utils.const import const from ..utils.redis_util import get_redis_conn_from_pool class Beautyleg7Spider(scrapy.Spider): name = "Beautyleg7Spider" category_list = ["siwameitui", "xingganmeinv", "weimeixiezhen", "ribenmeinv"] start_urls = [("http://www.beautyleg7.com/" + category) for category in category_list] const.REPEATED_THRESHOLD = 10 def __init__(self, name=None, **kwargs): super().__init__(name=None, **kwargs) self.db_session = None self.gevent_pool = Pool(32) self.redis_cmd = get_redis_conn_from_pool() self.ALBUM_URL_REDIS_KEY_PREFIX = "album_url" self.REDIS_LIMITER = ":" self.album_last_item_redis_unique_key = "" self.album_item = None self.album_image_item_list = [] self.album_image_relation_item = AlbumImageRelationItem() def start_requests(self): mysql_host = self.crawler.settings.get("MYSQL_HOST") mysql_port = self.crawler.settings.get("MYSQL_PORT") mysql_user = self.crawler.settings.get("MYSQL_USER") mysql_password = self.crawler.settings.get("MYSQL_PASSWORD") mysql_db_name = self.crawler.settings.get("MYSQL_DB_NAME") engine = create_engine( "mysql+mysqlconnector://{}:{}@{}:{}/{}".format(mysql_user, mysql_password, mysql_host, mysql_port, mysql_db_name), pool_recycle=180, echo=False, ) session_maker = sessionmaker(bind=engine) self.db_session = session_maker() for url in self.start_urls: yield scrapy.Request(url) def parse(self, response): if self.db_session is None: self.logger.error("db_session is None") return None repeated_count = 0 if response is None: self.logger.warn("响应为空，不做处理！") else: album_nodes = response.css(".pic .item") category = response.css(".sitepath a")[1].css("a::text").extract_first().strip() # 判断最后一页的最后主题是否被持久化 is_persisted_last_item = self.redis_cmd.get(self.album_last_item_redis_unique_key) is_last_item_finished = False if is_persisted_last_item is not None and int(is_persisted_last_item): is_last_item_finished = True self.logger.info("已持久化最后一页的最后主题：%s" % self.album_last_item_redis_unique_key) # 如果是最后一页则设置Redis存储key:“最后一页页码：最后一条主题url”，value：is_persisted（取值为0或1，默认为0） album_last_page_url = response.meta.get("album_last_page_url") if album_last_page_url is not None: album_last_page_url_last_item_redis_suffix = album_nodes[-1].css(".p a::attr(href)").extract_first() self.album_last_item_redis_unique_key = ( self.ALBUM_URL_REDIS_KEY_PREFIX + self.REDIS_LIMITER + self.sub_url_scheme(album_last_page_url, "") + self.REDIS_LIMITER + self.sub_url_scheme(album_last_page_url_last_item_redis_suffix, "") ) self.redis_cmd.setnx(self.album_last_item_redis_unique_key, 0) for album_node in album_nodes: album_url = album_node.css(".p a::attr(href)").extract_first().strip() # 判断当前主题url是否已持久化 is_persisted = self.redis_cmd.get(album_url) if is_persisted is not None and int(is_persisted): self.logger.info("Redis中该url album_url：%s已持久化" % album_url) continue album_url_object_id = self.get_md5(album_url) # 只有name不存在时，当前set操作才执行 self.redis_cmd.setnx(album_url, 0) count = 0 try: count = self.db_session.query(func.count()).filter(Album.album_url_object_id == album_url_object_id).first() if count: count = count[0] except Exception as e: self.logger.error("查询数据库异常，原因：{}".format(e)) finally: self.db_session.rollback() if count: self.logger.info("数据库已有该数据album_url_object_id：%s" % album_url_object_id) repeated_count += 1 # 只有name存在时，当前set操作才执行 self.redis_cmd.set(album_url, 1, xx=True) continue else: album_item = self.parse_album_item(album_node, album_url, album_url_object_id, category) yield response.follow(url=album_url, meta={"AlbumItem": album_item}, callback=self.parse_detail) # 提取下一页并交给scrapy下载 selector_list = response.css(".page li a::attr(href)") # 如果最后一页的最后一个主题url未被持久化则继续爬取 if not is_last_item_finished: if selector_list: last_page_url = None current_url_page = response.xpath('//li[@class="thisclass"]//text()').extract_first() # 如果当前页是第一页则获取最后一页url if current_url_page and int(current_url_page) == 1: last_page_url = selector_list[-1].extract() next_url = selector_list[-2].extract() if next_url == last_page_url: album_last_page_url = response.urljoin(last_page_url) self.logger.info("Last page：%s" % album_last_page_url) else: self.logger.info("Next page：%s" % response.urljoin(next_url)) yield response.follow(url=next_url, meta={"album_last_page_url": album_last_page_url}, callback=self.parse) else: self.logger.info("selector_list is None") self.logger.info("重复次数：%s" % repeated_count) else: self.logger.info("Stop crawler. None Next page!") def parse_album_item(self, album_node, album_url, album_url_object_id, category): album_title = album_node.css(".p a img::attr(alt)").extract_first().strip() cover_url = album_node.css(".p a img::attr(src)").extract_first().strip() regex = "\d+\.\d+.\d+\s+No\.\d+|\d+\-\d+-\d+\s+No\.\d+" number_group = re.findall(regex, album_title) if len(number_group) > 0: number = number_group[0] else: number = "No.unknown" create_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S") album_item = AlbumItem() album_item["category"] = category album_item["album_url"] = album_url album_item["album_url_object_id"] = album_url_object_id album_item["album_title"] = album_title album_item["cover_url"] = cover_url album_item["number"] = number album_item["create_date"] = create_date return album_item def parse_detail(self, response): self.album_item = response.meta.get("AlbumItem") self.album_image_relation_item["album_item"] = self.album_item self.parse_album_image_item(response) # 详情页分页链接,循环生成所有子页面的请求 relative_next_page_list = response.css(".page li a::attr(href)").extract() # 使用gevent协程池提升网络IO处理效率 next_page_threads = [ self.gevent_pool.spawn(self.get_album_image_item_list, response.urljoin(relative_next_page)) for relative_next_page in relative_next_page_list[2:-1] ] gevent.joinall(next_page_threads) self.album_image_relation_item["album_image_item_list"] = self.album_image_item_list # 重新初始化 self.album_image_item_list = [] yield self.album_image_relation_item def get_album_image_item_list(self, abs_next_page): """ 使用下页绝对路径同步请求 :param abs_next_page: :return: """ resp = requests.get(abs_next_page) if resp.status_code == 200: encoding = requests.utils.get_encodings_from_content(resp.text) resp.encoding = encoding[0] self.parse_album_image_item(etree.HTML(resp.text)) else: self.logger.warn("下载此页{}失败,返回的状态码为{}".format(abs_next_page, resp.status_code)) def parse_album_image_item(self, response): """ 解析item并返回给pipelines :param response: 如果response类型是继承自scrapy的TextResponse类则使用scrapy的Selector来解析，否则使用lxml来解析 :return: """ if isinstance(response, HtmlResponse): item_title = response.xpath('//div[@class="content"]/h1/text()').extract_first().strip() publish_date = response.xpath('//div[@class="tit"]/span/text()').extract_first().split(":")[1] image_link_list = response.css(".contents a img::attr(src)").extract() else: item_title = response.xpath('//div[@class="content"]/h1/text()')[0].strip() publish_date = response.xpath('//div[@class="tit"]/span/text()')[0].split(":")[1] image_link_list = response.xpath('//div[@class="contents"]/a/img') image_link_list = [image_link.attrib["src"] for image_link in image_link_list] regex = "\s?\w+[^\w]?" regex_group = re.findall(regex, item_title) stage_name = "unknown" if len(regex_group) > 0: str = regex_group[-1] if "[" in str: stage_name = str.split("[")[0].strip() elif "(" in str: stage_name = str.split("(")[0].strip() elif re.match("[^\d*]", str): stage_name = re.match("[^\d*]", str).group() # 详情页多个图片链接 for image_url in image_link_list: album_image_item = AlbumImageItem() album_image_item["item_url"] = image_url album_image_item["item_url_object_id"] = self.get_md5(image_url) item_url_list_json = "{}" album_image_item["item_url_list_json"] = item_url_list_json album_image_item["item_title"] = item_title album_image_item["stage_name"] = stage_name album_image_item["publish_date"] = publish_date self.album_image_item_list.append(album_image_item) return self.album_image_item_list @staticmethod def get_md5(param): if isinstance(param, str): param = param.encode() m = hashlib.md5() m.update(param) return m.hexdigest() @staticmethod def sub_url_scheme(website, replace_str): scheme_regex = "^(http://|https://)" return re.sub(scheme_regex, replace_str, website)
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bc4684d255a46427f708d8ce8bda2e12fb8c8ffe
# -*- coding: utf-8 -*- from route4me import Route4Me API_KEY = "11111111111111111111111111111111" def main(): r4m = Route4Me(API_KEY) route = r4m.route response = route.get_routes(limit=1, offset=0) if isinstance(response, dict) and "errors" in response.keys(): print(". ".join(response["errors"])) else: route_id = response[0]["route_id"] print("Route ID: {}".format(route_id)) response = route.get_route(route_id=route_id) if isinstance(response, dict) and "errors" in response.keys(): print(". ".join(response["errors"])) else: print("Original Route") print("Route ID: {}".format(response["route_id"])) for i, address in enumerate(response["addresses"]): print("Address #{}".format(i + 1)) print("\tAddress: {0}".format(address["address"])) print("\tRoute Destination ID: {0}".format(address["route_destination_id"])) route_destination_id = response["addresses"][1]["route_destination_id"] route_destination_id2 = response["addresses"][2]["route_destination_id"] data = { "route_destination_id": route_destination_id, "route_id": route_id, "addresses": [ { "route_destination_id": route_destination_id2, "sequence_no": 6, } ], } print("After Re-sequence Route") response = route.resequence_route(**data) print("Route ID: {}".format(response["route_id"])) for i, address in enumerate(response["addresses"]): print("Address #{}".format(i + 1)) print("\tAddress: {0}".format(address["address"])) print("\tRoute Destination ID: {0}".format(address["route_destination_id"])) if __name__ == "__main__": main()
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d015a1b27a3a9e7f5e6614da752137064000b905
#!/usr/bin/env python3 """Transfer learning with xception""" import tensorflow.keras as K from GPyOpt.methods import BayesianOptimization import pickle import os import numpy as np class my_model: """A model bassed on xception""" def make_model(self, param): """makes the model""" self.lr = param[0][0] dr = param[0][1] layer_units0 = param[0][2] layer_units1 = param[0][3] layer_units2 = param[0][4] def learning_rate(epoch): """The learning rate scheduler""" self.lr = self.lr / 1.00000001 return self.lr """Do not touch from here...""" # load data (X, Y), (X_test, Y_test) = K.datasets.cifar10.load_data() # uncomment for rapid test # X = X[0:256, :, :, :] # Y = Y[0:256, :] # X_test = X_test[0:256, :, :, :] # Y_test = Y_test[0:256, :] # preprocessing Y = K.utils.to_categorical(Y[:]) X = K.applications.xception.preprocess_input(X) Y_test = K.utils.to_categorical(Y_test[:]) X_test = K.applications.xception.preprocess_input(X_test) # data format df = "channels_last" # call backs save_best = K.callbacks.ModelCheckpoint( filepath="model_lr{:.2f}_dr{:.2f}_l0{}_l1{}_l2{}.h5".format(self.lr, dr, layer_units0, layer_units1, layer_units2), monitor="val_loss", save_best_only=True, ) early_stop = K.callbacks.EarlyStopping(monitor="val_loss", patience=7) learning_rate_0 = K.callbacks.LearningRateScheduler(learning_rate, verbose=1) # input layer and lambda layer save and load for faster training try: loaded_model = K.models.load_model("frozen_layers.h5") print("Loaded frozen layers!") except Exception as e: if isinstance(e, OSError): pass else: exit() print("Failed to load frozen layers.") inputs = K.Input(shape=(32, 32, 3)) l = K.layers.Lambda( lambda X: K.backend.resize_images(X, height_factor=7, width_factor=7, data_format="channels_last") )(inputs) # Transfer learning layers xception = K.applications.Xception(include_top=False, input_tensor=l, weights="imagenet", pooling="max") # freeze the resnet50 layers for layer in xception.layers: layer.trainable = False # get outputs outputs = xception.layers[-1].output outputs = K.layers.Dense(units=10, activation="softmax", kernel_initializer=K.initializers.he_normal())(outputs) # compile frozen model model = K.Model(inputs=inputs, outputs=outputs) model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) model.fit(X, Y, epochs=1, verbose=True, batch_size=128) model.save("frozen_layers.h5") loaded_model = K.models.load_model("frozen_layers.h5") except MemoryError("Try lowering the batch size"): exit() # set up new model if os.path.exists("X_inputs") and os.path.exists("X_test_inputs"): with open("X_inputs", "rb") as X_file: X = pickle.load(X_file) with open("X_test_inputs", "rb") as X_test_file: X_test = pickle.load(X_test_file) else: frozen_layers = K.Model(inputs=loaded_model.input, outputs=loaded_model.layers[-2].output) X = frozen_layers.predict(X, verbose=True) X_test = frozen_layers.predict(X_test, verbose=True) with open("X_inputs", "wb") as X_file: pickle.dump(X, X_file) with open("X_test_inputs", "wb") as X_test_file: pickle.dump(X_test, X_test_file) # inputs inputs = K.Input((2048,)) """... to here!!!""" # new layers here layer = K.layers.Dense(units=layer_units0, activation="relu", kernel_initializer=K.initializers.he_normal())(inputs) layer = K.layers.Dropout(dr)(layer) layer = K.layers.Dense(units=layer_units1, activation="relu", kernel_initializer=K.initializers.he_normal())(layer) # layer = K.layers.Dropout(dr)(layer) layer = K.layers.Dense(units=layer_units2, activation="relu", kernel_initializer=K.initializers.he_normal())(layer) # layer = K.layers.Dropout(dr)(layer) outputs = K.layers.Dense(units=10, activation="softmax", kernel_initializer=K.initializers.he_normal())(layer) model = K.Model(inputs=inputs, outputs=outputs) model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) # train h = model.fit( X, Y, validation_data=(X_test, Y_test), epochs=64, verbose=True, batch_size=128, shuffle=True, callbacks=[early_stop, learning_rate_0, save_best], ) val_accuracy = np.min(h.history["val_loss"]) return val_accuracy def opt(self): """the optimization function""" search_space = [ {"name": "lr", "type": "continuous", "domain": (0.01, 0.001)}, {"name": "dr", "type": "continuous", "domain": (0.1, 0.3)}, {"name": "layer_units0", "type": "discrete", "domain": (32, 64, 128, 256, 512)}, {"name": "layer_units1", "type": "discrete", "domain": (32, 64, 128, 256, 512)}, {"name": "layer_units2", "type": "discrete", "domain": (32, 64, 128, 256, 512)}, ] my_bayesian_opt = BayesianOptimization( self.make_model, domain=search_space, model_type="GP", initial_design_numdata=1, acquisition_type="EI", maximize=False, verbosity=True, ) print("==============================") my_bayesian_opt.run_optimization(max_iter=29, report_file="report", evaluations_file="evaluation", models_file="models") print("PLOTTING") my_bayesian_opt.plot_acquisition() my_bayesian_opt.plot_convergence() print("==============================") def preprocess_data(X, Y): """The data preprocessing""" Y_p = K.utils.to_categorical(Y[:]) X_p = K.applications.xception.preprocess_input(X) loaded_model = K.models.load_model("frozen_layers.h5") frozen_layers = K.Model(inputs=loaded_model.input, outputs=loaded_model.layers[-2].output) X_p = frozen_layers.predict(X_p, verbose=True) with open("Preprocessed_data_Xs", "wb") as my_file0: pickle.dump(X_p, my_file0) with open("Preprocessed_data_Ys", "wb") as my_file1: pickle.dump(Y_p, my_file1) return X_p, Y_p
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ef6f55bf27982f53441215da6822cfcdc80706a5
# -*- coding: utf-8 -*- __author__ = "Yun" __project__ = "DjangoBookTest2" # from django.template import Template, Context # from django.template.loader import get_template # from django.http import HttpResponse from django.shortcuts import render_to_response import datetime def current_datetime(request): # now = datetime.datetime.now() # t = Template("<html><body>It is now {{ current_date }}.</body></html>") # t = get_template('current_datetime.html') # html = t.render(Context({'current_date': now})) # return HttpResponse(html) # return render_to_response('current_datetime.html', {'current_date': now}) current_date = datetime.datetime.now() locals_prams = { "locals": locals(), } # locals_prams = {'current_date': now, 'name': 'YZhu', 'list': {'name': 'Qiqi', 'age': '29', 'gender': 'man'}} return render_to_response("current_datetime.html", locals_prams) def display_meta(request): context_dict = { "meta_dict": request.META, } return render_to_response("display_meta.html", context_dict)
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e616d14827beaa08ab08219421cbf7990cf163fd
#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.InsureAdmitDTO import InsureAdmitDTO class AlipayInsSceneEcommerceInsureCheckModel(object): def __init__(self): self._insure_admit_dto_list = None self._partner_org_id = None self._product_code = None self._scene_code = None self._user_client = None @property def insure_admit_dto_list(self): return self._insure_admit_dto_list @insure_admit_dto_list.setter def insure_admit_dto_list(self, value): if isinstance(value, list): self._insure_admit_dto_list = list() for i in value: if isinstance(i, InsureAdmitDTO): self._insure_admit_dto_list.append(i) else: self._insure_admit_dto_list.append(InsureAdmitDTO.from_alipay_dict(i)) @property def partner_org_id(self): return self._partner_org_id @partner_org_id.setter def partner_org_id(self, value): self._partner_org_id = value @property def product_code(self): return self._product_code @product_code.setter def product_code(self, value): self._product_code = value @property def scene_code(self): return self._scene_code @scene_code.setter def scene_code(self, value): self._scene_code = value @property def user_client(self): return self._user_client @user_client.setter def user_client(self, value): self._user_client = value def to_alipay_dict(self): params = dict() if self.insure_admit_dto_list: if isinstance(self.insure_admit_dto_list, list): for i in range(0, len(self.insure_admit_dto_list)): element = self.insure_admit_dto_list[i] if hasattr(element, "to_alipay_dict"): self.insure_admit_dto_list[i] = element.to_alipay_dict() if hasattr(self.insure_admit_dto_list, "to_alipay_dict"): params["insure_admit_dto_list"] = self.insure_admit_dto_list.to_alipay_dict() else: params["insure_admit_dto_list"] = self.insure_admit_dto_list if self.partner_org_id: if hasattr(self.partner_org_id, "to_alipay_dict"): params["partner_org_id"] = self.partner_org_id.to_alipay_dict() else: params["partner_org_id"] = self.partner_org_id if self.product_code: if hasattr(self.product_code, "to_alipay_dict"): params["product_code"] = self.product_code.to_alipay_dict() else: params["product_code"] = self.product_code if self.scene_code: if hasattr(self.scene_code, "to_alipay_dict"): params["scene_code"] = self.scene_code.to_alipay_dict() else: params["scene_code"] = self.scene_code if self.user_client: if hasattr(self.user_client, "to_alipay_dict"): params["user_client"] = self.user_client.to_alipay_dict() else: params["user_client"] = self.user_client return params @staticmethod def from_alipay_dict(d): if not d: return None o = AlipayInsSceneEcommerceInsureCheckModel() if "insure_admit_dto_list" in d: o.insure_admit_dto_list = d["insure_admit_dto_list"] if "partner_org_id" in d: o.partner_org_id = d["partner_org_id"] if "product_code" in d: o.product_code = d["product_code"] if "scene_code" in d: o.scene_code = d["scene_code"] if "user_client" in d: o.user_client = d["user_client"] return o
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c4ca4b5c77c3c912b44a4853be30298ec845c4fd
# str owog = "Delger" # len()- urt # lower()- jijigruuleh # upper()- tomruulah # capitalize()- ehnii useg tomruulah # replace()- temdegt solih print(owog.find("e")) print(owog.count("e")) print(owog[2:10]) a = 21 b = 21 if a > b: print("a too ih") elif a == b: print("tentsuu") else: print("b too ih") a, b = input().split() for i in range(a, b + 1): print(i)
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050e2207ac7331444d39305869c4b25bcbc53907
import pandas as pd from sklearn.preprocessing import MinMaxScaler # loading data from CSV training_data_df = pd.read_csv("sales_data_training.csv") test_data_df = pd.read_csv("sales_data_test.csv") # scaler scaler = MinMaxScaler(feature_range=(0, 1)) # scale both inputs and outputs scaled_training = scaler.fit_transform(training_data_df) scaled_testing = scaler.transform(test_data_df) # to bring it back to the original values print( "Note: total_earnings values were scaled by multiplying by {:.10f} and adding {:.6f}".format( scaler.scale_[8], scaler.min_[8] ) ) # create a new scaled dataframe object scaled_training_df = pd.DataFrame(scaled_training, columns=training_data_df.columns.values) scaled_testing_df = pd.DataFrame(scaled_testing, columns=test_data_df.columns.values) # save the scaled dataframe to new csv files scaled_training_df.to_csv("sales_data_training_scaled.csv", index=False) scaled_training_df.to_csv("sales_data_test_scaled.csv", index=False)
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21b295e28a7e4443ea116df1b22ff5074dca955a
n = int(input("nhap gia tri")) for i in range(1, n + 1): print(i)
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93737e4c409d0efb1ae2263cb60d4b03d9aad0d8
import os import shutil import argparse ap = argparse.ArgumentParser() ap.add_argument("-D", "--dir", required=False, help="Directory to sort") args = vars(ap.parse_args()) if args["dir"] == None: DIR = os.getcwd() elif os.path.exists(args["dir"]): DIR = args["dir"] for file in os.listdir(DIR): if not os.path.isdir(os.path.join(DIR, file)): name, ext = os.path.splitext(file) ext = ext[::-1][:-1][::-1] if os.path.exists(os.path.join(DIR, ext.upper())): shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.upper(), file)) else: os.mkdir(os.path.join(DIR, ext.upper())) shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.upper(), file))
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b5611c668a40e1735c92d6d00867885023ad713f
target = [] with open("IntegerArray.txt", "r") as f: target = f.readlines() for x in range(len(target)): target[x] = int(target[x]) def f(A): if len(A) == 1: return 0 else: rightStart = len(A) // 2 leftArray = A[0:rightStart] righArray = A[rightStart:] B, b = count_and_sort(leftArray) C, c = count_and_sort(righArray) D, d = count_and_sort_split(B, C) return b + c + d def count_and_sort(A): if len(A) == 1: return A, 0 elif len(A) == 2: if A[0] < A[1]: return A, 0 else: temp = A[0] A[0] = A[1] A[1] = temp return A, 1 else: rightStart = len(A) // 2 leftArray = A[0:rightStart] righArray = A[rightStart:] B, b = count_and_sort(leftArray) C, c = count_and_sort(righArray) D, d = count_and_sort_split(B, C) return D, b + c + d def count_and_sort_split(B, C): result = [] nums = 0 i = 0 j = 0 while i < len(B) or j < len(C): if i >= len(B): result = result + C[j:] break elif j >= len(C): result = result + B[i:] break if B[i] < C[j]: result.append(B[i]) i += 1 elif B[i] > C[j]: result.append(C[j]) nums = nums + len(B[i:]) j += 1 return result, nums print(f(target))
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""" generalised behaviour for actors and vacancies """ from mesa import Agent from random import shuffle import numpy as np class Entity(Agent): """ superclass for vacancy and actor agents not intended to be used on its own, but to inherit its methods to multiple other agents """ def __init__(self, unique_id, model): super().__init__(unique_id, model) self.type = "" # type of entity: vacancy, or actor self.position = "" # ID of current position self.log = [] # log of moves self.move_probability = None # for in-system moves; float [0,1] self.retire_probability = None # for leaving the system; float [0,1] self._next_state = None def pick_move(self): """ given a vector of probabilities that sums to one, pick which level you'll go to e.g. vector of probabilities = [0.3, 0.1, 0.3, 0.3] :return: the draw, an int """ cum_sum = np.cumsum(self.move_probability) cum_sum = np.insert(cum_sum, 0, 0) # throw random dart rd = np.random.uniform(0.0, 1.0) # see where dart hit m = np.asarray(cum_sum < rd).nonzero()[0] next_level = m[len(m) - 1] return next_level def get_next_position(self, next_level): """ randomly pick a position in some level and return its ID and the ID of its current occupant. :param next_level: int """ next_positions = list(self.model.positions[int(next_level)].values()) shuffle(next_positions) for p in next_positions: if p.dual[1] != self.type: # vacancies only pick positions occupied by actors, and vice versa self.model.desired_positions.append(p.unique_id) # mark position as desired return p.unique_id, p.dual[0] # return positions ID and ID of current dual/occupant def retire(self, other): """ swap with an agent and mark yourself as retired :param other: an Entity-class object """ self.swap(other) self.model.schedule.add(other) # put new entity into scheduler self.model.schedule.remove(self) # take yourself out of it self.model.retirees[self.type][self.model.schedule.steps] = self # mark yourself as retiree self.model.per_step_movement[self.type] += 1 def swap(self, other): """ swap positions with an entity :param other: an Entity-class object """ new_position = other.position # mark where you're going other.position = self.position # put swapee in your position other.log.append(other.position) # update swapee's log # update your old position's dual your_old_level = int(self.position[0]) self.model.positions[your_old_level][self.position].dual = [other.unique_id, other.type] self.position = new_position # take your new position self.log.append(self.position) # update your log # if you have a new position, update its dual if self.position != "": your_new_level = int(self.position[0]) self.model.positions[your_new_level][self.position].dual = [self.unique_id, self.type] # increment movement counters self.model.per_step_movement[self.type] += 1 def unmoving_update_log(self): """update own log if not moving.""" self.log.append(self.log[-1])
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46babde9c26a944c9d29121b6bbf89a32f242a81
import simple_draw as sd import random # sd.resolution = (1400, 900) # Prepare data for the sun function def sun_prepare(xpoint, ypoint, radius, color, angle): delta_list = [] radius_list = [] for delta in range(0, 360, angle): delta_list.append(delta) radius_list.append(random.randint(radius - 10, radius + 10)) return xpoint, ypoint, color, radius, delta_list, radius_list # Drawing the sun def sun(prepare_list): xpoint = prepare_list[0] ypoint = prepare_list[1] color = prepare_list[2] radius = prepare_list[3] delta_list = prepare_list[4] radius_list = prepare_list[5] sd.start_drawing() point = sd.get_point(xpoint, ypoint) sd.circle(center_position=point, radius=radius * 3.9, color=sd.background_color, width=0) sd.circle(center_position=point, radius=radius, color=color, width=0) for j, (delta, radius) in enumerate(zip(delta_list, radius_list)): v = sd.get_vector(start_point=point, angle=delta, width=6, length=random.randint(radius * 2, radius * 3)) v.draw(color) sd.finish_drawing() # sd.pause()
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e736991f364ba9ff709348e4b1f612b1e9673281
from flask import * app = Flask(__name__) from app import views from app import admin_views from app import usr_reg from app import cookie from app import db_connect
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18d3f58048b7e5d792eb2494ecc62bb158ac7407
from flask import Flask from flask import render_template from flask import make_response import json from lib import powerswitch app = Flask(__name__) @app.route("/") def hello_world(): return render_template("index.html") @app.route("/on/") def on(): state = powerswitch.on() return json.dumps(state) @app.route("/off/") def off(): state = powerswitch.off() return json.dumps(state) @app.route("/toggle/") def toggle(): state = powerswitch.toggle() return json.dumps(state) @app.route("/state/") def state(): state = powerswitch.state() return json.dumps(state) if __name__ == "__main__": powerswitch.on() app.run(host="0.0.0.0", port=80, debug=True)
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869284fa531a93c1b9812ed90a560d0bb2f87e97
# fonction pour voir quel est le plus grand entre l'energie limite et l'enerve potentiel def ep(m, h, el, g=9.8): E = m * h * g if E < el: print("le plus grand est : el") else: print("le plus grand est : E") ep(3, 4, 5) # fontion fibonaci 0 1 1 2 3 5 8 13 def fibonaci(n): for i in range( 0, n, ): j = 1 i = i + j j = i return fibonaci
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d126efa91b964a3a374d546bb860b39ae26dfa22
"""A tiny example binary for the native Python rules of Bazel.""" import unittest from bazel_tutorial.examples.py.lib import GetNumber from bazel_tutorial.examples.py.fibonacci.fib import Fib class TestGetNumber(unittest.TestCase): def test_ok(self): self.assertEqual(GetNumber(), 42) def test_fib(self): self.assertEqual(Fib(5), 8) if __name__ == "__main__": unittest.main()
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e582787a912f479830ed99575b2c6adb8088b4e5
from flask import Flask, request from flask import jsonify from preprocessing import QueryProcessor from flask_cors import CORS app = Flask(__name__) CORS(app) qp = QueryProcessor() @app.route("/search_general", methods=["POST"]) def query(): message = None searchQuery = request.json["searchQuery"] result = qp.generateQuery(searchQuery) response = jsonify(result) response.headers.add("Access-Control-Allow-Origin", "*") return response @app.route("/search_faceted", methods=["POST"]) def facQuery(): message = None facQuery = request.json["facQuery"] result = qp.advancedQuery(facQuery) response = jsonify(result) response.headers.add("Access-Control-Allow-Origin", "*") return response if __name__ == "__main__": app.run(debug=True)
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f8bf7e2d8f06bbd00f04047153833c07bf483fd3
from django.apps import AppConfig class PyrpgConfig(AppConfig): name = "PyRPG"
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48677d73f6489ce789884a9dff5d50c23f47d8b3
__author__ = "simon.hughes" from sklearn.feature_extraction import DictVectorizer from WindowFeatures import compute_middle_index from collections import Counter class WindowFeatureExtractor(object): """ A simple wrapper class that takes a number of window based feature extractor functions and applies them to a dataset of windows, and then vectorizes with the sklearn DictVectorizer class """ def __init__(self, feature_extractors, min_feat_frequency, sparse=True, feature_val=1): """ feature_extractors : list of fns feature extraction fns min_feat_frequency : int minimum frequency of features to retain sparse : boolean return a sparse numpy matrix or not """ self.feature_extractors = feature_extractors self.min_feat_frequency = min_feat_frequency self.vectorizer = DictVectorizer(sparse=sparse) self.feature_val = feature_val def fit(self, X, y=None): """ X : list of list of str list of word windows y : ignored returns : numpy array (sparse is sparse = True) """ feats = self.__extract_features_(X) return self.vectorizer.fit(feats) def transform(self, X, y=None): return self.vectorizer.transform(X, y) def fit_transform(self, X, y=None): feats = self.__extract_features_(X) return self.vectorizer.fit_transform(feats) def __extract_features_(self, X): if len(X) == 0: raise Exception("Empty list passed to WindowFeatureExtractor.fit") mid_ix = compute_middle_index(X[0]) all_feats = [] keys = [] for window in X: d = {} for fn in self.feature_extractors: fts = fn(window, mid_ix, self.feature_val) d.update(fts) keys.extend(d.keys()) all_feats.append(d) if self.min_feat_frequency <= 1: return all_feats """ Filter to at or above minimum feature frequency """ keyCnt = Counter(keys) frequent = set([k for k, v in keyCnt.items() if v >= self.min_feat_frequency]) freq_feats = [] for d in all_feats: freq_d = dict([(k, v) for k, v in d.items() if k in frequent]) freq_feats.append(freq_d) return freq_feats
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from packet import Packet from packetConstructor import PacketConstructor import threading import time class PacketSender: """ Packet represents a simulated UDP packet. """ # The next seq num for sent packets seq_num = 0 # The next seq num for acks that we're waiting for next_seq_num = 0 sent_packets = 0 acked_packets = [] acked_all_packets = False acked_packets_lock = threading.Lock() was_reset = False def reset(self): global seq_num global sent_packets global next_seq_num global acked_packets global acked_all_packets global acked_packets_lock seq_num = 0 sent_packets = 0 next_seq_num = 0 acked_packets = [] acked_all_packets = False acked_packets_lock = threading.Lock() def handle_ack(data): global acked_packets global seq_num global acked_all_packets global acked_packets_lock p = Packet.from_bytes(data) if not p.packet_type == PacketConstructor.ack_type: # TODO: handle NAKs here return print("received ack " + str(p.seq_num)) acked_packets_lock.acquire() if p.seq_num not in acked_packets: print("it's a new ack") acked_packets.append(p.seq_num) if len(acked_packets) == seq_num: print("got all acks") acked_all_packets = True else: print("len: " + str(len(acked_packets))) print("seq_num: " + str(seq_num)) acked_packets_lock.release() def await_acks(conn): print("awaiting acks") while not PacketSender.acked_all_packets: data, sender = conn.recvfrom(1024) threading.Thread(target=PacketSender.handle_ack, args=(data,)).start() def resend_packet_if_needed(conn, packet, destination): while not packet.seq_num in PacketSender.acked_packets and not PacketSender.was_reset: print("starting resend loop") time.sleep(0.5) acked_packets_lock.acquire() if not packet.seq_num in PacketSender.acked_packets and not PacketSender.was_reset: print("resending packet " + str(packet.seq_num)) conn.sendto(packet.to_bytes(), destination) acked_packets_lock.release() def spawn_resend_thread(conn, packet, destination): threading.Thread(target=PacketSender.resend_packet_if_needed, args=(conn, packet, destination)).start() @staticmethod def send_as_packets(data, conn, destination, peer_ip, peer_port): global sent_packets global acked_packets global next_seq_num global acked_all_packets global seq_num PacketSender.reset() max_payload_length = Packet.MAX_LEN - Packet.MIN_LEN curr = [0, 0] def nbytes(n): curr[0], curr[1] = curr[1], curr[1] + n return data[curr[0] : curr[1]] remaining_data = len(data) if remaining_data > 0: threading.Thread(target=PacketSender.await_acks, args=(conn,)).start() # While there's still data to be sent while remaining_data > 0: # While there are less packets in transit than the window size while sent_packets < PacketConstructor.window_size and remaining_data > 0: print("sending packet %d" % seq_num) if remaining_data > max_payload_length: p = Packet( packet_type=PacketConstructor.data_type, seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=peer_port, is_last_packet=False, payload=nbytes(max_payload_length), ) conn.sendto(p.to_bytes(), destination) sent_packets += 1 remaining_data -= max_payload_length seq_num += 1 PacketSender.spawn_resend_thread(conn, p, destination) print("not last packet") else: p = Packet( packet_type=PacketConstructor.data_type, seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=peer_port, is_last_packet=True, payload=nbytes(remaining_data), ) conn.sendto(p.to_bytes(), destination) sent_packets += 1 remaining_data -= remaining_data seq_num += 1 print("remaining data " + str(remaining_data)) print("is last packet") PacketSender.spawn_resend_thread(conn, p, destination) # Update the number of packets still in transit while next_seq_num in acked_packets: next_seq_num += 1 sent_packets -= 1 print("Waiting for acks") while not acked_all_packets: # Wait here until all packets have been acked pass print("RECEIVED ALL ACKS") PacketSender.was_reset = True
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24cdbbadc8ff1c7ad5d42eeb518cb6c2b34724a2
from openfermion import QubitOperator, FermionOperator from openfermion.transforms import jordan_wigner from src.utils import QasmUtils, MatrixUtils from src.ansatz_elements import AnsatzElement, DoubleExchange import itertools import numpy class EfficientDoubleExchange(AnsatzElement): def __init__(self, qubit_pair_1, qubit_pair_2, rescaled_parameter=False, parity_dependence=False, d_exc_correction=False): self.qubit_pair_1 = qubit_pair_1 self.qubit_pair_2 = qubit_pair_2 self.rescaled_parameter = rescaled_parameter self.parity_dependence = parity_dependence self.d_exc_correction = d_exc_correction super(EfficientDoubleExchange, self).__init__( element="d_exc {}, {}".format(qubit_pair_1, qubit_pair_2), element_type=str(self), n_var_parameters=1 ) @staticmethod def second_angle(x): if x == 0: return 0 else: tan_x = numpy.tan(x) tan_x_squared = tan_x**2 tan_y = (-tan_x_squared - 1 + numpy.sqrt(tan_x_squared**2 + 6 * tan_x_squared + 1)) / (2 * tan_x) return numpy.arctan(tan_y) # this method constructs an operation that acts approximately as a double partial exchange @staticmethod def double_exchange(angle, qubit_pair_1, qubit_pair_2, parity_dependence=False, d_exc_correction=False): assert len(qubit_pair_1) == 2 assert len(qubit_pair_2) == 2 theta_1 = numpy.pi / 2 - angle qasm = [""] # 1st exchange + 0-2 qasm.append(QasmUtils.controlled_xz(qubit_pair_2[0], qubit_pair_1[0])) qasm.append("ry({}) q[{}];\n".format(theta_1, qubit_pair_2[0])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_2[0])) qasm.append("ry({}) q[{}];\n".format(-theta_1, qubit_pair_2[0])) # 2nd exchange + 1-3 qasm.append(QasmUtils.controlled_xz(qubit_pair_2[1], qubit_pair_1[1])) qasm.append("ry({}) q[{}];\n".format(theta_1, qubit_pair_2[1])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[1], qubit_pair_2[1])) qasm.append("ry({}) q[{}];\n".format(-theta_1, qubit_pair_2[1])) # CZ gates qasm.append("cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1])) # qasm.append('cz q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1])) # correction 3rd order terms approximates the operation of a double exchange if d_exc_correction: angle_2 = DoubleExchange.second_angle(angle) # not correcting 3rd order terms approximates the operation of a double excitation (with 3rd order error terms) else: angle_2 = angle theta_2 = numpy.pi / 2 - angle_2 # 3rd exchange - 0-2 qasm.append("ry({}) q[{}];\n".format(theta_2, qubit_pair_2[0])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_2[0])) qasm.append("ry({}) q[{}];\n".format(-theta_2, qubit_pair_2[0])) qasm.append(QasmUtils.controlled_xz(qubit_pair_2[0], qubit_pair_1[0], reverse=True)) # 4th exchange -1-3 qasm.append("ry({}) q[{}];\n".format(theta_2, qubit_pair_2[1])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[1], qubit_pair_2[1])) qasm.append("ry({}) q[{}];\n".format(-theta_2, qubit_pair_2[1])) qasm.append(QasmUtils.controlled_xz(qubit_pair_2[1], qubit_pair_1[1], reverse=True)) # correcting for parameter sign if parity_dependence: # do not include the first qubit of the second pair parity_qubits = list(range(min(qubit_pair_1), max(qubit_pair_1))) + list( range(min(qubit_pair_2) + 1, max(qubit_pair_2)) ) # ladder of CNOT used to determine the parity cnot_ladder = [""] for i in range(len(parity_qubits) - 1): cnot_ladder.append("cx q[{}], q[{}];\n".format(parity_qubits[i], parity_qubits[i + 1])) if angle > 0: # applies a CZ correction in front, to get a negative sign for the excitation term, if the parity is 1 # (or the parity of "parity_qubits" is 0) front = [""] # this is the CZ that determines the sign of the excitation term front.append("cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1])) # this bit determines the parity and applies a CZ to negate the correction if the parity is wrong front += cnot_ladder front.append("x q[{}];\n".format(parity_qubits[-1])) front.append("cz q[{}], q[{}];\n".format(parity_qubits[-1], qubit_pair_2[0])) front.append("x q[{}];\n".format(parity_qubits[-1])) front += cnot_ladder[::-1] # .. positive sign for the excitation term, if the parity is 0 (or the parity of "parity_qubits" is 1) rear = [""] # .. sign correction rear.append("cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1])) # .. parity correction rear += cnot_ladder rear.append("cz q[{}], q[{}];\n".format(parity_qubits[-1], qubit_pair_2[0])) rear += cnot_ladder[::-1] # additional correction of states 010 and 110 rear.append("x q[{}];\n".format(qubit_pair_2[1])) rear.append("cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1])) rear.append("x q[{}];\n".format(qubit_pair_2[1])) qasm = front + qasm + rear else: front = [""] # sign correction front.append("cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1])) # parity correction front += cnot_ladder front.append("cz q[{}], q[{}];\n".format(parity_qubits[-1], qubit_pair_2[0])) front += cnot_ladder[::-1] rear = [""] # sign correction rear.append("cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1])) # parity correction rear += cnot_ladder rear.append("x q[{}];\n".format(parity_qubits[-1])) rear.append("cz q[{}], q[{}];\n".format(parity_qubits[-1], qubit_pair_2[0])) rear.append("x q[{}];\n".format(parity_qubits[-1])) rear += cnot_ladder[::-1] # 010 and 011 correction rear.append("x q[{}];\n".format(qubit_pair_2[1])) rear.append("cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1])) rear.append("x q[{}];\n".format(qubit_pair_2[1])) qasm = front + qasm + rear else: if angle > 0: # adding a correcting CZ gate at the end will result in a minus sign qasm.append("cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1])) # qasm.append('cz q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1])) else: # adding a correcting CZ gate at the front will result in a plus sign qasm = [ "cz q[{}], q[{}];\n".format(qubit_pair_2[0], qubit_pair_2[1]), ] + qasm # 'cz q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1]) return "".join(qasm) def get_qasm(self, var_parameters): assert len(var_parameters) == 1 parameter = var_parameters[0] # rescaled parameter (used for easier gradient optimization) if self.rescaled_parameter: if var_parameters[0] > 0: parameter = var_parameters[0] + numpy.tanh(var_parameters[0] ** 0.5) else: parameter = var_parameters[0] + numpy.tanh(-((-var_parameters[0]) ** 0.5)) return self.double_exchange( parameter, self.qubit_pair_1, self.qubit_pair_2, parity_dependence=self.parity_dependence, d_exc_correction=self.d_exc_correction, ) class EfficientDoubleExcitation2(AnsatzElement): def __init__(self, qubit_pair_1, qubit_pair_2): self.qubit_pair_1 = qubit_pair_1 self.qubit_pair_2 = qubit_pair_2 super(EfficientDoubleExcitation2, self).__init__( element="optimized_d_exc {}, {}".format(qubit_pair_1, qubit_pair_2), element_type=str(self), n_var_parameters=1 ) @staticmethod def efficient_double_excitation_2(angle, qubit_pair_1, qubit_pair_2): qasm = [""] theta = angle / 8 # determine the parity of the two pairs qasm.append("cx q[{}], q[{}];\n".format(*qubit_pair_1)) qasm.append("x q[{}];\n".format(qubit_pair_1[1])) qasm.append("cx q[{}], q[{}];\n".format(*qubit_pair_2)) qasm.append("x q[{}];\n".format(qubit_pair_2[1])) # apply a partial swap of qubits 0 and 2, controlled by 1 and 3 ## qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_2[0])) # # partial ccc_y operation qasm.append("rz({}) q[{}];\n".format(numpy.pi / 2, qubit_pair_1[0])) qasm.append("rx({}) q[{}];\n".format(theta, qubit_pair_1[0])) # + qasm.append("h q[{}];\n".format(qubit_pair_1[1])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_1[1])) # 0 1 qasm.append("h q[{}];\n".format(qubit_pair_1[1])) qasm.append("rx({}) q[{}];\n".format(-theta, qubit_pair_1[0])) # - qasm.append("h q[{}];\n".format(qubit_pair_2[1])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_2[1])) # 0 3 qasm.append("h q[{}];\n".format(qubit_pair_2[1])) qasm.append("rx({}) q[{}];\n".format(theta, qubit_pair_1[0])) # + qasm.append("h q[{}];\n".format(qubit_pair_1[1])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_1[1])) # 0 1 qasm.append("h q[{}];\n".format(qubit_pair_1[1])) qasm.append("rx({}) q[{}];\n".format(-theta, qubit_pair_1[0])) # - qasm.append("h q[{}];\n".format(qubit_pair_2[0])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_2[0])) # 0 2 qasm.append("h q[{}];\n".format(qubit_pair_2[0])) qasm.append("rx({}) q[{}];\n".format(theta, qubit_pair_1[0])) # + qasm.append("h q[{}];\n".format(qubit_pair_1[1])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_1[1])) # 0 1 qasm.append("h q[{}];\n".format(qubit_pair_1[1])) qasm.append("rx({}) q[{}];\n".format(-theta, qubit_pair_1[0])) # - qasm.append("h q[{}];\n".format(qubit_pair_2[1])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_2[1])) # 0 3 qasm.append("h q[{}];\n".format(qubit_pair_2[1])) qasm.append("rx({}) q[{}];\n".format(theta, qubit_pair_1[0])) # + qasm.append("h q[{}];\n".format(qubit_pair_1[1])) qasm.append("cx q[{}], q[{}];\n".format(qubit_pair_1[0], qubit_pair_1[1])) # 0 1 qasm.append("h q[{}];\n".format(qubit_pair_1[1])) qasm.append("rx({}) q[{}];\n".format(-theta, qubit_pair_1[0])) # - qasm.append("rz({}) q[{}];\n".format(-numpy.pi / 2, qubit_pair_1[0])) ##### test ##### # qasm.append('h q[{}];\n'.format(qubit_pair_2[0])) # qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_2[0])) # 0 2 # qasm.append('h q[{}];\n'.format(qubit_pair_2[0])) # ############### # partial ccc_y operation ############ to here qasm.append(QasmUtils.controlled_xz(qubit_pair_1[0], qubit_pair_2[0], reverse=True)) # correct for parity determination qasm.append("x q[{}];\n".format(qubit_pair_1[1])) qasm.append("cx q[{}], q[{}];\n".format(*qubit_pair_1)) qasm.append("x q[{}];\n".format(qubit_pair_2[1])) qasm.append("cx q[{}], q[{}];\n".format(*qubit_pair_2)) return "".join(qasm) def get_qasm(self, var_parameters): assert len(var_parameters) == 1 parameter = var_parameters[0] return self.efficient_double_excitation_2(parameter, self.qubit_pair_1, self.qubit_pair_2)
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2843845848747c723d670cd3a5fcb7127153ac7e
from flask import Flask from sim.toggle import ToggleSensor from sim.sensor import Sensor app = Flask(__name__) sensors = [ ToggleSensor(id="s-01", description="lampadina"), ToggleSensor(id="s-02", description="lampadina"), ToggleSensor(id="s-03", description="allarme atomico"), ToggleSensor(id="s-04", description="porta aperta"), Sensor(id="temperature-01", description="sensore di temperatura"), Sensor(id="umidita-01", description="sensore di umiditá"), Sensor(id="cleancode-01", description="sensore di bellezza del codice"), Sensor(id="luce-01", description="sensore di luce"), ] def search_index_by_id(sensor_id: str): for pos, sensor in enumerate(sensors): if sensor.id == sensor_id: return pos return -1 @app.route("/<sensor_id>", methods=["GET"]) def sensor_details(sensor_id): sensor_pos = search_index_by_id(sensor_id) if sensor_pos >= 0: return {"sensor": sensors[sensor_pos]} else: return {"kind": "error", "payload": f"Sensor {sensor_id} not found"} @app.route("/<sensor_id>/toggle", methods=["PUT"]) def set_sensor_value(sensor_id: str): sensor_pos = search_index_by_id(sensor_id) if sensor_pos >= 0 and isinstance(sensors[sensor_pos], ToggleSensor): sensors[sensor_pos].toggle() return {"kind": "error", "payload": f"Sensor {sensor_id} not found"} @app.route("/", methods=["GET"]) def hello_world(): for sensor in sensors: sensor.update() return {"sensors": sensors}
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# %load q03_skewness_log/build.py from scipy.stats import skew import pandas as pd import numpy as np data = pd.read_csv("data/train.csv") # Write code here: def skewness_log(df): df["SalePrice_New"] = np.log(df["SalePrice"]) df["GrLivArea_New"] = np.log(df["GrLivArea"]) skewed_slPri = skew(df["SalePrice_New"]) skewness_grLiv = skew(df["GrLivArea_New"]) return skewness_grLiv, skewed_slPri
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """Phase transition module""" import utils import datetime import itertools import numpy as np import recovery as rec import sampling as smp import graphs_signals as gs import pathos.multiprocessing as mp from tqdm import tqdm ## MAIN FUNCTIONS ## def grid_evaluation( param_list_one, param_list_two, param_eval, n_trials=16, aggr_method=np.mean, save_dir="data/", file_name="grid evaluation", save_to_disk=True, save_each=1000, chunksize=1.0, ): r""" Evaluates a grid of parameter pairs across repeated trials and aggregates the result. Parameters ---------- param_list_one : array_like List of values to test for the first parameter. param_list_two : array_like, optional List of values to test for the second parameter. Can be empty, in which case a one-dimensional grid is evaluated. param_eval : callable Must take an instance of parameter values and return an object that can be evaluated by `aggr_meth`. It should accept one input if `param_list_two` is empty, and two inputs otherwise. n_trials : int, optional Number of trials to run for each parameter pair. (default is `16`) aggr_method : callable, optional The aggregation method for the values returned by `patam_eval` on different trials for the same parameter pair. (default is :func:`numpy.mean`) save_dir : string, optional Directory onto which save the result. (default is 'data/') file_name : string, optional Optional name for the file. It is always prepended with the time stamp at the end of the grid evaluation. (default is 'grid evaluation') save_to_disk : bool, optional Whether to save the experiment to disk (True) or not (False). (default is `True`) save_each : int, optional Save the experiment each time `save_each` grid points are computed. (default is `1000`) chunksize : int The size of the chunks of jobs sent to each parallel worker. (default is `1`) Returns ------- dict A dictionary with the results of the experiment. """ if not list(param_list_two): # If `param_list_two` is empty params = param_list_one grid_shape = (len(param_list_one),) is_really_grid = False else: params = list(itertools.product(param_list_one, param_list_two)) grid_shape = (len(param_list_one), len(param_list_two)) is_really_grid = True def grid_fun(point): # Function to compute for each grid point trial_out = np.nan * np.ones((n_trials,)) for i in np.arange(n_trials): if is_really_grid: trial_out[i] = param_eval(point[0], point[1]) else: # If `param_list_two` is empty trial_out[i] = param_eval(point) return aggr_method(trial_out) n_grid_pts = len(params) # Recording procedure def record_experiment(grid): now = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") save_path = save_dir + now + " " + file_name + ".pkl" experiment = { "date": now, "rows": param_list_one, "cols": param_list_two, "n_trials": n_trials, "grid": np.reshape(grid, grid_shape), "path": save_path, } if save_to_disk: utils.save_obj(experiment, save_path) return experiment # Set a pool of workers nb_workers = min(mp.cpu_count(), 24) print("Working with {} processes.".format(nb_workers)) pool = mp.Pool(nb_workers) # Iterate `grid_fun` across workers it = pool.imap(grid_fun, params, chunksize=chunksize) grid = np.nan * np.ones((n_grid_pts,)) for idx, val in enumerate(tqdm(it, total=n_grid_pts)): grid[idx] = val # Make sure that we save after each couple of iterations if (idx >= save_each) and (idx % save_each == 0): experiment = record_experiment(grid) # Close pool pool.close() pool.join() experiment = record_experiment(grid) return experiment def line_evaluation(param_list, param_eval, file_name="line evaluation", **kwargs): r""" Evaluates a list of parameter pairs across repeated trials and aggregates the result. Parameters ---------- param_list : array_like List of values to test for parameter of interest. param_eval : callable Must take a parameter instance and return an object that can be evaluated by `aggr_meth` (see :func:`grid_evaluation`). file_name : string, optional Optional name for the file. (default is 'line evaluation') Returns ------- dict A dictionary with the results of the experiment. Notes ----- You can also explicitely set the arguments in :func:`grid_evaluation` in this function call. """ experiment = grid_evaluation( param_list_one=param_list, param_list_two=[], param_eval=param_eval, file_name=file_name, **kwargs ) experiment["line"] = experiment.pop("grid") experiment["cols"] = experiment.pop("rows") return experiment
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f97a892e6e0aa258ad917c4a73a66e89b0dc3253
# coding: utf-8 # In[1]: import sys sys.path.extend(["detection", "train"]) # from detection folder from MtcnnDetector import MtcnnDetector from detector import Detector from fcn_detector import FcnDetector # from train folder from model_factory import P_Net, R_Net, O_Net import config as config from preprocess.utils import iou import cv2 import os from os.path import join, split import numpy as np from tqdm import tqdm # In[ ]: # test_mode = config.test_mode test_mode = "ONet" thresh = [0.6, 0.7, 0.9] min_face_size = 24 stride = 2 detectors = [None, None, None] scale_factor = 0.79 # 模型放置位置 model_path = ["model/PNet/", "model/RNet/", "model/ONet"] batch_size = config.batches detectors[0] = FcnDetector(P_Net, model_path[0]) # detecotors for PNet if test_mode in ["RNet", "ONet"]: detectors[1] = Detector(R_Net, 24, batch_size[1], model_path[1]) if test_mode == "ONet": detectors[2] = Detector(O_Net, 48, batch_size[2], model_path[2]) # Use the three detectors to construct a mtcnn_detector = MtcnnDetector( detectors=detectors, min_face_size=min_face_size, stride=stride, threshold=thresh, scale_factor=scale_factor ) out_path = join("validate", test_mode) + "/" if config.input_mode == "1": # 选用图片 path = config.test_dir print(path) for item in tqdm(os.listdir(path)): img_path = os.path.join(path, item) img = cv2.imread(img_path) img_labeled = mtcnn_detector.detect_and_draw(img) cv2.imwrite(out_path + item, img_labeled) if config.input_mode == "2": cap = cv2.VideoCapture(0) fourcc = cv2.VideoWriter_fourcc(*"XVID") out = cv2.VideoWriter(out_path + "out.mp4", fourcc, 10, (640, 480)) while True: t1 = cv2.getTickCount() ret, frame = cap.read() if ret == True: boxes_c, landmarks = mtcnn_detector.detect(frame) t2 = cv2.getTickCount() t = (t2 - t1) / cv2.getTickFrequency() fps = 1.0 / t for i in range(boxes_c.shape[0]): bbox = boxes_c[i, :4] score = boxes_c[i, 4] corpbbox = [int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])] # 画人脸框 cv2.rectangle(frame, (corpbbox[0], corpbbox[1]), (corpbbox[2], corpbbox[3]), (255, 0, 0), 1) # 画置信度 cv2.putText( frame, "{:.2f}".format(score), (corpbbox[0], corpbbox[1] - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2 ) # 画fps值 cv2.putText( frame, "{:.4f}".format(t) + " " + "{:.3f}".format(fps), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2, ) # 画关键点 for i in range(landmarks.shape[0]): for j in range(len(landmarks[i]) // 2): cv2.circle(frame, (int(landmarks[i][2 * j]), int(int(landmarks[i][2 * j + 1]))), 2, (0, 0, 255)) a = out.write(frame) cv2.imshow("result", frame) if cv2.waitKey(1) & 0xFF == ord("q"): break else: break cap.release() out.release() cv2.destroyAllWindows()
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df40b0628d6a180a98cd385145ee7c65ecb78256
import os import tensorflow as tf import torch from tqdm import tqdm from glob import glob import numpy as np from collections.abc import Iterable from utils.hparams import HParam # from utils.audio import Audio # import librosa # python encoder_inference.py --in_dir training_libri_mel/train/ --gpu_str 5 # python tfrecord_producer.py --in_dir training_libri_mel/train/ --out xyz --gpu 6 # python tfrecord_producer.py --in_dir test_libri_mel/test/ --out xyz_mel_test --gpu 5 --need_mel --need_phase def bytes_feature(value): assert isinstance(value, Iterable) return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) def convert_to_example(target, mixed, speaker, target_phase=None, mixed_phase=None, target_mel=None, mixed_mel=None): raw_target = target.tostring() raw_mixed = mixed.tostring() raw_speaker = speaker.tostring() raw_target_phase = target_phase.tostring() if target_phase is not None else None raw_mixed_phase = mixed_phase.tostring() if mixed_phase is not None else None raw_target_mel = target_mel.tostring() if target_mel is not None else None raw_mixed_mel = mixed_mel.tostring() if mixed_mel is not None else None # TODO: handle the case where only have phase info but no mel info if target_mel is not None and mixed_mel is not None: if target_phase is not None and mixed_phase is not None: example = tf.train.Example( features=tf.train.Features( feature={ "target": bytes_feature([raw_target]), "mixed": bytes_feature([raw_mixed]), "speaker": bytes_feature([raw_speaker]), "target_phase": bytes_feature([raw_target_phase]), "mixed_phase": bytes_feature([raw_mixed_phase]), "target_mel": bytes_feature([raw_target_mel]), "mixed_mel": bytes_feature([raw_mixed_mel]), } ) ) else: example = tf.train.Example( features=tf.train.Features( feature={ "target": bytes_feature([raw_target]), "mixed": bytes_feature([raw_mixed]), "speaker": bytes_feature([raw_speaker]), "target_mel": bytes_feature([raw_target_mel]), "mixed_mel": bytes_feature([raw_mixed_mel]), } ) ) else: example = tf.train.Example( features=tf.train.Features( feature={ "target": bytes_feature([raw_target]), "mixed": bytes_feature([raw_mixed]), "speaker": bytes_feature([raw_speaker]), } ) ) return example class TFRecordProducer: def remove_list(self, list1, list2): i, j = 0, 0 tmp_list1 = [] tmp_list2 = [] while i < len(list1) and j < len(list2): item1 = int(list1[i].split("/")[-1].split("-")[0]) item2 = int(list2[j].split("/")[-1].split("-")[0]) if item1 == item2: tmp_list1.append(list1[i]) tmp_list2.append(list2[j]) i += 1 j += 1 elif item1 < item2: i += 1 else: j += 1 return tmp_list1, tmp_list2 def __init__(self, in_dir, hp, args, is_train): def find_all(file_format): return sorted(glob(os.path.join(self.data_dir, file_format))) self.in_dir = in_dir self.hp = hp self.args = args self.is_train = is_train # self.data_dir = hp.data.train_dir if is_train else hp.data.test_dir self.data_dir = in_dir self.dvec_list = find_all(hp.form.dvec_npy) self.target_wav_list = find_all(hp.form.target.wav) self.mixed_wav_list = find_all(hp.form.mixed.wav) self.target_mag_list = find_all(hp.form.target.mag) self.mixed_mag_list = find_all(hp.form.mixed.mag) self.target_phase_list = find_all(hp.form.target.phase) self.mixed_phase_list = find_all(hp.form.mixed.phase) self.target_mel_list = find_all(hp.form.target.mel) self.mixed_mel_list = find_all(hp.form.mixed.mel) _, self.target_wav_list = self.remove_list(self.dvec_list, self.target_wav_list) _, self.mixed_wav_list = self.remove_list(self.dvec_list, self.mixed_wav_list) _, self.target_mag_list = self.remove_list(self.dvec_list, self.target_mag_list) _, self.mixed_mag_list = self.remove_list(self.dvec_list, self.mixed_mag_list) _, self.target_phase_list = self.remove_list(self.dvec_list, self.target_phase_list) _, self.mixed_phase_list = self.remove_list(self.dvec_list, self.mixed_phase_list) _, self.target_mel_list = self.remove_list(self.dvec_list, self.target_mel_list) _, self.mixed_mel_list = self.remove_list(self.dvec_list, self.mixed_mel_list) print( len(self.dvec_list), len(self.target_wav_list), len(self.mixed_wav_list), len(self.target_mag_list), len(self.mixed_mag_list), len(self.target_phase_list), len(self.mixed_phase_list), len(self.target_mel_list), len(self.mixed_mel_list), ) assert ( len(self.dvec_list) == len(self.target_wav_list) == len(self.mixed_wav_list) == len(self.target_mag_list) == len(self.mixed_mag_list) ), "number of training files must match" assert len(self.dvec_list) != 0, "no training file found" def write_training_examples_to_tfrecords(self, filename, need_phase=False, need_mel=False, hp=None): # if hp is not None: # audio=Audio(hp) with tf.python_io.TFRecordWriter(filename) as writer: for i in tqdm(range(len(self.dvec_list))): target_mag = torch.load(self.target_mag_list[i]).numpy() mixed_mag = torch.load(self.mixed_mag_list[i]).numpy() dvec = np.load(self.dvec_list[i]) if need_phase: target_phase = torch.load(self.target_phase_list[i]).numpy() mixed_phase = torch.load(self.mixed_phase_list[i]).numpy() target_mel = torch.load(self.target_mel_list[i]).numpy() mixed_mel = torch.load(self.mixed_mel_list[i]).numpy() # mixed_wav = audio.spec2wav(mixed_mag, mixed_phase) # librosa.output.write_wav('mixed.wav', mixed_wav, 16000) writer.write( convert_to_example( target_mag, mixed_mag, dvec, target_phase, mixed_phase, target_mel, mixed_mel ).SerializeToString() ) elif need_mel: target_mel = torch.load(self.target_mel_list[i]).numpy() mixed_mel = torch.load(self.mixed_mel_list[i]).numpy() writer.write( convert_to_example(target_mag, mixed_mag, dvec, None, None, target_mel, mixed_mel).SerializeToString() ) else: writer.write(convert_to_example(target_mag, mixed_mag, dvec).SerializeToString()) import argparse parser = argparse.ArgumentParser() parser.add_argument("--in_dir", default=".", help="input glob str") parser.add_argument("-c", "--config", type=str, default="config/config.yaml", help="yaml file for configuration") parser.add_argument("--out", default=".", help="output prefix of tf record") parser.add_argument("--gpu", default=0, help="Path to model checkpoint") parser.add_argument("--need_phase", action="store_true") parser.add_argument("--need_mel", action="store_true") args = parser.parse_args() os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) in_dir = args.in_dir filename = args.out hp = HParam(args.config) tp = TFRecordProducer(in_dir, hp, args, True) tp.write_training_examples_to_tfrecords(filename, args.need_phase, args.need_mel, hp)
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ed7b29a4d7f3a48884434373418c3528f2f397ac
#!/usr/bin/python3 import os os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # or any {'0', '1', '2' os.environ["KERAS_BACKEND"] = "tensorflow" import numpy as np import sys from util import load_model from keras.preprocessing.text import hashing_trick from keras.preprocessing.sequence import pad_sequences from southpark.southpark_generative import string_one_hot, char_one_hot MODEL_NAME = "script_gen_demo_model" def main(): print("Loading model...") model, charset = load_model(MODEL_NAME) print(charset) seed_text = input("Enter a String: ").strip() print() generate_script(seed_text, model, charset) def generate_script(seed_text, model, charset): sys.stdout.write(seed_text) sys.stdout.flush() next_char = None should_stop = False while not should_stop: prev_char = next_char next_char = sample(model, seed_text, charset, temp=0.2) sys.stdout.write(next_char) sys.stdout.flush() if prev_char == "\n" and prev_char == next_char: should_stop = True def sample(model, string, charset, temp=1.0): inputs = [string_one_hot(string, charset)] inputs = pad_sequences(inputs, padding="post", maxlen=64) preds = model.predict(inputs)[0] return charset[sample_preds(preds, temp)] def sample_preds(results, temperature=1.0): # helper function to sample an index from a probability array if temperature <= 0.0: return np.argmax(results) # num_choices = results.shape[0] # (batch, outputs) probs = np.exp(np.log(results) / temperature) probs /= np.sum(probs) return np.random.choice(len(results), p=probs) # preds = np.asarray(preds).astype('float64') # preds = np.log(preds) / temperature # exp_preds = np.exp(preds) # preds = exp_preds / np.sum(exp_preds) # probas = np.random.multinomial(1, preds, 1) # # print(probas) # return np.argmax(probas) if __name__ == "__main__": main()
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b6d8a918659f733919fe3bb4be9037e36ad32386
import queue import sys import logging from superai.common import InitLog logger = logging.getLogger(__name__) # 2维到1维 def hwToidx(x: int, y: int, weight: int): return y * weight + x # 1维到2维 def idxTohw(idx, weight: int): return [idx % weight, idx // weight] # 10x10 cell idx 到 [x,y] def idxToXY(idx, cellw: int): curpoint = idxTohw(idx, cellw) curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10 return curpoint # 有向图 class Graph: def __init__(self, V: int, W: int): # 顶点数量 self.V = V # 边数量 self.E = 0 # 邻接表 self.adj = [] # 宽度 (虽然是一维的但是表示是二维的) self.W = W for i in range(V): nears = [] self.adj.append(nears) def AddEdge(self, v: int, w: int): self.adj[v].append(w) self.E += 1 def __str__(self): str = "" for idx, nears in enumerate(self.adj): str += "idx: {} nears: {}\n".format(idx, nears) return str # bfs class BreadthFirstPaths: def __init__(self, g: Graph, s: int): self.marked = [False] * g.V self.edgeTo = [0] * g.V self.s = s self.bfs(g, self.s) def bfs(self, g: Graph, s: int): q = queue.Queue() q.put(s) while q.qsize() != 0: v = q.get() for w in g.adj[v]: # 这个路径没有经过 if not self.marked[w]: self.edgeTo[w] = v self.marked[w] = True q.put(w) # print(w, idxTohw(w, 6)) def HasPathTo(self, v: int): return self.marked[v] def PathTo(self, v: int) -> [int]: result = [] if not self.HasPathTo(v): return result x = v while x != self.s: result.append(x) x = self.edgeTo[x] result.append(self.s) return result # 曼哈顿距离 def manhattanDistance(x, y): return sum(map(lambda i, j: abs(i - j), x, y)) # 欧几里得距离 def dist_between(a, b): return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2 # a* 4方位 class AStarPaths: def __init__(self, g: Graph, start: int, end: int): self.closedSet = [] self.openSet = [start] self.start = start self.end = end self.edgeTo = [0] * g.V self.marked = [False] * g.V # 实际距离 self.gScore = [sys.maxsize] * g.V self.gScore[start] = 0 # 估算到终点的距离 self.fScore = [sys.maxsize] * g.V self.fScore[start] = manhattanDistance(idxTohw(start, g.W), idxTohw(end, g.W)) self.astar(g) def astar(self, g: Graph): while len(self.openSet) > 0: current = min(self.openSet, key=lambda s: self.fScore[s]) if current == self.end: return self.openSet.remove(current) self.closedSet.append(current) for w in g.adj[current]: if w in self.closedSet: continue # 实际距离 tentativegScore = self.gScore[current] + manhattanDistance(idxTohw(current, g.W), idxTohw(w, g.W)) if tentativegScore < self.gScore[w]: self.edgeTo[w] = current self.marked[w] = True print("edgeTo ({}) -> ({})".format(idxTohw(current, g.W), idxTohw(w, g.W))) self.gScore[w] = tentativegScore self.fScore[w] = self.gScore[w] + manhattanDistance(idxTohw(w, g.W), idxTohw(self.end, g.W)) print("fScore[%d] manhattan: %d" % (w, self.fScore[w])) if w not in self.openSet: self.openSet.append(w) def HasPathTo(self, v: int): return self.marked[v] def PathTo(self, v: int): result = [] if not self.HasPathTo(v): return result x = v while x != self.start: result.append(x) x = self.edgeTo[x] result.append(self.start) return result def main(): InitLog() # 0,1,2,3,4 ... 一共12个顶点. width=6,height=4 graph = Graph(24, 6) graph.AddEdge(hwToidx(2, 0, 6), hwToidx(2, 1, 6)) graph.AddEdge(hwToidx(1, 1, 6), hwToidx(1, 2, 6)) graph.AddEdge(hwToidx(2, 1, 6), hwToidx(2, 0, 6)) graph.AddEdge(hwToidx(2, 1, 6), hwToidx(3, 1, 6)) graph.AddEdge(hwToidx(3, 1, 6), hwToidx(2, 1, 6)) graph.AddEdge(hwToidx(3, 1, 6), hwToidx(4, 1, 6)) graph.AddEdge(hwToidx(4, 1, 6), hwToidx(4, 2, 6)) graph.AddEdge(hwToidx(4, 1, 6), hwToidx(3, 1, 6)) graph.AddEdge(hwToidx(0, 2, 6), hwToidx(1, 2, 6)) graph.AddEdge(hwToidx(1, 2, 6), hwToidx(1, 3, 6)) graph.AddEdge(hwToidx(1, 2, 6), hwToidx(0, 2, 6)) graph.AddEdge(hwToidx(1, 2, 6), hwToidx(1, 1, 6)) graph.AddEdge(hwToidx(1, 2, 6), hwToidx(2, 2, 6)) graph.AddEdge(hwToidx(2, 2, 6), hwToidx(2, 3, 6)) graph.AddEdge(hwToidx(2, 2, 6), hwToidx(1, 2, 6)) graph.AddEdge(hwToidx(2, 2, 6), hwToidx(3, 2, 6)) graph.AddEdge(hwToidx(3, 2, 6), hwToidx(3, 3, 6)) graph.AddEdge(hwToidx(3, 2, 6), hwToidx(2, 2, 6)) graph.AddEdge(hwToidx(3, 2, 6), hwToidx(4, 2, 6)) graph.AddEdge(hwToidx(4, 2, 6), hwToidx(4, 3, 6)) graph.AddEdge(hwToidx(4, 2, 6), hwToidx(3, 2, 6)) graph.AddEdge(hwToidx(4, 2, 6), hwToidx(4, 1, 6)) graph.AddEdge(hwToidx(4, 2, 6), hwToidx(5, 2, 6)) graph.AddEdge(hwToidx(5, 2, 6), hwToidx(5, 3, 6)) graph.AddEdge(hwToidx(5, 2, 6), hwToidx(4, 2, 6)) graph.AddEdge(hwToidx(1, 3, 6), hwToidx(1, 2, 6)) graph.AddEdge(hwToidx(2, 3, 6), hwToidx(2, 2, 6)) graph.AddEdge(hwToidx(2, 3, 6), hwToidx(3, 3, 6)) graph.AddEdge(hwToidx(3, 3, 6), hwToidx(2, 3, 6)) graph.AddEdge(hwToidx(3, 3, 6), hwToidx(3, 2, 6)) graph.AddEdge(hwToidx(4, 3, 6), hwToidx(5, 3, 6)) graph.AddEdge(hwToidx(4, 3, 6), hwToidx(4, 2, 6)) graph.AddEdge(hwToidx(5, 3, 6), hwToidx(4, 3, 6)) graph.AddEdge(hwToidx(5, 3, 6), hwToidx(5, 2, 6)) print("图:") print(graph) print("广度优先:") bfs = BreadthFirstPaths(graph, 2) paths = bfs.PathTo(hwToidx(1, 1, 6)) for v in reversed(paths): print(v, idxTohw(v, 6)) # print(manhattanDistance([0, 2], [1, 2])) print("\na*寻径") astar = AStarPaths(graph, 2, 7) paths = astar.PathTo(hwToidx(1, 1, 6)) for v in reversed(paths): print(v, idxTohw(v, 6)) if __name__ == "__main__": main()
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7930bb813bd546747c7c65b661900939f5ba93f1
user_input = input() # abv>1>1>2>2asdasd exploded_str = user_input for n in range(len(user_input)): explosion_strength = 0 if user_input[n] == ">": explosion_strength += int(user_input[n + 1]) if user_input[n + explosion_strength] != ">": exploded_str = user_input[:n] + user_input[n + explosion_strength + 1 :] user_input = exploded_str else: explosion_strength += user_input[n + explosion_strength + 1]
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c6cf085330f47ffb139c5acc91d91e9758f5396a
from page_objects import PageObject, PageElement class MainPage(PageObject): level_menu_opened = False level_menu_created = False css_input = PageElement(css="input.input-strobe") level_text_span = PageElement(css="span.level-text") instruction_h2 = PageElement(css="h2.order") enter_button = PageElement(css="div.enter-button") level_menu = PageElement(xpath='//div[@class="level-menu-toggle-wrapper"]') def __init__(self, webdriver, root_uri=None): super(MainPage, self).__init__(webdriver, root_uri) # hack to initialize all the menu items self.open_level_menu() self.close_level_menu() def ensure_menu_created(self): if not self.level_menu_created: self.open_level_menu() self.close_level_menu() def open_level_menu(self): if not self.level_menu_opened: self.level_menu.click() self.level_menu_opened = True self.level_menu_created = True def close_level_menu(self): if self.level_menu_opened: self.level_menu.click() self.level_menu_opened = False def get_level_link(self, level_number): return PageElement(xpath='//span[@class="level-number" and text() = "{0}"]/..'.format(level_number)) def open_level(self, level_number): self.open_level_menu() self.get_level_link(level_number).click() def css_write(self, css): self.css_input = css self.enter_button.click() def do_level1(self): self.open_level(1) self.css_write("page") self.level1_link.click()
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c2ddf31bce4a5f3ae2b0d5455bbc9942f92bff40
import os os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" from keras.models import load_model from utils import resize_to_fit, clear_chunks, stack_windows from imutils import paths import numpy as np import imutils import cv2 as cv2 import pickle from tqdm import tqdm c1_correct = 0 c2_correct = 0 c3_correct = 0 c4_correct = 0 c5_correct = 0 total_correct = 0 incorrectly_segmented = 0 correct_guesses_dict = {} MODEL_FILENAME = "captcha_model.hdf5" MODEL_LABELS_FILENAME = "model_labels.dat" CAPTCHA_IMAGE_FOLDER = "test captchas" # Load up the model labels (so we can translate model predictions to actual letters) with open(MODEL_LABELS_FILENAME, "rb") as f: lb = pickle.load(f) # Load the trained neural network model = load_model(MODEL_FILENAME) for root, dirs, files in os.walk(CAPTCHA_IMAGE_FOLDER): for name in tqdm(files, desc="Solving captchas"): kernel = (5, 5) # load image image = cv2.imread(os.path.join(root, name)) image = cv2.cvtColor(image, cv2.COLOR_RGBA2GRAY) # add padding image = cv2.copyMakeBorder(image, 8, 8, 8, 8, cv2.BORDER_CONSTANT, None, 255) # blur k = np.ones((5, 5), np.float32) / 25 image = cv2.filter2D(image, -1, k) # threshhold image ret, image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV) # clear white dots clear_chunks(image, 0, 50) # erosion image = cv2.erode(image, kernel, iterations=1) # get contours contours = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] # segment letters letter_image_regions = [] # (x, y, w ,h) contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True) contours = contours[:5] for contour in contours: if cv2.contourArea(contour) < 60: continue (x, y, w, h) = cv2.boundingRect(contour) if w / h > 1.5: half_width = int(w / 2) letter_image_regions.append((x, y, half_width, h)) letter_image_regions.append((x + half_width, y, half_width, h)) else: letter_image_regions.append((x, y, w, h)) if len(letter_image_regions) != 5: incorrectly_segmented += 1 continue print(f"Found {len(letter_image_regions)} letter regions instead of 5 , the guess will likely be incorrect") letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0]) chars = [] i = 0 for x, y, w, h in letter_image_regions: letter = image[y - 2 : y + h + 2, x - 2 : x + w + 2] chars.append(letter) i += 1 predictions = [] for letter in chars: # Re-size the letter image to 20x20 pixels to match training data letter = resize_to_fit(letter, 20, 20) # Turn the single image into a 4d list of images to make Keras happy letter = np.expand_dims(letter, axis=2) letter = np.expand_dims(letter, axis=0) # Ask the neural network to make a prediction prediction = model.predict(letter) # Convert the one-hot-encoded prediction back to a normal letter letter_text = lb.inverse_transform(prediction)[0] predictions.append(letter_text) gc1, gc2, gc3, gc4, gc5 = predictions c1, c2, c3, c4, c5, e1, e2, e3, e4 = name correct_guesses = 0 if c1 == gc1: c1_correct += 1 correct_guesses += 1 if c2 == gc2: c2_correct += 1 correct_guesses += 1 if c3 == gc3: c3_correct += 1 correct_guesses += 1 if c4 == gc4: c4_correct += 1 correct_guesses += 1 if c5 == gc5: c5_correct += 1 correct_guesses += 1 if "".join(predictions) == "".join([c1, c2, c3, c4, c5]): total_correct += 1 n = correct_guesses_dict.get(correct_guesses, 0) + 1 correct_guesses_dict[correct_guesses] = n print(f"Prediction for {name}: {''.join(predictions)}") print(f"correct c1: {c1_correct}") print(f"correct c2: {c2_correct}") print(f"correct c3: {c3_correct}") print(f"correct c4: {c4_correct}") print(f"correct c5: {c5_correct}") print(f"correct total: {total_correct}") print(f"correctly segmented: {10000 - incorrectly_segmented}") print(correct_guesses_dict)
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75e6554ea3c327c87a2a65710a7f1d55e9933bb0
# Author: BeiYu # Github: https://github.com/beiyuouo # Date : 2021/2/21 21:57 # Description: __author__ = "BeiYu" from utils.init_env import set_seed from utils.options import * import os import logging import torch from torch import nn from torch import optim from torch.optim.lr_scheduler import MultiStepLR from torch.autograd import Variable from torch.utils.data import DataLoader from modules.seg_dataset import * from tqdm import tqdm import click import torch.nn.functional as F import numpy as np from modules.seg import PSPNet models = { "squeezenet": lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend="squeezenet", n_classes=3), "densenet": lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=1024, deep_features_size=512, backend="densenet", n_classes=3), "resnet18": lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend="resnet18", n_classes=3), "resnet34": lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend="resnet34", n_classes=3), "resnet50": lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend="resnet50", n_classes=3), "resnet101": lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend="resnet101", n_classes=3), "resnet152": lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend="resnet152", n_classes=3), } def build_network(snapshot, backend): epoch = 0 backend = backend.lower() net = models[backend]() # net = nn.DataParallel(net) if snapshot is not None: _, epoch = os.path.basename(snapshot).split("_") epoch = int(epoch) net.load_state_dict(torch.load(snapshot)) logging.info("Snapshot for epoch {} loaded from {}".format(epoch, snapshot)) net = net.cuda(0) return net, epoch def train(): args = get_args() # os.environ["CUDA_VISIBLE_DEVICES"] = gpu # net, starting_epoch = build_network(snapshot, backend) # data_path = os.path.abspath(os.path.expanduser(data_path)) # models_path = os.path.abspath(os.path.expanduser(models_path)) os.makedirs(args.model_path, exist_ok=True) set_seed(args.seed) """ To follow this training routine you need a DataLoader that yields the tuples of the following format: (Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where x - batch of input images, y - batch of groung truth seg maps, y_cls - batch of 1D tensors of dimensionality N: N total number of classes, y_cls[i, T] = 1 if class T is present in image i, 0 otherwise """ traindata = HeadSegData(args.seg_data_path, args.train_txt, train=True) train_loader = DataLoader(traindata, batch_size=args.seg_batch_size, shuffle=True, num_workers=1) net, _ = build_network(None, args.seg_backend) seg_criterion = nn.NLLLoss().cuda(0) cls_criterion = nn.BCEWithLogitsLoss().cuda(0) optimizer = optim.Adam(net.parameters(), lr=args.seg_lr) # scheduler = MultiStepLR(optimizer, milestones=[int(x) for x in milestones.split(',')]) print("start training...") net.train() total_loss = 0.0 for epoch in range(args.seg_epochs): if (epoch + 1) % 5 == 0: for group in optimizer.param_groups: group["lr"] *= 0.25 total_loss = 0.0 for i, (x, y, y_cls) in enumerate(train_loader): x, y, y_cls = x.cuda(0), y.cuda(0).long(), y_cls.cuda(0).float() out, out_cls = net(x) # print(x.shape, out.shape, out_cls.shape, y.shape, y_cls.shape) seg_loss = seg_criterion(out, y) cls_loss = cls_criterion(out_cls, y_cls) loss = seg_loss + args.seg_alpha * cls_loss total_loss += loss.item() if i % 50 == 0: status = "[batch:{0}/{1} epoch:{2}] loss = {3:0.5f}".format( i, len(traindata) // args.seg_batch_size, epoch + 1, loss.item() ) print(status) optimizer.zero_grad() loss.backward() optimizer.step() torch.save(net.state_dict(), os.path.join(args.model_path, f"{'seg'}_{args.seg_model}_{args.seg_backend}_{epoch}.pth")) print(f"epoch:{epoch} total_loss: {total_loss / len(traindata)}") if __name__ == "__main__": train()
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58f3b8c5470c765c81f27d39d9c28751a8c2b719
"""Ex026 Faça um programa que leia uma frase pelo teclado e mostre: Quantas vezes aparece a letra "A". Em que posição ela aparece a primeira vez. Em que posição ela aparece pela última vez.""" frase = str(input("Digite uma frase: ")).strip().lower() n_a = frase.count("a") f_a = frase.find("a") + 1 l_a = frase.rfind("a") - 1 print(f"Sua frase tem {n_a} letras a") print(f"A letra A aparece pela primeira vez na {f_a}° posição") print(f"A letra A apaerece pela ultima vez na {l_a}° posição")
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d83f2d9bb25a46bc7344b420ce65bf729165e6b9
from django.apps import AppConfig class FosAppConfig(AppConfig): name = "fos_app"
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d2b05c5653ca6c6b7219f6c0393e81c9425b5977
""" HLS: Check if Twin Granule Exists """ from typing import Dict import os import re import boto3 from botocore.errorfactory import ClientError from datetime import date s3 = boto3.client("s3") bucket = os.getenv("SENTINEL_INPUT_BUCKET", None) print(bucket) if bucket is None: raise Exception("No Input Bucket set") def handler(event: Dict, context: Dict): """AWS Lambda handler.""" granule = event.get("granule") prefix = granule[0:-6] print(prefix) response = s3.list_objects_v2( Bucket=bucket, Prefix=prefix, ) print(response) granules = [] contents = response["Contents"] for obj in contents: granules.append(obj["Key"][0:-4]) granule_str = ",".join(granules) output = { "granule": granule_str, } return output
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c73a199d1c1c1867f3d53ceebf614bc9b65c0d5e
from django.contrib import admin from ticket.models import Ticket, UserTicket, AuxiliaryTicket @admin.register(Ticket) class TicketAdmin(admin.ModelAdmin): pass @admin.register(AuxiliaryTicket) class AuxiliaryTicketAdmin(admin.ModelAdmin): pass @admin.register(UserTicket) class UserTicketAdmin(admin.ModelAdmin): pass
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e564e0d05c3c0e60f356422722803df510d9dd0b
import numpy as np import pandas as pd from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.metrics import precision_score, recall_score, f1_score from scipy.optimize import fsolve import numba from numba import njit, jit # @jit(parallel=True) def conventional_test(subject_array, typeII_error, typeI_error, repeat=1, seq=True): """ A function gives the test results to a subject array given the probability of type II error, the probability of Type I error, and the number of repeatition, and setting of sequence testing or not. Input: subject_array(Numpy Array): an array contains subject id and subject's condition (1 stands for infection and 0 stands for uninfection) typeII_error (float): probability of type II error typeI_error (float): probability of type I error repeat (int): the number of repetition seq (boolean): True stands for sequential testing. The test will end when the test result is positive or run up the number of repetition. False stands for simutanlous testing with majority voting. Output: test_result (Numpy Array): an array contains subjects' id and test results consum (int): the total test consumption """ # Sequential Testing if seq == True: consum = 0 test_result = np.zeros(subject_array.shape, dtype=int) random_table = np.random.uniform(0, 1, (subject_array.shape[0], repeat)) for i in range(len(subject_array)): temp = 0 j = 0 subject = subject_array[i, 1] while j < repeat and temp == 0: random_num = random_table[i, j] consum += 1 if subject == 1: temp = 1 if random_num > typeII_error else 0 else: temp = 1 if random_num < typeI_error else 0 j += 1 test_result[i, 0] = subject_array[i, 0] test_result[i, 1] = temp return test_result, consum # Simultanous Testing else: test_result = np.zeros(subject_array.shape, dtype=int) random_table = np.random.uniform(0, 1, (subject_array.shape[0], repeat)) for i in range(len(subject_array)): temp = 0 for j in range(repeat): temp_random = random_table[i, j] if subject_array[i, 1] == 1: temp_1 = 1 if temp_random > typeII_error else 0 elif subject_array[i, 1] == 0: temp_1 = 1 if temp_random < typeI_error else 0 temp += temp_1 temp = 1 if temp >= repeat / 2 else 0 test_result[i, 0] = subject_array[i, 0] test_result[i, 1] = temp return test_result, len(subject_array) * repeat @njit(parallel=True) def parallel_test(subject_array, typeII_error, typeI_error, num): test_result = np.zeros(subject_array.shape, dtype=int) random_table = np.random.uniform(0, 1, (subject_array.shape[0], num)) for i in range(len(subject_array)): subject = subject_array[i, 1] if subject == 1: temp = 1 if max(random_table[i, :]) > typeII_error else 0 elif subject == 0: temp = 1 if min(random_table[i, :]) < typeI_error else 0 test_result[i, 0] = subject_array[i, 0] test_result[i, 1] = temp return test_result, len(subject_array) * num, len(subject_array) * num def infection_rate_on_negative_batch(p, batch_size, typeII_error, typeI_error): """ Given infection rate, batch size, prob of type II error and prob of type I error, this function gives the infection rate on the negative batch. Input: p (float): the infection rate batch_size (int): the batch size typeII_error (float): the prob of type II error typeI_error (float): the prob of type I error Output: (float): the infection rate on the negative batch """ q = 1 - p r = typeII_error * (1 - q**batch_size) / ((1 - typeI_error) * q**batch_size + typeII_error * (1 - q**batch_size)) return p * r / (1 - q**batch_size) def infection_rate_on_positive_batch(p, batch_size, typeII_error, typeI_error): """ Given infection rate, batch size, prob of type II error and prob of type I error, this function gives the infection rate on the positive batch. Input: p (float): the infection rate batch_size (int): the batch size typeII_error (float): the prob of type II error typeI_error (float): the prob of type I error Output: (float): the infection rate on the positive batch """ q = 1 - p r = (1 - typeII_error) * (1 - q**batch_size) / (typeI_error * q**batch_size + (1 - typeII_error) * (1 - q**batch_size)) return p * r / (1 - q**batch_size) def one_batch_test_solver(prevalence_rate, typeII_error, typeI_error, n_initial_guess=2): """ A function gives (float) the best batch size for one batch test given the infection rate Inputs: prevalence_rate(float): infection rate typeII_error(float): the prob of type II error typeI_error(float): the prob of type I error n_initial_guess(float): the initial guess Output: (float): the optimal batch size """ q = 1 - prevalence_rate # To consistent with the notation of our document func = lambda n: n * q ** (n / 2) - (-(1 - typeII_error - typeI_error) * np.log(q)) ** (-1 / 2) # print(func(n_initial_guess)) n_solution = fsolve(func, n_initial_guess) return float(n_solution) def one_batch_test_int_solver(prevalence_rate, typeII_error, typeI_error, batch_limit, n_initial_guess=2): """ A function gives (int) the best batch size for one batch test given the infection rate Inputs: prevalence_rate(float): infection rate n_initial_guess(float): the initial guess typeII_error(float): the prob of type II error typeI_error(float): the prob of type I error n_initial_guess: batch_limit (int): the upper limit of batch size Output: (int): the optimal batch size """ sol_float = one_batch_test_solver(prevalence_rate, typeII_error, typeI_error, n_initial_guess) floor, ceil = np.floor(sol_float), np.ceil(sol_float) func = ( lambda batch_size: 1 / batch_size + 1 - typeII_error - (1 - typeII_error - typeI_error) * (1 - prevalence_rate) ** batch_size ) if func(floor) < func(ceil): temp = int(floor) else: temp = int(ceil) if temp <= batch_limit: return temp else: return int(batch_limit) def neg_pos_batch_split(subject_array, batch_size, typeII_error, typeI_error): """ A function gives a list of sujects on the negative batch(es), a list of subjects on the postive batch(es) and the test-kit consumption given the probability of type II error, the probability of Type I error. Input: subject_array (Numpy Array): an array contains subject id and subject's condition (1 stands for infection and 0 stands for uninfection) batch_size (int): batch size typeII_error (float): probability of type II error typeI_error (float): probability of type I error Output: neg_batch (Numpy Array): an array of subjects on the negative batch(es) pos_batch (Numpy Array): an array of subjects on the postive batch(es) test_consum (int): the number of test-kit consumptions """ neg_batch = [] pos_batch = [] test_consum = np.ceil(len(subject_array) / batch_size) random_table = np.random.uniform(0, 1, int(test_consum)) i = 0 for temp_batch in np.array_split(subject_array, test_consum): if 1 in (temp_batch[:, 1]): if random_table[i] > typeII_error: pos_batch.append(temp_batch) else: neg_batch.append(temp_batch) else: if random_table[i] > typeI_error: neg_batch.append(temp_batch) else: pos_batch.append(temp_batch) i += 1 neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([]) pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([]) return (neg_batch, pos_batch, test_consum) def helpfunction(subject_array, p, batch_size, typeII_error, typeI_error, batch_limit): """ The helpfunction is a handy function to give the list of subjects on the negative batch(es), the list of subjects on the postive batch(es), the test-kit consumption, the infection rate on the negative batches, the infection rate on the positive batches, the optimal batch size for negative batches and the optimal batch size for positive batches. Input: subject_array (Numpy Array): an array contains subject id and subject's condition (1 stands for infection and 0 stands for uninfection) p (float): Infection rate batch_size (int): batch size typeII_error (float): probability of type II error typeI_error (float): probability of type I error batch_limit (int): batch size upper limit Output: temp0 (Numpy Array): an array of subjects on the negative batch(es) temp1 (Numpy Array): an array of subjects on the postive batch(es) temp_con (int): the number of test-kit consumptions p0 (float): the infection rate on the negative batches p1 (float): the infection rate on the positive batches n0 (float): the optimal batch size for the negative batches n1 (float): the optimal batch size for the positive batches """ batch_size = min(batch_size, batch_limit) p0 = infection_rate_on_negative_batch(p, batch_size, typeII_error, typeI_error) p1 = infection_rate_on_positive_batch(p, batch_size, typeII_error, typeI_error) n0 = one_batch_test_int_solver(p0, typeII_error, typeI_error, batch_limit) n1 = one_batch_test_int_solver(p1, typeII_error, typeI_error, batch_limit) if subject_array == np.array([]): return (np.array([]), np.array([]), p0, p1, n0, n1) temp0, temp1, temp_con = neg_pos_batch_split(subject_array, batch_size, typeII_error, typeI_error) return (temp0, temp1, temp_con, p0, p1, n0, n1) def seq_test( subject_array, stop_rule, p, batch_size, typeII_error, typeI_error, repeat=1, prob_threshold=1, seq=True, batch_limit=32 ): """ A function gives the test results to a subject array and the total number of test-kit consumption and the individual testing number given the subject array, the stop rule, the batch size, the probability of type II error, the probability of Type I error, and the number of repeatition, the probability threshold, and setting of sequence testing or not. Input: subject_array(Numpy Array): an array contains subject id and subject's condition (1 stands for infection and 0 stands for uninfection) stop_rule (int): the number of postive batches to enter individual testing p (float): infection rate batch_size (int): batch size typeII_error (float): probability of type II error typeI_error (float): probability of type I error repeat (int): the number of repetition prob_threshold (float): if the infection rate of a batch is beyond prob_threshold, the subjects on that batch will enter individual testing phase seq (boolean): True stands for sequential testing. The test will end when the test result is positive or run up the number of repetition. False stands for simutanlous testing with majority voting. batch_limit (int): Output: result (Numpy Array): an array contains subjects' id and test results consum (int): the total test consumption individual_con (int): the test consumption for individual testings """ temp_list = [] neg_list = [] # renamed to negativeInfoList pos_list = [] # renamed to positiveInfoList consum = 0 temp = {"data": subject_array, "NB_Num": 0, "PB_Num": 0, "p": p, "batch_size": batch_size} temp_list.append(temp) new_list = [] neg_array = [] # renamed to negativeBatches pos_array = [] # renamed to positiveBatches while len(temp_list) > 0: for i in temp_list: temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction( i["data"], i["p"], i["batch_size"], typeII_error, typeI_error, batch_limit=batch_limit ) temp0 = {"data": temp0, "NB_Num": i["NB_Num"] + 1, "PB_Num": i["PB_Num"], "p": p0, "batch_size": n0} temp1 = {"data": temp1, "NB_Num": i["NB_Num"], "PB_Num": i["PB_Num"] + 1, "p": p1, "batch_size": n1} if len(temp0["data"]) > 0: if temp0["NB_Num"] >= stop_rule: neg_list.append(temp0) else: new_list.append(temp0) if len(temp1["data"]) > 0: if temp1["PB_Num"] >= stop_rule or temp1["p"] >= prob_threshold: pos_list.append(temp1) else: new_list.append(temp1) consum += temp_con temp_list = new_list new_list = [] for j in neg_list: neg_array.append(j["data"]) neg_array = np.concatenate(neg_array) for k in pos_list: pos_array.append(k["data"]) pos_array = np.concatenate(pos_array) neg_array[:, 1] = 0 individual_test, individual_con = conventional_test(pos_array, typeII_error, typeI_error, repeat, seq) pos_array = individual_test consum += individual_con result = np.concatenate((pos_array, neg_array)) result = result[result[:, 0].argsort()] result = result.astype("int64") return (result, consum, individual_con) def npv_score(y_true, y_pred): """ A function provides npv given the prediction and the truth """ tn, _, fn, _ = confusion_matrix(y_true=y_true, y_pred=y_pred).ravel() return tn / (tn + fn) def specificity_score(y_true, y_pred): """ A function provides specificty given the prediction and the truth """ tn, fp, _, _ = confusion_matrix(y_true=y_true, y_pred=y_pred).ravel() return tn / (tn + fp) @jit(parallel=True) def data_gen(size, p): """ data_gen provides a faster way to generate a random population with infection rate p. Input: size (int): the size of population p (float): the infection rate Output: test_array (array): the first column is for id and the second column is the condition, where 1 stands for infection and 0 stands for uninfection """ # print(np.random.get_state()[1][0]) random_table = np.random.binomial(size=size, p=p, n=1) test_array = np.zeros((size, 2), dtype=int) for i in range(size): test_array[i, 0] = i test_array[i, 1] = random_table[i] return test_array def test_result(data, seq_test, **kwargs): """ a helper function provides convenient results for a given test method with its **kwargs Input: data (array or list of arrays) seq_test (test_method object): could be seq_test, matrix_test and other test_method objects Output: result (DataFrame): a dataframe contains important evaluation metrics for the test method """ if isinstance(data, list) == False: pred, consum, ind_con = seq_test(data, **kwargs) result = { "acc": np.mean(pred[:, 1] == data[:, 1]), "sens": recall_score(data[:, 1], pred[:, 1]), "spec": specificity_score(data[:, 1], pred[:, 1]), "PPV": precision_score(data[:, 1], pred[:, 1]), "NPV": npv_score(data[:, 1], pred[:, 1]), "test_consum": consum, "ind_consum": ind_con, "batch_consum": consum - ind_con, } return result else: length = len(data) acc = np.zeros(length) sens = np.zeros(length) spec = np.zeros(length) ppv = np.zeros(length) npv = np.zeros(length) test_consum = np.zeros(length) ind_consum = np.zeros(length) batch_consum = np.zeros(length) for i in range(length): pred, consum, ind_con = seq_test(data[i], **kwargs) acc[i] = np.mean(pred[:, 1] == data[i][:, 1]) sens[i] = recall_score(data[i][:, 1], pred[:, 1]) spec[i] = specificity_score(data[i][:, 1], pred[:, 1]) ppv[i] = precision_score(data[i][:, 1], pred[:, 1]) npv[i] = npv_score(data[i][:, 1], pred[:, 1]) test_consum[i] = consum ind_consum[i] = ind_con batch_consum[i] = consum - ind_con result = { "acc": acc, "sens": sens, "spec": spec, "PPV": ppv, "NPV": npv, "test_consum": test_consum, "ind_consum": ind_consum, "batch_consum": batch_consum, } return pd.DataFrame(result) def matrix_test(subject_array, side_length, typeII_error, typeI_error, sq_repeat=1, ind_repeat=1, seq=True): """ This function provides the matrix testing results for a given subject array. Input: subject_array(Numpy Array): an array contains subject id and subject's condition (1 stands for infection and 0 stands for uninfection) side_length (int): the side length of the matrix testing typeII_error (float): probability of type II error typeI_error (float): probability of type I error sq_repeat (int): the number of parallel testing for the column/row batch testing ind_repeat (int): the number of potential individual testing for the positive crossings seq (boolean): True stands for sequential testing. The test will end when the test result is positive or run up the number of repetition. False stands for simutanlous testing with majority voting. Output: result (Numpy Array): an array contains subjects' id and test results consum (int): the total test consumption individual_con (int): the test consumption for individual testings """ matrix_test_num = len(subject_array) // (side_length**2) matrix_test_array = subject_array[0 : matrix_test_num * side_length**2, :] ind_test_array = subject_array[matrix_test_num * side_length**2 :, :] ind_idx = [] for temp_batch in np.array_split(matrix_test_array, matrix_test_num): temp_batch = temp_batch.reshape(side_length, side_length, 2) temp_row = [] temp_col = [] random_num_row = np.random.uniform(0, 1, sq_repeat) random_num_col = np.random.uniform(0, 1, sq_repeat) for i in range(side_length): if 1 in (temp_batch[i, :, 1]): if max(random_num_row) > typeII_error: temp_row.append(temp_batch[i, :, 0]) else: if min(random_num_row) < typeI_error: temp_row.append(temp_batch[i, :, 0]) if 1 in (temp_batch[:, i, 1]): if max(random_num_col) > typeII_error: temp_col.append(temp_batch[:, i, 0]) else: if min(random_num_col) < typeI_error: temp_col.append(temp_batch[:, i, 0]) ind_idx.append(np.intersect1d(temp_row, temp_col)) ind_idx = np.concatenate(ind_idx) ind_idx = ind_idx.astype("int") if len(ind_idx) == 0: neg_array = matrix_test_array else: mask = np.zeros(subject_array.shape[0], dtype=bool) mask[ind_idx] = True mask[matrix_test_num * side_length**2 :] = True ind_test_array = subject_array[mask, :] neg_array = subject_array[~mask, :] neg_array[:, 1] = 0 ind_test, ind_con = conventional_test(ind_test_array, typeII_error, typeI_error, repeat=ind_repeat, seq=seq) batch_test_num = matrix_test_num * 2 * side_length * sq_repeat result = np.concatenate((neg_array, ind_test)) result = result[result[:, 0].argsort()] return (result, batch_test_num + ind_con, ind_con) def parallel_batch_testing(subject_array, batch_size, typeII_error, typeI_error, parallel_num, ind_repeat, seq): """ This function provides the parallel batch testing results for a given subject array. Input: subject_array(Numpy Array): an array contains subject id and subject's condition (1 stands for infection and 0 stands for uninfection) batch_size (int): batch size typeII_error (float): probability of type II error typeI_error (float): probability of type I error parallel_num (int): the number of parallel testing for the batch testing ind_repeat (int): the number of potential individual testing for the positive batches seq (boolean): True stands for sequential testing. The test will end when the test result is positive or run up the number of repetition. False stands for simutanlous testing with majority voting. Output: result (Numpy Array): an array contains subjects' id and test results consum (int): the total test consumption individual_con (int): the test consumption for individual testings """ neg_batch = [] pos_batch = [] batch_consum = np.ceil(len(subject_array) / batch_size) * parallel_num for temp_batch in np.array_split(subject_array, np.ceil(len(subject_array) / batch_size)): random_table = np.random.uniform(0, 1, (1, parallel_num)) if 1 in (temp_batch[:, 1]): if random_table.max() > typeII_error: pos_batch.append(temp_batch) else: neg_batch.append(temp_batch) else: if random_table.min() < typeI_error: pos_batch.append(temp_batch) else: neg_batch.append(temp_batch) neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([]) pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([]) neg_batch[:, 1] = 0 individual_test, individual_con = conventional_test(pos_batch, typeII_error, typeI_error, repeat=ind_repeat, seq=seq) result = np.concatenate((individual_test, neg_batch)) result = result[result[:, 0].argsort()] result = result.astype("int64") return (result, batch_consum + individual_con, individual_con) def fixed_batch_seq_test( subject_array, stop_rule, p, batch_size, typeII_error, typeI_error, repeat, prob_threshold=0.3, seq=True ): """ This function provides the parallel batch testing results for a given subject array. Input: subject_array(Numpy Array): an array contains subject id and subject's condition (1 stands for infection and 0 stands for uninfection) stop_rule (int): the number of positive batches to enter the individual testing phase batch_size (int): batch size typeII_error (float): probability of type II error typeI_error (float): probability of type I error repeat (int): the number of potential individual testing for the positive crossings prob_threshold (float): if the infection rate of a batch is beyond prob_threshold, the subjects on that batch will enter individual testing phase seq (boolean): True stands for sequential testing. The test will end when the test result is positive or run up the number of repetition. False stands for simutanlous testing with majority voting. Output: result (Numpy Array): an array contains subjects' id and test results consum (int): the total test consumption individual_con (int): the test consumption for individual testings """ temp_list = [] neg_list = [] pos_list = [] consum = 0 temp = {"data": subject_array, "NB_Num": 0, "PB_Num": 0, "p": p, "batch_size": batch_size} temp_list.append(temp) new_list = [] neg_array = [] pos_array = [] while len(temp_list) > 0: for i in temp_list: temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i["data"], i["p"], i["batch_size"], typeII_error, typeI_error) temp0 = { "data": np.random.permutation(temp0), "NB_Num": i["NB_Num"] + 1, "PB_Num": i["PB_Num"], "p": p0, "batch_size": batch_size, } temp1 = { "data": np.random.permutation(temp1), "NB_Num": i["NB_Num"], "PB_Num": i["PB_Num"] + 1, "p": p1, "batch_size": batch_size, } if len(temp0["data"]) > 0: if temp0["NB_Num"] >= stop_rule: neg_list.append(temp0) else: new_list.append(temp0) if len(temp1["data"]) > 0: if temp1["PB_Num"] >= stop_rule or temp1["p"] >= prob_threshold: pos_list.append(temp1) else: new_list.append(temp1) consum += temp_con temp_list = new_list new_list = [] for j in neg_list: neg_array.append(j["data"]) neg_array = np.concatenate(neg_array) for k in pos_list: pos_array.append(k["data"]) pos_array = np.concatenate(pos_array) neg_array[:, 1] = 0 individual_test, individual_con = conventional_test(pos_array, typeII_error, typeI_error, repeat, seq) pos_array = individual_test consum += individual_con result = np.concatenate((pos_array, neg_array)) result = result[result[:, 0].argsort()] result = result.astype("int64") return (result, consum, individual_con) def name_fun(n): """ input: stopping rule output: finish nodes """ output = [] temp = [""] for i in range(2 * n - 1): temp_cur = [] for j in temp: candidate_pos = j + "+" candidate_neg = j + "-" if str.count(candidate_pos, "+") >= n: output.append(candidate_pos) else: temp_cur.append(candidate_pos) if str.count(candidate_neg, "-") >= n: output.append(candidate_neg) else: temp_cur.append(candidate_neg) temp = temp_cur neg_symbol = [x for x in output if str.count(x, "-") == n] pos_symbol = [x for x in output if str.count(x, "+") == n] return output, neg_symbol, pos_symbol def seq_test_with_node( subject_array, stop_rule, p, batch_size, typeII_error, typeI_error, repeat=1, prob_threshold=1, seq=True, batch_limit=32 ): """ A function gives the test results to a subject array and the total number of test-kit consumption and the individual testing number given the subject array, the stop rule, the batch size, the probability of type II error, the probability of Type I error, and the number of repeatition, the probability threshold, and setting of sequence testing or not. Input: subject_array(Numpy Array): an array contains subject id and subject's condition (1 stands for infection and 0 stands for uninfection) stop_rule (int): the number of postive batches to enter individual testing p (float): infection rate batch_size (int): batch size typeII_error (float): probability of type II error typeI_error (float): probability of type I error repeat (int): the number of repetition prob_threshold (float): if the infection rate of a batch is beyond prob_threshold, the subjects on that batch will enter individual testing phase seq (boolean): True stands for sequential testing. The test will end when the test result is positive or run up the number of repetition. False stands for simutanlous testing with majority voting. batch_limit (int): Output: result (Numpy Array): an array contains subjects' id and test results consum (int): the total test consumption individual_con (int): the test consumption for individual testings """ temp_list = [] neg_list = [] pos_list = [] batch_num_list = [] consum = 0 temp = {"data": subject_array, "NB_Num": 0, "PB_Num": 0, "p": p, "batch_size": batch_size, "node": ""} temp_list.append(temp) new_list = [] neg_array = [] neg_node = [] pos_node = [] pos_array = [] while len(temp_list) > 0: for i in temp_list: temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction( i["data"], i["p"], i["batch_size"], typeII_error, typeI_error, batch_limit=batch_limit ) temp0 = { "data": temp0, "NB_Num": i["NB_Num"] + 1, "PB_Num": i["PB_Num"], "p": p0, "batch_size": n0, "node": i["node"] + "-", } temp1 = { "data": temp1, "NB_Num": i["NB_Num"], "PB_Num": i["PB_Num"] + 1, "p": p1, "batch_size": n1, "node": i["node"] + "+", } if len(temp0["data"]) > 0: if temp0["NB_Num"] >= stop_rule: neg_list.append(temp0) else: new_list.append(temp0) if len(temp1["data"]) > 0: if temp1["PB_Num"] >= stop_rule or temp1["p"] >= prob_threshold: pos_list.append(temp1) else: new_list.append(temp1) consum += temp_con batch_num_list.append(consum) temp_list = new_list new_list = [] for j in neg_list: neg_array.append(j["data"]) temp = [[x, j["node"]] for x in j["data"][:, 0]] neg_node.append(temp) neg_array = np.concatenate(neg_array) # print(neg_array) # print(neg_node) # neg_node = np.concatenate(neg_node) for k in pos_list: pos_array.append(k["data"]) # pos_node.append(k['node']) # pos_node.append(np.column_stack((k['data'][:,0],np.repeat(k['node'], len(k['data']))))) temp = [[x, k["node"]] for x in k["data"][:, 0]] pos_node.append(temp) pos_array = np.concatenate(pos_array) # pos_node = np.concatenate(pos_node) neg_array[:, 1] = 0 individual_test, individual_con = conventional_test(pos_array, typeII_error, typeI_error, repeat, seq) pos_array = individual_test consum += individual_con result = np.concatenate((pos_array, neg_array)) # node = np.concatenate((pos_node, neg_node)) pos_node.extend(neg_node) node = pos_node node = sum(node, []) node.sort() node = [x[1] for x in node] # node = node[node[:,0].argsort()] result = result[result[:, 0].argsort()] result = result.astype("int64") return (result, consum, individual_con, node, batch_num_list)
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9bc13c608c079cbf23ed04f29edd1fd836214cde
from rest_framework import viewsets, mixins from .models import Comment, Post from .serializer import CommentSerializer, PostSerializer, AllCommentSerializer class PostViewSet(viewsets.ModelViewSet): serializer_class = PostSerializer queryset = Post.objects.all() class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins.RetrieveModelMixin): queryset = Comment.objects.all() def get_serializer_class(self): if self.action == "retrieve": if self.get_object().level < 3: return CommentSerializer return AllCommentSerializer
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b11e2837d3ba9c14770b8039186a2175adc41ea1
from .server import CanvasServer try: from .jupyter import JupyterCanvas, create_jupyter_canvas HAS_JUPYTER = True except: HAS_JUPYTER = False JupyterCanvas = None # type: ignore def http_server(file: str = None, host: str = "localhost", port: int = 5050) -> CanvasServer: """Creates a new HTTP server for displaying the network, using WebSockets to transmit data. The server will only start once its :meth:`~server.CanvasServer.start` method is called. After the server has started, the network can be viewed by opening a browser and navigating to the address ``http://localhost:5050/`` (change the port as necessary). :file: (Optional) The path to the HTML file which the server should display, relative to the current runtime directory. If unspecified, the default HTML file will be used. When creating a custom HTML interface, use the default file as a guide. :type file: str :port: (Optional) The port on which the server should start, defaulting to to 5050. Note that the next port (by default 5051) will also be used to transmit data through WebSockets. :type port: int """ return CanvasServer(file, host, port) def jupyter_canvas(buttons: bool = False) -> JupyterCanvas: """Creates a new :class:`~api.Canvas` which will dispatch and receive events through a Jupyter widget, and which can be displayed using the IPython ``display`` function. By default, the canvas size is (400, 250), and requires the ``ctrl``/``cmd`` to be held down while zooming.""" if HAS_JUPYTER: return create_jupyter_canvas(buttons=buttons) else: raise Exception("Jupyter is not installed")
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2da7892722afde5a6f87e3bd6d5763c895ac96c9
import json import os import ipdb from tqdm import tqdm import argparse from os import listdir from os.path import isfile, join import pickle import joblib from collections import Counter from shutil import copyfile import networkx as nx import spacy import nltk import numpy as np nltk.download("stopwords") nltk_stopwords = nltk.corpus.stopwords.words("english") data_path = "/home/joey.bose/dblp_papers_v11.txt" save_path_base = "/home/joey.bose/aminer_data/" load_path_rank_base = "/home/joey.bose/aminer_data_ranked/fos/" save_path_graph_base = "/home/joey.bose/aminer_data_ranked/graphs/" raw_save_path = "/home/joey.bose/aminer_data_ranked/aminer_raw.txt" spacy_nlp = spacy.load("en_core_web_sm") glove_path = "/home/joey.bose/docker_temp/meta-graph/meta-graph/glove.840B.300d.txt" class Lang: def __init__(self): super(Lang, self).__init__() self.word2index = {} self.word2count = {} self.index2word = {} self.n_words = 0 # Count default tokens def index_words(self, sentence): for word in sentence: self.index_word(word) def index_word(self, word): if word not in self.word2index: self.word2index[word] = self.n_words self.word2count[word] = 1 self.index2word[self.n_words] = word self.n_words += 1 else: self.word2count[word] += 1 def gen_embeddings(vocab, file, emb_size, emb_dim): """ Generate an initial embedding matrix for word_dict. If an embedding file is not given or a word is not in the embedding file, a randomly initialized vector will be used. """ # embeddings = np.random.randn(vocab.n_words, emb_size) * 0.01 embeddings = np.zeros((vocab.n_words, emb_size)) print("Embeddings: %d x %d" % (vocab.n_words, emb_size)) if file is not None: print("Loading embedding file: %s" % file) pre_trained = 0 for line in open(file).readlines(): sp = line.split() if len(sp) == emb_dim + 1: if sp[0] in vocab.word2index: pre_trained += 1 embeddings[vocab.word2index[sp[0]]] = [float(x) for x in sp[1:]] else: print(sp[0]) print("Pre-trained: %d (%.2f%%)" % (pre_trained, pre_trained * 100.0 / vocab.n_words)) return embeddings def process_raw_abstracts(vocab): with open(raw_save_path, "r", encoding="utf8") as f: for line in tqdm(f, total=13304586): tokens = nltk.tokenize.word_tokenize(line) tokens = [token for token in tokens if not token in nltk_stopwords] vocab.index_words(tokens) def get_node_embed(text, vocab): sum_embed = 0 for word in text: embed = embeddings[vocab.word2index[word]] sum_embed += embed return sum_embed def check_graph(G): total_nodes = len(G.nodes) no_emb_nodes = 0 nodes_to_delete = [] for node_str in G.nodes: try: emb = G.node[node_str]["emb"] except: no_emb_nodes += 1 nodes_to_delete.append(node_str) print("%d Nodes and %d missing nodes in G " % (total_nodes, no_emb_nodes)) G.remove_nodes_from(nodes_to_delete) return G def process_line(G, line, vocab=None): try: fos = data["fos"] abstract = data["indexed_abstract"] paper_id = data["id"] references_id = data["references"] text = list(abstract["InvertedIndex"].keys()) text = " ".join(text) if args.process_raw: with open(raw_save_path, "a+") as f: f.write(text) f.write("\n") """Create Node Embedding if Node doesn't exist """ if vocab is not None: tokens = nltk.tokenize.word_tokenize(text) tokens = [token for token in tokens if not token in nltk_stopwords] node_emb = get_node_embed(tokens, vocab) for field in fos: name = field["name"] for ref in references_id: G.add_edge(paper_id, ref) G.node[paper_id]["emb"] = node_emb except: return G return G if __name__ == "__main__": """ Create Aminer-Citation v-11 Graphs """ parser = argparse.ArgumentParser() parser.add_argument("--topk", type=int, default="100") parser.add_argument("--process_raw", action="store_true", default=False, help="Process Raw Data") parser.add_argument("--make_vocab", action="store_true", default=False, help="Create Vocab from the raw abstract data") args = parser.parse_args() onlyfiles = [f for f in listdir(load_path_rank_base) if isfile(join(load_path_rank_base, f))] vocab = Lang() if args.make_vocab: process_raw_abstracts(vocab) joblib.dump(vocab, "aminer_100_vocab.pkl") print("Done generating vocab") embeddings = gen_embeddings(vocab, file=glove_path, emb_size=300, emb_dim=300) joblib.dump(embeddings, "aminer_100_embed.pkl") print("Done") exit() else: vocab = joblib.load("aminer_100_vocab.pkl") embeddings = joblib.load("aminer_100_embed.pkl") for i, file_ in tqdm(enumerate(onlyfiles), total=len(onlyfiles)): file_path = load_path_rank_base + file_ G = nx.Graph() with open(file_path, "r", encoding="utf8") as f: for line in f: data = json.loads(line) G = process_line(G, data, vocab) G = check_graph(G) print("%s has %d Nodes and %d edges" % (file_, len(G), len(G.edges))) if not os.path.exists(save_path_graph_base): os.mkdir(save_path_graph_base) save_path_graph = save_path_graph_base + file_.split(".")[0] + "_graph.pkl" nx.write_gpickle(G, save_path_graph)
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e38ae7f91deed1be00e60b7516210ea1feefe23e
import sys import os from configparser import ConfigParser import logging from mod_argparse import setup_cli from checkers.IndexFile import DocumentIndex, ProgressNoteIndex from checkers import source_files from utilities import write_to_file, strip # , write_to_db_isok # import pandas as pd logger = logging.getLogger(__name__) DOCUMENTS = 1 PROGRESS_NOTES = 2 DOC_TYPE = { DOCUMENTS: { "file_type": "Document", "folder": "Documents", "class": DocumentIndex, "log": "d", "dates": ["CREATED_TIMESTAMP", "POST_DATE"], "converters": {"FILENAME": strip, "DISPLAY_DESC": strip, "DOC_COMMENT": strip, "DOCUMENT_TYPE": strip}, "staff_field": "displayname", }, PROGRESS_NOTES: { "file_type": "ProgressNote", "folder": "ProgressNotes", "class": ProgressNoteIndex, "log": "p", "dates": ["CREATED_TIMESTAMP"], "converters": {}, "staff_field": "login", }, } def folders_with_documents(pat_ids, main_dir_name, doc_prog_folder): str_pat_ids = [str(pat_id) for pat_id in pat_ids] str_pat_folder_names = [ os.path.join(main_dir_name, os.path.join(str_pat_id, doc_prog_folder)) for str_pat_id in str_pat_ids ] # Patients/*/Documents for pid, folder in zip(str_pat_ids, str_pat_folder_names): if os.path.isdir(folder) and len(os.listdir(folder)) > 1: yield pid, folder # # def write_db_isok(list, pid): # df = pd.concat(list) # return write_to_db_isok(df, pid) def df_chunksof_100(df): lendf = len(df) top100 = df.iloc[:, :100] df = df.iloc[:, 100:] yield top100, df # # def indxfiles_todb(config, pat_ids, main_dir_name, doctype_info): # idx_chkr_cls = doctype_info['class'].initialize(config) # log, indx_filename = doctype_info['log'], idx_chkr_cls.__name__ + '.csv' # # # df = pd.DataFrame() # list_ = [] # pid_start = 0 # pid_end = 0 # df_rows = 0 # # https://stackoverflow.com/questions/50689082/to-sql-pyodbc-count-field-incorrect-or-syntax-error # skipped_list = [] # for pid, pid_src_folder in folders_with_documents(pat_ids, main_dir_name, doctype_info['folder']): # index_file_path = os.path.join(pid_src_folder, indx_filename) # #int_pid = int(pid) # pid_end = int(pid) # try: # df = pd.read_csv(index_file_path, index_col=None, header=0, # converters=doctype_info['converters'], # # date_parser=pd.core.tools.datetimes.to_datetime, # # parse_dates=doctype_info['dates'] # ) # df['PID'] = pid # # #len_df = len(df) # # # if len_df > 50: # # df_1 = pd.concat(list_) # # if write_to_db_isok(df_1, f'{pid_start} - {pid_end}'): # # logger.info(f'Wrote to DB..upto pid: {pid} dfrows: {df_rows}') # # else: # # logger.error(f'Skipping batch because of errors : {pid_start} - {pid_end} dfrows: {df_rows} ') # # skipped_list.extend(range(pid_start, pid_end)) # # pid_start = pid_end + 1 # # # # else: # # list_.append(df) # # df_rows += len_df # # list_.append(df) # df_rows += len(df) # if len(list_) > 5 or df_rows > 30: # df = pd.concat(list_) # if write_to_db_isok(df, f'{pid_start} - {pid_end}'): # logger.info(f'Wrote to DB..upto pid: {pid} dfrows: {df_rows}') # else: # logger.error(f'Skipping batch because of errors : {pid_start} - {pid_end} dfrows: {df_rows} ') # skipped_list.extend(range(pid_start, pid_end)) # list_ = [] # pid_start = pid_end+1 # df_rows = 0 # # except pd.errors.ParserError as pe: # logger.error(f"Something went wrong (ParseError) pid:{pid}. skipping..") # list_ = [] # pid_start = pid_end + 1 # df_rows = 0 # continue # # if list_: # df = pd.concat(list_) # if write_to_db_isok(df, {pid_start} - {pid_end}): # logger.info(f'Wrote to DB..upto pid: {pid}') # # logger.warning(f'Skipped list of pids: {",".join([str(i) for i in skipped_list])}') def getvalid_src_dest_filepaths(config, pat_ids, main_dir_name, doctype_info): idx_chkr_cls = doctype_info["class"].initialize(config) log, indx_filename = doctype_info["log"], idx_chkr_cls.__name__ + ".csv" for pid, pid_src_folder in folders_with_documents(pat_ids, main_dir_name, doctype_info["folder"]): index_file_path = os.path.join(pid_src_folder, indx_filename) idxfile_data, built_src_filenames = idx_chkr_cls.get_idxfile_data(pid, index_file_path) if not idxfile_data: logger.debug(f"{pid}{log} | Issue(s) with IndexFile. Skipping..") continue not_in_indexbuilt_files, not_in_folderlisting = source_files.validate( pid, built_src_filenames, pid_src_folder, log, indx_filename ) if any([*not_in_indexbuilt_files, *not_in_folderlisting]): logger.debug(f"{pid}{log} | invalid folder. skipping") continue # . . . . . . . . . . . folder listing should match the file names built from index file . . . . . . . . for idxfile_row, built_src_file in zip(idxfile_data, built_src_filenames): destn_filename = idx_chkr_cls.get_destination_filename(idxfile_row) if not destn_filename: logger.error(f"{pid}{log} | Could not form the destination filename string") continue logger.info(f"{pid}{log} | src: {built_src_file} dest:{destn_filename}") yield pid, built_src_file, destn_filename def main(): config = setup_cli(sys.argv[1:]) logger.info(f"{sys.argv[0]} -f {config.file_type} -s {config.start_range} -e {config.end_range} -d {config.base_directory}") docinfo = DOC_TYPE[config.file_type] main_dir_name = os.path.join(config.base_directory, "Patients") results_doc = os.path.join(os.path.join(config.base_directory, "results"), docinfo["folder"]) logger.debug(f"results doc : {results_doc}") parser = ConfigParser() parser.read("./config/settings.indexfile.ini") outfile = os.path.join("output", f"{docinfo['folder']}.paths_pids.{config.start_range}-{config.end_range}.csv") if not os.path.isfile(outfile): with open(outfile, "w", newline="") as csv_file: write_to_file("pid", "src", "dest", csv_file, "size") # indxfiles_todb(parser, range(config.start_range, config.end_range), # main_dir_name, doctype_info) with open(outfile, "a", newline="") as csv_file: for p, s, d in getvalid_src_dest_filepaths(parser, range(config.start_range, config.end_range), main_dir_name, docinfo): full_src_path = f"{main_dir_name}{os.sep}{p}{os.sep}{docinfo['folder']}{os.sep}{s}" write_to_file(p, s, d, csv_file, os.path.getsize(full_src_path)) if __name__ == "__main__": main() # from checkers import sanitize # print(sanitize.fix_altdoctitle('None')) # print(sanitize.fix_doctitle('None')) # print(sanitize.fix_timestamp('None')) # print(sanitize.fix_pid('None')) # print(sanitize.fix_fileno('None')) # print(sanitize.fix_ext('...')) # print(sanitize.fix_altdoctitle(None)) # print(sanitize.fix_doctitle(None)) # print(sanitize.fix_timestamp(None)) # print(sanitize.fix_pid(None)) # print(sanitize.fix_fileno(None)) # print(sanitize.fix_ext('aa')) # print(sanitize.fix_ext('asd')) # print(sanitize.fix_ext('bbbb')) # print(sanitize.fix_ext('ccccc')) # print(sanitize.fix_pid('asdf')) # print(sanitize.fix_fileno(3.4))
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069338b188f3cf16357b2502cbb3130b69918bd9
from .cli import cli if __name__ == "__main__": exit(cli.main(prog_name="htmap"))
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8c539dbbb762717393b9a71ddca8eb3872890854
import re import os import pandas as pd instruments_file = os.path.abspath("instruments.csv") input_names_file = os.path.abspath("names.txt") output_names_file = os.path.abspath("names.csv") inst_name_file = os.path.abspath("name_instrument.csv") reg_ex = "; |, |\\*|\n" name_header = ["first_name", "last_name"] def process_names(): """ Opening, reading name file and building name array. """ with open(input_names_file, "r") as data: plaintext = data.read() name_array = plaintext.split("\n") # Final name list final_name_list = [] # Parsing different name formats and standardizing to create csv for name in name_array: if len(name.split(",")) == 2: temp_name_list = re.split(reg_ex, name) last_name = temp_name_list.pop() first_name = temp_name_list.pop() final_name_list.append(last_name + "," + first_name) elif len(name.split(" ")) == 2: final_name_list.append(name.replace(" ", ",")) elif len(name.split(" ")) == 3: temp_name_list = re.split(" ", name) last_name = temp_name_list.pop() middle_name = temp_name_list.pop() first_name = temp_name_list.pop() final_name_list.append(first_name + "," + middle_name + " " + last_name) else: final_name_list.append(name) # Writing final name list to a file with open(output_names_file, "w") as txt_file: txt_file.write("first_name,last_name" + "\n") for name in final_name_list: txt_file.write(name + "\n") # works with any number of elements in a line names_df = pd.read_csv(output_names_file, names=name_header, sep=",", engine="python")
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0db0daf9bea254cffaec1280cd13b2d70368cd94
import numpy.random as rnd import numpy as np B = 100000 N1 = 50 N2 = 50 p1mle = 0.3 p2mle = 0.4 taumle = p2mle - p1mle estimate = [] for i in range(B): p1 = 0.0 for j in range(N1): if rnd.uniform(0, 1) < p1mle: p1 += 1 p1 /= N1 p2 = 0.0 for j in range(N2): if rnd.uniform(0, 1) < p2mle: p2 += 1 p2 /= N2 estimate.append(p2 - p1) t = -10 estimate = np.array(estimate) allt = [0.01 * t for t in xrange(-5000, 5000)] target = 0.95 tol = 0.01 for t in allt: cur = np.mean(np.sqrt(N1 + N2) * (estimate - taumle) < t) if np.abs(target - cur) < tol: print(t) print(cur) break
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a90b7e44cc54d4f96a13e5e6e2d15b632d3c4983
import random import string import steembase import struct import steem from time import sleep from time import time from steem.transactionbuilder import TransactionBuilder from steembase import operations from steembase.transactions import SignedTransaction from resultthread import MyThread from charm.toolbox.pairinggroup import PairingGroup, ZR, G1, G2, GT, pair from charm.toolbox.secretutil import SecretUtil class GroupSignature: def __init__(self, groupObj): global util, group util = SecretUtil(groupObj, debug) self.group = groupObj def pkGen(self, h1str): gstr = "[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774149930982188956916913145008943931711059687988096415181819433817738, 8687587692191287108886119971783525001480020593934954052605681527814232399216375005546606067382536684351686344089456732201641997200939472924879001214689004]" g2str = "[7648994551207171188393784904797547917038803147671542540175090956205316897431443264058433935237605598252399113847934759009659621851760599508222321653067284, 922489308494109901795721463782161260386164061515796674638135394871842997698175772871045949554746517321480649326465484116060959631197509151923296896589720]" u0str = "[180015966842918451436547451263180245588308971597733548673037049536176684754209695288737508087729924028686259002375511049961436438196866049956546630518033, 1295050197915669955783867959538729894307963685491173858450359845766785488725907727220684060845012524740394664162328817669422178637925195059862486690053923]" u1str = "[2555472719769037960206282327195096320915753855199743796256065902544200822503613205017219993060986152240852358189992579821797745072366030183800897743028220, 7573705235093543416041007636313631591000596820214067724084077929638801811700093589294454562385664531190678890366928407286293582994146887505184778221562373]" u2str = "[6876276970903121931083294698771200898345396507892092532649392211995185517437159402176975528760594250374462299539306423347676182899798006533425047523984724, 5323739238507219125881988073888745575030677585404965990610324901624530474522642705344792075909082041735695801098770187248023797265998906693745587936574078]" u3str = "[6628726193389375981104409894060310698729022957801238449570622103067828518416602275957863668289683360250722835022304456841105526036470008237775051984811323, 862537748555943361001122447731987661405436458862545177179548603003392540530328380518694788420155531238391922289886044667763424887444361610972254938158280]" u4str = "[8157254219580822599577995921928211211847392705248772673869189421041858895589817404931780741226510985762564598862965174380020566416411083236239871342674775, 4736677719200783513058679582227494204159737596114643136852532046080608159561620208171676599501713934575216178076006396924589443776642926902969084668055006]" hstr = "[6248393417805371388321299785844751688345516419281230263497475615452026459314582553252281068616984105757749673095320346188725995701858182333525688832492249, 351368339412205819108519989143352052898751906937356995136442397753142226531384069336237369861919799955237545207977716196031001184146017796598836939617335]" nstr = "[75201312764006187596691102237923705656296213254701583615255122742135170369075831428394751330697143847448434841509551532135632624530360013837581615049543, 3886258599652934715331576083899336629981754505948456216299528998628273512432828729344158706718479567056972375128622026273382126529171409058157562418608963]" g = self.group.fromstr(gstr, 10, G1) g2 = self.group.fromstr(g2str, 10, G2) u0 = self.group.fromstr(u0str, 10, G2) u1 = self.group.fromstr(u1str, 10, G2) u2 = self.group.fromstr(u2str, 10, G2) u3 = self.group.fromstr(u3str, 10, G2) u4 = self.group.fromstr(u4str, 10, G2) h = self.group.fromstr(hstr, 10, G1) n = self.group.fromstr(nstr, 10, GT) h1 = self.group.fromstr(h1str, 10, G1) pk = {"g": g, "g2": g2, "u0": u0, "u1": u1, "u2": u2, "u3": u3, "u4": u4, "h": h, "n": n, "h1": h1} return pk def uskGen(self, usklist, pk, GID, UID, L, k): t1 = time() b0 = self.group.gen1_0(1) b3 = self.group.gen1_0(1) b4 = self.group.gen1_0(1) b5 = self.group.gen1_0(1) r2 = self.group.random(ZR) for i in range(k): b0 = b0 * (usklist[i]["b0"] ** L[i]) b3 = b3 * (usklist[i]["b3"] ** L[i]) b4 = b4 * (usklist[i]["b4"] ** L[i]) b5 = b5 * (usklist[i]["b5"] ** L[i]) b0 = b0 * (pk["u0"] * (pk["u1"] ** GID) * (pk["u2"] ** UID)) ** r2 b3 = b3 * (pk["u3"] ** r2) b4 = b4 * (pk["u4"] ** r2) b5 = b5 * (pk["g"] ** r2) usk = {"b0": b0, "b3": b3, "b4": b4, "b5": b5} t2 = time() with open("extracttime.txt", "a") as f: f.write(str(t2 - t1)) f.write("\n") return usk def LGen(self, n, k): L = [] I = self.group.random(ZR) J = self.group.random(ZR) for i in range(n): L.append(self.group.random(ZR)) L[i].set(1) I.set(i + 1) for j in range(1, k + 1): print(j) J.set(j) if (i + 1) != j: L[i] = L[i] * ((J) / (J - I)) return L def verifyUsk(self, usk, vk, pk, GID, UID): g = pk["g"] g2 = pk["g2"] u0 = pk["u0"] u1 = pk["u1"] u2 = pk["u2"] u3 = pk["u3"] u4 = pk["u4"] b0 = usk["b0"] b5 = usk["b5"] b3 = usk["b3"] b4 = usk["b4"] return ( pair(g, b0) == (pair(vk, g2) * pair(b5, u0) * pair(b5, u1**GID) * pair(b5, u2**UID)) and pair(g, b3) == pair(b5, u3) and pair(g, b4) == pair(b5, u4) ) def sign(self, title, usk, pk, GID, UID, groupID): t1 = time() m = self.group.hash(title) b0 = usk["b0"] b3 = usk["b3"] b4 = usk["b4"] b5 = usk["b5"] r4 = self.group.random(ZR) r3 = self.group.random(ZR) k = self.group.random(ZR) c0 = ( b0 * (b3**m) * (b4**r4) * ((pk["u0"] * (pk["u1"] ** GID) * (pk["u2"] ** UID) * (pk["u3"] ** m) * (pk["u4"] ** r4)) ** r3) ) c5 = b5 * (pk["g"] ** r3) c6 = (pk["u2"] ** UID) * (pk["u4"] ** r4) e1 = pk["g"] ** k e2 = (pk["u0"] * (pk["u1"] ** GID)) ** k e3 = (pk["n"] ** UID) * (pair(pk["h1"], pk["g2"]) ** k) # 产生pok f = pk["u0"] * (pk["u1"] ** GID) gp = pair(pk["h1"], pk["g2"]) k1 = self.group.random(ZR) k2 = self.group.random(ZR) k3 = self.group.random(ZR) r1 = (pk["u2"] ** k1) * (pk["u4"] ** k2) r2 = pk["g"] ** k3 r3 = f**k3 t4 = (pk["n"] ** k1) * (gp**k3) hashstr = str(r1) + str(r2) + str(r3) + str(t4) c = self.group.hash(hashstr) s1 = k1 + c * UID s2 = k2 + c * r4 s3 = k3 + c * k signature = {"c0": c0, "c5": c5, "c6": c6, "e1": e1, "e2": e2, "e3": e3, "c": c, "s1": s1, "s2": s2, "s3": s3} t2 = time() with open("gssigntime.txt", "a") as f: f.write(str(t2 - t1)) f.write("\n") print("gs time", t2 - t1) return signature def open(self, okliststr, L, k): t1 = time() oklist = [] for ok in okliststr: oklist.append({"ok1": self.group.fromstr(ok["ok1"], 10, GT), "ok2": self.group.fromstr(ok["ok2"], 10, GT)}) ok1 = self.group.gen1_0(1) ok2 = self.group.gen1_0(1) for i in range(k): ok1 = ok1 * (oklist[i]["ok1"] ** L[i]) ok2 = ok2 * (oklist[i]["ok2"] ** L[i]) t2 = time() with open("opentime.txt", "a") as f: f.write(str(t2 - t1)) f.write("\n") print("open time", t2 - t1) return ok1 / ok2 def get_usk(userID, GID, UID, h1str="", count=0): pk = {} for i in range(n): vkliststr.append(clientlist[i].get_vk()["vk"]) vklist.append(group_signature.group.fromstr(vkliststr[i], 10, G1)) uskliststr.append(clientlist[i].user_extract(userID)) usklist.append({}) usklist[i]["b0"] = group_signature.group.fromstr(uskliststr[i]["b0"], 10, G2) usklist[i]["b3"] = group_signature.group.fromstr(uskliststr[i]["b3"], 10, G2) usklist[i]["b4"] = group_signature.group.fromstr(uskliststr[i]["b4"], 10, G2) usklist[i]["b5"] = group_signature.group.fromstr(uskliststr[i]["b5"], 10, G1) print(usklist[i]) if h1str == "" or h1str == "0" or h1str == 0: h1str = clientlist[i].get_pk()["pk"] print("h1str", h1str) pk = group_signature.pkGen(h1str) print("pk---------------\n", pk) if group_signature.verifyUsk(usklist[i], vklist[i], pk, GID, UID): count = count + 1 else: print("key is invalide\n\n") usk = group_signature.uskGen(usklist, pk, GID, UID, L, k) print("usk---------------\n", usk) return pk, usk def get_lam(sig): okliststr = [] i = 0 for client in clientlist: okstr = client.get_ok(str(sig["e1"]), str(sig["e2"])) print(okstr) okliststr.append(okstr) i = i + 1 if i < k: print("the number of ok is not enough\n") return lam = group_signature.open(okliststr, L, k) return lam def tx_build_broad(op, steemd_instance, wallet_instance, account): tx = TransactionBuilder(steemd_instance=steemd_instance, wallet_instance=wallet_instance, no_broadcast=False) tx.appendOps(op) tx.appendSigner(account, "posting") tx.sign() # print("txsign",tx) re = tx.broadcast() return re def tx_build(op, steemd_instance, wallet_instance, account): tx = TransactionBuilder(steemd_instance=steemd_instance, wallet_instance=wallet_instance, no_broadcast=False) tx.appendOps(op) tx.appendSigner(account, "posting") tx.sign() # print("txsign",tx) # re = tx.broadcast() return tx def annoy_commit(account, usk, pk, GID, UID, title="paper_title", body="paper_body", groupID="computer"): annoy_author = "nya" # group signature ------title 必须 这里面是对title进行hash 然后使用usk对hash进行签名 sig = group_signature.sign(title, usk, pk, GID, UID, groupID) permlink = "".join(random.choices(string.digits, k=7)) print("permlink is " + permlink) op = operations.CommitPaper( **{ "account": account, "author": annoy_author, "permlink": permlink, "title": title, "body": body, "json_metadata": "", "c0": str(sig["c0"]), "c5": str(sig["c5"]), "c6": str(sig["c6"]), "e1": str(sig["e1"]), "e2": str(sig["e2"]), "e3": str(sig["e3"]), "c": str(sig["c"]), "s1": str(sig["s1"]), "s2": str(sig["s2"]), "s3": str(sig["s3"]), } ) print("commitop", op) return op, sig, permlink def open_op(account, sig, userID, permlink): lam = get_lam(sig) # E = (pk['n'] ** UID) * lam #计算出e3 即签名的e3 判断是否相等 op = operations.ApplyOpen( **{"account": account, "author": userID, "lambda": str(lam), "permlink": permlink, "json_metadata": ""} ) return op def annoy_commit_tx(account, usk, pk, GID, UID, steemd_instance, wallet_instance, title="paper_title", body="paper_body"): commitop, ssig, permlink = annoy_commit( account, usk, pk, GID, UID, title="paper_title", body="paper_body", groupID="computer" ) re = tx_build_broad(commitop, steemd_instance, wallet_instance, account) print("commit-re", re) return ssig, permlink def open_tx(account, ssig, userID, permlink, steemd_instance, wallet_instance): openop = open_op(account, ssig, userID, permlink) re = tx_build_broad(openop, steemd_instance, wallet_instance, account) print("open-re", re) # 一个节点的 并发产生交易 def one_mul_annoy_tx(account, usk, pk, UID, steemd, wallet): ssiglistone = [] permlinklistone = [] threads = [] for i in range(nodeTX): t = MyThread(annoy_commit_tx, args=(account, usk, pk, GID, UID, steemd, wallet)) threads.append(t) for t in threads: t.start() for t in threads: t.join() for t in threads: ssig, permlink = t.get_result() ssiglistone.append(ssig) permlinklistone.append(permlink) return ssiglistone, permlinklistone def one_mul_open_tx(account, ssiglistone, userID, permlinklistone, steemd, wallet): threads = [] for i in range(nodeTX): t = MyThread(open_tx, args=(account, ssiglistone[i], userID, permlinklistone[i], steemd, wallet)) threads.append(t) for t in threads: t.start() for t in threads: t.join() def mul_annoy_tx(usk, pk, UID): ssiglist = [] permlinklist = [] threads = [] for i in range(n): # t = MyThread(annoy_commit_tx, args=(accountlist[i], usk, pk, GID, UID, clientlist[i].steemd, clientlist[i].wallet)) t = MyThread(one_mul_annoy_tx, args=(accountlist[i], usk, pk, UID, clientlist[i].steemd, clientlist[i].wallet)) threads.append(t) for t in threads: t.start() for t in threads: t.join() for t in threads: ssig, permlink = t.get_result() ssiglist.append(ssig) permlinklist.append(permlink) return ssiglist, permlinklist # 多个节点， 每个节点并发 def mul_open_tx(ssiglist, permlinklist, userID): threads = [] for i in range(n): # t = MyThread(open_tx, # args=(accountlist[i], ssiglist[i], userID, permlinklist[i], clientlist[i].steemd, clientlist[i].wallet)) t = MyThread( one_mul_open_tx, args=(accountlist[i], ssiglist[i], userID, permlinklist[i], clientlist[i].steemd, clientlist[i].wallet), ) threads.append(t) for t in threads: t.start() for t in threads: t.join() # for t in threads: # t.get_result() # 仅创造tx 不广播 def creat_commit_tx(account, usk, pk, GID, UID, steemd_instance, wallet_instance, title="paper_title", body="paper_body"): commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID, title, body, groupID="computer") commit_tx = tx_build(commitop, steemd_instance, wallet_instance, account) return ssig, permlink, commit_tx def creat_num_commit_tx( num, account, usk, pk, GID, UID, steemd_instance, wallet_instance, ttitle="paper_title", tbody="paper_body" ): ssiglist = [] permlinklist = [] txlist = [] threads = [] for i in range(num): t = MyThread(creat_commit_tx, args=(account, usk, pk, GID, UID, steemd_instance, wallet_instance, ttitle, tbody)) threads.append(t) for t in threads: t.start() for t in threads: t.join() for t in threads: ssig, permlink, commit_tx = t.get_result() ssiglist.append(ssig) permlinklist.append(permlink) txlist.append(commit_tx) return ssiglist, permlinklist, txlist def creat_open_tx(account, ssig, userID, permlink, steemd_instance, wallet_instance): openop = open_op(account, ssig, userID, permlink) open_tx = tx_build(openop, steemd_instance, wallet_instance, account) return open_tx def creat_num_open_tx(num, account, ssiglist, userID, permlinklist, steemd_instance, wallet_instance): opentxlist = [] threads = [] for i in range(num): t = MyThread(creat_open_tx, args=(account, ssiglist[i], userID, permlinklist[i], steemd_instance, wallet_instance)) threads.append(t) for t in threads: t.start() for t in threads: t.join() for t in threads: opentx = t.get_result() opentxlist.append(opentx) return opentxlist def tx_broad(tx): tx.broadcast() def mul_tx_broad(txlist): threads = [] for tx in txlist: t = MyThread(tx_broad, args=(tx,)) threads.append(t) for t in threads: t.start() for t in threads: t.join() # public parma nodeTX = 5 k = 2 n = 3 # (k,n) # 节点地址 nodelist = ["http://101.76.208.83:8090", "http://101.76.208.83:8094", "http://101.76.208.83:8098"] accountlist = [ "initminer2", "zy1", "zy2", "zy3", "zy4", "zy5", "zy6", "zy7", "zy8", "zy9", "zy10", "zy11", "zy12", "zy13", "zy14", "zy15", "zy16", "zy17", "zy18", "zy19", "zy20", ] # 除了第一个 其他的都是posting key 5Hs4jcm5X4sanCnUKNFCjrq2irN8sH1Krzsb13Qd6DHqutZbhqu keylist = [ "5J3yMruND2TADZ7cZc6Cnp4VePrnehei2wvGdnLgf3aEj2nDGhc", "5Hs4jcm5X4sanCnUKNFCjrq2irN8sH1Krzsb13Qd6DHqutZbhqu", "5KPLLsQ3MuWgKvNYqAFRjziWZenBqefDhSe4K1uYuj8hT3zQoKv", ] debug = True # 群签名相关 groupobj = PairingGroup("SS512") group_signature = GroupSignature(groupobj) L = group_signature.LGen(n, k) # 密钥相关 clientlist = [] for i in range(n): clientlist.append(steem.Steem(nodes=[nodelist[i]], keys=keylist[i])) vkliststr = [] uskliststr = [] vklist = [] usklist = [] # steem testchain信息 steembase.chains.known_chains["TEST"] = { "chain_id": "18dcf0a285365fc58b71f18b3d3fec954aa0c141c44e4e5cb4cf777b9eab274e", "prefix": "TST", "steem_symbol": "TESTS", "sbd_symbol": "TBD", "vests_symbol": "VESTS", } groupID = "computer" GID = group_signature.group.hash(groupID) def main(): # 假设不存在不可用节点(无法判断节点状态) userID = "zhou" UID = group_signature.group.hash(userID) print("uid", UID) # 获取usk pk, usk = get_usk(userID, GID, UID) ssig, permlink = annoy_commit_tx( accountlist[0], usk, pk, GID, UID, clientlist[0].steemd, clientlist[0].wallet, title="paper_title", body="paper_body" ) sleep(3) open_tx(accountlist[0], ssig, userID, permlink, clientlist[0].steemd, clientlist[0].wallet) return if __name__ == "__main__": main() print("end")
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ee80169afd4741854eff8619822a857bbf757575
""" Created on 27 Mar 2015 @author: Jon """ import matplotlib.pyplot as plt from numerical_functions import Timer import numerical_functions.numba_funcs.indexing as indexing import numpy as np import unittest class Test(unittest.TestCase): def test_take(self): x = np.linspace(0, 100) idx = np.random.random_integers(0, 50, 20) result = indexing.take(x, idx) expected = np.take(x, idx) np.testing.assert_array_equal(expected, result) def test_take_comparison(self): x = np.arange(1e6) idx = np.random.random_integers(0, 1e5, 1e6) indexing.take(x, idx) np.take(x, idx) with Timer("numba") as nbtimer: indexing.take(x, idx) with Timer("numpy") as nptimer: np.take(x, idx) ratio = nbtimer.interval / nptimer.interval print("numba version of take took %0.2f as long as numpy" % ratio) def test_square_take(self): X = np.random.random_integers(0, 50, 25).reshape(5, 5) idx = np.arange(0, 4, 2) result = np.empty((idx.shape[0], idx.shape[0])) indexing.square_take_to_out(X, idx, result) print(result) expected = X.take(idx, axis=0).take(idx, axis=1) print(expected) np.testing.assert_array_equal(expected, result) def test_square_take_to_out(self): X = np.arange(25).reshape(5, 5) idx = np.arange(0, 4, 2) result = np.empty((idx.shape[0], idx.shape[0])) indexing.square_take_to_out(X, idx, result) print(result) expected = X.take(idx, axis=0).take(idx, axis=1) print(expected) np.testing.assert_array_equal(expected, result) def test_square_take_performance(self): X = np.arange(25).reshape(5, 5) idx = np.arange(0, 4, 2) result = np.empty((idx.shape[0], idx.shape[0])) indexing.square_take_to_out(X, idx, result) result2 = indexing.square_take(X, idx) np.testing.assert_array_equal(result, result2) num_tests = 1000 nbts = [] nbts2 = [] npts = [] ms = (10, 20, 40, 80, 160) # , 320, 640 ) for m in ms: X = np.arange(m * m).reshape(m, m) idx = np.random.random_integers(0, m - 1, m // 2) result = np.empty((idx.shape[0], idx.shape[0])) with Timer("numba") as nbt: for _ in range(num_tests): indexing.square_take_to_out(X, idx, result) nbts.append(nbt.interval) with Timer("numba2") as nbt: for _ in range(num_tests): r = indexing.square_take(X, idx) nbts2.append(nbt.interval) with Timer("numpy") as npt: for _ in range(num_tests): X.take(idx, axis=0).take(idx, axis=1) npts.append(npt.interval) plt.plot(ms, nbts, label="nb to out") plt.plot(ms, nbts2, label="nb new result") plt.plot(ms, npts, label="np") plt.title("square_take_to_out performance test") plt.legend(loc="center left", bbox_to_anchor=(1, 0.5)) plt.show() def test_square_and_rect_take_to_out(self): X = np.arange(100).reshape((10, 10)) idx0 = np.arange(0, 4, 2) idx1 = np.arange(4, 6) result = np.empty((idx0.shape[0], idx0.shape[0] + idx1.shape[0])) indexing.square_and_rect_take_to_out(X, idx0, idx1, result) np.testing.assert_array_equal(result[:, :2], indexing.square_take(X, idx0)) r2 = np.array([[4, 5], [24, 25]]) np.testing.assert_array_equal(r2, result[:, 2:]) def test_get_resample_indices(self): raw_index = np.arange(10) resampled_index = np.arange(1, 10, 2) result = indexing.get_resample_indices(raw_index, resampled_index) expected = np.arange(0, 10, 2) np.testing.assert_array_equal(expected, result) def test_take_upper_off_diagonal(self): X = np.array([[1, 2, 3], [np.nan, 5, 6], [np.nan, np.nan, 9]]) idx = np.array([0, 1]) expected = np.array([2]) actual = indexing.take_upper_off_diagonal(X, idx) np.testing.assert_array_equal(actual, expected) idx = np.array([1, 2]) expected = np.array([6]) actual = indexing.take_upper_off_diagonal(X, idx) np.testing.assert_array_equal(actual, expected) idx = np.array([0, 2]) expected = np.array([3]) actual = indexing.take_upper_off_diagonal(X, idx) np.testing.assert_array_equal(actual, expected) idx = np.array([0, 1, 2]) expected = np.array([2, 3, 6]) actual = indexing.take_upper_off_diagonal(X, idx) np.testing.assert_array_equal(actual, expected) if __name__ == "__main__": # import sys;sys.argv = ['', 'Test.testName'] unittest.main()
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ce6dba2f682b091249f3bbf362bead4b95fee1f4
""" .. currentmodule:: jotting .. automodule:: jotting.book :members: .. automodule:: jotting.to :members: .. automodule:: jotting.read :members: .. automodule:: jotting.style :members: """ from .book import book from . import style, to, read, dist
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bf8bbeb408cb75af314ef9f3907456036e731c0b
def solution(S): # write your code in Python 3.6 # Definitions log_sep = "," num_sep = "-" time_sep = ":" # Initialization from collections import defaultdict # defaultdict initialize missing key to default value -> 0 bill = defaultdict(int) total = defaultdict(int) calls = S.splitlines() maximal = 0 free_number = 0 for call in calls: # Parsing values hhmmss, number = call.split(log_sep) hh, mm, ss = hhmmss.split(time_sep) hh, mm, ss = int(hh), int(mm), int(ss) number = int(number.replace(num_sep, "")) # Call duration calculations minutes = mm + hh * 60 seconds = ss + minutes * 60 # Free number Rule total[number] += seconds if total[number] > maximal: # new maximal maximal = total[number] free_number = number elif total[number] == maximal: # in case of a tie... free_number = min(number, free_number) # Billing Rule if minutes < 5: bill[number] += seconds * 3 else: if ss > 0: started = 1 else: started = 0 bill[number] += (minutes + started) * 150 # Free number Rule enforcement bill[free_number] = 0 return sum(bill.values())
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35ae9c86594b50bbe4a67d2cc6b20efc6f6fdc64
# 配置我们文件所在目录的搜寻环境 import os, sys # 第一步先拿到当前文件的路径 file_path = os.path.abspath(__file__) # 第二步 根据这个路径去拿到这个文件所在目录的路径 dir_path = os.path.dirname(file_path) # 第三步：讲这个目录的路径添加到我们的搜寻环境当中 sys.path.append(dir_path) # 第四步，动态设置我们的setting文件 os.environ.setdefault("DJANGO_SETTINGS_MODULE", "gulishop.settings") # 第五步，让设置好的环境初始化生效 import django django.setup() # 这一行，不能放在上面 from goods.models import GoodsCategory from db_tools.data.category_data import row_data for lev1 in row_data: cat1 = GoodsCategory() cat1.name = lev1["name"] cat1.code = lev1["code"] if lev1["code"] else "" cat1.category_type = 1 cat1.save() for lev2 in lev1["sub_categorys"]: cat2 = GoodsCategory() cat2.name = lev2["name"] cat2.code = lev2["code"] if lev2["code"] else "" cat2.category_type = 2 cat2.parent_category = cat1 cat2.save() for lev3 in lev2["sub_categorys"]: cat3 = GoodsCategory() cat3.name = lev3["name"] cat3.code = lev3["code"] if lev3["code"] else "" cat3.category_type = 3 cat3.parent_category = cat2 cat3.save()
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d34159536e860719094a36cfc30ffb5fcae72a9a
# API End Points by Mitul import urllib.error, urllib.request, urllib.parse import json target = "http://py4e-data.dr-chuck.net/json?" local = input("Enter location: ") url = target + urllib.parse.urlencode({"address": local, "key": 42}) print("Retriving", url) data = urllib.request.urlopen(url).read() print("Retrived", len(data), "characters") js = json.loads(data) print(json.dumps(js, indent=4)) print("Place id", js["results"][0]["place_id"]) """Output: Enter location: >? UIUC Retriving http://py4e-data.dr-chuck.net/json?address=UIUC&key=42 Retrived 1720 characters { "results": [ { "access_points": [], "address_components": [ { "long_name": "Champaign", "short_name": "Champaign", "types": [ "locality", "political" ] }, { "long_name": "Champaign County", "short_name": "Champaign County", "types": [ "administrative_area_level_2", "political" ] }, { "long_name": "Illinois", "short_name": "IL", "types": [ "administrative_area_level_1", "political" ] }, { "long_name": "United States", "short_name": "US", "types": [ "country", "political" ] } ], "formatted_address": "Champaign, IL, USA", "geometry": { "location": { "lat": 40.1019523, "lng": -88.2271615 }, "location_type": "GEOMETRIC_CENTER", "viewport": { "northeast": { "lat": 40.1033012802915, "lng": -88.22581251970848 }, "southwest": { "lat": 40.1006033197085, "lng": -88.2285104802915 } } }, "place_id": "ChIJ6VUmqSTXDIgR-iZoBCUFPKU", "plus_code": { "compound_code": "4Q2F+Q4 Champaign, Champaign City Township, IL, United States", "global_code": "86GH4Q2F+Q4" }, "types": [ "establishment", "point_of_interest", "university" ] } ], "status": "OK" } Place id ChIJ6VUmqSTXDIgR-iZoBCUFPKU """
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c382b298cce8d7045d6ce8a84f90b3800dba7717
# Generated by Django 3.0.7 on 2020-06-15 15:26 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("products", "0003_auto_20200615_1225"), ] operations = [ migrations.AlterField( model_name="product", name="harmonizacao", field=models.TextField(null=True), ), migrations.AlterField( model_name="product", name="history", field=models.TextField(null=True), ), migrations.AlterField( model_name="product", name="premios", field=models.TextField(null=True), ), ]
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3372d98ff91d90558a87293d4032820b1662d60b
from django.conf.urls import patterns, url from riskDashboard2 import views urlpatterns = patterns( "", # url(r'getdata', views.vulnData, name='getdata'), url(r"appmanagement", views.appmanagement, name="appmanagement"), url(r"^.*", views.index, name="index"), )
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0465e33d65c2ce47ebffeec38db6908826bf4934
people = 20 cats = 30 dogs = 15 if people < cats: print("Too many cats") elif people > cats: print("Not many cats") else: print("we cannnot decide")
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5ddfeb49c16a7452c99126f1a837f3c0bed0ec10
import requests from urllib.parse import urlparse from bs4 import BeautifulSoup import re import datetime import random pages = set() # Retrieve a list of all Internal links foound on a page. def getInternalLinks(bs, includeUrl): includeUrl = f"{urlparse(includeUrl).scheme}://{urlparse(includeUrl).netloc}" internalLinks = [] # Finds all links thhat begin with a "/" for link in bs.find_all("a", href=re.compile("^(/|.*" + includeUrl + ")")): if link.attrs["href"] is not None: if link.attrs["href"] not in internalLinks: if link.attrs["href"].startswith("/"): internalLinks.append(includeUrl + link.attrs["href"]) else: internalLinks.append(link.attrs["href"]) return internalLinks # Retrieves a list of all external links found on a pagee. def getExternalLinks(bs, excludeUrl): externalLinks = [] # Finds all links that starts with "http" that do # not contain the current URL for link in bs.find_all("a", href=re.compile("^(http|www)((?!" + excludeUrl + ").)*$")): if link.attrs["href"] is not None: if link.attrs["href"] not in externalLinks: externalLinks.append(link.attrs["href"]) return externalLinks def getRandomExternalLink(startingPage): html = requests.get(startingPage) bs = BeautifulSoup(html.text, "html.parser") externalLinks = getExternalLinks(bs, urlparse(startingPage).netloc) if len(externalLinks) == 0: print("No external links, looking around the site for one.") domain = f"{urlparse(startingPage).scheme}://{urlparse(startingPage).netloc}" internalLinks = getInternalLinks(bs, domain) return getRandomExternalLink(internalLinks[random.randint(0, len(internalLinks) - 1)]) else: return externalLinks[random.randint(0, len(externalLinks) - 1)] # Collects a list of all external URLs found on the site allExtLinks = set() allIntLinks = set() def getAllExternalLinks(siteUrl): html = requests.get(siteUrl) domain = f"{urlparse(siteUrl).scheme}://{urlparse(siteUrl).netloc}" bs = BeautifulSoup(html.text, "html.parser") internalLinks = getInternalLinks(bs, domain) externalLinks = getExternalLinks(bs, domain) for link in externalLinks: if link not in allExtLinks: allExtLinks.add(link) print(link) for link in internalLinks: if link not in allIntLinks: allIntLinks.add(link) getAllExternalLinks(link) def followExternalOnly(startingSite): externalLink = getRandomExternalLink(startingSite) print(f"Random external link is: {externalLink}") followExternalOnly(externalLink)
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1e1f918ba24f5a5f13b9b01289ebfda65bae572d
def warshall_floyd(N): INF = 10**20 path = [[INF for _ in range(N + 1)] for _ in range(N + 1)] graph = get_graph() for i in range(N + 1): path[i][i] = 0 for g in graph: x = g[0] y = g[1] l = g[2] path[x][y] = path[y][x] = l for start in range(N + 1): for goal in range(N + 1): for way in range(N + 1): path[start][goal] = path[goal][start] = min(path[start][goal], path[start][way] + path[way][goal]) return path def get_graph(): graph = [input_as_int() for _ in range(M)] return graph def input_as_int(): return list(map(int, input().split())) R, C, K = input_as_int() N = int(input()) print(ans)
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04538cc5c9c68582cc9aa2959faae2d7547ab2ee
try: import xml.etree.cElementTree as ET except ImportError: import xml.etree.ElementTree as ET import data_helpers def write_to_file(file, line): file.write(line + "\n") def cat_map(): catmap = {} id = 1 f = open("cat") cat = set([s.strip() for s in list(f.readlines())]) for i in cat: catmap[i] = id id = id + 1 return catmap tree = ET.ElementTree(file="test.xml") root = tree.getroot() cnn = open("cnn", "a") lstm = open("lstm", "a") cat = open("cat", "a") for vespaadd in root: document = vespaadd.find("document") if document != None: subject = document.find("subject") content = document.find("content") maincat = document.find("maincat") if subject == None: continue if content == None: content = subject if maincat == None: continue write_to_file(cnn, data_helpers.clean_str(subject.text)) write_to_file(lstm, data_helpers.clean_str(content.text)) write_to_file(cat, data_helpers.clean_str(maincat.text)) cnn.close() lstm.close() cat.close()
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631323e79f4fb32611d7094af92cff8f923fa996
#!/bin/python3 def word_ladder(start_word, end_word, dictionary_file="words5.dict"): """ Returns a list satisfying the following properties: 1. the first element is `start_word` 2. the last element is `end_word` 3. elements at index i and i+1 are `_adjacent` 4. all elements are entries in the `dictionary_file` file For example, running the command ``` word_ladder('stone','money') ``` may give the output ``` ['stone', 'shone', 'phone', 'phony', 'peony', 'penny', 'benny', 'bonny', 'boney', 'money'] ``` but the possible outputs are not unique, so you may also get the output ``` ['stone', 'shone', 'shote', 'shots', 'soots', 'hoots', 'hooty', 'hooey', 'honey', 'money'] ``` (We cannot use doctests here because the outputs are not unique.) Whenever it is impossible to generate a word ladder between the two words, the function returns `None`. HINT: See <https://github.com/mikeizbicki/cmc-csci046/issues/472> for a discussion about a common memory management bug that causes the generated word ladders to be too long in some cases. """ def verify_word_ladder(ladder): """ Returns True if each entry of the input list is adjacent to its neighbors; otherwise returns False. >>> verify_word_ladder(['stone', 'shone', 'phone', 'phony']) True >>> verify_word_ladder(['stone', 'shone', 'phony']) False """ def _adjacent(word1, word2): """ Returns True if the input words differ by only a single character; returns False otherwise. >>> _adjacent('phone','phony') True >>> _adjacent('stone','money') False """
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from typing import Tuple class TreeNode: def __init__(self, x): self.val = x self.left = None self.right = None class Solution: def lcaDeepestLeaves(self, root: TreeNode) -> TreeNode: _, lca = self.get_lca(root, 0) return lca def get_lca(self, node: TreeNode, depth: int) -> Tuple[int, TreeNode]: if not node: return depth, node left_depth, left_lca = self.get_lca(node.left, depth + 1) right_depth, right_lca = self.get_lca(node.right, depth + 1) if left_depth == right_depth: return left_depth, node if left_depth > right_depth: return left_depth, left_lca return right_depth, right_lca
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371762a6e3f8b8ed14742a70a709da224ae6712b
#!/usr/bin/env python3 # -*- coding: utf-8 -*- import random """ 检查图数据和结构或者链表数据结构中是否存在环 https://www.youtube.com/watch?v=YKE4Vd1ysPI """ def get_all_edge(graph): result = [] for k, v in graph.items(): for i in v: if sorted((k, i)) not in result: result.append(sorted((k, i))) return result def bfs(graph, start): result = [] queue = [] seen = set() queue.append(start) seen.add(start) while len(queue): vertex = queue.pop(0) nodes = graph[vertex] for node in nodes: if node not in seen: queue.append(node) seen.add(node) result.append(vertex) return result if __name__ == "__main__": graph = { "0": ["1"], "1": ["0", "2", "3"], "2": ["1", "4", "5"], "3": ["1", "4"], "4": ["2", "3"], "5": ["2"], } all_edge = get_all_edge(graph) random.shuffle(all_edge) print(all_edge) print(bfs(graph, "0")) # ['0', '1', '2', '3', '4', '5'] """ ['0', '1', '2', '3', '4', '5'] [['1', '3'], ['3', '4'], ['2', '5'], ['1', '2'], ['0', '1'], ['2', '4']] ['1', '3'] disjoint set 1, 3 ['3', '4'] -> 一个点在集合中，另一个不在集合中 disjoint set 1, 3, 4 ['2', '5'] -> 两个点都不在同一集合中 disjoint set 1 1, 3, 4 disjoint set 2 2, 5 ['1', '2'] -> 两个点分别在不同的集合中，合并集合 disjoint set 1, 3, 4, 2, 5 ['0', '1'] -> 一个点在集合中，另一个不在集合中 disjoint set 1, 3, 4, 2, 5, 0 ['2', '4'] -> 两个点都在同一个集合中，说明有环 """ graph = { "0": ["1"], "1": ["0", "2", "3"], "2": ["1", "5"], "3": ["1", "4"], "4": ["3"], "5": ["2"], } all_edge = get_all_edge(graph) random.shuffle(all_edge) print(all_edge) print(bfs(graph, "0")) # ['0', '1', '2', '3', '4', '5'] """ [['3', '4'], ['1', '3'], ['0', '1'], ['1', '2'], ['2', '5']] ['0', '1', '2', '3', '5', '4'] ['3', '4'] disjoint set 3, 4 ['1', '3'] disjoint set 3, 4, 1 ['0', '1'] disjoint set 3, 4, 1, 0 ['1', '2'] disjoint set 3, 4, 1, 0, 2 ['2', '5'] disjoint set 3, 4, 1, 0, 2, 5 图中无环 """ graph = {"0": ["1"], "1": ["0", "2"], "2": ["1", "3"], "3": ["2"]} all_edge = get_all_edge(graph) random.shuffle(all_edge) print(all_edge) print(bfs(graph, "0")) """ [['2', '3'], ['0', '1'], ['1', '2']] ['0', '1', '2', '3'] ['2', '3'] disjoint set 2, 3 ['0', '1'] disjoint set1 2, 3 disjoint set2 0, 1 ['1', '2'] disjoint set 0, 1, 2, 3 链表中无环 """ graph = { "0": ["1"], "1": ["0", "2"], "2": ["1", "3"], "3": ["2", "4", "6"], "4": ["3", "5"], "5": ["4", "6"], "6": ["3", "5"], } all_edge = get_all_edge(graph) random.shuffle(all_edge) print(all_edge) print(bfs(graph, "0")) """ [['2', '3'], ['5', '6'], ['3', '4'], ['0', '1'], ['1', '2'], ['3', '6'], ['4', '5']] ['0', '1', '2', '3', '4', '6', '5'] ['2', '3'] disjoint set 2, 3 ['5', '6'] disjoint set1 2, 3 disjoint set2 5, 6 ['3', '4'] disjoint set1 2, 3, 4 disjoint set2 5, 6 ['0', '1'] disjoint set1 2, 3, 4 disjoint set2 5, 6 disjoint set3 0, 1 ['1', '2'] disjoint set1 2, 3, 4, 1, 0 disjoint set2 5, 6 ['3', '6'] disjoint set 2, 3, 4, 1, 0, 5, 6 ['4', '5'] 链表中有环 """
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7f7ebc6d3d69fbb19071c63a9ab235ad01f1d414
import sys sys.path.append("..") import helpers helpers.mask_busy_gpus(wait=False) import nltk import numpy as np nltk.download("brown") nltk.download("universal_tagset") data = nltk.corpus.brown.tagged_sents(tagset="universal") all_tags = ["#EOS#", "#UNK#", "ADV", "NOUN", "ADP", "PRON", "DET", ".", "PRT", "VERB", "X", "NUM", "CONJ", "ADJ"] data = np.array([[(word.lower(), tag) for word, tag in sentence] for sentence in data]) from sklearn.cross_validation import train_test_split train_data, test_data = train_test_split(data, test_size=0.25, random_state=42) from collections import Counter word_counts = Counter() for sentence in data: words, tags = zip(*sentence) word_counts.update(words) all_words = ["#EOS#", "#UNK#"] + list(list(zip(*word_counts.most_common(10000)))[0]) # print(all_words) # let's measure what fraction of data words are in the dictionary print("Coverage = %.5f" % (float(sum(word_counts[w] for w in all_words)) / sum(word_counts.values()))) from collections import defaultdict word_to_id = defaultdict(lambda: 1, {word: i for i, word in enumerate(all_words)}) tag_to_id = {tag: i for i, tag in enumerate(all_tags)} def to_matrix(lines, token_to_id, max_len=None, pad=0, dtype="int32", time_major=False): """Converts a list of names into rnn-digestable matrix with paddings added after the end""" max_len = max_len or max(map(len, lines)) matrix = np.empty([len(lines), max_len], dtype) matrix.fill(pad) for i in range(len(lines)): line_ix = list(map(token_to_id.__getitem__, lines[i]))[:max_len] matrix[i, : len(line_ix)] = line_ix return matrix.T if time_major else matrix batch_words, batch_tags = zip(*[zip(*sentence) for sentence in data[-3:]]) print("Word ids:") print(to_matrix(batch_words, word_to_id)) print("Tag ids:") print(to_matrix(batch_tags, tag_to_id)) import keras import keras.layers as L from keras.utils.np_utils import to_categorical BATCH_SIZE = 32 def generate_batches(sentences, batch_size=BATCH_SIZE, max_len=None, pad=0): assert isinstance(sentences, np.ndarray), "Make sure sentences is q numpy array" while True: indices = np.random.permutation(np.arange(len(sentences))) for start in range(0, len(indices) - 1, batch_size): batch_indices = indices[start : start + batch_size] batch_words, batch_tags = [], [] for sent in sentences[batch_indices]: words, tags = zip(*sent) batch_words.append(words) batch_tags.append(tags) batch_words = to_matrix(batch_words, word_to_id, max_len, pad) batch_tags = to_matrix(batch_tags, tag_to_id, max_len, pad) batch_tags_1hot = to_categorical(batch_tags, len(all_tags)).reshape(batch_tags.shape + (-1,)) yield batch_words, batch_tags_1hot def compute_test_accuracy(model): test_words, test_tags = zip(*[zip(*sentence) for sentence in test_data]) test_words, test_tags = to_matrix(test_words, word_to_id), to_matrix(test_tags, tag_to_id) # predict tag probabilities of shape [batch,time,n_tags] predicted_tag_probabilities = model.predict(test_words, verbose=1) predicted_tags = predicted_tag_probabilities.argmax(axis=-1) # compute accurary excluding padding numerator = np.sum(np.logical_and((predicted_tags == test_tags), (test_words != 0))) denominator = np.sum(test_words != 0) return float(numerator) / denominator class EvaluateAccuracy(keras.callbacks.Callback): def on_epoch_end(self, epoch, logs=None): sys.stdout.flush() print("\nMeasuring validation accuracy...") acc = compute_test_accuracy(self.model) print("\nValidation accuracy: %.5f\n" % acc) sys.stdout.flush() model = keras.models.Sequential() model = keras.models.Sequential() model.add(L.InputLayer([None], dtype="int32")) model.add(L.Embedding(len(all_words), 50)) model.add(L.TimeDistributed(L.Dense(96, activation="tanh"))) model.add(L.Dropout(0.25)) model.add(L.TimeDistributed(L.Dense(96, activation="tanh"))) model.add(L.Dropout(0.25)) # model.add(L.Conv1D(32,3,padding='same',activation='tanh')) model.add(L.Bidirectional(L.GRU(128, return_sequences=True, activation="tanh", recurrent_dropout=0.2, dropout=0.2))) model.add(L.TimeDistributed(L.Dense(128, activation="tanh"))) model.add(L.Dropout(0.25)) model.add(L.TimeDistributed(L.Dense(128, activation="tanh"))) model.add(L.Dropout(0.25)) model.add(L.Bidirectional(L.GRU(128, return_sequences=True, activation="tanh", recurrent_dropout=0.2, dropout=0.2))) model.add(L.TimeDistributed(L.Dense(128, activation="tanh"))) model.add(L.Dropout(0.25)) model.add(L.TimeDistributed(L.Dense(128, activation="tanh"))) model.add(L.Dropout(0.25)) # # model.add(L.Bidirectional(L.GRU(128, return_sequences=True, activation="tanh", recurrent_dropout=0.2, dropout=0.2))) model.add(L.Conv1D(128, 2, padding="same", activation="tanh")) model.add(L.Dropout(0.2)) model.add(L.Conv1D(128, 3, padding="same", activation="tanh")) model.add(L.Dropout(0.2)) model.add(L.Conv1D(128, 4, padding="same", activation="tanh")) model.add(L.TimeDistributed(L.Dense(256, activation="tanh"))) model.add(L.Dropout(0.25)) # model.add(L.TimeDistributed(L.Dense(128,activation='tanh'))) # model.add(L.Dropout(0.25)) stepwise_dense = L.Dense(len(all_tags), activation="softmax") stepwise_dense = L.TimeDistributed(stepwise_dense) model.add(stepwise_dense) model.summary() model.compile("adam", "categorical_crossentropy") model.fit_generator( generate_batches(train_data), len(train_data) / BATCH_SIZE, callbacks=[EvaluateAccuracy()], epochs=50, ) acc = compute_test_accuracy(model) print("\nFinal accuracy: %.5f" % acc) model.save_weights("LSTM_gpu_trained_weights_1layer.h5")
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8698aedc5c8671f46c73898a7188440254b79bbf
from abc import abstractmethod class Environment: @abstractmethod def __init__(self, agent): pass @abstractmethod def execute_step(self, n=1): pass @abstractmethod def execute_all(self): pass @abstractmethod def set_delay(self, delay): pass
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1be510e6715d21e814c48fe05496704e9a65d554
from end import Client c = Client()
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6b727cdfc684db4ba919cd5390fe45de43a806fe
import glob import xarray as xr from model_diagnostics import * data_root = "../data/synthetic/standard/" var_list = ["hs", "dp", "spr", "fp", "dir", "t0m1"] eke = 0.01 ########################## output = [] diagnostic_functions = [basic_stats] for var in var_list: grid_files = glob.glob(data_root + "gridded/*%s*%s.nc" % (eke, var)) for f in grid_files: output.append(analize_member(f, var, diagnostic_functions)) print("processing %s" % os.path.basename(f)) var = "hs" diagnostic_functions = [hs_spectral_slope] grid_files = glob.glob(data_root + "gridded/*%s*%s.nc" % (eke, var)) for f in grid_files: output.append(analize_member(f, var, diagnostic_functions)) print("processing %s" % os.path.basename(f)) var = "cur" diagnostic_functions = [flow_stats] grid_files = glob.glob(data_root + "gridded/*%s*%s.nc" % (eke, var)) for f in grid_files: output.append(analize_member(f, var, diagnostic_functions)) print("processing %s" % os.path.basename(f)) ds = xr.merge(output) df = ds.to_dataframe() df = df.reset_index() data = df.to_xarray() data.to_netcdf(path="../data/model_stats/S%s_gridded_stats.nc" % eke, mode="w")
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c66f4ee5719f764c8c713c23815302c00b6fb9af
import os from cs50 import SQL from flask import Flask, flash, redirect, render_template, request, session from flask_session import Session from tempfile import mkdtemp from werkzeug.exceptions import default_exceptions, HTTPException, InternalServerError from werkzeug.security import check_password_hash, generate_password_hash from helpers import apology, login_required, lookup, usd # Configure application app = Flask(__name__) # Ensure templates are auto-reloaded app.config["TEMPLATES_AUTO_RELOAD"] = True # Ensure responses aren't cached @app.after_request def after_request(response): response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate" response.headers["Expires"] = 0 response.headers["Pragma"] = "no-cache" return response # Custom filter app.jinja_env.filters["usd"] = usd # Configure session to use filesystem (instead of signed cookies) app.config["SESSION_FILE_DIR"] = mkdtemp() app.config["SESSION_PERMANENT"] = False app.config["SESSION_TYPE"] = "filesystem" Session(app) # Configure CS50 Library to use SQLite database db = SQL("sqlite:///finance.db") # Make sure API key is set if not os.environ.get("API_KEY"): raise RuntimeError("API_KEY not set") @app.route("/") @login_required def index(): """Show portfolio of stocks""" rows = db.execute( "SELECT symbol, SUM(transactions) as amount FROM record WHERE userID=? GROUP BY symbol HAVING transactions", session["user_id"], ) cash = db.execute("SELECT cash FROM users WHERE id=?", session["user_id"]) cash_ = cash[0]["cash"] # store all the data into a dict so its easier to pass in to html display = [] total_share = 0 for row in rows: symbol = str(row["symbol"]) print(symbol) name = lookup(symbol)["name"] shares = int(row["amount"]) price = float(lookup(symbol)["price"]) total = float(shares) * price total_share += total display.append({"symbol": symbol, "name": name, "shares": shares, "price": price, "total": total}) total_money = total_share + cash[0]["cash"] return render_template("index.html", display=display, total_money=total_money, cash=cash_) @app.route("/buy", methods=["GET", "POST"]) @login_required def buy(): """Buy shares of stock""" if request.method == "POST": # Ensure symbol was submitted if not request.form.get("symbol"): return apology("must provide symbol", 400) # Ensure shares was submitted elif not request.form.get("shares"): return apology("must provide shares", 400) if not request.form.get("shares").isdigit(): return apology("must be integer", 400) elif int(request.form.get("shares")) < 1: return apology("must be positive integer", 400) elif lookup(request.form.get("symbol")) == None: return apology("Must be a valid symbol", 400) # ensure money>price quote = lookup(request.form.get("symbol")) shares = request.form.get("shares") cash = db.execute("SELECT cash FROM users WHERE id=?", session["user_id"]) if cash[0]["cash"] < int(quote["price"]) * int(shares): return apology("You can't affort this/these", 400) # BUY, STORE DATA IN REPOSITORY AND RECORD # record this transaction db.execute( "INSERT INTO record(userID,transactions,symbol,price,t1) VALUES(?,?,?,?,strftime('%Y-%m-%d %H:%M:%S','now'))", session["user_id"], int(shares), quote["symbol"], float(quote["price"]), ) # deduct the cash total = int(quote["price"]) * int(shares) db.execute("UPDATE users SET cash=cash- (?) WHERE id=?", total, session["user_id"]) return redirect("/") else: return render_template("buy.html") @app.route("/history") @login_required def history(): """Show history of transactions""" rows = db.execute("SELECT * FROM record ORDER BY t1") return render_template("history.html", rows=rows) @app.route("/login", methods=["GET", "POST"]) def login(): """Log user in""" # Forget any user_id session.clear() # User reached route via POST (as by submitting a form via POST) if request.method == "POST": # Ensure username was submitted if not request.form.get("username"): return apology("must provide username", 403) # Ensure password was submitted elif not request.form.get("password"): return apology("must provide password", 403) # Query database for username rows = db.execute("SELECT * FROM users WHERE username = ?", request.form.get("username")) # Ensure username exists and password is correct if len(rows) != 1 or not check_password_hash(rows[0]["hash"], request.form.get("password")): return apology("invalid username and/or password", 403) # Remember which user has logged in session["user_id"] = rows[0]["id"] # Redirect user to home page return redirect("/") # User reached route via GET (as by clicking a link or via redirect) else: return render_template("login.html") @app.route("/logout") def logout(): """Log user out""" # Forget any user_id session.clear() # Redirect user to login form return redirect("/") @app.route("/quote", methods=["GET", "POST"]) @login_required def quote(): """Get stock quote.""" if request.method == "POST": quote = lookup(request.form.get("symbol")) if quote == None: return apology("Invalid symbol", 400) price = usd(quote["price"]) return render_template("quoted.html", quote=quote, price=price) else: return render_template("quote.html") @app.route("/register", methods=["GET", "POST"]) def register(): """Register user""" if request.method == "POST": # Ensure username was submitted if not request.form.get("username"): return apology("must provide username", 400) # Ensure password was submitted elif not request.form.get("password"): return apology("must provide password", 400) # Ensure comfirm password was submitted elif not request.form.get("confirmation"): return apology("must comfirm password", 400) # Ensure password matches elif request.form.get("confirmation") != request.form.get("password"): return apology("Password not matches", 400) # Ensure username is new(unique) rows = db.execute("SELECT * FROM users WHERE username = ?", request.form.get("username")) if len(rows) != 0: return apology("username used", 400) db.execute( "INSERT INTO users (username,hash) VALUES (?,?)", request.form.get("username"), generate_password_hash(request.form.get("password")), ) # Redirect user to home page return redirect("/") else: return render_template("register.html") @app.route("/sell", methods=["GET", "POST"]) @login_required def sell(): """Sell shares of stock""" if request.method == "POST": # parameter is not filled if not request.form.get("shares"): return apology("Please enter how much u want to sell", 400) # check if shares(amount) that are going to be sell less than owner's share. sell = request.form.get("symbol") shares = request.form.get("shares") amount = db.execute( "SELECT SUM(transactions) as amount FROM record WHERE userID=? AND symbol=? GROUP BY symbol HAVING transactions", session["user_id"], sell, ) if amount[0]["amount"] < int(shares): return apology("You dont own that much shares", 400) # record sell and add cash amount quote = lookup(sell) price = quote["price"] total = int(price) * int(shares) db.execute( "INSERT INTO record(userID,transactions,symbol,price,t1) VALUES(?,?,?,?,strftime('%s','now'))", session["user_id"], (int(shares) * -1), quote["symbol"], price, ) db.execute("UPDATE users SET cash=cash+ (?) WHERE id=?", total, session["user_id"]) return redirect("/") else: rows = db.execute( "SELECT symbol, SUM(transactions) as amount FROM record WHERE userID=? GROUP BY symbol HAVING transactions", session["user_id"], ) return render_template("sell.html", rows=rows) @app.route("/HAX", methods=["GET", "POST"]) @login_required def HAX(): # add free monei boiiii if request.method == "POST": total = request.form.get("HAX") db.execute("UPDATE users SET cash=cash+ (?) WHERE id=?", total, session["user_id"]) flash("HAX SUCCESSFULLY ACTIVATED!!!") return redirect("/") else: return render_template("HAX.html") def errorhandler(e): """Handle error""" if not isinstance(e, HTTPException): e = InternalServerError() return apology(e.name, e.code) # Listen for errors for code in default_exceptions: app.errorhandler(code)(errorhandler)
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097a87f7f1346e5db1599e59680232912348aef7
# -*- coding:utf-8 -*- from odoo import api, models, fields, _ class hrsalaryRule(models.Model): _inherit = "hr.salary.rule" is_tax_fdfp = fields.Boolean("Est un impôt FDFP")
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a126b1775ffe1ba1aebc288ce17fac8ada0b0756
import cv2 import numpy as np import pandas as pd import tkinter as tk import random from tkinter import * from tkinter import ttk from tkinter import messagebox from tkinter import Scale, Tk from tkinter.ttk import Notebook refPt = [] PtBGR = [] r = [] g = [] b = [] refPt = [] Serial = [] PtBGR = [] r1 = [] r2 = [] r3 = [] r4 = [] rate = [] rate2 = [] rate3 = [] r6 = [] r7 = [] r8 = [] r9 = [] add = [] add2 = [] add3 = [] color_name = [] locate = [] brand = [] boolean = False root = tk.Tk() root.geometry("400x200") root.configure(background="white") def quitScreen(): messagebox.showinfo("collecting data", "點擊視窗開始分析") root.destroy() root2 = Tk() root2.destroy() def getTextInput(): global result, result2 result = text.get(1.0, tk.END + "-1c") result2 = text2.get(1.0, tk.END + "-1c") img = PhotoImage(file="buttons/QJsmall.png") panel = tk.Label(root, image=img) panel.grid(row=0, column=0, columnspan=3) labelmode = tk.Label(root, text="請輸入圖片完整名稱\n ex:104432 w7.jpg", bg="white") labelmode.configure(font=("微軟正黑體", 10)) labelmode.grid(row=1) text = tk.Text(root, width=20, height=1) text.insert("insert", ".jpg") text.configure(font=("微軟正黑體", 10)) text.grid(row=1, column=2) labelmode2 = tk.Label(root, text="請輸入讀取資料庫名稱\n ex:PureColorBig.csv", bg="white") labelmode2.configure(font=("微軟正黑體", 10)) labelmode2.grid(row=2) text2 = tk.Text(root, width=20, height=1) text2.insert("insert", "PureColorBig.csv") text2.configure(font=("微軟正黑體", 10)) text2.grid(row=2, column=2) img_confirm = PhotoImage(file="buttons/confirm.png") img_start = PhotoImage(file="buttons/start.png") btnRead = tk.Button(root, image=img_confirm, text=" ", relief="flat", command=getTextInput) btnRead.grid(row=5, column=1) btnRead2 = tk.Button(root, image=img_start, text=" ", relief="flat", command=quitScreen) btnRead2.grid(row=5, column=2) root.mainloop() def Result_Print(): window = Tk() window.title("分析結果") window.geometry("600x900") frame2 = Frame(window) frame2.pack(fill="both") tablayout = Notebook(frame2) tablayout2 = Notebook(frame2) # 交叉配對 ntab1 = Frame(tablayout2) ntab1.pack(fill="both") for row in range(len(name_n)): for column in range(1): label = Label(ntab1, width=25, height=2, text=name_n[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=column, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) for row in range(len(name_n)): for column in range(1): label = Label(ntab1, width=5, height=2, text="%s" % rate_n[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) for row in range(len(name_n)): for column in range(1): label = Label(ntab1, width=12, height=2, text="% 相似程度", bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=2, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) tablayout2.add(ntab1, text="交叉配對結果") ntab2 = Frame(tablayout2) ntab2.pack(fill="both") for row in range(len(ncol)): for column in range(1): label = Label(ntab2, width=22, height=1, text=ncol[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=0, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) for row in range(len(ncol)): for column in range(1): label = Label(ntab2, width=22, height=1, text=row_nf3[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) tablayout2.add(ntab2, text="配方1") ntab3 = Frame(tablayout2) ntab3.pack(fill="both") for row in range(len(ncol)): for column in range(1): label = Label(ntab3, width=22, height=1, text=ncol[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=0, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) for row in range(len(ncol)): for column in range(1): label = Label(ntab3, width=22, height=1, text=row_nf32[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) tablayout2.add(ntab3, text="配方2") ntab4 = Frame(tablayout2) ntab4.pack(fill="both") for row in range(len(ncol)): for column in range(1): label = Label(ntab4, width=22, height=1, text=ncol[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=0, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) for row in range(len(ncol)): for column in range(1): label = Label(ntab4, width=22, height=1, text=row_nf33[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) tablayout2.add(ntab4, text="配方3") ntab5 = Frame(tablayout2) ntab5.pack(fill="both") for row in range(len(ncol)): for column in range(1): label = Label(ntab5, width=22, height=1, text=ncol[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=0, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) for row in range(len(ncol)): for column in range(1): label = Label(ntab5, width=22, height=1, text=row_nf3[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) ntab1.grid_columnconfigure(column, weight=1) tablayout2.add(ntab5, text="最接近配方") # 顏色分類 tab1 = Frame(tablayout) tab1.pack(fill="both") for row in range(len(name)): for column in range(1): label = Label(tab1, width=25, height=2, text=name[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=column, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) for row in range(len(name)): for column in range(1): label = Label(tab1, width=5, height=2, text="%s" % rate[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) for row in range(len(name)): for column in range(1): label = Label(tab1, width=12, height=2, text="% 相似程度", bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=2, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) tablayout.add(tab1, text="顏色分類結果") tab2 = Frame(tablayout) tab2.pack(fill="both") for row in range(len(col)): for column in range(1): label = Label(tab2, width=22, height=1, text=col[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=0, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) for row in range(len(col)): for column in range(1): label = Label(tab2, width=22, height=1, text=row_df3[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) tablayout.add(tab2, text="配方1") tab3 = Frame(tablayout) tab3.pack(fill="both") for row in range(len(col)): for column in range(1): label = Label(tab3, width=22, height=1, text=col[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=0, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) for row in range(len(col)): for column in range(1): label = Label(tab3, width=22, height=1, text=row_df32[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) tablayout.add(tab3, text="配方2") tab4 = Frame(tablayout) tab4.pack(fill="both") for row in range(len(col)): for column in range(1): label = Label(tab4, width=22, height=1, text=col[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=0, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) for row in range(len(col)): for column in range(1): label = Label(tab4, width=22, height=1, text=row_df33[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) tablayout.add(tab4, text="配方3") tab5 = Frame(tablayout) tab5.pack(fill="both") for row in range(len(col)): for column in range(1): label = Label(tab5, width=22, height=1, text=col[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=0, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) for row in range(len(col)): for column in range(1): label = Label(tab5, width=22, height=1, text=row_text[row], bg="black", fg="white", padx=1, pady=1) label.grid(row=row, column=1, sticky="nsew", padx=1, pady=1) tab1.grid_columnconfigure(column, weight=1) tablayout.add(tab5, text="最接近配方") tablayout.pack() tablayout2.pack() window.mainloop() def CircleCallback(event, x, y, flags, param): n = 8 global \ refPt, \ PtBGR, \ w, \ h, \ Serial, \ r1, \ r2, \ r3, \ r4, \ rate, \ rate2, \ rate3, \ r6, \ r7, \ r8, \ r9, \ add, \ add2, \ add3, \ color, \ b, \ g, \ r, \ df3, \ name, \ rate, \ col, \ row_text global row_df3, row_df32, row_df33, row_text2, row_nf3, row_nf32, nf3, row_nf33, name_n, rate_n, ncol if event == cv2.EVENT_LBUTTONDOWN: # 下面n代表取樣點數 若n越大則越精準一般不建議超過1000 n = 500 for c in range(0, n): c += 1 # 若n改變下面499改為n-1 ranx = random.randint(0, 499) rany = random.randint(0, 499) refPt.append((ranx, rany)) b, g, r = img[ranx, rany] PtBGR.append((b, g, r)) # print(PtBGR[0:n]) b = [x[0] for x in PtBGR] g = [x[1] for x in PtBGR] r = [x[2] for x in PtBGR] if len(refPt) == n: BAvr = round(sum(b[0:n]) / n) GAvr = round(sum(g[0:n]) / n) RAvr = round(sum(r[0:n]) / n) SumRGB = BAvr + GAvr + RAvr SumAvr = round(SumRGB / 3) color_def(BAvr, GAvr, RAvr) color_name.append(color) AvrRGB = {"R": RAvr, "G": GAvr, "B": BAvr, "Sum": SumRGB, "Avr": SumAvr, "color": color_name} df_test = pd.DataFrame(AvrRGB, index=[0]) dfread = pd.read_csv(".data base\\%s" % (result2)) dfread["A"] = round((dfread["R"] + dfread["G"] + dfread["B"]) / 3) dfread["S"] = dfread["R"] + dfread["G"] + dfread["B"] # 交叉比對法 nf = pd.DataFrame(list(zip(r, g, b)), columns=["R", "G", "B"]) nfread = dfread[["Serial no", "R", "G", "B"]] loan = pd.merge(nf, nfread) group = loan.groupby("Serial no") Newnf = group.count() Newnf["P"] = round((Newnf["R"] / Newnf["R"].sum()) * 100) Newnf = Newnf.sort_values(by=["R"], ascending=False) Rate = Newnf["P"].tolist() Newnf.columns = [" ".join(col).strip() for col in Newnf.columns.values] nf2 = pd.DataFrame(Newnf.to_records()) nf2 = nf2.head(5) print(nf2) if len(nf2["Serial no"]) == 0: i = 0 j = 0 k = 0 elif len(nf2["Serial no"]) == 1: i = nf2.at[0, "Serial no"] j = 0 k = 0 elif len(nf2["Serial no"]) == 2: i = nf2.at[0, "Serial no"] j = nf2.at[1, "Serial no"] k = 0 else: i = nf2.at[0, "Serial no"] j = nf2.at[1, "Serial no"] k = nf2.at[2, "Serial no"] print(k) nf3 = dfread.loc[(dfread["Serial no"] == i)].head(1) nf4 = dfread.loc[(dfread["Serial no"] == j)].head(1) nf5 = dfread.loc[(dfread["Serial no"] == k)].head(1) nf3 = nf3.drop(["R", "G", "B", "color", "A", "S"], axis=1) nf4 = nf4.drop(["R", "G", "B", "color", "A", "S"], axis=1) nf5 = nf5.drop(["R", "G", "B", "color", "A", "S"], axis=1) nf = pd.concat([nf3, nf4, nf5]) nf.to_csv(".data base\\test_result2.csv", index=False, encoding="utf_8_sig") print(nf) ncol = list(nf.columns) if len(nf2["Serial no"]) == 0: root = tk.Tk() root.withdraw() messagebox.showinfo("失敗", "未找到符合資料") elif len(nf2["Serial no"]) == 1: row_nf3 = nf3.iloc[0].tolist() row_nf32 = ["x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x"] row_nf33 = ["x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x"] elif len(nf2["Serial no"]) == 2: row_nf3 = nf3.iloc[0].tolist() row_nf32 = nf4.iloc[0].tolist() row_nf33 = ["x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "x"] else: row_nf3 = nf3.iloc[0].tolist() row_nf32 = nf4.iloc[0].tolist() print(row_nf32) row_nf33 = nf5.iloc[0].tolist() name_n = nf["Serial no"].tolist() rate_n = Rate # 顏色分類法 # (可以改)當需要寬鬆一點的比對，刪除下面一段的上下兩個''' """ newdf1=dfread.loc[(dfread['color']==color)|(dfread['A']==SumAvr)] newdf2=dfread.loc[(dfread['S']<=(SumRGB+2))&(dfread['S']>=(SumRGB-2))] newdf=pd.concat([newdf1, newdf2]) """ # (可以改)當需要嚴格一點的比對，刪除下面一段的上下兩個''' """ newdf=dfread.loc[(dfread['A']==SumAvr)|(dfread['S']==SumRGB)] newdf=newdf.loc[(newdf['color']==color)] """ # 並在下面一行的開頭加上# newdf = dfread.loc[(dfread["color"] == color) | (dfread["A"] == SumAvr) | (dfread["S"] == SumRGB)] newdf.insert(1, "Rdif", newdf[["R"]].add(-RAvr)) newdf.insert(2, "Gdif", newdf[["G"]].add(-GAvr)) newdf.insert(3, "Bdif", newdf[["B"]].add(-BAvr)) newdf.insert(4, "Adif", abs(newdf[["A"]].add(-SumAvr))) newdf.insert(5, "Sdif", abs(newdf[["S"]].add(-SumRGB))) df = newdf.sort_values(by=["Sdif", "Adif"], ascending=True).head(100) df.insert(1, "dalta", abs(df["Rdif"] + df["Gdif"] + df["Bdif"])) df = df.sort_values(by=["dalta"], ascending=True) data = df[["Serial no", "color"]] group = data.groupby("Serial no") datacount = group.count() df = df.merge(datacount, left_on="Serial no", right_index=True) df = df.sort_values(by=["color_y"], ascending=False) df3 = df.drop_duplicates("Serial no", keep="first", inplace=False).head() print(df3) df3.to_csv(".data base\\test_result.csv", index=False, encoding="utf_8_sig") if df3.empty == True: root = tk.Tk() root.withdraw() messagebox.showinfo("失敗", "未找到符合資料") elif len(df3) <= 2: root = tk.Tk() root.withdraw() messagebox.showinfo("失敗", "只找到少數資料\n 已存在test_result") else: Zero = df3.loc[(df3["Rdif"] == 0) & (df3["Gdif"] == 0) & (df3["Bdif"] == 0)] Zero = Zero.head(3) if Zero.empty == False: Zero = Zero.drop( ["R", "G", "B", "dalta", "Rdif", "Gdif", "Bdif", "A", "S", "Adif", "Sdif", "color_x", "color_y"], axis=1, ) name = df3["Serial no"].tolist() rate = df3["color_y"].tolist() col = list(Zero.columns) row_text = Zero.iloc[0].tolist() df3 = df3.drop( ["R", "G", "B", "dalta", "Rdif", "Gdif", "Bdif", "A", "S", "Adif", "Sdif", "color_x", "color_y"], axis=1, ) row_df3 = df3.iloc[0].tolist() row_df32 = df3.iloc[1].tolist() row_df33 = df3.iloc[2].tolist() Result_Print() print("0") print(Zero) else: filtdf = df3.loc[(df3["A"] >= SumAvr)] filtdf = filtdf.sort_values(by=["Rdif", "Gdif", "Bdif"]).head() Neg_filtdf = df3.loc[(df3["A"] < SumAvr)] Neg_filtdf = Neg_filtdf.sort_values(by=["Rdif", "Gdif", "Bdif"]).head() if Neg_filtdf.empty == True and filtdf.empty == True: root = tk.Tk() root.withdraw() messagebox.showinfo("失敗", "未找到符合資料") else: filtdf = filtdf.drop( [ "R", "G", "B", "dalta", "Rdif", "Gdif", "Bdif", "A", "S", "Adif", "Sdif", "color_x", "color_y", ], axis=1, ) name = df3["Serial no"].tolist() rate = df3["color_y"].tolist() col = list(filtdf.columns) row_text = filtdf.iloc[0].tolist() df3 = df3.drop( [ "R", "G", "B", "dalta", "Rdif", "Gdif", "Bdif", "A", "S", "Adif", "Sdif", "color_x", "color_y", ], axis=1, ) row_df3 = df3.iloc[0].tolist() row_df32 = df3.iloc[1].tolist() row_df33 = df3.iloc[2].tolist() Result_Print() print("最接近的為1", filtdf.head(1)) def color_def(BAvr, GAvr, RAvr): global color if abs(int(BAvr) - int(GAvr)) <= 1 and abs(int(BAvr) - int(RAvr)) <= 1: color = "White" return color elif BAvr >= GAvr and BAvr >= RAvr: if BAvr - GAvr > 3 and BAvr - RAvr >= 3: color = "Blue" return color elif BAvr - GAvr < 3: color = "Cyan" return color else: color = "Purple" return color elif GAvr >= RAvr and GAvr >= BAvr: if GAvr - RAvr > 3 or GAvr - BAvr > 3: color = "Green" return color elif GAvr - RAvr < 3: color = "Yellow" return color else: color = "Cyan" return color elif RAvr >= GAvr and RAvr >= BAvr: if RAvr - GAvr >= 3 and RAvr - BAvr >= 3: color = "Red" return color elif RAvr - GAvr < 3: color = "Yellow" return color else: color = "Purple" return color else: color = "White" # img=cv2.imdecode(np.fromfile(r"D:\桌面\JA Material\JA-material\pure\%s" % (result),dtype=np.uint8),-1) img = cv2.imdecode(np.fromfile(r".pure\%s" % (result), dtype=np.uint8), -1) cv2.namedWindow("mouse_callback") # bind the callback function to window cv2.setMouseCallback("mouse_callback", CircleCallback) def main(): while True: cv2.imshow("mouse_callback", img) if cv2.waitKey(20) == 27: break cv2.destroyAllWindows() if __name__ == "__main__": main()
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dca36de5556b120b8b93eac0ad7b971ad735d907
import numpy as np def GradientDescent(f, gradf, x0, epsilon, num_iter, line_search, disp=False, callback=None, **kwargs): x = x0.copy() iteration = 0 opt_arg = {"f": f, "grad_f": gradf} for key in kwargs: opt_arg[key] = kwargs[key] while True: gradient = -gradf(x) alpha = line_search(x, gradient, **opt_arg) x = x + alpha * gradient if callback is not None: callback(x) iteration += 1 if disp: print("Current function val =", f(x)) print("Current gradient norm = ", np.linalg.norm(gradf(x))) if np.linalg.norm(gradf(x)) < epsilon: break if iteration >= num_iter: break res = {"x": x, "num_iter": iteration, "tol": np.linalg.norm(gradf(x))} return res def backtracking(x, descent_dir, **kwargs): f = kwargs["f"] grad_f = kwargs["grad_f"] if kwargs["method"] == "Armijo": beta1 = kwargs["beta1"] rho = kwargs["rho"] alpha = 1 while f(x + alpha * descent_dir) >= f(x) + beta1 * alpha * grad_f(x).dot(descent_dir) or np.isnan( f(x + alpha * descent_dir) ): alpha *= rho return alpha
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import torch import torch.nn as nn import torch.nn.functional as F class Net(torch.nn.Module): def __init__(self, layer_sizes=[256, 128, 2], dropout_prob=None, device=None): super(Net, self).__init__() self.device = device if dropout_prob is not None and dropout_prob > 0.5: print("Are you sure dropout_prob is supposed to be greater than 0.5?") # Load Roberta self.roberta = torch.hub.load("pytorch/fairseq", "roberta.base", pretrained=True) for param in self.roberta.parameters(): param.requires_grad = False self.roberta.eval() # Load ResNet resnet_full = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True) self.resnet = torch.nn.Sequential(*list(resnet_full.children())[:-1]) # for param in self.resnet.parameters(): # param.requires_grad = False # self.resnet.eval() # self.lstm = nn.LSTM(input_size=768, hidden_size=768 * 2) # self.lstm.eval() # Layers self.bns = nn.ModuleList() self.fcs = nn.ModuleList() self.drops = None if dropout_prob is None else nn.ModuleList() prev_size = 2 * 512 + 2 * 768 + 2 * 10 + 2 * 2 for i, size in enumerate(layer_sizes): self.bns.append(nn.BatchNorm1d(prev_size)) self.fcs.append(nn.Linear(prev_size, size)) if dropout_prob is not None: self.drops.append(nn.Dropout(p=dropout_prob)) prev_size = size def forward(self, inputs): first_images = inputs["image1"].to(self.device) first_text = inputs["text1"] first_length = inputs["length1"].to(self.device) first_categories = inputs["categories1"].to(self.device) first_days_posted = inputs["days_posted1"].to(self.device) second_images = inputs["image2"].to(self.device) second_text = inputs["text2"] second_length = inputs["length2"].to(self.device) second_categories = inputs["categories2"].to(self.device) second_days_posted = inputs["days_posted2"].to(self.device) # Resnet image_tensor_one = self.resnet.forward(first_images) image_tensor_two = self.resnet.forward(second_images) # Roberta text_features1 = torch.Tensor() text_features2 = torch.Tensor() text_features1 = text_features1.to(self.device) text_features2 = text_features2.to(self.device) for text in first_text: first_tokens = self.roberta.encode(text)[:512] features = self.roberta.extract_features(first_tokens) feature_means = torch.mean(features, dim=1) # features = torch.reshape(features, (-1, 1,768)) # output, (hn, cn) = self.lstm(features) # cn = torch.reshape(cn, (1, 768 * 2)) text_features1 = torch.cat([text_features1, feature_means]) for text in second_text: second_tokens = self.roberta.encode(text)[:512] features = self.roberta.extract_features(second_tokens) # print("DIMENSION OF FEATURES ", features.shape) feature_means = torch.mean(features, dim=1) # features = torch.reshape(features, (-1, 1,768)) # output, (hn, cn) = self.lstm(features) # cn = torch.reshape(cn, (1, 768 * 2)) # print("DIMENSION OF FEATURES ", features.shape) text_features2 = torch.cat([text_features2, feature_means]) # Concatenated tensor concat_tensor = torch.cat((image_tensor_one, image_tensor_two), 1) concat_tensor = torch.squeeze(concat_tensor) concat_tensor = torch.cat((text_features1, text_features2, concat_tensor), 1) additional_features = torch.cat( [ torch.reshape(first_length, (-1, 1)), torch.reshape(second_length, (-1, 1)), torch.reshape(first_days_posted, (-1, 1)), torch.reshape(second_days_posted, (-1, 1)), ], dim=1, ) concat_tensor = torch.cat( [ concat_tensor, additional_features.float(), first_categories.float(), second_categories.float(), ], dim=1, ) x = concat_tensor zipped_layers = ( zip(self.bns, self.fcs, [None] * len(self.bns)) if self.drops is None else zip(self.bns, self.fcs, self.drops) ) for i, (bn, fc, drop) in enumerate(zipped_layers): x = bn(x) if drop is not None: x = drop(x) if i == len(self.bns) - 1: x = fc(x) else: x = F.relu(fc(x)) return x
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3479276d4769518aa60dcd4e1bb41a8a1a7d6517
import os from multiprocessing import Pool import glob import click import logging import pandas as pd from src.resampling.resampling import Resampler # Default paths path_in = "data/hecktor_nii/" path_out = "data/resampled/" path_bb = "data/bbox.csv" @click.command() @click.argument("input_folder", type=click.Path(exists=True), default=path_in) @click.argument("output_folder", type=click.Path(), default=path_out) @click.argument("bounding_boxes_file", type=click.Path(), default=path_bb) @click.option("--cores", type=click.INT, default=12, help="The number of workers for parallelization.") @click.option( "--resampling", type=click.FLOAT, nargs=3, default=(1, 1, 1), help="Expect 3 positive floats describing the output " "resolution of the resampling. To avoid resampling " "on one or more dimension a value of -1 can be fed " "e.g. --resampling 1.0 1.0 -1 will resample the x " "and y axis at 1 mm/px and left the z axis untouched.", ) @click.option("--order", type=click.INT, nargs=1, default=3, help="The order of the spline interpolation used to resample") def main(input_folder, output_folder, bounding_boxes_file, cores, resampling, order): """This command line interface allows to resample NIFTI files within a given bounding box contain in BOUNDING_BOXES_FILE. The images are resampled with spline interpolation of degree --order (default=3) and the segmentation are resampled by nearest neighbor interpolation. INPUT_FOLDER is the path of the folder containing the NIFTI to resample. OUTPUT_FOLDER is the path of the folder where to store the resampled NIFTI files. BOUNDING_BOXES_FILE is the path of the .csv file containing the bounding boxes of each patient. """ logger = logging.getLogger(__name__) logger.info("Resampling") if not os.path.exists(output_folder): os.mkdir(output_folder) print("resampling is {}".format(str(resampling))) bb_df = pd.read_csv(bounding_boxes_file) bb_df = bb_df.set_index("PatientID") files_list = [f for f in glob.glob(input_folder + "/**/*.nii.gz", recursive=True)] resampler = Resampler(bb_df, output_folder, order, resampling=resampling) with Pool(cores) as p: p.map(resampler, files_list) if __name__ == "__main__": log_fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" logging.basicConfig(level=logging.INFO, format=log_fmt) logging.captureWarnings(True) main()
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d5beff74e3746c77cbaf6b8233b822ed1a86701e
# Generated by Django 2.2.2 on 2021-01-23 04:11 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ("task", "0022_taskrecycle_create_date"), ] operations = [ migrations.RemoveField( model_name="ansibleextravars", name="playbook", ), migrations.RemoveField( model_name="ansibleplaybook", name="project", ), migrations.DeleteModel( name="CrontabTask", ), migrations.DeleteModel( name="TaskHistory", ), migrations.DeleteModel( name="TaskRecycle", ), migrations.RemoveField( model_name="taskscript", name="project", ), migrations.DeleteModel( name="AnsibleExtravars", ), migrations.DeleteModel( name="AnsiblePlaybook", ), migrations.DeleteModel( name="AnsibleProject", ), migrations.DeleteModel( name="TaskProject", ), migrations.DeleteModel( name="TaskScript", ), ]
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77763f501c6776969d2594f987e5d7ab7d4377fb
import time from PyQt5.QtCore import ( QThread, ) from common import attach_common from database_downloader import DatabaseDownload from ai_list_memorize import MemorizeList from ai_list_morpheme import MorphemeList from ai_list_ngram import NgramList from ai_list_none import NoneList from ai_bot_memorize import MemorizeBot from ai_bot_morpheme import MorphemeBot from ai_bot_ngram import NgramBot from ai_bot_none import NoneBot @attach_common class TalkBotThread(QThread): parent = None bot = None lister = None text_target = "" tokens_start_of_text = [] tokens_of_text = [] def run(self): self.start_database() self.update_bot_type() self.start_main_loop() def start_database(self): if self.config.ENABLED_DOWNLOAD: DatabaseDownload.remove_tempfiles() DatabaseDownload.set_url("") DatabaseDownload.do_download_html() else: DatabaseDownload.do_not_download_html() self.text_target = DatabaseDownload().get_outcome() def update_bot_type(self): parent = self.parent parent.is_bot_ready = False parent.show_bot_not_ready_msg() self.type_bot = parent.type_bot self.select_token_list() self.select_bot() parent.bot = self.bot parent.lister = self.lister parent.is_bot_ready = True parent.show_bot_ready_msg() self.output_bot_type() def output_bot_type(self): parent = self.parent msgs = ( "TalkBot:", " id: {}".format(parent.type_bot), " bot: {}".format(parent.bot.__class__.__name__), " lister: {}".format(parent.lister.__class__.__name__), " tokens: {}".format(str(parent.lister.get_token_list())[:60]), ) for msg in msgs: self.logger.w(msg) def select_token_list(self): if self.type_bot == 0: self.lister = NoneList( num_of_gram=self.config.DISABLE_NGRAM, text_target=self.text_target, ) self.tokens_start_of_text = self.lister.get_starting_token_list() self.tokens_of_text = self.lister.get_token_list() return if self.type_bot == 1: self.lister = NgramList( num_of_gram=3, text_target=self.text_target, ) self.tokens_start_of_text = self.lister.get_starting_token_list() self.tokens_of_text = self.lister.get_token_list() return if self.type_bot == 2: self.lister = MorphemeList( num_of_gram=self.config.DISABLE_NGRAM, text_target=self.text_target, ) self.tokens_start_of_text = self.lister.get_starting_token_list() self.tokens_of_text = self.lister.get_token_list() return if self.type_bot == 3: self.lister = MemorizeList( num_of_gram=self.config.DISABLE_NGRAM, text_target=self.text_target, ) self.tokens_start_of_text = self.lister.get_starting_token_list() self.tokens_of_text = self.lister.get_token_list() return err = self.type_bot raise Exception(err) def select_bot(self): if self.type_bot == 0: self.bot = NoneBot( starting_token_list=self.tokens_start_of_text, token_list=self.tokens_of_text, ) return if self.type_bot == 1: self.bot = NgramBot( starting_token_list=self.tokens_start_of_text, token_list=self.tokens_of_text, ) return if self.type_bot == 2: self.bot = MorphemeBot( starting_token_list=self.tokens_start_of_text, token_list=self.tokens_of_text, ) return if self.type_bot == 3: self.bot = MemorizeBot( starting_token_list=self.tokens_start_of_text, token_list=self.tokens_of_text, ) return err = self.type_bot raise Exception(err) def start_main_loop(self): parent = self.parent while True: time.sleep(0.2) if parent.is_app_close: break if parent.type_bot != self.type_bot: self.update_bot_type() continue parent.update_bot_msg_to_proper_latest_status() msg = "Stopped the database thread!" self.logger.w(msg) if __name__ == "__main__": from gui_talkbot import MainWindow TestClass = MainWindow import sys from PyQt5.QtWidgets import QApplication qapp = QApplication(sys.argv) window = TestClass() window.show() code = qapp.exec() sys.exit(code)
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d6a760774b45454c959c2932d7b28deee7f81872
# SPDX-License-Identifier: Apache-2.0 # Licensed to the Ed-Fi Alliance under one or more agreements. # The Ed-Fi Alliance licenses this file to you under the Apache License, Version 2.0. # See the LICENSE and NOTICES files in the project root for more information. import json from typing import Dict from pandas import DataFrame, concat, Series from edfi_google_classroom_extractor.mapping.constants import SOURCE_SYSTEM ACTIVITY_TYPE_STATE = "Submission State Change" ACTIVITY_TYPE_GRADE = "Submission Grade Change" def submissions_to_user_submission_activities_dfs( submissions_df: DataFrame, ) -> Dict[str, DataFrame]: """ Convert a Submission API DataFrame to a Dict of UserActivity UDM DataFrames grouped by source system section id. Parameters ---------- submissions_df: DataFrame is a Submission API DataFrame Returns ------- Dict[str, DataFrame] LMS UDM UserActivity DataFrames grouped by source system section id Notes ----- UserActivity DataFrame columns are: ActivityDateTime: The date/time the activity occurred ActivityStatus: The activity status ActivityTimeInMinutes: The total activity time in minutes ActivityType: The type of activity, here "Submission" or "Grade" AssignmentIdentifier: A unique numeric identifier assigned to the assignment Content: Content associated with the activity LMSSectionIdentifier: A unique numeric identifier assigned to the section SourceSystem: The system code or name providing the user activity data SourceSystemIdentifier: A unique number or alphanumeric code assigned to a user activity by the source system LMSUserIdentifier: A unique numeric identifier assigned to the user CreateDate: Date this record was created in the extractor LastModifiedDate: Date this record was last updated in the extractor """ assert "submissionHistory" in submissions_df.columns assert "id" in submissions_df.columns assert "courseId" in submissions_df.columns assert "courseWorkId" in submissions_df.columns # convert json-like submissionHistory string to list of dicts submissions_df["submissionHistory"] = submissions_df["submissionHistory"].apply( lambda json_like: json.loads(json_like.replace("'", '"')) ) submissions_df["AssignmentIdentifier"] = submissions_df[["courseId", "courseWorkId"]].agg("-".join, axis=1) submissions_df = submissions_df[ ["id", "courseId", "courseWorkId", "submissionHistory", "AssignmentIdentifier", "CreateDate", "LastModifiedDate"] ] # explode submissionHistory lists into rows with other columns duplicated history_df = submissions_df.explode(column="submissionHistory") # type: ignore # expand submissionHistory dicts (stateHistory and gradeHistory) into their own columns history_df = history_df["submissionHistory"].apply(Series).merge(history_df, left_index=True, right_index=True, how="outer") history_df.drop(columns=["submissionHistory"], inplace=True) # expand stateHistory (can assume exists, should always have at least one "CREATED" entry) user_submission_df = concat([history_df, history_df["stateHistory"].apply(Series)], axis=1) user_submission_df.dropna(subset=["stateHistory"], inplace=True) # enrich stateHistory user_submission_df["SourceSystemIdentifier"] = "S-" + user_submission_df[ ["courseId", "courseWorkId", "id", "stateTimestamp"] ].agg("-".join, axis=1) user_submission_df = user_submission_df[ [ "SourceSystemIdentifier", "AssignmentIdentifier", "stateTimestamp", "state", "courseId", "actorUserId", "CreateDate", "LastModifiedDate", ] ] user_submission_df = user_submission_df.rename( columns={ "stateTimestamp": "ActivityDateTime", "state": "ActivityStatus", "courseId": "LMSSectionIdentifier", "actorUserId": "LMSUserIdentifier", } ) user_submission_df["ActivityType"] = ACTIVITY_TYPE_STATE # expand gradeHistory if exists if "gradeHistory" in history_df: grade_history_df = concat([history_df, history_df["gradeHistory"].apply(Series)], axis=1) grade_history_df.dropna(subset=["gradeHistory"], inplace=True) # enrich gradeHistory grade_history_df["SourceSystemIdentifier"] = "G-" + grade_history_df[ ["courseId", "courseWorkId", "id", "gradeTimestamp"] ].agg("-".join, axis=1) grade_history_df = grade_history_df[ [ "SourceSystemIdentifier", "AssignmentIdentifier", "gradeTimestamp", "gradeChangeType", "courseId", "actorUserId", "CreateDate", "LastModifiedDate", ] ] grade_history_df = grade_history_df.rename( columns={ "gradeTimestamp": "ActivityDateTime", "gradeChangeType": "ActivityStatus", "courseId": "LMSSectionIdentifier", "actorUserId": "LMSUserIdentifier", } ) grade_history_df["ActivityType"] = ACTIVITY_TYPE_GRADE # combine with stateHistory user_submission_df = user_submission_df.append(grade_history_df) # teacher actions can show up on student histories and vice-versa user_submission_df.drop_duplicates(subset=["SourceSystemIdentifier"], inplace=True) # finish with common columns user_submission_df["ActivityTimeInMinutes"] = "" user_submission_df["Content"] = "" user_submission_df["SourceSystem"] = SOURCE_SYSTEM user_submission_df["SourceCreateDate"] = "" # No create date available from API user_submission_df["SourceLastModifiedDate"] = "" # No modified date available from API # group by section id as a Dict of DataFrames result: Dict[str, DataFrame] = dict(tuple(user_submission_df.groupby(["LMSSectionIdentifier"]))) return result
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63, 64, 74, 75, 77, 78, 8, 11, 17, 19, 21, 23, 29, 33, 35, 37, 39, 41, 43, 45, 50, 59, 61, 4, 7, 10, 13, 15, 19, 20, 25, 26, 27, 28, 4, 9, 10, 20, 21, 23, 25, 30, 31, 41, 42, 44, 45, 8, 10, 12, 14, 20, 26, 36, 38, 12, 14, 24, 34, 36, 12, 14, 19, 28, 30, 12, 14, 19, 21, 12, 14, 20, 22, 24, 12, 14, 19, 25, 27, 12, 14, 20, 24, 26, 12, 14, 18, 30, 32, 8, 10, 4, 6, 4, 9, 10, 20, 21, 23, 25, 30, 31, 41, 42, 44, 45, 51, 52, 8, 16, 18, 12, 14, 19, 28, 30, 32, 40, 48, 50, 12, 14, 19, 21, 23, 31, 37, 39, 12, 14, 20, 22, 24, 26, 27, 29, 36, 46, 48, 12, 14, 19, 25, 27, 29, 30, 32, 36, 46, 48, 8, 10, 4, 6, 4, 9, 10, 20, 21, 23, 25, 33, 37, 39, 41, 45, 50, 51, 55, 56, 61, 4, 6, 13, 19, 26, 29, 36, 4, 7, 9, 14, 21, 22, 25, 33, 34, 36, 37, 8, 14, 15, 22, 23, 25, 27, 34, 36, 43, 44, 46, 47, 55, 56, 58, 60, 65, 72, 75, 80, 81, 87, 90, 95, 96, 97, 98, 8, 14, 15, 22, 23, 25, 26, 30, 32, 33, 39, 42, 47, 54, 57, 65, 66, 70, 71, 8, 10, 13, 17, 23, 30, 8, 14, 15, 22, 23, 25, 27, 33, 39, 49, 51, 53, 55, 56, 58, 60, 66, 67, 74, 75, 77, 78, 12, 15, 21, 23, 25, 27, 33, 37, 39, 41, 43, 45, 47, 49, 54, 63, 65, 8, 11, 14, 17, 19, 23, 24, 29, 30, 31, 32, 8, 14, 15, 22, 23, 25, 27, 33, 34, 41, 42, 44, 45, 12, 14, 16, 18, 24, 30, 40, 42, 16, 18, 28, 38, 40, 16, 18, 23, 32, 34, 16, 18, 23, 29, 33, 35, 16, 18, 24, 26, 28, 16, 18, 23, 29, 31, 16, 18, 24, 28, 30, 16, 18, 22, 34, 36, 12, 14, 8, 10, 8, 14, 15, 22, 23, 25, 27, 33, 34, 41, 42, 44, 45, 51, 52, 12, 20, 22, 16, 18, 23, 32, 34, 36, 44, 52, 54, 16, 18, 23, 29, 33, 35, 37, 45, 51, 53, 16, 18, 24, 26, 28, 30, 31, 33, 40, 50, 52, 16, 18, 23, 29, 31, 33, 34, 36, 40, 50, 52, 12, 14, 8, 10, 8, 14, 15, 22, 23, 25, 27, 35, 39, 41, 43, 47, 52, 53, 57, 58, 60, 63, 8, 10, 18, 23, 29, 36, 8, 13, 14, 24, 25, 27, 29, 34, 35, 45, 46, 48, 49, 55, 56, 61, 62, 69, 70, 72, 73, 4, 6, 14, 22, 26, 31, 34, 37, 45, 50, 55, 59, 64, 65, 68, 70, 4, 9, 10, 20, 21, 23, 24, 28, 29, 39, 40, 46, 49, 55, 61, 71, 74, 82, 83, 87, 88, 4, 6, 13, 18, 25, 33, 4, 9, 10, 20, 21, 23, 25, 33, 39, 46, 48, 50, 53, 4, 9, 10, 20, 21, 23, 25, 32, 34, 36, 39, 4, 9, 10, 20, 21, 23, 25, 31, 37, 39, 41, 48, 49, 55, 4, 9, 10, 20, 21, 23, 25, 31, 37, 41, 43, 45, 48, 49, 51, 54, 61, 66, 76, 81, 85, 4, 9, 10, 20, 21, 23, 25, 31, 35, 47, 49, 51, 54, 55, 57, 60, 69, 74, 84, 89, 93, 4, 6, 12, 15, 23, 26, 29, 31, 36, 39, 44, 50, 4, 11, 12, 17, 18, 21, 22, 32, 33, 35, 40, 41, 46, 47, 51, 52, 62, 63, 65, 66, 73, 76, 77, 79, 86, 96, 98, 101, 4, 11, 18 ]
bd3b1263d7d657fe2edd3c7198f63821a3d1d1e5
import random from . import WaiterInterface class RandomIPv4Waiter(WaiterInterface): """ HostPortWaiter which generates random ipv4 adresses """ def __init__(self, options): self.ports = options["ports"] self.limit_generate = options.get("limit_generate", -1) def generator(self): while self.limit_generate != 0: randomIPv4 = generateRandomIPv4() yield (randomIPv4, self.ports) if self.limit_generate != -1: self.limit_generate -= 1 def generateRandomIPv4(): """ Helper method to generate a random ipv4 adress """ return ".".join(map(str, (random.randint(0, 255) for _ in range(4))))
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d2c31d9c3cc66b43966cfd852582539d4e4bea17
from selenium import webdriver import time import datetime import os import openpyxl as vb from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.common.action_chains import ActionChains def deconnexion(Chrome): """登陆""" """初始化""" global web, actions web = webdriver.Chrome(Chrome) # 公司电脑 # web = webdriver.Chrome(r'D:\python\webdrivers\chromedriver.exe') #自己的电脑 web.maximize_window() web.implicitly_wait(10) # 最大运行时间不超过10秒 web.get("http://www.wjw-cdc.com:8003/user-center-portal/login?redirect=%2Fmain") actions = ActionChains(web) """登录网页""" username = web.find_element_by_xpath("/html/body/div/div/div[1]/div/div[2]/form/div[1]/div/div[1]/input") # 获得账号和密码 password = web.find_element_by_xpath("/html/body/div/div/div[1]/div/div[2]/form/div[2]/div/div[2]/input") username.send_keys("15375429564") password.send_keys("cdc1234cdc") enter = web.find_element_by_xpath("/html/body/div/div/div[1]/div/div[2]/form/div[3]/div/button") enter.click() return 0 def menu_lien(): """跳转页面""" enter_into = web.find_element_by_xpath( "/html/body/div[1]/div/div[2]/section/div/div[1]/ul/li[1]/ul/li[2]/span/div[2]/section/article" ) enter_into.click() return 0 def confirm_area(city, area): """确定区域""" """点击区域""" enter_area = web.find_element_by_xpath("/html/body/div[1]/section/main/div/div[3]/div[1]/span/div/div[1]/input").click() """点击安徽省""" enter_on_on = web.find_element_by_class_name("el-cascader__dropdown") enter_on = enter_on_on.find_element_by_class_name("el-cascader-panel") try: enter_AnHui_on_on = enter_on.find_elements_by_class_name("el-scrollbar") enter_AnHui_on = enter_AnHui_on_on[0].find_element_by_class_name("el-scrollbar__view") except: time.sleep(1) enter_AnHui_on_on = enter_on.find_elements_by_class_name("el-scrollbar") enter_AnHui_on = enter_AnHui_on_on[0].find_element_by_class_name("el-scrollbar__view") enter_AnHui = enter_AnHui_on.find_element_by_tag_name("li") enter_AnHui_down = enter_AnHui.find_element_by_class_name("el-radio__input") web.execute_script("arguments[0].click();", enter_AnHui_down) """选择城市""" enter_on_on = web.find_element_by_class_name("el-cascader__dropdown") enter_on = enter_on_on.find_element_by_class_name("el-cascader-panel") try: enter_city_on_on = enter_on.find_elements_by_class_name("el-scrollbar") enter_city_on = enter_city_on_on[1].find_element_by_class_name("el-cascader-menu__wrap") except: time.sleep(1) enter_city_on_on = enter_on.find_elements_by_class_name("el-scrollbar") enter_city_on = enter_city_on_on[1].find_element_by_class_name("el-cascader-menu__wrap") enter_city = enter_city_on.find_elements_by_tag_name("li") for i in range(len(enter_city)): enter_on_on = web.find_element_by_class_name("el-cascader__dropdown") enter_on = enter_on_on.find_element_by_class_name("el-cascader-panel") enter_city_on_on = enter_on.find_elements_by_class_name("el-scrollbar") enter_city_on = enter_city_on_on[1].find_element_by_class_name("el-cascader-menu__wrap") enter_city = enter_city_on.find_elements_by_tag_name("li") if enter_city[i].text == city: enter_city_down = enter_city[i].find_element_by_class_name("el-radio__input") web.execute_script("arguments[0].click();", enter_city_down) break """选则区县""" enter_on_on = web.find_element_by_class_name("el-cascader__dropdown") enter_on = enter_on_on.find_element_by_class_name("el-cascader-panel") try: enter_area_on_on = enter_on.find_elements_by_class_name("el-scrollbar") enter_area_on = enter_area_on_on[2].find_element_by_class_name("el-cascader-menu__wrap") except: time.sleep(1) enter_area_on_on = enter_on.find_elements_by_class_name("el-scrollbar") enter_area_on = enter_area_on_on[2].find_element_by_class_name("el-cascader-menu__wrap") enter_area = enter_area_on.find_elements_by_tag_name("li") for i in range(len(enter_area)): enter_on_on = web.find_element_by_class_name("el-cascader__dropdown") enter_on = enter_on_on.find_element_by_class_name("el-cascader-panel") enter_area_on_on = enter_on.find_elements_by_class_name("el-scrollbar") enter_area_on = enter_area_on_on[2].find_element_by_class_name("el-cascader-menu__wrap") enter_area = enter_area_on.find_elements_by_tag_name("li") if enter_area[i].text == area: enter_area_down = enter_area[i].find_element_by_class_name("el-radio__input") web.execute_script("arguments[0].click();", enter_area_down) break return 0 def confirm_time_on(excel_time): if type(excel_time) == str: return str(excel_time) elif type(excel_time) == datetime.datetime: excel_time_2 = excel_time.strftime("%Y-%m-%d") return str(excel_time_2) def confirm_tiem(time): """确定时间""" time = confirm_time_on(time) enter_time = web.find_elements_by_class_name("el-range-input") for i in enter_time: i.send_keys(time) return 0 def confirm_cause(cause): """选则症状""" enter_symptom = web.find_element_by_xpath("/html/body/div[1]/section/main/div/div[3]/div[3]/span/div/div[2]/input").click() enter_on = web.find_element_by_class_name("is-multiple") enter_on_1 = enter_on.find_element_by_class_name("el-scrollbar") enter_on_symptom = enter_on_1.find_elements_by_tag_name("li") for i in range(len(enter_on_symptom)): enter_on = web.find_element_by_class_name("is-multiple") enter_on_symptom = enter_on.find_elements_by_tag_name("li") if enter_on_symptom[i].text == cause: enter_on_symptom[i].click() break return 0 def search(): """点击搜索""" enter_search = web.find_element_by_xpath("/html/body/div[1]/section/main/div/div[3]/button[1]").click() return 0 def reset(): """点击重置""" enter_reset = web.find_element_by_xpath("/html/body/div/section/main/div/div[3]/button[2]").click() return 0 def pending(): """待处理""" enter_pending = web.find_element_by_xpath("/html/body/div[1]/section/main/div/div[4]/div/div[1]/div/div/div/div[1]").click() return 0 def accomplish(): """已完成""" enter__accomplish = web.find_element_by_xpath( "/html/body/div[1]/section/main/div/div[4]/div/div[1]/div/div/div/div[3]" ).click() return 0 def download_cas(): """下载病例""" enter_download_cas = web.find_element_by_xpath("/html/body/div[1]/section/main/section/main/div[2]/ul/li[2]").click() enter_download_cas_1 = web.find_element_by_xpath( "/html/body/div[1]/section/main/section/main/div[2]/div/div[2]/div/div[1]/div/button[3]" ).click() return 0 def resetting_excel(cause, clinique, path="D:\林钟\下载"): """重命名病例""" try: files = os.listdir(path) src = path + "\\" + "外呼结果导出表格.xlsx" if cause == "发热伴畏寒|寒战": cause = "发热伴畏寒寒战'" if cause == "畏寒|寒战": cause = "畏寒寒战'" dst = path + "\\" + clinique + "--" + cause + ".xlsx" os.rename(src, dst) except FileExistsError: files = os.listdir(path) src = path + "\\" + "外呼结果导出表格.xlsx" if cause == "发热伴畏寒|寒战": cause = "发热伴畏寒寒战'" if cause == "畏寒|寒战": cause = "畏寒寒战'" dst = path + "\\" + clinique + "--" + cause + ".xlsx" os.remove(dst) os.rename(src, dst) return 0 def pagination(): # 获取当前界面一共有多少条数据 pagination__total = web.find_element_by_xpath("/html/body/div[1]/section/main/div/div[5]/span[1]") a = int(pagination__total.text[2:-2]) return a def search_data(cause, city, area, clinique, excel_time): """查找数据""" ls_2 = [] # 存储最终点击的元素，如果为空则说明没找到。 trlist_table_on = web.find_element_by_class_name("is-scrolling-none") trlist_table = trlist_table_on.find_element_by_class_name("el-table__body") trlist_tr = trlist_table.find_elements_by_tag_name("tr") for row in range(len(trlist_tr)): trlist_table = web.find_element_by_class_name("el-table__body") trlist_tr = trlist_table.find_elements_by_tag_name("tr") trlist_td = trlist_tr[row].find_elements_by_tag_name("td") i = 0 j = 0 ls = [] for col in range(len(trlist_td)): i += 1 if i == 2: ls.append(trlist_td[col].text) elif i == 3: ls.append(trlist_td[col].text) elif i == 7: ls.append(trlist_td[col]) elif i == 9: j = 1 ls.append((trlist_td[col])) trlist_td = trlist_tr[row].find_elements_by_tag_name("td") if ls[0] == cause: if ls[1] == ("安徽省/" + city + "/" + area + "/" + clinique): if j == 0: # ls[2].click() ls_2.append(ls[2]) elif j == 1: # ls[3].click() ls_2.append(ls[3]) return ls_2 def search_data_down(cause, clinique, path): """找到病例后的对病例进行一系列的处理""" """下载病例""" download_cas() """返回上一界面""" web.back() """点击重置""" reset() """点击待完成""" pending() """给病例重命名""" time.sleep(2) try: resetting_excel(cause, clinique, path) except FileNotFoundError: time.sleep(2) resetting_excel(cause, clinique, path) print(clinique + "--" + cause + "已下载完成！") def tourne_page(): enter_tourne_page = web.find_element_by_xpath("/html/body/div[1]/section/main/div/div[5]/button[2]/i").click() return "" def search_data_on(cause, city, area, clinique, excel_time, path): """核心处理流程""" time.sleep(2) number = pagination() """判断待处理下标是否为0""" if number == 0: """点击已完成""" accomplish() time.sleep(2) number_accmplish_1 = pagination() """判断已完成的下标是否为0""" if number_accmplish_1 == 0: """如果为0下载失败""" download_revers.append(clinique + "--" + cause + " 下载失败！") else: """不为0判断当前界面是否只有20条数据""" if 0 < number_accmplish_1 <= 20: """只有20条数据查找数据""" accomplish_search_data = search_data(cause, city, area, clinique, excel_time) if len(accomplish_search_data) == 0: """如果没找到结束""" download_revers.append(clinique + "--" + cause + " 下载失败！") reset() else: """如果找到则点击""" accomplish_search_data[0].click() search_data_down(cause, clinique, path) elif 20 < number_accmplish_1 <= 40: """多于20条数据""" accomplish_search_data = search_data(cause, city, area, clinique, excel_time) """判断第一页有没有查到""" if len(accomplish_search_data) == 0: """如果没找到翻页""" tourne_page() accomplish_search_data = search_data(cause, city, area, clinique, excel_time) """判断翻页后有没有找到""" if len(accomplish_search_data) == 0: """如果没找到存入列表""" download_revers.append(clinique + "--" + cause + " 下载失败！") reset() else: """找到后点击""" accomplish_search_data[0].click() search_data_down(cause, clinique, path) else: download_revers.append(clinique + "--" + cause + " 下载失败！") reset() else: """判断待处理里是否小于20条数据""" if 0 < number <= 20: """如果小于进行查找""" pending__search_data = search_data(cause, city, area, clinique, excel_time) """判断有没有找到""" if len(pending__search_data) == 0: """没找到""" """点击已完成""" accomplish() time.sleep(2) number_accmplish_1 = pagination() """判断已完成的下标是否为0""" if number_accmplish_1 == 0: """如果为0下载失败""" download_revers.append(clinique + "--" + cause + " 下载失败！") else: """不为0判断当前界面是否只有20条数据""" if 0 < number_accmplish_1 <= 20: """只有20条数据查找数据""" accomplish_search_data = search_data(cause, city, area, clinique, excel_time) if len(accomplish_search_data) == 0: """如果没找到结束""" download_revers.append(clinique + "--" + cause + " 下载失败！") reset() else: """如果找到则点击""" accomplish_search_data[0].click() search_data_down(cause, clinique, path) elif 20 < number_accmplish_1 <= 40: """多于20条数据""" accomplish_search_data = search_data(cause, city, area, clinique, excel_time) """判断第一页有没有查到""" if len(accomplish_search_data) == 0: """如果没找到翻页""" tourne_page() accomplish_search_data = search_data(cause, city, area, clinique, excel_time) """判断翻页后有没有找到""" if len(accomplish_search_data) == 0: """如果没找到存入列表""" download_revers.append(clinique + "--" + cause + " 下载失败！") reset() else: """找到后点击""" accomplish_search_data[0].click() search_data_down(cause, clinique, path) else: download_revers.append(clinique + "--" + cause + " 下载失败！") reset() else: """找到了""" pending__search_data[0].click() search_data_down(cause, clinique, path) # elif 20< number <= 40: # pending__search_data = search_data(cause, city, area, clinique, excel_time) # """判断有没有找到""" # if len(pending__search_data) == 0: if __name__ == "__main__": download_revers = [] """初始化""" url = input("请输入文件的绝对路径：") # 文件路径 path = "D:\林钟\下载" # 下载路径 Chrome = r"D:\PYthon\webdrivers\chromedriver.exe" # 驱动路径 time1 = time.time() """登录页面""" deconnexion(Chrome) print("已登陆") menu_lien() print("已跳转") """读取表格""" excel = vb.load_workbook(url) sheet = excel["1-每日监控告警明细"] subscript = 1 for i in sheet.iter_rows(min_row=2, max_row=101, max_col=1): for cell in i: if cell.value in ["3", 3, "高"]: """初始化数值""" cause = sheet["I" + str(cell.row)].value city = sheet["E" + str(cell.row)].value area = sheet["F" + str(cell.row)].value clinique = sheet["G" + str(cell.row)].value excel_time = sheet["D" + str(cell.row)].value """搜索""" try: confirm_area(city, area) confirm_tiem(excel_time) confirm_cause(cause) search() except: try: web.refresh() # 刷新方法 refresh print("刷新成功") confirm_area(city, area) confirm_tiem(excel_time) confirm_cause(cause) search() except Exception as e: print("刷新失败！", format(e)) """查找数据""" search_data_on(cause, city, area, clinique, excel_time, path) """打印最终结果""" print("") print("<-----------下面是下载失败的----------->") for i in download_revers: print(i) print("已全部下载完毕") time2 = time.time() print("用时：{:.2f} 秒".format(time2 - time1))
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2692, 51, 1828, 100, 2559, 100, 1186, 100, 16735, 4229, 935, 52, 1561, 52, 526, 96, 54, 11542, 1923, 52, 1850, 52, 526, 52, 526, 96, 56, 11542, 1654, 96, 54, 48958, 2956, 365, 303, 461, 244, 53, 499, 222, 610, 5672, 2284, 303, 1547, 22515, 7773, 4633, 3012, 303, 6621, 100, 4558, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 16735, 4229, 935, 52, 1561, 52, 526, 52, 1923, 52, 1850, 52, 526, 52, 526, 96, 56, 11542, 1654, 96, 55, 48958, 2956, 365, 303, 461, 244, 53, 499, 222, 610, 11354, 2284, 303, 1547, 17684, 11474, 3012, 303, 6621, 100, 9678, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 16735, 4229, 935, 52, 1561, 52, 526, 96, 54, 11542, 1923, 52, 1850, 52, 526, 52, 526, 96, 57, 11542, 526, 52, 526, 96, 54, 11542, 526, 52, 526, 52, 526, 52, 526, 96, 54, 48958, 2956, 365, 303, 461, 244, 53, 499, 222, 610, 40798, 2284, 303, 1547, 9125, 17030, 3012, 303, 6621, 523, 348, 998, 2883, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 16735, 45, 310, 2450, 935, 52, 1561, 52, 526, 96, 54, 11542, 1923, 52, 1850, 52, 526, 52, 526, 96, 57, 11542, 526, 52, 526, 96, 54, 11542, 526, 52, 526, 52, 526, 52, 526, 96, 56, 12035, 303, 6824, 2956, 365, 303, 461, 244, 53, 499, 222, 610, 5844, 100, 11258, 2284, 303, 1547, 20856, 35211, 7229, 3012, 303, 6621, 100, 5000, 100, 11258, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 16735, 4229, 935, 52, 1561, 52, 526, 96, 54, 11542, 1923, 52, 1850, 52, 1923, 52, 1850, 52, 526, 96, 55, 11542, 357, 52, 393, 96, 55, 48958, 2956, 365, 303, 6621, 100, 5000, 100, 11258, 100, 54, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 16735, 45, 310, 2450, 935, 52, 1561, 52, 526, 96, 54, 11542, 1923, 52, 1850, 52, 1923, 52, 1850, 52, 526, 96, 55, 11542, 526, 52, 526, 96, 55, 11542, 526, 52, 526, 96, 54, 11542, 526, 52, 1654, 96, 56, 12035, 303, 6824, 2956, 365, 303, 461, 244, 53, 499, 222, 610, 5672, 1073, 100, 15165, 45, 3192, 49, 37138, 2699, 49, 1536, 366, 73, 3509, 33253, 34256, 97, 20856, 10320, 303, 1547, 7773, 36970, 35211, 7229, 3012, 303, 1614, 63, 310, 2293, 299, 2355, 51, 26730, 45, 1005, 46, 310, 1581, 299, 1536, 494, 2724, 1508, 494, 332, 10514, 31851, 14009, 45031, 4461, 9745, 51, 22852, 39, 310, 434, 5758, 630, 332, 5312, 10239, 3154, 150, 8261, 261, 3022, 264, 129, 3022, 264, 24803, 582, 343, 5758, 299, 332, 5312, 10239, 3154, 150, 8261, 261, 3022, 264, 3022, 264, 24803, 7368, 310, 434, 5758, 630, 332, 8261, 261, 3022, 264, 129, 3022, 264, 24803, 582, 343, 5758, 299, 332, 8261, 261, 3022, 264, 3022, 264, 24803, 7368, 310, 7125, 299, 1536, 494, 2724, 1508, 494, 37138, 2699, 494, 7931, 39, 494, 5758, 494, 4956, 22852, 39, 310, 2355, 51, 13287, 45, 1634, 49, 7125, 46, 303, 2959, 2050, 7544, 943, 63, 310, 2293, 299, 2355, 51, 26730, 45, 1005, 46, 310, 1581, 299, 1536, 494, 2724, 1508, 494, 332, 10514, 31851, 14009, 45031, 4461, 9745, 51, 22852, 39, 310, 434, 5758, 630, 332, 5312, 10239, 3154, 150, 8261, 261, 3022, 264, 129, 3022, 264, 24803, 582, 343, 5758, 299, 332, 5312, 10239, 3154, 150, 8261, 261, 3022, 264, 3022, 264, 24803, 7368, 310, 434, 5758, 630, 332, 8261, 261, 3022, 264, 129, 3022, 264, 24803, 582, 343, 5758, 299, 332, 8261, 261, 3022, 264, 3022, 264, 24803, 7368, 310, 7125, 299, 1536, 494, 2724, 1508, 494, 37138, 2699, 494, 7931, 39, 494, 5758, 494, 4956, 22852, 39, 310, 2355, 51, 2564, 45, 6295, 46, 310, 2355, 51, 13287, 45, 1634, 49, 7125, 46, 465, 461, 244, 53, 499, 222, 610, 19611, 2284, 244, 607, 13614, 12380, 39197, 2288, 15107, 3052, 48157, 11316, 4857, 303, 19611, 523, 3025, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 16735, 4229, 935, 52, 1561, 52, 526, 96, 54, 11542, 1923, 52, 1850, 52, 526, 52, 526, 96, 58, 11542, 681, 96, 54, 25743, 303, 331, 299, 648, 45, 13252, 523, 3025, 51, 897, 96, 55, 6012, 55, 1156, 303, 461, 331, 499, 222, 610, 3252, 100, 624, 45, 3192, 49, 11353, 49, 5978, 49, 37138, 2699, 49, 32129, 100, 1021, 731, 303, 1547, 36528, 4857, 3012, 303, 12906, 100, 55, 299, 1627, 244, 607, 244, 18262, 42449, 22515, 1096, 14577, 1140, 7558, 23688, 8987, 19778, 9417, 26338, 1469, 303, 573, 687, 100, 1292, 100, 286, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 842, 100, 444, 459, 316, 50, 5828, 318, 50, 4129, 678, 303, 573, 687, 100, 1292, 299, 573, 687, 100, 1292, 100, 286, 51, 1828, 100, 2559, 100, 1186, 100, 842, 100, 444, 459, 361, 50, 1292, 523, 1561, 678, 303, 573, 687, 100, 378, 299, 573, 687, 100, 1292, 51, 1828, 100, 7620, 100, 1186, 100, 2159, 100, 444, 459, 378, 678, 303, 456, 2720, 347, 2189, 45, 1688, 45, 378, 687, 100, 378, 8485, 310, 573, 687, 100, 1292, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 842, 100, 444, 459, 361, 50, 1292, 523, 1561, 678, 310, 573, 687, 100, 378, 299, 573, 687, 100, 1292, 51, 1828, 100, 7620, 100, 1186, 100, 2159, 100, 444, 459, 378, 678, 310, 573, 687, 100, 689, 299, 573, 687, 100, 378, 96, 661, 1015, 1828, 100, 7620, 100, 1186, 100, 2159, 100, 444, 459, 689, 678, 310, 613, 299, 244, 53, 310, 614, 299, 244, 53, 310, 12906, 299, 1627, 310, 456, 956, 347, 2189, 45, 1688, 45, 378, 687, 100, 689, 8485, 343, 613, 1475, 244, 54, 343, 434, 613, 630, 244, 55, 63, 419, 12906, 51, 1713, 45, 378, 687, 100, 689, 96, 770, 1015, 897, 46, 343, 4378, 613, 630, 244, 56, 63, 419, 12906, 51, 1713, 45, 378, 687, 100, 689, 96, 770, 1015, 897, 46, 343, 4378, 613, 630, 244, 60, 63, 419, 12906, 51, 1713, 45, 378, 687, 100, 689, 96, 770, 1156, 343, 4378, 613, 630, 244, 62, 63, 419, 614, 299, 244, 54, 419, 12906, 51, 1713, 1181, 378, 687, 100, 689, 96, 770, 5310, 343, 573, 687, 100, 689, 299, 573, 687, 100, 378, 96, 661, 1015, 1828, 100, 7620, 100, 1186, 100, 2159, 100, 444, 459, 689, 678, 310, 434, 12906, 96, 53, 98, 630, 5758, 63, 343, 434, 12906, 96, 54, 98, 630, 2903, 9890, 3095, 159, 23698, 5694, 494, 11353, 494, 13198, 494, 5978, 494, 13198, 494, 37138, 2699, 731, 419, 434, 614, 630, 244, 53, 63, 547, 607, 12906, 96, 55, 1015, 2956, 365, 547, 12906, 100, 55, 51, 1713, 45, 3259, 96, 55, 1156, 419, 4378, 614, 630, 244, 54, 63, 547, 607, 12906, 96, 56, 1015, 2956, 365, 547, 12906, 100, 55, 51, 1713, 45, 3259, 96, 56, 1156, 303, 461, 12906, 100, 55, 499, 222, 610, 3252, 100, 624, 100, 1772, 45, 3192, 49, 37138, 2699, 49, 1536, 731, 303, 1547, 26338, 35211, 7229, 28674, 4317, 35211, 7229, 9966, 2288, 45417, 1096, 11474, 3012, 303, 1547, 20856, 35211, 7229, 3012, 303, 5844, 100, 11258, 365, 303, 1547, 8763, 4518, 2288, 39197, 3012, 303, 2692, 51, 994, 365, 303, 1547, 22515, 7773, 4633, 3012, 303, 5672, 365, 303, 1547, 22515, 17684, 17030, 3012, 303, 11354, 365, 303, 1547, 12862, 35211, 7229, 7773, 36970, 3012, 303, 1153, 51, 7860, 45, 55, 46, 303, 1614, 63, 310, 5672, 1073, 100, 15165, 45, 3192, 49, 37138, 2699, 49, 1536, 46, 303, 2959, 2050, 28010, 63, 310, 1153, 51, 7860, 45, 55, 46, 310, 5672, 1073, 100, 15165, 45, 3192, 49, 37138, 2699, 49, 1536, 46, 303, 1489, 45, 27764, 2699, 494, 7931, 39, 494, 5758, 494, 332, 9125, 20856, 17030, 11251, 678, 499, 222, 610, 391, 38363, 100, 1655, 2284, 303, 6621, 100, 471, 38363, 100, 1655, 299, 2692, 51, 1828, 100, 2559, 100, 1186, 100, 16735, 4229, 935, 52, 1561, 52, 526, 96, 54, 11542, 1923, 52, 1850, 52, 526, 52, 526, 96, 58, 11542, 1654, 96, 55, 11542, 110, 1912, 2956, 365, 303, 461, 3347, 499, 222, 610, 3252, 100, 624, 100, 286, 45, 3192, 49, 11353, 49, 5978, 49, 37138, 2699, 49, 32129, 100, 1021, 49, 1536, 731, 303, 1547, 43148, 11474, 39781, 3012, 303, 1153, 51, 7860, 45, 55, 46, 303, 1470, 299, 19611, 365, 303, 1547, 17829, 17684, 11474, 4316, 7044, 9271, 3397, 53, 3012, 303, 434, 1470, 630, 244, 53, 63, 310, 1547, 22515, 9125, 17030, 3012, 310, 40798, 365, 310, 1153, 51, 7860, 45, 55, 46, 310, 1470, 100, 4755, 336, 2883, 100, 54, 299, 19611, 365, 310, 1547, 17829, 9125, 17030, 1096, 4316, 7044, 9271, 3397, 53, 3012, 310, 434, 1470, 100, 4755, 336, 2883, 100, 54, 630, 244, 53, 63, 343, 1547, 7558, 3397, 53, 20856, 14236, 3012, 343, 5844, 100, 287, 5257, 51, 1713, 45, 27764, 2699, 494, 7931, 39, 494, 5758, 494, 332, 244, 20856, 14236, 11251, 678, 310, 832, 63, 343, 1547, 3065, 3397, 53, 17829, 12380, 39197, 9271, 27436, 55, 53, 11316, 4857, 3012, 343, 434, 244, 53, 350, 1470, 100, 4755, 336, 2883, 100, 54, 2527, 244, 55, 53, 63, 419, 1547, 27436, 55, 53, 11316, 4857, 36528, 4857, 3012, 419, 40798, 100, 1925, 100, 624, 299, 3252, 100, 624, 45, 3192, 49, 11353, 49, 5978, 49, 37138, 2699, 49, 32129, 100, 1021, 46, 419, 434, 2095, 45, 348, 998, 2883, 100, 1925, 100, 624, 46, 630, 244, 53, 63, 547, 1547, 7558, 9417, 26338, 24975, 3012, 547, 5844, 100, 287, 5257, 51, 1713, 45, 27764, 2699, 494, 7931, 39, 494, 5758, 494, 332, 244, 20856, 14236, 11251, 678, 547, 5672, 365, 419, 832, 63, 547, 1547, 7558, 26338, 8987, 22515, 3012, 547, 40798, 100, 1925, 100, 624, 96, 53, 1015, 2956, 365, 547, 3252, 100, 624, 100, 1772, 45, 3192, 49, 37138, 2699, 49, 1536, 46, 343, 4378, 244, 55, 53, 350, 1470, 100, 4755, 336, 2883, 100, 54, 2527, 244, 57, 53, 63, 419, 1547, 6525, 5024, 55, 53, 11316, 4857, 3012, 419, 40798, 100, 1925, 100, 624, 299, 3252, 100, 624, 45, 3192, 49, 11353, 49, 5978, 49, 37138, 2699, 49, 32129, 100, 1021, 46, 419, 1547, 17829, 26590, 9438, 3052, 11127, 8010, 4329, 3012, 419, 434, 2095, 45, 348, 998, 2883, 100, 1925, 100, 624, 46, 630, 244, 53, 63, 547, 1547, 7558, 9417, 26338, 34496, 9438, 3012, 547, 391, 38363, 100, 1655, 365, 547, 40798, 100, 1925, 100, 624, 299, 3252, 100, 624, 45, 3192, 49, 11353, 49, 5978, 49, 37138, 2699, 49, 32129, 100, 1021, 46, 547, 1547, 17829, 34496, 9438, 5054, 3052, 11127, 26338, 3012, 547, 434, 2095, 45, 348, 998, 2883, 100, 1925, 100, 624, 46, 630, 244, 53, 63, 733, 1547, 7558, 9417, 26338, 5487, 5039, 12150, 3012, 733, 5844, 100, 287, 5257, 51, 1713, 45, 27764, 2699, 494, 7931, 39, 494, 5758, 494, 332, 244, 20856, 14236, 11251, 678, 733, 5672, 365, 547, 832, 63, 733, 1547, 26338, 5054, 22515, 3012, 733, 40798, 100, 1925, 100, 624, 96, 53, 1015, 2956, 365, 733, 3252, 100, 624, 100, 1772, 45, 3192, 49, 37138, 2699, 49, 1536, 46, 343, 832, 63, 419, 5844, 100, 287, 5257, 51, 1713, 45, 27764, 2699, 494, 7931, 39, 494, 5758, 494, 332, 244, 20856, 14236, 11251, 678, 419, 5672, 365, 303, 832, 63, 310, 1547, 17829, 17684, 11474, 9407, 9271, 7333, 5024, 55, 53, 11316, 4857, 3012, 310, 434, 244, 53, 350, 1470, 2527, 244, 55, 53, 63, 343, 1547, 7558, 7333, 5024, 9966, 36528, 3012, 343, 11354, 523, 1925, 100, 624, 299, 3252, 100, 624, 45, 3192, 49, 11353, 49, 5978, 49, 37138, 2699, 49, 32129, 100, 1021, 46, 343, 1547, 17829, 3052, 11127, 26338, 3012, 343, 434, 2095, 45, 9678, 523, 1925, 100, 624, 46, 630, 244, 53, 63, 419, 1547, 9417, 26338, 3012, 419, 1547, 22515, 9125, 17030, 3012, 419, 40798, 365, 419, 1153, 51, 7860, 45, 55, 46, 419, 1470, 100, 4755, 336, 2883, 100, 54, 299, 19611, 365, 419, 1547, 17829, 9125, 17030, 1096, 4316, 7044, 9271, 3397, 53, 3012, 419, 434, 1470, 100, 4755, 336, 2883, 100, 54, 630, 244, 53, 63, 547, 1547, 7558, 3397, 53, 20856, 14236, 3012, 547, 5844, 100, 287, 5257, 51, 1713, 45, 27764, 2699, 494, 7931, 39, 494, 5758, 494, 332, 244, 20856, 14236, 11251, 678, 419, 832, 63, 547, 1547, 3065, 3397, 53, 17829, 12380, 39197, 9271, 27436, 55, 53, 11316, 4857, 3012, 547, 434, 244, 53, 350, 1470, 100, 4755, 336, 2883, 100, 54, 2527, 244, 55, 53, 63, 733, 1547, 27436, 55, 53, 11316, 4857, 36528, 4857, 3012, 733, 40798, 100, 1925, 100, 624, 299, 3252, 100, 624, 45, 3192, 49, 11353, 49, 5978, 49, 37138, 2699, 49, 32129, 100, 1021, 46, 733, 434, 2095, 45, 348, 998, 2883, 100, 1925, 100, 624, 46, 630, 244, 53, 63, 1031, 1547, 7558, 9417, 26338, 24975, 3012, 1031, 5844, 100, 287, 5257, 51, 1713, 45, 27764, 2699, 494, 7931, 39, 494, 5758, 494, 332, 244, 20856, 14236, 11251, 678, 1031, 5672, 365, 733, 832, 63, 1031, 1547, 7558, 26338, 8987, 22515, 3012, 1031, 40798, 100, 1925, 100, 624, 96, 53, 1015, 2956, 365, 1031, 3252, 100, 624, 100, 1772, 45, 3192, 49, 37138, 2699, 49, 1536, 46, 547, 4378, 244, 55, 53, 350, 1470, 100, 4755, 336, 2883, 100, 54, 2527, 244, 57, 53, 63, 733, 1547, 6525, 5024, 55, 53, 11316, 4857, 3012, 733, 40798, 100, 1925, 100, 624, 299, 3252, 100, 624, 45, 3192, 49, 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"æĿ¡", "æķ°æį®", "\"\"\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġaccomplish", "_", "search", "_", "data", "Ġ=", "Ġsearch", "_", "data", "(", "cause", ",", "Ġcity", ",", "Ġarea", ",", "Ġclin", "ique", ",", "Ġexcel", "_", "time", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"\"\"", "åĪ¤æĸŃ", "第ä¸Ģ", "页", "æľī", "没æľī", "æŁ¥", "åĪ°", "\"\"\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlen", "(", "ac", "comp", "lish", "_", "search", "_", "data", ")", "Ġ==", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"\"\"", "å¦Ĥæŀľ", "没", "æī¾åĪ°", "ç¿»", "页", "\"\"\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġto", "urne", "_", "page", "()", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġaccomplish", "_", "search", "_", "data", "Ġ=", "Ġsearch", "_", "data", "(", "cause", ",", "Ġcity", ",", "Ġarea", ",", "Ġclin", "ique", ",", "Ġexcel", "_", "time", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"\"\"", "åĪ¤æĸŃ", "ç¿»", "页", "åIJİ", "æľī", "没æľī", "æī¾åĪ°", "\"\"\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlen", "(", "ac", "comp", "lish", "_", 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"ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreset", "()", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"\"\"", "æī¾åĪ°", "äºĨ", "\"\"\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġpending", "__", "search", "_", "data", "[", "0", "].", "click", "()", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġsearch", "_", "data", "_", "down", "(", "cause", ",", "Ġclin", "ique", ",", "Ġpath", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġelif", "Ġ", "2", "0", "<", "Ġnumber", "Ġ<=", "Ġ", "4", "0", ":", "ĊĠĠĠĠĠĠĠ", "Ġ#", "ĠĠĠĠ", "Ġpending", "__", "search", "_", "data", "Ġ=", "Ġsearch", "_", "data", "(", "cause", ",", "Ġcity", ",", "Ġarea", ",", "Ġclin", "ique", ",", "Ġexcel", "_", "time", ")", "ĊĠĠĠĠĠĠĠ", "Ġ#", "ĠĠĠĠ", "Ġ\"\"\"", "åĪ¤æĸŃ", "æľī", "没æľī", "æī¾åĪ°", "\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġ#", "ĠĠĠĠ", "Ġif", "Ġlen", "(", "pending", "__", "search", "_", "data", ")", "Ġ==", "Ġ", "0", ":", "ĊĊ", "Ċ", "if", "Ġ__", "name", "__", "Ġ==", "Ġ\"__", "main", "__\":", "ĊĠĠĠ", "Ġdownload", "_", "re", "vers", "Ġ=", "Ġ[]", "ĊĠĠĠ", "Ġ\"\"\"", "åĪĿå§ĭåĮĸ", "\"\"\"", "ĊĠĠĠ", "Ġurl", "Ġ=", "Ġinput", "(\"", "请è¾ĵåħ¥", "æĸĩ件çļĦ", "ç»Ŀ", "对", "è·¯å¾Ħ", "ï¼ļ", "\")", "Ġ", "Ġ#", "Ġæĸĩ件", "è·¯å¾Ħ", "ĊĠĠĠ", "Ġpath", "Ġ=", "Ġ\"", "D", ":\\", "æŀĹ", "éĴŁ", "\\", "ä¸ĭè½½", "\"", "Ġ", "Ġ#", "Ġ", "ä¸ĭè½½", "è·¯å¾Ħ", "ĊĠĠĠ", "ĠChrome", "Ġ=", "Ġr", "\"", "D", ":\\", "PY", "thon", "\\", "web", "drivers", "\\", "chro", "med", "river", ".", "exe", "\"", "Ġ", "Ġ#", "Ġ", "驱åĬ¨", "è·¯å¾Ħ", "ĊĠĠĠ", "Ġtime", "1", "Ġ=", "Ġtime", ".", "time", "()", "ĊĠĠĠ", "Ġ\"\"\"", "çĻ»å½ķ", "页éĿ¢", "\"\"\"", "ĊĠĠĠ", "Ġde", "connexion", "(", "Chrome", ")", "ĊĠĠĠ", "Ġprint", "(\"", "å·²", "çĻ»", "éĻĨ", "\")", "ĊĠĠĠ", "Ġmenu", "_", "li", "en", "()", "ĊĠĠĠ", "Ġprint", "(\"", "å·²", "跳转", "\")", "ĊĊĠĠĠ", "Ġ\"\"\"", "读åıĸ", "表", "æł¼", "\"\"\"", "ĊĠĠĠ", "Ġexcel", "Ġ=", "Ġvb", ".", "load", "_", "workbook", "(", "url", ")", "ĊĠĠĠ", "Ġsheet", "Ġ=", "Ġexcel", "[\"", "1", "-", "æ¯ı", "æĹ¥", "çĽij", "æݧ", "åijĬ", "èѦ", "æĺİ", "ç»Ĩ", "\"]", "ĊĠĠĠ", "Ġsub", "script", "Ġ=", "Ġ", "1", "ĊĠĠĠ", "Ġfor", "Ġi", "Ġin", "Ġsheet", ".", "iter", "_", "rows", "(", "min", "_", "row", "=", "2", ",", "Ġmax", "_", "row", "=", "1", "0", "1", ",", "Ġmax", "_", "col", "=", "1", "):", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġcell", "Ġin", "Ġi", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġcell", ".", "value", "Ġin", "Ġ[\"", "3", "\",", "Ġ", "3", ",", "Ġ\"", "é«ĺ", "\"]:", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"\"\"", "åĪĿå§ĭåĮĸ", "æķ°", "åĢ¼", "\"\"\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġcause", "Ġ=", "Ġsheet", "[\"", "I", "\"", "Ġ+", "Ġstr", "(", "cell", ".", "row", ")].", "value", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġcity", "Ġ=", "Ġsheet", "[\"", "E", "\"", "Ġ+", "Ġstr", "(", "cell", ".", "row", ")].", "value", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġarea", "Ġ=", "Ġsheet", "[\"", "F", "\"", "Ġ+", "Ġstr", "(", "cell", ".", "row", ")].", "value", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġclin", "ique", "Ġ=", "Ġsheet", "[\"", "G", "\"", "Ġ+", "Ġstr", "(", "cell", ".", "row", ")].", "value", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġexcel", "_", "time", "Ġ=", "Ġsheet", "[\"", "D", "\"", "Ġ+", "Ġstr", "(", "cell", ".", "row", ")].", "value", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"\"\"", "æIJľç´¢", "\"\"\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtry", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġconfirm", "_", "area", "(", "city", ",", "Ġarea", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġconfirm", "_", "ti", "em", "(", "excel", "_", "time", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġconfirm", "_", "cause", "(", "cause", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġsearch", "()", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġexcept", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtry", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġweb", ".", "refresh", "()", "Ġ", "Ġ#", "Ġ", "åĪ·æĸ°", "æĸ¹æ³ķ", "Ġrefresh", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "åĪ·æĸ°", "æĪIJåĬŁ", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġconfirm", "_", "area", "(", "city", ",", "Ġarea", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġconfirm", "_", "ti", "em", "(", "excel", "_", "time", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġconfirm", "_", "cause", "(", "cause", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġsearch", "()", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġexcept", "ĠException", "Ġas", "Ġe", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "åĪ·æĸ°", "失败", "ï¼ģ", "\",", "Ġformat", "(", "e", "))", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"\"\"", "æŁ¥æī¾", "æķ°æį®", "\"\"\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġsearch", "_", "data", "_", "on", "(", "cause", ",", "Ġcity", ",", "Ġarea", ",", "Ġclin", "ique", ",", "Ġexcel", "_", "time", ",", "Ġpath", ")", "ĊĊĠĠĠ", "Ġ\"\"\"", "æīĵåį°", "æľĢç»Ī", "ç»ĵæŀľ", "\"\"\"", "ĊĠĠĠ", "Ġprint", "(\"\")", "ĊĠĠĠ", "Ġprint", "(\"<", "-----------", "ä¸ĭéĿ¢", "æĺ¯", "ä¸ĭè½½", "失败", "çļĦ", "----------", "->", "\")", "ĊĠĠĠ", "Ġfor", "Ġi", "Ġin", "Ġdownload", "_", "re", "vers", ":", "ĊĠĠĠĠĠĠĠ", "Ġprint", "(", "i", ")", "ĊĠĠĠ", "Ġprint", "(\"", "å·²", "åħ¨éĥ¨", "ä¸ĭè½½", "å®Į", "æ¯ķ", "\")", "ĊĠĠĠ", "Ġtime", "2", "Ġ=", "Ġtime", ".", "time", "()", "ĊĠĠĠ", "Ġprint", "(\"", "çĶ¨", "æŶ", "ï¼ļ", "{:", ".", "2", "f", "}", "Ġ", "ç§Ĵ", "\".", "format", "(", "time", "2", "Ġ-", "Ġtime", "1", "))", "Ċ" ]
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213ab22a269abc8180524462a8966e5d929ef7d1
import os import json import codecs import markdown from flask import current_app def get_json_file(filename, lang="en"): """ Get the contents of a JSON file. """ filepath = os.path.join(current_app.config["APP_PATH"], "data", filename) with open(filepath, "r") as f: return json.loads(f.read()) def get_markdown_file(name, lang="en"): """ Get the contents of a markdown file. """ filename_temp = "{0}_{1}.markdown" md_dir = os.path.join(current_app.config["APP_PATH"], "markdown") filepath = os.path.join(md_dir, filename_temp.format(name, lang)) if not os.path.isfile(filepath) and lang == "fr": filepath = os.path.join(md_dir, filename_temp.format(name, "en")) if not os.path.isfile(filepath): return None with codecs.open(filepath, mode="r", encoding="utf-8") as f: return markdown.markdown(f.read())
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398263b65fd98003f27020e46ae38e913dc5dd45
#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = "Brice Chou" import os import lib import sys import time import getopt import training try: import cv2 import h5py except Exception as e: error_info = "Please install h5py/cv2 tools first. Error: {}.\n".format(e) print("\033[0;31m%s\033[0m" % error_info) quit() class Usage(Exception): def __init__(self, msg): self.msg = msg def run(): # Set the window name window_name = __author__ # Get a reference to webcam #-1 (the last one) video_capture = cv2.VideoCapture(-1) # Initialize some variables unknown_folder_path = os.path.abspath("unknown") i = lib.get_file_max_number(unknown_folder_path) filerd = h5py.File("database/training_encodings.hdf5", "r") # Image encodings mode encodings_mode = "large" # Temp to save predict result name face_names = [] # Save the screen locations and encodings to find a person screen_locations = [] screen_encodings = [] # Save the training data from database training_names = [] training_eigenvalues = [] process_this_frame = True for key in filerd.keys(): training_names.append(filerd[key].name.split("/")[-1]) training_eigenvalues.append(filerd[key].value) filerd.close() while True: # Grab a single frame of video ret, frame = video_capture.read() # Resize frame of video to 1/4 size # for faster face recognition processing small_frame = cv2.resize(frame, (0, 0), fx=0.5, fy=0.5) # Only process every other frame of video to save time if process_this_frame: # Find all the faces and face encodings # in the current frame of video screen_locations = lib.face_locations(small_frame, 1, "hog") screen_encodings = lib.face_encodings(small_frame, None, 1, encodings_mode) face_names = [] # How manay faces in the screen detected_face_length = len(screen_locations) info = "We detected \033[0;32m{}\033[0m faces in the screen.\n" print(info.format(detected_face_length)) if detected_face_length >= 1: for screen_encoding in screen_encodings: # Compare the locations and get the face's name name = lib.compare_faces(training_eigenvalues, training_names, screen_encoding, 0.31) face_names.append(name) # Auto save the unknown images if "" == name: img_file_path = "{}/{}.jpg".format(unknown_folder_path, i) cv2.imwrite(img_file_path, frame) i += 1 time.sleep(0.15) process_this_frame = not process_this_frame # Display the results for (top, right, bottom, left), name in zip(screen_locations, face_names): # We detected in was scaled to 1/2 size top *= 2 right *= 2 bottom *= 2 left *= 2 # Draw a box around the face cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) if "" != name: # Draw a label with a name below the face # # cv2.cv.CV_FILLED cv2.rectangle(frame, (left - 60, bottom + 30), (right + 60, bottom - 10), (0, 0, 255), cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(frame, name, (left - 50, bottom + 20), font, 1, (255, 255, 255), 1) # Display the resulting image cv2.namedWindow(window_name, cv2.WND_PROP_FULLSCREEN) # cv2.cv.CV_WINDOW_FULLSCREEN cv2.setWindowProperty(window_name, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN) cv2.imshow(window_name, frame) key = cv2.waitKey(1) if key == ord("s"): label = "cache/{}.jpg".format(i) cv2.imwrite(label, frame) i += 1 elif key == ord("q"): break # Release handle to the webcam video_capture.release() cv2.destroyAllWindows() def main(argv=None): if argv is None: argv = sys.argv try: try: argv_list = argv[1:] opts, args = getopt.getopt(argv_list, "h", ["help"]) arg = argv_list[0] if "run" == arg: run() elif "save" == arg: training.save() elif "move" == arg: training.move() elif "detect" == arg: training.detect() elif "catch" == arg: if 2 == len(argv_list): training.catch(argv_list[1]) else: training.catch() elif "rotate" == arg: if 2 == len(argv_list): training.rotate(amount=int(argv_list[1])) else: training.rotate() except getopt.error, msg: raise Usage(msg) except Usage, err: print >> sys.stderr, err.msg print >> sys.stderr, "for help use --help" return 2 if __name__ == "__main__": lib.initial_project_folder() sys.exit(main())
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86ca94820c05b3f63f4a733b6d1fa7eb9dea6a5d
# generated from catkin/cmake/template/order_packages.context.py.in source_root_dir = "/home/songsong/image_transport_ws/src" whitelisted_packages = "".split(";") if "" != "" else [] blacklisted_packages = "".split(";") if "" != "" else [] underlay_workspaces = ( "/home/songsong/image_transport_ws/devel;/home/songsong/pibot_ros/ros_ws/devel;/home/songsong/catkin_ws/devel;/opt/ros/kinetic".split( ";" ) if "/home/songsong/image_transport_ws/devel;/home/songsong/pibot_ros/ros_ws/devel;/home/songsong/catkin_ws/devel;/opt/ros/kinetic" != "" else [] )
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1f27b697985c7417e6d8d978703175a415c6c57d
import math r = float(input()) p = int(input()) obim = 2 * r * math.pi ukupanPut = p * obim # centimetre pretvaramo u metre ukupanPut = ukupanPut * 0.01 print("%.2f" % ukupanPut)
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71a9c9b8f47dcfbecc154c44d5a72ddbd852145a
def randomizer(n, garrafa_vidro, lata_metal, copo_plastico, bola_papel, maça_organico): lixos = [garrafa_vidro, lata_metal, copo_plastico, bola_papel, maça_organico] return lixos[n]
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02e711dfc122007c74949cd9f86e2aeb9d334871
import numpy as np class Adaline: def __init__(self, eta=0.0001, n_iter=2000): self.eta = eta self.n_iter = n_iter self.error = [] def fit(self, X, Y): X = np.hstack((np.ones((X.shape[0], 1)), X)) self.w = np.random.uniform(-1, 1, (X.shape[1], 1)) for n in range(self.n_iter): y = X.dot(self.w) error = Y - y self.w += self.eta * X.T.dot(error) cost = 1.0 / 2 * np.sum(error**2) self.error.append(cost) return self def predict(self, X): X = np.hstack((np.ones((X.shape[0], 1)), X)) Y_hat = X.dot(self.w) return Y_hat
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6bf1d410a33e3b2535e39e4f8c5c7f8278b3de67
from PIL import Image from src import urbandictionary_api from src.card.cardDrawer import CardDrawer from src.card.cardModel import CardModel from src.repository import Repository from src.urbandictionary_api import get_random_word def save_card(word, image_path, filepath="data/cards/", filename=None): """Функция для генерации и сохранения изображения Возвращает filepath+filename Параметры: word - слово, чей контент будет на карточке image - задний фон изображения filepath - путь для хранения изображения filename - имя изображения """ content = urbandictionary_api.get_word_data(word) image = Image.open(image_path) rep = Repository() fonts = rep.fonts model = CardModel( content=content, image=image, auth_font=fonts.aut_font, cat_font=fonts.cat_font, def_font=fonts.def_font, ex_font=fonts.ex_font, rect_font=fonts.rect_font, word_font=fonts.word_font, thumb_font=fonts.thumb_font, ) card_drawer = CardDrawer(model) card_drawer.draw_card() path = card_drawer.save(filepath=filepath, filename=filename) return path if __name__ == "__main__": from random import randint save_card(get_random_word(), f"data/template/backgroundimages/bgimg ({randint(1, 9)}).jpg")
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a096e811e50e25e47a9b76b1f813c51f4307bbfe
import django_filters from .models import Drinks, Brand class DrinkFilter(django_filters.FilterSet): BRAND_CHOICES = tuple((brand.name, brand.name) for brand in Brand.objects.all()) name = django_filters.CharFilter(lookup_expr="icontains") price_lt = django_filters.NumberFilter(field_name="price", lookup_expr="lt") price_gt = django_filters.NumberFilter(field_name="price", lookup_expr="gt") likes_lt = django_filters.NumberFilter(field_name="likes", lookup_expr="lt") likes_gt = django_filters.NumberFilter(field_name="likes", lookup_expr="gt") brands = django_filters.MultipleChoiceFilter(field_name="brand__name", choices=BRAND_CHOICES) class Meta: model = Drinks fields = ["name", "brands"] """ f = F({'date_after': '2016-01-01', 'date_before': '2016-02-01'}) """
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3ea42e7ad5301314a39bf522280c084342cd18c5
from flask import render_template, request, Response from flask.views import MethodView, View from flask.views import View from repo import ClassifierRepo from services import PredictDigitService from settings import CLASSIFIER_STORAGE class IndexView(View): def dispatch_request(self): return render_template("index.html") class PredictDigitView(MethodView): def post(self): repo = ClassifierRepo(CLASSIFIER_STORAGE) service = PredictDigitService(repo) image_data_uri = request.json["image"] prediction = service.handle(image_data_uri) return Response(str(prediction).encode(), status=200)
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0c97569c77fb3598d83eba607960328bb2134dd2
from __future__ import print_function, division import os from os.path import exists, join, basename, dirname from os import makedirs import numpy as np import datetime import time import argparse import torch import torch.nn as nn import torch.optim as optim from lib.dataloader import DataLoader from lib.im_pair_dataset import ImagePairDataset from lib.normalization import NormalizeImageDict from lib.torch_util import save_checkpoint from lib.torch_util import BatchTensorToVars from lib.eval_util_dynamic import pfdataset_pck, pfpascal_val_dataloader # import DCCNet from models.model_dynamic import DCCNet from models.loss_dynamic import weak_loss # Seed and CUDA use_cuda = torch.cuda.is_available() torch.manual_seed(1) if use_cuda: torch.cuda.manual_seed(1) np.random.seed(1) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False print("DCCNet training script") # Argument parsing parser = argparse.ArgumentParser(description="Compute PF Pascal matches") parser.add_argument("--checkpoint", type=str, default="") parser.add_argument("--image_size", type=int, default=400) parser.add_argument("--dataset_image_path", type=str, default="datasets/pf-pascal/", help="path to PF Pascal dataset") parser.add_argument( "--dataset_csv_path", type=str, default="datasets/pf-pascal/image_pairs/", help="path to PF Pascal training csv" ) parser.add_argument("--num_epochs", type=int, default=5, help="number of training epochs") parser.add_argument("--batch_size", type=int, default=16, help="training batch size") parser.add_argument("--lr", type=float, default=0.0005, help="learning rate") parser.add_argument("--result_model_fn", type=str, default="checkpoint_adam", help="trained model filename") parser.add_argument("--result-model-dir", type=str, default="../model/checkpoints", help="path to trained models folder") parser.add_argument("--fe_finetune_params", type=int, default=0, help="number of layers to finetune") parser.add_argument("--exp_name", type=str, default="exp_delete", help="experiment name") # DCCNet args parser.add_argument("--ncons_kernel_sizes", nargs="+", type=int, default=[5, 5, 5], help="kernels sizes in neigh. cons.") parser.add_argument("--ncons_channels", nargs="+", type=int, default=[16, 16, 1], help="channels in neigh. cons") parser.add_argument("--sce_kernel_size", type=int, default=25, help="kernel size in sce.") parser.add_argument("--sce_hidden_dim", type=int, default=1024, help="hidden dim in sce") parser.add_argument( "--scaleloss_weight", type=float, default=1.0, help="whether use scale loss, if use the weight for scale loss" ) parser.add_argument( "--att_scale_ncons_kernel_sizes", nargs="+", type=int, default=[5, 5, 5], help="kernels sizes in dynamic fusion net." ) parser.add_argument( "--att_scale_ncons_channels", nargs="+", type=int, default=[16, 16, 1], help="channels in dynamic fusion net" ) args = parser.parse_args() print(args) # Create model print("Creating CNN model...") model = DCCNet( use_cuda=use_cuda, checkpoint=args.checkpoint, ncons_kernel_sizes=args.ncons_kernel_sizes, ncons_channels=args.ncons_channels, sce_kernel_size=args.sce_kernel_size, sce_hidden_dim=args.sce_hidden_dim, att_scale_ncons_kernel_sizes=args.att_scale_ncons_kernel_sizes, att_scale_ncons_channels=args.att_scale_ncons_channels, ) # Multi-GPU support model = nn.DataParallel(model) # Set which parts of the model to train if args.fe_finetune_params > 0: for i in range(args.fe_finetune_params): for p in model.module.FeatureExtraction.model[-1][-(i + 1)].parameters(): p.requires_grad = True print("Trainable parameters:") count = 0 for i, param in enumerate(model.named_parameters()): name, p = param if p.requires_grad: count += 1 print(str(count) + ": " + name + "\t" + str(p.shape) + "\t") print(model) # Optimizer print("using Adam optimizer") optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr) cnn_image_size = (args.image_size, args.image_size) Dataset = ImagePairDataset train_csv = "train_pairs.csv" # val_pairs_nocoords.csv: for compute loss, with flip column in csv, no coordinates # val_pairs.csv: for compute pck, with coordinates val_nocoordinates_csv = "val_pairs_nocoords.csv" val_csv = "image_pairs/val_pairs.csv" normalization_tnf = NormalizeImageDict(["source_image", "target_image"]) batch_preprocessing_fn = BatchTensorToVars(use_cuda=use_cuda) # Dataset and dataloader dataset = Dataset( transform=normalization_tnf, dataset_image_path=args.dataset_image_path, dataset_csv_path=args.dataset_csv_path, dataset_csv_file=train_csv, output_size=cnn_image_size, ) dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=0) dataset_val = Dataset( transform=normalization_tnf, dataset_image_path=args.dataset_image_path, dataset_csv_path=args.dataset_csv_path, dataset_csv_file=val_nocoordinates_csv, output_size=cnn_image_size, ) # compute val loss dataloader_val = DataLoader(dataset_val, batch_size=args.batch_size, shuffle=True, num_workers=4) # compute val pck dataloader_val_pck = pfpascal_val_dataloader( image_size=args.image_size, eval_dataset_path=args.dataset_image_path, csv_file=val_csv ) # load pfpascal val dataset # Define checkpoint name checkpoint_dir = os.path.join(args.result_model_dir, args.exp_name) checkpoint_name = os.path.join( args.result_model_dir, args.exp_name, datetime.datetime.now().strftime("%Y-%m-%d_%H:%M") + "_" + args.result_model_fn + ".pth.tar", ) log_name = os.path.join(args.result_model_dir, args.exp_name, "logmain_" + args.exp_name + ".txt") if not exists(dirname(log_name)): makedirs(dirname(log_name)) print("Checkpoint name: " + checkpoint_name) # Train best_val_pck = float("-inf") loss_fn = lambda model, batch: weak_loss(model, batch, normalization="softmax", scaleloss_weight=args.scaleloss_weight) # define epoch function def process_epoch(mode, epoch, model, loss_fn, optimizer, dataloader, batch_preprocessing_fn, use_cuda=True, log_interval=50): epoch_loss = 0 for batch_idx, batch in enumerate(dataloader): st = time.time() if mode == "train": optimizer.zero_grad() tnf_batch = batch_preprocessing_fn(batch) loss = loss_fn(model, tnf_batch) loss_np = loss.data.cpu().numpy()[0] # loss_np = loss.data.cpu().numpy() epoch_loss += loss_np if mode == "train": loss.backward() optimizer.step() else: loss = None if batch_idx % log_interval == 0: print( mode.capitalize() + " Epoch: {} [{}/{} ({:.0f}%)]\t\tLoss: {:.12f}\t\tcost time: {:.1f}".format( epoch, batch_idx, len(dataloader), 100.0 * batch_idx / len(dataloader), loss_np, time.time() - st ) ) epoch_loss /= len(dataloader) print(mode.capitalize() + " set: Average loss: {:.12f}".format(epoch_loss)) return epoch_loss train_loss = np.zeros(args.num_epochs) val_loss = np.zeros(args.num_epochs) val_pcks = np.zeros(args.num_epochs) model.module.FeatureExtraction.eval() print("Starting training...") for epoch in range(1, args.num_epochs + 1): st = time.time() train_loss_curepoch = process_epoch( "train", epoch, model, loss_fn, optimizer, dataloader, batch_preprocessing_fn, log_interval=1 ) time_train = time.time() - st st = time.time() val_loss_curepoch = process_epoch( "val", epoch, model, loss_fn, optimizer, dataloader_val, batch_preprocessing_fn, log_interval=1 ) time_valloss = time.time() - st st = time.time() val_pck_curepoch = pfdataset_pck(dataloader=dataloader_val_pck, model=model, verbose=False) time_valpck = time.time() - st train_loss[epoch - 1] = train_loss_curepoch val_loss[epoch - 1] = val_loss_curepoch val_pcks[epoch - 1] = val_pck_curepoch # remember best loss is_best = val_pcks[epoch - 1] > best_val_pck best_val_pck = max(val_pcks[epoch - 1], best_val_pck) save_checkpoint( { "epoch": epoch, "args": args, "state_dict": model.state_dict(), "optimizer": optimizer.state_dict(), "train_loss": train_loss, "val_loss": val_loss, "val_pck": val_pcks, "best_val_pck": best_val_pck, }, is_best, checkpoint_name, save_all_epochs=False, ) message = ( "Epoch{}\tTrain_loss{:.6f}\tcost time{:.1f}\tVal_loss{:.6f}\tcost time{:.1f}\tVal_pck{:.6f}\tcost time{:.1f}\n".format( epoch, train_loss_curepoch, time_train, val_loss_curepoch, time_valloss, val_pck_curepoch, time_valpck, ) ) print(message) with open(log_name, "a") as log_file: log_file.write("%s\n" % message) print("Done!")
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a65dfca1773c1e4101ebfb953e0f617a2c345695
def merge(self, intervals): intervals.sort() arr = [] for i in intervals: if len(arr) == 0 or arr[-1][1] < i[0]: arr.append(i) else: arr[-1][1] = max(arr[-1][1], i[1]) return arr
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49005500b299ca276f663fe8431bb955e5585bbd
import Net import mnist_parser import numpy as np # To use this model it is required to download the MNIST database # The donwloaded base is then needet parse to numpy using mnist_parser.parse_to_npy method # The files genetared using mnist_parser.parse_to_npy are then loaded using np.load in_values = np.load("MNIST/mnist_train_images.npy") out_values = np.load("MNIST/mnist_train_labels.npy") out_gt_numbers = mnist_parser.one_hots_to_ints(out_values) in_testing_values = np.load("MNIST/mnist_test_images.npy") out_testing_values = np.load("MNIST/mnist_test_labels.npy") out_gt_numbers_test = mnist_parser.one_hots_to_ints(out_testing_values) while True: net = Net.FeedForwardNet(input_count=784, layers=[100, 10], activation_function=Net.FeedForwardNet.leaky_relu) try: epoch_num = int(input("Epoch_num:")) batch_size = int(input("Batch_size:")) # 30 learning_rate = float(input("Learning rate:")) # 0.001 inertion_factor = float(input("Inertion factor:")) # 0.5 # max_error=float(input("Maximum error")) except: print("Parse error") continue for i in range(epoch_num): batch_in, batch_out = net.generate_random_batch(in_values, out_values, batch_size) net.forward_propagation(batch_in) net.backpropagation(batch_out, learning_rate=learning_rate, inertion_factor=inertion_factor) # print("X:",net.X[-1]) # net.stochastic_backpropagation(batch_out, learning_rate=learning_rate) if i % 50 == 0: print() output = net.forward_propagation(in_testing_values) if net.check_total_squared_error(output_values=out_testing_values, epsilon=1000, verbose=True): break output_numbers = mnist_parser.one_hots_to_ints(output) correct = np.sum(out_gt_numbers_test == output_numbers) print("Epoch: ", i, " br tocnih:", correct, "/", output_numbers.size, "(", correct / output_numbers.size, "%)") output = net.forward_propagation(in_testing_values) conf_mat = net.calculate_confusion_matrix(out_testing_values) output_numbers = mnist_parser.one_hots_to_ints(output) correct = np.sum(out_gt_numbers_test == output_numbers) print("Correct:", correct, "/", output_numbers.size, "(", correct / output_numbers.size, "%)") print(conf_mat) save = int(input("Save?(1/0)")) if save == 1: name = input("Save as?") net.save_state(name) exit = int(input("Exit?(1/0)")) if exit == 1: break
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219929d52b5f1a0690590e83b41d2b4f0b2b3a51
list = [3, 1, 2, 5, 4, 7, 6] def sort(list): for i in range(len(list) - 1): if list[i] > list[i + 1]: a = list[i] list[i] = list[i + 1] list[i + 1] = a print(list) sort(list)
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e884ce5878de75afe93085e2310b4b8d5953963a
""" Created on 13 Dec 2016 @author: hpcosta """ # https://www.hackerrank.com/challenges/backreferences-to-failed-groups regex = r"^\d{2}(-?)\d{2}\1\d{2}\1\d{2}$" # Do not delete 'r'. import re print(str(bool(re.search(regex, raw_input()))).lower()) # Task # # You have a test string S. # Your task is to write a regex which will match S, with following condition(s): # # S consists of 8 digits. # S may have "-" separator such that string S gets divided in 4 parts, with each part having exactly two digits. (Eg. 12-34-56-78) # Valid # # 12345678 # 12-34-56-87 # Invalid # # 1-234-56-78 # 12-45-7810
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302accfd5001a27c7bbe6081856d43dbec704168
import asyncio import logging import random from aiogram.dispatcher import FSMContext from aiogram.types import ContentTypes, Message, CallbackQuery from aiogram.utils.exceptions import BotBlocked import keyboards from data.config import ADMINS, ADMIN_CHAT_ID from keyboards.inline.activate_menu import active_menu_callback from loader import dp, db, storage from utils import text from utils.db_api import redis_commands from utils.jobs import cur_bot_info from utils.misc import rate_limit @dp.message_handler(commands="upload", user_id=ADMINS, state="*") async def upload_profile(command_msg: Message, state: FSMContext): profile_msg = command_msg.reply_to_message admin = command_msg.from_user param = command_msg.get_args() if not profile_msg: await command_msg.answer("Чтобы загрузить анкету сделай на неё REPLY") return elif param != "g" and param != "b": await command_msg.answer("Чтобы воспользоваться командой /upload нужно добавить параметры <b>b | g</b>") return other_bot = profile_msg.forward_from if not other_bot or other_bot.id != 1234060895: await profile_msg.reply("Загружать анкеты можно только из нашего БотаX :)") return elif (not profile_msg.photo and not profile_msg.video) or not profile_msg.caption: await profile_msg.reply("Загружать нужно именно анкету, а не части анкеты") return profile_data = text.get_parse_data(profile_msg.caption) if profile_msg.photo: media_id = profile_msg.photo[-1].file_id with_video = False else: media_id = profile_msg.video.file_id with_video = True profile_data.update( id=random.randint(1, 100000), username="f", media_id=media_id, with_video=with_video, sex=1 if param == "g" else 2 ) await db.add_user(**profile_data) await profile_msg.reply("Пользователь {}-{} успешно добавлен ✅".format(profile_data["user_nick"], profile_data["id"])) logging.info( f"Admin @{admin.username}-{admin.id} successfully added fake {profile_data['user_nick']}-{profile_data['id']} " ) @dp.message_handler(commands="get_msg_info", user_id=ADMINS, state="*") async def get_msg_info(command_msg: Message, state: FSMContext): msg = command_msg.reply_to_message await command_msg.delete() if not msg: await command_msg.answer("Нужно делать реплай на сообщение.") return state = await state.get_state() await msg.reply( f"Эхо в состоянии <code>{state}</code>.\n" f"\nСодержание сообщения:\n" f"\n<code>{msg}</code>\n" f"\ncontent_type = {msg.content_type}\n" f"\nentities={msg.entities}" ) @dp.message_handler(commands="ban_user", user_id=ADMINS, state="*") async def ban_user(command_msg: Message, state: FSMContext): ban_user_id = command_msg.get_args() admin = command_msg.from_user await command_msg.delete() if not ban_user_id or not ban_user_id.isdecimal(): await command_msg.answer(f"Формат команды: /ban_user user_id") return ban_user_id = int(ban_user_id) is_banned = await db.ban_user(ban_user_id) if not is_banned: await command_msg.answer(f"Пользователя с таким <user_id> не существует") return await redis_commands.ban_user(ban_user_id) await command_msg.answer("Пользователь({}) успешно забанен 😎".format(ban_user_id)) logging.info(f"Admin @{admin.username}-{admin.id} BAN USER-{ban_user_id}") @dp.message_handler(commands="unban_user", user_id=ADMINS, state="*") async def unban_user(command_msg: Message, state: FSMContext): unban_user_id = command_msg.get_args() admin = command_msg.from_user await command_msg.delete() if not unban_user_id or not unban_user_id.isdecimal(): await command_msg.answer(f"Формат команды: /unban_user user_id") return unban_user_id = int(unban_user_id) is_unbanned = await db.unban_user(unban_user_id) if not is_unbanned: await command_msg.answer(f"Пользователя с таким <user_id> не существует") return await redis_commands.unban_user(unban_user_id) await command_msg.answer("Пользователь({}) успешно разбанен 👻".format(unban_user_id)) logging.info(f"Admin @{admin.username}-{admin.id} UNBAN USER-{unban_user_id}") @dp.message_handler(commands="clean_old_likes", user_id=ADMINS, state="*") async def clean_old_likes(command_msg: Message, state: FSMContext): admin = command_msg.from_user await command_msg.delete() count = await db.clean_old_likes(interval=24) await command_msg.answer("Было успешно удалено {} старых лайков(за {} hours)".format(count, 24)) logging.info(f"Admin @{admin.username}-{admin.id} delete old likes(count={count})") @dp.message_handler(commands="say_to_all_now_go", user_id=ADMINS, state="*") async def say_to_all(command_msg: Message, state: FSMContext): admin = command_msg.from_user msg = command_msg.reply_to_message await command_msg.delete() if not msg: await command_msg.answer("Чтобы воспользоваться этой командой сделай REPLY") return active_user_ids = await db.get_all_users(active=True) # [375766905, 997319478] delete_bot_count = 0 for user_id in active_user_ids: try: await dp.bot.copy_message(chat_id=user_id, from_chat_id=command_msg.chat.id, message_id=msg.message_id) await asyncio.sleep(0.05) except BotBlocked as exc: await db.update_user(user_id, active=False) await redis_commands.clear_user(user_id) await redis_commands.clear_search_ids(user_id) delete_bot_count += 1 await msg.reply( "Сообщение успешно отправлено: оставили бот({}), заблокировали бот({})".format( len(active_user_ids) - delete_bot_count, delete_bot_count ) ) logging.info(f"Admin @{admin.username}-{admin.id} SAY TO ALL MSG(id={msg.message_id})") @dp.message_handler(commands="show_state_statistic", user_id=ADMINS, state="*") async def show_state_statistic(command_msg: Message, state: FSMContext): admin = command_msg.from_user statistic = dict() await command_msg.delete() states_list = await storage.get_states_list() for states_item in states_list: chat_id, user_id = states_item state_text = await storage.get_state(chat=chat_id, user=user_id, default="Deactivate bot") try: statistic[state_text] += 1 except KeyError: statistic.update({state_text: 1}) out_text = "<b>Статичктика по пользователям:</b>\n\n" for state_text, count_users in statistic.items(): out_text += f"В состоянии {state_text} — {count_users} пользователей\n\n" await command_msg.answer(out_text) logging.info(f"For Admin @{admin.username}-{admin.id} show state statistic") @rate_limit(3) @dp.message_handler(commands="show_info", user_id=ADMINS, state="*") async def show_info(command_msg: Message, state: FSMContext): admin = command_msg.from_user await command_msg.delete() await cur_bot_info(for_chat_id=command_msg.chat.id) logging.info(f"For admin @{admin.username}-{admin.id} SHOW INFO(command)") @dp.callback_query_handler(active_menu_callback.filter(), chat_id=ADMIN_CHAT_ID, state="*") async def change_active(call: CallbackQuery, state: FSMContext, callback_data: dict): active = not bool(int(callback_data["active"])) user_id = int(callback_data["user_id"]) admin = call.from_user profile_msg = call.message if active: await db.unban_user(user_id) await redis_commands.unban_user(user_id) else: await db.ban_user(user_id) await redis_commands.ban_user(user_id) await profile_msg.edit_reply_markup(keyboards.inline.get_activate_menu(user_id=user_id, active=active)) await call.answer() logging.info(f"Admin @{admin.username}-{admin.id} CHANGE ACTIVE FOR USER-{user_id} TO {active}")
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101 ]