Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

cb7c42230b22f0923e08c1027984a0c50e947b03

fb71611fb25fc0aec8d8026a95e4c5c39b4f360e

/hq/my_excepthook/test_excepthool_package/hq_excepthook.py

4fc03a0617045c66fddab2a291a838946ffa70ad

[]

no_license

jwzhoui/urllib1

16aaf7f49dd1d48f0d02fbba0268d56a84dae2d2

afed096587dedc8da6755a8939ad9ee83d1ee519

refs/heads/master

2020-03-25T17:28:26.995286

2018-11-08T10:15:23

143,979,127

0

1

null

UTF-8

Python

false

863

py

# encoding: utf-8 import sys import time import traceback from multiprocessing import Process sys.path.append('/opt/space/urllib1/') def quiet_errors(*args,**kwargs): err = ''.join(traceback.format_exception(*args,**kwargs)) print err # RedisCache.inset_exc_to_redis(err) from hq.my_excepthook.test_excepthool_package.redis_cache import def_inset_exc_to_redis def_inset_exc_to_redis(err) # 重写系统多进程Process的run方法 # def Process_run(self): # try: # if self._target: # self._target(*self._args, **self._kwargs) # except Exception: # print '走好巧多进程异常捕捉' # quiet_errors() # raise #======== # 一般捕捉定义全局异常捕获 sys.excepthook = quiet_errors sys.__excepthook__ = quiet_errors # 多进程捕捉 # Process.run = Process_run

[ "cclina@isoftstone.com" ]

cclina@isoftstone.com

f45845f3775295ca40384d997d67d25854ded28e

72a3977adc460ec70d0ebd8177cea1bb22a06cbf

/src/Classes/TennisSet.py

5a0109b5e881dab41bb21cd34e6c496b92627d7c

[]

no_license

andreistaicu1/TennisScoreSimulator

0ff6307ee1f1019a42fa70f5557cacb478c1d59e

62fc0cc21ade84526190b2c840b4dea79bfb32d6

refs/heads/master

2023-07-23T05:37:00.521304

2021-09-01T20:09:48

349,536,172

0

null

UTF-8

Python

false

3,374

py

from src.Classes.TennisGame import * from src.Classes.TennisTiebreak import * class TennisSet: def __init__(self, set_length, player1, player2, ad, serving, will_breaker): """ :param set_length: int - When the set stops :param player1: Players object - player 1 :param player2: Players object - player 2 :param ad: Boolean - true if there are ads :param serving: Boolean - true if player 1 serves first :param will_breaker: Boolean - True if there is a breaker in the last set """ self.set_length = set_length self.ad = ad self.player_array = [player1, player2] self.serving = serving self.will_breaker = will_breaker self.player1G = 0 self.player2G = 0 self.data = [] self.toText = {} self.winner = 0 self.setOver = False self.tiebreaker = False def play_set(self): """ Plays the set on its own :return: nothing """ while not self.setOver: self.tiebreaker = self.player1G == self.player2G and self.player1G == self.set_length if self.tiebreaker and self.will_breaker: new_tiebreak = TennisTiebreak(self.player_array[0], self.player_array[1], True, 7) new_tiebreak.play_breaker() self.iterate_set_breaker(new_tiebreak) else: new_game = TennisGame(self.player_array[0], self.player_array[1], self.serving, self.ad) new_game.play_game() self.iterate_set_game(new_game) self.serving = not self.serving def iterate_set_game(self, current_game): """ Given a game, updates internal data :param current_game: TennisGame object - game to be played :return: nothing """ if self.tiebreaker and self.will_breaker: return game_winner = current_game.winner if game_winner == 0: self.setOver = True elif game_winner == 1: self.player1G += 1 else: self.player2G += 1 self.data.append(current_game) if self.player1G >= self.set_length or self.player2G >= self.set_length: if self.player1G - self.player2G > 1: self.winner = 1 self.setOver = True elif self.player2G - self.player1G > 1: self.winner = 2 self.setOver = True def iterate_set_breaker(self, current_breaker): """ Given a tiebreaker updates internal data :param current_breaker: TennisTiebreak object :return: nothing """ if self.tiebreaker and self.will_breaker: self.winner = current_breaker.winner if self.winner == 0: self.setOver = True elif self.winner == 1: self.player1G += 1 else: self.player2G += 1 self.data.append(current_breaker) self.setOver = True def compile(self): """ Consolidates internal data in a dictionary that can be easily printed to a text file :return: nothing """ self.toText['serving'] = self.serving self.toText['will_breaker'] = self.will_breaker

[ "55329808+andreistaicu1@users.noreply.github.com" ]

55329808+andreistaicu1@users.noreply.github.com

e32442bb51f8c48f5ac0d4ec09fa256a766c99f4

053b48ff879d73e4cd8f507e1cdc2aea6431c8d9

/pythonYing/week03/exceise线程/p18_ProcessVsThread.py

12af688f98d553f89f8d16007db73124a4e9a1d2

[]

no_license

Masonnn/ApiTest

cb37c8741ffa0474d0ce000dad66b02569e10342

e9f30b9f0e74eb22489c02682e33ee8bf7a87bbf

refs/heads/master

2022-12-16T17:44:24.219411

2020-09-06T16:37:30

249,369,612

0

null

2022-12-08T11:29:17

2020-03-23T08:02:38

HTML

UTF-8

Python

false

1,847

py

# process vs thread import multiprocessing as mp def job(q): res = 0 for i in range(1000000): res += i + i ** 2 + i ** 3 q.put(res) # 多核 def multicore(): q = mp.Queue() p1 = mp.Process(target=job, args=(q,)) p2 = mp.Process(target=job, args=(q,)) p1.start() p2.start() p1.join() p2.join() res1 = q.get() res2 = q.get() print("multicore", res1 + res2) # 创建多线程mutithread # 接下来创建多线程程序，创建多线程和多进程有很多相似的地方。 # 首先import threading然后定义multithread()完成同样的任务 import threading as td def multithread(): q = mp.Queue() # thread可放入process同样的queue中 t1 = td.Thread(target=job, args=(q,)) t2 = td.Thread(target=job, args=(q,)) t1.start() t2.start() t1.join() t2.join() res1 = q.get() res2 = q.get() print("multiThread", res1 + res2) # 创建普通函数 def normal(): res = 0 for _ in range(2): for i in range(1000000): res += i + i ** 2 + i ** 3 print('normal: ', res) # 在上面例子中我们建立了两个进程或线程，均对job()进行了两次运算， # 所以在normal()中我们也让它循环两次 # 运行时间 import time if __name__ == "__main__": st = time.time() normal() st1 = time.time() print('normal time:', st1 - st) multithread() st2 = time.time() print('multithread time:', st2 - st1) multicore() print('multicore time:', time.time() - st2) # 普通/多线程/多进程的运行时间分别是1.41，1.47和0.75秒。 # 我们发现多核/多进程最快，说明在同时间运行了多个任务。 # 而多线程的运行时间居然比什么都不做的程序还要慢一点， # 说明多线程还是有一定的短板的（GIL）。

[ "lixq@weilaicheng.com" ]

lixq@weilaicheng.com

684bd46fe2d2944e322c3a422ed060208afcb062

96cbdf4762cdee018522b2b1e55e33ca1e4d1fef

/pyimagesearch/transform.py

3e5f7d79efbdc5bc77f7ecce6e6064640cfe4ac9

[]

no_license

JoeHowarth/GoScanner

09ad65c7ba422780d153b7e1ba4292554631f19e

f214819df8f29b73238ea0762c69be8ca01f1c35

refs/heads/master

2021-09-14T11:22:17.151850

2018-05-12T17:25:14

106,126,339

6

0

null

UTF-8

Python

false

2,435

py

# import the necessary packages import numpy as np import cv2 def order_points(pts): # initialzie a list of coordinates that will be ordered # such that the first entry in the list is the top-left, # the second entry is the top-right, the third is the # bottom-right, and the fourth is the bottom-left rect = np.zeros((4, 2), dtype = "float32") # the top-left point will have the smallest sum, whereas # the bottom-right point will have the largest sum s = pts.sum(axis = 1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] # now, compute the difference between the points, the # top-right point will have the smallest difference, # whereas the bottom-left will have the largest difference diff = np.diff(pts, axis = 1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] # return the ordered coordinates return rect def four_point_transform(image, pts, square=False): # obtain a consistent order of the points and unpack them # individually rect = order_points(pts) (tl, tr, br, bl) = rect # compute the width of the new image, which will be the # maximum distance between bottom-right and bottom-left # x-coordiates or the top-right and top-left x-coordinates widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) # compute the height of the new image, which will be the # maximum distance between the top-right and bottom-right # y-coordinates or the top-left and bottom-left y-coordinates heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) # now that we have the dimensions of the new image, construct # the set of destination points to obtain a "birds eye view", # (i.e. top-down view) of the image, again specifying points # in the top-left, top-right, bottom-right, and bottom-left # order if (square == True): big = max(maxWidth, maxHeight) maxWidth = big maxHeight = big dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") # compute the perspective transform matrix and then apply it M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # return the warped image return warped

[ "josephehowarth@gmail.com" ]

josephehowarth@gmail.com

9b67e572e5da326df863a9d611e338df54b02395

8132e93c74cb8c541ee037b5c85deb38af6b653a

/list_1/exercise_5.py

3c0af37d58907e91f76d1e7dace2cdafa9a190f6

[]

no_license

piotrkawa/data-mining

3a6cdacd4c1b059a469a6a3186eed0694d0a3b2c

c788365eeef801450b0f6bc7ea889e1484e9b53c

refs/heads/master

2022-04-03T11:08:50.896421

2020-02-11T16:48:47

212,835,771

0

null

UTF-8

Python

false

959

py

from wordcloud import WordCloud import utility import itertools import nltk import pdb from nltk.corpus import stopwords as nltk_stopwords import string from preprocessing import preprocess_text def create_pairs(words): return [(word, 1) for word in words] def group_words(pairs): word = lambda pair: pair[0] occurences = lambda pair: pair[1] pairs.sort() words_grouped = [(w, sum(1 for _ in g)) for w, g in itertools.groupby(pairs, key=word)] words_grouped.sort(key=occurences, reverse=True) return words_grouped if __name__ == '__main__': book = utility.get_text_file_as_list('shrek.txt') words = preprocess_text(book) pairs = create_pairs(words) grouped_words = group_words(pairs) wc = WordCloud(background_color="white", max_words=2000, contour_width=3, contour_color='steelblue') wc.generate_from_frequencies(dict(grouped_words[15:])) wc.to_file('clouds/book.png') pdb.set_trace()

[ "piotr.w.kawa@gmail.com" ]

piotr.w.kawa@gmail.com

be63e415ecf5e1d3a8f53e768d4c23c1d1643511

cca21b0ddca23665f886632a39a212d6b83b87c1

/virtual/classroom/views.py

07712f42f10a68880ba8e8500e4a6784453a72e1

[]

no_license

siumhossain/classroom

a8926621456d1e7ed77387fb8a5851825771a9d9

4afe9cdee2c58b71bd3711b042eae3f86172eaea

refs/heads/master

2023-02-02T08:28:14.958761

2020-12-24T14:58:59

323,007,793

0

null

UTF-8

Python

false

7,300

py

from django.shortcuts import render from django.urls import reverse_lazy from django.views.generic.list import ListView from django.views.generic.edit import CreateView, UpdateView,DeleteView from .models import Course from django.contrib.auth.mixins import LoginRequiredMixin,PermissionRequiredMixin from django.shortcuts import redirect, get_object_or_404 from django.views.generic.base import TemplateResponseMixin,View from .forms import ModuleFormSet from django.forms.models import modelform_factory from django.apps import apps from .models import Module, Content from braces.views import CsrfExemptMixin, JsonRequestResponseMixin from django.db.models import Count from .models import Subject from django.views.generic.detail import DetailView from students.forms import CourseEnrollForm # Create your views here. from django.views.generic.list import ListView from .models import Course class ManageCourseListView(ListView): model = Course template_name = 'courses/manage/course/list.html' def get_queryset(self): qs = super().get_queryset() return qs.filter(owner=self.request.user) class OwnerMixin(object): def get_queryset(self): qs = super().get_queryset() return qs.filter(owner=self.request.user) class OwnerEditMixin(object): def form_valid(self, form): form.instance.owner = self.request.user return super().form_valid(form) class OwnerCourseMixin(OwnerMixin): model = Course fields = ['subject', 'title', 'slug', 'overview'] success_url = reverse_lazy('manage_course_list') class OwnerCourseEditMixin(OwnerCourseMixin, OwnerEditMixin): template_name = 'courses/manage/course/form.html' class ManageCourseListView(OwnerCourseMixin, ListView): template_name = 'courses/manage/course/list.html' class CourseCreateView(OwnerCourseEditMixin, CreateView): pass class CourseUpdateView(OwnerCourseEditMixin, UpdateView): pass class CourseDeleteView(OwnerCourseMixin, DeleteView): template_name = 'courses/manage/course/delete.html' class OwnerCourseMixin(OwnerMixin,LoginRequiredMixin,PermissionRequiredMixin): model = Course fields = ['subject', 'title', 'slug', 'overview'] success_url = reverse_lazy('manage_course_list') class ManageCourseListView(OwnerCourseMixin, ListView): template_name = 'courses/manage/course/list.html' permission_required = 'courses.view_course' class CourseCreateView(OwnerCourseEditMixin, CreateView): permission_required = 'courses.add_course' class CourseUpdateView(OwnerCourseEditMixin, UpdateView): permission_required = 'courses.change_course' class CourseDeleteView(OwnerCourseMixin, DeleteView): template_name = 'courses/manage/course/delete.html' permission_required = 'courses.delete_course' class CourseModuleUpdateView(TemplateResponseMixin, View): template_name = 'courses/manage/module/formset.html' course = None def get_formset(self, data=None): return ModuleFormSet(instance=self.course,data=data) def dispatch(self, request, pk): self.course = get_object_or_404(Course,id=pk,owner=request.user) return super().dispatch(request, pk) def get(self, request, *args, **kwargs): formset = self.get_formset() return self.render_to_response({'course': self.course,'formset': formset}) def post(self, request, *args, **kwargs): formset = self.get_formset(data=request.POST) if formset.is_valid(): formset.save() return redirect('manage_course_list') return self.render_to_response({'course': self.course,'formset': formset}) class ContentCreateUpdateView(TemplateResponseMixin, View): module = None model = None obj = None template_name = 'courses/manage/content/form.html' def get_model(self, model_name): if model_name in ['text', 'video', 'image', 'file']: return apps.get_model(app_label='courses',model_name=model_name) return None def get_form(self, model, *args, **kwargs): Form = modelform_factory(model, exclude=['owner','order','created','updated']) return Form(*args, **kwargs) def dispatch(self, request, module_id, model_name, id=None): self.module = get_object_or_404(Module,id=module_id,course__owner=request.user) self.model = self.get_mode(model_name) if id: self.obj = get_object_or_404(self.model,id=id,owner=request.user) return super().dispatch(request, module_id, model_name, id) def get(self, request, module_id, model_name, id=None): form = self.get_form(self.model, instance=self.obj) return self.render_to_response({'form': form,'object': self.obj}) def post(self, request, module_id, model_name, id=None): form = self.get_form(self.model,instance=self.obj,data=request.POST,files=request.FILES) if form.is_valid(): obj = form.save(commit=False) obj.owner = request.user obj.save() if not id: # new content Content.objects.create(module=self.module,item=obj) return redirect('module_content_list', self.module.id) return self.render_to_response({'form': form,'object': self.obj}) class ContentDeleteView(View): def post(self, request, id): content = get_object_or_404(Content,id=id,module__course__owner=request.user) module = content.module content.item.delete() content.delete() return redirect('module_content_list', module.id) class ModuleContentListView(TemplateResponseMixin, View): template_name = 'courses/manage/module/content_list.html' def get(self, request, module_id): module = get_object_or_404(Module,id=module_id,course__owner=request.user) return self.render_to_response({'module': module}) class ModuleOrderView(CsrfExemptMixin,JsonRequestResponseMixin,View): def post(self, request): for id, order in self.request_json.items(): Module.objects.filter(id=id,course__owner=request.user).update(order=order) return self.render_json_response({'saved': 'OK'}) class ContentOrderView(CsrfExemptMixin,JsonRequestResponseMixin,View): def post(self, request): for id, order in self.request_json.items(): Content.objects.filter(id=id,module__course__owner=request.user).update(order=order) return self.render_json_response({'saved': 'OK'}) class CourseListView(TemplateResponseMixin, View): model = Course template_name = 'courses/course/list.html' def get(self, request, subject=None): subjects = Subject.objects.annotate(total_courses=Count('courses')) courses = Course.objects.annotate(total_modules=Count('modules')) if subject: subject = get_object_or_404(Subject, slug=subject) courses = courses.filter(subject=subject) return self.render_to_response({'subjects': subjects,'subject': subject,'courses': courses}) class CourseDetailView(DetailView): model = Course template_name = 'courses/course/detail.html' def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context['enroll_form'] = CourseEnrollForm(initial={'course':self.object}) return context

[ "sium.hossain@yahoo.com" ]

sium.hossain@yahoo.com

2f74ae3f7caac57b707a98584b6bdd4a40ded6f8

fd1dba8223ad1938916369b5eb721305ef197b30

/AtCoder/ABC/abc110/abc110c.py

b19744afbe63b3698d7e3487b7f15813a0167d39

[]

no_license

genkinanodesu/competitive

a3befd2f4127e2d41736655c8d0acfa9dc99c150

47003d545bcea848b409d60443655edb543d6ebb

refs/heads/master

2020-03-30T07:41:08.803867

2019-06-10T05:22:17

150,958,656

0

null

UTF-8

Python

false

326

py

S = input() T = input() n = len(S) X = [[] for _ in range(26)] Y = [[] for _ in range(26)] for i in range(n): s = ord(S[i]) - 97 t = ord(T[i]) - 97 X[s].append(i) Y[t].append(i) P = [tuple(x) for x in X] Q = [tuple(y) for y in Y] if set(P) == set(Q): print('Yes') else: print('No')

[ "s.genki0605@gmail.com" ]

s.genki0605@gmail.com

75db47364399ab750443e3afc703e376ca016a3a

b0984bc483f0e082975abb7610b4b4508764731d

/pythondispatchms/websetup/schema.py

35f6f96b61a9c4c8179ed07b9172c6753e6dc51e

[]

no_license

jordy33/python.dispatch.ms

42dd7b0f1d76742881111a71d9f8a897a6932ddc

e576b565e953871d56ee2f29a25a6f88b8ab120c

refs/heads/master

2020-09-23T06:10:14.817318

2019-12-02T16:51:20

225,424,389

0

null

UTF-8

Python

false

983

py

# -*- coding: utf-8 -*- """Setup the python.dispatch.ms application""" from __future__ import print_function from tg import config import transaction def setup_schema(command, conf, vars): """Place any commands to setup pythondispatchms here""" # Load the models # <websetup.websetup.schema.before.model.import> from pythondispatchms import model # <websetup.websetup.schema.after.model.import> # <websetup.websetup.schema.before.metadata.create_all> print("Creating tables") model.metadata.create_all(bind=config['tg.app_globals'].sa_engine) # <websetup.websetup.schema.after.metadata.create_all> transaction.commit() print('Initializing Migrations') import alembic.config alembic_cfg = alembic.config.Config() alembic_cfg.set_main_option("script_location", "migration") alembic_cfg.set_main_option("sqlalchemy.url", config['sqlalchemy.url']) import alembic.command alembic.command.stamp(alembic_cfg, "head")

[ "jorgemacias@Jorges-Mac-mini.local" ]

jorgemacias@Jorges-Mac-mini.local

1c1aa42436ef6a2988456af22d8dd172cb127e53

b9c579de7fdca8de76e00b4a912450b338e53b41

/Banner_Connections/Initialize_Oracle_Connection.py

63f10562b1831380044a3bdd027ce81ccf795501

[]

no_license

nmbenzo/ISSS_SMS_Prod

5bd5ede93369bdd158230a5a993a0554fcb2ecaf

dd1797b6614077a716e6df464ba865b400fcca2c

refs/heads/master

2022-12-22T04:41:27.276600

2019-08-21T22:38:46

184,464,467

0

null

UTF-8

Python

false

2,096

py

import sys, os import cx_Oracle import traceback import Banner_Connections.ODSP_Creds as creds import Banner_Connections.queries as query def banner_odsp_handler(): """Function to initialize an object from the cx_Oracle Connection class :returns a cx_Oracle Connection object if valid credentials exist otherwise prints a traceback error""" host = creds.host port = creds.port sid = creds.sid username = creds.username password = creds.password try: dsn = cx_Oracle.makedsn(host, port, sid) #print(dsn) connection = cx_Oracle.Connection("%s/%s@%s" % (username, password, dsn)) return connection except cx_Oracle.DatabaseError as exc: error, = exc.args print(sys.stderr, "Oracle-Error-Code:", error.code) print(sys.stderr, "Oracle-Error-Message:", error.message) tb = traceback.format_exc() return tb def banner_ODSP_tele(connection, query_name): """The banner_ODSP_tele function takes in an connection argument to connect to ODSP. It then accepts a specific query as the second argument and returns the query results""" cursor = connection.cursor() cursor.execute(query_name) try: query_result = [(area_code, number) for area_code, number in cursor] cleaned_number = [''.join(number) for number in query_result] return cleaned_number finally: cursor.close() connection.close() def banner_ODSP_emails(connection, query_name): """The banner_ODSP_tele function takes in an connection argument to connect to ODSP. It then accepts a specific query as the second argument and returns the query results""" cursor = connection.cursor() cursor.execute(query_name) try: if query_name != query.active_emails: query_result = [email[0] for email in cursor] cleaned_email = ''.join(email for email in query_result) return cleaned_email else: query_result = [email[0] for email in cursor] return query_result finally: cursor.close() connection.close() #if __name__ == "__main__": # print(banner_ODSP_emails(banner_odsp_handler(), query.active_emails))

[ "nmbenzo@gmail.com" ]

nmbenzo@gmail.com

7d92969bf5b2b8ec520a58f55d136bb8b5bb5568

5bfe0ddb8953711886c10ba636d3eeba934f6ae9

/6 zigzag.py

3bbc6ee51018e565701f4d5b1171c6bc532d19c6

[]

no_license

Alexis374/LeetCode

db1fd8348ff012a55582ee6c430a64308b0b9807

1b3ed37a87002891e55c627adb66e9a03e903061

refs/heads/master

2021-01-22T06:45:07.129877

2015-04-26T13:06:33

30,817,691

0

null

UTF-8

Python

false

1,071

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''' The string "PAYPALISHIRING" is written in a zigzag pattern on a given number of rows like this: (you may want to display this pattern in a fixed font for better legibility) P A H N A P L S I I G Y I R And then read line by line: "PAHNAPLSIIGYIR" Write the code that will take a string and make this conversion given a number of rows: string convert(string text, int nRows); convert("PAYPALISHIRING", 3) should return "PAHNAPLSIIGYIR". 边界情况是只有一行的时候。其他情况是存哈希表 ''' def convert(s, numRows): result_dict = {} for i in range(numRows): result_dict[i]=[] print result_dict sign = 1 index = 0 for ch in s: if numRows==1: return s result_dict[index].append(ch) index+= sign if index == numRows: sign = -1 index -= 2 if index == -1: sign =1 index = 1 result = '' for i in range(numRows): result = result + ''.join(result_dict[i]) return result print convert('ab',1)

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import pytest from .main import multiply def test_multiply(): assert multiply(2, 3) == 6 assert multiply(-3, 5) == -15 assert multiply(0, 4) == 0 with pytest.raises(TypeError): multiply("abc", 1.2)

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#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Sun Dec 9 14:19:45 2018 @author: user """ import numpy as np #import matplotlib.pyplot as plt from scipy.signal import butter, lfilter from scipy import signal from qcore import timeseries from scipy import integrate import os def readGP_2(loc, fname): """ Convinience function for reading files in the Graves and Pitarka format """ with open("/".join([loc, fname]), 'r') as f: lines = f.readlines() data = [] for line in lines[2:]: data.append([float(val) for val in line.split()]) data=np.concatenate(data) line1=lines[1].split() num_pts=float(line1[0]) dt=float(line1[1]) shift=float(line1[4]) return data, num_pts, dt, shift def computeFourier(accTimeSeries, dt, duration): #computes fourier spectra for acceleration time series # TODO: compute original number of points (setting to default for now) change to npts = len(accTimeSeries) npts = len(accTimeSeries) npts_FFT = int(np.ceil(duration)/dt) # compute number of points for efficient FFT ft_len = int(2.0 ** np.ceil(np.log(npts_FFT) / np.log(2.0))) if npts > ft_len: accTimeSeries = accTimeSeries[:ft_len] npts = len(accTimeSeries) # Apply hanning taper to last 5% of motion ntap = int(npts * 0.05) accTimeSeries[npts - ntap:] *= np.hanning(ntap * 2 + 1)[ntap + 1:] # increase time series length with zeroes for FFT accForFFT = np.pad(accTimeSeries, (0, ft_len - len(accTimeSeries)), 'constant', constant_values=(0,0)) ft = np.fft.rfft(accForFFT) # compute frequencies at which fourier amplitudes are computed ft_freq = np.arange(0, ft_len / 2 + 1) * ( 1.0 / (ft_len * dt)) return ft, ft_freq def butter_bandpass(lowcut, highcut, fs, order): nyq = 0.5 * fs low = lowcut / nyq high = highcut / nyq b, a = butter(order, [low, high], btype='band') return b, a def butter_bandpass_filter(data, lowcut, highcut, fs, order): b, a = butter_bandpass(lowcut, highcut, fs, order=order) y = lfilter(b, a, data) return y ###################################### def normpdf_python(x, mu, sigma): return 1/(sigma*np.sqrt(2*np.pi))*np.exp(-1*(x-mu)**2/2*sigma**2) ############################################## #def source_adj_1(stat_data_S,stat_data_O,num_pts,dt,wd): # # """ # Measure TauP value and construct Jp signal # """ # t = np.arange(num_pts)*dt # # ts=np.flip(-t[1:], axis=0) # lTime = np.concatenate((ts,t), axis=0) # w = normpdf_python(lTime, 0, 0.5) # wp = w/max(w) # # stat_data_S = np.multiply(stat_data_S,wd) # stat_data_O = np.multiply(stat_data_O,wd) # # Dis_S = np.cumsum(stat_data_S)*dt # Dis_O = np.cumsum(stat_data_O)*dt # # Dis_S = np.multiply(signal.tukey(int(num_pts),0.1),Dis_S) # Dis_O = np.multiply(signal.tukey(int(num_pts),0.1),Dis_O) # ## Dis_S = signal.detrend(Dis_S) ## Dis_O = signal.detrend(Dis_O) # # corr=np.correlate(Dis_O,Dis_S,"full") # wx=np.multiply(wp,corr) # # In=np.argmax(wx) # TauP=lTime[In] # # Jp_inv_norm=1/dt*np.sum(np.multiply(stat_data_S,stat_data_S)) # Jp = TauP*np.flip(stat_data_S, axis=0)*Jp_inv_norm # # Source = Jp # # return Source ############################################## def source_adj_ncc(stat_data_Sf,stat_data_Of,num_pts, delta_T,dt): """ Normalized correlation coefficient and optimal time shift """ num_delta_t = int(delta_T/dt) td = np.arange(-num_delta_t,num_delta_t)*dt ncc_array=np.zeros(len(td)) for it in range(0,len(td)): stat_data_O_shift = np.zeros(num_pts); n_shift=int(np.abs((td[it]/dt))); # if td[it]<0: # stat_data_0_O_shift[n_shift:num_pts] = stat_data_0_Of[0:num_pts-n_shift] # else: # stat_data_0_O_shift[0:num_pts-n_shift] = stat_data_0_Of[n_shift:num_pts] if td[it]<0: stat_data_O_shift[0:num_pts-n_shift] = stat_data_Of[n_shift:num_pts] else: stat_data_O_shift[n_shift:num_pts] = stat_data_Of[0:num_pts-n_shift] rwm1_arr=np.multiply(stat_data_Sf,stat_data_O_shift) rwm2_arr=np.square((stat_data_Sf)) rwm3_arr=np.square((stat_data_O_shift)) rwm1=integrate.simps(rwm1_arr,t) rwm2=integrate.simps(rwm2_arr,t) rwm3=integrate.simps(rwm3_arr,t) # rwm1 = np.sum(rwm1_arr) # rwm2 = np.sum(rwm2_arr) # rwm3 = np.sum(rwm3_arr) ncc_array[it]=rwm1/((rwm2*rwm3)**0.5) ncc_max = np.max(ncc_array); id_max = np.argmax(ncc_array); td_max = td[id_max]; Jp_inv_norm=1/dt*np.sum(np.multiply(stat_data_Sf,stat_data_Sf)) # Jp = TauP*np.flip(stat_data_S, axis=0)*Jp_inv_norm Jp = -td_max*np.flip(stat_data_Sf, axis=0)*Jp_inv_norm Source = Jp return Source def write_adj_source(s1,v1,mainfolder,mainfolder_source,source): with open("/".join([mainfolder, s1]), 'r') as f: lines = f.readlines() tline1= lines[0] tline2= lines[1] filename1=mainfolder_source+v1 print(filename1) fid = open(filename1,'w') fid.write("%s" %(tline1)) fid.write("%s" %(tline2)) lt=len(source) count=0 while (count+1)*6<lt: fid.write("%10f%10f%10f%10f%10f%10f%s" %(source[count*6],source[count*6+1],source[count*6+2],source[count*6+3],source[count*6+4],source[count*6+5],'\n')) count+=1 ii=lt-count*6 i=0 while (i<ii): i+=1 fid.write("%10f%s" %(source[lt-ii+i-1],'\n')) fid.close() return def write_adj_source_ts(s1,v1,mainfolder,mainfolder_source,source,dt): #filename1=mainfolder_source+v1 vs1=v1.split('.') timeseries.seis2txt(source,dt,mainfolder_source,vs1[0],vs1[1]) return def read_source(source_file): with open(source_file, 'r') as f: lines = f.readlines() line0=lines[0].split() nShot=int(line0[0]) S=np.zeros((nShot,3)) for i in range(1,nShot+1): line_i=lines[i].split() S[i-1,0]=int(line_i[0]) S[i-1,1]=int(line_i[1]) S[i-1,2]=int(line_i[2]) return nShot, S def read_stat_name(station_file): with open(station_file, 'r') as f: lines = f.readlines() line0=lines[0].split() nRec=int(line0[0]) R=np.zeros((nRec,3)) statnames = [] for i in range(1,nRec+1): line_i=lines[i].split() R[i-1,0]=int(line_i[0]) R[i-1,1]=int(line_i[1]) R[i-1,2]=int(line_i[2]) statnames.append(line_i[3]) return nRec, R, statnames def rms(stat_data): num_pts=len(stat_data) D = (np.sum(np.square(stat_data))/num_pts)**0.5 return stat_data/D def read_flexwin(filename): with open(filename, 'r') as f: lines = f.readlines() if(len(lines)>2): line2=lines[2].split() t_on=float(line2[1]) t_off=float(line2[2]) td_shift=float(line2[3]) cc=float(line2[4]) else: t_on = 0; t_off = 0; td_shift = 0; cc = 0; return t_on, t_off, td_shift, cc def winpad(lt,t_off,t_on,pad): """ Sin taper window """ #pad=5 if(t_on<10): t_on=10 if(t_off>lt-10): t_off=lt-10 L=t_off-t_on+2*pad # window=signal.gaussian(30, 4) # window1=signal.resample(window,L,axis=0, window=None) # window=np.linspace(1, 1, L))) window=np.ones((L)) #x=np.arange(0,pad,1) x=np.linspace(0, np.pi/2, pad) sinx=np.sin(x) window[0:pad] = sinx window[L-pad:L] = sinx[::-1] print('lt='+str(lt)) ar1=np.zeros((t_on-pad)) ar2=np.zeros((lt-t_off-pad)) window_pad0 = np.concatenate((ar1,window)) window_pad = np.concatenate((window_pad0,ar2)) return window_pad def time_shift_emod3d(data,delay_Time,dt): n_pts = len(data) ndelay_Time = int(delay_Time/(dt)) data_shift = np.zeros(data.shape) data_shift[0:n_pts-ndelay_Time] = data[ndelay_Time:n_pts] return data_shift ################################################### #statnames = ['A1' ,'A2' ,'A3' ,'A4' ,'A5' ,'A6' , 'A7', 'B1' ,'B2' ,'B3' ,'B4' ,'B5' ,'B6' , 'B7','C1' ,'C2' ,'C3' ,'C4' ,'C5' ,'C6' ,'C7','D1' ,'D2' ,'D3' ,'D4' ,'D5' ,'D6' ,'D7','E1' ,'E2' ,'E3' ,'E4' ,'E5' ,'E6' ,'E7','F1' ,'F2' ,'F3' ,'F4' ,'F5' ,'F6','F7','G1' ,'G2' ,'G3' ,'G4' ,'G5' ,'G6','G7'] station_file = '../../../StatInfo/STATION.txt' nRec, R, statnames = read_stat_name(station_file) #statnames=statnames[0:10] print('statnames') print(statnames) GV=['.090','.000','.ver'] GV_ascii=['.x','.y','.z'] mainfolder='../../Vel_es/Vel_es_i/' mainfolder_o='../../Vel_ob/Vel_ob_i/' mainfolder_source='../../../AdjSims/V3.0.7-a2a_xyz/Adj-InputAscii/' os.system('rm ../../../AdjSims/V3.0.7-a2a_xyz/Adj-InputAscii/*.*') print(mainfolder_o) _, num_pts, dt, shift = readGP_2('../../Vel_ob/Vel_ob_i','CBGS.000') num_pts=int(num_pts) t = np.arange(num_pts)*dt ############/nesi/nobackup/nesi00213/RunFolder/tdn27/rgraves/Adjoint/Syn_VMs/Kernels/######################### fs = 1/dt lowcut = 0.05 #highcut = 0.05 highcut = 0.1 #ndelay_T=int((3/0.1)/(dt)) #delta_T=10 delta_T=20 flo=0.1 delay_Time=(3/flo) fc = highcut # Cut-off frequency of the filter w = fc / (fs / 2) # Normalize the frequency b, a = signal.butter(4, w, 'low') source_file='../../../StatInfo/SOURCE.txt' nShot, S = read_source(source_file) wr = np.loadtxt('../../../../Kernels/Iters/iter1/Dump/geo_correlation.txt') #wr_arr = np.loadtxt('../../../../Kernels/Iters/iter1/Dump/geo_correlation.txt') #wr=np.reshape(wr_arr,[nRec,nShot]) #wr=np.ones([nRec,nShot]) ################################ fi1=open('iShot.dat','r') ishot=int(np.fromfile(fi1,dtype='int64')) fi1.close() print('ishot='+str(ishot)) #R_ishot_arr=np.loadtxt('../../../../Kernels/Iters/iter1/Dump/R_ishot_'+str(ishot)+'.txt') #R_all_arr=np.loadtxt('../../../../Kernels/index_all_ncc_gt005_t_end_corrected.txt') R_all_arr=np.loadtxt('../../../../Kernels/index_all_ncc_gt005_tshift_20s_new.txt') R_all=R_all_arr.reshape([nRec,3,nShot]) R_Time_record_arr = np.loadtxt('../../../../Kernels/R_Time_record_148s_dh_2km.txt') R_Time_record = R_Time_record_arr.reshape([2,nShot,nRec]) for i,statname in enumerate(statnames): #print('ireceiver='+str(i)) distance=((R[i,1]-S[ishot-1,1])**2+(R[i,2]-S[ishot-1,2])**2+(R[i,0]-S[ishot-1,0])**2)**(0.5) # source_x=np.zeros(num_pts) # source_y=np.zeros(num_pts) # source_z=np.zeros(num_pts) for k in range(0,3): source_adj=np.zeros(num_pts) s0=statname+GV[k] v0=statname+GV_ascii[k] if((distance<200) and (distance>0) and (R_all[i,k,ishot-1]==1)): print('ireceiver='+str(i)) wr_ij = wr[nRec*(ishot-1)+i] #wr_ij = wr[nShot*(i)+ishot-1] #wr_ij = wr[i,ishot-1] stat_data_0_S_org = timeseries.read_ascii(mainfolder+s0) stat_data_0_S = time_shift_emod3d(stat_data_0_S_org,delay_Time,dt) stat_data_0_S = np.multiply(signal.tukey(int(num_pts),0.1),stat_data_0_S) stat_data_0_O = timeseries.read_ascii(mainfolder_o+s0) stat_data_0_O = np.multiply(signal.tukey(int(num_pts),0.1),stat_data_0_O) #stat_data_0_S = signal.detrend(stat_data_0_S) #stat_data_0_O = signal.detrend(stat_data_0_O) # stat_data_0_S = signal.filtfilt(b, a, stat_data_0_S) # stat_data_0_O = signal.filtfilt(b, a, stat_data_0_O) stat_data_0_S = butter_bandpass_filter(stat_data_0_S, lowcut, highcut, fs, order=4) stat_data_0_O = butter_bandpass_filter(stat_data_0_O, lowcut, highcut, fs, order=4) #stat_data_0_O = np.multiply(signal.tukey(int(num_pts),1.0),stat_data_0_O) stat_data_0_S = rms(stat_data_0_S)*wr_ij stat_data_0_O = rms(stat_data_0_O)*wr_ij #Parameters for window df=1/dt lt=num_pts pad=5 #flexwin e_s_c_name = str(ishot)+'.'+s0+'.win' #filename = '../../../../Kernels/ALL_WINs/'+e_s_c_name filename = '../../../../Kernels/ALL_WINs_Tshift_20s/'+e_s_c_name t_on, t_off, td_shift, cc = read_flexwin(filename) #if (t_off> R_Time_record[1,ishot-1,i]): # t_off = R_Time_record[1,ishot-1,i] tx_on=int(t_on/dt) tx_off=int(t_off/dt) #Window for isolated filter wd=winpad(lt,tx_off,tx_on,pad) stat_data_0_S = np.multiply(stat_data_0_S,wd) stat_data_0_O = np.multiply(stat_data_0_O,wd) source_adj=source_adj_ncc(stat_data_0_S,stat_data_0_O,num_pts, delta_T,dt) write_adj_source_ts(s0,v0,mainfolder,mainfolder_source,source_adj,dt)

[ "andrei.nguyen@canterbury.ac.nz" ]

andrei.nguyen@canterbury.ac.nz

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euanramsay/python_movie_play

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import webbrowser class Movie(): def __init__(self, movie_title, movie_storyline, poster_image, trailer_youtube): """Template of an instance of Movie Args: movie_title (str): The title of the movie movie_storyline(str): A short description of the movies plot movie_image(str): A URL link to an image of the movie poster trailer_youtube(str): A URL link to the movie trailer """ self.title = movie_title self.storyline = movie_storyline self.poster_image_url = poster_image self.trailer_youtube_url = trailer_youtube def show_trailer(self): """Opens the trailer URL in a browser """ webbrowser.open(self.trailer_youtube_url)

[ "euancramsay@aol.com" ]

euancramsay@aol.com

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[]

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hspearman/spotify_library_serializer

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import json from flask import render_template, redirect, Response, Blueprint, session, request, logging from werkzeug.exceptions import abort from spotify_utility import build_authorization_url, refresh_token, is_token_existent, is_token_expired, get_token, \ get_tracks api = Blueprint('api', __name__) # # Protects against CSRF with every request # @api.before_request def check_csrf_token(): if request.method == "POST": token = session.pop('_csrf_token', None) if not token or token != request.form.get('_csrf_token'): abort(400) # # Selects template to render for site's default URL # @api.route('/') def index(): template = 'index.html' # If token exists ... if is_token_existent(): # If token expired, try to refresh if is_token_expired(): try: refresh_token() # If refresh fails, fall-back on default template except: pass # Otherwise, select authorized template else: template = 'index_authorized.html' return render_template(template) # # Redirects user to spotify authorization endpoint # @api.route( '/authorize', methods=['POST']) def authorize(): # Redirect user to spotify authorize page spotify_url = build_authorization_url() return redirect(spotify_url) # # Acts as callback for spotify's authorization workflow # When called, exchanges code for access token # @api.route('/login') def login(): try: get_token() # Ignore failure (workflow restarts on redirect) except: pass return redirect("/") # # Acts as callback for spotify's authorization workflow # When called, exchanges code for access token # @api.route( '/logout', methods=['POST']) def logout(): session.clear() return redirect("/") # # Get user's spotify library # @api.route('/library') def get_library(): # Try to get user's tracks try: tracks = get_tracks() return Response( json.dumps(tracks), headers={ 'Content-Disposition': 'attachment;filename=library.json', 'Content-Type': 'application/json' }) # On error, redirect to index except: return redirect("/")

[ "hspearman92@gmail.com" ]

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permissive

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2023-04-26T13:50:23.207329

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import json from django.core.exceptions import ValidationError from django.utils.translation import gettext_lazy as _ from django.utils.deconstruct import deconstructible from django.template.defaultfilters import filesizeformat def validate_monday(value): """Проверяет является ли дата понедельником. """ if value.weekday() != 0: raise ValidationError( _("This date should be a Monday.") ) def validate_sunday(value): """Проверяет является ли дата воскресеньем. """ if value.weekday() != 6: raise ValidationError( _("This date should be a Sunday.") ) def validate_slug(value): """Проверяет поле slug на допустимые значения. """ if value.lower() in ('create', 'update', 'delete'): raise ValidationError( _('Slug must not be "%(slug)s"'), params={'slug': value, }, ) def validate_positive(value): """Проверяет числовое поле на значения большее 0. """ if value <= 0: raise ValidationError( _('This value must be grater than zero'), ) def validate_json(value): """Проверяет текстовое поле на соответствие формату json. """ try: if value: json.loads(value) except ValueError as e: raise ValidationError( _('An error was founded in %(value)s template: %(message)s'), params={'value': value, 'message': e, }, )

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crowmurk@gmail.com

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epirevolve/workscheduler

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# -*- coding: utf-8 -*- from datetime import datetime def is_overlap(a_from: datetime, a_to: datetime, b_from: datetime, b_to: datetime): return (b_from <= a_from <= b_to) or (b_from <= a_to <= b_to)

[ "epirevolve@gmail.com" ]

epirevolve@gmail.com

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[]

no_license

svonton/discord_bot

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import discord import datetime import random import asyncio from discord.ext import commands bot = commands.Bot(command_prefix='$') def read_token(): with open("token.txt", "r") as f: lines = f.readlines() return lines[0].strip() def dembel(): td = datetime.date.today() dds = "2020-12-15" d = dds.split('-') dd = datetime.date(int(d[0]), int(d[1]), int(d[2])) resd = dd - td return str(resd).split()[0] def store_img(imgLink): link = imgLink.split(" ") with open("imgLink.txt", "r") as f: existLines = f.readlines() if link[1]+"\n" in existLines: return 1 else: with open("imgLink.txt", "a") as f: f.write(f"""{link[1]}\n""") return 0 def get_img(imgNumber): with open("imgLink.txt", "r") as f: lines = f.readlines() return lines[imgNumber].strip() def img_file_size(): with open("imgLink.txt", "r") as f: lines = f.readlines() return len(lines) game_gunlet = "" def gun_gunlet(message): global game_gunlet game_name = str(message.content).split(" ") game_gunlet += game_name[1] + "+" return game_gunlet token = read_token() client = discord.Client() @client.event async def on_ready(): await client.change_presence(status=discord.Status.online, activity=discord.Game("гольф промеж твоих булок")) @client.event async def on_message(message): global game_gunlet if message.content.startswith("!help"): embed = discord.Embed(title="Помощь по боту", description="Итак гаврики вот вам парочку команд") embed.add_field(name="!t", value="Оставшиеся дни до дембеля") embed.add_field(name="!s url", value="Сохраняет картинку по сылке") embed.add_field(name="!o", value="Показывает картинку по порядковуму номеру") embed.add_field(name="!r", value="Выпуливает рандомную картинку") embed.add_field(name="!ga", value="Добавляет игру в рулетку") embed.add_field(name="!gc", value="Чистит рулетку") embed.add_field(name="!gs", value="Показывает победителя рулетки") await message.channel.send(content=None, embed=embed) elif message.content.startswith("!t"): await message.channel.send(f"""{dembel()}""") elif message.content.startswith("пидр"): await message.channel.send(f"""скорее {message.author} пидр""") elif message.content.startswith("!s "): if "https" in message.content and len(message.content) > 10: if "twitch.tv" in message.content or "youtube" in message.content: await message.channel.send("не кидай сюда хню") else: if store_img(str(message.content)) == 0: await message.channel.send("забрал") else: await message.channel.send("изображение уже есть мудила") elif message.content.startswith("!o "): num = str(message.content).split(" ") await message.channel.send(get_img(int(num[1]))) elif message.content.startswith("!r"): await message.channel.send(get_img(random.randint(0, img_file_size()-1))) elif message.content.startswith("!ga "): if str(message.content) == "!ga": await message.channel.send("Напиши название дурашка") else: await message.channel.send(f"""Игра {str(message.content).split(" ")[1]} добавлена в обойму""") await message.channel.send(f"""Текущие игры в обойме: {gun_gunlet(message)}""") elif message.content.startswith("!gc"): game_gunlet = "" await message.channel.send("Магазин разряжен") elif message.content.startswith("!gs"): if game_gunlet == "": await message.channel.send("Ты куда стреляешь обойма то пустая") else: winner = game_gunlet.split("+") await message.channel.send(f"""Победитель: {winner[random.randint(0,len(winner)-2)]}""") await message.channel.send("Если кончил стрелять то разряди магазин с помощью !gc") client.run(token)

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import codecademylib3_seaborn import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier import matplotlib.pyplot as plt flags = pd.read_csv('flags.csv',header =0) print(flags.columns) print(flags.head()) labels = flags[['Landmass']] data = flags[["Red", "Green", "Blue", "Gold", "White", "Black", "Orange", "Circles", "Crosses","Saltires","Quarters","Sunstars", "Crescent","Triangle"]] train_data,test_data,train_labels,test_labels = train_test_split(data,labels,random_state=1) scores = [] for i in range(1,20): tree = DecisionTreeClassifier(random_state=1,max_depth =i) tree.fit(train_data,train_labels) score = tree.score(test_data,test_labels) scores.append(score) plt.plot(range(1,20),scores) plt.show()

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noreply@github.com

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gatoatigrado/vim-complete-es-server

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from __future__ import absolute_import from __future__ import print_function # Fix Flask-autoloaded module path import sys if "/code" not in sys.path: sys.path.insert(0, "/code") import flask import re import simplejson from collections import namedtuple from es_complete import es_client from es_complete import es_query from es_complete import es_index_data from es_complete import text_analysis app = flask.Flask(__name__) AutocompleteRequest = namedtuple("AutocompleteRequest", [ "current_line", "current_column", "lines", "word_being_completed" ]) SPACES_RE = re.compile(ur'[^\w\d\_]+') def simple_buffer_complete(request): if request.word_being_completed: words = text_analysis.get_all_words("\n".join(request.lines)) return [ w for w in words if w.startswith(request.word_being_completed) ] else: return [] @app.route('/complete') def complete(): body = flask.request.get_json(force=True) request = AutocompleteRequest(**body) basics = simple_buffer_complete(request) result = ( simple_buffer_complete(request)[:10] + es_query.elasticsearch_complete(request) ) return simplejson.dumps(result) @app.route("/index-raw", methods=["POST"]) def index_raw(): es_index_data.index_data(flask.request.get_data()) return "Success!" @app.route("/clear-index", methods=["POST"]) def clear_index(): es_client.recreate_index() return "Success!" if __name__ == '__main__': app.run(host='0.0.0.0', port=18013, debug=True)

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/Exercício 8 - verificação de aprendizagem.py

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eliasssantana/logicaProgramacao

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print("=-" * 30) e = 'Exercício' print(e.center(50)) print("=-" * 30) # 8) ''' - Crie um programa que leia nome, ano de nascimento e carteira de trabalho e cadastre-os (com idade) em um dicionário. Se por acaso a CTPS for diferente de 0, o dicionário receberá também o ano de contratação e o salário. Calcule e acrescente , além da idade , com quantos anos a pessoa vai se aposentar. Considere que o trabalhador deve contribuir por 35 anos para se aposentar. ''' def aposentadoria(): pessoa = {} nome = input("Digite o seu nome:\n") ano_nascimento = int(input("Digite o ano de seu nascimento:\n")) carteira_trabalho = int(input("Digite o número da sua carteira de trabalho:\n")) pessoa['nome'] = nome pessoa['nascimento'] = ano_nascimento if carteira_trabalho != 0: pessoa['ano de contratação'] = int(input("Digite o ano de sua contratação:\n")) pessoa['salário'] = float(input('Digite o seu salário:\n')) pessoa['CTPS'] = carteira_trabalho else: pass idade = 2021 - ano_nascimento pessoa['idade'] = idade aposentadoria = pessoa['ano de contratação'] + 35 pessoa['idade_aposentadoria'] = (aposentadoria - 2021) + idade pessoa['aposentadoria'] = aposentadoria for k,v in pessoa.items(): print(f'{k} - {v}') aposentadoria()

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/Servicio-Nica-Ventas/Nivel3/nica-ventas/app/app.py

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LISSETTE-QUINTERO/Servicio-Nica-Ventas

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from flask import Flask, jsonify, request, escape import os from flask_mysqldb import MySQL from worklog import Worklog import redis app = Flask(__name__) app.config['MYSQL_HOST'] = os.environ['DATABASE_HOST'] app.config['MYSQL_USER'] = os.environ['DATABASE_USER'] app.config['MYSQL_PASSWORD'] = os.environ['DATABASE_PASSWORD'] app.config['MYSQL_DB'] = os.environ['DATABASE_NAME'] mysql = MySQL(app) redis_cli = redis.Redis(host=os.environ['REDIS_LOCATION'], port=os.environ['REDIS_PORT']) @app.route('/active', methods=['GET']) def get_active(): #try: country = request.args.get('country') city = request.args.get('city'); key = country.lower() + '_' + city.lower() state = redis_cli.get(key) if state: response = { "country":country, "city":city, "active":bool(state), "cache":"hit" } else: wl=Worklog(mysql,app.logger) js=wl.find_location(country,city) if js is None: response = {"mensaje":"Registro no identificado"} else: redis_cli.set(key,escape(js[2])) response = { "country":js[0], "city":js[1], "active":bool(js[2]), "cache":"miss" } return jsonify(response) # except: # return jsonify({"mensaje":"Error Verifique URL"}) @app.route('/active', methods=['POST']) def post_active(): try: payload = request.get_json() wl = Worklog(mysql, app.logger) js=wl.find_location(payload['country'],payload['city']) if js is None: wl.save_location(**payload) response = { "mensaje":"Registro guardado", "country":payload['country'], "city":payload['city'] } else: response = {"mensaje":"Registro existente"} return jsonify(response) except: return jsonify({"mensaje": "error"}) @app.route('/active', methods=['PUT']) def put_active(): try: payload = request.get_json() auth = request.headers.get("authorization", None) if not auth: return jsonify('Token no enviado') elif auth != "Bearer 2234hj234h2kkjjh42kjj2b20asd6918": return jsonify('Token no autorizado') else: wl = Worklog(mysql, app.logger) wl.state_location(**payload) response= { "mensaje": "Registro actualizado", "token": auth, "country": payload['country'], "city": payload['city'], "active": payload['active'] } return jsonify(response) except: return jsonify({"mensaje": "error"}) if __name__ == '__main__': app.run(debug=True, host='0.0.0.0')

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2023-08-13T02:42:54.275525

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import torch from torchvision import transforms from torch.utils.data import DataLoader from matplotlib import pyplot as plt import numpy as np from . import config from .datasets import RealImageDataset from . import models from .logger import Logger from ._utils import train_d_model, train_g_model, save_samples, show_samples, denormalize class Generator: def __init__( self, model_name: str = None, ): self.logger = Logger(self.__class__.__name__) self.logger.info(f'model path: {config.path.models / model_name}') self.model_path = str(config.path.models / model_name) self.model = None def load_model(self): self.model = models.Generator().to(config.device) self.model.load_state_dict( torch.load(self.model_path) ) self.model.eval() self.logger.debug('model was loaded successfully') def save_model(self): torch.save( self.model.state_dict(), self.model_path ) self.logger.debug('model was saved successfully') def generate( self, seed: int = None, latent_vector: torch.Tensor = None ): if seed is not None: torch.manual_seed(seed) if latent_vector is None: latent_vector = torch.randn( 1, config.data.latent_vector_size, device=config.device, ) img = self.model(latent_vector).squeeze().detach().cpu().numpy() return np.transpose(denormalize(img), (1, 2, 0)) def train( self, start_from_checkpoint=True, ): self.logger.info('started training new model') self.logger.info(f'using device: {config.device}') # prepare data dataset = RealImageDataset( config.path.training_dataset, transform=transforms.Compose( [ transforms.Resize(config.data.image_size), transforms.CenterCrop(config.data.image_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ), ) data_loader = DataLoader( dataset=dataset, batch_size=config.training.batch_size, shuffle=True, drop_last=True, num_workers=4, ) # prepare models d_model = models.Discriminator().to(config.device) g_model = models.Generator().to(config.device) # link models with optimizers d_optimizer = torch.optim.Adam( params=d_model.parameters(), lr=config.training.d_learning_rate, betas=(0.5, 0.9), ) g_optimizer = torch.optim.Adam( params=g_model.parameters(), lr=config.training.g_learning_rate, betas=(0.5, 0.9), ) # prepare to record dataset plots d_losses = [] g_losses = [] fixed_latent_vector = torch.randn( config.training.sample_num, config.data.latent_vector_size, device=config.device, ) start_epoch = 0 if start_from_checkpoint: try: checkpoint = torch.load(config.path.checkpoint, map_location='cpu') d_model.load_state_dict(checkpoint['d_model_state_dict']) g_model.load_state_dict(checkpoint['g_model_state_dict']) d_optimizer.load_state_dict(checkpoint['d_optimizer_state_dict']) g_optimizer.load_state_dict(checkpoint['g_optimizer_state_dict']) d_losses = checkpoint['d_losses'] g_losses = checkpoint['g_losses'] fixed_latent_vector = checkpoint['fixed_latent_vector'].to(config.device) start_epoch = checkpoint['epoch'] + 1 torch.set_rng_state(checkpoint['rng_state']) except FileNotFoundError: self.logger.warning('Checkpoint not found') # train for epoch in range(start_epoch, config.training.epochs, ): print(f'\nEpoch: {epoch + 1}') for idx, (real_images, _) in enumerate(data_loader): # show_samples(real_images) real_images = real_images.to(config.device) print(f'\rProcess: {100 * (idx + 1) / len(data_loader): .2f}%', end='') d_loss = None for _ in range(config.training.d_loop_num): d_loss = train_d_model( d_model=d_model, g_model=g_model, real_images=real_images, d_optimizer=d_optimizer, ) d_losses.append(d_loss) torch.cuda.empty_cache() g_loss = None for _ in range(config.training.g_loop_num): g_loss = train_g_model( g_model=g_model, d_model=d_model, g_optimizer=g_optimizer, ) g_losses.append(g_loss) torch.cuda.empty_cache() print( f"\n" f"Discriminator loss: {d_losses[-1]}\n" f"Generator loss: {g_losses[-1]}\n" ) # save losses plot plt.title("Generator and Discriminator Loss During Training") plt.plot(g_losses, label="generator") plt.plot(d_losses, label="discriminator") plt.xlabel("iterations") plt.ylabel("Loss") plt.legend() plt.savefig(fname=str(config.path.training_plots / 'losses.jpg')) plt.clf() # save samples g_model.eval() save_samples( file_name=f'E{epoch + 1}.jpg', samples=g_model(fixed_latent_vector) ) g_model.train() self.model = g_model self.save_model() checkpoint = dict() checkpoint['d_model_state_dict'] = d_model.state_dict() checkpoint['g_model_state_dict'] = g_model.state_dict() checkpoint['d_optimizer_state_dict'] = d_optimizer.state_dict() checkpoint['g_optimizer_state_dict'] = g_optimizer.state_dict() checkpoint['d_losses'] = d_losses checkpoint['g_losses'] = g_losses checkpoint['fixed_latent_vector'] = fixed_latent_vector checkpoint['epoch'] = epoch checkpoint['rng_state'] = torch.get_rng_state() torch.save(checkpoint, config.path.checkpoint) self.model.eval()

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from subprocess import call, Popen, PIPE, check_output print(call("ls -l", shell=True)) print(check_output("ls -l", shell=True).decode()) pipe1 = Popen("ls -l", stdout=PIPE, shell=True) pipe2 = Popen("wc -l", stdin=pipe1.stdout, stdout=PIPE, shell=True) print(pipe2.stdout.read().decode())

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d-halverson/Walgreens-Scraper

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# ---------------------------------------------------------------------------- # pyglet # Copyright (c) 2006-2008 Alex Holkner # Copyright (c) 2008-2020 pyglet contributors # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * 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. # * Neither the name of pyglet nor the names of its # contributors may be used to endorse or promote products # derived from this software without specific prior written # permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "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 THE # COPYRIGHT OWNER OR 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. # ---------------------------------------------------------------------------- from pyglet import gl, compat_platform from pyglet.gl import gl_info from pyglet.gl import glu_info class Config: """Graphics configuration. A Config stores the preferences for OpenGL attributes such as the number of auxilliary buffers, size of the colour and depth buffers, double buffering, stencilling, multi- and super-sampling, and so on. Different platforms support a different set of attributes, so these are set with a string key and a value which is integer or boolean. :Ivariables: `double_buffer` : bool Specify the presence of a back-buffer for every color buffer. `stereo` : bool Specify the presence of separate left and right buffer sets. `buffer_size` : int Total bits per sample per color buffer. `aux_buffers` : int The number of auxilliary color buffers. `sample_buffers` : int The number of multisample buffers. `samples` : int The number of samples per pixel, or 0 if there are no multisample buffers. `red_size` : int Bits per sample per buffer devoted to the red component. `green_size` : int Bits per sample per buffer devoted to the green component. `blue_size` : int Bits per sample per buffer devoted to the blue component. `alpha_size` : int Bits per sample per buffer devoted to the alpha component. `depth_size` : int Bits per sample in the depth buffer. `stencil_size` : int Bits per sample in the stencil buffer. `accum_red_size` : int Bits per pixel devoted to the red component in the accumulation buffer. `accum_green_size` : int Bits per pixel devoted to the green component in the accumulation buffer. `accum_blue_size` : int Bits per pixel devoted to the blue component in the accumulation buffer. `accum_alpha_size` : int Bits per pixel devoted to the alpha component in the accumulation buffer. """ _attribute_names = [ 'double_buffer', 'stereo', 'buffer_size', 'aux_buffers', 'sample_buffers', 'samples', 'red_size', 'green_size', 'blue_size', 'alpha_size', 'depth_size', 'stencil_size', 'accum_red_size', 'accum_green_size', 'accum_blue_size', 'accum_alpha_size', 'major_version', 'minor_version', 'forward_compatible', 'debug' ] major_version = None minor_version = None forward_compatible = None debug = None def __init__(self, **kwargs): """Create a template config with the given attributes. Specify attributes as keyword arguments, for example:: template = Config(double_buffer=True) """ for name in self._attribute_names: if name in kwargs: setattr(self, name, kwargs[name]) else: setattr(self, name, None) def requires_gl_3(self): if self.major_version is not None and self.major_version >= 3: return True if self.forward_compatible or self.debug: return True return False def get_gl_attributes(self): """Return a list of attributes set on this config. :rtype: list of tuple ``(name, value)`` :return: All attributes, with unset attributes having a value of ``None``. """ return [(name, getattr(self, name)) for name in self._attribute_names] def match(self, canvas): """Return a list of matching complete configs for the given canvas. .. versionadded:: 1.2 :Parameters: `canvas` : `Canvas` Display to host contexts created from the config. :rtype: list of `CanvasConfig` """ raise NotImplementedError('abstract') def create_context(self, share): """Create a GL context that satisifies this configuration. :deprecated: Use `CanvasConfig.create_context`. :Parameters: `share` : `Context` If not None, a context with which to share objects with. :rtype: `Context` :return: The new context. """ raise gl.ConfigException('This config cannot be used to create contexts. ' 'Use Config.match to created a CanvasConfig') def is_complete(self): """Determine if this config is complete and able to create a context. Configs created directly are not complete, they can only serve as templates for retrieving a supported config from the system. For example, `pyglet.window.Screen.get_matching_configs` returns complete configs. :deprecated: Use ``isinstance(config, CanvasConfig)``. :rtype: bool :return: True if the config is complete and can create a context. """ return isinstance(self, CanvasConfig) def __repr__(self): import pprint return '%s(%s)' % (self.__class__.__name__, pprint.pformat(self.get_gl_attributes())) class CanvasConfig(Config): """OpenGL configuration for a particular canvas. Use `Config.match` to obtain an instance of this class. .. versionadded:: 1.2 :Ivariables: `canvas` : `Canvas` The canvas this config is valid on. """ def __init__(self, canvas, base_config): self.canvas = canvas self.major_version = base_config.major_version self.minor_version = base_config.minor_version self.forward_compatible = base_config.forward_compatible self.debug = base_config.debug def compatible(self, canvas): raise NotImplementedError('abstract') def create_context(self, share): """Create a GL context that satisifies this configuration. :Parameters: `share` : `Context` If not None, a context with which to share objects with. :rtype: `Context` :return: The new context. """ raise NotImplementedError('abstract') def is_complete(self): return True class ObjectSpace: def __init__(self): # Textures and buffers scheduled for deletion # the next time this object space is active. self._doomed_textures = [] self._doomed_buffers = [] class Context: """OpenGL context for drawing. Use `CanvasConfig.create_context` to create a context. :Ivariables: `object_space` : `ObjectSpace` An object which is shared between all contexts that share GL objects. """ #: Context share behaviour indicating that objects should not be #: shared with existing contexts. CONTEXT_SHARE_NONE = None #: Context share behaviour indicating that objects are shared with #: the most recently created context (the default). CONTEXT_SHARE_EXISTING = 1 # Used for error checking, True if currently within a glBegin/End block. # Ignored if error checking is disabled. _gl_begin = False # gl_info.GLInfo instance, filled in on first set_current _info = None # List of (attr, check) for each driver/device-specific workaround that is # implemented. The `attr` attribute on this context is set to the result # of evaluating `check(gl_info)` the first time this context is used. _workaround_checks = [ # GDI Generic renderer on Windows does not implement # GL_UNPACK_ROW_LENGTH correctly. ('_workaround_unpack_row_length', lambda info: info.get_renderer() == 'GDI Generic'), # Reportedly segfaults in text_input.py example with # "ATI Radeon X1600 OpenGL Engine" # glGenBuffers not exported by # "ATI Radeon X1270 x86/MMX/3DNow!/SSE2" # "RADEON XPRESS 200M Series x86/MMX/3DNow!/SSE2" # glGenBuffers not exported by # "Intel 965/963 Graphics Media Accelerator" ('_workaround_vbo', lambda info: (info.get_renderer().startswith('ATI Radeon X') or info.get_renderer().startswith('RADEON XPRESS 200M') or info.get_renderer() == 'Intel 965/963 Graphics Media Accelerator')), # Some ATI cards on OS X start drawing from a VBO before it's written # to. In these cases pyglet needs to call glFinish() to flush the # pipeline after updating a buffer but before rendering. ('_workaround_vbo_finish', lambda info: ('ATI' in info.get_renderer() and info.have_version(1, 5) and compat_platform == 'darwin')), ] def __init__(self, config, context_share=None): self.config = config self.context_share = context_share self.canvas = None if context_share: self.object_space = context_share.object_space else: self.object_space = ObjectSpace() def __repr__(self): return '%s()' % self.__class__.__name__ def attach(self, canvas): if self.canvas is not None: self.detach() if not self.config.compatible(canvas): raise RuntimeError('Cannot attach %r to %r' % (canvas, self)) self.canvas = canvas def detach(self): self.canvas = None def set_current(self): if not self.canvas: raise RuntimeError('Canvas has not been attached') # XXX not per-thread gl.current_context = self # XXX gl_info.set_active_context() glu_info.set_active_context() # Implement workarounds if not self._info: self._info = gl_info.GLInfo() self._info.set_active_context() for attr, check in self._workaround_checks: setattr(self, attr, check(self._info)) # Release textures and buffers on this context scheduled for deletion. # Note that the garbage collector may introduce a race condition, # so operate on a copy of the textures/buffers and remove the deleted # items using list slicing (which is an atomic operation) if self.object_space._doomed_textures: textures = self.object_space._doomed_textures[:] textures = (gl.GLuint * len(textures))(*textures) gl.glDeleteTextures(len(textures), textures) self.object_space._doomed_textures[0:len(textures)] = [] if self.object_space._doomed_buffers: buffers = self.object_space._doomed_buffers[:] buffers = (gl.GLuint * len(buffers))(*buffers) gl.glDeleteBuffers(len(buffers), buffers) self.object_space._doomed_buffers[0:len(buffers)] = [] def destroy(self): """Release the context. The context will not be useable after being destroyed. Each platform has its own convention for releasing the context and the buffer(s) that depend on it in the correct order; this should never be called by an application. """ self.detach() if gl.current_context is self: gl.current_context = None gl_info.remove_active_context() # Switch back to shadow context. if gl._shadow_window is not None: gl._shadow_window.switch_to() def delete_texture(self, texture_id): """Safely delete a texture belonging to this context. Usually, the texture is released immediately using ``glDeleteTextures``, however if another context that does not share this context's object space is currently active, the deletion will be deferred until an appropriate context is activated. :Parameters: `texture_id` : int The OpenGL name of the texture to delete. """ if self.object_space is gl.current_context.object_space: id = gl.GLuint(texture_id) gl.glDeleteTextures(1, id) else: self.object_space._doomed_textures.append(texture_id) def delete_buffer(self, buffer_id): """Safely delete a buffer object belonging to this context. This method behaves similarly to :py:func:`~pyglet.text.document.AbstractDocument.delete_texture`, though for ``glDeleteBuffers`` instead of ``glDeleteTextures``. :Parameters: `buffer_id` : int The OpenGL name of the buffer to delete. .. versionadded:: 1.1 """ if self.object_space is gl.current_context.object_space and False: id = gl.GLuint(buffer_id) gl.glDeleteBuffers(1, id) else: self.object_space._doomed_buffers.append(buffer_id) def get_info(self): """Get the OpenGL information for this context. .. versionadded:: 1.2 :rtype: `GLInfo` """ return self._info

[ "drew.m.halverson@gmail.com" ]

drew.m.halverson@gmail.com

e6d5eb58168ade63b3dec20ddcaa54dadfbbe80f

23ac473ba6ca03412e825d939676a08f6a2e6546

/modules/exploit-0.1/commands.py

565ba6aee271dad9935cdad2e5aa40dfd7c4ab5d

[]

no_license

bwingu/app01back

710c77c35bcb9a27e75a27304a26ca46546b260a

b5ef894ecfd43d59e6e8d4b5215a445fd0b19f97

refs/heads/master

2021-01-18T10:53:37.988723

2012-02-08T21:11:09

3,304,899

0

null

UTF-8

Python

false

827

py

# Here you can create play commands that are specific to the module, and extend existing commands MODULE = 'exploit' # Commands that are specific to your module COMMANDS = ['exploit:hello'] def execute(**kargs): command = kargs.get("command") app = kargs.get("app") args = kargs.get("args") env = kargs.get("env") if command == "exploit:hello": print "~ Hello" # This will be executed before any command (new, run...) def before(**kargs): command = kargs.get("command") app = kargs.get("app") args = kargs.get("args") env = kargs.get("env") # This will be executed after any command (new, run...) def after(**kargs): command = kargs.get("command") app = kargs.get("app") args = kargs.get("args") env = kargs.get("env") if command == "new": pass

[ "louis.sebastien@gmail.com" ]

louis.sebastien@gmail.com

72237823847f45db31b97d3e26d00c5ab4caf5a6

276c7120ce431ed8fae93c70b3ed2dedc9bc9301

/resources/events.py

b64397111ff50dfb1d43e130675fbcd85b57b071

[]

no_license

notinuse1234/zoom_zoom_zow

8f82c6deb1e826bd1b918a5715915457659aedc8

bc0e94bec6b62dcc0da410e9f25be787b11d5a78

refs/heads/master

2022-01-27T05:14:59.446663

2019-05-06T18:32:16

null

0

null

UTF-8

Python

false

210

py

import pygame as pg class Events(): @staticmethod @property def BEGINSWING(): return pg.USEREVENT + 1 @staticmethod @property def ENDSWING(): return pg.USEREVENT + 2

[ "ahester@cisco.com" ]

ahester@cisco.com

1938abebcd0e1a343f13385337a8f7233e24c2f7

359881c00ce4faf756fe2cf3b503452c0bcf313a

/raterprojectapi/serializers/ratings.py

3614aefc0cc68bf7977060010f57ea5de9d85a9c

[]

no_license

jshearon/rater-project

1bd8fe2080faa24e2b2dd2217ae1474132aeb61a

a67142018e822c57ad80ba6589e2e054abc42bcf

refs/heads/main

2023-02-28T04:30:11.916983

2021-01-27T00:56:21

314,429,265

0

null

2021-01-27T00:52:43

2020-11-20T02:50:38

Python

UTF-8

Python

false

713

py

from rest_framework import serializers from raterprojectapi.models import Ratings from rest_framework.validators import UniqueTogetherValidator class RatingSerializer(serializers.ModelSerializer): class Meta: model = Ratings validators = [ UniqueTogetherValidator( queryset=Ratings.objects.all(), fields=['player', 'game'], message="This player has already rated this game" ) ] player = serializers.PrimaryKeyRelatedField(many=False, read_only=True) game = serializers.PrimaryKeyRelatedField(many=False, read_only=True) fields = ('id', 'rating_value', 'player', 'game') depth = 0

[ "jonathan.shearon@gmail.com" ]

jonathan.shearon@gmail.com

90cfe476dff2e715a46fde91c5b2163d5eb304ca

b7c68fac8bb7b3b372a76988f3efe63c4f2526a5

/tensorflow-experiments.py

754f456975768457874ff4d4c0127e1405d60391

[]

no_license

misrasiddhant/Python

5b87feaae7e47766621e4453969c39e2030137bf

6bd4afb6b614613c7462e36a24ffd852c5ec82d1

refs/heads/master

2023-04-19T17:59:56.141669

2021-05-01T22:50:22

163,623,424

0

null

UTF-8

Python

false

1,208

py

# -*- coding: utf-8 -*- """ Created on Sat Sep 23 17:51:28 2017 @author: Siddhant Misra """ import tensorflow as tf """ a = tf.constant(3.0) b= tf.constant(4.0, dtype = tf.float32) print(a,b) session = tf.Session() session.run([a,b]) sum = tf.add(a,b) print(sum) session.run(sum) a = tf.placeholder(dtype = tf.float32) b = tf.placeholder(dtype = tf.float32) c = tf.add(a,b) session.run(c,{a:2,b:5}) session.run(c,{a:[2,4],b:[5,9]}) session.run(c,{a:[[2,4],[3,5]],b:[[2,3],[5,9]]}) """ session = tf.Session() m = tf.Variable([.3], dtype = tf.float32) c = tf.Variable([-.3], dtype = tf.float32) x = tf.placeholder(dtype = tf.float32) y = tf.placeholder(dtype = tf.float32) linear_model = m*x+c init= tf.global_variables_initializer() session.run(init) session.run(linear_model, {x:[1,2,3,4,5]}) SqErr = tf.squared_difference(linear_model ,y) loss = tf.reduce_sum(SqErr) session.run(loss,{x:[1,2,3,4,5],y:[2,6,10,14,18]}) print(loss) optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) session.run(init) for i in range(100000): session.run(train,{x:[1,2,3,4,5],y:[2,6,10,14,18]}) session.run([m,c]) session.run(loss,{x:[1,2,3,4,5],y:[2,6,10,14,18]})

[ "misrasiddhant@yahoo.co.in" ]

misrasiddhant@yahoo.co.in

ef06e79576ab02091941bdb71d690f0d88a6d32d

9fa4b5f59dd1089e27ff4a4148e59eab76930ce8

/Lesson2/day2_gaussian.py

763939d697f7f5b65145aea3f70d3a1bac3a5f46

[]

no_license

butlerkend/DrexelCodingLessons

1dc469e14c651a555bd28eb4a7010661b3ec90d9

c98b04c57ba1216a71d02e1920c5b142a7bcee79

refs/heads/master

2022-12-13T09:59:28.963756

2020-09-11T14:58:24

294,724,529

0

null

2020-09-11T14:57:07

2020-09-11T14:57:06

null

UTF-8

Python

false

356

py

import math as m #choose variables x = float(input("Choose x: ")) m_var = float(input("Choose m: ")) s = float(input("Choose s: ")) print("x is %d" % x) print("m is %d" % m_var) print("s is %d" % s) #calculate f = (1./m.sqrt(2 * m.pi)) * m.exp(-1*(1./2)*(((x-m_var)/float(s))**2)) #print answer print("therefore, the Guassian is computed as") print(f)

[ "scl63@drexel.edu" ]

scl63@drexel.edu

4a04f161cd2987c6ca772ac5ef11c4953ecbb7ec

cfa35dc2ea93ee0eceb2399a9e6112e987579c09

/stonesoup/metricgenerator/__init__.py

580303c8a8d1dce6e8550f6f212b7afe198d89c9

[ "LicenseRef-scancode-proprietary-license", "MIT", "LicenseRef-scancode-unknown-license-reference", "Apache-2.0", "Python-2.0", "LicenseRef-scancode-secret-labs-2011" ]

permissive

dstl/Stone-Soup

227e6a9e6fbdceca14af3f0259f311ec74095597

f24090cc919b3b590b84f965a3884ed1293d181d

refs/heads/main

2023-09-01T14:33:14.626428

2023-09-01T11:35:46

98,420,803

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126

MIT

2023-09-14T14:55:34

2017-07-26T12:34:28

Python

UTF-8

Python

false

65

py

from .base import MetricGenerator __all__ = ['MetricGenerator']

[ "sdhiscocks@dstl.gov.uk" ]

sdhiscocks@dstl.gov.uk

ac2e687c0c4a12c9903eb7d92a3010cc767d746d

055badce36d00effa7f36c43bf4c3c9fc0cd3642

/streamFootballNew.py

504b135de8c66a59718c955e1c0383ee58384417

[]

no_license

sgtrouge/Adsfluence

f4bbb3b684b8fb08c5ce642ea8535222123bd2c8

23843f03aded1915cf3f885c9198129abaf73bbc

refs/heads/master

2021-01-11T06:16:36.206828

2016-12-12T20:50:45

72,179,452

0

null

2016-11-21T03:55:41

2016-10-28T06:19:30

Python

UTF-8

Python

false

4,413

py

#!/usr/bin/env python # -*- coding: UTF-8 -*- # https://www.dataquest.io/blog/streaming-data-python/ import time import tweepy import json import csv from getpass import getpass from textwrap import TextWrapper # Keywords track_Michigan = ['GoBlue', 'Ann Arbor', 'UMich', 'Michigan Wolverines', 'BigHouse', 'University of Michigan', 'Duderstadt', 'Yost arena', 'Crisler Center'] import csv w = open("newFootball3rd.txt", "r") initContent = w.read() count = len(initContent.split('}{')) w.close() w = open("newFootball3rd.txt", "a") # Read through old user list to ensure we only filter # tweets of existing user all_user_id = {} def readOldUser(filename): f = open(filename, 'r') global all_user_id data = f.read() splits = data.split('}{') splits[0] = splits[0][1:] # For some reason the last one isn't built correctly, need to append ]] splits[-1] = splits[-1][:-1] +']]' count = 0 for js in splits: jss = '{' + js + '}' try: tweet_map = json.loads(jss) user = tweet_map["user_id"] all_user_id[int(user)] = True if "retweeted_author_id" in tweet_map: retweeted_author_id = tweet_map['retweeted_author_id'] all_user_id[int(retweeted_author_id)] = True count += 1 except: pass print len(all_user_id) f.close() # extract a row of info into csv from status # Features: user_id, source_id if RT, content, location of user, # of RT, # of followers, # of followees, # timestamp, tweet ID def writeAsJSON(status): global all_user_id global count print count if int(status.author.id) not in all_user_id: return rowInfo = {} rowInfo['content'] = status.text rowInfo['user_id'] = status.author.id rowInfo['user_follower_count'] = status.author.followers_count rowInfo['user_location'] = status.author.location rowInfo['retweet_count'] = status.retweet_count rowInfo['timestamp'] = status.timestamp_ms if hasattr(status, 'retweeted_status'): if int(status.retweeted_status.author.id) not in all_user_id: return rowInfo['retweeted_author_id'] = status.retweeted_status.author.id rowInfo['retweeted_author_followers_count'] = status.retweeted_status.author.followers_count rowInfo['retweeted_author_location'] = status.retweeted_status.author.location rowInfo['retweeted_favorite_count'] = status.retweeted_status.favorite_count global w count = count + 1 print count json.dump(rowInfo, w) class StreamWatcherListener(tweepy.StreamListener): status_wrapper = TextWrapper(width=60, initial_indent=' ', subsequent_indent=' ') def on_status(self, status): try: print self.status_wrapper.fill(status.text) writeAsJSON(status) print '\n %s %s via %s\n' % (status.author.screen_name, status.created_at, status.source) except: # Catch any unicode errors while printing to console # and just ignore them to avoid breaking application. pass def on_error(self, status_code): print 'An error has occured! Status code = %s' % status_code return True # keep stream alive def on_timeout(self): print 'Snoozing Zzzzzz' def main(): readOldUser('resultBackup.txt') access_token = "308609794-UnsFrbl4fcBQsOzbG5sqliFMKowhOlzRmLHVeBdp" access_token_secret = "BwjfaD1QgiF0wEzMdyBDsLLEnYXeXLpLqgVcru4oU9QLB" consumer_key = "Tzui94xupQIdChpAD7shh6DVo" consumer_secret = "uMRrB7tH7YHgANboYa3wB1U0HHGm2g51j9Aj7QG9XVZnLgRlv9" auth = tweepy.auth.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) stream = tweepy.Stream(auth, StreamWatcherListener(), timeout=None) # Prompt for mode of streaming valid_modes = ['sample', 'filter'] while True: mode = raw_input('Mode? [sample/filter] ') if mode in valid_modes: break print 'Invalid mode! Try again.' if mode == 'sample': stream.sample() elif mode == 'filter': track_list = track_Michigan print track_list stream.filter([], track_list, languages=['en']) if __name__ == '__main__': try: main() except KeyboardInterrupt: print '\nGoodbye!' w.close()

[ "nghia.vo.0509@gmail.com" ]

nghia.vo.0509@gmail.com

1fffe8e845e8dadbebbcb8cc480060849bec6a19

e8815ba621e72a015e3567e736aa7d5c6d05dd71

/experiment_datasets_creator.py

1150f93b1d38d2fc3ef76503ef6d083bd3c97b17

[]

no_license

oserikov/nn_harmony_np

5f810114481402a56b9349dfff77af47939ee9d8

b93c19bcd625eea3716dc964bc962af84e21fb4b

refs/heads/master

2021-07-01T16:40:07.420621

2020-10-05T08:01:07

179,607,321

0

null

UTF-8

Python

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6,566

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import numpy as np from phonology_tool import PhonologyTool from nn_model import NNModel, ModelStateLogDTO from typing import List, Callable import csv # 1. vow vs cons # 2. +front vs -front # 3. voiced stop consonant detection # 4. start cons cluster # 5. 2nd in cons cluster # 6. +front harmony TODO # 7. -front_harmony TODO class ExperimentCreator: def __init__(self, nn_model: NNModel, dataset: List[str], phonology_tool: PhonologyTool): self.nn_model = nn_model self.dataset = dataset self.phon_tool = phonology_tool def construct_unigram_dataset(self, char2target_fun: Callable, nn_feature_extractor_fun: Callable): training_data = [] for word in self.dataset: nn_features = self.get_nn_features_for_word(word) word_training_data = [] for nn_feature in nn_features: word_training_data.append((nn_feature_extractor_fun(nn_feature), char2target_fun(nn_feature.char))) training_data.extend(word_training_data) return training_data def construct_ngram_dataset(self, ngram2target_fun: Callable, nn_feature_extractor_fun: Callable, ngram_len: int): training_data = [] for word in self.dataset: nn_features = self.get_nn_features_for_word(word) word_training_data = [] for idx, nn_feature in enumerate(nn_features[:-ngram_len + 1]): ngram = ''.join([f.char for f in nn_features[idx:idx+ngram_len]]) tmp_features = [{"ngram": ngram}] for idx, tmp_feature in enumerate(nn_features[idx:idx+ngram_len]): tmp_features.append({f"char_{idx}_"+k: v for entry in nn_feature_extractor_fun(tmp_feature) for k, v in entry.items()}) word_training_data.append((tmp_features, ngram2target_fun(ngram))) # word_training_data.append((nn_feature_extractor_fun(nn_feature), ngram2target_fun(nn_feature.char))) training_data.extend(word_training_data) return training_data def front_harmony_dataset(self): return self.construct_ngram_dataset(self.phon_tool.shows_front_harmony, self.extract_all_nn_features, 4) def round_feature_dataset(self): return self.construct_unigram_dataset(self.phon_tool.is_round, self.extract_all_nn_features) def vov_vs_cons_dataset(self): return self.construct_unigram_dataset(self.phon_tool.is_vowel, self.extract_all_nn_features) def front_feature_dataset(self): return self.construct_unigram_dataset(self.phon_tool.is_front, self.extract_all_nn_features) def voiced_stop_consonant_dataset(self): return self.construct_unigram_dataset(self.phon_tool.is_voiced_stop_consonant, self.extract_all_nn_features) def second_consonant_in_cluster_dataset(self): nn_feature_extractor_fun = self.extract_all_nn_features training_data = [] for word in self.dataset: nn_features = self.get_nn_features_for_word(word) nn_feature = nn_features[0] word_training_data = [(nn_feature_extractor_fun(nn_feature), False)] previous_is_first_consonant_in_cluster = self.phon_tool.is_consonant(nn_feature.char) previous_is_vowel = self.phon_tool.is_vowel(nn_feature.char) for nn_feature in nn_features: word_training_data.append((nn_feature_extractor_fun(nn_feature), self.phon_tool.is_consonant(nn_feature.char) and previous_is_first_consonant_in_cluster)) previous_is_first_consonant_in_cluster = self.phon_tool.is_consonant(nn_feature.char) \ and previous_is_vowel previous_is_vowel = self.phon_tool.is_vowel(nn_feature.char) training_data.extend(word_training_data) return training_data def is_starting_consonant_cluster_dataset(self): nn_feature_extractor_fun = self.extract_all_nn_features training_data = [] for word in self.dataset: nn_features = self.get_nn_features_for_word(word) nn_feature = nn_features[0] word_training_data = [(nn_feature_extractor_fun(nn_feature), self.phon_tool.is_consonant(nn_feature.char))] previous_is_vowel = self.phon_tool.is_vowel(nn_feature.char) for nn_feature in nn_features: word_training_data.append((nn_feature_extractor_fun(nn_feature), self.phon_tool.is_consonant(nn_feature.char) and previous_is_vowel)) previous_is_vowel = self.phon_tool.is_vowel(nn_feature.char) training_data.extend(word_training_data) return training_data def extract_all_nn_features(self, nn_feature: ModelStateLogDTO): return nn_feature.as_dict() def get_nn_features_for_word(self, word) -> List[ModelStateLogDTO]: return self.nn_model.run_model_on_word(word) @staticmethod def train_features_to_single_dict(dataset_train_features_list): res = {} for idx, d in enumerate(dataset_train_features_list): for k, v in d.items(): res[f"{idx}_" + k] = v return res @staticmethod def make_dataset_pretty(dataset): pretty_dataset = [] for (train_entry, target_entry) in dataset: pretty_dataset.append((ExperimentCreator.train_features_to_single_dict(train_entry), target_entry)) return pretty_dataset @staticmethod def save_dataset_to_tsv(dataset, dataset_fn): dataset_to_single_dicts_list = [] for entry in dataset: new_entry = entry[0].copy() new_entry["TARGET"] = entry[1] dataset_to_single_dicts_list.append(new_entry) dataset_keys = list(dataset_to_single_dicts_list[0].keys()) with open(dataset_fn, 'w', encoding="utf-8", newline='') as dataset_f: dictWriter = csv.DictWriter(dataset_f, dataset_to_single_dicts_list[0].keys(), delimiter='\t') dictWriter.writeheader() dictWriter.writerows(dataset_to_single_dicts_list) # print('\t'.join(dataset_keys + ["target"]), file=dataset_f) # for (features, target) in dataset: # print('\t'.join([features[key] for key in dataset_keys] + [target]), file=dataset_f)

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##Config file for lifetime_spyrelet.py in spyre/spyre/spyrelet/ # Device List devices = { 'vna':[ 'lantz.drivers.VNA.P9371A', ['TCPIP0::DESKTOP-ER250Q8::hislip0,4880::INSTR'], {} ] } # Experiment List spyrelets = { 'freqSweep':[ 'spyre.spyrelets.cavity_spyrelet.Record', {'vna': 'vna'}, {} ], }

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# USAGE # python pdestrianTracker.py --input pedestrians.mp4 # python pdestrianTracker.py --input pedestrians.mp4 --output output.avi import argparse import cv2 import imutils import numpy as np import torch import time import pymongo from deep_sort import DeepSort # import the necessary packages from pyimagesearch.detectorSwitcher import get_detector from tracking import CentroidTracker from utils.parser import get_config from datetime import datetime from influxdb_client import InfluxDBClient, Point, WritePrecision from influxdb_client.client.write_api import SYNCHRONOUS # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--input", type=str, default="pedestrians.mp4", help="path to (optional) input video file") ap.add_argument("-o", "--output", type=str, default="output.avi", help="path to (optional) output video file") ap.add_argument("-d", "--display", type=int, default=1, help="whether or not output frame should be displayed") ap.add_argument("-a", "--algorithm", type=str, default="detr", help="choose what kind of algorithm should be used for people detection") ap.add_argument("-c", "--coords", type=str, default="[(427, 0), (700, 0), (530, 318), (1, 198)]", help="comma seperated list of source points") ap.add_argument("-s", "--size", type=str, default="[700,400]", help="coma separated tuple describing height and width of transformed birdview image") ap.add_argument("-t", "--time", type=int, default=0, help="set the initial timestamp of video stream start in unix format in nanoseconds") ap.add_argument("r" "--object", type=str, default="person", help="objects to detect and track") args = vars(ap.parse_args()) # initialize the video stream and pointer to output video file print("[INFO] accessing video stream...") vs = cv2.VideoCapture(args["input"] if args["input"] else 0) initial_time = args["time"] if args["time"] > 0 else time.time_ns() writer = None trajectories = {} ct = CentroidTracker() mongoclient = pymongo.MongoClient("mongodb://localhost:27017/") mongodb = mongoclient["massmove"] mongocol = mongodb["points"] token = "uw400lj_tKeWjbTdwM4VJz_qZ2MnpsOh5zeBdP3BKS7Au4NaOVSpePcd1Zj47bsNdBtmqCt9Gf5u1UHvWiFYgg==" org = "ikillforfood@gmail.com" bucket = "points" influxclient = InfluxDBClient(url="https://westeurope-1.azure.cloud2.influxdata.com", token=token) write_api = influxclient.write_api(write_options=SYNCHRONOUS) cfg = get_config() cfg.merge_from_file("deep_sort/configs/deep_sort.yaml") deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT, max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, use_cuda=True) def draw_markers(): global i, bbox, cX, cY, id color = (0, 255, 0) if len(outputs) > 0: bbox_xyxy = outputs[:, :4] identities = outputs[:, -1] # loop over the results for (i, bbox) in enumerate(bbox_xyxy): x1, y1, x2, y2 = [int(i) for i in bbox] cX = int((x1 + x2) / 2) cY = int((y1 + y2) / 2) id = int(identities[i]) if identities is not None else 0 label = '{}{:d}'.format("", id) t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0] # if the index pair exists within the violation set, then # update the color # if i in violate: # color = (0, 0, 255) # draw the centroid coordinates of the person, cv2.circle(frame, (cX, cY), 5, color, 1) cv2.circle(frame, (cX, y2), 5, color, 1) cv2.line(frame, (cX, cY), (cX, y2), color, 1) cv2.putText(frame, label, (x1, y1 + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2) draw_markers_on_birdview(cX, color, y2, id) def draw_markers_on_birdview(cX, color, y2, id): # calculate pedestrians position from birdview p = (cX, y2) px = (matrix[0][0] * p[0] + matrix[0][1] * p[1] + matrix[0][2]) / ( matrix[2][0] * p[0] + matrix[2][1] * p[1] + matrix[2][2]) py = (matrix[1][0] * p[0] + matrix[1][1] * p[1] + matrix[1][2]) / ( matrix[2][0] * p[0] + matrix[2][1] * p[1] + matrix[2][2]) p_after = (int(px), int(py)) cv2.circle(birdview, p_after, 5, color, 1) cv2.circle(birdview, p_after, 30, color, 1) add_to_pedestrian_trajectory(id, p_after) def add_to_pedestrian_trajectory(id, p_after): if id in trajectories: trajectories[id].add(p_after) else: trajectory = set() trajectory.add(p_after) trajectories[id] = trajectory (x, y) = p_after point = Point("mem").tag("user", id).field("point", f'[{x}, {y}]').time(datetime.utcnow(), WritePrecision.NS) write_api.write(bucket, org, point) dict = { "id": id, "point": [x, y], "time": timestamp } mongocol.insert_one(dict) def calculate_bird_view(): global h, w, size, matrix, birdview pts = np.array(eval(args["coords"]), dtype="float32") (h, w) = frame.shape[:2] if args["size"] != "": size = np.array(eval(args["size"]), dtype="int") (h, w) = size[:2] pts_dst = np.float32([[0, 0], [w, 0], [w, h], [0, h]]) matrix = cv2.getPerspectiveTransform(pts, pts_dst) # birdview = np.zeros((h, w, 3), np.uint8) birdview = cv2.warpPerspective(frame, matrix, (w, h)); def calculate_bboxs_confs(): global i, bbox, w, h, cX, cY for (i, bbox) in enumerate(boxes): # extract the bounding box and centroid coordinates, then # initialize the color of the annotation startX = bbox[0] startY = bbox[1] w = bbox[2] h = bbox[3] cX = startX + int(w / 2) cY = startY + int(h / 2) bbox_xcycwh.append([cX, cY, w, h]) confs.append([scores[i]]) def track(boxes): bboxes = [] for (i, bbox) in enumerate(boxes): startX = bbox[0] startY = bbox[1] endX = startX + bbox[2] endY = startY + bbox[3] box = np.array([startX, startY, endX, endY]) bboxes.append(box.astype("int")) objects = ct.update(bboxes) return objects def draw_markers_alternate(): global text color = (0, 255, 0) # loop over the tracked objects for (objectID, centroid) in outputs.items(): # draw both the ID of the object and the centroid of the # object on the output frame text = "ID {}".format(objectID) cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 0), -1) draw_markers_on_birdview(centroid[0], color, centroid[1], objectID) # loop over the frames from the video stream while True: # read the next frame from the file (grabbed, frame) = vs.read() timestamp = initial_time + (vs.get(cv2.CAP_PROP_POS_MSEC) * 1000000) # if the frame was not grabbed, then we have reached the end # of the stream if not grabbed: break # resize the frame and then detect people (and only people) in it frame = imutils.resize(frame, width=700) calculate_bird_view() detector = get_detector(args["algorithm"]) boxes, scores = detector.detect(frame) bbox_xcycwh = [] confs = [] calculate_bboxs_confs() xywhs = torch.Tensor(bbox_xcycwh) confss = torch.Tensor(scores) # Pass detections to deepsort #outputs = deepsort.update(xywhs, confss, frame) outputs = track(boxes) # initialize the set of indexes that violate the minimum social # distance violate = set() #draw_markers() draw_markers_alternate() # draw the total number of social distancing violations on the # output frame text = "Social Distancing Violations: {}".format(len(violate)) cv2.putText(frame, text, (10, frame.shape[0] - 25), cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3) # check to see if the output frame should be displayed to our # screen if args["display"] > 0: # show the output frame cv2.imshow("Frame", frame) cv2.imshow("Birdview", birdview) key = cv2.waitKey(1) & 0xFF # if the `q` key was pressed, break from the loop if key == ord("q"): break # if an output video file path has been supplied and the video # writer has not been initialized, do so now if args["output"] != "" and writer is None: # initialize our video writer fourcc = cv2.VideoWriter_fourcc(*"MJPG") writer = cv2.VideoWriter(args["output"], fourcc, 25, (frame.shape[1], frame.shape[0]), True) # if the video writer is not None, write the frame to the output # video file if writer is not None: writer.write(frame)

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import sys from itertools import combinations class Queue(): def __init__(self): self.front = 0 self.rear = 0 self.list = [] self.pop_count = 0 def append(self, x): self.list.append(x) self.rear += 1 def pop(self): res = self.list[self.front] self.front += 1 self.pop_count += 1 return res def empty(self): return len(self.list) == self.pop_count res = 0 rl = lambda: sys.stdin.readline() N, M = map(int, rl().split()) all_map = [] virus = [] zero = [] virus_num = sys.maxsize for i in range(N): tmp = list(map(int, rl().split())) for j, v in enumerate(tmp): if v == 2: virus.append((i, j)) elif v == 0: zero.append((i, j)) all_map.append(tmp) row_dir, col_dir = [1, 0, -1, 0], [0, 1, 0, -1] wall_comb = combinations(zero, 3) for wall in wall_comb: # visited = copy.deepcopy(all_map) visited = [] for i in range(N): tmp = [] for j in range(M): tmp.append(all_map[i][j]) visited.append(tmp) for w in wall: visited[w[0]][w[1]] = 1 v_num = 0 queue = Queue() for v in virus: queue.append(v) while queue.empty() == False: r, c = queue.pop() v_num += 1 if v_num > virus_num: break for i in range(4): new_r, new_c = r + row_dir[i], c + col_dir[i] if (0 <= new_r < N) and (0 <= new_c < M): if visited[new_r][new_c] == 0: queue.append((new_r, new_c)) visited[new_r][new_c] = 2 cnt, v_cnt = 0, 0 for i in range(N): for j in range(M): if visited[i][j] == 0: cnt += 1 if visited[i][j] == 2: v_cnt += 1 if cnt > res: res = cnt virus_num = v_cnt print(res)

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# Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Configurations for the server. """ import os from google.cloud import logging def is_production(): return 'DASHBOARD_FRONTEND_PRODUCTION' in os.environ def setup_logging(): """Connects the default logger to Google Cloud Logging. Only logs at INFO level or higher will be captured. """ client = logging.Client() client.get_default_handler() client.setup_logging() def get_dashboard_oauth_client_id(): """Gets the client ID used to authenticate with Identity-Aware Proxy from the environment variable DASHBOARD_OAUTH_CLIENT_ID.""" return os.environ.get('DASHBOARD_OAUTH_CLIENT_ID')

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import pickle ######## 바이너리 파일 (파일을 열어도 내용을 알아 볼 수 없다) f = open("test.txt","wb") data = {1:'python', 2: 'you need'} pickle.dump(data, f) f.close() f = open("test.txt","rb") data = pickle.load(f) print(data) f.close() ######## 텍스트파일 (아스키코드를 이용한 파일 , 내용을 보고 읽을 수 있다) f = open("test2.txt","w") f.write("{1:'python', 2: 'you need'}") f.close() f = open("test2.txt","r") print(f.read()) f.close()

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from testapp.models import UserModel, ActivityPeroidModel from rest_framework import serializers class ActivitySerializer(serializers.ModelSerializer): class Meta: model = ActivityPeroidModel fields = ['start_time', 'end_time'] class UserSerializer(serializers.ModelSerializer): activity_periods = ActivitySerializer(read_only=True,many=True) class Meta: model = UserModel fields = '__all__'

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# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import proto # type: ignore from google.ads.googleads.v6.enums.types import product_bidding_category_level from google.ads.googleads.v6.enums.types import product_bidding_category_status __protobuf__ = proto.module( package='google.ads.googleads.v6.resources', marshal='google.ads.googleads.v6', manifest={ 'ProductBiddingCategoryConstant', }, ) class ProductBiddingCategoryConstant(proto.Message): r"""A Product Bidding Category. Attributes: resource_name (str): Output only. The resource name of the product bidding category. Product bidding category resource names have the form: ``productBiddingCategoryConstants/{country_code}~{level}~{id}`` id (int): Output only. ID of the product bidding category. This ID is equivalent to the google_product_category ID as described in this article: https://support.google.com/merchants/answer/6324436. country_code (str): Output only. Two-letter upper-case country code of the product bidding category. product_bidding_category_constant_parent (str): Output only. Resource name of the parent product bidding category. level (google.ads.googleads.v6.enums.types.ProductBiddingCategoryLevelEnum.ProductBiddingCategoryLevel): Output only. Level of the product bidding category. status (google.ads.googleads.v6.enums.types.ProductBiddingCategoryStatusEnum.ProductBiddingCategoryStatus): Output only. Status of the product bidding category. language_code (str): Output only. Language code of the product bidding category. localized_name (str): Output only. Display value of the product bidding category localized according to language_code. """ resource_name = proto.Field(proto.STRING, number=1) id = proto.Field(proto.INT64, number=10, optional=True) country_code = proto.Field(proto.STRING, number=11, optional=True) product_bidding_category_constant_parent = proto.Field(proto.STRING, number=12, optional=True) level = proto.Field(proto.ENUM, number=5, enum=product_bidding_category_level.ProductBiddingCategoryLevelEnum.ProductBiddingCategoryLevel, ) status = proto.Field(proto.ENUM, number=6, enum=product_bidding_category_status.ProductBiddingCategoryStatusEnum.ProductBiddingCategoryStatus, ) language_code = proto.Field(proto.STRING, number=13, optional=True) localized_name = proto.Field(proto.STRING, number=14, optional=True) __all__ = tuple(sorted(__protobuf__.manifest))

[ "bazel-bot-development[bot]@users.noreply.github.com" ]

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/开班笔记/python基础部分/day02/code/test.py

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#!/usr/bin/python3 print('这是我的python第一条语句') print('我现在开始学python') print('这是最后一条语句')

[ "yabing_ji@163.com" ]

yabing_ji@163.com

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/BASES/OLD/3_2_CAC_CC_SPLTED_CSDS/tx_no_gui.py

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#!/usr/bin/env python2 # -*- coding: utf-8 -*- ################################################## # GNU Radio Python Flow Graph # Title: Tx No Gui # Author: andresilva # GNU Radio version: 3.7.13.5 ################################################## from gnuradio import blocks from gnuradio import digital from gnuradio import eng_notation from gnuradio import fec from gnuradio import gr from gnuradio import uhd from gnuradio.eng_option import eng_option from gnuradio.filter import firdes from gnuradio.filter import pfb from optparse import OptionParser import insert_vec_cpp import pmt import random import time class tx_no_gui(gr.top_block): def __init__(self, puncpat='11'): gr.top_block.__init__(self, "Tx No Gui") ################################################## # Parameters ################################################## self.puncpat = puncpat ################################################## # Variables ################################################## self.sps = sps = 4 self.nfilts = nfilts = 32 self.eb = eb = 0.22 self.tx_rrc_taps = tx_rrc_taps = firdes.root_raised_cosine(nfilts, nfilts, 1.0, eb, 5*sps*nfilts) self.taps_per_filt = taps_per_filt = len(tx_rrc_taps)/nfilts self.samp_rate_array_MCR = samp_rate_array_MCR = [7500000,4000000,3750000,3000000,2500000,2000000,1500000,1000000,937500,882352,833333,714285,533333,500000,421052,400000,380952] self.vector = vector = [int(random.random()*4) for i in range(49600)] self.variable_qtgui_range_0 = variable_qtgui_range_0 = 50 self.samp_rate = samp_rate = samp_rate_array_MCR[1] self.rate = rate = 2 self.polys = polys = [109, 79] self.pld_enc = pld_enc = map( (lambda a: fec.ccsds_encoder_make(440, 0, fec.CC_TERMINATED)), range(0,16) ); self.pld_const = pld_const = digital.constellation_rect(([0.707+0.707j, -0.707+0.707j, -0.707-0.707j, 0.707-0.707j]), ([0, 1, 2, 3]), 4, 2, 2, 1, 1).base() self.pld_const.gen_soft_dec_lut(8) self.k = k = 7 self.frequencia_usrp = frequencia_usrp = 484e6 self.filt_delay = filt_delay = 1+(taps_per_filt-1)/2 self.MCR = MCR = "master_clock_rate=60e6" ################################################## # Blocks ################################################## self.uhd_usrp_sink_0_0 = uhd.usrp_sink( ",".join(("serial=F5EAE1", MCR)), uhd.stream_args( cpu_format="fc32", channels=range(1), ), ) self.uhd_usrp_sink_0_0.set_samp_rate(samp_rate) self.uhd_usrp_sink_0_0.set_time_now(uhd.time_spec(time.time()), uhd.ALL_MBOARDS) self.uhd_usrp_sink_0_0.set_center_freq(frequencia_usrp, 0) self.uhd_usrp_sink_0_0.set_gain(variable_qtgui_range_0, 0) self.uhd_usrp_sink_0_0.set_antenna('TX/RX', 0) self.pfb_arb_resampler_xxx_0 = pfb.arb_resampler_ccf( sps, taps=(tx_rrc_taps), flt_size=nfilts) self.pfb_arb_resampler_xxx_0.declare_sample_delay(filt_delay) self.insert_vec_cpp_new_vec_0 = insert_vec_cpp.new_vec((vector)) self.fec_extended_encoder_0 = fec.extended_encoder(encoder_obj_list=pld_enc, threading='capillary', puncpat=puncpat) self.digital_map_bb_1_0 = digital.map_bb((pld_const.pre_diff_code())) self.digital_diff_encoder_bb_0 = digital.diff_encoder_bb(4) self.digital_chunks_to_symbols_xx_0_0 = digital.chunks_to_symbols_bc((pld_const.points()), 1) self.blocks_vector_source_x_0_0_0 = blocks.vector_source_b([0], True, 1, []) self.blocks_stream_to_tagged_stream_0_0_0 = blocks.stream_to_tagged_stream(gr.sizeof_char, 1, 992, "packet_len") self.blocks_stream_mux_0_1_0 = blocks.stream_mux(gr.sizeof_char*1, (96, 896)) self.blocks_stream_mux_0_0 = blocks.stream_mux(gr.sizeof_char*1, (892, 4)) self.blocks_repack_bits_bb_1_0_0_1 = blocks.repack_bits_bb(8, 1, '', False, gr.GR_MSB_FIRST) self.blocks_repack_bits_bb_1_0_0_0 = blocks.repack_bits_bb(1, 2, "packet_len", False, gr.GR_MSB_FIRST) self.blocks_multiply_const_vxx_1 = blocks.multiply_const_vcc((0.7, )) self.blocks_file_source_0_0_1_0_0_0 = blocks.file_source(gr.sizeof_char*1, '/home/andre/Desktop/Files_To_Transmit/video_lion.mpeg', False) self.blocks_file_source_0_0_1_0_0_0.set_begin_tag(pmt.PMT_NIL) self.acode_1104 = blocks.vector_source_b([0x1, 0x0, 0x1, 0x0, 0x1, 0x1, 0x0, 0x0, 0x1, 0x1, 0x0, 0x1, 0x1, 0x1, 0x0, 0x1, 0x1, 0x0, 0x1, 0x0, 0x0, 0x1, 0x0, 0x0, 0x1, 0x1, 0x1, 0x0, 0x0, 0x0, 0x1, 0x0, 0x1, 0x1, 0x1, 0x1, 0x0, 0x0, 0x1, 0x0, 0x1, 0x0, 0x0, 0x0, 0x1, 0x1, 0x0, 0x0, 0x0, 0x0, 0x1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x1, 0x1, 0x1, 0x1, 0x1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x1, 0x1, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x1, 0x1, 0x1, 0x0, 0x0, 0x0, 0x0], True, 1, []) ################################################## # Connections ################################################## self.connect((self.acode_1104, 0), (self.blocks_stream_mux_0_1_0, 0)) self.connect((self.blocks_file_source_0_0_1_0_0_0, 0), (self.blocks_repack_bits_bb_1_0_0_1, 0)) self.connect((self.blocks_multiply_const_vxx_1, 0), (self.uhd_usrp_sink_0_0, 0)) self.connect((self.blocks_repack_bits_bb_1_0_0_0, 0), (self.insert_vec_cpp_new_vec_0, 0)) self.connect((self.blocks_repack_bits_bb_1_0_0_1, 0), (self.fec_extended_encoder_0, 0)) self.connect((self.blocks_stream_mux_0_0, 0), (self.blocks_stream_mux_0_1_0, 1)) self.connect((self.blocks_stream_mux_0_1_0, 0), (self.blocks_stream_to_tagged_stream_0_0_0, 0)) self.connect((self.blocks_stream_to_tagged_stream_0_0_0, 0), (self.blocks_repack_bits_bb_1_0_0_0, 0)) self.connect((self.blocks_vector_source_x_0_0_0, 0), (self.blocks_stream_mux_0_0, 1)) self.connect((self.digital_chunks_to_symbols_xx_0_0, 0), (self.pfb_arb_resampler_xxx_0, 0)) self.connect((self.digital_diff_encoder_bb_0, 0), (self.digital_chunks_to_symbols_xx_0_0, 0)) self.connect((self.digital_map_bb_1_0, 0), (self.digital_diff_encoder_bb_0, 0)) self.connect((self.fec_extended_encoder_0, 0), (self.blocks_stream_mux_0_0, 0)) self.connect((self.insert_vec_cpp_new_vec_0, 0), (self.digital_map_bb_1_0, 0)) self.connect((self.pfb_arb_resampler_xxx_0, 0), (self.blocks_multiply_const_vxx_1, 0)) def get_puncpat(self): return self.puncpat def set_puncpat(self, puncpat): self.puncpat = puncpat def get_sps(self): return self.sps def set_sps(self, sps): self.sps = sps self.pfb_arb_resampler_xxx_0.set_rate(self.sps) def get_nfilts(self): return self.nfilts def set_nfilts(self, nfilts): self.nfilts = nfilts self.set_taps_per_filt(len(self.tx_rrc_taps)/self.nfilts) def get_eb(self): return self.eb def set_eb(self, eb): self.eb = eb def get_tx_rrc_taps(self): return self.tx_rrc_taps def set_tx_rrc_taps(self, tx_rrc_taps): self.tx_rrc_taps = tx_rrc_taps self.set_taps_per_filt(len(self.tx_rrc_taps)/self.nfilts) self.pfb_arb_resampler_xxx_0.set_taps((self.tx_rrc_taps)) def get_taps_per_filt(self): return self.taps_per_filt def set_taps_per_filt(self, taps_per_filt): self.taps_per_filt = taps_per_filt self.set_filt_delay(1+(self.taps_per_filt-1)/2) def get_samp_rate_array_MCR(self): return self.samp_rate_array_MCR def set_samp_rate_array_MCR(self, samp_rate_array_MCR): self.samp_rate_array_MCR = samp_rate_array_MCR self.set_samp_rate(self.samp_rate_array_MCR[1]) def get_vector(self): return self.vector def set_vector(self, vector): self.vector = vector def get_variable_qtgui_range_0(self): return self.variable_qtgui_range_0 def set_variable_qtgui_range_0(self, variable_qtgui_range_0): self.variable_qtgui_range_0 = variable_qtgui_range_0 self.uhd_usrp_sink_0_0.set_gain(self.variable_qtgui_range_0, 0) def get_samp_rate(self): return self.samp_rate def set_samp_rate(self, samp_rate): self.samp_rate = samp_rate self.uhd_usrp_sink_0_0.set_samp_rate(self.samp_rate) def get_rate(self): return self.rate def set_rate(self, rate): self.rate = rate def get_polys(self): return self.polys def set_polys(self, polys): self.polys = polys def get_pld_enc(self): return self.pld_enc def set_pld_enc(self, pld_enc): self.pld_enc = pld_enc def get_pld_const(self): return self.pld_const def set_pld_const(self, pld_const): self.pld_const = pld_const def get_k(self): return self.k def set_k(self, k): self.k = k def get_frequencia_usrp(self): return self.frequencia_usrp def set_frequencia_usrp(self, frequencia_usrp): self.frequencia_usrp = frequencia_usrp self.uhd_usrp_sink_0_0.set_center_freq(self.frequencia_usrp, 0) def get_filt_delay(self): return self.filt_delay def set_filt_delay(self, filt_delay): self.filt_delay = filt_delay def get_MCR(self): return self.MCR def set_MCR(self, MCR): self.MCR = MCR def argument_parser(): parser = OptionParser(usage="%prog: [options]", option_class=eng_option) parser.add_option( "", "--puncpat", dest="puncpat", type="string", default='11', help="Set puncpat [default=%default]") return parser def main(top_block_cls=tx_no_gui, options=None): if options is None: options, _ = argument_parser().parse_args() tb = top_block_cls(puncpat=options.puncpat) tb.start() tb.wait() if __name__ == '__main__': main()

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import scannerpy import scannerpy.stdlib.parsers as parsers import scannerpy.stdlib.writers as writers import scannerpy.stdlib.bboxes as bboxes class BBoxNMSKernel(scannerpy.Kernel): def __init__(self, config, protobufs): self.protobufs = protobufs args = protobufs.BBoxNMSArgs() args.ParseFromString(config) self.scale = args.scale def close(self): pass def execute(self, input_columns): bboxes_list = [] for c in input_columns: bboxes_list += parsers.bboxes(c, self.protobufs) nmsed_bboxes = bboxes.nms(bboxes_list, 0.1) return writers.bboxes([nmsed_bboxes], self.protobufs) KERNEL = BBoxNMSKernel

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import logging from typing import Dict, List, Tuple import aiosqlite from melati.server.address_manager import ( BUCKET_SIZE, NEW_BUCKET_COUNT, NEW_BUCKETS_PER_ADDRESS, AddressManager, ExtendedPeerInfo, ) log = logging.getLogger(__name__) class AddressManagerStore: """ Metadata table: - private key - new table count - tried table count Nodes table: * Maps entries from new/tried table to unique node ids. - node_id - IP, port, together with the IP, port of the source peer. New table: * Stores node_id, bucket for each occurrence in the new table of an entry. * Once we know the buckets, we can also deduce the bucket positions. Every other information, such as tried_matrix, map_addr, map_info, random_pos, be deduced and it is not explicitly stored, instead it is recalculated. """ db: aiosqlite.Connection @classmethod async def create(cls, connection) -> "AddressManagerStore": self = cls() self.db = connection await self.db.commit() await self.db.execute("pragma journal_mode=wal") await self.db.execute("pragma synchronous=2") await self.db.execute("CREATE TABLE IF NOT EXISTS peer_metadata(key text,value text)") await self.db.commit() await self.db.execute("CREATE TABLE IF NOT EXISTS peer_nodes(node_id int,value text)") await self.db.commit() await self.db.execute("CREATE TABLE IF NOT EXISTS peer_new_table(node_id int,bucket int)") await self.db.commit() return self async def clear(self) -> None: cursor = await self.db.execute("DELETE from peer_metadata") await cursor.close() cursor = await self.db.execute("DELETE from peer_nodes") await cursor.close() cursor = await self.db.execute("DELETE from peer_new_table") await cursor.close() await self.db.commit() async def get_metadata(self) -> Dict[str, str]: cursor = await self.db.execute("SELECT key, value from peer_metadata") metadata = await cursor.fetchall() await cursor.close() return {key: value for key, value in metadata} async def is_empty(self) -> bool: metadata = await self.get_metadata() if "key" not in metadata: return True if int(metadata.get("new_count", 0)) > 0: return False if int(metadata.get("tried_count", 0)) > 0: return False return True async def get_nodes(self) -> List[Tuple[int, ExtendedPeerInfo]]: cursor = await self.db.execute("SELECT node_id, value from peer_nodes") nodes_id = await cursor.fetchall() await cursor.close() return [(node_id, ExtendedPeerInfo.from_string(info_str)) for node_id, info_str in nodes_id] async def get_new_table(self) -> List[Tuple[int, int]]: cursor = await self.db.execute("SELECT node_id, bucket from peer_new_table") entries = await cursor.fetchall() await cursor.close() return [(node_id, bucket) for node_id, bucket in entries] async def set_metadata(self, metadata) -> None: for key, value in metadata: cursor = await self.db.execute( "INSERT OR REPLACE INTO peer_metadata VALUES(?, ?)", (key, value), ) await cursor.close() await self.db.commit() async def set_nodes(self, node_list) -> None: for node_id, peer_info in node_list: cursor = await self.db.execute( "INSERT OR REPLACE INTO peer_nodes VALUES(?, ?)", (node_id, peer_info.to_string()), ) await cursor.close() await self.db.commit() async def set_new_table(self, entries) -> None: for node_id, bucket in entries: cursor = await self.db.execute( "INSERT OR REPLACE INTO peer_new_table VALUES(?, ?)", (node_id, bucket), ) await cursor.close() await self.db.commit() async def serialize(self, address_manager: AddressManager): metadata = [] nodes = [] new_table_entries = [] metadata.append(("key", str(address_manager.key))) unique_ids = {} count_ids = 0 for node_id, info in address_manager.map_info.items(): unique_ids[node_id] = count_ids if info.ref_count > 0: assert count_ids != address_manager.new_count nodes.append((count_ids, info)) count_ids += 1 metadata.append(("new_count", str(count_ids))) tried_ids = 0 for node_id, info in address_manager.map_info.items(): if info.is_tried: assert info is not None assert tried_ids != address_manager.tried_count nodes.append((count_ids, info)) count_ids += 1 tried_ids += 1 metadata.append(("tried_count", str(tried_ids))) for bucket in range(NEW_BUCKET_COUNT): for i in range(BUCKET_SIZE): if address_manager.new_matrix[bucket][i] != -1: index = unique_ids[address_manager.new_matrix[bucket][i]] new_table_entries.append((index, bucket)) await self.clear() await self.set_metadata(metadata) await self.set_nodes(nodes) await self.set_new_table(new_table_entries) async def deserialize(self) -> AddressManager: address_manager = AddressManager() metadata = await self.get_metadata() nodes = await self.get_nodes() new_table_entries = await self.get_new_table() address_manager.clear() address_manager.key = int(metadata["key"]) address_manager.new_count = int(metadata["new_count"]) # address_manager.tried_count = int(metadata["tried_count"]) address_manager.tried_count = 0 new_table_nodes = [(node_id, info) for node_id, info in nodes if node_id < address_manager.new_count] for n, info in new_table_nodes: address_manager.map_addr[info.peer_info.host] = n address_manager.map_info[n] = info info.random_pos = len(address_manager.random_pos) address_manager.random_pos.append(n) address_manager.id_count = len(new_table_nodes) tried_table_nodes = [(node_id, info) for node_id, info in nodes if node_id >= address_manager.new_count] # lost_count = 0 for node_id, info in tried_table_nodes: tried_bucket = info.get_tried_bucket(address_manager.key) tried_bucket_pos = info.get_bucket_position(address_manager.key, False, tried_bucket) if address_manager.tried_matrix[tried_bucket][tried_bucket_pos] == -1: info.random_pos = len(address_manager.random_pos) info.is_tried = True id_count = address_manager.id_count address_manager.random_pos.append(id_count) address_manager.map_info[id_count] = info address_manager.map_addr[info.peer_info.host] = id_count address_manager.tried_matrix[tried_bucket][tried_bucket_pos] = id_count address_manager.id_count += 1 address_manager.tried_count += 1 # else: # lost_count += 1 # address_manager.tried_count -= lost_count for node_id, bucket in new_table_entries: if node_id >= 0 and node_id < address_manager.new_count: info = address_manager.map_info[node_id] bucket_pos = info.get_bucket_position(address_manager.key, True, bucket) if address_manager.new_matrix[bucket][bucket_pos] == -1 and info.ref_count < NEW_BUCKETS_PER_ADDRESS: info.ref_count += 1 address_manager.new_matrix[bucket][bucket_pos] = node_id for node_id, info in list(address_manager.map_info.items()): if not info.is_tried and info.ref_count == 0: address_manager.delete_new_entry_(node_id) address_manager.load_used_table_positions() return address_manager

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from functools import reduce import itertools import math def pi(N): ' 计算pi的值 ' # step 1: 创建一个奇数序列: 1, 3, 5, 7, 9, ... # step 2: 取该序列的前N项: 1, 3, 5, 7, 9, ..., 2*N-1. # step 3: 添加正负符号并用4除: 4/1, -4/3, 4/5, -4/7, 4/9, ... # step 4: 求和: natuals = itertools.count(1,2) ns = itertools.takewhile(lambda x:x<=2*N-1,natuals) return reduce(lambda x,y:x+y , list(map(lambda x:(pow(-1,((x + 1) / 2 - 1)))*4/x, list(ns)))) # return list(ns) if __name__ == "__main__": print(pi(10)) print(pi(100)) print(pi(1000)) print(pi(10000))

[ "dengdanchaoren@gmail.com" ]

dengdanchaoren@gmail.com

85cd55ec06dd7935e25dd335edf8a21bac1969f2

4f41a3faa073d0d4f672601add08edbfbb32c762

/application/sqlalchemy/schema.py

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gterranova/timetracker

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refs/heads/master

2021-01-12T14:40:27.162638

2012-04-11T19:33:47

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py

# schema.py # Copyright (C) 2005, 2006, 2007, 2008, 2009, 2010 Michael Bayer mike_mp@zzzcomputing.com # # This module is part of SQLAlchemy and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """The schema module provides the building blocks for database metadata. Each element within this module describes a database entity which can be created and dropped, or is otherwise part of such an entity. Examples include tables, columns, sequences, and indexes. All entities are subclasses of :class:`~sqlalchemy.schema.SchemaItem`, and as defined in this module they are intended to be agnostic of any vendor-specific constructs. A collection of entities are grouped into a unit called :class:`~sqlalchemy.schema.MetaData`. MetaData serves as a logical grouping of schema elements, and can also be associated with an actual database connection such that operations involving the contained elements can contact the database as needed. Two of the elements here also build upon their "syntactic" counterparts, which are defined in :class:`~sqlalchemy.sql.expression.`, specifically :class:`~sqlalchemy.schema.Table` and :class:`~sqlalchemy.schema.Column`. Since these objects are part of the SQL expression language, they are usable as components in SQL expressions. """ import re, inspect from sqlalchemy import exc, util, dialects from sqlalchemy.sql import expression, visitors URL = None __all__ = ['SchemaItem', 'Table', 'Column', 'ForeignKey', 'Sequence', 'Index', 'ForeignKeyConstraint', 'PrimaryKeyConstraint', 'CheckConstraint', 'UniqueConstraint', 'DefaultGenerator', 'Constraint', 'MetaData', 'ThreadLocalMetaData', 'SchemaVisitor', 'PassiveDefault', 'DefaultClause', 'FetchedValue', 'ColumnDefault', 'DDL', 'CreateTable', 'DropTable', 'CreateSequence', 'DropSequence', 'AddConstraint', 'DropConstraint', ] __all__.sort() RETAIN_SCHEMA = util.symbol('retain_schema') class SchemaItem(visitors.Visitable): """Base class for items that define a database schema.""" __visit_name__ = 'schema_item' quote = None def _init_items(self, *args): """Initialize the list of child items for this SchemaItem.""" for item in args: if item is not None: item._set_parent(self) def _set_parent(self, parent): """Associate with this SchemaItem's parent object.""" raise NotImplementedError() def get_children(self, **kwargs): """used to allow SchemaVisitor access""" return [] def __repr__(self): return "%s()" % self.__class__.__name__ @util.memoized_property def info(self): return {} def _get_table_key(name, schema): if schema is None: return name else: return schema + "." + name class Table(SchemaItem, expression.TableClause): """Represent a table in a database. e.g.:: mytable = Table("mytable", metadata, Column('mytable_id', Integer, primary_key=True), Column('value', String(50)) ) The Table object constructs a unique instance of itself based on its name within the given MetaData object. Constructor arguments are as follows: :param name: The name of this table as represented in the database. This property, along with the *schema*, indicates the *singleton identity* of this table in relation to its parent :class:`MetaData`. Additional calls to :class:`Table` with the same name, metadata, and schema name will return the same :class:`Table` object. Names which contain no upper case characters will be treated as case insensitive names, and will not be quoted unless they are a reserved word. Names with any number of upper case characters will be quoted and sent exactly. Note that this behavior applies even for databases which standardize upper case names as case insensitive such as Oracle. :param metadata: a :class:`MetaData` object which will contain this table. The metadata is used as a point of association of this table with other tables which are referenced via foreign key. It also may be used to associate this table with a particular :class:`~sqlalchemy.engine.base.Connectable`. :param \*args: Additional positional arguments are used primarily to add the list of :class:`Column` objects contained within this table. Similar to the style of a CREATE TABLE statement, other :class:`SchemaItem` constructs may be added here, including :class:`PrimaryKeyConstraint`, and :class:`ForeignKeyConstraint`. :param autoload: Defaults to False: the Columns for this table should be reflected from the database. Usually there will be no Column objects in the constructor if this property is set. :param autoload_with: If autoload==True, this is an optional Engine or Connection instance to be used for the table reflection. If ``None``, the underlying MetaData's bound connectable will be used. :param implicit_returning: True by default - indicates that RETURNING can be used by default to fetch newly inserted primary key values, for backends which support this. Note that create_engine() also provides an implicit_returning flag. :param include_columns: A list of strings indicating a subset of columns to be loaded via the ``autoload`` operation; table columns who aren't present in this list will not be represented on the resulting ``Table`` object. Defaults to ``None`` which indicates all columns should be reflected. :param info: A dictionary which defaults to ``{}``. A space to store application specific data. This must be a dictionary. :param mustexist: When ``True``, indicates that this Table must already be present in the given :class:`MetaData`` collection. :param prefixes: A list of strings to insert after CREATE in the CREATE TABLE statement. They will be separated by spaces. :param quote: Force quoting of this table's name on or off, corresponding to ``True`` or ``False``. When left at its default of ``None``, the column identifier will be quoted according to whether the name is case sensitive (identifiers with at least one upper case character are treated as case sensitive), or if it's a reserved word. This flag is only needed to force quoting of a reserved word which is not known by the SQLAlchemy dialect. :param quote_schema: same as 'quote' but applies to the schema identifier. :param schema: The *schema name* for this table, which is required if the table resides in a schema other than the default selected schema for the engine's database connection. Defaults to ``None``. :param useexisting: When ``True``, indicates that if this Table is already present in the given :class:`MetaData`, apply further arguments within the constructor to the existing :class:`Table`. If this flag is not set, an error is raised when the parameters of an existing :class:`Table` are overwritten. """ __visit_name__ = 'table' ddl_events = ('before-create', 'after-create', 'before-drop', 'after-drop') def __new__(cls, *args, **kw): if not args: # python3k pickle seems to call this return object.__new__(cls) try: name, metadata, args = args[0], args[1], args[2:] except IndexError: raise TypeError("Table() takes at least two arguments") schema = kw.get('schema', None) useexisting = kw.pop('useexisting', False) mustexist = kw.pop('mustexist', False) key = _get_table_key(name, schema) if key in metadata.tables: if not useexisting and bool(args): raise exc.InvalidRequestError( "Table '%s' is already defined for this MetaData instance. " "Specify 'useexisting=True' to redefine options and " "columns on an existing Table object." % key) table = metadata.tables[key] table._init_existing(*args, **kw) return table else: if mustexist: raise exc.InvalidRequestError( "Table '%s' not defined" % (key)) metadata.tables[key] = table = object.__new__(cls) try: table._init(name, metadata, *args, **kw) return table except: metadata.tables.pop(key) raise def __init__(self, *args, **kw): # __init__ is overridden to prevent __new__ from # calling the superclass constructor. pass def _init(self, name, metadata, *args, **kwargs): super(Table, self).__init__(name) self.metadata = metadata self.schema = kwargs.pop('schema', None) self.indexes = set() self.constraints = set() self._columns = expression.ColumnCollection() self._set_primary_key(PrimaryKeyConstraint()) self._foreign_keys = util.OrderedSet() self._extra_dependencies = set() self.ddl_listeners = util.defaultdict(list) self.kwargs = {} if self.schema is not None: self.fullname = "%s.%s" % (self.schema, self.name) else: self.fullname = self.name autoload = kwargs.pop('autoload', False) autoload_with = kwargs.pop('autoload_with', None) include_columns = kwargs.pop('include_columns', None) self.implicit_returning = kwargs.pop('implicit_returning', True) self.quote = kwargs.pop('quote', None) self.quote_schema = kwargs.pop('quote_schema', None) if 'info' in kwargs: self.info = kwargs.pop('info') self._prefixes = kwargs.pop('prefixes', []) self._extra_kwargs(**kwargs) # load column definitions from the database if 'autoload' is defined # we do it after the table is in the singleton dictionary to support # circular foreign keys if autoload: if autoload_with: autoload_with.reflecttable(self, include_columns=include_columns) else: _bind_or_error(metadata, msg="No engine is bound to this Table's MetaData. " "Pass an engine to the Table via " "autoload_with=<someengine>, " "or associate the MetaData with an engine via " "metadata.bind=<someengine>").\ reflecttable(self, include_columns=include_columns) # initialize all the column, etc. objects. done after reflection to # allow user-overrides self._init_items(*args) def _init_existing(self, *args, **kwargs): autoload = kwargs.pop('autoload', False) autoload_with = kwargs.pop('autoload_with', None) schema = kwargs.pop('schema', None) if schema and schema != self.schema: raise exc.ArgumentError( "Can't change schema of existing table from '%s' to '%s'", (self.schema, schema)) include_columns = kwargs.pop('include_columns', None) if include_columns: for c in self.c: if c.name not in include_columns: self.c.remove(c) for key in ('quote', 'quote_schema'): if key in kwargs: setattr(self, key, kwargs.pop(key)) if 'info' in kwargs: self.info = kwargs.pop('info') self._extra_kwargs(**kwargs) self._init_items(*args) def _extra_kwargs(self, **kwargs): # validate remaining kwargs that they all specify DB prefixes if len([k for k in kwargs if not re.match(r'^(?:%s)_' % '|'.join(dialects.__all__), k)]): raise TypeError( "Invalid argument(s) for Table: %r" % kwargs.keys()) self.kwargs.update(kwargs) def _set_primary_key(self, pk): if getattr(self, '_primary_key', None) in self.constraints: self.constraints.remove(self._primary_key) self._primary_key = pk self.constraints.add(pk) for c in pk.columns: c.primary_key = True @util.memoized_property def _autoincrement_column(self): for col in self.primary_key: if col.autoincrement and \ isinstance(col.type, types.Integer) and \ not col.foreign_keys and \ isinstance(col.default, (type(None), Sequence)): return col @property def key(self): return _get_table_key(self.name, self.schema) @property def primary_key(self): return self._primary_key def __repr__(self): return "Table(%s)" % ', '.join( [repr(self.name)] + [repr(self.metadata)] + [repr(x) for x in self.columns] + ["%s=%s" % (k, repr(getattr(self, k))) for k in ['schema']]) def __str__(self): return _get_table_key(self.description, self.schema) @property def bind(self): """Return the connectable associated with this Table.""" return self.metadata and self.metadata.bind or None def add_is_dependent_on(self, table): """Add a 'dependency' for this Table. This is another Table object which must be created first before this one can, or dropped after this one. Usually, dependencies between tables are determined via ForeignKey objects. However, for other situations that create dependencies outside of foreign keys (rules, inheriting), this method can manually establish such a link. """ self._extra_dependencies.add(table) def append_column(self, column): """Append a ``Column`` to this ``Table``.""" column._set_parent(self) def append_constraint(self, constraint): """Append a ``Constraint`` to this ``Table``.""" constraint._set_parent(self) def append_ddl_listener(self, event, listener): """Append a DDL event listener to this ``Table``. The ``listener`` callable will be triggered when this ``Table`` is created or dropped, either directly before or after the DDL is issued to the database. The listener may modify the Table, but may not abort the event itself. Arguments are: event One of ``Table.ddl_events``; e.g. 'before-create', 'after-create', 'before-drop' or 'after-drop'. listener A callable, invoked with three positional arguments: event The event currently being handled target The ``Table`` object being created or dropped bind The ``Connection`` bueing used for DDL execution. Listeners are added to the Table's ``ddl_listeners`` attribute. """ if event not in self.ddl_events: raise LookupError(event) self.ddl_listeners[event].append(listener) def _set_parent(self, metadata): metadata.tables[_get_table_key(self.name, self.schema)] = self self.metadata = metadata def get_children(self, column_collections=True, schema_visitor=False, **kwargs): if not schema_visitor: return expression.TableClause.get_children( self, column_collections=column_collections, **kwargs) else: if column_collections: return list(self.columns) else: return [] def exists(self, bind=None): """Return True if this table exists.""" if bind is None: bind = _bind_or_error(self) return bind.run_callable(bind.dialect.has_table, self.name, schema=self.schema) def create(self, bind=None, checkfirst=False): """Issue a ``CREATE`` statement for this table. See also ``metadata.create_all()``. """ if bind is None: bind = _bind_or_error(self) bind.create(self, checkfirst=checkfirst) def drop(self, bind=None, checkfirst=False): """Issue a ``DROP`` statement for this table. See also ``metadata.drop_all()``. """ if bind is None: bind = _bind_or_error(self) bind.drop(self, checkfirst=checkfirst) def tometadata(self, metadata, schema=RETAIN_SCHEMA): """Return a copy of this ``Table`` associated with a different ``MetaData``.""" try: if schema is RETAIN_SCHEMA: schema = self.schema key = _get_table_key(self.name, schema) return metadata.tables[key] except KeyError: args = [] for c in self.columns: args.append(c.copy(schema=schema)) for c in self.constraints: args.append(c.copy(schema=schema)) return Table(self.name, metadata, schema=schema, *args) class Column(SchemaItem, expression.ColumnClause): """Represents a column in a database table.""" __visit_name__ = 'column' def __init__(self, *args, **kwargs): """ Construct a new ``Column`` object. :param name: The name of this column as represented in the database. This argument may be the first positional argument, or specified via keyword. Names which contain no upper case characters will be treated as case insensitive names, and will not be quoted unless they are a reserved word. Names with any number of upper case characters will be quoted and sent exactly. Note that this behavior applies even for databases which standardize upper case names as case insensitive such as Oracle. The name field may be omitted at construction time and applied later, at any time before the Column is associated with a :class:`Table`. This is to support convenient usage within the :mod:`~sqlalchemy.ext.declarative` extension. :param type\_: The column's type, indicated using an instance which subclasses :class:`~sqlalchemy.types.AbstractType`. If no arguments are required for the type, the class of the type can be sent as well, e.g.:: # use a type with arguments Column('data', String(50)) # use no arguments Column('level', Integer) The ``type`` argument may be the second positional argument or specified by keyword. There is partial support for automatic detection of the type based on that of a :class:`ForeignKey` associated with this column, if the type is specified as ``None``. However, this feature is not fully implemented and may not function in all cases. :param \*args: Additional positional arguments include various :class:`SchemaItem` derived constructs which will be applied as options to the column. These include instances of :class:`Constraint`, :class:`ForeignKey`, :class:`ColumnDefault`, and :class:`Sequence`. In some cases an equivalent keyword argument is available such as ``server_default``, ``default`` and ``unique``. :param autoincrement: This flag may be set to ``False`` to indicate an integer primary key column that should not be considered to be the "autoincrement" column, that is the integer primary key column which generates values implicitly upon INSERT and whose value is usually returned via the DBAPI cursor.lastrowid attribute. It defaults to ``True`` to satisfy the common use case of a table with a single integer primary key column. If the table has a composite primary key consisting of more than one integer column, set this flag to True only on the column that should be considered "autoincrement". The setting *only* has an effect for columns which are: * Integer derived (i.e. INT, SMALLINT, BIGINT) * Part of the primary key * Are not referenced by any foreign keys * have no server side or client side defaults (with the exception of Postgresql SERIAL). The setting has these two effects on columns that meet the above criteria: * DDL issued for the column will include database-specific keywords intended to signify this column as an "autoincrement" column, such as AUTO INCREMENT on MySQL, SERIAL on Postgresql, and IDENTITY on MS-SQL. It does *not* issue AUTOINCREMENT for SQLite since this is a special SQLite flag that is not required for autoincrementing behavior. See the SQLite dialect documentation for information on SQLite's AUTOINCREMENT. * The column will be considered to be available as cursor.lastrowid or equivalent, for those dialects which "post fetch" newly inserted identifiers after a row has been inserted (SQLite, MySQL, MS-SQL). It does not have any effect in this regard for databases that use sequences to generate primary key identifiers (i.e. Firebird, Postgresql, Oracle). :param default: A scalar, Python callable, or :class:`~sqlalchemy.sql.expression.ClauseElement` representing the *default value* for this column, which will be invoked upon insert if this column is otherwise not specified in the VALUES clause of the insert. This is a shortcut to using :class:`ColumnDefault` as a positional argument. Contrast this argument to ``server_default`` which creates a default generator on the database side. :param doc: optional String that can be used by the ORM or similar to document attributes. This attribute does not render SQL comments (a future attribute 'comment' will achieve that). :param key: An optional string identifier which will identify this ``Column`` object on the :class:`Table`. When a key is provided, this is the only identifier referencing the ``Column`` within the application, including ORM attribute mapping; the ``name`` field is used only when rendering SQL. :param index: When ``True``, indicates that the column is indexed. This is a shortcut for using a :class:`Index` construct on the table. To specify indexes with explicit names or indexes that contain multiple columns, use the :class:`Index` construct instead. :param info: A dictionary which defaults to ``{}``. A space to store application specific data. This must be a dictionary. :param nullable: If set to the default of ``True``, indicates the column will be rendered as allowing NULL, else it's rendered as NOT NULL. This parameter is only used when issuing CREATE TABLE statements. :param onupdate: A scalar, Python callable, or :class:`~sqlalchemy.sql.expression.ClauseElement` representing a default value to be applied to the column within UPDATE statements, which wil be invoked upon update if this column is not present in the SET clause of the update. This is a shortcut to using :class:`ColumnDefault` as a positional argument with ``for_update=True``. :param primary_key: If ``True``, marks this column as a primary key column. Multiple columns can have this flag set to specify composite primary keys. As an alternative, the primary key of a :class:`Table` can be specified via an explicit :class:`PrimaryKeyConstraint` object. :param server_default: A :class:`FetchedValue` instance, str, Unicode or :func:`~sqlalchemy.sql.expression.text` construct representing the DDL DEFAULT value for the column. String types will be emitted as-is, surrounded by single quotes:: Column('x', Text, server_default="val") x TEXT DEFAULT 'val' A :func:`~sqlalchemy.sql.expression.text` expression will be rendered as-is, without quotes:: Column('y', DateTime, server_default=text('NOW()'))0 y DATETIME DEFAULT NOW() Strings and text() will be converted into a :class:`DefaultClause` object upon initialization. Use :class:`FetchedValue` to indicate that an already-existing column will generate a default value on the database side which will be available to SQLAlchemy for post-fetch after inserts. This construct does not specify any DDL and the implementation is left to the database, such as via a trigger. :param server_onupdate: A :class:`FetchedValue` instance representing a database-side default generation function. This indicates to SQLAlchemy that a newly generated value will be available after updates. This construct does not specify any DDL and the implementation is left to the database, such as via a trigger. :param quote: Force quoting of this column's name on or off, corresponding to ``True`` or ``False``. When left at its default of ``None``, the column identifier will be quoted according to whether the name is case sensitive (identifiers with at least one upper case character are treated as case sensitive), or if it's a reserved word. This flag is only needed to force quoting of a reserved word which is not known by the SQLAlchemy dialect. :param unique: When ``True``, indicates that this column contains a unique constraint, or if ``index`` is ``True`` as well, indicates that the :class:`Index` should be created with the unique flag. To specify multiple columns in the constraint/index or to specify an explicit name, use the :class:`UniqueConstraint` or :class:`Index` constructs explicitly. """ name = kwargs.pop('name', None) type_ = kwargs.pop('type_', None) args = list(args) if args: if isinstance(args[0], basestring): if name is not None: raise exc.ArgumentError( "May not pass name positionally and as a keyword.") name = args.pop(0) if args: coltype = args[0] if (isinstance(coltype, types.AbstractType) or (isinstance(coltype, type) and issubclass(coltype, types.AbstractType))): if type_ is not None: raise exc.ArgumentError( "May not pass type_ positionally and as a keyword.") type_ = args.pop(0) no_type = type_ is None super(Column, self).__init__(name, None, type_) self.key = kwargs.pop('key', name) self.primary_key = kwargs.pop('primary_key', False) self.nullable = kwargs.pop('nullable', not self.primary_key) self.default = kwargs.pop('default', None) self.server_default = kwargs.pop('server_default', None) self.server_onupdate = kwargs.pop('server_onupdate', None) self.index = kwargs.pop('index', None) self.unique = kwargs.pop('unique', None) self.quote = kwargs.pop('quote', None) self.doc = kwargs.pop('doc', None) self.onupdate = kwargs.pop('onupdate', None) self.autoincrement = kwargs.pop('autoincrement', True) self.constraints = set() self.foreign_keys = util.OrderedSet() self._table_events = set() # check if this Column is proxying another column if '_proxies' in kwargs: self.proxies = kwargs.pop('_proxies') # otherwise, add DDL-related events elif isinstance(self.type, types.SchemaType): self.type._set_parent(self) if self.default is not None: if isinstance(self.default, (ColumnDefault, Sequence)): args.append(self.default) else: args.append(ColumnDefault(self.default)) if self.server_default is not None: if isinstance(self.server_default, FetchedValue): args.append(self.server_default) else: args.append(DefaultClause(self.server_default)) if self.onupdate is not None: if isinstance(self.onupdate, (ColumnDefault, Sequence)): args.append(self.onupdate) else: args.append(ColumnDefault(self.onupdate, for_update=True)) if self.server_onupdate is not None: if isinstance(self.server_onupdate, FetchedValue): args.append(self.server_default) else: args.append(DefaultClause(self.server_onupdate, for_update=True)) self._init_items(*args) if not self.foreign_keys and no_type: raise exc.ArgumentError("'type' is required on Column objects " "which have no foreign keys.") util.set_creation_order(self) if 'info' in kwargs: self.info = kwargs.pop('info') if kwargs: raise exc.ArgumentError( "Unknown arguments passed to Column: " + repr(kwargs.keys())) def __str__(self): if self.name is None: return "(no name)" elif self.table is not None: if self.table.named_with_column: return (self.table.description + "." + self.description) else: return self.description else: return self.description def references(self, column): """Return True if this Column references the given column via foreign key.""" for fk in self.foreign_keys: if fk.references(column.table): return True else: return False def append_foreign_key(self, fk): fk._set_parent(self) def __repr__(self): kwarg = [] if self.key != self.name: kwarg.append('key') if self.primary_key: kwarg.append('primary_key') if not self.nullable: kwarg.append('nullable') if self.onupdate: kwarg.append('onupdate') if self.default: kwarg.append('default') if self.server_default: kwarg.append('server_default') return "Column(%s)" % ', '.join( [repr(self.name)] + [repr(self.type)] + [repr(x) for x in self.foreign_keys if x is not None] + [repr(x) for x in self.constraints] + [(self.table is not None and "table=<%s>" % self.table.description or "")] + ["%s=%s" % (k, repr(getattr(self, k))) for k in kwarg]) def _set_parent(self, table): if self.name is None: raise exc.ArgumentError( "Column must be constructed with a name or assign .name " "before adding to a Table.") if self.key is None: self.key = self.name if getattr(self, 'table', None) is not None: raise exc.ArgumentError("this Column already has a table!") if self.key in table._columns: col = table._columns.get(self.key) for fk in col.foreign_keys: col.foreign_keys.remove(fk) table.foreign_keys.remove(fk) table.constraints.remove(fk.constraint) table._columns.replace(self) if self.primary_key: table.primary_key._replace(self) elif self.key in table.primary_key: raise exc.ArgumentError( "Trying to redefine primary-key column '%s' as a " "non-primary-key column on table '%s'" % ( self.key, table.fullname)) self.table = table if self.index: if isinstance(self.index, basestring): raise exc.ArgumentError( "The 'index' keyword argument on Column is boolean only. " "To create indexes with a specific name, create an " "explicit Index object external to the Table.") Index('ix_%s' % self._label, self, unique=self.unique) elif self.unique: if isinstance(self.unique, basestring): raise exc.ArgumentError( "The 'unique' keyword argument on Column is boolean only. " "To create unique constraints or indexes with a specific " "name, append an explicit UniqueConstraint to the Table's " "list of elements, or create an explicit Index object " "external to the Table.") table.append_constraint(UniqueConstraint(self.key)) for fn in self._table_events: fn(table, self) del self._table_events def _on_table_attach(self, fn): if self.table is not None: fn(self.table, self) else: self._table_events.add(fn) def copy(self, **kw): """Create a copy of this ``Column``, unitialized. This is used in ``Table.tometadata``. """ # Constraint objects plus non-constraint-bound ForeignKey objects args = \ [c.copy(**kw) for c in self.constraints] + \ [c.copy(**kw) for c in self.foreign_keys if not c.constraint] c = Column( name=self.name, type_=self.type, key = self.key, primary_key = self.primary_key, nullable = self.nullable, quote=self.quote, index=self.index, autoincrement=self.autoincrement, default=self.default, server_default=self.server_default, onupdate=self.onupdate, server_onupdate=self.server_onupdate, *args ) if hasattr(self, '_table_events'): c._table_events = list(self._table_events) return c def _make_proxy(self, selectable, name=None): """Create a *proxy* for this column. This is a copy of this ``Column`` referenced by a different parent (such as an alias or select statement). The column should be used only in select scenarios, as its full DDL/default information is not transferred. """ fk = [ForeignKey(f.column) for f in self.foreign_keys] c = self._constructor( name or self.name, self.type, key = name or self.key, primary_key = self.primary_key, nullable = self.nullable, quote=self.quote, _proxies=[self], *fk) c.table = selectable selectable.columns.add(c) if self.primary_key: selectable.primary_key.add(c) for fn in c._table_events: fn(selectable, c) del c._table_events return c def get_children(self, schema_visitor=False, **kwargs): if schema_visitor: return [x for x in (self.default, self.onupdate) if x is not None] + \ list(self.foreign_keys) + list(self.constraints) else: return expression.ColumnClause.get_children(self, **kwargs) class ForeignKey(SchemaItem): """Defines a dependency between two columns. ``ForeignKey`` is specified as an argument to a :class:`Column` object, e.g.:: t = Table("remote_table", metadata, Column("remote_id", ForeignKey("main_table.id")) ) Note that ``ForeignKey`` is only a marker object that defines a dependency between two columns. The actual constraint is in all cases represented by the :class:`ForeignKeyConstraint` object. This object will be generated automatically when a ``ForeignKey`` is associated with a :class:`Column` which in turn is associated with a :class:`Table`. Conversely, when :class:`ForeignKeyConstraint` is applied to a :class:`Table`, ``ForeignKey`` markers are automatically generated to be present on each associated :class:`Column`, which are also associated with the constraint object. Note that you cannot define a "composite" foreign key constraint, that is a constraint between a grouping of multiple parent/child columns, using ``ForeignKey`` objects. To define this grouping, the :class:`ForeignKeyConstraint` object must be used, and applied to the :class:`Table`. The associated ``ForeignKey`` objects are created automatically. The ``ForeignKey`` objects associated with an individual :class:`Column` object are available in the `foreign_keys` collection of that column. Further examples of foreign key configuration are in :ref:`metadata_foreignkeys`. """ __visit_name__ = 'foreign_key' def __init__(self, column, _constraint=None, use_alter=False, name=None, onupdate=None, ondelete=None, deferrable=None, initially=None, link_to_name=False): """ Construct a column-level FOREIGN KEY. The :class:`ForeignKey` object when constructed generates a :class:`ForeignKeyConstraint` which is associated with the parent :class:`Table` object's collection of constraints. :param column: A single target column for the key relationship. A :class:`Column` object or a column name as a string: ``tablename.columnkey`` or ``schema.tablename.columnkey``. ``columnkey`` is the ``key`` which has been assigned to the column (defaults to the column name itself), unless ``link_to_name`` is ``True`` in which case the rendered name of the column is used. :param name: Optional string. An in-database name for the key if `constraint` is not provided. :param onupdate: Optional string. If set, emit ON UPDATE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. :param ondelete: Optional string. If set, emit ON DELETE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. :param deferrable: Optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. :param initially: Optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. :param link_to_name: if True, the string name given in ``column`` is the rendered name of the referenced column, not its locally assigned ``key``. :param use_alter: passed to the underlying :class:`ForeignKeyConstraint` to indicate the constraint should be generated/dropped externally from the CREATE TABLE/ DROP TABLE statement. See that classes' constructor for details. """ self._colspec = column # the linked ForeignKeyConstraint. # ForeignKey will create this when parent Column # is attached to a Table, *or* ForeignKeyConstraint # object passes itself in when creating ForeignKey # markers. self.constraint = _constraint self.use_alter = use_alter self.name = name self.onupdate = onupdate self.ondelete = ondelete self.deferrable = deferrable self.initially = initially self.link_to_name = link_to_name def __repr__(self): return "ForeignKey(%r)" % self._get_colspec() def copy(self, schema=None): """Produce a copy of this ForeignKey object.""" return ForeignKey( self._get_colspec(schema=schema), use_alter=self.use_alter, name=self.name, onupdate=self.onupdate, ondelete=self.ondelete, deferrable=self.deferrable, initially=self.initially, link_to_name=self.link_to_name ) def _get_colspec(self, schema=None): if schema: return schema + "." + self.column.table.name + "." + self.column.key elif isinstance(self._colspec, basestring): return self._colspec elif hasattr(self._colspec, '__clause_element__'): _column = self._colspec.__clause_element__() else: _column = self._colspec return "%s.%s" % (_column.table.fullname, _column.key) target_fullname = property(_get_colspec) def references(self, table): """Return True if the given table is referenced by this ForeignKey.""" return table.corresponding_column(self.column) is not None def get_referent(self, table): """Return the column in the given table referenced by this ForeignKey. Returns None if this ``ForeignKey`` does not reference the given table. """ return table.corresponding_column(self.column) @util.memoized_property def column(self): # ForeignKey inits its remote column as late as possible, so tables # can be defined without dependencies if isinstance(self._colspec, basestring): # locate the parent table this foreign key is attached to. we # use the "original" column which our parent column represents # (its a list of columns/other ColumnElements if the parent # table is a UNION) for c in self.parent.base_columns: if isinstance(c, Column): parenttable = c.table break else: raise exc.ArgumentError( "Parent column '%s' does not descend from a " "table-attached Column" % str(self.parent)) m = self._colspec.split('.') if m is None: raise exc.ArgumentError( "Invalid foreign key column specification: %s" % self._colspec) # A FK between column 'bar' and table 'foo' can be # specified as 'foo', 'foo.bar', 'dbo.foo.bar', # 'otherdb.dbo.foo.bar'. Once we have the column name and # the table name, treat everything else as the schema # name. Some databases (e.g. Sybase) support # inter-database foreign keys. See tickets#1341 and -- # indirectly related -- Ticket #594. This assumes that '.' # will never appear *within* any component of the FK. (schema, tname, colname) = (None, None, None) if (len(m) == 1): tname = m.pop() else: colname = m.pop() tname = m.pop() if (len(m) > 0): schema = '.'.join(m) if _get_table_key(tname, schema) not in parenttable.metadata: raise exc.NoReferencedTableError( "Could not find table '%s' with which to generate a " "foreign key" % tname) table = Table(tname, parenttable.metadata, mustexist=True, schema=schema) _column = None if colname is None: # colname is None in the case that ForeignKey argument # was specified as table name only, in which case we # match the column name to the same column on the # parent. key = self.parent _column = table.c.get(self.parent.key, None) elif self.link_to_name: key = colname for c in table.c: if c.name == colname: _column = c else: key = colname _column = table.c.get(colname, None) if _column is None: raise exc.NoReferencedColumnError( "Could not create ForeignKey '%s' on table '%s': " "table '%s' has no column named '%s'" % ( self._colspec, parenttable.name, table.name, key)) elif hasattr(self._colspec, '__clause_element__'): _column = self._colspec.__clause_element__() else: _column = self._colspec # propagate TypeEngine to parent if it didn't have one if isinstance(self.parent.type, types.NullType): self.parent.type = _column.type return _column def _set_parent(self, column): if hasattr(self, 'parent'): if self.parent is column: return raise exc.InvalidRequestError("This ForeignKey already has a parent !") self.parent = column self.parent.foreign_keys.add(self) self.parent._on_table_attach(self._set_table) def _set_table(self, table, column): # standalone ForeignKey - create ForeignKeyConstraint # on the hosting Table when attached to the Table. if self.constraint is None and isinstance(table, Table): self.constraint = ForeignKeyConstraint( [], [], use_alter=self.use_alter, name=self.name, onupdate=self.onupdate, ondelete=self.ondelete, deferrable=self.deferrable, initially=self.initially, ) self.constraint._elements[self.parent] = self self.constraint._set_parent(table) table.foreign_keys.add(self) class DefaultGenerator(SchemaItem): """Base class for column *default* values.""" __visit_name__ = 'default_generator' is_sequence = False def __init__(self, for_update=False): self.for_update = for_update def _set_parent(self, column): self.column = column if self.for_update: self.column.onupdate = self else: self.column.default = self def execute(self, bind=None, **kwargs): if bind is None: bind = _bind_or_error(self) return bind._execute_default(self, **kwargs) @property def bind(self): """Return the connectable associated with this default.""" if getattr(self, 'column', None) is not None: return self.column.table.bind else: return None def __repr__(self): return "DefaultGenerator()" class ColumnDefault(DefaultGenerator): """A plain default value on a column. This could correspond to a constant, a callable function, or a SQL clause. """ def __init__(self, arg, **kwargs): super(ColumnDefault, self).__init__(**kwargs) if isinstance(arg, FetchedValue): raise exc.ArgumentError( "ColumnDefault may not be a server-side default type.") if util.callable(arg): arg = self._maybe_wrap_callable(arg) self.arg = arg @util.memoized_property def is_callable(self): return util.callable(self.arg) @util.memoized_property def is_clause_element(self): return isinstance(self.arg, expression.ClauseElement) @util.memoized_property def is_scalar(self): return not self.is_callable and not self.is_clause_element and not self.is_sequence def _maybe_wrap_callable(self, fn): """Backward compat: Wrap callables that don't accept a context.""" if inspect.isfunction(fn): inspectable = fn elif inspect.isclass(fn): inspectable = fn.__init__ elif hasattr(fn, '__call__'): inspectable = fn.__call__ else: # probably not inspectable, try anyways. inspectable = fn try: argspec = inspect.getargspec(inspectable) except TypeError: return lambda ctx: fn() positionals = len(argspec[0]) # Py3K compat - no unbound methods if inspect.ismethod(inspectable) or inspect.isclass(fn): positionals -= 1 if positionals == 0: return lambda ctx: fn() defaulted = argspec[3] is not None and len(argspec[3]) or 0 if positionals - defaulted > 1: raise exc.ArgumentError( "ColumnDefault Python function takes zero or one " "positional arguments") return fn def _visit_name(self): if self.for_update: return "column_onupdate" else: return "column_default" __visit_name__ = property(_visit_name) def __repr__(self): return "ColumnDefault(%r)" % self.arg class Sequence(DefaultGenerator): """Represents a named database sequence.""" __visit_name__ = 'sequence' is_sequence = True def __init__(self, name, start=None, increment=None, schema=None, optional=False, quote=None, metadata=None, for_update=False): super(Sequence, self).__init__(for_update=for_update) self.name = name self.start = start self.increment = increment self.optional = optional self.quote = quote self.schema = schema self.metadata = metadata @util.memoized_property def is_callable(self): return False @util.memoized_property def is_clause_element(self): return False def __repr__(self): return "Sequence(%s)" % ', '.join( [repr(self.name)] + ["%s=%s" % (k, repr(getattr(self, k))) for k in ['start', 'increment', 'optional']]) def _set_parent(self, column): super(Sequence, self)._set_parent(column) column._on_table_attach(self._set_table) def _set_table(self, table, column): self.metadata = table.metadata @property def bind(self): if self.metadata: return self.metadata.bind else: return None def create(self, bind=None, checkfirst=True): """Creates this sequence in the database.""" if bind is None: bind = _bind_or_error(self) bind.create(self, checkfirst=checkfirst) def drop(self, bind=None, checkfirst=True): """Drops this sequence from the database.""" if bind is None: bind = _bind_or_error(self) bind.drop(self, checkfirst=checkfirst) class FetchedValue(object): """A default that takes effect on the database side.""" def __init__(self, for_update=False): self.for_update = for_update def _set_parent(self, column): self.column = column if self.for_update: self.column.server_onupdate = self else: self.column.server_default = self def __repr__(self): return 'FetchedValue(for_update=%r)' % self.for_update class DefaultClause(FetchedValue): """A DDL-specified DEFAULT column value.""" def __init__(self, arg, for_update=False): util.assert_arg_type(arg, (basestring, expression.ClauseElement, expression._TextClause), 'arg') super(DefaultClause, self).__init__(for_update) self.arg = arg def __repr__(self): return "DefaultClause(%r, for_update=%r)" % (self.arg, self.for_update) class PassiveDefault(DefaultClause): def __init__(self, *arg, **kw): util.warn_deprecated("PassiveDefault is deprecated. Use DefaultClause.") DefaultClause.__init__(self, *arg, **kw) class Constraint(SchemaItem): """A table-level SQL constraint.""" __visit_name__ = 'constraint' def __init__(self, name=None, deferrable=None, initially=None, _create_rule=None): """Create a SQL constraint. name Optional, the in-database name of this ``Constraint``. deferrable Optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. initially Optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. _create_rule a callable which is passed the DDLCompiler object during compilation. Returns True or False to signal inline generation of this Constraint. The AddConstraint and DropConstraint DDL constructs provide DDLElement's more comprehensive "conditional DDL" approach that is passed a database connection when DDL is being issued. _create_rule is instead called during any CREATE TABLE compilation, where there may not be any transaction/connection in progress. However, it allows conditional compilation of the constraint even for backends which do not support addition of constraints through ALTER TABLE, which currently includes SQLite. _create_rule is used by some types to create constraints. Currently, its call signature is subject to change at any time. """ self.name = name self.deferrable = deferrable self.initially = initially self._create_rule = _create_rule @property def table(self): try: if isinstance(self.parent, Table): return self.parent except AttributeError: pass raise exc.InvalidRequestError("This constraint is not bound to a table. Did you mean to call table.add_constraint(constraint) ?") def _set_parent(self, parent): self.parent = parent parent.constraints.add(self) def copy(self, **kw): raise NotImplementedError() class ColumnCollectionConstraint(Constraint): """A constraint that proxies a ColumnCollection.""" def __init__(self, *columns, **kw): """ \*columns A sequence of column names or Column objects. name Optional, the in-database name of this constraint. deferrable Optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. initially Optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. """ super(ColumnCollectionConstraint, self).__init__(**kw) self.columns = expression.ColumnCollection() self._pending_colargs = [_to_schema_column_or_string(c) for c in columns] if self._pending_colargs and \ isinstance(self._pending_colargs[0], Column) and \ self._pending_colargs[0].table is not None: self._set_parent(self._pending_colargs[0].table) def _set_parent(self, table): super(ColumnCollectionConstraint, self)._set_parent(table) for col in self._pending_colargs: if isinstance(col, basestring): col = table.c[col] self.columns.add(col) def __contains__(self, x): return x in self.columns def copy(self, **kw): return self.__class__(name=self.name, deferrable=self.deferrable, initially=self.initially, *self.columns.keys()) def contains_column(self, col): return self.columns.contains_column(col) def __iter__(self): return iter(self.columns) def __len__(self): return len(self.columns) class CheckConstraint(Constraint): """A table- or column-level CHECK constraint. Can be included in the definition of a Table or Column. """ def __init__(self, sqltext, name=None, deferrable=None, initially=None, table=None, _create_rule=None): """Construct a CHECK constraint. sqltext A string containing the constraint definition, which will be used verbatim, or a SQL expression construct. name Optional, the in-database name of the constraint. deferrable Optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. initially Optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. """ super(CheckConstraint, self).__init__(name, deferrable, initially, _create_rule) self.sqltext = expression._literal_as_text(sqltext) if table is not None: self._set_parent(table) def __visit_name__(self): if isinstance(self.parent, Table): return "check_constraint" else: return "column_check_constraint" __visit_name__ = property(__visit_name__) def copy(self, **kw): return CheckConstraint(self.sqltext, name=self.name) class ForeignKeyConstraint(Constraint): """A table-level FOREIGN KEY constraint. Defines a single column or composite FOREIGN KEY ... REFERENCES constraint. For a no-frills, single column foreign key, adding a :class:`ForeignKey` to the definition of a :class:`Column` is a shorthand equivalent for an unnamed, single column :class:`ForeignKeyConstraint`. Examples of foreign key configuration are in :ref:`metadata_foreignkeys`. """ __visit_name__ = 'foreign_key_constraint' def __init__(self, columns, refcolumns, name=None, onupdate=None, ondelete=None, deferrable=None, initially=None, use_alter=False, link_to_name=False, table=None): """Construct a composite-capable FOREIGN KEY. :param columns: A sequence of local column names. The named columns must be defined and present in the parent Table. The names should match the ``key`` given to each column (defaults to the name) unless ``link_to_name`` is True. :param refcolumns: A sequence of foreign column names or Column objects. The columns must all be located within the same Table. :param name: Optional, the in-database name of the key. :param onupdate: Optional string. If set, emit ON UPDATE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. :param ondelete: Optional string. If set, emit ON DELETE <value> when issuing DDL for this constraint. Typical values include CASCADE, DELETE and RESTRICT. :param deferrable: Optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint. :param initially: Optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint. :param link_to_name: if True, the string name given in ``column`` is the rendered name of the referenced column, not its locally assigned ``key``. :param use_alter: If True, do not emit the DDL for this constraint as part of the CREATE TABLE definition. Instead, generate it via an ALTER TABLE statement issued after the full collection of tables have been created, and drop it via an ALTER TABLE statement before the full collection of tables are dropped. This is shorthand for the usage of :class:`AddConstraint` and :class:`DropConstraint` applied as "after-create" and "before-drop" events on the MetaData object. This is normally used to generate/drop constraints on objects that are mutually dependent on each other. """ super(ForeignKeyConstraint, self).__init__(name, deferrable, initially) self.onupdate = onupdate self.ondelete = ondelete self.link_to_name = link_to_name if self.name is None and use_alter: raise exc.ArgumentError("Alterable Constraint requires a name") self.use_alter = use_alter self._elements = util.OrderedDict() # standalone ForeignKeyConstraint - create # associated ForeignKey objects which will be applied to hosted # Column objects (in col.foreign_keys), either now or when attached # to the Table for string-specified names for col, refcol in zip(columns, refcolumns): self._elements[col] = ForeignKey( refcol, _constraint=self, name=self.name, onupdate=self.onupdate, ondelete=self.ondelete, use_alter=self.use_alter, link_to_name=self.link_to_name ) if table is not None: self._set_parent(table) @property def columns(self): return self._elements.keys() @property def elements(self): return self._elements.values() def _set_parent(self, table): super(ForeignKeyConstraint, self)._set_parent(table) for col, fk in self._elements.iteritems(): # string-specified column names now get # resolved to Column objects if isinstance(col, basestring): col = table.c[col] fk._set_parent(col) if self.use_alter: def supports_alter(ddl, event, schema_item, bind, **kw): return table in set(kw['tables']) and bind.dialect.supports_alter AddConstraint(self, on=supports_alter).execute_at('after-create', table.metadata) DropConstraint(self, on=supports_alter).execute_at('before-drop', table.metadata) def copy(self, **kw): return ForeignKeyConstraint( [x.parent.name for x in self._elements.values()], [x._get_colspec(**kw) for x in self._elements.values()], name=self.name, onupdate=self.onupdate, ondelete=self.ondelete, use_alter=self.use_alter, deferrable=self.deferrable, initially=self.initially, link_to_name=self.link_to_name ) class PrimaryKeyConstraint(ColumnCollectionConstraint): """A table-level PRIMARY KEY constraint. Defines a single column or composite PRIMARY KEY constraint. For a no-frills primary key, adding ``primary_key=True`` to one or more ``Column`` definitions is a shorthand equivalent for an unnamed single- or multiple-column PrimaryKeyConstraint. """ __visit_name__ = 'primary_key_constraint' def _set_parent(self, table): super(PrimaryKeyConstraint, self)._set_parent(table) table._set_primary_key(self) def _replace(self, col): self.columns.replace(col) class UniqueConstraint(ColumnCollectionConstraint): """A table-level UNIQUE constraint. Defines a single column or composite UNIQUE constraint. For a no-frills, single column constraint, adding ``unique=True`` to the ``Column`` definition is a shorthand equivalent for an unnamed, single column UniqueConstraint. """ __visit_name__ = 'unique_constraint' class Index(SchemaItem): """A table-level INDEX. Defines a composite (one or more column) INDEX. For a no-frills, single column index, adding ``index=True`` to the ``Column`` definition is a shorthand equivalent for an unnamed, single column Index. """ __visit_name__ = 'index' def __init__(self, name, *columns, **kwargs): """Construct an index object. Arguments are: name The name of the index \*columns Columns to include in the index. All columns must belong to the same table. \**kwargs Keyword arguments include: unique Defaults to False: create a unique index. postgresql_where Defaults to None: create a partial index when using PostgreSQL """ self.name = name self.columns = expression.ColumnCollection() self.table = None self.unique = kwargs.pop('unique', False) self.kwargs = kwargs for column in columns: column = _to_schema_column(column) if self.table is None: self._set_parent(column.table) elif column.table != self.table: # all columns muse be from same table raise exc.ArgumentError( "All index columns must be from same table. " "%s is from %s not %s" % (column, column.table, self.table)) self.columns.add(column) def _set_parent(self, table): self.table = table table.indexes.add(self) @property def bind(self): """Return the connectable associated with this Index.""" return self.table.bind def create(self, bind=None): if bind is None: bind = _bind_or_error(self) bind.create(self) return self def drop(self, bind=None): if bind is None: bind = _bind_or_error(self) bind.drop(self) def __repr__(self): return 'Index("%s", %s%s)' % (self.name, ', '.join(repr(c) for c in self.columns), (self.unique and ', unique=True') or '') class MetaData(SchemaItem): """A collection of Tables and their associated schema constructs. Holds a collection of Tables and an optional binding to an ``Engine`` or ``Connection``. If bound, the :class:`~sqlalchemy.schema.Table` objects in the collection and their columns may participate in implicit SQL execution. The `Table` objects themselves are stored in the `metadata.tables` dictionary. The ``bind`` property may be assigned to dynamically. A common pattern is to start unbound and then bind later when an engine is available:: metadata = MetaData() # define tables Table('mytable', metadata, ...) # connect to an engine later, perhaps after loading a URL from a # configuration file metadata.bind = an_engine MetaData is a thread-safe object after tables have been explicitly defined or loaded via reflection. .. index:: single: thread safety; MetaData """ __visit_name__ = 'metadata' ddl_events = ('before-create', 'after-create', 'before-drop', 'after-drop') def __init__(self, bind=None, reflect=False): """Create a new MetaData object. bind An Engine or Connection to bind to. May also be a string or URL instance, these are passed to create_engine() and this MetaData will be bound to the resulting engine. reflect Optional, automatically load all tables from the bound database. Defaults to False. ``bind`` is required when this option is set. For finer control over loaded tables, use the ``reflect`` method of ``MetaData``. """ self.tables = {} self.bind = bind self.metadata = self self.ddl_listeners = util.defaultdict(list) if reflect: if not bind: raise exc.ArgumentError( "A bind must be supplied in conjunction with reflect=True") self.reflect() def __repr__(self): return 'MetaData(%r)' % self.bind def __contains__(self, table_or_key): if not isinstance(table_or_key, basestring): table_or_key = table_or_key.key return table_or_key in self.tables def __getstate__(self): return {'tables': self.tables} def __setstate__(self, state): self.tables = state['tables'] self._bind = None def is_bound(self): """True if this MetaData is bound to an Engine or Connection.""" return self._bind is not None def bind(self): """An Engine or Connection to which this MetaData is bound. This property may be assigned an ``Engine`` or ``Connection``, or assigned a string or URL to automatically create a basic ``Engine`` for this bind with ``create_engine()``. """ return self._bind def _bind_to(self, bind): """Bind this MetaData to an Engine, Connection, string or URL.""" global URL if URL is None: from sqlalchemy.engine.url import URL if isinstance(bind, (basestring, URL)): from sqlalchemy import create_engine self._bind = create_engine(bind) else: self._bind = bind bind = property(bind, _bind_to) def clear(self): """Clear all Table objects from this MetaData.""" # TODO: why have clear()/remove() but not all # other accesors/mutators for the tables dict ? self.tables.clear() def remove(self, table): """Remove the given Table object from this MetaData.""" # TODO: scan all other tables and remove FK _column del self.tables[table.key] @property def sorted_tables(self): """Returns a list of ``Table`` objects sorted in order of dependency. """ from sqlalchemy.sql.util import sort_tables return sort_tables(self.tables.itervalues()) def reflect(self, bind=None, schema=None, only=None): """Load all available table definitions from the database. Automatically creates ``Table`` entries in this ``MetaData`` for any table available in the database but not yet present in the ``MetaData``. May be called multiple times to pick up tables recently added to the database, however no special action is taken if a table in this ``MetaData`` no longer exists in the database. bind A :class:`~sqlalchemy.engine.base.Connectable` used to access the database; if None, uses the existing bind on this ``MetaData``, if any. schema Optional, query and reflect tables from an alterate schema. only Optional. Load only a sub-set of available named tables. May be specified as a sequence of names or a callable. If a sequence of names is provided, only those tables will be reflected. An error is raised if a table is requested but not available. Named tables already present in this ``MetaData`` are ignored. If a callable is provided, it will be used as a boolean predicate to filter the list of potential table names. The callable is called with a table name and this ``MetaData`` instance as positional arguments and should return a true value for any table to reflect. """ reflect_opts = {'autoload': True} if bind is None: bind = _bind_or_error(self) conn = None else: reflect_opts['autoload_with'] = bind conn = bind.contextual_connect() if schema is not None: reflect_opts['schema'] = schema available = util.OrderedSet(bind.engine.table_names(schema, connection=conn)) current = set(self.tables.iterkeys()) if only is None: load = [name for name in available if name not in current] elif util.callable(only): load = [name for name in available if name not in current and only(name, self)] else: missing = [name for name in only if name not in available] if missing: s = schema and (" schema '%s'" % schema) or '' raise exc.InvalidRequestError( 'Could not reflect: requested table(s) not available ' 'in %s%s: (%s)' % (bind.engine.url, s, ', '.join(missing))) load = [name for name in only if name not in current] for name in load: Table(name, self, **reflect_opts) def append_ddl_listener(self, event, listener): """Append a DDL event listener to this ``MetaData``. The ``listener`` callable will be triggered when this ``MetaData`` is involved in DDL creates or drops, and will be invoked either before all Table-related actions or after. Arguments are: event One of ``MetaData.ddl_events``; 'before-create', 'after-create', 'before-drop' or 'after-drop'. listener A callable, invoked with three positional arguments: event The event currently being handled target The ``MetaData`` object being operated upon bind The ``Connection`` bueing used for DDL execution. Listeners are added to the MetaData's ``ddl_listeners`` attribute. Note: MetaData listeners are invoked even when ``Tables`` are created in isolation. This may change in a future release. I.e.:: # triggers all MetaData and Table listeners: metadata.create_all() # triggers MetaData listeners too: some.table.create() """ if event not in self.ddl_events: raise LookupError(event) self.ddl_listeners[event].append(listener) def create_all(self, bind=None, tables=None, checkfirst=True): """Create all tables stored in this metadata. Conditional by default, will not attempt to recreate tables already present in the target database. bind A :class:`~sqlalchemy.engine.base.Connectable` used to access the database; if None, uses the existing bind on this ``MetaData``, if any. tables Optional list of ``Table`` objects, which is a subset of the total tables in the ``MetaData`` (others are ignored). checkfirst Defaults to True, don't issue CREATEs for tables already present in the target database. """ if bind is None: bind = _bind_or_error(self) bind.create(self, checkfirst=checkfirst, tables=tables) def drop_all(self, bind=None, tables=None, checkfirst=True): """Drop all tables stored in this metadata. Conditional by default, will not attempt to drop tables not present in the target database. bind A :class:`~sqlalchemy.engine.base.Connectable` used to access the database; if None, uses the existing bind on this ``MetaData``, if any. tables Optional list of ``Table`` objects, which is a subset of the total tables in the ``MetaData`` (others are ignored). checkfirst Defaults to True, only issue DROPs for tables confirmed to be present in the target database. """ if bind is None: bind = _bind_or_error(self) bind.drop(self, checkfirst=checkfirst, tables=tables) class ThreadLocalMetaData(MetaData): """A MetaData variant that presents a different ``bind`` in every thread. Makes the ``bind`` property of the MetaData a thread-local value, allowing this collection of tables to be bound to different ``Engine`` implementations or connections in each thread. The ThreadLocalMetaData starts off bound to None in each thread. Binds must be made explicitly by assigning to the ``bind`` property or using ``connect()``. You can also re-bind dynamically multiple times per thread, just like a regular ``MetaData``. """ __visit_name__ = 'metadata' def __init__(self): """Construct a ThreadLocalMetaData.""" self.context = util.threading.local() self.__engines = {} super(ThreadLocalMetaData, self).__init__() def bind(self): """The bound Engine or Connection for this thread. This property may be assigned an Engine or Connection, or assigned a string or URL to automatically create a basic Engine for this bind with ``create_engine()``.""" return getattr(self.context, '_engine', None) def _bind_to(self, bind): """Bind to a Connectable in the caller's thread.""" global URL if URL is None: from sqlalchemy.engine.url import URL if isinstance(bind, (basestring, URL)): try: self.context._engine = self.__engines[bind] except KeyError: from sqlalchemy import create_engine e = create_engine(bind) self.__engines[bind] = e self.context._engine = e else: # TODO: this is squirrely. we shouldnt have to hold onto engines # in a case like this if bind not in self.__engines: self.__engines[bind] = bind self.context._engine = bind bind = property(bind, _bind_to) def is_bound(self): """True if there is a bind for this thread.""" return (hasattr(self.context, '_engine') and self.context._engine is not None) def dispose(self): """Dispose all bound engines, in all thread contexts.""" for e in self.__engines.itervalues(): if hasattr(e, 'dispose'): e.dispose() class SchemaVisitor(visitors.ClauseVisitor): """Define the visiting for ``SchemaItem`` objects.""" __traverse_options__ = {'schema_visitor':True} class DDLElement(expression.Executable, expression.ClauseElement): """Base class for DDL expression constructs.""" _execution_options = expression.Executable.\ _execution_options.union({'autocommit':True}) target = None on = None def execute(self, bind=None, target=None): """Execute this DDL immediately. Executes the DDL statement in isolation using the supplied :class:`~sqlalchemy.engine.base.Connectable` or :class:`~sqlalchemy.engine.base.Connectable` assigned to the ``.bind`` property, if not supplied. If the DDL has a conditional ``on`` criteria, it will be invoked with None as the event. bind Optional, an ``Engine`` or ``Connection``. If not supplied, a valid :class:`~sqlalchemy.engine.base.Connectable` must be present in the ``.bind`` property. target Optional, defaults to None. The target SchemaItem for the execute call. Will be passed to the ``on`` callable if any, and may also provide string expansion data for the statement. See ``execute_at`` for more information. """ if bind is None: bind = _bind_or_error(self) if self._should_execute(None, target, bind): return bind.execute(self.against(target)) else: bind.engine.logger.info("DDL execution skipped, criteria not met.") def execute_at(self, event, target): """Link execution of this DDL to the DDL lifecycle of a SchemaItem. Links this ``DDLElement`` to a ``Table`` or ``MetaData`` instance, executing it when that schema item is created or dropped. The DDL statement will be executed using the same Connection and transactional context as the Table create/drop itself. The ``.bind`` property of this statement is ignored. event One of the events defined in the schema item's ``.ddl_events``; e.g. 'before-create', 'after-create', 'before-drop' or 'after-drop' target The Table or MetaData instance for which this DDLElement will be associated with. A DDLElement instance can be linked to any number of schema items. ``execute_at`` builds on the ``append_ddl_listener`` interface of MetaDta and Table objects. Caveat: Creating or dropping a Table in isolation will also trigger any DDL set to ``execute_at`` that Table's MetaData. This may change in a future release. """ if not hasattr(target, 'ddl_listeners'): raise exc.ArgumentError( "%s does not support DDL events" % type(target).__name__) if event not in target.ddl_events: raise exc.ArgumentError( "Unknown event, expected one of (%s), got '%r'" % (', '.join(target.ddl_events), event)) target.ddl_listeners[event].append(self) return self @expression._generative def against(self, target): """Return a copy of this DDL against a specific schema item.""" self.target = target def __call__(self, event, target, bind, **kw): """Execute the DDL as a ddl_listener.""" if self._should_execute(event, target, bind, **kw): return bind.execute(self.against(target)) def _check_ddl_on(self, on): if (on is not None and (not isinstance(on, (basestring, tuple, list, set)) and not util.callable(on))): raise exc.ArgumentError( "Expected the name of a database dialect, a tuple of names, or a callable for " "'on' criteria, got type '%s'." % type(on).__name__) def _should_execute(self, event, target, bind, **kw): if self.on is None: return True elif isinstance(self.on, basestring): return self.on == bind.engine.name elif isinstance(self.on, (tuple, list, set)): return bind.engine.name in self.on else: return self.on(self, event, target, bind, **kw) def bind(self): if self._bind: return self._bind def _set_bind(self, bind): self._bind = bind bind = property(bind, _set_bind) def _generate(self): s = self.__class__.__new__(self.__class__) s.__dict__ = self.__dict__.copy() return s def _compiler(self, dialect, **kw): """Return a compiler appropriate for this ClauseElement, given a Dialect.""" return dialect.ddl_compiler(dialect, self, **kw) class DDL(DDLElement): """A literal DDL statement. Specifies literal SQL DDL to be executed by the database. DDL objects can be attached to ``Tables`` or ``MetaData`` instances, conditionally executing SQL as part of the DDL lifecycle of those schema items. Basic templating support allows a single DDL instance to handle repetitive tasks for multiple tables. Examples:: tbl = Table('users', metadata, Column('uid', Integer)) # ... DDL('DROP TRIGGER users_trigger').execute_at('before-create', tbl) spow = DDL('ALTER TABLE %(table)s SET secretpowers TRUE', on='somedb') spow.execute_at('after-create', tbl) drop_spow = DDL('ALTER TABLE users SET secretpowers FALSE') connection.execute(drop_spow) When operating on Table events, the following ``statement`` string substitions are available:: %(table)s - the Table name, with any required quoting applied %(schema)s - the schema name, with any required quoting applied %(fullname)s - the Table name including schema, quoted if needed The DDL's ``context``, if any, will be combined with the standard substutions noted above. Keys present in the context will override the standard substitutions. """ __visit_name__ = "ddl" def __init__(self, statement, on=None, context=None, bind=None): """Create a DDL statement. statement A string or unicode string to be executed. Statements will be processed with Python's string formatting operator. See the ``context`` argument and the ``execute_at`` method. A literal '%' in a statement must be escaped as '%%'. SQL bind parameters are not available in DDL statements. on Optional filtering criteria. May be a string, tuple or a callable predicate. If a string, it will be compared to the name of the executing database dialect:: DDL('something', on='postgresql') If a tuple, specifies multiple dialect names:: DDL('something', on=('postgresql', 'mysql')) If a callable, it will be invoked with four positional arguments as well as optional keyword arguments: ddl This DDL element. event The name of the event that has triggered this DDL, such as 'after-create' Will be None if the DDL is executed explicitly. target The ``Table`` or ``MetaData`` object which is the target of this event. May be None if the DDL is executed explicitly. connection The ``Connection`` being used for DDL execution \**kw Keyword arguments which may be sent include: tables - a list of Table objects which are to be created/ dropped within a MetaData.create_all() or drop_all() method call. If the callable returns a true value, the DDL statement will be executed. context Optional dictionary, defaults to None. These values will be available for use in string substitutions on the DDL statement. bind Optional. A :class:`~sqlalchemy.engine.base.Connectable`, used by default when ``execute()`` is invoked without a bind argument. """ if not isinstance(statement, basestring): raise exc.ArgumentError( "Expected a string or unicode SQL statement, got '%r'" % statement) self.statement = statement self.context = context or {} self._check_ddl_on(on) self.on = on self._bind = bind def __repr__(self): return '<%s@%s; %s>' % ( type(self).__name__, id(self), ', '.join([repr(self.statement)] + ['%s=%r' % (key, getattr(self, key)) for key in ('on', 'context') if getattr(self, key)])) def _to_schema_column(element): if hasattr(element, '__clause_element__'): element = element.__clause_element__() if not isinstance(element, Column): raise exc.ArgumentError("schema.Column object expected") return element def _to_schema_column_or_string(element): if hasattr(element, '__clause_element__'): element = element.__clause_element__() return element class _CreateDropBase(DDLElement): """Base class for DDL constucts that represent CREATE and DROP or equivalents. The common theme of _CreateDropBase is a single ``element`` attribute which refers to the element to be created or dropped. """ def __init__(self, element, on=None, bind=None): self.element = element self._check_ddl_on(on) self.on = on self.bind = bind def _create_rule_disable(self, compiler): """Allow disable of _create_rule using a callable. Pass to _create_rule using util.portable_instancemethod(self._create_rule_disable) to retain serializability. """ return False class CreateTable(_CreateDropBase): """Represent a CREATE TABLE statement.""" __visit_name__ = "create_table" class DropTable(_CreateDropBase): """Represent a DROP TABLE statement.""" __visit_name__ = "drop_table" class CreateSequence(_CreateDropBase): """Represent a CREATE SEQUENCE statement.""" __visit_name__ = "create_sequence" class DropSequence(_CreateDropBase): """Represent a DROP SEQUENCE statement.""" __visit_name__ = "drop_sequence" class CreateIndex(_CreateDropBase): """Represent a CREATE INDEX statement.""" __visit_name__ = "create_index" class DropIndex(_CreateDropBase): """Represent a DROP INDEX statement.""" __visit_name__ = "drop_index" class AddConstraint(_CreateDropBase): """Represent an ALTER TABLE ADD CONSTRAINT statement.""" __visit_name__ = "add_constraint" def __init__(self, element, *args, **kw): super(AddConstraint, self).__init__(element, *args, **kw) element._create_rule = util.portable_instancemethod(self._create_rule_disable) class DropConstraint(_CreateDropBase): """Represent an ALTER TABLE DROP CONSTRAINT statement.""" __visit_name__ = "drop_constraint" def __init__(self, element, cascade=False, **kw): self.cascade = cascade super(DropConstraint, self).__init__(element, **kw) element._create_rule = util.portable_instancemethod(self._create_rule_disable) def _bind_or_error(schemaitem, msg=None): bind = schemaitem.bind if not bind: name = schemaitem.__class__.__name__ label = getattr(schemaitem, 'fullname', getattr(schemaitem, 'name', None)) if label: item = '%s %r' % (name, label) else: item = name if isinstance(schemaitem, (MetaData, DDL)): bindable = "the %s's .bind" % name else: bindable = "this %s's .metadata.bind" % name if msg is None: msg = ('The %s is not bound to an Engine or Connection. ' 'Execution can not proceed without a database to execute ' 'against. Either execute with an explicit connection or ' 'assign %s to enable implicit execution.') % (item, bindable) raise exc.UnboundExecutionError(msg) return bind

[ "gterranova@GTERRANOVA.9ren.org" ]

gterranova@GTERRANOVA.9ren.org

af6d319381a3634ed79b1ce5fbc909e292583f02

77ce46ae4198108326e7606f4f4d0bb7d59c6555

/rpcenable/async.py

00a6c44db3df31e5888f2a2ab600366c592b5d6e

[]

no_license

mtrdesign/django-rpcenable

5ca87287ccef99ef7c8f6221a48ab8daf55b9228

7e8836699f5cc7c140a154946246f4ab2403ad39

refs/heads/master

2020-09-22T02:16:37.600130

2013-12-09T18:12:09

6,478,013

2

0

null

2013-12-09T13:09:12

2012-10-31T17:38:02

Python

UTF-8

Python

false

784

py

import atexit import Queue import threading import functools from django.core.mail import mail_admins def _worker(): while True: func, args, kwargs = _queue.get() try: func(*args, **kwargs) except: import traceback details = traceback.format_exc() mail_admins('Background process exception', details) finally: _queue.task_done() # so we can join at exit def postpone(f): @functools.wraps(f) def wrapper (*args, **kwargs): _queue.put((f, args, kwargs)) return wrapper _queue = Queue.Queue() _thread = threading.Thread(target=_worker) _thread.daemon = True _thread.start() def _cleanup(): _queue.join() # so we don't exit too soon atexit.register(_cleanup)

[ "tie@TTOP.(none)" ]

tie@TTOP.(none)

915bb39fd2f17f7a5a22a7b3fbb3525926ae7f63

e7f8acff7948cf6618043ec7b998867b7c6ad831

/python/pythonday5/pythonday5_5_文件操作.py

b822ba838138be00185e46990a9ce22e8e4143ea

[]

no_license

z778107203/pengzihao1999.github.io

9b97ecefba6557f1f815a5f17da33118ddf78b37

d683ba31772fb3a42085cb44ee0601637bcdd84e

refs/heads/master

2022-11-13T10:44:00.988950

2019-10-08T15:29:04

null

0

null

UTF-8

Python

false

813

py

""" r :以只读方式打开文件，文件的指针将会放在文件的开头，如果文件不存在抛出异常 w :以只写方式打开文件，如果文件存在会被覆盖，如果文件不存在，创建新文件 a :以追加方式打开文件，如果文件存在，文件指针将会放在文件的结尾，如果不存在，创建新文件进行写入 r+:以读写方式打开文件，文件的指针会放在文件的开头，如果文件不存在，抛出异常 w+:以读写方式打开文件，如果文件存在会被覆盖，如果文件不存在，创建新文件 a+：以读写方式打开文件，如果文件已经存在，文件指针将会放在文件的结尾，如果文件不存在，创建新文件进行写入 """ file = open("wenjian") text = file.read() print(text) file.close()

[ "916811138@qq.com" ]

916811138@qq.com

9eb70544daa471de18bc1c32603379098b98bf4d

1d6fbc4eecad619a889682ee026082e46e501475

/Control.py

9994cc22acad8a03307af8c85b14214a2f01028b

[]

no_license

matheus-osorio/project_opener

740ddf40f2a9a03b743081d6dc9c083f25bcdb07

ceb6a97a58c255c4f8e4cc085410510091de94f3

refs/heads/main

2023-04-18T00:31:27.435748

2021-05-09T19:00:32

361,580,307

0

null

UTF-8

Python

false

2,097

py

from configs.Organizer import Organizer class Control: def run(self,obj,path): st = obj['starter'] self.Organizer = Organizer(path) response = self[st](obj) txt = f''' RESPONSE TYPE: {response['type']} -------------- RESPONSE MESSAGE: {response['text']} ''' print(txt) def new(self,obj): return self.Organizer.new_project(**obj['params']) def edit(self,obj): params = obj['params'] proj_hash = params['hash'] del params['hash'] return self.Organizer.edit_project(proj_hash,obj['params']) def list(self,obj): return self.Organizer.list_projects() def delete(self,obj): return self.Organizer.delete_project(obj['params']['hash']) def define(self,obj): params = obj['params'] name = params['name'][0] content = params['content'] return self.Organizer.define_variable(name,content) def change_hash_length(self,obj): try: num = int(obj['params']['content']) except ValueError: raise Exception('With this function type must be Integer') except: raise Exception('Something went wrong...') return self.Organizer.change_sys_variable('hash_length',num) def change_standart_folder(self,obj): return self.Organizer.change_sys_variable('standart_folder',obj['params']['content']) def change_standart_editor(self,obj): return self.Organizer.change_sys_variable('standart_editor',obj['params']['content']) def change_replacement_policy(self,obj): content = obj['params']['content'] if content not in ['replace','ignore']: raise Exception('content must be either "replace" or "ignore".') return self.Organizer.change_sys_variable('duplicate_policy',content) def open(self,obj): name = obj['params']['name'][0] return self.Organizer.open_project(name) def __getitem__(self,attr): return self.__getattribute__(attr)

[ "matheuscerqueir@gmail.com" ]

matheuscerqueir@gmail.com

fabdb59965535befc9dac9f98e452ca2667188c8

3331bef496806453a89f469d0a93b71d42f4d0d9

/clase9.py

f7d374d056bf1741423d19a3e59ce08bd36d63f2

[]

no_license

katherinelasluisa/Clase9

56ebda90ef2b63cec07f362851c145a91b3328f5

e353edb3c960b52ffb9ed110a3d3f61363381bb8

refs/heads/master

2021-01-09T20:40:06.425243

2016-06-16T14:54:28

61,290,633

0

null

UTF-8

Python

false

287

py

#trabajo en clase segundo emi import time print (time.localtime()) #time.struct_time(tm_year=2020, tm_mon=2, tm_mday=23, tm_hour=22, tm_min=18, tm_sec=39, tm_wday=0, tm_yday=73, tm_isdst=0) t=time.localtime() year=t[1] month= t[2] day= t[3] wday=t[4] min=t[5] print (year) print (month)

[ "katherinelasluisa7@gmail.com" ]

katherinelasluisa7@gmail.com

37a4bed3bf5ad368c0622bb623e70c8852cd6ba3

c0239d75a8199ec84ad683f945c21785c1b59386

/dingtalk/api/rest/CorpDingTaskCreateRequest.py

ebe77db44bea52c850f1888fb9ce57aede6aae7f

[]

no_license

luss613/oauth_dingtalk

9f253a75ce914c577dbabfb84e97fd883e80e04b

1e2554642d2b16c642a031670d08efa4a74e8252

refs/heads/master

2023-04-23T01:16:33.450821

2020-06-18T08:22:57

264,966,287

1

null

2020-06-18T08:31:24

2020-05-18T14:33:25

Python

UTF-8

Python

false

332

py

''' Created by auto_sdk on 2018.07.25 ''' from dingtalk.api.base import RestApi class CorpDingTaskCreateRequest(RestApi): def __init__(self,url=None): RestApi.__init__(self,url) self.task_send_v_o = None def getHttpMethod(self): return 'POST' def getapiname(self): return 'dingtalk.corp.ding.task.create'

[ "paul.lu@belstar.com.cn" ]

paul.lu@belstar.com.cn

62f564b7edaf6eb32c0ccc8850acdb734cae478b

487d6bbdd801d37478734ba5a96a670abada4021

/MyWagtailWebsite/resume/migrations/0001_initial.py

47d5a3c05ca09cca7ee3ab1e97374da7e3619feb

[]

no_license

Munnu/PersonalWebsite-Wagtail

5414218f2b24933a462ac95dbc40de9fd449d772

950cf8d43e0b68c7f4445100c94ca67f738b477f

refs/heads/master

2022-12-23T08:05:05.421092

2018-09-21T06:43:15

84,692,087

0

null

2022-12-07T23:48:00

2017-03-12T01:41:09

Python

UTF-8

Python

false

892

py

# -*- coding: utf-8 -*- # Generated by Django 1.11.1 on 2017-07-20 07:03 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion import wagtail.wagtailcore.fields class Migration(migrations.Migration): initial = True dependencies = [ ('wagtailcore', '0033_remove_golive_expiry_help_text'), ] operations = [ migrations.CreateModel( name='Resume', fields=[ ('page_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='wagtailcore.Page')), ('body', wagtail.wagtailcore.fields.RichTextField(blank=True)), ], options={ 'abstract': False, }, bases=('wagtailcore.page',), ), ]

[ "moniqueblake4@gmail.com" ]

moniqueblake4@gmail.com

2752abedd7b48de52b2fddab35c8f2b31dad7226

3cb4c484912f540632edd5d0446df1867a68ce62

/src/pytorch_impl/cnn_pdtb_arg_multiclass_jl.py

6ead21d65aaf3bf957a0adf344db0e9af4c9b6cb

[ "MIT" ]

permissive

siddharthvaria/WordPair-CNN

a91307c35d8d163299b2a09dbe971e69873ef866

d54cef994e49615e6bf9d911c11cb5992862bcce

refs/heads/master

2022-03-25T07:51:47.634344

2019-08-10T04:10:18

197,980,051

2

0

null

UTF-8

Python

false

18,457

py

import argparse import time import math import os import pickle import numpy as np np.random.seed(57697) from sklearn.metrics import classification_report from sklearn.utils import shuffle from pytorch_impl.cnn_pdtb_classifier_utils_pytorch import get_W, post_process, print_params, get_time_stamp from pytorch_impl.cnn_pdtb_classifier_utils_pytorch import get_class_weights, get_class_names, get_binary_class_index from pytorch_impl.cnn_pdtb_classifier_utils_pytorch import load_data_for_mtl, compute_metrices import torch torch.manual_seed(57697) import torch.nn as nn import torch.nn.functional as F class LossCompute: "A Loss compute and train function." def __init__(self, clf_criterion, opt = None): self.clf_criterion = clf_criterion self.opt = opt def __call__(self, Y, clf_logits, only_return_losses = False): # Classification loss clf_losses = self.clf_criterion(clf_logits, Y) # only_return_losses: true for validation and test if only_return_losses: return clf_losses train_loss = clf_losses.sum() train_loss.backward() if self.opt is not None: # Performs a single optimization step self.opt.step() self.opt.zero_grad() return train_loss.item() class PDTB_Classifier(nn.Module): def __init__(self, args): super(PDTB_Classifier, self).__init__() self.args = args # word embedding layer self.word_embed = nn.Embedding(args.W.shape[0], args.W.shape[1]) self.word_embed.weight.data.copy_(torch.from_numpy(args.W)) if args.emb_static: self.word_embed.weight.requires_grad = False # pos embedding layer self.pos_embed = nn.Embedding(args.P.shape[0], args.P.shape[1]) self.pos_embed.weight.data.copy_(torch.from_numpy(args.P)) self.pos_embed.weight.requires_grad = False emb_dim = args.W.shape[1] + args.P.shape[1] # convolutional layers for arg self.conv_layers_arg = nn.ModuleList([nn.Conv2d(1, args.nfmaps_arg, (K, emb_dim), stride = (1, 1)) for K in args.fsz_arg]) # initialize conv_layers_arg for conv_layer in self.conv_layers_arg: # nn.init.xavier_uniform_(conv_layer.weight, gain = nn.init.calculate_gain('relu')) nn.init.xavier_uniform(conv_layer.weight, gain = nn.init.calculate_gain('relu')) conv_layer.bias.data.fill_(0) # nn.init.zeros_(conv_layer.bias) # # # dense layers for arg # dense_layers_arg = [] # for i, D in enumerate(args.dsz_arg): # if i == 0: # dense_layers_arg.append(nn.Linear(len(args.fsz_arg) * args.nfmaps_arg, D)) # else: # dense_layers_arg.append(nn.Linear(args.dsz_arg[i - 1], D)) # # self.dense_layers_arg = nn.ModuleList(dense_layers_arg) # # # initialize dense_layers_arg # for dense_layer in self.dense_layers_arg: # nn.init.xavier_uniform_(dense_layer.weight, gain = nn.init.calculate_gain('relu')) # dense_layer.bias.data.fill_(0) # # nn.init.zeros_(dense_layer.bias) # define gate1 self.dense_arg_cap = nn.Linear(2 * len(args.fsz_arg) * args.nfmaps_arg, args.gate_units_arg) # nn.init.xavier_uniform_(self.dense_arg_cap.weight, gain = nn.init.calculate_gain('relu')) nn.init.xavier_uniform(self.dense_arg_cap.weight, gain = nn.init.calculate_gain('relu')) self.dense_arg_cap.bias.data.fill_(0) self.dense_arg_gate = nn.Linear(2 * len(args.fsz_arg) * args.nfmaps_arg, args.gate_units_arg) # nn.init.xavier_uniform_(self.dense_arg_gate.weight, gain = 1) nn.init.xavier_uniform(self.dense_arg_gate.weight, gain = 1) self.dense_arg_gate.bias.data.fill_(0) # classification layer for imp self.clf_layer_imp = nn.Linear(args.gate_units_arg, args.nclasses) # nn.init.xavier_uniform_(self.clf_layer_imp.weight, gain = 1) nn.init.xavier_uniform(self.clf_layer_imp.weight, gain = 1) self.clf_layer_imp.bias.data.fill_(0) # classification layer for exp self.clf_layer_exp = nn.Linear(args.gate_units_arg, args.nclasses) # nn.init.xavier_uniform_(self.clf_layer_exp.weight, gain = 1) nn.init.xavier_uniform(self.clf_layer_exp.weight, gain = 1) self.clf_layer_exp.bias.data.fill_(0) def forward(self, X): X_larg, X_lpos, X_rarg, X_rpos, is_imp = X h_arg_vecs = [] for x in [(X_larg, X_lpos), (X_rarg, X_rpos)]: x_w, x_p = x x_w = self.word_embed(x_w) # (batch_size, seq_len, dim) x_p = self.pos_embed(x_p) x_w_p = torch.cat([x_w, x_p], 2) x_w_p = x_w_p.unsqueeze(1) # (batch_size, 1, seq_len, dim) x_convs = [F.relu(conv_layer(x_w_p)).squeeze(3) for conv_layer in self.conv_layers_arg] # At this point x_convs is [(batch_size, nfmaps_arg, seq_len_new), ...]*len(fsz_arg) x_max_pools = [F.max_pool1d(xi, xi.size(2)).squeeze(2) for xi in x_convs] # [(batch_size, nfmaps_arg), ...]*len(fsz_arg) x_max_pool = torch.cat(x_max_pools, 1) x_max_pool = nn.Dropout(self.args.dropout_p)(x_max_pool) h_arg_vecs.append(x_max_pool) h_arg_vec = torch.cat(h_arg_vecs, 1) h_arg_cap = F.relu(self.dense_arg_cap(h_arg_vec)) h_arg_gate = torch.sigmoid(self.dense_arg_gate(h_arg_vec)) h_clf_in = h_arg_cap * h_arg_gate h_out_imp = self.clf_layer_imp(nn.Dropout(self.args.dropout_p)(h_clf_in)) h_out_exp = self.clf_layer_exp(nn.Dropout(self.args.dropout_p)(h_clf_in)) is_exp = 1 - is_imp # TODO: cast is_imp and is_exp to float tensor h_out = is_imp.unsqueeze(1).expand_as(h_out_imp).float() * h_out_imp + is_exp.unsqueeze(1).expand_as(h_out_exp).float() * h_out_exp return h_out def iter_data(datas, batch_size = 200): n = int(math.ceil(float(len(datas[0])) / batch_size)) * batch_size for i in range(0, n, batch_size): if len(datas) == 1: yield datas[0][i:i + batch_size] else: yield [d[i:i + batch_size] for d in datas] def train(train_set, val_set, args, run_id): # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # device = torch.device("cpu") train_y = [train_set[-1][ii][0] for ii in range(len(train_set[-1]))] # At this point, train set labels will be a list where for each example, we have only one label train_set = train_set[:-1] train_set.append(train_y) imp_indices_val = np.where(val_set[-2] == 1)[0] exp_indices_val = np.where(val_set[-2] == 0)[0] y_val_imp = val_set[-1][imp_indices_val] y_val_exp = val_set[-1][exp_indices_val] val_set = val_set[:-1] # model clf = PDTB_Classifier(args) # clf = clf.to(device) clf = clf.cuda() # class_weights = torch.from_numpy(np.asarray(args.class_weights, dtype = 'float32')).to(device) class_weights = torch.from_numpy(np.asarray(args.class_weights, dtype = 'float32')).cuda() # This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class. criterion = nn.CrossEntropyLoss(weight = class_weights) print('List of trainable parameters: ') for name, param in clf.named_parameters(): # for name, param in clf.parameters(): if param.requires_grad: print(name) model_opt = torch.optim.Adam(filter(lambda p: p.requires_grad, clf.parameters()), lr = args.lr, weight_decay = args.l2_weight) compute_loss_fct = LossCompute(criterion, model_opt) n_epochs = 0 best_val_perf = None if args.conv_metric in ['f1' , 'acc']: best_val_perf = 0 else: best_val_perf = np.inf patience = args.patience for i in range(args.n_epochs): n_epochs += 1 print("running epoch", i) # actual training starts here start_time = time.time() tr_loss = 0 batch_count = 0 for _train_set in iter_data(shuffle(*train_set, random_state = np.random), batch_size = args.batch_size): clf.train() # _train_set = [torch.tensor(data, dtype = torch.long).to(device) for data in _train_set] _train_set = [torch.LongTensor(data).cuda() for data in _train_set] y_tr = _train_set[-1] _train_set = _train_set[:-1] clf_logits = clf(_train_set) tr_loss += compute_loss_fct(y_tr, clf_logits) batch_count += 1 tr_loss /= batch_count print('epoch: %i, training time: %.2f secs, train loss: %.4f' % (n_epochs, time.time() - start_time, tr_loss)) logits = [] with torch.no_grad(): clf.eval() for _val_set in iter_data(val_set, batch_size = args.batch_size): # _val_set = [torch.tensor(data, dtype = torch.long).to(device) for data in _val_set] _val_set = [torch.LongTensor(data).cuda() for data in _val_set] clf_logits = clf(_val_set) logits.append(clf_logits.to("cpu").numpy()) logits = np.concatenate(logits, 0) logits_imp = logits[imp_indices_val] logits_exp = logits[exp_indices_val] # At present the logits are not probabilities so will only work for multiclass clf _, y_true_val_imp, y_pred_val_imp = post_process(-1, logits_imp, y_val_imp, args.multilabel, args.binarize, args.binary_class) val_acc_imp, val_macro_f1_imp, val_micro_f1_imp = compute_metrices(y_true_val_imp, y_pred_val_imp, binary_class = args.binary_class, binarize = args.binarize) _, y_true_val_exp, y_pred_val_exp = post_process(-1, logits_exp, y_val_exp, args.multilabel, args.binarize, args.binary_class) val_acc_exp, val_macro_f1_exp, val_micro_f1_exp = compute_metrices(y_true_val_exp, y_pred_val_exp, binary_class = args.binary_class, binarize = args.binarize) print('Imp val acc: %.2f, Imp val micro f1: %.2f, Imp val macro f1: %.2f' % (val_acc_imp * 100.0, val_micro_f1_imp * 100.0, val_macro_f1_imp * 100.0)) print('Exp val acc: %.2f, Exp val micro f1: %.2f, Exp val macro f1: %.2f' % (val_acc_exp * 100.0, val_micro_f1_exp * 100.0, val_macro_f1_exp * 100.0)) if args.conv_metric == 'f1': val_perf = val_macro_f1_imp elif args.conv_metric == 'acc': val_perf = val_acc_imp if (args.conv_metric in ['f1', 'acc'] and val_perf > best_val_perf): best_val_perf = val_perf patience = args.patience print('Saving the best model . . .') path = os.path.join(args.output_dir, 'best_params_{0}'.format(run_id)) torch.save(clf.state_dict(), path) else: patience -= 1 if patience <= 0: print ('Early stopping . . .') break def test(test_set, args, run_id): imp_indices_test = np.where(test_set[-2] == 1)[0] exp_indices_test = np.where(test_set[-2] == 0)[0] y_test_imp = test_set[-1][imp_indices_test] y_test_exp = test_set[-1][exp_indices_test] test_set = test_set[:-1] # model clf = PDTB_Classifier(args) clf.load_state_dict(torch.load(os.path.join(args.output_dir, 'best_params_{0}'.format(run_id)))) # clf = clf.to(device) clf = clf.cuda() logits = [] with torch.no_grad(): clf.eval() for _test_set in iter_data(test_set, batch_size = args.batch_size): # _test_set = [torch.tensor(data, dtype = torch.long).to(device) for data in _test_set] _test_set = [torch.LongTensor(data).cuda() for data in _test_set] clf_logits = clf(_test_set) logits.append(clf_logits.to("cpu").numpy()) logits = np.concatenate(logits, 0) logits_imp = logits[imp_indices_test] logits_exp = logits[exp_indices_test] # At present the logits are not probabilities so will only work for multiclass clf _, y_true_test_imp, y_pred_test_imp = post_process(-1, logits_imp, y_test_imp, args.multilabel, args.binarize, args.binary_class) test_acc_imp, test_macro_f1_imp, test_micro_f1_imp = compute_metrices(y_true_test_imp, y_pred_test_imp, binary_class = args.binary_class, binarize = args.binarize) _, y_true_test_exp, y_pred_test_exp = post_process(-1, logits_exp, y_test_exp, args.multilabel, args.binarize, args.binary_class) test_acc_exp, test_macro_f1_exp, test_micro_f1_exp = compute_metrices(y_true_test_exp, y_pred_test_exp, binary_class = args.binary_class, binarize = args.binarize) print('Imp test acc: %.2f, Imp test micro f1: %.2f, Imp test macro f1: %.2f' % (test_acc_imp * 100.0, test_micro_f1_imp * 100.0, test_macro_f1_imp * 100.0)) print('Exp test acc: %.2f, Exp test micro f1: %.2f, Exp test macro f1: %.2f' % (test_acc_exp * 100.0, test_micro_f1_exp * 100.0, test_macro_f1_exp * 100.0)) pickle.dump([y_true_test_imp, y_pred_test_imp, _ , _, args.class_names], open(os.path.join(args.output_dir, 'best_prediction_imp_' + args.timestamp + '_' + str(run_id) + '.p'), 'wb')) pickle.dump([y_true_test_exp, y_pred_test_exp, _ , _, args.class_names], open(os.path.join(args.output_dir, 'best_prediction_exp_' + args.timestamp + '_' + str(run_id) + '.p'), 'wb')) print('############################### IMP ###############################') print(classification_report(y_true_test_imp, y_pred_test_imp, target_names = args.class_names, labels = range(len(args.class_names)), digits = 4)) print('############################### EXP ###############################') print(classification_report(y_true_test_exp, y_pred_test_exp, target_names = args.class_names, labels = range(len(args.class_names)), digits = 4)) return test_macro_f1_imp, test_acc_imp, test_macro_f1_exp, test_acc_exp def main(): ts = get_time_stamp(args.input_file) filter_hs_arg = [2, 3, 4, 5] train_data, val_data, test_data, class_dict, examples_per_class = load_data_for_mtl(args.input_file, filter_hs_arg[-1], 2) # [X_larg, X_rarg, X_lpos, X_rpos, X_lner, X_rner, X_wp, X_wp_rev, X_wp_pos, X_wp_rev_pos, X_wp_ner, X_wp_rev_ner, is_imp, y] X_tr_larg, X_tr_rarg, X_tr_lpos, X_tr_rpos, _, _, _, _, _, _, _, _, is_imp_tr, Y_tr_all = train_data X_val_larg, X_val_rarg, X_val_lpos, X_val_rpos, _, _, _, _, _, _, _, _, is_imp_val, Y_val_all = val_data X_te_larg, X_te_rarg, X_te_lpos, X_te_rpos, _, _, _, _, _, _, _, _, is_imp_te, Y_te_all = test_data train_set = [X_tr_larg, X_tr_lpos, X_tr_rarg, X_tr_rpos, is_imp_tr, Y_tr_all] val_set = [X_val_larg, X_val_lpos, X_val_rarg, X_val_rpos, is_imp_val, Y_val_all] test_set = [X_te_larg, X_te_lpos, X_te_rarg, X_te_rpos, is_imp_te, Y_te_all] args.emb_static = True args.reg_emb = False # should regularize embeddings or not. Obviously when emb_static is True, reg_emb will be false args.fsz_arg = filter_hs_arg args.nfmaps_arg = 50 args.dsz_arg = [] args.gate_units_arg = 300 args.nclasses = len(class_dict) args.binarize = True if len(class_dict) == 2 else False args.binary_class = get_binary_class_index(class_dict) args.dropout_p = 0.5 args.l2_weight = 1e-4 args.batch_size = 200 args.class_names = get_class_names(class_dict) args.n_epochs = 30 args.patience = 5 args.timestamp = ts print_params(args) word2idx, pos2idx, _ = pickle.load(open(args.encoder_file, "rb")) args.W = get_W(args.w2v_file, word2idx) args.P = np.identity(len(pos2idx)) args.class_weights = get_class_weights(class_dict, examples_per_class) print('Class weights:') print(args.class_weights) f_scores_imp = [] accuracies_imp = [] f_scores_exp = [] accuracies_exp = [] runs = range(0, 5) for run_id in runs: print ('Run: ', run_id) train(train_set, val_set, args, run_id) f1_imp, acc_imp, f1_exp, acc_exp = test(test_set, args, run_id) f_scores_imp.append(f1_imp) accuracies_imp.append(acc_imp) f_scores_exp.append(f1_exp) accuracies_exp.append(acc_exp) print('avg f1 (imp): %s' % (str(np.mean(f_scores_imp)))) print('avg acc (imp): %s' % (str(np.mean(accuracies_imp)))) print('avg f1 (exp): %s' % (str(np.mean(f_scores_exp)))) print('avg acc (exp): %s' % (str(np.mean(accuracies_exp)))) if __name__ == "__main__": parser = argparse.ArgumentParser(description = '') parser.add_argument('input_file', help = 'pickled input file generated using \'preprocess_pdtb_relations.py\'') parser.add_argument('encoder_file', default = None, type = str, help = 'WordEncoder file') parser.add_argument('w2v_file', type = str, default = None, help = 'GoogleNews-vectors-negative300.bin') # GoogleNews-vectors-negative300.bin parser.add_argument('output_dir', help = 'directory where you want to save the best model and predictions file') parser.add_argument('--conv_metric', default = 'f1', type = str, help = '\'f1\', \'acc\'') parser.add_argument('--multilabel', default = True, type = bool, help = 'If True, multilabel evaluation will be done on val and test sets') parser.add_argument('--trained_weights_file', default = None, type = str, help = 'file containing the trained model weights') parser.add_argument('--opt', default = 'adam', type = str, help = 'opt to use') parser.add_argument('--lr', default = 0.0005, type = float, help = 'learning rate to use') args = parser.parse_args() main()

[ "varia.siddharth@gmail.com" ]

varia.siddharth@gmail.com

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/etc/MOPSO-ZDT2/ZDT2-1.py

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[]

no_license

dqyi11/SVNBackup

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''' Created on Jan 26, 2014 @author: daqing_yi ''' if __name__ == '__main__': from PerformanceAnalyzer import *; import sys; trial_time = 30; figFolder = sys.path[0] + "\\zdt2"; caseName = "ZDT2"; fileList1 = []; fileList2 = []; fileList3 = []; fileList4 = []; for tt in range(trial_time): filename1 = "ZDT2-"+str(tt)+"--Div.txt"; filename2 = "ZDT2-"+str(tt)+"--AD.txt"; filename3 = "ZDT2-"+str(tt)+"--Spread.txt"; filename4 = "ZDT2-"+str(tt)+"--Efficiency.txt"; fileList1.append(filename1); fileList2.append(filename2); fileList3.append(filename3); fileList4.append(filename4); analyzer1 = PerformanceAnalyzer(fileList1, figFolder, "Diversity", 10); analyzer1.genData(); analyzer1.plot(caseName); analyzer1.dump(caseName); analyzer2 = PerformanceAnalyzer(fileList2, figFolder, "Distance", 10); analyzer2.genData(); analyzer2.plot(caseName); analyzer2.dump(caseName); analyzer3 = PerformanceAnalyzer(fileList3, figFolder, "Spread", 10); analyzer3.genData(); analyzer3.plot(caseName); analyzer3.dump(caseName); analyzer4 = PerformanceAnalyzer(fileList4, figFolder, "Efficiency", 10); analyzer4.genData(); analyzer4.plot(caseName); analyzer4.dump(caseName);

[ "walter@e224401c-0ce2-47f2-81f6-2da1fe30fd39" ]

walter@e224401c-0ce2-47f2-81f6-2da1fe30fd39

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842a7c01a33270766fd382c787b0616066522694

/LinkedList/Leco1290.py

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[]

no_license

BubbleMa123/Leetcode

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refs/heads/main

2023-04-02T05:36:32.856999

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# Definition for singly-linked list. class ListNode: def __init__(self, x): self.val = x self.next = None # class Solution: # def getDecimalValue(self, head: ListNode) -> int: # nums = [] # while head: # nums.append(head.val) # head = head.next # num = 0 # for i in range(len(nums) - 1, -1, -1): # num += nums[i] * 2 ** (len(nums) - 1 - i) # return num class Solution: def getDecimalValue(self, head: ListNode) -> int: num = 0 while head: num = num * 2 + head.val head = head.next return num

[ "76720145+BubbleMa123@users.noreply.github.com" ]

76720145+BubbleMa123@users.noreply.github.com

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/webapp/api/resources/podcast_resources.py

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[]

no_license

asm-products/podato-web

90bc3463ee4581d97a7863522ac9f308dfb82d1d

e4693c232a25fa4003a2cc8de17327b9fca2fd2a

refs/heads/master

2021-01-20T23:31:53.813211

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import urllib from flask import abort from flask_restplus import Resource from flask_restplus import fields from flask_restplus import abort from webapp.utils import AttributeHider from webapp.api.oauth import oauth from webapp.api.oauth import AuthorizationRequired from webapp.api.blueprint import api from webapp.api.representations import podcast_full_fields, podcast_fields from webapp.podcasts import Podcast ns = api.namespace("podcasts") @ns.route("/<path:podcastId>", endpoint="podcast") @api.doc(params={"podcastId":"A podcast's id (the same as its URL. If the API returns a podcast with a different URL, it means the podcast has moved."}) class PodcastResource(Resource): """Resource that represents a podcast.""" @api.marshal_with(podcast_full_fields) @api.doc(id="getPodcast") def get(self, podcastId): """Get a podcast by id.""" podcastId = urllib.unquote(podcastId) podcast = Podcast.get_by_url(podcastId) if podcast == None: abort(404, message="Podcast not found: %s" % podcastId) return podcast queryParser = api.parser() queryParser.add_argument(name="order", required=False, location="args", default="subscriptions") queryParser.add_argument(name="category", required=False, location="args") queryParser.add_argument(name="author", required=False, location="args") queryParser.add_argument(name="language", required=False, location="args") queryParser.add_argument(name="page", default=1, type=int) queryParser.add_argument(name="per_page", default=30, type=int) @ns.route("/") class PodcastQueryResource(Resource): """Resource representing the collection of al podcasts.""" @api.marshal_with(podcast_fields, as_list=True) @api.doc(id="query", parser=queryParser) def get(self): """Query for podcasts.""" args = queryParser.parse_args() query = Podcast.objects if args.get("order"): query = query.order_by(args.get('order')) if args.get("category"): query = query.filter(categories=args.get("category")) if args.get("author"): query = query.filter(author=args.get("author")) if args.get("language"): query = query.filter(language=args.get("language")) return query.paginate(page=args["page"], per_page=args["per_page"]).items

[ "frederikcreemers@gmail.com" ]

frederikcreemers@gmail.com

7e504807ecaf06d5ef595d1ea58a05ab471ee2b1

a90547c558f666b8f6717d735b5cd89552e5fc20

/animal.py

3858b40150c65651677991c69535a52a34333eca

[]

no_license

Maniss-ai/PythonGame

8400eaa3bfdc25983141418b0b03b7299e2f8649

faf76a7f06b60e37357811cdc26d5c0aff681fd8

refs/heads/main

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import os import random import game_config as gc from pygame import image, transform animal_count = dict((a, 0) for a in gc.ASSET_FILES) def availabe_animals(): return [a for a, c in animal_count.items() if c < 2] class Animal: def __init__(self, index): self.index = index self.row = index // gc.NUM_TILES_SIDE self.col = index % gc.NUM_TILES_SIDE self.name = random.choice(availabe_animals()) animal_count[self.name] += 1 self.image_path = os.path.join(gc.ASSET_DIR, self.name) self.image = image.load(self.image_path) self.image = transform.scale(self.image, (gc.IMAGE_SIZE - 2*gc.MARGIN, gc.IMAGE_SIZE - 2*gc.MARGIN)) self.box = self.image.copy() self.box.fill((200, 200, 200)) self.skip = False

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javierramon23/Python-Crash-Course

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''' http://pymotw.com '''

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# Generated by Django 2.1.7 on 2019-06-09 20:49 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('telaPrincipal', '0016_auto_20190609_2034'), ] operations = [ migrations.AlterField( model_name='hsmtarefasgerais', name='id_hsmemprocesso', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='telaPrincipal.HSMEmProcesso'), ), ]

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from parallel import * from utils import * import sys home = sys.argv[1] project = sys.argv[2] gene_gtf = sys.argv[3] TE_gtf = sys.argv[4] threads = int(sys.argv[5]) # cpu per cmd batch_size = int(sys.argv[6]) def reads_assign_on_gene(path, summary, bam_in, experiment): cmd0 = 'echo "processing %s"\ncd %s' % (experiment, path) cmd1 = '''time featureCounts \ -a %s \ --extraAttributes gene_id,gene_name,gene_type \ -T %s \ -o %s \ -R BAM %s''' % (gene_gtf, threads, summary, bam_in) cmd2 = "mv %s.featureCounts.bam %s.gene_assigned.bam" % (bam_in, experiment) cmd3 = "time samtools sort -@ 24 %s.gene_assigned.bam -o %s.gene_assigned.sorted.bam" % ( experiment, experiment) cmd4 = "time samtools index %s.gene_assigned.sorted.bam" % (experiment) return (cmd0, cmd1, cmd2, cmd3, cmd4) def reads_assign_on_TE(path, summary, bam_in, experiment): cmd0 = "cd %s" % path cmd1 = '''time featureCounts \ -a %s \ --extraAttributes gene_id,family_id,class_id \ -T %s \ -o %s \ -R BAM %s''' % (TE_gtf, threads, summary, bam_in) cmd2 = "mv %s.featureCounts.bam %s.TE_assigned.bam" % (bam_in, experiment) cmd3 = "time samtools sort -@ 24 %s.TE_assigned.bam -o %s.TE_assigned.sorted.bam" % ( experiment, experiment) cmd4 = "time samtools index %s.TE_assigned.sorted.bam" % (experiment) return (cmd0, cmd1, cmd2, cmd3, cmd4) def main(): cmds = [] for experiment in get_experiment(home, project): path = os.path.join(home, "data", project, experiment) bam_in = "%sAligned.sortedByCoord.out.bam" % experiment summary_gene = "%s.gene_assigned" % experiment summary_te = "%s.TE_assigned" % experiment cmd_tuple = reads_assign_on_gene(path, summary_gene, bam_in, experiment) cmds.append(cmd_tuple) cmd_tuple = reads_assign_on_TE(path, summary_te, bam_in, experiment) cmds.append(cmd_tuple) cmd_list = make_parallel(cmds, batch_size) for i in range(len(cmd_list)): fo = open(os.path.join(home, "04-assignment-%s.pbs" %(i+1)), 'w') fo.write('''#!/bin/sh #PBS -N preprocess_s4-b%s #PBS -o preprocess_s4-b%s.log #PBS -e preprocess_s4-b%s.err #PBS -q middle #PBS -l nodes=1:ppn=%s #PBS -l mem=10G module load samtools module load Anaconda3 cd %s echo "step4 Reads assignment, processing batch-%s"\n ''' % (i+1, i+1, i+1, threads, home, i+1)) for cmd_tuple in cmd_list[i]: for cmd in cmd_tuple: fo.write(cmd+"\n") fo.write("\n\n") fo.close() if __name__ == '__main__': main()

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jarninggau@gmail.com

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/cs231n/rnn_layers.py

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FengYen-Chang/cs231-assignment-3

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2020-04-17T08:43:18.241622

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import numpy as np """ This file defines layer types that are commonly used for recurrent neural networks. """ def rnn_step_forward(x, prev_h, Wx, Wh, b): """ Run the forward pass for a single timestep of a vanilla RNN that uses a tanh activation function. The input data has dimension D, the hidden state has dimension H, and we use a minibatch size of N. Inputs: - x: Input data for this timestep, of shape (N, D). - prev_h: Hidden state from previous timestep, of shape (N, H) - Wx: Weight matrix for input-to-hidden connections, of shape (D, H) - Wh: Weight matrix for hidden-to-hidden connections, of shape (H, H) - b: Biases of shape (H,) Returns a tuple of: - next_h: Next hidden state, of shape (N, H) - cache: Tuple of values needed for the backward pass. """ next_h, cache = None, None ############################################################################## # TODO: Implement a single forward step for the vanilla RNN. Store the next # # hidden state and any values you need for the backward pass in the next_h # # and cache variables respectively. # ############################################################################## #pass #self.h = np.tanh(np.dot(self.W_hh, self.h) + np.dot(self.W_xh, x)) next_h = np.tanh((prev_h.dot(Wh) + b) + x.dot(Wx)) cache = (x, prev_h, Wx, Wh, b, next_h) ############################################################################## # END OF YOUR CODE # ############################################################################## return next_h, cache def rnn_step_backward(dnext_h, cache): """ Backward pass for a single timestep of a vanilla RNN. Inputs: - dnext_h: Gradient of loss with respect to next hidden state - cache: Cache object from the forward pass Returns a tuple of: - dx: Gradients of input data, of shape (N, D) - dprev_h: Gradients of previous hidden state, of shape (N, H) - dWx: Gradients of input-to-hidden weights, of shape (D, H) - dWh: Gradients of hidden-to-hidden weights, of shape (H, H) - db: Gradients of bias vector, of shape (H,) """ dx, dprev_h, dWx, dWh, db = None, None, None, None, None ############################################################################## # TODO: Implement the backward pass for a single step of a vanilla RNN. # # # # HINT: For the tanh function, you can compute the local derivative in terms # # of the output value from tanh. # ############################################################################## x, prev_h, Wx, Wh, b, next_h = cache #pass dnext_h = (1 - next_h * next_h) * dnext_h db = np.sum(dnext_h, axis=0, keepdims=True) dprev_h = dnext_h.dot(Wh.T) dx = dnext_h.dot(Wx.T) dWx = x.T.dot(dnext_h) dWh = prev_h.T.dot(dnext_h) db = db.reshape(db.shape[0] * db.shape[1]) #print db.shape ############################################################################## # END OF YOUR CODE # ############################################################################## return dx, dprev_h, dWx, dWh, db def rnn_forward(x, h0, Wx, Wh, b): """ Run a vanilla RNN forward on an entire sequence of data. We assume an input sequence composed of T vectors, each of dimension D. The RNN uses a hidden size of H, and we work over a minibatch containing N sequences. After running the RNN forward, we return the hidden states for all timesteps. Inputs: - x: Input data for the entire timeseries, of shape (N, T, D). - h0: Initial hidden state, of shape (N, H) - Wx: Weight matrix for input-to-hidden connections, of shape (D, H) - Wh: Weight matrix for hidden-to-hidden connections, of shape (H, H) - b: Biases of shape (H,) Returns a tuple of: - h: Hidden states for the entire timeseries, of shape (N, T, H). - cache: Values needed in the backward pass """ h, cache = None, None ############################################################################## # TODO: Implement forward pass for a vanilla RNN running on a sequence of # # input data. You should use the rnn_step_forward function that you defined # # above. # ############################################################################## #pass N, T, D = x.shape H = Wh.shape[0] swap_x = x.swapaxes(0, 1) h = np.zeros(N * T * H) h = h.reshape(T, N, H) for i in range(T): _x = swap_x[i, :, :] if i == 0 : h[i, :, :], _ = rnn_step_forward(_x, h0, Wx, Wh, b) else : h[i, :, :], _ = rnn_step_forward(_x, h[(i - 1), :, :], Wx, Wh, b) h = h.swapaxes(0, 1) cache = (x, h0, Wx, Wh, b, h) #print h.shape ############################################################################## # END OF YOUR CODE # ############################################################################## return h, cache def rnn_backward(dh, cache): """ Compute the backward pass for a vanilla RNN over an entire sequence of data. Inputs: - dh: Upstream gradients of all hidden states, of shape (N, T, H) Returns a tuple of: - dx: Gradient of inputs, of shape (N, T, D) - dh0: Gradient of initial hidden state, of shape (N, H) - dWx: Gradient of input-to-hidden weights, of shape (D, H) - dWh: Gradient of hidden-to-hidden weights, of shape (H, H) - db: Gradient of biases, of shape (H,) """ dx, dh0, dWx, dWh, db = None, None, None, None, None ############################################################################## # TODO: Implement the backward pass for a vanilla RNN running an entire # # sequence of data. You should use the rnn_step_backward function that you # # defined above. # ############################################################################## #pass x, h0, Wx, Wh, b, h = cache x_swap = x.swapaxes(0, 1) dh_swap = dh.swapaxes(0, 1) h = h.swapaxes(0, 1) N, T, H = x.shape dx = np.zeros(x_swap.shape) dWh = np.zeros(Wh.shape) dWx = np.zeros(Wx.shape) db = np.zeros(b.shape) d_h = np.zeros(h0.shape) for i in range(T) : if i == (T - 1): _cache = (x_swap[(T - 1 - i), :, :], h0, Wx, Wh, b, h[(T - 1 - i), :, :]) d_x, dh0, _dWx, _dWh, _db = rnn_step_backward((dh_swap[(T - 1 - i), :, :] + d_h), _cache) else : _cache = (x_swap[(T - 1 - i), :, :], h[(T - 2 - i), :, :],Wx, Wh, b, h[(T - 1 - i), :, :]) d_x, d_h, _dWx, _dWh, _db = rnn_step_backward((dh_swap[(T - 1 - i), :, :] + d_h), _cache) #dh_swap[(T - 2 - i), :, :] += d_h dx[(T - 1 - i) ,: , :] = d_x dWh += _dWh dWx += _dWx db += _db dx = dx.swapaxes(0, 1) ############################################################################## # END OF YOUR CODE # ############################################################################## return dx, dh0, dWx, dWh, db def word_embedding_forward(x, W): """ Forward pass for word embeddings. We operate on minibatches of size N where each sequence has length T. We assume a vocabulary of V words, assigning each to a vector of dimension D. Inputs: - x: Integer array of shape (N, T) giving indices of words. Each element idx of x muxt be in the range 0 <= idx < V. - W: Weight matrix of shape (V, D) giving word vectors for all words. Returns a tuple of: - out: Array of shape (N, T, D) giving word vectors for all input words. - cache: Values needed for the backward pass """ out, cache = None, None ############################################################################## # TODO: Implement the forward pass for word embeddings. # # # # HINT: This should be very simple. # ############################################################################## #pass N, T = x.shape V, D = W.shape out = np.zeros(N * T * D).reshape(N, T, D) for i in range(N) : out[i, :, :] = W[x[i, :]] cache = (x, W) ############################################################################## # END OF YOUR CODE # ############################################################################## return out, cache def word_embedding_backward(dout, cache): """ Backward pass for word embeddings. We cannot back-propagate into the words since they are integers, so we only return gradient for the word embedding matrix. HINT: Look up the function np.add.at Inputs: - dout: Upstream gradients of shape (N, T, D) - cache: Values from the forward pass Returns: - dW: Gradient of word embedding matrix, of shape (V, D). """ dW = None ############################################################################## # TODO: Implement the backward pass for word embeddings. # # # # HINT: Look up the function np.add.at # ############################################################################## #pass x, W = cache N, T, D = dout.shape dW = np.zeros(W.shape) for i in range(N): indices = x[i, :] np.add.at(dW, indices, dout[i, :, :]) ############################################################################## # END OF YOUR CODE # ############################################################################## return dW def sigmoid(x): """ A numerically stable version of the logistic sigmoid function. """ pos_mask = (x >= 0) neg_mask = (x < 0) z = np.zeros_like(x) z[pos_mask] = np.exp(-x[pos_mask]) z[neg_mask] = np.exp(x[neg_mask]) top = np.ones_like(x) top[neg_mask] = z[neg_mask] return top / (1 + z) def lstm_step_forward(x, prev_h, prev_c, Wx, Wh, b): """ Forward pass for a single timestep of an LSTM. The input data has dimension D, the hidden state has dimension H, and we use a minibatch size of N. Inputs: - x: Input data, of shape (N, D) - prev_h: Previous hidden state, of shape (N, H) - prev_c: previous cell state, of shape (N, H) - Wx: Input-to-hidden weights, of shape (D, 4H) - Wh: Hidden-to-hidden weights, of shape (H, 4H) - b: Biases, of shape (4H,) Returns a tuple of: - next_h: Next hidden state, of shape (N, H) - next_c: Next cell state, of shape (N, H) - cache: Tuple of values needed for backward pass. """ next_h, next_c, cache = None, None, None ############################################################################# # TODO: Implement the forward pass for a single timestep of an LSTM. # # You may want to use the numerically stable sigmoid implementation above. # ############################################################################# #pass _, H = prev_h.shape a = x.dot(Wx) + prev_h.dot(Wh) + b i, f, o, g = sigmoid(a[:, 0: H]), sigmoid(a[:, H: 2 * H]), sigmoid(a[:, 2 * H: 3 * H]), np.tanh(a[:, 3 * H: 4 * H]) next_c = f * prev_c + i * g next_h = o * np.tanh(next_c) cache = (x, prev_h, prev_c, i, f, o, g, next_c, Wx, Wh, b) ############################################################################## # END OF YOUR CODE # ############################################################################## return next_h, next_c, cache def lstm_step_backward(dnext_h, dnext_c, cache): """ Backward pass for a single timestep of an LSTM. Inputs: - dnext_h: Gradients of next hidden state, of shape (N, H) - dnext_c: Gradients of next cell state, of shape (N, H) - cache: Values from the forward pass Returns a tuple of: - dx: Gradient of input data, of shape (N, D) - dprev_h: Gradient of previous hidden state, of shape (N, H) - dprev_c: Gradient of previous cell state, of shape (N, H) - dWx: Gradient of input-to-hidden weights, of shape (D, 4H) - dWh: Gradient of hidden-to-hidden weights, of shape (H, 4H) - db: Gradient of biases, of shape (4H,) """ dx, dprev_h, dprev_c, dWx, dWh, db = None, None, None, None, None, None ############################################################################# # TODO: Implement the backward pass for a single timestep of an LSTM. # # # # HINT: For sigmoid and tanh you can compute local derivatives in terms of # # the output value from the nonlinearity. # ############################################################################# #pass x, prev_h, prev_c, i, f, o, g, next_c, Wx, Wh, b = cache do = dnext_h * np.tanh(next_c) dnext_c += (1 - np.tanh(next_c) * np.tanh(next_c)) * o * dnext_h df = dnext_c * prev_c dprev_c = dnext_c * f di = dnext_c * g dg = dnext_c * i do = o * (1 - o) * do df = f * (1 - f) * df di = i * (1 - i) * di dg = (1 - g * g) * dg N, H = dnext_h.shape _H = 4 * H da = np.zeros(N * _H).reshape(N, _H) da[:, 0: H], da[:, H: 2 * H], da[:, 2 * H: 3 * H], da[:, 3 * H: 4 * H] = di, df, do, dg dx = da.dot(Wx.T) dWx = x.T.dot(da) dprev_h = da.dot(Wh.T) dWh = prev_h.T.dot(da) db = np.sum(da, axis=0, keepdims=True) ############################################################################## # END OF YOUR CODE # ############################################################################## return dx, dprev_h, dprev_c, dWx, dWh, db def lstm_forward(x, h0, Wx, Wh, b): """ Forward pass for an LSTM over an entire sequence of data. We assume an input sequence composed of T vectors, each of dimension D. The LSTM uses a hidden size of H, and we work over a minibatch containing N sequences. After running the LSTM forward, we return the hidden states for all timesteps. Note that the initial cell state is passed as input, but the initial cell state is set to zero. Also note that the cell state is not returned; it is an internal variable to the LSTM and is not accessed from outside. Inputs: - x: Input data of shape (N, T, D) - h0: Initial hidden state of shape (N, H) - Wx: Weights for input-to-hidden connections, of shape (D, 4H) - Wh: Weights for hidden-to-hidden connections, of shape (H, 4H) - b: Biases of shape (4H,) Returns a tuple of: - h: Hidden states for all timesteps of all sequences, of shape (N, T, H) - cache: Values needed for the backward pass. """ h, cache = None, None ############################################################################# # TODO: Implement the forward pass for an LSTM over an entire timeseries. # # You should use the lstm_step_forward function that you just defined. # ############################################################################# #pass N, T, D = x.shape _, H = h0.shape cache = [None] * T x_swap = x.swapaxes(0, 1) prev_h = h0 prev_c = np.zeros(h0.shape) h = np.zeros(N * T * H).reshape(T, N, H) for i in range(T) : _x = x_swap[i, :, :] prev_h, prev_c, cache[i] = lstm_step_forward(_x, prev_h, prev_c, Wx, Wh, b) h[i, :, :] = prev_h h = h.swapaxes(0, 1) ############################################################################## # END OF YOUR CODE # ############################################################################## return h, cache def lstm_backward(dh, cache): """ Backward pass for an LSTM over an entire sequence of data.] Inputs: - dh: Upstream gradients of hidden states, of shape (N, T, H) - cache: Values from the forward pass Returns a tuple of: - dx: Gradient of input data of shape (N, T, D) - dh0: Gradient of initial hidden state of shape (N, H) - dWx: Gradient of input-to-hidden weight matrix of shape (D, 4H) - dWh: Gradient of hidden-to-hidden weight matrix of shape (H, 4H) - db: Gradient of biases, of shape (4H,) """ dx, dh0, dWx, dWh, db = None, None, None, None, None ############################################################################# # TODO: Implement the backward pass for an LSTM over an entire timeseries. # # You should use the lstm_step_backward function that you just defined. # ############################################################################# #pass N, T, H = dh.shape dnext_c = np.zeros(N * H).reshape(N, H) x, _, _, _, _, _, _, _, _, _, _ = cache[0] _, D = x.shape dx = np.zeros(N * T * D).reshape(T, N, D) dh_swap = dh.swapaxes(0, 1) _dprev_h = np.zeros(N * H).reshape(N, H) _dprev_c = np.zeros(N * H).reshape(N, H) dWx = np.zeros(4 * D * H).reshape(D, 4 * H) dWh = np.zeros(4 * H * H).reshape(H, 4 * H) db = np.zeros(4 * H).reshape(1, 4 * H) for i in range(T) : dnext_h = dh_swap[(T - 1 - i), :, :] _dx, _dprev_h, _dprev_c, _dWx, _dWh, _db = lstm_step_backward(dnext_h + _dprev_h, dnext_c + _dprev_c, cache[T - 1 - i]) dx[(T - 1 - i), :, :] = _dx dWx += _dWx dWh += _dWh db += _db db = db.reshape(4 * H) dx = dx.swapaxes(0, 1) dh0 = _dprev_h ############################################################################## # END OF YOUR CODE # ############################################################################## return dx, dh0, dWx, dWh, db def temporal_affine_forward(x, w, b): """ Forward pass for a temporal affine layer. The input is a set of D-dimensional vectors arranged into a minibatch of N timeseries, each of length T. We use an affine function to transform each of those vectors into a new vector of dimension M. Inputs: - x: Input data of shape (N, T, D) - w: Weights of shape (D, M) - b: Biases of shape (M,) Returns a tuple of: - out: Output data of shape (N, T, M) - cache: Values needed for the backward pass """ N, T, D = x.shape M = b.shape[0] out = x.reshape(N * T, D).dot(w).reshape(N, T, M) + b cache = x, w, b, out return out, cache def temporal_affine_backward(dout, cache): """ Backward pass for temporal affine layer. Input: - dout: Upstream gradients of shape (N, T, M) - cache: Values from forward pass Returns a tuple of: - dx: Gradient of input, of shape (N, T, D) - dw: Gradient of weights, of shape (D, M) - db: Gradient of biases, of shape (M,) """ x, w, b, out = cache N, T, D = x.shape M = b.shape[0] dx = dout.reshape(N * T, M).dot(w.T).reshape(N, T, D) dw = dout.reshape(N * T, M).T.dot(x.reshape(N * T, D)).T db = dout.sum(axis=(0, 1)) return dx, dw, db def temporal_softmax_loss(x, y, mask, verbose=False): """ A temporal version of softmax loss for use in RNNs. We assume that we are making predictions over a vocabulary of size V for each timestep of a timeseries of length T, over a minibatch of size N. The input x gives scores for all vocabulary elements at all timesteps, and y gives the indices of the ground-truth element at each timestep. We use a cross-entropy loss at each timestep, summing the loss over all timesteps and averaging across the minibatch. As an additional complication, we may want to ignore the model output at some timesteps, since sequences of different length may have been combined into a minibatch and padded with NULL tokens. The optional mask argument tells us which elements should contribute to the loss. Inputs: - x: Input scores, of shape (N, T, V) - y: Ground-truth indices, of shape (N, T) where each element is in the range 0 <= y[i, t] < V - mask: Boolean array of shape (N, T) where mask[i, t] tells whether or not the scores at x[i, t] should contribute to the loss. Returns a tuple of: - loss: Scalar giving loss - dx: Gradient of loss with respect to scores x. """ N, T, V = x.shape x_flat = x.reshape(N * T, V) y_flat = y.reshape(N * T) mask_flat = mask.reshape(N * T) probs = np.exp(x_flat - np.max(x_flat, axis=1, keepdims=True)) probs /= np.sum(probs, axis=1, keepdims=True) loss = -np.sum(mask_flat * np.log(probs[np.arange(N * T), y_flat])) / N dx_flat = probs.copy() dx_flat[np.arange(N * T), y_flat] -= 1 dx_flat /= N dx_flat *= mask_flat[:, None] if verbose: print ('dx_flat: ', dx_flat.shape) dx = dx_flat.reshape(N, T, V) return loss, dx

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from db.db import conn from model.guruh import Guruh class GuruhRepo: cur = conn.cursor() def getAll(self): sql = "SELECT id, nom, yunalish, yil FROM guruh;" self.cur.execute(sql) tplist = self.cur.fetchall() return list(map(Guruh, tplist)) def getById(self, id): sql = "SELECT id, nom, yunalish, yil FROM guruh WHERE id = %s;" self.cur.execute(sql, [id]) return Guruh(self.cur.fetch()) def getByYunalish(self, yunalish_id): sql = "SELECT id, nom, yunalish, yil FROM guruh WHERE yunalish = %s;" try: self.cur.execute(sql, [yunalish_id]) tplist = self.cur.fetchall() return list(map(Guruh, tplist)) except: return False def add(self, g): sql = "INSERT INTO guruh(nom, yunalish, yil) VALUES(%s, %s, %s)" self.cur.execute(sql, [g.nom, g.yunalish, g.yil]) def update(self, g): sql = "UPDATE public.guruh SET nom = %s, yunalish = %s, yil = %s WHERE id = %s" self.cur.execute(sql, [g.nom, g.yunalish, g.yil, g.id]) return True def deleteById(self, g): sql = "DELETE FROM guruh WHERE id = %s" self.cur.execute(sql, [g])

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def same_first_last(nums): if len(nums) >= 1 and nums[0] == nums[len(nums)-1]: return True return False

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# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://doc.scrapy.org/en/latest/topics/items.html import scrapy class HitadScraperItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() pass

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import pytest from system.utils import * from hypothesis import settings, given, strategies, Phase, Verbosity from hypothesis.strategies import composite from string import printable, ascii_letters import hashlib import copy import os import sys @composite def strategy_for_req_data(draw): reqid = draw(strategies.integers().filter(lambda x: 0 < x < 999999999999999)) reqtype = draw(strategies.integers().filter(lambda x: x not in [6, 7, 119, 20001])) data = draw( strategies.recursive( strategies.dictionaries( strategies.text(printable, min_size=1), strategies.text(printable, min_size=1), min_size=1, max_size=5 ), lambda x: strategies.dictionaries(strategies.text(printable, min_size=1), x, min_size=1, max_size=3) ) ) return reqid, reqtype, data @pytest.mark.usefixtures('docker_setup_and_teardown') class TestPropertyBasedSuite: @pytest.mark.skip('example') @settings(deadline=None, max_examples=100) @given(var_bin=strategies.binary(5, 25).filter(lambda x: x != b'\x00\x00\x00\x00\x00'), # <<< filter var_char=strategies.characters('S').filter(lambda x: x not in ['@', '#', '$']), # <<< filter var_text=strategies.text(ascii_letters, min_size=10, max_size=10).map(lambda x: x.lower()), # <<< map var_rec=strategies.recursive(strategies.integers() | strategies.floats(), lambda children: strategies.lists(children, min_size=3) | strategies.dictionaries( strategies.text(printable), children, min_size=3), max_leaves=10), var_dt_lists= strategies.integers(1, 5).flatmap(lambda x: strategies.lists(strategies.datetimes(), x, x))) # <<< flatmap @pytest.mark.asyncio async def test_case_strategies(self, var_bin, var_char, var_text, var_rec, var_dt_lists): print() print(var_bin) print(var_char) print(var_text) print(var_rec) print(var_dt_lists) print('-'*25) @settings(deadline=None, max_examples=1000, verbosity=Verbosity.verbose) @given(reqid=strategies.integers(min_value=1, max_value=999999999999999), dest=strategies.text(ascii_letters, min_size=16, max_size=16), # verkey=strategies.text(ascii_letters, min_size=32, max_size=32), alias=strategies.text(min_size=1, max_size=10000)) @pytest.mark.asyncio async def test_case_nym(self, pool_handler, wallet_handler, get_default_trustee, reqid, dest, alias): trustee_did, trustee_vk = get_default_trustee roles = ['0', '2', '101', '201'] req = { 'protocolVersion': 2, 'reqId': reqid, 'identifier': trustee_did, 'operation': { 'type': '1', 'dest': base58.b58encode(dest).decode(), # 'verkey': base58.b58encode(verkey).decode(), 'role': random.choice(roles), 'alias': alias } } res = json.loads( await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, json.dumps(req)) ) print(req) print(res) assert res['op'] == 'REPLY' @settings(deadline=None, max_examples=250) @given(reqid=strategies.integers(min_value=1, max_value=999999999999999), xhash=strategies.text().map(lambda x: hashlib.sha256(x.encode()).hexdigest()), key=strategies.text(printable), value=strategies.text(printable), enc=strategies.text(min_size=1)) @pytest.mark.asyncio async def test_case_attrib(self, pool_handler, wallet_handler, get_default_trustee, reqid, xhash, key, value, enc): trustee_did, trustee_vk = get_default_trustee target_did, target_vk = await did.create_and_store_my_did(wallet_handler, '{}') res = await send_nym(pool_handler, wallet_handler, trustee_did, target_did, target_vk) assert res['op'] == 'REPLY' req_base = { 'protocolVersion': 2, 'identifier': target_did, 'operation': { 'type': '100', 'dest': target_did } } req1 = copy.deepcopy(req_base) req1['reqId'] = reqid + 1 req1['operation']['hash'] = xhash res1 = json.loads( await ledger.sign_and_submit_request(pool_handler, wallet_handler, target_did, json.dumps(req1)) ) print(req1) print(res1) assert res1['op'] == 'REPLY' req2 = copy.deepcopy(req_base) req2['reqId'] = reqid + 2 req2['operation']['raw'] = json.dumps({key: value}) res2 = json.loads( await ledger.sign_and_submit_request(pool_handler, wallet_handler, target_did, json.dumps(req2)) ) print(req2) print(res2) assert res2['op'] == 'REPLY' req3 = copy.deepcopy(req_base) req3['reqId'] = reqid + 3 req3['operation']['enc'] = enc res3 = json.loads( await ledger.sign_and_submit_request(pool_handler, wallet_handler, target_did, json.dumps(req3)) ) print(req3) print(res3) assert res3['op'] == 'REPLY' @settings(deadline=None, max_examples=250) @given(reqid=strategies.integers(min_value=1, max_value=999999999999999), version=strategies.floats(min_value=0.1, max_value=999.999), name=strategies.text(min_size=1), attrs=strategies.lists(strategies.text(min_size=1), min_size=1, max_size=125)) @pytest.mark.asyncio async def test_case_schema(self, pool_handler, wallet_handler, get_default_trustee, reqid, version, name, attrs): trustee_did, trustee_vk = get_default_trustee creator_did, creator_vk = await did.create_and_store_my_did(wallet_handler, '{}') res = await send_nym(pool_handler, wallet_handler, trustee_did, creator_did, creator_vk, None, 'TRUSTEE') assert res['op'] == 'REPLY' req = { 'protocolVersion': 2, 'reqId': reqid, 'identifier': creator_did, 'operation': { 'type': '101', 'data': { 'version': str(version), 'name': name, 'attr_names': attrs } } } res = json.loads( await ledger.sign_and_submit_request(pool_handler, wallet_handler, creator_did, json.dumps(req)) ) print(req) print(res) assert res['op'] == 'REPLY' @settings(deadline=None, max_examples=250, verbosity=Verbosity.verbose) @given(reqid=strategies.integers(min_value=1, max_value=999999999999999), tag=strategies.text(printable, min_size=1), primary=strategies.recursive( strategies.dictionaries( strategies.text(printable, min_size=1), strategies.text(printable, min_size=1), min_size=1, max_size=3), lambda x: strategies.dictionaries(strategies.text(printable, min_size=1), x, min_size=1, max_size=3) )) @pytest.mark.asyncio async def test_case_cred_def(self, pool_handler, wallet_handler, get_default_trustee, reqid, tag, primary): trustee_did, trustee_vk = get_default_trustee creator_did, creator_vk = await did.create_and_store_my_did(wallet_handler, '{}') res = await send_nym(pool_handler, wallet_handler, trustee_did, creator_did, creator_vk, None, 'TRUSTEE') assert res['op'] == 'REPLY' schema_id, res = await send_schema\ (pool_handler, wallet_handler, creator_did, random_string(10), '1.0', json.dumps(['attribute'])) assert res['op'] == 'REPLY' await asyncio.sleep(1) res = await get_schema(pool_handler, wallet_handler, creator_did, schema_id) schema_id, schema_json = await ledger.parse_get_schema_response(json.dumps(res)) req = { 'protocolVersion': 2, 'reqId': reqid, 'identifier': creator_did, 'operation': { 'type': '102', 'ref': json.loads(schema_json)['seqNo'], 'signature_type': 'CL', 'tag': tag, 'data': { 'primary': primary } } } res = json.loads( await ledger.sign_and_submit_request(pool_handler, wallet_handler, creator_did, json.dumps(req)) ) print(res) assert res['op'] == 'REPLY' @settings(deadline=None, max_examples=10000, verbosity=Verbosity.verbose) @given(reqid=strategies.integers(min_value=1, max_value=999999999999999), # TODO fine-tune operation structure operation=strategies.recursive(strategies.dictionaries( strategies.text(printable, min_size=1), strategies.text(printable, min_size=1), min_size=1, max_size=5), lambda x: strategies.dictionaries(strategies.text(printable, min_size=1), x, min_size=1, max_size=3))) @pytest.mark.asyncio async def test_case_random_req_op(self, pool_handler, wallet_handler, get_default_trustee, reqid, operation): trustee_did, trustee_vk = get_default_trustee req = { 'protocolVersion': 2, 'reqId': reqid, 'identifier': trustee_did, 'operation': operation } # client-side validation with pytest.raises(IndyError): await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, json.dumps(req)) @settings(deadline=None, max_examples=10000, verbosity=Verbosity.verbose) # @given(reqid=strategies.integers(min_value=1, max_value=999999999999999), # _type=strategies.integers().filter(lambda x: x not in [6, 7, 119, 20001]), # # TODO fine-tune data structure # data=strategies.recursive(strategies.dictionaries( # strategies.text(printable, min_size=1), strategies.text(printable, min_size=1), # min_size=1, max_size=5), # lambda x: strategies.dictionaries(strategies.text(printable, min_size=1), x, min_size=1, max_size=3))) @given(values=strategy_for_req_data()) @pytest.mark.asyncio async def test_case_random_req_data( self, pool_handler, wallet_handler, get_default_trustee, values ): trustee_did, trustee_vk = get_default_trustee req = { 'protocolVersion': 2, 'reqId': values[0], 'identifier': trustee_did, 'operation': { 'type': str(values[1]), 'data': values[2] } } print(req) res = json.loads( await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, json.dumps(req)) ) print(res) # server-side static validation try: assert res['op'] == 'REQNACK' except KeyError: res = {k: json.loads(v) for k, v in res.items()} assert all([v['op'] == 'REQNACK' for k, v in res.items()])

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class MadgraphCardsGenerator_ttbar(object): def __init__(self, number_initializer): self.number = number_initializer def Generator(self): aux = ["set group_subprocesses Auto" ,"set ignore_six_quark_processes False" ,"set gauge unitary" ,"set complex_mass_scheme False" ,"import model sm" ,"define p = g u c d s u~ c~ d~ s~" ,"define j = g u c d s u~ c~ d~ s~" ,"define l+ = e+ mu+" ,"define l- = e- mu-" ,"define vl = ve vm vt" ,"define vl~ = ve~ vm~ vt~" ,"generate p p > t t~ > l- vl~ b b~ j j" ,"add process p p > t t~ > l+ vl b b~ j j" ,"output /data/atlas/dbetalhc/exphys/ttbar_events/ttbar" + str(self.number) ,"launch -i" ,"multi_run 1" ,"pythia=ON" ,"pgs=OFF" ,"delphes=ON" ,"set run_card ptj 40" ,"set cut_decays True" ,"set ptb 40" ,"set ptl 40" ,"set ebeam1 7000" ,"set ebeam2 7000" ,"set nevents 50000" ,"print_results --path=/data/atlas/dbetalhc/exphys/ttbar_events/ttbar" + str(self.number) + "/cs.txt --format=short"] return aux class MadgraphCardsGenerator_tW(object): def __init__(self, number_initializer): self.number = number_initializer def Generator(self): aux = ["set group_subprocesses Auto" ,"set ignore_six_quark_processes False" ,"set gauge unitary" ,"set complex_mass_scheme False" ,"import model sm" ,"define p = g u c d s b u~ c~ d~ s~ b~" ,"define j = g u c d s u~ c~ d~ s~" ,"define l+ = e+ mu+" ,"define l- = e- mu-" ,"define vl = ve vm vt" ,"define vl~ = ve~ vm~ vt~" ,"generate p p > t w-" ,"add process p p > t~ w+" ,"output /data/atlas/dbetalhc/exphys/tW_events/tW" + str(self.number) ,"launch -i" ,"multi_run 1" ,"pythia=ON" ,"pgs=OFF" ,"delphes=ON" ,"set run_card ptj 40" ,"set cut_decays True" ,"set ptb 40" ,"set ptl 40" ,"set ebeam1 7000" ,"set ebeam2 7000" ,"set nevents 10000" ,"print_results --path=/data/atlas/dbetalhc/exphys/tW_events/tW" + str(self.number) + "/cs.txt --format=short"] return aux class MadgraphCardsGenerator_WW(object): def __init__(self, number_initializer): self.number = number_initializer def Generator(self): aux = ["set group_subprocesses Auto" ,"set ignore_six_quark_processes False" ,"set gauge unitary" ,"set complex_mass_scheme False" ,"import model sm" ,"define p = g u c d s u~ c~ d~ s~" ,"define j = g u c d s u~ c~ d~ s~" ,"define l+ = e+ mu+" ,"define l- = e- mu-" ,"define vl = ve vm vt" ,"define vl~ = ve~ vm~ vt~" ,"generate p p > w+ w-" ,"output /data/atlas/dbetalhc/exphys/WW_events/WW" + str(self.number) ,"launch -i" ,"multi_run 1" ,"pythia=ON" ,"pgs=OFF" ,"delphes=ON" ,"set ptj 40" ,"set ptb 40" ,"set ptl 40" ,"set ebeam1 7000" ,"set ebeam2 7000" ,"set nevents 50000" ,"print_results --path=/data/atlas/dbetalhc/exphys/WW_events/WW" + str(self.number) + "/cs.txt --format=short"] return aux for i in range(0, 11): obj = [MadgraphCardsGenerator_ttbar(i), MadgraphCardsGenerator_tW(i), MadgraphCardsGenerator_WW(i)] paths = ["/user/e/exphys02/F-sicaExperimental/ExpPhysFinalProyect/CharginoPairProduction/ttbar_cards/" + "ttbar" + str(i) + ".dat" ,"/user/e/exphys02/F-sicaExperimental/ExpPhysFinalProyect/CharginoPairProduction/tW_cards/" + "tW" + str(i) + ".dat" ,"/user/e/exphys02/F-sicaExperimental/ExpPhysFinalProyect/CharginoPairProduction/WW_cards/" + "WW" + str(i) + ".dat"] for j in range(len(obj)): textfile = open(paths[j], "w") for k in range(len(obj[j].Generator())): textfile.write(obj[j].Generator()[k] + "\n") textfile.close()

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# -*- coding: utf-8 -*- """ django_async_test.tests.testcase ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Tests for :py:class:`django_async_test.TestCase`. """ import unittest from django.test import TestCase from django_async_test.tests.testapp.models import ModelWithBasicField class TestCaseTestCase(TestCase): def assertTests(self, tests): suite = unittest.TestSuite() suite.addTests(tests) result = unittest.TestResult() suite.run(result) if len(result.errors) > 0: for testcase, traceback in result.errors: print(traceback) if len(result.failures) > 0: for testcase, traceback in result.failures: print(traceback) self.assertEqual(len(result.errors), 0) self.assertEqual(len(result.failures), 0) def test_transaction_support(self): """ Test transaction support of :py:class:`django_async_test.TestCase`. """ from django_async_test.tests.testapp.util import DummyTestCase self.assertTests([ DummyTestCase('test_transaction_support'), DummyTestCase('test_transaction_support')] ) self.assertEqual(ModelWithBasicField.objects.count(), 0) def test_coroutine(self): """ Test coroutine support of :py:class:`django_async_test.TestCase`. """ from django_async_test.tests.testapp.util import DummyTestCase self.assertTests([DummyTestCase('test_coroutine')]) def test_transactional_coroutine(self): """ Test transactional coroutine support of :py:class:`django_async_test.TestCase`.. """ from django_async_test.tests.testapp.util import DummyTestCase self.assertTests([ DummyTestCase('test_transactional_coroutine'), DummyTestCase('test_transactional_coroutine')] ) self.assertEqual(ModelWithBasicField.objects.count(), 0)

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import heapq def kthSmallest(iterable, k): smallest = [] heapq.heapify(smallest) for value in iterable: if (len(smallest) < k): heapq.heappush(smallest, -value) else: heapq.heappushpop(smallest, -value) if (len(smallest) < k): return None return -smallest[0] arr = list(map(int, input().split())) print(kthSmallest(arr,3))

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""" Program: challenge8_4class.py Author: Michael Rouse Date: 12/12/13 Description: Class object for challenge8_4.py """ class Critter(object): """A virtual pet""" def __init__(self, name, hunger=0, boredom=0): self.name = name self.hunger = hunger self.boredom = boredom def __pass_time(self): self.hunger += 1 self.boredom += 1 def __str__(self): return "" @property def mood(self): unhappiness = self.hunger + self.boredom if unhappiness < 5: m = "happy" elif 5 <= unhappiness <= 10: m = "okay" elif 11 <= unhappiness <= 15: m = "frustrated" else: m = "mad" return m def talk(self): print(self.name + " is " + self.mood + ".") self.__pass_time() def eat(self, food=4): print("Brruppp! " + self.name + " has been feed.") self.hunger -= food if self.hunger < 0: self.hunger = 0 self.__pass_time() def play(self, fun=4): print("Wheee! " + self.name + " had lots of fun.") self.boredom -= fun if self.boredom < 0: self.boredom = 0 self.__pass_time()

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# -*- coding: utf-8 -*- # Copyright (c) 2013 Daniel Prokscha # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. from array import array import usb # USB device ID of FS20 PCS. ID_PRODUCT = 0xe015 ID_VENDOR = 0x18ef # I/O endpoints. ENDPOINT_READ = 0x81 ENDPOINT_WRITE = 0x01 # Possible data frames. DATAFRAME_SEND_ONCE = '\x01\x06\xf1' DATAFRAME_SEND_MULTIPLE = '\x01\x07\xf2' DATAFRAME_STOP_MULTIPLE_SENDING = '\x01\x01\xf3' DATAFRAME_VERSION = '\x01\x01\xf0' # Possible response codes. RESPONSE_DATAFRAME_UNKNOWN = 0x02 RESPONSE_DATAFRAME_MISMATCH = 0x03 RESPONSE_FIRMWARE_REQUEST_OK = 0x01 RESPONSE_OK = 0x00 RESPONSE_STOP_MULTIPLE_SENDING_OK = 0x04 RESPONSE_STOP_MULTIPLE_SENDING_NOT_SENT = 0x05 class PCS: """ Handles I/O of FS20 PCS. """ def _get_device(self): """ Returns FS20 PCS device instance. Returns: >>> self._get_device() <usb.core.Device object> Raises: DeviceNotFound: If FS20 PCS is not connected or can't be found. """ device = usb.core.find(idVendor=ID_VENDOR, idProduct=ID_PRODUCT) if device is None: raise DeviceNotFound('FS20 PCS not found.') # Set configuration if there is no active one. try: device.get_active_configuration() except Exception: device.set_configuration() # Force I/O if device seems to be busy. try: device.detach_kernel_driver(0) except Exception: pass return device def _get_raw_address(self, address): """ Returns a raw address. Args: address: Byte string which represents a fully qualified address. Returns: >>> self._get_raw_address('\xff\xff\xff') '\xff\xff\xff' Raises: InvalidInput: If more or less then 3 bytes given. """ if 3 == len(address): return address raise InvalidInput('Invalid address given (3 bytes expected).') def _get_raw_command(self, command): """ Returns a raw command, which is always two bytes long. Args: command: Byte string which represents a fully qualified command. Returns: >>> self._get_raw_address('\x00\x00') '\x00\x00' >>> self._get_raw_address('\x01') '\x01\x00' Raises: InvalidInput: If more then two or less then one bytes given. """ if 2 == len(command): return command elif 1 == len(command): return command + '\x00' raise InvalidInput('Invalid command given (1-2 bytes expected).') def _get_raw_interval(self, interval): """ Returns a raw interval. Args: interval: Integer value which represents an interval. Returns: >>> self._get_raw_interval(15) '\x0f' Raises: InvalidInput: If the given interval is not between 1 and 255. """ if 1 <= int(interval) <= 255: return chr(int(interval)) raise InvalidInput('Invalid interval given (1-255 expected).') def _get_response(self): """ Returns the response of FS20 PCS (after sending commands). Returns: >>> self._get_response() '\x00\x00' >>> self._get_response() '\x01\x10' Raises: DeviceCommandUnknown: If an unknown command was sent to FS20 PCS. DeviceCommandMismatch: If FS20 PCS can't handle the sent command. DeviceInvalidResponse: If FS20 PCS returns an invalid response. """ try: response = self._get_device().read(ENDPOINT_READ, 5, timeout=500) except Exception: response = '' if response[0:3] == array('B', [0x02, 0x03, 0xa0]): if response[3] in [RESPONSE_STOP_MULTIPLE_SENDING_OK, RESPONSE_STOP_MULTIPLE_SENDING_NOT_SENT, RESPONSE_FIRMWARE_REQUEST_OK, RESPONSE_OK]: return response[3:5] elif RESPONSE_DATAFRAME_UNKNOWN == response[3]: raise DeviceDataframeUnknown('Unknown data frame sent to device.') elif RESPONSE_DATAFRAME_MISMATCH == response[3]: raise DeviceDataframeMismatch('Device can not handle data frame.') raise DeviceInvalidResponse('Invalid response from device.') def _write(self, dataframe, with_response=True): """ Writes the given data frame to FS20 PCS. Args: dataframe: Byte string which represents a fully qualified data frame. with_response: Boolean value whether to get a response from the sent command. Returns: Depends from the given data frame. """ self._get_device().write(ENDPOINT_WRITE, dataframe) if with_response: return self._get_response() return array('B', [RESPONSE_OK, 0]) def get_version(self): """ Returns the firmware version of FS20 PCS. Returns: >>> self.get_version() 'v1.7' """ version = str(self._write(DATAFRAME_VERSION)[1]) return 'v%s.%s' % (version[0], version[1]) def send_multiple(self, address, command, time='\x00', interval=1): """ Sends the given command multiple for the given address. Args: address: Byte string which represents a fully qualified address. command: Byte string which represents a fully qualified command. time: Byte string which represents a fully qualified time. interval: Interval between 1 and 255 how often the command should be sent. Returns: >>> self.send_multiple('\x00\x00\x00', '\x10', 10) '\x00' """ return self._write( DATAFRAME_SEND_MULTIPLE + self._get_raw_address(address) + self._get_raw_command(command + time) + self._get_raw_interval(interval) , False )[0] def send_once(self, address, command, time='\x00'): """ Sends the given command once for the given address. Args: address: Byte string which represents a fully qualified address. command: Byte string which represents a fully qualified command. time: Byte string which represents a fully qualified time. Returns: >>> self.send('\x00\x00\x00', '\x10') '\x00' """ return self._write( DATAFRAME_SEND_ONCE + self._get_raw_address(address) + self._get_raw_command(command + time) )[0] def stop_multiple_sending(self): """ Stops instantly the multiple sending of a command. Returns: >>> self.stop_multiple_sending() '\x04' """ return self._write(DATAFRAME_STOP_MULTIPLE_SENDING)[0] # Module exceptions. class DeviceDataframeMismatch(Exception): pass class DeviceDataframeUnknown(Exception): pass class DeviceInvalidResponse(Exception): pass class DeviceNotFound(Exception): pass class InvalidInput(Exception): pass

[ "dp.online@googlemail.com" ]

dp.online@googlemail.com

eabfec2e4c0257175b2f88f159573dc90713903f

faaad3f79c5409ba87c32648562097a611884800

/app/app/migrations/0008_auto__add_field_partner_enabled.py

a3d5859a39c05f0df938dd399d231cd774ed6a0c

[]

no_license

ahguerilla/movements

d320cf4e59549f9aebb9c534ce4ae9c468189915

a2065b65ff96391571390d4d44744566b5f298ac

refs/heads/master

2020-12-29T02:32:05.568280

2018-05-11T16:22:00

55,590,490

0

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UTF-8

Python

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# -*- coding: utf-8 -*- from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding field 'Partner.enabled' db.add_column(u'app_partner', 'enabled', self.gf('django.db.models.fields.BooleanField')(default=True), keep_default=False) def backwards(self, orm): # Deleting field 'Partner.enabled' db.delete_column(u'app_partner', 'enabled') models = { u'app.menuextension': { 'Meta': {'object_name': 'MenuExtension'}, 'extended_object': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['cms.Page']", 'unique': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'public_extension': ('django.db.models.fields.related.OneToOneField', [], {'related_name': "'draft_extension'", 'unique': 'True', 'null': 'True', 'to': u"orm['app.MenuExtension']"}), 'show_on_footer_menu': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'show_on_top_menu': ('django.db.models.fields.BooleanField', [], {'default': 'False'}) }, u'app.newslettersignups': { 'Meta': {'ordering': "('-registered_date',)", 'object_name': 'NewsletterSignups'}, 'email': ('django.db.models.fields.CharField', [], {'max_length': '300'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'registered_date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}) }, u'app.notificationping': { 'Meta': {'object_name': 'NotificationPing'}, 'completed': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'send_email_to': ('django.db.models.fields.EmailField', [], {'max_length': '75'}) }, u'app.partner': { 'Meta': {'object_name': 'Partner'}, 'enabled': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'logo': ('django.db.models.fields.files.ImageField', [], {'max_length': '100'}), 'text': ('django.db.models.fields.TextField', [], {}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, u'app.safevpnlink': { 'Meta': {'object_name': 'SafeVPNLink', '_ormbases': ['cms.CMSPlugin']}, 'base_url': ('django.db.models.fields.CharField', [], {'max_length': '200'}), u'cmsplugin_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['cms.CMSPlugin']", 'unique': 'True', 'primary_key': 'True'}), 'key': ('django.db.models.fields.CharField', [], {'max_length': '10'}), 'link_text': ('django.db.models.fields.CharField', [], {'max_length': '500'}) }, 'cms.cmsplugin': { 'Meta': {'object_name': 'CMSPlugin'}, 'changed_date': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'creation_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'language': ('django.db.models.fields.CharField', [], {'max_length': '15', 'db_index': 'True'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.CMSPlugin']", 'null': 'True', 'blank': 'True'}), 'placeholder': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.Placeholder']", 'null': 'True'}), 'plugin_type': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}), 'position': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'blank': 'True'}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}) }, 'cms.page': { 'Meta': {'ordering': "('tree_id', 'lft')", 'unique_together': "(('publisher_is_draft', 'application_namespace'), ('reverse_id', 'site', 'publisher_is_draft'))", 'object_name': 'Page'}, 'application_namespace': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'application_urls': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '200', 'null': 'True', 'blank': 'True'}), 'changed_by': ('django.db.models.fields.CharField', [], {'max_length': '70'}), 'changed_date': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'created_by': ('django.db.models.fields.CharField', [], {'max_length': '70'}), 'creation_date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'in_navigation': ('django.db.models.fields.BooleanField', [], {'default': 'True', 'db_index': 'True'}), 'is_home': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True'}), 'languages': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'limit_visibility_in_menu': ('django.db.models.fields.SmallIntegerField', [], {'default': 'None', 'null': 'True', 'db_index': 'True', 'blank': 'True'}), 'login_required': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'navigation_extenders': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '80', 'null': 'True', 'blank': 'True'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'children'", 'null': 'True', 'to': "orm['cms.Page']"}), 'placeholders': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['cms.Placeholder']", 'symmetrical': 'False'}), 'publication_date': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'publication_end_date': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'publisher_is_draft': ('django.db.models.fields.BooleanField', [], {'default': 'True', 'db_index': 'True'}), 'publisher_public': ('django.db.models.fields.related.OneToOneField', [], {'related_name': "'publisher_draft'", 'unique': 'True', 'null': 'True', 'to': "orm['cms.Page']"}), 'reverse_id': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '40', 'null': 'True', 'blank': 'True'}), 'revision_id': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'djangocms_pages'", 'to': u"orm['sites.Site']"}), 'soft_root': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True'}), 'template': ('django.db.models.fields.CharField', [], {'default': "'INHERIT'", 'max_length': '100'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'xframe_options': ('django.db.models.fields.IntegerField', [], {'default': '0'}) }, 'cms.placeholder': { 'Meta': {'object_name': 'Placeholder'}, 'default_width': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'slot': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}) }, u'sites.site': { 'Meta': {'ordering': "(u'domain',)", 'object_name': 'Site', 'db_table': "u'django_site'"}, 'domain': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) } } complete_apps = ['app']

[ "bazerk@gmail.com" ]

bazerk@gmail.com

48dbfd7b8a9ff2b153aa35b2ec8208aca946ed0f

851c1b7b5cabc660d1dac448fb61746d1f66237e

/week02/week02-03-succ.py

2ea9219e796488a14f5f81f48858978165312898

[]

no_license

ntut0002/coding_365

0c89edf3d193380c64015800e6a646553a9047be

d5b0b01333943cc611fb1c5ea9713cf6ffcf246f

refs/heads/master

2020-03-24T03:17:03.127353

2018-08-11T04:21:03

142,412,517

0

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UTF-8

Python

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py

type1or2 = int(input()) a = int(input()) if type1or2 == 1 : for j in range(1, (a//2+1)+1 ): for i in range(1,j+1): print(i,end='') print() for j in range((a//2), 0, -1): for i in range(1,j+1): print(i,end='') print() ######type2 L = [] if type1or2 == 2 : for j in range(1, (a//2+1)+1): for i in range(j, 0, -1): L.append(i) L2 = list(map(str,L)) L3 = ''.join(L2) print('%s%s'%(((a//2+1)-j)*'.',L3)) L = [] for j in range((a//2), 0, -1): for i in range(j, 0, -1): L.append(i) L2 = list(map(str,L)) L3 = ''.join(L2) print('%s%s'%(((a//2+1)-j)*'.',L3)) L = []

[ "ntut0002@gmail.com" ]

ntut0002@gmail.com

2151cceac149e0509db788b0da44d68c4d1cd4cb

3e24611b7315b5ad588b2128570f1341b9c968e8

/Pseudo_Finder.py

2d5054ccbc1b1928f339f8fd026680b8d0102af6

[ "BSD-2-Clause" ]

permissive

bioCKO/lpp_Script

dc327be88c7d12243e25557f7da68d963917aa90

0cb2eedb48d4afa25abc2ed7231eb1fdd9baecc2

refs/heads/master

2022-02-27T12:35:05.979231

2019-08-27T05:56:33

null

0

null

UTF-8

Python

false

4,854

py

#!/usr/bin/env python #coding:utf-8 """ Author: --<> Purpose: Created: 2015/10/19 """ from lpp import * import os from optparse import OptionParser def check_path( path ): if not os.path.exists(path): os.makedirs( path ) return os.path.abspath(path)+'/' def GBLASTA( protein,assemblyresult,output ): #os.system("""makeblastdb -in %s -title Assem -parse_seqids -out Assem -dbtype nucl"""%(assemblyresult)) COMMAND = open("gblasta_run.bat",'w') RAW = fasta_check(open(protein,'rU')) i=0 for t,s in RAW: i+=1 COMMAND.write(""" genblast -P blast -q $input -t %s -o $output """%(assemblyresult)) os.system(""" Genblast_Run.py -i %s -s %s -c %s -o %s """%( protein,COMMAND.name, i,output ) ) def ParseGblasta(gbaresult,genewiseruncommand): COMMAND = open(genewiseruncommand,'w') cache_path = check_path("CACHE/") i=0 data_cache_hash = {} GBA = block_reading(open(gbaresult,'rU'), re.escape("//******************END*******************//") ) i=0 for e_b in GBA: i+=1 k=0 gb_block = re.split("\n\n+", e_b) if "for query:" not in e_b: continue proteinid = re.search("for query\:\s+(\S+)", e_b).group(1) for align in gb_block[1:]: if "gene cover" not in align: continue aligndata = re.search("cover\:\d+$(\S+)\%$\|score:([^\|]+)", align) perc = float(aligndata.group(1)) score = float(aligndata.group(2)) if perc >=80: i+=1 if i not in data_cache_hash: PRO= open(cache_path+'%s.pep'%(i),'w') PRO.write(proteinseqHash[proteinid]) data_cache_hash[i] = [PRO.name] k+=1 NUC = open(cache_path+'%s_%s.nuc'%(i,k),'w') align_detail = align.split("\n")[0] align_detail_list = align_detail.split("|") subject_detail = align_detail_list[1] scaffold_name = subject_detail.split(":")[0] direct = align_detail_list[2] scaffoldStart,scaffoldEND = subject_detail.split(":")[1].split("..") scaffoldStart=int(scaffoldStart) scaffoldEND = int(scaffoldEND) if scaffoldStart<10000: scaffoldStart = 0 else: scaffoldStart =scaffoldStart -10000 scaffoldEND = scaffoldEND+10000 NUC.write(">"+scaffold_name+"__%s\n"%(scaffoldStart)+assemblyseqHash[scaffold_name][scaffoldStart:scaffoldEND]+'\n') commandline = """Genewise_Psuedeo.py -p %s -n %s -o %s.result.gff"""%(PRO.name,NUC.name,i) if direct =="-": commandline += " -d" COMMAND.write(commandline+'\n') COMMAND.close() os.system( "cat %s | parallel -j 64"%(COMMAND.name) ) os.system( "cat *.result.gff > %s"%(output) ) os.system(" rm *.result.gff") #os.system("cat %s| parallel -j %s >genewise.out") if __name__=='__main__': usage = '''usage: python2.7 %prog [options] Kmer Kmer is a list of K value you want,e.g [ 1, 2, 3, 4 ]''' parser = OptionParser(usage =usage ) parser.add_option("-c", "--CPU", action="store", dest="cpu", type='int', default = 60, help="CPU number for each thread") parser.add_option("-p", "--pro", action="store", dest="protein", help="protein sequence!!") parser.add_option("-a", "--assembly", action="store", dest="assembly", help="Assemblied Genome!!") parser.add_option("-o", "--out", action="store", dest="output", default = 'genewise.out', help="The output file you want!!") (options, args) = parser.parse_args() cpu = options.cpu protein = options.protein assembly = options.assembly output = options.output assemblyseqHash = {} for t,s in fasta_check(open(assembly,'rU')): t = t.split()[0][1:] s = re.sub("\s+",'',s) assemblyseqHash[t]=s proteinseqHash = {} for t,s in fasta_check(open(protein,'rU')): proteinseqHash[t.split()[0][1:]] = t+s GBLASTA(protein, assembly,"geneblasta.out") ParseGblasta("geneblasta.out", "genewise.command") os.remove("genewise.command") os.system("rm CACHE -rf") os.system("rm cache -rf") os.system( "rm *.xml")

[ "409511038@qq.com" ]

409511038@qq.com

e1d5e4dea12c8224a49304457ec39b4f8a764fc5

ba2da5470dd9f78907880e51fbe029ea784afe44

/smh_microbit/orientation/pc/visualization.py

decc8983cb2d4be3a2ad61b7284b7cd447833bc8

[]

no_license

samuelmh/microbit_tests

a678847ab4a8d0805118b826c6ee45cc6f5b629d

cbf36c08f1bcef9659aec221e40679623005bcdd

refs/heads/master

2020-04-03T17:29:56.362199

2018-01-26T08:20:47

null

0

null

UTF-8

Python

false

2,446

py

# -*- coding: utf-8 -*- """ """ import sys import vispy vispy.use(app='pyglet', gl=None) from vispy import app, gloo from vispy.visuals import CubeVisual, transforms from vispy.color import Color from utils.orientation import Orientation from utils.USB_data import USBData class Canvas(app.Canvas): def __init__(self, connection, orientation): self.con = connection self.orientation = orientation app.Canvas.__init__(self, 'Cube', keys='interactive', size=(400, 400)) self.cube = CubeVisual((7.0, 4.0, 0.3), color=Color(color='grey', alpha=0.1, clip=False), edge_color="black") # Create a TransformSystem that will tell the visual how to draw self.cube_transform = transforms.MatrixTransform() self.cube.transform = self.cube_transform self._timer = app.Timer('0.05', connect=self.on_timer, start=True) self.show() def on_close(self, event): self.con.close() def on_resize(self, event): # Set canvas viewport and reconfigure visual transforms to match. vp = (0, 0, self.physical_size[0], self.physical_size[1]) self.context.set_viewport(*vp) self.cube.transforms.configure(canvas=self, viewport=vp) def on_draw(self, event): gloo.set_viewport(0, 0, *self.physical_size) gloo.clear('white', depth=True) self.cube.draw() def on_timer(self, event): data = connection.get_data() if data: roll, pitch, yaw = orientation.data2roll_pitch_yaw(data) # print("{}\t{}\t{}".format( *map(round,(roll,pitch,yaw)))) self.cube_transform.reset() self.cube_transform.rotate(pitch, (1, 0, 0)) # Pitch self.cube_transform.rotate(roll, (0, 1, 0)) # Roll self.cube_transform.rotate(yaw, (0, 0, 1)) # Yaw self.cube_transform.scale((20, 20, 0.001)) self.cube_transform.translate((200, 200)) self.update() if __name__ == '__main__': connection = USBData(port="/dev/ttyACM0", baudrate=115200) connection.start() print("Calibration\nSet your device on the origin and press ENTER.\n") input("Waiting...") c_roll, c_pitch, c_yaw = Orientation._data2roll_pitch_yaw(connection.get_data()) orientation = Orientation(c_roll, c_pitch, c_yaw) win = Canvas(connection, orientation) win.show() if sys.flags.interactive != 1: win.app.run()

[ "samuel.munoz@beeva.com" ]

samuel.munoz@beeva.com

aa1712a6023130a39815c00d100059139f19ba7f

975aebf5e7200da9d053838f483811a685fe33b1

/atm.py

0f9619ea19707630a88bb3784fbc3cbc9fa73bf0

[]

no_license

Aaditya123-apple/Project-100

126df1ce1d05b1c67d9f767de064bce0ae267635

41f5b80251a14e0aac44107c512d36ba671c3358

refs/heads/main

2023-06-30T11:48:40.167339

2021-08-05T10:25:09

392,999,097

0

null

UTF-8

Python

false

865

py

class ATM (): def __init__(self, CardNumber, PIN): self.CardNumber=CardNumber self.PIN=PIN def Withdrawal(self, Amount): new_Amount=1000000 - Amount print('You have withdrawn:'+str(Amount)) def BalanceEnquiry(self): print('You have 1 million dollars in your account') def main(): CardNumber = input("insert your card number:- ") PIN = input("enter your pin number:- ") new_user = ATM(CardNumber ,PIN) print("Choose your activity ") print("1.BalanceEnquriy 2.withdrawl") activity = int(input("Enter activity number :- ")) if (activity == 1): new_user.BalanceEnquiry() elif (activity == 2): amount = int(input("Enter the amount:- ")) new_user.Withdrawal(amount) else: print("Enter a valid number") main()

[ "noreply@github.com" ]

noreply@github.com

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/Dictionaries And HashMaps/Count Triplets.py

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[]

no_license

NipunaMadhushan/HackerRank-Interview-Preparation-Kit

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refs/heads/main

2023-04-08T04:38:06.152719

2021-04-16T17:28:51

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#!/bin/python3 import math import os import random import re import sys def countTriplets(arr, r): count = 0 dict = {} dictPairs = {} for i in reversed(arr): if i*r in dictPairs: count += dictPairs[i*r] if i*r in dict: dictPairs[i] = dictPairs.get(i, 0) + dict[i*r] dict[i] = dict.get(i, 0) + 1 return count if __name__ == '__main__': n, r = map(int, input().strip().split()) arr = list(map(int, input().strip().split())) ans = countTriplets(arr, r) print(ans)

[ "noreply@github.com" ]

noreply@github.com

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/Chapter 6 - Program 6-18.py

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[]

no_license

ScottSko/Python---Pearson---Third-Edition---Chapter-6

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b9174127dc051a8f2de9efd25ee7e9df613b25f0

refs/heads/master

2021-01-16T18:32:18.385119

2017-08-12T03:33:52

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py

import os def main(): found = False search = input("Enter a description to search for: ") #new_qty = int(input("Enter the new quantity: ")) friend_file = open("friends.txt", 'r') temp_file = open("temp.txt", 'w') descr = friend_file.readline() while descr != '': #qty = float(friend_file.readline()) descr = descr.rstrip('\n') if descr == search: new_descr = input("What would you like the new name to be? ") temp_file.write(new_descr + '\n') #temp_file.write(str(new_qty + '\n')) found = True else: temp_file.write(descr + '\n') #temp_file.write(str(new_qty + '\n')) descr = friend_file.readline() friend_file.close() temp_file.close() os.remove('friends.txt') os.rename("temp.txt", 'friends.txt') if found: print("The file has been updated.") else: print("That item was not found in the file.") main()

[ "noreply@github.com" ]

noreply@github.com

89e1b8099d3c6daef52ccd5790833d74358328b6

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/posts/migrations/0001_initial.py

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[]

no_license

NataliiaSubotyshyna/mb

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de02ce0931a0e535abb9e5d71fd126bb9334f861

refs/heads/master

2023-04-15T07:31:46.214771

2021-04-26T14:26:32

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# Generated by Django 3.2 on 2021-04-25 10:53 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Post', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('text', models.TextField()), ], ), ]

[ "natasubotyshyna@gmail.com" ]

natasubotyshyna@gmail.com

f5461736c28ce7d1d94133cafcb5474ecda9b94b

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/scripts/g_to_cterm.py

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[]

no_license

queyenth/dotfiles

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refs/heads/master

2023-04-01T01:02:50.709996

2023-03-15T09:24:39

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import sys from grapefruit import Color from x256 import x256 def html2xterm256(color): r, g, b = Color.HtmlToRgb(color) r = int(r*255) g = int(g*255) b = int(b*255) return x256.from_rgb(r, g, b) color = input() while color != "exit": print(html2xterm256(color)) color = input()

[ "queyenth@gmail.com" ]

queyenth@gmail.com

75909f671b2af23833756e70594ad8826be0566b

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/apps/customers/apps.py

db4a5bceb5549a6c1fd188ada0df9f9bc611c4fa

[]

no_license

canionlabs/MESBack

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69c94e8a41644a34782f0174b1caa21f8b17167a

refs/heads/master

2022-01-23T14:14:12.526381

2020-02-13T22:28:50

151,326,773

0

null

2022-01-21T20:13:55

2018-10-02T21:37:32

Python

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py

from django.apps import AppConfig class CustomersConfig(AppConfig): name = 'apps.customers' verbose_name = 'Clientes'

[ "caiovictor31@live.com" ]

caiovictor31@live.com

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/Labs/Lab2.py

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no_license

akaHEPTA/IntroToAlgorithm

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refs/heads/master

2022-12-23T01:23:56.810110

2020-09-26T08:07:58

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""" Basic python list problems -- no loops. """ def first_last6(nums): """ Given a list of ints, return True if 6 appears as either the first or last element in the list. The list will be length 1 or more. """ if len(nums) >= 1: # Check the length of list is enough if nums[0] == 6 or nums[-1] == 6: # Compare the first or last element is 6 return True return False def same_first_last(nums): """ Given a list of ints, return True if the list is length 1 or more, and the first element and the last element are equal. """ if len(nums) >= 1: # Check the length of list is enough if nums[0] == nums[-1]: # And check the first and the last elements are the same return True return False def common_end(a, b): """ Given 2 lists of ints, a and b, return True if they have the same first element or they have the same last element. Both lists will be length 1 or more. """ if len(a) >= 1 and len(b) >= 1: # Check the length of lists if a[0] == b[0] or a[-1] == b[-1]: # First or last element match return True return False def sum3(nums): """ Given a list of ints length 3, return the sum of all the elements. """ return nums[0] + nums[1] + nums[2] # using loop is not allowed def rotate_left3(nums): """ Given a list of ints length 3, return a list with the elements "rotated left" so {1, 2, 3} yields {2, 3, 1}. """ nums.append(nums.pop(0)) return nums def reverse3(nums): """ Given a list of ints length 3, return a new list with the elements in reverse order, so {1, 2, 3} becomes {3, 2, 1}. """ temp = nums[0] nums[0] = nums[2] nums[2] = temp return nums def max_ends3(nums): """ Given a list of ints length 3, figure out which is larger, the first or last element in the list, and set all the other elements to be that value. Return the changed list. """ if nums[0] > nums[2]: return [nums[0], nums[0], nums[0]] elif nums[2] > nums[0]: return [nums[2], nums[2], nums[2]] else: # else means the fist and the last elements' value is same return [nums[0], nums[0], nums[0]] def make_ends(nums): """ Given a list of ints, return a new list length 2 containing the first and last elements from the original list. The original list will be length 1 or more. """ if len(nums) >= 1: return [nums[0], nums[-1]] else: return "The list length is not enough." def has23(nums): """ Given an int list length 2, return True if it contains a 2 or a 3. """ if nums[0] == 2 or nums[1] == 2 or nums[0] == 3 or nums[1] == 3: return True return False

[ "abelsteiger@gmail.com" ]

abelsteiger@gmail.com

59926ccc37b0d67ba5ff8f1b19411c38efcf66ff

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/TP2/my_code/zDataManager.py

d352f4a494c593b97ead2a972c6162a52d1e155c

[]

no_license

eric-aubinais/info232

f2fc0658d1f10a633853a780882fbffc22f1be0f

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refs/heads/master

2020-04-19T17:42:59.229564

2019-02-08T15:48:26

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2019-01-30T12:55:24

2019-01-30T12:55:23

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""" Created on Sat Mar 11 08:04:23 2017 Last revised: Feb 2, 2019 @author: isabelleguyon This is an example of program that reads data and has a few display methods. Add more views of the data getting inspired by previous lessons: Histograms of single variables Data matrix heat map Correlation matric heat map Add methods of exploratory data analysis and visualization: PCA or tSNE two-way hierachical clustering (combine with heat maps) The same class could be used to visualize prediction results, by replacing X by the predicted values (the end of the transformation chain): For regression, you can plot Y as a function of X. plot the residual a function of X. For classification, you can show the histograms of X for each Y value. show ROC curves. For both: provide a table of scores and error bars. """ # Add the sample code in the path mypath = "../ingestion_program" from sys import argv, path from os.path import abspath import os path.append(abspath(mypath)) # Graphic routines import seaborn as sns; sns.set() import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # Red, lime, blue cm = LinearSegmentedColormap.from_list('rgb', colors, N=3) # Data types import pandas as pd import numpy as np import warnings with warnings.catch_warnings(): warnings.filterwarnings("ignore",category=DeprecationWarning) # Converter class import data_converter # Mother class import data_manager # Typical score from sklearn.metrics import accuracy_score class DataManager(data_manager.DataManager): '''This class reads and displays data. With class inheritance, we do not need to redefine the constructor, unless we want to add or change some data members. ''' def __init__(self, basename="", input_dir=""): ''' New contructor.''' super(DataManager, self).__init__(basename, input_dir) # We added new members: self.feat_name = self.loadName (os.path.join(self.input_dir, basename + '_feat.name')) self.label_name = self.loadName (os.path.join(self.input_dir, basename + '_label.name')) def loadName (self, filename, verbose=False): ''' Get the variable name''' if verbose: print("========= Reading " + filename) name_list = [] if os.path.isfile(filename): name_list = data_converter.file_to_array (filename, verbose=False) else: n=self.info['feat_num'] name_list = [self.info['feat_name']]*n name_list = np.array(name_list).ravel() return name_list def __str__(self): val = "DataManager : " + self.basename + "\ninfo:\n" for item in self.info: val = val + "\t" + item + " = " + str(self.info[item]) + "\n" val = val + "data:\n" val = val + "\tX_train = array" + str(self.data['X_train'].shape) + "\n" val = val + "\tY_train = array" + str(self.data['Y_train'].shape) + "\n" val = val + "\tX_valid = array" + str(self.data['X_valid'].shape) + "\n" val = val + "\tY_valid = array" + str(self.data['Y_valid'].shape) + "\n" val = val + "\tX_test = array" + str(self.data['X_test'].shape) + "\n" val = val + "\tY_test = array" + str(self.data['Y_test'].shape) + "\n" val = val + "feat_type:\tarray" + str(self.feat_type.shape) + "\n" val = val + "feat_idx:\tarray" + str(self.feat_idx.shape) + "\n" # These 2 lines are new: val = val + "feat_name:\tarray" + str(self.feat_name.shape) + "\n" val = val + "label_name:\tarray" + str(self.label_name.shape) + "\n" return val def toDF(self, set_name): ''' Change a given data subset to a data Panda's frame. set_name is 'train', 'valid' or 'test'.''' DF = pd.DataFrame(self.data['X_'+set_name]) # For training examples, we can add the target values as # a last column: this is convenient to use seaborn # Look at http://seaborn.pydata.org/tutorial/axis_grids.html for other ideas if set_name == 'train': Y = self.data['Y_train'] DF = DF.assign(target=Y) # We modified the constructor to add self.feat_name, so we can also: # 1) Add a header to the data frame DF.columns=np.append(self.feat_name, 'target') # 2) Replace the numeric categories by the class labels DF = DF.replace({'target': dict(zip(np.arange(len(self.label_name)), self.label_name))}) return DF ##### HERE YOU CAN IMPLEMENT YOUR OWN METHODS ##### def DataStats(self, set_name): ''' Display simple data statistics.''' DF = self.toDF(set_name) return DF.describe() # Return something better def DataHist(self, set_name): ''' Show histograms.''' DF = self.toDF(set_name) return DF.hist(figsize = (10,10), bins = 50, layout = (3,2)) # Return something better def ShowScatter(self, set_name): ''' Show scatter plots.''' DF = self.toDF(set_name) if set_name == 'train': return sns.pairplot(DF, hue = "target") # Return something better else: return sns.pairplot(DF) # Return something better def ShowSomethingElse(self): ''' Surprise me.''' # For your project proposal, provide # a sketch with what you intend to do written in English (or French) is OK. pass ##### END OF YOUR OWN METHODS ###################### def ClfScatter(self, clf, dim1=0, dim2=1, title=''): '''(self, clf, dim1=0, dim2=1, title='') Split the training data into 1/2 for training and 1/2 for testing. Display decision function and training or test examples. clf: a classifier with at least a fit and a predict method like a sckit-learn classifier. dim1 and dim2: chosen features. title: Figure title. Returns: Test accuracy. ''' X = self.data['X_train'] Y = self.data['Y_train'] F = self.feat_name # Split the data ntr=round(X.shape[0]/2) nte=X.shape[0]-ntr Xtr = X[0:ntr, (dim1,dim2)] Ytr = Y[0:ntr] Xte = X[ntr+1:ntr+nte, (dim1,dim2)] Yte = Y[ntr+1:ntr+nte] # Fit model in chosen dimensions clf.fit(Xtr, Ytr) # Compute the training score Yhat_tr = clf.predict(Xtr) training_accuracy = accuracy_score(Ytr, Yhat_tr) # Compute the test score Yhat_te = clf.predict(Xte) test_accuracy = accuracy_score(Yte, Yhat_te) # Define a mesh x_min, x_max = Xtr[:, 0].min() - 1, Xtr[:, 0].max() + 1 y_min, y_max = Xtr[:, 1].min() - 1, Xtr[:, 1].max() + 1 h = 0.1 # step xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Xgene = np.c_[xx.ravel(), yy.ravel()] # Make your predictions on all mesh grid points (test points) Yhat = clf.predict(Xgene) # Make contour plot for all points in mesh Yhat = Yhat.reshape(xx.shape) plt.subplot(1, 2, 1) plt.contourf(xx, yy, Yhat, cmap=plt.cm.Paired) # Overlay scatter plot of training examples plt.scatter(Xtr[:, 0], Xtr[:, 1], c=Ytr, cmap=cm) plt.title('{}: training accuracy = {:5.2f}'.format(title, training_accuracy)) plt.xlabel(F[dim1]) plt.ylabel(F[dim2]) plt.subplot(1, 2, 2) plt.contourf(xx, yy, Yhat, cmap=plt.cm.Paired) # Overlay scatter plot of test examples plt.scatter(Xte[:, 0], Xte[:, 1], c=Yte, cmap=cm) plt.title('{}: test accuracy = {:5.2f}'.format(title, test_accuracy)) plt.xlabel(F[dim1]) plt.ylabel(F[dim2]) plt.subplots_adjust(left = 0, right = 1.5, bottom=0, top = 1, wspace=0.2) plt.show() return test_accuracy if __name__=="__main__": # You can use this to run this file as a script and test the DataManager if len(argv)==1: # Use the default input and output directories if no arguments are provided input_dir = "../public_data" output_dir = "../results" else: input_dir = argv[1] output_dir = argv[2]; print("Using input_dir: " + input_dir) print("Using output_dir: " + output_dir) basename = 'Iris' D = DataManager(basename, input_dir) print(D) D.DataStats('train')

[ "eric.aubinais@u-psud.fr" ]

eric.aubinais@u-psud.fr

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rudresh04thakur/7_30_GBT_PY

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2020-04-15T20:10:53.772642

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/home/rudresh/anaconda3/lib/python3.7/sre_parse.py

[ "rudresh04thakur@gmail.com" ]

rudresh04thakur@gmail.com

fe01b55d1a445146aff5fa8d9d11d9ab0fec0471

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/MDA/manage.py

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[]

no_license

Abhi5123/intern33

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refs/heads/master

2022-11-27T08:35:00.285031

2020-08-04T16:38:13

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

[ "62535575+Abhi-hue5123@users.noreply.github.com" ]

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/UnitTest_addevent.py

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[]

no_license

anushamanda/8220assign4waves-tests

231c0ec190b174fd5fd0735c1d2975e57ae8e6df

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refs/heads/master

2022-05-24T18:27:57.693544

2020-05-01T05:43:02

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import unittest import time from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from selenium.common.exceptions import NoSuchElementException class waves_Test9(unittest.TestCase): def setUp(self): self.driver = webdriver.Chrome() def test_addevent(self): driver = self.driver driver.maximize_window() driver.get("https://wavesfit.herokuapp.com/") elem = driver.find_element_by_xpath('//*[@id="myNavbar"]/ul/li[2]/a/b').click() #time.sleep(0.5) elem = driver.find_element_by_id("id_username") elem.send_keys("SamRuth") elem = driver.find_element_by_id("id_password") elem.send_keys("sam1") elem = driver.find_element_by_xpath('//*[@id="app-layout"]/div/div/div/div/div/div/div/div[2]/div/form/p[3]/input').click() time.sleep(2) elem = driver.find_element_by_xpath('//*[@id="app-layout"]/nav/div/div[1]/p/a[3]/b').click() elem = driver.find_element_by_xpath('//*[@id="listings"]/div/div/div[2]/table/tbody/tr[1]/td[3]/a/span').click() time.sleep(2) elem = driver.find_element_by_id("id_event_name") elem.send_keys("Weight lifting") elem = driver.find_element_by_id("id_trainer_name") elem.send_keys("sri vidya") elem = driver.find_element_by_id("id_branch") elem.send_keys("72nd st omaha, NE waves branch1") elem = driver.find_element_by_id("id_description") elem.send_keys("power lifting is a great muscle building session") elem = driver.find_element_by_xpath('//*[@id="app-layout"]/div/div/div/form/button').click() try: time.sleep(2) elem = driver.find_element_by_xpath('//*[@id="listings"]/div/div/div[1]/h2') assert True except NoSuchElementException: self.fail("Login Failed") assert False def tearDown(self): self.driver.close() if __name__ == "__main__": unittest.main()

[ "amanda@unomaha.edu" ]

amanda@unomaha.edu

bab6d662deb64c019535c2f9d445ba7d16dddda6

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/dogehouse/client.py

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[ "MIT" ]

permissive

IdlyBond/dogehouse.py

a8007ab8952755f88e490194613242b428d5475f

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refs/heads/main

2023-04-04T13:10:21.543626

2021-03-25T16:02:27

351,272,314

0

MIT

2021-03-25T01:23:49

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UTF-8

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# -*- coding: utf-8 -*- # MIT License # Copyright (c) 2021 Arthur # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import asyncio import websockets from uuid import uuid4 from json import loads, dumps from inspect import signature from logging import info, debug from typing import Awaitable, List, Union from websockets.exceptions import ConnectionClosedOK, ConnectionClosedError from .utils import Repr from .entities import User, Room, UserPreview, Message, BaseUser from .config import apiUrl, heartbeatInterval, topPublicRoomsInterval from .exceptions import NoConnectionException, InvalidAccessToken, InvalidSize, NotEnoughArguments, CommandNotFound listeners = {} commands = {} def event(func: Awaitable): """ Create an event listener for dogehouse. Example: class Client(dogehouse.DogeClient): @dogehouse.event async def on_ready(self): print(f"Logged in as {self.user.username}") if __name__ == "__main__": Client("token", "refresh_token").run() """ listeners[func.__name__.lower()] = [func, False] return func def command(name: str = None): """ Create a new command for dogehouse. Example: class Client(dogehouse.DogeClient): @dogehouse.command async def hello(self, ctx): await self.send(f"Hello {ctx.author.mention}") if __name__ == "__main__": Client("token", "refresh_token").run() """ def wrapper(func: Awaitable): commands[(name if name else func.__name__).lower()] = [func, False] return func return wrapper class DogeClient(Repr): """Represents your Dogehouse client.""" def __init__(self, token: str, refresh_token: str, *, room: str = None, muted: bool = False, reconnect_voice: bool = False, prefix: Union[str, List[str]] = "!"): """ Initialize your Dogehouse client Args: token (str): Your super secret client token. refresh_token (str): Your super secret client refresh token. room (int, optional): The room your client should join. Defaults to None. muted (bool, optional): Wether or not the client should be muted. Defaults to False. reconnect_voice (bool, optional): When the client disconnects from the voice server, should it try to reconnect. Defaults to False. prefix (List of strings or a string): The bot prefix. """ self.user = None self.room = room self.rooms = [] self.prefix = prefix self.__token = token self.__refresh_token = refresh_token self.__socket = None self.__active = False self.__muted = muted self.__reconnect_voice = reconnect_voice self.__listeners = listeners self.__fetches = {} self.__commands = commands async def __fetch(self, op: str, data: dict): fetch = str(uuid4()) await self.__send(op, data, fetch_id=fetch) self.__fetches[fetch] = op async def __send(self, opcode: str, data: dict, *, fetch_id: str = None): """Internal websocket sender method.""" raw_data = dict(op=opcode, d=data) if fetch_id: raw_data["fetchId"] = fetch_id await self.__socket.send(dumps(raw_data)) async def __main(self, loop): """This instance handles the websocket connections.""" async def event_loop(): async def execute_listener(listener: str, *args): listener = self.__listeners.get(listener.lower()) if listener: asyncio.ensure_future(listener[0](*args) if listener[1] else listener[0](self, *args)) async def execute_command(command_name: str, ctx: Message, *args): command = self.__commands.get(command_name.lower()) if command: arguments = [] params = {} parameters = list(signature(command[0]).parameters.items()) if not command[1]: arguments.append(self) parameters.pop(0) if parameters: arguments.append(ctx) parameters.pop(0) for idx, (key, param) in enumerate(parameters): value = args[idx] if param.kind == param.KEYWORD_ONLY: value = " ".join(args[idx::]) params[key] = value try: asyncio.ensure_future(command[0](*arguments, **params)) except TypeError: raise NotEnoughArguments( f"Not enough arguments were provided in command `{command_name}`.") else: raise CommandNotFound( f"The requested command `{command_name}` does not exist.") info("Dogehouse: Starting event listener loop") while self.__active: res = loads(await self.__socket.recv()) op = res if isinstance(res, str) else res.get("op") if op == "auth-good": info("Dogehouse: Received client ready") self.user = User.from_dict(res["d"]["user"]) await execute_listener("on_ready") elif op == "new-tokens": info("Dogehouse: Received new authorization tokens") self.__token = res["d"]["accessToken"] self.__refresh_token = res["d"]["refreshToken"] elif op == "fetch_done": fetch = self.__fetches.get(res.get("fetchId"), False) if fetch: del self.__fetches[res.get("fetchId")] if fetch == "get_top_public_rooms": info("Dogehouse: Received new rooms") self.rooms = list( map(Room.from_dict, res["d"]["rooms"])) await execute_listener("on_rooms_fetch") elif fetch == "create_room": info("Dogehouse: Created new room") self.room = Room.from_dict(res["d"]["room"]) elif op == "you-joined-as-speaker": await execute_listener("on_room_join", True) elif op == "join_room_done": self.room = Room.from_dict(res["d"]["room"]) await execute_listener("on_room_join", False) elif op == "new_user_join_room": await execute_listener("on_user_join", User.from_dict(res["d"]["user"])) elif op == "user_left_room": await execute_listener("on_user_leave", res["d"]["userId"]) elif op == "new_chat_msg": msg = Message.from_dict(res["d"]["msg"]) await execute_listener("on_message", msg) if msg.author.id == self.user.id: return try: async def handle_command(prefix: str): if msg.content.startswith(prefix) and len(msg.content) > len(prefix) + 1: splitted = msg.content[len(prefix)::].split(" ") await execute_command(splitted[0], msg, *splitted[1::]) return True return False prefixes = [] if isinstance(self.prefix, str): prefixes.append(self.prefix) else: prefixes = self.prefix for prefix in prefixes: if await handle_command(prefix): break except Exception as e: await execute_listener("on_error", e) elif op == "message_deleted": await execute_listener("on_message_delete", res["d"]["deleterId"], res["d"]["messageId"]) elif op == "speaker_removed": await execute_listener("on_speaker_delete", res["d"]["userId"], res["d"]["roomId"], res["d"]["muteMap"], res["d"]["raiseHandMap"]) elif op == "chat_user_banned": await execute_listener("on_user_ban", res["d"]["userId"]) elif op == "hand_raised": await execute_listener("on_speaker_request", res["d"]["userId"], res["d"]["roomId"]) async def heartbeat(): debug("Dogehouse: Starting heartbeat") while self.__active: await self.__socket.send("ping") await asyncio.sleep(heartbeatInterval) async def get_top_rooms_loop(): debug("Dogehouse: Starting to get all rooms") while self.__active and not self.room: await self.get_top_public_rooms() await asyncio.sleep(topPublicRoomsInterval) try: info("Dogehouse: Connecting with Dogehouse websocket") async with websockets.connect(apiUrl) as ws: info("Dogehouse: Websocket connection established successfully") self.__active = True self.__socket = ws info("Dogehouse: Attemting to authenticate") await self.__send('auth', { "accessToken": self.__token, "refreshToken": self.__refresh_token, "reconnectToVoice": self.__reconnect_voice, "muted": self.__muted, "currentRoomId": self.room, "platform": "dogehouse.py" }) info("Dogehouse: Successfully authenticated") event_loop_task = loop.create_task(event_loop()) get_top_rooms_task = loop.create_task(get_top_rooms_loop()) await heartbeat() await event_loop_task() await get_top_rooms_task() except ConnectionClosedOK: info("Dogehouse: Websocket connection closed peacefully") self.__active = False except ConnectionClosedError as e: if (e.code == 4004): raise InvalidAccessToken() def run(self): """Establishes a connection to the websocket servers.""" loop = asyncio.get_event_loop() loop.run_until_complete(self.__main(loop)) loop.close() async def close(self): """ Closes the established connection. Raises: NoConnectionException: No connection has been established yet. Aka got nothing to close. """ if not isinstance(self.__socket, websockets.WebSocketClientProtocol): raise NoConnectionException() self.__active = False def listener(self, name: str = None): """ Create an event listener for dogehouse. Args: name (str, optional): The name of the event. Defaults to the function name. Example: client = dogehouse.DogeClient("token", "refresh_token") @client.listener() async def on_ready(): print(f"Logged in as {self.user.username}") client.run() # Or: client = dogehouse.DogeClient("token", "refresh_token") @client.listener(name="on_ready") async def bot_has_started(): print(f"Logged in as {self.user.username}") client.run() """ def decorator(func: Awaitable): self.__listeners[(name if name else func.__name__).lower()] = [ func, True] return func return decorator def command(self, name: str = None): """ Create an command for dogehouse. Args: name (str, optional): The name of the command. Defaults to the function name. Example: client = dogehouse.DogeClient("token", "refresh_token") @client.command() async def hello(ctx): await client.send(f"Hello {ctx.author.mention}") client.run() # Or: client = dogehouse.DogeClient("token", "refresh_token") @client.listener(name="hello") async def hello_command(ctx): await client.send(f"Hello {ctx.author.mention}") client.run() """ def decorator(func: Awaitable): self.__commands[(name if name else func.__name__).lower()] = [ func, True] return func return decorator async def get_top_public_rooms(self, *, cursor=0) -> None: """ Manually send a request to update the client rooms property. This method gets triggered every X seconds. (Stated in dogehouse.config.topPublicRoomsInterval) Args: # TODO: Add cursor description cursor (int, optional): [description]. Defaults to 0. """ await self.__fetch("get_top_public_rooms", dict(cursor=cursor)) async def create_room(self, name: str, description: str = "", *, public=True) -> None: """ Creates a room, when the room is created a request will be sent to join the room. When the client joins the room the `on_room_join` event will be triggered. Args: name (str): The name for room. description (str): The description for the room. public (bool, optional): Wether or not the room should be publicly visible. Defaults to True. """ if 2 <= len(name) <= 60: return await self.__fetch("create_room", dict(name=name, description=description, privacy="public" if public else "private")) raise InvalidSize( "The `name` property length should be 2-60 characters long.") async def join_room(self, id: str) -> None: """ Send a request to join a room as a listener. Args: id (str): The ID of the room you want to join. """ await self.__send("join_room", dict(roomId=id)) async def send(self, message: str, *, whisper: List[str] = []) -> None: """ Send a message to the current room. Args: message (str): The message that should be sent. whisper (List[str], optional): A collection of user id's who should only see the message. Defaults to []. Raises: NoConnectionException: Gets thrown when the client hasn't joined a room yet. """ if not self.room: raise NoConnectionException("No room has been joined yet!") def parse_message(): tokens = [] for token in message.split(" "): t, v = "text", token if v.startswith("@") and len(v) >= 3: t = "mention" v = v[1:] elif v.startswith("http") and len(v) >= 8: t = "link" elif v.startswith(":") and v.endswith(":") and len(v) >= 3: t = "emote" v = v[1:-1] tokens.append(dict(t=t, v=v)) return tokens await self.__send("send_room_chat_msg", dict(whisperedTo=whisper, tokens=parse_message())) async def ask_to_speak(self): """ Request in the current room to speak. Raises: NoConnectionException: Gets raised when no room has been joined yet. """ if not self.room: raise NoConnectionException("No room has been joined yet.") await self.__send("ask_to_speak", {}) async def make_mod(self, user: Union[User, BaseUser, UserPreview]): """ Make a user in the room moderator. Args: user (Union[User, BaseUser, UserPreview]): The user which should be promoted to room moderator. """ await self.__send("change_mod_status", dict(userId=user.id, value=True)) async def unmod(self, user: Union[User, BaseUser, UserPreview]): """ Remove a user their room moderator permissions. Args: user (Union[User, BaseUser, UserPreview]): The user from which his permissions should be taken. """ await self.__send("change_mod_status", dict(userId=user.id, value=False)) async def make_admin(self, user: Union[User, BaseUser, UserPreview]): """ Make a user the room administrator/owner. NOTE: This action is irreversable. Args: user (Union[User, BaseUser, UserPreview]): The user which should be promoted to room admin. """ await self.__send("change_room_creator", dict(userId=user.id)) async def set_listener(self, user: Union[User, BaseUser, UserPreview] = None): """ Force a user to be a listener. Args: user (Union[User, BaseUser, UserPreview], optional): The user which should become a Listener. Defaults to the client. """ if not user: user = self.user await self.__send("set_listener", dict(userId=user.id)) async def ban_chat(self, user: Union[User, BaseUser, UserPreview]): """ Ban a user from speaking in the room. NOTE: This action can not be undone. Args: user (Union[User, BaseUser, UserPreview]): The user from which their chat permissions should be taken. """ await self.__send("ban_from_room_chat", dict(userId=user.id)) async def ban(self, user: Union[User, BaseUser, UserPreview]): """ Bans a user from a room. Args: user (Union[User, BaseUser, UserPreview]): The user who should be banned. """ await self.__send("block_from_room", dict(userId=user.id)) async def unban(self, user: Union[User, BaseUser, UserPreview]): """ Unban a user from the room. Args: user (Union[User, BaseUser, UserPreview]): The user who should be unbanned. """ await self.__send("unban_from_room", dict(userId=user.id), fetch_id=uuid4()) async def add_speaker(self, user: Union[User, BaseUser, UserPreview]): """ Accept a speaker request from a user. Args: user (Union[User, BaseUser, UserPreview]): The user who will has to be accepted. """ await self.__send("add_speaker", dict(userId=user.id)) async def delete_message(self, id: str, user_id: str): """ Deletes a message that has been sent by a user. Args: id (str): The id of the message that should be removed. user_id (str): The author of that message. """ await self.__send("delete_room_chat_message", dict(messageId=id, userId=user_id))

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/归并排序优化版.py

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chenglu66/sort

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refs/heads/master

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# -*- coding: utf-8 -*- """ Created on Fri May 5 21:25:52 2017 @author: Lenovo-Y430p """ #非递归实现 def mergesorted(a,p,r): if p<r: mid=(p+r)//2 mergesorted(a,p,mid) mergesorted(a,mid+1,r) merge(a,p,mid,r) def merge(a,p,q,r): a1=a[p:q+1] a2=a[q+1:r+1] a1.append(88888) a2.append(88888) i=0;j=0 for k in range(p,r+1): if a1[i]<= a2[j]: a[k]=a1[i] i+=1 else: a[k]=a2[j] j+=1 def main(): a=[2, 4, 3, 5, 6, 6, 7, 7, 7, 8, 8, 9, 44, 56, 65] b=merge1(a,0,14) print(a) mergesorted(a,0,14) print(b) print(a) #递归实现 def merge1(a,left,right): if left==right: return [a[left]] if left<right: mid=(left+right)//2 A=merge1(a,left,mid) B=merge1(a,mid+1,right) return sort(A,B) def sort(A,B): temp=[] A.append(88888)#加入一个最大值因为两个list最多不会超过一个 B.append(88888) i=0;j=0 for k in range(len(A)+len(B)-2): if A[i]<= B[j]: temp.append(A[i]) i+=1 else: temp.append(B[j]) j+=1 return temp if __name__=='__main__': main()

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/lib/func_mAP_v2.py

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zealot5209/FPN_SeNet50

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#!/usr/bin/python # -*- coding: UTF-8 -*- import os import os.path as osp import sys import shutil import glob import cPickle from xml.dom.minidom import parse import xml.dom.minidom import numpy as np import BBoxXmlTool as bxt import xml.etree.ElementTree as ET # ======================================= # check_neg(gtxmlPath,resultxmlPath): # add_neg(gtxmlPath,resultxmlPath): # cleandata(xmlpath): # savetxt_extractxml(objcls,resultxmlPath): # parse_rec(filename): # voc_ap(rec, prec, use_07_metric=False): # voc_eval( ):该函数主要实现单一类别的AP计算 # do_mAP_eval(setNeg, gtxmlPath, resultxmlPath): # ======================================= # ======================================= def check_neg(gtxmlPath,resultxmlPath): afiles = [] bfiles = [] for root, dirs, files in os.walk(gtxmlPath): print gtxmlPath, 'All files numbers:', len(files) for f in files: afiles.append(root + f) for root, dirs, files in os.walk(resultxmlPath): print resultxmlPath, 'All files numbers:', len(files) for f in files: bfiles.append(root + f) # 去掉afiles中文件名的gtxmlPath (拿A,B相同的路径\文件名,做成集合,去找交集) gtxmlPathlen = len(gtxmlPath) aafiles = [] for f in afiles: aafiles.append(f[gtxmlPathlen:]) # 去掉bfiles中文件名的resultxmlPath resultxmlPathlen = len(resultxmlPath) bbfiles = [] for f in bfiles: bbfiles.append(f[resultxmlPathlen:]) afiles = aafiles bfiles = bbfiles setA = set(afiles) setB = set(bfiles) # 处理仅出现在results_XML目录中的文件 onlyFiles = setA ^ setB # aonlyFiles = [] bonlyFiles = [] for of in onlyFiles: if of in afiles: aonlyFiles.append(of) elif of in bfiles: bonlyFiles.append(of) print gtxmlPath, 'only files numbers:', len(aonlyFiles) print resultxmlPath, 'only files numbers:', len(bonlyFiles) xmlNegPath = os.path.dirname(resultxmlPath) + "/resxmlWithoutNeg" if not (osp.exists(xmlNegPath)): os.mkdir(xmlNegPath) bothfiles = setA & setB for line2 in bothfiles: linetmp = line2.strip() # line = line.strip() + '.jpg' oldname = resultxmlPath + "/" + linetmp newname = xmlNegPath + "/" + linetmp shutil.copyfile(oldname, newname) # xmlnames = os.listdir(gtxmlPath) tmpdir = os.path.dirname(gtxmlPath) imglistPath = tmpdir + '/imglist' + '.txt' imglistfile = open(imglistPath, 'w+') if (len(bothfiles) > 0): for xmlname in bothfiles: tmp1 = xmlname.split(".") tmp2 = tmp1[0] imglistfile.write(tmp2) imglistfile.write('\n') imglistfile.close() resultxmlPath = xmlNegPath return imglistPath, gtxmlPath, resultxmlPath # return imglistPath # ====================================== def add_neg(gtxmlPath,resultxmlPath): afiles = [] bfiles = [] for root, dirs, files in os.walk(gtxmlPath): print gtxmlPath, 'All files numbers:', len(files) for f in files: afiles.append(root + f) for root, dirs, files in os.walk(resultxmlPath): print resultxmlPath, 'All files numbers:', len(files) for f in files: bfiles.append(root + f) # 去掉afiles中文件名的gtxmlPath (拿A,B相同的路径\文件名,做成集合,去找交集) gtxmlPathlen = len(gtxmlPath) aafiles = [] for f in afiles: aafiles.append(f[gtxmlPathlen:]) # 去掉bfiles中文件名的resultxmlPath resultxmlPathlen = len(resultxmlPath) bbfiles = [] for f in bfiles: bbfiles.append(f[resultxmlPathlen:]) afiles = aafiles bfiles = bbfiles setA = set(afiles) setB = set(bfiles) # 处理仅出现在results_XML目录中的文件 bonlyFiles = setB-setA xmlNegPath = os.path.dirname(gtxmlPath) + "/gtxmlwithNeg" if not (os.path.exists(xmlNegPath)): os.mkdir(xmlNegPath) for line in bonlyFiles: XMLBBox = bxt.IMGBBox(line) # XMLBBox = bxt.IMGBBox(img_path=img_path) IMGbbox函数的作用是？？？ xml_save_path = os.path.join(xmlNegPath, os.path.splitext(line)[0] + '.xml') XMLBBox.saveXML(save_path=xml_save_path) for line2 in afiles: linetmp = line2.strip() # line = line.strip() + '.jpg' oldname = gtxmlPath + "/" + linetmp newname = xmlNegPath + "/" + linetmp shutil.copyfile(oldname, newname) imglistPath = os.path.dirname(gtxmlPath) + '/imglist' + '.txt' imglistfile = open(imglistPath, 'w+') if (len(bfiles) > 0): for xmlname in bfiles: tmp1 = xmlname.split(".") tmp2 = tmp1[0] imglistfile.write(tmp2) imglistfile.write('\n') imglistfile.close() gtxmlPath = xmlNegPath return imglistPath, gtxmlPath, resultxmlPath # ====================================== def cleandata(gtxmlpath): xml_list = os.listdir(gtxmlpath) minw = 10000 minh = 10000 maxw = 0 maxh = 0 meanw = 0 meanh = 0 # counter = 0 img_counter = 0 bbox_counter = 0 cls_dict = {} for idx, fname in enumerate(xml_list): xml_path = osp.join(gtxmlpath, fname) tmp_img = bxt.IMGBBox(xml_path=xml_path) minw = min(minw, tmp_img.width) minh = min(minh, tmp_img.height) maxw = max(maxw, tmp_img.width) maxh = max(maxh, tmp_img.height) # counter += 1 meanw += tmp_img.width meanh += tmp_img.height bbox_num = len(tmp_img.bboxes) # if bbox_num > 0 and not tmp_img.img is None : if bbox_num > 0: # print "-- [%d/%d] %s %d" % (idx, img_num, fname, bbox_num) # 记录每一个类别的框的个数 for tmpidx, item in enumerate(tmp_img.bboxes): if cls_dict.has_key(item.name) : cls_dict[item.name] += 1 else: cls_dict[item.name] = 1 if item.name == 'portable_other' : tmp_img.bboxes[tmpidx].name = 'portable_side' img_counter += 1 bbox_counter += bbox_num else: print "no bbox: " + fname # print "bbox num :", bbox_counter # print "img num :", img_counter print "%s :" % (gtxmlpath) print "cls :", (cls_dict) return cls_dict # ========================================== def savetxt_extractxml(objcls,resultxmlPath): # dirPath = '/home/ubuntu/workfile_guoc/pkg_mAP/results_xml' # outPath = '/home/ubuntu/workfile_guoc/pkg_mAP/output_txt/hammer.txt' outdirPath = os.path.dirname(resultxmlPath) # os.mknod(outdirPath + objcls +'.txt3') outtmpPath = outdirPath + '/tmpcompTxt' if not (os.path.exists(outtmpPath)): os.mkdir(outtmpPath) outPath = outtmpPath + '/' + objcls + '.txt' dirPath = resultxmlPath if (os.path.exists(dirPath)): # filenames = os.listdir(dirPath) filenames = glob.glob(dirPath + '//*.xml') # filenames = glob.glob(r'/home/ubuntu/workfile_guoc/pkg_mAP/results_xml1/*.xml') fileout = open(outPath, 'w+') # fileout = open(outPath) for filename in filenames: # 使用minidom解析器打开 XML 文档 DOMTree = xml.dom.minidom.parse(filename) collection = DOMTree.documentElement if collection.hasAttribute("annotation"): print "Root element : %s" % collection.getAttribute("annotation") # 在集合中获取所有对象 objects = collection.getElementsByTagName("object") # 查找每个object的详细信息 for object in objects: name = object.getElementsByTagName('name')[0] namestr = name.childNodes[0].data if namestr == objcls: score = object.getElementsByTagName('score')[0] scorestr = score.childNodes[0].data bndbox = object.getElementsByTagName("bndbox") bndbox_xmin = bndbox[0].getElementsByTagName('xmin')[0] bndbox_ymin = bndbox[0].getElementsByTagName('ymin')[0] bndbox_xmax = bndbox[0].getElementsByTagName('xmax')[0] bndbox_ymax = bndbox[0].getElementsByTagName('ymax')[0] bndbox_xminstr = bndbox_xmin.childNodes[0].data bndbox_yminstr = bndbox_ymin.childNodes[0].data bndbox_xmaxstr = bndbox_xmax.childNodes[0].data bndbox_ymaxstr = bndbox_ymax.childNodes[0].data filebasename = os.path.basename(filename) temp = [filebasename, scorestr, bndbox_xminstr, bndbox_yminstr, bndbox_xmaxstr, bndbox_ymaxstr] # temp1 = ' '.join(temp) # ' '.join(temp) fileout.writelines(' '.join(temp)) fileout.write('\n') fileout.close() return outPath # ==================================================== def parse_rec(filename): """ Parse a PASCAL VOC xml file """ tree = ET.parse(filename) objects = [] for obj in tree.findall('object'): obj_struct = {} obj_struct['name'] = obj.find('name').text obj_struct['pose'] = obj.find('pose').text obj_struct['truncated'] = int(obj.find('truncated').text) obj_struct['difficult'] = int(obj.find('difficult').text) bbox = obj.find('bndbox') obj_struct['bbox'] = [int(bbox.find('xmin').text), int(bbox.find('ymin').text), int(bbox.find('xmax').text), int(bbox.find('ymax').text)] objects.append(obj_struct) return objects def voc_ap(rec, prec, use_07_metric=False): """ ap = voc_ap(rec, prec, [use_07_metric]) Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11 point method (default:False). """ if use_07_metric: # 11 point metric ap = 0. for t in np.arange(0., 1.1, 0.1): if np.sum(rec >= t) == 0: p = 0 else: p = np.max(prec[rec >= t]) ap = ap + p / 11. else: # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.], rec, [1.])) mpre = np.concatenate(([0.], prec, [0.])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def voc_eval(detpath, #该函数主要实现单一类别的AP计算 annopath, imagesetfile, classname, cachedir, ovthresh=0.5, # ovthresh=0.5 use_07_metric=False): """rec, prec, ap = voc_eval(detpath, annopath, imagesetfile, classname, [ovthresh], [use_07_metric]) Top level function that does the PASCAL VOC evaluation. detpath: Path to detections detpath.format(classname) should produce the detection results file. annopath: Path to annotations annopath.format(imagename) should be the xml annotations file. imagesetfile: Text file containing the list of images, one image per line. classname: Category name (duh) cachedir: Directory for caching the annotations [ovthresh]: Overlap threshold (default = 0.5) [use_07_metric]: Whether to use VOC07's 11 point AP computation (default False) """ # assumes detections are in detpath.format(classname) # assumes annotations are in annopath.format(imagename) # assumes imagesetfile is a text file with each line an image name # cachedir caches the annotations in a pickle file # first load gt if not os.path.isdir(cachedir): os.mkdir(cachedir) cachefile = os.path.join(cachedir, 'annots.pkl') # read list of images with open(imagesetfile, 'r') as f: lines = f.readlines() imagenames = [x.strip() for x in lines] if not os.path.isfile(cachefile): # load annots recs = {} for i, imagename in enumerate(imagenames): recs[imagename] = parse_rec(annopath.format(imagename)) if i % 100 == 0: print 'Reading annotation for {:d}/{:d}'.format( i + 1, len(imagenames)) # save print 'Saving cached annotations to {:s}'.format(cachefile) with open(cachefile, 'w') as f: cPickle.dump(recs, f) #根据cpickle模块对recs进行序列化操作 else: # load with open(cachefile, 'r') as f: recs = cPickle.load(f) # extract gt objects for this class：从groundtruth_xml文件夹中提取单一类别的矩形框 class_recs = {} npos = 0 # 根据imgname和clasname，从xml文件中抽取出对应的矩形框object for imagename in imagenames: R = [obj for obj in recs[imagename] if obj['name'] == classname] bbox = np.array([x['bbox'] for x in R]) difficult = np.array([x['difficult'] for x in R]).astype(np.bool) det = [False] * len(R) npos = npos + sum(~difficult) # class_recs是一个结构体，用来存放某张图片的某个类别的信息 class_recs[imagename] = {'bbox': bbox, 'difficult': difficult, 'det': det} # read dets：读取检测结果 detfile = detpath.format(classname) with open(detfile, 'r') as f: lines = f.readlines() splitlines = [x.strip().split(' ') for x in lines] # image_ids = [x[0] for x in splitlines] confidence = np.array([float(x[1]) for x in splitlines]) BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) # sort by confidence：对单一类别的检测目标，进行降序排序 sorted_ind = np.argsort(-confidence) sorted_scores = np.sort(-confidence) BB = BB[sorted_ind, :] image_ids = [image_ids[x] for x in sorted_ind] # go down dets and mark TPs and FPs： nd = len(image_ids) tp = np.zeros(nd) fp = np.zeros(nd) for d in range(nd): # R = class_recs[(image_ids[d].split('.'))[0]] # imagenames没有.xml后缀名，image_ids是有.xml后缀名的，所以报错！！！ R = class_recs[(image_ids[d].split('.'))[0]] bb = BB[d, :].astype(float) ovmax = -np.inf BBGT = R['bbox'].astype(float) if BBGT.size > 0: # compute overlaps # intersection ixmin = np.maximum(BBGT[:, 0], bb[0]) iymin = np.maximum(BBGT[:, 1], bb[1]) ixmax = np.minimum(BBGT[:, 2], bb[2]) iymax = np.minimum(BBGT[:, 3], bb[3]) iw = np.maximum(ixmax - ixmin + 1., 0.) ih = np.maximum(iymax - iymin + 1., 0.) inters = iw * ih # union uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + (BBGT[:, 2] - BBGT[:, 0] + 1.) * (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) overlaps = inters / uni ovmax = np.max(overlaps) jmax = np.argmax(overlaps) if ovmax > ovthresh: if not R['difficult'][jmax]: if not R['det'][jmax]: tp[d] = 1. R['det'][jmax] = 1 else: fp[d] = 1. else: fp[d] = 1. # compute precision recall fp = np.cumsum(fp) tp = np.cumsum(tp) if (npos == 0): npos = 1e-14 rec = tp / float(npos) # avoid divide by zero in case the first detection matches a difficult # ground truth prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) ap = voc_ap(rec, prec, use_07_metric) return rec, prec, ap # =================================================== def do_mAP_eval(setNeg, gtxmlPath, resultxmlPath): if setNeg == '0': imagesetfile, gtxmlPath , resultxmlPath = check_neg(gtxmlPath, resultxmlPath) else: imagesetfile, gtxmlPath, resultxmlPath = add_neg(gtxmlPath, resultxmlPath) annopath = gtxmlPath + '/{:s}.xml' # 此处方法 # cachedir = os.path.join(self._devkit_path, 'annotations_cache') cachedir = os.path.join(os.path.dirname(gtxmlPath), 'annotations_cache') aps = [] # The PASCAL VOC metric changed in 2010 # use_07_metric = True if int(self._year) < 2010 else False use_07_metric = False print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No') # self_classes = ('__background__', 'spray', 'hammer', 'knife') classes_dict = cleandata(gtxmlPath) classes_dict_ref = cleandata(resultxmlPath) self_classes = classes_dict.keys() self_classes.append('__background__') # 此处需要调用cleandata脚本去求出classes矩阵,在类别矩阵需要添加“_background_” # self_classes = cleanData(gtxmlPath,resultxmlPath) dict_mAP = {} for i, cls in enumerate(self_classes): # 此处需要调用classes if cls == '__background__': continue # filename = self._get_voc_results_file_template().format(cls) filename = savetxt_extractxml(cls, resultxmlPath) clsFilePath = os.path.dirname(resultxmlPath) + '/tmpcompTxt' + '/' + cls + '.txt' if osp.exists(clsFilePath)==False or len(open(filename).readlines())==0: # rec = 0 # prec = 0 ap = 0 else: rec, prec, ap = voc_eval( filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, use_07_metric=use_07_metric) aps += [ap] print('AP for {} = {:.4f}'.format(cls, ap)) dict_mAP[cls] = ap print('Mean AP = {:.4f}'.format(np.mean(aps))) # dict_mAP['Mean AP'] = np.mean(aps) mAP_output = os.path.dirname(gtxmlPath) + '/mAP_output' + '.txt' mAP_os = open(mAP_output, 'w+') for key, value in dict_mAP.items(): mAP_os.write('AP for: ' + key + '：' + ('%.4f' % value)) mAP_os.write('\n') mAP_os.write('Mean AP: ' + ('%.4f' % np.mean(aps))) mAP_os.close() print('--------------------------------------------------------------') print('Results:') for ap in aps: print('{:.3f}'.format(ap)) print('{:.3f}'.format(np.mean(aps))) print('--------------------------------------------------------------') print('') print('--------------------------------------------------------------') if osp.exists(cachedir): # os.removedirs(cachedir) shutil.rmtree(cachedir) # if osp.exists(os.path.dirname(resultxmlPath) + '/tmpcompTxt'): # # os.removedirs(os.path.dirname(resultxmlPath) + '/tmpcompTxt') # shutil.rmtree(os.path.dirname(resultxmlPath) + '/tmpcompTxt') if setNeg == '0': # os.removedirs(resultxmlPath) shutil.rmtree(resultxmlPath) # else: # os.removedirs(gtxmlPath) shutil.rmtree(gtxmlPath) # ============================================= # usage：调用do_mAP_eval(groundxmlpath,resultxmlpath)函数，即可输出结果 if __name__ == '__main__': if len(sys.argv) == 4: setNeg = sys.argv[1] gtxmlPath = sys.argv[2] resultxmlPath = sys.argv[3] do_mAP_eval(setNeg, gtxmlPath, resultxmlPath) print("done!") # ==============================================

[ "noreply@github.com" ]

noreply@github.com

207c707157fd441286ecf9952084a3c11def6be1

9c8fdfa389eaaf2df4c8ba0e3072d94671b5a622

/0163. Missing Ranges.py

dbf13be4a24913568795bb380bbbac50fd487f69

[]

no_license

aidardarmesh/leetcode2

41b64695afa850f9cc7847158abb6f2e8dc9abcd

4cf03307c5caeccaa87ccce249322bd02397f489

refs/heads/master

2023-02-27T11:22:09.803298

2021-02-07T06:47:35

264,491,702

0

null

UTF-8

Python

false

480

py

from typing import * class Solution: def findMissingRanges(self, nums: List[int], lower: int, upper: int) -> List[str]: res = [] nums = [lower-1] + nums + [upper+1] for i in range(len(nums)-1): delta = nums[i+1] - nums[i] if delta == 2: res.append(str(nums[i]+1)) elif delta > 2: res.append(str(nums[i]+1) + '->' + str(nums[i+1]-1)) return res

[ "darmesh.aidar@gmail.com" ]

darmesh.aidar@gmail.com

f572f19251815ab1c976e75b0a87211c5c643151

74686d2f8f66ac9f69f5f0240cfaa786738b0efd

/mysite/cinema/tst.py

263b83b0a722cf95ff1775762cde074931e54d81

[]

no_license

ColumbusCoders/Tamilsite

fc8c0051e2300f3aab4a7fcb28251ca4985d1215

9bbd251cabf6fcfde0027e06cb5a2f2a61f413c2

refs/heads/master

2020-03-28T11:40:38.901298

2018-09-11T23:34:37

148,238,057

0

null

UTF-8

Python

false

233

py

import sys sys.path.append("/Users/saravananveeramani/Coding/pythonprj/mysite") #print (sys.path) import common as f1 arr = ["http://www.espncricinfo.com/rss/content/story/feeds/6.xml"] t=f1.GetParseResults(f1.cinema_urls); print t

[ "43099277+ColumbusCoders@users.noreply.github.com" ]

43099277+ColumbusCoders@users.noreply.github.com

4f881705ead8533f4b36e3d548cee5f34fd1eb24

ca3563857ea6cfa40125eeb64a582249f20b0e73

/pages/base_page.py

aaf66bb97655a8efc603034df270c819b17765c6

[]

no_license

l3sombre/final_project_stepik

6ffb6c053aab1d7c5a8565e58198e801f3d0f460

80a6b903754371a8fc66fe006ee013a0b9143251

refs/heads/main

2023-02-16T18:51:41.577462

2021-01-17T16:27:05

320,839,216

1

0

null

UTF-8

Python

false

2,622

py

from selenium.common.exceptions import NoSuchElementException from selenium.common.exceptions import NoAlertPresentException from selenium.common.exceptions import TimeoutException from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from .locators import BasePageLocators import math class BasePage(): def __init__(self, browser, url, timeout=4): self.browser = browser self.url = url self.browser.implicitly_wait(timeout) def go_to_login_page(self): link = self.browser.find_element(*BasePageLocators.LOGIN_LINK) link.click() def go_to_basket_page(self): link = self.browser.find_element(*BasePageLocators.BASKET_LINK) link.click() def is_disappeared(self, how, what, timeout=4): try: WebDriverWait(self.browser, timeout, 1, TimeoutException).\ until_not(EC.presence_of_element_located((how, what))) except TimeoutException: return False return True def is_element_present(self, how, what): try: self.browser.find_element(how, what) except (NoSuchElementException): return False return True def is_not_element_present(self, how, what, timeout=4): try: WebDriverWait(self.browser, timeout).until(EC.presence_of_element_located((how, what))) except TimeoutException: return True return False def open(self): self.browser.get(self.url) def should_be_login_link(self): assert self.is_element_present(*BasePageLocators.LOGIN_LINK), \ "Login link is not presented." def should_be_basket_link(self): assert self.is_element_present(*BasePageLocators.BASKET_LINK), \ "Basket link is not presented." def should_be_authorized_user(self): assert self.is_element_present(*BasePageLocators.USER_ICON), \ "User icon is not presented, probably unauthorised user" def solve_quiz_and_get_code(self): WebDriverWait(self.browser, 3).until(EC.alert_is_present()) alert = self.browser.switch_to.alert x = alert.text.split(" ")[2] answer = str(math.log(abs((12 * math.sin(float(x)))))) alert.send_keys(answer) alert.accept() try: alert = self.browser.switch_to.alert alert_text = alert.text print(f"Your code: {alert_text}") alert.accept() except NoAlertPresentException: print("No second alert presented")

[ "karinhizhnyak@gmail.com" ]

karinhizhnyak@gmail.com

9c9dbf9b2db26b8b664feaa06c4b5878f07a34c8

63ede8ffd8642847ef3e100218f2962ef6a4fe26

/venv/Scripts/easy_install-3.7-script.py

7e56a26623c52b43de8882e129e9eb548018385f

[]

no_license

Heytec/dashjuly

e406968ac111670d31ec3a7ca494b93438c76120

add5d3c361cbcc552a99508919df1c4037fd6bb8

refs/heads/master

2020-06-29T22:48:16.282015

2019-08-05T11:57:04

200,646,922

0

null

UTF-8

Python

false

457

py

#!C:\Users\t\PycharmProjects\dash_graph\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==39.1.0','console_scripts','easy_install-3.7' __requires__ = 'setuptools==39.1.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==39.1.0', 'console_scripts', 'easy_install-3.7')() )

[ "johnmuchirim@gmail.com" ]

johnmuchirim@gmail.com

565722b55ed51c31b9185847c9602271b24781d0

d17475288410d0f4df1c12dc7f487090c4a0731f

/Different_City_Time/Write_in_mongoDB.py

8a7b022a5db0f243399cf01e5f088b53876fc4fc

[]

no_license

ExileSaber/Internship-training

e86ecea8dc41051d1ebaaf2733f0cf57fba1833c

b8e419663332566004100ede7b657549d8549b10

refs/heads/master

2020-07-03T20:01:16.443478

2019-08-13T03:07:34

202,033,414

1

0

null

UTF-8

Python

false

529

py

import pymongo client = pymongo.MongoClient(host='localhost', port=27017) def write_in_mongoDB(Time_list, keyword): city = keyword + "房价时间分布" db = client[city] string = 'two_year' collection = db[string] flag_1 = 0 flag_2 = 0 for time in Time_list: flag_1 = flag_1 + 1 if collection.insert_one(time): flag_2 = flag_2 + 1 print('Time list saved to Mongo') print('一共 ' + str(flag_1) + ' 条数据，存储成功数据条数为：' + str(flag_2))

[ "ExileSaber@users.com" ]

ExileSaber@users.com

7408c2daf47695470fc23b16881676211fd053f3

4fc3080a23a9eed7f6ac5d6f3996f9f388fb3e11

/mysite/polls/views.py

becedb731343c57e3d8d6724d2ae486fcda7cdd8

[]

no_license

wangbobby/PollsSystem

b249382152614039f3ea6ec75dc7ed29c8ca9b1c

d7ab8876b485f87ec81d18c334c1017b685b4a2e

refs/heads/master

2020-03-24T05:06:54.173693

2018-07-26T17:52:55

142,475,166

0

null

UTF-8

Python

false

1,762

py

from django.shortcuts import render, get_object_or_404 # Create your views here. from django.template import loader from django.http import HttpResponse from .models import Question def index(request): # print(request) # print("Path: " + request.path) # print(request.method) # print(request.COOKIES) # print(request.session) # print(request.FILES) # print(request.GET) # print(request.POST) # return HttpResponse("Hello, world. You're at the polls index") """ latest_question_list = Question.objects.order_by('-pub_date')[0:5] # output = ', '.join([q.question_text for q in latest_question_list]) template = loader.get_template('polls/index.html') context = { 'latest_question_list': latest_question_list } # return HttpResponse(output) return HttpResponse(template.render(context, request)) """ latest_question_list = Question.objects.order_by('-pub_date')[0:5] context = {'latest_question_list': latest_question_list} return render(request, 'polls/index.html', context) # def detail(request, question_id): # return HttpResponse("You're looking at question %s." %question_id) def detail(request, question_id): print(request) # return HttpResponse("You're looking at question %s." % question_id) """ try: question = Question.objects.get(pk=question_id) except Question.DoesNotExist: raise Http404("Qustion does not exist") """ question = get_object_or_404(Question, pk=question_id) return render(request, 'polls/detail.html', {'question': question}) def results(request, question_id): print(request) response = "You're looking at the results of question %s." return HttpResponse(response %question_id) def vote(request, question_id): print(request) return HttpResponse("You're voting on question %s." %question_id)

[ "wangyanyao@gmail.com" ]

wangyanyao@gmail.com

aa45d1380fe3d4470534e60abe11b65beee60370

22ae4a4e1192ef95b3547b27bf5aa2d0dbf94da3

/team_test.py

a3496980b16753d2e34dd3d3758afc42ba012cf1

[]

no_license

jibryllbrink/SuperheroTeam

ae799d236d5c9faaaa5acb7e7eed7c041b8e1d2d

3baa36133402a8004aa175bc584c8f0a7939f745

refs/heads/master

2020-10-02T08:26:31.382237

2019-12-13T11:52:16

227,739,317

0

null

UTF-8

Python

false

10,593

py

import pytest import io import sys import superheroes import math import random # Helper Function def capture_console_output(function_body): # _io.StringIO object string_io = io.StringIO() sys.stdout = string_io function_body() sys.stdout = sys.__stdout__ return string_io.getvalue() def create_armor(): armors = [ "Calculator", "Laser Shield", "Invisibility", "SFPD Strike Force", "Social Workers", "Face Paint", "Damaskus Shield", "Bamboo Wall", "Forced Projection", "Thick Fog", "Wall of Will", "Wall of Walls", "Obamacare", "Thick Goo"] name = armors[random.randint(0, len(armors) - 1)] power = random.randint(23, 700000) return superheroes.Armor(name, power) def create_weapon(): weapons = [ "Antimatter Gun", "Star Cannon", "Black Hole Ram Jet", "Laser Sword", "Laser Cannon", "Ion Accellerated Disc Drive", "Superhuman Strength", "Blinding Lights", "Ferociousness", "Speed of Hermes", "Lightning Bolts"] name = weapons[random.randint(0, len(weapons) - 1)] power = random.randint(27, 700000) return superheroes.Weapon(name, power) def create_ability(): abilities = [ "Alien Attack", "Science", "Star Power", "Immortality", "Grandmas Cookies", "Blinding Strength", "Cute Kittens", "Team Morale", "Luck", "Obsequious Destruction", "The Kraken", "The Fire of A Million Suns", "Team Spirit", "Canada"] name = abilities[random.randint(0, len(abilities) - 1)] power = random.randint(45, 700000) return superheroes.Ability(name, power) def build_hero(num_of_weapons=0, num_of_armor=0, num_of_abilities=0): heroes = [ "Athena", "Jodie Foster", "Christina Aguilera", "Gamora", "Supergirl", "Wonder Woman", "Batgirl", "Carmen Sandiego", "Okoye", "America Chavez", "Cat Woman", "White Canary", "Nakia", "Mera", "Iris West", "Quake", "Wasp", "Storm", "Black Widow", "San Luis Obispo", "Ted Kennedy", "San Francisco", "Bananas"] weapons = [] armors = [] for _ in range(num_of_weapons): weapons.append(create_weapon()) for _ in range(num_of_armor): armors.append(create_armor()) for _ in range(num_of_abilities): weapons.append(create_ability()) name = random.choice(heroes) hero = superheroes.Hero(name) for item in weapons: hero.add_ability(item) for armor in armors: hero.add_armor(armor) return hero def create_hero(max_strength=100, weapons=False, armors=False, health=False): heroes = [ "Athena", "Jodie Foster", "Christina Aguilera", "Gamora", "Supergirl", "Wonder Woman", "Batgirl", "Carmen Sandiego", "Okoye", "America Chavez", "Cat Woman", "White Canary", "Nakia", "Mera", "Iris West", "Quake", "Wasp", "Storm", "Black Widow", "San Luis Obispo", "Ted Kennedy", "San Francisco", "Bananas"] name = heroes[random.randint(0, len(heroes) - 1)] if health: power = health else: power = random.randint(3, 700000) hero = superheroes.Hero(name, power) if weapons and armors: for weapon in weapons: hero.add_ability(weapon) for armor in armors: hero.add_armor(armor) if armors and not weapons: for armor in armors: hero.add_armor(armor) return hero def create_team(heroes=[]): teams = [ "Orchids", "Red", "Blue", "Cheese Steaks", "Warriors", "49ers", "Marvel", "DC", "Rat Pack", "The Little Red Riding Hoods", "Team One", "Generic Team", "X-men", "Team Two", "Golden Champions", "Vegan Protectors", "The Cardinals", "Winky Bears", "Steelsmiths", "Boilermakers", "Nincompoops"] name = teams[random.randint(0, len(teams) - 1)] team = superheroes.Team(name) if len(heroes) > 0: for member in heroes: team.add_hero(member) return team def create_set(): armor_pieces = random.randint(1, 300) weapon_pieces = random.randint(1, 300) ability_ct = random.randint(1, 300) armors = [] abilities = [] for _ in range(0, armor_pieces): armors.append(create_armor()) for _ in range(0, weapon_pieces): abilities.append(create_weapon()) for _ in range(0, ability_ct): abilities.append(create_ability()) hero_set = {'weapons': abilities, 'armors': armors} return hero_set # def test_armor(): # armor = superheroes.Hero("The Ring", 200) # for _ in range(0, 500): # defense = armor.defend() # assert (defense <= 200 and defense >= 0) def test_hero_default_health(): jodie = superheroes.Hero("Jodie Foster") assert jodie.current_health == 100 def test_hero_init_new_health(): hero = superheroes.Hero("Jodie Foster", 600) assert hero.current_health == 600 def test_hero_start_health(): hero = superheroes.Hero("Jodie Foster", 300) assert hero.starting_health == 300 def test_hero_defense(): jodie = superheroes.Hero("Jodie Foster") gauntlets = superheroes.Armor("Gauntlets", 30) jodie.add_armor(gauntlets) defense = jodie.defend(10) assert defense >= 0 and defense <= 30 def test_hero_defense_mean_value(): athena = superheroes.Hero("Athena") strength = random.randint(400, 30000) big_strength = superheroes.Armor("Overwhelming Shield", strength) athena.add_armor(big_strength) calculated_mean = strength // 2 iterations = 8000 total_attack = 0 accepted_window = 400 for _ in range(iterations): attack_value = athena.defend() assert attack_value >= 0 and attack_value <= strength total_attack += attack_value actual_mean = total_attack / iterations print("Max Allowed: {}".format(strength)) print("Defenses Tested: {}".format(iterations)) print("Mean -- calculated: {} | actual: {}".format(calculated_mean, actual_mean)) print( "Acceptable deviation from mean: {} | Current deviation from mean: {}".format( accepted_window, abs( calculated_mean - actual_mean))) print( "Acceptable Min: {} | Acceptable Max: {}".format( actual_mean - accepted_window, actual_mean + accepted_window)) assert actual_mean <= calculated_mean + \ accepted_window and actual_mean >= calculated_mean - accepted_window def test_hero_defense_standard_deviation(): willow_waffle = superheroes.Hero("Willow Waffle") strength = random.randint(400, 30000) willow = superheroes.Armor("Willowness", strength) willow_waffle.add_armor(willow) defenses = list() total_defend = 0 number_tests = 100 for _ in range(number_tests): defense = willow_waffle.defend() defenses.append(defense) total_defend += defense mean = total_defend / number_tests # Get Square Deviations for index, value in enumerate(defenses): defenses[index] = math.pow(value - mean, 2) standard_dev = math.sqrt(sum(defenses) / len(defenses)) print("Hero Armor must block with random value.") print("Standard Deviation Cannot be 0.") assert standard_dev != 0.0 def test_dead_hero_defense(): hero = superheroes.Hero("Vlaad", 0) garlic = superheroes.Armor("Garlic", 30000) hero.add_ability(garlic) assert hero.defend() == 0 def test_hero_equip_armor(): jodie = superheroes.Hero("Jodie Foster") gauntlets = superheroes.Armor("Gauntlets", 30) jodie.add_armor(gauntlets) assert len(jodie.armors) == 1 assert jodie.armors[0].name == "Gauntlets" def test_hero_defend_multi_armor(): jodie = superheroes.Hero("Jodie Foster") gauntlets = superheroes.Armor("Gauntlets", 4000) science = superheroes.Armor("Science", 9000) jodie.add_armor(gauntlets) jodie.add_armor(science) defend = jodie.defend() assert defend <= 13000 and defend >= 0 # Test Team def test_team_attack(): team_one = superheroes.Team("One") jodie = superheroes.Hero("Jodie Foster") aliens = superheroes.Ability("Alien Friends", 10000) jodie.add_ability(aliens) team_one.add_hero(jodie) team_two = superheroes.Team("Two") athena = superheroes.Hero("Athena") socks = superheroes.Armor("Socks", 10) athena.add_armor(socks) team_two.add_hero(athena) assert team_two.heroes[0].current_health == 100 team_one.attack(team_two) assert team_two.heroes[0].current_health <= 0 def test_team_attack_kills(): team_one = superheroes.Team("One") jodie = superheroes.Hero("Jodie Foster") aliens = superheroes.Ability("Alien Friends", 10000) jodie.add_ability(aliens) team_one.add_hero(jodie) team_two = superheroes.Team("Two") athena = superheroes.Hero("Athena") socks = superheroes.Armor("Socks", 10) athena.add_armor(socks) team_two.add_hero(athena) assert team_one.heroes[0].kills == 0 team_one.attack(team_two) assert team_one.heroes[0].kills == 1 def test_team_attack_deaths(): team_one = superheroes.Team("One") jodie = superheroes.Hero("Jodie Foster") aliens = superheroes.Ability("Alien Friends", 10000) jodie.add_ability(aliens) team_one.add_hero(jodie) team_two = superheroes.Team("Two") athena = superheroes.Hero("Athena") socks = superheroes.Armor("Socks", 10) athena.add_armor(socks) team_two.add_hero(athena) assert team_two.heroes[0].deaths == 0 team_one.attack(team_two) assert team_two.heroes[0].deaths == 1 def test_revive_heroes(): heroes = [] for _ in range(0, 20): heroes.append(build_hero(4, 4, 4)) team_one = superheroes.Team("One") for hero in heroes: team_one.add_hero(hero) for hero in team_one.heroes: hero.current_health == 12 team_one.revive_heroes() for hero in team_one.heroes: assert hero.current_health == 100

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# coding=utf-8 """SSM automation document to increase Lambda memory size""" from pytest_bdd import ( scenario ) @scenario('../features/change_concurrency_limit.feature', 'Change Concurrency limit of Lambda Function') def test_change_concurrency_limit(): """Create AWS resources using CloudFormation template and execute SSM automation document.""" @scenario('../features/change_concurrency_limit.feature', 'Set Concurrency limit out of account limits') def test_concurrency_limit_out_of_quota(): """Create AWS resources using CloudFormation template and execute SSM automation document."""

[ "semiond@amazon.com" ]

semiond@amazon.com

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#copy of j25 import numpy as np from collections import namedtuple from functools import partial from PIL import Image import data_transforms import data_iterators import pathfinder import utils import app import torch import torchvision import torch.optim as optim import torch.nn as nn import torch.nn.init import torch.nn.functional as F import math restart_from_save = None rng = np.random.RandomState(42) # transformations p_transform = {'patch_size': (256, 256), 'channels': 4, 'n_labels': 17} p_augmentation = { 'rot90_values': [0, 1, 2, 3], 'flip': [0, 1] } channel_zmuv_stats = { 'avg': [4970.55, 4245.35, 3064.64, 6360.08], 'std': [1785.79, 1576.31, 1661.19, 1841.09]} # data preparation function def data_prep_function_train(x, p_transform=p_transform, p_augmentation=p_augmentation, **kwargs): x = np.array(x,dtype=np.float32) x = data_transforms.channel_zmuv(x, img_stats=channel_zmuv_stats, no_channels=4) x = data_transforms.random_lossless(x, p_augmentation, rng) return x def data_prep_function_valid(x, p_transform=p_transform, **kwargs): x = np.array(x, dtype=np.float32) x = data_transforms.channel_zmuv(x, img_stats=channel_zmuv_stats, no_channels=4) return x def label_prep_function(x): #cut out the label return x # data iterators batch_size = 32 nbatches_chunk = 1 chunk_size = batch_size * nbatches_chunk folds = app.make_stratified_split(no_folds=10) #for checking if folds are equal over multiple config files for fold in folds: print sum(fold) train_ids = folds[1] + folds[2] + folds[3] + folds[4] + folds[5] + folds[0] + folds[7] + folds[8] + folds[9] valid_ids = folds[6] all_ids = folds[0] + folds[1] + folds[2] + folds[3] + folds[4] + folds[5] + folds[6] + folds[7] + folds[8] + folds[9] bad_ids = [] train_ids = [x for x in train_ids if x not in bad_ids] valid_ids = [x for x in valid_ids if x not in bad_ids] test_ids = np.arange(40669) test2_ids = np.arange(20522) train_data_iterator = data_iterators.DataGenerator(dataset='train', batch_size=chunk_size, img_ids = train_ids, p_transform=p_transform, data_prep_fun = data_prep_function_train, label_prep_fun = label_prep_function, rng=rng, full_batch=True, random=True, infinite=True) feat_data_iterator = data_iterators.DataGenerator(dataset='train', batch_size=chunk_size, img_ids = all_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=True, infinite=False) valid_data_iterator = data_iterators.DataGenerator(dataset='train', batch_size=chunk_size, img_ids = valid_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=True, infinite=False) test_data_iterator = data_iterators.DataGenerator(dataset='test', batch_size=chunk_size, img_ids = test_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=False, infinite=False) test2_data_iterator = data_iterators.DataGenerator(dataset='test2', batch_size=chunk_size, img_ids = test2_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=False, infinite=False) import tta tta = tta.LosslessTTA(p_augmentation) tta_test_data_iterator = data_iterators.TTADataGenerator(dataset='test', tta = tta, duplicate_label = False, img_ids = test_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=False, infinite=False) tta_test2_data_iterator = data_iterators.TTADataGenerator(dataset='test2', tta = tta, duplicate_label = False, img_ids = test2_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=False, infinite=False) tta_valid_data_iterator = data_iterators.TTADataGenerator(dataset='train', tta = tta, duplicate_label = True, batch_size=chunk_size, img_ids = valid_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=True, infinite=False) tta_train_data_iterator = data_iterators.TTADataGenerator(dataset='train', tta = tta, duplicate_label = True, batch_size=chunk_size, img_ids = train_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=True, infinite=False) tta_all_data_iterator = data_iterators.TTADataGenerator(dataset='train', tta = tta, duplicate_label = True, batch_size=chunk_size, img_ids = all_ids, p_transform=p_transform, data_prep_fun = data_prep_function_valid, label_prep_fun = label_prep_function, rng=rng, full_batch=False, random=True, infinite=False) nchunks_per_epoch = train_data_iterator.nsamples / chunk_size max_nchunks = nchunks_per_epoch * 60 validate_every = int(0.5 * nchunks_per_epoch) save_every = int(10 * nchunks_per_epoch) learning_rate_schedule = { 0: 5e-2, int(max_nchunks * 0.2): 2e-2, int(max_nchunks * 0.4): 1e-2, int(max_nchunks * 0.6): 3e-3, int(max_nchunks * 0.8): 1e-3 } # model from collections import OrderedDict class MyDenseNet(nn.Module): def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000): super(MyDenseNet, self).__init__() # First convolution self.features = nn.Sequential(OrderedDict([ ('conv0', nn.Conv2d(4, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)), ('norm0', nn.BatchNorm2d(num_init_features)), ('relu0', nn.ReLU(inplace=True)), ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), ])) # Each denseblock num_features = num_init_features self.blocks = [] final_num_features = 0 for i, num_layers in enumerate(block_config): block = torchvision.models.densenet._DenseBlock(num_layers=num_layers, num_input_features=num_features, bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate) self.features.add_module('denseblock%d' % (i + 1), block) self.blocks.append(block) num_features = num_features + num_layers * growth_rate if i != len(block_config) - 1: trans = torchvision.models.densenet._Transition(num_input_features=num_features, num_output_features=num_features // 2) self.features.add_module('transition%d' % (i + 1), trans) num_features = num_features // 2 # Final batch norm self.features.add_module('norm5', nn.BatchNorm2d(num_features)) self.classifier_drop = nn.Dropout(p=0.5) # Linear layer self.classifier = nn.Linear(num_features, num_classes) def forward(self, x, feat=False): features = self.features(x) out = F.relu(features, inplace=True) out = self.classifier_drop(out) out = F.avg_pool2d(out, kernel_size=7).view(features.size(0), -1) if feat: return out out = self.classifier(out) return out def my_densenet121(pretrained=False, **kwargs): r"""Densenet-121 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>` Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = MyDenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16)) if pretrained: model.load_state_dict(torch.utils.model_zoo.load_url(torchvision.models.densenet.model_urls['densenet121'])) return model class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.densenet = my_densenet121(pretrained=False) self.densenet.apply(weight_init) self.densenet.classifier = nn.Linear(self.densenet.classifier.in_features, p_transform["n_labels"]) self.densenet.classifier.weight.data.zero_() def forward(self, x, feat=False): if feat: return self.densenet(x,feat) else: x = self.densenet(x) return F.sigmoid(x) def weight_init(m): if isinstance(m,nn.Conv2d): m.weight.data=nn.init.orthogonal(m.weight.data) def build_model(): net = Net() return namedtuple('Model', [ 'l_out'])( net ) # loss class MultiLoss(torch.nn.modules.loss._Loss): def __init__(self, weight): super(MultiLoss, self).__init__() self.weight = weight def forward(self, input, target): torch.nn.modules.loss._assert_no_grad(target) weighted = (self.weight*target)*(input-target)**2 +(1-target)*(input-target)**2 return torch.mean(weighted) def build_objective(): return MultiLoss(5.0) def build_objective2(): return MultiLoss(1.0) def score(gts, preds): return app.f2_score_arr(gts, preds) # updates def build_updates(model, learning_rate): return optim.SGD(model.parameters(), lr=learning_rate,momentum=0.9,weight_decay=0.0002)

[ "frederic.godin@ugent.be" ]

frederic.godin@ugent.be

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d25f2c9baac039664eb39843048ad02a1a6a1084

/DANN_py3-master/main.py

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Jinsung-Jeon/DomainAdaptation

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refs/heads/master

2021-05-18T14:54:09.510102

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import random import os import torch.backends.cudnn as cudnn import torch.optim as optim import torch.utils.data from data_loader import GetLoader from torchvision import datasets from torchvision import transforms from model import CNNModel import numpy as np from test import test source_dataset_name = 'CIFAR10' target_dataset_name = 'STL10' source_image_root = os.path.join('dataset', source_dataset_name) target_image_root = os.path.join('dataset', target_dataset_name) model_root = 'models' cuda = True cudnn.benchmark = True lr = 1e-3 batch_size = 128 image_size = 32 n_epoch = 50 manual_seed = random.randint(1, 10000) random.seed(manual_seed) torch.manual_seed(manual_seed) # load data img_transform_source = transforms.Compose([ transforms.Resize(image_size), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2430, 0.2610)) ]) img_transform_target = transforms.Compose([ transforms.Resize(image_size), transforms.ToTensor(), transforms.Normalize((0.4467, 0.4398, 0.4066), (0.2603, 0.2565, 0.2712)) ]) dataset_source = datasets.CIFAR10( root='dataset', train=True, transform=img_transform_source, download=True ) from modify_cifar_stl import modify_cifar modify_cifar(dataset_source) dataloader_source = torch.utils.data.DataLoader( dataset=dataset_source, batch_size=batch_size, shuffle=True, num_workers=0) train_list = os.path.join(target_image_root, 'svhn_train_labels.txt') dataset_target = datasets.STL10( root='dataset', transform=img_transform_target, download=True ) from modify_cifar_stl import modify_stl modify_stl(dataset_target) dataloader_target = torch.utils.data.DataLoader( dataset=dataset_target, batch_size=batch_size, shuffle=True, num_workers=0) # load model my_net = CNNModel() # setup optimizer optimizer = optim.Adam(my_net.parameters(), lr=lr) loss_class = torch.nn.NLLLoss() loss_domain = torch.nn.NLLLoss() if cuda: my_net = my_net.cuda() loss_class = loss_class.cuda() loss_domain = loss_domain.cuda() for p in my_net.parameters(): p.requires_grad = True # training for epoch in range(n_epoch): len_dataloader = min(len(dataloader_source), len(dataloader_target)) data_source_iter = iter(dataloader_source) data_target_iter = iter(dataloader_target) i = 0 while i < len_dataloader: p = float(i + epoch * len_dataloader) / n_epoch / len_dataloader #alpha = 2. / (1. + np.exp(-10 * p)) - 1 alpha = 1 # training model using source data data_source = data_source_iter.next() s_img, s_label = data_source my_net.zero_grad() batch_size = len(s_label) input_img = torch.FloatTensor(batch_size, 3, image_size, image_size) class_label = torch.LongTensor(batch_size) domain_label = torch.zeros(batch_size) domain_label = domain_label.long() if cuda: s_img = s_img.cuda() s_label = s_label.cuda() input_img = input_img.cuda() class_label = class_label.cuda() domain_label = domain_label.cuda() input_img.resize_as_(s_img).copy_(s_img) class_label.resize_as_(s_label).copy_(s_label) class_output, domain_output = my_net(input_data=input_img, alpha=alpha) err_s_label = loss_class(class_output, class_label) err_s_domain = loss_domain(domain_output, domain_label) # training model using target data data_target = data_target_iter.next() t_img, _ = data_target batch_size = len(t_img) input_img = torch.FloatTensor(batch_size, 3, image_size, image_size) domain_label = torch.ones(batch_size) domain_label = domain_label.long() if cuda: t_img = t_img.cuda() input_img = input_img.cuda() domain_label = domain_label.cuda() input_img.resize_as_(t_img).copy_(t_img) _, domain_output = my_net(input_data=input_img, alpha=alpha) err_t_domain = loss_domain(domain_output, domain_label) err = err_t_domain + err_s_domain + err_s_label err.backward() optimizer.step() i += 1 print ('epoch: %d [iter: %d / all %d], err_s_label: %f, err_s_domain: %f, err_t_domain: %f' \ % (epoch, i, len_dataloader, err_s_label.data.cpu().numpy(), err_s_domain.data.cpu().numpy(), err_t_domain.data.cpu().item())) print('lets save') torch.save(my_net, '{0}/cifar_stl_model_epoch_{1}.pth'.format(model_root, epoch)) test(source_dataset_name, epoch) print('source done!') test(target_dataset_name, epoch) print('target done!') print('done')

[ "jjsjjs0902@naver.com" ]

jjsjjs0902@naver.com

4a84f62d878637adbdc7231f34f39011cb2eb011

5563fc38a479bf31b158e22ad381bcc1ef6677df

/triangles.py

cac783538a7e501568406903122530725b621395

[]

no_license

MonRes/tester_school_day5

e6a1d84bc32342e0e03061208458581ac4357f59

985fdb344bf7009c4ba3cd50910ba6b9b9fa172e

refs/heads/master

2020-03-19T05:13:38.891646

2018-06-03T14:30:07

135,911,381

0

null

UTF-8

Python

false

585

py

a = 2 b = 4 c = 4 if a>0 and b>0 and c>0: if a + b > c and a + c > b and b + c > a: print ("da się utworzyć trójkąt") else: print ("nie da się") else: print("nie da się") #lub preferowana wersja if a <= 0 or b <= 0 or c <= 0: print ('nie da się utworzyć trójkąta - któras długość jest ujemna') elif a + b > c and a + c > b and b + c > a: print ('Da się utworzyć trójkąt') else: print ('nie da się utworzyć trójkąta') #mozna z powtarzającego się warunku utworzyć zmienną np. length_negative = a <= 0 or b<= 0 c <= 0

[ "Restek87@gmail.com" ]

Restek87@gmail.com

06a768b10284ec7d0ca364d50ef7abfd9a2060ff

358aaf68f3c60ebbbd86b3bc66d4e6c098bcb39e

/fonts/wonder16_8x16.py

ff96b7c5170caead9f8c94e725a350e50d913b60

[ "MIT" ]

permissive

ccccmagicboy/st7735_mpy

d2de0046abd81978d5176dace45a40758377af82

b15f1bde69fbe6e0eb4931c57e71c136d8e7f024

refs/heads/master

2022-08-28T23:18:04.353733

2020-05-28T04:19:21

254,869,035

7

0

null

UTF-8

Python

false

6,756

py

"""converted from ..\fonts\WONDER16__8x16.bin """ WIDTH = 8 HEIGHT = 16 FIRST = 0x20 LAST = 0x7f _FONT =\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x18\x3c\x3c\x3c\x3c\x18\x18\x00\x18\x18\x00\x00\x00\x00'\ b'\x00\x36\x36\x36\x36\x14\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x6c\x6c\x6c\xfe\x6c\x6c\xfe\x6c\x6c\x6c\x00\x00\x00\x00'\ b'\x00\x00\x18\x18\x7c\xc6\xc0\x78\x3c\x06\xc6\x7c\x18\x18\x00\x00'\ b'\x00\x00\x00\x00\x00\x62\x66\x0c\x18\x30\x66\xc6\x00\x00\x00\x00'\ b'\x00\x00\x38\x6c\x38\x30\x76\x7e\xcc\xcc\xcc\x76\x00\x00\x00\x00'\ b'\x00\x0c\x0c\x0c\x18\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x0c\x18\x30\x30\x30\x30\x30\x30\x18\x0c\x00\x00\x00\x00'\ b'\x00\x00\x30\x18\x0c\x0c\x0c\x0c\x0c\x0c\x18\x30\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x6c\x38\xfe\x38\x6c\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x18\x18\x7e\x18\x18\x00\x00\x00\x00\x00\x00'\ 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b'\x00\x00\x00\x00\x00\x0c\x0c\x00\x00\x0c\x0c\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x0c\x0c\x00\x00\x0c\x0c\x0c\x18\x00\x00\x00'\ b'\x00\x00\x00\x0c\x18\x30\x60\xc0\x60\x30\x18\x0c\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\xfe\x00\xfe\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x60\x30\x18\x0c\x06\x0c\x18\x30\x60\x00\x00\x00\x00'\ b'\x00\x00\x7c\xc6\xc6\x0c\x18\x18\x18\x00\x18\x18\x00\x00\x00\x00'\ b'\x00\x00\x7c\xc6\xc6\xc6\xde\xde\xde\xdc\xc0\x7e\x00\x00\x00\x00'\ b'\x00\x00\x38\x6c\xc6\xc6\xc6\xfe\xc6\xc6\xc6\xc6\x00\x00\x00\x00'\ b'\x00\x00\xfc\x66\x66\x66\x7c\x66\x66\x66\x66\xfc\x00\x00\x00\x00'\ b'\x00\x00\x3c\x66\xc2\xc0\xc0\xc0\xc0\xc2\x66\x3c\x00\x00\x00\x00'\ b'\x00\x00\xf8\x6c\x66\x66\x66\x66\x66\x66\x6c\xf8\x00\x00\x00\x00'\ b'\x00\x00\xfe\x66\x60\x60\x7c\x60\x60\x60\x66\xfe\x00\x00\x00\x00'\ b'\x00\x00\xfe\x66\x60\x60\x7c\x60\x60\x60\x60\xf0\x00\x00\x00\x00'\ b'\x00\x00\x7c\xc6\xc6\xc0\xc0\xc0\xce\xc6\xc6\x7c\x00\x00\x00\x00'\ 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b'\x00\x00\xc6\xc6\xc6\xc6\xc6\xc6\xc6\x6c\x38\x10\x00\x00\x00\x00'\ b'\x00\x00\xc6\xc6\xc6\xd6\xd6\xd6\xfe\xee\xc6\xc6\x00\x00\x00\x00'\ b'\x00\x00\xc6\xc6\xc6\x6c\x38\x38\x6c\xc6\xc6\xc6\x00\x00\x00\x00'\ b'\x00\x00\x66\x66\x66\x66\x66\x3c\x18\x18\x18\x3c\x00\x00\x00\x00'\ b'\x00\x00\xfe\xc6\x86\x0c\x18\x30\x60\xc2\xc6\xfe\x00\x00\x00\x00'\ b'\x00\x00\x7c\x60\x60\x60\x60\x60\x60\x60\x60\x7c\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x80\xc0\x60\x30\x18\x0c\x06\x02\x00\x00\x00\x00'\ b'\x00\x00\x7c\x0c\x0c\x0c\x0c\x0c\x0c\x0c\x0c\x7c\x00\x00\x00\x00'\ b'\x00\x10\x38\x6c\xc6\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff'\ b'\x00\x18\x18\x18\x0c\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x78\x0c\x7c\xcc\xcc\xdc\x76\x00\x00\x00\x00'\ b'\x00\x00\xe0\x60\x60\x7c\x66\x66\x66\x66\x66\xfc\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x7c\xc6\xc0\xc0\xc0\xc6\x7c\x00\x00\x00\x00'\ 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b'\x00\x00\x00\x00\x00\xdc\x66\x60\x60\x60\x60\xf0\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x7c\xc6\xc0\x7c\x06\xc6\x7c\x00\x00\x00\x00'\ b'\x00\x00\x30\x30\x30\xfc\x30\x30\x30\x30\x36\x1c\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\xcc\xcc\xcc\xcc\xcc\xcc\x76\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\xc6\xc6\xc6\xc6\x6c\x38\x10\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\xc6\xc6\xd6\xd6\xd6\xfe\x6c\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\xc6\xc6\x6c\x38\x6c\xc6\xc6\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\xc6\xc6\xc6\xc6\xce\x76\x06\xc6\x7c\x00\x00'\ b'\x00\x00\x00\x00\x00\xfe\x86\x0c\x18\x30\x62\xfe\x00\x00\x00\x00'\ b'\x00\x00\x0e\x18\x18\x18\x70\x18\x18\x18\x18\x0e\x00\x00\x00\x00'\ b'\x00\x00\x18\x18\x18\x18\x00\x18\x18\x18\x18\x18\x00\x00\x00\x00'\ b'\x00\x00\x70\x18\x18\x18\x0e\x18\x18\x18\x18\x70\x00\x00\x00\x00'\ b'\x00\x00\x76\xdc\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'\ b'\x00\x00\x00\x00\x00\x10\x38\x38\x6c\x6c\xfe\x00\x00\x00\x00\x00'\ FONT = memoryview(_FONT)

[ "cuiwei_cv@163.com" ]

cuiwei_cv@163.com