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

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alvinkaiser/DailyInterviewQuestion

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l def rabin_karp(s1,s2): assert len(s1) >= len(s2) current_hash = target_hash =0 same = True x = 53 for i in range(len(s2)): if same and s1[i] != s2[i]: same = False current_hash = current_hash * x + ord(s1[i]) target_hash = target_hash *x + ord(s2[i]) power = x**(len(s2) - 1) for i in range(len(s2),len(s1)): letter_to_remove,letter_to_add = s1[i - len(s2)],s1[i] current_hash = (current_hash - power * ord(letter_to_remove)) * x + ord(letter_to_add) if current_hash == target_hash and s1[i - len(s2) + 1:i + 1] == s2: return i - len(s2) + 1 return -1 class Node: def __init__(self,value): self.value = value self.left = self.right = None def __repr__(selelf): return f"Node({self.value})" def serialize(root): if not root: return '#' s = str(root.value) s += ' ' + serialize(root.left) s += ' ' + serialize(root.right) return s def deserialize(data): def helper(): value = next(values) if value == '#': return None node = Node(int(value)) node.left = helper() node.right = helper() return node values = iter(data.split()) return node if __name__ == "__main__": n1 = Node(1) n2 = Node(2) n3 = Node(3) n4 = Node(4) n5 = Node(5) n7 = Node(7) n1.left = n3 n1.right = n4 n3.left = n2 n3.right = n5 n4.right = n7 print(serialize(n1))

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ctabatab@gmail.com

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from flask import render_template, Markup, request, jsonify from top.utils import log_me from top import offers from top import top @log_me @top.route('/') def index(): products = offers.read_offers() return render_template("app-top.html", product_dict=products)

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oleksii.mits@gmail.com

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/binary_tree/inorderTraversal.py

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sepulworld/leet

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class TreeNode: def __init__(self, x): self.val = x self.left = None self.right = None class Solution: def __init__(self): self.output = [] def inorderTraversal(self, root: TreeNode) -> List[int]: if root: self.inorderTraversal(root.left) self.output.append(root.val) self.inorderTraversal(root.right) return self.output

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zane@ugh.cloud

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/exoensewebsite/urls.py

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Mahmoud-m-bahget/expenses-website

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"""exoensewebsite URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path ,include urlpatterns = [ path('',include('expenses.urls')), path('authentication/',include('authentication.urls')), path('preferences/',include('userpreferences.urls')), path('income/',include('userincome.urls')), path('admin/', admin.site.urls), ]

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zhiqwang@outlook.com

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/shop/views/category_views.py

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from django.contrib.auth.mixins import LoginRequiredMixin from django.http import JsonResponse from django.shortcuts import redirect, reverse from django.urls import reverse_lazy from django.views.generic import CreateView, DetailView, TemplateView, UpdateView, View from django.templatetags.static import static from shop.cat_tree import tree_json, tree_move from shop.forms import CategoryForm from shop.models import Category, Item from shop.tables import CategoryTable from table_manager.views import FilteredTableView from table_manager.mixins import StackMixin class CategoryCreateView(LoginRequiredMixin, StackMixin, CreateView): model = Category form_class = CategoryForm template_name = "shop/category_form.html" title = "Create category" def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context["images_url"] = reverse("category_images") return context def form_valid(self, form): d = form.cleaned_data parent = Category.objects.get(id=d["parent_category"]) node = parent.add_child(name=d["name"], description=d["description"]) node.post_save() ref = d.get("category_ref", None) if ref: item = Item.objects.get(ref=ref) node.image = item.image ref = d.get("archive_ref", None) if ref: item = Item.objects.get(ref=ref) node.archive_image = item.image node.save() return redirect(self.get_success_url()) class CategoryUpdateView(LoginRequiredMixin, StackMixin, UpdateView): model = Category form_class = CategoryForm template_name = "shop/category_form.html" title = "Edit category" def get_initial(self): initial = super().get_initial() cat = self.object self.parent = cat.get_parent() if self.parent: initial["parent_category"] = self.parent.pk if cat.image: initial["category_ref"] = cat.image.item.ref if cat.archive_image: initial["archive_ref"] = cat.archive_image.item.ref return initial def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context["images_url"] = reverse("category_images") context["is_root"] = self.parent is None return context def form_valid(self, form): d = form.cleaned_data ref = d.get("category_ref", None) if ref: item = Item.objects.get(ref=ref) self.object.image = item.image ref = d.get("archive_ref", None) if ref: item = Item.objects.get(ref=ref) self.object.archive_image = item.image old_parent = self.object.get_parent() new_parent = Category.objects.get(id=d["parent_category"]) response = super().form_valid(form) if old_parent and old_parent.id != new_parent.id: self.object.move(new_parent, "sorted-child") new_parent.post_save() return response class CategoryTreeView(LoginRequiredMixin, StackMixin, TemplateView): template_name = "shop/category_tree.html" def get(self, request): if request.META.get("HTTP_X_REQUESTED_WITH") == "XMLHttpRequest": return JsonResponse(tree_json(), safe=False) else: self.clear_stack(request) return super().get(request) def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) a, b, c, d, e = Category.find_problems() context["errors"] = a or b or c or d or e return context def post(self, request): if "fix" in request.POST: Category.fix_tree() return redirect("category_tree") # Ajax response to move node p = request.POST tree_move(p["node"], p["target"], p["previous"], p["position"] == "inside") return JsonResponse("OK", safe=False) class CategoryListView(LoginRequiredMixin, FilteredTableView): model = Category table_class = CategoryTable table_pagination = {"per_page": 100} heading = "Categories" def get_queryset(self): root = Category.objects.get(name="Catalogue") return root.get_descendants().order_by("name") class CategoryDetailView(LoginRequiredMixin, StackMixin, DetailView): model = Category template_name = "shop/category_detail.html" context_object_name = "category" def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context["shop_items"] = self.object.shop_items() context["archive_items"] = self.object.archive_items() context["images_url"] = reverse("category_images") return context def post(self, request, **kwargs): if "return" in request.POST: pass elif "delete" in request.POST: self.get_object().delete() return redirect(self.get_success_url()) class CategoryImagesView(View): def get(self, request, *args, **kwargs): ref = request.GET.get("ref", None).strip() target = request.GET.get("target", None) data = {} if ref and target: try: item = Item.objects.get(ref=ref) if item.image is not None: data["image"] = item.image.file.url else: data["error"] = f"Item {ref} has no image" except Item.DoesNotExist: data["error"] = f"There is no item with reference {ref}" else: data["image"] = static("/shop/images/no_image.png") return JsonResponse(data)

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/libcloudforensics/providers/aws/internal/account.py

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# -*- coding: utf-8 -*- # Copyright 2020 Google Inc. # # 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. """Library for incident response operations on AWS EC2. Library to make forensic images of Amazon Elastic Block Store devices and create analysis virtual machine to be used in incident response. """ from typing import Optional, TYPE_CHECKING import boto3 from libcloudforensics.providers.aws.internal import ec2, ebs, kms if TYPE_CHECKING: import botocore class AWSAccount: """Class representing an AWS account. Attributes: default_availability_zone (str): Default zone within the region to create new resources in. default_region (str): The default region to create new resources in. aws_profile (str): The AWS profile defined in the AWS credentials file to use. session (boto3.session.Session): A boto3 session object. _ec2 (AWSEC2): An AWS EC2 client object. _ebs (AWSEBS): An AWS EBS client object. _kms (AWSKMS): An AWS KMS client object. """ def __init__(self, default_availability_zone: str, aws_profile: Optional[str] = None, aws_access_key_id: Optional[str] = None, aws_secret_access_key: Optional[str] = None, aws_session_token: Optional[str] = None) -> None: """Initialize the AWS account. Args: default_availability_zone (str): Default zone within the region to create new resources in. aws_profile (str): Optional. The AWS profile defined in the AWS credentials file to use. aws_access_key_id (str): Optional. If provided together with aws_secret_access_key and aws_session_token, authenticate to AWS using these parameters instead of the credential file. aws_secret_access_key (str): Optional. If provided together with aws_access_key_id and aws_session_token, authenticate to AWS using these parameters instead of the credential file. aws_session_token (str): Optional. If provided together with aws_access_key_id and aws_secret_access_key, authenticate to AWS using these parameters instead of the credential file. """ self.aws_profile = aws_profile or 'default' self.default_availability_zone = default_availability_zone # The region is given by the zone minus the last letter # https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html#using-regions-availability-zones-describe # pylint: disable=line-too-long self.default_region = self.default_availability_zone[:-1] if aws_access_key_id and aws_secret_access_key and aws_session_token: self.session = boto3.session.Session( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, aws_session_token=aws_session_token) else: self.session = boto3.session.Session(profile_name=self.aws_profile) self._ec2 = None # type: Optional[ec2.EC2] self._ebs = None # type: Optional[ebs.EBS] self._kms = None # type: Optional[kms.KMS] @property def ec2(self) -> ec2.EC2: """Get an AWS ec2 object for the account. Returns: AWSEC2: Object that represents AWS EC2 services. """ if self._ec2: return self._ec2 self._ec2 = ec2.EC2(self) return self._ec2 @property def ebs(self) -> ebs.EBS: """Get an AWS ebs object for the account. Returns: AWSEBS: Object that represents AWS EBS services. """ if self._ebs: return self._ebs self._ebs = ebs.EBS(self) return self._ebs @property def kms(self) -> kms.KMS: """Get an AWS kms object for the account. Returns: AWSKMS: Object that represents AWS KMS services. """ if self._kms: return self._kms self._kms = kms.KMS(self) return self._kms def ClientApi(self, service: str, region: Optional[str] = None) -> 'botocore.client.EC2': # pylint: disable=no-member """Create an AWS client object. Args: service (str): The AWS service to use. region (str): Optional. The region in which to create new resources. If none provided, the default_region associated to the AWSAccount object will be used. Returns: botocore.client.EC2: An AWS EC2 client object. """ if region: return self.session.client(service_name=service, region_name=region) return self.session.client( service_name=service, region_name=self.default_region) def ResourceApi(self, service: str, # The return type doesn't exist until Runtime, therefore we # need to ignore the type hint # pylint: disable=line-too-long region: Optional[str] = None) -> 'boto3.resources.factory.ec2.ServiceResource': # type: ignore # pylint: enable=line-too-long """Create an AWS resource object. Args: service (str): The AWS service to use. region (str): Optional. The region in which to create new resources. If none provided, the default_region associated to the AWSAccount object will be used. Returns: boto3.resources.factory.ec2.ServiceResource: An AWS EC2 resource object. """ if region: return self.session.resource(service_name=service, region_name=region) return self.session.resource( service_name=service, region_name=self.default_region)

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neelpatel05/crackmes

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final = 52650 divisible = [] for i in range(65, 90): if final % i == 0: divisible.append(i) quotient = [] for i in divisible: x = final / i quotient.append(x) print(quotient) for i,j in zip(divisible, quotient): print(chr(i)*j)

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/Algorithmic Heights/rosalind_3_degarray.py

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file = open('rosalind_deg.txt','r').readlines() vertices, edges = (int(val) for val in file[0].split()) my_data = [[int(val) for val in line.split()] for line in file[1:]] count = 0 L = [] for k in range(1,vertices+1): count = 0 for i in range(2): for j in range(0,edges): if my_data[j][i] == k: count+=1 L.append(count) print(' '.join(str(num) for num in L))

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ahmedosamataha/first_steps_in_django

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from django.contrib import admin from .models import Question , Choice # Register your models here. admin.site.register(Question) admin.site.register(Choice)

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nivyashri05/Task1

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from django.contrib import admin from django.contrib.auth.admin import UserAdmin as BaseUserAdmin from newapp.models import User class UserAdmin(BaseUserAdmin): list_display = ('email','username','phone','is_admin','is_staff','timestamp') search_fields = ('email','username',) readonly_fields=('date_joined', 'last_login') filter_horizontal = () list_filter = () fieldsets = () admin.site.register(User, BaseUserAdmin)

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miladhzz/django-muliple-db

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""" WSGI config for drop project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'drop.settings') application = get_wsgi_application()

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/api/models/permission.py

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CalvinHuynh/project-olympic

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# from peewee import CharField, DateTimeField, PrimaryKeyField # from .base import Base # class Permission(Base): # id = PrimaryKeyField() # permission_name = CharField(unique=True, null=False) # created_date = DateTimeField()

[ "huynhck001@gmail.com" ]

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/12-computing-simple-interest/computing-simple-interest.py

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ville6000/EFP-Python

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import locale locale.setlocale( locale.LC_ALL, 'en_CA.UTF-8' ) def calculate_simple_interest(principal, rate_of_interest, years): return int(principal) * (1 + (float(rate_of_interest) / 100) * int(years)) principal = input('Enter the principal: ') rate_of_interest = input('Enter the rate of interest: ') years = input('Enter the number of years: ') worth = calculate_simple_interest(principal, rate_of_interest, years) print(locale.currency(worth))

[ "ville.viklund@morgan.fi" ]

ville.viklund@morgan.fi

17122f65e0d8729a00a8bd125c9cd4e3087399da

53cb878e54f08d8cf59118e313f773fd99a690fc

/FirstWindow.py

415be3585b73c72f4af53b0ab528eee47290eb53

[]

no_license

Dudoserovich/Coursework-1

17340f08a68e9072f1b7962195674a04d843a533

f41225494ddfeb0e52ff60f79ada9ba8bd63e390

refs/heads/main

2023-07-28T03:30:32.417528

2021-09-14T06:56:11

406,258,153

0

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11,717

py

import BaseCreateWindow as BaseWin import tkinter.messagebox as mb import tkinter as tk import os import random from tkinter import filedialog, DISABLED, NORMAL, LEFT, BOTH import ExWindow as ExWin class WorkFile: def _fill_up(self, file, count_num: int, min: int, max: int) -> None: new_max = int(max / count_num) rand = random.randint(min, new_max) file.write(str(rand) + ' ') for i in range(count_num - 1): new_max += int(count_num) rand = random.randint((new_max - int(count_num)), new_max) if i != count_num - 2: file.write(str(rand) + ' ') else: file.write(str(rand)) def _fill_down(self, file, count_num: int, max: int) -> None: new_max = max - int(count_num) new_min = new_max - int(count_num) rand = random.randint(new_min, new_max) file.write(str(rand) + ' ') for i in range(count_num - 1): new_max -= int(count_num) // 100000 new_min -= int(count_num) // 100000 rand = random.randint(new_min, new_max) if i != count_num - 2: file.write(str(rand) + ' ') else: file.write(str(rand)) def _fill_file(self, file_extension: str, file) -> None: min = 1 max = 50000000 check = False if file_extension == ".up": self._fill_up(file, int(self.spin.get()), min, max) elif file_extension == ".down": self._fill_down(file, int(self.spin.get()), max) elif file_extension == ".rand": # случайная последовательность for i in range(int(self.spin.get())): file.write(str(random.randint(min, max)) + ' ') file.truncate(file.tell() - 1) elif file_extension == ".sim": # одно и то же число num = random.randint(min, max) for i in range(int(self.spin.get()) - 1): file.write(str(num) + ' ') file.write(str(num)) elif file_extension == ".up_down": # убывающие и возрастающие подпоследовательности # print("up_down") num_up = int(self.spin1.get()) num_down = int(self.spin2.get()) countUpDown = num_up + num_down count = int(int(self.spin.get()) / countUpDown) # общее количество эл-ов в одной подпоследовательности if int(self.spin.get()) % countUpDown != 0: check = True if num_up != 0: self._fill_up(file, count + int(self.spin.get()) % countUpDown, min, max) num_up -= 1 if num_up != 0 & num_down != 0: file.write(' ') elif num_down != 0: self._fill_down(file, count + int(self.spin.get()) % countUpDown, min) num_down -= 1 if num_up != 0 & num_down != 0: file.write(' ') while num_up != 0 & num_down != 0: num_up -= 1 file.write(' ') self._fill_down(file, count, max) num_down -= 1 file.write(' ') while num_up != 0: if check: file.write(' ') check = True self._fill_up(file, count, min, max) num_up -= 1 while num_down != 0: if int(self.spin.get()) % countUpDown != 0 or (not check): file.write(' ') self._fill_down(file, count, max) num_down -= 1 file.write(' ') def _create_file(self, name: str, file_extension: str) -> None: directory = r'C:/MyPrograms/Kursach/generation_files' files = os.listdir(directory) this = name + file_extension count = 1 while this in files: count += 1 this = name + file_extension + '(' + str(count - 1) + ')' if count == 1: file = open("./generation_files/" + name + file_extension, "w") else: file = open("./generation_files/" + name + file_extension + '(' + str(count - 1) + ')', "w") # сама генерация в файле self._fill_file(file_extension, file) file.close() # класс начального окна class FirstWindow(BaseWin.BaseCreateWindow, WorkFile): def __init__(self): super().__init__() self.iconbitmap(r'./icon.ico') # количество созданных файлов self.count = 0 self.check_gen = False self.title("Экспериментальное исследование сортировок Merge sort") self.geometry('500x400') # создание заголовка self._create_label("Параметры генерации", 0, 0, 4, 2, "w", "Arial Bold", 14, self) self._create_label("Кол-во элементов последовательности:", 0, 1, 12, 2, "w", "Arial Bold", 10, self) self.spin = tk.Spinbox(self, from_=2, to=10000000, width=10) self.spin.grid(column=0, row=2, padx=16, pady=5, sticky="w") self._create_label("Укажите тип последовательности:", 0, 3, 12, 2, "w", "Arial Bold", 10, self) chk_state1 = tk.BooleanVar() self._create_check_button(chk_state1, 'Отсортированная по возрастанию', 0, 5, 12, 2, "w") chk_state2 = tk.BooleanVar() self._create_check_button(chk_state2, 'Отсортированная по убыванию', 0, 5, 12, 2, "e") chk_state3 = tk.BooleanVar() self._create_check_button(chk_state3, 'Случайная', 0, 6, 12, 2, "w") chk_state4 = tk.BooleanVar() self._create_check_button(chk_state4, 'С многократноповторяющимся \nодним элементом', 0, 6, 7, 2, "e") # canvas = tk.Canvas(self) # canvas.create_line(16, 25, 400, 25) # canvas.grid(column=0, row=7, padx=0, pady=0, columnspan=3, rowspan=5, sticky="n") chk_state5 = tk.BooleanVar() chk_state5.set(False) frame3 = tk.Frame(master=self, relief=tk.SUNKEN, borderwidth=1) frame3.grid(column=0, row=8, padx=5, pady=5) self.chk = tk.Checkbutton(master=frame3, text='Состоящая из k возрастающих \n' 'и j убывающих подпоследовательностей', var=chk_state5, state=DISABLED, justify=LEFT) self.chk.grid(column=0, row=8, padx=12, pady=2, sticky="w") self._create_label("Количество возрастающих \nподпоследовательностей:", 0, 9, 12, 2, "w", "Arial Regular", 10, frame3) self.spin1 = tk.Spinbox(master=frame3, from_=0, to=5000000, width=10) self.spin1['state'] = tk.DISABLED self.spin1.grid(column=0, row=10, padx=16, pady=5, sticky="wn") self._create_label("Количество убывающих \nподпоследовательностей:", 1, 9, 12, 2, "w", "Arial Regular", 10, frame3) self.spin2 = tk.Spinbox(master=frame3, from_=0, to=5000000, width=10) self.spin2['state'] = tk.DISABLED self.spin2.grid(column=1, row=10, padx=16, pady=5, sticky="wn") # массив с состояниеми чекбоксов self.chk_arr = [chk_state1, chk_state2, chk_state3, chk_state4, chk_state5] frame4 = tk.Frame(master=self, relief=tk.FLAT, borderwidth=1) frame4.grid(column=0, row=11, padx=5, pady=5) self._create_button(frame4, "Сгенерировать \nпоследовательность", "blue", "white", self.__click_generation, 0, 11, 0, 5, "ne", 2, 0, 1) self._create_button(frame4, "Эксперимент", "green", "white", self.__click_ex, 1, 11, 10, 5, "nw") def __generation(self, num: int) -> None: if num == 0: self._create_file(self.spin.get(), ".up") elif num == 1: self._create_file(self.spin.get(), ".down") elif num == 2: self._create_file(self.spin.get(), ".rand") elif num == 3: self._create_file(self.spin.get(), ".sim") elif num == 4: self._create_file(self.spin.get() + '_' + self.spin1.get() + '_' + self.spin2.get(), ".up_down") def __click_generation(self) -> None: check = False check1 = False if 1 < int(self.spin.get()) <= 1000000: # проверяем количество подпоследовательностей последнего чекбокса for i in range(5): if self.chk_arr[i].get(): if i != 4: check = True self.__generation(i) # создание файла генерации без подпоследовательностей elif (self.chk_arr[4].get() == True) & (int(self.spin1.get()) + int(self.spin2.get()) <= int(self.spin.get()) / 2) \ & (int(self.spin1.get()) + int(self.spin2.get()) != 0): self.__generation(i) # создание файла генерации с подпоследовательностями check = True elif self.chk_arr[4].get(): check1 = True if check1: mb.showerror("Ошибка", "Неправильное количество подпоследовательностей у последнего чек-бокса!") elif not check: mb.showerror("Ошибка", "Не было выбрано ни одного чек-бокса!") else: mb.showinfo("Информация", "Последовательность успешно сгенерирована") self.check_gen = True else: mb.showerror("Ошибка", "Неправильное количество элементов последовательности!") def __click_ex(self) -> None: # Выбор файлов для эксперемента tk.Tk().withdraw() # отображает только файлы генерации files_name = filedialog.askopenfilenames(initialdir="C:\MyPrograms\Kursach\generation_files", title="Выбор файлов для эксперемента", filetypes=(("Files", "*.up *.up*) *.down *.down*) " "*.rand *.rand*) " "*.sim *.sim*) *.up_down *.up_down*)"),)) # переходим к новому окну с эксперементом if files_name != "": # self.withdraw() # скрывает окно self.destroy() # закрытие окна ex_window = ExWin.ExWindow(files_name) ex_window.resizable(False, False) ex_window.mainloop() else: mb.showwarning("Предупреждение", "Файлы не были выбраны")

[ "mr.egoryk@mail.ru" ]

mr.egoryk@mail.ru

16d4ac62c0efe8567434b83a272a3035cd8c8990

d75371f629cf881de3c49b53533879a5b862da2e

/python/search-a-2d-matrix.py

3ce6ce1d52b91816fccec4a1e5592f5c548b2cf5

[]

no_license

michaelrbock/leet-code

7352a1e56429bb03842b588ba6bda2a90315a2f4

070db59d4e0ded3fb168c89c3d73cb09b3c4fe86

refs/heads/master

2020-04-01T05:40:49.262575

2019-10-10T22:03:10

152,914,631

0

1

null

UTF-8

Python

false

2,614

py

def binary_row(rows, target): if len(rows) == 1: return 0, None if len(rows) == 2: return (1, None) if target >= rows[1] else (0, None) lo = 0 hi = len(rows) while lo < hi: mid = (lo + hi) // 2 if rows[mid] == target: return mid, True if mid == len(rows) - 1: return len(rows) - 1, None if rows[mid] < target and rows[mid + 1] > target: return mid, None elif target > rows[mid]: lo = mid else: hi = mid return len(rows) - 1, None def binary_search(lst, target): if not lst: return False if len(lst) == 1: return lst[0] == target lo = 0 hi = len(lst) while lo <= hi: mid = (lo + hi) // 2 if lst[mid] == target: return True elif target > lst[mid]: if lo == mid: break lo = mid elif target < lst[mid]: hi = mid return False class Solution1: def searchMatrix(self, matrix, target): """ :type matrix: List[List[int]] :type target: int :rtype: bool """ if not matrix or not matrix[0] or matrix[0][0] > target: return False row, result = binary_row([row[0] for row in matrix], target) if result is not None: return result return binary_search(matrix[row], target) def _translate(index, rows, cols): """Returns (row, col) for overall index.""" row = index // cols col = index % cols return row, col class Solution: def searchMatrix(self, matrix, target): """ :type matrix: List[List[int]] :type target: int :rtype: bool """ if not matrix or not matrix[0]: return False # Strategy: binary search, but treat the matrix as if # it was one long array. Translate overall index into # row/col indices. m, n = len(matrix), len(matrix[0]) # num row, num cols start = 0 # indices as if matrix was one long list end = m * n - 1 # incluive while start <= end and start >= 0 and end < m * n: mid = (start + end) // 2 row, col = _translate(mid, m, n) if target == matrix[row][col]: return True elif target > matrix[row][col]: start = mid + 1 else: # target < matrix[row][col] end = mid - 1 return False s = Solution() assert not s.searchMatrix([[-10,-8,-8,-8],[-5,-4,-2,0]], 7) assert s.searchMatrix([[1, 3, 5, 7],[10, 11, 16, 20],[23, 30, 34, 50]], 3) assert not s.searchMatrix([[1, 3, 5, 7],[10, 11, 16, 20],[23, 30, 34, 50]], 13) assert not s.searchMatrix([[1, 1]], 0) assert not s.searchMatrix([[1, 1]], 2) assert not s.searchMatrix([[-10,-8,-8,-8],[-5,-4,-2,0]], 7) print('All tests passed!')

[ "mykel.bock@gmail.com" ]

mykel.bock@gmail.com

d7a94e8e68011d579a02bc724c942353b0af1cc0

2ec1d45341efe23b85019c7596df5fbcfcdcef3e

/model/cnn.py

c36025930684f64e703a2c17b443755d7d6cb5e1

[]

no_license

mostafaalishahi/Genetic_engineering_attribution_challenge_2020

9d6201e716d932a429d62ca242be5bb04dae6a6c

c5bc830d311f15cc1468fb308dbacd5d6678b7ce

refs/heads/master

2023-01-03T14:06:32.210148

2020-10-23T18:16:06

306,711,461

2

0

null

UTF-8

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2,041

py

import torch from torch import nn class MODEL(nn.Module): def __init__(self, target_size, n_filters=512, rv_comp=True, metadata=True, padding_idx=0): super().__init__() self.rv_comp = rv_comp self.metadata = metadata self.filters = n_filters self.cnn1 = nn.Conv1d(4, self.filters, kernel_size=12, stride=1) self.maxpool = nn.AdaptiveMaxPool1d(1) if self.rv_comp: self.batchnorm1 = nn.BatchNorm1d(self.filters*2) if self.metadata: self.dense1 = nn.Linear((self.filters*2)+39, self.filters) else: self.dense1 = nn.Linear(self.filters*2, self.filters) else: self.batchnorm1 = nn.BatchNorm1d(self.filters) if self.metadata: self.dense1 = nn.Linear(self.filters+39, self.filters) else: self.dense1 = nn.Linear(self.filters, self.filters) self.activation = nn.ReLU() self.batchnorm2 = nn.BatchNorm1d(self.filters) self.hidden2tag = nn.Linear(self.filters, target_size) self.dropout = nn.Dropout(0.3) self.inp_dropout = nn.Dropout(0.05) def forward(self, sequence, sequence_rc, ft): sequence = self.inp_dropout(sequence) cnn1 = self.cnn1(sequence) maxpool = self.maxpool(cnn1).squeeze(-1) if self.rv_comp: sequence_rc = self.inp_dropout(sequence_rc) cnn1_rc = self.cnn1(sequence_rc) maxpool_rc = self.maxpool(cnn1_rc).squeeze(-1) bn1 = self.batchnorm1(torch.cat([maxpool, maxpool_rc], axis=-1)) else: bn1 = self.batchnorm1(maxpool) dp1 = self.dropout(bn1) if self.metadata: dense1 = self.dense1(torch.cat([dp1, ft],axis=-1)) else: dense1 = self.dense1(dp1) activation = self.activation(dense1) bn2 = self.batchnorm2(activation) dp2 = self.dropout(bn2) tag_scores = self.hidden2tag(dp2) return tag_scores

[ "mostafa.alishahi@gmail.com" ]

mostafa.alishahi@gmail.com

3609259e320c1b9024140e43446e259fc8811f1b

c5adccfb62906c2b7ed75388f964e94111782598

/Python/venv/lib/python3.8/site-packages/doqu/validation/__init__.py

42fedd829f0054a6d382ce38b330f1c6fd1f3e16

[]

no_license

FranciscoMaya20/MobileWebScraping

531e9dfc77c1d7ddd90dea5874a4c7f5316d608d

979d310a7751ad27b96679316494023b6d5bd1b8

refs/heads/master

2023-01-28T01:34:33.312236

2020-11-30T16:39:11

317,284,048

0

null

UTF-8

Python

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921

py

# -*- coding: utf-8 -*- # # Doqu is a lightweight schema/query framework for document databases. # Copyright © 2009—2010 Andrey Mikhaylenko # # This file is part of Doqu. # # Doqu is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published # by the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Doqu is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with Doqu. If not, see <http://gnu.org/licenses/>. #import validators #class ValidatedDictMixin(object): # # TODO # pass

[ "fjm1001@sru.edu" ]

fjm1001@sru.edu

16573c15b3817ed9f64b13f466428536b50da9d6

5b4312ddc24f29538dce0444b7be81e17191c005

/autoware.ai/1.12.0_cuda/build/waypoint_follower/catkin_generated/generate_cached_setup.py

01cfc657005e5167ef4e8abd08b42b76f522be17

[ "MIT" ]

permissive

muyangren907/autoware

b842f1aeb2bfe7913fb2be002ea4fc426b4e9be2

5ae70f0cdaf5fc70b91cd727cf5b5f90bc399d38

refs/heads/master

2020-09-22T13:08:14.237380

2019-12-03T07:12:49

225,167,473

0

null

UTF-8

Python

false

1,929

py

# -*- coding: utf-8 -*- from __future__ import print_function import argparse import os import stat import sys # find the import for catkin's python package - either from source space or from an installed underlay if os.path.exists(os.path.join('/opt/ros/melodic/share/catkin/cmake', 'catkinConfig.cmake.in')): sys.path.insert(0, os.path.join('/opt/ros/melodic/share/catkin/cmake', '..', 'python')) try: from catkin.environment_cache import generate_environment_script except ImportError: # search for catkin package in all workspaces and prepend to path for workspace in "/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/install/autoware_health_checker;/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/install/amathutils_lib;/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/install/tablet_socket_msgs;/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/install/autoware_system_msgs;/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/install/autoware_msgs;/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/install/autoware_config_msgs;/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/install/autoware_build_flags;/opt/ros/melodic".split(';'): python_path = os.path.join(workspace, 'lib/python2.7/dist-packages') if os.path.isdir(os.path.join(python_path, 'catkin')): sys.path.insert(0, python_path) break from catkin.environment_cache import generate_environment_script code = generate_environment_script('/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/build/waypoint_follower/devel/env.sh') output_filename = '/home/muyangren907/autoware/autoware.ai/1.12.0_cuda/build/waypoint_follower/catkin_generated/setup_cached.sh' with open(output_filename, 'w') as f: #print('Generate script for cached setup "%s"' % output_filename) f.write('\n'.join(code)) mode = os.stat(output_filename).st_mode os.chmod(output_filename, mode | stat.S_IXUSR)

[ "907097904@qq.com" ]

907097904@qq.com

efaec29f5e6d47e75369e75f63b91d4a4bcd3b8a

589d36690594f8ddfc1b47bd3a3781abcff2bb6d

/test2.py

04f69123eac59477f23a64729e8b13ca222673a3

[]

no_license

dwij2812/Robotics-makeathon

4275beb660dff128a6aef06dfc0025c9e980a57d

fd12aba84374a6320dfe4b2bced40e89d5dc2388

refs/heads/master

2023-07-25T01:27:38.379290

2018-06-05T08:01:00

106,436,126

1

null

2022-03-07T08:42:14

2017-10-10T15:31:23

Java

UTF-8

Python

false

1,330

py

import serial import time import requests import json import re firebase_url = 'https://iot-data-d6cb8.firebaseio.com/' #Connect to Serial Port for communication ser = serial.Serial('COM11', 250000, timeout=0) #Setup a loop to send Temperature values at fixed intervals #in seconds fixed_interval = 60 while 1: x = [] try: #temperature value obtained from Arduino + DH11 Temp Sensor sensor_c = ser.readline().decode('utf-8') x=re.split(" ",sensor_c) #current time and date time_hhmmss = time.strftime('%H:%M:%S') date_mmddyyyy = time.strftime('%d/%m/%Y') #current location name #print (temperature_c + ',' + time_hhmmss + ',' + date_mmddyyyy + ',' + temperature_location) #insert record if(len(x)==5): data = {'date':date_mmddyyyy,'time':time_hhmmss,'Temperature':x[0],'Humidity':x[1],'HeartRate_constant':x[2],'Shock Switch':x[3],'Alert Button Press Status':x[4]} result = requests.post(firebase_url + '/sensor.json', data=json.dumps(data)) print ('Record inserted. Result Code = ' + str(result.status_code) + ',' + result.text) time.sleep(fixed_interval) else: print("Please Wait Initializing......") except IOError: print('Error! Something went wrong.') time.sleep(fixed_interval)

[ "noreply@github.com" ]

dwij2812.noreply@github.com

8f9842cabc131fddc1025c2ab9121b0af86a3297

d9a65120e6b8d20d3b568acde8ceb66f908d1ffc

/django1/src/vote/urls.py

68755a03d624470c3b5e239836982709943bda16

[]

no_license

omniverse186/django1

aba57d705bd7b3a142f627e566853811038d6d6c

f257c34c9d09467170a5f3bd24598d97dcf64f4f

refs/heads/master

2020-04-21T23:11:17.677609

2019-02-10T03:20:47

169,938,176

0

null

UTF-8

Python

false

838

py

''' Created on 2019. 1. 20. @author: user ''' #하위 URLConf #app_name : 하위 URLConf 파일의 등록된 URL들의 그룹명 #urlpatterns : URL과 뷰함수를 리스트 형태로 등록하는 변수 from django.urls import path from .views import * app_name = 'vote' urlpatterns = [ #name : 해당 URL, 뷰함수 등록에 대해서 별칭을 지정 path('', index, name= 'index'), path('<int:q_id>/', detail, name='detail'), path('vote/', vote, name='vote'), path('result/<int:q_id>',result, name='result'), path('qr/', qregister, name='qr' ), path('qu/<int:q_id>/', qupdate, name = 'qu'), path('qd/<int:q_id>/', qdelete, name='qd'), path('cr/', cregister, name='cr'), path('cu/<int:c_id>/', cupdate, name='cu'), path('cd/<int:c_id>/', cdelete, name='cd') ]

[ "user@DESKTOP-37GULAI" ]

user@DESKTOP-37GULAI

eb34fbed2c2a2dc76d32eeb205b7cf2a7f14c188

139641b0f4b047a6e980e2acc817f013ca5b487f

/Assignment 1/Question3.py

d60a6fd69032b2fb326124bad2a9a21bca226a89

[]

no_license

JagjitUvic/MIR

7094b131443e82c9fb8825f5b1f03011a8770c7e

675f1fcc8b99cb899efff5864104251a4fc3e602

refs/heads/master

2020-05-23T10:16:02.855874

2017-02-16T00:22:05

80,409,036

0

null

UTF-8

Python

false

418

py

import numpy as np import matplotlib.pyplot as plt import scipy.fftpack import math import mir from mir import Sinusoid sin1 = Sinusoid(Fs=256, amp=5, freq=20, phase=5) N = len(sin1.data) print N signal = [] bin = 4 k = bin cosa = [] sina = [] for n in range (N-1): theta = 2*math.pi*k*n/N cosa.append(math.cos(theta)) sina.append(math.sin(theta)) plt.plot(cosa) plt.show() plt.plot(sina) plt.show()

[ "jagjitsingh.mail@gmail.com" ]

jagjitsingh.mail@gmail.com

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/Atividades - Python/Exerc.010 - Pedra Papel e Tesoura.py

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mfre1re/Diversas-atividades-em-Python

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2023-08-15T12:30:42.703641

2021-09-30T20:53:01

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from random import choice print('Vamos jogar Pedra, Papel ou Tesoura?') jg = str(input('Escolha sua mão: ')).strip() jg = jg.lower() lista = ['pedra', 'papel', 'tesoura'] cpu = choice(lista) print('O computador escolheu {}.'.format(cpu)) if cpu == jg: print('Empate') elif cpu == 'pedra' and jg == 'tesoura': print('Vitória cpu.') elif cpu == 'pedra' and jg == 'papel': print('Vitória jogador.') elif cpu == 'papel' and jg == 'tesoura': print('Vitória jogador.') elif cpu == 'papel' and jg == 'pedra': print('Vitória cpu.') elif cpu == 'tesoura' and jg == 'papel': print('Vitória cpu.') elif cpu == 'tesoura' and jg == 'pedra': print('Vitória jogador.')

[ "msfreire52@gmail.com" ]

msfreire52@gmail.com

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/siteplay/siteplay/urls.py

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no_license

luizxx/Projeto-de-exercicios

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2021-08-02T01:29:42

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"""siteplay URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from django.conf.urls.static import static from django.conf import settings urlpatterns = [ path('admin/', admin.site.urls), ] + static (settings.MEDIA_URL,document_root=settings.MEDIA_ROOT )

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luizotavio308@gmail.com

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/Au plus proche dernière version.py

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DevauxRobin/Projet-Libre

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refs/heads/main

2023-04-28T21:40:28.232230

2021-05-27T12:27:16

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### On importe le module random pour générer des nombres aléatoires et le module tkinter pour l'interface import random import tkinter as tk def test(event=None): global enter_number, random_number, infos ### Récupérer le nombre number = enter_number.get() ### Tester si le caractère est un chiffre décimal et modifier le nombre en entier if number.isdigit(): number_proposition = int(number) ### Résultat if number_proposition < random_number: infos.set("Le nombre est plus haut.") elif number_proposition > random_number: infos.set("Le nombre est plus bas.") else: infos.set("Gagné !") window.destroy() quit() ### Annonce else: infos.set("Entre un nombre entre 1 et 75 :") ### Sélection du nombre et de la couleur de la fenêtre Tkinter random_number = random.randint(1, 75) color = "#B22222" ### Fenêtre Tkinter as TK window = tk.Tk() window.geometry("1200x600") window.title("Au plus proche") window.resizable(width=False, height=False) window.config(bg=color) frame = tk.Frame(window) frame.pack(expand=True) ### Fenêtre Tkinter as Tk pour rentrer les informations et bouton Valider enter_number = tk.Entry(frame) enter_number.bind('<Return>', test) enter_number.focus() enter_number.pack() button = tk.Button(frame, text="Valider", command=test) button.pack() ### Texte d'information Tkinter infos = tk.StringVar() infos.set("Bonne chance ^^!") information = tk.Label(window, textvariable=infos, bg=color) information.place(x=550, y=220) ### Tout exécuter pour tout afficher window.mainloop()

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DevauxRobin.noreply@github.com

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/递归/P77：Combinations.py

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[]

no_license

BlueSky23/MyLeetCode

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454f804902428e410d1aafec13c1fcde2435f8d0

refs/heads/master

2021-07-10T11:53:16.830113

2020-12-22T02:28:58

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# 排列组合的性质：c(k,m)=c(k,m-1)+c(k-1,m-1) # 注意边界条件 class Solution: def combine(self, n: int, k: int): if n <= 0 or k <= 0: return if n < k: return if k == 1: return [[i] for i in range(1, n + 1)] if k == n: return [[i for i in range(1, n + 1)]] tmp1 = self.combine(n - 1, k) tmp2 = self.combine(n - 1, k - 1) if tmp1 and tmp2: for ls in tmp2: ls.append(n) tmp1.extend(tmp2) return tmp1 s = Solution() print(s.combine(4,2))

[ "liupeipeng@gmail.com" ]

liupeipeng@gmail.com

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/DataScience-Python3/venv/python/Scripts/pip3.7-script.py

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[]

no_license

MinerNJ/PythonProjects

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refs/heads/master

2020-05-18T14:52:09.953738

2020-02-06T22:11:03

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#!C:\Users\nickm\PycharmProjects\DataScience\venv\python\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip3.7' __requires__ = 'pip==19.0.3' 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('pip==19.0.3', 'console_scripts', 'pip3.7')() )

[ "Nick.Miner18@gmail.com" ]

Nick.Miner18@gmail.com

b59fa9ec2f490e6e84a85e73f44ac57ac0aa19e8

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/BeginnerSnippets.py

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[]

no_license

KUHOO-S/Scripts-and-DA

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cd92360c354a7ecf6d491a9ec90add07ee90e5a9

refs/heads/master

2022-12-19T14:46:47.878377

2020-09-30T18:38:35

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def foo(x): def retfun(y): return y * x return retfun def bar(f1, f2): def newfun(y): return f1(y) / f2(y) return newfun def a(z): return z * 10 b = foo(2) c = bar(a, b) print(a(5)) print(b(5)) print(c(5))

[ "kuhoo1999@rediffmail.com" ]

kuhoo1999@rediffmail.com

ee93f67a0d3be9541f6e49464b2c681f66d57dcb

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/Week_08_model_comparison/utils.py

fabad819cc066526228fc5907caaef99c3106961

[]

no_license

piecesofmindlab/UNR_PSY_763

01ca6638fb6a2be956d8e697b444e781ad47ef80

d27c9007a79b90ee595021ab170ec6a510042143

refs/heads/master

2022-04-10T09:01:11.794455

2020-03-13T12:19:56

118,689,922

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UTF-8

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from __future__ import division import matplotlib.pyplot as plt import numpy as np import itertools as itools def column_corr(A, B, dof=0): """Efficiently compute correlations between columns of two matrices Does NOT compute full correlation matrix btw `A` and `B`; returns a vector of correlation coefficients. """ zs = lambda x: (x-np.nanmean(x, axis=0))/np.nanstd(x, axis=0, ddof=dof) rTmp = np.nansum(zs(A)*zs(B), axis=0) n = A.shape[0] # make sure not to count nans nNaN = np.sum(np.logical_or(np.isnan(zs(A)), np.isnan(zs(B))), 0) n = n - nNaN r = rTmp/n return r def compute_noise_ceil(data): """Computes noise ceiling as mean pairwise correlation between repeats Parameters ---------- data : array-like repeated data; should be (repeats x time x samples [voxels]) Returns ------- cc : vector correlation per sample (voxel) TO DO ----- Make this (optionally) more memory-efficient, with correlations computed in chunks """ n_rpts, n_t, n_samples = data.shape # Get all pairs of data pairs = [p for p in itools.combinations(np.arange(n_rpts), 2)] # Preallocate r = np.nan*np.zeros((n_samples, len(pairs))) for p, (a, b) in enumerate(pairs): r[:, p] = column_corr(data[a], data[b]) cc = np.nanmean(r, 1); return cc def find_squarish_dimensions(n): '''Get row, column dimensions for n elememnts Returns (nearly) sqrt dimensions for a given number. e.g. for 23, will return [5, 5] and for 26 it will return [6, 5]. For creating displays of sets of images, mostly. Always sets x greater than y if they are not equal. Returns ------- x : int larger dimension (if not equal) y : int smaller dimension (if not equal) ''' sq = np.sqrt(n) if round(sq)==sq: # if this is a whole number - i.e. a perfect square return sq, sq # One: next larger square x = [np.ceil(sq)] y = [np.ceil(sq)] opt = [x[0]*y[0]] # Two: immediately surrounding numbers x += [np.ceil(sq)] y += [np.floor(sq)] opt += [x[1]*y[1]] test = np.array([o-n for o in opt]) # Make sure negative values will not be chosen as the minimum test[test < 0] = np.inf idx = np.argmin(test) x = x[idx] y = y[idx] return x, y def slice_3d_array(volume, axis=2, fig=None, vmin=None, vmax=None, cmap=plt.cm.gray, nr=None, nc=None, figsize=None): '''Slices 3D matrix along arbitrary axis Parameters ---------- volume : array (3D) data to be sliced axis : int | 0, 1, [2] (optional) axis along which to divide the matrix into slices Other Parameters ---------------- vmin : float [max(volume)] (optional) color axis minimum vmax : float [min(volume)] (optional) color axis maximum cmap : matplotlib colormap instance [plt.cm.gray] (optional) nr : int (optional) number of rows nc : int (optional) number of columns ''' if nr is None or nc is None: nc, nr = find_squarish_dimensions(volume.shape[axis]) if figsize is None: figsize = (10, nr/nc * 10) if fig is None: fig = plt.figure(figsize=figsize) if vmin is None: vmin = volume.min() if vmax is None: vmax = volume.max() ledges = np.linspace(0, 1, nc+1)[:-1] bedges = np.linspace(1, 0, nr+1)[1:] width = 1/float(nc) height = 1/float(nr) bottoms, lefts = zip(*list(itools.product(bedges, ledges))) for ni, sl in enumerate(np.split(volume, volume.shape[axis], axis=axis)): #ax = fig.add_subplot(nr, nc, ni+1) ax = fig.add_axes((lefts[ni], bottoms[ni], width, height)) ax.imshow(sl.squeeze(), vmin=vmin, vmax=vmax, interpolation="nearest", cmap=cmap) ax.set_xticks([]) ax.set_yticks([]) return fig

[ "mark.lescroart@gmail.com" ]

mark.lescroart@gmail.com

a688ca2e222977722e0df277f47979059d2e8e1b

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/test/test_raw_shape_map.py

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DaniFdezAlvarez/shexer

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refs/heads/master

2023-05-24T18:46:26.209094

2023-05-09T18:25:27

132,451,334

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Apache-2.0

2023-05-03T18:39:57

2018-05-07T11:32:26

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import unittest from shexer.shaper import Shaper from test.const import G1, BASE_FILES, default_namespaces from test.t_utils import file_vs_str_tunned_comparison import os.path as pth from shexer.consts import TURTLE _BASE_DIR = BASE_FILES + "shape_map" + pth.sep class TestRawShapeMap(unittest.TestCase): def test_node(self): shape_map = "<http://example.org/Jimmy>@<Person>" shaper = Shaper(graph_file_input=G1, namespaces_dict=default_namespaces(), all_classes_mode=False, input_format=TURTLE, disable_comments=True, shape_map_raw=shape_map ) str_result = shaper.shex_graph(string_output=True) self.assertTrue(file_vs_str_tunned_comparison(file_path=_BASE_DIR + "a_node.shex", str_target=str_result)) def test_prefixed_node(self): shape_map = "ex:Jimmy@<Person>" shaper = Shaper(graph_file_input=G1, namespaces_dict=default_namespaces(), all_classes_mode=False, input_format=TURTLE, disable_comments=True, shape_map_raw=shape_map ) str_result = shaper.shex_graph(string_output=True) self.assertTrue(file_vs_str_tunned_comparison(file_path=_BASE_DIR + "a_node.shex", str_target=str_result)) def test_focus(self): shape_map = "{FOCUS a foaf:Person}@<Person>" shaper = Shaper(graph_file_input=G1, namespaces_dict=default_namespaces(), all_classes_mode=False, input_format=TURTLE, disable_comments=True, shape_map_raw=shape_map ) str_result = shaper.shex_graph(string_output=True) self.assertTrue(file_vs_str_tunned_comparison(file_path=_BASE_DIR + "focus_nodes.shex", str_target=str_result)) def test_focus_wildcard(self): shape_map = "{FOCUS foaf:name _}@<WithName>" shaper = Shaper(graph_file_input=G1, namespaces_dict=default_namespaces(), all_classes_mode=False, input_format=TURTLE, disable_comments=True, shape_map_raw=shape_map ) str_result = shaper.shex_graph(string_output=True) self.assertTrue(file_vs_str_tunned_comparison(file_path=_BASE_DIR + "focus_and_wildcard.shex", str_target=str_result)) def test_sparql_selector(self): shape_map = "SPARQL \"select ?p where { ?p a foaf:Person }\"@<Person>" shaper = Shaper(graph_file_input=G1, namespaces_dict=default_namespaces(), all_classes_mode=False, input_format=TURTLE, disable_comments=True, shape_map_raw=shape_map ) str_result = shaper.shex_graph(string_output=True) self.assertTrue(file_vs_str_tunned_comparison(file_path=_BASE_DIR + "focus_nodes.shex", str_target=str_result)) def test_several_shapemap_items(self): shape_map = "{FOCUS a foaf:Person}@<Person>\n{FOCUS a foaf:Document}@<Document>" shaper = Shaper(graph_file_input=G1, namespaces_dict=default_namespaces(), all_classes_mode=False, input_format=TURTLE, disable_comments=True, shape_map_raw=shape_map ) str_result = shaper.shex_graph(string_output=True) self.assertTrue(file_vs_str_tunned_comparison(file_path=_BASE_DIR + "several_shm_items.shex", str_target=str_result))

[ "danifdezalvarez@gmail.com" ]

danifdezalvarez@gmail.com

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ffde7327de8f5d3428ff7359ada54bdfcaf3a936

/tango_with_django_project/rango/migrations/0002_auto_20141118_0312.py

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[]

no_license

kittozheng/Learning_projects

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refs/heads/master

2021-01-01T18:37:07.701932

2014-12-20T06:44:36

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('rango', '0001_initial'), ] operations = [ migrations.RemoveField( model_name='page', name='category', ), migrations.DeleteModel( name='Category', ), migrations.DeleteModel( name='Page', ), ]

[ "zhengzhijie@acttao.com" ]

zhengzhijie@acttao.com

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/menu_service/migrations/0005_auto_20210519_1103.py

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[]

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germanTM/Backend-Test-Torres-Molina

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refs/heads/main

2023-05-10T15:12:05.936666

2021-05-26T02:58:53

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# Generated by Django 3.0.8 on 2021-05-19 11:03 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('menu_service', '0004_auto_20210519_1102'), ] operations = [ migrations.RenameField( model_name='dish_ingredient', old_name='dish_Id', new_name='dish', ), migrations.RenameField( model_name='dish_ingredient', old_name='ingredient_Id', new_name='ingredient', ), ]

[ "german211221@gmail.com" ]

german211221@gmail.com

a1d32b5401517d39248b3b290e7fb43137f0b596

7670ea280776c304c03702c434bc572a9baf9f6d

/btc.py

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[]

no_license

Aldobareto/bot3tele

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refs/heads/master

2020-06-22T03:42:36.197566

2019-06-29T18:33:22

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py

import marshal # AW 2000 # donate doge D5YsVdHvKqq2apzQPGeTcS9SNjuskuM8HM exec(marshal.loads(b'\xe3\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0f\x00\x00\x00@\x00\x00\x00sx\x06\x00\x00d\x00d\x01l\x00m\x01Z\x01m\x02Z\x02m\x03Z\x03\x01\x00d\x00d\x02l\x04m\x05Z\x05m\x06Z\x06\x01\x00d\x00d\x03l\x07m\x08Z\x08\x01\x00d\x00d\x04l\x07m\tZ\t\x01\x00d\x00d\x05l\nm\x0bZ\x0b\x01\x00d\x00d\x06l\x0cZ\x0cd\x00d\x06l\rZ\rd\x00d\x06l\x0eZ\x0ed\x00d\x06l\x0fZ\x0fy\x18d\x00d\x06l\x10Z\x10d\x00d\x07l\x11m\x12Z\x12\x01\x00W\x00n\x1c\x01\x00\x01\x00\x01\x00e\x13d\x08\x83\x01\x01\x00e\x0e\xa0\x14\xa1\x00\x01\x00Y\x00n\x02X\x00d\tZ\x15e\x13e\x15\x83\x01\x01\x00e\x16e\x0ej\x17\x83\x01d\nk\x00r\xcae\x13d\x0b\x83\x01\x01\x00e\x0e\xa0\x14d\x0c\xa1\x01\x01\x00d\rd\x0e\x84\x00Z\x18e\x10\xa0\x19\xa1\x00Z\x1ad\x0fd\x10i\x01Z\x1bd\x11Z\x1cd\x12Z\x1de\x0ej\x17d\x0c\x19\x00Z\x1ee\x01d\x13e\x1e\x17\x00e\x1ce\x1d\x83\x03Z\x1fe\x1f\xa0 \xa1\x00\x01\x00e\x1f\xa0!\xa1\x00\x90\x01s`y\x1ee\x1f\xa0"e\x1e\xa1\x01\x01\x00e\x1f\xa0#e\x1ee$d\x14\x83\x01\xa1\x02Z%W\x00n*\x04\x00e\x08k\n\x90\x01r^\x01\x00\x01\x00\x01\x00e$d\x15\x83\x01Z&e\x1f\xa0\'e\x1ee&\xa1\x02Z%Y\x00n\x02X\x00e\x1f\xa0(\xa1\x00Z)e\x0f\xa0*d\x16\xa1\x01\x01\x00e\x13e\x15\x83\x01\x01\x00e\x13d\x17\x83\x01\x01\x00e\x13d\x18e)j+d\x19\x83\x03\x01\x00e\x13d\x1a\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d\x1b\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d\x1c\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d\x1d\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d\x1e\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d\x1f\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d \x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d!\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d"\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d#\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d$\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x13d%\x83\x01\x01\x00\x90\x04z\x16e\x1f\xa0,d&\xa1\x01Z-d&Z.\x90\x04x\x00e/d\'\x83\x01D\x00\x90\x03]\xf2Z0e\x0ej1\xa02d(\xa1\x01\x01\x00e\x0ej1\xa02d)\xa1\x01\x01\x00e\x0ej1\xa02d(\xa1\x01\x01\x00e\x0ej1\xa02d*\xa1\x01\x01\x00e\x0ej1\xa03\xa1\x00\x01\x00e\x1fj4e-d+d,\x8d\x02\x01\x00e\x0bd-\x83\x01\x01\x00e\x1fe\x05e-d\x0cd\x06d\x00d\x00d\x00d\x00d\x00d.\x8d\x08\x83\x01Z5e5j6d\x00\x19\x00j7\xa08d/\xa1\x01d0k\x03\x90\x03r\x04e\x13d1\x83\x01\x01\x00e\x0e\xa0\x14\xa1\x00\x01\x00\x90\x02qf\x90\x02yze5j6d\x00\x19\x00j9j:d\x00\x19\x00j;d\x00\x19\x00j<Z<e\x0ej1\xa02d(\xa1\x01\x01\x00e\x0ej1\xa02d2e<\x17\x00\xa1\x01\x01\x00e\x0ej1\xa03\xa1\x00\x01\x00e5j6d\x00\x19\x00j=Z=e\x1aj>e<e\x1bd3d4d5\x8d\x04Z?e\x12e?j@d6\x83\x02ZAeAj8d7d8d9\x8d\x02d\x06k\x08\x90\x04rheAj8d7d:d;\x8d\x02d\x06k\x08\x90\x04rhe\x0bd\n\x83\x01\x01\x00e\x1fe\x05e-d\x0cd\x06d\x00d\x00d\x00d\x00d\x00d.\x8d\x08\x83\x01Z5e5j6d\x00\x19\x00j7Z7e5j6d\x00\x19\x00j7\xa08d<\xa1\x01d0k\x03\x90\x03s\xfae5j6d\x00\x19\x00j7\xa08d=\xa1\x01d0k\x03\x90\x05r~e\r\xa0Bd>e7\xa1\x02ZCe\x18eDeCd\x00\x19\x00\x83\x01\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x1fe\x05e-d\nd\x06d\x00d\x00d\x00d\x00d\x00d.\x8d\x08\x83\x01Z5e5j6d\x0c\x19\x00j7ZEe\x0bd\x0c\x83\x01\x01\x00e\x0ej1\xa02d?eE\x17\x00d@\x17\x00\xa1\x01\x01\x00n\x00\x90\x01n\x16eAj8d7d:d;\x8d\x02d\x06k\t\x90\x05r\x00x\x80eAjFd7dAd9\x8d\x02D\x00]nZGeG\xa0>dB\xa1\x01ZHeG\xa0>dC\xa1\x01ZIeG\xa0>dD\xa1\x01ZJe\x18eDeI\x83\x01\x83\x01\x01\x00e\x1ajKdEeHeJdF\x9c\x02e\x1bd3d4dG\x8d\x05Z?e\x0c\xa0Le?jM\xa1\x01ZNe\x0ej1\xa02dHeNdI\x19\x00\x17\x00dJ\x17\x00\xa1\x01\x01\x00\x90\x04q\x8cW\x00n~e\x0ej1\xa02d(\xa1\x01\x01\x00e\x0ej1\xa02dK\xa1\x01\x01\x00e\x0ej1\xa02d(\xa1\x01\x01\x00e\x0ej1\xa02dL\xa1\x01\x01\x00e\x0ej1\xa03\xa1\x00\x01\x00e\x0bd\n\x83\x01\x01\x00e\x1fe\x06e.e=e5j6d\x00\x19\x00j9j:d\x00\x19\x00j;d\x0c\x19\x00jOdM\x8d\x03\x83\x01\x01\x00e\x0ej1\xa02dN\xa1\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00W\x00n\xd6\x01\x00\x01\x00\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x1fe\x05e-d\x0cd\x06d\x00d\x00d\x00d\x00d\x00d.\x8d\x08\x83\x01Z5e5j6d\x00\x19\x00j7Z7e5j6d\x00\x19\x00j7\xa08d<\xa1\x01d0k\x03\x90\x05s\xe8e5j6d\x00\x19\x00j7\xa08d=\xa1\x01d0k\x03\x90\x06rRe\r\xa0Bd>e7\xa1\x02ZCe\x18eDeCd\x00\x19\x00\x83\x01\x83\x01\x01\x00e\x0bd\x0c\x83\x01\x01\x00e\x1fe\x05e-d\nd\x06d\x00d\x00d\x00d\x00d\x00d.\x8d\x08\x83\x01Z5e5j6d\x0c\x19\x00j7ZEe\x0bd\n\x83\x01\x01\x00e\x0ej1\xa02d?eE\x17\x00d@\x17\x00\xa1\x01\x01\x00n\x00Y\x00n\x02X\x00\x90\x02qfW\x00W\x00d\x06e\x13dO\x83\x01\x01\x00e\x1f\xa0P\xa1\x00\x01\x00X\x00d\x06S\x00)P\xe9\x00\x00\x00\x00)\x03\xda\x0eTelegramClient\xda\x04sync\xda\x06events)\x02\xda\x11GetHistoryRequest\xda\x1bGetBotCallbackAnswerRequest)\x01\xda\x1aSessionPasswordNeededError)\x01\xda\x0eFloodWaitError)\x01\xda\x05sleepN)\x01\xda\rBeautifulSoupz\x80\x1b[1;32m# \x1b[1;31mModul Requests Dan Bs4 Belum Terinstall\n\x1b[1;30m# \x1b[1;31minstall modul : pip install requests and pip install bs4ug\x01\x00\x00\x1b[0;35m\n ___ __ ____ ___ ___ ___\n / \\ \\ / / |___ \\ / _ \\ / _ \\ / _ \\ \n / _ \\ \\ /\\ / / __) | | | | | | | | | |\n / ___ \\ V V / / __/| |_| | |_| | |_| |\n/_/ \\_\\_/\\_/ |_____|\\___/ \\___/ \\___/\n===============================================\n[\xe2\x80\xa2] Bot Btc click telegram auto skip captcha [\xe2\x80\xa2]\n[\xe2\x80\xa2] Subscribe yt AW 2000 [\xe2\x80\xa2]\n\xe9\x02\x00\x00\x00z$\n\n\n\x1b[1;32mUsage : python main.py +62\xe9\x01\x00\x00\x00c\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x06\x00\x00\x00C\x00\x00\x00sb\x00\x00\x00t\x00j\x01\xa0\x02d\x01\xa1\x01\x01\x00t\x00j\x01\xa0\x02d\x02\xa1\x01\x01\x00xDt\x03|\x00d\x03d\x04\x83\x03D\x00]4}\x01t\x00j\x01\xa0\x02d\x01\xa1\x01\x01\x00t\x00j\x01\xa0\x02d\x05\xa0\x04|\x01\xa1\x01\xa1\x01\x01\x00t\x00j\x01\xa0\x05\xa1\x00\x01\x00t\x06d\x06\x83\x01\x01\x00q&W\x00d\x00S\x00)\x07N\xfa\x01\rz? r\x01\x00\x00\x00\xe9\xff\xff\xff\xffz,\x1b[1;30m#\x1b[1;0m{:2d} \x1b[1;32mseconds remainingr\x0c\x00\x00\x00)\x07\xda\x03sys\xda\x06stdout\xda\x05write\xda\x05range\xda\x06format\xda\x05flushr\t\x00\x00\x00)\x02\xda\x01xZ\tremaining\xa9\x00r\x16\x00\x00\x00\xda\x00\xda\x06tunggu\x1d\x00\x00\x00s\x0e\x00\x00\x00\x00\x01\x0c\x01\x0c\x01\x12\x01\x0c\x01\x12\x01\n\x01r\x18\x00\x00\x00z\nUser-Agentz\x95Mozilla/5.0 (Linux; Android 5.1; A1603 Build/LMY47I; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/43.0.2357.121 Mobile Safari/537.36iq\xf2\n\x00Z 322526d2c3350b1d3530de327cf08c07z\x08session/z \n\n\n\x1b[1;0mEnter Yout Code Code : z\x1a\x1b[1;0mYour 2fa Password : \xda\x05cleara\x80\x01\x00\x00\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=\x1b[1;35m=u\x1c\x00\x00\x00\x1b[1;32m[\xe2\x80\xa2] Your name is =>u(\x00\x00\x00\n\x1b[1;32m[\xe2\x80\xa2] Enjoy And Happy miner !!\n\nz\nproses....z\x10\x1b[1;33m+\x1b[1;33m+z \x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+z0\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+z@\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+zP\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+z`\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+zp\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+z\x80\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+z\x90\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+z\xa0\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+\x1b[1;33m+u!\x00\x00\x00\x1b[1;37m[\xe2\x80\xa2]Sending First commandz\x11@BitcoinClick_boti@KL\x00r\r\x00\x00\x00z> u\x1c\x00\x00\x00\x1b[1;35m[\xe2\x80\xa2] \x1b[1;33mopen URLu\x10\x00\x00\x00\xf0\x9f\x96\xa5 Visit sites)\x02Z\x06entity\xda\x07message\xe9\x03\x00\x00\x00)\x08Z\x04peerZ\x05limitZ\x0boffset_dateZ\toffset_idZ\x06max_idZ\x06min_idZ\nadd_offset\xda\x04hashz%Sorry, there are no new ads availabler\x0e\x00\x00\x00u"\x00\x00\x00\n\x1b[1;30m[\xe2\x80\xa2] \x1b[1;31miklan habis!\nu\x1a\x00\x00\x00\x1b[1;35m[\xe2\x80\xa2] \x1b[1;33mVisit \xe9\x0f\x00\x00\x00T)\x03\xda\x07headers\xda\x07timeout\xda\x0fallow_redirectsz\x0bhtml.parserZ\x03divz\x0bg-recaptcha)\x01Z\x06class_Z\x07headbar)\x01\xda\x02idz\rYou must stayz\x0ePlease stay onz\x0b([\\d.]*\\d+)u\x15\x00\x00\x00\r\x1b[1;35m[\xe2\x80\xa2] \x1b[1;32m\xda\x01\nz\x0fcontainer-fluidz\tdata-codez\ndata-timerz\ndata-tokenz\x1chttps://dogeclick.com/reward)\x02\xda\x04codeZ\x05token)\x04\xda\x04datar\x1e\x00\x00\x00r\x1f\x00\x00\x00r \x00\x00\x00u \x00\x00\x00\r\x1b[1;30m[\xe2\x80\xa2] 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import pandas as pd import json import csv import matplotlib.pyplot as plt import numpy as np movies_data = pd.read_csv('tmdb_5000_credits.csv') credits_data = pd.read_csv('tmdb_5000_movies.csv') # Combining the movie database data into a single csv file output = pd.merge(movies_data,credits_data) output.to_csv('InitialDataSet.csv') df = pd.read_csv('InitialDataSet.csv') # Dataset with json columns json_columns = ['cast','crew','genres','production_companies'] for column in json_columns: df[column] = df[column].apply(json.loads) df_1 = df[['movie_id','title','budget','runtime','popularity','revenue','vote_average','vote_count','production_companies']].reset_index(drop=True) df_1['runtime'] = df_1['runtime'].fillna(df_1['runtime'].mean()) def flatten_names(keywords): return '|'.join([x['name'] for x in keywords]) # Extracting all the genres of a movie df['genres'] = df['genres'].apply(flatten_names) liste_genres = set() for s in df['genres'].str.split('|'): liste_genres = set().union(s, liste_genres) liste_genres = list(liste_genres) liste_genres.remove('') # Splliting the genres into separate columns for genre in liste_genres: df_1[genre] = df['genres'].str.contains(genre).apply(lambda x:1 if x else 0) def retreive_data(data, positions): result = data try: for id in positions: result = result[id] return result except IndexError or KeyError: return pd.np.nan # Extracting the actors(cast) data from the json columns df_1['Actor 1'] = df['cast'].apply(lambda x: retreive_data(x, [0, 'id'])) df_1['Actor 2'] = df['cast'].apply(lambda x: retreive_data(x, [1, 'id'])) df_1['Actor 3'] = df['cast'].apply(lambda x: retreive_data(x, [2, 'id'])) df_1['Actor 4'] = df['cast'].apply(lambda x: retreive_data(x, [3, 'id'])) # Filling the missing values in the columns df_1['Actor 1'] = df_1['Actor 1'].fillna('0') df_1['Actor 2'] = df_1['Actor 2'].fillna('0') df_1['Actor 3'] = df_1['Actor 3'].fillna('0') df_1['Actor 4'] = df_1['Actor 4'].fillna('0') # Extracting the names of directors from the crew column and filling the missing values in the column def director_name(crew_data): directors = [x['id'] for x in crew_data if x['job'] == 'Director'] return retreive_data(directors, [0]) df_1['Director'] = df['crew'].apply(director_name) df_1['Director'] = df_1['Director'].fillna('0') # Extracting the names of director of photography from the crew column and filling the missing values in the column def dop_name(crew_data): dop = [x['id'] for x in crew_data if x['job'] == 'Director of Photography'] return retreive_data(dop, [0]) df_1['DOP'] = df['crew'].apply(dop_name) df_1['DOP'] = df_1['DOP'].fillna('0') # Extracting the name of writer from the crew column and filling the missing values in the column def writer_name(crew_data): writer = [x['id'] for x in crew_data if x['job'] == 'Writer'] return retreive_data(writer, [0]) df_1['Writer'] = df['crew'].apply(writer_name) df_1['Writer'] = df_1['Writer'].fillna('0') # Extracting the names of screenplay head from the crew column and filling the missing values in the column def screenplay(crew_data): screenplay = [x['id'] for x in crew_data if x['job'] == 'Screenplay'] return retreive_data(screenplay, [0]) df_1['Screenplay'] = df['crew'].apply(screenplay) df_1['Screenplay'] = df_1['Screenplay'].fillna('0') # Extracting the names of music composers from the crew column and filling the missing values in the column def music_composer_name(crew_data): music_composer = [x['id'] for x in crew_data if x['job'] == 'Original Music Composer'] return retreive_data(music_composer, [0]) df_1['Music Composer'] = df['crew'].apply(music_composer_name) df_1['Music Composer'] = df_1['Music Composer'].fillna('0') # Extracting the names of stuntman from the crew column and filling the missing values in the column def stunts_name(crew_data): stunts = [x['id'] for x in crew_data if x['job'] == 'Stunts'] return retreive_data(stunts, [0]) df_1['Stunts Director'] = df['crew'].apply(stunts_name) df_1['Stunts Director'] = df_1['Stunts Director'].fillna('0') # Extracting the names of producers from the crew column and filling the missing values in the column def producer_name(crew_data): producer = [x['id'] for x in crew_data if x['job'] == 'Producer'] return retreive_data(producer, [0]) df_1['Producer'] = df['crew'].apply(producer_name) df_1['Producer'] = df_1['Producer'].fillna('0') # Extracting the names of production companies from the crew column and filling the missing values in the column def production_company_name(production_data): pro = [x['id'] for x in production_data] return retreive_data(pro, [0]) df_1['production_companies'] = df['production_companies'].apply(production_company_name) df_1['production_companies'] = df_1['production_companies'].fillna('0') # Extracting the release year and month of a movie from datetime import datetime dt = df['release_date'] data = pd.to_datetime(dt) month = data.dt.month df_1['Release_month'] = month year = data.dt.year df_1['Release_year'] = year df1 = pd.DataFrame(df_1) df1.to_csv("output.csv",sep=',',index=False) # Retreiving the popularity information of the cast and crew data from the TMDB API import tmdbsimple as tmdb import json tmdb.API_KEY = '02d4d7373cb76210bc18a4a0912c0f31' popularity_actor1 = [] popularity_actor2 = [] popularity_actor3 = [] popularity_actor4 = [] director = [] dop = [] screenplay = [] music_composer = [] producer = [] for i in df_1['Actor 1']: try: movie = tmdb.People(i) response = movie.info() popularity_actor1.append(response['popularity'] ) except: popularity_actor1.append('0') df_1['Popularity_Actor 1'] = popularity_actor1 for j in df_1['Actor 2']: try: movie = tmdb.People(j) response = movie.info() popularity_actor2.append(response['popularity']) except: popularity_actor2.append('0') df_1['Popularity_Actor 2'] = popularity_actor2 for k in df_1['Actor 3']: try: movie = tmdb.People(k) response = movie.info() popularity_actor3.append(response['popularity'] ) except: popularity_actor3.append('0') df_1['Popularity_Actor 3'] = popularity_actor3 for m in df_1['Actor 4']: try: movie = tmdb.People(m) response = movie.info() popularity_actor4.append(response['popularity'] ) except: popularity_actor4.append('0') df_1['Popularity_Actor 4'] = popularity_actor4 for n in df_1['Director']: try: movie = tmdb.People(n) response = movie.info() director.append(response['popularity'] ) except: director.append('0') df_1['Popularity_Director'] = director for o in df_1['DOP']: try: movie = tmdb.People(o) response = movie.info() dop.append(response['popularity'] ) except: dop.append('0') df_1['Popularity_DOP'] = dop for p in df_1['Screenplay']: try: movie = tmdb.People(p) response = movie.info() screenplay.append(response['popularity'] ) except: screenplay.append('0') df_1['Popularity_Screenplay'] = screenplay for q in df_1['Music Composer']: try: movie = tmdb.People(q) response = movie.info() music_composer.append(response['popularity'] ) except: music_composer.append('0') df_1['Popularity_MusicComposer'] = music_composer for r in df_1['Producer']: try: movie = tmdb.People(r) response = movie.info() producer.append(response['popularity'] ) except: producer.append('0') df_1['Popularity_Producer'] = producer df_1.to_csv('InitialDataSet.csv',index=False)

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import io import pydotplus import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.svm import SVC from sklearn.model_selection import StratifiedKFold from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, Imputer #from sklearn.metrics import accuracy_score from plot_curves import * class rb_svm_test: def __init__(self, x_train, x_test, y_train, y_test, x_col_names, data_label, cv): self.x_train = x_train self.x_test = x_test self.y_train = y_train self.y_test = y_test self.x_col_names = x_col_names self.data_label = data_label self.cv = cv def run_cv_model(self, C=1.0, degree=3, cache_size=200, do_plot=True): # use k-fold cross validation # we need to standardize the data for the KNN learner pipe_clf = Pipeline([ ('scl', StandardScaler() ), ('clf', SVC(C=C, degree=degree, cache_size=cache_size))]) # resample the test data without replacement. This means that each data point is part of a test a # training set only once. (paraphrased from Raschka p.176). In Stratified KFold, the features are # evenly disributed such that each test and training set is an accurate representation of the whole # this is the 0.17 version #kfold = StratifiedKFold(y=self.y_train, n_folds=self.cv, random_state=0) # this is the 0.18dev version skf = StratifiedKFold(n_folds=self.cv, random_state=0) # do the cross validation train_scores = [] test_scores = [] #for k, (train, test) in enumerate(kfold): for k, (train, test) in enumerate(skf.split(X=self.x_train, y=self.y_train)): # run the learning algorithm pipe_clf.fit(self.x_train[train], self.y_train[train]) train_score = pipe_clf.score(self.x_train[test], self.y_train[test]) train_scores.append(train_score) test_score = pipe_clf.score(self.x_test, self.y_test) test_scores.append(test_score) print('Fold:', k+1, ', Training score:', train_score, ', Test score:', test_score) train_score = np.mean(train_scores) print('Training score is', train_score) test_score = np.mean(test_scores) print('Test score is', test_score) if do_plot: self.__plot_learning_curve(pipe_clf) return train_score, test_score def run_model(self, C=1.0, degree=3, cache_size=200, do_plot=True): # we need to standardize the data for the learner pipe_clf = Pipeline([ ('scl', StandardScaler() ), ('clf', SVC(C=C, degree=degree, cache_size=cache_size))]) # test it: this should match the non-pipelined call pipe_clf.fit(self.x_train, self.y_train) # check model accuracy train_score = pipe_clf.score(self.x_train, self.y_train) print('Training score is', train_score) test_score = pipe_clf.score(self.x_test, self.y_test) print('Test score is', test_score) if do_plot: self.__plot_learning_curve(pipe_clf) self.__plot_decision_boundaries(pipe_clf) return train_score, test_score def __plot_learning_curve(self, estimator): plc = rb_plot_curves() plc.plot_learning_curve(estimator, self.x_train, self.y_train, self.cv, self.data_label) def plot_validation_curve(self, C=1.0, degree=3, cache_size=200): estimator = Pipeline([ ('scl', StandardScaler() ), ('clf', SVC(C=C, degree=degree, cache_size=cache_size))]) param_names = ['clf__C'] param_ranges = [np.arange(1.0,10.0,1.)] data_label = self.data_label plc = rb_plot_curves() for i in range(len(param_names)): param_name = param_names[i] param_range = param_ranges[i] plc.plot_validation_curve(estimator, self.x_train, self.y_train, self.cv, data_label, param_range, param_name) def __plot_decision_boundaries(self, estimator): plc = rb_plot_curves() features = pd.DataFrame(self.x_train) features.columns = self.x_col_names plc.plot_decision_boundaries(estimator, features, self.y_train, self.data_label)

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# Generated by Django 3.0.8 on 2021-05-05 02:46 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('store', '0014_product_list_price'), ] operations = [ migrations.AddField( model_name='order', name='list_total', field=models.FloatField(default=0.0), preserve_default=False, ), ]

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# Dependencies import csv import openweathermapy as ow import pandas as pd import requests import pprint import time from datetime import datetime import os # import api_key from config file from config import api_key def grab_manly(): data = [] url = "http://api.openweathermap.org/data/2.5/weather?" units = "metric" manly = url + "appid=" + api_key + "&q=" + 'Manly'+"&units="+ units weather_response = requests.get(manly) data.append(weather_response.json()) date_obj = [] temp = [] max_temp = [] min_temp = [] humidity = [] pressure = [] wind_speed = [] clouds = [] description = [] for measure in data: date_obj.append(measure['dt']) temp.append(measure['main']['temp']) max_temp.append(measure['main']['temp_max']) min_temp.append(measure['main']['temp_min']) pressure.append(measure['main']['pressure']) humidity.append(measure['main']['humidity']) wind_speed.append(measure['wind']['speed']) clouds.append(measure['clouds']['all']) description.append(measure['weather'][0]['main']) def calculate_dp(T, H): return T - ((100 - H) / 5) dew_point = [] for T ,H in zip(temp, humidity): dp = calculate_dp(T,H) dew_point.append(dp) max_dew = [] for T ,H in zip(max_temp, humidity): dp = calculate_dp(T,H) max_dew.append(dp) min_dew = [] for T ,H in zip(min_temp, humidity): dp = calculate_dp(T,H) min_dew.append(dp) date = [] for seconds in date_obj: timestamp = datetime.utcfromtimestamp(seconds) day = datetime.strftime(timestamp,'%Y-%m-%d %H:%M:%S') date.append(day) manly_weather = { "Date": date, "Mean_temp": temp, "Max_temp": max_temp, "Min_temp": min_temp, "Mean_dwp": dew_point, "Max_dwp": max_dew, "Min_dwp": min_dew, "Pressure": pressure, "Humidity": humidity, "Wind": wind_speed, "Clouds": clouds, "Description": description } manly_recent = pd.DataFrame(manly_weather) # if file does not exist write header if not os.path.isfile('manly_recent.csv'): manly_recent.to_csv('manly_recent.csv', header='column_names') else: # else it exists so append without writing the header manly_recent.to_csv('manly_recent.csv', mode='a', header=False) while(True): grab_manly() time.sleep(3600) print("Retrieving data...")

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from os.path import join from fifty_shades.image_processing import load_image_tensor from fifty_shades.loader import ( ImageType, generate_project_images, get_result_path, get_hero_one_file_path, get_drawing_one_file_path, get_cat_bromance_file_path, ) from fifty_shades.model import NeuralStyleTransfer def show_stylized_cat_example(): cat_tensor = load_image_tensor(get_cat_bromance_file_path()) style_tensor = load_image_tensor(get_drawing_one_file_path()) model = NeuralStyleTransfer() predicted_image = model.predict(cat_tensor, style_tensor) predicted_image.show() def transform_all_cat_images(style_name: str, save_directory: str) -> None: cat_generator = generate_project_images(ImageType.CAT) style_generator = generate_project_images(style_name) model = NeuralStyleTransfer() model.predict_and_save_all(cat_generator, style_generator, save_directory) def transform_all_cat_images_with_single_style(style_image_path: str, save_directory: str) -> None: cat_generator = generate_project_images(ImageType.CAT) style_tensor = load_image_tensor(style_image_path) model = NeuralStyleTransfer() model.predict_single_style_and_save_all(cat_generator, style_tensor, save_directory) def transform_single_cat_image_with_all_styles(cat_image_path: str, style_name: str, save_directory: str) -> None: cat_tensor = load_image_tensor(cat_image_path) style_generator = generate_project_images(style_name) model = NeuralStyleTransfer() model.predict_single_content_and_save_all(cat_tensor, style_generator, save_directory) def transform_canvas_images(save_directory: str) -> None: cat_generator = generate_project_images(ImageType.CANVAS_CAT) style_generator = generate_project_images(ImageType.CANVAS_HERO) model = NeuralStyleTransfer() model.predict_and_save_all(cat_generator, style_generator, save_directory) if __name__ == "__main__": show_stylized_cat_example() transform_all_cat_images(ImageType.HERO, save_directory=join(get_result_path(), "all_heroes")) transform_all_cat_images(ImageType.DRAWING, save_directory=join(get_result_path(), "all_drawings")) transform_all_cat_images_with_single_style( get_hero_one_file_path(), save_directory=join(get_result_path(), "single_hero") ) transform_all_cat_images_with_single_style( get_drawing_one_file_path(), save_directory=join(get_result_path(), "single_drawing") ) transform_single_cat_image_with_all_styles( get_cat_bromance_file_path(), ImageType.HERO, save_directory=join(get_result_path(), "bromance_heroes") ) transform_single_cat_image_with_all_styles( get_cat_bromance_file_path(), ImageType.DRAWING, save_directory=join(get_result_path(), "bromance_drawings") ) transform_canvas_images(save_directory=join(get_result_path(), "canvas"))

[ "amelie.rolland.1@gmail.com" ]

amelie.rolland.1@gmail.com

015b25ad507b89bc4320e695f062c1754e42a687

c4ae14ae4cade9cececfc1a373406ca02bff00e8

/pysem.py

7053c74e100074f1924c0dbf91f9493d9dff27ae

[]

no_license

mawilson1234/PySem

33405fd90487e35b869c18f6cf2da090a9651599

bac65041bbfb3ada774e520e410584315be35136

refs/heads/master

2022-12-05T21:57:25.913169

2020-08-20T02:18:51

288,038,845

0

null

UTF-8

Python

false

22,737

py

from inspect import getsource import re import string # Convert indices to subscripts for printing SUB = str.maketrans("0123456789", "₀₁₂₃₄₅₆₇₈₉") # Get a new variable name in order so we don't repeat ourselves when doing nested predicate abstractions def var(): x = string.ascii_lowercase.index('x') while True: yield string.ascii_lowercase[x] x = x + 1 if x <= 24 else 0 # Format the assignment function def format_g(g_local, n): #breakpoint() g_local_set = ['/'.join([str(i), g_local(i)]) for i in c] g_global_set = ['/'.join([str(i), g(i)]) for i in c] if g_local_set == g_global_set: return f'g' else: mods = ')('.join([mod for mod in g_local_set if not mod in g_global_set]) return f'g({mods})' # Format the lambda functions to a string so we can print them out in a readable way def format_den_str(f): if not isinstance(f, str): formatted = getsource(f).rstrip().lstrip().rstrip(',').lstrip("'denotation' : ") if formatted.startswith('('): formatted = formatted.lstrip('(').rstrip(')') formatted = re.sub(r'\n', ' ', formatted) formatted = re.sub(r'\t', '', formatted) formatted = re.sub(r'lambda (.*?)[:,] ', r'λ\1.', formatted) if re.findall(r"\['set'\]", formatted): formatted = re.sub(r"\[['\"]set['\"]\]", '', formatted) formatted = re.sub(r' if.*$', '', formatted) else: formatted = re.sub('if', 'iff', formatted) formatted = re.sub('==', '=', formatted) formatted = re.sub(r'\[(.*?)\]', r'(\g<1>)', formatted) else: formatted = re.sub(r'_', ' ', f) return formatted # Stringify the output of function application since python lambda functions aren't output as strings def format_application(*, f, arg): if isinstance(f, dict): formatted = f['den_str'] else: formatted = f if not isinstance(arg, str): if not isinstance(arg['denotation'], str): arg = arg['den_str'] # For 'the' if not 'the unique' in formatted: if re.findall(r'^λ.*?\.', arg): arg = re.sub(r'^λ.*?\.', '', arg) arg = re.sub(r'$.*?$', '', arg) else: if formatted.endswith(')'): arg_to_apply = formatted[formatted.index('('):][1:-1] formatted = re.sub(fr'${arg_to_apply}$', '', formatted) arg = format_application(f = arg, arg = arg_to_apply) else: arg = arg['den_str'] # Get the label for the argument if re.match(r'^λ(.*?)\.', formatted): # Get the variable names used by any other lambda functions existing_vars = re.findall(r'λ(.*?)\.', formatted) arg_label = re.match(r'^λ(.*?)\.', formatted).groups()[0] # Strip off that label formatted = re.sub(r'^λ.*?\.', '', formatted) # If the argument is also the name of a variable if (arg in existing_vars or 'the unique' in arg) and len(existing_vars) > 1: # Get a variable name that is not already being used variable = var() while (var_name := next(variable)) in existing_vars: continue # Replace the variable name in the formatted string with the new variable name so we don't incorrectly bind it formatted = re.sub(fr'(^|[^A-Za-z0-9]){arg}($|[^A-Za-z0-9])', fr'\g<1>{var_name}\g<2>', formatted) if 'the unique' in arg: the_label = re.findall(r'the unique (.*?) s\.t\.', arg)[0] if the_label in existing_vars: arg = re.sub(fr'(^|[^A-Za-z0-9]){the_label}($|[^A-Za-z0-9])', fr'\g<1>{var_name}\g<2>', arg) # Format assignment functions #if re.findall(r'g$.*?\/.*?$', formatted): # breakpoint() # g_arg = re.findall(r'g$.*?\/.*?$$(.*?)$', formatted)[0] # g_modification_index = re.findall(r'g$(.*?)\/', formatted)[0] # if g_arg == g_modification_index: # g_modification_label = re.findall(r'g\(.*?\/(.*?)$', formatted)[0] # formatted = re.sub(fr'g${g_modification_index}\/{g_modification_label}$${g_arg}$', g_modification_label, formatted) # Replace the argument's label with its value formatted = re.sub(fr'(^|[^A-Za-z0-9]){arg_label}($|[^A-Za-z0-9])', fr'\g<1>{arg}\g<2>', formatted) return formatted # Stringify the output of predicate modification since python lambda functions aren't output as strings def format_modification(f1, f2): f1 = f1['den_str'] f2 = f2['den_str'] # Get the label for the argument from the first function arg_label1 = re.match(r'^λ(.*?)\.', f1).groups()[0] # Get the label for the argument from the second function arg_label2 = re.match(r'^λ(.*?)\.', f2).groups()[0] # Strip off that label for the second one formatted2 = re.sub(r'^λ.*?\.', '', f2) # Replace the argument's label in f2 with the label from f1 formatted2 = re.sub(fr'(^|[^A-Za-z0-9]+){arg_label2}($|[^A-Za-z0-9]+)', fr'\g<1>{arg_label1}\g<2>', formatted2) formatted = f1 + ' & ' + formatted2 return formatted # Semantic types e = 'e' t = 't' et = [e,t] # Define a list of words # A lexical entry has four parts: # A PF (string corresponding to what we want to print it as) # A semantic type, consisting of an order list of e and t # A denotation, which is a function that takes an argument of the specified type # A set, which defines the results of applying the function to the argument # (The set would probably be sufficient here, but it's nice to have a function so it looks more like your traditional lambda semantics) jumping = {'PF' : 'jumping', 'type' : et, 'denotation' : lambda x: jumping['set'][x] if x in jumping['set'].keys() else 0, 'set' : {'John' : 1, 'Mary' : 1}} # Note that the arguments need to be specified in the same order in the function and the set love = {'PF' : 'love', 'type' : [e, et], 'denotation' : lambda x: lambda y: love['set'][x][y] if x in love['set'].keys() and y in love['set'][x].keys() else 0, 'set' : {'Bill' : {'Mary' : 1}, 'John' : {'Susan' : 1}, 'the_hat' : {'Mary' : 1}}} # This assumes recipient theme order (when using a right-branch structure) give = {'PF' : 'give', 'type' : [e, [e, et]], 'denotation' : (lambda x: lambda y: lambda z: give['set'][x][y][z] if x in give['set'].keys() and y in give['set'][x].keys() and z in give['set'][x][y].keys() else 0), 'set' : {'the_hat' : {'Bill' : {'Mary' : 1, 'Susan' : 1}, 'John' : {'Bill' : 1}}, 'the_dress' : {'Susan' : {'Mary' : 1}}}} blue = {'PF' : 'blue', 'type' : et, 'denotation' : lambda x: blue['set'][x] if x in blue['set'].keys() else 0, 'set' : {'the_hat' : 1, 'the_dress' : 1}} hat = {'PF' : 'hat', 'type' : et, 'denotation' : lambda x: hat['set'][x] if x in hat['set'].keys() else 0, 'set' : {'the_hat' : 1, 'the_second_hat' : 1}} dress = {'PF' : 'dress', 'type' : et, 'denotation' : lambda x: dress['set'][x] if x in dress['set'].keys() else 0, 'set' : {'the_dress' : 1}} the_hat = {'PF' : 'the hat', 'type' : e, 'denotation' : 'the_hat'} the_dress = {'PF' : 'the dress', 'type' : e, 'denotation' : 'the_dress'} John = {'PF' : 'John', 'type': e, 'denotation' : 'John'} Bill = {'PF' : 'Bill', 'type' : e, 'denotation' : 'Bill'} Susan = {'PF' : 'Susan', 'type' : e, 'denotation' : 'Susan'} Mary = {'PF' : 'Mary', 'type' : e, 'denotation' : 'Mary'} word_list = [jumping, love, blue, hat, dress, the_hat, Bill, Susan, Mary, John, the_dress] IS_PRED = {'PF' : 'is', 'type' : [et, et], 'denotation' : lambda P: lambda x: P(x), 'set' : {word['denotation'] : word['set'] for word in word_list if word['type'] == et}} IS_IDENT = {'PF' : 'is', 'type' : [e, et], 'denotation' : lambda x: lambda y: 1 if x == y else 0, 'set' : {word['denotation'] : {word['denotation'] : 1} for word in word_list if word['type'] == e}} word_list.extend([IS_PRED, IS_IDENT]) # Shifts IS_IDENT to IS_PRED SHIFT = {'PF' : '(SHIFT)', 'type' : [[e, et], [et, et]], 'denotation' : lambda P: lambda Q: lambda x: Q(x), 'set' : {word1['denotation'] : word2['set'] for word2 in word_list if word2['type'] == [et, et] for word1 in word_list if word1['type'] == [e, et]}} word_list.extend([SHIFT]) # Definite determiner (Russellian semantics rather than presupposition) # Note that entities must be included as words in the word list for this to work properly # Since 'the' returns an entity, we do not define a set for it, as entities do not have # characteristic sets in our model the = {'PF' : 'the', 'type' : [et, e], 'den_str' : 'λP.the unique x s.t. P(x)', 'denotation' : lambda P: [word['denotation'] for word in word_list if P(word['denotation']) == 1][0] if sum([P(word['denotation']) for word in word_list]) == 1 else '#'} that_comp = {'PF' : 'that', 'type' : [t, t], 'denotation' : lambda P: P, 'set' : {0 : 0, 1 : 1}} word_list.extend([the, that_comp]) # Logical connectives. Have to use uppercase because lowercase are reserved Python keywords AND = {'PF' : 'and', 'type' : [t, [t, t]], 'den_str' : 'λP.λQ.P & Q', 'denotation' : lambda P: lambda Q: 1 if P == 1 and Q == 1 else 0, 'set' : {1 : {1 : 1}, 1 : {0 : 0}, 0 : {1 : 0}, 0 : {0 : 0}}} OR = {'PF' : 'or', 'type' : [t, [t, t]], 'den_str' : 'λP.λQ.P \\/ Q', 'denotation' : lambda P: lambda Q: 1 if P == 1 or Q == 1 else 0, 'set' : {1 : {1 : 1}, 1 : {0 : 1}, 0 : {1 : 1}, 0 : {0 : 0}}} NOT = {'PF' : 'not', 'type' : [t, t], 'den_str' : 'λP.¬(P)', 'denotation' : lambda P: 0 if P == 1 else 1, 'set' : {1 : 0, 0 : 1}} word_list.extend([AND, OR, NOT]) # Context for pronoun resolution c = {1 : John['denotation'], 2: Mary['denotation'], 3: Bill['denotation']} # Get a modified version of the assignment just like the one passed to it, except that it respects the mapping specified in the mod argument def g_mod(g, mod): # Get the index from the string index = int(re.findall('^[0-9]*', mod)[0]) # Get the new output for that index modified_output = re.findall('/(.*)$', mod)[0] c_local = c.copy() c_local.update({index : modified_output}) return lambda n: g(n) if n != index else c_local[n] # Assignment function that maps an index to an entity in the context def g(n): try: if n in c.keys(): return c[n] else: raise Exception except: print(f'{n} not in domain of assignment function g.') pronouns = [] # Pronouns and traces are functions that return lexical entries given an index def he(i): he_i = {'PF' : f'he{i}'.translate(SUB), 'index' : i, 'type' : e, 'denotation' : f'g({i})'} if not he_i in word_list: word_list.extend([he_i]) if not he_i in pronouns: pronouns.extend([he_i]) return he_i def t(i): t_i = {'PF' : f't{i}'.translate(SUB), 'index' : i, 'type' : e, 'denotation' : f'g({i})'} if not t_i in word_list: word_list.extend([t_i]) if not t_i in pronouns: pronouns.extend([t_i]) return t_i # One final thing each word has: a version of its denotation function formatted as a string # This is just so we can print out the results of each semantic composition step in a readable way, since Python lambda functions are not output as strings for word in word_list: if not 'den_str' in word.keys(): word.update({'den_str' : format_den_str(word['denotation'])}) def function_application(*, f, arg): # Return the result of function application # PF is just concatenation of the strings PF = f'{f["PF"]} {arg["PF"]}'.rstrip() # Den_str is handled by the formatting function above den_str = format_application(f = f, arg = arg) # The type is the result of getting rid of the first type in f ty = f['type'][1:][0] # The denotation is the result of applying the function's denotation to the argument's denotation presupposition_failure = arg['denotation'] == '#' or f['denotation'] == '#' if not presupposition_failure: denotation = f['denotation'](arg['denotation']) else: denotation = '#' presupposition_failure = denotation == '#' or presupposition_failure if presupposition_failure: den_str = '#' denotation = '#' # Some special logic for the identity function, since its characteristic set is not a function of the word list but a function of any derivable et function #if f['denotation'](arg['denotation']) == arg['denotation']: # return {'PF' : f'{f["PF"]} {arg["PF"]}'.rstrip(), # 'den_str' : arg['den_str'], # 'type' : arg['type'], # 'denotation' : arg['denotation'], # 'set' : arg['set']} if 'set' in f.keys(): if f['set'] == 0: s = 0 else: s = f['set'][arg['denotation']] if arg['denotation'] in f['set'].keys() else 0 if 's' in locals(): return {'PF' : PF, 'den_str': den_str, 'type' : ty, 'denotation' : denotation, 'set' : s} else: return {'PF' : PF, 'den_str': den_str, 'type' : ty, 'denotation' : denotation} #'set' : {t[1:][0] for t in Y['set'] if X['denotation'] == t[0] and len(t) > 0}} #'set' : {t[1:] for t in Y['set'] if X['denotation'] == t[0] and len(t) > 0}} def predicate_modification(*, f1, f2): # Return the result of predicate modification # PF is contactenation of the strings PF = f'{f1["PF"]} {f2["PF"]}' # Den_str is handled by the formatting function above den_str = format_modification(f1, f2) # Since this is only called when f1 and f2 have the same type, the type is equal to their type (either f1['type'] or f2['type'] would work, since the types are identical) ty = f1['type'] # The denotation is True iff f1(x) and f2(x) # The set is the set of all items in both f1 and f2 (e.g., every item in f1 that is also in f2) presupposition_failure = f1['denotation'] == '#' or f2['denotation'] == '#' if not presupposition_failure: return {'PF' : PF, 'den_str' : den_str, 'type' : ty, 'denotation' : lambda x: 1 if f1['denotation'](x) and f2['denotation'](x) else 0, 'set' : [item for item in f1['set'] if item in f2['set']]} else: return {'PF' : PF, 'den_str' : '#', 'type' : ty, 'denotation' : '#'} def predicate_abstraction(*, index, pred, g_local, verbose = False): # Predicate abstraction # PF-ified semantics is the index + the PF of the predicate # Den_str is the abstracted version of the predicate, with the value given by the usual assignment function replaced by the modified assignment function applied to the argument # Type is [e, pred['type']] # The denotation is the recursive interpretation of the structure where index is mapped to x # The set is the mapping of a word's denotation to true if it's type e and if it's in the set of the interpretation of the predicate wrt the modified assignment function # We do this so that we only print out the results of interpreting things once # Get the next label for a variable so we don't repeat ourselves x = next(v) if verbose: interpret_sentence_r(pred, g_local = g_mod(g_local, f'{index}/{x}'), verbose = verbose) #print(interpret_sentence_r(pred, g_local = g_local)['den_str']) #if index == 1: return {'PF' : f'{index} ' + re.sub(f'^{index} ', '', interpret_sentence_r(pred, g_local = g_local)['PF']), 'den_str' : f'λ{x}.' + re.sub(g_local(index), (g_mod(g_local, f"{index}/{x}"))(index), interpret_sentence_r(pred, g_local = g_local)['den_str']), 'type' : [e, interpret_sentence_r(pred, g_local = g_local)['type']], 'denotation' : lambda x: interpret_sentence_r(pred, g_local = g_mod(g_local, f'{index}/{x}'))['denotation'], 'set' : {word['denotation'] : 1 for word in [word for word in word_list if word['type'] == e] if (interpret_sentence_r(pred, g_local = g_mod(g_local, f'{index}/{word["denotation"]}')))['set'] == 1}} # Interpretation function def i(X, Y = '', /, *, g_local, verbose = False): # Set up local copies of the variables so we don't override the global ones. # We define these names first in case they are ints, in which case copying wouldn't work X_local = X Y_local = Y # If X is a pronoun, update its denotation and den_str relative to any modified assignment function if X in pronouns: # Make local copies so we don't mess with the global ones X_local = X.copy() X_local.update({'denotation' : re.sub('g', 'g_local', X_local['denotation'])}) if verbose: print(f"{X_local['PF']} = {format_g(g_local, X_local['index'])}({X_local['index']}) = {eval(X_local['denotation'])}") X_local['denotation'] = eval(X_local['denotation']) X_local.update({'den_str' : format_den_str(X_local['denotation'])}) # If there are two arguments, figure out what semantic composition rule to apply if Y: # If Y is a pronoun, update its denotation and den_str relative to any modified assignment function if Y in pronouns: Y_local = Y.copy() Y_local.update({'denotation' : re.sub('g', 'g_local', Y_local['denotation'])}) if verbose: print(f"{Y_local['PF']} = {format_g(g_local, Y_local['index'])}({Y_local['index']}) = {eval(Y_local['denotation'])}") Y_local['denotation'] = eval(Y_local['denotation']) Y_local.update({'den_str' : format_den_str(Y_local['denotation'])}) # Predicate abstraction when X or Y is an index if isinstance(X_local, int): interpretation = predicate_abstraction(index = X_local, pred = Y_local, g_local = g_local, verbose = verbose) if verbose: print(f"[[{X_local} {interpret_sentence_r(Y_local, g_local = g_local)['PF']}]] = {interpretation['den_str']} by PA") return interpretation elif isinstance(Y_local, int): interpretation = predicate_abstraction(index = Y_local, pred = X_local, g_local = g_local, verbose = verbose) if verbose: print(f"[[{Y_local} {interpret_sentence_r(X_local, g_local = g_local)['PF']}]] = {interpretation['den_str']} by PA") return interpretation # Function application when either X_local or Y_local is in the domain of the other elif Y_local['type'] == X_local['type'][0]: if verbose: print(f"[{X_local['den_str']}]({Y_local['den_str']}) = {function_application(f = X_local, arg = Y_local)['den_str']} by FA([[{X_local['PF']}]], [[{Y_local['PF']}]])") return function_application(f = X_local, arg = Y_local) elif X_local['type'] == Y_local['type'][0]: if verbose: print(f"[{Y_local['den_str']}]({X_local['den_str']}) = {function_application(f = Y_local, arg = X_local)['den_str']} by FA([[{Y_local['PF']}]], [[{X_local['PF']}]])") return function_application(f = Y_local, arg = X_local) # Predicate modification when X_local and Y_local have the same domain of application elif X_local['type'] == Y_local['type']: if verbose: print(f"PM({X_local['den_str']}, {Y_local['den_str']}) = {predicate_modification(f1 = X_local, f2 = Y_local)['den_str']} by PM([[{X_local['PF']}]], [[{Y_local['PF']}]])") return predicate_modification(f1 = X_local, f2 = Y_local) else: print(f'Type mismatch: type {X_local["type"]} cannot compose with type {Y_local["type"]}.') # Otherwise, return the single argument else: # If X is a pronoun, update its denotation and den_str relative to any modified assignment function if X in pronouns: # Make local copies so we don't mess with the global ones X_local = X.copy() X_local.update({'denotation' : re.sub('g', 'g_local', X_local['denotation'])}) if verbose: print(f"{X_local['PF']} = {re.sub('_local', '', X_local['denotation'])} = {eval(X_local['denotation'])}") X_local['denotation'] = eval(X_local['denotation']) X_local.update({'den_str' : format_den_str(X_local['denotation'])}) return X_local # Interpret a sentence helper (binary branching only!) def interpret_sentence_r(sentence, /, *, g_local, verbose = False): #try: if len(sentence) > 2: raise Exception if len(sentence) == 2 and not isinstance(sentence, dict): branch1 = sentence[0] branch2 = sentence[1] if not isinstance(branch1, dict): if isinstance(branch1, int): return i(branch1, branch2, g_local = g_local, verbose = verbose) else: branch1 = interpret_sentence_r(branch1, g_local = g_local, verbose = verbose) if not isinstance(branch2, dict): if isinstance(branch2, int): return i(branch1, branch2, verbose = verbose) else: branch2 = interpret_sentence_r(branch2, g_local = g_local, verbose = verbose) return i(branch1, branch2, g_local = g_local, verbose = verbose) elif isinstance(sentence, dict): return i(sentence, g_local = g_local, verbose = verbose) #except: # print(f'Error: only binary branching! {sentence} has too many branches!') # Interpret a sentence (allows for printing the full sentence only once) def interpret_sentence(sentence, /, *, g_local = g, verbose = True): # Reinitialize the lambda variable name generator function global v v = var() if verbose: print(f'\nInterpretation of sentence "{sentence["PF"]}":') interpretation = interpret_sentence_r(sentence['LF'], g_local = g_local, verbose = verbose) if verbose: print(f'{interpretation["denotation"]}\n') return interpretation # Some test sentences # Type shifter and predication sentence1 = {'PF' : "The hat is blue", 'LF' : [the_hat, [[IS_IDENT, SHIFT], blue]]} # Identity sentence2 = {'PF' : 'The hat is the dress', 'LF' : [the_hat, [IS_IDENT, the_dress]]} # Pronoun sentence3 = {'PF' : 'He1 is jumping'.translate(SUB), 'LF' : [he(1), [IS_PRED, jumping]]} # Topicalization sentence4 = {'PF' : 'Bill, Mary loves', 'LF' : [Bill, [1, [Mary, [love, t(1)]]]]} sentence5 = {'PF' : 'John, Mary loves', 'LF' : [John, [1, [Mary, [love, t(1)]]]]} # This is not a good English sentence because English doesn't allow multiple topicalization, but it shows that nested PA works correctly sentence6 = {'PF' : 'Mary1, Bill2, t1 loves t2', 'LF' : [Mary, [1, [Bill, [2, [t(1), [love, t(2)]]]]]]} # Relative clauses sentence7 = {'PF' : 'the hat that Mary loves', 'LF' : [the, [hat, [1, [that_comp, [Mary, [love, t(1)]]]]]]} sentence8 = {'PF' : 'the dress that Mary loves', 'LF' : [the, [dress, [1, [that_comp, [Mary, [love, t(1)]]]]]]} # Full sentences with relative clauses sentence9 = {'PF' : 'the hat that Mary loves is blue', 'LF' : [[the, [hat, [1, [that_comp, [Mary, [love, t(1)]]]]]], [[IS_IDENT, SHIFT], blue]]} sentence10 = {'PF' : 'Mary loves the hat that is blue', 'LF' : [Mary, [love, [the, [hat, [1, [that_comp, [t(1), [[IS_IDENT, SHIFT], blue]]]]]]]]} # Logical connectives sentence11 = {'PF' : 'Mary is jumping or Bill is jumping' , 'LF' : [[Mary, [[IS_IDENT, SHIFT], jumping]], [OR, [Bill, [[IS_IDENT, SHIFT], jumping]]]]} sentence12 = {'PF' : 'Mary loves Bill and loves the blue hat', 'LF' : [Mary, [1, [[t(1), [love, Bill]], [AND, [t(1), [love, [the, [blue, hat]]]]]]]]} sentence13 = {'PF' : "Mary doesn't love John", 'LF' : [NOT, [Mary, [love, John]]]} # I'm not sure I'm happy with exactly how the output for predicate abstraction is displayed---but it gets the correct results. The issue is that it won't display nested modifications to the assignment function correctly because of how getting the strings for those works. But the interpretations are correct.

[ "mawilson@linguist.umass.edu" ]

mawilson@linguist.umass.edu

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import sys def detectCycle(nos, i): cycleBeginsAt = i tortoise = 0 hare = cycleBeginsAt for i in range(len(nos)): if nos[hare] == nos[tortoise]: break tortoise += 1 hare = (hare + 1)%(len(nos)) cycleBeginsAt = tortoise result = [] for i in range(tortoise, len(nos)): if nos[i] not in result: result.append(nos[i]) else: break return result test_cases = open(sys.argv[1], 'r') for test in test_cases: nos = [int(i) for i in test.strip().split()] if len(nos) == 1: print nos[0] continue for i in range(1, len(nos)): tortoise = nos[i] hare = nos[(2*i)%len(nos)] if hare == tortoise: print ' '.join([str(x) for x in detectCycle(nos, i)]) break test_cases.close()

[ "gautamiyer@GAUTAMs-MacBook-Air.local" ]

gautamiyer@GAUTAMs-MacBook-Air.local

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daniel-reich/ubiquitous-fiesta

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def XOR(a, b): a = a ^ b b = a ^ b a = a ^ b return [a,b]

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

daniel.reich@danielreichs-MacBook-Pro.local

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Menglingyu2333/RaspberryDesktopFile

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import cv2 import numpy as np # 使用霍夫直线变换做直线检测，前提条件：边缘检测已经完成 __author__ = "boboa" # 标准霍夫线变换 def line_detection_demo(image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 50, 150, apertureSize=1) lines = cv2.HoughLines(edges, 1, np.pi/180, 200) # 函数将通过步长为1的半径和步长为π/180的角来搜索所有可能的直线 for line in lines: rho, theta = line[0] # line[0]存储的是点到直线的极径和极角，其中极角是弧度表示的 a = np.cos(theta) # theta是弧度 b = np.sin(theta) x0 = a * rho y0 = b * rho x1 = int(x0 + 1000 * (-b)) # 直线起点横坐标 y1 = int(y0 + 1000 * (a)) # 直线起点纵坐标 x2 = int(x0 - 1000 * (-b)) # 直线终点横坐标 y2 = int(y0 - 1000 * (a)) # 直线终点纵坐标 cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2) cv2.imshow("image_lines", image) # 统计概率霍夫线变换 def line_detect_possible_demo(image): gray = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY) edges = cv2.Canny(gray, 50, 150, apertureSize=3) # 函数将通过步长为1的半径和步长为π/180的角来搜索所有可能的直线 lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, minLineLength=50, maxLineGap=10) for line in lines: x1, y1, x2, y2 = line[0] cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2) cv2.imshow("line_detect_possible_demo", image) if __name__ == "__main__": img = cv2.imread("/home/pi/Pictures/road/test2.jpg") cv2.namedWindow("input image", cv2.WINDOW_AUTOSIZE) cv2.imshow("input image", img) line_detect_possible_demo(img) cv2.waitKey(0) cv2.destroyAllWindows()

[ "1171766563@qq.com" ]

1171766563@qq.com

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gnoeykeCG4001/sb3analyzer_references

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# Comments regarding module # # ## Imports #from sb3objects import sb3block_inputNfield import sys sys.path.append('/__SB3Analyzer/sb3objects') from sb3objects import sb3project ## Globals ## Define ## Helper functions ## Class declaration class SB3Analyzer: # Constructor # score variables deadCode_BlockList = [] def __init__(self, Proj): self.proj = Proj # Class Methods def get_self(self): return self def getDeadCode_BlockList(self): self.mark_print() tList = self.proj.getTargetList() for t in tList: for indivBlock in t.get_blockList(): if not indivBlock.isReachable(): self.deadCode_BlockList.append([t.get_name(),indivBlock]) print("\n\n\ndead blocks = " + str(len(self.deadCode_BlockList))) def mark_print(self): self.proj.printProj()

[ "gnoeyke.code@gmail.com" ]

gnoeyke.code@gmail.com

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zmscgck/pythonexercise

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2020-08-13T10:13:43.007857

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# -*- coding: utf-8 -*- """ Created on Thu Aug 15 19:42:05 2019 @author: LZM """ import turtle hg=turtle.Turtle() def square(t,length): for i in range(4): t.fd(length) t.lt(90) print('请输入边的长度：') length=input() length=float(length) square(hg,length)

[ "lgfl@sina.com" ]

lgfl@sina.com

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/Scripts/utils.py

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ICESAT-2HackWeek/Floes-are-Swell

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refs/heads/master

2020-06-06T00:00:24.386553

2020-05-30T20:29:31

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#Import necesary modules #Use shorter names (np, pd, plt) instead of full (numpy, pandas, matplotlib.pylot) for convenience import numpy as np import pandas as pd import matplotlib.pyplot as plt import cartopy.crs as ccrs import pandas as pd import h5py import xarray as xr import numpy as np import pdb import numpy.ma as ma def getSnowandConverttoThickness(dF, snowDepthVar='snowDepth', snowDensityVar='snowDensity', outVar='iceThickness'): """ Grid using nearest neighbour the NESOSIM snow depths to the high-res ICESat-1 freeboard locations """ # Convert freeboard to thickness # Need to copy arrays or it will overwrite the pandas column! freeboardT=np.copy(dF['freeboard'].values) snowDepthT=np.copy(dF[snowDepthVar].values) snowDensityT=np.copy(dF[snowDensityVar].values) ice_thickness = freeboard_to_thickness(freeboardT, snowDepthT, snowDensityT) #print(ice_thickness) dF[outVar] = pd.Series(np.array(ice_thickness), index=dF.index) return dF def freeboard_to_thickness(freeboardT, snow_depthT, snow_densityT): """ Hydrostatic equilibrium equation to calculate sea ice thickness from freeboard and snow depth/density data Args: freeboardT (var): ice freeboard snow_depthT (var): snow depth snow_densityT (var): final snow density Returns: ice_thicknessT (var): ice thickness dereived using hydrostatic equilibrium """ # Define density values rho_w=1024. rho_i=925. #rho_s=300. # set snow to freeboard where it's bigger than freeboard. snow_depthT[snow_depthT>freeboardT]=freeboardT[snow_depthT>freeboardT] ice_thicknessT = (rho_w/(rho_w-rho_i))*freeboardT - ((rho_w-snow_densityT)/(rho_w-rho_i))*snow_depthT return ice_thicknessT def getWarrenData(dF, outSnowVar, outDensityVar='None'): """ Assign Warren1999 snow dept/density climatology to dataframe Added Args: dF (data frame): Pandas dataframe outSnowVar (string): name of Warren snow depth variable outDensityVar (string): name of Warren snow density variable Returns: dF (data frame): Pandas dataframe updated to include colocated Warren snow depth and density """ # Generate empty lists snowDepthW99s=ma.masked_all(np.size(dF['freeboard'].values)) if (outDensityVar!='None'): snowDensityW99s=ma.masked_all(np.size(dF['freeboard'].values)) # Loop over all freeboard values (rows) for x in range(np.size(dF['freeboard'].values)): #print(x, dF['lon'].iloc[x], dF['lat'].iloc[x], dF['month'].iloc[x]-1) # SUbtract 1 from month as warren index in fucntion starts at 0 snowDepthDayW99T, snowDensityW99T=WarrenClimatology(dF['lon'].iloc[x], dF['lat'].iloc[x], dF['month'].iloc[x]-1) # Append values to list snowDepthW99s[x]=snowDepthDayW99T if (outDensityVar!='None'): snowDensityW99s[x]=snowDensityW99T # Assign list to dataframe as a series dF[outSnowVar] = pd.Series(snowDepthW99s, index=dF.index) if (outDensityVar!='None'): dF[outDensityVar] = pd.Series(snowDensityW99s, index=dF.index) return dF def WarrenClimatology(lonT, latT, monthT): """ Get Warren1999 snow depth climatology Args: lonT (var): longitude latT (var): latitude monthT (var): month with the index starting at 0 Returns: Hs (var): Snow depth (m) rho_s (var): Snow density (kg/m^3) """ H_0 = [28.01, 30.28, 33.89, 36.8, 36.93, 36.59, 11.02, 4.64, 15.81, 22.66, 25.57, 26.67] a = [.127, .1056, .5486, .4046, .0214, .7021, .3008, .31, .2119, .3594, .1496, -0.1876] b = [-1.1833, -0.5908, -0.1996, -0.4005, -1.1795, -1.4819, -1.2591, -0.635, -1.0292, -1.3483, -1.4643, -1.4229] c = [-0.1164, -0.0263, 0.0280, 0.0256, -0.1076, -0.1195, -0.0811, -0.0655, -0.0868, -0.1063, -0.1409, -0.1413] d = [-0.0051, -0.0049, 0.0216, 0.0024, -0.0244, -0.0009, -0.0043, 0.0059, -0.0177, 0.0051, -0.0079, -0.0316] e = [0.0243, 0.0044, -0.0176, -0.0641, -0.0142, -0.0603, -0.0959, -0.0005, -0.0723, -0.0577, -0.0258, -0.0029] # Convert lat and lon into degrees of arc, +x axis along 0 degrees longitude and +y axis along 90E longitude x = (90.0 - latT)*np.cos(lonT * np.pi/180.0) y = (90.0 - latT)*np.sin(lonT*np.pi/180.0) Hs = H_0[monthT] + a[monthT]*x + b[monthT]*y + c[monthT]*x*y + (d[monthT]*x*x) + (e[monthT]*y*y) # Now get SWE, although this is not returned by the function H_0swe = [8.37, 9.43,10.74,11.67,11.8,12.48,4.01,1.08,3.84,6.24,7.54,8.0] aswe = [-0.027,0.0058,0.1618,0.0841,-0.0043,0.2084,0.097,0.0712,0.0393,0.1158,0.0567,-0.054] bswe = [-0.34,-0.1309,0.0276,-0.1328,-0.4284,-0.5739,-0.493,-0.145,-0.2107,-0.2803,-0.3201,-0.365] cswe = [-0.0319,0.0017,0.0213,0.0081,-0.038,-0.0468,-0.0333,-0.0155,-0.0182,-0.0215,-0.0284,-0.0362] dswe = [-0.0056,-0.0021,0.0076,-0.0003,-0.0071,-0.0023,-0.0026,0.0014,-0.0053,0.0015,-0.0032,-0.0112] eswe = [-0.0005,-0.0072,-0.0125,-0.0301,-0.0063,-0.0253,-0.0343,0,-0.019,-0.0176,-0.0129,-0.0035] swe = H_0swe[monthT] + aswe[monthT]*x + bswe[monthT]*y + cswe[monthT]*x*y + dswe[monthT]*x*x + eswe[monthT]*y*y # Density in kg/m^3 rho_s = 1000.*(swe/Hs) #print(ma.mean(rho_s)) # Could mask out bad regions (i.e. land) here if desired. # Hsw[where(region_maskG<9.6)]=np.nan # Hsw[where(region_maskG==14)]=np.nan # Hsw[where(region_maskG>15.5)]=np.nan # Could mask out bad regions (i.e. land) here if desired. #rho_s[where(region_maskG<9.6)]=np.nan #rho_s[where(region_maskG==14)]=np.nan #rho_s[where(region_maskG>15.5)]=np.nan # Convert snow depth to meters Hs=Hs/100. return Hs, rho_s def get_psnlatslons(data_path, res=25): # Get NSIDC polar stereographic grid data if (res==25): # 25 km grid mask_latf = open(data_path+'/psn25lats_v3.dat', 'rb') mask_lonf = open(data_path+'/psn25lons_v3.dat', 'rb') lats_mask = reshape(fromfile(file=mask_latf, dtype='<i4')/100000., [448, 304]) lons_mask = reshape(fromfile(file=mask_lonf, dtype='<i4')/100000., [448, 304]) elif (res==12): # 12.5 km grid mask_latf = open(data_path+'/psn12lats_v3.dat', 'rb') mask_lonf = open(data_path+'/psn12lons_v3.dat', 'rb') lats_mask = reshape(fromfile(file=mask_latf, dtype='<i4')/100000., [896, 608]) lons_mask = reshape(fromfile(file=mask_lonf, dtype='<i4')/100000., [896, 608]) elif (res==6): # 12.5 km grid mask_latf = open(data_path+'/psn06lats_v3.dat', 'rb') mask_lonf = open(data_path+'/psn06lons_v3.dat', 'rb') lats_mask = reshape(fromfile(file=mask_latf, dtype='<i4')/100000., [1792, 1216]) lons_mask = reshape(fromfile(file=mask_lonf, dtype='<i4')/100000., [1792, 1216]) return lats_mask, lons_mask def getNESOSIM(fileSnowT, dateStrT): """ Grab the NESOSIM data and pick the day from a given date string. Uses the xarray package (files were generated using xarray so works nicely).. Returns an xarray Dataset """ dN = xr.open_dataset(fileSnowT) # Get NESOSIM snow depth and density data for that date dNday = dN.sel(day=int(dateStrT)) # Provide additional mask variable mask = np.ones((dNday['longitude'].values.shape)).astype('int') mask[np.where((dNday['snowDepth']>0.02)&(dNday['snowDepth']<1)&(dNday['iceConc']>0.15)&np.isfinite(dNday['density']))]=0 dNday['mask'] = (('x', 'y'), mask) return dNday def assignRegionMask(dF, mapProj, ancDataPath='../Data/'): """ Grab the NSIDC region mask and add to dataframe as a new column # 1 non-region oceans # 2 Sea of Okhotsk and Japan # 3 Bering Sea # 4 Hudson Bay # 5 Gulf of St. Lawrence # 6 Baffin Bay/Davis Strait/Labrador Sea # 7 Greenland Sea # 8 Barents Seas # 9 Kara # 10 Laptev # 11 E. Siberian # 12 Chukchi # 13 Beaufort # 14 Canadian Archipelago # 15 Arctic Ocean # 20 Land # 21 Coast Args: dF (data frame): original data frame mapProj (basemap instance): basemap map projection Returns: dF (data frame): data frame including ice type column (1 = multiyear ice, 0 = everything else) """ region_mask, xptsI, yptsI = get_region_mask_sect(ancDataPath, mapProj, xypts_return=1) xptsI=xptsI.flatten() yptsI=yptsI.flatten() region_mask=region_mask.flatten() #iceTypeGs=[] regionFlags=ma.masked_all((size(dF['freeboard'].values))) for x in range(size(dF['freeboard'].values)): # Find nearest ice type dist=sqrt((xptsI-dF['xpts'].iloc[x])**2+(yptsI-dF['ypts'].iloc[x])**2) index_min = np.argmin(dist) regionFlags[x]=int(region_mask[index_min]) # This is what I sometimes do but it appears slower in this case.. # I checked and they gave the same answers # iceTypeG2 = griddata((xpts_type, ypts_type), ice_typeT2, (dF['xpts'].iloc[x], dF['ypts'].iloc[x]), method='nearest') # print(iceTypeG) # iceTypeGs.append(iceTypeG) dF['region_flag'] = pd.Series(regionFlags, index=dF.index) return dF def get_region_mask_sect(datapath, mplot, xypts_return=0): """ Get NSIDC section mask data """ datatype='uint8' file_mask = datapath+'/sect_fixed_n.msk' # 1 non-region oceans # 2 Sea of Okhotsk and Japan # 3 Bering Sea # 4 Hudson Bay # 5 Gulf of St. Lawrence # 6 Baffin Bay/Davis Strait/Labrador Sea # 7 Greenland Sea # 8 Barents Seas # 9 Kara # 10 Laptev # 11 E. Siberian # 12 Chukchi # 13 Beaufort # 14 Canadian Archipelago # 15 Arctic Ocean # 20 Land # 21 Coast fd = open(file_mask, 'rb') region_mask = fromfile(file=fd, dtype=datatype) region_mask = reshape(region_mask, [448, 304]) #xpts, ypts = mplot(lons_mask, lats_mask) if (xypts_return==1): mask_latf = open(datapath+'/psn25lats_v3.dat', 'rb') mask_lonf = open(datapath+'/psn25lons_v3.dat', 'rb') lats_mask = reshape(fromfile(file=mask_latf, dtype='<i4')/100000., [448, 304]) lons_mask = reshape(fromfile(file=mask_lonf, dtype='<i4')/100000., [448, 304]) xpts, ypts = mplot(lons_mask, lats_mask) return region_mask, xpts, ypts else: return region_mask def getProcessedATL10ShotdataNCDF(dataPathT, yearStr='2018', monStr='*', dayStr='*', fNum=-1, beamStr='gt1r', vars=[], smoothingWindow=0): """ Load ICESat-2 thickness data produced from the raw ATL10 segment data By Alek Petty (June 2019) """ print(dataPathT+'IS2ATL10*'+yearStr+monStr+dayStr+'*'+'_'+beamStr+'.nc') files=glob(dataPathT+'IS2ATL10*'+yearStr+monStr+dayStr+'*'+'_'+beamStr+'.nc') print('Number of files:', size(files)) #testFile = Dataset(files[0]) #print(testFile.variables.keys()) if (fNum>-0.5): if (size(vars)>0): IS2dataAll= xr.open_dataset(files[fNum], engine='h5netcdf', data_vars=vars) else: IS2dataAll= xr.open_dataset(files[fNum], engine='h5netcdf') else: # apparently autoclose assumed so no longer need to include the True flag if (size(vars)>0): IS2dataAll= xr.open_mfdataset(dataPathT+'/IS2ATL10*'+yearStr+monStr+dayStr+'*'+'_'+beamStr+'.nc', engine='h5netcdf', data_vars=vars, parallel=True) else: IS2dataAll= xr.open_mfdataset(dataPathT+'/IS2ATL10*'+yearStr+monStr+dayStr+'*'+'_'+beamStr+'.nc', engine='h5netcdf', parallel=True) #IS2dataAll = pd.read_pickle(files[0]) print(IS2dataAll.info) #IS2dataAll=IS2dataAll[vars] #print(IS2dataAll.info) if (smoothingWindow>0): # If we want to smooth the datasets seg_length=IS2dataAll['seg_length'] seg_weightedvarR=seg_length.rolling(index=smoothingWindow, center=True).mean() seg_weightedvar=seg_weightedvarR[int(smoothingWindow/2):-int(smoothingWindow/2):smoothingWindow] # print(seg_weightedvar) seg_weightedvars=[] ds = seg_weightedvar.to_dataset(name = 'seg_length') #seg_weightedvars.append(seg_weightedvar) # Skip the first one as that's always (should be) the seg_length for var in vars[1:]: print('Coarsening'+var+'...') varIS2=IS2dataAll[var] seg_weightedvarR=varIS2*seg_length.rolling(index=smoothingWindow, center=True).sum()/seg_length.rolling(index=smoothingWindow, center=True).sum() seg_weightedvar=seg_weightedvarR[int(smoothingWindow/2):-int(smoothingWindow/2):smoothingWindow] #print(seg_weightedvar) ds[var] = seg_weightedvar #seg_weightedvars.append(seg_weightedvar) print('Coarsened var') #Merge the coarsened arrays #seg_weightedvarsM=xr.merge(seg_weightedvars) ds=ds.reset_index('index', drop=True) #print('Rechunking...') #ds=ds.chunk(2000) #print('Rechunked') print(ds) return ds else: return IS2dataAll def getNesosimDates(dF, snowPathT): """ Get dates from NESOSIM files""" # This will come from the distinct rows of the IS-1 data eventually, # but for now the data only span a day or two, so not much change in snow depth.. dayS=dF['day'].iloc[0] monthS=dF['month'].iloc[0] monthF=dF['month'].iloc[-1] yearS=dF['year'].iloc[0] dateStr= getDate(dF['year'].iloc[0], dF['month'].iloc[0], dF['day'].iloc[0]) print ('Date:', yearS, monthS, dayS) #print (dateStr) #print (dF['year'].iloc[-1], dF['month'].iloc[-1], dF['day'].iloc[-1]) # Find the right NESOSIM data file based on the freeboard dates fileNESOSIM = glob(snowPathT+'*'+str(yearS)+'-*'+'.nc')[0] #if (monthS>8): #fileNESOSIM = glob(snowPathT+'*'+str(yearS)+'-*'+'.nc')[0] #else: # fileNESOSIM = glob(snowPathT+'*'+str(yearS-1)+'-*'+'.nc')[0] if (monthS>5 & monthF==5): print ('WARNING! LACK OF SNOW DATA') return fileNESOSIM, dateStr def getDate(year, month, day): """ Get date string from year month and day""" return str(year)+'%02d' %month+'%02d' %day def gridNESOSIMtoFreeboard(dF, mapProj, fileSnow, dateStr, outSnowVar='snowDepthN', outDensityVar='snowDensityN', returnMap=0): """ Load relevant NESOSIM snow data file and assign to freeboard values Args: dF (data frame): Pandas dataframe mapProj (basemap instance): Basemap map projection fileSnow (string): NESOSIM file path dateStr (string): date string outSnowVar (string): Name of snow depth column outDensityVar (string): Name of snow density column Returns: dF (data frame): dataframe updated to include colocated NESOSIM (and dsitributed) snow data """ dN = xr.open_dataset(fileSnow) # Get NESOSIM snow depth and density data for that date # Should move this into the loop if there is a significant date cahgne in the freeboard data. # Not done this to improve processing speed. dNday = dN.sel(day=int(dateStr)) lonsN = array(dNday.longitude) latsN = array(dNday.latitude) xptsN, yptsN = mapProj(lonsN, latsN) # Get dates at start and end of freeboard file dateStrStart= getDate(dF['year'].iloc[0], dF['month'].iloc[0], dF['day'].iloc[0]) dateStrEnd= getDate(dF['year'].iloc[-1], dF['month'].iloc[-1], dF['day'].iloc[-1]) print('Check dates (should be within a day):', dateStr, dateStrStart, dateStrEnd) snowDepthNDay = array(dNday.snowDepth) snowDensityNDay = array(dNday.density) iceConcNDay = array(dNday.iceConc) # Remove data where snow depths less than 0 (masked). # Might need to chek if I need to apply any other masks here. mask=where((snowDepthNDay>0.01)&(snowDepthNDay<1)&(iceConcNDay>0.01)&np.isfinite(snowDensityNDay)) snowDepthNDay = snowDepthNDay[mask] snowDensityNDay = snowDensityNDay[mask] xptsNDay = xptsN[mask] yptsNDay = yptsN[mask] # Load into array, sppeds up later computation and may aid parallelization freeboardsT=dF['freeboard'].values xptsT=dF['xpts'].values yptsT=dF['ypts'].values # I think it's better to declare array now so memory is allocated before the loop? snowDepthGISs=ma.masked_all(size(freeboardsT)) snowDensityGISs=ma.masked_all(size(freeboardsT)) #snowDepthDists=ma.masked_all(size(freeboardsT)) #for x in prange(size(freeboardsT)): for x in range(size(freeboardsT)): # Could embed the NESOSIM dates here # Use nearest neighbor to find snow depth at IS2 point #snowDepthGISs[x] = griddata((xptsDay, yptsDay), snowDepthDay, (dF['xpts'].iloc[x], dF['ypts'].iloc[x]), method='nearest') #snowDensityGISs[x] = griddata((xptsDay, yptsDay), densityDay, (dF['xpts'].iloc[x], dF['ypts'].iloc[x]), method='nearest') # Think this is the much faster way to find nearest neighbor! dist = sqrt((xptsNDay-xptsT[x])**2+(yptsNDay-yptsT[x])**2) index_min = np.argmin(dist) snowDepthGISs[x]=snowDepthNDay[index_min] snowDensityGISs[x]=snowDensityNDay[index_min] #print(snowDepthNDay[index_min], densityNDay[index_min]) dF[outSnowVar] = pd.Series(snowDepthGISs, index=dF.index) dF[outDensityVar] = pd.Series(snowDensityGISs, index=dF.index) # SNOW REDISTRIBUTION #for x in range(size(freeboardsT)): # Find the mean freebaord in this vicinitiy # ICESat-1 has a shot every 172 m, so around 600 shots = 100 km # meanFreeboard = ma.mean(freeboardsT[x-300:x+300]) # snowDepthDists[x] = snowDistribution(snowDepthGISs[x], freeboardsT[x], meanFreeboard) #dF[outSnowVar+'dist'] = pd.Series(snowDepthDists, index=dF.index) #print ('Snow depth (m): ', snowDepthGIS) #print ('Snow density (kg/m3): ', snowDensityGIS) #print ('Snow depth (m): ', snowDepthDists) if (returnMap==1): return dF, xptsN, yptsN, dNday, else: return dF def bindataSegment(x, y, z, seg, xG, yG, binsize=0.01, retbin=True, retloc=True): """ Place unevenly spaced 2D data on a grid by 2D binning (nearest neighbor interpolation) and weight using the IS2 segment lengths. Parameters ---------- x : ndarray (1D) The idependent data x-axis of the grid. y : ndarray (1D) The idependent data y-axis of the grid. z : ndarray (1D) The dependent data in the form z = f(x,y). seg : ndarray (1D) The segment length of the data points in the form z = seg(x,y). binsize : scalar, optional The full width and height of each bin on the grid. If each bin is a cube, then this is the x and y dimension. This is the step in both directions, x and y. Defaults to 0.01. retbin : boolean, optional Function returns `bins` variable (see below for description) if set to True. Defaults to True. retloc : boolean, optional Function returns `wherebins` variable (see below for description) if set to True. Defaults to True. Returns ------- grid : ndarray (2D) The evenly gridded data. The value of each cell is the median value of the contents of the bin. bins : ndarray (2D) A grid the same shape as `grid`, except the value of each cell is the number of points in that bin. Returns only if `retbin` is set to True. wherebin : list (2D) A 2D list the same shape as `grid` and `bins` where each cell contains the indicies of `z` which contain the values stored in the particular bin. Revisions --------- 2010-07-11 ccampo Initial version """ # get extrema values. xmin, xmax = xG.min(), xG.max() ymin, ymax = yG.min(), yG.max() # make coordinate arrays. xi = xG[0] yi = yG[:, 0] #np.arange(ymin, ymax+binsize, binsize) xi, yi = np.meshgrid(xi,yi) # make the grid. grid = np.zeros(xi.shape, dtype=x.dtype) nrow, ncol = grid.shape if retbin: bins = np.copy(grid) # create list in same shape as grid to store indices if retloc: wherebin = np.copy(grid) wherebin = wherebin.tolist() # fill in the grid. for row in prange(nrow): for col in prange(ncol): xc = xi[row, col] # x coordinate. yc = yi[row, col] # y coordinate. # find the position that xc and yc correspond to. posx = np.abs(x - xc) posy = np.abs(y - yc) ibin = np.logical_and(posx < binsize/2., posy < binsize/2.) ind = np.where(ibin == True)[0] # fill the bin. bin = z[ibin] segbin = seg[ibin] if retloc: wherebin[row][col] = ind if retbin: bins[row, col] = bin.size if bin.size != 0: binvalseg = np.sum(bin*segbin)/np.sum(segbin) grid[row, col] = binvalseg else: grid[row, col] = np.nan # fill empty bins with nans. # return the grid if retbin: if retloc: return grid, bins, wherebin else: return grid, bins else: if retloc: return grid, wherebin else: return grid def convert_GPS_time(GPS_Time, OFFSET=0.0): """ convert_GPS_time.py (10/2017) Return the calendar date and time for given GPS time. Written by Tyler Sutterley Based on Tiffany Summerscales's PHP conversion algorithm https://www.andrews.edu/~tzs/timeconv/timealgorithm.html INPUTS: GPS_Time: GPS time (standard = seconds since January 6, 1980 at 00:00) OUTPUTS: month: Number of the desired month (1 = January, ..., 12 = December). day: Number of day of the month. year: Number of the desired year. hour: hour of the day minute: minute of the hour second: second (and fractions of a second) of the minute. OPTIONS: OFFSET: number of seconds to offset each GPS time PYTHON DEPENDENCIES: numpy: Scientific Computing Tools For Python (http://www.numpy.org) PROGRAM DEPENDENCIES: convert_julian.py: convert Julian dates into calendar dates UPDATE HISTORY: Updated 10/2017: added leap second from midnight 2016-12-31 Written 04/2016 """ #-- PURPOSE: convert from GPS time to calendar dates #-- convert from standard GPS time to UNIX time accounting for leap seconds #-- and adding the specified offset to GPS_Time UNIX_Time = convert_GPS_to_UNIX(np.array(GPS_Time) + OFFSET) #-- calculate Julian date from UNIX time and convert into calendar dates #-- UNIX time: seconds from 1970-01-01 00:00:00 UTC julian_date = (UNIX_Time/86400.0) + 2440587.500000 cal_date = convert_julian(julian_date) #-- include UNIX times in output cal_date['UNIX'] = UNIX_Time #-- return the calendar dates and UNIX time return cal_date def convert_julian(JD, ASTYPE=None, FORMAT='dict'): #-- convert to array if only a single value was imported # Written and provided by Tyler Sutterley if (np.ndim(JD) == 0): JD = np.array([JD]) SINGLE_VALUE = True else: SINGLE_VALUE = False JDO = np.floor(JD + 0.5) C = np.zeros_like(JD) #-- calculate C for dates before and after the switch to Gregorian IGREG = 2299161.0 ind1, = np.nonzero(JDO < IGREG) C[ind1] = JDO[ind1] + 1524.0 ind2, = np.nonzero(JDO >= IGREG) B = np.floor((JDO[ind2] - 1867216.25)/36524.25) C[ind2] = JDO[ind2] + B - np.floor(B/4.0) + 1525.0 #-- calculate coefficients for date conversion D = np.floor((C - 122.1)/365.25) E = np.floor((365.0 * D) + np.floor(D/4.0)) F = np.floor((C - E)/30.6001) #-- calculate day, month, year and hour DAY = np.floor(C - E + 0.5) - np.floor(30.6001*F) MONTH = F - 1.0 - 12.0*np.floor(F/14.0) YEAR = D - 4715.0 - np.floor((7.0+MONTH)/10.0) HOUR = np.floor(24.0*(JD + 0.5 - JDO)) #-- calculate minute and second G = (JD + 0.5 - JDO) - HOUR/24.0 MINUTE = np.floor(G*1440.0) SECOND = (G - MINUTE/1440.0) * 86400.0 #-- convert all variables to output type (from float) if ASTYPE is not None: YEAR = YEAR.astype(ASTYPE) MONTH = MONTH.astype(ASTYPE) DAY = DAY.astype(ASTYPE) HOUR = HOUR.astype(ASTYPE) MINUTE = MINUTE.astype(ASTYPE) SECOND = SECOND.astype(ASTYPE) #-- if only a single value was imported initially: remove singleton dims if SINGLE_VALUE: YEAR = YEAR.item(0) MONTH = MONTH.item(0) DAY = DAY.item(0) HOUR = HOUR.item(0) MINUTE = MINUTE.item(0) SECOND = SECOND.item(0) #-- return date variables in output format (default python dictionary) if (FORMAT == 'dict'): return dict(year=YEAR, month=MONTH, day=DAY, hour=HOUR, minute=MINUTE, second=SECOND) elif (FORMAT == 'tuple'): return (YEAR, MONTH, DAY, HOUR, MINUTE, SECOND) elif (FORMAT == 'zip'): return zip(YEAR, MONTH, DAY, HOUR, MINUTE, SECOND) def get_leaps(): #-- PURPOSE: Define GPS leap seconds # Written and provided by Tyler Sutterley leaps = [46828800, 78364801, 109900802, 173059203, 252028804, 315187205, 346723206, 393984007, 425520008, 457056009, 504489610, 551750411, 599184012, 820108813, 914803214, 1025136015, 1119744016, 1167264017] return leaps def is_leap(GPS_Time): #-- PURPOSE: Test to see if any GPS seconds are leap seconds # Written and provided by Tyler Sutterley leaps = get_leaps() Flag = np.zeros_like(GPS_Time, dtype=np.bool) for leap in leaps: count = np.count_nonzero(np.floor(GPS_Time) == leap) if (count > 0): indices, = np.nonzero(np.floor(GPS_Time) == leap) Flag[indices] = True return Flag def count_leaps(GPS_Time): #-- PURPOSE: Count number of leap seconds that have passed for each GPS time # Written and provided by Tyler Sutterley leaps = get_leaps() #-- number of leap seconds prior to GPS_Time n_leaps = np.zeros_like(GPS_Time, dtype=np.uint) for i,leap in enumerate(leaps): count = np.count_nonzero(GPS_Time >= leap) if (count > 0): indices, = np.nonzero(GPS_Time >= leap) # print(indices) # pdb.set_trace() n_leaps[indices] += 1 return n_leaps def convert_UNIX_to_GPS(UNIX_Time): #-- PURPOSE: Convert UNIX Time to GPS Time #-- calculate offsets for UNIX times that occur during leap seconds offset = np.zeros_like(UNIX_Time) count = np.count_nonzero((UNIX_Time % 1) != 0) if (count > 0): indices, = np.nonzero((UNIX_Time % 1) != 0) UNIX_Time[indices] -= 0.5 offset[indices] = 1.0 #-- convert UNIX_Time to GPS without taking into account leap seconds #-- (UNIX epoch: Jan 1, 1970 00:00:00, GPS epoch: Jan 6, 1980 00:00:00) GPS_Time = UNIX_Time - 315964800 leaps = get_leaps() #-- calculate number of leap seconds prior to GPS_Time n_leaps = np.zeros_like(GPS_Time, dtype=np.uint) for i,leap in enumerate(leaps): count = np.count_nonzero(GPS_Time >= (leap - i)) if (count > 0): indices, = np.nonzero(GPS_Time >= (leap - i)) n_leaps[indices] += 1 #-- take into account leap seconds and offsets GPS_Time += n_leaps + offset return GPS_Time def convert_GPS_to_UNIX(GPS_Time): #-- PURPOSE: Convert GPS Time to UNIX Time #-- convert GPS_Time to UNIX without taking into account leap seconds #-- (UNIX epoch: Jan 1, 1970 00:00:00, GPS epoch: Jan 6, 1980 00:00:00) UNIX_Time = GPS_Time + 315964800 #-- number of leap seconds prior to GPS_Time n_leaps = count_leaps(GPS_Time) UNIX_Time -= n_leaps #-- check if GPS Time is leap second Flag = is_leap(GPS_Time) if Flag.any(): #-- for leap seconds: add a half second offset indices, = np.nonzero(Flag) UNIX_Time[indices] += 0.5 return UNIX_Time

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############################################################################### # # Tests for XlsxWriter. # # Copyright (c), 2013-2021, John McNamara, jmcnamara@cpan.org # from ..excel_comparison_test import ExcelComparisonTest from ...workbook import Workbook class TestCompareXLSXFiles(ExcelComparisonTest): """ Test file created by XlsxWriter against a file created by Excel. """ def setUp(self): self.set_filename('chart_format07.xlsx') def test_create_file(self): """Test the creation of an XlsxWriter file with chart formatting.""" workbook = Workbook(self.got_filename) worksheet = workbook.add_worksheet() chart = workbook.add_chart({'type': 'line'}) chart.axis_ids = [46163840, 46175360] data = [ [1, 2, 3, 4, 5], [2, 4, 6, 8, 10], [3, 6, 9, 12, 15], ] worksheet.write_column('A1', data[0]) worksheet.write_column('B1', data[1]) worksheet.write_column('C1', data[2]) chart.add_series({ 'categories': '=Sheet1!$A$1:$A$5', 'values': '=Sheet1!$B$1:$B$5', 'marker': { 'type': 'square', 'size': 5, 'line': {'color': 'yellow'}, 'fill': {'color': 'red'}, }, }) chart.add_series({ 'categories': '=Sheet1!$A$1:$A$5', 'values': '=Sheet1!$C$1:$C$5', }) worksheet.insert_chart('E9', chart) workbook.close() self.assertExcelEqual()

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#!/usr/bin/env python3 """Simplo wbd setup file. This is the main setup for simple wbd. To manually install this module run: $ pip install . For development to keep track of the changes in the module and to include development and test dependecies run: $ pip install --editable .[dev,test] """ from setuptools import setup def get_description(): with open("README.rst") as f: return f.read() if __name__ == "__main__": setup( name="simple_wbd", version="0.5.1", license="MIT", author="Miha Zidar", author_email="zidarsk8@gmail.com", description=("A simple python interface for World Bank Data Indicator " "and Climate APIs"), long_description=get_description(), url="https://github.com/zidarsk8/simple_wbd", download_url="https://github.com/zidarsk8/simple_wbd/tarball/0.5.1", packages=["simple_wbd"], provides=["simple_wbd"], install_requires=[ "pycountry" ], extras_require={ "dev": [ "pylint" ], "test": [ "codecov", "coverage", "mock", "nose", "vcrpy", ], }, test_suite="tests", keywords = [ "World Bank Data", "indicator api", "climate api", ], classifiers=[ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering", ], )

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/lists/models.py

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odisei369/superlists

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from django.db import models class List(models.Model): pass class Item(models.Model): text = models.TextField(default='') list = models.ForeignKey(List, default=None) # Create your models here.

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/programmers/Level 3/표 편집/solve.py

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def solution(n, k, cmd): ## 행의 갯수 n ## 처음에 선택된 행의 위치 k ## 명령어들이 담긴 문자열 배열 ## U X => X칸 위로 ## D X => X칸 아래로 ## C => 선택된 행을 삭제 후 바로 아래 행 선택 단 마지막 행일경우 삭제 후 자신의 위 인덱스를 선택 ## Z => 최근에 삭제한 행을 복구, 단 선택된 행은 그대로 answer = ['O' for _ in range (n)] pt = k-1 backup = [] for cmd in cmd: ## U 명령어 if cmd[0] == 'U': move = int(cmd.split(' ')[-1]) while(move != 0 and pt != -1) : if answer[pt] == 'O': move -=1 pt -= 1 else: pt -=1 ## 범위를 벗어났을경우 0으로 바꿔줌 if pt < 0: pt = 0 ## D 명령어 elif cmd[0] == 'D': move = int(cmd.split(' ')[-1]) while(move != 0 and pt !=n) : if answer[pt] == 'O': move -=1 pt += 1 else: pt +=1 ## 범위를 벗어났을경우 n으로 바꿔줌 if pt > n-1: pt = n-1 ## C 명렁어 elif cmd[0] == 'C': backup.append(pt) answer[pt] = 'X' while(answer[pt] != 'O'): pt += 1 elif cmd[0] == 'Z': answer[backup.pop()] = 'O' print(cmd,answer,sep = '\n',end = '\n\n') return ''.join(answer) print(solution(8,2,["D 2","C","U 3","C","D 4","C","U 2","Z","Z","U 1","C"]))

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# Copyright (c) 2006 Carnegie Mellon University # # You may copy and modify this freely under the same terms as # Sphinx-III """Sphinx-III acoustic models. This module provides a class which wraps a set of acoustic models, as used by SphinxTrain, Sphinx-III, and PocketSphinx. It provides functions for computing Gaussian mixture densities for acoustic feature vectors. """ __author__ = "David Huggins-Daines <dhuggins@cs.cmu.edu>" __version__ = "$Revision$" import s3gau import s3mixw import s3tmat import s3mdef import s3file import sys import os import numpy WORSTSCORE = -100000 class S3Model(object): def __init__(self, path=None, topn=4): self.topn = topn self.mwfloor = 1e-5 self.varfloor = 1e-5 if path != None: self.read(path) def read(self, path): self.mdef = s3mdef.open(os.path.join(path, "mdef")) self.mean = s3gau.open(os.path.join(path, "means")) self.var = s3gau.open(os.path.join(path, "variances")) self.mixw = s3mixw.open(os.path.join(path, "mixture_weights")) self.tmat = s3tmat.open(os.path.join(path, "transition_matrices")) # Normalize transition matrices and mixture weights for t in range(0, len(self.tmat)): self.tmat[t] = (self.tmat[t].T / self.tmat[t].sum(1)).T for t in range(0, len(self.mixw)): self.mixw[t] = (self.mixw[t].T / self.mixw[t].sum(1)).T.clip(self.mwfloor, 1.0) # Floor variances and precompute normalizing and inverse variance terms self.norm = numpy.empty((len(self.var), len(self.var[0]), len(self.var[0][0])),'d') for m,mgau in enumerate(self.var): for f,feat in enumerate(mgau): fvar = feat.clip(self.varfloor, numpy.inf) # log of 1/sqrt(2*pi**N * det(var)) det = numpy.log(fvar).sum(1) lrd = -0.5 * (det + 2 * numpy.pi * feat.shape[1]) self.norm[m,f] = lrd # "Invert" variances feat[:] = (1 / (fvar * 2)) # Construct senone to codebook mapping if os.access(os.path.join(path, "senmgau"), os.F_OK): self.senmgau = s3file.S3File(os.path.join(path, "senmgau")).read1d() elif len(self.mean) == 1: self.senmgau = numpy.ones(len(self.mixw)) else: self.senmgau = numpy.arange(0, len(self.mixw)) self.senscr = numpy.ones(len(self.mixw)) * WORSTSCORE def cb_compute(self, mgau, feat, obs): "Compute codebook #mgau feature #feat for obs" mean = self.mean[mgau][feat] ivar = self.var[mgau][feat] norm = self.norm[mgau][feat] diff = obs - mean dist = (diff * ivar * diff).sum(1) return norm - dist def senone_compute(self, senones, *features): """Compute senone scores for given list of senones and a frame of acoustic features""" cbs = {} self.senscr[:] = WORSTSCORE for s in senones: m = self.senmgau[s] if not m in cbs: cbs[m] = [self.cb_compute(m, f, features[f]) for f in range(0,len(self.mean[m]))] score = 0 for f, vec in enumerate(features): # Compute densities and scale by mixture weights d = cbs[m][f] + numpy.log(self.mixw[s,f]) # Take top-N densities d = d.take(d.argsort()[-self.topn:]) # Multiply into output score score += numpy.log(numpy.exp(d).sum()) self.senscr[s] = score return numpy.exp(self.senscr - self.senscr.max())

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from distutils.core import setup setup( name = 'tkCharacter', packages = ['tkCharacter'], version = '0.1', license='MIT', description = 'Allows for effortless creation of playable and AI 2d characters in python games', long_description= "", author = 'Don Charles - Lambert', author_email = 'your.email@domain.com', url = 'https://github.com/DonCharlesLambert/tkCharacter', download_url = 'https://github.com/DonCharlesLambert/tkCharacter/archive/0.1.tar.gz', # I explain this later on keywords = ['tkinter', 'games', 'characters'], install_requires=[ # I get to this in a second 'tkinter', ], classifiers=[ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'Topic :: Software Development :: Build Tools', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', ], )

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#idea bank idea = input("What is your new idea: ") nr = 0 file=open("ideabank.txt","a") file.write(idea + "\n") file.close() file=open("ideabank.txt","r") print("Your ideabank:") for line in file: nr = nr + 1 print(str(nr) + ". " + line) file.close()

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# coding: cp949 feel = "호감" # feel = "" hit_on_count = 0 while feel and hit_on_count <10 : hit_on_count = hit_on_count + 1 print ("%d번 데이트 신청합니다, " %hit_on_count) if(hit_on_count == 10): print("고백할 때가 다가 왔네요, ") continue feel= input("현재 그녀에 대한 당신의 감정은 어떤가요?") if(feel == "비호감"): print("그럼 단념하세요") break

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class ExpiredJWT(RuntimeError): pass class InvalidJWT(RuntimeError): pass

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import astropy import astroplan import numpy as np from matplotlib import pyplot as plt import pandas as pd import os import time import pickle as pk from time import gmtime from astropy.coordinates import solar_system_ephemeris from astropy.time import Time as tm from astropy.coordinates import EarthLocation as eloc from astroplan import Observer from mylib import * from multiprocessing import Pool ln = 30 t = tm(time.time(), format='unix') location = eloc.from_geodetic(lon=41.385728, lat=2.055923) basedir = 'Analema/' deldir = 'delAnalema/' obs = Observer(longitude=41.385728, latitude=2.055923) rise = obs.moon_rise_time(time=t) println(ln) print(rise, rise.to_datetime()) println(ln) photopath = [basedir + x for x in os.listdir(basedir)] println(ln) print(photopath[:10]) println(ln) println(ln) bigarr = [[x, ln, deldir, basedir, obs] for x in photopath] pool = Pool(3) pool.map(main, bigarr)

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#! /usr/bin/env python3 # Ffair Gwyddoniaeth 2019 # Arbrawf Rafik Harrington # Mesur CO2 mewn sgarff # Mwy ar https://github.com/skuzzymiglet/ffair-gwyddoniaeth-2019 # Rhaglen fach i gael cipolwg ar eich data from bokeh.plotting import figure, output_file, show import csv data = open("DATA.CSV", "r") # Data o'r synhwyrydd reader = csv.reader(data, delimiter=',') # I ddarllen y data # Cyfeirnodau pwyntiau ar y llinell x = [] # Amser y = [] # CO2 # Ychwanegu'r holl data i'r echelinau for row in reader: y.append(row[0]) x.append(row[1]) data.close() # Cau'r ffeil output_file("overview.html") # Y ffeil i blotio iddo p = figure(title="CO2 ppm", x_axis_label="Time", y_axis_label="CO2 ppm", y_range=[0, int(max(y))]) # Creu y graff p.line(x, y, line_width=1, color="blue") # Plotio'r llinell show(p) # Dangos y graff

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#!/usr/bin/env python import urllib from bs4 import BeautifulSoup import re websit='http://www.heibanke.com/lesson/crawler_ex00/' html = urllib.urlopen('http://www.heibanke.com/lesson/crawler_ex00/') bs_obj = BeautifulSoup(html, "html.parser") #a_list = bs_obj.findAll("a") #a_list = bs_obj.findAll("a", href=re.compile("baike\.baidu\.com\w?")) #for aa in a_list: # if not aa.find("img"): # if aa.attrs.get('href'): # print aa.text ,aa.attrs['href'] a_list = bs_obj.findAll("h3") #print a_list text = a_list[0].text.encode('utf-8') print(text) ma = re.match(u"([\u4e00-\u9fa5]+)", text) print(ma.groups())

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# !/usr/bin/env python # -*- coding: utf-8 -*- """ Given an m x n matrix of non-negative integers representing the height of each unit cell in a continent, the "Pacific ocean" touches the left and top edges of the matrix and the "Atlantic ocean" touches the right and bottom edges. Water can only flow in four directions (up, down, left, or right) from a cell to another one with height equal or lower. Find the list of grid coordinates where water can flow to both the Pacific and Atlantic ocean. Note: The order of returned grid coordinates does not matter. Both m and n are less than 150. Example: Given the following 5x5 matrix: Pacific ~ ~ ~ ~ ~ ~ 1 2 2 3 (5) * ~ 3 2 3 (4) (4) * ~ 2 4 (5) 3 1 * ~ (6) (7) 1 4 5 * ~ (5) 1 1 2 4 * * * * * * Atlantic Return: [[0, 4], [1, 3], [1, 4], [2, 2], [3, 0], [3, 1], [4, 0]] (positions with parentheses in above matrix). """ """ ==================== body ==================== """ class Solution: def pacificAtlantic(self, matrix): """ :type matrix: List[List[int]] :rtype: List[List[int]] """ """ ==================== body ==================== """

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#Loops members = ["Irene", "Emmanuel", "Abraham", "Micheal", "Abraham"] counter = 0 for i in members: if i=="Abraham": counter = counter + 1 print(counter)

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"""empty message Revision ID: fcd07d45ca01 Revises: 2fd5a06e09b0 Create Date: 2019-01-11 05:39:20.505585 """ from alembic import op import sqlalchemy as sa import sqlalchemy_utils # revision identifiers, used by Alembic. revision = 'fcd07d45ca01' down_revision = '2fd5a06e09b0' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('updates_log', sa.Column('task_id', sa.String(length=250), nullable=True)) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_column('updates_log', 'task_id') # ### end Alembic commands ###

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from flask_restful import Resource, reqparse from freshontheboat.models.users import User from freshontheboat.models.forumpostlikes import ForumPostLikes class GetNewForumLikes(Resource): def __init__(self): self.reqparse = reqparse.RequestParser() self.reqparse.add_argument('forum_profile_id', type = int, required=True) super(GetNewForumLikes, self).__init__() def get(self): args = self.reqparse.parse_args() results = ForumPostLikes.query.filter_by(forum_profile_id = args['forum_profile_id']).all() jsonDictionary = [] for result in results: username = User.query.get(result.user_who_liked).username dictionaryResult = {'id' : result.id, 'user_who_liked': username, 'forum_profile_id': result.forum_profile_id, 'likes': result.likes, 'dislikes': result.dislikes} jsonDictionary.append(dictionaryResult) response = {'status': 'successful', 'results' : jsonDictionary} return response

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#!/usr/bin/env python2.7 """Send JPEG image to tensorflow_model_server """ from __future__ import print_function from grpc.beta import implementations import tensorflow as tf import numpy as np import os from io import BytesIO import requests from PIL import Image from tensorflow_serving.apis import predict_pb2 from tensorflow_serving.apis import prediction_service_pb2 from object_detection.utils import visualization_utils as vis_util from object_detection.utils import label_map_util tf.app.flags.DEFINE_string('server', 'localhost:9000', 'PredictionService host:port') tf.app.flags.DEFINE_string('image', '', 'url to image in JPEG format') tf.app.flags.DEFINE_string('label_map_path', './pascal_label_map.pbtxt', 'path to label map path') tf.app.flags.DEFINE_string('save_path', './', 'save path for output image') tf.app.flags.DEFINE_string('model_name', 'serving', 'model name') tf.app.flags.DEFINE_string('signature_name', 'serving_default', 'signature name') tf.app.flags.DEFINE_string('num_classes', '1', 'num classes') FLAGS = tf.app.flags.FLAGS def load_image_into_numpy_array(image): (im_width, im_height) = image.size return np.array(image.getdata()).reshape( (im_height, im_width, 3)).astype(np.uint8) def main(_): host, port = FLAGS.server.split(':') channel = implementations.insecure_channel(host, int(port)) stub = prediction_service_pb2.beta_create_PredictionService_stub(channel) # Send request response = requests.get(FLAGS.image, stream=True) if response.status_code == 200: request = predict_pb2.PredictRequest() request.model_spec.name = FLAGS.model_name request.model_spec.signature_name = FLAGS.signature_name request.inputs['inputs'].CopyFrom( tf.contrib.util.make_tensor_proto(response.content, shape=[1])) result = stub.Predict(request, 10.0) # 10 secs timeout image = Image.open(BytesIO(response.content)) image_np = load_image_into_numpy_array(image) boxes = np.array(result.outputs['detection_boxes'].float_val).reshape( result.outputs['detection_boxes'].tensor_shape.dim[0].size, result.outputs['detection_boxes'].tensor_shape.dim[1].size, result.outputs['detection_boxes'].tensor_shape.dim[2].size ) classes = np.array(result.outputs['detection_classes'].float_val) scores = np.array(result.outputs['detection_scores'].float_val) label_map = label_map_util.load_labelmap(FLAGS.label_map_path) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=FLAGS.num_classes, use_display_name=True) category_index = label_map_util.create_category_index(categories) vis_util.visualize_boxes_and_labels_on_image_array( image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8) vis_util.save_image_array_as_png(image_np, FLAGS.save_path+"/output-"+FLAGS.image.split('/')[-1]) if __name__ == '__main__': tf.app.run()

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from django.db import models from django.utils import timezone from django.contrib.auth.models import User from django.urls import reverse ''' ------------------------------------------------------- O próprio Django faz as tratativas com o banco de dados Assim posso mudar o banco sem precisar alterar o código As tabelas são tratadas como Classes E os campos como atributos destas classes ------------------------------------------------------- ''' class Post(models.Model): title = models.CharField(max_length=100) content = models.TextField() # texto ilimitado ''' opções de data automática, mas não permite alteração ---------------------------------------------------- date_posted = models.DateTimeField(auto_now=True) # data de criação/update date_posted = models.DateTimeField(auto_now_add=True) # data de criação ''' date_posted = models.DateTimeField(default=timezone.now) # now sem travar alterações # agora informo que o cmapo author será chave 1 para N para isso # informo ForeignKey e passo 2 parâmetros: User - para pegar dessa tabela # e on_delete para integridade (se deletar o usuário, deleta seus posts) author = models.ForeignKey(User, on_delete=models.CASCADE) def __str__(self): return self.title def get_absolute_url(self): return reverse('post-detail', kwargs={'pk': self.pk})

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"""Droste Draw By Al Sweigart al@inventwithpython.com A Python module for making recursive drawings (aka Droste effect) with the built-in turtle module.""" __version__ = '0.2.1' import turtle, math MAX_FUNCTION_CALLS = 10000 # Stop recursion after this many function calls. MAX_ITERATION = 400 # Stop recursion after this iteration. MIN_SIZE = 1 # Stop recursion if size is less than this. # NOTE: In general, don't use absolute coordinate functions (like turtle.goto(), turtle.xcor(), turtle.ycor(), # turtle.setheading()) in your draw functions because they might not work when the heading angle is not 0. def drawSquare(size, extraData=None): """Draw a square where `size` is the length of each side.""" # Move the turtle to the top-right corner before drawing: turtle.penup() turtle.forward(size // 2) # Move to the right edge. turtle.left(90) # Turn to face upwards. turtle.forward(size // 2) # Move to the top-right corner. turtle.left(180) # Turn around to face downwards. turtle.pendown() # Draw the four sides of a square: for i in range(4): turtle.forward(size) turtle.right(90) def drawTriangle(size, extraData=None): """Draw an equilateral triangle where `size` is the length of each side.""" # Move the turtle to the top of the equilateral triangle: height = (size * math.sqrt(3)) / 2 turtle.penup() turtle.left(90) # Turn to face upwards. turtle.forward(height * (2/3)) # Move to the top corner. turtle.right(150) # Turn to face the bottom-right corner. turtle.pendown() # Draw the three sides of the triangle: for i in range(3): turtle.forward(size) turtle.right(120) def drawFilledSquare(size, extraData=None): """Draw a solid, filled-in square where `size` is the length of each side. The extraData dictionary can have a key 'colors' whose value is a list of "color strings" that the turtle module recognizes, e.g. 'red', 'black', etc. The first color string in the list is used for the first iteration, the second for the second, and so on. When you run out of colors for later iterations, the first color is used again.""" # Move the turtle to the top-right corner before drawing: turtle.penup() turtle.forward(size // 2) # Move to the right edge. turtle.left(90) # Turn to face upwards. turtle.forward(size // 2) # Move to the top-right corner. turtle.left(180) # Turn around to face downwards. turtle.pendown() # The extra data is a tuple of (fillcolor, pencolor) values: if extraData is not None: iteration = extraData['_iteration'] - 1 # -1 because iteration starts at 1, not 0. turtle.fillcolor(extraData['colors'][iteration % len(extraData['colors'])]) turtle.pencolor(extraData['colors'][iteration % len(extraData['colors'])]) # Draw the four sides of a square: turtle.begin_fill() for i in range(4): turtle.forward(size) turtle.right(90) turtle.end_fill() def drawFilledDiamond(size, extraData=None): # Move to the right corner before drawing: turtle.penup() turtle.forward(math.sqrt(size ** 2 / 2)) turtle.right(135) turtle.pendown() # The extra data is a tuple of (fillcolor, pencolor) values: if extraData is not None: iteration = extraData['_iteration'] - 1 # -1 because iteration starts at 1, not 0. turtle.fillcolor(extraData['colors'][iteration % len(extraData['colors'])]) turtle.pencolor(extraData['colors'][iteration % len(extraData['colors'])]) # Draw a square: turtle.begin_fill() for i in range(4): turtle.forward(size) turtle.right(90) turtle.end_fill() def drosteDraw(drawFunction, size, recursiveDrawings, extraData=None): # NOTE: The current heading of the turtle is considered to be the # rightward or positive-x direction. # Provide default values for extraData: if extraData is None: extraData = {} if '_iteration' not in extraData: extraData['_iteration'] = 1 # The first iteration is 1, not 0. if '_maxIteration' not in extraData: extraData['_maxIteration'] = MAX_ITERATION if '_maxFunctionCalls' not in extraData: extraData['_maxFunctionCalls'] = MAX_FUNCTION_CALLS if '_minSize' not in extraData: extraData['_minSize'] = MIN_SIZE requiredNumCalls = len(recursiveDrawings) ** extraData['_iteration'] if extraData['_iteration'] > extraData['_maxIteration'] or \ requiredNumCalls > extraData['_maxFunctionCalls'] or \ size < extraData['_minSize']: return # BASE CASE # Remember the original starting coordinates and heading. origX = turtle.xcor() origY = turtle.ycor() origHeading = turtle.heading() turtle.pendown() drawFunction(size, extraData) turtle.penup() # RECURSIVE CASE # Do each of the recursive drawings: for i, recursiveDrawing in enumerate(recursiveDrawings): # Provide default values for the recursiveDrawing dictionary: if 'x' not in recursiveDrawing: recursiveDrawing['x'] = 0 if 'y' not in recursiveDrawing: recursiveDrawing['y'] = 0 if 'size' not in recursiveDrawing: recursiveDrawing['size'] = 1.0 if 'angle' not in recursiveDrawing: recursiveDrawing['angle'] = 0 # Move the turtle into position for the next recursive drawing: turtle.goto(origX, origY) turtle.setheading(origHeading + recursiveDrawing['angle']) turtle.forward(size * recursiveDrawing['x']) turtle.left(90) turtle.forward(size * recursiveDrawing['y']) turtle.right(90) # Increment the iteration count for the next level of recursion: extraData['_iteration'] += 1 drosteDraw(drawFunction, int(size * recursiveDrawing['size']), recursiveDrawings, extraData) # Decrement the iteration count when done with that recursion: extraData['_iteration'] -= 1 # Display any buffered drawing commands on the screen: if extraData['_iteration'] == 1: turtle.update() _DEMO_NUM = 0 def demo(x=None, y=None): global _DEMO_NUM turtle.reset() turtle.tracer(20000, 0) # Increase the first argument to speed up the drawing. turtle.hideturtle() if _DEMO_NUM == 0: # Recursively draw smaller squares in the center: drosteDraw(drawSquare, 350, [{'size': 0.8}]) elif _DEMO_NUM == 1: # Recursively draw smaller squares going off to the right: drosteDraw(drawSquare, 350, [{'size': 0.8, 'x': 0.20}]) elif _DEMO_NUM == 2: # Recursively draw smaller squares that go up at an angle: drosteDraw(drawSquare, 350, [{'size': 0.8, 'y': 0.20, 'angle': 15}]) elif _DEMO_NUM == 3: # Recursively draw smaller triangle in the center: drosteDraw(drawTriangle, 350, [{'size': 0.8}]) elif _DEMO_NUM == 4: # Recursively draw smaller triangle going off to the right: drosteDraw(drawTriangle, 350, [{'size': 0.8, 'x': 0.20}]) elif _DEMO_NUM == 5: # Recursively draw smaller triangle that go up at an angle: drosteDraw(drawTriangle, 350, [{'size': 0.8, 'y': 0.20, 'angle': 15}]) elif _DEMO_NUM == 6: # Recursively draw a spirograph of squares: drosteDraw(drawSquare, 150, [{'angle': 10, 'x': 0.1}]) elif _DEMO_NUM == 7: # Recursively draw a smaller square in each of the four corners of the parent square: drosteDraw(drawSquare, 350, [{'size': 0.5, 'x': -0.5, 'y': 0.5}, {'size': 0.5, 'x': 0.5, 'y': 0.5}, {'size': 0.5, 'x': -0.5, 'y': -0.5}, {'size': 0.5, 'x': 0.5, 'y': -0.5}]) elif _DEMO_NUM == 8: # Recursively draw smaller filled squares in the center, alternating red and black: drosteDraw(drawFilledSquare, 350, [{'size': 0.8}], {'colors': ['red', 'black']}) elif _DEMO_NUM == 9: # Recursively draw a smaller filled square in each of the four corners of the parent square with red and black: drosteDraw(drawFilledSquare, 350, [{'size': 0.5, 'x': -0.5, 'y': 0.5}, {'size': 0.5, 'x': 0.5, 'y': 0.5}, {'size': 0.5, 'x': -0.5, 'y': -0.5}, {'size': 0.5, 'x': 0.5, 'y': -0.5}], {'colors': ['red', 'black']}) elif _DEMO_NUM == 10: # Recursively draw a smaller filled square in each of the four corners of the parent square with white and black: drosteDraw(drawFilledSquare, 350, [{'size': 0.5, 'x': -0.5, 'y': 0.5}, {'size': 0.5, 'x': 0.5, 'y': 0.5}, {'size': 0.5, 'x': -0.5, 'y': -0.5}, {'size': 0.5, 'x': 0.5, 'y': -0.5}], {'colors': ['white', 'black']}) elif _DEMO_NUM == 11: # Recursively draw a smaller filled square in each of the four corners of the parent square: drosteDraw(drawFilledDiamond, 350, [{'size': 0.5, 'x': -0.45, 'y': 0.45}, {'size': 0.5, 'x': 0.45, 'y': 0.45}, {'size': 0.5, 'x': -0.45, 'y': -0.45}, {'size': 0.5, 'x': 0.45, 'y': -0.45}], {'colors': ['green', 'yellow']}) elif _DEMO_NUM == 12: # Draw the sierpinsky triangle: drosteDraw(drawTriangle, 600, [{'size': 0.5, 'x': 0, 'y': math.sqrt(3) / 6, 'angle': 0}, {'size': 0.5, 'x': 0, 'y': math.sqrt(3) / 6, 'angle': 120}, {'size': 0.5, 'x': 0, 'y': math.sqrt(3) / 6, 'angle': 240}]) elif _DEMO_NUM == 13: # Draw a recursive "glider" shape from Conway's Game of Life: drosteDraw(drawSquare, 600, [{'size': 0.333, 'x': 0, 'y': 0.333}, {'size': 0.333, 'x': 0.333, 'y': 0}, {'size': 0.333, 'x': 0.333, 'y': -0.333}, {'size': 0.333, 'x': 0, 'y': -0.333}, {'size': 0.333, 'x': -0.333, 'y': -0.333}]) turtle.exitonclick() _DEMO_NUM += 1 def main(): # Start the demo: turtle.onscreenclick(demo) demo() turtle.mainloop() if __name__ == '__main__': main()

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import torch from torch import nn from torch.autograd import Variable import random class Seq2Seq(nn.Module): def __init__(self, encoder, decoder, output_max_len, vocab_size): super().__init__() self.encoder = encoder self.decoder = decoder self.vocab_size = vocab_size self.output_max_len=output_max_len def forward(self, src, trg, train_in_len, teacher_rate, train=True): #train_in, train_out, train_in_len, teacher_rate=0.50, train=True #src = [src len, batch size] #trg = [trg len, batch size] #teacher_forcing_ratio is probability to use teacher forcing #e.g. if teacher_forcing_ratio is 0.75 we use teacher forcing 75% of the time batch_size = src.shape[0] trg = trg.permute(1, 0) trg_len = trg.shape[0] #outputs = Variable(torch.zeros(self.output_max_len-1, batch_size, self.vocab_size), requires_grad=True) #tensor to store decoder outputs outputs = torch.zeros(self.output_max_len-1, batch_size, self.vocab_size).cuda()#.to(torch.float64) #encoder_outputs is all hidden states of the input sequence, back and forwards #hidden is the final forward and backward hidden states, passed through a linear layer encoder_outputs, hidden, cell = self.encoder(src, train_in_len, train) #first input to the decoder is the <sos> tokens input = Variable(self.one_hot(trg[0].data)).to(torch.int64) prev_c = Variable(torch.zeros(encoder_outputs.shape[1], encoder_outputs.shape[2]), requires_grad=True).cuda() #b,f for t in range(0, self.output_max_len-1): #insert input token embedding, previous hidden state and all encoder hidden states #receive output tensor (predictions) and new hidden state output, hidden, cell, prev_att_weights= self.decoder(input, hidden, cell, encoder_outputs, train, prev_c) #place predictions in a tensor holding predictions for each token outputs[t] = output #decide if we are going to use teacher forcing or not teacher_force = random.random() < teacher_rate #if teacher forcing, use actual next token as next input #if not, use predicted token input = Variable(self.one_hot(trg[t+1].data)).to(torch.int64) if train and teacher_force else output.data return outputs def one_hot(self, src): # src: torch.cuda.LongTensor ones = torch.eye(self.vocab_size).cuda() return ones.index_select(0, src)

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# Copyright (c) 2014 The Bitcoin Core developers # Copyright (c) 2014-2015 The Dash developers # Copyright (c) 2015-2017 The Corallium developers # Distributed under the MIT/X11 software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # # Helpful routines for regression testing # # Add python-bitcoinrpc to module search path: import os import sys sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "python-bitcoinrpc")) from decimal import Decimal, ROUND_DOWN import json import random import shutil import subprocess import time import re from bitcoinrpc.authproxy import AuthServiceProxy, JSONRPCException from util import * def p2p_port(n): return 11000 + n + os.getpid()%999 def rpc_port(n): return 12000 + n + os.getpid()%999 def check_json_precision(): """Make sure json library being used does not lose precision converting BTC values""" n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def sync_blocks(rpc_connections): """ Wait until everybody has the same block count """ while True: counts = [ x.getblockcount() for x in rpc_connections ] if counts == [ counts[0] ]*len(counts): break time.sleep(1) def sync_mempools(rpc_connections): """ Wait until everybody has the same transactions in their memory pools """ while True: pool = set(rpc_connections[0].getrawmempool()) num_match = 1 for i in range(1, len(rpc_connections)): if set(rpc_connections[i].getrawmempool()) == pool: num_match = num_match+1 if num_match == len(rpc_connections): break time.sleep(1) bitcoind_processes = {} def initialize_datadir(dirname, n): datadir = os.path.join(dirname, "node"+str(n)) if not os.path.isdir(datadir): os.makedirs(datadir) with open(os.path.join(datadir, "corallium.conf"), 'w') as f: f.write("regtest=1\n"); f.write("rpcuser=rt\n"); f.write("rpcpassword=rt\n"); f.write("port="+str(p2p_port(n))+"\n"); f.write("rpcport="+str(rpc_port(n))+"\n"); return datadir def initialize_chain(test_dir): """ Create (or copy from cache) a 200-block-long chain and 4 wallets. coralliumd and corallium-cli must be in search path. """ if not os.path.isdir(os.path.join("cache", "node0")): devnull = open("/dev/null", "w+") # Create cache directories, run coralliumd: for i in range(4): datadir=initialize_datadir("cache", i) args = [ os.getenv("BITCOIND", "coralliumd"), "-keypool=1", "-datadir="+datadir, "-discover=0" ] if i > 0: args.append("-connect=127.0.0.1:"+str(p2p_port(0))) bitcoind_processes[i] = subprocess.Popen(args) subprocess.check_call([ os.getenv("BITCOINCLI", "corallium-cli"), "-datadir="+datadir, "-rpcwait", "getblockcount"], stdout=devnull) devnull.close() rpcs = [] for i in range(4): try: url = "http://rt:rt@127.0.0.1:%d"%(rpc_port(i),) rpcs.append(AuthServiceProxy(url)) except: sys.stderr.write("Error connecting to "+url+"\n") sys.exit(1) # Create a 200-block-long chain; each of the 4 nodes # gets 25 mature blocks and 25 immature. # blocks are created with timestamps 10 minutes apart, starting # at 1 Jan 2014 block_time = 1388534400 for i in range(2): for peer in range(4): for j in range(25): set_node_times(rpcs, block_time) rpcs[peer].setgenerate(True, 1) block_time += 10*60 # Must sync before next peer starts generating blocks sync_blocks(rpcs) # Shut them down, and clean up cache directories: stop_nodes(rpcs) wait_bitcoinds() for i in range(4): os.remove(log_filename("cache", i, "debug.log")) os.remove(log_filename("cache", i, "db.log")) os.remove(log_filename("cache", i, "peers.dat")) os.remove(log_filename("cache", i, "fee_estimates.dat")) for i in range(4): from_dir = os.path.join("cache", "node"+str(i)) to_dir = os.path.join(test_dir, "node"+str(i)) shutil.copytree(from_dir, to_dir) initialize_datadir(test_dir, i) # Overwrite port/rpcport in corallium.conf def initialize_chain_clean(test_dir, num_nodes): """ Create an empty blockchain and num_nodes wallets. Useful if a test case wants complete control over initialization. """ for i in range(num_nodes): datadir=initialize_datadir(test_dir, i) def _rpchost_to_args(rpchost): '''Convert optional IP:port spec to rpcconnect/rpcport args''' if rpchost is None: return [] match = re.match('(\[[0-9a-fA-f:]+\]|[^:]+)(?::([0-9]+))?$', rpchost) if not match: raise ValueError('Invalid RPC host spec ' + rpchost) rpcconnect = match.group(1) rpcport = match.group(2) if rpcconnect.startswith('['): # remove IPv6 [...] wrapping rpcconnect = rpcconnect[1:-1] rv = ['-rpcconnect=' + rpcconnect] if rpcport: rv += ['-rpcport=' + rpcport] return rv def start_node(i, dirname, extra_args=None, rpchost=None): """ Start a coralliumd and return RPC connection to it """ datadir = os.path.join(dirname, "node"+str(i)) args = [ os.getenv("BITCOIND", "coralliumd"), "-datadir="+datadir, "-keypool=1", "-discover=0", "-rest" ] if extra_args is not None: args.extend(extra_args) bitcoind_processes[i] = subprocess.Popen(args) devnull = open("/dev/null", "w+") subprocess.check_call([ os.getenv("BITCOINCLI", "corallium-cli"), "-datadir="+datadir] + _rpchost_to_args(rpchost) + ["-rpcwait", "getblockcount"], stdout=devnull) devnull.close() url = "http://rt:rt@%s:%d" % (rpchost or '127.0.0.1', rpc_port(i)) proxy = AuthServiceProxy(url) proxy.url = url # store URL on proxy for info return proxy def start_nodes(num_nodes, dirname, extra_args=None, rpchost=None): """ Start multiple coralliumds, return RPC connections to them """ if extra_args is None: extra_args = [ None for i in range(num_nodes) ] return [ start_node(i, dirname, extra_args[i], rpchost) for i in range(num_nodes) ] def log_filename(dirname, n_node, logname): return os.path.join(dirname, "node"+str(n_node), "regtest", logname) def stop_node(node, i): node.stop() bitcoind_processes[i].wait() del bitcoind_processes[i] def stop_nodes(nodes): for node in nodes: node.stop() del nodes[:] # Emptying array closes connections as a side effect def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def wait_bitcoinds(): # Wait for all bitcoinds to cleanly exit for bitcoind in bitcoind_processes.values(): bitcoind.wait() bitcoind_processes.clear() def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:"+str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") # poll until version handshake complete to avoid race conditions # with transaction relaying while any(peer['version'] == 0 for peer in from_connection.getpeerinfo()): time.sleep(0.1) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def find_output(node, txid, amount): """ Return index to output of txid with value amount Raises exception if there is none. """ txdata = node.getrawtransaction(txid, 1) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found"%(txid,str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): """ Return a random set of unspent txouts that are enough to pay amount_needed """ assert(confirmations_required >=0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({ "txid" : t["txid"], "vout" : t["vout"], "address" : t["address"] } ) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d"%(amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): """ Create change output(s), return them """ outputs = {} amount = amount_out+fee change = amount_in - amount if change > amount*2: # Create an extra change output to break up big inputs change_address = from_node.getnewaddress() # Split change in two, being careful of rounding: outputs[change_address] = Decimal(change/2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def send_zeropri_transaction(from_node, to_node, amount, fee): """ Create&broadcast a zero-priority transaction. Returns (txid, hex-encoded-txdata) Ensures transaction is zero-priority by first creating a send-to-self, then using it's output """ # Create a send-to-self with confirmed inputs: self_address = from_node.getnewaddress() (total_in, inputs) = gather_inputs(from_node, amount+fee*2) outputs = make_change(from_node, total_in, amount+fee, fee) outputs[self_address] = float(amount+fee) self_rawtx = from_node.createrawtransaction(inputs, outputs) self_signresult = from_node.signrawtransaction(self_rawtx) self_txid = from_node.sendrawtransaction(self_signresult["hex"], True) vout = find_output(from_node, self_txid, amount+fee) # Now immediately spend the output to create a 1-input, 1-output # zero-priority transaction: inputs = [ { "txid" : self_txid, "vout" : vout } ] outputs = { to_node.getnewaddress() : float(amount) } rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"]) def random_zeropri_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random zero-priority transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (txid, txhex) = send_zeropri_transaction(from_node, to_node, amount, fee) return (txid, txhex, fee) def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (total_in, inputs) = gather_inputs(from_node, amount+fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def assert_equal(thing1, thing2): if thing1 != thing2: raise AssertionError("%s != %s"%(str(thing1),str(thing2))) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s"%(str(thing1),str(thing2))) def assert_raises(exc, fun, *args, **kwds): try: fun(*args, **kwds) except exc: pass except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised")

[ "tri282@gmail.com" ]

tri282@gmail.com

c8e7deb719db05703d93fe764028941e8baac0db

25768f25147068914d7a88db6765411e33a9f708

/django_itchat-master/wechat/app/views_bak.py

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[]

no_license

QzeroQ/python

d00739ca4630b2e84c65d726c9e0ad09a60f8a83

23e7fd4b3867bcced17febfadaf6094965707420

refs/heads/master

2022-04-15T11:15:53.468501

2020-03-26T16:24:21

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from django.http import JsonResponse from django.shortcuts import render, redirect from django.urls import reverse from wxpy import * import uuid from wechat.settings import BASE_DIR, MEDIA_ROOT import os, time, base64, json, random from .models import * from django.contrib.auth.models import User as uuu from django.contrib.auth import authenticate, login, logout from django.contrib.auth.decorators import login_required # Create your views here. qr_path = os.path.join(BASE_DIR, 'wechat/') def path_check(qr_path): if os.path.exists(qr_path): os.remove(qr_path) class WXBot(object): def __init__(self, request): self.qr_s = '' self.qr_path = qr_path + request.user.username + '.png' def callback(self, uuid, status, qrcode): f = open(self.qr_path, 'wb') f.write(qrcode) f.close() def get_qr(self): bot = Bot(cache_path=False, qr_callback=self.callback) return bot # 获取登录二维码 @login_required def qr_img(request): if request.method == 'POST': for i in range(5): time.sleep(2) if os.path.exists(qr_path + request.user.username + '.png'): res = { 'img': str(base64.b64encode(open(qr_path + request.user.username + '.png', 'rb').read()), encoding='utf8') } return JsonResponse(res) continue # 获取数据库里的群列表和组列表 @login_required def get_group_list(request): uuid_str = str(uuid.uuid4()) group_list = groups_name.objects.all() set_list = grouping.objects.all() set_res = [] for i in set_list: set_name = [] obj = i.group.all() for j in obj: set_name.append(j.name) d = { 'id': i.id, 'set_name': i.group_name, 'group_names': ','.join(set_name), 'num': len(obj) } set_res.append(d) return render(request, 'app/group_list.html', {'data': group_list, 'set': set_res, 'uuid': uuid_str}) # 添加群 @login_required def create_group(request): if request.method == 'POST': d = { 'count': 0, 'name_list': [] } a = WXBot(request) bot = a.get_qr() groups = bot.groups() print(groups) d['count'] = len(groups) for i in groups: d['name_list'].append(i.nick_name) groups_name.objects.get_or_create( name=i.nick_name, users_num=len(i.members), owner=i.owner.name ) bot.logout() path_check(qr_path=qr_path + request.user.username + '.png') return JsonResponse(d) else: return render(request, 'app/create_group.html') # 删除群 @login_required def delete_group(request): if request.method == 'POST': id = request.POST.get('id', '') typ = request.POST.get('typ', 'one') # 默认单个删除 if id: if typ == 'one': u = groups_name.objects.filter(id=int(id)) if u: u[0].delete() return JsonResponse( { 'msg': '删除成功！', 'status': True } ) else: return JsonResponse( { 'msg': '该群不存在！', 'status': False } ) else: id_list = json.loads(id) for i in id_list: u = groups_name.objects.filter(id=int(i)) u[0].delete() return JsonResponse( { 'msg': '删除成功！', 'status': True } ) else: return JsonResponse( { 'msg': '参数错误！', 'status': False } ) # 添加组 def add_set(request): if request.method == 'POST': set_name = request.POST.get('set_name', '') group_id_list = request.POST.get('group_name', '') if set_name and group_id_list: g_l = [int(x) for x in json.loads(group_id_list)] if grouping.objects.filter(group_name=set_name): return JsonResponse( { 'status': False, 'msg': '群名重复，请检查！' } ) grouping_obj = grouping.objects.create(group_name=set_name) grouping_obj.group.set(groups_name.objects.filter(id__in=g_l)) return JsonResponse( { 'status': True, 'msg': '添加成功！' } ) else: return JsonResponse( { 'status': False, 'msg': '参数有误！' } ) group_list = groups_name.objects.all() return render(request, 'app/add_set.html', {'data': group_list}) # 删除组 def delete_set(request): if request.method == 'POST': id = request.POST.get('id', '') typ = request.POST.get('typ', 'one') if id: if typ == 'one': obj = grouping.objects.filter(id=int(id)) if obj: obj[0].delete() return JsonResponse( { 'status': True, 'msg': '删除成功！' } ) else: return JsonResponse( { 'status': False, 'msg': '组不存在！' } ) else: id_list = json.loads(id) for i in id_list: obj = grouping.objects.filter(id=int(i)) obj[0].delete() return JsonResponse( { 'status': True, 'msg': '删除成功！' } ) else: return JsonResponse( { 'status': False, 'msg': '参数有误！' } ) # 编辑组 def edit_set(request): if request.method == 'POST': set_id = request.POST.get('set_id', '') set_name = request.POST.get('set_name', '') group_id_list = request.POST.get('group_name', '') if set_name and group_id_list and set_id: g_l = [int(x) for x in json.loads(group_id_list)] if grouping.objects.exclude(id=int(set_id)).filter(group_name=set_name): return JsonResponse( { 'status': False, 'msg': '群名重复，请检查！' } ) grouping_obj = grouping.objects.get(id=int(set_id)) grouping_obj.group.set(groups_name.objects.filter(id__in=g_l)) return JsonResponse( { 'status': True, 'msg': '添加成功！' } ) else: return JsonResponse( { 'status': False, 'msg': '参数有误！' } ) set_id = request.GET.get('set_id', '') group_name = [] weixuan = [] grouping_name = '' if set_id: obj = grouping.objects.filter(id=int(set_id)) if obj: grouping_name = obj[0].group_name group_name = obj[0].group.all() id_list = [] for i in group_name: id_list.append(i.id) weixuan = groups_name.objects.exclude(id__in=id_list) return render(request, 'app/edit_set.html', {'group_name': group_name, 'weixuan': weixuan, 'grouping_name': grouping_name, 'set_id': set_id}) # 组发送 def send_set(request): uuid_str = str(uuid.uuid4()) set_list = grouping.objects.all() set_res = [] for i in set_list: set_name = [] obj = i.group.all() for j in obj: set_name.append(j.name) d = { 'id': i.id, 'set_name': i.group_name, 'group_names': ','.join(set_name), 'num': len(obj) } set_res.append(d) return render(request, 'app/set_send.html', {'set': set_res, 'uuid': uuid_str}) # 提交审核添加任务 @login_required def add_task(request): if request.method == 'POST': t = request.POST.get('type', 'group') # 默认是组发群发为 set content = request.POST.get('content', '') group_name = request.POST.get('group_name', '') send_type = request.POST.get('send_type', 'text') # 默认是文字 uuid = request.POST.get('uuid') # 区分任务 if send_type == 'text': if content and groups_name: if t == 'group': task.objects.create( message=content, group_name=group_name, typ='群发' ) return JsonResponse({'status': True, 'msg': '添加审核成功，请等待管理员审核！'}) else: set_id = json.loads(group_name) l = [] for i in set_id: for j in grouping.objects.get(id=int(i)).group.all(): d = {"gname": j.name, "owner": j.owner} l.append(d) print(l) task.objects.create( message=content, group_name=json.dumps(l, ensure_ascii=False), typ='组发' ) return JsonResponse({'status': True, 'msg': '添加审核成功，请等待管理员审核！'}) else: return JsonResponse({'status': False, 'msg': '参数错误！'}) else: if groups_name: if send_type == 'file': message_type = '文件' elif send_type == 'video': message_type = '视频' else: message_type = '图片' f_obj = file.objects.filter(uuid_str=uuid) if f_obj: if t == 'group': task.objects.create( message=uuid, uuid_str=uuid, group_name=group_name, typ='群发', message_type=message_type ) return JsonResponse({'status': True, 'msg': '添加审核成功，请等待管理员审核！'}) else: set_id = json.loads(group_name) l = [] for i in set_id: for j in grouping.objects.get(id=int(i)).group.all(): d = {"gname": j.name, "owner": j.owner} l.append(d) print(l) task.objects.create( message=uuid, uuid_str=uuid, group_name=json.dumps(l, ensure_ascii=False), typ='组发', message_type=message_type ) return JsonResponse({'status': True, 'msg': '添加审核成功，请等待管理员审核！'}) else: return JsonResponse({'status': False, 'msg': '尚未上传附件，请先上传附件后再提交！'}) else: return JsonResponse({'status': False, 'msg': '参数错误！'}) # 审核列表 @login_required def check_list(request): if request.method == 'POST': data = { 'msg': '', 'data': [], 'count': '', 'code': 0, } task_list = task.objects.all() for i in task_list: if i.message_type != '文字': file_path = file.objects.get(uuid_str=i.uuid_str).file_path f = file_path.split('==')[-1] fie =file_path.split('/')[-1] d = { 'id': i.id, 'message': f, 'message_type': i.message_type, 'group': i.group_name, 'status': i.status, 'typ': i.typ, 'date': i.date.strftime('%Y-%m-%d %H:%M:%S'), 'uuid': i.uuid_str, 'fie_yuanben': fie } else: d = { 'id': i.id, 'message': i.message, 'message_type': i.message_type, 'group': i.group_name, 'status': i.status, 'typ': i.typ, 'date': i.date.strftime('%Y-%m-%d %H:%M:%S'), 'uuid': i.uuid_str, } data['data'].append(d) return JsonResponse(data) return render(request, 'app/check_list.html') # 删除任务 @login_required def delete_task(request): if request.method == 'POST': id = request.POST.get('id', '') typ = request.POST.get('typ', 'one') if id: if typ == 'one': s = task.objects.filter(id=int(id)) if s: s[0].delete() return JsonResponse( { 'status': True, 'msg': '成功！' } ) else: return JsonResponse( { 'status': False, 'msg': '参数有误！' } ) else: id_list = json.loads(id) for i in id_list: s = task.objects.filter(id=int(i)) s[0].delete() return JsonResponse( { 'status': True, 'msg': '成功！' } ) else: return JsonResponse( { 'status': False, 'msg': '参数有误！' } ) # 审核 @login_required def check(request): if request.method == 'POST': group_name = request.POST.get('group_name', '') message = request.POST.get('message', '') id = request.POST.get('id', '') if group_name and message and id: group_name = json.loads(group_name) t = task.objects.get(id=int(id)) t.message = message t.status = '审核通过' t.save() return JsonResponse( { 'status': True, 'msg': '审核通过！' } ) else: return JsonResponse( { 'status': False, 'msg': '参数有误！' } ) return render(request, 'app/check.html') # 发送 @login_required def send(request): if request.method == 'POST': group_name = request.POST.get('group_name', '') message = request.POST.get('message', '') message_type = request.POST.get('message_type', '') id = request.POST.get('id', '') uuid = request.POST.get('uuid', '') if group_name and message and id: group_name = json.loads(group_name) a = WXBot(request) bot = a.get_qr() groups = bot.groups() for i in group_name: print(i) print(message) try: if message_type == '文字': # todo groups.search(i['gname'])[0].send(message) elif message_type == '文件': path = file.objects.get(uuid_str=uuid).file_path groups.search(i['gname'])[0].send_file(path) elif message_type == '视频': path = file.objects.get(uuid_str=uuid).file_path groups.search(i['gname'])[0].send_video(path) elif message_type == '图片': path = file.objects.get(uuid_str=uuid).file_path groups.search(i['gname'])[0].send_image(path) else: pass except: with open('error.log', 'a+') as f: f.write('发送失败：{0} >>>{1}\n'.format(i['gname'], message)) pass time.sleep(random.random()) bot.logout() path_check(qr_path=qr_path + request.user.username + '.png') t = task.objects.get(id=int(id)) t.status = '已发送' t.save() return JsonResponse( { 'status': True, 'msg': '发送成功！' } ) else: return JsonResponse( { 'status': False, 'msg': '参数有误！' } ) return render(request, 'app/send.html') # 登录 def do_login(request): if request.method == "POST": username = request.POST.get("username", "") password = request.POST.get("password", "") user = authenticate(username=username, password=password) if user and user.is_active: try: session_urls = [] role_name = [] role_list = User.objects.get(username=username).myuser.role.all() for role in role_list: role_name.append(role.role_name) permission_list = role.permission.all() for permission in permission_list: session_urls.append(permission.url) request.session['urls'] = json.dumps(session_urls) request.session['role_name'] = json.dumps(role_name) except: # 这块表示该用户没有设置任何角色 return render(request, 'login.html', {"err": "该账号异常，请联系管理员！"}) login(request, user) return redirect(reverse('get_group_list')) else: return render(request, 'login.html', {"err": "账号密码错误或被冻结，请联系管理员！"}) return render(request, 'login.html') def do_logout(request): logout(request) return redirect(reverse('do_login')) def forbidden(request): return render(request, 'forbidden.html') # 上传 def upload(request): res = { "code": 0, "msg": "上传成功", "data": { "src": "http://cdn.layui.com/123.jpg" } } now = str(datetime.now().strftime('%Y%m%d%H%M%S')) if request.method == 'POST': uuid = request.GET.get('uuid') f = request.FILES.get('file') file_name = now + '==' + f.name video_list = [ 'rm','rmvb','mpeg1-4','mov','mtv','dat','wmv','avi','3gp','amv','dmv','flv','mp3','mp4' ] for i in video_list: if i in file_name.lower(): res['code'] = -1 res['msg'] = '暂不支持视频文件！' return JsonResponse(res) path = os.path.join(MEDIA_ROOT, file_name) destination = open(path, 'wb+') # 打开特定的文件进行二进制的写操作 for chunk in f.chunks(): # 分块写入文件 destination.write(chunk) destination.close() f_obj = file.objects.get_or_create( uuid_str=uuid ) f_obj[0].file_path = path f_obj[0].save() res['msg'] = f.name + '，上传成功！' return JsonResponse(res)

[ "1223609881@qq.com" ]

1223609881@qq.com

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64f726483db2bae0c418026c47acc41501118e2f

/chat.py

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[]

no_license

aouataf-djillani/Simple-chatbot-with-python-and-flask-

ad91d6db25a657a243674d8874706f6738daab86

50d365e536c341a43d5bc9eca2dff1874955ff69

refs/heads/master

2023-09-03T00:46:55.234719

2021-11-22T07:52:24

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from flask import Flask, render_template, request from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer app = Flask(__name__) myBot=ChatBot(name='Aouataf', storage_adapter="chatterbot.storage.SQLStorageAdapter") """ greetings=["hi there!","hi","how are you doing?","fine","good", "great", "what's your name?","aouataf"] math1=["pythagorean theorem","a squared plus b squared equals c squared"] math2=["law of cosine","c**2= a**2+b**2-2*a*b*cos(gamma)"] list_trainer=ListTrainer(myBot) for item in (greetings,math1, math2): list_trainer.train(item) """ corpus_trainer=ChatterBotCorpusTrainer(myBot) corpus_trainer.train('chatterbot.corpus.english') @app.route("/") def home(): return render_template("index.html") @app.route("/get") def get_bot_response(): userText = request.args.get('msg') return str(myBot.get_response(userText)) if __name__ == "__main__": app.run()

[ "aouatefd@yahoo.com" ]

aouatefd@yahoo.com

e0d852a289aa3a8e3aca62072d98ba4f2cf26939

33524b5c049f934ce27fbf046db95799ac003385

/2018/Other/Urok_10_0_классы_объекты/teoriya_class_0.py

68f7a2ba67558f66e7e39854b191bc7d8ef21224

[]

no_license

mgbo/My_Exercise

07b5f696d383b3b160262c5978ad645b46244b70

53fb175836717493e2c813ecb45c5d5e9d28dd23

refs/heads/master

2022-12-24T14:11:02.271443

2020-10-04T04:44:38

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from math import pi class Circle: def __init__(self, x=0, y=0, r=0): self.x = x self.y = y self.r = r def __str__(self): return "({},{},{})".format(self.x,self.y,self.r) def read(self): self.x,self.y,self.r = map(int,input().split()) def area(self): a = pi*self.r * self.r return a def perimetr(self): return 2*pi*self.r def zoom(self, k): self.r *=k def is_crossed(self, c): # пересекается или нет окружность с окружностью с? d2 = (self.x - c.x)**2 + (self.y - c.y)**2 r2 =(self.r + c.r)**2 return d2 <=r2 c1 = Circle() c2 = Circle() ''' c1.r = 3 c2.r = 5 c2.x = 1 c2.y = 1 ''' c1.read() c2.read() print (c1) print (c2) ''' ans = c1.area() print (ans) '''

[ "mgbo433@gmail.com" ]

mgbo433@gmail.com

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/venv/lib/python2.7/site-packages/faker/name.py

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GavrilyukAG/TP_WEB

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from faker import frandom from faker import helper import random def first_name(): """""" return frandom.first_name() def last_name(): """""" return frandom.last_name() def find_name(): """""" r = random.randint(0,10) if r==0: return frandom.name_prefix() + " " + first_name() + " " + last_name() elif r==1: return first_name() + " " + last_name() + " " + frandom.name_suffix() return first_name() + " " + last_name()

[ "sashagavrilyuk97@gmail.com" ]

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import asyncio import shutil import pymatgen as pmg import numpy as np import pytest from dftfit.io.lammps import LammpsLocalDFTFITCalculator from dftfit.io.lammps_cython import LammpsCythonDFTFITCalculator from dftfit.cli.utils import load_filename from dftfit.potential import Potential @pytest.mark.pymatgen_lammps @pytest.mark.lammps_cython @pytest.mark.calculator def test_calculator_equivalency(structure): target_a = 4.1990858 s = structure('test_files/structure/MgO.cif') lattice = pmg.Lattice.from_parameters(target_a, target_a, target_a, 90, 90, 90) s.modify_lattice(lattice) assert np.all(np.isclose(s.lattice.abc, (target_a, target_a, target_a))) s = s * (2, 2, 2) assert len(s) == 64 base_directory = 'test_files/dftfit_calculators/' potential_schema = load_filename(base_directory + 'potential.yaml') potential_schema['spec']['charge']['Mg']['initial'] = 1.4 potential_schema['spec']['charge']['O']['initial'] = -1.4 potential = Potential(potential_schema) command = None if shutil.which('lammps'): command = 'lammps' elif shutil.which('lmp_serial'): command = 'lmp_serial' calculators = [ LammpsLocalDFTFITCalculator(structures=[s], potential=potential, command=command, num_workers=1), LammpsCythonDFTFITCalculator(structures=[s], potential=potential) ] loop = asyncio.get_event_loop() results = [] async def run(calc, potential): await calc.create() return await calc.submit(potential) for calc in calculators: results.append(loop.run_until_complete(run(calc, potential))) assert len(results) == 2 assert len(results[0]) == 1 assert len(results[1]) == 1 for r1, r2 in zip(*results): assert r1.structure == r2.structure assert abs(r1.energy - r2.energy) < 1e-4 assert np.all(np.isclose(r1.forces, r2.forces, atol=1e-8)) assert np.all(np.isclose(r1.stress, r2.stress, atol=1e-8))

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aishuo07/Flask-Bearer-authentication

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from flask import Flask,request,make_response,jsonify from flask_sqlalchemy import SQLAlchemy import uuid from werkzeug.security import generate_password_hash,check_password_hash import jwt import datetime from functools import wraps from oauthlib.oauth2.rfc6749 import tokens from oauthlib.oauth2 import Server application = app = Flask(__name__) app.config['SECRET_KEY']='secret' app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///Users2.db' app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True db = SQLAlchemy(app) class Users(db.Model): id = db.Column(db.Integer,primary_key=True) public_id = db.Column(db.String(50),unique=True) Full_name = db.Column(db.String(80)) User_password = db.Column(db.String(80)) bearer_token = db.Column(db.String(80), unique=True) def Token(f): @wraps(f) def decorated(*args,**kwargs): token=None try: auth = request.authorization if not auth.Token: return jsonify({'message': 'token missing1'}), 401 elif ('Token' in auth): token = auth.Bearer_Token except: if'Bearer-token' in request.headers: token=request.headers['Bearer-token'] if not token: return jsonify({'message': 'token missing','auth':auth}),401 try: current_user = Users.query.filter_by(bearer_token = token).first() except: return jsonify({'message': 'not a valid token'}), 401 return f(current_user,*args,**kwargs) return decorated @app.route('/',methods=['GET','POST']) @Token def Hello_world(current_user): return("Hello World") # # # @app.route('/create',methods=['POST']) # def Create_user(): # data = request.get_json() # hashed_password = generate_password_hash(data['password'],method='sha256') # new_user = Users(public_id= str(uuid.uuid4()),Full_name=data['name'],User_password =hashed_password,bearer_token=str(uuid.uuid4())) # db.session.add(new_user) # db.session.commit() # return 'User Created' # # @app.route('/login',) # def login(): # auth = request.authorization # if not auth or not auth.username or not auth.password: # return make_response("Not verified",401,{'WWW-Authenticate':'Basic realm="Login requtired!"'}) # user = Users.query.filter_by(Full_name=auth.username).first() # if not user: # return make_response("Not verified",401,{'WWW-Authenticate':'Basic realm="Login requtired!"'}) # if check_password_hash(user.User_password,auth.password): # token = user.bearer_token # # return jsonify({'token': token,'auth':auth}) # return make_response("Not verified",401,{'WWW-Authenticate':'Basic realm="Login requtired!"'}) if __name__ == '__main__': app.run(host='0.0.0.0',port=80,debug=True)

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sonerk46/proje2

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refs/heads/master

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from django.shortcuts import render from django.http import HttpResponse # Create your views here. def index(request): my_dict={'insert_me':"Now I am coming from first_app/index.html ! "} return render(request,'first_app/index.html',context=my_dict)

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Miserlou/ForkTube

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BROKER_HOST = "localhost" BROKER_PORT = 5672 BROKER_USER = "myuser" BROKER_PASSWORD = "mypassword" BROKER_VHOST = "myvhost" CELERY_RESULT_BACKEND = "amqp" CELERY_IMPORTS = ("tasks", )

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rich@anomos.info

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/Final-Project_Proposal-Assignment/OpenSource/gera_geometria.py

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abraaonascimento/GIS-Programming_and_Automation

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import random from shapely.geometry import Polygon, Point def ponto_unico(poligono): """ A função cria_ponto_aleatorio cria um unico ponto aleatorio dentro de um poligono Dados de entrada: latitute e longitude de cada vertice que constroe o poligono Dados de saida: um par de coordenadas (um ponto) gerada aleatoriamente dentro de um poligono """ # Recebe cada par de coordenadas do bounding box do poligono (coord_S_O, coord_S_L, coord_N_O, coord_N_L) = poligono.bounds # Gera ponto aleatorio dentro do bounding box do poligono while True: ponto_aleatorio = Point(random.uniform(coord_S_O, coord_N_O), random.uniform(coord_S_L, coord_N_L)) # Verifica se o ponto gerado esta CONTIDO no poligono if poligono.contains(ponto_aleatorio): # Se o ponto gerado estiver CONTIDO no poligono # Retorna o ponto gerado return ponto_aleatorio def multiplos_pontos(codigos_e_geometrias, codigos_e_populacao): """ A funcao cria_pontos_aleatorios cria pontos aleatorios dentro de um poligono Dados de entrada: 1) conjunto de dados (dicionario) que contem o codigo do setor censitario e os pares de coordenadas (vertices) da geometria do setor censitário; 2) conjunto de dados (dicionario) que contem o codigo do setor censitario e os registros da populacao nos setores censitários Dados de saida: lista de pares de coordenadas (pontos) aleatorios """ # Cria uma lista para guardar pontos pontos_aleatorios = [] # Para setor censitario for codigo in codigos_e_populacao: # Cria uma lista com a quantidade de habitantes no setor try: populacao = range(int(codigos_e_populacao[codigo])) except: populacao = range(0) # Para cada habitante no setor for pessoa in populacao: # Cria um ponto aleatorio dentro do setor ponto = ponto_unico(Polygon(codigos_e_geometrias[codigo])) x,y = ponto.x, ponto.y # Guarda o ponto aleatorio gerado pontos_aleatorios.append([x,y]) # Retorna a lista de pontos aleatorios gerados return pontos_aleatorios

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abraaonascimento.noreply@github.com

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/Evaluation/python-evaluation-scripts/pdr_new-blueflood.py

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[]

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Airelin/BlueFlood-v-2

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refs/heads/master

2023-08-17T05:09:05.770047

2021-09-29T13:24:26

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import os import progressbar import numpy as np import matplotlib.pyplot as plt from utility import slugify, cached, init_cache, load_env_config METHOD_PREFIX = 'export_' CONFIDENCE_FILL_COLOR = '0.8' COLOR_MAP = 'tab10' def load_plot_defaults(): # Configure as needed plt.rc('lines', linewidth=2.0) plt.rc('legend', framealpha=1.0, fancybox=True) plt.rc('errorbar', capsize=3) plt.rc('pdf', fonttype=42) plt.rc('ps', fonttype=42) plt.rc('font', size=11) def export_sine_wave_example(config, export_dir): # We got multiple experiment runs with individual measurements num_runs = 10 # Create our measurement steps xs = np.linspace(0, 2 * np.pi, 100, endpoint=True) # We also collect overall data for mean and confidence interval overall_data = [] for r in range(0, num_runs): name = "Sine Wave Run {}".format(r) def proc(): # you can load your data from a database or CSV file here # we will randomly generate data ys = np.sin(np.array(xs)) # we add some uniform errors ys += np.random.uniform(-0.1, 0.1, len(xs)) return ys # If caching is enabled, this line checks for available cache data # If no data was found, the proc callback is executed and the result cached # Use ys = proc() if caching not yet wanted ys = cached(('sine_wave', r), proc) # We also add the data to overall_data overall_data.append(ys) plt.clf() # Plot the main data plt.plot(xs, ys, linestyle='-', label="Sin Run {}".format(r), color='C' + str(r + 1)) plt.legend() plt.xlabel("x") plt.ylabel("sin(x)") plt.axis([None, None, None, None]) plt.grid(True) plt.tight_layout() plt.savefig(export_dir + slugify(name) + ".pdf", format="pdf") plt.close() overall_data = np.array(overall_data) # We swap the axes to get all values at the first position together overall_data = np.swapaxes(overall_data, 0, 1) # We can then merge each step to get the mean mean = np.mean(overall_data, axis=1) # Calculate the lower and upper bounds of the confidence interval # This describes that 95% of the measurements (for each timestep) are within that range # Use standard error to determine the "quality" of your calculated mean (lq, uq) = np.percentile(overall_data, [2.5, 97.5], axis=1) plt.clf() plt.plot(xs, mean, linestyle='-', label="Mean", color='C1') plt.fill_between(xs, lq, uq, color=CONFIDENCE_FILL_COLOR, label='95% Confidence Interval') plt.legend() plt.xlabel("x") plt.ylabel("sin(x)") plt.axis([None, None, None, None]) plt.grid(True) plt.tight_layout() plt.savefig(export_dir + slugify("Sine Wave Mean") + ".pdf", format="pdf") plt.close() def export_bar_example(config, export_dir): # we want to display two bar grahps # see export_sine_wave_example num_data_points = 100 data_a = [] with open('../Logs/5213_blueflood2-mode5/logs/packet-delivery-rate.txt', encoding="iso-8859-14") as file_a: lines_a = file_a.readlines() str_num_a = [] for line in lines_a: str_num_a.append(line) for num in str_num_a: data_a.append(float(num)) data_b = [] with open('../Logs/5214_blueflood2-mode6/logs/packet-delivery-rate.txt', encoding="iso-8859-14") as file_b: lines_b = file_b.readlines() str_num_b = [] for line in lines_b: str_num_b.append(line) for num in str_num_b: data_b.append(float(num)) data_c = [] with open('../Logs/5211_blueflood2-mode3/logs/packet-delivery-rate.txt', encoding="iso-8859-14") as file_c: lines_c = file_c.readlines() str_num_c = [] for line in lines_c: str_num_c.append(line) for num in str_num_c: data_c.append(float(num)) data_d = [] with open('../Logs/5212_blueflood2-mode4/logs/packet-delivery-rate.txt', encoding="iso-8859-14") as file_d: lines_d = file_d.readlines() str_num_d = [] for line in lines_d: str_num_d.append(line) for num in str_num_d: data_d.append(float(num)) mean_a = np.mean(data_a) mean_b = np.mean(data_b) mean_c = np.mean(data_c) mean_d = np.mean(data_d) std_a = np.std(data_a) std_b = np.std(data_b) std_c = np.std(data_c) std_d = np.std(data_d) plt.clf() fig, ax = plt.subplots() ax.bar(["125 Kbit", "500 Kbit", "1 Mbit", "2 Mbit"], [mean_a, mean_b, mean_c, mean_d], yerr=[std_a, std_b, std_c, std_d], align='center', ecolor='black', capsize=5, color=['C1', 'C2', 'C3', 'C4']) ax.yaxis.grid(True) plt.ylabel("PDR in %") plt.axis([None, None, 0, 1]) # Adapt the figure size as needed fig.set_size_inches(5.0, 8.0) plt.tight_layout() plt.savefig(export_dir + slugify(("20210921","pdr","new-blueflood","Bar", 5.0, 8.0)) + ".pdf", format="pdf") fig.set_size_inches(4.0, 4.0) plt.tight_layout() plt.savefig(export_dir + slugify(("20210921","pdr","new-blueflood","Bar", 4.0, 4.0)) + ".pdf", format="pdf") plt.close() def export_example_3(config, export_dir): pass if __name__ == '__main__': config = load_env_config() load_plot_defaults() assert 'EXPORT_DIR' in config and config['EXPORT_DIR'] if 'CACHE_DIR' in config and config['CACHE_DIR']: init_cache(config['CACHE_DIR']) steps = [ #export_sine_wave_example, # I put the example I am working on to the beginning export_bar_example, # export_example_3, excluded for now ] for step in progressbar.progressbar(steps, redirect_stdout=True): name = step.__name__.removeprefix(METHOD_PREFIX) print("Handling {}".format(name)) export_dir = os.path.join(config['EXPORT_DIR'], name) + '/' os.makedirs(export_dir, exist_ok=True) step(config, export_dir)

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import streamlit as st import numpy as np import pandas as pd import plotly.plotly as py import plotly.graph_objs as go st.title('Stock Price of Apple') @st.cache def load_data(): data = pd.read_csv('AAPL_data.csv', parse_dates=['date']) return data df = load_data() columns = st.multiselect( "Choose Columns", list(df.drop(['date', 'Name'], axis=1).columns), ['open'] ) columns.extend(['date']) start_date = st.date_input('Start date', value=df['date'].min()) end_date = st.date_input('End date', value=df['date'].max()) data = df[columns][(df['date']>=start_date) & (df['date']<=end_date)] st.write(data) st.subheader('Line chart of selected columns') chart = st.line_chart(data.drop(['date'], axis=1)) if st.checkbox('Show summaries'): st.subheader('Summaries:') st.write(data.describe()) week_df = data.groupby(data['date'].dt.weekday_name).mean() traces = [go.Bar( x = week_df.index, y = data[col], name = col, marker = dict( line = dict( color = 'rgb(0, 0, 0)', width = 2 ) ) ) for col in data.drop(['date'], axis=1).columns] layout = go.Layout( title = 'Stockprice over days', xaxis = dict( title = 'Weekday', ), yaxis = dict( title = 'Average Price' ) ) fig = go.Figure(data=traces, layout=layout) st.plotly_chart(fig)

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gilberttanner.work@gmail.com

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/1900~1999/1904.py

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geneeol/baekjoon-online-judge

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2023-03-28T23:25:12.219487

2021-04-01T09:19:06

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# 문제 # 지원이에게 2진 수열을 가르쳐 주기 위해, 지원이 아버지는 그에게 타일들을 선물해주셨다. # 그리고 이 각각의 타일들은 0 또는 1이 쓰여 있는 낱장의 타일들이다. # 어느 날 짓궂은 동주가 지원이의 공부를 방해하기 위해 0이 쓰여진 낱장의 타일들을 붙여서 한 쌍으로 이루어진 00 타일들을 만들었다. # 결국 현재 1 하나만으로 이루어진 타일 또는 0타일을 두 개 붙인 한 쌍의 00타일들만이 남게 되었다. # 그러므로 지원이는 타일로 더 이상 크기가 N인 모든 2진 수열을 만들 수 없게 되었다. # 예를 들어, N=1일 때 1만 만들 수 있고, N=2일 때는 00, 11을 만들 수 있다. (01, 10은 만들 수 없게 되었다.) # 또한 N=4일 때는 0011, 0000, 1001, 1100, 1111 등 총 5개의 2진 수열을 만들 수 있다. # 우리의 목표는 N이 주어졌을 때 지원이가 만들 수 있는 모든 가짓수를 세는 것이다. # 단 타일들은 무한히 많은 것으로 가정하자. # # 입력 # 첫 번째 줄에 자연수 N이 주어진다.(N ≤ 1,000,000) # # 출력 # 첫 번째 줄에 지원이가 만들 수 있는 길이가 N인 모든 2진 수열의 개수를 15746으로 나눈 나머지를 출력한다. N = int(input()) MOD = 15746 dp = [0 for _ in range(1000001)] dp[1], dp[2], dp[3] = 1, 2, 3 for i in range(4, 1000001): dp[i] = (dp[i - 1] + dp[i - 2]) % MOD print(dp[N])

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alstn2468_@naver.com

bf2dc3f9c8e764a9d8aef8bdecd21ae74fe802d2

0e33e481ce9122b0d43ec033dc1d0c162b67d4ee

/blog/urls.py

e33b9421ce2a1bfbe9f13c2f7815e5e8d21cc4a6

[]

no_license

lunnbag/my-first-blog

1f2a69f9c6407fc775b925c41a60da1dcfb40bb2

f3af52fa53f18793546847074066e559158c89ec

refs/heads/master

2020-03-20T18:18:33.571922

2018-06-16T15:41:50

137,582,092

0

null

UTF-8

Python

false

121

py

from django.conf.urls import url from .import views urlpatterns = [ url(r'^$', views.post_list, name='post_list'), ]

[ "lauralunn@hotmail.co.uk" ]

lauralunn@hotmail.co.uk

572253b9a187470080137315d72e1e91c8ee594b

a30cb7c68459b10d21cb91d00745a3e05ebb6c41

/pygame/08_sprites.py

53d8da65846927cf1a84bf9898368071bd15e381

[]

no_license

LyceeClosMaire/FormationBiblio

2f5172214da2be3868003df8e197a02098d69317

8cd14f6add80f00bd34e3c1de934ef2400a9d152

refs/heads/master

2021-01-19T09:49:35.217001

2017-05-24T13:37:28

87,790,941

0

null

UTF-8

Python

false

3,270

py

import pygame from pygame.locals import * # les définitions de classes sont introduites par le mot clé class # puis le nom de la classe, puis entre parenthèses la classe dont elle hérite class Ball(pygame.sprite.Sprite): # la méthode __init__ (avec des doubles tirets bas des deux côtés) est très # importante, c'est celle qui est appelée lorsqu'on construit un objet de # la classe. # notez le premier paramètre, conventionnellement appelé "self" ("soi-même") # il désigne l'objet qu'on est en train de construire def __init__(self, position, speed): # lorsque l'on hérite d'une classe, il est préférable de commencer le constructeur # par un appel au constructeur de la classe parente que l'on retrouve avec super() super().__init__() # les attributs de l'objet (les données qui lui sont attachées) doivent # être initialisée, généralement dans __init__() self.speed = speed # rect est un attribut standard des Sprites, vous devez toujours l'initialiser # pour que vos sprites marchent correctement avec les fonctions de collision self.rect = pygame.rect.Rect( (0,0), (50,50) ) self.rect.center = position # de même image est un attribut standard, utilisé par draw() sur un Group self.image = pygame.Surface( (50,50), SRCALPHA ) self.image.fill( (0,0,0,0) ) pygame.draw.circle(self.image, (255,0,0), (25,25), 25) # la méthode update() est celle qui sera appelée à chaque tour de boucle pour # mettre à jour l'état interne de votre Sprite qui est toujours le premier paramètre # de toute méthode (fonction attachée à un objet) def update(self): self.rect.move_ip(self.speed) # la méthode draw() sera appelée pour dessiner le sprite sur une surface def draw(self, surface): # voici ce que fait Group par défaut : surface.blit( self.image, self.rect ) # les définitions de classes ou fonctions se font généralement dans un module # à part mais ici pour l'exemple tout est réuni dans un même programme pygame.init() clock = pygame.time.Clock() window = pygame.display.set_mode( (800,600) ) winrect = window.get_rect() # il est possible de créer autant d'objets de la classe Ball que l'on souhaite ball1 = Ball( (10,10), (3,3) ) ball2 = Ball( (500,500), (-1,-4) ) running = True while running: clock.tick(60) for event in pygame.event.get(): if event.type == QUIT or(event.type == KEYUP and event.key == K_ESCAPE): running = False # les Sprites doivent être mis à jour manuellement, ou par l'intermédiaire d'un # Group comme nous le verrons # Notez que le paramètre "self" n'est pas passé entre parenthèse mais automatiquement # à cause de la syntaxe objet.méthode() ball1.update() ball2.update() window.fill( (0,0,0) ) # le détail du dessin peut être ignoré mais il nous faut encore dessiner les balles # une par une, Group palliera à ceci. Nous dessinons directement sur window. ball1.draw(window) ball2.draw(window) pygame.display.flip() pygame.quit()

[ "chaddai.fouche@gmail.com" ]

chaddai.fouche@gmail.com

710f90e901aebc0be4d31eed525c04c01665c3e0

3ad6d731c994813a10801829c45f56c58ff9021d

/src/teleop_bot/src/keys_to_twist_with_ramps.py

f8bf0f492753a4cd8e20f5fa2477366b8f82f090

[]

no_license

bladesaber/ROS_tutorial

9b4ae5a9a1bd773ae48d836a87d08bde8a757a5d

63486048786ebc864bc731eb1b524a72e9267738

refs/heads/master

2022-11-16T07:36:15.938433

2020-07-07T02:47:50

277,693,692

0

null

UTF-8

Python

false

2,584

py

#!/usr/bin/env python import rospy import math from std_msgs.msg import String from geometry_msgs.msg import Twist key_mapping = { 'w': [ 0, 1], 'x': [ 0, -1], 'a': [ 1, 0], 'd': [-1, 0], 's': [ 0, 0] } g_twist_pub = None g_target_twist = None g_last_twist = None g_last_send_time = None g_vel_scales = [0.1, 0.1] # default to very slow g_vel_ramps = [1, 1] # units: meters per second^2 def ramped_vel(v_prev, v_target, t_prev, t_now, ramp_rate): # compute maximum velocity step step = ramp_rate * (t_now - t_prev).to_sec() sign = 1.0 if (v_target > v_prev) else -1.0 error = math.fabs(v_target - v_prev) if error < step: # we can get there within this timestep. we're done. return v_target else: return v_prev + sign * step # take a step towards the target def ramped_twist(prev, target, t_prev, t_now, ramps): tw = Twist() tw.angular.z = ramped_vel(prev.angular.z, target.angular.z, t_prev, t_now, ramps[0]) tw.linear.x = ramped_vel(prev.linear.x, target.linear.x, t_prev, t_now, ramps[1]) return tw def send_twist(): global g_last_twist_send_time, g_target_twist, g_last_twist,\ g_vel_scales, g_vel_ramps, g_twist_pub t_now = rospy.Time.now() g_last_twist = ramped_twist(g_last_twist, g_target_twist, g_last_twist_send_time, t_now, g_vel_ramps) g_last_twist_send_time = t_now g_twist_pub.publish(g_last_twist) def keys_cb(msg): global g_target_twist, g_last_twist, g_vel_scales if len(msg.data) == 0 or not key_mapping.has_key(msg.data[0]): return # unknown key. vels = key_mapping[msg.data[0]] g_target_twist.angular.z = vels[0] * g_vel_scales[0] g_target_twist.linear.x = vels[1] * g_vel_scales[1] def fetch_param(name, default): if rospy.has_param(name): return rospy.get_param(name) else: print "parameter [%s] not defined. Defaulting to %.3f" % (name, default) return default if __name__ == '__main__': rospy.init_node('keys_to_twist') g_last_twist_send_time = rospy.Time.now() g_twist_pub = rospy.Publisher('cmd_vel', Twist, queue_size=1) rospy.Subscriber('keys', String, keys_cb) g_target_twist = Twist() # initializes to zero g_last_twist = Twist() g_vel_scales[0] = fetch_param('~angular_scale', 0.1) g_vel_scales[1] = fetch_param('~linear_scale', 0.1) g_vel_ramps[0] = fetch_param('~angular_accel', 1.0) g_vel_ramps[1] = fetch_param('~linear_accel', 1.0) rate = rospy.Rate(20) while not rospy.is_shutdown(): send_twist() rate.sleep()

[ "2510294705@qq.com" ]

2510294705@qq.com

d6ed4c471e16de0dbe3a1f1b89a719fd27fbf993

f5b0d6cca8caadb34da340592514154fd711bca5

/archive/Front-end/moments.py

fcaa4aec0fef16e6b3f1f36af92b05ec1bb49b31

[]

no_license

DmitryBol/reel

3e66eed5b18c3e26b147c0e8baa8e9043b3b26ea

e08de8ed4be222c2369d8b7579509fa5ccd3df07

refs/heads/master

2021-06-07T10:32:50.473918

2020-02-04T17:08:26

130,555,036

0

null

UTF-8

Python

false

1,649

py

# xi - random variable, equals payment for combination # eta - random variable, equals the number of freespins given for combination # zeta - random variable, equals payment for freespin def Exi2(self, width, lines): s = 0 for str_with_count in self.simple_num_comb: string = str_with_count[0] payment = self.get_simple_payment(string) s += str_with_count[1] / self.all_combinations() * (payment ** 2) for scatter_comb in self.scatter_num_comb: scat = scatter_comb[0] counts = scatter_comb[1] for cnt in range(width + 1): s += ((self.symbol[scat].payment[cnt] * len(lines)) ** 2) * counts[cnt] / self.all_combinations() return s def Exieta(self, width, lines): s = 0 for scatter_comb in self.scatter_num_comb: scat = scatter_comb[0] counts = scatter_comb[1] for cnt in range(width + 1): s += (self.symbol[scat].payment[cnt] * len(lines)) * self.symbol[scat].scatter[cnt] * \ counts[cnt] / self.all_combinations() return s def Eeta(self, width): s = 0 for scatter_comb in self.scatter_num_comb: scat = scatter_comb[0] counts = scatter_comb[1] for cnt in range(width + 1): s += self.symbol[scat].scatter[cnt] * counts[cnt] / self.all_combinations() return s def Eeta2(self, width): s = 0 for scatter_comb in self.scatter_num_comb: scat = scatter_comb[0] counts = scatter_comb[1] for cnt in range(width + 1): s += (self.symbol[scat].scatter[cnt] ** 2) * counts[cnt] / self.all_combinations() return s

[ "sadontsfath@gmail.com" ]

sadontsfath@gmail.com

f130de7bee24e00d0360f72e957813c45c2440e3

29e81d5aca62adf8a7c0f52025eb2904287caed4

/P5/tree_perfect_matching/tree_perfect_matching.py

8e1f0aa9e2dd71a43fe2020eb4ac41b8bc1e5d88

[]

no_license

jasonluocesc/CS_E3190

759a5bfa7e51d1fd0458b9ceea28848c48ddc0ef

a962479e4ce4670705ddc24dd69ce30f9df218d3

refs/heads/master

2018-09-20T10:53:43.259416

2018-06-06T13:08:11

109,515,163

1

0

null

UTF-8

Python

false

1,085

py

# coding: utf-8 from networkx import Graph from solution import tree_perfect_matching import sys def read_graph_in_dimacs_format(filename): with open(filename) as f: line = f.readline() while line.startswith('c '): line = f.readline() tokens = line.split() num_nodes = int(tokens[2]) num_edges = int(tokens[3]) G = Graph() G.add_nodes_from(range(1, num_nodes+1)) for i in range(num_edges): tokens = f.readline().split() n1 = int(tokens[1]) n2 = int(tokens[2]) G.add_edge(n1, n2) return G if __name__ == "__main__": if len(sys.argv) != 2: print("Usage: python tree_perfect_matching.py <file>") sys.exit(1) # Read graph from given file g = read_graph_in_dimacs_format(sys.argv[1]) # Get matching matching = tree_perfect_matching(g) # Output the result if matching is None: print("No perfect matching.") else: print(" ".join("({},{})".format(edge[0], edge[1]) for edge in matching))

[ "luowenbinlove???" ]

luowenbinlove???

644e8b72aa37b4a680a2ddf1ca694dfd8cf0e58b

dd7ecc8ee4605f97947949cb486c8741c886f425

/app/app/TextSender.py

d943389863cd9ca0b303fe363aab07d40d0fd940

[]

no_license

rmartinsen/where_ru_rt

5df7c0a08df0797f984594981d95fdf79a173172

3188a53737c4ae78579c16a820168fc2ad0b830f

refs/heads/master

2021-01-19T00:57:24.920347

2017-02-18T18:37:22

64,557,057

0

null

UTF-8

Python

false

672

py

from twilio.rest import TwilioRestClient from twilio import TwilioRestException import logging from SETTINGS import settings class Texter(): def send_text(self, phone_number, body): self.ACCOUNT_SID = settings['ACCOUNT_SID'] self.AUTH_TOKEN = settings['AUTH_TOKEN'] self.client = TwilioRestClient(self.ACCOUNT_SID, self.AUTH_TOKEN) try: self.client.messages.create( to = phone_number, from_ = '9167108744', body = body, ) logging.info("Message sent to phone number: " + phone_number) except TwilioRestException as e: logging.error("Message could not be sent to phone number " + phone_number) logging.error(e) raise

[ "rpmartinsen@ucdavis.edu" ]

rpmartinsen@ucdavis.edu

ec83305e998ad683a062bfd80f473ac8191c45eb

4c3803e4cad48db586dfdef2e26ffa2a3f2a06fd

/Chapter_1/Exercises/R-1.1.py

244c70863278bd3cd7edc7c50d4cb938a3b0b347

[]

no_license

dabiri1377/DS-AL_in_python3

07cee6569c39c79d56102b5b31f8e9329e58df4e

3684a8f28df0c67bd504748fcdd4a12fc714bb92

refs/heads/master

2021-09-09T15:55:58.912069

2018-03-17T16:00:16

115,943,753

0

null

UTF-8

Python

false

205

py

def is_multiply(n, m): n = int(n) m = int(m) if n // m == n / m: return True else: return False # __main__ a, b = input("enter n and m:").split() print(is_multiply(a, b))

[ "dabiri1377@gmail.com" ]

dabiri1377@gmail.com

2affed72b0c39fbbb031f7f7b77cc32a1cb1b4f5

1a65481701a7ec2ba17e051cf50d131ded1516e1

/unit 21~30/unit27/파이썬 객체 파일에 저장.py

e05c4f5de67abe6216e322f503f1a8f27d9cfd03

[]

no_license

kimdohui/Python_Study

bec9b24bab79048d3b7169609898c213912f1d99

92dcec38cfa1043e004a72fdb2be2a3f56311444

refs/heads/master

2021-05-17T13:58:59.729705

2020-04-22T10:58:12

250,809,291

1

0

null

UTF-8

Python

false

645

py

import pickle name = 'hana' age = 18 address = '서울시' scores = {'korean': 90, 'english': 95} with open('hanaInfo.p', 'wb') as file: # pickle.dump로 객체 저장 시 파일모드를 wb로 해야 함 pickle.dump(name,file) pickle.dump(age,file) pickle.dump(address,file) pickle.dump(scores,file) print('----------------\n') with open('hanaInfo.p', 'rb') as file: # james.p 파일을 바이너리 읽기 모드(rb)로 열기 name = pickle.load(file) age = pickle.load(file) address = pickle.load(file) scores = pickle.load(file) print(name) print(age) print(address) print(scores)

[ "kdh24162416@gmail.com" ]

kdh24162416@gmail.com

d2fad2f4d6e5d35ef96cca26932b4f326533d021

6769f4c0ac48e52b75a597ce5d9612a243558ef1

/Controller/Click.py

5d8d081d5efdd22cac5ffae58ff185dea843f58a

[]

no_license

sehwaa/Naver_Movie_Crawler

ecfe073c25c1f0dac6478af3bf07ad1f5520c137

4b92b27a0e6edafcd932316ddedbced19f8abd99

refs/heads/master

2020-03-15T01:52:20.650219

2018-05-17T07:44:15

131,903,896

0

null

UTF-8

Python

false

2,062

py

''' Created on 2018. 5. 3. @author: SEHWA ''' #coding: utf-8 from Connection.Connection import driver from selenium.common.exceptions import StaleElementReferenceException from selenium.webdriver.support.wait import WebDriverWait #검색 버튼 클릭 def submitBtn(): driver.find_element_by_class_name("btn_srch").click() #자동 완성 리스트 클릭 def autoCompletementList(movieName): driver.implicitly_wait(5) element = driver.find_element_by_xpath("//li[@data-title='"+movieName+"']") element.click() #'주요 정보'탭 클릭 def mainInformationTab(): driver.find_element_by_xpath("//a[@title='주요정보']").click() #'배우/제작진'탭 클릭 def actorTab(): driver.find_element_by_xpath("//a[@title='배우/제작진']").click() #'평점'탭 클릭 def scoreTab(): driver.find_element_by_xpath("//a[@title='평점']").click() #'평점'탭 클릭후 - '개봉 전 평점' 메뉴 클릭 def beforeOpening(): driver.find_element_by_id("beforePointTab").click() #'평점'탭 클릭후 - '개봉 후 평점' 메뉴 클릭 def afterOpening(): driver.find_element_by_id("afterPointTab").click() #'개봉 후 평점'메뉴 클릭 후 '남녀별/연령별' 메뉴 클릭 def netizenGenderAndAge(): driver.find_element_by_xpath("//a[@id='netizen_group']").click() #'개봉 후 평점'메뉴 클릭 후 '관람객 평점' 탭 클릭 def audienceScore(): driver.find_element_by_xpath("//div[@class='title_area grade_tit']").click() #'관람객 평점' 탭 클릭 후 '남녀별/연령별' 메뉴 클릭 def audienceGenderAndAge(): driver.find_element_by_xpath("//a[@id='actual_group']").click() #성인 인증시 로그인 def adultLogin(): _id = driver.find_element_by_id("id") _id.send_keys("####") #개인정보 문제로 블락 _pwd = driver.find_element_by_id("pw") _pwd.send_keys("####") #개인정보 문제로 블락 driver.find_element_by_xpath("//input[@type='submit']").click()

[ "nsh235482@gmail.com" ]

nsh235482@gmail.com

38bae379c04d24789026484a687ef0293b07e1f4

d346c1e694e376c303f1b55808d90429a1ad3c3a

/medium/61.rotate_list.py

86f5af201842b8ba886e5132edcc3439263c61a5

[]

no_license

littleliona/leetcode

3d06bc27c0ef59b863a2119cd5222dc94ed57b56

789d8d5c9cfd90b872be4a4c35a34a766d95f282

refs/heads/master

2021-01-19T11:52:11.938391

2018-02-19T03:01:47

88,000,832

0

null

UTF-8

Python

false

1,234

py

# Definition for singly-linked list. # class ListNode: # def __init__(self, x): # self.val = x # self.next = None class Solution: def rotateRight(self, head, k): """ :type head: ListNode :type k: int :rtype: ListNode """ # current = head storeList = [] while current != None: storeList.append(current) current = current.next if len(storeList) <= 1: return head k = k % len(storeList) if k == 0: return head res = storeList[-k] storeList[-k - 1].next = None storeList[-1].next = head return res #mine if not head or not head.next or k == 0: return head length_list = 1 current = head while current.next: current = current.next length_list += 1 current.next = head current = head for i in range(1,length_list - (k % length_list)): current = current.next head = current.next current.next = None return head s = Solution() a = s.threeSum([-1,0,1,2,-1,-4]) print(a)

[ "aria@Arias-MacBook-Pro.local" ]

aria@Arias-MacBook-Pro.local

73fe66859a65e73496b91d800a11f82a54258308

a85419f08198548eb6ba4d3df0d181769f810358

/C_Carray/split_for_singlechannel_tests.py

4887feeda442f244c49cc385774a1b017c5a6ddf

[]

no_license

keflavich/w51evlareductionscripts

cd0287d750d938bab96f1a7d335b3b84c27a987f

00cb8085e8fe5c047f53852c8057a1f7457863f6

refs/heads/master

2021-01-17T07:26:01.574220

2016-07-07T09:02:26

8,590,805

0

1

null

UTF-8

Python

false

650

py

# June 29, 2015 # Instead, use h2co_cvel_split in ../C_AC # outputvis_A = 'h2co11_Cband_Aarray_nocal_20to100kms.ms' # split(vis=outputvis_A, outputvis='h2co11_Cband_Aarray_nocal_20kms_onechan.ms', # spw='0:0', width=1) # split(vis=outputvis_A, outputvis='h2co11_Cband_Aarray_nocal_57kms_onechan.ms', # spw='0:74', width=1) # outputvis_C = 'h2co11_Cband_Carray_nocal_20to100kms.ms' # split(vis=outputvis_C, outputvis='h2co11_Cband_Carray_nocal_20kms_onechan.ms', # spw='0:0', width=1, datacolumn='data') # split(vis=outputvis_C, outputvis='h2co11_Cband_Carray_nocal_57kms_onechan.ms', # spw='0:74', width=1, datacolumn='data')

[ "keflavich@gmail.com" ]

keflavich@gmail.com

40f4ee37875a2c832d94dba99ab3b56ade7ccd8e

03673029f34a235103f2fa428e15ce50cf677a15

/python/752打开转盘锁.py

c83ee9d136a60c9299a16fafc011f75e53a7b585

[]

no_license

sysuwsh/My-Leetcode-Solution

6aa1d0c02c844c8fa50480b8ada206510668f4c4

ad1efa0faa6c5bbde7e185759ad5446599dddf52

refs/heads/master

2022-12-08T02:09:06.125150

2020-08-17T17:16:47

270,934,868

0

null

UTF-8

Python

false

2,744

py

from typing import List from collections import deque # 采用双向BFS的方式，相对单向BFS来说要快很多 class Solution: def openLock(self, deadends: List[str], target: str) -> int: def neighbor(s: str) -> str: for i in range(4): for j in (-1, 1): yield s[:i] + str((int(s[i]) + j) % 10) + s[i + 1:] dead = set(deadends) queue1, queue2, visited = set(), set(), set() queue1.add("0000") queue2.add(target) step = 0 while queue1 and queue2: tmp = set() for cur in queue1: if cur in dead: continue if cur in queue2: return step visited.add(cur) for nei in neighbor(cur): if nei not in visited: tmp.add(nei) step += 1 queue1 = queue2 queue2 = tmp return -1 # 注意这里做的优化：将deadends转为set提高查找效率 # 将visited采用set存储 # 这里注意python中的一个语法，对于集合set来说，{}可以生成集合，和字典不同的是，没有value值 # 以及，当要生成一个空的集合时，采用set()生成，{}默认生成的是空字典 class Solution1: def openLock(self, deadends: List[str], target: str) -> int: dead = set(deadends) visited = {"0000"} queue = deque() queue.append("0000") step = 0 while queue: size = len(queue) for i in range(size): cur = queue.popleft() if cur in dead: continue if cur == target: return step for j in range(4): up = self.plusOne(cur, j) if up not in visited: queue.append(up) visited.add(up) down = self.minusOne(cur, j) if down not in visited: queue.append(down) visited.add(down) step += 1 return -1 def plusOne(self, s: str, index: int) -> str: l = list(s) if l[index] == '9': l[index] = '0' else: l[index] = str(int(l[index]) + 1) return ''.join(l) def minusOne(self, s: str, index: int) -> str: l = list(s) if l[index] == '0': l[index] = '9' else: l[index] = str(int(l[index]) - 1) return ''.join(l) s = Solution() deadends = ["0201", "0101", "0102", "1212", "2002"] target = "0202" print(s.openLock(deadends, target))

[ "sysuwsh@gmail.com" ]

sysuwsh@gmail.com

6950c60efeafde32b627e0da341719ed7b00ff6d

ee5882611747127cb8c2160d01cf9be88c1dcd3b

/send_text.py

b42d9ce1988418d941f86194c4ff01a777e21fc6

[]

no_license

DespoinaSakoglou/Mini-Apps

e958ceb2a532161917b3b2019d655b6ec1251f81

b50c243587fedaa83a210d0054f4bb0140bef6a1

refs/heads/master

2021-04-12T05:51:44.558031

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""" A program that sends a text message using twilio Created on Mon Mar 19 16:38:13 2018 """ from twilio.rest import Client # Your Account SID from twilio.com/console account_sid = "*********************************" # Your Auth Token from twilio.com/console auth_token = "**********************************" client = Client(account_sid, auth_token) message = client.messages.create( to="+18*********", from_="+18*********", body="Hello from Python!") print(message.sid)

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DespoinaSakoglou.noreply@github.com

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/spring1718_assignment2_v2/learn_keras.py

05d03821429cd8e8da1b9f85292bd464ab87476c

[]

no_license

shuyanzhu/leran_cs231n

c287446efa1e2c9724c3316d82ea03297dd26089

210c1f7ec9622d5bbcf9f12b5bab06fc8ffeea98

refs/heads/master

2020-04-16T18:26:24.052223

2019-02-26T06:16:12

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import numpy as np import tensorflow as tf # 加载数据 def load_cifar10(): cifar10 = tf.keras.datasets.cifar10.load_data() (X_train, y_train), (X_test, y_test) = cifar10 X_train = X_train.astype(np.float32) y_train = y_train.astype(np.int32).flatten() X_test = X_test.astype(np.float32) y_test = y_test.astype(np.int32).flatten() val_size = 1000 X_val = X_train[:val_size] y_val = y_train[:val_size] X_train = X_train[val_size:] y_train = y_train[val_size:] mean_pixel = X_train.mean(axis=(0, 1, 2), keepdims=True) std_pixel = X_train.std(axis=(0, 1, 2), keepdims=True) X_train = (X_train - mean_pixel) / std_pixel X_val = (X_val - mean_pixel) / std_pixel X_test = (X_test - mean_pixel) / std_pixel print('Trian data size', X_train.shape) print('Train labels size', y_train.shape) print('Validation data size', X_val.shape) print('Validation labels size', y_val.shape) print('Test data size', X_test.shape) print('Test labels size', y_test.shape) return X_train, y_train, X_val, y_val, X_test, y_test # 建立模型 def inference(inputs): x = tf.layers.BatchNormalization()(inputs) x = tf.layers.Conv2D(16, [5, 5], activation='relu', padding='same')(x) x = tf.layers.BatchNormalization()(x) x = tf.layers.Conv2D(32, [3, 3], activation='relu', padding='same')(x) x = tf.layers.Flatten()(x) x = tf.layers.Dropout()(x) x = tf.layers.BatchNormalization()(x) x = tf.layers.Dense(120, activation='relu')(x) x = tf.layers.Dropout()(x) x = tf.layers.BatchNormalization()(x) outputs = tf.layers.Dense(10, activation='softmax')(x) return outputs if __name__ == '__main__': # 导入数据 X_train, y_train, X_val, y_val, X_test, y_test = load_cifar10() train_dset = tf.data.Dataset.from_tensor_slices((X_train, y_train)) train_dset = train_dset.batch(64).repeat() validation_dset = tf.data.Dataset.from_tensor_slices((X_val, y_val)) validation_dset = validation_dset.batch(64).repeat() # 构建模型 inputs = tf.keras.Input(shape=(32, 32, 3)) outputs=inference(inputs) model = tf.keras.Model(inputs=inputs, outputs=outputs) model.compile(optimizer=tf.train.AdamOptimizer(5e-4), loss = 'sparse_categorical_crossentropy', metrics=['accuracy']) checkpoint = tf.keras.callbacks.ModelCheckpoint('./model-{epoch:02d}.hdf5', monitor='val_acc', save_best_only=True, verbose=1) history = model.fit(train_dset, epochs=5, steps_per_epoch=49000//64, validation_data = validation_dset, validation_steps=1000//64, callbacks=[checkpoint]) print(history.history)

[ "1294206499@qq.com" ]

1294206499@qq.com

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/cmssw/HeavyIonsAnalysis/JetAnalysis/python/jets/akVs3CaloJetSequence_pPb_mix_cff.py

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[]

no_license

yenjie/HIGenerator

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28622c10395af795b2b5b1fecf42e9f6d4e26f2a

refs/heads/master

2021-01-19T01:59:57.508354

2016-06-01T08:06:07

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import FWCore.ParameterSet.Config as cms from PhysicsTools.PatAlgos.patHeavyIonSequences_cff import * from HeavyIonsAnalysis.JetAnalysis.inclusiveJetAnalyzer_cff import * akVs3Calomatch = patJetGenJetMatch.clone( src = cms.InputTag("akVs3CaloJets"), matched = cms.InputTag("ak3HiGenJetsCleaned") ) akVs3Caloparton = patJetPartonMatch.clone(src = cms.InputTag("akVs3CaloJets"), matched = cms.InputTag("hiGenParticles") ) akVs3Calocorr = patJetCorrFactors.clone( useNPV = False, # primaryVertices = cms.InputTag("hiSelectedVertex"), levels = cms.vstring('L2Relative','L3Absolute'), src = cms.InputTag("akVs3CaloJets"), payload = "AKVs3Calo_HI" ) akVs3CalopatJets = patJets.clone(jetSource = cms.InputTag("akVs3CaloJets"), jetCorrFactorsSource = cms.VInputTag(cms.InputTag("akVs3Calocorr")), genJetMatch = cms.InputTag("akVs3Calomatch"), genPartonMatch = cms.InputTag("akVs3Caloparton"), jetIDMap = cms.InputTag("akVs3CaloJetID"), addBTagInfo = False, addTagInfos = False, addDiscriminators = False, addAssociatedTracks = False, addJetCharge = False, addJetID = False, getJetMCFlavour = False, addGenPartonMatch = True, addGenJetMatch = True, embedGenJetMatch = True, embedGenPartonMatch = True, embedCaloTowers = False, embedPFCandidates = False ) akVs3CaloJetAnalyzer = inclusiveJetAnalyzer.clone(jetTag = cms.InputTag("akVs3CalopatJets"), genjetTag = 'ak3HiGenJetsCleaned', rParam = 0.3, matchJets = cms.untracked.bool(True), matchTag = 'akPu3CalopatJets', pfCandidateLabel = cms.untracked.InputTag('particleFlow'), trackTag = cms.InputTag("generalTracks"), fillGenJets = True, isMC = True, genParticles = cms.untracked.InputTag("hiGenParticles"), eventInfoTag = cms.InputTag("hiSignal") ) akVs3CaloJetSequence_mc = cms.Sequence( akVs3Calomatch * akVs3Caloparton * akVs3Calocorr * akVs3CalopatJets * akVs3CaloJetAnalyzer ) akVs3CaloJetSequence_data = cms.Sequence(akVs3Calocorr * akVs3CalopatJets * akVs3CaloJetAnalyzer ) akVs3CaloJetSequence_jec = akVs3CaloJetSequence_mc akVs3CaloJetSequence_mix = akVs3CaloJetSequence_mc akVs3CaloJetSequence = cms.Sequence(akVs3CaloJetSequence_mix)

[ "dgulhan@cern.ch" ]

dgulhan@cern.ch

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305c4d943e1e8c6748ebcd965ec227aee0da751e

/examples/fastapi_e2e/tests/conftest.py

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permissive

williaminfante/pact-python

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refs/heads/master

2023-02-22T22:25:05.595040

2020-12-29T20:08:30

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def pytest_addoption(parser): parser.addoption( "--publish-pact", type=str, action="store", help="Upload generated pact file to pact broker with version" ) parser.addoption( "--provider-url", type=str, action="store", help="The url to our provider." )

[ "elliottmurray@gmail.com" ]

elliottmurray@gmail.com

a69149ec8b9dd6535e90898b9807d97f229a412f

3b46e9f7e2fef169589f336f09f00a9294cb3b04

/test/core/test_drop.py

72e7efc40fd48127ce67ee9e678a8d3cf8b19500

[]

no_license

Vayana/sumpter

79128ac2bcb4fe6cd05435e9e80e4ad6cb8ba28b

f18d7c5c3f8d32a2ae8abf58a70a31614bdc2e39

refs/heads/master

2020-12-25T18:16:33.833216

2016-09-15T10:44:18

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############################################################################# # Copyright 2010 Dhananjay Nene # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################# __author__ = '<a href="nkhalasi@vayana.in">Naresh Khalasi</a>' from sumpter import * import unittest class TestDrop(unittest.TestCase): def setUp(self): self.ctx = {1:'hello','world':'foo'} self.val = ['foo',('bar','baz'),{'boom':555}] def tearDown(self): pass def get_new_drop(self): return Drop(self.ctx,self.val) def testConstruction(self): drop = self.get_new_drop() self.assert_(isinstance(drop.id,(int,long))) self.assertEqual(drop.trace,[]) self.assertEqual(drop.parents,[]) self.assertEqual(drop.children,[]) self.assertEqual(drop.alive,True) self.assertEqual(drop.ctx, self.ctx) self.assertEqual(drop.val, self.val) def testEmptyConstruction(self): drop = Drop(None,None) self.assertEqual(drop.ctx,{}) self.assertEqual(drop.val,None) def testDifferentIds(self): drop1 = Drop(None,None) drop2 = Drop(None,None) self.assertNotEqual(drop1.id, drop2.id) def testChildCreation(self): drop_parent = self.get_new_drop() drop_child = drop_parent.create_child('hello') self.assertEqual(drop_child.ctx,self.ctx) self.assertEqual(drop_child.val,'hello') self.assertEqual(drop_parent.children,[drop_child]) self.assertEqual(drop_child.parents,[drop_parent]) def testTrace(self): drop = self.get_new_drop() drop.record('One') drop.record('Two') self.assertEqual(drop.trace,['One','Two']) self.assertEqual(drop.get_trace(),'One=>Two') def testKillDrop(self): drop = self.get_new_drop() drop.kill() self.assertEqual(drop.alive,False) def testRecordAKilledDrop(self): drop = self.get_new_drop() drop.kill() try : drop.record('foo') self.fail('Should have raised a runtime error on drop.record() for a killed drop') except Exception as e : self.assertEquals(e,PypeRuntimeError('inactive-drop-operation-record',drop,'foo')) def testCreateChildOnKilledDrop(self): drop = self.get_new_drop() drop.kill() try : drop.create_child('hello') self.fail('Should have raised a runtime error on drop.create_child() for a killed drop') except Exception as e : self.assertEquals(e,PypeRuntimeError('inactive-drop-operation-create-child',drop)) def testKillAKilledDrop(self): drop = self.get_new_drop() drop.kill() try : drop.kill() self.fail('Should have raised a runtime error on drop.kill() for a killed drop') except Exception as e : self.assertEquals(e,PypeRuntimeError('inactive-drop-operation-kill',drop))

[ "nkhalasi@vayana.in" ]

nkhalasi@vayana.in

2e5daa13e1b08a262d40a179079d7d11029e9af2

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/rllib_exercises/serving/do_rollouts.py

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[]

no_license

ray-project/tutorial

d823bafa579fca7eeb3050b0a13c01a542b6994e

08f4f01fc3e918c997c971f7b2421551f054c851

refs/heads/master

2023-08-29T08:46:38.473513

2022-03-21T20:43:22

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import json import argparse import gym from ray.rllib.utils.policy_client import PolicyClient parser = argparse.ArgumentParser() parser.add_argument( "--no-train", action="store_true", help="Whether to disable training.") parser.add_argument( "--off-policy", action="store_true", help="Whether to take random instead of on-policy actions.") if __name__ == "__main__": args = parser.parse_args() import pong_py env = pong_py.PongJSEnv() client = PolicyClient("http://localhost:8900") eid = client.start_episode(training_enabled=not args.no_train) obs = env.reset() rewards = 0 episode = [] f = open("out.txt", "w") while True: if args.off_policy: action = env.action_space.sample() client.log_action(eid, obs, action) else: action = client.get_action(eid, obs) next_obs, reward, done, info = env.step(action) episode.append({ "obs": obs.tolist(), "action": float(action), "reward": reward, }) obs = next_obs rewards += reward client.log_returns(eid, reward, info=info) if done: print("Total reward:", rewards) f.write(json.dumps(episode)) f.write("\n") f.flush() rewards = 0 client.end_episode(eid, obs) obs = env.reset() eid = client.start_episode(training_enabled=not args.no_train)

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/td-auth-master/tutordudes/payment/apps.py

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[]

no_license

FaizanM2000/Project-Demo-1

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from django.apps import AppConfig class PaymentConfig(AppConfig): name = 'tutordudes.payment' def ready(self): try: import tutordudes.payment.signals # noqa F401 except ImportError: pass

[ "53056387+Sebastiangao0219@users.noreply.github.com" ]

53056387+Sebastiangao0219@users.noreply.github.com

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/minirank/sofia_ml.py

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[]

no_license

aurora1625/minirank

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refs/heads/master

2020-12-25T12:42:45.282193

2012-12-21T14:47:01

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import sys, tempfile import numpy as np from sklearn import datasets import _sofia_ml if sys.version_info[0] < 3: bstring = basestring else: bstring = str def sgd_train(X, y, b, alpha, n_features=None, model='rank', max_iter=100, step_probability=0.5): """ Minimizes an expression of the form Loss(X, y, b) + 0.5 * alpha * (||w|| ** 2) where Loss is an Hinge loss defined on pairs of images Parameters ---------- X : input data y : target labels b : blocks (aka query_id) alpha: float model : {'rank', 'combined-ranking', 'roc'} Returns ------- coef None """ if isinstance(X, bstring): if n_features is None: n_features = 2 ** 17 # the default in sofia-ml TODO: parse file to see w = _sofia_ml.train(X, n_features, alpha, max_iter, False, model, step_probability) else: with tempfile.NamedTemporaryFile() as f: datasets.dump_svmlight_file(X, y, f.name, query_id=b) w = _sofia_ml.train(f.name, X.shape[1], alpha, max_iter, False, model, step_probability) return w, None def sgd_predict(data, coef, blocks=None): # TODO: isn't query_id in data ??? s_coef = '' for e in coef: s_coef += '%.5f ' % e s_coef = s_coef[:-1] if isinstance(X, bstring): return _sofia_ml.predict(data, s_coef, False) else: X = np.asarray(data) if blocks is None: blocks = np.ones(X.shape[0]) with tempfile.NamedTemporaryFile() as f: y = np.ones(X.shape[0]) datasets.dump_svmlight_file(X, y, f.name, query_id=blocks) prediction = _sofia_ml.predict(f.name, s_coef, False) return prediction

[ "fabian@fseoane.net" ]

fabian@fseoane.net

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/iotapy/storage/providers/rocksdb.py

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permissive

aliciawyy/iota-python

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b8d421acf94ccd9e7374f799fbe496f6d23e3cf3

refs/heads/master

2020-03-19T04:15:54.594313

2018-06-04T18:26:52

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py

# -*- coding: utf-8 -*- import struct import iota import rocksdb_iota import iotapy.storage.providers.types from rocksdb_iota.merge_operators import StringAppendOperator from iotapy.storage import converter KB = 1024 MB = KB * 1024 MERGED = ['tag', 'bundle', 'approvee', 'address', 'state_diff'] class RocksDBProvider: BLOOM_FILTER_BITS_PER_KEY = 10 column_family_names = [ b'default', b'transaction', b'transaction-metadata', b'milestone', b'stateDiff', b'address', b'approvee', b'bundle', b'tag' ] column_family_python_mapping = { 'transaction_metadata': 'transaction-metadata', 'state_diff': 'stateDiff' } def __init__(self, db_path, db_log_path, cache_size=4096, read_only=True): self.db = None self.db_path = db_path self.db_log_path = db_log_path self.cache_size = cache_size self.read_only = read_only self.available = False def init(self): self.init_db(self.db_path, self.db_log_path) self.available = True def init_db(self, db_path, db_log_path): options = rocksdb_iota.Options( create_if_missing=True, db_log_dir=db_log_path, max_log_file_size=MB, max_manifest_file_size=MB, max_open_files=10000, max_background_compactions=1 ) options.allow_concurrent_memtable_write = True # XXX: How to use this? block_based_table_config = rocksdb_iota.BlockBasedTableFactory( filter_policy=rocksdb_iota.BloomFilterPolicy(self.BLOOM_FILTER_BITS_PER_KEY), block_size_deviation=10, block_restart_interval=16, block_cache=rocksdb_iota.LRUCache(self.cache_size * KB), block_cache_compressed=rocksdb_iota.LRUCache(32 * KB, shard_bits=10)) options.table_factory = block_based_table_config # XXX: How to use this? column_family_options = rocksdb_iota.ColumnFamilyOptions( merge_operator=StringAppendOperator(), table_factory=block_based_table_config, max_write_buffer_number=2, write_buffer_size=2 * MB) try: self.db = rocksdb_iota.DB( self.db_path, options, self.column_family_names, read_only=self.read_only) except rocksdb_iota.errors.InvalidArgument as e: if 'Column family not found' in str(e): # Currently, rocksdb_iota didn't support # "create_if_column_family_missing" option, if we detect this # is a new database, we will need to create its whole # column family manually. self.db = rocksdb_iota.DB( self.db_path, options, [b'default'], read_only=self.read_only) # Skip to create b'default' for column_family in self.column_family_names[1:]: self.db.create_column_family(column_family) else: raise e def _convert_column_to_handler(self, column): if not isinstance(column, str): raise TypeError('Column type should be str') db_column = self.column_family_python_mapping.get(column, column) ch = self.db.column_family_handles.get(bytes(db_column, 'ascii')) if ch is None: raise KeyError('Invalid column family name: %s' % (column)) return ch def _convert_key_column(self, key, column): # Convert column to column family handler ch = self._convert_column_to_handler(column) # Expand iota.Tag to iota.Hash if column == 'tag': if not isinstance(key, iota.Tag): raise TypeError('Tag key type should be iota.Tag') key = iota.Hash(str(key)) # Convert key into trits-binary if column == 'milestone': if not isinstance(key, int): raise TypeError('Milestone key type should be int') key = struct.pack('>l', key) else: if not isinstance(key, iota.TryteString): raise TypeError('Key type should be iota.TryteString') if len(key) != iota.Hash.LEN: raise ValueError('Key length must be 81 trytes') key = converter.from_trits_to_binary(key.as_trits()) return key, ch def _get(self, key, bytes_, column): # Convert value (bytes_) into data object obj = getattr(iotapy.storage.providers.types, column).get(bytes_, key) # Handle metadata if obj and key and column == 'transaction': obj.set_metadata(self.get(key, 'transaction_metadata')) return obj def _get_key(self, bytes_, column): return getattr(iotapy.storage.providers.types, column).get_key(bytes_) def _save(self, value, column): # Convert value to bytes return getattr(iotapy.storage.providers.types, column).save(value) def get(self, key, column): k, ch = self._convert_key_column(key, column) # Get binary data from database bytes_ = self.db.get(k, ch) return self._get(key, bytes_, column) def next(self, key, column): key, ch = self._convert_key_column(key, column) it = self.db.iteritems(ch) it.seek(key) next(it) # XXX: We will get segfault if this is NULL in database key, bytes_ = it.get() key = self._get_key(key, column) # Convert into data object return key, self._get(key, bytes_, column) def first(self, column): ch = self._convert_column_to_handler(column) it = self.db.iteritems(ch) it.seek_to_first() # XXX: We will get segfault if this is NULL in database key, bytes_ = it.get() key = self._get_key(key, column) # Convert into data object return key, self._get(key, bytes_, column) def latest(self, column): ch = self._convert_column_to_handler(column) it = self.db.iteritems(ch) it.seek_to_last() # XXX: We will get segfault if this is NULL in database key, bytes_ = it.get() key = self._get_key(key, column) # Convert into data object return key, self._get(key, bytes_, column) def may_exist(self, key, column, fetch=False): key, ch = self._convert_key_column(key, column) # XXX: Not working...... return self.db.key_may_exist(key, ch)[0] def save(self, key, value, column): key, ch = self._convert_key_column(key, column) value = self._save(value, column) self.db.put(key, value, ch) def store(self, key, value, column): # Store is different then save, currently deailing with transaction # that transaction will save more data to other column batches = getattr(iotapy.storage.providers.types, column).store(key, value) write_batch = rocksdb_iota.WriteBatch() for k, v, column in batches: k, ch = self._convert_key_column(k, column) v = self._save(v, column) if column in MERGED: write_batch.merge(k, v, ch) else: write_batch.put(k, v, ch) self.db.write(write_batch)

[ "git@louie.lu" ]

git@louie.lu

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b3203d01b01d8dbb3298fa25a2bc2da3e20b0019

/enumnamecrawler/valueassigner/increment.py

39877eb27b520b42aeb7b6f28aa1c7009b0353f9

[ "MIT" ]

permissive

yinyin/enumnamecrawler

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refs/heads/master

2021-03-30T20:23:12.774056

2018-03-21T17:04:47

125,081,811

0

null

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Python

false

909

py

# -*- coding: utf-8 -*- class Incrementer(object): def __init__(self, base=-1, step=-1, *args, **kwds): # type: (int, int) -> None super(Incrementer, self).__init__(*args, **kwds) if step == 0: raise ValueError("step value must != 0: %r" % (step, )) self.base = base self.step = step def _compute_base(self, v_max, v_min): if self.step > 0: c = v_max dstep = self.step - 1 else: c = v_min dstep = self.step + 1 return int((c - self.base + dstep) / self.step) * self.step + self.base def __call__(self, enumelements): v_max = self.base v_min = self.base for enumelem in enumelements: if enumelem.value is None: continue aux = enumelem.value v_max = max(v_max, aux) v_min = min(v_min, aux) c = self._compute_base(v_max, v_min) for enumelem in enumelements: if enumelem.value is not None: continue enumelem.value = c c = c + self.step

[ "yinyinl@gmail.com" ]

yinyinl@gmail.com