anton-l/python_sample_10k · Datasets at Hugging Face

JanaRankova/pyladies

16,217,796,512,512

9c56992d10b6aeef854ae5854a83f784dc90a02f

f44a783125105e858843082a2b526d613ba0be01

/06_ulohy/piskvorky1.py

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[]

no_license

https://github.com/JanaRankova/pyladies

ab09f1392212f72f2952e73525a93be0ede2484d

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refs/heads/master

2020-08-15T15:07:46.326357

2019-12-30T18:04:58

215,361,294

0

null

false

2019-12-30T18:05:00

2019-10-15T17:47:29

2019-12-30T18:02:58

2019-12-30T18:04:59

42

0

Python

false

hracie_pole = '--------------------' def vyhodnot(hracie_pole): """Vezme momentalny stav hracieho pola a podla podmienok vo funkcii, urci vyhercu.""" stav = '' if 'xxx' in hracie_pole: stav = 'x' elif 'ooo' in hracie_pole: stav = 'o' elif '-' not in hracie_pole: stav = '!' else: stav = '-' return stav print(vyhodnot(hracie_pole))

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piskvorky1.py

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emaballarin/RADLER

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/src/radler_ae_pretrain.py

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[ "MIT" ]

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https://github.com/emaballarin/RADLER

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refs/heads/master

2020-06-28T18:26:01.567885

2020-06-08T03:28:56

200,308,007

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# ---------------------------------------------------------------------------- # # # # RADLER ~ (adversarially) Robust Adversarial Distributional LEaRner # # # # |> Pretraining through AutoEncoding <| # # # # (C) 2019-* Emanuele Ballarin <emanuele@ballarin.cc> # # (C) 2019-* AI-CPS@UniTS Laboratory (a.k.a. Bortolussi Group) # # # # Distribution: MIT License # # (Full text: https://github.com/emaballarin/RADLER/blob/master/LICENSE) # # # # Eventually-updated version: https://github.com/emaballarin/RADLER # # # # ---------------------------------------------------------------------------- # # Adapted from W. Falcon: # https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09 # ------- # # IMPORTS # # ------- # from __future__ import print_function import torch import torch as th from torch.utils.data import DataLoader from torch.utils.data.dataset import Dataset import pytorch_lightning as pl if __name__ == "__main__": import architectures as myarchs import weights_util as wutil else: from src import architectures as myarchs from src import weights_util as wutil # -------------------- # # NETWORK ARCHITECTURE # # -------------------- # # Building blocks implemented in another module # ------------------- # # DATASET BOILERPLATE # # ------------------- # # A fake dataset composed all of the same in-out pair, which is the only example (to be autoencoded) class MyStupidDataset(Dataset): def __init__(self): # Such example: self.my_single_example = ( wutil.dictmodel_flatten(th.load("mnist_cnn_small.pt"), th_device="cuda") .clone() .detach() ) def __getitem__(self, index): return self.my_single_example, self.my_single_example def __len__(self): return 1000 # AUTOENCODER (PyTorch Lightning module; just by putting E & D scaffolds together) class Autoencoder(pl.LightningModule): def __init__(self, data_size=20522, code_size=2): super(Autoencoder, self).__init__() self.data_size = data_size self.code_size = code_size self.encoder = myarchs.AE_Encoder(data_size, code_size) self.decoder = myarchs.AE_Decoder(code_size, data_size) def forward(self, x): # Encoding module x = self.encoder(x) # Decoding module x = self.decoder(x) return x def encode(self, x): return self.encoder(x) def decode(self, code): return self.decoder(code) def MSE_loss(self, given_in, given_out): return (torch.nn.MSELoss(reduction="sum"))(given_in, given_out) def training_step(self, train_batch, batch_idx): x, y = train_batch copied_input = self.forward(x) loss = self.MSE_loss(copied_input, y) logs = {"train_loss": loss} return {"loss": loss, "log": logs} # def validation_step(self, val_batch, batch_idx): # x, y = val_batch # copied_input = self.forward(x) # loss = self.MSE_loss(copied_input, y) # return {"val_loss": loss} # # def validation_end(self, outputs): # avg_loss = torch.stack([x["val_loss"] for x in outputs]).mean() # tensorboard_logs = {"val_loss": avg_loss} # return {"avg_val_loss": avg_loss, "log": tensorboard_logs} def train_dataloader(self): my_dataset = MyStupidDataset() my_train = DataLoader(my_dataset, shuffle=False, batch_size=64) return my_train # def val_dataloader(self): # my_dataset = MyStupidDataset() # my_val = DataLoader(my_dataset, shuffle=False, batch_size=64) # return my_val # def test_dataloader(self): # my_dataset = MyStupidDataset() # my_test = DataLoader(my_dataset, shuffle=False, batch_size=64) # return my_test def configure_optimizers(self): # The lightningModule HAS the parameters # (remember that we had the __init__ and forward method but we're just not showing it here) optimizer = torch.optim.Adam(self.parameters(), lr=1e-3, weight_decay=1e-5) return optimizer # train model = Autoencoder() trainer = pl.Trainer( # We are forcing pure overfitting here; still # we don't want too much of it. max_epochs=15, # Autostopped, eventually (but it's balanced after all) gpus=1, ) trainer.fit(model) # For some curious reason (a.k.a. purposeful overfitting), the decoding part is # almost centered at [0.0, 0.0] to produce the best replication of the in-out # Save model (decoding part) th.save(model.decoder.state_dict(), "bottleneck.pt")

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radler_ae_pretrain.py

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Tecplot/handyscripts

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/python/examples/doc_examples/data/dataset_variable_names_tuple.py

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https://github.com/Tecplot/handyscripts

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refs/heads/master

2023-08-22T15:29:22.629644

2023-08-12T01:19:59

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2022-01-13T01:11:02

2018-09-21T22:47:23

2022-01-12T19:41:56

2022-01-13T01:11:01

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from os import path import tecplot as tp examples_dir = tp.session.tecplot_examples_directory() datafile = path.join(examples_dir,'SimpleData','DownDraft.plt') dataset = tp.data.load_tecplot(datafile) result = tp.data.query.probe_at_position(0,0.1,0.3) data = dataset.VariablesNamedTuple(*result.data) # prints: (RHO, E) = (1.17, 252930.37) msg = '(RHO, E) = ({:.2f}, {:.2f})' print(msg.format(data.RHO, data.E))

UTF-8

Python

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415

py

314

dataset_variable_names_tuple.py

287

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qingshangithub/Smart-car-tracking-with-opencv

19,026,705,149,551

458c9de713b120f8e0d9a237eea7de21fa71b2fb

c5e2ca3242cf86c4d6d9e5cff65763784aaaa708

/NNCProject/Lower/motor_multiPro/speedsensor/test.py

48db76f53a3cb325a45c095b83a8b459520f291c

[]

no_license

https://github.com/qingshangithub/Smart-car-tracking-with-opencv

bed8a3417102572963dc35bd6bdb80e226a93142

9d11d9b3f22acfc0f24002e6b420cbdc5d95f9cf

refs/heads/master

2021-04-26T22:57:11.912021

2018-03-05T10:35:30

123,901,874

0

null

import pigpio from time import sleep from multiprocessing import Process p = pigpio.pi() p.set_mode(17, pigpio.INPUT) def cbf(g, l, tick): print(tick) cb = p.callback(17, pigpio.RISING_EDGE, cbf) if __name__ == '__main__': sleep(20)

UTF-8

Python

false

244

py

40

test.py

23

0.668033

0.643443

0

13

17.769231

44

usyyy/python

17,557,826,309,209

8a4bf4df04f08e0b9b6458f4ae30f9f027d76afb

80cc3be9871b9d306dcebb1df7ddcbf9284a9524

/fluent-python/my-code/01-python-data-model/01-a-pythonic-deck.py

dbcb61a4ef3eb3acadb3506cfc408f0ae2e4ecac

[]

no_license

https://github.com/usyyy/python

c4d79cb4211eaece960d64649a83db1060df30fa

9532c5b95036342d9345c4ed2d55b16f507dade6

refs/heads/master

2020-01-23T22:01:02.662730

2017-12-06T22:37:02

74,713,627

0

null

import collections from random import choice from math import hypot # Example 1.1 - A deck as a sequence of cards Card = collections.namedtuple('Card', ['rank', 'suit']) class FrenchDeck: ranks = [str(n) for n in range(2, 11)] + list('JQKA') suits = 'spades diamonds clubs hearts'.split() def __init__(self): self._cards = [Card(rank, suit) for suit in self.suits for rank in self.ranks] def __len__(self): return len(self._cards) def __getitem__(self, position): return self._cards[position] # beer_card = Card('7', 'diamonds') beer_card # deck = FrenchDeck() len(deck) # deck[0] deck[-1] # choice(deck) choice(deck) choice(deck) # deck[:3] deck[12::13] # pick the aces by starting on 12 and skipping 13 cards at a time # for card in deck: print(card) for card in reversed(deck): print(card) # Card('Q', 'hearts') in deck Card('7', 'beasts') in deck # suit_values = dict(spades=3, hearts=2, diamonds=1, clubs=0) def spades_high(card): rank_value = FrenchDeck.ranks.index(card.rank) return rank_value * len(suit_values) + suit_values[card.suit] for card in sorted(deck, key=spades_high): print(card) # Example 1.2 - A Simple 2d vector class class Vector: def __init__(self, x=0, y=0): self.x = x self.y = y # string representation of the object # %r returns 1 not '1' since they are needed as integers def __repr__(self): return 'Vector (%r, %r)' % (self.x, self.y) def __abs__(self): return hypot(self.x, self.y) def __bool__(self): # TODO: How does abs(self) return 0.0 (I've checked that it does) return bool(abs(self)) def __add__(self, other): x = self.x + other.y y = self.y + other.y return Vector(x, y) def __mul__(self, scalar): return Vector(self.x * scalar, self.y * scalar)

UTF-8

Python

false

1,892

py

59

01-a-pythonic-deck.py

53

0.609937

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detian08/mcl

3,547,642,993,752

ecf7afa8cc47355febbf1968a8783586d0099af2

99e57f00fcaf4469c1c1b79f2d17176aaef9a790

/loan/wizard/__init__.py

73594a5199bed8d87c64e42931c7ccf1dc9fa32c

[]

no_license

https://github.com/detian08/mcl

d007ffd0e869f3bd9a8c74bc8473119901f0de2a

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refs/heads/master

2022-03-23T19:36:29.608645

2019-12-11T10:15:50

null

0

null

from . import hr_loan_wizard

UTF-8

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29

py

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__init__.py

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1

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akbarlintang/perumahan

16,449,724,778,184

e981ad2971bf95bb5ad26fb4b7a1eb93a1732c2b

73f6ba42a793d18ad5b4c44cfdc278e51aa1b9b0

/perum/views.py

e8828e9cea66ce406f19303c074bfd490595440b

[]

no_license

https://github.com/akbarlintang/perumahan

e14eb922a86c76581d8faae5700ff21e83ba13ee

66c908a382bc32e9b9abc69b3a6f22eab12d8d2c

refs/heads/main

2022-12-30T08:11:23.856824

2020-10-22T15:33:30

306,377,878

0

null

from django.shortcuts import render, redirect from django.http import HttpResponse from django.db.models import Sum from django.forms import inlineformset_factory from django.contrib.auth.forms import UserCreationForm from django.contrib.auth import authenticate, login, logout from django.contrib.auth.decorators import login_required from django.contrib.auth.models import Group from django.contrib import messages from .models import * from .forms import * from .filters import * from .decorators import * import datetime # Create your views here. @unauthenticated_user def loginPage(request): if request.method == 'POST': username = request.POST.get('username') password = request.POST.get('password') user = authenticate(request, username=username, password=password) if user is not None: login(request, user) return redirect('home') else: messages.info(request, 'Username atau Password salah!') context = {} return render(request, 'perum/login.html', context) def logoutUser(request): logout(request) return redirect('login') @unauthenticated_user def registerPage(request): form = CreateUserForm() if request.method == 'POST': form = CreateUserForm(request.POST) if form.is_valid(): user = form.save() username = form.cleaned_data.get('username') group = Group.objects.get(name='customer') user.groups.add(group) Pelanggan.objects.create( user=user, ) messages.success(request, 'Akun berhasil dibuat untuk ' + username) return redirect('login') context = {'form':form} return render(request, 'perum/register.html', context) @login_required(login_url='login') @admin_only def home(request): pelanggans = Pelanggan.objects.all() administrasis = Administrasi.objects.all() today = datetime.date.today() bulan = Administrasi.objects.filter(tanggal__year=today.year, tanggal__month=today.month).aggregate(Sum('biaya_angsur'))['biaya_angsur__sum'] tahun = Administrasi.objects.filter(tanggal__year=today.year).aggregate(Sum('biaya_angsur'))['biaya_angsur__sum'] context = {'pelanggans':pelanggans, 'bulan':bulan, 'tahun':tahun, 'administrasis':administrasis} return render(request, 'perum/dashboard.html', context) @login_required(login_url='login') def unit(request): units = Unit.objects.all().order_by('no_unit') unitFilter = UnitFilter(request.GET, queryset=units) units = unitFilter.qs context = {'units':units, 'unitFilter':unitFilter} return render(request, 'perum/unit.html', context) @login_required(login_url='login') @allowed_users(allowed_roles=['admin']) def pelanggan(request): pelanggans = Pelanggan.objects.all() pelangganFilter = PelangganFilter(request.GET, queryset=pelanggans) pelanggans = pelangganFilter.qs context = {'pelanggans':pelanggans, 'pelangganFilter':pelangganFilter} return render(request, 'perum/pelanggan.html', context) @login_required(login_url='login') @allowed_users(allowed_roles=['admin']) def administrasi(request): administrasis = Administrasi.objects.all() administrasiFilter = AdministrasiFilter(request.GET, queryset=administrasis) administrasis = administrasiFilter.qs context = {'administrasis':administrasis, 'administrasiFilter':administrasiFilter} return render(request, 'perum/administrasi.html', context) def pemesanan(request): pemesanan = Booking.objects.all().order_by('tanggal') context = {'pemesanan':pemesanan} return render(request, 'perum/pemesanan.html', context) def akun(request, pk): akun = Pelanggan.objects.get(id=pk) pelanggans = Pelanggan.objects.filter(id=pk) adm = Administrasi.objects.filter(nama_id=pk) context = {'pelanggans':pelanggans, 'akun':akun, 'adm':adm} return render(request, 'perum/akun.html', context) @login_required(login_url='login') @allowed_users(allowed_roles=['customer']) def profil(request): nama = request.user.profile.nama no_telp = request.user.profile.no_telp email = request.user.profile.email no_unit = request.user.profile.no_unit context = {'nama':nama, 'no_telp':no_telp, 'email':email, 'no_unit':no_unit} return render(request, 'perum/profil.html', context) @login_required(login_url='login') def angsuran(request, pk): adm = Administrasi.objects.filter(nama_id=pk) context = {'adm':adm} return render(request, 'perum/angsuran.html', context) def infoUnit(request, pk): unit = Unit.objects.filter(id=pk) context = {'unit':unit} return render(request, 'perum/info_unit.html', context) def createBooking(request, pk): unit = Booking.objects.filter(id=pk) form = BookingForm() if request.method == 'POST': form = BookingForm(request.POST) if form.is_valid(): form.save() return redirect('/unit') context = {'form':form, 'unit':unit} return render(request, 'perum/form_booking.html', context) def createPelanggan(request): form = PelangganForm() if request.method == 'POST': form = PelangganForm(request.POST) if form.is_valid(): form.save() return redirect('home') context = {'form':form} return render(request, 'perum/form_pelanggan.html', context) def ubahPelanggan(request, pk): pelanggan = Pelanggan.objects.get(id=pk) form = PelangganForm(instance=pelanggan) if request.method == 'POST': form = PelangganForm(request.POST, instance=pelanggan) if form.is_valid(): form.save() return redirect('home') context = {'form':form} return render(request, 'perum/form_pelanggan.html', context) def hapusPelanggan(request, pk): pelanggan = Pelanggan.objects.get(id=pk) if request.method == "POST": pelanggan.delete() return redirect('pelanggan') context = {'pelanggan':pelanggan} return render(request, 'perum/hapus_pelanggan.html', context) def createUnit(request): form = UnitForm() if request.method == 'POST': form = UnitForm(request.POST) if form.is_valid(): form.save() return redirect('/unit') context = {'form':form} return render(request, 'perum/form_unit.html', context) def ubahUnit(request, pk): unit = Unit.objects.get(id=pk) form = UnitForm(instance=unit) if request.method == 'POST': form = UnitForm(request.POST, instance=unit) if form.is_valid(): form.save() return redirect('/unit') context = {'form':form} return render(request, 'perum/form_unit.html', context) def hapusUnit(request, pk): unit = Unit.objects.get(id=pk) if request.method == "POST": unit.delete() return redirect('/unit') context = {'unit':unit} return render(request, 'perum/hapus_unit.html', context) def hapusPemesanan(request, pk): pemesanan = Booking.objects.get(id=pk) if request.method == "POST": pemesanan.delete() return redirect('/pemesanan') context = {'pemesanan':pemesanan} return render(request, 'perum/hapus_pemesanan.html', context) def createAdministrasi(request): form = AdministrasiForm() if request.method == 'POST': form = AdministrasiForm(request.POST) if form.is_valid(): form.save() return redirect('/administrasi') context = {'form':form} return render(request, 'perum/form_administrasi.html', context) def pembayaran(request, pk): administrasis = Administrasi.objects.filter(id=pk) context = {'administrasis':administrasis} return render(request, 'perum/pembayaran.html', context) def pemasukan(request): today = datetime.datetime.today() bulan = Administrasi.objects.filter(tanggal__year=today.year).filter(tanggal__month=today.month).aggregate(Sum('biaya_angsur')) tahun = Administrasi.objects.filter(tanggal__year=today.year).aggregate(Sum('biaya_angsur')) hari = Administrasi.objects.filter(tanggal__year=today, tanggal__month=today, tanggal__date=today).aggregate(Sum('biaya_angsur'))

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andrebola/main-recsys-cocoplaya

9,191,230,041,151

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/model_main.py

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[ "Apache-2.0" ]

permissive

https://github.com/andrebola/main-recsys-cocoplaya

eec1f05186d7acaaebb72a40030126b95ccdb1ce

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refs/heads/master

2022-12-14T23:42:16.614712

2018-06-30T13:32:09

139,248,485

3

0

Apache-2.0

false

2021-06-01T22:20:20

2018-06-30T12:40:58

2019-04-18T06:01:25

2021-06-01T22:20:18

8

3

0

1

Python

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import pickle import os import re import json import numpy as np import datetime from sklearn.feature_extraction.text import CountVectorizer, HashingVectorizer from sklearn.feature_extraction import DictVectorizer from sklearn.preprocessing import MinMaxScaler, QuantileTransformer, LabelBinarizer from sklearn.decomposition import TruncatedSVD from scipy import sparse from lightfm import LightFM from collections import defaultdict SEED = 10 def normalize_name(name): name = name.lower() name = re.sub(r"[.,\/#!$%\^\*;:{}=\_`~()@]", ' ', name) name = re.sub(r'\s+', ' ', name).strip() return name def process_mpd(playlists_path, target_playlists, output_file, prev_songs_window): max_prev_song = 0 previous_tracks = defaultdict(lambda: defaultdict(int)) playlists_tracks = [] playlists = [] playlists_extra = {'name': []} filenames = os.listdir(playlists_path) for filename in sorted(filenames): if filename.startswith("mpd.slice.") and filename.endswith(".json"): fullpath = os.sep.join((playlists_path, filename)) f = open(fullpath) js = f.read() f.close() mpd_slice = json.loads(js) for playlist in mpd_slice['playlists']: nname = normalize_name(playlist['name']) playlists_extra['name'].append(nname) tracks = defaultdict(int) sorted_tracks = sorted(playlist['tracks'], key=lambda k: k['pos']) prev_track = [] for track in sorted_tracks: tracks[track['track_uri']] += 1 curr_prev_tracks = len(prev_track) for i, song_in_window in enumerate(prev_track): previous_tracks[song_in_window][track['track_uri']] += (i+1)/curr_prev_tracks previous_tracks[track['track_uri']][song_in_window] += (i+1)/curr_prev_tracks #previous_tracks[song_in_window][track['track_uri']] += 1 #previous_tracks[track['track_uri']][song_in_window] += 1 max_prev_song = max(max_prev_song, previous_tracks[track['track_uri']][song_in_window]) max_prev_song = max(max_prev_song, previous_tracks[song_in_window][track['track_uri']]) if len(prev_track) == prev_songs_window: prev_track.pop(0) prev_track.append(track['track_uri']) playlists_tracks.append(tracks) playlists.append(str(playlist['pid'])) top_pop = [] for i in previous_tracks.keys(): top_pop.append((i, np.sum(list(previous_tracks[i].values())))) top_pop = sorted(top_pop, key=lambda x:x[1], reverse=True)[:10000] top_pop = [t[0] for t in top_pop] # Add playlists on testing set test_playlists = [] test_playlists_tracks = [] target = json.load(open(target_playlists)) train_playlists_count = len(playlists) test_playlists_recommended_sum = [] for playlist in target["playlists"]: nname = "" if 'name' in playlist: nname = normalize_name(playlist['name']) playlists_extra['name'].append(nname) playlists.append(str(playlist['pid'])) test_playlists.append(str(playlist['pid'])) if len(playlist['tracks']) == 0: test_playlists_recommended_sum.append(top_pop) test_playlists_tracks.append({}) continue tracks = defaultdict(int) for track in playlist['tracks']: tracks[track['track_uri']] += 1 #playlists_tracks.append(tracks) test_playlists_tracks.append(tracks) recommended_pop = defaultdict(list) for t in tracks.keys(): for pt in previous_tracks[t].keys(): if pt not in tracks: recommended_pop[pt].append(previous_tracks[t][pt] /max_prev_song) recommended_pop_sum = [(t, np.sum(recommended_pop[t])) for t in recommended_pop.keys()] recommended_pop_sum = sorted(recommended_pop_sum, key=lambda x:x[1], reverse=True) recommended_pop_sum = [t[0] for t in recommended_pop_sum] test_playlists_recommended_sum.append(recommended_pop_sum) print ("Data loaded. Creating features matrix") dv = DictVectorizer() interaction_matrix = dv.fit_transform(playlists_tracks+[{}]*10000) lb = LabelBinarizer(sparse_output=True) pfeat_train = lb.fit_transform(playlists_extra['name'][:1000000]+[""]*10000) pfeat_test = lb.transform(playlists_extra['name']) print ("pfeat_train", pfeat_train.shape) print ("pfeat_test", pfeat_test.shape) playlist_features = pfeat_train # Need to hstack playlist_features eye = sparse.eye(playlist_features.shape[0], playlist_features.shape[0]).tocsr() playlist_features_concat = sparse.hstack((eye, playlist_features)) print ("Features matrix created. Training model") model = LightFM(loss='warp', no_components=200, max_sampled=30, item_alpha=1e-06, user_alpha=1e-06, random_state=SEED) model = model.fit(interaction_matrix, user_features=playlist_features_concat, epochs=150, num_threads=32) # freeze the gradient and optimize held-out users model.item_embedding_gradients = np.finfo(np.float32).max * np.ones_like(model.item_embedding_gradients) model.item_bias_gradients = np.finfo(np.float32).max * np.ones_like(model.item_bias_gradients) model.item_alpha = 0.0 model.user_alpha = 0.0 model.user_embedding_gradients[:1000000,:] = np.finfo(np.float32).max * np.ones_like(model.user_embedding_gradients[:1000000,:]) model.user_bias_gradients[:1000000] = np.finfo(np.float32).max * np.ones_like(model.user_bias_gradients[:1000000]) # Use the trained model to get a representation of the playlists on challenge set interaction_matrix = dv.transform(playlists_tracks+test_playlists_tracks) playlist_features = pfeat_test playlist_features_concat = sparse.hstack((eye, playlist_features)) model.user_embeddings[-10000:] = ((model.random_state.rand(10000, model.no_components) - 0.5) / model.no_components).astype(np.float32) model = model.fit_partial(interaction_matrix, user_features=playlist_features_concat, epochs=150, num_threads=32) print ("Model Trained") user_biases, user_embeddings = model.get_user_representations(playlist_features_concat) item_biases, item_embeddings = model.get_item_representations() fuse_perc = 0.7 with open(output_file, 'w') as fout: print('team_info,cocoplaya,main,andres.ferraro@upf.edu', file=fout) for i, playlist in enumerate(test_playlists): playlist_pos = train_playlists_count+i y_pred = user_embeddings[playlist_pos].dot(item_embeddings.T) + item_biases topn = np.argsort(-y_pred)[:len(test_playlists_tracks[i])+4000] rets = [(dv.feature_names_[t], float(y_pred[t])) for t in topn] songids = [s for s, _ in rets if s not in test_playlists_tracks[i]] songids_dict = {s:1 for s in songids} max_score = max(len(songids), len(test_playlists_recommended_sum[i])) pop_sum = {s:(max_score - p) for p,s in enumerate(test_playlists_recommended_sum[i])} fuse_sum = [] for p, s in enumerate(songids): pop_val_sum = 0 if s in pop_sum: pop_val_sum = pop_sum[s] fuse_sum.append((s,((max_score - p)*fuse_perc + pop_val_sum*(1-fuse_perc) ) / 2)) for s in pop_sum.keys(): if s not in songids_dict: fuse_sum.append((s,(pop_sum[s]*(1-fuse_perc) ) / 2)) fuse_sum = sorted(fuse_sum, key=lambda x:x[1], reverse=True) print(' , '.join([playlist] + [x[0] for x in fuse_sum[:500]]), file=fout) if __name__ == '__main__': playlists_file = './mpd/data/' target_playlists = 'eval_data/challenge_set.json' output_file = 'output/output_main_final_sum_window_10.csv' process_mpd(playlists_file, target_playlists, output_file, 10)

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import tensorflow as tf import numpy as np import dic import rnn_functions import display_functions import matplotlib.pyplot as plt from sklearn.metrics import r2_score as r2 data_path = '../recon_q_examples/data/Exam52004/Series5/recon_data' mask_path = '../recon_q_examples/data/Exam52004/Series5/mask.dat' map_path = '../recon_q_examples/data/Exam52004/Series5/dm_qmaps.dat' dl_path = '../recon_q_examples/data/Exam52004/Series5/nn_qmaps.dat' size = 200 Nreps = 1000 series = dic.read_mrf_data(data_path, Nreps, size) mask = dic.load_relaxation_times_map(mask_path, size, method=2) dm_map = dic.load_relaxation_times_map(map_path, size, method=0) dl_map = dic.load_relaxation_times_map(dl_path, size, method=1) num_in = 100 num_fc = 64 timesteps = 10 num_hidden = 8 num_out = 2 times_max = np.array([4., .6]) #times_max = np.array([4., 1.]) X = tf.placeholder("float", [None, timesteps, num_in]) net = rnn_functions.LSTM(X, timesteps, num_hidden, num_out, activation=tf.sigmoid, fc=True, tr=True) #out = times_max * net out = [times_max * net_ for net_ in net] saver = tf.train.Saver() epoch = 1940 with tf.Session() as sess: rnn_functions.load_lstm(saver, sess, epoch, '_sigmoid_mape_tr') times = sess.run(out, feed_dict={X: series[:timesteps*num_in, :].T.reshape((series.shape[1], timesteps, num_in))}) img = times[9].reshape((size, size, 2), order='C') #img = times.reshape((size, size, 2), order='C') img[:, :, 0] *= mask * 1e3 img[:, :, 1] *= mask * 1e3 img_dl = dl_map * 1e3 img_dl[:, :, 0] = mask * img_dl[:, :, 0].T img_dl[:, :, 1] = mask * img_dl[:, :, 1].T img_dm = dm_map * 1e3 img_dm[:, :, 0] = img_dm[:, :, 0].T img_dm[:, :, 1] = img_dm[:, :, 1].T true_t1 = np.array([604, 596,1448, 1262, 444, 754, 903, 1276, 1034, 745, 1160, 966]) true_t2 = np.array([95, 136, 390, 184, 154, 116, 137, 204, 167, 157, 214, 224]) corners = display_functions.detect_phantom_tubes(img, mask, 28, 2) img_gt, _, _ = display_functions.compare_to_gt(img, mask, corners, 28, true_t1, true_t2) fig_t1, ax_t1, fig_t2, ax_t2 = display_functions.plot_results(img, phantom=True, gt=img_gt) fig_t1_dm, ax_t1_dm, fig_t2_dm, ax_t2_dm = display_functions.plot_comparison_method(img_dm, img, phantom=True, gt=img_gt) fig_t1_dl, ax_t1_dl, fig_t2_dl, ax_t2_dl = display_functions.plot_comparison_method(img_dl, img, phantom=True, gt=img_gt, method=1) #display_functions.draw_bounding_boxes(ax_t1, corners, 28) #display_functions.draw_bounding_boxes(ax_t2, corners, 28) plt.rc('xtick', labelsize=16) plt.rc('ytick', labelsize=16) imgs = [] for k in range(len(times)): img = times[k].reshape((size, size, 2), order='C') img[:, :, 0] *= mask * 1e3 img[:, :, 1] *= mask * 1e3 imgs.append(img) #fig_tr, ax_tr = plt.subplots(10, 6, figsize=(18, 30)) #for k in range(len(imgs)): # t1 = ax_tr[k][0].imshow(imgs[k][:, :, 0], cmap='hot', vmax=4000, vmin=0) # t1_err = ax_tr[k][1].imshow(np.abs(imgs[k][:, :, 0] - img_dm[:, :, 0])/(img_dm[:, :, 0] + 1e-6) * 1e2, cmap='Reds', vmax=100, vmin=0) # ax_tr[k][2].scatter(img_dm[:, :, 0], imgs[k][:, :, 0], c='b', marker='.', alpha=0.1) # t2 = ax_tr[k][3].imshow(imgs[k][:, :, 1], cmap='copper', vmax=300, vmin=0) # t2_err = ax_tr[k][4].imshow(np.abs(imgs[k][:, :, 1] - img_dm[:, :, 1])/(img_dm[:, :, 1] + 1e-6) * 1e2, cmap='Reds', vmax=100, vmin=0) # ax_tr[k][5].scatter(img_dm[:, :, 1], imgs[k][:, :, 1], c='b', marker='.', alpha=0.1) # r2_t1 = r2(img_dm[:, :, 0], imgs[k][:, :, 0]) # r2_t2 = r2(img_dm[:, :, 1], imgs[k][:, :, 1]) # ax_tr[k][0].text(-35, 100, r'\Huge {:d}'.format((k+1))) # ax_tr[k][2].text(1, 3550, r'\Large R2 = {:5f}'.format(r2_t1)) # ax_tr[k][2].set_xlabel(r'\Large DM (ms)') # ax_tr[k][2].set_ylabel(r'\Large LSTM (ms)') # ax_tr[k][2].set_xbound(lower=0, upper=4000) # ax_tr[k][2].set_ybound(lower=0, upper=4000) # ax_tr[k][2].plot([x for x in range(4000)], [x for x in range(4000)], 'g--') # asp = np.diff(ax_tr[k][2].get_xlim())[0] / np.diff(ax_tr[k][2].get_ylim())[0] # ax_tr[k][2].set_aspect(asp) # ax_tr[k][2].ticklabel_format(style='sci', axis='both', scilimits=(3, 3)) # ax_tr[k][0].set_axis_off() # ax_tr[k][1].set_axis_off() # ax_tr[k][5].text(1, 550, r'\Large R2 = {:5f}'.format(r2_t2)) # ax_tr[k][5].set_xlabel(r'\Large DM (ms)') # ax_tr[k][5].set_ylabel(r'\Large LSTM (ms)') # ax_tr[k][5].set_xbound(lower=0, upper=600) # ax_tr[k][5].set_ybound(lower=0, upper=600) # ax_tr[k][5].plot([x for x in range(600)], [x for x in range(600)], 'g--') # asp = np.diff(ax_tr[k][5].get_xlim())[0] / np.diff(ax_tr[k][5].get_ylim())[0] # ax_tr[k][5].set_aspect(asp) # ax_tr[k][5].ticklabel_format(style='sci', axis='both', scilimits=(2, 2)) # ax_tr[k][5].set_xticks(ax_tr[k][5].get_yticks()[1:-1]) # ax_tr[k][3].set_axis_off() # ax_tr[k][4].set_axis_off() #fig_tr.colorbar(t1, fraction=0.05, pad=-0.05, ax=ax_tr[9][0], orientation='horizontal') #fig_tr.colorbar(t1_err, fraction=0.05, pad=-0.05, ax=ax_tr[9][1], orientation='horizontal') #fig_tr.colorbar(t2, fraction=0.05, pad=-0.05, ax=ax_tr[9][3], orientation='horizontal') #fig_tr.colorbar(t2_err, fraction=0.05, pad=-0.05, ax=ax_tr[9][4], orientation='horizontal') #ax_tr[0][0].set_title(r'\Huge \textbf{T1 (ms)}') #ax_tr[0][3].set_title(r'\Huge \textbf{T2 (ms)}') #ax_tr[0][1].set_title(r'\Huge \textbf{T1 Error (\%)') #ax_tr[0][4].set_title(r'\Huge \textbf{T2 Error (\%)') #fig_comp, ax_comp = plt.subplots(2, 4, figsize=(20, 10)) #t1 = ax_comp[0][0].imshow(imgs[9][:, :, 0], cmap='hot', vmax=4000, vmin=0) #t1_err = ax_comp[0][2].imshow(np.abs(imgs[9][:, :, 0] - img_dm[:, :, 0])/(img_dm[:, :, 0] + 1e-6) * 1e2, cmap='Reds', vmax=100, vmin=0) #t1_dm = ax_comp[0][1].imshow(img_dm[:, :, 0], cmap='hot', vmax=4000, vmin=0) #ax_comp[0][3].scatter(img_dm[:, :, 0], imgs[9][:, :, 0], c='b', marker='.', alpha=0.1) #t2 = ax_comp[1][0].imshow(imgs[9][:, :, 1], cmap='copper', vmax=300, vmin=0) #t2_err = ax_comp[1][2].imshow(np.abs(imgs[9][:, :, 1] - img_dm[:, :, 1])/(img_dm[:, :, 1] + 1e-6) * 1e2, cmap='Reds', vmax=100, vmin=0) #t2_dm = ax_comp[1][1].imshow(img_dm[:, :, 1], cmap='copper', vmax=300, vmin=0) #ax_comp[1][3].scatter(img_dm[:, :, 1], imgs[9][:, :, 1], c='b', marker='.', alpha=0.1) #r2_t1 = r2(img_dm[:, :, 0], imgs[9][:, :, 0]) #r2_t2 = r2(img_dm[:, :, 1], imgs[9][:, :, 1]) #ax_comp[0][3].text(1, 3550, r'\huge R2 = {:5f}'.format(r2_t1)) #ax_comp[0][3].set_xlabel(r'\huge DM (ms)') #ax_comp[0][3].set_ylabel(r'\huge LSTM (ms)') #ax_comp[0][3].set_xbound(lower=0, upper=4000) #ax_comp[0][3].set_ybound(lower=0, upper=4000) #ax_comp[0][3].plot([x for x in range(4000)], [x for x in range(4000)], 'g--') #ax_comp[0][0].set_axis_off() #ax_comp[0][1].set_axis_off() #ax_comp[0][2].set_axis_off() #ax_comp[1][3].text(1, 550, r'\huge R2 = {:5f}'.format(r2_t2)) #ax_comp[1][3].set_xlabel(r'\huge Dictionary matching (ms)') #ax_comp[1][3].set_ylabel(r'\huge LSTM (ms)') #ax_comp[1][3].set_xbound(lower=0, upper=600) #ax_comp[1][3].set_ybound(lower=0, upper=600) #ax_comp[1][3].plot([x for x in range(600)], [x for x in range(600)], 'g--') #ax_comp[1][0].set_axis_off() #ax_comp[1][1].set_axis_off() #ax_comp[1][2].set_axis_off() #fig_comp.colorbar(t1, fraction=0.05, pad=-0.05, ax=ax_comp[0][0], orientation='horizontal') #fig_comp.colorbar(t1_dm, fraction=0.05, pad=-0.05, ax=ax_comp[0][1], orientation='horizontal') #fig_comp.colorbar(t1_err, fraction=0.05, pad=-0.05, ax=ax_comp[0][2], orientation='horizontal') #fig_comp.colorbar(t2, fraction=0.05, pad=-0.05, ax=ax_comp[1][0], orientation='horizontal') #fig_comp.colorbar(t2_dm, fraction=0.05, pad=-0.05, ax=ax_comp[1][1], orientation='horizontal') #fig_comp.colorbar(t2_err, fraction=0.05, pad=-0.05, ax=ax_comp[1][2], orientation='horizontal') #ax_comp[0][0].text(-80, 100, r'\Huge \textbf{T1 (ms)}') #ax_comp[1][0].text(-80, 100, r'\Huge \textbf{T2 (ms)}') #ax_comp[0][0].set_title(r'\Huge \textbf{LSTM}') #ax_comp[0][1].set_title(r'\Huge \textbf{DM}') #ax_comp[0][2].set_title(r'\Huge \textbf{Absolute Percentage Error')

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#Created by pramod G #All imports from time import time,sleep import argparse import torch from torch import nn, optim import torch.nn.functional as F from torchvision import datasets, transforms, models import numpy as np import matplotlib.pyplot as plt from PIL import Image import json #for keeping workspace live during non activity from workspace_utils import active_session # Main program function defined below def main(): start_time = time() #taking the input in_arg = get_input_args() #print(in_arg.hidden_units, in_arg.dir) #Set which device to run on if in_arg.gpu: if torch.cuda.is_available(): device = torch.device("cuda:0") else: print("Sorry but there is no gpu available" ) print("Program is terminated") exit() else: device = torch.device("cpu") #Load the desired model loaded_model = loadCheckpoint(in_arg.checkpointpath,device) #print("Accuracy of newly loaded model :") #Acurracy(loaded_model,device) #read catagories names catnames = in_arg.category_names with open(catnames, 'r') as f: cat_to_name = json.load(f) #preprocess the data probs, classes = predict(in_arg.img, loaded_model, device , in_arg.top_k ) probs = probs.cpu() probs = probs.numpy() Topprob = probs.argmax() print("The most likely image class is {} and it's associated probability is {}".format(classes[Topprob],probs[0][Topprob])) print("The top {} classes along with associated probabilities are {}, {}".format(in_arg.top_k ,classes,probs)) TopKClasses = [cat_to_name[clas] for clas in classes] for i,clas in enumerate(TopKClasses): print(" The top {} class probably is {}".format(i+1,clas)) end_time = time() ttime = end_time - start_time hh = round(ttime/(3600)) mm = round((ttime%3600)/60) ss = round((ttime%3600)%60) tot_time = "{}:{}:{}".format(hh,mm,ss) print("\n** Total Elapsed Runtime:", tot_time) def get_input_args(): parser = argparse.ArgumentParser(description = " This program is for predicting when given a image by specifying path to Imagefile and a checkpoint to create model from ") parser.add_argument( 'img',type = str, default = 'flowers/test/100/image_07899.jpg', metavar='' , help = 'This is to specify Path to the imagefile need to be predicted (default if running from workspace- \'paind-project/flowers/test/100/image_07899.jpg\')') parser.add_argument( 'checkpointpath', type = str, default = 'CLcheckpoint.pth', metavar='' , help = 'This is to specify checkpoint from which tained model will be loaded (default-\'CLcheckpoint.pth\')') parser.add_argument( '----category_names',type = str, default = 'cat_to_name.json', metavar='' , help = 'This is to specify to load a JSON file that maps the class values to other category names (default-\'cat_to_name.json.pth\')') parser.add_argument( '--top_k', type = int, default = 5, metavar='' , help = 'This is to specify no of top probabilities you want to predict (default-\'5\')') #for flags like to be trained on gpu or not like that group = parser.add_mutually_exclusive_group() group.add_argument( '--gpu', action = 'store_true' , help = 'This is to set the flag if you want to use gpu or not(default if not specified-\'cpu\'') arg = parser.parse_args() return arg def preprocess(dir): #transforms for testing sets Pre_transforms = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])]) image = Image.open(dir) # Load the datasets with ImageFolder PreImage = Pre_transforms(image)[:3,:,:] return PreImage def predict(image_path, model , device ,topk): ''' Predict the class (or classes) of an image using a trained deep learning model. ''' # TODO: Implement the code to predict the class from an image file image = preprocess(image_path) #print(image.shape) image.unsqueeze_(0) #print(image.shape) image = image image = image.to(device) #model.type(torch.DoubleTensor) model.to(device) model.eval() with torch.no_grad(): output = model.forward(image) prob = torch.exp(output) probs , indices = torch.topk(prob , topk) indice = indices.cpu() indice = indice.numpy()[0] # Convert indices to classes idx_to_class = {val: key for key, val in model.class_to_idx.items()} #print(idx_to_class) classes = [idx_to_class[index] for index in indice] return probs , classes #function that loads a checkpoint and rebuilds the model def loadCheckpoint(filepath,device): if device == 'cuda:0': checkpoint = torch.load(filepath) else: checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage) arch = checkpoint['model'] model = models.__dict__[arch](pretrained=True) if(arch == 'resnet50'): model.fc = checkpoint['classifier'] if(arch == 'densenet121'): model.classifier = checkpoint['classifier'] if(arch == 'vgg16'): model.classifier = checkpoint['classifier'] model.load_state_dict(checkpoint['state_Dict']) #optimizer = checkpoint['optimizer_state_dict'] epochs = checkpoint['epochs'] model.class_to_idx = checkpoint['class_to_idx'] for param in model.parameters(): param.requires_grad = False #model.to() return model print("The model is loaded from {} file..".format(filepath)) #Defining Classifier with network class style class Network(nn.Module): def __init__(self,input_size, hiddenlayers, output_size, drop_p = 0.5): super().__init__() self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hiddenlayers[0])]) layersizes = zip(hiddenlayers[:-1],hiddenlayers[1:]) #print(layersizes) self.hidden_layers.extend([nn.Linear(h1,h2) for h1,h2 in layersizes]) self.output = nn.Linear(hiddenlayers[-1],output_size) self.dropout = nn.Dropout(p = drop_p) def forward(self , x): for lin in self.hidden_layers: x = F.relu(lin(x)) x = self.dropout(x) x = self.output(x) return F.log_softmax(x, dim= 1) if __name__ == "__main__": main()

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Python

false

6,961

py

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ByteLorde/RSEnterprise

13,958,643,741,172

044256da2e98d9101c671edc4e7b257061064509

f4fcceabcadc8dfb4d06cad2e8a5a6535253c0b5

/src/base/modules/Drawable/Label/Label.py

b2484e6d65d936cdc802b29239a121b3ca8e94ea

[]

no_license

https://github.com/ByteLorde/RSEnterprise

7fa56ce9a78ed6e6609a1045cc2c805ab277000d

7ceb1e46339dc6c8f0e212fcfc95edf25aac7fcf

refs/heads/master

2020-03-23T14:15:37.691099

2019-09-04T18:15:16

141,665,879

0

1

null

false

2018-08-08T02:14:01

2018-07-20T05:05:32

2018-07-25T01:05:42

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Python

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null

import cv2 from src.base.modules.Drawable.Color.Color import Color class Label: HERSHEY_SIMPLEX = 0 HERSHEY_PLAIN = 1 HERSHEY_DUPLEX = 2 HERSHEY_COMPLEX = 3 HERSHEY_TRIPLEX = 4 HERSHEY_COMPLEX_SMALL = 5 HERSHEY_SCRIPT_SIMPLEX = 6 HERSHEY_SCRIPT_COMPLEX = 7 FONT_ITALIC = 16 def __init__(self, text, color=Color.GREEN, style=HERSHEY_SIMPLEX, scale=1, thickness=1): self.text = text self.color = color self.style = style self.scale = scale self.thickness = thickness def getText(self): return self.text def setText(self, text): self.text = text def getTextSize(self): return cv2.getTextSize(self.text, self.style, self.scale, self.thickness) def drawComponent(self, image): cv2.putText(image, self.text, (x, y), self.style, self.scale, self.color, self.thickness) def getThickness(self): return self.thickness def setThickness(self, thickness): self.thickness = thickness def getScale(self): return self.scale def setScale(self, scale): self.scale = scale def getStyle(self): return self.style def setStyle(self, style): self.style = style def getColor(self): return self.color def setColor(self, color): self.color = color

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typemegan/Python

18,124,762,005,692

bfb6cdd76314a13dba63f9e91d7096ff7d8805fe

af4d2a0cefb304b5f38a4f0c170b799d7bb8f059

/CorePythonProgramming_2nd/ch02_start/print_test.py

839073f2c92b972766270b7df77ae28380b530b7

[]

no_license

https://github.com/typemegan/Python

e577842447c81dcb980198b33f844b4fab888517

9bfc48ddc3b050e629a9b84b41df57f35f990d2e

refs/heads/master

2020-12-13T18:25:13.379953

2019-06-07T04:01:24

37,420,605

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#coding:utf-8 num = 10 decorate = '*'*num + 'ex%d' + '*'*num #-----------------ex8：逗号----------------- title = decorate % 8 print title print "Mary had a little lamb." print "Its fleece was white as %s." % 'snow' print "And everywhere that Mary went." print "."*10 end1 = 'C' end2 = 'h' end3 = 'e' end4 = 'e' end5 = 's' end6 = 'e' end7 = 'B' end8 = 'u' end9 = 'r' end10 = 'g' end11 = 'e' end12 = 'r' #不换行输出，print后跟逗号 #当要print内容过长时，可用逗号‘，’来换行输入 print end1 + end2 + end3 + end4 + end5 + end6, #末尾的逗号表示不换行，空格后继续输出下一行 print end7 + end8 + end9 + end10 + end11 + end12 print "hello", print "Python", print "!" print "" #------------------ex9：三引号--------------- title = decorate % 9 print title days = "Mon Tue Wed Thu Fri Sat Sun" months = "\nJan\nFeb\nMar\nApr\nMay\nJun\nJul\nAug" print "Here are the days:",days print "Here are the months:",months print "Here are the months:%r" % months #输出：'\nJan\nFeb\nMar\nApr\nMay\nJun\nJul\nAug' #%r只输出原始数据，不对转义符后内容进行转换 #多行输入并多行输出 #成对三双引号包含所有内容"""xxx""" print """ There's something on here. With the three double-quotes. We'll be able to type as much as we like. Even 4 lines if we want,or 5, or 6. """ #多行输入但输出一行 print( "test", "example", "is right?" ) #加‘\n’后仍是只输出一行 print( "hello\n", "Python\n", "how are you today\n" ) print "" #-----------------ex10 转义符--------------- title = decorate % 10 print title #转义单/双引号 print "single quote 'i'" #‘i’ print "double quote -"u"." #u没有打印出来 print "single quote \'i\'" #‘i’ print "double quote -\"u\"." #-"u". print "" print "I am 6'2\" tall." print 'I am 6\'2" tall.' print "" tabby_cat = "\tI'm tabbed in." persian_cat = "I'm split\na line." backslash_cat = "I'm \\ a \\ cat." #在三引号中可以不对引号转义 #三单引号或三双引号：效果一样 fat_cat = ''' I'll do a list: \t* Cat food \t* Fishies \t* Ca[nip\n\t* Grass \t* 'cake' \t* 'dessert:"chocolate"' \t* "drinks:'milk'" \t* "..." ''' print tabby_cat print persian_cat print backslash_cat print fat_cat quote_escape = "escape single:\'%s\'\rescape double:\"my little %s.\"\n" print '%%r-:%r' % (quote_escape % ("cat","cat")) print "%%s-:%s" % (quote_escape % ("dog",'dog')) #while True: # for i in ["/","-","|","\\","|"]: # print "%s\r" % i,

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Python

false

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274

print_test.py

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fossabot/Airflow-DAG

7,679,401,533,495

dbfc6c003c1468115b1a63beb9d9a4ba791f5b70

09eebd3cadc58f0765602d29f603db4528f2873e

/dags/bash_test_triggers.py

b49ffb5cc86d228aae535787c603cbac57da3517

[ "Apache-2.0" ]

permissive

https://github.com/fossabot/Airflow-DAG

ef52675b770870e36e579e8816a19626f00deac9

feb015251093afbb2d51325e56f7764a1ad8cdc9

refs/heads/master

2020-06-17T07:02:04.223922

2019-07-08T15:22:39

195,838,983

0

Apache-2.0

true

2019-07-08T15:22:34

2019-07-06T21:17:41

2019-07-06T21:17:40

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from airflow import DAG from airflow.operators.bash_operator import BashOperator from datetime import datetime, timedelta # Define the DAG... # Create the default arguments from airflow.operators.dagrun_operator import TriggerDagRunOperator default_args = { 'owner': 'hashmap-airflow', 'depends_on_past': False, 'start_date': datetime(2019, 7, 3), 'retries': 0, 'retry_delay': timedelta(minutes=1), } # create the DAG instance dag_layer_1 = DAG(dag_id='hw_bash_layer_1', default_args=default_args, schedule_interval=timedelta(1)) dag_layer_2 = DAG(dag_id='hw_bash_layer_2', default_args=default_args, schedule_interval=None) dag_layer_3 = DAG(dag_id='hw_bash_layer_3', default_args=default_args, schedule_interval=None) # Set start data # These are passed in as args. Seems that they aren't sent that way is a bug. dag_layer_1.start_date = default_args['start_date'] dag_layer_2.start_date = default_args['start_date'] dag_layer_3.start_date = default_args['start_date'] # This path is used in the code below. This should identify where the code is # being executed from. path = '/Users/johnaven/Sandbox/bash_dag_example' # STDOUT 'Hello World' with redirect to out.txt create_file= BashOperator( task_id='save-bash', bash_command='echo "Hello John" > {path}/out_tr.txt'.format(path=path) ) # print the contents of out.txt to STDOUT print_file=BashOperator( task_id='print-file', bash_command='cat {path}/out_tr.txt'.format(path=path) ) # clone/copy the data into another file copy_file=BashOperator( task_id='copy-file', bash_command='cp {path}/out_tr.txt {path}/out_tr_copy.txt'.format(path=path) ) # delete the files that were created delete_files = BashOperator( task_id='delete-files', bash_command='rm -f {path}/out_tr.txt && rm -f {path}/out_tr_copy.txt'.format(path=path) ) # Create Triggers trigger_layer_2 = TriggerDagRunOperator( task_id='trigger-layer2', trigger_dag_id='hw_bash_layer_2' ) trigger_layer_3 = TriggerDagRunOperator( task_id='trigger-layer-3', trigger_dag_id='hw_bash_layer_3' ) # Assign the operators to a DAG create_file.dag = dag_layer_1 trigger_layer_2.dag = dag_layer_1 print_file.dag = dag_layer_2 copy_file.dag = dag_layer_2 trigger_layer_3.dag = dag_layer_2 delete_files.dag = dag_layer_3 # Set any upstream requirements - e.g. especially for the triggers trigger_layer_2.set_upstream(task_or_task_list=[create_file]) trigger_layer_3.set_upstream(task_or_task_list=[print_file, copy_file])

UTF-8

Python

false

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py

5

bash_test_triggers.py

2

0.67754

0.664792

0

90

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92

DaiJitao/web_spammer_detection

15,693,810,537,107

2a9d6814c583645399bdfb8a9f75b5443eb5a202

a04c62fe53387f41e893c86d3603f65ff1a70ae0

/server/data_annotation/semantic_similarity.py

bf6650eda6d2ef46c5bf211ce9ba7427dc9d9551

[]

no_license

https://github.com/DaiJitao/web_spammer_detection

233f2b228cff6a1c71745179ee313f9daaf287cb

5d6c72dbdabdbb97ae0bf05468838a435dc4b3a5

refs/heads/master

2020-05-25T23:29:21.309615

2019-07-22T01:01:07

188,034,797

0

null

from conf.config import stop_words_file, fanChengCheng_db, redis_host, redis_port, incsv_fanChengCheng, zhaiTianLin_db, \ incsv_haiTianLin, jueDiQiuSheng_db, incsv_jueDiQiuSheng, zhangDanFeng_db, incsv_zhangDanFeng import jieba_fast as jieba from tools.utils import list_all_users_text, RedisClient, list_all_users, Indicators import redis import pickle ''' 链接redis ''' client = redis.Redis(host=redis_host, port=redis_port, db=fanChengCheng_db, decode_responses=True) ''' 数据标注 ''' class SemanticSimilarity(object): def __init__(self): pass def cut(self, text): ''' 分词 :param text: :return: ''' try: d = [line.rstrip() for line in open(stop_words_file, mode='r', encoding='utf-8')] # 停用词 stop_words = {}.fromkeys(d) cut_words = jieba.cut(text) except Exception as e: print(e) cut_words_clean = [] for seg in cut_words: if seg not in stop_words: cut_words_clean.append(seg) # 去除停用词 return cut_words_clean def common_words(self, cutwds_lst1, cutwds_lst2): ''' 找出共现词 :param cutwds_lst1: :param cutwds_lst2: :return: [] ''' cmn_words = [] tmp = {}.fromkeys(cutwds_lst2) for words in cutwds_lst1: if words in tmp: cmn_words.append(words) return cmn_words def short_words_num(self, lst1, lst2): return min(len(lst1), len(lst2)) def ratio(self, texts, thred=0.8): ''' 计算语义重复率 :param texts: [ doc, doc ] :return: ''' cut_texts = [] # 对所有文本进行分词 for text in texts: seg = self.cut(text) cut_texts.append(seg) ratios = [] # 该用户的比率 size = len(cut_texts) for i in range(size): for j in range(i + 1, size): common_words_ = self.common_words(cut_texts[i], cut_texts[j]) n = self.short_words_num(cut_texts[i], cut_texts[j]) if n != 0: ratio = len(common_words_) / n else: ratio = 0.0 if ratio > thred: ratio = float("%.3f" % ratio) ratios.append(ratio) return ratios class EventSemanticSim(): ''' 统计每个事件的语义重复率 ''' def __init__(self, user_file, user_db): self.user_file = user_file self.user_client = RedisClient(host=redis_host, port=redis_port, db=user_db) def save_redis(self): ss = SemanticSimilarity() # 初始化 all_users = list_all_users(self.user_file) for key in all_users: texts = list_all_users_text(self.user_file, key) # 获取所有文本 rs = ss.ratio(texts) if len(rs) > 0: rs = [str(i) for i in rs] # 转换为字符串 temp = ",".join(rs) else: temp = "低于80%" uid = str(key) + "_" + Indicators.semantic # print(uid, " rs:", rs, " temp:", temp) self.user_client.set(uid, temp) print("写入语义重复率redis成功！") if __name__ == "__main__": # event = EventSemanticSim(user_file=incsv_fanChengCheng, user_db=fanChengCheng_db) # event.save_redis() event = EventSemanticSim(user_file=incsv_zhangDanFeng, user_db=zhangDanFeng_db) event.save_redis() event = EventSemanticSim(user_file=incsv_haiTianLin, user_db=zhaiTianLin_db) event.save_redis() event = EventSemanticSim(user_file=incsv_jueDiQiuSheng, user_db=jueDiQiuSheng_db) event.save_redis()

UTF-8

Python

false

3,808

py

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semantic_similarity.py

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sanyamjain335/SNS-Assignment

14,293,651,166,186

0bf98e82a9de440af428448c3e633aee30776ad0

5d2c6c4072510d1883ee30ddf7da2c263598c47c

/serverlib.py

503371a426ed80babb4ebb5ea28fca97cfb24437

[]

no_license

https://github.com/sanyamjain335/SNS-Assignment

0ce1f3d2433872b9b4d97d0f3ca1e4684d826654

3e5d4b39eba7b8c6f46f5ce5676570bcd50487e8

refs/heads/main

2023-02-25T02:46:41.875400

2021-01-27T13:34:51

null

0

null

import clientlib import socket,pickle import json import random import crypto import time HOST = '127.0.0.1' PORT = 54005 class group: def __init__(self,name): self.grname = name self.membercount=0 self.memberdic = {} self.nounce=str(random.randint(0,655365)) def addmember(self,name,port): self.memberdic[name]=port self.membercount=self.membercount+1 return self.nounce def getportlist(self): return [self.memberdic[name] for name in self.memberdic.keys()] def message(self,conn,data): if data['type'] =='file': chunk_array=[] while(True): msg=conn.recv(1024) if not msg: break chunk_array.append(msg) for port in self.getportlist(): if port==data['initiator-port']: continue client_sock= socket.socket(socket.AF_INET,socket.SOCK_STREAM) client_sock.connect(('127.0.0.1',port)) messageObj={} messageObj['groupname'] = self.grname if data['type'] =='text': messageObj['type'] = 'text' messageObj['encrypted'] = data['msg'] client_sock.sendall(pickle.dumps(messageObj)) else: messageObj['type'] = 'file' messageObj['filename']=data['filename'] client_sock.sendall(pickle.dumps(messageObj)) for item in chunk_array: time.sleep(1) client_sock.sendall(item) if data['type']=='file': chunk_array.clear() client_sock.close() class server: def __init__(self): self.s= socket.socket(socket.AF_INET,socket.SOCK_STREAM) self.s.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR, 1) self.s.bind((HOST,PORT)) self.clientlist={} self.grouplist={} self.s.listen() while True: conn,addr=self.s.accept() data = pickle.loads(conn.recv(1024)) if data['choice']=='signin': if data['name'] in self.clientlist.keys() and self.clientlist[data['name']]['pswd']==data['pswd'] and 'online' not in self.clientlist[data['name']].keys(): self.clientlist[data['name']]['online']=1 conn.send(b"1") else: conn.send(b"0") if data['choice']=='signup': if(data['name'] in self.clientlist.keys()): conn.send(b'0') else: self.clientlist[data['name']]=data print(data['name']) conn.send(b"1") if data['choice']=='get-client-port': print(data['name']) print(self.clientlist[data['name']]) data['port'] = self.clientlist[data['name']]['port'] conn.sendall(pickle.dumps(data)) if data['choice']=='join-group': if data['groupname'] in self.grouplist.keys(): self.grouplist[data['groupname']].addmember(data['name'],data['port']) conn.sendall(self.grouplist[data['groupname']].nounce.encode()) else: g = group(data['groupname']) g.addmember(data['name'],data['port']) self.grouplist[data['groupname']]=g conn.sendall(g.nounce.encode()) print([name for name in self.grouplist.keys()]) if data['choice']=='list-group': print([name for name in self.grouplist.keys()]) groupstring = [[self.grouplist[name].grname,self.grouplist[name].membercount] for name in self.grouplist.keys()] conn.sendall(str(groupstring).encode()) if data['choice']=='message-group': print(data) g = self.grouplist[data['groupname']] if(data['name'] in g.memberdic.keys()): g.message(conn,data) conn.close()

UTF-8

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false

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GuilhermeVBeira/Aulas-Django

5,076,651,351,419

6edfb7e032f7c49a38e8e464c7e6e7df6747afe3

af3eae2d55e83622bdbeb2188079d2feb98eb617

/aula11/apps.py

3fe432c7a4d3917373e18b06eb82b338d9f30f86

[]

no_license

https://github.com/GuilhermeVBeira/Aulas-Django

16d1582040d83dd5b6b8f6194a8261be63a95569

80e784fdc2d8a6903b44463f8b4ade514bd809f1

refs/heads/master

2022-12-06T09:03:27.858413

2021-06-09T19:32:36

240,728,073

2

null

false

2022-11-22T05:26:37

2020-02-15T14:36:02

2021-06-09T19:32:39

2022-11-22T05:26:34

981

2

0

2

Python

false

from django.apps import AppConfig class Aula11Config(AppConfig): name = "aula11" def ready(self): import aula11.signals

UTF-8

Python

false

139

py

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apps.py

75

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massimo-nocentini/microkanrenpy

10,024,453,677,728

63eb372d08729adb11b5de4c71f5968728509e00

6960e497327f51d99e82acf899fa45b4bc38af62

/src/sexp_test.py

afba8136c973807a09072b3644012801ad5ef273

[]

no_license

https://github.com/massimo-nocentini/microkanrenpy

6a635f02d6bb645e1d0eb0b2abc5282934a49589

62c71ae015c0ecc0330d70a2eeb6084430a5bb47

refs/heads/master

2021-06-07T06:02:06.137934

2020-12-04T10:47:31

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import unittest from functools import partialmethod from muk.sexp import * class sexp_tests(unittest.TestCase): def test_null_list(self): self.isomorphism(l=[], c=[]) def test_singleton_proper_list_to_cons(self): self.isomorphism(l=[1], c=cons(1, [])) def test_plain_proper_list_to_cons(self): self.isomorphism(l=[1,2,3], c=cons(1, cons(2, cons(3, [])))) def test_plain_improper_list_to_cons(self): self.isomorphism(l=(1,2,3), c=cons(1, cons(2, 3))) def test_nested_improper_list_to_cons(self): self.isomorphism(l=(1,[2,3], 4), c=cons(1, cons(cons(2, cons(3, [])), 4))) def test_more_nested_improper_list_to_cons(self): self.isomorphism(l=([3],(4,5), 6), c=cons(cons(3, []), cons(cons(4, 5), 6))) def test_shadow_proper_list_using_improper_list_notation(self): # pay attention, this is not an isomorphism, the next test shows the # natural way of writing, without shadowing. The broken direction is # represented by function `cons_to_list` which doesn't shadow objs it # produces. self.assertEqual(list_to_cons(([3],(4,5), [6])), cons(cons(3, []), cons(cons(4, 5), cons(6, [])))) def test_more_nested_improper_lists_into_proper_list_to_cons(self): self.isomorphism(l=[[3],(4,5), 6], c=cons(cons(3, []), cons(cons(4, 5), cons(6, [])))) def test_invalid_improper_list(self): with self.assertRaises(ImproperListError): list_to_cons(l=(3,)) def test_invalid_improper_cons(self): with self.assertRaises(ImproperListError): cons_to_list(c=cons(3, ())) def isomorphism(self, l, c): self.assertEqual(c, list_to_cons(l)) self.assertEqual(l, cons_to_list(c)) def test_tuple_wrapping_and_ctor_call(self): class A(tuple): __int__ = partialmethod(sum) a = (1,2,3,4) # vanilla tuple obj self.assertEqual(tuple, type(a)) self.assertEqual(A, type(A(a))) self.assertEqual(10, int(A(a)))

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yuexishuihan/yuexishuihan.github.io

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/Socket通信/udp聊天_1.py

62b1fba846d6337957fd982ea6f082e5f2632ae3

[]

no_license

https://github.com/yuexishuihan/yuexishuihan.github.io

dbe862cea471d756d7770168aab9b82794c63a96

ad9af9cc9481e0fdd4695b8c872f5c2ddd9ea51b

refs/heads/master

2020-04-09T06:31:27.285478

2019-02-27T08:31:31

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null

import socket def send_msg(udp_socket): '''发送消息''' # 获取对方的IP/PORT dest_ip = input("请输入对方IP：") dest_port = int(input("请输入对方端口号：")) # socket套接字可以同时进行收发数据 # 2.使用套接字发送数据 # 从键盘获取数据 send_date = input("请输入要发送的内容：") udp_socket.sendto(send_date.encode("utf-8"),(dest_ip,dest_port)) def recv_msg(udp_socket): '''接收消息''' # 3.使用套接字接收数据 recv_data = udp_socket.recvfrom(1024) print("%s:%s" % (str(recv_data[1]),recv_data[0].decode("utf-8"))) def main(): # 1.创建udp套接字 udp_socket = socket.socket(socket.AF_INET,socket.SOCK_DGRAM) # 绑定信息 udp_socket.bind("",8080) # 循环处理 while True: print("-----xx聊天室------") print("1.发送消息") print("2.接收消息") print("0.退出聊天室") op = input("请输入功能序号：") if op == "1": # 发送 send_msg(udp_socket) elif op == "2" # 接收并显示 recv_msg(udp_socket) elif op == "0": break else: print("输入有误请重新输入。。。") # 4.关闭套接字 udp_socket.close() if __name__ == "__main__": main()

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udp聊天_1.py

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wlWarren/mail_classifizer

3,530,463,141,284

207730e79a2c2a57bfa23dd338c0c3b309663b9a

959983112b37db56c0ee7f0e3940f9aad99fd169

/word_vector.py

037ae9ef56a1b2ee2667adb855c53c546f2fbea5

[]

no_license

https://github.com/wlWarren/mail_classifizer

3e102d89e02e241faf2be78fb84cc3e274ee6716

68202f7b88d229e22a309cfe24501fa81a5006be

refs/heads/master

2021-08-30T05:36:09.306868

2017-12-16T06:28:00

114,438,000

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# encoding:utf-8 import jieba import jieba.posseg as pseg import sklearn.feature_extraction.text import json from scipy import sparse, io from sklearn.externals import joblib # 非 tf-idf 词向量 class Counter_Vectorizer(sklearn.feature_extraction.text.CountVectorizer): def build_analyzer(self): def analyzer(doc): # 去标点 words = pseg.cut(doc) new_doc = ''.join(w.word for w in words if w.flag != 'x') words = jieba.cut(new_doc) return words return analyzer # 用tf-idf生成词向量 class TfidfVectorizer(sklearn.feature_extraction.text.TfidfVectorizer): def build_analyzer(self): # 生成词向量前需要进行切词 def analyzer(doc): # 将标点符号去掉 words = pseg.cut(doc) new_doc = ''.join(w.word for w in words if w.flag != 'x') words = jieba.cut(new_doc) return words return analyzer # 生成词向量并进行存储 def vector_word(): with open('RawData/train_content_5000.json', 'r') as f: content = json.load(f) with open('RawData/train_label_5000.json', 'r') as f: label = json.load(f) vec_tfidf = TfidfVectorizer(min_df=2, max_df=0.8,max_features=2000) tfidf = vec_tfidf.fit(content) # 存储分词模型 # joblib.dump(tfidf,'model/word_vector_model_60w.pkl') data_tfidf = tfidf.transform(content) data_tfidf_dense = data_tfidf.todense() name_tfidf_feature = vec_tfidf.get_feature_names() io.mmwrite('XGBoost/word_vector/word_vector.mtx', data_tfidf) ''' # 稀疏矩阵存储 io.mmwrite('word_vector/word_vector.mtx', data_tfidf) with open('word_vector/train_label.json', 'w') as f: json.dump(label, f) # 存入特征词 with open('word_vector/vector_type.json', 'w') as f: json.dump(name_tfidf_feature, f) ''' def dispose_new_doc(): tfidf = joblib.load('word_vector_model.pkl') doc = '您好！紫荆x号本周日x日妇女节有活动，女士到场都有花送，小孩有礼物，下午x:xx还会有抽奖活动哦，有兴趣可过来玩噢！联系人:黄秀秀。x' transform_document = [doc] new_data_tfidf = (tfidf.transform(transform_document)).todense() print (new_data_tfidf) if '__main__' == __name__: vector_word() print ('word_vector Finish') # dispose_new_doc()

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dellielo/katatest

3,221,225,484,093

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b882ffe25d7aadd0286bb9f00d8d7df0b3ad29db

/tests/test_katatest.py

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https://github.com/dellielo/katatest

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83f4499a0e3f9d950ebc6c8bd02604181836d23a

refs/heads/master

2020-09-09T15:07:34.091054

2019-11-18T13:09:46

221,474,622

0

null

from katatest.cli import main def test_basics(): assert_equal(0, price([])) assert_equal(8, price([1])) assert_equal(8, price([2])) assert_equal(8, price([3])) assert_equal(8, price([4])) assert_equal(8 * 3, price([1, 1, 1])) def test_simple_discount(): assert_equal(8 * 2 * 0.95, price([0, 1])) assert_equal(8 * 3 * 0.9, price([0, 2, 4])) assert_equal(8 * 4 * 0.8, price([0, 1, 2, 4])) assert_equal(8 * 5 * 0.75, price([0, 1, 2, 3, 4])) def test_several_discount(): assert_equal(8 + (8 * 2 * 0.95), price([0, 0, 1])) assert_equal(2 * (8 * 2 * 0.95), price([0, 0, 1, 1])) assert_equal((8 * 4 * 0.8) + (8 * 2 * 0.95), price([0, 0, 1, 2, 2, 3])) assert_equal(8 + (8 * 5 * 0.75), price([0, 1, 1, 2, 3, 4])) def test_edge_cases(): assert_equal(2 * (8 * 4 * 0.8), price([0, 0, 1, 1, 2, 2, 3, 4])) assert_equal(3 * (8 * 5 * 0.75) + 2 * (8 * 4 * 0.8), price([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4])) def test_main(): main([])

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caiwjohn/embedded_pred

8,615,704,428,635

7a955cc141a044f71276acf6f740b228647d0cc6

ec2dd6cb985439bd8f70be558900a8e2f119f691

/get_embeddings.py

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refs/heads/master

2022-07-17T22:46:51.432284

2020-05-18T19:09:29

null

0

null

from keras.models import Model, load_model from load_data import load_csv, get_onehot import numpy as np import csv #Saves <ex_per_class> sample embeddings per class #EDIT THESE PARAMETERS (see README)------------------------------------- model_name = 'my_model' num_classes = 100 ex_per_class = 100 data_file = 'my_dir/test.csv' name_file = 'my_dir/dna_100class_names.csv' #a csv of (class number, name) for each class out_file = 'my_dir/embed_'+model_name+'.csv' is_dna_data = True mask = True mask_len = 113 seq_len = 4500 model_file = 'model_dir/'+model_name+'.h5' #---------------------------------------------------------------------- model = load_model(model_file) embed_model = Model(inputs=model.input, outputs=model.get_layer("lstm_2").output) embed_model.summary() counts = np.zeros(num_classes) data = load_csv(data_file) chosen_data = [] for (x, y) in data: if counts[y] < ex_per_class: chosen_data.append((x,y)) counts[y] += 1 x, y, m = get_onehot(chosen_data, None, is_dna_data=is_dna_data, seq_len=seq_len, mask_len=mask_len if mask else None) embed = embed_model.predict([x,m] if mask else x) print(embed.shape) names = dict() with open(name_file, 'r') as infile: r = csv.reader(infile) for row in r: y = int(row[0]) names[y] = row[1] with open(out_file, 'w') as outfile: w = csv.writer(outfile) for (i, (x, y)) in enumerate(chosen_data): w.writerow([y,names[y]]+embed[i].tolist())

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ten2net/Leetcode-solution

13,365,938,256,520

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/116. Populating Next Right Pointers in Each Node.py

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refs/heads/master

2021-01-21T20:29:42.570931

2016-12-06T10:29:18

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# Definition for binary tree with next pointer. # class TreeLinkNode(object): # def __init__(self, x): # self.val = x # self.left = None # self.right = None # self.next = None class Solution(object): def connect(self, root): """ :type root: TreeLinkNode :rtype: nothing """ level_order = [] from collections import deque queue = deque() if root is not None: queue.append(root) while len(queue) > 0: top = queue.popleft() level_order.append(top) if top.left is not None: queue.append(top.left) if top.right is not None: queue.append(top.right) cnt = 1 level = 1 for i in range(len(level_order)): node = level_order[i] cnt -= 1 if cnt == 0: node.next = None level *= 2 cnt = level else: node.next = level_order[i+1]

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116. Populating Next Right Pointers in Each Node.py

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TKhyarn/flappy_unity_api

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f0dad14660be9c32208cbeaeae287504cad11b68

/run.py

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no_license

https://github.com/TKhyarn/flappy_unity_api

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refs/heads/master

2021-08-16T22:53:34.155948

2017-11-20T13:27:58

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#!flask/bin/python from app import app # not suitable for prod environment app.run(host='0.0.0.0', debug=True)

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we333/python_study

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[]

no_license

https://github.com/we333/python_study

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2016-07-17T10:53:57

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#!usr/bin/env python #_*_ coding:utf-8 _*_ ''' Created on 2016年7月12日 @author: wantone '''

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archit-dwevedi/parking_lot

13,907,104,130,496

3f3e328804aff3eb5ee034e8fa65260baca9627f

e33c8af70e79622b0f4818854c13e5e55aaab437

/parking/managers.py

a91f0d05a24d78e084a31797a37db0e7f2241e17

[]

no_license

https://github.com/archit-dwevedi/parking_lot

a4f275b147e5cdba2a7cc364e0537815582d2737

e6b5244ec6740b0b32c67ec2434713dcf0312931

refs/heads/main

2023-07-17T05:13:47.097863

2021-08-09T13:50:59

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from django.db import models class ParkingQuerySet(models.QuerySet): def get_empty_parking_space(self, parking, vechile_type): from .models import ParkingSpace return ParkingSpace.objects.filter( parking=parking, vechile_type=vechile_type, parked_car__isnull=True ).first() def get_parked_car_space(self, parking, vechile): from .models import ParkingSpace return ParkingSpace.objects.filter(parking=parking, parked_car=vechile).first() class ParkingManager(models.Manager): def get_queryset(self): return ParkingQuerySet(self.model, using=self._db) def park_car(self, parking, vechile): if not parking: raise Exception("Parking not found") if not vechile: raise Exception("Vechile is not found") parked_space = self.all().get_parked_car_space(parking, vechile) if parked_space: raise Exception("Car is already parked") space = self.all().get_empty_parking_space(parking, vechile.vechile_type) if not space: raise Exception("No Parking spots are empty") space.parked_car = vechile space.save() return space def exit_car(self, parking, vechile): if not parking: raise Exception("Parking not found") if not vechile: raise Exception("Vechile is not found") parked_space = self.all().get_parked_car_space(parking, vechile) if not parked_space: raise Exception("Car is not parked") parked_space.parked_car = None parked_space.save() return parked_space class RateManager(models.Manager): def get_price_for_parked_car(self, parking, vechile_type, hours): rate = self.filter( parking=parking, vechile_type=vechile_type, start_hours__lte=hours, end_hours__gte=hours ).first() if not rate: return 100 return rate.price

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tzyl/hackerrank-python

9,783,935,517,607

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/week_of_code/30/candy_replenishing_robot.py

a505f80439fd9717a6e06f4686f09ffe22b588f2

[]

no_license

https://github.com/tzyl/hackerrank-python

71a80cbd1a0c931e5112e7c0cdb2e339c4e50eb3

f3f2990de738403bccaedd4005368aa3456b94d3

refs/heads/master

2021-05-14T11:32:44.268500

2018-01-05T12:49:59

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null

n, t = input().strip().split(' ') n, t = [int(n), int(t)] c = [int(s) for s in input().strip().split()] candies = n added = 0 for i in range(t - 1): candies -= c[i] if candies < 5: added += n - candies candies = n print(added)

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candy_replenishing_robot.py

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leocvml/chatBot

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/chatbotFinalProject/discriminator.py

1975fd8724a8b23fae44ac0ff26c4e577ca1b476

[]

no_license

https://github.com/leocvml/chatBot

a97198e83ecdb59bacd7481d56119ecc6bf548fc

38863f7fa737e4dfb762d186d215dda75c921955

refs/heads/master

2021-07-05T06:04:58.287319

2019-01-24T03:12:10

131,238,684

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import sys sys.path.append('..') import collections import mxnet as mx from mxnet import autograd, gluon, init, metric, nd from mxnet.gluon import loss as gloss, nn, rnn from mxnet.contrib import text import os import random import zipfile demo = True if demo: with zipfile.ZipFile('data/aclImdb_tiny.zip', 'r') as zin: zin.extractall('data/') def readIMDB(dir_url, seg='train'): pos_or_neg = ['pos', 'neg'] data = [] for label in pos_or_neg: files = os.listdir( 'data/' + dir_url + '/' + seg + '/' + label + '/') for file in files: with open('data/' + dir_url + '/' + seg + '/' + label + '/' + file, 'r', encoding='utf8') as rf: review = rf.read().replace('\n', '') if label == 'pos': data.append([review, 1]) elif label == 'neg': data.append([review, 0]) return data if demo: train_data = readIMDB('aclImdb_tiny/', 'train') test_data = readIMDB('aclImdb_tiny/', 'test') else: train_data = readIMDB('aclImdb/', 'train') test_data = readIMDB('aclImdb/', 'test') random.shuffle(train_data) random.shuffle(test_data) def tokenizer(text): return [tok.lower() for tok in text.split(' ')] train_tokenized = [] for review, score in train_data: train_tokenized.append(tokenizer(review)) test_tokenized = [] for review, score in test_data: test_tokenized.append(tokenizer(review)) token_counter = collections.Counter() def count_token(train_tokenized): for sample in train_tokenized: for token in sample: if token not in token_counter: token_counter[token] = 1 else: token_counter[token] += 1 count_token(train_tokenized) vocab = text.vocab.Vocabulary(token_counter, unknown_token='<unk>', reserved_tokens=None) def encode_samples(tokenized_samples, vocab): features = [] for sample in tokenized_samples: feature = [] for token in sample: if token in vocab.token_to_idx: feature.append(vocab.token_to_idx[token]) else: feature.append(0) features.append(feature) return features def pad_samples(features, maxlen=500, padding=0): padded_features = [] for feature in features: if len(feature) > maxlen: padded_feature = feature[:maxlen] else: padded_feature = feature # 添加 PAD 符号使每个序列等长（长度为 maxlen ）。 while len(padded_feature) < maxlen: padded_feature.append(padding) padded_features.append(padded_feature) return padded_features ctx = mx.gpu() train_features = encode_samples(train_tokenized, vocab) test_features = encode_samples(test_tokenized, vocab) train_features = nd.array(pad_samples(train_features, 500, 0), ctx=ctx) test_features = nd.array(pad_samples(test_features, 500, 0), ctx=ctx) train_labels = nd.array([score for _, score in train_data], ctx=ctx) test_labels = nd.array([score for _, score in test_data], ctx=ctx) # print(train_features[0]) # print(train_labels[0]) glove_embedding = text.embedding.create( 'glove', pretrained_file_name='glove.6B.100d.txt', vocabulary=vocab) class SentimentNet(nn.Block): def __init__(self, vocab, embed_size, num_hiddens, num_layers, bidirectional, **kwargs): super(SentimentNet, self).__init__(**kwargs) with self.name_scope(): self.embedding = nn.Embedding(len(vocab), embed_size) self.encoder = rnn.LSTM(num_hiddens, num_layers=num_layers, bidirectional=bidirectional, input_size=embed_size) self.decoder = nn.Dense(num_outputs, flatten=False) def forward(self, inputs, begin_state=None): embeddings = self.embedding(inputs) states = self.encoder(embeddings) # 连结初始时间步和最终时间步的隐藏状态。 encoding = nd.concat(states[0], states[-1]) outputs = self.decoder(encoding) return outputs num_outputs = 2 lr = 0.1 num_epochs = 100 batch_size = 10 embed_size = 100 num_hiddens = 100 num_layers = 2 bidirectional = True net = SentimentNet(vocab, embed_size, num_hiddens, num_layers, bidirectional) net.initialize(init.Xavier(), ctx=ctx) # 设置 embedding 层的 weight 为预训练的词向量。 net.embedding.weight.set_data(glove_embedding.idx_to_vec.as_in_context(ctx)) # 训练中不迭代词向量（net.embedding中的模型参数）。 net.embedding.collect_params().setattr('grad_req', 'null') trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr}) loss = gloss.SoftmaxCrossEntropyLoss() # def eval_model(features, labels): # l_sum = 0 # l_n = 0 # accuracy = metric.Accuracy() # for i in range(features.shape[0] // batch_size): # X = features[i*batch_size : (i+1)*batch_size].as_in_context(ctx).T # y = labels[i*batch_size :(i+1)*batch_size].as_in_context(ctx).T # output = net(X) # l = loss(output, y) # l_sum += l.sum().asscalar() # l_n += l.size # accuracy.update(preds=nd.argmax(output, axis=1), labels=y) # return l_sum / l_n, accuracy.get()[1] # net.load_params('classfication.params') # for epoch in range(1, num_epochs + 1): # for i in range(train_features.shape[0] // batch_size): # X = train_features[i*batch_size : (i+1)*batch_size].as_in_context( # ctx).T # y = train_labels[i*batch_size : (i+1)*batch_size].as_in_context( # ctx).T # with autograd.record(): # l = loss(net(X), y) # l.backward() # trainer.step(batch_size) # train_loss, train_acc = eval_model(train_features, train_labels) # test_loss, test_acc = eval_model(test_features, test_labels) # print('epoch %d, train loss %.6f, acc %.2f; test loss %.6f, acc %.2f' # % (epoch, train_loss, train_acc, test_loss, test_acc)) # net.save_params('classfication.params') # def inference(set): # feature = [] # for token in set: # if token in vocab.token_to_idx: # feature.append(vocab.token_to_idx[token]) # else: # feature.append(0) # return feature # # # review = ['i', 'think', 'it', 'is','low'] # inf_data = inference(review) # # # print(nd.argmax(net(nd.reshape( # nd.array( inf_data, ctx=ctx), # shape=(-1, 1))), axis=1).asscalar())

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atlpatchin/bookstore

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/bookstores/comments/urls.py

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from django.conf.urls import url from cart import views urlpatterns = [ ]

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Perf-Org-5KRepos/aerial_wildlife_detection

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/projectCreation/migrate_aide.py

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''' Run this file whenever you update AIDE to bring your existing project setup up-to-date with respect to changes due to newer versions. 2019-20 Benjamin Kellenberger ''' import os import argparse MODIFICATIONS_sql = [ 'ALTER TABLE {schema}.annotation ADD COLUMN IF NOT EXISTS meta VARCHAR; ALTER TABLE {schema}.image_user ADD COLUMN IF NOT EXISTS meta VARCHAR;', 'ALTER TABLE {schema}.labelclass ADD COLUMN IF NOT EXISTS keystroke SMALLINT UNIQUE;', 'ALTER TABLE {schema}.image ADD COLUMN IF NOT EXISTS last_requested TIMESTAMPTZ;', 'ALTER TABLE {schema}.image_user ADD COLUMN IF NOT EXISTS num_interactions INTEGER NOT NULL DEFAULT 0;', 'ALTER TABLE {schema}.annotation ADD COLUMN IF NOT EXISTS autoConverted boolean;', 'ALTER TABLE {schema}.image_user ADD COLUMN IF NOT EXISTS first_checked TIMESTAMPTZ;', 'ALTER TABLE {schema}.image_user ADD COLUMN IF NOT EXISTS total_time_required BIGINT;' ] if __name__ == '__main__': parser = argparse.ArgumentParser(description='Update AIDE database structure.') parser.add_argument('--settings_filepath', type=str, default='config/settings.ini', const=1, nargs='?', help='Manual specification of the directory of the settings.ini file; only considered if environment variable unset (default: "config/settings.ini").') args = parser.parse_args() if not 'AIDE_CONFIG_PATH' in os.environ: os.environ['AIDE_CONFIG_PATH'] = str(args.settings_filepath) from util.configDef import Config from modules import Database config = Config() dbConn = Database(config) if dbConn.connectionPool is None: raise Exception('Error connecting to database.') dbSchema = config.getProperty('Database', 'schema') # make modifications one at a time for mod in MODIFICATIONS_sql: dbConn.execute(mod.format(schema=dbSchema), None, None) print('Project {} is now up-to-date for the latest changes in AIDE.'.format(config.getProperty('Project', 'projectName')))

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StanfordAHA/lake

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import kratos from lake.modules.passthru import * from lake.top.lake_top import * from lake.modules.sram_stub import * #lake_top = LakeTop(16) #lake_top_src = kratos.verilog(lake_top, optimize_passthrough=False) #print(lake_top_src["LakeTop"]) sramstub = SRAMStub(16, 512) sramstub_src = kratos.verilog(sramstub, optimize_passthrough=False) print(sramstub_src["SRAMStub"])

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echo9527git/jpress_selenium

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/util/my_js_utils.py

d6d5c7f1a5dc429c429b1bfb03094486b6e76da0

[]

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from selenium import webdriver from time import sleep,strftime,localtime,time import os def _set_index(index=None): """ index判断赋值 :param index: :return: """ if index is None: index = 0 else: index = index return index def clear(driver: webdriver, css,index=None, describe=None): """ 用途：用来清除输入框的内容 :param driver: webdriver :param css: css选择器 :param index: 列表的下标 :param describe: 描述信息 :return: """ js = """var elm = document.querySelectorAll("{css}")[{index}]; elm.style.border="2px solid red"; elm.value = "";""".format(css=css, index=_set_index(index)) driver.execute_script(js) def input(value,driver: webdriver, css,index=None, describe=None): """ 用途：输入框中输入内容 :param self: :param value:待输入的数据 :param driver: webdriver :param css: css选择器 :param index: 列表的下标 :param describe: 描述信息 :return: """ js = """var elm = document.querySelectorAll("{css}")[{index}]; elm.style.border="2px solid red"; elm.value = "{value}";""".format(css=css, index=_set_index(index), value=value) driver.execute_script(js) def click(driver: webdriver, css,index=None, describe=None): """ 用途：由于web自动化的最大问题就是稳定性比较差，有些时候使用selenium无法点击元素，因此我们可以使用JS实现元素的点击操作 :param self: :param driver: webdriver :param css: css选择器 :param index: 列表的下标 :param describe: 描述信息 :return: """ js = """var elm = document.querySelectorAll("{css}")[{index}]; elm.style.border="2px solid red"; elm.click();""".format(css=css, index=_set_index(index)) driver.execute_script(js) def remove_attribute(attribute,driver: webdriver, css,index=None, describe=None): """ 用途：以下方法可以删除元素的任何属性，主要用来移除时间控件的readonly属性 :param attribute:元素的某个属性，比如readonly，value，name等 :param driver: webdriver :param css: css选择器 :param index: 列表的下标 :param describe: 描述信息 :return: """ # _index_(index) js = """ var elm = document.querySelectorAll("{css}")[{index}]; elm.removeAttribute("{attr}"); """.format(css=css, index=_set_index(index), attr=attribute) driver.execute_script(js) def remove_attr(element, attribute,driver: webdriver): """ 用途：以下方法可以删除元素的任何属性 :param element: 需要被删除属性的控件 :param attribute: 需要删除的属性 :param driver: webdriver :return: """ js = """ arguments[0].removeAttribute("{attr}"); """.format(attr=attribute) driver.execute_script(js, element) def scroll_to_xy(driver: webdriver,x, y): ''' 用途：通过制定xy坐标来滑动web页面 :param driver: webdriver :param x:屏幕向右移动的距离 :param y:屏幕向下移动的距离 :return: 1、滚动到文档中的某个坐标 window.scrollTo(x-coord,y-coord ) window.scrollTo(options) ·x-coord 是文档中的横轴坐标。 ·y-coord 是文档中的纵轴坐标。 ·options 是一个包含三个属性的对象: ·top 等同于 y-coord ·left 等同于 x-coord ·behavior 类型String,表示滚动行为,支持参数 smooth(平滑滚动),instant(瞬间滚动),默认值auto,实测效果等同于instant 例子： window.scrollTo( 0, 1000 ); // 设置滚动行为改为平滑的滚动 window.scrollTo({ top: 1000, behavior: "smooth" }); ''' js = """ window.scrollTo("{}", "{}") """.format(x, y) driver.execute_script(js) # TODO：没整明白到底怎么个滑动法 def window_scroll(driver: webdriver,element, x, y): """ 用途：指定元素移动的某位置 :param driver: webdriver :param element: 指定元素 :param x:屏幕向右移动的距离 :param y:屏幕向下移动的距离 :return: """ js = """ arguments[0].scrollTo("{}", "{}") """.format(x, y) driver.execute_script(js, element) def scroll_to_element(driver: webdriver,element): """ 用途：滚动屏幕使得元素上下、左右居中 :param driver: :param element: :return: """ js = "arguments[0].scrollIntoView({behavior: 'smooth', block: 'center', inline: 'center'});" driver.execute_script(js, element) def height_light(driver: webdriver, css,index=0): """ 用途：方便用户查看当前操作的是哪个页面元素，也方便测试人员定位问题 :param driver: webdriver :param css: css选择器 :param index: 列表的下标 :return: """ js = """ var element = document.querySelectorAll("{css}")[{index}]; element.style.border="4px solid red"; """.format(css=css, index=index) driver.execute_script(js) def height_lig(driver: webdriver,element): """ 用途：指定元素高亮 :param driver: webdriver :param element: 需要高亮的元素 :return: """ js = """ arguments[0].style.border="2px solid red"; """ driver.execute_script(js, element) def save_screenshot(driver: webdriver,sub_filename,element,describe=None): """ 用途：对当前屏幕截图并保存到当前路径，同时标记被操作的元素为高亮，图片文件名格式为：当前时间+sub_filename.png :param driver: webdriver :param sub_filename: 文件的部分名称，为了区分建议用selector命名 :param element: 需要被高亮的元素 :param describe: 方法的描述，同时也作为png图片文件名的一部分 :return: """ name = os.path.abspath('screenshot')+'/' # name = "C:/Users/Administrator/PycharmProjects/haige_selenium/screenshot/" st = strftime("%Y-%m-%d-%H-%M-%S",localtime(time())) file_name = name+st+'-'+sub_filename+describe+".png" height_lig(driver,element) driver.save_screenshot(file_name);

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# Created by mire on 7/21/21. Copyright 2021, All rights reserved. import shap import methods supported_methods = { 'shap': methods.Shape, 'lime': methods.Lime, 'kernelshap': methods.KernelShap } class Explainer: def __init__(self, name): assert (name in supported_methods[name].keys(), f'This Method is not supported at the moment. Methods support are {list(supported_methods.keys())}') self.name = name self.method = lambda clf, data: supported_methods[name](clf, data)

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Meet2147/yt_download

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2023-04-30T08:48:23.971366

2021-05-09T03:30:30

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from flask import Flask, render_template, request, session, redirect, send_file, url_for from pytube import YouTube app = Flask(__name__) app.config['SECRET_KEY'] = "AlmastYogi" def download(url): video = url.streams.first() filepath = video.download() return filepath @app.route("/", methods=['GET', 'POST']) def index(): if request.method == 'POST': session['link'] = request.form.get('url') url = YouTube(session['link']) return render_template("see_video.html",url=url) return render_template("index.html") @app.route("/see_video", methods=['GET','POST']) def see_video(): if request.method == 'POST': url = YouTube(session['link']) itag = request.form.get('itag') video = url.streams.get_by_itag(itag) filename = video.download() return send_file(filename, as_attachment=True) return redirect(url_for('index'))

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cliftonpalmer/python-diff

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/diff.py

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no_license

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refs/heads/master

2020-04-12T12:28:31.423480

2019-10-18T01:43:36

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def index_by_newlines(a, sign): return [ [i, v, sign] for i,v in enumerate(a.split("\n")) ] def diff(a, b): # separate left and right into arrays indexed by line a = index_by_newlines(a, '-') b = index_by_newlines(b, '+') # remove matching lines for va in a[:]: for vb in b: if va[1] == vb[1]: a.remove(va) b.remove(vb) continue # print in line index order with right or left symbols, + or - c = a + b c.sort(key=lambda v: v[0]) return c a = """a b c""" b = """a a x b""" for v in diff(a,b): print(v[2] + v[1])

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JayjeetAtGithub/spack

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# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack.package import * class PyGeocube(PythonPackage): """Tool to convert geopandas vector data into rasterized xarray data.""" homepage = "https://github.com/corteva/geocube" pypi = "geocube/geocube-0.0.17.tar.gz" maintainers = ["adamjstewart"] version("0.3.2", sha256="71ff0228f1ef44e3a649d29a045ff7e2a2094a5cfca30fadab8f88f4ec23a41d") version("0.3.1", sha256="5c97131010cd8d556a5fad2a3824452120640ac33a6a45b6ca9ee3c28f2e266f") version("0.0.17", sha256="bf8da0fa96d772ebaea0b98bafa0ba5b8639669d5feb07465d4255af177bddc0") depends_on("python@3.7:", type=("build", "run")) depends_on("python@3.8:", when="@0.1.1:", type=("build", "run")) depends_on("py-setuptools", type="build") depends_on("py-appdirs", type=("build", "run")) depends_on("py-click@6.0:", type=("build", "run")) depends_on("py-datacube", when="@:0.1", type=("build", "run")) depends_on("py-geopandas@0.7:", type=("build", "run")) depends_on("py-odc-geo", when="@0.2:", type=("build", "run")) depends_on("py-rasterio", type=("build", "run")) depends_on("py-rioxarray@0.4:", type=("build", "run")) depends_on("py-scipy", when="@0.0.18:", type=("build", "run")) depends_on("py-xarray@0.17:", type=("build", "run")) depends_on("py-pyproj@2:", type=("build", "run")) depends_on("py-numpy@1.20:", when="@0.3:", type=("build", "run"))

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creedasaurus/stoopclub_Chat

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be14609a6755c502f1b7798f4ede942b793dff64

98d7ba89d515f3abd8ad0812d61baf3063a166ba

/hello_stoopers.py

f13c6206a2aaf34ae4ebaf1d01ff03826e990be7

[]

no_license

https://github.com/creedasaurus/stoopclub_Chat

ff5b6c57d61bb05c59e4bcc360d1c3c272c0a4e9

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refs/heads/master

2016-08-11T14:56:34.252603

2015-11-04T08:16:55

45,523,127

0

null

newNumber = 26 print "Hello Stoop Troopers"

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Python

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py

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hello_stoopers.py

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0.790698

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dimonrtm/ml_

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/Программирование на Python/Погружение в Python/Задания/Неделя1/Задание1/solution.py

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[]

no_license

https://github.com/dimonrtm/ml_

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refs/heads/master

2020-08-31T05:20:43.477108

2020-07-04T18:22:21

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null

import sys digit_string=sys.argv[1] sum=0 for number in digit_string: sum+=int(number) print(sum)

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solution.py

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Plavit/cs50ai

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/Project 2a - Pagerank/pagerank.py

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[]

no_license

https://github.com/Plavit/cs50ai

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refs/heads/master

2023-02-10T12:12:37.347996

2021-01-03T19:12:17

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2020-12-21T22:21:31

2021-01-02T11:09:11

2021-01-02T11:09:22

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import copy import os import random import re import sys DAMPING = 0.85 SAMPLES = 10000 def main(): if len(sys.argv) != 2: sys.exit("Usage: python pagerank.py corpus") corpus = crawl(sys.argv[1]) ranks = sample_pagerank(corpus, DAMPING, SAMPLES) print(f"PageRank Results from Sampling (n = {SAMPLES})") for page in sorted(ranks): print(f" {page}: {ranks[page]:.4f}") ranks = iterate_pagerank(corpus, DAMPING) print(f"PageRank Results from Iteration") for page in sorted(ranks): print(f" {page}: {ranks[page]:.4f}") def crawl(directory): """ Parse a directory of HTML pages and check for links to other pages. Return a dictionary where each key is a page, and values are a list of all other pages in the corpus that are linked to by the page. """ pages = dict() # Extract all links from HTML files for filename in os.listdir(directory): if not filename.endswith(".html"): continue with open(os.path.join(directory, filename)) as f: contents = f.read() links = re.findall(r"<a\s+(?:[^>]*?)href=\"([^\"]*)\"", contents) pages[filename] = set(links) - {filename} # Only include links to other pages in the corpus for filename in pages: pages[filename] = set( link for link in pages[filename] if link in pages ) return pages def transition_model(corpus, page, damping_factor): """ Return a probability distribution over which page to visit next, given a current page. With probability `damping_factor`, choose a link at random linked to by `page`. With probability `1 - damping_factor`, choose a link at random chosen from all pages in the corpus. """ # Variable setup target = {} links = corpus[page] # Without links, each page has same probability if not links: for x in corpus: target[x] = 1.0 / len(corpus) else: # Otherwise, with probability `1 - damping_factor`, choose # a link at random chosen from all pages in the corpus. for x in corpus: target[x] = (1 - damping_factor) / len(corpus) # plus, add another probability per link: with probability `damping_factor`, choose a link at random # linked to by `page` for x in links: target[x] += damping_factor / len(links) # Debug - total sum # total=0 # for y in corpus: # total+=target[y] # print(total) # Return a probability distribution over which page to visit next return target def sample_pagerank(corpus, damping_factor, n): """ Return PageRank values for each page by sampling `n` pages according to transition model, starting with a page at random. Return a dictionary where keys are page names, and values are their estimated PageRank value (a value between 0 and 1). All PageRank values should sum to 1. """ # Variable setup page_ranks = {}.fromkeys(corpus.keys(), 0) target_page = random.choices(list(corpus.keys()))[0] # For n pages as defined by input for i in range(1, n): # Sample transition model, starting with random target page initialized before current_dist = transition_model(corpus, target_page, damping_factor) for page in page_ranks: # For each page, add pagerank value page_ranks[page] = (((i - 1) * page_ranks[page]) + current_dist[page]) / i target_page = random.choices(list(page_ranks.keys()), weights=list(page_ranks.values()), k=1)[0] # Debug - total sum total = 0 for page in page_ranks: total += page_ranks[page] print("Debug 1, total sum: ", total) # Return a dictionary where keys are page names, and values are # their estimated PageRank value (a value between 0 and 1). All # PageRank values should sum to 1. return page_ranks def iterate_pagerank(corpus, damping_factor): """ Return PageRank values for each page by iteratively updating PageRank values until convergence. Return a dictionary where keys are page names, and values are their estimated PageRank value (a value between 0 and 1). All PageRank values should sum to 1. """ # Variable setup total_pages = len(corpus) page_ranks = {}.fromkeys(corpus.keys(), 1.0 / total_pages) too_rough = True # Until reaching set precision threshold, iterate while too_rough: old_distribution = copy.deepcopy(page_ranks) # PageRank values for each page by iteratively updating # PageRank values until convergence, with same rules as previous function for page in corpus: # Determine link weight for page link_weight = 0 for p in corpus: if page in corpus[p]: link_weight += page_ranks[p] / len(corpus[p]) # Iteratively add pagerank to pages page_ranks[page] = ((1 - damping_factor) / total_pages) + (damping_factor * link_weight) # Until estimate is precise enough too_rough = (abs(old_distribution[page] - page_ranks[page]) > 0.0001) # Debug - total sum total = 0 for page in page_ranks: total += page_ranks[page] print("Debug 2, total sum: ", total) # Return a dictionary where keys are page names, and values are # their estimated PageRank value (a value between 0 and 1). All # PageRank values should sum to 1. (should be very similar in results to previous function) return page_ranks if __name__ == "__main__": main()

UTF-8

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py

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pagerank.py

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yomhub/Tensorflow_research

4,174,708,258,760

baf840af748c2230fef4231bff5f47bedaa51311

de86f9f9dd620212c96fc3bbc28bdbc7432aa237

/lib/tflib/log_tools.py

7fc6066e1c69ff10ecfe8c364bac1338fc3ec9fa

[]

no_license

https://github.com/yomhub/Tensorflow_research

235fa5513abeea64e44291e6705fb136cf108af4

2f8102039168ade5481745e4aa59c7e6a0cba59b

refs/heads/master

2022-11-24T05:08:11.009741

2020-06-14T01:21:54

280,804,212

0

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import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from matplotlib import colors as mcolors from matplotlib.collections import PolyCollection from datetime import datetime from mpl_toolkits.mplot3d import Axes3D __DEF_LINE_STY = [ 'solid', # _____ 'dotted', # ....... 'dashdot', # __.__.__. 'dashed', # __ __ __ ] __DEF_COLORS = [ 'r','b','g','c','y','m'] def str2time(instr): ymd,hms=instr.split('-') return datetime(int(ymd[:4]), int(ymd[4:6]), int(ymd[6:]), int(hms[:2]), int(hms[2:4]), int(hms[4:6])) def str2num(instr): return [int(s) for s in instr.split() if s.isdigit()] def auto_scalar(dic_data, step=0, logname=None): if(type(dic_data)==list): if(logname==None): logname="auto_scalar" cont = 0 for itm in dic_data: tf.summary.scalar(logname+"_list_{}".format(cont),itm,step=step) cont += 1 elif(type(dic_data)==dict): for itname in dic_data: tf.summary.scalar(itname,dic_data[itname],step=step) else: if(logname==None): logname="auto_scalar" tf.summary.scalar(logname,dic_data,step=step) def auto_image(img_data, name=None, step=0, max_outputs=None, description=None): """ Args: img_data: tensor with shape (h,w,3 or 1) or (N,h,w,3 or 1) name: log name step: int of step max_outputs: int of max_outputs """ if(len(img_data)==3): img_data = tf.reshape(img_data,[1,]+img_data.shape) if(tf.reduce_max(img_data)>1.0): img_data = img_data / 256.0 max_outputs = img_data.shape[0] if max_outputs==None else max_outputs name = "auto_image" if name==None else name tf.summary.image(name,img_data,step,max_outputs,description) def auto_histogram(dic_data, step=0, logname=None): if(type(dic_data)==list): if(logname==None): logname="auto_scalar" cont = 0 for itm in dic_data: tf.summary.histogram(logname+"_list_{}".format(cont),itm,step=step) cont += 1 elif(type(dic_data)==dict): for itname in dic_data: tf.summary.histogram(itname,dic_data[itname],step=step) else: if(logname==None): logname="auto_scalar" tf.summary.histogram(logname,dic_data,step=step) def save_image(img, savedir): if(len(img.shape)==4): img = tf.reshape(img,img.shape[1:]) tf.io.write_file(savedir,tf.io.encode_jpeg(tf.cast(img,tf.uint8))) def plt_func_lines(funcs,xarry=None,cols=None,figure=None,fig_size=None,save_name=None): """ Draw plt by function Args: fig_size: width, height """ fg = plt.figure(figsize=fig_size) if(figure==None)else figure if(xarry==None): xarry=np.range(-5.0,5.0,100) if(type(funcs)!=list): funcs = [funcs] for i in range(len(funcs)): fg.plot( xarry,funcs[i](xarry), color=__DEF_COLORS[i%len(__DEF_COLORS)], linewidth=1.0, linestyle=__DEF_LINE_STY[i%len(__DEF_LINE_STY)]) if(save_name!=None):fg.savefig(save_name) return fg def plt_points_lines(points,xarry=None,xcfg=None,figure=None,fig_size=None,save_name=None): """ Draw plt by function Args: points: 1D array (Ny,) with [y0,y1...]: if xarry is [x0,x1...], use xarry. Or calculate xarry by xcfg 2D array (2,Nyx) with [[y0,y1...],[x0,x1...]] List of 1D/2D arrays: draw multi lines in figure xarry: 1D array (Nx,) with [x0,x1...] xcfg: if points is 1D and xarry==None, (start,end) will be use """ fg = plt.figure(figsize=fig_size) if(figure==None)else figure if(type(points)!=list): points = [points] for i in range(len(points)): if(len(points[i].shape)==1): if(xarry!=None and xarry.shape[0]==points[i].shape[0]): xs = xarry elif(xcfg!=None): xs = np.linspace(xcfg[0],xcfg[1],points[i].shape[0]) ys = points[i] else: xs = points[i][1] ys = points[i][0] fg.plot(xs,ys, color=__DEF_COLORS[i%len(__DEF_COLORS)], linewidth=1.0, linestyle=__DEF_LINE_STY[i%len(__DEF_LINE_STY)]) if(save_name!=None):fg.savefig(save_name) return fg def rf_helper(net_list,ord_len,panding=True): """ Args: net_list: list pramaters of network [kernel size, stride size] ord_num: int, coordinate range panding: True or False Print coordinate in each layer """ if(type(net_list)!=list or len(net_list)<2):return panding = bool(panding) ord_len = int(ord_len) rf_st = [[1,1]] cod_table = np.arange(ord_len,dtype=np.int) cod_table = np.stack((cod_table,cod_table),axis=-1) cod_table = [cod_table.tolist()] for i in range(len(net_list)): rf,st = rf_st[i] ksize,strsize = net_list[i] crf = rf + (ksize-1)*st cst = st*strsize rf_st.append([crf,cst]) p_harf_k = int(ksize/2) if((ksize-int(ksize/2)*2)!=0)else int(ksize/2)-1 harf_k = ksize - 1 - p_harf_k max_cod = len(cod_table[i])-1 stp = 0 if panding else p_harf_k edp = max_cod if panding else max_cod - harf_k tmp = [] while(stp<edp): c_ctp = max(0,stp-p_harf_k) c_edp = min(max_cod,stp + harf_k) tmp.append([cod_table[i][c_ctp][0],cod_table[i][c_edp][1]]) stp+=strsize cod_table.append(tmp) return rf_st,cod_table def resize_visualize_helper(img,model,gtbox=None,mask=None): """ Helper for feature part in unet in resize task. Args: img: input image gtbox: (N,4) with [y1,x1,y2,x2] in [0,1] mask: pixel mask module: model with output {} """ linewidth = 1.3 plt.figure(figsize=(8,4)) # fg = plt.figure(figsize=(8,4)) plt.subplot(3,3,1, # figure=fg ) divnum = 3*3-1 base_scale = 32 # vgg net based scale if(type(img)!=list):img=[img] if(gtbox!=None and type(gtbox)!=list):gtbox=[gtbox] if(mask!=None and type(mask)!=list):mask=[mask] for j in range(divnum): plt.subplot(3,3,j+1, # figure=fg ) plt.xlabel('layer', # figure=fg ) plt.ylabel('energy', # figure=fg ) plt.title('Scaler {}/{}'.format(j+1,divnum), # figure=fg ) dx=np.arange(5) for i in range(len(img)): coe_x = int(max(int(img[i].shape[-2]/base_scale),divnum)/divnum) coe_y = int(max(int(img[i].shape[-3]/base_scale),divnum)/divnum) for j in range(divnum): img_size = [coe_y*base_scale,coe_x*base_scale] tmp = tf.image.resize(img[i],img_size) rt = model(tmp) mp = tf.cast(rt['scr'][:,:,:,1]>rt['scr'][:,:,:,0],tf.float32) mp = tf.reshape(mp,mp.shape+[1]) mp = tf.broadcast_to(mp,mp.shape[:-1]+[3]) mp = tf.concat([mp,tf.image.resize(tmp,mp.shape[-3:-1])/255.0],axis=2) tf.summary.image( name = 'Score|Img image size {}.'.format(img_size), data = mp,step=0) # if(dx==None):dx=np.arange(len(rt['ftlist'])) dmin = [] dmean = [] dmax = [] for o in range(len(rt['ftlist'])): dmin += [tf.reduce_min(rt['ftlist'][o]).numpy()] dmax += [tf.reduce_max(rt['ftlist'][o]).numpy()] dmean += [tf.reduce_mean(rt['ftlist'][o]).numpy()] dmin = np.asarray(dmin) dmean = np.asarray(dmean) dmax = np.asarray(dmax) plt.subplot(3,3,j+1, # figure=fg ) plt.plot(dx,dmean, color=__DEF_COLORS[i%len(__DEF_COLORS)], linewidth=linewidth, linestyle=__DEF_LINE_STY[0], label='mean', # figure=fg ) plt.plot(dx,dmin, color=__DEF_COLORS[i%len(__DEF_COLORS)], linewidth=linewidth, linestyle=__DEF_LINE_STY[1], label='min', # figure=fg ) plt.plot(dx,dmax, color=__DEF_COLORS[i%len(__DEF_COLORS)], linewidth=linewidth, linestyle=__DEF_LINE_STY[2], label='max', # figure=fg ) plt.show() plt.savefig('logfig.png') # fg.show() print("") def sequence_visualize_helper(img,model,gtbox=None,mask=None): """ Helper for feature part in unet in sequence task. Args: img: list of image sequence gtbox: (N,4) with [y1,x1,y2,x2] in [0,1] mask: pixel mask module: model with output {} """ def polygon_under_graph(xlist, ylist): ''' Construct the vertex list which defines the polygon filling the space under the (xlist, ylist) line graph. Assumes the xs are in ascending order. ''' return [(xlist[0], 0.)] + list(zip(xlist, ylist)) + [(xlist[-1], 0.)] def cc(arg): ''' Shorthand to convert 'named' colors to rgba format at 60% opacity. ''' return mcolors.to_rgba(arg, alpha=0.6) fig_min = plt.figure(num='min') fig_max = plt.figure(num='max') fig_mean = plt.figure(num='mean') # mean, min, max ax_min = fig_min.gca(projection='3d') ax_max = fig_max.gca(projection='3d') ax_mean = fig_mean.gca(projection='3d') # ax1 = fig.add_subplot(1,3,1,projection='3d') # ax2 = fig.add_subplot(1,3,2,projection='3d') # ax3 = fig.add_subplot(1,3,3,projection='3d') linewidth = 1.3 if(type(img)!=list):img=[img] if(gtbox!=None and type(gtbox)!=list):gtbox=[gtbox] if(mask!=None and type(mask)!=list):mask=[mask] dmin = [] dmean = [] dmax = [] for i in range(len(img)): rt = model(img[i]) mp = tf.cast(rt['scr'][:,:,:,1]>rt['scr'][:,:,:,0],tf.float32) mp = tf.reshape(mp,mp.shape+[1]) mp = tf.broadcast_to(mp,mp.shape[:-1]+[3]) mp = tf.concat([ mp, # tf.broadcast_to(rt['mask'],rt['mask'].shape[:-1]+[3]), tf.image.resize(img[i],mp.shape[-3:-1])/255.0], axis=2) tf.summary.image( name = 'Score|Edg|Img image.', data = mp,step = i, max_outputs=20 ) tf.keras.preprocessing.image.save_img( path='fg{}.jpg'.format(i), x=tf.reshape(mp,mp.shape[1:]).numpy(), # scale=False, ) tmp_dmin = [] tmp_dmax = [] tmp_dmean = [] for j in range(len(rt['ftlist'])): tmp_dmin += [tf.reduce_min(rt['ftlist'][j]).numpy()] tmp_dmax += [tf.reduce_max(rt['ftlist'][j]).numpy()] tmp_dmean += [tf.reduce_mean(rt['ftlist'][j]).numpy()] dmin += [tmp_dmin] dmax += [tmp_dmax] dmean += [tmp_dmean] # convert d[img][layer] to d[layer][img] dmin = np.asarray(dmin).transpose((1,0)) dmax = np.asarray(dmax).transpose((1,0)) dmean = np.asarray(dmean).transpose((1,0)) zs = range(dmin.shape[0]) # layers num xs = np.arange(len(img)) verts_min = [] verts_max = [] verts_mean = [] cols = [] for i in zs: verts_min.append(polygon_under_graph(xs,dmin[i])) verts_max.append(polygon_under_graph(xs,dmax[i])) verts_mean.append(polygon_under_graph(xs,dmean[i])) cols += [cc(__DEF_COLORS[i%len(__DEF_COLORS)])] poly_min = PolyCollection(verts_min, facecolors=cols) poly_max = PolyCollection(verts_max, facecolors=cols) poly_mean = PolyCollection(verts_mean, facecolors=cols) ax_min.add_collection3d(poly_min, zs=zs, zdir='y') ax_min.set_xlabel('Images') ax_min.set_xlim(0, len(img)) ax_min.set_ylabel('Layers') ax_min.set_ylim(0, dmin.shape[0]) ax_min.set_zlabel('Mean') ax_min.set_zlim(dmin.min()-1.0, dmin.max()+1.0) ax_max.add_collection3d(poly_max, zs=zs, zdir='y') ax_max.set_xlabel('Images') ax_max.set_xlim(0, len(img)) ax_max.set_ylabel('Layers') ax_max.set_ylim(0, dmin.shape[0]) ax_max.set_zlabel('Max') ax_max.set_zlim(dmax.min()-1.0, dmax.max()+1.0) ax_mean.add_collection3d(poly_mean, zs=zs, zdir='y') ax_mean.set_xlabel('Images') ax_mean.set_xlim(0, len(img)) ax_mean.set_ylabel('Layers') ax_mean.set_ylim(0, dmin.shape[0]) ax_mean.set_zlabel('Mean') ax_mean.set_zlim(dmean.min()-1.0, dmean.max()+1.0) # plt.show() # plt.savefig('logfig.png') # fg.show() print("")

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luoxuwei/Compiler

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/pythonvm/test_case/27_test_allocate_instance.py

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[]

no_license

https://github.com/luoxuwei/Compiler

cceee6af426ba9d9b2f13d22fcf6e06a692953ba

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refs/heads/master

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2022-11-26T16:29:00

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null

a = int() print(a) b = str() print(b) c = list() print(c) d = dict() print(d)

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27_test_allocate_instance.py

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ajevnisek/FederatedLearning

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5e6895248c79c6f5860e371edb9d325aa26f92f2

/models/influence.py

8f0427a4c3780fd17d901050b4d16f2634a05511

[]

no_license

https://github.com/ajevnisek/FederatedLearning

c613e743ef1368b75e77d0cd69419745e3e40e8e

6330befb1629a5f652bc1c1fea80f1830aec00a0

refs/heads/master

2022-04-13T16:29:10.895522

2019-12-21T08:12:05

null

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import six import torch from torch.autograd import grad from torch.autograd import Variable import torch.nn.functional as F from models import utility import argparse import copy import numpy as np from utils.options import args_parser import pickle from torch.utils import data from torch.utils.data import Dataset from torch.autograd import Variable import torch.nn.functional as F import random def hvp(y, w, v): first_grads = grad(y, w, retain_graph=True, create_graph=True) grad_v = 0 for g, v in six.moves.zip(first_grads, v): grad_v += torch.sum(g * v) grad(grad_v, w, create_graph=True) return grad(grad_v, w, create_graph=True) def grad_z(z, t, model, gpu=-,create_graph=True): device = torch.device('cuda:{}'.format(gpu) if torch.cuda.is_available() and gpu != -1 else 'cpu') model.eval() z, t = Variable(z, volatile=False).to(device), Variable(t, volatile=False).to(device) y = model(z) loss = F.nll_loss(y, t, weight=None, reduction='mean') return list(grad(loss, list(model.parameters()), create_graph=create_graph)) def stest(v,model,z_loader,gpu,damp=0.01,scale=25.0,repeat=5): h_estimate=v.copy() train_set=z_loader device = torch.device('cuda:{}'.format(gpu) if torch.cuda.is_available() and gpu != -1 else 'cpu') for i in utility.create_progressbar(repeat, desc='s_test'): j=random.randint(0,len(z_loader)) data, target= train_set.dataset[j] data = train_set.collate_fn([data]) target= train_set.collate_fn([target]) x, t = Variable(data, volatile=False).to(device), Variable(target, volatile=False).to(device) y = model(x) loss = F.nll_loss(y, t, weight=None, reduction='mean') hv = hvp(loss, list(model.parameters()), h_estimate) h_estimate = [_v + (1 - damp) * h_estimate - _hv / scale for _v, h_estimate, _hv in six.moves.zip(v, h_estimate, hv)] return h_estimate

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codingpurush/class11python

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#PRINT Nth FIBONACCI NUMBER x=int(input()) a=-1 b=1 c=a+b for i in range(2,x+1): #print(c) a=b b=c c=a+b if(i==x): print(c)

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fibonnacci_Nth.py

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#!/usr/bin/env python # coding: utf-8 from _train import data, X, y, pipeline, pipeline_for_encoded_data,encoded_X import pandas as pd import sklearn import numpy as np from lime import lime_tabular import dalex as dx model = pipeline X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.80) model.fit(X_train, y_train) # create an explainer for the model: exp = dx.Explainer(model, X, y, label = "Lung's Cancer MLP Pipeline") # BreakDown and BreakDownInt methods def BreakDown(number_of_observation): bd = exp.predict_parts(pd.DataFrame(X_test.iloc[number_of_observation,:]).T, type='break_down') bd.plot() def BreakDownI(number_of_observation): bd_interactions = exp.predict_parts(pd.DataFrame(X_test.iloc[number_of_observation,:]).T, type='break_down_interactions') bd_interactions.plot() #SHAP def Shap(number_of_observation): sh = exp.predict_parts(pd.DataFrame(X_test.iloc[number_of_observation,:]).T, type='shap', B = 10) sh.plot(bar_width = 16) Shap(4) # sh.result.loc[sh.result.B == 0, ] # #lime # # preparing categorical_features for lime method # categorical_features = [3,7,9,10] # categorical_names = {} # categorical_names = {} # for feature in categorical_features: # Y = X.copy() # le = sklearn.preprocessing.LabelEncoder() # Y.iloc[:, feature] = Y.iloc[:, feature].astype(str) # le.fit(Y.iloc[:, feature]) # # Y.iloc[:, feature] = le.transform(Y.iloc[:, feature]) # categorical_names[feature] = le.classes_ # stadia = [float('nan'), 'IA1', 'IA2', 'IA3', 'IB', 'IIA', 'IIB', 'IIIA', 'IIIB', 'IVA', 'IVB'] # categorical_names.update({11:np.array(stadia, dtype=object)}) # categorical_features2 = [3,7,9,10,11] # encoder = lambda x: model.named_steps["encoder"].transform(x) # scaler = lambda x: model.named_steps["scaler"].transform(x) # predict_fn = lambda x: model.named_steps["nn"].predict_proba(x) # X_train_enc = encoder(X_train) # X_test_enc = encoder(X_test) # X_train_sc = scaler(X_train_enc) # X_test_sc = scaler(X_test_enc) # explainer_lime = lime_tabular.LimeTabularExplainer(X_train_sc,class_names=["NO", "YES"], # feature_names=X_train.columns, # categorical_features=categorical_features2, # categorical_names=categorical_names, # verbose=False) # def Lime(number_of_observation): # exp_lime = explainer_lime.explain_instance(X_test_sc[number_of_observation],predict_fn) # exp_lime.show_in_notebook(show_table=True, show_all=False) # Lime(4) def CeterisParibus(number_of_observation): cp = exp.predict_profile(pd.DataFrame(X_test.iloc[number_of_observation,:]).T) cp.plot() CeterisParibus(4) def VariableImp(): vi = exp.model_parts() vi.plot(max_vars=10) VariableImp() def PartialDp(): pdp_num = exp.model_profile(type = 'partial') pdp_num.result["_label_"] = 'pdp' pdp_num.plot() PartialDp()

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Alpine-DAV/ascent

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############################################################################### # Copyright (c) Lawrence Livermore National Security, LLC and other Ascent # Project developers. See top-level LICENSE AND COPYRIGHT files for dates and # other details. No copyright assignment is required to contribute to Ascent. ############################################################################### import conduit import numpy as np # # Using hierarchical paths imposes a tree structure # n = conduit.Node() n["dir1/dir2/val1"] = 100.5; print(n.to_yaml())

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enverygtlr/simple-Ray-tracer-in-webgl

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def genPairs(n): for i in range(0, n): for j in range(i+1, n): yield (i, j) for p in genPairs(5): print(p)

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williamy1996/AutoExpression

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refs/heads/master

2022-12-14T20:22:37.529136

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import argparse import os import sys import time import pickle import numpy as np import pandas as pd import json import pickle as pkl from sklearn.datasets import load_iris from sklearn.metrics import balanced_accuracy_score from sklearn.model_selection import train_test_split from solnml.utils.data_manager import DataManager from solnml.estimators import Classifier class Ensemble_models: def __init__(self,ensemble_info,mdl_list): self.ensemble_info = ensemble_info self.model_list = mdl_list def predict_proba(self,test_x): #ONLY FOR CLF MODEL if(self.ensemble_info['task_type'] == 'RGS'): print('Regression model does not have \'predict_proba\'.') return 'Regression model does not have \'predict_proba\'.' if(self.ensemble_info['ensemble_method']=='none'): femdl = self.model_list[0] y_predict = predict_proba_from_path(femdl,test_x) return y_predict if(self.ensemble_info['ensemble_method']=='bagging'): y_predict = [] for femdl in self.model_list: y_predict.append(predict_proba_from_path(femdl,test_x)) y_predict = np.array(y_predict) return np.average(y_predict,axis=0) if(self.ensemble_info['ensemble_method']=='ensemble_selection'): y_predict = [] weights = np.array(pd.read_json(self.ensemble_info['ensemble_weights']))[:,0] i = 0 for femdl in self.model_list: y_predict.append(predict_proba_from_path(femdl,test_x)*weights[i]) i+=1 y_predict = np.array(y_predict) return np.sum(y_predict,axis=0) if(self.ensemble_info['ensemble_method']=='stacking'): meta_learner = pickle.load(open(self.ensemble_info['meta_learner_path'],'rb')) kfold = self.ensemble_info['kfold'] femdl = self.model_list[0] y_predict = predict_proba_from_path(femdl,test_x) n_dim = y_predict.shape[1] sample_dim = y_predict.shape[0] y_predict = [] if(n_dim==2): n_dim = 1 i=0 for femdl in self.model_list: if(i == 0): new_sumpredict = np.zeros([sample_dim,n_dim]) new_predict = predict_proba_from_path(femdl,test_x) if(n_dim==1): new_predict = new_predict[:,1:] new_sumpredict = new_sumpredict + new_predict/kfold i+=1 if(i==kfold): i=0 y_predict.append(new_sumpredict) y_predict = np.hstack(y_predict) y_pred = meta_learner.predict_proba(y_predict) return y_pred if(self.ensemble_info['ensemble_method']=='blending'): meta_learner = pickle.load(open(self.ensemble_info['meta_learner_path'],'rb')) femdl = self.model_list[0] y_predict = predict_proba_from_path(femdl,test_x) n_dim = y_predict.shape[1] if(n_dim==2): n_dim = 1 y_predict = [] for femdl in self.model_list: new_predict = predict_proba_from_path(femdl,test_x) if(n_dim==1): new_predict = new_predict[:,1:] y_predict.append(new_predict) y_predict = np.hstack(y_predict) y_pred = meta_learner.predict_proba(y_predict) return y_pred def predict(self,test_x): if(self.ensemble_info['task_type'] == 'CLF'): return np.argmax(self.predict_proba(test_x),axis=1) if(self.ensemble_info['ensemble_method']=='none'): femdl = self.model_list[0] y_predict = predict_from_path(femdl,test_x) return y_predict if(self.ensemble_info['ensemble_method']=='bagging'): y_predict = [] for femdl in self.model_list: y_predict.append(predict_from_path(femdl,test_x)) y_predict = np.array(y_predict) return np.average(y_predict,axis=0) if(self.ensemble_info['ensemble_method']=='ensemble_selection'): y_predict = [] weights = np.array(pd.read_json(self.ensemble_info['ensemble_weights']))[:,0] i = 0 for femdl in self.model_list: y_predict.append(predict_from_path(femdl,test_x)*weights[i]) i+=1 y_predict = np.array(y_predict) return np.sum(y_predict,axis=0) if(self.ensemble_info['ensemble_method']=='stacking'): meta_learner = pickle.load(open(self.ensemble_info['meta_learner_path'],'rb')) kfold = self.ensemble_info['kfold'] femdl = self.model_list[0] y_predict = predict_from_path(femdl,test_x) n_dim = y_predict.shape[1] sample_dim = y_predict.shape[0] y_predict = [] if(n_dim==2): n_dim = 1 i=0 for femdl in self.model_list: if(i == 0): new_sumpredict = np.zeros([sample_dim,n_dim]) new_predict = predict_from_path(femdl,test_x) if(n_dim==1): new_predict = new_predict[:,1:] new_sumpredict = new_sumpredict + new_predict/kfold i+=1 if(i==kfold): i=0 y_predict.append(new_sumpredict) y_predict = np.hstack(y_predict) y_pred = meta_learner.predict(y_predict) return y_pred if(self.ensemble_info['ensemble_method']=='blending'): meta_learner = pickle.load(open(self.ensemble_info['meta_learner_path'],'rb')) femdl = self.model_list[0] y_predict = predict_from_path(femdl,test_x) n_dim = y_predict.shape[1] if(n_dim==2): n_dim = 1 y_predict = [] for femdl in self.model_list: new_predict = predict_from_path(femdl,test_x) if(n_dim==1): new_predict = new_predict[:,1:] y_predict.append(new_predict) y_predict = np.hstack(y_predict) y_pred = meta_learner.predict(y_predict) return y_pred def save_model(mdl,save_dir): mdl_list = '' fe_list = '' if not os.path.exists(save_dir): os.makedirs(save_dir) info = mdl.get_ens_model_info() if(info is None): f_ens_info = open(save_dir +'/ens_info','w') ens_dict = {} ens_dict['ensemble_method'] = 'none' f_ens_info.write(json.dumps(ens_dict)) f_ens_info.close() os.system('cp '+ mdl.best_algo_path + ' '+save_dir +'/') os.system('cp '+ mdl.best_fe_path + ' '+save_dir +'/') f_mdl_list = open(save_dir +'/model_list','w') f_mdl_list.write(os.path.basename(mdl.best_algo_path)) f_mdl_list.close() f_fe_list = open(save_dir +'/fe_list','w') f_fe_list.write(os.path.basename(mdl.best_fe_path)) f_fe_list.close() return f_ens_info = open(save_dir +'/ens_info','w') ens_dict = {} if(mdl.task_type == 4): ens_dict['task_type'] = 'RGS' else: ens_dict['task_type'] = 'CLF' ens_met = info['ensemble_method'] ens_dict['ensemble_method'] = ens_met if(ens_met=='bagging'): f_ens_info.write(json.dumps(ens_dict)) if(ens_met=='ensemble_selection'): ens_dict['ensemble_weights'] = pd.DataFrame(info['ensemble_weights']).to_json() f_ens_info.write(json.dumps(ens_dict)) if(ens_met=='stacking'): meta_learner_path = save_dir +'/'+os.path.basename(info['meta_learner_path']) os.system('cp '+ info['meta_learner_path'] + ' '+save_dir +'/') ens_dict['meta_learner_path'] = meta_learner_path ens_dict['kfold'] = info['kfold'] f_ens_info.write(json.dumps(ens_dict)) if(ens_met=='blending'): meta_learner_path = save_dir +'/'+os.path.basename(info['meta_learner_path']) os.system('cp '+ info['meta_learner_path'] + ' '+save_dir +'/') ens_dict['meta_learner_path'] = meta_learner_path f_ens_info.write(json.dumps(ens_dict)) f_ens_info.close() if(ens_met=='stacking'): for conf in info['config']: for partpath in conf[-2]: os.system('cp '+ partpath + ' '+save_dir +'/') mdl_list += (os.path.basename(partpath)+'\n') for partpath in conf[-1]: os.system('cp '+ partpath + ' '+save_dir +'/') fe_list += (os.path.basename(partpath)+'\n') else: for conf in info['config']: os.system('cp '+ conf[-2] + ' '+save_dir +'/') os.system('cp '+ conf[-1] + ' '+save_dir +'/') mdl_list += (os.path.basename(conf[-2])+'\n') fe_list += (os.path.basename(conf[-1])+'\n') f_mdl_list = open(save_dir +'/model_list','w') f_mdl_list.write(mdl_list) f_mdl_list.close() f_fe_list = open(save_dir +'/fe_list','w') f_fe_list.write(fe_list) f_fe_list.close() def predict_proba_from_path(femdl,test_x): fe = femdl[0].replace('\n','') base_fe = pickle.load(open(fe,'rb')) test_x_tf = base_fe.operate(test_x) mdl = femdl[1].replace('\n','') base_model = pickle.load(open(mdl,'rb')) return base_model.predict_proba(test_x_tf) def predict_from_path(femdl,test_x): fe = femdl[0].replace('\n','') base_fe = pickle.load(open(fe,'rb')) test_x_tf = base_fe.operate(test_x) mdl = femdl[1].replace('\n','') base_model = pickle.load(open(mdl,'rb')) return base_model.predict(test_x_tf) def load_model(save_dir): f_ens_info = open(save_dir +'/ens_info','r') ens_info = json.loads(f_ens_info.read()) f_ens_info.close() mdl_list = [] f_mdl_list = open(save_dir +'/model_list','r') for mdl in f_mdl_list: mdl.replace('\n','') mdl_list.append(save_dir +'/'+mdl) f_mdl_list.close() fe_list = [] f_fe_list = open(save_dir +'/fe_list','r') for fe in f_fe_list: fe.replace('\n','') fe_list.append(save_dir +'/'+fe) f_fe_list.close() mdl_list = [[fe_list[i],mdl_list[i]] for i in range(len(mdl_list))] return Ensemble_models(ens_info,mdl_list) class bio_models(): def __init__(self,ensemble_info, mdl_list, imp_ope_list, fb): self.ensemble_info = ensemble_info self.mdl_list = mdl_list self.fb = fb self.imp_ope_list = imp_ope_list def predict_proba(self,test_x): #ONLY FOR CLF MODEL if self.ensemble_info['task_type'] == 'RGS': print('Regression model does not have \'predict_proba\'.') return 'Regression model does not have \'predict_proba\'.' if self.fb is not None: test_x = self.fb.transform(test_x) y_pred = None for key in self.mdl_list: if(np.sum(np.isnan(test_x)) > 0): test_x_filled = self.imp_ope_list[key].fit_transform(test_x) else: test_x_filled = test_x if y_pred is None: y_pred = self.mdl_list[key].predict_proba(test_x_filled) else: y_pred += self.mdl_list[key].predict_proba(test_x_filled) y_pred = y_pred/len(self.mdl_list) return y_pred def predict(self,test_x): if self.ensemble_info['task_type'] == 'CLF': return np.argmax(self.predict_proba(test_x),axis=1) if self.fb is not None: test_x = self.fb.transform(test_x) y_pred = None for key in self.mdl_list: if(np.sum(np.isnan(test_x)) > 0): test_x_filled = self.imp_ope_list[key].fit_transform(test_x) else: test_x_filled = test_x if y_pred is None: y_pred = self.mdl_list[key].predict(test_x_filled) else: y_pred += self.mdl_list[key].predict(test_x_filled) y_pred = y_pred/len(self.mdl_list) return y_pred def save(biomdl, save_dir, task_type): print("PLEASE SAVE THE MODEL IN A NEW FOLDER OR AN EMPTY FOLDER") if not os.path.exists(save_dir): os.makedirs(save_dir) f_ens_info = open(save_dir +'/ens_info','w') ens_dict = {} ens_dict['task_type'] = task_type ens_dict['impute_method'] = biomdl.impute_method f_ens_info.write(json.dumps(ens_dict)) if not os.path.exists(save_dir): os.makedirs(save_dir) if biomdl.fb_operator is not None: pkl.dump(biomdl.fb_operator,open(save_dir+'/fb.pkl','wb')) for key in biomdl.impute_method: pkl.dump(biomdl.impute_operator[key],open(save_dir+'/'+key+'.pkl','wb')) save_model(biomdl.mdl[key],save_dir+'/'+key+'_models') def load(save_dir): f_ens_info = open(save_dir +'/ens_info','r') ens_info = json.loads(f_ens_info.read()) f_ens_info.close() mdl_list = {} imp_ope_list = {} if os.path.exists(save_dir+'/fb.pkl'): fb = pkl.load(open(save_dir+'/fb.pkl','rb')) else: fb = None for method in ens_info['impute_method']: mdl_list[method] = load_model(save_dir+'/'+method+'_models') imp_ope_list[method] = pkl.load(open(save_dir+'/'+method+'.pkl','rb')) return bio_models(ens_info,mdl_list,imp_ope_list,fb)

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null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon May 6 20:18:30 2019 @author: hechen """ import pandas as pd import numpy as np import matplotlib.pyplot as plt import os current_path = os.path.dirname(__file__) csv_path = current_path + '/2016_02_10_0000_1687_export.csv' Data = pd.read_csv(csv_path, ';') """ ['Unix Timestamp [ms]', 'Time [Europe/Amsterdam]', 'Battery - Avg cell voltage [mV]', 'Battery - Avg cell temp [°C]', 'Battery - Max. charge current [A]', 'Battery - Max. discharge current [A]', 'Battery - Cell nr. max volt', 'Battery - Max cell voltage [mV]', 'Battery - Cell nr. min volt', 'Battery - Min cell voltage [mV]', 'Battery - Max. charge voltage [V]', 'Battery - Current [A]', 'Battery - Battery power [kW]', 'Battery - Voltage [V]', 'Battery - State of Charge [%]', 'Battery - State of Health [%]', 'Primove - Charger wayside present', 'Primove - Charging state', 'Primove - Charging current [A]', 'Primove - Pick-up temp [°C]', 'Primove - Rectrifier temp [°C]', 'Primove - Charging voltage [V]', 'Primove - Pick-up position [mm]', 'Primove - Pick-up position control', 'Battery - TCU compressor status', 'Battery - TCU heater status', 'Vehicle - Accelerator pedal position [%]', 'Vehicle - Accelerator pedal switch', 'Engine - Actual Engine Torque [%]', 'Vehicle - Ambient Air Temperature [°C]', 'Air pressure - Pressure front axle left [bar]', 'Air pressure - Pressure front axle right [bar]', 'Air pressure - Pressure rear axle left [bar]', 'Air pressure - Pressure rear axle right [bar]', 'Air pressure - Pressure brake circuit 1 [bar]', 'Air pressure - Pressure brake circuit 2 [bar]', 'Vehicle - Brake pedal switch', 'Vehicle - Current Gear', 'Engine - Driver Engine Torque demand [%]', 'Doors - Enable status Doors 1', 'Doors - Enable status Doors 2', 'Doors - Enable status Doors 3', 'Doors - Enable status Doors 4', 'Doors - Enable status Doors 5', 'Engine - Motor Speed RPM [RPM]', 'Vehicle - High resolution vehicle distance [km]', 'Doors - Lock status Doors 1', 'Doors - Lock status Doors 2', 'Doors - Lock status Doors 3', 'Doors - Lock status Doors 4', 'Doors - Lock status Doors 5', 'Doors - Open status Doors 1', 'Doors - Open status Doors 2', 'Doors - Open status Doors 3', 'Doors - Open status Doors 4', 'Doors - Open status Doors 5', 'Doors - Positions Doors', 'Doors - Status Doors', 'Vehicle - Tachograph Speed [km/h]', 'Vehicle - Wheel based Speed [km/h]', 'IVH - Altitude [m]', 'IVH - GPS Course [°]', 'IVH - GPS position', 'IVH - GPS speed [km/h]', 'IVH - Satellites', 'IVH - 24V Battery [V]', 'IVH - Online status', 'HVAC - AC compressor state [%]', 'Auxiliary - Air compressor state', 'Vehicle - Brake pedal position [%]', 'HVAC - Cabin air temp [°C]', 'HVAC - Cabin air temp setpoint [°C]', 'HVAC - Condenser fan state', 'HVAC - Evaporator fan state', 'HVAC - Floor air heater state', 'Vehicle - Ignition State', 'HVAC - Outlet air temperature floor unit [°C]', 'HVAC - Outlet air temperature roof unit [°C]', 'HVAC - Recirculation Air flap position [%]', 'Auxiliary - Steering pump state', 'HVAC - Water heater outlet temp [°C]', 'Auxiliary - Total Power 24V [kW]', 'Auxiliary - Total Power HV [kW]', 'Powertrain - Inverter temperature [°C]', 'Powertrain - Motor temperatur status', 'Powertrain - Traction Power [kW]', 'Vehicle - Vehicle state'], dtype='object') """ #%% obversation # Modifications to the data or indices of the copy will # not be reflected in the original object X = Data.copy(deep=True) # feststellen simpling frequency print(X['Time [Europe/Amsterdam]'][0:40]) #%% # slice operation, simple every 20 points X = X[0::20] ''' print(X['Time [Europe/Amsterdam]'][0:40]) X = X[0::20] print(X['Time [Europe/Amsterdam]'][0:50]) X['Unix Timestamp [ms]'][0:20] sjdada X['Unix Timestamp [ms]'][0:20] pd.to_datetime(1455072686227) pd.to_datetime(1455072686) pd.to_datetime(1455072686227,unit='s') pd.to_datetime(1455072686227,unit='ms') X['Unix Timestamp [ms]'][0:20] pd.to_datetime(1455072686227,unit='ms') print(X['Time [Europe/Amsterdam]'][0:50]) pd.to_datetime(1455072686227,unit='ms') pd.to_datetime(1455072686227/1000,unit='s') 1455072686227/1000 int(1455072686227/1000) pd.to_datetime(1455072686227//1000,unit='s') pd.to_datetime(X['Unix Timestamp [ms]'][0:30]//1000,unit='s') ''' #%% #time = Data['seconds'] #time = (time-time.iloc[0]) / 60 #my_x_ticks = np.arange(time.iloc[0], time.iloc[-1], 1) # #plt.plot(time, Data['GPS Speed']) #plt.grid(axis='x') #plt.xticks(my_x_ticks)

UTF-8

Python

false

4,958

py

3

data_load_bus.py

2

0.614918

0.567415

0

131

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huyanhai/spider-music

6,906,307,414,796

ba43709f4da2e19307b3ba604caaeb92b52b38ba

42fb81f32a776809079aa343e4dd7d521b1998b0

/sider_scrapy/spiders/music.py

c8ceb5fc3d5d4892b9da50491bb83772320901fc

[]

no_license

https://github.com/huyanhai/spider-music

8849d1ebbc4060219ad70fde2268d00c55330a4b

cfc87b6daadfc9ecc238e835e1f1079fa866d94d

refs/heads/master

2020-06-28T09:28:09.290872

2019-08-05T08:57:16

null

0

null

# -*- coding: utf-8 -*- import scrapy import requests import json from scrapy.linkextractors import LinkExtractor from scrapy.spiders import CrawlSpider, Rule from sider_scrapy.items import QqmusicItem class MusicSpider(CrawlSpider): name = 'music' allowed_domains = ['y.qq.com'] start_urls = ['https://y.qq.com/n/yqq/album/003L8BAv3UNXWx.html'] # def __init__(self): # self.song_data = [] rules = ( Rule(LinkExtractor(allow=r'album/.+\.html'),callback='parse_item', follow=False), ) def start_requests(self): URL = 'https://u.y.qq.com/cgi-bin/musicu.fcg' formdata = { '-':'getUCGI12417882050721563', 'g_tk':'155229945', 'format':'json', 'inCharset':'utf8', 'outCharset':'utf-8', 'notice':'0', 'platform':'yqq.json', 'needNewCode':'0', } for item in range(1,5): item = item*20 for ids in range(1,7): formdata['data'] = '{"new_album":{"module":"newalbum.NewAlbumServer","method":"get_new_album_info","param":{"area":%d,"start":0,"num":%d}},"new_album_tag":{"module":"newalbum.NewAlbumServer","method":"get_new_album_area","param":{}},"comm":{"ct":24,"cv":0}}' %(ids,item) result = requests.get(URL,params=formdata) datas = json.loads(result.text) for id in datas['new_album']['data']['albums']: url = 'https://y.qq.com/n/yqq/album/%s.html' %id['mid'] self.start_urls.append(url) yield self.make_requests_from_url(url) def parse_item(self, response): URL_LINK = "https://u.y.qq.com/cgi-bin/musicu.fcg" URL_LYR = "https://c.y.qq.com/lyric/fcgi-bin/fcg_query_lyric_new.fcg" result = response song_mid = result.xpath("//span[@class='songlist__songname_txt']/a/@href").get().split('/')[-1].split('.')[0] singer_name = result.xpath("//div[@class='data__cont']/div[@class='data__singer']/a/text()").get() song_name = result.xpath("//div[@class='data__cont']//h1/text()").get() more_data = result.xpath("//div[@class='data__cont']//li/text()").getall() company = result.xpath("//div[@class='data__cont']//li/a/text()").get() post = response.urljoin(result.xpath("//span[@class='data__cover']/img/@src").get()) more_data = list(map(lambda x:x.split('：')[1],more_data)) schools = more_data[0] language = more_data[1] send_time = more_data[2] song_type = None qq_pages = 'https://y.qq.com/n/yqq/album/%s.html' %song_mid formdata_link = { '-':'getplaysongvkey', 'g_tk':'155229945', 'loginUin': '810839700', 'hostUin':'0', 'format':'json', 'inCharset':'utf8', 'outCharset':'utf-8', 'notice':'0', 'platform':'yqq.json', 'needNewCode':'0', 'data':'{"req":{"module":"CDN.SrfCdnDispatchServer","method":"GetCdnDispatch","param":{"guid":"986239112","calltype":0,"userip":""}},"req_0":{"module":"vkey.GetVkeyServer","method":"CgiGetVkey","param":{"guid":"986239112","songmid":["%s"],"songtype":[0],"uin":"810839700","loginflag":1,"platform":"20"}},"comm":{"uin":810839700,"format":"json","ct":24,"cv":0}}' %song_mid } formdata_lry = { '-':'MusicJsonCallback_lrc', 'g_tk':'155229945', 'songmid':song_mid, 'format':'json', 'inCharset':'utf8', 'outCharset':'utf-8', 'notice':'0', 'platform':'yqq.json', 'needNewCode':'0', } headers = { 'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36', 'Referer':'https://y.qq.com/portal/player.html' } if len(more_data) > 4: song_type = more_data[4] print('正在爬取歌曲%s' %song_name) res_link = requests.get(URL_LINK,params=formdata_link) # 获取歌曲地址 res_lyr = requests.get(URL_LYR,params=formdata_lry,headers=headers) # 获取歌词 datas_link = json.loads(res_link.text)['req_0']['data'] datas_lyr = json.loads(res_lyr.text)['lyric'] song_link = 'http://isure.stream.qqmusic.qq.com/' + datas_link['midurlinfo'][0]['purl'] item = QqmusicItem(singer_name=singer_name,song_name=song_name,schools=schools,language=language,send_time=send_time,song_type=song_type,company=company,post=post,song_mid=song_mid,song_link=song_link,qq_pages=qq_pages,lyric=datas_lyr) yield item

UTF-8

Python

false

4,734

py

8

music.py

6

0.556809

0.521702

0

99

46.484848

383

Dituohgasirre/python

9,698,036,202,454

346616db94cff25f16ee05ffd974b5924403425e

8e7a2b9efbc0d25111f01f4cddb781961032685a

/python-1025/python/1_base_syntax/homework.py

56368582436dac9d44afcfe1e4ce98f4cc01c885

[]

no_license

https://github.com/Dituohgasirre/python

e044aa2e1fb2233b6ccd59701b834ab01e4e24c2

05f036d2723f75cd89e4412aaed7ee0ba5d3a502

refs/heads/master

2023-06-03T13:50:18.641433

2021-06-17T10:23:40

366,942,423

0

null

#!/usr/bin/env python3 import kyo import menu def ball(index, args): """ 一个袋子里有3个红球， 3个绿球，6个黄球，一次从袋子里取6个球列出所有可能的颜色组合 """ r = 0 y = 0 while r < 4: g = 0 while g < 4: y = 6 - r - g print("红球: %d, 绿球: %d, 黄球: %d" % (r, g, y)) g += 1 r += 1 def mul9x9(index, args): """ 9x9乘法 """ i = 1 while i < 10: j = 1 while j <= i: if i % 2 == 0: print("%d X %d = %2d " % (i, j, i * j), end='') else: print("\033[32;40m%d X %d = %2d\033[0m " % (i, j, i * j), end='') j += 1 print() i += 1 def blackFri(index, args): """ 已知1900年一月一号是星期一问今年有多少个黑色星期五(黑色星期五每个月13号是星期五) """ year = int(input("请输入年: ")) days = kyo.countYearDays(1900, year) + 13 m = 1 while m <= 12: if (days + kyo.countMonthDays(year, m)) % 7 == 5: print("%d 年 %d 月 13号是黑色星期五!" % (year, m)) m += 1 def fish(index, args): """ 某人从2000年一月一号开始过着3天打鱼两天晒网的日子输入年月日判断此人在打鱼还是晒网 """ def core(year, month, day): day += kyo.countYearDays(2000, year) + kyo.countMonthDays(year, month) return "打鱼" if 1 <= day % 5 <= 3 else "晒网" try: print(core(*kyo.checkDate(input("请输入年月日: ").split('-')))) # print(core(*kyo.checkDate(input("请输入年月日: ")))) except: print("日期不合法....") # print(core(*[int(x) for x in input("请输入年月日:").split()])) # year, month, day = [int(x) for x in input("请输入年月日: ").split()] # print(core(year, month, day)) #自动测试代码 # for i in range(1, 16): # print("%d-%d-%d: " % (year, month, i), core(year, month, i)) def main(): def end(index, args): print("--------------------------------------------") print("本题测试完成, 回车继续, q为退出: ", end='') return True if input() == 'q' else False menuList = menu.add("拿球", ball) menu.add("买鸡", items=menuList) menu.add("9x9乘法表", mul9x9, items=menuList) menu.add("黑色星期五", blackFri, items=menuList) menu.add("打鱼晒网", fish, items=menuList) menu.add("最大公约数", items=menuList) menu.add("4, 5, 6, 7组合数", items=menuList) menu.add("输出1-1/2+1/3-1/4+1/5...+1/99-1/100结果", items=menuList) menu.add("日历", items=menuList) menu.add("回文数", items=menuList) menu.add("完数", items=menuList) menu.add("水仙花数", items=menuList) menu.add("八进制转十进制", items=menuList) menu.add("菜单打印图形", items=menuList) menu.add("把八进制数转化为十进制数输出", items=menuList) menu.add("实现菜单打印图形", items=menuList) menu.add("退出", items=menuList) menu.run(menuList, "====== 第一天练习题 ======", end) if __name__ == "__main__": main()

UTF-8

Python

false

3,271

py

347

homework.py

244

0.501279

0.465839

0

108

24.333333

81

iluoxw/OurServer

9,938,554,340,682

a511da40c36be7c0eff00be83b3c64be0c43be17

2ced9f032b61792d81aced126dfeb1d40eeb065e

/User/migrations/0001_initial.py

bdf95fc8d9e65dcad2418f1dafc2004765c6de32

[]

no_license

https://github.com/iluoxw/OurServer

c35e2d334df86f278156a2f32479caf353ad1646

a3a76f1c8b61bd81c9a64774d7b0c3717755c9cb

refs/heads/master

2020-05-21T03:48:50.868345

2017-03-10T14:28:10

84,567,482

0

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='Group', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('name', models.CharField(max_length=32)), ('delete_flag', models.CharField(max_length=4)), ], ), migrations.CreateModel( name='GroupPrivileges', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('groupId', models.IntegerField()), ('privilegesId', models.IntegerField()), ('delete_flag', models.CharField(max_length=4)), ], ), migrations.CreateModel( name='Privileges', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('name', models.CharField(max_length=32)), ('description', models.TextField()), ('delete_flag', models.CharField(max_length=4)), ], ), migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('username', models.CharField(max_length=32)), ('name', models.CharField(max_length=32)), ('password', models.CharField(max_length=32)), ('delete_flag', models.CharField(max_length=4)), ], ), migrations.CreateModel( name='UserGroup', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('userId', models.IntegerField()), ('groupId', models.IntegerField()), ('delete_flag', models.CharField(max_length=4)), ], ), migrations.CreateModel( name='UserPrivileges', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('userId', models.IntegerField()), ('privilegesId', models.IntegerField()), ('delete_flag', models.CharField(max_length=4)), ], ), ]

UTF-8

Python

false

2,614

py

4

0001_initial.py

3

0.521423

0.51492

0

67

38.014925

114

ygzylmz/theNextTrace

9,105,330,692,341

d6f7ae3fbb5de7652b1d1a67ef23609644c0b94c

7447ddef42bb3dc859a48d8a05e7b4f0832ecb55

/blog/migrations/0025_auto_20201014_1845.py

306cd6472ae4cc678c76a93f84e27e1063f8aa4d

[]

no_license

https://github.com/ygzylmz/theNextTrace

f72feca5402b203afea91a3fe8d719d2e1ed9744

f46efcb9575c83d865ee6b841943ea0292a1f094

refs/heads/master

2022-12-31T11:36:35.116518

2020-10-22T14:20:44

null

0

null

# -*- coding: utf-8 -*- # Generated by Django 1.10 on 2020-10-14 18:45 from __future__ import unicode_literals import ckeditor.fields from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('blog', '0024_auto_20200914_1043'), ] operations = [ migrations.AlterField( model_name='blogdizi', name='content', field=ckeditor.fields.RichTextField(max_length=10000, null=True, verbose_name='İcerik'), ), migrations.AlterField( model_name='blogfilm', name='content', field=ckeditor.fields.RichTextField(max_length=10000, null=True, verbose_name='İcerik'), ), migrations.AlterField( model_name='bloggezi', name='content', field=ckeditor.fields.RichTextField(max_length=10000, null=True, verbose_name='İcerik'), ), migrations.AlterField( model_name='bloghayat', name='content', field=ckeditor.fields.RichTextField(max_length=10000, null=True, verbose_name='İcerik'), ), ]

UTF-8

Python

false

1,137

py

22

0025_auto_20201014_1845.py

17

0.599294

0.553398

0

36

30.472222

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arnabs542/Leetcode-38

16,080,357,596,918

dcf8bd6b88034b5eb09ab3480fa86aaddcdd933d

c6ec292a52ea54499a35a7ec7bc042a9fd56b1aa

/Python/90.py

06ec0bed0222cb95586ce2e133a8a11258bb8a3d

[]

no_license

https://github.com/arnabs542/Leetcode-38

ad585353d569d863613e90edb82ea80097e9ca6c

b75b06fa1551f5e4d8a559ef64e1ac29db79c083

refs/heads/master

2023-02-01T01:18:45.851097

2020-12-19T03:46:26

null

0

null

class Solution: def subsetsWithDup(self, nums: List[int]) -> List[List[int]]: nums.sort() self.results = [] visited = [False] * len(nums) self.dfs(nums, visited, [], 0) return self.results def dfs(self, nums, visited, prev, start): self.results.append(prev) if start == len(nums): return for i in range(start, len(nums)): if i > 0 and nums[i - 1] == nums[i] and not visited[i - 1]: continue visited[i] = True self.dfs(nums, visited, prev + [nums[i]], i + 1) visited[i] = False

UTF-8

Python

false

658

py

364

90.py

362

0.484802

0.477204

0

19

32.947368

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nusaibsqli/TCPShell

13,176,959,697,811

a73239e6da0227a91cf3eff6380aa0454122ad18

c31ba668f35095075186b3bfd9250c97a6b2d80c

/generate_password.py

d0cc6b91394f2b210eabb78f2a849429109b1e54

[]

no_license

https://github.com/nusaibsqli/TCPShell

1e765b04cc740e92483784c0c6d4d9e659cc6047

9a15b36d0cceac6d46580058003b7093842ca705

refs/heads/master

2021-09-15T12:50:22.202323

2018-06-02T01:47:50

null

0

null

clrPasswd = raw_input("Enter Password: ") encPasswd = '' for char in clrPasswd: _tmp = (ord(char) << 1) | (ord(char) >> 7) #print _tmp encPasswd += chr(_tmp) encPasswd = '\\x' + '\\x'.join(x.encode('hex') for x in encPasswd) print encPasswd

UTF-8

Python

false

248

py

5

generate_password.py

1

0.616935

0.608871

0.008065

11

21.454545

66

shuuki4/MachineLearningInAction

5,291,399,734,393

45f5f8dba6fe2d64b122628bc792e21bdfd6773a

661399426fed233f508430a211cc952339faa7da

/ID3DecisionTree+TreePlot.py

b189a01efc1334020afbd111c5b3ebcf889d750f

[]

no_license

https://github.com/shuuki4/MachineLearningInAction

1dd1776ae962c0a8e28bb0115e72d369e30f0bbd

6d539aa099593d555fd704f9707b9bdb24274fd5

refs/heads/master

2021-03-12T22:55:02.952965

2015-09-13T16:13:44

42,063,251

0

1

null

## This code generates an ID3 Decision Tree and plot this tree by matplotlib ## ## TreePlotting Codes are from text, rest by YuKiSa ## from numpy import * import matplotlib.pyplot as plt from math import log ## Tree Plot Section ## ## tree plot codes are from MLinA text ## ## @author : Peter Harington ## decisionNode = dict(boxstyle="sawtooth", fc="0.8") leafNode = dict(boxstyle="round4", fc="0.8") arrow_args = dict(arrowstyle="<-") def getNumLeafs(myTree): numLeafs = 0 firstStr = myTree.keys()[0] secondDict = myTree[firstStr] for key in secondDict.keys(): if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes numLeafs += getNumLeafs(secondDict[key]) else: numLeafs +=1 return numLeafs def getTreeDepth(myTree): maxDepth = 0 firstStr = myTree.keys()[0] secondDict = myTree[firstStr] for key in secondDict.keys(): if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes thisDepth = 1 + getTreeDepth(secondDict[key]) else: thisDepth = 1 if thisDepth > maxDepth: maxDepth = thisDepth return maxDepth def plotNode(nodeTxt, centerPt, parentPt, nodeType): createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction', xytext=centerPt, textcoords='axes fraction', va="center", ha="center", bbox=nodeType, arrowprops=arrow_args ) def plotMidText(cntrPt, parentPt, txtString): xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0] yMid = (parentPt[1]-cntrPt[1])/2.0 + cntrPt[1] createPlot.ax1.text(xMid, yMid, txtString, va="center", ha="center", rotation=30) def plotTree(myTree, parentPt, nodeTxt):#if the first key tells you what feat was split on numLeafs = getNumLeafs(myTree) #this determines the x width of this tree depth = getTreeDepth(myTree) firstStr = myTree.keys()[0] #the text label for this node should be this cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff) plotMidText(cntrPt, parentPt, nodeTxt) plotNode(firstStr, cntrPt, parentPt, decisionNode) secondDict = myTree[firstStr] plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD for key in secondDict.keys(): if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes plotTree(secondDict[key],cntrPt,str(key)) #recursion else: #it's a leaf node print the leaf node plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode) plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key)) plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD #if you do get a dictonary you know it's a tree, and the first element will be another dict def createPlot(inTree): fig = plt.figure(1, facecolor='white') fig.clf() axprops = dict(xticks=[], yticks=[]) createPlot.ax1 = plt.subplot(111, frameon=False, **axprops) #no ticks #createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses plotTree.totalW = float(getNumLeafs(inTree)) plotTree.totalD = float(getTreeDepth(inTree)) plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0; plotTree(inTree, (0.5,1.0), '') plt.show() ## ID3 Decision Tree Section ## ## dataSet = [[feature1, feature2, ..., featureN, label], ... [feature1, ..., label]] def calcShannonEnt(dataSet) : m = len(dataSet) labelCount = {}; for featureVec in dataSet : labelCount[featureVec[-1]] = labelCount.get(featureVec[-1], 0.0)+1.0; totalEnt = 0.0; for label in labelCount.keys() : p = labelCount[label]/float(m) totalEnt -= p*log(p, 2) return totalEnt def dataSplit(dataSet, divFeatureIndex, value) : returnList = [] for featureVec in dataSet : if featureVec[divFeatureIndex]==value : newfeatVec = featureVec[:divFeatureIndex] newfeatVec.extend(featureVec[divFeatureIndex+1:]) returnList.append(newfeatVec) return returnList def chooseBestFeature(dataSet) : bestEntropy = calcShannonEnt(dataSet) bestFeatureIndex = -1 for i in range(len(dataSet[0])-1) : featList = [example[i] for example in dataSet] # fetch all available featureList featList = set(featList) nowEntropy = 0.0 for feature in featList : nowEntropy += calcShannonEnt(dataSplit(dataSet, i, feature)) if (bestEntropy > nowEntropy) : bestEntropy = nowEntropy bestFeatureIndex = i return bestFeatureIndex def majorityVote(dataSet) : labelCount = {} bestCount = -1 bestLabel = dataSet[0][-1] for vec in dataSet : label = vec[-1] labelCount[label] = labelCount.get(label, 0)+1 if bestCount < labelCount[label] : bestCount = labelCount[label] bestLabel = label return bestLabel def generateTree(dataSet, labels) : ## stop condition ## # condition 1 : no more benefit on dividing bestFeatureIndex = chooseBestFeature(dataSet) if bestFeatureIndex==-1 : return majorityVote(dataSet) # condition 2 : only one kind left kindList = [vec[-1] for vec in dataSet] kindSet = set(kindList) if (len(kindSet)==1) : return kindSet[0] # condition 3 : no more labels if (len(labels)==0) : return majorityVote(dataSet) returnTree = {} returnTreeElement = {} availableFeatureValSet = [vec[bestFeatureIndex] for vec in dataSet] availableFeatureValSet = set(availableFeatureValSet) newLabels = labels[:bestFeatureIndex] newLabels.extend(labels[bestFeatureIndex+1:]) for val in availableFeatureValSet : returnTreeElement[val] = generateTree(dataSplit(dataSet, bestFeatureIndex, val), newLabels) returnTree[labels[bestFeatureIndex]] = returnTreeElement return returnTree def myData() : return [[1,1,'yes'], [1,1,'yes'], [1,0,'no'], [0,1,'no'], [0,1,'no']] print createPlot( generateTree(myData(), ['feature1', 'feature2']))

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[]

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2016-09-14T04:51:14.652863

2016-05-07T18:49:00

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import urllib import json url="2015-2016/bl.json" uh=urllib.urlopen(url) data=uh.read() js=json.loads(data) i=0 ganador=None perdedor=None puntoslocal=0 puntosvisitante=0 sumavisitante=0 puntos=0 sumalocal=0 def getPuntos(equipo, afavor, encontra): puntos=0 if afavor > encontra: puntos=3 ganador=equipo perdedor=contrario elif afavor==encontra: puntos=1 ganador="Empate" perdedor=ganador else: puntos=0 ganador=contrario perdedor=equipo return puntos def getPuntosAcumulados(puntoslocal, puntos): puntoslocal=puntoslocal+puntos return puntoslocal jornadainput=raw_input("Ingrese la jornada: ") jornada=int(jornadainput)-1 equipoinput=raw_input("Ingrese el equipo: ") if jornada <0 or jornada >=34: print "No se jugó la jornada "+jornadainput+" en ese torneo. Verifique y vuelva a ingresar." jornada=None else: for fecha in range(0,jornada+1): jornadascompletas=js['rounds'][fecha]['name'] for i in range(0,9): equipo=js['rounds'][fecha]['matches'][i]['team1']['name'] afavor=js['rounds'][fecha]['matches'][i]['score1'] contrario=js['rounds'][fecha]['matches'][i]['team2']['name'] encontra=js['rounds'][fecha]['matches'][i]['score2'] if equipo>0 and equipo==equipoinput: puntoslocal=getPuntos(equipo, afavor, encontra) sumalocal=getPuntosAcumulados(sumalocal, puntoslocal) if equipo>0 and contrario==equipoinput: puntosvisitante=getPuntos(contrario, encontra, afavor) sumavisitante=getPuntosAcumulados(sumavisitante, puntosvisitante) sumapuntos=sumalocal+sumavisitante print "El equipo "+equipoinput+" sumaba "+str(sumalocal)+" como local a la "+jornadascompletas print "El equipo "+equipoinput+" sumaba "+str(sumavisitante)+" como visitante a la "+jornadascompletas print "El equipo "+equipoinput+" sumaba "+str(sumapuntos)+" puntos en total a la "+jornadascompletas uh.close()

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bljson-puntos.py

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fooofei/py_pieces

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[]

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refs/heads/master

2021-01-23T19:12:16.586391

2021-01-11T03:10:57

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# coding=utf-8 ''' use itertools.tee to copy an generator 适合多个 iterator 步差不大的, <10 的 1 生成两个 generator 但是实例并没有增加 2 两个 generator 持有同一份实例的引用在一个 generator 中修改了一个实例另一个 generator 也会受到修改影响 3 官方警告使用 itertools.tee 分裂之后母 generator 不再使用 https://docs.python.org/2/library/itertools.html 4 官方建议如果其中 1 个 iterator 走的太快其他的 iterator 没走使用 list() 比 tee() 更快 ''' import itertools import sys import unittest import copy count = 0 class Instance(object): def __init__(self, v): global count print('instance init {}'.format(count)) count += 1 self._v = v @property def v(self): return self._v def __del__(self): print('instance del') def __str__(self): return super(Instance, self).__str__() def __repr__(self): return '{}'.format(self.v) class MyTestCase(unittest.TestCase): def test_fetch_iterator_value(self): a = [Instance(i) for i in range(10)] ai = iter(a) self.assertEqual(a, list(ai)) self.assertEqual([], list(ai)) def test_tee_iterator1(self): a = [Instance(i) for i in range(10)] ai = iter(a) ai1, ai2 = itertools.tee(ai) self.assertEqual(a, list(ai1)) self.assertEqual([], list(ai)) def test_tee_iterator2(self): a = [Instance(i) for i in range(10)] ai = iter(a) ai1, ai2 = itertools.tee(ai) self.assertEqual(a, list(ai1)) self.assertEqual(a, list(ai2)) def test_tee_iterator3(self): a = [Instance(i) for i in range(10)] ai = iter(a) ai1, ai2 = itertools.tee(ai) self.assertEqual(a, list(ai)) self.assertEqual([], list(ai1)) self.assertEqual([], list(ai2)) def test_tee_iterator4(self): a = [Instance(i) for i in range(10)] ai = iter(a) ai1, ai2 = itertools.tee(ai) # this will effect a and list(ai2) for v in ai1: if v.v == 5: v._v = 55 self.assertEqual(a, list(ai2)) if __name__ == '__main__': unittest.main()

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/Lecture9/python_dynamics.py

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# coding=utf-8 """ Module aimed to demonstrate dynamic nature of Python's code. """ import datetime class Lecture: def __init__(self, no): self.no = no if datetime.datetime.now().hour > 14: def __str__(self): return f"Lecture {self.no} has finished." else: def __str__(self): return f"Lecture {self.no} has started." if __name__ == '__main__': l9 = Lecture(9) print(l9)

UTF-8

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python_dynamics.py

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inverseTrig/leet_code

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/202_happy_number.py

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[]

no_license

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refs/heads/master

2021-12-21T11:30:09.755104

2021-12-16T04:46:10

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class Solution: def isHappy(self, n: int) -> bool: seen = set() while n != 1: nstr = str(n) nxt = 0 for each in nstr: nxt += int(each) ** 2 n = nxt if n in seen: return False else: seen.add(n) return True sol = Solution() print(sol.isHappy(n=19)) print(sol.isHappy(n=2))

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202_happy_number.py

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coralhalperin/slides

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/python/examples/lists/master_mind.py

7286bcf73ed3ebe72aa9feed42b8c8b66d710cf2

[]

no_license

https://github.com/coralhalperin/slides

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refs/heads/main

2023-01-04T14:02:11.406990

2020-10-25T06:20:07

null

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null

import random width = 4 USED = '_' hidden = random.sample(range(10), width) # print(hidden) while True: # print(hidden) inp = input("your guess ({} digits):".format(width)) if inp == 'x': print("Bye") exit() if len(inp) != width: print("We need exactly {} characters".format(width)) continue guess = [] for cr in inp: guess.append(int(cr)) # guess = list(map(int, inp)) # print(guess) if hidden == guess: print("Match!") break my_hidden = hidden[:] my_guess = guess[:] result = '' for i in range(width): if my_hidden[i] == my_guess[i]: result += '*' my_hidden[i] = USED my_guess[i] = USED for i in range(width): if my_guess[i] == USED: continue if my_guess[i] in my_hidden: loc = my_hidden.index(my_guess[i]) my_hidden[loc] = USED guess[i] = USED result += '+' print(''.join(result))

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master_mind.py

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ethz-asl/libpointmatcher

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/examples/python/icp_simple.py

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refs/heads/master

2023-09-02T20:24:16.436314

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BSD-3-Clause

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C++

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# Code example for ICP taking 2 points clouds (2D or 3D) relatively close # and computing the transformation between them. from pypointmatcher import pointmatcher as pm PM = pm.PointMatcher DP = PM.DataPoints # Path of output directory (default: tests/icp_simple/) # The output directory must already exist # Leave empty to save in the current directory output_base_directory = "tests/icp_simple/" # Name of output files (default: test) output_base_file = "test" # Toggle to switch between 2D and 3D clouds is_3D = True if is_3D: # Load 3D point clouds ref = DP(DP.load('../data/car_cloud400.csv')) data = DP(DP.load('../data/car_cloud401.csv')) test_base = "3D" else: # Load 2D point clouds ref = DP(DP.load('../data/2D_twoBoxes.csv')) data = DP(DP.load('../data/2D_oneBox.csv')) test_base = "2D" # Create the default ICP algorithm icp = PM.ICP() # See the implementation of setDefault() to create a custom ICP algorithm icp.setDefault() # Compute the transformation to express data in ref T = icp(data, ref) # Transform data to express it in ref data_out = DP(data) icp.transformations.apply(data_out, T) # Save files to see the results ref.save(f"{output_base_directory + test_base}_{output_base_file}_ref.vtk") data.save(f"{output_base_directory + test_base}_{output_base_file}_data_in.vtk") data_out.save(f"{output_base_directory + test_base}_{output_base_file}_data_out.vtk") print(f"Final {test_base} transformations:\n{T}\n".replace("[", " ").replace("]", " "))

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py

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icp_simple.py

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rajesh305k/python_programs_9.26.2021

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/Ram/Assignment/Day4/assignment1.py

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[]

no_license

https://github.com/rajesh305k/python_programs_9.26.2021

58e63e6ff5b5a83436e75f5e2ee52c8570ee9df0

61a69cd1e9ec0d91a36d1cb5501db11c0d017778

refs/heads/main

2023-08-24T08:11:14.789908

2021-10-30T04:47:44

null

0

null

amount=int(input("Enter amount :")) # 1.Write a program to find credited amount,balance using **if else and operators.** # Note:variables:**amount,balance,credited_amount**, # Formula: **credited_amount=amount**,**balance+=credited_amount**, # **amount** should be greater than zero balance=10000 if(amount!=0): if(amount>0): credited_amount=amount balance+=credited_amount print("Balance : ",balance) print("credited_amount : ",credited_amount) elif(abs(amount)<balance): debited_amount=abs(amount) balance-=debited_amount print("Debited Amount :",debited_amount) print("balance Amount :",balance) else: print("you have eceeded your limit") else: print("Please enter a valid amount")

UTF-8

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py

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assignment1.py

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34.5

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angkoonhian/hacknroll2021

11,364,483,487,053

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/word_generator.py

b8f45d921f64bf2671d82f671c211d86025f7f39

[]

no_license

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9f1da6503cd7b5872f11e8e4cbc3c4d7f001c20c

refs/heads/main

2023-02-17T07:13:27.781828

2021-01-09T03:01:10

325,207,862

0

2

null

from transformers import GPT2Tokenizer from transformers import TFGPT2Model, TFGPT2LMHeadModel import tensorflow as tf import numpy as np import os #Install the GPT-2 model/tokenizer and load it into memory def load_model_tokenizer_GPT2(): """ Loads GPT-2 model from local memory. Replace with gpt2 """ dir_path = os.path.dirname(os.path.realpath(__file__)) tokenizer = GPT2Tokenizer.from_pretrained(f'{dir_path}\\gpt2_model') model = TFGPT2LMHeadModel.from_pretrained(f'{dir_path}\\gpt2_model') return tokenizer, model #Tokenizer any given text and return def tokenize_text(tokenizer, text): #Using tensorflow backend #Removing space if text[-1] == " ": text = text[:-1] tokenized = tokenizer.encode(text, return_tensors='tf') return tokenized #Next Word algorithm def next_word_prediction(tokenizer, model, text, num_results = 3): tokens = tokenize_text(tokenizer, text) output = model(tokens) #Returns the logits of predictions for the last word in the sequence next_word_logits = output.logits[:, -1, :] softmaxed_next_word = tf.nn.softmax(next_word_logits) most_likely_words = tf.math.top_k(softmaxed_next_word, num_results) prob_most_likely_words = np.array(most_likely_words.values).squeeze() index_most_likely_words = np.array(most_likely_words.indices).squeeze() prob_word_dic = {} for i in range(num_results): prob = prob_most_likely_words[i] word = tokenizer.decode(int(index_most_likely_words[i])) prob_word_dic["word" + str(i)] = word return prob_word_dic

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word_generator.py

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taketakeyyy/atcoder

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/abc/170/b.py

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[]

no_license

https://github.com/taketakeyyy/atcoder

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refs/heads/master

2023-09-04T16:53:55.172945

2023-09-04T07:25:59

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null

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2019-04-21T07:39:45

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Python

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# -*- coding:utf-8 -*- def solve(): X, Y = list(map(int, input().split())) for kame in range(0, X+1): turu = X - kame if kame*4 + turu*2 == Y: print("Yes") return print("No") if __name__ == "__main__": solve()

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gustcomer/pynnystock_analyser

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/pynnystock/strategies/stophighpre/ParametersSHP.py

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[]

no_license

https://github.com/gustcomer/pynnystock_analyser

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refs/heads/master

2023-03-22T01:39:35.920679

2021-03-12T20:54:03

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0

null

from ...Parameters import Parameters class ParametersSHP(Parameters): def __init__(self): super().__init__() # inicializa tudo mas algumas variaveis da super vão ficar de fora. Bad software engineering practice # repetimos a declaração de alguns parâmetros só pra enfatizar. Bad practice. self.short_after = 0.1 self.exit_target = 0.3 self.exit_stop_margin = 0.1 # se high do pre for $5.00, stop vai ficar em $5.50 def setAlgoParameters( self, short_after = 0.1, exit_target = 0.3, exit_stop_margin = 0.1): self.short_after = short_after self.exit_target = exit_target self.exit_stop_margin = exit_stop_margin def __repr__(self): s='PARÂMETROS PARA ALGORITMO DO TIPO STOP AT HIGH OF PRE-MARKET\n' s = s + 'FILTERING PARAMETERS\n' s = s + f"prevol_threshold: {self.prevol_threshold}\n" s = s + f"open_dolar_threshold: {self.open_dolar_threshold}\n" s = s + f"gap_threshold: {self.gap_threshold}\n" s = s + f"F_low_threshold: {self.F_low_threshold}\n" s = s + f"F_high_threshold: {self.F_high_threshold}\n" s = s + f"\n" s = s + f'TRADING PARAMETERS\n' s = s + f"short_after: {self.short_after}\n" s = s + f"exit_target: {self.exit_target}\n" s = s + f"exit_stop_margin: {self.exit_stop_margin}\n" s = s + f"\n" s = s + f'SIMULATION PARAMETERS\n' s = s + f"start_money: {self.start_money}\n" s = s + f"allocation: {self.allocation}\n" s = s + f"locate_fee: {self.locate_fee}\n" s = s + f"commission: {self.commission}\n" return s

UTF-8

Python

false

1,527

py

49

ParametersSHP.py

32

0.648258

0.636423

0

46

32.086957

122

chitakeo/Nagayama_2019

3,238,405,389,670

88e51b77d5fd511bc52c2f9fb042fe57b1be7ade

cc9a8298c4ce507d092c4bc83fd3659ea37a519d

/cat/cat4.py

d16914357d74f76b970d3de42e04a22b76dd8b0e

[]

no_license

https://github.com/chitakeo/Nagayama_2019

2d86eed47aeceb536aebc893b1bc0f14feab9799

29bd6316a73ed1da2c3967db689d81beec6bcbae

refs/heads/master

2020-12-28T01:46:48.511229

2020-02-16T13:58:16

238,141,590

0

null

false

2020-02-16T13:58:17

2020-02-04T06:46:53

2020-02-16T09:38:13

2020-02-16T13:58:16

31,374

0

Python

false

import numpy as np import cv2 import os from matplotlib import pyplot as plt kan_file_path = '../save/fe.jpg' #完成図保存のファイルパス&ファイル名 mach_file_path = '../save/zikken.jpg' #マッチング結果保存のファイルパス&ファイル名 pazu_file_path = '../pazu/kousiki.png' #パズルのファイルパス&ファイル名 pice_file_path = '../pazu' #ピースのファイルパス&ファイル名 pice = ['/rt.png', '/rrb.png', '/lt.png', '/lb.png'] #4隅の画像のファイルパス #SIFT def sif(gray1, gray2): sift = cv2.xfeatures2d.SIFT_create() kp1, des1 = sift.detectAndCompute(gray1,None) kp2, des2 = sift.detectAndCompute(gray2,None) bf = cv2.BFMatcher() matches =bf.knnMatch(des1,des2, k=2) good = [] for m,n in matches: if m.distance < 0.75*n.distance: good.append([m]) good = sorted(good, key = lambda x:x[0].distance) return good, kp1, kp2 #マッチング後の組み立て def create(img1t,img1,img2,gray1,kp1,kp2, n): img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good[:5],None,flags=2) cv2.imwrite(mach_file_path, img3) img1_pt = [list(map(int, kp1[m[0].queryIdx].pt)) for m in good] img2_pt = [list(map(int, kp2[m[0].trainIdx].pt)) for m in good] #ここからマッチングがうまくいったと仮定している #元画像の縦と横の距離を計算 img1_hei = img1_pt[0][1] - img1_pt[1][1] img1_wei = img1_pt[0][0] - img1_pt[1][0] #ピースの縦と横の距離を計算 img2_hei = img2_pt[0][1] - img2_pt[1][1] img2_wei = img2_pt[0][0] - img2_pt[1][0] #ピースと元画像の縦と横の比率を計算 height2, weight2 = img2.shape[:2] if img1_hei == 0 and img2_hei == 0: if img1_wei == 0 and img2_wei == 0: pass else: wei = abs(img2_wei) / abs(img1_wei) img2 = cv2.resize(img2, (int(weight2/wei), int(height2))) elif img1_wei == 0 and img2_wei == 0: hei = abs(img2_hei) / abs(img1_hei) img2 = cv2.resize(img2, (int(weight2), int(height2/hei))) elif img1_pt[0][1] != img2_pt[0][1] and img1_pt[0][0] != img2_pt[0][0]: wei = abs(img2_wei) / abs(img1_wei) hei = abs(img2_hei) / abs(img1_hei) img2 = cv2.resize(img2, (int(weight2/wei), int(height2/hei))) height, weight = img1t.shape[:2] #全体画像と同じ大きさの画像を作成、あるなら作らない if os.path.isfile(kan_file_path) : imageArray = cv2.imread(kan_file_path) else: imageArray = np.ones((height, weight, 3), np.uint8)*255 height2,weight2 = img2.shape[:2] hei41 = int(height / 4) hei43 = int(hei41 * 3) wei41 = int(weight / 4) wei43 = int(wei41 * 3) #nが0の時が左上,1の時左下、２の時右上、３の時右下 if n == 0: h1 = 0 h2 = height2 w1 = 0 w2 = weight2 elif n == 1: h1 = height - height2 h2 = height w1 = 0 w2 = weight2 elif n == 2: h1 = 0 h2 = height2 w1 = weight - weight2 w2 = weight elif n == 3: h1 = height - height2 h2 = height w1 = weight - weight2 w2 = weight imageArray[h1 : h2 , w1 : w2 ] = img2 cv2.imwrite(kan_file_path, imageArray) return imageArray #4隅の画像がどこか探す def find4(gray1): good = [] max_len = 0 for i in range(4): f = pice_file_path + pice[i] img2_1 = cv2.imread(f) gray2_1 = cv2.cvtColor(img2_1, cv2.COLOR_BGR2GRAY) check = [] check, kp3, kp4 = sif(gray1, gray2_1) print(len(check)) if max_len < len(check): max_len = len(check) img2 = img2_1 good = check kp1 = kp3 kp2 = kp4 return good, img2, kp1, kp2 if __name__ == '__main__': img1 = cv2.imread(pazu_file_path) # 元画像 height, weight = img1.shape[:2] hei41 = int(height / 4) hei43 = int(hei41 * 3) wei41 = int(weight / 4) wei43 = int(wei41 * 3) img1_lt = img1[0 : hei41 , 0 : wei41] img1_lb = img1[hei43 : height , 0 : wei41] img1_rt = img1[0 : hei41 , wei43 : weight] img1_rb = img1[hei43 : height , wei43 : weight] coner = [img1_lt, img1_lb, img1_rt, img1_rb] #左上、左下、右上、右下の順番にして for n in range(4): img1_tk = coner[n] gray1 = cv2.cvtColor(img1_tk, cv2.COLOR_BGR2GRAY) good = [] good, img2, kp1, kp2 = find4(gray1) dst = create(img1,img1_tk, img2, gray1, kp1, kp2, n) while(1): cv2.namedWindow('image', cv2.WINDOW_NORMAL) cv2.imshow('image',dst) if cv2.waitKey(20) & 0xFF == 27: break cv2.destroyAllWindows()

UTF-8

Python

false

4,345

py

7

cat4.py

6

0.63109

0.55272

0

158

23.556962

72

WillNye/duolingo_x

8,761,733,285,529

004d1ccd7375c327d089998c19e25b353ba201c9

7ea6d43d98813d9839f3a32e136ca04933922737

/training/models.py

f6c449e19cd157838fb18952520d0f1452f6a4a8

[]

no_license

https://github.com/WillNye/duolingo_x

d5e60749acc7e75fa0aca9994cc9ad83695dd211

923f38de89c60e0f8c945df4a13b360c1369f960

refs/heads/master

2021-01-13T13:04:21.456410

2017-01-14T03:01:05

78,687,778

0

null

from django.db import models from django.contrib.auth.models import User from datetime import datetime as dt class Language(models.Model): name = models.CharField(max_length=250, null=False) audio_base = models.CharField(max_length=250, null=False) def __str__(self): return self.name class Phrase(models.Model): english_translation = models.CharField(max_length=750, null=False) foreign_translation = models.CharField(max_length=750, null=False) language = models.ForeignKey(Language, null=False) audio_id = models.CharField(max_length=250, null=False) def __str__(self): return self.english_translation class PhraseStats(models.Model): user = models.ForeignKey(User, null=False) phrase = models.ForeignKey(Phrase, null=False) is_make_streak = models.BooleanField(default=False, null=False) streak_number = models.IntegerField(default=0, null=False) last_heard = models.DateTimeField(default=dt.now(), null=False) def __str__(self): if self.is_make_streak: stat_str = "Made {} in a row" else: stat_str = "Missed {} in a row" return stat_str.format(str(self.streak_number))

UTF-8

Python

false

1,205

py

7

models.py

5

0.685477

0.672199

0

37

31.540541

70

daniel-l-merhi/Python-and-ROS-Coursework

14,259,291,435,794

b4f792aad8b88d7cd9b93f0b552da2b2bc259752

5508e79fb43398f0622d0df986354f530fe6032c

/Python for Robotics/robot_control/get_laser_method.py

1e4af28b341ce1452c0b3f24c7c2b8ee7d520468

[]

no_license

https://github.com/daniel-l-merhi/Python-and-ROS-Coursework

14ac226379da6e8a9a0319244166e7a712df7f88

bc9956b264f5388184c0388d7106d89c162d718b

refs/heads/main

2023-08-06T12:51:35.236840

2021-10-09T02:25:00

415,173,258

0

null

from robot_control_class import RobotControl rc = RobotControl(robot_name="summit") def return_laser(a, b, c): laser_list = [rc.get_laser_summit(a), rc.get_laser_summit(b), rc.get_laser_summit(c)] return laser_list l = return_laser(0, 360, 400) print (l)

UTF-8

Python

false

267

py

43

get_laser_method.py

31

0.692884

0.666667

0

12

21.333333

89

vigman-xxxw/python-code

936,302,905,518

bd629e12fc3d65e0111493f1f9b86c728db02632

16d33ca26751e50d518f79bcb8f1c05dd7e79f7b

/二分法.py

20af27a85f4cb0833f444355126f9c70e5b7a8a2

[]

no_license

https://github.com/vigman-xxxw/python-code

6017fac6c7c019384957c63b38ef76b6919726d3

004d18576065ba94044659d4b83a361675a0f178

refs/heads/master

2023-08-19T01:53:52.624302

2021-10-11T09:03:53

null

0

null

# 给定数组A 目标值T 查找T在A中位置 def binary_search(arr, key): start = 0 end = len(arr) - 1 while start <= end: mid = int((start + end) / 2) if arr[mid] < key: start = mid + 1 elif arr[mid] > key: end = mid - 1 else: return mid return -1 list1 = [i for i in range(100)] print(list1) b = binary_search(list1, 5)

UTF-8

Python

false

413

py

66

二分法.py

66

0.49354

0.459948

0

19

19.315789

36

MarcoBorsato/AOC2020

13,795,434,976,011

8c9334e962500e0e8f12851fdd6e0af220e892dc

ec56e52fdb18c80c2a6eda74a66a8e6e6229c6f5

/AOCday6.py

e958cbfd2d7d764ca9ed32d4b73abbb15a120ee8

[]

no_license

https://github.com/MarcoBorsato/AOC2020

7d3a8896a60e737b574b3a8b0cffe7820228f95f

f813048f233b7833aedd1ebe126b0bd1cd50502c

refs/heads/master

2023-03-19T16:02:15.871020

2021-03-10T22:27:29

344,621,179

0

1

null

def readInput(input): data = [] person = [] line = "" for i in input.readlines(): i = i.strip() if i == "": data.append(person) person = [] else: person.append(i) data.append(person) return data def countAnswers1(input: list[str]) -> int: data = readInput(input) answers = [] stripped = "" count = 0 for i in data: print(i) for s in "".join(i): if s not in stripped: stripped += s count += len(stripped) stripped = "" return count def countAnswers2(data: list[str]) -> int: data = readInput(data) count = 0 result = 0 for i in data: for j in range(len(i)): if j == 0: answer = set(i[j]) else: answer = answer.intersection(i[j]) count += len(answer) return count if __name__ == '__main__': input4 = open('inputday6.txt') print(countAnswers2(input4))

UTF-8

Python

false

1,023

py

7

AOCday6.py

7

0.482893

0.473118

0

47

20.765957

50

nasawz/wiki.catke

5,841,155,567,762

9cefe78c2e5477a86cc64fbe92e11912f89e6104

9933cb7f69b8e13a970901012fa3672120d944a3

/diy/syncdb.py

261cc82eba45f63affc579aae1d3ec5f2a9f3fef

[]

no_license

https://github.com/nasawz/wiki.catke

a78689f1e67fd48c134e95276552012f2d4c61dc

35d43fe9df92d9413f564b40e18028a931a6e652

refs/heads/master

2020-02-03T15:21:18.574729

2013-02-04T08:36:56

null

0

null

# coding=utf-8 ''' 同步数据表 @author vfasky@gmail.com ''' import os import sys # 设置系统编码为utf8 reload(sys) sys.setdefaultencoding('utf8') # 加入第三方类库搜索路径 sys.path.append(os.path.join(os.path.dirname(__file__), 'modules')) # 加载程序配置 from wiki import config # 引入Database from xcat import Database # 加载数据库配置 Database.load_config( config.settings['database'].get(config.settings['run_mode'], False) ) Database.connect() import wiki.models import wiki.models.wiki for m in dir(wiki.models): model = getattr(wiki.models, m) if m != 'Model' and str(type(model)) == "<class 'peewee.BaseModel'>": if model.table_exists() == False: try: model.create_table() except Exception, e: pass elif str(type(model)) == "<type 'module'>": for m2 in dir(model): model2 = getattr(model, m2) if m2 not in ('Model') and str(type(model2)) == "<class 'peewee.BaseModel'>": if model2.table_exists() == False: try: model2.create_table() except Exception, e: pass # 执行安装 if 0 == wiki.models.Role.select().count(): ar = wiki.models.Role() ar.code = 'admin' ar.name = '管理者' ar.save() ar = wiki.models.Role() ar.code = 'user' ar.name = '用户' ar.save() Database.close()

UTF-8

Python

false

1,487

py

35

syncdb.py

24

0.566403

0.558507

0

66

20.106061

89

SuhashisAcharya/automation

7,524,782,746,848

24ab24e059f116b78bc9da4dd37522d3be4ba4a0

8bb594bed8e458019d828864cbcd548c8d538e5a

/Infrastructure/validation_matrix_server/media_stream_validator/media_stream_validator

562257f35b581e879f25745a0b2d9b544f58e55a

[]

no_license

https://github.com/SuhashisAcharya/automation

ff1b8e6ffbd573a2c4edd95575627333fefd0128

db312f4fc168d297fc027298c865aa3e462d5d25

refs/heads/master

2020-03-03T00:38:22.010621

2016-06-06T05:31:00

65,850,723

0

null

#!/usr/bin/python # -*- coding: utf-8 -*- import os,sys import pexpect import json import re from time import gmtime, strftime #print len(sys.argv) #print str(sys.argv) if ( 6 > len(sys.argv) ): print 'Insufficient number of commands' sys.exit(1) manifest_url=sys.argv[1] apple_ip=sys.argv[2] apple_user=sys.argv[3] apple_pwd=sys.argv[4] automation_user=sys.argv[5] status_file_path=sys.argv[6] def media_stream_validator(manifest_url, apple_ip, apple_user, apple_pwd, status_file_path): #mediastreamvalidator -O /Users/rebaca/ http://192.168.4.128/AZ_HLS_FILE_BASED_TC2/master/master.m3u8 #hlsreport.py validation_data.json cmd = '/usr/local/bin/mediastreamvalidator -O /var/log/validation_data.json ' + manifest_url (command_output1, exitstatus) = \ pexpect.run("ssh " + apple_user + "@" + apple_ip + " '" + cmd + "'", \ events={'Password':'' + apple_pwd + '\n'}, \ timeout=660, withexitstatus=1) cmd = '/Users/rebaca/hlsreport_updated.py /var/log/validation_data.json' (command_output2, exitstatus) = \ pexpect.run("ssh " + apple_user + "@" + apple_ip + " '" + cmd + "'", \ events={'Password':'' + apple_pwd + '\n'}, \ timeout=660, withexitstatus=1) cmd = 'cat /var/log/validation_data_1.json' (command_output, exitstatus) = \ pexpect.run("ssh " + apple_user + "@" + apple_ip + " '" + cmd + "'", \ events={'Password':'' + apple_pwd + '\n'}, \ timeout=660, withexitstatus=1) status_file_and_path = status_file_path + '/' + 'MSV_' + automation_user; status_file_and_path = status_file_and_path + strftime("_%m-%d-%H-%M-%S", gmtime()) status_file_and_path = status_file_and_path + '.log' file_des = open(status_file_and_path,'w') file_des.write(command_output) hls_report_json_data=command_output.splitlines()[1] must_fix_errors_present = re.search(r'"errorStatusCode": -1', command_output, re.I) should_fix_errors_present = re.search(r'"errorStatusCode": 0', command_output, re.I) hls_report_json_object=json.loads(hls_report_json_data) if must_fix_errors_present: hls_report_json_object["must_fix_errors_present"] = "YES" else: hls_report_json_object["must_fix_errors_present"] = "NO" if should_fix_errors_present: hls_report_json_object["should_fix_errors_present"] = "YES" else: hls_report_json_object["should_fix_errors_present"] = "NO" mustfix_shouldfix_msg_json_obj=json.loads("{}") ''' message_list=hls_report_json_object["messages"] shouldfix_msg_list_obj = [] mustfix_msg_list_obj = [] if message_list == NONE: pass else: for read_message in message_list message_type=read_message["errorStatusCode"] if message_type == "-1" mustfix_msg_list_obj.append(read_message["errorComment"]) elif message_type == "0" shouldfix_msg_list_obj.append(read_message["errorComment"]) mustfix_shouldfix_msg_json_obj["mustfix_messages"]=mustfix_msg_list_obj mustfix_shouldfix_msg_json_obj["shouldfix_messages"]=shouldfix_msg_list_obj ''' #hls_report_json_object=json.loads(hls_report_json_data) mustfix_shouldfix_msg_json_obj=json.loads("{}") # Getting messages from top level hierarchy message_list=hls_report_json_object["messages"] shouldfix_msg_list_obj = [] mustfix_msg_list_obj = [] if message_list == None: pass else: for read_message in message_list: message_type=read_message["errorStatusCode"] if message_type == -1: mustfix_msg_list_obj.append(read_message["errorComment"]) elif message_type == 0: shouldfix_msg_list_obj.append(read_message["errorComment"]) message_object=json.loads("{}") message_object["mustfix_messages"]=mustfix_msg_list_obj message_object["shouldfix_messages"]=shouldfix_msg_list_obj mustfix_shouldfix_msg_json_obj["messages"]=message_object # Getting messages from each varient and creating json object varients_list=hls_report_json_object["variants"] varients_list_created=[] #print json.dumps(varients_list) if varients_list == None: pass else: for read_varient in varients_list: message_list=read_varient["messages"] bitrate=read_varient["measuredMaxBitrate"] shouldfix_msg_list_obj = [] mustfix_msg_list_obj = [] message_object=json.loads("{}") if message_list == None: pass else: for read_message in message_list: message_type=read_message["errorStatusCode"] if message_type == -1: mustfix_msg_list_obj.append(read_message["errorComment"]) elif message_type == 0: shouldfix_msg_list_obj.append(read_message["errorComment"]) message_object["playlistMaxBitrate"]=bitrate message_object["mustfix_messages"]=mustfix_msg_list_obj message_object["shouldfix_messages"]=shouldfix_msg_list_obj varients_list_created.append(message_object) mustfix_shouldfix_msg_json_obj["variants"]=varients_list_created if must_fix_errors_present: mustfix_shouldfix_msg_json_obj["must_fix_errors_present"] = "YES" else: mustfix_shouldfix_msg_json_obj["must_fix_errors_present"] = "NO" if should_fix_errors_present: mustfix_shouldfix_msg_json_obj["should_fix_errors_present"] = "YES" else: mustfix_shouldfix_msg_json_obj["should_fix_errors_present"] = "NO" print json.dumps(mustfix_shouldfix_msg_json_obj) #end of creating json object contains list of all the messages if must_fix_errors_present or should_fix_errors_present: print 'FAIL' else: print 'PASS' #print json.dumps(hls_report_json_object) ''' wrong_playlist = re.search(r'Failed to download playlist.*', command_output, re.I) valid_playlist = re.search(r'Playlist.*OK', command_output, re.I) not_run = re.search(r': command not found', command_output, re.I) if wrong_playlist: #print 'Invalid Playlist Provided: %s' %(wrong_playlist.group()) print 'FAIL' elif valid_playlist: fatal_msg = re.search(r'fatal.*', command_output, re.I) error_msg = re.search(r'error.*', command_output, re.I) bfr_error = re.search(r'error.*Decreasing DTS were detected in track 0.*', command_output, re.I) fail_msg = re.search(r'fail.*', command_output, re.I) warn_msg = re.search(r'warning.*', command_output, re.I) #print 'bfr_error: %s' %(bfr_error.group())) #print 'error_msg: %s' %(error_msg.group())) if error_msg and bfr_error: if error_msg.group() == bfr_error.group(): bframe_input_error = True else: bframe_input_error = False if fatal_msg: pass #print 'Fatal Message: %s' %(fatal_msg.group()) if error_msg: pass #print 'Error Message: %s' %(error_msg.group()) if fail_msg: pass #print 'Failure Message: %s' %(fail_msg.group()) if warn_msg: pass #print 'Warning Message: %s' %(warn_msg.group()) if fatal_msg or (error_msg and bframe_input_error == False) or fail_msg: print 'FAIL' else: print 'PASS' else: print 'NOT_PERFORMED' ''' #media_stream_validator('http://192.168.3.222/automation/main/mbnebfr/5/master/dummy.m3u8', '192.168.4.113', 'rebaca', 'rebaca') media_stream_validator(manifest_url, apple_ip, apple_user, apple_pwd, status_file_path)

UTF-8

Python

false

8,037

134

media_stream_validator

79

0.59898

0.59027

0

232

33.642241

128

devesh37/HackerRankProblems

10,067,403,355,660

363e8330029df04f82b127159adf0093172ff0ef

d65f58cb47b308dc7130a4033c5ec1de61d9814f

/Datastructure_HackerRank/Array/arrayRotation.py

5dd164efb2caa0a39fa9a4d00aa6c25fb74120db

[]

no_license

https://github.com/devesh37/HackerRankProblems

1b1fee329d2fae31dea524d272d5c1b596e107ed

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#!/bin/python3 import math import os import random import re import sys def left_rotation(a,n,r): array=[0]*n i=0 while(i<n): array[(i+n-r)%n]=a[i] i+=1 for i in array: print(i,end=' ') if __name__ == '__main__': nd = input().split() n = int(nd[0]) r = int(nd[1]) a = list(map(int, input().rstrip().split())) left_rotation(a,n,r)

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kss2153/hubspot-mr

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/src/app/db.py

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https://github.com/kss2153/hubspot-mr

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2019-09-24T22:19:20

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import os from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker db_user = os.environ.get('CLOUD_SQL_USERNAME') db_password = os.environ.get('CLOUD_SQL_PASSWORD') db_name = os.environ.get('CLOUD_SQL_DATABASE_NAME') db_host = os.environ.get('CLOUD_SQL_HOST') engine_url = 'mysql+pymysql://{}:{}@{}/{}'.format(db_user, db_password, db_host, db_name) engine = create_engine(engine_url, pool_size=3) SqlSession = sessionmaker(bind=engine)

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mennthor/tdepps

13,125,420,065,650

ccd34e57f587e6658f7ea61f5529d79ca90d2248

490cd642d522ab512ab435580ea92bbae789414c

/tdepps/grb/llh.py

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[]

no_license

https://github.com/mennthor/tdepps

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100035f95321506057677806d25f1695cfea58a9

refs/heads/master

2021-09-22T21:05:21.801613

2018-08-06T09:38:26

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# coding: utf-8 from __future__ import division, absolute_import import math import numpy as np import scipy.optimize as sco from ..base import BaseLLH, BaseMultiLLH from ..utils import fill_dict_defaults, all_equal, dict_map class GRBLLH(BaseLLH): """ Stacking GRB LLH Stacking weights are a-priori fixed with w_theo * w_dec and only a single signal strength parameter ns is fitted. """ def __init__(self, llh_model, llh_opts=None): """ Parameters ---------- llh_model : BaseModel instance Model providing LLH args and signal over background ratio. llh_opts : dict, optional LLH options: - 'sob_rel_eps', optional: Relative threshold under which a single signal over background ratio is considered zero for speed reasons. - 'sob_abs_eps', optional: Absolute threshold under which a single signal over background ratio is considered zero for speed reasons. - 'ns_bounds', optional: ``[lo, hi]`` bounds for the ``ns`` fit parameter. - 'minimizer', optional: String selecting a scipy minizer. - 'minimizer_opts', optional: Options dict for the scipy minimizer. """ self._needed_args = ["src_w_dec", "src_w_theo", "nb"] self.model = llh_model self.llh_opts = llh_opts @property def needed_args(self): return self._needed_args @property def model(self): return self._model @model.setter def model(self, model): if not all_equal(self._needed_args, model.provided_args): raise(KeyError("Model `provided_args` don't match `needed_args`.")) # Cache fixed src weights over background estimation, shape (nsrcs, 1) args = model.get_args() src_w = args["src_w_dec"] * args["src_w_theo"] # Shape is (1, nsrcs) for stacking GRB LLH self._src_w_over_nb = ((src_w / np.sum(src_w)) / args["nb"])[:, None] self._model = model @property def llh_opts(self): return self._llh_opts.copy() @llh_opts.setter def llh_opts(self, llh_opts): required_keys = [] opt_keys = { "sob_rel_eps": 0, "sob_abs_eps": 1e-3, "ns_bounds": [0., None], "minimizer": "L-BFGS-B", "minimizer_opts": { "ftol": 1e-15, "gtol": 1e-10, "maxiter": int(1e3) }, } llh_opts = fill_dict_defaults(llh_opts, required_keys, opt_keys) if (llh_opts["sob_rel_eps"] < 0 or llh_opts["sob_rel_eps"] > 1): raise ValueError("'sob_rel_eps' must be in [0, 1]") if llh_opts["sob_abs_eps"] < 0: raise ValueError("'sob_abs_eps' must be >= 0.") if len(llh_opts["ns_bounds"]) != 2: raise ValueError("'ns_bounds' must be `[lo, hi]`.") if type(llh_opts["minimizer_opts"]) is not dict: raise ValueError("'minimizer_opts' must be a dictionary.") self._llh_opts = llh_opts def lnllh_ratio(self, ns, X, band_select=True): """ Public method wrapper """ sob = self._soverb(X, band_select=band_select) return self._lnllh_ratio(ns, sob) def fit_lnllh_ratio(self, ns0, X, band_select=True): """ Fit TS with optimized analytic cases """ def _neglnllh(ns, sob): """ Wrapper for minimizing the negative lnLLH ratio """ lnllh, lnllh_grad = self._lnllh_ratio(ns, sob) return -lnllh, -lnllh_grad if len(X) == 0: # Fit is always 0 if no events are given return 0., 0. # Get the best fit parameter and TS. Analytic cases are handled: # For nevts = [1 | 2] we get a [linear | quadratic] equation to solve. sob = self._soverb(X, band_select=band_select) nevts = len(sob) # Test again, because we applied some threshold cuts if nevts == 0: return 0., 0. elif nevts == 1: sob = sob[0] if sob <= 1.: # sob <= 1 => ns <= 0, so fit will be 0 return 0., 0. else: ns = 1. - (1. / sob) ts = 2. * (-ns + math.log(sob)) return ns, ts elif nevts == 2: sum_sob = sob[0] + sob[1] if sum_sob <= 1.: # More complicated to show but same as above return 0., 0. else: a = 1. / (sob[0] * sob[1]) c = sum_sob * a ns = 1. - 0.5 * c + math.sqrt(c * c / 4. - a + 1.) ts = 2. * (-ns + np.sum(np.log1p(ns * sob))) return ns, ts else: # Fit other cases res = sco.minimize(fun=_neglnllh, x0=[ns0], jac=True, args=(sob,), bounds=[self._llh_opts["ns_bounds"]], method=self._llh_opts["minimizer"], options=self._llh_opts["minimizer_opts"]) ns, ts = res.x[0], -res.fun[0] if ts < 0.: # Some times the minimizer doesn't go all the way to 0., so # TS vals might end up negative for a truly zero fit result ts = 0. return ns, ts def _soverb(self, X, band_select=True): """ Make an additional cut on small sob values to save time """ if len(X) == 0: # With no events given, we can skip this step return np.empty(0, dtype=np.float) # Stacking case: Weighted signal sum per source sob = self._model.get_soverb(X, band_select=band_select) sob = np.sum(sob * self._src_w_over_nb, axis=0) if len(sob) < 1: return np.empty(0) # Apply a SoB ratio cut, to save computation time on events that don't # contribute anyway. We have a relative and an absolute threshold sob_max = np.amax(sob) if sob_max > 0: sob_rel_mask = (sob / sob_max) < self._llh_opts["sob_rel_eps"] else: sob_rel_mask = np.zeros_like(sob, dtype=bool) sob_abs_mask = sob < self._llh_opts["sob_abs_eps"] return sob[~(sob_rel_mask | sob_abs_mask)] def _lnllh_ratio(self, ns, sob): """ Calculate TS = 2 * ln(L1 / L0) """ x = ns * sob ts = 2. * (-ns + np.sum(np.log1p(x))) # Gradient in ns (chain rule: ln(ns * a + 1)' = 1 / (ns * a + 1) * a) ns_grad = 2. * (-1. + np.sum(sob / (x + 1.))) return ts, np.array([ns_grad]) class MultiGRBLLH(BaseMultiLLH): """ Class holding multiple GRBLLH objects, implementing the combined GRBLLH from all single GRBLLHs. """ def __init__(self, llh_opts=None): self._ns_weights = None self.llh_opts = llh_opts @property def names(self): return list(self._llhs.keys()) @property def llhs(self): return self._llhs @property def model(self): return dict_map(lambda key, llh: llh.model, self._llhs) @property def needed_args(self): return dict_map(lambda key, llh: llh.needed_args, self._llhs) @property def llh_opts(self): return self._llh_opts.copy() @llh_opts.setter def llh_opts(self, llh_opts): required_keys = [] opt_keys = { "ns_bounds": [0., None], "minimizer": "L-BFGS-B", "minimizer_opts": { "ftol": 1e-15, "gtol": 1e-10, "maxiter": int(1e3) }, } llh_opts = fill_dict_defaults(llh_opts, required_keys, opt_keys) if len(llh_opts["ns_bounds"]) != 2: raise ValueError("'ns_bounds' must be `[lo, hi]`.") if type(llh_opts["minimizer_opts"]) is not dict: raise ValueError("'minimizer_opts' must be a dictionary.") self._llh_opts = llh_opts def fit(self, llhs): """ Takes multiple single GRBLLHs in a dict and manages them. Parameters ---------- llhs : dict LLHs to be managed by this multi LLH class. Names must match with dict keys of provided multi-injector data. """ for name, llh in llhs.items(): if not isinstance(llh, GRBLLH): raise ValueError("LLH object " + "`{}` is not of type `GRBLLH`.".format(name)) # Cache ns plit weights used in combined LLH evaluation self._ns_weights = self._ns_split_weights(llhs) self._llhs = llhs return def lnllh_ratio(self, ns, X): """ Combine LLH contribution from fitted single LLH instances. Parameters ---------- ns : float Total expected signal events ``ns``. X : dict of recarrays Fixed data to evaluate the LHL at """ # Loop over ln-LLHs and add their contribution ts = 0. ns_grad = 0. # Add up LLHs for each single LLH for key, llh in self._llhs.items(): ns_w = self._ns_weights[key] ts_i, ns_grad_i = llh.lnllh_ratio(ns=ns * ns_w, X=X[key]) ts += ts_i ns_grad += ns_grad_i * ns_w # Chain rule return ts, ns_grad def fit_lnllh_ratio(self, ns0, X): """ Fit single ns parameter simultaneously for all LLHs. TODO: This relies on calls into private LLH methods directly using sob for speed reasons. Maybe we can change that. """ def _neglnllh(ns, sob_dict): """ Multi LLH wrapper directly using a dict of sob values """ ts = 0. ns_grad = 0. for key, sob in sob_dict.items(): ts_i, ns_grad_i = self._llhs[key]._lnllh_ratio( ns * self._ns_weights[key], sob) ts -= ts_i ns_grad -= ns_grad_i * self._ns_weights[key] # Chain rule return ts, ns_grad # No events given for any LLH, fit is zero if sum(map(len, X.values())) == 0: return 0., 0. # Get soverb separately for all LLHs sob = [] # sob_dict is only used if we fit, because we need sob unweighted there sob_dict = {} for key, llh in self._llhs.items(): sob_i = llh._soverb(X[key]) sob.append(self._ns_weights[key] * sob_i) if len(sob_i) > 0: # If sob is empty for a LLH, it would return (0, [0]) anyway, # so just add the existing ones. ns_weights are added in # correctly in the fit function later sob_dict[key] = sob_i sob = np.concatenate(sob) nevts = len(sob) # Test again, because we may have applied sob threshold cuts per LLH if nevts == 0: return 0., 0. elif nevts == 1: # Same case as in single LLH because sob is multi year weighted sob = sob[0] if sob <= 1.: # sob <= 1 => ns <= 0, so fit will be 0 return 0., 0. else: ns = 1. - (1. / sob) ts = 2. * (-ns + math.log(sob)) return ns, ts elif nevts == 2: # Same case as in single LLH because sob is multi year weighted sum_sob = sob[0] + sob[1] if sum_sob <= 1.: # More complicated to show but same as above return 0., 0. else: a = 1. / (sob[0] * sob[1]) c = sum_sob * a ns = 1. - 0.5 * c + math.sqrt(c * c / 4. - a + 1.) ts = 2. * (-ns + np.sum(np.log1p(ns * sob))) return ns, ts else: # Fit other cases res = sco.minimize(fun=_neglnllh, x0=[ns0], jac=True, args=(sob_dict,), bounds=[self._llh_opts["ns_bounds"]], method=self._llh_opts["minimizer"], options=self._llh_opts["minimizer_opts"]) if not res.success: def _neglnllh_numgrad(ns, sob_dict): """ Use numerical gradient if LINESRCH problem arises. """ return _neglnllh(ns, sob_dict)[0] res = sco.minimize(fun=_neglnllh_numgrad, x0=[ns0], jac=False, args=(sob_dict,), bounds=[self._llh_opts["ns_bounds"]], method=self._llh_opts["minimizer"], options=self._llh_opts["minimizer_opts"]) ns, ts = res.x[0], -res.fun[0] if ts < 0.: # Some times the minimizer doesn't go all the way to 0., so # TS vals might end up negative for a truly zero fit result ts = 0. return ns, ts def _ns_split_weights(self, llhs): """ Set up the ``ns`` splitting weights: The weights simply renormalize the source weights for all single LLHs over all samples. Parameters ---------- llhs : dict of LLH instances Single LLH instances that shall be combined. Returns ------- ns_weigths : dict of array-like Weight per LLH to split up ``ns`` among different samples. """ ns_weights = {} ns_w_sum = 0 for key, llh in llhs.items(): args = llh.model.get_args() ns_weights[key] = np.sum(args["src_w_dec"] * args["src_w_theo"]) ns_w_sum += ns_weights[key] # Normalize weights over all sample source weights return dict_map(lambda key, nsw: nsw / ns_w_sum, ns_weights)

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wikimedia/revscoring

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/revscoring/features/bytes/revision_oriented.py

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https://github.com/wikimedia/revscoring

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from revscoring.datasources import revision_oriented from revscoring.dependencies import DependentSet from ..meta import aggregators from . import datasources name = "bytes.revision" class Revision(DependentSet): def __init__(self, name, revision_datasources): super().__init__(name) self.length = aggregators.len( revision_datasources.bytes, name=name + ".length" ) "`int` : The length of the revision content in bytes" if hasattr(revision_datasources, "parent"): self.parent = Revision( name + ".parent", revision_datasources.parent ) """ :class:`revscoring.features.bytes.Revision` : The parent (aka "previous") revision of the page. """ revision = Revision(name, datasources.Revision(name, revision_oriented.revision)) """ Represents the base revision of interest. Implements this a basic structure: * revision: :class:`~revscoring.features.bytes.Revision` * parent: :class:`~revscoring.features.bytes.Revision` """

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diweiqiang/PRIS

7,464,653,160,914

436a03719bf2ba13659b101b5783418a1d3a3b9f

7576879de06944ab501ef8875ec02aaab15d2e47

/peris_model.py

b4a7c8d019c2d149cc37f4c173f6fd933584a2c9

[]

no_license

https://github.com/diweiqiang/PRIS

4ed89540c4731a4e6b3591410796885b5287c9af

2e691ac82e6f91ae118c0cbb80aba6d2f337c052

refs/heads/master

2022-04-21T10:01:00.071643

2020-04-24T04:22:21

null

0

null

import tensorflow as tf import time from utils import Eval import numpy as np from sampler import * from mylayers import RCEncoding, EuclideanDistillation samplers = [SamplerModel, PopularSamplerModel, ClusterSamplerModel, ClusterPopularSamplerModel, ExactSamplerModel] class EvaluateCallback(tf.keras.callbacks.Callback): def __init__(self, round_): self.round = round_ super(EvaluateCallback, self).__init__() def on_epoch_begin(self, epoch, logs=None): self.starttime = time.time() def on_epoch_end(self, epoch, logs=None): elapsed = time.time() - self.starttime print('Epoch={} - {}s - loss={:.4f}'.format(self.round + epoch + 1, int(elapsed), logs['loss'])) def compute_loss(pred, prob, weighted): if weighted: importance = tf.nn.softmax(tf.negative(pred) - tf.log(prob)) else: importance = tf.nn.softmax(tf.ones_like(pred)) weight_loss = tf.multiply(importance, tf.negative(tf.log_sigmoid(pred))) loss = tf.reduce_sum(weight_loss, -1, keepdims=True) return loss def identity_loss(y_true, y_pred): return tf.reduce_mean(y_pred - 0 * y_true) class PerisModel: def __init__(self, config): user_id = tf.keras.Input(shape=(1,), name='user_id') pos_id = tf.keras.Input(shape=(1,), name='pos_id') neg_id = tf.keras.Input(shape=(config.neg_num,), name='neg_id') neg_prob = tf.keras.Input(shape=(config.neg_num,), name='neg_prob', dtype='float32') item_embed_layer = tf.keras.layers.Embedding(config.num_item, config.d, name='item_embedding', embeddings_initializer=tf.keras.initializers.glorot_normal(), activity_regularizer=tf.keras.regularizers.l2(config.coef / config.batch_size)) user_embed = tf.keras.layers.Embedding(config.num_user, config.d, name='user_embedding', embeddings_initializer=tf.keras.initializers.glorot_normal(), activity_regularizer=tf.keras.regularizers.l2(config.coef / config.batch_size))(user_id) pos_item_embed = item_embed_layer(pos_id) neg_item_embed = item_embed_layer(neg_id) pos_score = tf.keras.layers.dot([user_embed, pos_item_embed], axes=-1) neg_score = tf.keras.layers.dot([user_embed, neg_item_embed], axes=-1) ruij = tf.keras.layers.Flatten()(tf.keras.layers.subtract([pos_score, neg_score])) loss = tf.keras.layers.Lambda(lambda x: compute_loss(*x, config.weighted))([ruij, neg_prob]) self.model = tf.keras.Model(inputs=[user_id, pos_id, neg_id, neg_prob], outputs=loss) self.model.compile(loss=identity_loss, optimizer=tf.keras.optimizers.Adam(lr=config.learning_rate)) self.config = config def get_uv(self): user_embed = self.model.get_layer('user_embedding') item_embed = self.model.get_layer('item_embedding') u = user_embed.get_weights()[0] v = item_embed.get_weights()[0] return u, v def fit(self, train): steps_per_epoch = int((train.nnz + self.config.batch_size - 1) / self.config.batch_size) opt_para = [{}, {'mode': self.config.mode}, {'num_clusters': self.config.num_clusters}, {'num_clusters': self.config.num_clusters, 'mode': self.config.mode}, {}] if self.config.sampler in {0, 1}: sampler = samplers[self.config.sampler](train, **opt_para[self.config.sampler])\ .negative_sampler(neg=self.config.neg_num) dataset = IO.construct_dataset(sampler, self.config.neg_num).shuffle(50000)\ .batch(self.config.batch_size).repeat(self.config.epochs) self.model.fit(dataset, epochs=self.config.epochs, steps_per_epoch=steps_per_epoch, verbose=0, callbacks=[EvaluateCallback(0)]) elif self.config.sampler in {2, 3, 4}: sampler = samplers[self.config.sampler].__bases__[0](train, **opt_para[self.config.sampler % 2])\ .negative_sampler(neg=self.config.neg_num) for i in range(int(self.config.epochs / self.config.epochs_)): dataset = IO.construct_dataset(sampler, self.config.neg_num).shuffle(50000)\ .batch(self.config.batch_size).repeat(self.config.epochs_) self.model.fit(dataset, epochs=self.config.epochs_, steps_per_epoch=steps_per_epoch, verbose=0, callbacks=[EvaluateCallback(i * self.config.epochs_)]) u, v = self.get_uv() sampler = samplers[self.config.sampler](train, {'U': u, 'V': v}, **opt_para[self.config.sampler])\ .negative_sampler(self.config.neg_num) def evaluate(self, train, test): m, n = train.shape u, v = self.get_uv() users = np.random.choice(m, min(m, 50000), False) m = Eval.evaluate_item(train[users, :], test[users, :], u[users, :], v, topk=-1) return m class PerisJointModel: def __init__(self, config): user_id = tf.keras.Input(shape=(1,), name='user_id') pos_id = tf.keras.Input(shape=(1,), name='pos_id') neg_id = tf.keras.Input(shape=(config.neg_num,), name='neg_id') neg_prob = tf.keras.Input(shape=(config.neg_num,), name='neg_prob', dtype='float32') item_embed_layer = tf.keras.layers.Embedding(config.num_item, config.d, name='item_embedding', embeddings_initializer=tf.keras.initializers.glorot_normal(), activity_regularizer=tf.keras.regularizers.l2(config.coef / config.batch_size)) user_embed = tf.keras.layers.Embedding(config.num_user, config.d, name='user_embedding', embeddings_initializer=tf.keras.initializers.glorot_normal(), activity_regularizer=tf.keras.regularizers.l2(config.coef / config.batch_size))(user_id) pos_item_embed = item_embed_layer(pos_id) neg_item_embed = item_embed_layer(neg_id) pos_score = tf.keras.layers.dot([user_embed, pos_item_embed], axes=-1) neg_score = tf.keras.layers.dot([user_embed, neg_item_embed], axes=-1) ruij = tf.keras.layers.Flatten()(tf.keras.layers.subtract([pos_score, neg_score])) loss = tf.keras.layers.Lambda(lambda x: compute_loss(*x, config.weighted))([ruij, neg_prob]) num_clusters = config.num_clusters reg = tf.keras.layers.ActivityRegularization(l2=config.coef2 / config.batch_size) dist = EuclideanDistillation(coef=config.coef_kd) def transform(x): return reg(dist(x)) stop_grad = tf.keras.layers.Lambda(lambda x: tf.stop_gradient(x)) num_codewords = [num_clusters] item_rce_layer = RCEncoding(num_codewords, att_mode='bilinear', rnn_mode='none', name='rcencoding') pos_item_embed_stop = stop_grad(pos_item_embed) neg_item_embed_stop = stop_grad(neg_item_embed) pos_item_embed_, pos_item_cluster_idx = item_rce_layer(pos_item_embed_stop) neg_item_embed_, _ = item_rce_layer(neg_item_embed_stop) user_embed_ = tf.keras.layers.Dense(config.d, use_bias=False, name='user_dense', activity_regularizer=tf.keras.regularizers.l2(config.coef2 / config.batch_size))(stop_grad(user_embed)) pos_score_ = tf.keras.layers.dot([user_embed_, transform([pos_item_embed_stop, pos_item_embed_])], axes=-1) neg_score_ = tf.keras.layers.dot([user_embed_, transform([neg_item_embed_stop, neg_item_embed_])], axes=-1) ruij_ = tf.keras.layers.Flatten()(tf.keras.layers.subtract([pos_score_, neg_score_])) loss_ = tf.keras.layers.Lambda(lambda x: compute_loss(*x, config.weighted))([ruij_, neg_prob]) loss2 = tf.keras.layers.Lambda(lambda x: x[0] + x[1])([loss, loss_]) self.model = tf.keras.Model(inputs=[user_id, pos_id, neg_id, neg_prob], outputs=loss2) self.model.compile(loss=identity_loss, optimizer=tf.keras.optimizers.Adam()) self.config = config def get_cluster(self, m, n): cluster = self.model.get_layer('rcencoding') model_pred_item_cluster = tf.keras.Model(inputs=self.model.input[1], outputs=cluster.output[1]) user_embed_layer = self.model.get_layer('user_dense') model_pred_user = tf.keras.Model(inputs=self.model.input[0], outputs=user_embed_layer.output) item_code = np.squeeze(model_pred_item_cluster.predict(np.arange(n))) item_center = cluster.get_weights()[0] U = np.squeeze(model_pred_user.predict(np.arange(m))) return U, item_code, item_center def get_uv(self): user_embed = self.model.get_layer('user_embedding') item_embed = self.model.get_layer('item_embedding') U = user_embed.get_weights()[0] V = item_embed.get_weights()[0] return U, V def fit(self, train): steps_per_epoch = int((train.nnz + self.config.batch_size - 1) / self.config.batch_size) opt_para = {} if self.config.sampler == 2 else {'mode': self.config.mode} if self.config.sampler in {2, 3}: sampler = samplers[self.config.sampler].__bases__[0](train, **opt_para)\ .negative_sampler(neg=self.config.neg_num) for i in range(int(self.config.epochs / self.config.epochs_)): dataset = IO.construct_dataset(sampler, self.config.neg_num).shuffle(50000)\ .batch(self.config.batch_size).repeat(self.config.epochs_) self.model.fit(dataset, epochs=self.config.epochs_, steps_per_epoch=steps_per_epoch, verbose=0, callbacks=[EvaluateCallback(i * self.config.epochs_)]) u, code, center = self.get_cluster(self.config.num_user, self.config.num_item) sampler = ClusterPopularSamplerModel(train, {'U': u, 'code': code, 'center': center}, **opt_para)\ .negative_sampler(self.config.neg_num) def evaluate(self, train, test): m, n = train.shape u, v = self.get_uv() users = np.random.choice(m, min(m, 50000), False) m = Eval.evaluate_item(train[users, :], test[users, :], u[users, :], v, topk=-1) return m

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#coding=utf8 import itchat # tuling plugin can be get here: # https://github.com/littlecodersh/EasierLife/tree/master/Plugins/Tuling from tuling import get_response @itchat.msg_register('Text') def text_reply(msg): if u'作者' in msg['Text'] or u'主人' in msg['Text']: return u'找陈蒙 421235586@qq.com' elif u'同盟会群' in msg['Text'] or u'同盟会' in msg['Text']: itchat.send('@img@/Users/koudai232/PycharmProjects/python_pycharm/Python/itchat_test/img/tongmenghui_group.png', msg['FromUserName']) return u'长按扫码进群，有问题请@陈蒙或者群主' elif u'白领活动' in msg['Text']: itchat.send('@img@/Users/koudai232/PycharmProjects/python_pycharm/Python/itchat_test/img/funbailing.jpg', msg['FromUserName']) # there should be a picture return u'长按扫码关注，有问题联系@陈蒙' elif u'亲子活动' in msg['Text']: itchat.send('@img@/Users/koudai232/PycharmProjects/python_pycharm/Python/itchat_test/img/funmili.jpg', msg['FromUserName']) # there should be a picture return u'长按扫码关注，有问题联系@陈蒙' else: return get_response(msg['Text']) @itchat.msg_register(['Picture', 'Recording', 'Attachment', 'Video']) def atta_reply(msg): return (u'很好，'+{ 'Picture': u'图片', 'Recording': u'录音', 'Attachment': u'附件', 'Video': u'视频', }.get(msg['Type']) + u'已转发给王珂') # download function is: msg['Text'](msg['FileName']) @itchat.msg_register(['Map', 'Card', 'Note', 'Sharing']) def mm_reply(msg): if msg['Type'] == 'Map': return u'收到位置分享' elif msg['Type'] == 'Sharing': return u'收到分享' + msg['Text'] elif msg['Type'] == 'Note': return u'收到：' + msg['Text'] elif msg['Type'] == 'Card': return u'收到好友信息：' + msg['Text']['Alias'] @itchat.msg_register('Text', isGroupChat = True) def group_reply(msg): if msg['isAt']: return u'@%s\u2005%s' % (msg['ActualNickName'], get_response(msg['Text']) or u'收到：' + msg['Text']) @itchat.msg_register('Friends') def add_friend(msg): itchat.add_friend(**msg['Text']) itchat.send_msg(u'可设置自定义回复消息、图片甚至文件，比如：\n' + u'同盟会入群：回复同盟会群\n' + u'亲子活动：回复亲子活动\n' + u'白领活动：回复白领活动\n' + u'有问题反馈：回复有问题然后发送到邮箱即可', msg['RecommendInfo']['UserName']) itchat.auto_login(True, enableCmdQR=False) itchat.run()

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import matplotlib.pyplot as plt import numpy as np from pandas_datareader import data as wb STCK = wb.DataReader('VFINX', data_source='yahoo', start='2019-1-1') STCK['log_return'] = np.log(STCK['Adj Close'] / STCK['Adj Close'].shift(1)) print (STCK) #print(STCK['log_return']) STCK['log_return'].plot(figsize = (8,5)) plt.show() # use this to show in a graph. # multiplied by 250 becasue there is an average of 250 days on a calendar year. So just a small trick.. avg_returns_a = STCK['log_return'].mean() * 250 print ('log return is : ' + str(round(avg_returns_a, 2) * 100) + '%')

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from __future__ import unicode_literals import logging import warnings import weakref import spotify import spotify.connection import spotify.player import spotify.social from spotify import ffi, lib, serialized, utils __all__ = ["Session", "SessionEvent"] logger = logging.getLogger(__name__) class Session(utils.EventEmitter): """The Spotify session. If no ``config`` is provided, the default config is used. The session object will emit a number of events. See :class:`SessionEvent` for a list of all available events and how to connect your own listener functions up to get called when the events happens. .. warning:: You can only have one :class:`Session` instance per process. This is a libspotify limitation. If you create a second :class:`Session` instance in the same process pyspotify will raise a :exc:`RuntimeError` with the message "Session has already been initialized". :param config: the session config :type config: :class:`Config` or :class:`None` """ @serialized def __init__(self, config=None): super(Session, self).__init__() if spotify._session_instance is not None: raise RuntimeError("Session has already been initialized") if config is not None: self.config = config else: self.config = spotify.Config() if self.config.application_key is None: self.config.load_application_key_file() sp_session_ptr = ffi.new("sp_session **") spotify.Error.maybe_raise( lib.sp_session_create(self.config._sp_session_config, sp_session_ptr) ) self._sp_session = ffi.gc(sp_session_ptr[0], lib.sp_session_release) self._cache = weakref.WeakValueDictionary() self._emitters = [] self._callback_handles = set() self.connection = spotify.connection.Connection(self) self.offline = spotify.offline.Offline(self) self.player = spotify.player.Player(self) self.social = spotify.social.Social(self) spotify._session_instance = self _cache = None """A mapping from sp_* objects to their corresponding Python instances. The ``_cached`` helper constructors on wrapper objects use this cache for finding and returning existing alive wrapper objects for the sp_* object it is about to create a wrapper for. The cache *does not* keep objects alive. It's only a means for looking up the objects if they are kept alive somewhere else in the application. Internal attribute. """ _emitters = None """A list of event emitters with attached listeners. When an event emitter has attached event listeners, we must keep the emitter alive for as long as the listeners are attached. This is achieved by adding them to this list. When creating wrapper objects around sp_* objects we must also return the existing wrapper objects instead of creating new ones so that the set of event listeners on the wrapper object can be modified. This is achieved with a combination of this list and the :attr:`_cache` mapping. Internal attribute. """ _callback_handles = None """A set of handles returned by :meth:`spotify.ffi.new_handle`. These must be kept alive for the handle to remain valid until the callback arrives, even if the end user does not maintain a reference to the object the callback works on. Internal attribute. """ config = None """A :class:`Config` instance with the current configuration. Once the session has been created, changing the attributes of this object will generally have no effect. """ connection = None """An :class:`~spotify.connection.Connection` instance for controlling the connection to the Spotify servers.""" offline = None """An :class:`~spotify.offline.Offline` instance for controlling offline sync.""" player = None """A :class:`~spotify.player.Player` instance for controlling playback.""" social = None """A :class:`~spotify.social.Social` instance for controlling social sharing.""" def login(self, username, password=None, remember_me=False, blob=None): """Authenticate to Spotify's servers. You can login with one of two combinations: - ``username`` and ``password`` - ``username`` and ``blob`` To get the ``blob`` string, you must once log in with ``username`` and ``password``. You'll then get the ``blob`` string passed to the :attr:`~SessionCallbacks.credentials_blob_updated` callback. If you set ``remember_me`` to :class:`True`, you can later login to the same account without providing any ``username`` or credentials by calling :meth:`relogin`. """ username = utils.to_char(username) if password is not None: password = utils.to_char(password) blob = ffi.NULL elif blob is not None: password = ffi.NULL blob = utils.to_char(blob) else: raise AttributeError("password or blob is required to login") spotify.Error.maybe_raise( lib.sp_session_login( self._sp_session, username, password, bool(remember_me), blob ) ) def logout(self): """Log out the current user. If you logged in with the ``remember_me`` argument set to :class:`True`, you will also need to call :meth:`forget_me` to completely remove all credentials of the user that was logged in. """ spotify.Error.maybe_raise(lib.sp_session_logout(self._sp_session)) @property def remembered_user_name(self): """The username of the remembered user from a previous :meth:`login` call.""" return utils.get_with_growing_buffer( lib.sp_session_remembered_user, self._sp_session ) def relogin(self): """Relogin as the remembered user. To be able do this, you must previously have logged in with :meth:`login` with the ``remember_me`` argument set to :class:`True`. To check what user you'll be logged in as if you call this method, see :attr:`remembered_user_name`. """ spotify.Error.maybe_raise(lib.sp_session_relogin(self._sp_session)) def forget_me(self): """Forget the remembered user from a previous :meth:`login` call.""" spotify.Error.maybe_raise(lib.sp_session_forget_me(self._sp_session)) @property @serialized def user(self): """The logged in :class:`User`.""" sp_user = lib.sp_session_user(self._sp_session) if sp_user == ffi.NULL: return None return spotify.User(self, sp_user=sp_user, add_ref=True) @property @serialized def user_name(self): """The username of the logged in user.""" return utils.to_unicode(lib.sp_session_user_name(self._sp_session)) @property @serialized def user_country(self): """The country of the currently logged in user. The :attr:`~SessionEvent.OFFLINE_STATUS_UPDATED` event is emitted on the session object when this changes. """ return utils.to_country(lib.sp_session_user_country(self._sp_session)) @property @serialized def playlist_container(self): """The :class:`PlaylistContainer` for the currently logged in user. .. warning:: The playlists API was broken at 2018-05-24 by a server-side change made by Spotify. The functionality was never restored. Please use the Spotify Web API to work with playlists. """ warnings.warn( "Spotify broke the libspotify playlists API 2018-05-24 " "and never restored it. " "Please use the Spotify Web API to work with playlists." ) sp_playlistcontainer = lib.sp_session_playlistcontainer(self._sp_session) if sp_playlistcontainer == ffi.NULL: return None return spotify.PlaylistContainer._cached( self, sp_playlistcontainer, add_ref=True ) @property def inbox(self): """The inbox :class:`Playlist` for the currently logged in user. .. warning:: The playlists API was broken at 2018-05-24 by a server-side change made by Spotify. The functionality was never restored. Please use the Spotify Web API to work with playlists. """ warnings.warn( "Spotify broke the libspotify playlists API 2018-05-24 " "and never restored it. " "Please use the Spotify Web API to work with playlists." ) sp_playlist = lib.sp_session_inbox_create(self._sp_session) if sp_playlist == ffi.NULL: return None return spotify.Playlist._cached(self, sp_playlist=sp_playlist, add_ref=False) def set_cache_size(self, size): """Set maximum size in MB for libspotify's cache. If set to 0 (the default), up to 10% of the free disk space will be used.""" spotify.Error.maybe_raise(lib.sp_session_set_cache_size(self._sp_session, size)) def flush_caches(self): """Write all cached data to disk. libspotify does this regularly and on logout, so you should never need to call this method yourself. """ spotify.Error.maybe_raise(lib.sp_session_flush_caches(self._sp_session)) def preferred_bitrate(self, bitrate): """Set preferred :class:`Bitrate` for music streaming.""" spotify.Error.maybe_raise( lib.sp_session_preferred_bitrate(self._sp_session, bitrate) ) def preferred_offline_bitrate(self, bitrate, allow_resync=False): """Set preferred :class:`Bitrate` for offline sync. If ``allow_resync`` is :class:`True` libspotify may resynchronize already synced tracks. """ spotify.Error.maybe_raise( lib.sp_session_preferred_offline_bitrate( self._sp_session, bitrate, allow_resync ) ) @property def volume_normalization(self): """Whether volume normalization is active or not. Set to :class:`True` or :class:`False` to change. """ return bool(lib.sp_session_get_volume_normalization(self._sp_session)) @volume_normalization.setter def volume_normalization(self, value): spotify.Error.maybe_raise( lib.sp_session_set_volume_normalization(self._sp_session, value) ) def process_events(self): """Process pending events in libspotify. This method must be called for most callbacks to be called. Without calling this method, you'll only get the callbacks that are called from internal libspotify threads. When the :attr:`~SessionEvent.NOTIFY_MAIN_THREAD` event is emitted (from an internal libspotify thread), it's your job to make sure this method is called (from the thread you use for accessing Spotify), so that further callbacks can be triggered (from the same thread). pyspotify provides an :class:`~spotify.EventLoop` that you can use for processing events when needed. """ next_timeout = ffi.new("int *") spotify.Error.maybe_raise( lib.sp_session_process_events(self._sp_session, next_timeout) ) return next_timeout[0] def inbox_post_tracks(self, canonical_username, tracks, message, callback=None): """Post a ``message`` and one or more ``tracks`` to the inbox of the user with the given ``canonical_username``. ``tracks`` can be a single :class:`~spotify.Track` or a list of :class:`~spotify.Track` objects. Returns an :class:`InboxPostResult` that can be used to check if the request completed successfully. If callback isn't :class:`None`, it is called with an :class:`InboxPostResult` instance when the request has completed. """ return spotify.InboxPostResult( self, canonical_username, tracks, message, callback ) def get_starred(self, canonical_username=None): """Get the starred :class:`Playlist` for the user with ``canonical_username``. .. warning:: The playlists API was broken at 2018-05-24 by a server-side change made by Spotify. The functionality was never restored. Please use the Spotify Web API to work with playlists. If ``canonical_username`` isn't specified, the starred playlist for the currently logged in user is returned. """ warnings.warn( "Spotify broke the libspotify playlists API 2018-05-24 " "and never restored it. " "Please use the Spotify Web API to work with playlists." ) if canonical_username is None: sp_playlist = lib.sp_session_starred_create(self._sp_session) else: sp_playlist = lib.sp_session_starred_for_user_create( self._sp_session, utils.to_bytes(canonical_username) ) if sp_playlist == ffi.NULL: return None return spotify.Playlist._cached(self, sp_playlist, add_ref=False) def get_published_playlists(self, canonical_username=None): """Get the :class:`PlaylistContainer` of published playlists for the user with ``canonical_username``. .. warning:: The playlists API was broken at 2018-05-24 by a server-side change made by Spotify. The functionality was never restored. Please use the Spotify Web API to work with playlists. If ``canonical_username`` isn't specified, the published container for the currently logged in user is returned. """ warnings.warn( "Spotify broke the libspotify playlists API 2018-05-24 " "and never restored it. " "Please use the Spotify Web API to work with playlists." ) if canonical_username is None: canonical_username = ffi.NULL else: canonical_username = utils.to_bytes(canonical_username) sp_playlistcontainer = lib.sp_session_publishedcontainer_for_user_create( self._sp_session, canonical_username ) if sp_playlistcontainer == ffi.NULL: return None return spotify.PlaylistContainer._cached( self, sp_playlistcontainer, add_ref=False ) def get_link(self, uri): """ Get :class:`Link` from any Spotify URI. A link can be created from a string containing a Spotify URI on the form ``spotify:...``. Example:: >>> session = spotify.Session() # ... >>> session.get_link( ... 'spotify:track:2Foc5Q5nqNiosCNqttzHof') Link('spotify:track:2Foc5Q5nqNiosCNqttzHof') >>> session.get_link( ... 'http://open.spotify.com/track/4wl1dK5dHGp3Ig51stvxb0') Link('spotify:track:4wl1dK5dHGp3Ig51stvxb0') """ return spotify.Link(self, uri=uri) def get_track(self, uri): """ Get :class:`Track` from a Spotify track URI. Example:: >>> session = spotify.Session() # ... >>> track = session.get_track( ... 'spotify:track:2Foc5Q5nqNiosCNqttzHof') >>> track.load().name u'Get Lucky' """ return spotify.Track(self, uri=uri) def get_local_track(self, artist=None, title=None, album=None, length=None): """ Get :class:`Track` for a local track. Spotify's official clients supports adding your local music files to Spotify so they can be played in the Spotify client. These are not synced with Spotify's servers or between your devices and there is not trace of them in your Spotify user account. The exception is when you add one of these local tracks to a playlist or mark them as starred. This creates a "local track" which pyspotify also will be able to observe. "Local tracks" can be recognized in several ways: - The track's URI will be of the form ``spotify:local:ARTIST:ALBUM:TITLE:LENGTH_IN_SECONDS``. Any of the parts in all caps can be left out if there is no information available. That is, ``spotify:local::::`` is a valid local track URI. - :attr:`Link.type` will be :class:`LinkType.LOCALTRACK` for the track's link. - :attr:`Track.is_local` will be :class:`True` for the track. This method can be used to create local tracks that can be starred or added to playlists. ``artist`` may be an artist name. ``title`` may be a track name. ``album`` may be an album name. ``length`` may be a track length in milliseconds. Note that when creating a local track you provide the length in milliseconds, while the local track URI contains the length in seconds. """ if artist is None: artist = "" if title is None: title = "" if album is None: album = "" if length is None: length = -1 artist = utils.to_char(artist) title = utils.to_char(title) album = utils.to_char(album) sp_track = lib.sp_localtrack_create(artist, title, album, length) return spotify.Track(self, sp_track=sp_track, add_ref=False) def get_album(self, uri): """ Get :class:`Album` from a Spotify album URI. Example:: >>> session = spotify.Session() # ... >>> album = session.get_album( ... 'spotify:album:6wXDbHLesy6zWqQawAa91d') >>> album.load().name u'Forward / Return' """ return spotify.Album(self, uri=uri) def get_artist(self, uri): """ Get :class:`Artist` from a Spotify artist URI. Example:: >>> session = spotify.Session() # ... >>> artist = session.get_artist( ... 'spotify:artist:22xRIphSN7IkPVbErICu7s') >>> artist.load().name u'Rob Dougan' """ return spotify.Artist(self, uri=uri) def get_playlist(self, uri): """ Get :class:`Playlist` from a Spotify playlist URI. .. warning:: The playlists API was broken at 2018-05-24 by a server-side change made by Spotify. The functionality was never restored. Please use the Spotify Web API to work with playlists. Example:: >>> session = spotify.Session() # ... >>> playlist = session.get_playlist( ... 'spotify:user:fiat500c:playlist:54k50VZdvtnIPt4d8RBCmZ') >>> playlist.load().name u'500C feelgood playlist' """ warnings.warn( "Spotify broke the libspotify playlists API 2018-05-24 " "and never restored it. " "Please use the Spotify Web API to work with playlists." ) return spotify.Playlist(self, uri=uri) def get_user(self, uri): """ Get :class:`User` from a Spotify user URI. Example:: >>> session = spotify.Session() # ... >>> user = session.get_user('spotify:user:jodal') >>> user.load().display_name u'jodal' """ return spotify.User(self, uri=uri) def get_image(self, uri, callback=None): """ Get :class:`Image` from a Spotify image URI. If ``callback`` isn't :class:`None`, it is expected to be a callable that accepts a single argument, an :class:`Image` instance, when the image is done loading. Example:: >>> session = spotify.Session() # ... >>> image = session.get_image( ... 'spotify:image:a0bdcbe11b5cd126968e519b5ed1050b0e8183d0') >>> image.load().data_uri[:50] u'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEBLAEsAAD' """ return spotify.Image(self, uri=uri, callback=callback) def search( self, query, callback=None, track_offset=0, track_count=20, album_offset=0, album_count=20, artist_offset=0, artist_count=20, playlist_offset=0, playlist_count=20, search_type=None, ): """ Search Spotify for tracks, albums, artists, and playlists matching ``query``. .. warning:: The search API was broken at 2016-02-03 by a server-side change made by Spotify. The functionality was never restored. Please use the Spotify Web API to perform searches. The ``query`` string can be free format, or use some prefixes like ``title:`` and ``artist:`` to limit what to match on. There is no official docs on the search query format, but there's a `Spotify blog post <https://www.spotify.com/blog/archives/2008/01/22/searching-spotify/>`_ from 2008 with some examples. If ``callback`` isn't :class:`None`, it is expected to be a callable that accepts a single argument, a :class:`Search` instance, when the search completes. The ``*_offset`` and ``*_count`` arguments can be used to retrieve more search results. libspotify will currently not respect ``*_count`` values higher than 200, though this may change at any time as the limit isn't documented in any official docs. If you want to retrieve more than 200 results, you'll have to search multiple times with different ``*_offset`` values. See the ``*_total`` attributes on the :class:`Search` to see how many results exists, and to figure out how many searches you'll need to make to retrieve everything. ``search_type`` is a :class:`SearchType` value. It defaults to :attr:`SearchType.STANDARD`. Returns a :class:`Search` instance. """ raise Exception( "Spotify broke the libspotify search API 2016-02-03 " "and never restored it." ) def get_toplist( self, type=None, region=None, canonical_username=None, callback=None ): """Get a :class:`Toplist` of artists, albums, or tracks that are the currently most popular worldwide or in a specific region. ``type`` is a :class:`ToplistType` instance that specifies the type of toplist to create. ``region`` is either a :class:`ToplistRegion` instance, or a 2-letter ISO 3166-1 country code as a unicode string, that specifies the geographical region to create a toplist for. If ``region`` is :attr:`ToplistRegion.USER` and ``canonical_username`` isn't specified, the region of the current user will be used. If ``canonical_username`` is specified, the region of the specified user will be used instead. If ``callback`` isn't :class:`None`, it is expected to be a callable that accepts a single argument, a :class:`Toplist` instance, when the toplist request completes. Example:: >>> import spotify >>> session = spotify.Session() # ... >>> toplist = session.get_toplist( ... type=spotify.ToplistType.TRACKS, region='US') >>> toplist.load() >>> len(toplist.tracks) 100 >>> len(toplist.artists) 0 >>> toplist.tracks[0] Track(u'spotify:track:2dLLR6qlu5UJ5gk0dKz0h3') """ return spotify.Toplist( self, type=type, region=region, canonical_username=canonical_username, callback=callback, ) class SessionEvent(object): """Session events. Using the :class:`Session` object, you can register listener functions to be called when various session related events occurs. This class enumerates the available events and the arguments your listener functions will be called with. Example usage:: import spotify def logged_in(session, error_type): if error_type is spotify.ErrorType.OK: print('Logged in as %s' % session.user) else: print('Login failed: %s' % error_type) session = spotify.Session() session.on(spotify.SessionEvent.LOGGED_IN, logged_in) session.login('alice', 's3cret') All events will cause debug log statements to be emitted, even if no listeners are registered. Thus, there is no need to register listener functions just to log that they're called. """ LOGGED_IN = "logged_in" """Called when login has completed. Note that even if login has succeeded, that does not mean that you're online yet as libspotify may have cached enough information to let you authenticate with Spotify while offline. This event should be used to get notified about login errors. To get notified about the authentication and connection state, refer to the :attr:`SessionEvent.CONNECTION_STATE_UPDATED` event. :param session: the current session :type session: :class:`Session` :param error_type: the login error type :type error_type: :class:`ErrorType` """ LOGGED_OUT = "logged_out" """Called when logout has completed or there is a permanent connection error. :param session: the current session :type session: :class:`Session` """ METADATA_UPDATED = "metadata_updated" """Called when some metadata has been updated. There is no way to know what metadata was updated, so you'll have to refresh all you metadata caches. :param session: the current session :type session: :class:`Session` """ CONNECTION_ERROR = "connection_error" """Called when there is a connection error and libspotify has problems reconnecting to the Spotify service. May be called repeatedly as long as the problem persists. Will be called with an :attr:`ErrorType.OK` error when the problem is resolved. :param session: the current session :type session: :class:`Session` :param error_type: the connection error type :type error_type: :class:`ErrorType` """ MESSAGE_TO_USER = "message_to_user" """Called when libspotify wants to show a message to the end user. :param session: the current session :type session: :class:`Session` :param data: the message :type data: text """ NOTIFY_MAIN_THREAD = "notify_main_thread" """Called when processing on the main thread is needed. When this is called, you should call :meth:`~Session.process_events` from your main thread. Failure to do so may cause request timeouts, or a lost connection. .. warning:: This event is emitted from an internal libspotify thread. Thus, your event listener must not block, and must use proper synchronization around anything it does. :param session: the current session :type session: :class:`Session` """ MUSIC_DELIVERY = "music_delivery" """Called when there is decompressed audio data available. If the function returns a lower number of frames consumed than ``num_frames``, libspotify will retry delivery of the unconsumed frames in about 100ms. This can be used for rate limiting if libspotify is giving you audio data too fast. .. note:: You can register at most one event listener for this event. .. warning:: This event is emitted from an internal libspotify thread. Thus, your event listener must not block, and must use proper synchronization around anything it does. :param session: the current session :type session: :class:`Session` :param audio_format: the audio format :type audio_format: :class:`AudioFormat` :param frames: the audio frames :type frames: bytestring :param num_frames: the number of frames :type num_frames: int :returns: the number of frames consumed """ PLAY_TOKEN_LOST = "play_token_lost" """Music has been paused because an account only allows music to be played from one location simultaneously. When this event is emitted, you should pause playback. :param session: the current session :type session: :class:`Session` """ LOG_MESSAGE = "log_message" """Called when libspotify have something to log. Note that pyspotify logs this for you, so you'll probably never need to register a listener for this event. :param session: the current session :type session: :class:`Session` :param data: the message :type data: text """ END_OF_TRACK = "end_of_track" """Called when all audio data for the current track has been delivered. :param session: the current session :type session: :class:`Session` """ STREAMING_ERROR = "streaming_error" """Called when audio streaming cannot start or continue. :param session: the current session :type session: :class:`Session` :param error_type: the streaming error type :type error_type: :class:`ErrorType` """ USER_INFO_UPDATED = "user_info_updated" """Called when anything related to :class:`User` objects is updated. :param session: the current session :type session: :class:`Session` """ START_PLAYBACK = "start_playback" """Called when audio playback should start. You need to implement a listener for the :attr:`GET_AUDIO_BUFFER_STATS` event for the :attr:`START_PLAYBACK` event to be useful. .. warning:: This event is emitted from an internal libspotify thread. Thus, your event listener must not block, and must use proper synchronization around anything it does. :param session: the current session :type session: :class:`Session` """ STOP_PLAYBACK = "stop_playback" """Called when audio playback should stop. You need to implement a listener for the :attr:`GET_AUDIO_BUFFER_STATS` event for the :attr:`STOP_PLAYBACK` event to be useful. .. warning:: This event is emitted from an internal libspotify thread. Thus, your event listener must not block, and must use proper synchronization around anything it does. :param session: the current session :type session: :class:`Session` """ GET_AUDIO_BUFFER_STATS = "get_audio_buffer_stats" """Called to query the application about its audio buffer. .. note:: You can register at most one event listener for this event. .. warning:: This event is emitted from an internal libspotify thread. Thus, your event listener must not block, and must use proper synchronization around anything it does. :param session: the current session :type session: :class:`Session` :returns: an :class:`AudioBufferStats` instance """ OFFLINE_STATUS_UPDATED = "offline_status_updated" """Called when offline sync status is updated. :param session: the current session :type session: :class:`Session` """ CREDENTIALS_BLOB_UPDATED = "credentials_blob_updated" """Called when storable credentials have been updated, typically right after login. The ``blob`` argument can be stored and later passed to :meth:`~Session.login` to login without storing the user's password. :param session: the current session :type session: :class:`Session` :param blob: the authentication blob :type blob: bytestring """ CONNECTION_STATE_UPDATED = "connection_state_updated" """Called when the connection state is updated. The connection state includes login, logout, offline mode, etc. :param session: the current session :type session: :class:`Session` """ SCROBBLE_ERROR = "scrobble_error" """Called when there is a scrobble error event. :param session: the current session :type session: :class:`Session` :param error_type: the scrobble error type :type error_type: :class:`ErrorType` """ PRIVATE_SESSION_MODE_CHANGED = "private_session_mode_changed" """Called when there is a change in the private session mode. :param session: the current session :type session: :class:`Session` :param is_private: whether the session is private :type is_private: bool """ class _SessionCallbacks(object): """Internal class.""" @classmethod def get_struct(cls): return ffi.new( "sp_session_callbacks *", { "logged_in": cls.logged_in, "logged_out": cls.logged_out, "metadata_updated": cls.metadata_updated, "connection_error": cls.connection_error, "message_to_user": cls.message_to_user, "notify_main_thread": cls.notify_main_thread, "music_delivery": cls.music_delivery, "play_token_lost": cls.play_token_lost, "log_message": cls.log_message, "end_of_track": cls.end_of_track, "streaming_error": cls.streaming_error, "userinfo_updated": cls.user_info_updated, "start_playback": cls.start_playback, "stop_playback": cls.stop_playback, "get_audio_buffer_stats": cls.get_audio_buffer_stats, "offline_status_updated": cls.offline_status_updated, "credentials_blob_updated": cls.credentials_blob_updated, "connectionstate_updated": cls.connection_state_updated, "scrobble_error": cls.scrobble_error, "private_session_mode_changed": cls.private_session_mode_changed, }, ) # XXX Avoid use of the spotify._session_instance global in the following # callbacks. @staticmethod @ffi.callback("void(sp_session *, sp_error)") def logged_in(sp_session, sp_error): if not spotify._session_instance: return error_type = spotify.ErrorType(sp_error) if error_type == spotify.ErrorType.OK: logger.info("Spotify logged in") else: logger.error("Spotify login error: %r", error_type) spotify._session_instance.emit( SessionEvent.LOGGED_IN, spotify._session_instance, error_type ) @staticmethod @ffi.callback("void(sp_session *)") def logged_out(sp_session): if not spotify._session_instance: return logger.info("Spotify logged out") spotify._session_instance.emit( SessionEvent.LOGGED_OUT, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *)") def metadata_updated(sp_session): if not spotify._session_instance: return logger.debug("Metadata updated") spotify._session_instance.emit( SessionEvent.METADATA_UPDATED, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *, sp_error)") def connection_error(sp_session, sp_error): if not spotify._session_instance: return error_type = spotify.ErrorType(sp_error) logger.error("Spotify connection error: %r", error_type) spotify._session_instance.emit( SessionEvent.CONNECTION_ERROR, spotify._session_instance, error_type ) @staticmethod @ffi.callback("void(sp_session *, const char *)") def message_to_user(sp_session, data): if not spotify._session_instance: return data = utils.to_unicode(data).strip() logger.debug("Message to user: %s", data) spotify._session_instance.emit( SessionEvent.MESSAGE_TO_USER, spotify._session_instance, data ) @staticmethod @ffi.callback("void(sp_session *)") def notify_main_thread(sp_session): if not spotify._session_instance: return logger.debug("Notify main thread") spotify._session_instance.emit( SessionEvent.NOTIFY_MAIN_THREAD, spotify._session_instance ) @staticmethod @ffi.callback("int(sp_session *, const sp_audioformat *, const void *, int)") def music_delivery(sp_session, sp_audioformat, frames, num_frames): if not spotify._session_instance: return 0 if spotify._session_instance.num_listeners(SessionEvent.MUSIC_DELIVERY) == 0: logger.debug("Music delivery, but no listener") return 0 audio_format = spotify.AudioFormat(sp_audioformat) frames_buffer = ffi.buffer(frames, audio_format.frame_size() * num_frames) frames_bytes = frames_buffer[:] num_frames_consumed = spotify._session_instance.call( SessionEvent.MUSIC_DELIVERY, spotify._session_instance, audio_format, frames_bytes, num_frames, ) logger.debug( "Music delivery of %d frames, %d consumed", num_frames, num_frames_consumed, ) return num_frames_consumed @staticmethod @ffi.callback("void(sp_session *)") def play_token_lost(sp_session): if not spotify._session_instance: return logger.debug("Play token lost") spotify._session_instance.emit( SessionEvent.PLAY_TOKEN_LOST, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *, const char *)") def log_message(sp_session, data): if not spotify._session_instance: return data = utils.to_unicode(data).strip() logger.debug("libspotify log message: %s", data) spotify._session_instance.emit( SessionEvent.LOG_MESSAGE, spotify._session_instance, data ) @staticmethod @ffi.callback("void(sp_session *)") def end_of_track(sp_session): if not spotify._session_instance: return logger.debug("End of track") spotify._session_instance.emit( SessionEvent.END_OF_TRACK, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *, sp_error)") def streaming_error(sp_session, sp_error): if not spotify._session_instance: return error_type = spotify.ErrorType(sp_error) logger.error("Spotify streaming error: %r", error_type) spotify._session_instance.emit( SessionEvent.STREAMING_ERROR, spotify._session_instance, error_type ) @staticmethod @ffi.callback("void(sp_session *)") def user_info_updated(sp_session): if not spotify._session_instance: return logger.debug("User info updated") spotify._session_instance.emit( SessionEvent.USER_INFO_UPDATED, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *)") def start_playback(sp_session): if not spotify._session_instance: return logger.debug("Start playback called") spotify._session_instance.emit( SessionEvent.START_PLAYBACK, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *)") def stop_playback(sp_session): if not spotify._session_instance: return logger.debug("Stop playback called") spotify._session_instance.emit( SessionEvent.STOP_PLAYBACK, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *, sp_audio_buffer_stats *)") def get_audio_buffer_stats(sp_session, sp_audio_buffer_stats): if not spotify._session_instance: return if ( spotify._session_instance.num_listeners(SessionEvent.GET_AUDIO_BUFFER_STATS) == 0 ): logger.debug("Audio buffer stats requested, but no listener") return logger.debug("Audio buffer stats requested") stats = spotify._session_instance.call( SessionEvent.GET_AUDIO_BUFFER_STATS, spotify._session_instance ) sp_audio_buffer_stats.samples = stats.samples sp_audio_buffer_stats.stutter = stats.stutter @staticmethod @ffi.callback("void(sp_session *)") def offline_status_updated(sp_session): if not spotify._session_instance: return logger.debug("Offline status updated") spotify._session_instance.emit( SessionEvent.OFFLINE_STATUS_UPDATED, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *, const char *)") def credentials_blob_updated(sp_session, data): if not spotify._session_instance: return data = ffi.string(data) logger.debug("Credentials blob updated: %r", data) spotify._session_instance.emit( SessionEvent.CREDENTIALS_BLOB_UPDATED, spotify._session_instance, data, ) @staticmethod @ffi.callback("void(sp_session *)") def connection_state_updated(sp_session): if not spotify._session_instance: return logger.debug("Connection state updated") spotify._session_instance.emit( SessionEvent.CONNECTION_STATE_UPDATED, spotify._session_instance ) @staticmethod @ffi.callback("void(sp_session *, sp_error)") def scrobble_error(sp_session, sp_error): if not spotify._session_instance: return error_type = spotify.ErrorType(sp_error) logger.error("Spotify scrobble error: %r", error_type) spotify._session_instance.emit( SessionEvent.SCROBBLE_ERROR, spotify._session_instance, error_type ) @staticmethod @ffi.callback("void(sp_session *, bool)") def private_session_mode_changed(sp_session, is_private): if not spotify._session_instance: return is_private = bool(is_private) status = "private" if is_private else "public" logger.debug("Private session mode changed: %s", status) spotify._session_instance.emit( SessionEvent.PRIVATE_SESSION_MODE_CHANGED, spotify._session_instance, is_private, )

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luyaochi/mycrawlerlib

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[]

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https://github.com/luyaochi/mycrawlerlib

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#!/usr/bin/python # -*-coding:utf-8-*- class frontier: def __init__(self,seed = None): self.list_frontier = [] self.init_frontier(seed) def init_frontier(self,seed): if seed == None: self.load_frontierFromDb() else: self.add_frontier(seed) return self.list_frontier def show_frontier(self): return self.list_frontier def add_frontier(self,seed): if seed not in self.list_frontier: self.list_frontier.append(seed) def del_frontier(self): return self.list_frontier.pop(0) def show_first_frontier(self): if len(self.list_frontier) > 0: return self.list_frontier[0] return '' def len_frontier(self): return len(self.list_frontier)

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jamesjarlathlong/beeview_gateway

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https://github.com/jamesjarlathlong/beeview_gateway

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import scipy.fftpack as spfft import numpy as np import seaborn as sns import matplotlib import matplotlib.pyplot as plt import fourier_basis as ft import cvxpy as cvx import itertools import math import numpy as np import random import sys import fourier_basis as ft def downsample(data,ri): return data[ri] def reconstruct(downsampled, basis, ri): A = basis[ri] n =len(basis) vx = cvx.Variable(n) objective = cvx.Minimize(cvx.norm(vx, 1)) constraints = [A*vx == downsampled] prob = cvx.Problem(objective, constraints) result = prob.solve(verbose=False) return vx def fourier_denoising(lamda,downsampled, basis, ri): A = basis[ri] n =len(basis) w = cvx.Variable(n) loss = cvx.sum_squares(A*w-downsampled)/2 + lamda * cvx.norm(w,1) problem = cvx.Problem(cvx.Minimize(loss)) result = problem.solve(verbose=True) return w def freq_and_time(A, ri, signal, method): vx = method(signal[ri], A, ri) x = np.array(vx.value) x = np.squeeze(x) sig = np.dot(A,x) return x, sig def fourier_cs(signal, downsample_factor, method=reconstruct): n = len(signal) m =int(n//downsample_factor) A = np.array(ft.t(ft.zmean_real_dft(n))) ri = np.random.choice(n, m, replace=False) # random sample of indices f, t = freq_and_time(A, ri, signal, method=method) return f,t if __name__ == "__main__": data = np.loadtxt(sys.argv[1]) factor = int(sys.argv[2])

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xflows/cf_weka

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#!/usr/bin/env python # -*- coding: utf-8 -*- __authors__ = 'vid, daleksovski' import classification import evaluation import utilities import preprocessing # # CLASSIFICATION ALGORITHMS # def decision_tree_j48(input_dict): """Decision Tree learner J48""" p = input_dict['params'] return {'learner': classification.j48(p)} # '-C 0.25 -M 2' def naive_bayes(input_dict): """Naive Bayes learner""" p = input_dict['params'] return {'learner': classification.naive_bayes(p)} def random_forest(input_dict): """Random Forest learner""" p = input_dict['params'] return {'learner': classification.random_forest(p)} def multilayer_perceptron(input_dict): """MLP Neural-network learner""" p = input_dict['params'] return {'learner': classification.multilayer_perceptron(p)} def smo(input_dict): """SVM learner""" p = input_dict['params'] return {'learner': classification.smo(p)} def logistic_regression_weka(input_dict): """Logistic Regression learner""" p = input_dict['params'] return {'learner': classification.logistic(p)} def rules_zeror(input_dict): """rulesZeroR Rule learner""" p = input_dict['params'] return {'learner': classification.rules_zeror(p)} def rules_jripper(input_dict): """Rule learner JRipper""" p = input_dict['params'] return {'learner': classification.rules_jrip(p)} def knn(input_dict): """K-Nearest-Neighbours learner IBk""" p = input_dict['params'] return {'learner': classification.ibk(p)} def random_tree(input_dict): """Random Tree learner""" p = input_dict['params'] return {'learner': classification.random_tree(p)} def rep_tree(input_dict): """Reduced Error Pruning tree""" p = input_dict['params'] return {'learner': classification.rep_tree(p)} def k_star(input_dict): """K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function""" p = input_dict['params'] return {'learner': classification.k_star(p)} # # PREPROCESSING # def feature_selection(input_dict): """Correlation-based Feature Subset Selection""" instances = input_dict['instances'] output_dict = {} output_dict['selected'] = preprocessing.correlation_basedfeat_sel(instances) return output_dict def normalize(input_dict): """Normalizes all numeric values in the given dataset""" instances = input_dict['instances'] output_dict = {} # 1,0 -> normalize to [0,1]; 2,-1 then to [-1,1] output_dict['normalized'] = preprocessing.normalize(instances, '-S 2.0 -T -1.0') return output_dict # # EVALUATION # def apply_mapped_classifier_get_instances(input_dict): """An advanced version of the Apply Classifier method""" sclassifier = input_dict['classifier'] soriginalInstances = input_dict['original_training_instances'] sinstances = input_dict['instances'] instances, report = evaluation.apply_mapped_classifier_get_instances(sclassifier, soriginalInstances, sinstances) output_dict = {'instances': instances, 'mapping_report': report} return output_dict # # UTILITIES # def export_dataset_to_arff(input_dict): """Export Dataset to an ARFF Textual Format""" arff_file_contents = utilities.export_dataset_to_arff(input_dict['dataset']) file_out = open('myfile', 'w') file_out.write(arff_file_contents) file_out.close() output_dict = {} output_dict['file_out'] = file_out return output_dict def import_dataset_from_arff(input_dict): """Imports Dataset From an ARFF Textual Format""" arff = input_dict['arff'] output_dict = {} output_dict['instances'] = utilities.import_dataset_from_arff(arff) return output_dict def load_uci(input_dict): """Loads a UCI dataset""" arff_file = input_dict['filename'] output_dict = {} output_dict['data'] = utilities.load_uci_dataset_weka(arff_file) return output_dict

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4ar0n/fifthtutorial

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2023-02-18T23:04:56.162587

2021-01-16T14:31:47

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from pprint import pprint from random import * from datetime import datetime person_mapping_dict = { 1:"Aaron", 2:"Vincent", 3:"Sir Ying", 4:"Jason", 5:"Billy" } good_mapping_dict = { 45:"banana", 46:"apple", 47:"melon", 48:"orange" } price_mapping_table = { 45:4, 46:6, 47:20, 48:3, } data=[[datetime(2020, 1, 6), 3, 45], [datetime(2020, 1, 10), 1, 48], [datetime(2020, 1, 28), 3, 45], [datetime(2020, 2, 2), 4, 48], [datetime(2020, 2, 15), 3, 45], [datetime(2020, 2, 23), 4, 45], [datetime(2020, 3, 5), 4, 45], [datetime(2020, 3, 6), 2, 45], [datetime(2020, 3, 8), 2, 48], [datetime(2020, 3, 20), 1, 45], [datetime(2020, 4, 26), 4, 47], [datetime(2020, 4, 28), 3, 46], [datetime(2020, 5, 17), 1, 47], [datetime(2020, 5, 18), 1, 47], [datetime(2020, 5, 28), 3, 47], [datetime(2020, 6, 9), 2, 46], [datetime(2020, 6, 12), 2, 46], [datetime(2020, 6, 16), 2, 47], [datetime(2020, 6, 22), 5, 48], [datetime(2020, 6, 24), 5, 48], [datetime(2020, 7, 4), 4, 46], [datetime(2020, 7, 9), 4, 45], [datetime(2020, 7, 20), 5, 48], [datetime(2020, 7, 22), 4, 45], [datetime(2020, 7, 25), 2, 47], [datetime(2020, 7, 26), 5, 45], [datetime(2020, 7, 28), 3, 46], [datetime(2020, 7, 28), 4, 48], [datetime(2020, 8, 2), 5, 46], [datetime(2020, 8, 3), 4, 46], [datetime(2020, 8, 6), 1, 48], [datetime(2020, 8, 7), 3, 48], [datetime(2020, 8, 28), 1, 48], [datetime(2020, 9, 3), 4, 48], [datetime(2020, 9, 26), 1, 47], [datetime(2020, 9, 28), 3, 47], [datetime(2020, 10, 7), 3, 47], [datetime(2020, 10, 9), 1, 46], [datetime(2020, 10, 20), 3, 47], [datetime(2020, 10, 22), 1, 46], [datetime(2020, 10, 22), 2, 48], [datetime(2020, 10, 23), 3, 48], [datetime(2020, 10, 27), 3, 48], [datetime(2020, 11, 1), 2, 47], [datetime(2020, 11, 17), 4, 46], [datetime(2020, 11, 24), 2, 48], [datetime(2020, 12, 5), 1, 45], [datetime(2020, 12, 10), 4, 45], [datetime(2020, 12, 28), 5, 46]] Question 1a Write a script to show the transactions in the data above. e.g.: 'Aaron has bought a/an orange at a price of 3 on 2020-01-10.' Give the total revenue at then as a summary: "Total Revenue: 40" Question 1b Write a FUNCTION to show filtered transactions by name_id in the above. def transaction_filter(1): ... print and return transactions-of-name_id=1 Question 1c (*advanced) Write a FUNCTION to show filtered transactions by name_id , good_id and month, *PLUS your function need to capable of showing all transactions or filter just good_id or just name_id etc.

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P-R-McWhirter/augment_height

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/augment_height.py

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refs/heads/master

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2020-03-05T17:36:03

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# Imports from __future__ import print_function import numpy as np import pandas as pd import os from sklearn.model_selection import train_test_split from sklearn import preprocessing from sklearn.metrics import confusion_matrix import gc import itertools import cv2 import argparse gc.enable() ap = argparse.ArgumentParser() ap.add_argument("-s", "--start", type=float, required=True, help="starting height of data") ap.add_argument("-e", "--end", type=float, required=True, help="ending height of data") ap.add_argument("-t", "--type", type=str, required=True, help="file extension of data") args = vars(ap.parse_args()) start = args["start"] end = args["end"] filetype = args["type"] ratio = start/end color = [0, 0, 0] imgs = [] cwd = os.getcwd() for file in os.listdir(cwd): if file.endswith(filetype): imgs.append(file) new_folder = 'data_' + str(int(start)) + '_' + str(int(end)) if not os.path.exists(new_folder): os.makedirs(new_folder) for file in imgs: img = cv2.imread(file) newsize_x = int(img.shape[0] * ratio) newsize_y = int(img.shape[1] * ratio) resize = cv2.resize(img, dsize=(newsize_y, newsize_x), interpolation=cv2.INTER_CUBIC) delta_w = img.shape[1] - resize.shape[1] delta_h = img.shape[0] - resize.shape[0] top, bottom = delta_h//2, delta_h-(delta_h//2) left, right = delta_w//2, delta_w-(delta_w//2) new_im = cv2.copyMakeBorder(resize, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) cv2.imwrite(new_folder + '/' + file, new_im) labels = pd.read_csv(cwd + "/" + file[:-4] + ".txt", sep = " ", header = None).values labels[:,1] = (labels[:,1] - 0.5) * ratio + 0.5 labels[:,2] = (labels[:,2] - 0.5) * ratio + 0.5 labels[:,3] = labels[:,3] * ratio labels[:,4] = labels[:,4] * ratio np.savetxt(new_folder + '/' + file[:-4] + ".txt", labels, delimiter = ' ', fmt='%i %1.6f %1.6f %1.6f %1.6f')

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manhcntt21/TextNormSeq2Seq

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2020-02-03T02:09:01

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NOASSERTION

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import random def get_repleace_character(): repleace_character = {} # repleace_character['ch'] = ['tr'] # repleace_character['tr'] = ['ch'] repleace_character['l'] = ['n'] repleace_character['n'] = ['l'] repleace_character['x'] = ['s'] repleace_character['s'] = ['x'] repleace_character['r'] = ['d', 'gi'] repleace_character['d'] = ['r', 'gi'] # repleace_character['gi'] = ['d', 'r'] repleace_character['c'] = ['q', 'k'] repleace_character['k'] = ['q', 'c'] repleace_character['q'] = ['c', 'k'] repleace_character['i'] = ['y'] repleace_character['y'] = ['i'] repleace_character['_'] = ['_'] return repleace_character def get_prox_keys(): array_prox = {} array_prox['a'] = ['q', 'w', 'z', 'x', 's'] array_prox['b'] = ['v', 'f', 'g', 'h', 'n', ' '] array_prox['c'] = ['x', 's', 'd', 'f', 'v'] array_prox['d'] = ['x', 's', 'w', 'e', 'r', 'f', 'v', 'c'] array_prox['e'] = ['w', 's', 'd', 'f', 'r'] array_prox['f'] = ['c', 'd', 'e', 'r', 't', 'g', 'b', 'v'] array_prox['g'] = ['r', 'f', 'v', 't', 'b', 'y', 'h', 'n'] array_prox['h'] = ['b', 'g', 't', 'y', 'u', 'j', 'm', 'n'] array_prox['i'] = ['u', 'j', 'k', 'l', 'o'] array_prox['j'] = ['n', 'h', 'y', 'u', 'i', 'k', 'm'] array_prox['k'] = ['u', 'j', 'm', 'l', 'o'] array_prox['l'] = ['p', 'o', 'i', 'k', 'm'] array_prox['m'] = ['n', 'h', 'j', 'k', 'l'] array_prox['n'] = ['b', 'g', 'h', 'j', 'm'] array_prox['o'] = ['i', 'k', 'l', 'p'] array_prox['p'] = ['o', 'l'] array_prox['q'] = ['w', 'a'] array_prox['r'] = ['e', 'd', 'f', 'g', 't'] array_prox['s'] = ['q', 'w', 'e', 'z', 'x', 'c'] array_prox['t'] = ['r', 'f', 'g', 'h', 'y'] array_prox['u'] = ['y', 'h', 'j', 'k', 'i'] array_prox['v'] = ['', 'c', 'd', 'f', 'g', 'b'] array_prox['w'] = ['q', 'a', 's', 'd', 'e'] array_prox['x'] = ['z', 'a', 's', 'd', 'c'] array_prox['y'] = ['t', 'g', 'h', 'j', 'u'] array_prox['z'] = ['x', 's', 'a'] array_prox['1'] = ['q', 'w'] array_prox['2'] = ['q', 'w', 'e'] array_prox['3'] = ['w', 'e', 'r'] array_prox['4'] = ['e', 'r', 't'] array_prox['5'] = ['r', 't', 'y'] array_prox['6'] = ['t', 'y', 'u'] array_prox['7'] = ['y', 'u', 'i'] array_prox['8'] = ['u', 'i', 'o'] array_prox['9'] = ['i', 'o', 'p'] array_prox['0'] = ['o', 'p'] array_prox['_'] = ['_'] return array_prox def add_noise(word): # i = random.randint(0,len(word)-1) # op = random.randint(0, 30) i = 0 op = 4 if op == 0: return word[:i] + word[i+1:] if op == 1: i += 1 return word[:i-1] + word[i:i+1] + word[i-1:i] + word[i+1:] if op == 2 or op == 3: try: # print(op) print(random.choice(repleace_character[word[i]])) return word[:i] + random.choice(repleace_character[word[i]]) + word[i+1:] # thay doi dau except: return word # print(random.choice(get_prox_keys[word[i]])) # return word[:i] + random.choice(get_prox_keys[word[i]]) + word[i+1:] try: tmp = get_prox_keys() tmp1 = random.choice(tmp[word[i]]) print(tmp1) return word[:i] + tmp1 + word[i+1:] #default is keyboard errors except : # print(word) return word if __name__ == '__main__': # a = '[anh' # b = add_noise(a) # print(b) # b = get_prox_keys() a = 10 b = 'string' c = str(a) print(type(a)) print(type(c))

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g2thend/proxypool

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[]

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refs/heads/master

2020-12-14T17:43:12.544796

2020-01-19T02:42:25

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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 12/20/19 2:19 PM # @Author : yon # @Email : @qq.com # @File : imorter # 手动录入代理 # 未测试 from proxypool.db import sqlitedb importer = sqlitedb() def importproxy(proxy): result = importer.add(proxy) print(proxy) print('录入成功' if result else '录入失败') def scan(): print('请输入代理, 输入exit退出读入') while True: proxy = input("代理ip,格式为 ip:port") protocol = input("代理协议:http或https") temp = (proxy, protocol) if proxy == 'exit': break set(list(temp)) if __name__ == '__main__': scan()

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ezeutno/PycharmProject

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/Data Project/Jude_Assignment/Alien_Invasion(New)/earth.py

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[]

no_license

https://github.com/ezeutno/PycharmProject

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refs/heads/master

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import pygame class Earth(): def __init__ (self,ai_setting,screen): self.screen = screen self.ai_setting = ai_setting x = self.ai_setting self.image = pygame.image.load('Data_base\\earth.bmp') self.rect = self.image.get_rect() self.screen_rect = screen.get_rect() self.rect.centerx = self.screen_rect.centerx self.rect.bottom = self.screen_rect.bottom def blitme(self): self.screen.blit(self.image, self.rect)

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JohnLZeller/F310_Gamepad_Parser

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refs/heads/master

2021-01-21T05:06:02.317722

2013-02-12T23:22:49

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null

############ Logitech F310 Gamepad Controller GUI - gui_main.py ########## # Original Author: John Zeller # Description: GUI_Main displays the values being stored in the states # python dictionary in a very simple labeled Tkinter GUI # to make them easily viewable. import Tkinter as tk # Native Python GUI Framework import time class GUI(): def __init__(self, states): self.states = states # Save the states in self.states self.root = tk.Tk() self.root.title('Logitech F310 Gamepad Controller Output Display') self.root.geometry('600x750') # Create and Position Title Labels self.label_Time = tk.Label(self.root, text="GUI Running for... ").grid(row=0, padx=10, pady=20) self.label_Time_s = tk.Label(self.root, text="seconds").grid(row=0, column=2) self.label_Buttons_Title = tk.Label(self.root, text="-- Buttons --").grid(columnspan=2, pady=10, sticky="E") self.label_A = tk.Label(self.root, text="A").grid(row=2, padx=5, sticky="E") self.label_B = tk.Label(self.root, text="B").grid(row=3, padx=5, sticky="E") self.label_X = tk.Label(self.root, text="X").grid(row=4, padx=5, sticky="E") self.label_Y = tk.Label(self.root, text="Y").grid(row=5, padx=5, sticky="E") self.label_Back = tk.Label(self.root, text="Back").grid(row=6, padx=5, sticky="E") self.label_Start = tk.Label(self.root, text="Start").grid(row=7, padx=5, sticky="E") self.label_Middle = tk.Label(self.root, text="Middle").grid(row=8, padx=5, sticky="E") self.label_Left = tk.Label(self.root, text="Left").grid(row=9, padx=5, sticky="E") self.label_Right = tk.Label(self.root, text="Right").grid(row=10, padx=5, sticky="E") self.label_Up = tk.Label(self.root, text="Up").grid(row=11, padx=5, sticky="E") self.label_Down = tk.Label(self.root, text="Down").grid(row=12, padx=5, sticky="E") self.label_LB = tk.Label(self.root, text="LB").grid(row=13, padx=5, sticky="E") self.label_RB = tk.Label(self.root, text="RB").grid(row=14, padx=5, sticky="E") self.label_LJButton = tk.Label(self.root, text="LJ/Button").grid(row=15, padx=5, sticky="E") self.label_RJButton = tk.Label(self.root, text="RJ/Button").grid(row=16, padx=5, sticky="E") self.label_Joys_Title = tk.Label(self.root, text="-- Joys --").grid(columnspan=2, pady=10, sticky="E") self.label_LT = tk.Label(self.root, text="LT").grid(row=18, padx=5, sticky="E") self.label_RT = tk.Label(self.root, text="RT").grid(row=19, padx=5, sticky="E") self.label_LJLeft = tk.Label(self.root, text="LJ/Left").grid(row=20, padx=5, sticky="E") self.label_LJRight = tk.Label(self.root, text="LJ/Right").grid(row=21, padx=5, sticky="E") self.label_LJUp = tk.Label(self.root, text="LJ/Up").grid(row=22, padx=5, sticky="E") self.label_LJDown = tk.Label(self.root, text="LJ/Down").grid(row=23, padx=5, sticky="E") self.label_RJLeft = tk.Label(self.root, text="RJ/Left").grid(row=24, padx=5, sticky="E") self.label_RJRight = tk.Label(self.root, text="RJ/Right").grid(row=25, padx=5, sticky="E") self.label_RJUp = tk.Label(self.root, text="RJ/Up").grid(row=26, padx=5, sticky="E") self.label_RJDown = tk.Label(self.root, text="RJ/Down").grid(row=27, padx=5, sticky="E") self.label_Packet = tk.Label(self.root, text="Packets coming from controller (Bytes 0-7)").grid(row=29, \ column=2, columnspan=8, pady=10, sticky="W") self.label_Packet0 = tk.Label(self.root, text="Byte 0").grid(row=30, column=1, padx=5, sticky="E") self.label_Packet1 = tk.Label(self.root, text="Byte 1").grid(row=30, column=2, padx=5, sticky="E") self.label_Packet2 = tk.Label(self.root, text="Byte 2").grid(row=30, column=3, padx=5, sticky="E") self.label_Packet3 = tk.Label(self.root, text="Byte 3").grid(row=30, column=4, padx=5, sticky="E") self.label_Packet4 = tk.Label(self.root, text="Byte 4").grid(row=30, column=5, padx=5, sticky="E") self.label_Packet5 = tk.Label(self.root, text="Byte 5").grid(row=30, column=6, padx=5, sticky="E") self.label_Packet6 = tk.Label(self.root, text="Byte 6").grid(row=30, column=7, padx=5, sticky="E") self.label_Packet7 = tk.Label(self.root, text="Byte 7").grid(row=30, column=8, padx=5, sticky="E") self.label_HEX = tk.Label(self.root, text="HEX --->").grid(row=31, column=0, padx=5, sticky="E") self.label_DECIMAL = tk.Label(self.root, text="DECIMAL --->").grid(row=32, column=0, padx=5, sticky="E") # Create Dynamic Variable Labels self.variable_Time = tk.Label(text="") self.variable_Blank = tk.Label(text="") self.variable_A = tk.Label(text="") self.variable_B = tk.Label(text="") self.variable_X = tk.Label(text="") self.variable_Y = tk.Label(text="") self.variable_Back = tk.Label(text="") self.variable_Start = tk.Label(text="") self.variable_Middle = tk.Label(text="") self.variable_Left = tk.Label(text="") self.variable_Right = tk.Label(text="") self.variable_Up = tk.Label(text="") self.variable_Down = tk.Label(text="") self.variable_LB = tk.Label(text="") self.variable_RB = tk.Label(text="") self.variable_LJButton = tk.Label(text="") self.variable_RJButton = tk.Label(text="") self.variable_LT = tk.Label(text="") self.variable_RT = tk.Label(text="") self.variable_LJLeft = tk.Label(text="") self.variable_LJRight = tk.Label(text="") self.variable_LJUp = tk.Label(text="") self.variable_LJDown = tk.Label(text="") self.variable_RJLeft = tk.Label(text="") self.variable_RJRight = tk.Label(text="") self.variable_RJUp = tk.Label(text="") self.variable_RJDown = tk.Label(text="") self.variable_Packet0 = tk.Label(text="") self.variable_Packet1 = tk.Label(text="") self.variable_Packet2 = tk.Label(text="") self.variable_Packet3 = tk.Label(text="") self.variable_Packet4 = tk.Label(text="") self.variable_Packet5 = tk.Label(text="") self.variable_Packet6 = tk.Label(text="") self.variable_Packet7 = tk.Label(text="") self.variable_Packet0_INT = tk.Label(text="") self.variable_Packet1_INT = tk.Label(text="") self.variable_Packet2_INT = tk.Label(text="") self.variable_Packet3_INT = tk.Label(text="") self.variable_Packet4_INT = tk.Label(text="") self.variable_Packet5_INT = tk.Label(text="") self.variable_Packet6_INT = tk.Label(text="") self.variable_Packet7_INT = tk.Label(text="") # Position Dynamic Variable Labels self.variable_Time.grid(row=0, column=1) self.variable_A.grid(row=2, column=1) self.variable_B.grid(row=3, column=1) self.variable_X.grid(row=4, column=1) self.variable_Y.grid(row=5, column=1) self.variable_Back.grid(row=6, column=1) self.variable_Start.grid(row=7, column=1) self.variable_Middle.grid(row=8, column=1) self.variable_Left.grid(row=9, column=1) self.variable_Right.grid(row=10, column=1) self.variable_Up.grid(row=11, column=1) self.variable_Down.grid(row=12, column=1) self.variable_LB.grid(row=13, column=1) self.variable_RB.grid(row=14, column=1) self.variable_LJButton.grid(row=15, column=1) self.variable_RJButton.grid(row=16, column=1) self.variable_LT.grid(row=18, column=1) self.variable_RT.grid(row=19, column=1) self.variable_LJLeft.grid(row=20, column=1) self.variable_LJRight.grid(row=21, column=1) self.variable_LJUp.grid(row=22, column=1) self.variable_LJDown.grid(row=23, column=1) self.variable_RJLeft.grid(row=24, column=1) self.variable_RJRight.grid(row=25, column=1) self.variable_RJUp.grid(row=26, column=1) self.variable_RJDown.grid(row=27, column=1) self.variable_Packet0.grid(row=31, column=1, sticky="E") self.variable_Packet1.grid(row=31, column=2, sticky="E") self.variable_Packet2.grid(row=31, column=3, sticky="E") self.variable_Packet3.grid(row=31, column=4, sticky="E") self.variable_Packet4.grid(row=31, column=5, sticky="E") self.variable_Packet5.grid(row=31, column=6, sticky="E") self.variable_Packet6.grid(row=31, column=7, sticky="E") self.variable_Packet7.grid(row=31, column=8, sticky="E") self.variable_Packet0_INT.grid(row=32, column=1, sticky="E") self.variable_Packet1_INT.grid(row=32, column=2, sticky="E") self.variable_Packet2_INT.grid(row=32, column=3, sticky="E") self.variable_Packet3_INT.grid(row=32, column=4, sticky="E") self.variable_Packet4_INT.grid(row=32, column=5, sticky="E") self.variable_Packet5_INT.grid(row=32, column=6, sticky="E") self.variable_Packet6_INT.grid(row=32, column=7, sticky="E") self.variable_Packet7_INT.grid(row=32, column=8, sticky="E") # Start tracking time self.start_time = time.time() # Run update_label() self.update_labels() self.root.mainloop() # Once here, begin main loop again def update_labels(self): time_counter = time.time() - self.start_time time_counter = round(time_counter, 0) # Configure Labels with Updates self.variable_Time.configure(text=str(time_counter)) self.variable_A.configure(text=str(self.states['A'])) self.variable_B.configure(text=str(self.states['B'])) self.variable_X.configure(text=str(self.states['X'])) self.variable_Y.configure(text=str(self.states['Y'])) self.variable_Back.configure(text=str(self.states['Back'])) self.variable_Start.configure(text=str(self.states['Start'])) self.variable_Middle.configure(text=str(self.states['Middle'])) self.variable_Left.configure(text=str(self.states['Left'])) self.variable_Right.configure(text=str(self.states['Right'])) self.variable_Up.configure(text=str(self.states['Up'])) self.variable_Down.configure(text=str(self.states['Down'])) self.variable_LB.configure(text=str(self.states['LB'])) self.variable_RB.configure(text=str(self.states['RB'])) self.variable_LJButton.configure(text=str(self.states['LJ/Button'])) self.variable_RJButton.configure(text=str(self.states['RJ/Button'])) self.variable_LT.configure(text=str(self.states['LT'])) self.variable_RT.configure(text=str(self.states['RT'])) self.variable_LJLeft.configure(text=str(self.states['LJ/Left'])) self.variable_LJRight.configure(text=str(self.states['LJ/Right'])) self.variable_LJUp.configure(text=str(self.states['LJ/Up'])) self.variable_LJDown.configure(text=str(self.states['LJ/Down'])) self.variable_RJLeft.configure(text=str(self.states['RJ/Left'])) self.variable_RJRight.configure(text=str(self.states['RJ/Right'])) self.variable_RJUp.configure(text=str(self.states['RJ/Up'])) self.variable_RJDown.configure(text=str(self.states['RJ/Down'])) self.variable_Packet0.configure(text=self.states['Byte0']) self.variable_Packet1.configure(text=self.states['Byte1']) self.variable_Packet2.configure(text=self.states['Byte2']) self.variable_Packet3.configure(text=self.states['Byte3']) self.variable_Packet4.configure(text=self.states['Byte4']) self.variable_Packet5.configure(text=self.states['Byte5']) self.variable_Packet6.configure(text=self.states['Byte6']) self.variable_Packet7.configure(text=self.states['Byte7']) self.variable_Packet0_INT.configure(text=str(self.states['Byte0/INT'])) self.variable_Packet1_INT.configure(text=str(self.states['Byte1/INT'])) self.variable_Packet2_INT.configure(text=str(self.states['Byte2/INT'])) self.variable_Packet3_INT.configure(text=str(self.states['Byte3/INT'])) self.variable_Packet4_INT.configure(text=str(self.states['Byte4/INT'])) self.variable_Packet5_INT.configure(text=str(self.states['Byte5/INT'])) self.variable_Packet6_INT.configure(text=str(self.states['Byte6/INT'])) self.variable_Packet7_INT.configure(text=str(self.states['Byte7/INT'])) self.root.after(10, self.update_labels)

UTF-8

Python

false

12,513

py

2

gui_main.py

2

0.638376

0.611204

0

197

62.522843

116

rinoshinme/detection_yolo

16,329,465,678,604

747623a8ea175bf2e5caeb8bcf165ba9dfb92be8

42245303c0914e9923a67f7e5763f354627e1d7a

/yolo/yolov1.py

c39617f182d1eae1b8988db4b408ca628d3c094f

[]

no_license

https://github.com/rinoshinme/detection_yolo

0d68170136da908d6b7e384c2b9538f0017411e2

8c367d31cca4361aff1eacd6c96e544d5497ccbd

refs/heads/master

2020-07-02T05:04:44.489451

2019-08-09T09:55:15

201,423,587

0

null

import tensorflow as tf import numpy as np from utils.layers import leaky_relu from config.yolov1_config import cfg slim = tf.contrib.slim class YOLOv1(object): def __init__(self, is_training=True): self.classes = cfg.CLASSES self.num_classes = len(self.classes) self.image_size = cfg.IMAGE_SIZE self.cell_size = cfg.CELL_SIZE self.boxes_per_cell = cfg.BOXES_PER_CELL self.output_size = (self.cell_size * self.cell_size) * \ (self.num_classes + self.boxes_per_cell * 5) self.scale = 1.0 * self.image_size / self.cell_size self.boundary1 = 0 self.boundary2 = 0 self.object_scale = cfg.OBJECT_SCALE self.noobject_scale = cfg.NOOBJECT_SCALE self.class_scale = cfg.CLASS_SCALE self.coord_scale = cfg.COORD_SCALE self.learning_rate = cfg.LEARNING_RATE self.batch_size = cfg.BATCH_SIZE self.alpha = cfg.ALPHA self.is_training = is_training def build_network(self, images, num_output, keep_prob=0.5): with tf.variable_scope('yolo'): net = tf.pad(images, np.array([[0, 0], [3, 3], [3, 3], [0, 0]]), name='pad_1') net = tf.layers.conv2d(net, 64, [3, 3], [2, 2], padding='valid', activation=leaky_relu(self.alpha), name='conv_2') return net def loss_layer(self): pass if __name__ == '__main__': model = YOLOv1()

UTF-8

Python

false

1,507

py

22

yolov1.py

17

0.569343

0.5501

0

44

33.25

90

javerdejo/UMACamp_sensors

2,860,448,223,835

c448651a88c86378291100a365e153a7d13bfd8a

a00fc0006530b637f1439982b85a8f0b2fa12cc4

/sensors/bluetooth/settings.py

c2f6e0c67e41426ee864f4edbedca1b8d61510e7

[]

no_license

https://github.com/javerdejo/UMACamp_sensors

c272aa240a1f02f4b4e2aefa12218d3de8442996

05d6315ca085bbf2e77688a02eef18ea2ec10c23

refs/heads/master

2021-01-11T18:57:20.384878

2017-05-09T08:44:57

79,280,292

0

null

false

2017-03-03T13:34:02

2017-01-17T22:46:44

2017-02-24T10:26:42

2017-03-03T13:32:59

47

0

2

Python

null

BT_SCANNER_DURATION = 10 #seconds BT_SCANNER_LOGGING = "/var/log/bluetooth.log" BT_SCANNER_DATAHTML = "/var/log/bluetooth_data.log" BT_SCANNER_LOGPATH = "/bluetooht_sensed"

UTF-8

Python

false

173

py

12

settings.py

11

0.751445

0.739884

0

4

42.25

51

vtheno/python-monad

1,176,821,040,834

1da93c0cef33f802d0edcc0b358370fffddb1848

e8e36611267126c6add47d8a66175ec5a30961e9

/infix.py

2aea38426855a4c12725ba23b3cd8d102807f023

[ "MIT" ]

permissive

https://github.com/vtheno/python-monad

666a044e5bc011e44b74440b725ba7dfabe83c20

781407a1b3eca7fb0beb0899136f4859f5e77540

refs/heads/master

2021-05-26T19:56:16.386606

2014-01-24T15:18:43

106,650,047

1

0

null

true

2017-10-12T05:47:57

2017-10-12T05:45:52

2014-01-24T15:18:49

152

0

null

""" Infix hack from: http://code.activestate.com/recipes/384122/ """ # pylint: disable=R0903 class Infix(object): """ Infix decorator. For instance: >>> @Infix >>> def mult(x, y): ... return x * y >>> 2 |mult| 4 8 >>> 2 <<mult>> 4 8 """ def __init__(self, function): self.function = function def __ror__(self, other): return Infix(lambda x, self=self, other=other: self.function(other, x)) def __or__(self, other): return self.function(other) def __rlshift__(self, other): return self.__or__(other) def __rshift__(self, other): return self.__ror__(other) def __call__(self, left, right): return self.function(left, right)

UTF-8

Python

false

749

py

7

infix.py

7

0.543391

0.522029

0

36

19.805556

79

rimvydaszilinskas/printer

1,906,965,484,100

befe3f1fc2053a6cb2cfc2b279ed089ea285068e

786a05b62e0a6bded2568194d80570e2c1f58df1

/lib/templates.py

fa1b98e6018f3c3ad7b1a34193944e0a97a6fcec

[]

no_license

https://github.com/rimvydaszilinskas/printer

ac31d05bd4c747dd793c5e3c5182573f922e7940

4a443c049dfc2fdb690f3c079393a7f2aad28058

refs/heads/master

2022-05-24T04:22:19.652929

2020-04-16T11:40:19

171,291,657

0

null

import os import urllib.request def load_image(template): print(template) return open("/home/pi/printer/templates/" + template) def cleanup_templates(): for root, dirs, files in os.walk("/home/pi/printer/templates"): for filename in files: if filename != "default.png" and filename != "template.bmp": os.remove("/home/pi/printer/templates/" + filename) def download_template(url, event_id): urllib.request.urlretrieve(url, "/home/pi/printer/templates/" + event_id + ".png") return "/home/pi/printer/templates/" + event_id + ".png" def template_exist(event_id): return os.path.isfile("/home/pi/printer/templates/" + event_id + ".png")

UTF-8

Python

false

697

py

14

templates.py

9

0.662841

0

19

35.736842

86

wkdewey/learnpythonthehardway

13,812,614,850,581

e62e78f693e95d5dd4ae8971cebc808c10cc6f90

2ff2cb79b2ea556514ceb9f41c55734f30f4b5fe

/ex17.py

d0cb3a3c4481920873d422c7617e7874a178acce

[]

no_license

https://github.com/wkdewey/learnpythonthehardway

4ccc7dc02aeac09d42d36344961f539703cc008a

284643d0ad52abfc826492c6f9a3cdbd9dcd8b50

refs/heads/master

2020-03-23T02:31:25.530269

2018-07-14T21:06:54

140,978,281

0

null

from sys import argv; from os.path import exists; script, from_file, to_file = argv;print "Copying from %s to %s" % (from_file, to_file); indata = open(from_file).read(); out_file = open(to_file, 'w').write(indata) x = len("12345") print "%d" % x

UTF-8

Python

false

247

py

27

ex17.py

27

0.663968

0.643725

0

3

81.333333

214

rdghosal/WaveNative

16,329,465,659,863

551c9608375bf82c17d00c2df5bb24e15885a4bc

4c71c8a38429a39da78509c617923f7911b07bab

/api/services/user.py

9168764d00abc470547274c229d3a57ec5be182b

[]

no_license

https://github.com/rdghosal/WaveNative

4b4e184070b9e992c9379df647e66a64cfb19e4b

340fdb8e354daf87915d8e4725e510dd222cc4cc

refs/heads/master

2023-01-12T05:49:39.878089

2020-02-16T02:16:31

196,894,015

0

null

false

2023-01-07T14:39:10

2019-07-14T23:40:55

2020-02-16T02:16:37

2023-01-07T14:39:09

7,756

0

25

JavaScript

false

from api import db from api.models.user import User from werkzeug.security import check_password_hash, generate_password_hash def check_form_data(form): """Takes data used for registration and evaluates whether info is original""" if form["password"] != form["confirmation"] or \ db.session.query(User).filter(User.username == form["username"]).count() > 0: return False return True def add_user(form): """Adds user to database""" # Add new user data db.session.add(User( username=form["username"], hashed=generate_password_hash(form["password"]), age=form["age"], country=form["country"] )) # Commit change to db db.session.commit() def get_user_info(form): """Checks login credentials against database""" result = db.session.query(User).filter(User.username == form["username"]).first() if not result or not check_password_hash(result.hashed, form["password"]): return None return { "id": result.id, "username": result.username }

UTF-8

Python

false

1,071

py

41

user.py

29

0.645191

0.644258

0

37

27.972973

85

mhyttsten/GeneralMLRepoPython

11,596,411,703,049

52be2f8821d9fa77f71892998fb48654208274e6

c790d516ae4fcb5a83010a08f9756313afd170a4

/TF_Old/Ref_TFFileIO.py

99032f755f0b4f9e56c2268ec7855c949877969c

[]

no_license

https://github.com/mhyttsten/GeneralMLRepoPython

1ccbf0c7fbd85ec981b8d4d417ab673dcaa2e8ec

c5d3bfb115601a3f162e8b089b0a1527ac0ba139

refs/heads/master

2021-09-06T05:20:07.597283

2018-02-02T18:16:06

null

0

null

import tensorflow as tf def input_pipeline(filenames, batch_size, num_epochs=None): filename_queue = tf.train.string_input_producer(filenames, num_epochs=num_epochs, shuffle=True) reader = tf.TextLineReader() key, record_string = reader.read(filename_queue) f1, f2, f3, label = tf.decode_csv(record_string, record_defaults=[["a"], ["b"], ["c"], ["d"]]) features = tf.stack([f1, f2, f3]) min_after_dequeue = 10000 capacity = min_after_dequeue + 3*batch_size feature_batch, label_batch = tf.train.shuffle_batch( [features, label], num_threads=16, batch_size=batch_size, capacity=capacity, min_after_dequeue=min_after_dequeue, allow_smaller_final_batch=True) return feature_batch, label_batch get_batch = input_pipeline( filenames=[("csv_file%d.csv" % i) for i in range(4)], batch_size=3, num_epochs=2) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) coord = tf.train.Coordinator() sess = tf.Session() writer = tf.summary.FileWriter('tb_report', sess.graph) sess.run(init_op) threads = tf.train.start_queue_runners(sess=sess, coord=coord) try: while not coord.should_stop(): (features, labels) = sess.run(get_batch) for i in range(features.shape[0]): print "f: " + str(features[i]) + ", l: " + labels[i] print 'features: {}, labels: {}'.format(str(features.shape), str(labels.shape)) except tf.errors.OutOfRangeError, e: print 'Done training -- epoch limit reached' finally: coord.request_stop() coord.join(threads) writer.close()

UTF-8

Python

false

1,621

py

45

Ref_TFFileIO.py

45

0.660703

0.649599

0

45

35.044444

99

sneha8412/back-end-inspiration-board

18,657,337,937,933

efdb86bfd1b5c23c64eacf5538a0d0b5921d26f2

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2021-06-29T02:59:24

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from flask import Blueprint, request, jsonify, make_response from app import db from .models.board import Board from .models.card import Card import os # example_bp = Blueprint('example_bp', __name__) card_bp = Blueprint("cards", __name__, url_prefix="/cards") @card_bp.route("", methods=["POST"], strict_slashes=False) def create_a_card(): request_body = request.get.json() if "message" not in request_body: return jsonify(details="invalid data"), 400 new_card = card.from_json(request_body) db.session.add(new_card) db.session.commit() return new_card.to_json_card(), 201 @card_bp.route("", methods=["DELETE"], strict_slashes=False) def delete_a_card(): pass #update a card # @card_bp.route("", methods=["PUT"], strict_slashes=False) # def like_a_card(): # pass @card_bp.route("", methods=["GET"], strict_slashes=False) def get_all_cards(): pass

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OverLordGoldDragon/StackExchangeAnswers

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/SignalProcessing/Q76636 - filters - Why is x(n) - x(n - 1) + x(n + 2) lowpass/main.py

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import numpy as np import matplotlib.pyplot as plt def plot(x, title): fig = plt.figure() plt.plot(np.abs(x)) plt.scatter(np.arange(len(x)), np.abs(x), s=10) plt.title(title, weight='bold', fontsize=18, loc='left') return fig def plot_T(x, Tmax): if Tmax == 0: title = "|H(w)|: x(n)" elif Tmax == 1: title = "|H(w)|: x(n) - x(n - 1)" elif Tmax == 2: title = "|H(w)|: x(n) - x(n - 1) + x(n - 2)" else: title = "|H(w)|: x(n) - x(n - 1) + x(n - 2) - ... x(n - %s)" % Tmax fig = plot(x, title, scatter=1) plt.ylim(-.05, 1.05) plt.savefig(f'im{Tmax}.png', bbox_inches='tight') plt.close(fig) def csoid(f): return (np.cos(2*np.pi* f * t) - np.sin(2*np.pi* f * t) * 1j) #%%# Direct frequency response ############################################### N = 32 t = np.linspace(0, 1, N, 0) for Tmax in range(N): x = np.sum([(-1)**T * csoid(T) for T in range(Tmax + 1)], axis=0) x /= np.abs(x).max() plot_T(x, Tmax) #%%# WGN example ############################################################# def plot_and_save(x, title, savepath): fig = plot(x, title) plt.savefig(savepath, bbox_inches='tight') plt.close(fig) np.random.seed(69) x = np.random.randn(32) xf0 = np.fft.fft(x) x = x - np.roll(x, 1) + np.roll(x, 2) xf1 = np.fft.fft(x) plot_and_save(xf0, "|X(w)|: x(n)", "WGN0.png") plot_and_save(xf1, "|X(w)|: x(n) - x(n - 1) + x(n - 2)", "WGN1.png")

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YannisYao/12306Plus

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/business/check_captcha.py

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https://github.com/YannisYao/12306Plus

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2021-05-11T06:13:25.597472

2018-01-19T07:58:07

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#校验验证码 from business.damatuWeb import DamatuApi from business.captcha_download import download_captcha import json from business.urlcontants import UrlContants from business.middleproxy import MiddleProxy from bs4 import BeautifulSoup #坐标识别式验证码 CAPTCHA_TYPE = 310 #坐标偏移量 OFFSET_X = 0 OFFSET_Y = 30 def get_result_points(ret,result): result_point = '' if ret == 0: if result is not None: captcha_point = result.split('|') for point in captcha_point: if point is not None: pointX = point.split(',')[0] pointY = point.split(',')[1] pointX = int(pointX) - OFFSET_X pointY = int(pointY) - OFFSET_Y result_point = result_point + str(pointX) + ',' + str(pointY)+',' return result_point.rstrip(',') else: print('打码故障！错误代码：%s' % ret) return None def check_captcha_request(param,url=UrlContants.CAPTCHA_CHECK): params={'answer':param, 'login_site':'E', 'rand':'sjrand'} s = MiddleProxy.getSession().post(url, params=params,headers=MiddleProxy.headers_xhr) if s.status_code == 200 and s.headers['Content-Type'] == 'application/json;charset=UTF-8': content = str(s.content, encoding='utf-8') jres = json.loads(content) if jres['result_code'] == '4': return (True,jres['result_message']) else: return (False,jres['result_message']) elif s.status_code == 200 and s.headers['Content-Type'] == 'text/html': bsObj = BeautifulSoup(s.content,'html.parser') err_msg = bsObj.find('div', {'class': 'err_text'}).find('li', {'id': 'err_bot'}).get_text().strip() err_msg = err_msg[:err_msg.index('！')+2] return (False,err_msg) def check_captcha(): #存储验证码图片 image_path = download_captcha() if image_path is not None: damatu = DamatuApi(MiddleProxy.getDm2User(),MiddleProxy.getDm2Pwd()) #使用打码兔获取验证码 print(damatu.getBalance()) ret,result,id = damatu.decode(image_path,310) print('打码兔提供--->'+result) #获取转换后的坐标 result_points = get_result_points(ret,result) print('打码兔坐标转换--->'+result_points) #发起验证码验证 check_result = check_captcha_request(result_points) if check_result['result_code'] == '4': #此处后续可以进行数据库存储，因12306验证码图片不是动态生成的，可以存储对应的答案，减少对打码平台的依赖 return True else: return False

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