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

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#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * class FrequencyRuleDetail(object): def __init__(self): self._frequency_duration = None self._frequency_type = None self._total_times = None self._used_times = None @property def frequency_duration(self): return self._frequency_duration @frequency_duration.setter def frequency_duration(self, value): self._frequency_duration = value @property def frequency_type(self): return self._frequency_type @frequency_type.setter def frequency_type(self, value): self._frequency_type = value @property def total_times(self): return self._total_times @total_times.setter def total_times(self, value): self._total_times = value @property def used_times(self): return self._used_times @used_times.setter def used_times(self, value): self._used_times = value def to_alipay_dict(self): params = dict() if self.frequency_duration: if hasattr(self.frequency_duration, 'to_alipay_dict'): params['frequency_duration'] = self.frequency_duration.to_alipay_dict() else: params['frequency_duration'] = self.frequency_duration if self.frequency_type: if hasattr(self.frequency_type, 'to_alipay_dict'): params['frequency_type'] = self.frequency_type.to_alipay_dict() else: params['frequency_type'] = self.frequency_type if self.total_times: if hasattr(self.total_times, 'to_alipay_dict'): params['total_times'] = self.total_times.to_alipay_dict() else: params['total_times'] = self.total_times if self.used_times: if hasattr(self.used_times, 'to_alipay_dict'): params['used_times'] = self.used_times.to_alipay_dict() else: params['used_times'] = self.used_times return params @staticmethod def from_alipay_dict(d): if not d: return None o = FrequencyRuleDetail() if 'frequency_duration' in d: o.frequency_duration = d['frequency_duration'] if 'frequency_type' in d: o.frequency_type = d['frequency_type'] if 'total_times' in d: o.total_times = d['total_times'] if 'used_times' in d: o.used_times = d['used_times'] return o

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# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html class NewsfeedPipeline(object): def process_item(self, item, spider): return item

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app = Tk() app.title("TVN Game Show") app.geometry('300x100+200+100') b1 = Button(app, text = "Correct!", width = 10) b1.pack(side = 'left', padx = 10, pady = 10) b2 = Button(app, text = "Wrong!", width = 10) b2.pack(side = 'right', padx = 10, pady = 10) app.mainloop()

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# -*- coding:utf-8 -*- def mergeSort(alist): print("Splitting ",alist) if len(alist) > 1: mid = len(alist) // 2 lefthalf = alist[:mid] righthalf = alist[mid:] mergeSort(lefthalf) mergeSort(righthalf) i=0 j=0 k=0 while i < len(lefthalf) and j < len(righthalf): if lefthalf[i] < righthalf[j]: alist[k] = lefthalf[i] i = i + 1 else: alist[k]=righthalf[j] j=j+1 k=k+1 while i < len(lefthalf): alist[k]=lefthalf[i] i=i+1 k=k+1 while j < len(righthalf): alist[k]=righthalf[j] j=j+1 k=k+1 print("Merging ",alist) alist = [54,26,93,17,77,31,44,55,20] mergeSort(alist) print(alist)

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import numpy as np import matplotlib.pyplot as plt import seaborn as sns import csv TAKE_NEW_DATA = False FILENAME = "data/experiment1_data2_3.csv" if TAKE_NEW_DATA: import smu s = smu.smu() f = open(FILENAME, "wb") writer = csv.writer(f) v_in = np.linspace(0.35, 0.9, 255) i_b = [] i_e = [] s.set_voltage(2, 0.) for v in v_in: s.set_voltage(1, v) s.autorange(1) i_b.append(s.get_current(1)) i_e.append(-s.get_current(2)) s.set_voltage(1, 0.) data = zip(v_in, i_b, i_e) writer.writerow(["V_in(Ch1)", "I_b(Ch1)", "I_e(Ch2)"]) writer.writerows(data) f.close() x = v_in y1 = i_b y2 = i_e if not TAKE_NEW_DATA: with open(FILENAME, 'r') as f: reader = csv.reader(f) x = [] y1 = [] y2 = [] for i, row in enumerate(reader): if i == 0 : continue x.append(row[0]) y1.append(row[1]) y2.append(row[2]) if True: plt.plot(x, y1, '.', label="i_b") plt.figure() plt.plot(x, y2, '.', label="i_e") plt.legend() plt.xlabel("Voltage") plt.ylabel("Current") plt.show()

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""" Django settings for pdfimport project. Generated by 'django-admin startproject' using Django 3.0.3. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '^k+5+0a4k9prdr%v809&x8v^uw7h@50kl!yw^yib!u4&$ni7-_' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['*'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'home', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'pdfimport.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'pdfimport.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ DATA_UPLOAD_MAX_MEMORY_SIZE = 26214400 AUTH_USER_MODEL = 'home.User' STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR,'static/') MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media')

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def change(b): print(id(b)) # 指向的是同一个对象 b = 10 print(id(b)) # 一个新对象 a = 1 print(id(a)) change(a) print(id(a))

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""" Escribir un programa que almacene la cadena de caracteres contraseña en una variable, pregunte al usuario por la contraseña hasta que introduzca la contraseña correcta. """ def run(): password = str(input("Ingresa contraseña: ")) re_password = str(input("Repite la contraseña: ")) while password != re_password: print("Error contraseñas no coincen") password = str(input("Ingresa nuevamente la contraseña: ")) re_password = str(input("Repite la contraseña: ")) if password == re_password: print(f"Felicidades ambas contraseñas coinciden: {password}") break if __name__ == '__main__': run()

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#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import itertools import os from typing import Dict, List, Tuple, Union import torch import torch.nn as nn from pytext.data.tensorizers import LabelTensorizer, Tensorizer, TokenTensorizer from pytext.models.decoders import DecoderBase from pytext.models.decoders.mlp_decoder import MLPDecoder from pytext.models.embeddings import EmbeddingBase, EmbeddingList, WordEmbedding from pytext.models.model import BaseModel from pytext.models.module import create_module from pytext.models.output_layers import ClassificationOutputLayer, OutputLayerBase from pytext.models.representations.bilstm_doc_attention import BiLSTMDocAttention from pytext.models.representations.docnn import DocNNRepresentation from pytext.models.representations.representation_base import RepresentationBase from scipy.special import comb class BasePairwiseModel(BaseModel): """ A base classification model that scores a pair of texts. Subclasses need to implement the from_config, forward and save_modules. """ __EXPANSIBLE__ = True class Config(BaseModel.Config): decoder: MLPDecoder.Config = MLPDecoder.Config() output_layer: ClassificationOutputLayer.Config = ( ClassificationOutputLayer.Config() ) encode_relations: bool = True def __init__( self, decoder: DecoderBase, output_layer: OutputLayerBase, encode_relations: bool, ) -> None: super().__init__() self.decoder = decoder self.output_layer = output_layer self.encode_relations = encode_relations @classmethod def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]): raise NotImplementedError def forward( self, input1: Tuple[torch.Tensor, ...], input2: Tuple[torch.Tensor, ...] ): raise NotImplementedError def save_modules(self, base_path: str = "", suffix: str = ""): raise NotImplementedError @classmethod def _create_decoder( cls, config: Config, representations: nn.ModuleList, tensorizers: Dict[str, Tensorizer], ): labels = tensorizers["labels"].vocab num_reps = len(representations) rep_dim = representations[0].representation_dim decoder_in_dim = num_reps * rep_dim if config.encode_relations: decoder_in_dim += 2 * comb(num_reps, 2, exact=True) * rep_dim decoder = create_module( config.decoder, in_dim=decoder_in_dim, out_dim=len(labels) ) return decoder @classmethod def _encode_relations(cls, encodings: List[torch.Tensor]) -> List[torch.Tensor]: for rep_l, rep_r in itertools.combinations(encodings, 2): encodings.append(torch.abs(rep_l - rep_r)) encodings.append(rep_l * rep_r) return encodings def _save_modules(self, modules: nn.ModuleList, base_path: str, suffix: str): super().save_modules(base_path, suffix) # Special case to also save the multi-representations separately, if needed. for module in modules: if getattr(module.config, "save_path", None): path = module.config.save_path + suffix if base_path: path = os.path.join(base_path, path) print( f"Saving state of module {type(module).__name__} " f"to {path} ..." ) torch.save(module.state_dict(), path) class PairwiseModel(BasePairwiseModel): """ A classification model that scores a pair of texts, for example, a model for natural language inference. The model shares embedding space (so it doesn't support pairs of texts where left and right are in different languages). It uses bidirectional LSTM or CNN to represent the two documents, and concatenates them along with their absolute difference and elementwise product. This concatenated pair representation is passed to a multi-layer perceptron to decode to label/target space. See https://arxiv.org/pdf/1705.02364.pdf for more details. It can be instantiated just like any other :class:`~Model`. """ EMBEDDINGS = ["embedding"] INPUTS_PAIR = [["tokens1"], ["tokens2"]] class Config(BasePairwiseModel.Config): """ Attributes: encode_relations (bool): if `false`, return the concatenation of the two representations; if `true`, also concatenate their pairwise absolute difference and pairwise elementwise product (à la arXiv:1705.02364). Default: `true`. tied_representation: whether to use the same representation, with tied weights, for all the input subrepresentations. Default: `true`. """ class ModelInput(BasePairwiseModel.Config.ModelInput): tokens1: TokenTensorizer.Config = TokenTensorizer.Config(column="text1") tokens2: TokenTensorizer.Config = TokenTensorizer.Config(column="text2") labels: LabelTensorizer.Config = LabelTensorizer.Config() inputs: ModelInput = ModelInput() embedding: WordEmbedding.Config = WordEmbedding.Config() representation: Union[ BiLSTMDocAttention.Config, DocNNRepresentation.Config ] = BiLSTMDocAttention.Config() shared_representations: bool = True def __init__( self, embeddings: nn.ModuleList, representations: nn.ModuleList, decoder: MLPDecoder, output_layer: ClassificationOutputLayer, encode_relations: bool, ) -> None: super().__init__(decoder, output_layer, encode_relations) self.embeddings = embeddings self.representations = representations # from_config and helper function @classmethod def _create_embedding(cls, config, tensorizer) -> EmbeddingBase: return create_module(config, None, tensorizer) @classmethod def _create_embeddings( cls, config: Config, tensorizers: Dict[str, Tensorizer] ) -> nn.ModuleList: embeddings = [] for inputs in cls.INPUTS_PAIR: embedding_list = [] for emb, input in zip(cls.EMBEDDINGS, inputs): if hasattr(config, emb) and input in tensorizers: embedding_list.append( cls._create_embedding(getattr(config, emb), tensorizers[input]) ) if len(embedding_list) == 1: embeddings.append(embedding_list[0]) else: embeddings.append(EmbeddingList(embeddings=embedding_list, concat=True)) return nn.ModuleList(embeddings) @classmethod def _create_representations(cls, config: Config, embeddings: nn.ModuleList): if config.shared_representations: # create representation once and used for all embeddings embedding_dim = embeddings[0].embedding_dim representations = nn.ModuleList( itertools.repeat( create_module(config.representation, embed_dim=embedding_dim), len(embeddings), ) ) else: representations = nn.ModuleList( [ create_module( config.representation, embed_dim=embedding.embedding_dim ) for embedding in embeddings ] ) return representations @classmethod def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]): embeddings = cls._create_embeddings(config, tensorizers) representations = cls._create_representations(config, embeddings) decoder = cls._create_decoder(config, representations, tensorizers) output_layer = create_module( config.output_layer, labels=tensorizers["labels"].vocab ) return cls( embeddings, representations, decoder, output_layer, config.encode_relations ) def arrange_model_inputs(self, tensor_dict): return tensor_dict["tokens1"][:2], tensor_dict["tokens2"][:2] def arrange_targets(self, tensor_dict): return tensor_dict["labels"] # _encode and helper functions @classmethod def _represent_helper( cls, rep: RepresentationBase, embs: torch.Tensor, lens: torch.Tensor ) -> torch.Tensor: representation = rep(embs, lens) if isinstance(representation, tuple): return representation[0] return representation @classmethod def _represent_sort( cls, embeddings: List[torch.Tensor], lengths: List[torch.Tensor], represention_modules: nn.ModuleList, ) -> List[torch.Tensor]: """ Apply the representations computations in `self.representations` to the sentence representations in `embeddings`. Internally, it sorts the sentences in `embeddings` by the number of tokens for packing efficiency, where the number of tokens is in `lengths`, and undoes the sort after applying the representations to preserve the original ordering of sentences. Assumes that the leftmost sentences are already sorted by number of tokens. """ if isinstance(represention_modules[0], BiLSTMDocAttention): # The leftmost inputs already come sorted by length. The others need to # be sorted as well, for packing. We do it manually. sorted_inputs = [(embeddings[0], lengths[0])] sorted_indices = [None] for embs, lens in zip(embeddings[1:], lengths[1:]): lens_sorted, sorted_idx = lens.sort(descending=True) embs_sorted = embs[sorted_idx] sorted_inputs.append((embs_sorted, lens_sorted)) sorted_indices.append(sorted_idx) representations = [ cls._represent_helper(rep, embs, lens) for rep, (embs, lens) in zip(represention_modules, sorted_inputs) ] # Put the inputs back in the original order, so they still match up to # each other as well as the targets. unsorted_representations = [representations[0]] for sorted_idx, rep in zip(sorted_indices[1:], representations[1:]): _, unsorted_idx = sorted_idx.sort() unsorted_representations.append(rep[unsorted_idx]) return unsorted_representations else: return [ cls._represent_helper(rep, embs, lens) for rep, (embs, lens) in zip( represention_modules, zip(embeddings, lengths) ) ] def _represent(self, embeddings: List[torch.Tensor], seq_lens: List[torch.Tensor]): representations = self._represent_sort( embeddings, seq_lens, self.representations ) if self.encode_relations: representations = self._encode_relations(representations) return torch.cat(representations, -1) def forward( self, input1: Tuple[torch.Tensor, ...], input2: Tuple[torch.Tensor, ...] ) -> torch.Tensor: token_tups, seq_lens = (input1[:-1], input2[:-1]), (input1[-1], input2[-1]) embeddings = [ emb(*token_tup) for emb, token_tup in zip(self.embeddings, token_tups) ] representation = self._represent(embeddings, seq_lens) return self.decoder(representation) def save_modules(self, base_path: str = "", suffix: str = ""): self._save_modules(self.representations, base_path, suffix)

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'''Дан список результатов попыток одного спортсмена для некоторого соревнования. Написать функцию, которая считает сколько за сессию был установлен новый рекорд, т.е. текущее значение превышает значение максимального. Например Имеем список результатов.: scores = [10, 5, 20, 20, 4, 5, 2, 25, 1]. В данном случае ответ: 2.''' def adds(): scores = input("Введите список результатов:").split(' ') print('Список результатов \n', scores) count = 0 rec = scores[0] for ind, el in enumerate(scores): if int(el) > int(rec): rec = el count += 1 return 'Количество рекордов:', count print(adds())

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/contrib/testgen/base58.py

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GroinGuy/GroinCoin-GXG

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refs/heads/master

2020-05-21T14:04:39.761147

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# Copyright (c) 2012-2017 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' Groincoin base58 encoding and decoding. Based on https://groincointalk.org/index.php?topic=1026.0 (public domain) ''' import hashlib # for compatibility with following code... class SHA256: new = hashlib.sha256 if str != bytes: # Python 3.x def ord(c): return c def chr(n): return bytes( (n,) ) __b58chars = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz' __b58base = len(__b58chars) b58chars = __b58chars def b58encode(v): """ encode v, which is a string of bytes, to base58. """ long_value = 0 for (i, c) in enumerate(v[::-1]): long_value += (256**i) * ord(c) result = '' while long_value >= __b58base: div, mod = divmod(long_value, __b58base) result = __b58chars[mod] + result long_value = div result = __b58chars[long_value] + result # Groincoin does a little leading-zero-compression: # leading 0-bytes in the input become leading-1s nPad = 0 for c in v: if c == '\0': nPad += 1 else: break return (__b58chars[0]*nPad) + result def b58decode(v, length = None): """ decode v into a string of len bytes """ long_value = 0 for (i, c) in enumerate(v[::-1]): long_value += __b58chars.find(c) * (__b58base**i) result = bytes() while long_value >= 256: div, mod = divmod(long_value, 256) result = chr(mod) + result long_value = div result = chr(long_value) + result nPad = 0 for c in v: if c == __b58chars[0]: nPad += 1 else: break result = chr(0)*nPad + result if length is not None and len(result) != length: return None return result def checksum(v): """Return 32-bit checksum based on SHA256""" return SHA256.new(SHA256.new(v).digest()).digest()[0:4] def b58encode_chk(v): """b58encode a string, with 32-bit checksum""" return b58encode(v + checksum(v)) def b58decode_chk(v): """decode a base58 string, check and remove checksum""" result = b58decode(v) if result is None: return None if result[-4:] == checksum(result[:-4]): return result[:-4] else: return None def get_bcaddress_version(strAddress): """ Returns None if strAddress is invalid. Otherwise returns integer version of address. """ addr = b58decode_chk(strAddress) if addr is None or len(addr)!=21: return None version = addr[0] return ord(version) if __name__ == '__main__': # Test case (from http://gitorious.org/groincoin/python-base58.git) assert get_bcaddress_version('15VjRaDX9zpbA8LVnbrCAFzrVzN7ixHNsC') is 0 _ohai = 'o hai'.encode('ascii') _tmp = b58encode(_ohai) assert _tmp == 'DYB3oMS' assert b58decode(_tmp, 5) == _ohai print("Tests passed")

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mendozg@gmx.com

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/dynamic_programming/CtCI_8_5_recursive_multiply/Solution.py

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[]

no_license

chopdev/leetcode_tasks

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2023-08-28T04:46:01.534860

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# Recursive Multiply: Write a recursive function to multiply two positive integers without using # the * operator (or / operator). You can use addition, subtraction, and bit shifting, but you should # minimize the number of those operations. # The idea is to divide smaller number on two, until it's 1 # when it's one, 1 * number = number # 17 * 20 # (9 + 8) * 20 # (5 + 4 + 4 + 4) *20 # (3 + 2 + 2 + 2 + 2 + 2 + 2 + 2) * 20 # (1 + 2 + 1 + 1 + 1... + 1) * 20 # (1 + 1 + 1 ... + 1) * 20 # My solution # if N*M then O(log(min(N, M))) is a space complexity for recursion tree # O(logN) time def multiply(num1: int, num2: int) -> int: smaller = num1 if num1 >= num2 else num2 bigger = num2 if num1 >= num2 else num1 return multiply_rec(smaller, bigger) def multiply_rec(smaller: int, bigger: int) -> int: if smaller == 0: return 0 if smaller == 1: return bigger rest = 0 if (smaller & 1 == 1): # if smaller is odd rest = bigger smaller -= 1 # remove one to make it even, but remember that 1 in rest sum = multiply_rec(smaller >> 1, bigger) # divide smaller on 2 return sum + sum + rest # multiply back on 2 print(multiply(0, 0)) print(multiply(11, 0)) print(multiply(1, 2)) print(multiply(2, 2)) print(multiply(17, 21)) # 357 print(multiply(22, 10)) print(multiply(17, 20))

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taras.plavin@gmail.com

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/wsgi/openshift/photo_album/views.py

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sagoyanfisic/openshift-cloudinary-django-sample

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import json from django.http import HttpResponse from django.shortcuts import render from django.views.decorators.csrf import csrf_exempt from cloudinary import api from .models import Profile def filter_nones(d): return dict((k,v) for k,v in d.iteritems() if v is not None) def list(request): defaults = dict(format="jpg", height=150, width=150) samples = [ dict(width = 120, height =120 , crop = 'thumb', gravity = 'face', radius = 'max', effect = ""), ] samples = [filter_nones(dict(defaults, **sample)) for sample in samples] return render(request, 'list.html', dict(photos=Profile.objects.all(), samples=samples))

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sagoyanfisic1@gmail.com

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/mirrormirror.py

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[]

no_license

mdhruv/mirrormirror

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refs/heads/master

2021-01-20T11:17:07.023343

2019-02-07T03:43:53

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import os import urllib import jinja2 import webapp2 JINJA_ENVIRONMENT = jinja2.Environment( loader=jinja2.FileSystemLoader(os.path.dirname(__file__)), extensions=['jinja2.ext.autoescape'], autoescape=True) class MainPage(webapp2.RequestHandler): def get(self): greeting = 'Meow Meow Meow' template_values = { 'greeting': greeting, } template = JINJA_ENVIRONMENT.get_template('index.html') self.response.write(template.render(template_values)) app = webapp2.WSGIApplication([ ('/', MainPage), ], debug=True)

[ "mikhail@Mikhails-MacBook-Pro.local" ]

mikhail@Mikhails-MacBook-Pro.local

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/mapss/celery.py

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[]

no_license

MPI-MAPSS/MAPSSdb

512656b5d0c73ae019ea3acbcaee12617470cd77

982394c95c615a135ec6171c819c3f6eb29b56f7

refs/heads/master

2023-04-16T06:04:10.286241

2023-02-11T13:34:48

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from __future__ import absolute_import, unicode_literals import os from celery import Celery import platform if platform.system().lower() == "windows": os.environ.setdefault("FORKED_BY_MULTIPROCESSING", "1") os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mapss.settings') app = Celery('mapss') app.config_from_object('django.conf:settings', namespace='CELERY') app.autodiscover_tasks()

[ "root@mpi-arches.server.shh.mpg.de" ]

root@mpi-arches.server.shh.mpg.de

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/Day6/wordcount/mycount/views.py

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[]

no_license

roseline0912/LikeLion

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9f2a3386544bd6e7f928494328ff8d8ca9d6feb1

refs/heads/master

2022-11-13T03:14:13.307939

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from django.shortcuts import render # Create your views here. def index(request): context ={} if request.GET.get("myword"): mysentence = request.GET.get("myword") context["wordlen"] = len(mysentence.replace(" ", "")) myword = mysentence.split(" ") checkcnt = dict() for i in myword: if i in checkcnt: checkcnt[i] += 1 else: checkcnt[i] = 1 context["checkcnt"] =checkcnt return render(request,'index.html',context)

[ "roseline0912@likelion.org" ]

roseline0912@likelion.org

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/src/collective/saml2/testing.py

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[]

no_license

collective/collective.saml2

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refs/heads/master

2023-08-28T11:40:53.034492

2022-11-22T14:51:23

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2014-01-12T23:18:22

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from plone.app.testing import PLONE_FIXTURE from plone.app.testing import PloneSandboxLayer from plone.app.testing import IntegrationTesting from plone.app.testing import applyProfile from zope.configuration import xmlconfig class CollectiveSAML2(PloneSandboxLayer): defaultBases = (PLONE_FIXTURE, ) def setUpZope(self, app, configurationContext): # Load ZCML for this package import collective.saml2 xmlconfig.file('configure.zcml', collective.saml2, context=configurationContext) def setUpPloneSite(self, portal): applyProfile(portal, 'collective.saml2:default') COLLECTIVE_SAML2_FIXTURE = CollectiveSAML2() COLLECTIVE_SAML2_INTEGRATION_TESTING = \ IntegrationTesting(bases=(COLLECTIVE_SAML2_FIXTURE, ), name="CollectiveSAML2:Integration")

[ "software@pretaweb.com" ]

software@pretaweb.com

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/leet/0904-fruit-into-baskets/fruit-into-baskets.py

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[]

no_license

lugy-bupt/algorithm

550a28060615d824de34c66c117f76a83e07b708

43b663c125dfa1f78a0936f5caa601407cfb39f4

refs/heads/master

2020-04-10T12:22:39.765248

2019-07-09T08:09:18

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class Solution: def totalFruit(self, tree: List[int]) -> int: def getFruit(start: int) -> int: ret = 0 dicts = {} for i in range(start, len(tree)): if tree[i] not in dicts and len(dicts) == 2: break dicts[tree[i]] = dicts.get(tree[i], 0) + 1 for i in dicts.values(): ret += i return ret ret = 0 i = 0 while i < len(tree): if len(tree) - i <= ret: break ret = max(ret, getFruit(i)) while i + 1 < len(tree) and tree[i] == tree[i + 1]: i += 1 i += 1 return ret

[ "noreply@github.com" ]

lugy-bupt.noreply@github.com

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/src/mbox_move.py

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[ "MIT" ]

permissive

pzia/keepmydatas

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refs/heads/master

2021-05-19T22:48:15.389461

2020-12-25T11:24:39

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#!/usr/bin/env python # -*- coding: utf-8 -*- """Parse tree, find matching MBOX files, and move them info a new folder""" import KmdCmd import KmdFiles import KmdMbox import os import re import logging class KmdFilesMove(KmdCmd.KmdCommand): regexp = None def extendParser(self): super(KmdFilesMove, self).extendParser() #Extend parser self.parser.add_argument('tree', metavar='</path/to/tree>', nargs=1, help='The source tree') self.parser.add_argument('regexp', metavar='<regexp>', default='.*(thunderbird|mozilla|message|mail|evolution|mbox|imap|lotus).*', nargs=1, help='Perl regexp matching files PATH (Example : .*thundebird.*') self.parser.add_argument('folder', metavar='</path/to/dest>', nargs=1, help='Folder to put matching files') def run(self): self.regexp = re.compile(self.args.regexp[0], re.IGNORECASE) logging.info("Parsing %s", self.args.tree[0]) for root, _, files in os.walk(self.args.tree[0]): logging.debug("Walking in %s", root) for name in files: pname = os.path.join(root, name) if self.regexp.match(pname) and KmdMbox.isFileMbox(pname): logging.debug("Found %s", name) dname = os.path.join(self.args.folder[0], name) KmdFiles.fileMoveRename(pname, dname, self.args.doit) if __name__ == "__main__": cmd = KmdFilesMove(__doc__) cmd.run()

[ "paparazzia@gmail.com" ]

paparazzia@gmail.com

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/testnmap/libnmap.py

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[]

no_license

eroa/reconator

022d64b59882427064738bbf242b82a56c8de760

33c767155508ec49a5536977f8e16a06823c3853

refs/heads/master

2021-10-27T04:15:20.383146

2019-04-15T22:20:48

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#!/usr/bin/env python # -*- coding: utf-8 -*- import libnmap from libnmap.process import NmapProcess from libnmap.parser import NmapParser, NmapParserException import os import sys import subprocess import multiprocessing from multiprocessing import Process, Queue # start a new nmap scan on localhost with some specific options def do_scan(targets, options): parsed = None nmproc = NmapProcess(targets, options) rc = nmproc.run() if rc != 0: print("nmap scan failed: {0}".format(nmproc.stderr)) print(type(nmproc.stdout)) try: parsed = NmapParser.parse(nmproc.stdout) except NmapParserException as e: print("Exception raised while parsing scan: {0}".format(e.msg)) return parsed # print scan results from a nmap report def print_scan(nmap_report): print("Starting Nmap {0} ( http://nmap.org ) at {1}".format( nmap_report.version, nmap_report.started)) for host in nmap_report.hosts: if len(host.hostnames): tmp_host = host.hostnames.pop() else: tmp_host = host.address print("Nmap scan report for {0} ({1})".format( tmp_host, host.address)) print("Host is {0}.".format(host.status)) print(" PORT STATE SERVICE") for serv in host.services: pserv = "{0:>5s}/{1:3s} {2:12s} {3}".format( str(serv.port), serv.protocol, serv.state, serv.service) if len(serv.banner): pserv += " ({0})".format(serv.banner) print(pserv) print(nmap_report.summary) print'# Reconator #' print'#go, drink coffee#' if __name__ == "__main__": f =open('sys.argv[1]' 'r') if sys.argv.count() < 1: print "usage : recontor.py iplist.txt" else: for scanip in f: jobs = [] report = do_scan(scanip, "-sS -sV -sC -O -p- -T4 -nvvv -Pn ") # p = multiprocessing.Process(targets=do_scan,args=(scanip,)) # jobs.append(p) if report: print_scan(report) else: print("No results returned")

[ "toxic@t28.io" ]

toxic@t28.io

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/Meshless_Python/rascunho.py

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[]

no_license

CatarinaPinheiro/MetodosSemMalha

98470dd7baaf027245133458fef69ce07e425254

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refs/heads/master

2020-03-14T12:23:54.916183

2018-12-09T13:22:58

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null

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import numpy as np import weightmatrix as wm from numpy import linalg as la import basismatrix as bm import basis as b import coefficients as c import minimumradius as mr r_data = [[0, 1], [0.5, 1.5], [1, 2], [1.5, 2.5], [2, 3], [2.5, 3.5], [3, 4], [3.5, 4.5], [4, 5]] contour_point = [[0, 0], [4, 4]] radius_approximation = mr.get_radius(r_data, r_data[4], 3, contour_point) print(radius_approximation) base = b.pde_basis(1)[0] P = bm.create_basis(base, r_data, r_data[4], radius_approximation) Pt = np.transpose(P) Peso = wm.W(r_data, r_data[4], radius_approximation) print('P = ', P) print('Pt = ', Pt) print('W = ', Peso) A = Pt @ Peso @ P print('A = ', A) B_matrix = Pt @ Peso pt = bm.create_basis(base, [r_data[4]]) print('pt = ', pt) phi = pt @ la.inv(A) @ B_matrix print('phi = ', phi) coef = c.coefficients(r_data, r_data[4], 1, contour_point) print('coefficients = ', coef) dptd_ = bm.create_basis(b.pde_basis(1)[1], [r_data[4]]) dWd_ = wm.W(r_data, r_data[4], radius_approximation, { 'order': 1, 'var': 'x' }) dAd_ = Pt @ dWd_ @ P dBd_ = Pt @ dWd_ invA = la.inv(A) d1 = dptd_ @ invA @ B_matrix - pt @ invA @ dAd_ @ invA @ B_matrix + pt @ invA @ dBd_ print('derivative = ', d1) pde_differential_x = { # Information about pde equation 'order': 1, # order of pde 'var': 'x', # Variable 'base1': b.pde_basis(1)[1], 'base2': b.pde_basis(1)[3] } dcoef_1 = c.coefficients(r_data, r_data[4], 1, contour_point, pde_differential_x) print('coefficients derivative = ', dcoef_1) dptd_2 = bm.create_basis(b.pde_basis(1)[4], [r_data[4]]) d2Wd_ = wm.W(r_data, r_data[4], radius_approximation, { 'order': 2, 'var': 'y' }) d2Bd_2 = Pt @ d2Wd_ d2Ad_2 = d2Bd_2 @ P d2 = dptd_2 @ invA @ B_matrix + np.array([[ 2]]) @ pt @ invA @ dAd_ @ invA @ dAd_ @ invA @ B_matrix - pt @ invA @ d2Ad_2 @ invA @ B_matrix + pt @ invA @ d2Bd_2 - np.array( [[2]]) @ dptd_ @ invA @ dAd_ @ invA @ B_matrix + np.array([[2]]) @ dptd_ @ invA @ dBd_ - np.array( [[2]]) @ pt @ invA @ dAd_ @ invA @ dBd_ print('d2 =', d2) pde_differential_xx = { # Information about pde equation 'order': 2, # order of pde 'var': 'x', # Variable 'base1': b.pde_basis(1)[1], 'base2': b.pde_basis(1)[4] } coef_2 = c.coefficients(r_data, r_data[4], 1, contour_point, pde_differential_xx) print('coef_2 = ', coef_2) print(np.log(2)) print('phi = ', phi) print('coefficients = ', coef)

[ "catarina_pine@hotmail.com" ]

catarina_pine@hotmail.com

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/polymerxtal/visualize.py

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lengxupa/polymerxtal

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refs/heads/master

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""" Functions for visualization of molecules """ import numpy as np import sys from mpl_toolkits.mplot3d import Axes3D try: from ovito.io import import_file from ovito.vis import Viewport use_ovito = True except: use_ovito = False try: import matplotlib.pyplot as plt except: from polymerxtal.io import check_nanohub use_nanohub = check_nanohub() if not (use_ovito and use_nanohub): import matplotlib.pyplot as plt from polymerxtal.data import atom_colors def draw_molecule(coordinates, symbols, draw_bonds=None, save_location=None, dpi=300): # Draw a picture of a molecule using matplotlib. # Create figure fig = plt.figure() ax = fig.add_subplot(111, projection="3d") # Get colors - based on atom name colors = [] for atom in symbols: colors.append(atom_colors[atom]) size = np.array(plt.rcParams["lines.markersize"] ** 2) * 200 / (len(coordinates)) ax.scatter( coordinates[:, 0], coordinates[:, 1], coordinates[:, 2], marker="o", edgecolors="k", facecolors=colors, alpha=1, s=size, ) # Draw bonds if draw_bonds: for atoms, bond_length in draw_bonds.items(): atom1 = atoms[0] atom2 = atoms[1] ax.plot( coordinates[[atom1, atom2], 0], coordinates[[atom1, atom2], 1], coordinates[[atom1, atom2], 2], color="k", ) # Save figure if save_location: plt.savefig(save_location, dpi=dpi, graph_min=0, graph_max=2) return ax def bond_histogram(bond_list, save_location=None, dpi=300, graph_min=0, graph_max=2): # Draw a histogram of bond lengths based on a bond_list (output from build_bond_list function) lengths = [] for atoms, bond_length in bond_list.items(): lengths.append(bond_length) bins = np.linspace(graph_min, graph_max) fig = plt.figure() ax = fig.add_subplot(111) plt.xlabel("Bond Length (angstrom)") plt.ylabel("Number of Bonds") ax.hist(lengths, bins=bins) # Save figure if save_location: plt.savefig(save_location, dpi=dpi) return ax def ovito_view(sample_path, filename, view="Perspective"): """ Use the package ovito to make visualizaitons of molecules. Parameters ---------- sample_path : str The path of the file to visualize filename : str The name of the output file image view : str (optional) The view to use """ if use_ovito: # Import the sample file. pipeline = import_file(sample_path) pipeline.source.data.cell.vis.enabled = False pipeline.source.data.particles.vis.radius = 0.5 pipeline.add_to_scene() vp = Viewport() if view == "Perspective": vp.type = Viewport.Type.Perspective vp.camera_dir = (-1, -1, -1) elif view == "Ortho": vp.type = Viewport.Type.Ortho vp.camera_dir = (-1, -1, -1) elif view == "Top": vp.type = Viewport.Type.Top elif view == "Bottom": vp.type = Viewport.Type.Bottom elif view == "Front": vp.type = Viewport.Type.Front elif view == "Back": vp.type = Viewport.Type.Back elif view == "Left": vp.type = Viewport.Type.Left elif view == "Right": vp.type = Viewport.Type.Right vp.zoom_all() vp.render_image( size=(800, 600), filename=filename, background=(0, 0, 0), frame=0 ) pipeline.remove_from_scene() else: print( "Cannot use function ovito_view - the package ovito is not installed or cannot be found." ) def main(sample_path, filename, view): ovito_view(sample_path, filename, view=view) if __name__ == "__main__": main(sys.argv[1], sys.argv[2], sys.argv[3])

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#!/usr/bin/env python3 # Copyright (c) 2018-2019 The Unit-e developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """ Test Snapshot Creation This test checks the following scenarios: 1. node generates snapshots with the expected interval 2. node keeps up to 5 snapshots 3. node keeps at least 3 finalized snapshots """ from test_framework.regtest_mnemonics import regtest_mnemonics from test_framework.test_framework import UnitETestFramework from test_framework.util import ( assert_equal, wait_until, sync_blocks, assert_finalizationstate, ) class SnapshotCreationTest(UnitETestFramework): def set_test_params(self): self.setup_clean_chain = True self.extra_args = [ ['-validating=1'], [], ] self.num_nodes = len(self.extra_args) def run_test(self): def verify_snapshot_result(res): if 'snapshot_hash' not in res: return False if 'valid' not in res: return False return res['valid'] is True def has_valid_snapshot_for_height(node, height): res = node.getblocksnapshot(node.getblockhash(height)) return verify_snapshot_result(res) validator = self.nodes[0] node = self.nodes[1] validator.importmasterkey(regtest_mnemonics[0]['mnemonics']) node.importmasterkey(regtest_mnemonics[1]['mnemonics']) node.generatetoaddress(1, node.getnewaddress('', 'bech32')) # IBD # test 1. node generates snapshots with the expected interval node.generatetoaddress(23, node.getnewaddress('', 'bech32')) wait_until(lambda: len(node.listsnapshots()) == 5) assert has_valid_snapshot_for_height(node, 4) assert has_valid_snapshot_for_height(node, 9) assert has_valid_snapshot_for_height(node, 14) assert has_valid_snapshot_for_height(node, 19) assert has_valid_snapshot_for_height(node, 24) # test 2. node keeps up to 5 snapshots node.generatetoaddress(5, node.getnewaddress('', 'bech32')) assert_equal(node.getblockcount(), 29) wait_until(lambda: has_valid_snapshot_for_height(node, 29), timeout=10) assert_equal(len(node.listsnapshots()), 5) assert has_valid_snapshot_for_height(node, 4) is False assert has_valid_snapshot_for_height(node, 9) assert has_valid_snapshot_for_height(node, 14) assert has_valid_snapshot_for_height(node, 19) assert has_valid_snapshot_for_height(node, 24) # disable instant justification payto = validator.getnewaddress("", "legacy") txid = validator.deposit(payto, 1500) self.wait_for_transaction(txid, 10) self.stop_node(validator.index) node.generatetoaddress(12, node.getnewaddress('', 'bech32')) assert_equal(node.getblockcount(), 41) assert_finalizationstate(node, {'currentDynasty': 6, 'currentEpoch': 9, 'lastJustifiedEpoch': 7, 'lastFinalizedEpoch': 6, 'validators': 1}) wait_until(lambda: has_valid_snapshot_for_height(node, 39), timeout=10) assert_equal(len(node.listsnapshots()), 5) assert has_valid_snapshot_for_height(node, 9) is False assert has_valid_snapshot_for_height(node, 14) is False assert node.getblocksnapshot(node.getblockhash(19))['snapshot_finalized'] assert node.getblocksnapshot(node.getblockhash(24))['snapshot_finalized'] assert node.getblocksnapshot(node.getblockhash(29))['snapshot_finalized'] assert node.getblocksnapshot(node.getblockhash(34))['snapshot_finalized'] is False assert node.getblocksnapshot(node.getblockhash(39))['snapshot_finalized'] is False # test 3. node keeps at least 2 finalized snapshots node.generatetoaddress(9, node.getnewaddress('', 'bech32')) wait_until(lambda: has_valid_snapshot_for_height(node, 49), timeout=10) assert_equal(len(node.listsnapshots()), 5) assert has_valid_snapshot_for_height(node, 19) is False assert node.getblocksnapshot(node.getblockhash(24))['snapshot_finalized'] assert node.getblocksnapshot(node.getblockhash(29))['snapshot_finalized'] assert has_valid_snapshot_for_height(node, 34) is False assert node.getblocksnapshot(node.getblockhash(39))['snapshot_finalized'] is False assert node.getblocksnapshot(node.getblockhash(44))['snapshot_finalized'] is False assert node.getblocksnapshot(node.getblockhash(49))['snapshot_finalized'] is False node.generatetoaddress(5, node.getnewaddress('', 'bech32')) wait_until(lambda: has_valid_snapshot_for_height(node, 54), timeout=10) assert_equal(len(node.listsnapshots()), 5) assert node.getblocksnapshot(node.getblockhash(24))['snapshot_finalized'] assert node.getblocksnapshot(node.getblockhash(29))['snapshot_finalized'] assert has_valid_snapshot_for_height(node, 39) is False assert node.getblocksnapshot(node.getblockhash(44))['snapshot_finalized'] is False assert node.getblocksnapshot(node.getblockhash(49))['snapshot_finalized'] is False node.generatetoaddress(5, node.getnewaddress('', 'bech32')) wait_until(lambda: has_valid_snapshot_for_height(node, 59), timeout=10) assert_equal(len(node.listsnapshots()), 5) assert node.getblocksnapshot(node.getblockhash(24))['snapshot_finalized'] assert node.getblocksnapshot(node.getblockhash(29))['snapshot_finalized'] assert has_valid_snapshot_for_height(node, 44) is False assert node.getblocksnapshot(node.getblockhash(49))['snapshot_finalized'] is False assert node.getblocksnapshot(node.getblockhash(54))['snapshot_finalized'] is False if __name__ == '__main__': SnapshotCreationTest().main()

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import numbers # print(numbers.Equation.fromSpecal(23,25,numbers.OpperationFactory.divide).factors) n = numbers.Number.fromFloat(20) n2 = numbers.Number.fromFloat(5) print(n.value,n.fractData) # print(n2.factors) # n3 = n/100 # n3 = numbers.Fraction.fromSpecal(1000,10) f = n / n2 # print(f.value, f.fractData) # print(n.value,n2.value) # f = f + numbers.Number.fromFloat(9/-5) # f = f + numbers.Number.fromInt(-1000) f = f - n*n2 print(f.value, f.fractData)

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import tensorflow as tf import tensorflow.contrib.slim.nets as nets import numpy as np slim = tf.contrib.slim resnet = nets.resnet_v1 if __name__ == '__main__': output_shape = 85164 batch_size = 64 image = tf.placeholder(name='input_x', shape=[None, 224, 224, 3], dtype=tf.float32) labels = tf.placeholder(name='input_label', shape=[None, output_shape], dtype=tf.float32) with slim.arg_scope(nets.resnet_utils.resnet_arg_scope()): resnet_50, end_points = resnet.resnet_v1_50(inputs=image, num_classes=output_shape, scope='resnet_v1_50') prob = tf.squeeze(resnet_50, axis=[1, 2]) probabilities = tf.reduce_mean(tf.nn.softmax(prob, dim=-1)) losses = tf.norm(tf.subtract(probabilities, labels)) train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(losses) sess = tf.Session() saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) while True: datasets = np.random.randn(batch_size, 224, 224, 3).astype(np.float32) datasets_labels = np.random.randn(batch_size, output_shape).astype(np.float32) losses_val, _ = sess.run([losses, train_op], feed_dict={image: datasets, labels: datasets_labels}) print(losses_val)

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/ALE_Example1/Controllers_For_Example/Chicken_Controller4/Chicken_Controller4.py

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from ALEFramework.LeggedPredator import LeggedPredator class Chicken(LeggedPredator): def __init__(self): mdNames = ['leg1', 'leg2', 'leg3', 'leg4'] turnSpeed = 1.0 forSpeed = 1.0 objName = 'chicken4' self.food = ['grain', 'grain1', 'grain2', 'grain3'] super().__init__(mdNames, forSpeed, objName) self.legs = self.getMotorDevices() self.infPos = float('inf') self.multiMoveMotorPos(self.legs, self.infPos) self.setMultiMotorVel(self.legs, 1) self.setMaxEnergy(100000) self.setEnergy(100000) self.setConsumptionEnergy(10000) self.setFocusAngle(0.1) def behaviour(self): while self.robot.step(self.timestep) != -1: self.energy = self.energy -1 if self.energy > 600000: self.moveForward() isObstacle = self.checkObstacle() if isObstacle: self.avoidObstacle(isObstacle) else: self.predBehaviour() collided = self.checkEnergyCollision(self.food) if collided: self.eat(collided) chicken = Chicken() chicken.behaviour()

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[]

no_license

TheBroMoe/pramp-interview-questions

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''' [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20] ] [1,2,3,...] output_array = [] 1. traverse first list from 0 to n append to output_array stop searching 2. append last element of each list 3. append first to last 4. append first elemnt of each list 5. repeat 1. ignore last element psuedo-code: 1. traversing left to right (left_col, right_col) 2. up to down (top_row, bot_row) 3. right to left (right_col, left_col) 4. down to up (bot_row, top_row) O(n) time O(n) space R = numbers of rows C = # cols ''' def spiral_copy(inputMatrix): total_col = len(inputMatrix[0]) total_row = len(inputMatrix) left_col = 0 right_col = total_col top_row = 0 bot_row = total_row output = [] while top_row <= bot_row and left_col <= right_col: for i in range(left_col, right_col): output.append(inputMatrix[top_row][i]) top_row += 1 for j in range(top_row, bot_row): output.append(inputMatrix[j][right_col]) right_col -= 1 if top_row <= bot_row: for i in range(right_col, left_col): output.append(inputMatrix[bot_row][i]) bot_row -= 1 if left_col <= right_col: for j in range(bot_row , top_row): output.append(inputMatrix[j][left_col]) left_col += 1 return output

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kebbi@ualberta.ca

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/dennisivy/settings.py

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""" Django settings for dennisivy project. Generated by 'django-admin startproject' using Django 3.0.2. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'hrj48*mobkzo)s6q+2wfqcg=_=@5c38n&sb4_vlp#bb^zyp_pi' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = False ALLOWED_HOSTS = ['yohannisayana.herokuapp.com', '127.0.0.1'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'base.apps.BaseConfig', 'crispy_forms', 'django_filters', 'ckeditor', 'ckeditor_uploader', 'storages', ] CRISPY_TEMPLATE_PACK = 'bootstrap4' MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'whitenoise.middleware.WhiteNoiseMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'dennisivy.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(BASE_DIR, 'templates'), ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'dennisivy.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), 'HOST': 'database endpoint', 'PORT': 'database port' } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' MEDIA_URL = '/images/' STATICFILES_DIRS = [ os.path.join(BASE_DIR, 'static') ] MEDIA_ROOT = os.path.join(BASE_DIR, 'static/images') STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles') EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' EMAIL_HOST = 'smtp.gmail.com' EMAIL_PORT = 587 EMAIL_USE_TLS = True EMAIL_HOST_USER = "joelawro@gmail.com" EMAIL_HOST_PASSWORD = 'johnny ceo of gmail' CKEDITOR_UPLOAD_PATH = 'uploads/' CKEDITOR_CONFIGS = { 'default': { 'toolbar': 'full', 'height': 300, 'width': '100%', }, } AWS_QUERYSTRING_AUTH = False #STATICFILES_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage' #DEFAULT_FILE_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage' #AWS_ACCESS_KEY_ID = 'YOUR-AWS-ACCESS-KEY-ID' #AWS_SECRET_ACCESS_KEY = 'YOUR_AWS-SECRET-ACCESS-KEY' #AWS_STORAGE_BUCKET_NAME = 'YOU_BUCKET_NAME' #AWS_S3_FILE_OVERWRITE = False #AWS_DEFAULT_ACL = None

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#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2018 Vit Novotny <witiko@mail.muni.cz> # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html """ This module provides a namespace for functions that use the Levenshtein distance. """ from itertools import islice import logging from math import floor from gensim.similarities.termsim import TermSimilarityIndex logger = logging.getLogger(__name__) def levdist(t1, t2, max_distance=float("inf")): """Get the Levenshtein distance between two terms. Return the Levenshtein distance between two terms. The distance is a number between <1.0, inf>, higher is less similar. Parameters ---------- t1 : {bytes, str, unicode} The first compared term. t2 : {bytes, str, unicode} The second compared term. max_distance : {int, float}, optional If you don't care about distances larger than a known threshold, a more efficient code path can be taken. For terms that are clearly "too far apart", we will not compute the distance exactly, but we will return `max(len(t1), len(t2))` more quickly, meaning "more than `max_distance`". Default: always compute distance exactly, no threshold clipping. Returns ------- int The Levenshtein distance between `t1` and `t2`. """ import Levenshtein distance = Levenshtein.distance(t1, t2) if distance > max_distance: return max(len(t1), len(t2)) return distance def levsim(t1, t2, alpha=1.8, beta=5.0, min_similarity=0.0): """Get the Levenshtein similarity between two terms. Return the Levenshtein similarity between two terms. The similarity is a number between <0.0, 1.0>, higher is more similar. Parameters ---------- t1 : {bytes, str, unicode} The first compared term. t2 : {bytes, str, unicode} The second compared term. alpha : float, optional The multiplicative factor alpha defined by Charlet and Damnati (2017). beta : float, optional The exponential factor beta defined by Charlet and Damnati (2017). min_similarity : {int, float}, optional If you don't care about similarities smaller than a known threshold, a more efficient code path can be taken. For terms that are clearly "too far apart", we will not compute the distance exactly, but we will return zero more quickly, meaning "less than `min_similarity`". Default: always compute similarity exactly, no threshold clipping. Returns ------- float The Levenshtein similarity between `t1` and `t2`. Notes ----- This notion of Levenshtein similarity was first defined in section 2.2 of `Delphine Charlet and Geraldine Damnati, "SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering", 2017 <http://www.aclweb.org/anthology/S/S17/S17-2051.pdf>`_. """ assert alpha >= 0 assert beta >= 0 max_lengths = max(len(t1), len(t2)) if max_lengths == 0: return 1.0 min_similarity = float(max(min(min_similarity, 1.0), 0.0)) max_distance = int(floor(max_lengths * (1 - (min_similarity / alpha) ** (1 / beta)))) distance = levdist(t1, t2, max_distance) similarity = alpha * (1 - distance * 1.0 / max_lengths)**beta return similarity class LevenshteinSimilarityIndex(TermSimilarityIndex): """ Computes Levenshtein similarities between terms and retrieves most similar terms for a given term. Notes ----- This is a naive implementation that iteratively computes pointwise Levenshtein similarities between individual terms. Using this implementation to compute the similarity of all terms in real-world dictionaries such as the English Wikipedia will take years. Parameters ---------- dictionary : :class:`~gensim.corpora.dictionary.Dictionary` A dictionary that specifies the considered terms. alpha : float, optional The multiplicative factor alpha defined by Charlet and Damnati (2017). beta : float, optional The exponential factor beta defined by Charlet and Damnati (2017). threshold : float, optional Only terms more similar than `threshold` are considered when retrieving the most similar terms for a given term. See Also -------- :func:`gensim.similarities.levenshtein.levsim` The Levenshtein similarity. :class:`~gensim.similarities.termsim.SparseTermSimilarityMatrix` Build a term similarity matrix and compute the Soft Cosine Measure. """ def __init__(self, dictionary, alpha=1.8, beta=5.0, threshold=0.0): self.dictionary = dictionary self.alpha = alpha self.beta = beta self.threshold = threshold super(LevenshteinSimilarityIndex, self).__init__() def most_similar(self, t1, topn=10): similarities = ( (levsim(t1, t2, self.alpha, self.beta, self.threshold), t2) for t2 in self.dictionary.values() if t1 != t2 ) most_similar = ( (t2, similarity) for (similarity, t2) in sorted(similarities, reverse=True) if similarity > 0 ) return islice(most_similar, topn)

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from django.conf import settings from rest_framework.routers import DefaultRouter, SimpleRouter from vapor_manager.users.api.views import UserViewSet if settings.DEBUG: router = DefaultRouter() else: router = SimpleRouter() router.register("users", UserViewSet) app_name = "api" urlpatterns = router.urls

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from typing import Generator from .parser import Parser from .code_writer import CodeWriter class Translator: def __init__(self, input_file: str): self.parser = Parser(input_file) self.code_writer = CodeWriter(input_file) def translate(self) -> Generator[str, None, None]: for inst in self.parser.parse(): yield self.code_writer.write(inst)

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"""Resolvers based on Twitter user profile data.""" import re import warnings from names import * from resolver import AbstractResolver, register STATE_RE = re.compile(r'.+,\s*(\w+)') NORMALIZATION_RE = re.compile(r'\s+|\W') def normalize(location_name, preserve_commas=False): """Normalize *location_name* by stripping punctuation and collapsing runs of whitespace, and return the normalized name.""" def replace(match): if preserve_commas and ',' in match.group(0): return ',' return ' ' return NORMALIZATION_RE.sub(replace, location_name).strip().lower() @register('profile') class ProfileResolver(AbstractResolver): """A resolver that locates a tweet by matching the tweet author's profile location against known locations.""" name = 'profile' def __init__(self): self.location_name_to_location = {} def add_location(self, location): aliases = list(location.aliases) aliases_already_added = set() for alias in aliases: if alias in aliases_already_added: continue #if alias in self.location_name_to_location: #warnings.warn('Duplicate location name "%s"' % alias) else: self.location_name_to_location[alias] = location # Additionally add a normalized version of the alias # stripped of punctuation, and with runs of whitespace # reduced to single spaces. normalized = normalize(alias) if normalized != alias: aliases.append(normalized) aliases_already_added.add(alias) def resolve_tweet(self, tweet): import sys location_string = tweet.get('user', {}).get('location', '') if not location_string: return None normalized = normalize(location_string) if normalized in self.location_name_to_location: return (False, self.location_name_to_location[normalized]) # Try again with commas. normalized = normalize(location_string, preserve_commas=True) match = STATE_RE.search(normalized) if match: after_comma = match.group(1) location_name = None if after_comma in US_STATES or after_comma in COUNTRIES: location_name = after_comma elif after_comma in US_STATE_ABBREVIATIONS: location_name = US_STATE_ABBREVIATIONS[after_comma] elif after_comma in COUNTRY_CODES: location_name = COUNTRY_CODES[after_comma] if location_name in self.location_name_to_location: return (False, self.location_name_to_location[location_name]) return None

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#!/usr/bin/env python # -*- coding:utf-8 -*- """ Copyright (C) 2019. Huawei Technologies Co., Ltd. All rights reserved. This program is free software; you can redistribute it and/or modify it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache License for more details at http://www.apache.org/licenses/LICENSE-2.0 sigmoid_grad """ from functools import reduce as reduce_ins from te import tvm from te import platform as tbe_platform import te.lang.dynamic from topi import generic from te.platform.shape_classifier import classify from te.platform.shape_classifier import Mode from te.utils.op_utils import KERNEL_NAME from te.utils.op_utils import REQUIRED_INPUT from te.utils.op_utils import REQUIRED_OUTPUT from te.utils.op_utils import check_dtype from te.utils.op_utils import check_op_params from te.utils.op_utils import variable_shape from te.utils.op_utils import broadcast_shapes from te.utils.op_utils import check_elewise_shape_range from te.utils.error_manager import error_manager_vector from te.utils.op_utils import refine_shapes_for_broadcast from te.utils.error_manager import error_manager_vector # General limitation of the reduce size for input shape: 2**30 SHAPE_SIZE_LIMIT = 2 ** 30 # pylint: disable=locally-disabled,too-many-arguments,unused-argument def sigmoid_grad_compute(x, y, z, kernel_name="sigmoid_grad"): """ algorithm : sigmoid grad compute sigmoid_grad = (sigmoid - sigmoid*sigmoid)*grad Parameters: ---------- x : a tensor of input data y : a tensor of grad z : output dict kernel_name : cce kernel name, default value is "sigmoid_grad" Returns ------- a tenosr """ dtype = x.dtype.lower() cast_support = tbe_platform.cce_conf.api_check_support( "te.lang.cce.cast_to", "f322f16") if dtype == "float32" and not cast_support: error_detail = "float32 transfer to float16 is only supported on mini and cloud platform!" error_manager_vector.raise_err_two_input_dtype_invalid(kernel_name, "x", 'y', error_detail) vmul_support = tbe_platform.cce_conf.api_check_support( "te.lang.cce.vmul", "float32") vsub_support = tbe_platform.cce_conf.api_check_support( "te.lang.cce.vsub", "float32") if dtype == "float16": x = te.lang.dynamic.cast_to(x, "float32") y = te.lang.dynamic.cast_to(y, "float32") sigmoid_square = te.lang.dynamic.vmul(x, x) if dtype == "float32" and not vmul_support: sigmoid_square = te.lang.dynamic.cast_to(sigmoid_square, "float16") tensor_sub = te.lang.dynamic.vsub(x, sigmoid_square) if dtype == "float32" and not vsub_support: tensor_sub = te.lang.dynamic.cast_to(tensor_sub, "float16") res = te.lang.dynamic.vmul(tensor_sub, y) if dtype == "float16": res = te.lang.dynamic.cast_to(res, "float16") return res @te.op.register_operator("SigmoidGrad") @check_op_params(REQUIRED_INPUT, REQUIRED_INPUT, REQUIRED_OUTPUT, KERNEL_NAME) def sigmoid_grad(x, dx, out, kernel_name="sigmoid_grad"): """ do sigmoid grad sigmoid_grad = (sigmoid - sigmoid*sigmoid)*grad Parameters: ---------- x : dictionary shape of sigmoid input dx : dictionary shape of grad out: dictionary output kernel_name : cce kernel name, default value is "sigmoid_grad_cce" Returns ------- None """ x_dtype = x.get("dtype").lower() dx_dtype = dx.get("dtype").lower() check_list = ("float16", "float32") check_dtype(x_dtype, check_list, param_name="input_x") check_dtype(dx_dtype, check_list, param_name="input_dx") check_elewise_shape_range([x, dx], support_broadcast=False) if x_dtype != dx_dtype: error_manager_vector.raise_err_inputs_dtype_not_equal(kernel_name, "x", "dx", x_dtype, dx_dtype) ins = classify([x, dx], Mode.ELEWISE) schedules, tensors = [], [] for (sig, dx) in ins: with te.op.compute(): shape_sig, shape_dx = variable_shape([sig, dx], support_broadcast=False) shape_sig, shape_dx = refine_shapes_for_broadcast(shape_sig, shape_dx) tensor_sig = tvm.placeholder(shape_sig, x_dtype, "tensor_x") tensor_dx = tvm.placeholder(shape_dx, dx_dtype, "tensor_dx") res = sigmoid_grad_compute(tensor_sig, tensor_dx, out, kernel_name) tensors.append([tensor_sig, tensor_dx, res]) with tvm.target.cce(): sch = generic.auto_schedule(res) schedules.append(sch) config = {"name": kernel_name, "tensor_list": tensors} te.lang.dynamic.build(schedules, config)

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""" Django settings for agenda project. Generated by 'django-admin startproject' using Django 3.1.7. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'z(b1x*9w5*nt=12+ya@hn=qx+*q7om7o+v-_3rd)wm3-e+7!ep' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'core' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'agenda.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'agenda.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/'

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""" WSGI config for students_control project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'students_control.settings') application = get_wsgi_application()

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# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class Plan(Model): """Plan for the resource. :param name: The plan ID. :type name: str :param publisher: The publisher ID. :type publisher: str :param product: The offer ID. :type product: str :param promotion_code: The promotion code. :type promotion_code: str :param version: The plan's version. :type version: str """ _attribute_map = { 'name': {'key': 'name', 'type': 'str'}, 'publisher': {'key': 'publisher', 'type': 'str'}, 'product': {'key': 'product', 'type': 'str'}, 'promotion_code': {'key': 'promotionCode', 'type': 'str'}, 'version': {'key': 'version', 'type': 'str'}, } def __init__(self, **kwargs): super(Plan, self).__init__(**kwargs) self.name = kwargs.get('name', None) self.publisher = kwargs.get('publisher', None) self.product = kwargs.get('product', None) self.promotion_code = kwargs.get('promotion_code', None) self.version = kwargs.get('version', None)

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from django.contrib import admin from django.urls import path,include from basic_app import views app_name = 'basic_app' urlpatterns = [ path('user_login',views.user_login,name='user_login'), path('register',views.register,name='register') ]

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# share https://youtu.be/x084tfX4JnI # subscribed by armor-boop as @varunpariharr ig handel #plz watch this video https://youtu.be/x084tfX4JnI from codehouse and subscribe for further amazing videos related to coding programming import random r = random.randint(1,20) while(True): inp = int(input()) if(inp<r): print("oops,try a greater number") elif(inp>r): print("oops,try a smaller number") else: print("congrats you choosed write number") break;

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#!/home/yamen/WorkSpace/Django1.9/Development/myenv/bin/python2.7 # -*- coding: utf-8 -*- import re import sys from setuptools.command.easy_install import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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# Generated by Django 2.2.4 on 2020-05-24 21:10 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('lofd', '0048_book_cover_image'), ] operations = [ migrations.CreateModel( name='Watch', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(blank=True, default='', max_length=200, null=True)), ('cover_image', models.ImageField(blank=True, null=True, upload_to='movies')), ], ), migrations.DeleteModel( name='Movie', ), migrations.DeleteModel( name='Series', ), ]

[ "onder.yilmz@gmail.com" ]

onder.yilmz@gmail.com

f802008470611389229998d8cb6b02f793426a34

aadb2d7175273bd99520998d3970456c86c15de9

/binder.py

eaf321def1404c14d1b5a4cfb4f72084d69330cb

[ "BSD-3-Clause" ]

permissive

gahughes/Apertif-Visualization-Example-Docker

94bc9a8bf5a2a1dd392f8b45182022ba217b6078

c541a0660df2c5427d10b9576fde20b99b806cd4

refs/heads/main

2023-08-17T00:45:48.888234

2021-09-27T11:20:21

410,800,386

0

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UTF-8

Python

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844

py

"""Install code on Binder. This script is executed from Dockerfile configuration file It installs software dependencies declared in environment.yml in the docker container built for the Binder service. """ import yaml import conda.cli import subprocess import sys with open("environment.yml") as stream: content = yaml.safe_load(stream) for chan in content['channels']: print("RUN mamba config --add channels {}".format(chan)) conda.cli.main('mamba', 'config', '--add', 'channels', chan) for pack in content['dependencies']: if isinstance(pack, str): print("RUN mamba install -q -y {}".format(pack)) conda.cli.main('conda', 'install', '-y', '-q', pack) else: print("RUN pip install {}".format(pack['pip'][0])) subprocess.call([sys.executable, "-m", "pip", "install", pack['pip'][0]])

[ "gareth.hughe@cta-observatory.org" ]

gareth.hughe@cta-observatory.org

0328bd33ee83d556bf57c8a718f7cbaa035b7531

5d058397ba3f90d3c9864076dd2908dd43c0b70a

/weather.py

dcd73f7d42fe71cbd97e27bb95e50569b80bd5cb

[]

no_license

R-Yamaga/git_lesson

2604628086f368656d39a98520da504cdcb43299

08b588f7996ad23378865792c81d2dd6bd1bf223

refs/heads/master

2023-03-07T01:25:27.536331

2021-02-20T06:41:40

340,559,376

0

null

UTF-8

Python

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493

py

import requests import json from pprint import pprint API_KEY = "de0493ad02b5f68f7866c1ea3a8819e4" BASE_URL = "http://api.openweathermap.org/data/2.5/weather?units=metric&lang=ja" city = "Kobe,jp" response = requests.get(BASE_URL + "&q={}&APPID={}".format(city, API_KEY)) weather_data = json.loads(response.text) # pprint(weather_data) city = weather_data['name'] weather = weather_data['weather'][0]['description'] result = "{}の天気は{}です。".format(city, weather) print(result)

[ "yamaga.3939.love@gmail.com" ]

yamaga.3939.love@gmail.com

9f77649ff464ce56850ba4c2959988042dc566e6

941c37c639551010fb99dfa29d0da15589a86cf9

/scripts/classification.py

4c4e0d0bbdf830389fd32ea0745963c6607179cd

[]

no_license

rpezoa/multiW_analysis

0d28ed6b9bfb5b335f6e19aaab62f941f893792f

d0d6909fecc19c2e1f547f4efc6fb4b59c05f039

refs/heads/main

2023-01-21T03:39:06.961507

2020-11-27T19:27:39

314,274,267

0

null

UTF-8

Python

false

2,547

py

import numpy as np from sklearn.ensemble import RandomForestClassifier import lightgbm as lgb import time from sklearn.model_selection import RandomizedSearchCV def classification(X_train, y_train): print("Running classification model ...") start = time.time() d_train = lgb.Dataset(X_train, label=y_train) params = {} params['learning_rate'] = 0.003 params['boosting_type'] = 'gbdt' params['objective'] = 'binary' params['metric'] = '' params['sub_feature'] = 0.5 params['num_leaves'] = 10 params['min_data'] = 50 params['max_depth'] = 10 clf = lgb.train(params, d_train, 100) end = time.time() print(end - start) return clf def classification_RF(X_train, y_train): print("Running classification model ...") start = time.time() # random forest model creation rfc = RandomForestClassifier() clf = rfc.fit(X_train,y_train) print("clf.classes_", clf.classes_) end = time.time() print(end - start) return clf def classification_RF_with_tunning(X_train, y_train,weights=None): print("Running classification model ...") # Number of trees in random forest n_estimators = [int(x) for x in np.linspace(start = 10, stop = 40, num = 10)] # 200, 2000, 100 # Number of features to consider at every split max_features = ['auto', 'sqrt'] # Maximum number of levels in tree max_depth = [int(x) for x in np.linspace(1, 3, num = 3)] max_depth.append(None) # Minimum number of samples required to split a node min_samples_split = [2, 5, 10] # Minimum number of samples required at each leaf node min_samples_leaf = [1, 2, 4] # Method of selecting samples for training each tree bootstrap = [True, False] random_grid = {'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth, 'min_samples_split': min_samples_split, 'min_samples_leaf': min_samples_leaf, 'bootstrap': bootstrap} start = time.time() rfc = RandomForestClassifier() # random forest model creation rf_random = RandomizedSearchCV(estimator = rfc, param_distributions = random_grid, n_iter = 10, cv = 3, verbose=2, random_state=42, n_jobs = -1) rf_random.fit(X_train,y_train, sample_weight=weights) clf = rf_random.best_estimator_ #clf = clf.fit(X_train,y_train) print("clf.classes_", clf.classes_) end = time.time() print(end - start) return clf

[ "rpezoarivera@gmail.com" ]

rpezoarivera@gmail.com

e656df746553b3c6a62b4a2c65f1bf14cff21bed

bf842db27fe35c8f4e659682326b4b77bf9b5726

/embedding_glove.py

7b28503d2ee92628ce560bb9ee0343695e4bcd71

[]

no_license

palarunava/deep-learning-exercises

79135db506fadd8acd8fe775802e987c642066c8

92e195b96a77f731cb767f153308fe05e19a1cbd

refs/heads/master

2020-04-17T07:38:17.959265

2019-02-13T07:49:24

166,377,913

0

null

UTF-8

Python

false

2,063

py

from numpy import array from numpy import asarray from numpy import zeros from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.models import Sequential from keras.layers import Dense from keras.layers import Flatten from keras.layers import Embedding #%% # define documents docs = ['Well done!', 'Good work', 'Great effort', 'nice work', 'Excellent!', 'Weak', 'Poor effort!', 'not good', 'poor work', 'Could have done better.'] # define class labels labels = array([1,1,1,1,1,0,0,0,0,0]) #%% # prepare tokenizer t = Tokenizer() t.fit_on_texts(docs) vocab_size = len(t.word_index) + 1 # integer encode the documents encoded_docs = t.texts_to_sequences(docs) print(encoded_docs) #%% # pad documents to a max length of 4 words max_length = 4 padded_docs = pad_sequences(encoded_docs, maxlen=max_length, padding='post') print(padded_docs) #%% # load the whole embedding into memory embeddings_index = dict() f = open('./data/glove.6B/glove.6B.100d.txt', encoding='utf8') for line in f: values = line.split() word = values[0] coefs = asarray(values[1:], dtype='float32') embeddings_index[word] = coefs f.close() print('Loaded %s word vectors.' % len(embeddings_index)) #%% # create a weight matrix for words in training docs embedding_matrix = zeros((vocab_size, 100)) for word, i in t.word_index.items(): embedding_vector = embeddings_index.get(word) if embedding_vector is not None: embedding_matrix[i] = embedding_vector #%% # define model model = Sequential() e = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=4, trainable=False) model.add(e) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) # compile the model model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc']) # summarize the model print(model.summary()) #%% # fit the model model.fit(padded_docs, labels, epochs=50, verbose=0) #%% # evaluate the model loss, accuracy = model.evaluate(padded_docs, labels, verbose=0) print('Accuracy: %f' % (accuracy*100))

[ "pal.arunava83@yahoo.com" ]

pal.arunava83@yahoo.com

4162fc60669d9da300db7416d8d71205d1582779

9f1039075cc611198a988034429afed6ec6d7408

/tensorflow-stubs/errors/__init__.pyi

27774c85b4c35c1cb30753e12fefd8de51da2622

[]

no_license

matangover/tensorflow-stubs

9422fbb1cb3a3638958d621461291c315f9c6ec2

664bd995ef24f05ba2b3867d979d23ee845cb652

refs/heads/master

2020-05-23T12:03:40.996675

2019-05-15T06:21:43

186,748,093

0

null

UTF-8

Python

false

1,614

pyi

# Stubs for tensorflow.errors (Python 3) # # NOTE: This dynamically typed stub was automatically generated by stubgen. from tensorflow.python import OpError as OpError from tensorflow.python.framework.errors import AbortedError as AbortedError, AlreadyExistsError as AlreadyExistsError, CancelledError as CancelledError, DataLossError as DataLossError, DeadlineExceededError as DeadlineExceededError, FailedPreconditionError as FailedPreconditionError, InternalError as InternalError, InvalidArgumentError as InvalidArgumentError, NotFoundError as NotFoundError, OutOfRangeError as OutOfRangeError, PermissionDeniedError as PermissionDeniedError, ResourceExhaustedError as ResourceExhaustedError, UnauthenticatedError as UnauthenticatedError, UnavailableError as UnavailableError, UnimplementedError as UnimplementedError, UnknownError as UnknownError, error_code_from_exception_type as error_code_from_exception_type, exception_type_from_error_code as exception_type_from_error_code, raise_exception_on_not_ok_status as raise_exception_on_not_ok_status from tensorflow.python.framework.errors_impl import ABORTED as ABORTED, ALREADY_EXISTS as ALREADY_EXISTS, CANCELLED as CANCELLED, DATA_LOSS as DATA_LOSS, DEADLINE_EXCEEDED as DEADLINE_EXCEEDED, FAILED_PRECONDITION as FAILED_PRECONDITION, INTERNAL as INTERNAL, INVALID_ARGUMENT as INVALID_ARGUMENT, NOT_FOUND as NOT_FOUND, OK as OK, OUT_OF_RANGE as OUT_OF_RANGE, PERMISSION_DENIED as PERMISSION_DENIED, RESOURCE_EXHAUSTED as RESOURCE_EXHAUSTED, UNAUTHENTICATED as UNAUTHENTICATED, UNAVAILABLE as UNAVAILABLE, UNIMPLEMENTED as UNIMPLEMENTED, UNKNOWN as UNKNOWN

[ "matangover@gmail.com" ]

matangover@gmail.com

1e6046772828baa97b1ac0a5d2a410f1e5a74fb2

a885bfe825f42129155841c71b146ed9e254f285

/concurrency/process_io.py

128ed4ca10ad8bc6f63078bdee6667031c470824

[]

no_license

stackeric/practical-python

72c3a0210c0206c66ad608cf7f31da8261a3dca4

e6f12189b59953da0887afd85cbac6c576406265

refs/heads/master

2022-12-02T18:36:38.159509

2020-08-25T11:12:51

287,465,888

0

null

UTF-8

Python

false

506

py

import time from multiprocessing import Process, Queue def block_task(q): time.sleep(1) q.put("Done") def main(): n = 4 tasks = [] q = Queue() result = [] for i in range(n): p = Process(target=block_task, args=(q,)) tasks.append(p) for i in tasks: i.start() for i in tasks: res = q.get() result.append(res) for i in tasks: i.join() for r in result: print(r) if __name__ == "__main__": main()

[ "eric@ericde" ]

eric@ericde

606bcb6ee4260f59f029fccb186d5191429fe6bf

de84f9d44b59aa39a1612c683cf2de150ef59e9b

/qurl/migrations/0001_initial.py

43c0b8f99a45263bfb2a764182ab58f5e645ba99

[]

no_license

evrenesat/uygulamatik

25d7617d4ae6c10623b30d4a57731242efa9a4a7

c90da279c6571549e35f51e097524752d9cc2936

refs/heads/master

2021-01-19T05:39:26.908079

2014-06-05T21:57:00

65,093,382

1

0

null

UTF-8

Python

false

1,381

py

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'QRcode' db.create_table(u'qurl_qrcode', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('url', self.gf('django.db.models.fields.URLField')(max_length=200)), ('code', self.gf('django.db.models.fields.CharField')(max_length=20, null=True, blank=True)), ('timestamp', self.gf('django.db.models.fields.DateTimeField')(default=datetime.datetime.now)), )) db.send_create_signal(u'qurl', ['QRcode']) def backwards(self, orm): # Deleting model 'QRcode' db.delete_table(u'qurl_qrcode') models = { u'qurl.qrcode': { 'Meta': {'ordering': "['timestamp']", 'object_name': 'QRcode'}, 'code': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'timestamp': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'url': ('django.db.models.fields.URLField', [], {'max_length': '200'}) } } complete_apps = ['qurl']

[ "esat.ozkan@grow-is.com" ]

esat.ozkan@grow-is.com

d61f4f643c07997b94870339b9e90ad794a8f9fc

4769bb5482141b84898a33c4376394389d540c15

/docker/models/services.py

590729eb19fa03d913f1306d0c27de12ab258293

[]

no_license

oraluisarias/dockerPSM

a92052a3f0641acdb57b94aba26934754927ddcc

4b66ffb9ff061c1661b880fa8f62a6bec94464c0

refs/heads/master

2021-01-21T18:58:24.671171

2017-05-22T21:55:06

92,103,006

0

1

null

UTF-8

Python

false

7,961

py

import copy from docker.errors import create_unexpected_kwargs_error from docker.types import TaskTemplate, ContainerSpec from .resource import Model, Collection class Service(Model): """A service.""" id_attribute = 'ID' @property def name(self): """The service's name.""" return self.attrs['Spec']['Name'] @property def version(self): """ The version number of the service. If this is not the same as the server, the :py:meth:`update` function will not work and you will need to call :py:meth:`reload` before calling it again. """ return self.attrs.get('Version').get('Index') def remove(self): """ Stop and remove the service. Raises: :py:class:`docker.errors.APIError` If the server returns an error. """ return self.client.api.remove_service(self.id) def tasks(self, filters=None): """ List the tasks in this service. Args: filters (dict): A map of filters to process on the tasks list. Valid filters: ``id``, ``name``, ``node``, ``label``, and ``desired-state``. Returns: (:py:class:`list`): List of task dictionaries. Raises: :py:class:`docker.errors.APIError` If the server returns an error. """ if filters is None: filters = {} filters['service'] = self.id return self.client.api.tasks(filters=filters) def update(self, **kwargs): """ Update a service's configuration. Similar to the ``docker service update`` command. Takes the same parameters as :py:meth:`~ServiceCollection.create`. Raises: :py:class:`docker.errors.APIError` If the server returns an error. """ # Image is required, so if it hasn't been set, use current image if 'image' not in kwargs: spec = self.attrs['Spec']['TaskTemplate']['ContainerSpec'] kwargs['image'] = spec['Image'] create_kwargs = _get_create_service_kwargs('update', kwargs) return self.client.api.update_service( self.id, self.version, **create_kwargs ) class ServiceCollection(Collection): """Services on the Docker server.""" model = Service def create(self, image, command=None, **kwargs): """ Create a service. Similar to the ``docker service create`` command. Args: image (str): The image name to use for the containers. command (list of str or str): Command to run. args (list of str): Arguments to the command. constraints (list of str): Placement constraints. container_labels (dict): Labels to apply to the container. endpoint_spec (EndpointSpec): Properties that can be configured to access and load balance a service. Default: ``None``. env (list of str): Environment variables, in the form ``KEY=val``. hostname (string): Hostname to set on the container. labels (dict): Labels to apply to the service. log_driver (str): Log driver to use for containers. log_driver_options (dict): Log driver options. mode (str): Scheduling mode for the service (``replicated`` or ``global``). Defaults to ``replicated``. mounts (list of str): Mounts for the containers, in the form ``source:target:options``, where options is either ``ro`` or ``rw``. name (str): Name to give to the service. networks (list of str): List of network names or IDs to attach the service to. Default: ``None``. resources (Resources): Resource limits and reservations. restart_policy (RestartPolicy): Restart policy for containers. secrets (list of :py:class:`docker.types.SecretReference`): List of secrets accessible to containers for this service. stop_grace_period (int): Amount of time to wait for containers to terminate before forcefully killing them. update_config (UpdateConfig): Specification for the update strategy of the service. Default: ``None`` user (str): User to run commands as. workdir (str): Working directory for commands to run. Returns: (:py:class:`Service`) The created service. Raises: :py:class:`docker.errors.APIError` If the server returns an error. """ kwargs['image'] = image kwargs['command'] = command create_kwargs = _get_create_service_kwargs('create', kwargs) service_id = self.client.api.create_service(**create_kwargs) return self.get(service_id) def get(self, service_id): """ Get a service. Args: service_id (str): The ID of the service. Returns: (:py:class:`Service`): The service. Raises: :py:class:`docker.errors.NotFound` If the service does not exist. :py:class:`docker.errors.APIError` If the server returns an error. """ return self.prepare_model(self.client.api.inspect_service(service_id)) def list(self, **kwargs): """ List services. Args: filters (dict): Filters to process on the nodes list. Valid filters: ``id`` and ``name``. Default: ``None``. Returns: (list of :py:class:`Service`): The services. Raises: :py:class:`docker.errors.APIError` If the server returns an error. """ return [ self.prepare_model(s) for s in self.client.api.services(**kwargs) ] # kwargs to copy straight over to ContainerSpec CONTAINER_SPEC_KWARGS = [ 'image', 'command', 'args', 'env', 'hostname', 'workdir', 'user', 'labels', 'mounts', 'stop_grace_period', 'secrets', ] # kwargs to copy straight over to TaskTemplate TASK_TEMPLATE_KWARGS = [ 'resources', 'restart_policy', ] # kwargs to copy straight over to create_service CREATE_SERVICE_KWARGS = [ 'name', 'labels', 'mode', 'update_config', 'networks', 'endpoint_spec', ] def _get_create_service_kwargs(func_name, kwargs): # Copy over things which can be copied directly create_kwargs = {} for key in copy.copy(kwargs): if key in CREATE_SERVICE_KWARGS: create_kwargs[key] = kwargs.pop(key) container_spec_kwargs = {} for key in copy.copy(kwargs): if key in CONTAINER_SPEC_KWARGS: container_spec_kwargs[key] = kwargs.pop(key) task_template_kwargs = {} for key in copy.copy(kwargs): if key in TASK_TEMPLATE_KWARGS: task_template_kwargs[key] = kwargs.pop(key) if 'container_labels' in kwargs: container_spec_kwargs['labels'] = kwargs.pop('container_labels') if 'constraints' in kwargs: task_template_kwargs['placement'] = { 'Constraints': kwargs.pop('constraints') } if 'log_driver' in kwargs: task_template_kwargs['log_driver'] = { 'Name': kwargs.pop('log_driver'), 'Options': kwargs.pop('log_driver_options', {}) } # All kwargs should have been consumed by this point, so raise # error if any are left if kwargs: raise create_unexpected_kwargs_error(func_name, kwargs) container_spec = ContainerSpec(**container_spec_kwargs) task_template_kwargs['container_spec'] = container_spec create_kwargs['task_template'] = TaskTemplate(**task_template_kwargs) return create_kwargs

[ "tflores" ]

tflores

86d517542aae9fb3b1f8c499f26b5e9fd3831df4

48040e0159e4c8471923edc1e89260f1350f73d2

/forms/autoCreateShifts.py

4a38ba876a8012b6084bcab6d1ea15326551492c

[]

no_license

flurl/lagerManager

496214fd841987882e8debbdccc5e81d76727f01

f7617b9dddc3ff175e44bb847583fbffb933c716

refs/heads/master

2020-05-21T15:19:07.969152

2019-06-17T15:40:16

5,766,718

0

null

UTF-8

Python

false

4,509

py

# -*- coding: utf-8 -*- from PyQt4 import QtCore, QtGui, QtSql from CONSTANTS import * import DBConnection import config from lib.GlobalConfig import globalConf from forms.formBase import FormBase from ui.forms.autoCreateShiftsForm_gui import Ui_AutoCreateShiftsForm import datetime class AutoCreateShiftsForm(FormBase): uiClass = Ui_AutoCreateShiftsForm ident = 'autoCreateShifts' def __init__(self, parent, *args, **kwargs): super(AutoCreateShiftsForm, self).__init__(parent, *args, **kwargs) self.parent = parent def setupUi(self): super(AutoCreateShiftsForm, self).setupUi() self.ui.dateTimeEdit_shift.setDate(QtCore.QDate.currentDate()) try: lastImportURL = config.config['connection'][DBConnection.connName]['last_shift_import_url'] except KeyError: lastImportURL = '' self.ui.lineEdit_URL.setText(lastImportURL) try: lastImportedShiftDateTime = globalConf['last_imported_shift_datetime'] except KeyError: lastImportedShiftDateTime = QtCore.QDateTime.currentDateTime().toTime_t() dt = QtCore.QDateTime() dt.setTime_t(lastImportedShiftDateTime) self.ui.dateTimeEdit_ignoreBefore.setDateTime(dt) def setupSignals(self): super(AutoCreateShiftsForm, self).setupSignals() self.connect(self.ui.pushButton_createShifts, QtCore.SIGNAL('clicked()'), self.createShifts) def createShifts(self): idx = self.ui.tabWidget.currentIndex() if idx == 0: self.createRecurringShifts() elif idx == 1: self.importShifts() def createRecurringShifts(self): recurrence = self.ui.comboBox_recurrence.currentIndex() count = self.ui.spinBox_recurrenceCount.value() name = self.ui.lineEdit_shiftName.text() beginDate = self.ui.dateTimeEdit_shift.date() beginTime = self.ui.dateTimeEdit_shift.time() self.beginTransaction() for i in range(count): date = beginDate if recurrence == 0: date = date.addDays(i*1) elif recurrence == 1: date = date.addDays(i*7) elif recurrence == 2: date = date.addMonths(i*1) elif recurrence == 3: date = date.addYears(i*1) if not self.createShift(name, date, beginTime): self.rollback() QtGui.QMessageBox.critical(self, u'Schichterstellung fehlgeschlagen', u'Die Schichten konnten nicht erstellt werden!\nBitte kontaktieren Sie Ihren Datenbank Administrator') self.commit() QtGui.QMessageBox.information(self, u'Schichterstellung erfolgreich', u'Die Schichten wurden erfolgreich erstellt') return True def importShifts(self): from lib.feedparser import feedparser url = unicode(self.ui.lineEdit_URL.text()) feed = feedparser.parse(url) self.beginTransaction() maxDateTime = ignoreBefore = self.ui.dateTimeEdit_ignoreBefore.dateTime() for item in feed['items']: name = item['title'] dateTime = item['published'][:-6] #strip the last 6 chars for easier parsing below locale = QtCore.QLocale(QtCore.QLocale.English, QtCore.QLocale.UnitedStates) dateTime = locale.toDateTime(dateTime, 'ddd, dd MMM yyyy hh:mm:ss') # Sat, 29 Dec 2018 22:00:00 if dateTime <= ignoreBefore: continue beginDate = dateTime.date() beginTime = dateTime.time() if dateTime > maxDateTime: maxDateTime = dateTime if not self.createShift(name, beginDate, beginTime): self.rollback() QtGui.QMessageBox.critical(self, u'Schichterstellung fehlgeschlagen', u'Die Schichten konnten nicht erstellt werden!\nBitte kontaktieren Sie Ihren Datenbank Administrator') self.commit() QtGui.QMessageBox.information(self, u'Schichterstellung erfolgreich', u'Die Schichten wurden erfolgreich import') try: tmp = config.config['connection'] except KeyError: config.config['connection'] = {DBConnection.connName: {}} config.config['connection'][DBConnection.connName]['last_shift_import_url'] = url config.config.write() globalConf.setValueI('last_imported_shift_datetime', maxDateTime.toTime_t()) return True def createShift(self, name, date, beginTime): query = QtSql.QSqlQuery() query.prepare('insert into veranstaltungen (ver_datum, ver_bezeichnung, ver_beginn) values (?, ?, ?)') query.addBindValue(date) query.addBindValue(name) query.addBindValue(beginTime) query.exec_() if query.lastError().isValid(): print "Error while creating shift!", query.lastError().text() return False #update the parent dialog self.parent.model.select() return True

[ "it@postgarage.at" ]

it@postgarage.at

5f1f8f1639525065d366c6762675a3cc642d30c2

84bb72a8618116c6e61353b73b744a5271bc99aa

/scripts/cloud/ppo_gcp.py

50607139b0024b559f136bac0d5f06aeebdfe3e7

[]

no_license

ndalton12/AMPED

46916b109e19e88831150bde6ee8a5358bc91323

d93d5379ab91ace92d9c2f112ea5a9d1881fd0cb

refs/heads/master

2023-05-26T18:27:52.487607

2020-12-16T01:35:43

310,496,290

0

null

UTF-8

Python

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873

py

import ray from ray import tune from ray.rllib.agents.ppo import PPOTrainer from src.common.env_wrappers import register_super_mario_env def train(): register_super_mario_env() ray.init(address="auto") tune.run( PPOTrainer, config={ "env": "super_mario", "framework": "torch", "num_workers": 4, "log_level": "INFO", "seed": 1337, "num_envs_per_worker": 5, "entropy_coeff": 0.01, "kl_coeff": 0.0, "num_sgd_iter": 2, "num_gpus": 1, "vf_share_layers": False, }, sync_config=tune.SyncConfig(upload_dir="gs://amp-results"), stop={"training_iteration": 500}, checkpoint_freq=500, checkpoint_at_end=True, #resume=True, ) if __name__ == "__main__": train()

[ "niall.dalton12@gmail.com" ]

niall.dalton12@gmail.com

c73146252b7243a3e4200c4ac7e3b724ebbf5db5

8d79780c26453efd8d0a8ed5b1bea3ad30d54fa2

/signs/commands/stopwords.py

5c11110480e379df3c92bf4e39bbc89c8cb0c4b8

[ "MIT" ]

permissive

autonomio/signs

90d217bb873e8417b5c55c6e53fe7551cb18833d

0ae345464d21d144e43f1e342ceb07783fe70a4f

refs/heads/master

2023-08-03T10:57:52.638763

2023-07-29T11:17:00

138,289,276

13

3

MIT

2023-07-29T11:17:02

2018-06-22T10:23:32

Python

UTF-8

Python

false

2,570

py

class Stopwords: def __init__(self, docs, common_stopwords=True, add_stopwords=[], min_length=2, max_threshold=10): '''Accepts as input a list-of-lists where each sublist is a document represented in tokens. docs : list (of lists) Tokenized documents. common_stopwords : bool If a comprehensive list of stopwords should be used. If set to False then add_stopwords can't be empty. add_stopwords : list or None If a list of words is provided, then those will be used as well as common_stopwords unless it's set to False. min_lenght : int Drop all words below this length. max_threshold : int Keep all words that are at least this long. ''' import string self.common_stopwords = common_stopwords self.add_stopwords = add_stopwords self.min_length = min_length self.max_threshold = max_threshold self.string = string self.stopwords = self.stopword_index() self.docs = docs for i in range(len(docs)): self.docs[i] = self.check_stopwords(docs[i]) def stopword_index(self): import numpy as np out = [] if self.common_stopwords is True: from signs.utils.stopwords import stopwords stopword_list = np.unique(stopwords()).tolist() stopword_list + self.add_stopwords else: stopword_list = self.add_stopwords for word in stopword_list: if len(word) > 1: out.append(word.lower()) stopword_dict = {} for word in out: if word[0] in self.string.ascii_letters: try: stopword_dict[word[0]].append(word) except KeyError: stopword_dict[word[0]] = [word] return stopword_dict def check_stopwords(self, doc): out = [] for word in doc: # always keep words longer than 10 characters if len(word) >= self.max_threshold: out.append(word) # always pass words shorter than 2 characters elif len(word) <= self.min_length: continue elif word[0] not in self.stopwords.keys(): out.append(word) elif word not in self.stopwords[word[0]]: out.append(word) return out

[ "mailme@mikkokotila.com" ]

mailme@mikkokotila.com

5c6512d6f30eb845155ab18b6ca2551e3db2692e

80cab747b3a1ed9639ba45b86d1c6871c4d23fd0

/fb_mybot/apps.py

0bf4eb4f779c4b7d9328d96a6bd738725c1c5c1b

[]

no_license

HadesSama/fuujisamytestbot

efd43440661e3e83ee26ea8af80a7260a60a3b50

4aaeda1c8e2195914b33a04de8435bb94991f715

refs/heads/master

2021-01-20T10:28:28.965635

2017-03-04T23:47:56

83,932,497

0

null

UTF-8

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false

90

py

from django.apps import AppConfig class FbMybotConfig(AppConfig): name = 'fb_mybot'

[ "samybensbih@gmail.com" ]

samybensbih@gmail.com

c403caa88f7b02079838aa6f7f4193a522735a2a

8e41c877244f04c79cb0828e582f23fc19fce31c

/david/modules/works/model/work.py

eac66b28f1eccb686aed670193c1ec97f8e21f71

[ "MIT" ]

permissive

ktmud/david

8214e0dd3c6b74f696e4d9a038eadc2e8e22223b

4b8d6f804b73cdfa1a8ddf784077fa9a39f1e36f

refs/heads/master

2016-08-05T22:56:42.548100

2014-01-19T11:57:18

2014-01-19T12:05:18

13,518,612

2

0

null

UTF-8

Python

false

1,398

py

# -*- coding: utf-8 -*- from config import SITE_ROOT from david.core.db import db, orm, func, CatLimitedQuery, UidMixin from david.core.accounts import User from david.core.attachment import MediaMixin from david.lib.utils import truncate, striptags from david.ext.babel import lazy_gettext as _ from david.modules.artist.model import Artist K_WORK = 210 class Work(db.Model, UidMixin, MediaMixin): kind = K_WORK kind_name = 'work' id = db.Column(db.Integer, primary_key=True) cat = db.Column('cat', db.SmallInteger, index=True, nullable=False) title = db.Column(db.String(255), nullable=False) owner_id = db.Column(db.Integer, nullable=False) artist_id = db.Column(db.Integer, nullable=False) desc = db.Column(db.Text()) pubdate = db.Column(db.Date) create_at = db.Column(db.DateTime, default=func.now()) update_at = db.Column(db.DateTime, default=func.now(), onupdate=func.utc_timestamp()) deleted = db.Column(db.Boolean, default=False) query_class= CatLimitedQuery @property def _DEFAULT_PIC(self): return self.extended_self._DEFAULT_PIC @property def cat_id(self): return self.cat cat_name = 'work' catname = property(lambda x: _(x.cat_name)) @property def artist(self): return Artist.get(self.artist_id) def url(self): return SITE_ROOT + 'work/' + self.slug

[ "jyyjcc@gmail.com" ]

jyyjcc@gmail.com

0278c67c002e9408e6651d0eb061d1606c8955f0

782ea192b3d7bb6392488c841ec60392a7dd5569

/home/models.py

bba6bb452fe7dd171fca3d8843eacceb472fd124

[]

no_license

GM1957/mysite

9526658800b2deefedb1da07d5e152acfe9a618e

ee2dcdd8b9c6c3243c7b615ae0f72c55ec745cb1

refs/heads/master

2022-12-22T08:40:21.846110

2020-10-03T17:17:54

300,935,268

0

null

2020-10-03T17:56:46

2020-10-03T17:15:53

JavaScript

UTF-8

Python

false

1,606

py

from django.db import models from django.contrib.auth.models import User from django.utils.timezone import now # Create your models here. class Product(models.Model): productUniquename = models.CharField(max_length=200) product_id = models.AutoField(primary_key=True) product_name = models.CharField(max_length=200,unique = True) description = models.CharField(max_length=1000) pub_date = models.DateField(null = True,blank=True,default=now) image = models.ImageField(upload_to="home/images") #slug = models.CharField(max_length=200) def __str__(self): return self.product_name class Contact(models.Model): contactsno = models.AutoField(primary_key = True) name = models.CharField(max_length=300) phonenumber = models.CharField(max_length=13) email = models.CharField(max_length=100) content = models.TextField() timeStamp = models.DateTimeField(auto_now_add=True, blank=True) def __str__(self): return self.name class userdata(models.Model): user = models.OneToOneField(User,on_delete=models.CASCADE,primary_key=True) profile_pic = models.ImageField(upload_to="home/images/userprofileimages",default='home/images/userprofileimages/defaultuser.png') def __str__(self): return str(self.user) # class userinfo(models.Model): # user = models.OneToOneField(User,null=True,on_delete=models.CASCADE) # profile_pic = models.ImageField(upload_to="home/images/userprofileimages",blank=True,default='home/images/userprofileimages/defaultuser.png') # def __str__(self): # return str(self.user)

[ "Tanmoybhowmik03@gmail.com" ]

Tanmoybhowmik03@gmail.com

23db1f0ed3b70bb1a06804fbf9ed2880e100f1f4

c2d533b32c258280293be3f3bf20afe7fd77f1ac

/home/debian/brewing/log.py

bae24f68a12a10a641c07a4bb0a83aa3fc8f7447

[]

no_license

leograba/final_paper_tcc

26bc1621272cbb587a750a7eb994b5e2b80428da

9743055eea2e6245d68295da7897e6e7a43d1b34

refs/heads/master

2021-01-18T16:26:42.147756

2016-05-29T19:51:07

50,020,934

0

null

UTF-8

Python

false

25

py

import temp temp.tlog()

[ "leogveiga@gmail.com" ]

leogveiga@gmail.com

0b7d0093a662db29643feb27131176aa436c1bb7

29aa0266f6cb5578730cc44e6f792a7eeb6b01a5

/src/big_o_notation.py

1f93e368b1dcd5f8d3e3a83fd41aa3210bdf92c2

[]

no_license

Kandelonius/python-lectures-LS

7ad0e1690dd5e474dbb6f9595fa488c1b58ef2aa

1d81a4e52238ae06894050168bb713760e47a804

refs/heads/master

2022-12-16T14:02:33.638479

2020-09-23T22:29:31

286,515,557

0

null

UTF-8

Python

false

1,149

py

my_list = [1, 2, 3, 4, 5] longer_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] def print_list(arr): # O(n) linear for n in arr: print(n) print_list(my_list) def print_pairs(items): # O(n^2) quadratic for item_one in items: for item_two in items: print(item_one, item_two) def my_func(arr): a = 1 b = 2 c = a * b for x in range(1000): print(x) for thing in arr: x = 10 y = 20 z = x * y print(thing) my_func(my_list) my_func(longer_list) # "on the order of" def two_loops(arr): # still O(n) linear for x in range(1000000000): z = x * x print(z) for thing in arr: print(thing) for thing_again in arr: print(thing_again) # def simple_recurse(n): # if n <= 1: # return n # simple_recurse(n) # # def weird_recurse(num): # if n <= 1: # return num # simple_recurse(num - 1) # simple_recurse(num - 1) # simple_recurse(num - 1) def two_n_demo(n): if n == 0: return 1 a = two_n_demo(n - 1) b = two_n_demo(n - 1) return a + b two_n_demo(3)

[ "shane.kaestner@yahoo.com" ]

shane.kaestner@yahoo.com

8ad2d0a9ef495db9e47b6c1c13825bd57cc2fef6

4c00d6993f7d934fd57314df275e37ce3d231c39

/defrag.py

66ad04e6883ba9651f624597344fb2d9349cca59

[]

no_license

cbhl/cusec12

56ce830d70ba9deee92fba745260bcca1a0bd1c3

0967a471833fc8f2d55fd8464b96ddadb195ad67

refs/heads/master

2016-09-06T04:10:12.421705

2012-01-21T04:55:42

3,220,147

3

2

null

UTF-8

Python

false

1,927

py

#!/usr/bin/env python ''' Defrag CUSEC Coding Competition Waterloo Software Engineering @author Michael Chang, Alice Yuan, Joshua Kaplin, Aaron Morais ''' import defrag_scorer from strategy import * class TimeoutException(Exception): pass if __name__ == '__main__': import argparse import signal def timeout_handler(signum, frame): raise TimeoutException() parser = argparse.ArgumentParser( description='defragments a fictional file system' ) parser.add_argument( '--input', required=False, help='Input file for the disk (if not specified, will read from disk.txt)' ) args = parser.parse_args() signal.signal(signal.SIGALRM, timeout_handler) signal.alarm(295) # five minutes, minus a five second buffer # signal.alarm(30) try: inputFilename = 'disk.txt' if args.input == None else args.input strategies = [] #strategies.append(BaseStrategy.BaseStrategy()) strategies.append(MoveToFrontStrategy.MoveToFrontStrategy()) strategies.append(MoveToEndStrategy.MoveToEndStrategy()) strategies.append(QuickSortBaseStrategy.QuickSortBaseStrategy()) # strategies.append(SleepStrategy.SleepStrategy()) bestResult = [] disk = defrag_scorer.loadDisk(inputFilename) bestScore = defrag_scorer.calculateScore(disk) for strategy in strategies: disk = defrag_scorer.loadDisk(inputFilename) final_disk = strategy.calculate(disk) result = strategy.result() score = defrag_scorer.calculateScore(final_disk) if score > bestScore or len(result) < len(bestResult): bestResult = result bestScore = score except TimeoutException: pass; # fall through to returning the result # output for line in bestResult: print line

[ "m9chang@uwaterloo.ca" ]

m9chang@uwaterloo.ca

84ffc63b90733910ea156b66f03e2393e38acd03

f0d713996eb095bcdc701f3fab0a8110b8541cbb

/9CWPv99o4EjZgHnkq_20.py

51d78dfb8d43ec2f17ffec1bef69618a23bddc68

[]

no_license

daniel-reich/turbo-robot

feda6c0523bb83ab8954b6d06302bfec5b16ebdf

a7a25c63097674c0a81675eed7e6b763785f1c41

refs/heads/main

2023-03-26T01:55:14.210264

2021-03-23T16:08:01

350,773,815

0

null

UTF-8

Python

false

937

py

""" Write a function that divides a list into chunks such that the sum of each chunk is `<= n`. Start from the left side of the list and move to the right. ### Examples divide([1, 2, 3, 4, 1, 0, 2, 2], 5) ➞ [[1, 2], [3], [4, 1, 0], [2, 2]] divide([1, 0, 1, 1, -1, 0, 0], 1) ➞ [[1, 0], [1], [1, -1, 0, 0]] divide([2, 1, 0, -1, 0, 0, 2, 1, 3], 3) ➞ [[2, 1, 0, -1, 0, 0], [2, 1], [3]] ### Notes * The max of the list will always be smaller than or equal to `n`. * Use the **greedy approach** when solving the problem (e.g. fit as many elements you can into a chunk as long as you satisfy the sum constraint). """ def divide(lst, n): r, add = [], [] i = 0 for x in lst: if i + x <= n: add.append(x) i += x else: r.append(add) add = [] add.append(x) i = x r.append(add) return r

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

daniel.reich@danielreichs-MacBook-Pro.local

2018a3d9a6c42fdf513d453e9e9e752d29105e14

e62513f731a7e0c0f93df82fad4a7c391ae79503

/examples/crypto_ticker.py

28c1b0cb756302b1f0204ca0b27fe9326392f9ba

[ "MIT" ]

permissive

bjarnekvae/pymobitec-flipdot

173157c6954d014b87ad444405466a1157f52d11

b51545e8b1b792c2c4e970cf9ce8b207ffabe9c2

refs/heads/master

2021-06-25T11:27:28.342520

2021-01-09T00:54:09

171,110,798

4

0

null

UTF-8

Python

false

1,188

py

import serial import time import pymobitec_flipdot as flipdot import cryptocompare import RPi.GPIO as GPIO crypto_currency = 'ADA' fiat_currency = 'USD' LED_flash_threshold = 0.005 # % price change per period GPIO.setwarnings(False) GPIO.setmode(GPIO.BCM) GPIO.setup(18, GPIO.OUT) GPIO.output(18, GPIO.HIGH) prev_price = 1.0 with serial.Serial('/dev/ttyS0', 4800, timeout=1) as ser: while True: price = cryptocompare.get_price(crypto_currency, curr=fiat_currency) if price is not None: price = price[crypto_currency]['USD'] + 0.00001 # extra digits to fill out display else: time.sleep(60) continue if price > 1.0: price_str = str(price)[:5] else: price_str = str(price)[1:6] msg = flipdot.set_text(price_str, 1, 0, flipdot.Fonts.numbers_14px) ser.write(msg) if (price-prev_price)/prev_price > LED_flash_threshold: for i in range(3): GPIO.output(18, GPIO.LOW) time.sleep(0.04) GPIO.output(18, GPIO.HIGH) time.sleep(0.06) prev_price = price time.sleep(60)

[ "bjarnekvae@gmail.com" ]

bjarnekvae@gmail.com

d537973b9f91bc317cd461a50404c9e0a259fd8f

4f380f74e0b0992427e5aa5d7c0bfc7ce841c98f

/main.py

138fa0aeeb06cf83cd4f653c068ee9a201fd0a3b

[]

no_license

MurrayC7/DeepISP

1c4512087ce3ea40e274dd340a51e9f9500488c4

303507174c240a9f5048a76fdaa6045069f9d395

refs/heads/master

2022-04-03T20:17:36.407947

2019-11-28T07:49:17

215,030,415

1

0

null

UTF-8

Python

false

801

py

import argparse import os import tensorflow as tf tf.set_random_seed(819) from .model import Unet parser = argparse.ArgumentParser(description='deepisp') parser.add_argument('--dataset_dir', dest='dataset_dir', default='fivek', help='path of the dataset') args = parser.parse_args() def main(): if not os.path.exists(args.checkpoint_dir): os.makedirs(args.checkpoint_dir) if not os.path.exists(args.result_dir): os.makedirs(args.result_dir) tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True with tf.Session(config=tfconfig, graph=tf.get_default_graph()) as sess: model = Unet(sess, args) model.train(args) if args.phase == 'train' else model.test(args) if __name__ == '__main__': tf.app.run()

[ "330784960@qq.com" ]

330784960@qq.com

6b2b48dfde1b45519659285e8341242c9d50a5b9

4fe0dae5fcac233b087326b907513b3fb390e87c

/qidian_login/qidian_login/spiders/bookshelf.py

789234414771125ffa7c77e946ca1cd94bd3de48

[]

no_license

cjg1994/crawl_practice

9b91efb1e237b91c2714828f10a2fa7c4e5c452b

8fc62c03b30dcce6b52b152ae03358aa9159be9a

refs/heads/master

2022-11-19T14:56:51.947369

2020-07-13T13:18:56

279,276,943

0

null

UTF-8

Python

false

2,260

py

# -*- coding: utf-8 -*- """ 通过browsercookie可以获取chrome浏览器存在本地的各个网站的cookie对象,< Cookie key=value domain> 构造一个cookie字典,通过请求发送实现自动登录 """ import scrapy import browsercookie #browsercookie库的使用 from qidian_login.items import QidianLoginItem cookie='Cookie: _csrfToken=W3JqtsH9COGyqaCjsvl3TgJe8E6YbrpcA2CAWen3; newstatisticUUID=1571035550_1737766613; ywkey=ywzElAb4Jnjy; ywguid=851000542593; ywopenid=A47F8ADC20F3492F392A7F56C0DBF5DB; e1=%7B%22pid%22%3A%22qd_P_my_bookshelf%22%2C%22eid%22%3A%22qd_M194%22%2C%22l3%22%3A1%2C%22l2%22%3A2%2C%22l1%22%3A3%7D; e2=%7B%22pid%22%3A%22qd_P_my_bookshelf%22%2C%22eid%22%3A%22qd_M194%22%2C%22l3%22%3A1%2C%22l2%22%3A2%2C%22l1%22%3A3%7D' class BookshelfSpider(scrapy.Spider): name = 'bookshelf' def __init__(self): cookiejar=browsercookie.chrome()#调用chrome方法,返回的是一个cookiejar.Cookiejar类 self.cookie_dict={} for cookie in cookiejar:#cookiejar可以直接遍历,里面是一个个形如 <Cookie key=value domain>的cookie对象 #访问该对象的属性cookie.name cookie.value cookie.value if cookie.domain==".qidian.com": if cookie.name in ["_csrfToken", "newstatisticUUID", "ywkey", "ywguid", "ywopenid", "e1", "e2"] : self.cookie_dict[cookie.name]=cookie.value def start_requests(self): url="https://my.qidian.com/bookcase?targetTab=tabTarget1" #个人书架的网址 yield scrapy.Request(url,cookies=self.cookie_dict) def parse(self, response): book_list=response.xpath('//table[@id="shelfTable"]/tbody/tr') for book in book_list: item=QidianLoginItem() item["category"]=book.xpath('td[2]/span/b/a/text()').extract()[0] item["title"]=book.xpath('td[2]/span/b/a/text()').extract()[1] item["update"]=book.xpath('td[3]/text()').extract()[0] item["author"]=book.xpath('td[4]/a/text()').extract()[0] yield item

[ "532223911@qq.com" ]

532223911@qq.com

148be16b7639ef836ce0d724e7b939cb91f4eb7d

e1a2ddd7e382a450f75f47a366f740e807bf6d99

/area.py

6d46b185c2688b712e294169f7740fbd61845300

[]

no_license

Padmajaya123/simple-python-programs

ddb37b410800b1601c640d907e1a13deba8f40c7

28a40e0ce2764fb7ab33ad234c6de668ed99dc04

refs/heads/master

2022-09-29T04:16:10.854631

2020-06-02T04:11:24

null

0

null

UTF-8

Python

false

228

py

a=int(input("enter the length=")) b=int(input("enter the breadth=")) as1=0 sr=0 c=0.0 as1=a*a sr=a*b r=int(input("enter radius=")) c=3.14*r*r print("Area of square=",as1) print("Area of rectangle=",sr) print("Area of circle=",c)

[ "saipadmarekha@gmail.com" ]

saipadmarekha@gmail.com

a9982fa549dd8e35fbd52c6ff5c868688b657c02

a9eec5b1dbccb40f4bf4daad61bf4a1e669218b0

/api/poc-api/apps/api/v1/notifications/views.py

dcc6713ef86124cf761773813675d06e3f887ecf

[]

no_license

Gaiachain-Ltd/Charcoal-Web-Panel

f515a92ee6660e3bf53026465449227aa5255bee

18ab01b3bb0906e4ebf9fd1864a1e37562ce53cf

refs/heads/master

2023-01-20T09:16:48.327582

2020-11-25T11:58:09

315,596,568

0

null

UTF-8

Python

false

544

py

from rest_framework.viewsets import ReadOnlyModelViewSet from apps.notifications.models import Notification from config.swagger_schema import CustomSchema from .serializers import NotificationSerializer class NotificationViewSet(ReadOnlyModelViewSet): serializer_class = NotificationSerializer schema = CustomSchema() def get_queryset(self): user = self.request.user queryset = Notification.objects.filter(users=user).exclude(read_by=user) user.read_notifications.add(*queryset) return queryset

[ "sbondar@milosolutions.com" ]

sbondar@milosolutions.com

d3fc9110d178c1cc4d088cd664454800902db469

3c2cc8910c4a333a44d2d7b22489ef8d5ddb6a13

/src/zvt/domain/quotes/stock/stock_1mon_hfq_kdata.py

eef6e6ec4b3b171e2a4a4a850183a722c32f1e8f

[ "MIT" ]

permissive

zvtvz/zvt

6341dc765177b1e99727207f1608b730cbbb705a

03aee869fd432bb933d59ba419401cfc11501392

refs/heads/master

2023-08-28T10:05:29.185590

2023-08-01T10:19:03

179,451,497

2,782

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MIT

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# -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1monHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1mon_hfq_kdata" register_schema( providers=["joinquant", "em"], db_name="stock_1mon_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1monHfqKdata"]

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import threading from customauth.models import MyUser from codeRS.models import Solved, Problem class CodeProcessor(threading.Thread): def __init__(self,request): threading.Thread.__init__(self) self.waiting = [] self.success = [] self.failed = [] self.request = request def run(self): if request.user.is_anonymous: return redirect("/login/") if request.method == 'GET' or request.method == 'POST': try: test_bubble = False print('See noi: ',os.getcwd()) id_ = request.GET['id'] path_problem = os.getcwd()+os.sep+'problems'+os.sep+str(id_) run_path = path_problem + os.sep + 'run.exe' out_path = path_problem + os.sep + 'out.txt' f = open(path_problem+os.sep+'starter.txt','r') starter = f.read() f.close() if request.method == 'POST': fname='' test_bubble = True code_content = request.POST.get('task','') if Problem.objects.get(pk=int(id_)).language == 'cpp': fname='submit.cpp' else: fname='submit.py' f_code = open(path_problem + os.sep + fname,'w') f_code.write(code_content) f_code.close() test_folder_path = path_problem + os.sep + 'TestCases' testfile_path = path_problem + os.sep + 'TestCases' + os.sep + 'test.txt' is_compile_sucess = False if Problem.objects.get(pk = int(id_)).language=='cpp': status = os.system('g++ ' + path_problem + os.sep + fname +' -o ' + run_path) if status == 1: messages.error(request,'Compilation error occurred') else: messages.success(request,'Compiled Successfully') is_compile_sucess = True print(run_path) print(testfile_path) print(out_path) os.system(run_path + '< ' + testfile_path + ' >' + out_path) elif Problem.objects.get(pk = int(id_)).language=='python': status = os.system('python ' + path_problem + os.sep + fname + '< '+ testfile_path + ' >' + out_path) if status == 1: messages.error(request,'Compilation error occurred') else: is_compile_sucess = True messages.success(request,'Compiled Successfully') if is_compile_sucess: f_ref = open(test_folder_path + os.sep + 'ref.txt','r') ref_list = f_ref.read().splitlines() f_ref.close() f_out = open(out_path,'r') output_list = f_out.read().splitlines() f_out.close() for i in range(len(ref_list)): try: ref = ref_list[i] out = output_list[i] if ref == out: print(' Output ',i, 'passed') else: print('MISMATCH') except IndexError: print('Mismatch') if ref_list == output_list: User = MyUser.objects.get(pk=request.user.id) User.score += Problem.objects.get(pk=int(id_)).score User.save() solved = Solved(uid = request.user, pid = Problem.objects.get(pk=int(id_))) solved.save()

[ "satviksingh28@gmail.com" ]

satviksingh28@gmail.com

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n = int(input("Enter the first number to find HCF of Number : ")) m = int(input("Enter the second number to find HCF of Number : ")) def find_hcf(x,y): if x>y: s = y else: s = x for i in range(1 , s+1): if x % i== 0 and y % i == 0: hcf = i return hcf print("the H.C.F. of", n ,"amd", m,"is", find_hcf(n,m))

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vishnu83550@gmail.com

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/learn_motif.py

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naterich2/oops_meme

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#!/usr/bin/env python # Written by: Nathan Richman <nate@nrichman.dev> # For BMI 776 (Advanced Bioinformatics) at UW-Madison # Spring 2020 # Part of homework 1, this file contains classes and a main function to run the MEME algorithm # with an OOPS model and exhaustive subsequence method as a starting point. # Framework code with a main function and the Argparser was provided. import argparse, os, sys import numpy as np import pandas as pd import collections import seq_logo import matplotlib.pyplot as plt # You can choose to write classes in other python files # and import them here. class MEME(object): """Class for the EM algorithm""" def __init__(self, sequences, width, pseudocount): """TODO: to be defined. Parameters ---------- sequences : array-like Array of sequences width: int Width of sequence to look for pseudocount: int Pseudocount to use when running the M-step """ self._sequences = sequences if width > (len(sequences[0]) + 1)/2: raise ValueError("Width must be less than (len(sequence) + 1)/2") self._width = width self._pseudo = pseudocount # Number of starting positions, L - W + 1 self._starting_pos = len(sequences[0]) - width + 1 # Probability matrix, first column is background, starting at position 1 is the first position on the motif, initialize to all 0's # TODO check dimension on p, additionally check indexing on n_ck, p_ck p_ck and n_ck are 1-indexed since 0 is background, the rest is 0-indexed self._P = pd.DataFrame(data=np.zeros((4,width+1)),index=['A','C','G','T'],columns=list(range(width + 1))) # Matrix of starting position probabilities for each sequence, initialize to 0 self._Z = np.zeros((len(sequences),self._starting_pos)) @property def p(self): """Getter for _p Returns -------- p_matrix """ return self._P @property def z(self): """Getter for _z Returns -------- z_matrix """ return self._Z def _normalize(self,to_normalize): """_normalize: Helper function that normalizes a distribution in log-space so that all probabilities in the distribution sum to 1 in real space Parameters ---------- to_normalize : 1D array Distribution in log-space to normalize Returns ------- Normalized distribution in real space """ # https://stats.stackexchange.com/questions/6616/converting-normalizing-very-small-likelihood-values-to-probability # For a precision of \eta = 10^-d for d digits of precision with n likelihoods # 1. Subtract the maximum likelihood from all logs. # 2. Throw away values less than the logarithm of \eta/n # # \eta = 10^-16 for 16 decimals # ln(\eta / n) = ln(\eta) - ln(n) thresh = np.log(10**-16) - np.log(len(to_normalize)) my_max = np.max(to_normalize) # If the value is too small set it to float("-inf") which will successfully evaluat to 0 through the exp function vals = [np.exp(a - my_max) if (a - my_max) > thresh else float("-inf") for a in to_normalize ] vals = vals/np.sum(vals) return vals def test(self,seq): counts = collections.Counter(''.join(self._sequences)) p_matrix = pd.DataFrame(data=np.zeros_like(self._P),index=['A','C','G','T'],columns=list(range(self._width + 1))) ############ Populate p_c_k, k > 0 # Go through bases, and set probability of the found character at that position equal to 2/5, and the others to 1/5 counts_seq = collections.Counter(seq) for i,base in enumerate(seq): for letter in p_matrix.index: if letter == base: p_matrix.loc[base,i+1] = (self._pseudo + 1)/(self._pseudo*4 + 1) else: p_matrix.loc[letter,i+1] = (self._pseudo)/(self._pseudo*4 + 1) ############ Populate p_c_k, k = 0 n_0 = {} for base in ['A','C','G','T']: # n_c_0 = total # of c's in dataset - count of c's in motif n_0[base] = (counts[base] - counts_seq[base]) my_sum = sum(n_0.values()) for i,base in enumerate(['A','C','G','T']): # p_matrix_0 = (n_c_0 + pseudocount)/sum n_b_0 + 4*pseudocount p_matrix.iloc[i,0] = (n_0[base] + self._pseudo)/(my_sum + self._pseudo*4) #TODO: Find what to do with background for now set all to .25 # p_matrix.loc[:,0] = .25 log_likelihood, p_matrix, z_matrix = self._run_em(p_matrix) self._P = p_matrix self._Z = z_matrix print("New starting position log-likelihood of: {:.3f}".format(log_likelihood)) print("Finished exhaustive subsequence, starting EM") likelihood = self.run_em() def exhaustive_subsequence(self,iterations=1): """Initialize the EM algzorithm by using the exhaustive-subsequence method: run EM for a specified number of iterations on each possible length W subsequence of the input. Parameters ---------- iterations : int Default: 1 Number of iterations to run the EM algorithm on each distinct subsequence Returns ------- p : array-like Matrix of probabities of letter c in column k of the highest likelihood starting position Additionally, sets self._p with this matrix """ # Find total number of characters in Dataset: counts = collections.Counter(''.join(self._sequences)) # Find unique set of length W subsequences so we don't have any redundencies unique = set() for sequence in self._sequences: for start in range(self._starting_pos): # Iterate through the starting positions and make a substring at the starting position and ending at that position plus the width unique.add(sequence[start:(start+self._width)]) # Now that we have the set, we can iterate through all the unique sequences and set their p-matrix # Keep track of best log-likelihood best = float("-inf") best_seq = '' print("Total number of subsequences to evaluate: ",len(unique)) evaluated = 0 # my_j = 0 for seq in unique: # if my_j < 500: # print(my_j) # my_j += 1 # continue p_matrix = pd.DataFrame(data=np.zeros_like(self._P),index=['A','C','G','T'],columns=list(range(self._width + 1))) ############ Populate p_c_k, k > 0 # Go through bases, and set probability of the found character at that position equal to 2/5, and the others to 1/5 for i,base in enumerate(seq): for letter in p_matrix.index: if letter == base: p_matrix.loc[base,i+1] = (self._pseudo + 1)/(self._pseudo*4 + 1) else: p_matrix.loc[letter,i+1] = (self._pseudo)/(self._pseudo*4 + 1) ############ Populate p_c_k, k = 0 n_0 = {} # Number of A's C's G's and T's in the sequence. counts_seq = collections.Counter(seq) for base in ['A','C','G','T']: # n_c_0 = total # of c's in dataset - count of c's in motif n_0[base] = (counts[base] - counts_seq[base]) my_sum = sum(n_0.values()) for i,base in enumerate(['A','C','G','T']): # p_matrix_0 = (n_c_0 + pseudocount)/sum n_b_0 + 4*pseudocount p_matrix.iloc[i,0] = (n_0[base] + self._pseudo)/(my_sum + self._pseudo*4) #TODO: Find what to do with background for now set all to .25 # p_matrix.loc[:,0] = .25 log_likelihood, p_matrix, z_matrix = self._run_em(p_matrix) evaluated += 1 if log_likelihood > best: self._P = p_matrix self._Z = z_matrix best = log_likelihood best_seq = seq print("{:s} log-likelihood of: {:.3f}, best log-likelihood of: {:.3f}, {:s}. {} left to evaluate".format(seq,log_likelihood,best,best_seq,len(unique) - evaluated)) print("Finished exhaustive subsequence, starting EM") likelihood = self.run_em() # easy test passed def _calc_seq_prob(self,seq,z,p_matrix): """_calculate_seq_prob calculates the log probability of a sequence given a sequence (seq), the motif starting position (z), and a probability matrix (p_matrix) Parameters ---------- seq : string Sequence of whcih to calculate the probability z : int Starting position of motif in sequence p_matrix : array-like probability matrix of character c in row k of matrix, where the 0th row is the background Raise ------- ValueError when z > L - W + 1 Returns ------- log-probability of the sequence """ if z > (len(seq) - (p_matrix.shape[1] - 1)): raise ValueError("z must be less than L - W") total = 0 for i,base in enumerate(seq): # Background if i < z or i > (z+(p_matrix.shape[1]-1)-1): total += np.log(p_matrix.loc[base,0]) else: # 0th index of the motif is at column 1 in the p_matrix so use i - z + 1 total += np.log(p_matrix.loc[base,(i - z + 1)]) return total # easy test passed def _calc_seq_prob_sum(self,seq,p_matrix): """Calculate the sum of sequence probabilities over all z \in {0,...,(L - W)} Parameters ---------- seq : string Sequence to calculate sum on p_matrix : array-like probability matrix for character c in column k of the motif, with background being in column 0 Returns ------- tuple: (log-probability, [individual probabilities]) [0]: log-probability of the sum of sequence probabilities for all z \in {0,...,(L - W)} [1]: list of the probability of the sequence at the individual starts """ # Initialize total at -inf corresponding to 0 probability total = float("-inf") individual = [0]*(len(seq) - (p_matrix.shape[1] - 1) + 1) # Iterate through all starting positions: {0,...,(L - W)} for z in range(len(seq) - (p_matrix.shape[1] - 1) + 1): # log(x + y) = x' + log(1 + exp(y' - x')), where ' denotes log-space # x' is supposed to be larger, i.e. closer to 0, and it starts at 0 so it should be larger # Define the total as x and y as the new sequence to add on prob = self._calc_seq_prob(seq,z,p_matrix) individual[z] = prob #if z == 0: total = prob + np.log(1 + np.exp(total - prob)) #else: # total = total + np.log(1 + np.exp(prob - total)) return (total,individual) def _calc_log_likelihood(self,p_matrix,z_matrix): """TODO: Docstring for _calc_log_likelihood. Parameters ---------- p_matrix : TODO z_matrix : TODO Returns ------- Log-likelihood of data and starting positions given current parameters """ # Normalize z matrix so we use the max percentage as the starting position. # i.e. convert .1 .1 .1 .7 to 0 0 0 1 # for i,row in enumerate(z_matrix): # max_index = np.argmax(row) # z_matrix[i,:] = np.zeros_like(row) # z_matriz[i,max_index] = 1 # total = 0 # n = len(self._sequences) m = z_matrix.shape[1] # # Enumerate all sequences # for i, seq in enumerate(self._sequences): # # Enumerate all starting positions # for j, col in enumerate(z_matrix[i,:]): # # Calculate sequence prob # total += (z_matrix[i,j]*self._calc_seq_prob(seq,j,p_matrix) + n*np.log(1/m)) total = 0 for i,seq in enumerate(self._sequences): total += self._calc_seq_prob_sum(seq,p_matrix)[0] - np.log(m) return total def run_em(self): """Run the EM algorithm until convergence Returns ------- float: current log-likelihood """ current = float("-inf") i = 1 print(self._P) while(True): log_likelihood,p_matrix,z_matrix = self._run_em(self._P) i += 1 if (current - log_likelihood > -.05) and (current - log_likelihood < 0): current = log_likelihood print("Difference less than -.05. EM converged. Log-likelihood: ", log_likelihood) break elif log_likelihood > current: current = log_likelihood self._P = p_matrix self._Z = z_matrix print("Finished iteration: ", i," \tLog-likelihood: ",current) else: print(self._P) raise ValueError("log-likelihood not increasing, something is wrong...") return current def _run_em(self,p_matrix): """TODO: Docstring for _run_em. Parameters ---------- p_matrix : array-like Current matrix of probabilities for character c at position k of the motif, index starting at 1 since index 0 is background 0 1 2 .... W A 0.0 0.0 C 0.0 0.0 G 0.0 0.0 T 0.0 0.0 Returns ------- tuple of (log_likelihood, p_matrix, z_matrix) """ ################### Initialization ############# # Use local versions of p_matrix and z_matrix n_matrix _p = pd.DataFrame().reindex_like(self._P).fillna(0) _z = np.zeros_like(self._Z) _n = pd.DataFrame().reindex_like(self._P).fillna(0) ################### E-Step ##################### # Compute Z_{i,j}^t seq_sums = [0]*len(self._sequences) for i,sequence in enumerate(self._sequences): sequence_sum,individuals = self._calc_seq_prob_sum(sequence,p_matrix) seq_sums[i] = sequence_sum # Set the values for the sequence by dividing the individual probability by the sum, using subtraction in log-space and normalize _z[i,:] = self._normalize([(x - sequence_sum) for x in individuals]) ################### M-Step ##################### ##### populate n_matrix for i, sequence in enumerate(self._sequences): for j, base in enumerate(sequence): ######### The following only calculates n_c,k where k > 0 # Add to the sum across sequences of the sum over positions where c appears # For each position we want k to correspond to the number of positions that each character could be at if it were part of a motif. i.e. the character at position 0 in the sequence can't be the second position in the motif. if j < (self._width - 1): k = j + 1 my_range = range(1,k+1) elif j > (len(sequence) - self._width): #k = width - (distance from the end) k = self._width - ((len(sequence) - 1) - j) my_range = range(k,self._width+1) else: my_range = range(1,self._width+1) # my_range represents a range over all the positions k in a motif that the character could be in for k in my_range: #j needs to equal where the starting position would be # if k = 1, starting pos = j # if k = 2, starting pos = j - 1 #starting_pos = j - (k - 1) starting_pos = j - (k - 1) # _n.loc[base,k] += _z[j,starting_pos] _n.loc[base,k] += _z[i,starting_pos] ###### k = 0 # Populate n_c,0 with n_C (total # of c's in data set) _n.loc[base,0] += 1 # After this nested loop, have one loop to subtract the sume of n_c,k for all k for base in range(4): _n.iloc[base,0] -= np.sum(_n.iloc[base,1:]) ##### calculate p_matrix from n_matrix for base in ['A','C','G','T']: for position in range(self._P.shape[1]): _p.loc[base,position] = (_n.loc[base,position] + self._pseudo)/(np.sum(_n.loc[:,position]) + self._pseudo*4) log_likelihood = self._calc_log_likelihood(_p,_z) return (log_likelihood, _p, _z) ################ Testing #sequences = [] #with open('example1.txt', 'r') as infile: # for line in infile: # sequences.append(line.replace('\n','')) #test = MEME(sequences, 3, 1) ## Main # This is the main function provided for you. # Define additional functions to implement MEME def main(args): # Parse input arguments # It is generally good practice to validate the input arguments, e.g., # verify that the input and output filenames are provided if args.sub_name == 'meme': seq_file_path = args.sequences_filename W = args.width model_file_path = args.model position_file_path = args.positions subseq_file_path = args.subseqs sequences = [] with open(seq_file_path, 'r') as infile: for line in infile: sequences.append(line.replace('\n','')) my_meme = MEME(sequences, W, 1) if args.start and len(args.start) == args.width: if len(args.start) != args.width: raise ValueError('Start must be same length as width') my_meme.test(args.start) else: my_meme.exhaustive_subsequence() positions = [] subseqs = [] for i,seq in enumerate(my_meme.z): positions.append(np.argmax(seq)) subseqs.append(sequences[i][np.argmax(seq):(np.argmax(seq) + W + 1)]) with open(position_file_path,'w') as out_file: for position in positions: print(position, file=out_file) with open(subseq_file_path,'w') as out_file: for sequence in subseqs: print(sequence, file=out_file) with open(model_file_path, 'w') as out_file: print(my_meme.p.to_csv(header=False,sep='\t'),file=out_file) print(my_meme.p.to_csv(header=False,sep='\t',float_format="%.3f")) if args.graph: heights = seq_logo.seq_logo(my_meme.p) plots = [0]*my_meme.p.shape[0] for letter in range(my_meme.p.shape[0]): plots[letter] = plt.bar(list(range(my_meme.p.shape[1])), my_meme.p.iloc[letter,:],bottom=(np.sum(my_meme.p.iloc[0:letter,:]) if letter != 0 else 0))[0] plt.legend(plots,('A','C','G','T')) plt.ylim([0,2]) plt.ylabel('Bits') plt.show() elif args.sub_name == 'logo': model_filename = args.model_filename # Read file as csv with tab separator, treat the first column as the row names, and drop columns that are NaN model = pd.read_csv(model_filename,header=None,sep='\t',index_col=0).dropna(axis=1) heights = seq_logo.seq_logo(model) plots = [0]*model.shape[0] if args.graph: for letter in range(model.shape[0]): plots[letter] = plt.bar(list(range(model.shape[1])), model.iloc[letter,:],bottom=(np.sum(model.iloc[0:letter,:]) if letter != 0 else 0))[0] plt.legend(plots,('A','C','G','T')) plt.ylim([0,2]) plt.ylabel('Bits') plt.show() else: print(heights) # Note: this syntax checks if the Python file is being run as the main program # and will not execute if the module is imported into a different module if __name__ == "__main__": # Note: this example shows named command line arguments. See the argparse # documentation for positional arguments and other examples. parser = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) subparsers = parser.add_subparsers(title='subcommands',dest='sub_name') parser_em = subparsers.add_parser('meme',description=__doc__,help='Run meme algorithm') parser_logo = subparsers.add_parser('logo',description=__doc__,help='Compute sequence logo') parser_logo.add_argument('model_filename', help='model file path.', type=str) parser_logo.add_argument('--graph', help='Graph model?', action='store_true', default=False) parser_em.add_argument('sequences_filename', help='sequences file path.', type=str) parser_em.add_argument('--width', help='width of the motif.', type=int, default=6) parser_em.add_argument('--graph', help='Graph model?', action='store_true', default=False) parser_em.add_argument('--start', help='Motif possibility to start with, must be same length as --width.', type=str, default=None) parser_em.add_argument('--model', help='model output file path.', type=str, default='model.txt') parser_em.add_argument('--positions', help='position output file path.', type=str, default='positions.txt') parser_em.add_argument('--subseqs', help='subsequence output file path.', type=str, default='subseqs.txt') args = parser.parse_args() # Note: this simply calls the main function above, which we could have # given any name main(args)

[ "nate.rich2@gmail.com" ]

nate.rich2@gmail.com

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from django.db import models from django.utils import timezone class Post(models.Model): author = models.ForeignKey('auth.User') title = models.CharField(max_length=200) text = models.TextField() created_date = models.DateTimeField(default=timezone.now()) published_date = models.DateTimeField(blank=True, null=True) def publish(self): self.published_date = timezone.now() self.save() def __str__(self): return self.title

[ "mperesp1990@gmail.com" ]

mperesp1990@gmail.com

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[]

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irs1318dev/irsScouting2017

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cf2f0f75495916a74f3246e5119fa945f281c279

refs/heads/master

2021-01-16T00:28:41.380053

2020-02-26T04:29:54

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"""Commonly used settings """ # Server settings host = "localhost" port = "5432" # Production Database Settings user = "irs1318" pw = "irs1318" db = "scouting" def_event = ("turing", "2017") # Root Settings root_user = "postgres" root_pw = "irs1318" root_db = "postgres" # Test Settings test_user = "irs1318test" test_pw = "irs1318test" test_db = "scouting_test" test_event = ("test_event", "2017")

[ "stacy.k.irwin@gmail.com" ]

stacy.k.irwin@gmail.com

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prabhjyotsingh95@gmail.com

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scurry222/AirBnB_clone_v2

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#!/usr/bin/python3 """ starts a web application """ from flask import Flask app = Flask(__name__) @app.route('/', strict_slashes=False) def hello_flask(): """ returns intro string """ return "Hello HBNB!" if __name__ == "__main__": app.run()

[ "scurry222@gmail.com" ]

scurry222@gmail.com

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kevin-fang/PillUp

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2023-01-06T20:12:57.328896

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from Utilities.NotificationCenter import NotificationCenter as Notification from main import socketio import json @Notification.notify_on('dispense') def dispense(patient, medicine): try: data = patient.to_json() data['medicine'] = medicine.to_json() data = json.dumps(data) socketio.emit('dispense', data, broadcast=True, namespace='/stream') except: pass @Notification.notify_on('refill') def dispense(patient, medicine): try: data = patient.to_json() data['medicine'] = medicine.to_json() data = json.dumps(data) socketio.emit('refill', data, broadcast=True, namespace='/stream') except: pass @socketio.on('connect', namespace='/stream') def handle_connect(): pass

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sh.taslim@gmail.com

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2023-01-10T04:31:52.694859

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# # This file is part of LiteX. # # Copyright (c) 2015-2019 Florent Kermarrec <florent@enjoy-digital.fr> # Copyright (c) 2019 msloniewski <marcin.sloniewski@gmail.com> # SPDX-License-Identifier: BSD-2-Clause import os from litex.build.generic_platform import GenericPlatform from litex.build.altera import common, quartus # AlteraPlatform ----------------------------------------------------------------------------------- class AlteraPlatform(GenericPlatform): bitstream_ext = ".sof" create_rbf = True def __init__(self, *args, toolchain="quartus", **kwargs): GenericPlatform.__init__(self, *args, **kwargs) self.ips = set() if toolchain == "quartus": self.toolchain = quartus.AlteraQuartusToolchain() else: raise ValueError("Unknown toolchain") def add_ip(self, filename): self.ips.add((os.path.abspath(filename))) def get_verilog(self, *args, special_overrides=dict(), **kwargs): so = dict(common.altera_special_overrides) so.update(special_overrides) return GenericPlatform.get_verilog(self, *args, special_overrides=so, **kwargs) def build(self, *args, **kwargs): return self.toolchain.build(self, *args, **kwargs) def add_period_constraint(self, clk, period): if clk is None: return if hasattr(clk, "p"): clk = clk.p self.toolchain.add_period_constraint(self, clk, period) def add_false_path_constraint(self, from_, to): if hasattr(from_, "p"): from_ = from_.p if hasattr(to, "p"): to = to.p self.toolchain.add_false_path_constraint(self, from_, to)

[ "florent@enjoy-digital.fr" ]

florent@enjoy-digital.fr

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/tests/callable/publisher_function/test_subscriber_staticmethod.py

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[ "Apache-2.0" ]

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basecue/cue

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from types import SimpleNamespace import pytest from cue import publisher, subscribe @pytest.fixture def setup(): @publisher def event(text: str, flag: bool = True): return text, flag class Subscriber: subscribers = SimpleNamespace( on_event_staticmethod=[], ) @subscribe(event) @staticmethod def on_event_staticmethod(text: str, flag: bool = True): Subscriber.subscribers.on_event_staticmethod.append((text, flag)) return event, Subscriber def test(setup): event, Subscriber = setup return_value = event('text', flag=False) assert return_value == ("text", False) assert Subscriber.subscribers.on_event_staticmethod == [ ('text', False) ] def test_subscriber_instance(setup): event, Subscriber = setup subscriber = Subscriber() subscriber_2 = Subscriber() return_value = event('text', flag=False) assert return_value == ("text", False) assert Subscriber.subscribers.on_event_staticmethod == [ ('text', False) ]

[ "noreply@github.com" ]

basecue.noreply@github.com

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/qida/models.py

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[]

no_license

liubq919/VisualizeLogStatistics

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refs/heads/master

2021-01-10T08:44:49.284076

2016-03-09T02:58:56

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# -*- coding: utf-8 -*- from mongoengine import * __author__ = 'liubq' class EveryFifteenMinutes(EmbeddedDocument): hour = IntField(required=True) counter1 = IntField(required=True, default=0) counter2 = IntField(required=True, default=0) counter3 = IntField(required=True, default=0) counter4 = IntField(required=True, default=0) class QidaEveryFifMinLoginCount(Document): date = StringField(required=True) info = ListField(EmbeddedDocumentField(EveryFifteenMinutes)) class QidaOSInfo(Document): date = StringField(required=True) # desktop os windowsxp = IntField(required=True, default=0) windowsvista = IntField(required=True, default=0) windows7 = IntField(required=True, default=0) windows8 = IntField(required=True, default=0) windows8_1 = IntField(required=True, default=0) windows10 = IntField(required=True, default=0) windowsOthers = IntField(required=True, default=0) windows = IntField(required=True, default=0) osx = IntField(required=True, default=0) # mobile/tablet os iPhone = IntField(required=True, default=0) iPad = IntField(required=True, default=0) android = IntField(required=True, default=0) # other os osOthers = IntField(required=True, default=0) meta = { 'collection': 'qida_os_info' } class QidaBrowserInfo(Document): date = StringField(required=True) ie6 = IntField(required=True, default=0) ie7 = IntField(required=True, default=0) ie8 = IntField(required=True, default=0) ie9 = IntField(required=True, default=0) ie10 = IntField(required=True, default=0) ie11 = IntField(required=True, default=0) edge = IntField(required=True, default=0) firefox = IntField(required=True, default=0) chrome = IntField(required=True, default=0) safari = IntField(required=True, default=0) others = IntField(required=True, default=0) class QidaLoginCount(Document): date = StringField(required=True) times = IntField(required=True, default=0)

[ "liubq919@163.com" ]

liubq919@163.com

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refs/heads/master

2023-09-05T02:45:56.225937

2023-08-28T22:19:24

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2020-05-05T12:13:49

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# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from skywalking import Layer, Component from skywalking.trace.carrier import Carrier from skywalking.trace.context import get_context from skywalking.trace.tags import TagMqBroker, TagMqTopic, TagMqQueue link_vector = ['https://pypi.org/project/aiormq/'] support_matrix = { 'aiormq': { '>=3.7': ['6.3', '6.4'] } } note = """""" def install(): from aiormq import Channel from aiormq.tools import awaitable try: from pamqp import commands as spec # aiormq v6.4.1 except ImportError: from pamqp import specification as spec # aiormq v3.3.1 async def _sw_basic_publish(self, body, exchange='', routing_key='', properties=None, **kwargs): url = self.connection.url peer = f'{url.host}:{url.port}' if url.port else url.host context = get_context() with context.new_exit_span(op=f'RabbitMQ/Topic/{exchange}/Queue/{routing_key}/Producer', peer=peer, component=Component.RabbitmqProducer) as span: span.tag(TagMqBroker(peer)) span.tag(TagMqTopic(exchange)) span.tag(TagMqQueue(routing_key)) span.layer = Layer.MQ carrier = span.inject() if properties is None: properties = spec.Basic.Properties(delivery_mode=1) headers = getattr(properties, 'headers', None) if headers is None: headers = properties.headers = {} for item in carrier: headers[item.key] = item.val return await _basic_publish(self, body, exchange=exchange, routing_key=routing_key, properties=properties, **kwargs) async def _sw_basic_consume(self, queue, consumer_callback, *args, **kwargs): async def _callback(msg): context = get_context() url = self.connection.url peer = f'{url.host}:{url.port}' if url.port else url.host exchange = msg.delivery.exchange routing_key = msg.delivery.routing_key headers = msg.header.properties.headers carrier = Carrier() for item in carrier: if item.key in headers: val = headers.get(item.key) if val is not None: item.val = val if isinstance(val, str) else val.decode() with context.new_entry_span(op='RabbitMQ/Topic/' + exchange + '/Queue/' + routing_key + '/Consumer' or '', carrier=carrier) as span: span.layer = Layer.MQ span.component = Component.RabbitmqConsumer span.tag(TagMqBroker(peer)) span.tag(TagMqTopic(exchange)) span.tag(TagMqQueue(routing_key)) return await awaitable(consumer_callback)(msg) return await _basic_consume(self, queue, _callback, *args, **kwargs) _basic_publish = Channel.basic_publish _basic_consume = Channel.basic_consume Channel.basic_publish = _sw_basic_publish Channel.basic_consume = _sw_basic_consume

[ "noreply@github.com" ]

apache.noreply@github.com

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/test/test2.py

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[]

no_license

changyubiao/myalgorithm-demo

ee9c8f559d61604ec8f4805785adf1a569d031a4

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refs/heads/master

2022-12-08T18:47:23.681537

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @Time : 2018/12/16 09:55 @File : test2.py @Author : frank.chang@shoufuyou.com info = [ {"name": "laoda", "age": 12, "height": "175"}, {"name": "laoda", "age": 22, "height": "180"}, {"name": "laoda", "age": 42, "height": "160"}, ] """ from operator import itemgetter, attrgetter class Student: def __init__(self, name, age, score): self.name = name self.age = age self.score = score def __str__(self): return '%s(name:%s,age:%s,score:%s)' % (self.__class__.__name__, self.name, self.age, self.score) __repr__ = __str__ if __name__ == '__main__': std1 = Student("A", 11, 23) std2 = Student("B", 13, 10) std3 = Student("C", 16, 15) std4 = Student("D", 34, 4) students = [std1, std2, std3, std4] students.sort(key=lambda student: student.score, reverse=True) # print(students) for item in students: print(item) print(sorted(students, key=attrgetter('age'), reverse=True))

[ "frank.chang@shoufuyou.com" ]

frank.chang@shoufuyou.com

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/store/views/contact.py

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[]

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Manishkd04/py-estore

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refs/heads/master

2023-02-02T02:47:05.885373

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from django.shortcuts import render, redirect from django.contrib.auth.hashers import check_password from store.models.customer import Customer from django.views import View from store.models.product import Product, Contact from store.models.orders import Order class Contactview(View): def get(self, request): return render(request, 'contact.html') def post (self, request): first_name = request.POST.get('first_name') last_name = request.POST.get('last_name') email = request.POST.get('email') phone = request.POST.get('phone') desc = request.POST.get('desc') if request.method=="POST": contact = Contact(first_name=first_name, last_name=last_name, email=email, phone=phone, desc=desc) contact.save() return render(request, 'contact.html') # return redirect('/contact') # def post(self, request):

[ "nashkumard5@gmail.com" ]

nashkumard5@gmail.com

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/numpy_bressert/ch02_scipy/solutions_to_funcs.py

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[]

no_license

olegzinkevich/programming_books_notes_and_codes

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refs/heads/main

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# With data modeling and fitting under our belts, we can move on to finding solutions, # such as “What is the root of a function?” or “Where do two functions intersect?” SciPy # provides an arsenal of tools to do this in the optimize module. # Let’s start simply, by solving for the root of an equation (see Figure 3-4). Here we will # use scipy.optimize.fsolve from scipy.optimize import fsolve import numpy as np # Find the roots of a function. line = lambda x: x + 3 solution = fsolve(line, -2) print(solution) # Finding the intersection points between two equations is nearly as simple.3 # Defining function to simplify intersection solution def findIntersection(func1, func2, x0): return fsolve(lambda x : func1(x) - func2(x), x0) # Defining functions that will intersect funky = lambda x : np.cos(x / 5) * np.sin(x / 2) line = lambda x : 0.01 * x - 0.5 # Defining range and getting solutions on intersection points x = np.linspace(0,45,10000) result = findIntersection(funky, line, [15, 20, 30, 35, 40, 45]) # Printing out results for x and y print(result, line(result))

[ "zinkevicholeg@gmail.com" ]

zinkevicholeg@gmail.com

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/listings/migrations/0001_initial.py

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[]

no_license

ToughTiger/tbre

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refs/heads/master

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# Generated by Django 3.2 on 2021-04-20 09:30 import datetime from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ('realtors', '0001_initial'), ] operations = [ migrations.CreateModel( name='Listing', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=120)), ('address', models.CharField(max_length=120)), ('city', models.CharField(max_length=120)), ('state', models.CharField(blank=True, max_length=20)), ('pincode', models.CharField(max_length=20)), ('descripting', models.TextField(max_length=120)), ('price', models.IntegerField(default=0)), ('bedrooms', models.DecimalField(decimal_places=2, max_digits=2)), ('bathrooms', models.IntegerField()), ('parking', models.IntegerField(default=0)), ('builtupArea', models.DecimalField(decimal_places=2, max_digits=2)), ('carpetArea', models.DecimalField(decimal_places=2, max_digits=2)), ('photo_main', models.ImageField(upload_to='photos/%Y/%m/%d/')), ('photo_1', models.ImageField(blank=True, upload_to='photos/%Y/%m/%d/')), ('photo_2', models.ImageField(blank=True, upload_to='photos/%Y/%m/%d/')), ('photo_3', models.ImageField(blank=True, upload_to='photos/%Y/%m/%d/')), ('photo_4', models.ImageField(blank=True, upload_to='photos/%Y/%m/%d/')), ('photo_5', models.ImageField(blank=True, upload_to='photos/%Y/%m/%d/')), ('photo_6', models.ImageField(blank=True, upload_to='photos/%Y/%m/%d/')), ('is_published', models.BooleanField(default=True)), ('list_date', models.DateTimeField(blank=True, default=datetime.datetime.now)), ('realtor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='realtors.realtor')), ], ), ]

[ "santosh.cerdak@gmail.com" ]

santosh.cerdak@gmail.com

dcd3c1fcde72e282819c6cf23917385107266f14

7d25b5877a615cad87ce4b606db1f06dba05afa6

/convert_to_snake_case.py

6bde2e631f695f88e174a6e78b8cd8732583e520

[]

no_license

Thomd209/Convert_to_snake_case

c32b06c7edf9f27b6a0240d70e56a55b9a2b12bf

e2c6597f882daeede3a3f674cde3e4f109ba9beb

refs/heads/main

2023-04-04T06:49:16.845828

2021-04-14T21:37:53

358,048,163

0

null

UTF-8

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455

py

def convert_to_snake_case(s): snake_case = '' for i in range(len(s)): if i == len(s) - 1: snake_case += s[i] continue if s[i + 1].isupper() and s[i + 1].isalpha(): snake_case += s[i] + '_' else: snake_case += s[i] snake_case = snake_case.lower() return snake_case camel_case = input() result = convert_to_snake_case(camel_case) print(result)

[ "thomd209@gmail.com" ]

thomd209@gmail.com

e624584bc90951ce86e2b1625a67dc7648748b9f

5c90e8542121f110d595805116afc3ae9863f6bd

/Ada_LSN/operations.py

2efaca044053bf8901745863f945de769b6df2ee

[]

no_license

philaWu/SDL-Skeleton

46d710f317a820a3bb193d218f96948534ddfd50

554bbfd4775422343ce7d7789ca22720a298d359

refs/heads/main

2023-06-26T09:01:31.518831

2021-07-26T10:48:18

null

0

null

UTF-8

Python

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py

import torch import torch.nn as nn import torch.nn.functional as F OPS = { 'skip': lambda C, stride: Identity(), 'conv1': lambda C, stride: Conv(C, C, 1, stride, 0), 'conv3': lambda C, stride: Conv(C, C, 3, stride, 1), 'conv5': lambda C, stride: Conv(C, C, 5, stride, 2), 'dconv3_2': lambda C, stride: DilConv(C, C, 3, stride, 2, 2), 'dconv3_4': lambda C, stride: DilConv(C, C, 3, stride, 4, 4), 'dconv3_8': lambda C, stride: DilConv(C, C, 3, stride, 8, 8), 'dconv5_2': lambda C, stride: DilConv(C, C, 5, stride, 4, 2), 'dconv5_4': lambda C, stride: DilConv(C, C, 5, stride, 8, 4), 'dconv5_8': lambda C, stride: DilConv(C, C, 5, stride, 16, 8), } class ReLUConvBN(nn.Module): def __init__(self, C_in, C_out, kernel_size, stride, padding, inplace=True, affine=True): super(ReLUConvBN, self).__init__() self.op = nn.Sequential( nn.ReLU(inplace=inplace), nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding), # nn.BatchNorm2d(C_out) ) # for layer in self.op.modules(): # if isinstance(layer, nn.Conv2d): # nn.init.xavier_normal_(layer.weight) def forward(self, x): return self.op(x) class DilConv(nn.Module): def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, inplace=False, affine=True): super(DilConv, self).__init__() self.op = nn.Sequential( nn.ReLU(inplace=inplace), nn.Conv2d(C_in, C_out, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation), # nn.BatchNorm2d(C_out, affine=affine), ) # for layer in self.op.modules(): # if isinstance(layer, nn.Conv2d): # nn.init.xavier_normal_(layer.weight) def forward(self, x): return self.op(x) class Conv(nn.Module): def __init__(self, C_in, C_out, kernel_size, stride, padding, inplace=False, affine=True): super(Conv, self).__init__() self.op = nn.Sequential( nn.ReLU(inplace=inplace), nn.Conv2d(C_in, C_out, kernel_size=kernel_size, stride=stride, padding=padding), # nn.BatchNorm2d(C_out), # memory overflow if use this BN ) # for layer in self.op.modules(): # if isinstance(layer, nn.Conv2d): # nn.init.xavier_normal_(layer.weight) def forward(self, x): return self.op(x) class Identity(nn.Module): def __init__(self): super(Identity, self).__init__() def forward(self, x): return x class FactorizedReduce(nn.Module): def __init__(self, C_in, C_out, inplace=True, affine=True): super(FactorizedReduce, self).__init__() assert C_out % 2 == 0 self.relu = nn.ReLU(inplace=inplace) self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0) self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0) # self.bn = nn.BatchNorm2d(C_out) def forward(self, x): x = self.relu(x) out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1) # out = self.bn(out) return out

[ "noreply@github.com" ]

philaWu.noreply@github.com

2d80f265944c4dd17736144a3090e076a6c797d3

d59cb746bb746c4ed87d5b222c7bde0688d95456

/todo/tasks/migrations/0002_task_done.py

6de5bb6d3ee400fa11fed9db9dd5bb0b502e3514

[]

no_license

quiqueporta/devscola-djangorest

0eea64f30b49554c05bd508767be38ea4ab9cc41

4121e7a7c82721c730e70f042d95d6f4696da814

refs/heads/master

2020-03-27T06:19:36.761044

2018-08-25T13:27:09

2018-08-25T13:34:47

146,097,543

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UTF-8

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380

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# Generated by Django 2.1 on 2018-08-25 12:07 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('tasks', '0001_initial'), ] operations = [ migrations.AddField( model_name='task', name='done', field=models.BooleanField(blank=True, default=False), ), ]

[ "quiqueporta@gmail.com" ]

quiqueporta@gmail.com

23f10a81cd196cdcebaab2c24f29d41451239302

ee409492c8e6370ee883b6fa430fde3ec0bd7a73

/sys/day8/file.py

be6f36086354d38bdacffc5daae7cf8dde891547

[]

no_license

n30curry/python

0e9aa681e94dafe6b39d703e751a80891ac4e708

8ab8e37fec6ae320e5c2f647182e4a24165dcf8d

refs/heads/master

2020-11-25T21:38:36.949822

2016-09-07T02:09:59

66,701,574

0

null

UTF-8

Python

false

88

py

#!/usr/bin/python import re a = open('dict.txt','r') b = a.readlines() print type(b)

[ "1070380869@qq.com" ]

1070380869@qq.com

083a6080a763233b225992d29eee723297afd443

3a5d2b0b840f138b1813649bf54626ab3bdc66da

/admin_login.py

86c0bab40c7bf029b3c63bef50ae59340ffded92

[]

no_license

deadline-dealt/ip_project

37532d67e85f066ee7c43c0c09e62f298a5f4f9d

b088b722f57bec8886591699a925a27084bd0d56

refs/heads/main

2023-03-24T07:49:43.380894

2021-03-29T16:18:48

352,696,787

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UTF-8

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py

import tkinter as tk from tkinter import * from tkinter import messagebox from PIL import ImageTk,Image root=tk.Tk() root.title("Admin Login") root.geometry("1000x500+200+100") #==========background_img================== photo = ImageTk.PhotoImage(Image.open("/home/varun/varun-files/ip project001/images/loginp.jpg")) root=Label(image=photo) root.place(x=0,y=0,relwidth=1,relheight=1) #===========username=============== username=Label(text="username",font=("times new roman",18,"bold","italic")) username.place(x=75,y=208) username_input=Entry(font=("times new roman",18)) username_input.place(x=240,y=208) #===========Password============ Password=Label(text="Password",font=("times new roman",18,"bold","italic")) Password.place(x=75,y=260) passwrd_input=Entry(show="*",font=("times new roman",18)) passwrd_input.place(x=240,y=260) #==========Login-work============== def Login(): if (username_input.get()=="nithish" and passwrd_input.get()=="nithish"): messagebox.showinfo("","sucessfully logined") messagebox.showinfo("good day","welcome back Nithish") import admin_menu.py elif (username_input.get()=="varun" and passwrd_input.get()=="varun"): messagebox.showinfo("","sucessfully logined") messagebox.showinfo("good day","welcome back Varun") import admin_menu.py elif (username_input.get()=="harshavardhan" and passwrd_input.get()=="harshavardhan"): messagebox.showinfo("","sucessfully logined") messagebox.showinfo("good day","welcome back Harshavardhan") import admin_menu.py else: messagebox.showinfo("error","Incorrect username/password") #========login_button==========0 Login_bt=Button(text="Login",command= Login ,bg="white",fg="red",font=("",13,"bold")) Login_bt.place(x=260,y=320) root.mainloop()

[ "noreply@github.com" ]

deadline-dealt.noreply@github.com

7b0b098c83e893f44397072c1651e786b859c43e

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/dynn/data/mnist.py

126220df89f2992934441526595b87c08313e0e0

[ "MIT" ]

permissive

pmichel31415/dynn

984b0fdfe45bf77f079248d1678d1aaf0dbf3649

7e780c4dcf928cdff5f2e52210409d2775ca7796

refs/heads/master

2022-11-01T08:58:53.432236

2018-12-11T18:06:15

106,301,897

1

MIT

2022-10-26T17:18:51

2017-10-09T15:32:02

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UTF-8

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#!/usr/bin/env python3 """ MNIST ^^^^^ Various functions for accessing the `MNIST <http://yann.lecun.com/exdb/mnist/>`_ dataset. """ import os import struct import gzip from io import BytesIO import array import numpy as np from .data_util import download_if_not_there mnist_url = "http://yann.lecun.com/exdb/mnist/" mnist_files = { "train_img": "train-images-idx3-ubyte.gz", "train_lbl": "train-labels-idx1-ubyte.gz", "test_img": "t10k-images-idx3-ubyte.gz", "test_lbl": "t10k-labels-idx1-ubyte.gz", } def download_mnist(path=".", force=False): """Downloads MNIST from "http://yann.lecun.com/exdb/mnist/" Args: path (str, optional): Local folder (defaults to ".") force (bool, optional): Force the redownload even if the files are already at ``path`` """ # Download all files sequentially for filename in mnist_files.values(): download_if_not_there(filename, mnist_url, path, force=force) def read_mnist(split, path): """Iterates over the MNIST dataset Example: .. code-block:: python for image in read_mnist("train", "/path/to/mnist"): train(image) Args: split (str): Either ``"training"`` or ``"test"`` path (str): Path to the folder containing the ``*-ubyte`` files Returns: tuple: image, label """ # Adapted from https://gist.github.com/akesling/5358964 if not (split is "test" or split is "train"): raise ValueError("split must be \"train\" or \"test\"") abs_path = os.path.abspath(path) fname_img = os.path.join(abs_path, mnist_files[f"{split}_img"]) fname_lbl = os.path.join(abs_path, mnist_files[f"{split}_lbl"]) with open(fname_lbl, "rb") as zflbl: flbl = BytesIO(gzip.decompress(zflbl.read())) _, _ = struct.unpack(">II", flbl.read(8)) data = array.array("B", flbl.read()) lbl = np.asarray(data, dtype=np.uint8) with open(fname_img, "rb") as zfimg: fimg = BytesIO(gzip.decompress(zfimg.read())) _, _, rows, cols = struct.unpack(">IIII", fimg.read(16)) data = array.array("B", fimg.read()) img = np.multiply( np.asarray(data, dtype=np.uint8).reshape(len(lbl), rows, cols, 1), 1.0 / 255.0 ) def get_img(idx): return (img[idx], lbl[idx]) for i in range(len(lbl)): yield get_img(i) def load_mnist(path): """Loads the MNIST dataset Returns MNIST as a dictionary. Example: .. code-block:: python mnist = load_mnist(".") # Train images and labels train_imgs, train_labels = mnist["train"] # Test images and labels test_imgs, test_labels = mnist["test"] The images are represented as numpy arrays and the labels as integers. Args: path (str): Path to the folder containing the ``*-ubyte.gz`` files Returns: dict: MNIST dataset """ splits = {} # Read data for split in ["train", "test"]: data = list(read_mnist("train", path)) images = [img for img, _ in data] labels = [lbl for _, lbl in data] splits[split] = (images, labels) return splits

[ "pmichel31415@gmail.com" ]

pmichel31415@gmail.com

e41d656de70cbce44fc05b492b78337f13b6036b

4bad9a1d18d0c0071b4a894fa0e181a4a77fece8

/app/plans/ramp.py

c13924d7e307464d3db41876dbfa6b9f510a5945

[]

no_license

bauersmatthew/olf

ae2d91514fb31664325cb62a05dea77baed224aa

aa472d1d68014976532532460308789aceb94e73

refs/heads/main

2023-07-22T16:45:25.107692

2021-09-10T04:12:43

404,792,153

1

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from errors import * import random import copy def order_from_spec(spec): if isinstance(spec, str): return [spec] elif isinstance(spec, list): shuffle = False items = spec else: assert isinstance(spec, dict) shuffle = spec.get('shuffle', False) items = spec['items'] items = copy.copy(items) # shallow ok if shuffle: random.shuffle(items) out = [] for x in items: out += order_from_spec(x) return out def default_order_spec(names): pfo = None if 'pfo' in names: pfo = 'pfo' elif 'paraffin' in names: pfo = 'paraffin' if pfo is None: return [names] return [pfo, {'shuffle': True, 'items': [x for x in names if x != pfo]}] def try_typecasting(cfg, *args): a = [] for k, ty in args: try: a.append(ty(cfg[k])) except: raise ParseError(f'Failed to typecast param: {k}') return tuple(a) def deep_update(a, b): for k, v in b.items(): if k not in a: a[k] = v else: va = a[k] if isinstance(va, dict): assert isinstance(v, dict) deep_update(va, v) else: a[k] = v class VialOdor: def __init__(self, odors, carrier_rate, odor_rate): self.order = sorted(odors, key=lambda o: o.conc_num()) self.carrier_rate = carrier_rate self.odor_rate = odor_rate def insert_steps(self, plan, prestimt, poststimt, stimt): for o in self.order: plan.push(f'Odor: {o}') plan += [ AdjustOdorFlowStep(o, self.carrier_rate, self.odor_rate), WaitStep(prestimt), OdorStep(o, stimt), WaitStep(poststimt) ] plan.pop() def parse_float_or_percent(x, of_what): if isinstance(x, str): assert x[-1] == '%' prop = float(x[:-1])/100 return prop*of_what else: return float(x) class GasOdor: def __init__(self, odor, cfg): self.odor = odor tot = float(cfg['total-flow']) oflows = [] for x in cfg['odor-flows']: oflows.append(parse_float_or_percent(x, tot)) cflows = [tot-x for x in oflows] self.rates = sorted( list(zip(cflows, oflows)), key = lambda x: x[1] ) def insert_steps(self, plan, prestimt, poststimt, stimt): for carrier_rate, odor_rate in self.rates: pc = round(100*odor_rate/(odor_rate+carrier_rate)) plan.push(f'Odor: {self.odor} at {pc}%') plan += [ AdjustOdorFlowStep(self.odor, carrier_rate, odor_rate), WaitStep(prestimt), OdorStep(self.odor, stimt), WaitStep(poststimt) ] @plan('Concentration Ramp', 'ramp') def concentration_ramp_plan(odors, cfg_): names = list(set([o.name for o in odors])) cfg = { 'order': default_order_spec(names), 'gasses': {}, 'nblocks': 3, 'timing': { 'stim': 2.0, 'begin-movie': 30.0, 'pre-stim': 30.0, 'post-stim': 30.0, 'rest': 120.0 }, 'default-flow': { 'total-flow': 2000.0, 'odor-flow': '10%' } } deep_update(cfg, cfg_) nb, timing = try_typecasting(cfg, ('nblocks', int), ('timing', dict) ) stimt, begint, prestimt, poststimt, restt = try_typecasting(timing, ('stim', float), ('begin-movie', float), ('pre-stim', float), ('post-stim', float), ('rest', float) ) default_flow_cfg = dict(cfg['default-flow']) vial_total_rate, = try_typecasting(default_flow_cfg, ('total-flow', float)) vial_odor_rate = parse_float_or_percent(default_flow_cfg['odor-flow'], vial_total_rate) vial_carrier_rate = vial_total_rate - vial_odor_rate order_spec = order_from_spec(cfg['order']) order = [] for x in order_spec: assert x in names relevant = [o for o in odors if o.name == x] if x in cfg['gasses']: assert len(relevant) == 1 order.append(GasOdor(relevant[0], cfg['gasses'][x])) else: assert len(relevant) > 0 order.append(VialOdor(relevant, vial_carrier_rate, vial_odor_rate)) sl = StepList() for i in range(nb): sl.push(f'Block {i+1}') sl += [ MirrorDownStep(), ScopeOnStep() ] if i == 0: sl.append(WaitStep(begint)) for o in order: o.insert_steps(sl, prestimt, poststimt, stimt) sl += [ ScopeOffStep(), MirrorUpStep() ] if i+1 < nb: sl.append(WaitStep(restt)) sl.pop() return sl

[ "bauer.s.matthew@gmail.com" ]

bauer.s.matthew@gmail.com

43d980dd88b22f0291fd9f0ef7279c68ae1ca292

3417f1ad3580519829172af19d7ce01e7972b4b7

/manage.py

1dd101fa79b5ad02c51c906177c5f98e305ff091

[]

no_license

min-afk/noob

b54eec904da9e5beb8459a74d1201c299314d673

0af044509878dfd9c04b179957daf67e33750f81

refs/heads/master

2021-03-29T11:31:09.623974

2020-03-17T12:04:00

247,950,510

0

null

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

[ "minotodor@gmail.com" ]

minotodor@gmail.com

634a8fda1b878503bf27d2455a97bc7b1ebf595a

e5450be6c3a071b81b57ac640b756f707b44cc92

/server/resources/portfolio/education.py

6218f0af78db05b0f31680a66a1010f2043ba15a

[]

no_license

karmadilos/portfolio-market

9fec4c5b0de6c8d9d894601076f3de7de569d64b

2e82c5aff59a607843e78e60b361fec97fb05d8a

refs/heads/master

2023-04-09T16:19:50.945833

2021-03-08T12:44:32

2021-03-08T12:44:40

362,552,909

0

null

UTF-8

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from flask import jsonify, request from flask_restful import reqparse, abort, Api, Resource from flask_jwt_extended import jwt_required from flask_jwt_extended import get_jwt_identity from database.models.education import Education from database.db import db from flask_jwt_extended import JWTManager import datetime import maya jwt = JWTManager() keys = [ "id", "user_id", "school_name", "major", "status", "create_date", "updated_date", ] # 토큰 방식으로 구현 후, post/put/delete 작업은 # 토큰 검증방식을 거친 후 접근할 수 있도록 처리하는 것 필요 class EducationApi(Resource): def get(self, user_id, id=None): # id값이 주어지는 경우에는 하나의 데이터만 return, 200 if not id: educations = ( db.session.query(Education) .filter_by(user_id=user_id) .order_by(Education.create_date) .all() ) else: educations = db.session.query(Education).filter_by(user_id=user_id, id=id) # 데이터가 없는 초기의 경우에는 빈 배열만 return, 200 if not educations: jsonify(status="success", data=[]) result = [{key: getattr(v, key) for key in keys} for v in educations] return jsonify( status="success", educations=result, ) @jwt_required() def post(self, user_id): if get_jwt_identity() != int(user_id): abort(401, status="fail", message="접근 권한이 없습니다.") # school_name, major, status = dict(request.get_json(force=True)).values() # print(request.header.get("csrf-access-token")) print(request.cookies, request.headers) education = Education(user_id) db.session.add(education) db.session.commit() return jsonify( status="success", result={key: getattr(education, key) for key in keys}, ) @jwt_required() def put(self, user_id, id=None): if get_jwt_identity() != int(user_id): abort(401, status="fail", message="접근 권한이 없습니다.") # 여러개의 데이터를 동시에 수정한다. (data에 배열로 수정 내용을 입력받음) data = request.get_json(force=True) print(data) for v in data: v["updated_date"] = datetime.datetime.utcnow() v["create_date"] = maya.parse(v["create_date"]).datetime() db.session.query(Education).filter_by(id=v["id"]).update(v) db.session.commit() return jsonify( status="success", result={"id": list(map(lambda x: x["id"], data))}, ) @jwt_required() def delete(self, user_id, id): if get_jwt_identity() != int(user_id): abort(401, status="fail", message="접근 권한이 없습니다.") if not id: abort(400, status="fail", message="삭제할 데이터가 없습니다.") education = Education.query.filter_by(user_id=user_id, id=id).first() # 해당 학위 정보가 존재하지 않으면 response 400 if not education: abort(400, status="fail", message="잘못된 ID입니다.") # 삭제할 학위 정보가 존재하면 삭제 후 response 200 db.session.delete(education) db.session.commit() return jsonify( status="success", result={"id": id, "user_id": user_id}, )

[ "swj960515@gmail.com" ]

swj960515@gmail.com

b62c10fe3f323f7df0fb2e4d3ffbe716839c228a

d1c0c6f9f2a9297639825138f8a62aa7a6e71a03

/PythonMoora/management/hasiltes/views.py

c6ffdf481e9ca2fd892353945ad9fb7e86a81cb7

[]

no_license

yunuslon/SPKDataSiswa

bd147280c4f141e193554da499aa45ccf9219a4c

e2af29d7d6777460d285207f11ebb6c7d2b926c1

refs/heads/master

2020-04-08T02:01:47.689577

2018-12-19T07:55:32

158,919,739

0

null

UTF-8

Python

false

3,919

py

from django.shortcuts import render, redirect, get_list_or_404 from django.views.generic import View from django.http import HttpResponse from django.contrib import messages from orm.models import HasilTes,Siswa from .forms import HasilTesForm from library.view import ManagementAccessView # Create your views here. class ListHasilTesView(ManagementAccessView): def get(self, request): template = 'hasiltes/index.html' form = HasilTesForm(request.POST or None) hasiltes = HasilTes.objects.all() data = { 'form_mode' : 'add', 'form' : form, 'siswa' : Siswa.objects.all(), 'hasiltes' : hasiltes, } return render(request, template, data) class SaveHasilTesView(ManagementAccessView): def post(self, request): template = 'hasiltes/index.html' form = HasilTesForm(request.POST or None) if form.is_valid(): # dari ngambil data # sampai simpan data hasiltes = HasilTes() hasiltes.siswa = form.cleaned_data['siswa'] hasiltes.mata_pelajaran = form.cleaned_data['mata_pelajaran'] hasiltes.nilai = form.cleaned_data['nilai'] messages.add_message(request, messages.INFO, 'Data Berhasil Disimpan') hasiltes.save() return redirect('hasiltes:view') else: hasiltes = HasilTes.objects.all() data = { 'form': form, 'hasiltes': hasiltes, } messages.add_message(request, messages.INFO, 'Data Gagal Disimpan !!') return render(request, template, data) class EditHasilTesView(ManagementAccessView): template = 'hasiltes/edit.html' def get(self, request, id): hasiltes = HasilTes.objects.filter(id=id) if not hasiltes.exists(): return redirect('hasiltes:view') hasiltes = hasiltes.first() initial = { 'id': hasiltes.id, 'mata_pelajaran': hasiltes.mata_pelajaran, 'nilai': hasiltes.nilai, 'siswa': hasiltes.siswa, } form = HasilTesForm(initial=initial) hasiltes = HasilTes.objects.all() data = { 'id':id, 'form': form, 'form_mode' : 'edit', 'hasiltes' : hasiltes, } return render(request, self.template, data) class UpdateHasilTesView(ManagementAccessView): def post(self, request): template = "hasiltes/index.html" form = HasilTesForm(request.POST or None) if form.is_valid(): id = form.cleaned_data['id'] hasiltes = HasilTes.objects.get(pk=id) hasiltes.mata_pelajaran = form.cleaned_data['mata_pelajaran'] hasiltes.nilai = form.cleaned_data['nilai'] hasiltes.siswa = form.cleaned_data['siswa'] messages.add_message(request, messages.INFO, 'Data Berhasil Diupdate') hasiltes.save(force_update=True) return redirect('hasiltes:view') else: hasiltes = HasilTes.objects.all() data = { 'form_mode':'edit', 'form': form, 'hasiltes': hasiltes, } messages.add_message(request, messages.INFO, 'Data Gagal Diupdate !!') # return render(request, template, data) return HttpResponse(form.errors) class HapusHasilTesView(ManagementAccessView): def get(self, request, id): hasiltes = HasilTes.objects.filter(id=id) if hasiltes.exists(): hasiltes.first().delete() messages.add_message(request, messages.INFO, 'Data Berhasil Dihapus') return redirect('hasiltes:view') else: messages.add_message(request, messages.INFO, 'Data Gagal Dihapus !!')

[ "yunuslon97@gmail.com" ]

yunuslon97@gmail.com

a32cbb4f180530ca941c558c1b528e2e3c1b2831

3f992958e8897a957e3b99c1391f48821fdcfb68

/Tests/Test_InputUserData.py

1ac375b7603157d74f5a1d3a01593690872e242d

[]

no_license

GlubokinV/ProgramSort

d36143898b8f88591906944ea2698a7685009998

49d7d5d44c383dbc22f1367e1c8e06f5d78537b8

refs/heads/main

2023-04-14T01:07:18.039866

2021-04-15T18:29:54

357,791,372

0

null

UTF-8

Python

false

1,641

py

import unittest from Program.InputUserData import DataInput class TestExample(unittest.TestCase): """Тестирование функции подготовки вводимых данных для обработки""" def test_example_only_KZS(self): """Разбивает строку из КЗС на отдельные символы?""" x = DataInput() res = x.example('ЗКЗС') self.assertEqual(res, ['З', 'К', 'З', 'С']) def test_example_spec_char(self): """Разбивает строку на отдельные символы, если есть спец. символы?""" x = DataInput() res = x.example('З!@#') self.assertEqual(res, ['З', '!', '@', '#']) def test_example_exceptions_not_KZS(self): """Появляется исключение, при вводе строки без КЗС?""" x = DataInput() self.assertRaises(ValueError, x.example, '!@#') def test_example_exceptions_empty_line(self): """Появляется исключение, при вводе пустой строки?""" x = DataInput() self.assertRaises(ValueError, x.example, []) def test_example_long_line(self): """Разбивает длинную строку на отдельные символы?""" x = DataInput() res = x.example('З!@sd;о;;;;qqqqqqqq') self.assertEqual(res, ['З', '!', '@', 's', 'd', ';', 'о', ';', ';', ';', ';', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q']) if __name__ == '__main__': unittest.main()

[ "glubokin_vyacheslav@mail.ru" ]

glubokin_vyacheslav@mail.ru

ee1e1f57e91c9660d5c3c00a1b93a25981c9b7af

82fce9aae9e855a73f4e92d750e6a8df2ef877a5

/Lab/venv/lib/python3.8/site-packages/OpenGL/raw/GLES1/OES/stencil8.py

412215b93288aa6a7ffd69e2b7144d4f8c58a803

[]

no_license

BartoszRudnik/GK

1294f7708902e867dacd7da591b9f2e741bfe9e5

6dc09184a3af07143b9729e42a6f62f13da50128

refs/heads/main

2023-02-20T19:02:12.408974

2021-01-22T10:51:14

307,847,589

0

null

UTF-8

Python

false

464

py

'''Autogenerated by xml_generate script, do not edit!''' from OpenGL import platform as _p from OpenGL.constant import Constant as _C # Code generation uses this # End users want this... from OpenGL.raw.GLES1 import _errors _EXTENSION_NAME = 'GLES1_OES_stencil8' def _f(function): return _p.createFunction(function, _p.PLATFORM.GLES1, 'GLES1_OES_stencil8', error_checker=_errors._error_checker) GL_STENCIL_INDEX8_OES = _C('GL_STENCIL_INDEX8_OES', 0x8D48)

[ "rudnik49@gmail.com" ]

rudnik49@gmail.com

3ecc132007c95e7d84efb6b8054cfa19c6580661

2f18521276c42457d09ed2470d5c663e28f47504

/triangle.py

d28eaf844529e5dc99b8991c930a7c56204d79f5

[]

no_license

benpulido21/python_prueba_isep

6240734de9aac0e9cbe9637e01712e10578f5f10

575340fa8b20bd0d68494509e96a38d30897606e

refs/heads/main

2023-01-06T18:10:46.727378

2020-11-10T02:16:05

311,506,046

0

null

UTF-8

Python

false

575

py

#declaramos la clase triangulo con el constructor y sus atributos privados class Triangulo(object): def __init__(self): self.__a = 6 self.__b = 10 self.__c = 6 #metodo para calcular la altura y area de un triangulo e imprimirlo def CalcularArea(self): a = self.__a b = self.__b c = self.__c h = a*b/c area = b*h/2 print("El area del triangulo es: ",area) #instanciamos el clase y objeto y llamamos a la funcion calcular area calculo = Triangulo() calculo.CalcularArea()

[ "noreply@github.com" ]

benpulido21.noreply@github.com

0fd7e4600c1f60ad50ddd026cfbcf63facd62174

28a462a28f443c285ca5efec181ebe36b147c167

/tests/compile/basic/es2019/IdentifierReference[0,0].EarlyErrors.spec

83d1d21d575792c488f8b800f94658ad98179c23

[ "BSD-3-Clause", "BSD-2-Clause" ]

permissive

kaist-plrg/jstar

63e71f9156860dc21cccc33a9f6c638dfee448ea

1282919127ea18a7e40c7a55e63a1ddaaf7d9db4

refs/heads/main

2022-07-22T08:12:34.947712

2022-02-27T04:19:33

2022-02-27T11:06:14

384,045,526

6

4

NOASSERTION

2022-02-27T11:05:26

2021-07-08T07:53:21

Python

UTF-8

Python

false

316

spec

<li> It is a Syntax Error if this production has a <sub>[Yield]</sub> parameter and StringValue of |Identifier| is `"yield"`. </li> <li> It is a Syntax Error if this production has an <sub>[Await]</sub> parameter and StringValue of |Identifier| is `"await"`. </li>

[ "h2oche22@gmail.com" ]

h2oche22@gmail.com

cd3fe391a3c4e68535080180aaa0f1eeff2ee745

4a4a4653bc70bb25241c532307e8fbb2142fee92

/helloworld.py

bac76f0f7208476e12978f924dd2f1c2c564d4b7

[]

no_license

v202009/PROG1700-basics-demo-da

4830daa5792fe3eb0e43fa7f6e29d9d91d3f510a

7a7e2e1863843bde4482e642de9f245e8eee79d8

refs/heads/master

2022-12-17T14:48:12.920194

2020-09-22T14:54:49

297,675,683

0

null

UTF-8

Python

false

65

py

print("Hello World!") print("Hello, again!") print("Goodbye...")

[ "w0432542@nscc.ca" ]

w0432542@nscc.ca

1b3b2b81385f029cf071465b5a67afbfcc29d78e

5de16bedbdabbd552dcebdeec3d2be30b59c80e3

/Array Partition I/solution.py

7d7a8e6875fca546f93cf66fffa754e5d02d2abf

[]

no_license

kimjaspermui/LeetCode

39f6aa61a27e7bf2aac7de940941ec376f3640e8

c01002206fcc1b3ed35d1ba1e83dffdff5fc16a5

refs/heads/master

2020-12-02T07:51:49.508145

2018-07-15T04:21:48

96,737,433

0

null

UTF-8

Python

false

394

py

class Solution: def arrayPairSum(self, nums): """ :type nums: List[int] :rtype: int """ # sort the list first nums = sorted(nums) # get the sum by pairing them from the left tempSum = 0 for i in range(0, len(nums), 2): tempSum += min(nums[i], nums[i+1]) return tempSum

[ "kmui@ucsd.edu" ]

kmui@ucsd.edu

40468665411f90fee120f8434b3ccd804495257c

66d25596e2165da03a40e6f31c59789d27d71572

/ban/commands/auth.py

e89df21f79ef2f27edbe8cdc087963ab72e72c8b

[]

no_license

christopheprudent/ban

72336bfaf1e5bc24477f00ada2aa1bd37c12d614

187a2e7719cb2d7ef64a35c22975cea3686097bb

refs/heads/master

2021-01-18T11:23:38.890962

2016-05-11T16:39:42

null

0

null

UTF-8

Python

false

1,139

py

from ban.auth.models import Token, User from ban.commands import command, reporter from ban.core import context from . import helpers @command @helpers.session def dummytoken(**kwargs): """Create a dummy token for dev.""" session = context.get('session') Token.delete().where(Token.access_token == 'token').execute() Token.create(session=session.pk, access_token="token", expires_in=3600*24) reporter.notice('Created token', 'token') @command def createuser(username=None, email=None, is_staff=False, **kwargs): """Create a user. is_staff set user staff """ if not username: username = helpers.prompt('Username') if not email: email = helpers.prompt('Email') password = helpers.prompt('Password', confirmation=True, hidden=True) validator = User.validator(username=username, email=email) if not validator.errors: user = validator.save() user.set_password(password) if is_staff: user.is_staff = True user.save() reporter.notice('Created', user) else: reporter.error('Errored', validator.errors)

[ "yb@enix.org" ]

yb@enix.org

8a57fcfb46d097525f0644f0772dd6101d30b6e8

491727fd417d9012055601b322a2a85ae0f09fa7

/quoteShare/main/urls.py

6e55634ce2ed20f5264a3adb439c327eba0655d3

[]

no_license

neumeye9/quoteShare

77f49e393a4744a7b2314dec6664bf51be32155b

796e9c8d1806f2cfa08e72a9b4e4417ac001880d

refs/heads/master

2021-06-28T04:14:02.274981

2017-09-18T16:49:42

103,965,774

0

null

UTF-8

Python

false

833

py

"""main URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.10/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url, include from django.contrib import admin urlpatterns = [ url(r'^admin/', admin.site.urls), ] urlpatterns = [ url(r'^', include('apps.belt_app.urls')) ]

[ "noreply@github.com" ]

neumeye9.noreply@github.com

2429dd6e0f7ede974b1e9f1a3904282d0dd48edf

5c89de8b2b6ceaabd7c094514fd96440577a4347

/profiles/phylosift_gene_filter.py

4c2f80e0a7d27a301d9e03467a6b8269375a6010

[]

no_license

Ecogenomics/PhylogeneticM

0d959d1bfc6b5abf8a112efd2a45114d22a6ee38

463243f5e02a32d0427dca784c9c20bb264a07f6

refs/heads/master

2020-06-03T13:59:50.113576

2013-03-11T06:06:30

5,761,180

1

0

null

UTF-8

Python

false

8,272

py

import os import sys import psycopg2 as pg import xml.etree.ElementTree as ET import common valid_configs = [('individual', type(None), "Create individual FASTA files for each marker instead of a concatenated alignment."), ('taxonomy', type(''), "Filter for organisms with this taxonomy (internal genome tree taxonomy)"), ('reject_missing_taxonomy', type(None), "Reject any genomes that have not got an assigned taxonomy."), ('gene_count_threshold', type(0), "Reject any genomes with a phylosift gene count below this threshold.")] def GetValidConfigOptions(): return valid_configs def MakeTreeData(GenomeDatabase, marker_set_id, list_of_genome_ids, directory, prefix=None, config_dict=None): """ TODO - This function is less ugly than before, but it needs to be cleaned up. """ if not os.path.isdir(directory): GenomeDatabase.ReportError("Directory doesn't exist: " + directory) return None if not common.CheckPassedConfigsAgainstKnownConfigs(config_dict, GetValidConfigOptions()): return None cur = GenomeDatabase.conn.cursor() # Get the total number of markers in this marker set total_marker_count = len(GenomeDatabase.GetMarkerIdListFromMarkerSetId(marker_set_id)) # For each genome, get the number of markers in this marker set that genome contains genome_gene_counts = GenomeDatabase.GetAlignedMarkersCountForGenomeFromMarkerSetId(marker_set_id) # This filter needs to know about phylosift markers, calculate them if they haven't been calculated for genome_id in list_of_genome_ids: uncalculated_markers = GenomeDatabase.FindUncalculatedMarkersForGenomeId(genome_id, GenomeDatabase.GetMarkerIdListFromMarkerSetId(1)) if len(uncalculated_markers) > 0: if GenomeDatabase.debugMode: print "Calculating markers for ", genome_id print uncalculated_markers GenomeDatabase.RecalculateMarkersForGenome(genome_id, uncalculated_markers) # For each genome, get the number of phylosift markers it contains phylosift_gene_counts = GenomeDatabase.GetAlignedMarkersCountForGenomeFromMarkerSetId(1) # For all of the markers, get the expected marker size. cur.execute("SELECT markers.id, markers.database_specific_id, size " + "FROM markers, marker_set_contents " + "WHERE set_id = %s " + "AND marker_id = markers.id " "ORDER by markers.id", (marker_set_id,)) chosen_markers = dict() for marker_id, database_specific_id, size in cur: chosen_markers[marker_id] = {'database_specific_id': database_specific_id, 'size': size} individual_marker_fasta = dict() genome_info = dict() if prefix is None: prefix = "genome_tree_data" gg_fh = open(os.path.join(directory, prefix + "_concatenated.greengenes"), 'wb') fasta_concat_fh = open(os.path.join(directory, prefix + "_concatenated.faa"), 'wb') # Run through each of the genomes and make the magic happen. for genome_id in list_of_genome_ids: cur.execute("SELECT tree_id, name, XMLSERIALIZE(document metadata as text), username "+ "FROM genomes, users "+ "WHERE users.id = owner_id "+ "AND genomes.id = %s", (genome_id,)) result = cur.fetchone() if not result: continue (tree_id, name, xmlstr, owner) = result # Check if complete. if 'gene_count_threshold' in config_dict and phylosift_gene_counts[genome_id] < int(config_dict['gene_count_threshold']): sys.stderr.write("WARNING: Genome %s has < %i markers (%i) in the database and will be missing from the output files.\n" % (tree_id, int(config_dict['gene_count_threshold']), phylosift_gene_counts[genome_id])) continue # Populate genome info genome_info['markers'] = dict() genome_info['name'] = name genome_info['tree_id'] = tree_id genome_info['xmlstr'] = xmlstr genome_info['owner'] = owner # update XML metadata genome_info.update(common.GetInternalMetadataDictFromXMLString(xmlstr)) # Test taxonomy taxonomy_success = True if not genome_info['internal_tax']: if 'reject_missing_taxonomy' in config_dict: taxonomy_success = False else: if ('taxonomy' in config_dict and config_dict['taxonomy']): if (genome_info['internal_tax'].find(config_dict['taxonomy']) < 0): taxonomy_success = False if not taxonomy_success: if GenomeDatabase.debugMode: sys.stderr.write("%s (%s) not included. Filtered at taxonomy stage (taxonomy: %s)\n" % (genome_info['tree_id'], genome_info['name'], genome_info['internal_tax'])) sys.stderr.flush() continue cur.execute("SELECT aligned_markers.marker_id, sequence " + "FROM aligned_markers, marker_set_contents "+ "WHERE marker_set_contents.marker_id = aligned_markers.marker_id " + "AND genome_id = %s " + "AND sequence is NOT NULL "+ "AND set_id = %s ", (genome_id, marker_set_id)) if (cur.rowcount == 0): sys.stderr.write("WARNING: Genome %s has no markers for this marker set in the database and will be missing from the output files.\n" % tree_id) sys.stderr.flush() continue for marker_id, sequence in cur: genome_info['markers'][marker_id] = sequence aligned_seq = ''; for marker_id in chosen_markers.keys(): if marker_id in genome_info['markers']: sequence = genome_info['markers'][marker_id] fasta_outstr = ">%s\n%s\n" % (genome_info['tree_id'], sequence) try: individual_marker_fasta[marker_id].append(fasta_outstr) except KeyError: individual_marker_fasta[marker_id] = [fasta_outstr] else: sequence = chosen_markers[marker_id]['size'] * '-' aligned_seq += sequence fasta_outstr = ">%s\n%s\n" % (genome_info['tree_id'], aligned_seq) gg_list = ["BEGIN", "db_name=%s" % genome_info['tree_id'], "organism=%s" % genome_info['name'], "prokMSA_id=%s" % genome_info['tree_id'], "owner=%s" % genome_info['owner'], "genome_tree_tax_string=%s" % genome_info['internal_tax'], "greengenes_tax_string=%s" % genome_info['gg_tax'], "core_list_status=%s" % genome_info['core_list_status'], "remark=%iof%i" % (genome_gene_counts[genome_id], total_marker_count), "warning=", "aligned_seq=%s" % (aligned_seq), "END"] gg_outstr = "\n".join(gg_list) + "\n\n"; fasta_concat_fh.write(fasta_outstr) gg_fh.write(gg_outstr) gg_fh.close() fasta_concat_fh.close() if "individual" in config_dict: for marker_id in chosen_markers.keys(): fasta_individual_fh = open(os.path.join(directory, prefix + "_" + str(marker_id) + "_" + chosen_markers[marker_id]['database_specific_id'] + ".faa"), 'wb') fasta_individual_fh.write(''.join(individual_marker_fasta[marker_id])) fasta_individual_fh.close() return True

[ "a.skarshewski@uq.edu.au" ]

a.skarshewski@uq.edu.au

0262980918b061d33ebd285f6a64c74430421acf

d08f165c3dc46a8736588306e9e7c8964e22c7f8

/Inheritance_practice.py

155a3ae4a096145cf5aafda3763306bafce8a401

[]

no_license

vermadev54/Python-oops

fa2b12389612cd222852041e8d76792d57ebee68

d1a1d44117c34efa950b2397718d10d5ef268e86

refs/heads/master

2021-07-12T05:23:31.785691

2020-08-16T06:37:15

192,698,129

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py

class student(object): __city="patna" def __init__(self,name,email,roll,father_name): self.name=name self._roll=roll self.email=email self.__father_name=father_name def __str__(self): return "{},{},{}".format(self.name,self.roll,self.email) def __gat_father_name(self): return self.__father_name def get_fname(self): return self.__gat_father_name(),self.__city class school_employee(student): def __init__(self,name,email,emp_id): super().__init__(name, email,roll=None,father_name=None) self.emp_id=emp_id def __str__(self): return "name: {},email: {},emp_id: {}".format(self.name, self.email,self.emp_id) stu1=student("jainendra","jain@gmail.com","17","sid") print(stu1.__dict__) print(stu1.get_fname()) stu1.get_fname() emp1=school_employee("bhanu","bhanu@gmail.com","2020123") print (emp1.__dict__) print(emp1._roll)

[ "kumar" ]

kumar