Datasets:

anton-l

/

python_sample_10k

like 0

ShaGodJi/ButterflyServer

2ac0dc6895b85343cb7021d984baa5f5e0d03422

b5b91fb3b4616bfccef258c68374554a5d4d72cd

/butterflyServer/views.py

19a667454a3909e545e97d7e6cdfbfb93fb844d2

no_license

https://github.com/ShaGodJi/ButterflyServer

c71f24c051f022bce942b1b285eef05176dc84eb

55b6445626d250e145c98ded6e20d2bb982e6ff9

refs/heads/master

import json import os from django import forms from django.http import HttpResponse from django.shortcuts import render from textblob.formats import JSON from butterflyServer.label_image import get_label from django.views.decorators.csrf import csrf_exempt class FileForm(forms.Form): username = forms.CharField() file = forms.FileField() # for creating file input def handle_uploaded_file(f, username): directory = 'static/upload/' + username + '/' if not os.path.exists(directory): os.makedirs(directory) with open(directory + f.name, 'wb+') as destination: for chunk in f.chunks(): destination.write(chunk) return directory + f.name @csrf_exempt def getFile(request): if request.method == 'POST': fileForm = FileForm(request.POST, request.FILES) if fileForm.is_valid(): file_path = handle_uploaded_file(request.FILES['file'], request.POST['username']) result_dict = get_label({ 'labels': 'butterflyServer/output_labels.txt', 'graph': 'butterflyServer/output_graph.pb', 'input_layer': 'Placeholder', 'image': file_path, 'output_layer': 'final_result' }) result_json = [] for each in result_dict: a = {} a['name'] = each a['value'] = result_dict[each] # print(a,'a') result_json.append(a) sorted_result = sorted(result_json, key = lambda i: -i['value']) print(file_path, sorted_result) if(float(sorted_result[0]['value'])<0.30): return HttpResponse('error') else: return HttpResponse(json.dumps(str(sorted_result[0]['name']))) return HttpResponse("false") else: fileForm = FileForm() return render(request, "index.html", {'form': fileForm})

UTF-8

Python

py

views.py

el1s7/sanic-api-kit

d5a4dd08e558b8dc3b6a83bb8a912dbfaa687647

1a5e8948a3ba9df36a94489ac3f560aa960f107f

/api/middlewares/__init__.py

023b5a60064a3f85a3161775a34545611c426841

no_license

https://github.com/el1s7/sanic-api-kit

7bb1729b0fc122ae18f93f99bde2cde549cd9191

55516680fcc34121df0a18ef545919b3fd444832

refs/heads/master

from api.models.auth import auth def auth_verify(request): request.ctx.session = auth.verify( request.token, request.headers.get("user-agent"), request.ip )

UTF-8

Python

py

__init__.py

hosseinghorbanzadeh/A-hybrid-method-of-link-prediction-in-directed-graphs

27c703d59b66956cb76591211835ff4f19a23d82

ef5f48eac538382f5e29cbb08a2980017cd0a177

/supervis/Method.py

9d8ae4a093242d9e4bf8fab37ac932b3192c5580

no_license

https://github.com/hosseinghorbanzadeh/A-hybrid-method-of-link-prediction-in-directed-graphs

e2792447666060ca89e66511786079026a600a69

7a12f9fd458a05a2c38770e49cd0a4f8a3a3d09d

refs/heads/main

# -*- coding: utf-8 -*- """ Created on Mon Aug 2 17:03:56 2021 @author: hossein """ import networkx as nx import pandas as pd import numpy as np import ast import matplotlib.pyplot as plt import itertools as IT import random from measure.AADirect import * from measure.CN_Hub_Auth import * from measure.RADir import * from measure.triads_motif_measure import * from measure.n2vLP import * #======================= #====================== def RandomNumber(Low,Hight,Num): L=[] i=0 while i<Num: r=random.randint(Low,Hight) if(r not in L): L.append(r) i+=1 return L def common_out_neighbors(g, i, j): return set(g.successors(i)).intersection(g.successors(j)) def common_in_neighbors(g, i, j): return set(g.predecessors(i)).intersection(g.predecessors(j)) #=============================Retuen_Miss_Link======================================================== def Retuen_Miss_Link(WholeGraph,alpha): TraningGraph=WholeGraph.copy() edges=WholeGraph.edges() Number=int(len(edges)*alpha) sample=iterSample(edges,Number) TraningGraph.remove_edges_from(sample) set1=set(WholeGraph.nodes()) set2=set(TraningGraph.nodes()) set3=set1.difference(set2) list1=list(set3) if list1!=[]: for i in range(len(list1)): TraningGraph.add_node(list1[i]) G2=nx.difference(WholeGraph,TraningGraph) missLink=list(G2.edges()) return missLink,WholeGraph,TraningGraph #======================================================================================================== def Weight_sample(WholeGraph,Nnmber_Negative): Nonedges=iterSample(nx.non_edges(WholeGraph),Nnmber_Negative) return Nonedges #=========================================================================================== def iterSample(iterable, samplesize): results = [] for i, v in enumerate(iterable): r = random.randint(0, i) if r < samplesize: if i < samplesize: results.insert(r, v) # add first samplesize items in random order else: results[r] = v # at a decreasing rate, replace random items if len(results) < samplesize: raise ValueError("Sample larger than population.") return results #Input non_edges That is iterable Output list #=========================================================================================== def Final_Result_Multi_Dataset1(WholeGraph,TraningGraph,L,Non,miss): ResultAUC_Dic={} ResultPER_Dic={} '''if(bigGraph=='no'): miss,Non,G1,G2=MissLink_and_NonExistentLink(WholeGraph_DF.values,TraningGraph_DF.values) elif(bigGraph=='yes'): miss,Non,G1,G2=MissLink_and_NonExistentLink_for_big_Graph(WholeGraph_DF.values,TraningGraph_DF.values,num_nonEdges) ''' G1=TraningGraph print('miss',len(miss)) ######################################Number Negative############################# CE=len(G1.edges())-1 NE=len(Non)-1 Nnmber_Negative=(CE/(CE+NE))*NE Number_Positive=(NE/(CE+NE))*CE list1=RandomNumber(1,NE,Nnmber_Negative) NonExistent=[] for w in list1: NonExistent.append(Non[w]) print('CE',CE) print('NonExistent',len(NonExistent)) print('miss',len(miss)) ############################################################## Class=[] for i in range(len(miss)): Class.append(1) for j in range(len(NonExistent)): Class.append(0) ################################adamic_adar_index####################################### print('#####################AADricted Graph####################################') SmissLinkAADir=list(adamic_adar_index(G1,miss,TyppeGraph='DirGhraphIn-Out')) NonExistentAAdir=list(adamic_adar_index(G1,NonExistent,TyppeGraph='DirGhraphIn-Out')) data1=pd.DataFrame(SmissLinkAADir+NonExistentAAdir,columns=['Node1','Node2','AA-in-out']) SmissLinkAADir=list(adamic_adar_index(G1,miss,TyppeGraph='DirGhraphIn')) NonExistentAAdir=list(adamic_adar_index(G1,NonExistent,TyppeGraph='DirGhraphIn')) mix=SmissLinkAADir+NonExistentAAdir CN=[k[2] for k in mix] data1['AA-in']=CN SmissLinkAADir=list(adamic_adar_index(G1,miss,TyppeGraph='DirGhraphOut')) NonExistentAAdir=list(adamic_adar_index(G1,NonExistent,TyppeGraph='DirGhraphOut')) mix=SmissLinkAADir+NonExistentAAdir CN=[k[2] for k in mix] data1['AA-Out']=CN print('#######################################') ################################adamic_adar_index####################################### #########################Common nightboar############################################### print('lin pridiction Using Common nightboar') CN_MissLink = [(e[0],e[1],len(list(common_out_neighbors(G1,e[0],e[1])))+len(list(common_in_neighbors(G1,e[0],e[1])))) for e in miss] print('common_out_neighbors') CN_nonexiteEdges=[(e[0],e[1],len(list(common_out_neighbors(G1,e[0],e[1])))+len(list(common_in_neighbors(G1,e[0],e[1])))) for e in NonExistent] mix=CN_MissLink+CN_nonexiteEdges CN=[k[2] for k in mix] data1['CN-inout']=CN CN_MissLink = [(e[0],e[1],len(list(common_out_neighbors(G1,e[0],e[1])))) for e in miss] CN_nonexiteEdges=[(e[0],e[1],len(list(common_out_neighbors(G1,e[0],e[1])))) for e in NonExistent] mix=CN_MissLink+CN_nonexiteEdges CN=[k[2] for k in mix] data1['CND-out']=CN CN_MissLink = [(e[0],e[1],len(list(common_in_neighbors(G1,e[0],e[1])))) for e in miss] CN_nonexiteEdges=[(e[0],e[1],len(list(common_in_neighbors(G1,e[0],e[1])))) for e in NonExistent] mix=CN_MissLink+CN_nonexiteEdges CN=[k[2] for k in mix] data1['CND-in']=CN #########################Common nightboar############################################### ############################## Resource Allocation############################## print('Resource Allocation') SmissLinkAADir=list(resource_allocation_index(G1,miss,TyppeGraph='DirGhraphIn-Out')) print('Len SmissLinkAADir',len(SmissLinkAADir)) NonExistentAAdir=list(resource_allocation_index(G1,NonExistent,TyppeGraph='DirGhraphIn-Out')) mix=SmissLinkAADir+NonExistentAAdir #print('RA',len(mix)) commRA=[k[2] for k in mix] data1['RA-in-out']=commRA print('Resource Allocation in') SmissLinkAADir=list(resource_allocation_index(G1,miss,TyppeGraph='DirGhraphIn')) NonExistentAAdir=list(resource_allocation_index(G1,NonExistent,TyppeGraph='DirGhraphIn')) mix=SmissLinkAADir+NonExistentAAdir commRA=[k[2] for k in mix] data1['RA-in']=commRA print('Resource Allocation out') SmissLinkAADir=list(resource_allocation_index(G1,miss,TyppeGraph='DirGhraphOut')) NonExistentAAdir=list(resource_allocation_index(G1,NonExistent,TyppeGraph='DirGhraphOut')) mix=SmissLinkAADir+NonExistentAAdir commRA=[k[2] for k in mix] data1['RA-Out']=commRA ##############################AucAndPre_co_ni_With_Pageranking######################### dic_miss,dic_NonExistent=CN_HA_AH(G1,miss,NonExistent) mix=merge_two_dicts(dic_miss,dic_NonExistent) #print('CN_HA_AH',len(mix)) CN_Hun_Auth1=[mix.get(p) for p in mix] data1['CN_HA_AH']=CN_Hun_Auth1 dic_miss,dic_NonExistent=CN_H_A(G1,miss,NonExistent) mix=merge_two_dicts(dic_miss,dic_NonExistent) CN_Hun_Auth1=[mix.get(p) for p in mix] data1['CN_H_A']=CN_Hun_Auth1 dic_miss,dic_NonExistent=CN_A_H(G1,miss,NonExistent) mix=merge_two_dicts(dic_miss,dic_NonExistent) CN_Hun_Auth1=[mix.get(p) for p in mix] data1['CN_A_H']=CN_Hun_Auth1 ##############################AucAndPre_co_ni_With_Pageranking######################### ###########################quadr_motif_measure######################################### print('################################mofit#########################') Mofit_miss=Measure_MotifsTriad(G1,miss) print('#####################') #print('Len Miss',len(miss)) #print('Len Mofit_miss',len(Mofit_miss)) Mofit_NonExistent=Measure_MotifsTriad(G1,NonExistent) mix=Mofit_miss+Mofit_NonExistent print(len(mix)) CN_MOfit=[k[2] for k in mix] data1['Motifs_triads']=CN_MOfit print('################################mofit#########################') ##############################AucAndPre_co_ni_With_Pageranking######################### ###########################n2v######################################### print('#############################N2V##############################') Miss_Scoure=n2vlp(G1,miss) Non_Scoure=n2vlp(G1,NonExistent) mix=Miss_Scoure+Non_Scoure print(len(mix)) CN_n2v=[k[2] for k in mix] data1['N2V']=CN_n2v ###########################n2v######################################### data1['Class']=Class strName="MLExcel"+" "+L+".xlsx" print(strName) print(data1) '''data1.to_excel(strName, sheet_name='MLExcel')''' return data1

UTF-8

Python

py

Method.py

siki-aayush/Implementation-of-Advanced-Algorithms-Using-Python

2593164d84359b5768edadcbf82919c780743896

ebaa9207f0ce1c86c694b88045c784a5e1c687bb

/Edge_Disjoint_Path.py

c9e6c756954b3770a090043216d5edba8410e998

no_license

https://github.com/siki-aayush/Implementation-of-Advanced-Algorithms-Using-Python

bd8c7b1c53cc3621ed5e621940c5f0b18c980d3b

75ddd6441bbf8ab9782145c0b009b2c2f5c8e4e9

refs/heads/master

n = int(input("Enter the number of nodes : ")) graph = {} for i in range(0,n): j = input("Enter the edges flowing from the node : ") temp=j.split(",") graph[str(i)]=temp matrix = [[0 for x in range(n)] for y in range(n)] #print(graph) for i in graph.keys(): j = graph.get(i) for k in j: if k is not '': matrix[int(i)][int(k)]=1 print(matrix) def dfs(a,current,dest,path,f): if current==dest: path.append(current) else: path.append(current) temp=a[current] j=-1 for i in temp: j+=1 if i>0 and (j not in path) and (dest not in path): dfs(a,j,dest,path,f) f.append(current) return f source_capacity=0 sink_capacity=0 for i in range(0,n): source_capacity+=matrix[0][i] sink_capacity+=matrix[i][n-1] max_flow=min(source_capacity,sink_capacity) flow=0 disjoint_paths=[] while flow<max_flow: path=[] path=dfs(matrix,0,n-1,path,[]) if (n-1) in path: ind = path.index(n-1) path=path[ind:] path.reverse() disjoint_paths.append(path) flows=[] for i in range(0,len(path)-1): row=path[i] col=path[i+1] flows.append(matrix[row][col]) f=min(flows) flow+=f for i in range(0,len(path)-1): row=path[i] col=path[i+1] matrix[row][col]-=f matrix[col][row]+=f else: break print("Maximum number of edge-disjoint paths are :",flow) print("Disjoint Paths are : ") for i in disjoint_paths: print(i)

UTF-8

Python

py

Edge_Disjoint_Path.py

yiclwh/Facebook-Interview-Coding

ea14e73556131eba674422ea954c5661e738917e

83607de1e707e955f8e7be0432fa638d709ee832

/binary search.py

1753a3dd1a699a73a14425c1e26218d8bfb80b96

no_license

https://github.com/yiclwh/Facebook-Interview-Coding

b5b225f4a5bcef6286f498655a7a50b081e89e63

587238fbb66b0824b944d43a12d37c9adcdb02e7

refs/heads/master

可以找不比target小的第一个数(大于等于) 最后换成end 找比target 小的第一个数 def binarysearch(array, target): start, end = 0, len(array)-1 while start <= end: mid = start + (end-start)//2 if array[mid] < target: start = mid + 1 else: end = mid - 1 return start public static void PrintIndicesForValue(int[] numbers, int target) { if (numbers == null) return; int low = 0, high = numbers.length - 1; // get the start index of target number int startIndex = -1; while (low <= high) { int mid = (high - low) / 2 + low; if (numbers[mid] > target) { high = mid - 1; } else if (numbers[mid] == target) { startIndex = mid; high = mid - 1; } else low = mid + 1; } // get the end index of target number int endIndex = -1; low = 0; high = numbers.length - 1; while (low <= high) { int mid = (high - low) / 2 + low; if (numbers[mid] > target) { high = mid - 1; } else if (numbers[mid] == target) { endIndex = mid; low = mid + 1; } else low = mid + 1; } if (startIndex != -1 && endIndex != -1){ for(int i=0; i+startIndex<=endIndex;i++){ if(i>0) System.out.print(','); System.out.print(i+startIndex); } } }

UTF-8

Python

py

binary search.py

insightcs/CenterNet

e049d9d686bc095ce7401955abcf17d59a605ead

2d0a1cc732760bbbd55f68a6483c5b65fcc5b046

/detector/utils/decode.py

415b1507bf480feb5931595ab245b415c59e5e02

permissive

https://github.com/insightcs/CenterNet

82cb6d927e80d32849fca9b8f0705f2ee3b93676

277ab04321285023ce4f0f2e7d81ef52252d4019

refs/heads/master

MIT

from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch import torch.nn as nn from utils.utils import _gather_feat, _tranpose_and_gather_feat def _nms(heat, kernel=3): pad = (kernel - 1) // 2 hmax = nn.functional.max_pool2d( heat, (kernel, kernel), stride=1, padding=pad) keep = (hmax == heat).float() return heat * keep def _left_aggregate(heat): ''' heat: batchsize x channels x h x w ''' shape = heat.shape heat = heat.reshape(-1, heat.shape[3]) heat = heat.transpose(1, 0).contiguous() ret = heat.clone() for i in range(1, heat.shape[0]): inds = (heat[i] >= heat[i - 1]) ret[i] += ret[i - 1] * inds.float() return (ret - heat).transpose(1, 0).reshape(shape) def _right_aggregate(heat): ''' heat: batchsize x channels x h x w ''' shape = heat.shape heat = heat.reshape(-1, heat.shape[3]) heat = heat.transpose(1, 0).contiguous() ret = heat.clone() for i in range(heat.shape[0] - 2, -1, -1): inds = (heat[i] >= heat[i +1]) ret[i] += ret[i + 1] * inds.float() return (ret - heat).transpose(1, 0).reshape(shape) def _top_aggregate(heat): ''' heat: batchsize x channels x h x w ''' heat = heat.transpose(3, 2) shape = heat.shape heat = heat.reshape(-1, heat.shape[3]) heat = heat.transpose(1, 0).contiguous() ret = heat.clone() for i in range(1, heat.shape[0]): inds = (heat[i] >= heat[i - 1]) ret[i] += ret[i - 1] * inds.float() return (ret - heat).transpose(1, 0).reshape(shape).transpose(3, 2) def _bottom_aggregate(heat): ''' heat: batchsize x channels x h x w ''' heat = heat.transpose(3, 2) shape = heat.shape heat = heat.reshape(-1, heat.shape[3]) heat = heat.transpose(1, 0).contiguous() ret = heat.clone() for i in range(heat.shape[0] - 2, -1, -1): inds = (heat[i] >= heat[i + 1]) ret[i] += ret[i + 1] * inds.float() return (ret - heat).transpose(1, 0).reshape(shape).transpose(3, 2) def _h_aggregate(heat, aggr_weight=0.1): return aggr_weight * _left_aggregate(heat) + \ aggr_weight * _right_aggregate(heat) + heat def _v_aggregate(heat, aggr_weight=0.1): return aggr_weight * _top_aggregate(heat) + \ aggr_weight * _bottom_aggregate(heat) + heat ''' # Slow for large number of categories def _topk(scores, K=40): batch, cat, height, width = scores.size() topk_scores, topk_inds = torch.topk(scores.view(batch, -1), K) topk_clses = (topk_inds / (height * width)).int() topk_inds = topk_inds % (height * width) topk_ys = (topk_inds / width).int().float() topk_xs = (topk_inds % width).int().float() return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs ''' def _topk_channel(scores, K=40): batch, cat, height, width = scores.size() topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K) topk_inds = topk_inds % (height * width) topk_ys = (topk_inds / width).int().float() topk_xs = (topk_inds % width).int().float() return topk_scores, topk_inds, topk_ys, topk_xs def _topk(scores, K=40): batch, cat, height, width = scores.size() topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K) topk_inds = topk_inds % (height * width) topk_ys = (topk_inds / width).int().float() topk_xs = (topk_inds % width).int().float() topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K) topk_clses = (topk_ind / K).int() topk_inds = _gather_feat( topk_inds.view(batch, -1, 1), topk_ind).view(batch, K) topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K) topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K) return topk_score, topk_inds, topk_clses, topk_ys, topk_xs def ctdet_decode(heat, wh, reg=None, cat_spec_wh=False, K=100): batch, cat, height, width = heat.size() # heat = torch.sigmoid(heat) # perform nms on heatmaps heat = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) if reg is not None: reg = _tranpose_and_gather_feat(reg, inds) reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) + reg[:, :, 0:1] ys = ys.view(batch, K, 1) + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) + 0.5 ys = ys.view(batch, K, 1) + 0.5 wh = _tranpose_and_gather_feat(wh, inds) if cat_spec_wh: wh = wh.view(batch, K, cat, 2) clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 2).long() wh = wh.gather(2, clses_ind).view(batch, K, 2) else: wh = wh.view(batch, K, 2) clses = clses.view(batch, K, 1).float() scores = scores.view(batch, K, 1) bboxes = torch.cat([xs - wh[..., 0:1] / 2, ys - wh[..., 1:2] / 2, xs + wh[..., 0:1] / 2, ys + wh[..., 1:2] / 2], dim=2) detections = torch.cat([bboxes, scores, clses], dim=2) return detections

UTF-8

Python

py

decode.py

pytsite/pytsite

8b0cce6b74052cb59fb7dc8423cc79e456699248

1bc7053e7582e43bdd4b943c5700677e07449d3c

/pytsite/maintenance/__init__.py

a095c4d37f4ca965860074efe8f236f8aa7a4c88

permissive

https://github.com/pytsite/pytsite

f92eaa041d85b245fbfcdff44224b24da5d9b73a

e4896722709607bda88b4a69400dcde4bf7e5f0a

refs/heads/master

"""PytSite Maintenance """ __author__ = 'Oleksandr Shepetko' __email__ = 'a@shepetko.com' __license__ = 'MIT' # Public API from ._api import enable, disable, is_enabled def _init(): from pytsite import console, lang from . import _console_command lang.register_package(__name__) console.register_command(_console_command.Maintenance()) _init()

UTF-8

Python

py

__init__.py

dockrexter/Design-Patterns-Python

5313a44ebcef80b1602608e879876c208cced8cb

8b3fe40844e180d71989a7a953adfe303f3d6e24

/mvc.py

a8fb3ce604c0a26db95b138edf7c8fe8e3e51e39

no_license

https://github.com/dockrexter/Design-Patterns-Python

fe24fb2bc2e3588ea2860fe63cebed022b2f7469

0b8c57b9450c9ffc2823ca9c6f0e548f5dd0c071

refs/heads/master

class model: def __init__(self,name): self.name=name def returning(self,message): if message == "name": return self.name else: return "required information not available" class view: def __init__(self,message): print (message) class controller(model,view): def __init__(self,request): information=model("ghost").returning(request) view(information) controller("name")

UTF-8

Python

py

mvc.py

The-Cracker-Technology/Katana

c72ed548d6a991c267038cc6ff63f948e5336b0f

ebe6c75ee675f45e078c4c655946c6031d9657a7

/Modes/Gmode.py

6c63cf5104af7ea428a6a835601db9d0e91082f1

permissive

https://github.com/The-Cracker-Technology/Katana

a8818463c167707d25d8d9cccf170c5a0f453f6d

a54a2e35927cd73505d675920d93b1d8c04a274b

refs/heads/master

# @name: Katana-dorkscanner # @repo: https://github.com/adnane-X-tebbaa/Katana # @author: Adnane tebbaa (AXT) # Main Google Dorking file V2.0 """ MIT License Copyright (c) 2020 adnane tebbaa Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import requests import proxybroker from googlesearch import search import sys import sys from termcolor import colored, cprint import warnings import random from http import cookiejar class BlockAll(cookiejar.CookiePolicy): return_ok = set_ok = domain_return_ok = path_return_ok = lambda self, *args, **kwargs: False netscape = True rfc2965 = hide_cookie2 = False def fxn(): warnings.warn("deprecated", DeprecationWarning) with warnings.catch_warnings(): warnings.simplefilter("ignore") fxn() def clear(): return os.system('cls' if os.name == 'nt' else 'clear') print ("") A = """ ,_._._._._._._._._|__________________________________________________________ |G|o|o|g|l|e|_|_|_|_________________________________________________________/ | Katana dork scanner (Katana-ds V1.5.3) coded by adnane-X-tebbaa Google Mode """ print ("") print(A) TLD = ["com","com.tw","co.in","be","de","co.uk","co.ma","dz","ru","ca"] s = requests.Session() s.cookies.set_policy(BlockAll()) alpha = input (colored('[>] Please set a Dork : ', 'green' )) query = alpha beta = random.choice(TLD) for gamma in search(query, tld=beta, num=10 , stop=95 , pause=2): print(colored ('[+] Found > ' ,'yellow') + (gamma) ) print(colored ('[+] Done ' ,'green')) print(colored ('[! >] delete .google-cookie file in Katana DIR ' ,'red'))

UTF-8

Python

py

Gmode.py

aspc/mainsite

81a926ae23813ae67f088113ecf6fc9448d16b7d

65ec50c5bc1bd4948de96cf6a8b6fee82badacef

/aspc/files/models.py

2c537888cd5ea278613ef15da82146003ee0a07f

permissive

https://github.com/aspc/mainsite

eab3e75e2e701881bae1f3e1c998c5b9f76ae761

a6ccee0bb921147b7f630d65e01371e451aa3c54

refs/heads/master

MIT

JavaScript

from django.db import models class ImageUpload(models.Model): name = models.CharField(max_length=100) image = models.ImageField() class FileUpload(models.Model): name = models.CharField(max_length=100) _file = models.FileField() # `file` is a built-in Python function

UTF-8

Python

py

models.py

csnoboa/sistemas_distribuidos_at1

47cfc83cb7df00e424d9b127b00bf77593db7cde

cb212d1e2ca56c6fd2c11523d98b897032a8aa18

/script_usuario.py

97b0fbf7e46884de3de3f5a2a6685a6e2775b88c

no_license

https://github.com/csnoboa/sistemas_distribuidos_at1

dff41780ffe9b453895a823a5edc63cc80c83de1

e53325c3a5c848b0b53ad22ed0ec620b744096f6

refs/heads/master

import sys import Pyro5.api from cliente import Cliente import time server = Pyro5.api.Proxy("PYRONAME:server.enquete") nome = input("Digite o nome do usuario: ") cliente = Cliente(nome, server) cliente.cadastrar() while True: resp = input("Ver menu? \n1 - Sim \n2 - Não\n") if resp == "1": resp = input(""" O que deseja fazer? 0 - Atualizar ( Receber enquetes ) 1 - Criar enquete 2 - Consultar enquete """) if resp == "1": resp = input("Qual enquete pronta deseja criar? de 1 a 3\n") if resp == "1": cliente.cria_enquete("mais saches", "casa", [ { "dia": "sabado", "horario": "13:00", "votos": 0 }, { "dia": "sabado", "horario": "18:00", "votos": 0 }, { "dia": "domingo", "horario": "13:00", "votos": 0 }, ], 100) if resp == "2": cliente.cria_enquete("mais ração", "casa", [ { "dia": "sexta", "horario": "13:00", "votos": 0 }, { "dia": "sabado", "horario": "18:00", "votos": 0 }, ], 15) if resp == "3": cliente.cria_enquete("mais brinquedos", "casa", [ { "dia": "segunda", "horario": "10:00", "votos": 0 }, { "dia": "terça", "horario": "18:00", "votos": 0 }, ], 60) elif resp == "2": titulo = input("Qual o nome da enquete que quer consultar?\n") cliente.ver_enquete(titulo) else: time.sleep(15) else: time.sleep(120)

UTF-8

Python

py

script_usuario.py

Kowenjko/Python_Homework_14_Kowenjko

3975a80b7c4f8f369000dd6c4d62ddade2010850

740c5c83800ab6bd54fe3a977c4af723bc354ab2

/unregisteredCustomer.py

4175ba3cc7ae8563073c1422996062baef6e70e4

no_license

https://github.com/Kowenjko/Python_Homework_14_Kowenjko

1ffa41f0e96c82372e0b55591986ddfae864a7c3

5e8fc6f3488018c223bf9f978f1040efa5c8ecf3

refs/heads/master

import psycopg2 from settings import * from connection import Connection class UnregisteredCuctomer(Connection): def register_self(self, login, password, data): self._register(login, password, 'customer') table = 'customer' result = self._postData(table, data) return result def get_product_info(self, category='', selector='',): """ category must be one of the item from the list: ['product_name','country_name', 'category_name'] """ categoryes = ['product_name', 'country_name', 'category_name'] table = ('product p',) fields = ( """p.id, p.product_name ,p.unit_price,c.country_name,pc.category_name """,) fieldNames = ["id", "product_name", "unit_price", "country_name", "category_name"] if category and category in categoryes and selector: where = f"""where {category} = '{selector}'""" else: where = '' selector = f""" inner join country c on c.id =p.country_id inner join product_category pc on pc.id =p.product_catagery_id {where}""" result = self._getData(table, fields, selector) changeRes = [] for item in result: cort = {} for index, element in enumerate(item): cort[fieldNames[index]] = element changeRes.append(cort) return changeRes if __name__ == '__main__': regCust = UnregisteredCuctomer() orders = regCust.get_product_info() print(orders) # ---------------------------------- # data = [{ # 'city_id': 2, # 'first_name': "Loman", # 'last_name': "Mipol" # }] # put = regCust.register_self('Petro', 'Petrow', data) # print(put)

UTF-8

Python

py

unregisteredCustomer.py

libbyandhelen/nn_arch_search

63b7f96ee494f108651a37c58d893e6ce28fa8b3

2ad545b3784e523889cc8be91608b3f8fe53cf75

/NASsearch/acquisition_func.py

62ea5b7c5939e7add0c8838de381653c922c7458

no_license

https://github.com/libbyandhelen/nn_arch_search

707767516140322a64f956bb79bfc74ecfc69b7c

0df1af91ff794d7017bd38bcbd758df5b3d6a091

refs/heads/master

""" module for acquisition function""" import numpy as np from scipy.stats import norm from Kernel_optimization.all_gp import GaussianProcess class AcquisitionFunc: """ class for acquisition function expected improvement in this case """ def __init__(self, X_train, y_train, current_optimal, mode, trade_off): """ :param mode: pi: probability of improvement, ei: expected improvement, lcb: lower confident bound :param trade_off: a parameter to control the trade off between exploiting and exploring :param model_type: gp: gaussian process, rf: random forest """ self.X_train = X_train self.y_train = y_train self.current_optimal = current_optimal self.mode = mode or "ei" self.trade_off = trade_off or 0.1 self.model = GaussianProcess(80) def compute(self, X_test, weight_file): y_means, y_vars, y_stds = self.model.fit_predict(self.X_train, X_test, self.y_train, weight_file) z = (y_means - self.current_optimal - self.trade_off) / y_stds if self.mode == "ei": result = y_stds * (z * norm.cdf(z) + norm.pdf(z)) elif self.mode == "pi": result = norm.cdf(z) elif self.mode == "ucb": result = y_means + self.trade_off * y_stds else: result = - (y_means - self.trade_off * y_stds) return result, y_means, y_stds

UTF-8

Python

py

acquisition_func.py

milton96/practicas

72aabc5a22d865d0c4b8275d51be0a313b8cad7d

eb37d1ae40953bb7498e88e97e52f2c9debaae10

/Algorithms/Sorting/Bubble Sort/difference_max_min.py

2c2dc85435780b235dd9ffae6c32acd906ca814b

no_license

https://github.com/milton96/practicas

141ef1c5240dbd95f122c0f670f1a0e5ed61a9a4

1c9e5b7098b9f2b665e9b3e38f8913a2784ea2d1

refs/heads/master

# Obtiene la diferencia entre la suma máxima y la suma mínima de un array, # usando n-m elementos para calcular cada una t = int(input()) for k in range(t): n, m = map(int, input().split()) a = list(map(int, input().split())) #Solución implementando ordenamiento con burbuja # for i in range(n-1): # for j in range(n-i-1): # if a[j] >= a[j+1]: # b = a[j] # a[j] = a[j+1] # a[j+1] = b # print(sum(a[m:n])-sum(a[0:n-m])) #Solución implementada usando el metodo de ordenación por defecto de python a.sort() i = 0 max = 0 min = 0 while i < (n-m): min += a[i] i += 1 i = 0 while i < (n-m): max += a[n-i-1] i += 1 print(max-min)

UTF-8

Python

py

difference_max_min.py

ashiqur0202/Python-Projects

ba03002610ffd4413fcdd9510fc62f1812b6718a

21281f2124348f122894522ff8ce512c2c210568

/Heads Tails.py

0188e27a15e701d81020ec2aebf35d527daf5193

no_license

https://github.com/ashiqur0202/Python-Projects

624097c93c59d4c28ecf1b919089de390141cdc9

467df5be1064dbc365e798b9e8fb52cc435f489f

refs/heads/master

Python

print("/// Heads and Tails ///") import random numbers = int(input("enter mumbers: \n")) random.seed(numbers) random_numbers = random.randint(0, 1) if random_numbers == 1: print("Heats") else: print("Tails")

UTF-8

Python

py

Heads Tails.py

Cheburashka303/edrenbaton

90b6883980033bfcde8b92d14c8dfa802b4c88f0

7c6adf3115e720abc0039ffafac3850abea9d2b3

/yoperniiteatr.py

5abe2a309d0613a407d94854845025c2b71e8e73

permissive

https://github.com/Cheburashka303/edrenbaton

2389270d9907b110ef02561743e8bbe6eefed671

55891919b6039a427991cbdb978b07b7f63a8d64

refs/heads/main

"""Функция кодирования в код Морзе будет называться encode_to_morse(text). Функция декодирования называется decode_from_morse(code).""" from encode import encode_to_morse from decode import decode_from_morse

UTF-8

Python

py

yoperniiteatr.py

markWJJ/Open_domain_QA

e1dee17b99b955c03f72e8b156230c27264f3bf6

f303abd351b5502138a8812f65e4ba7701faf485

/R_Net_QA/Data_r_net/Data_deal_no_array.py

55746f7197dea6f18ceb27a167a3f5ee6262183a

no_license

https://github.com/markWJJ/Open_domain_QA

55b8c992de615fc00dbd8c264fd6d2f50df77df8

a06bb9511f832d6f30a67b4a420c4f2de0d7b14d

refs/heads/master

import numpy as np import pickle import os global PATH import sys PATH=os.path.split(os.path.realpath(__file__))[0] class DataDealRNet(object): def __init__(self,train_path,dev_path,test_path,dim,batch_size,Q_len,P_len,flag): self.train_path=train_path self.dev_path=dev_path self.test_path=test_path self.dim=dim self.Q_len=Q_len self.P_len=P_len self.batch_size=batch_size if flag=="train_new": self.vocab=self.get_vocab() pickle.dump(self.vocab,open(PATH+"/vocab.p",'wb')) elif flag=="test" or flag=="train": self.vocab=pickle.load(open(PATH+"/vocab.p",'rb')) self.index=0 def get_vocab(self): ''' 构造字典 :return: ''' train_file=open(PATH+self.train_path,'r') test_file=open(PATH+self.dev_path,'r') dev_file=open(PATH+self.test_path,'r') vocab={"NONE":0} index=1 for ele in train_file: ele.replace("\n","") ele1=ele.replace("\t\t"," ") ws=ele1.split(" ") for w in ws: w=w.lower() if w not in vocab: vocab[w]=index index+=1 for ele in test_file: ele1=ele.replace(" "," ").replace("\n","") for w in ele1.split(" "): w=w.lower() if w not in vocab: vocab[w]=index index+=1 for ele in dev_file: ele1=ele.replace(" "," ").replace("\n","") for w in ele1.split(" "): w=w.lower() if w not in vocab: vocab[w]=index index+=1 train_file.close() dev_file.close() test_file.close() return vocab def sent2vec(self,sent,max_len): ''' 根据vocab将句子转换为向量 :param sent: :return: ''' sent=str(sent).replace("\n","") sent_list=[] real_len=len(sent.split(" ")) for word in sent.split(" "): word=word.lower() if word in self.vocab: sent_list.append(self.vocab[word]) else: sent_list.append(0) if len(sent_list)>=max_len: new_sent_list=sent_list[0:max_len] else: new_sent_list=sent_list ss=[0]*(max_len-len(sent_list)) new_sent_list.extend(ss) sent_vec=np.array(new_sent_list) return sent_vec,real_len def shuffle(self,Q,A,label): ''' 将矩阵X打乱 :param x: :return: ''' ss=list(range(Q.shape[0])) np.random.shuffle(ss) new_Q=np.zeros_like(Q) new_A=np.zeros_like(A) new_label=np.zeros_like(label) for i in range(Q.shape[0]): new_Q[i]=Q[ss[i]] new_A[i]=A[ss[i]] new_label[i]=label[ss[i]] return new_Q,new_A,new_label def get_ev_ans(self,sentence): ''' 获取 envience and answer_label :param sentence: :return: ''' env_list=[] ans_list=[] for e in sentence.split(" "): try: env_list.append(e.split("/")[0]) ans_list.append(self.label_dict[str(e.split("/")[1])]) except: pass return " ".join(env_list),ans_list def next_batch(self): ''' 获取训练机的下一个batch :return: ''' train_file=open(PATH+self.train_path,'r') Q_list=[] P_list=[] label_list=[] train_sentcens=train_file.readlines() file_size=len(train_sentcens) Q_len_list=[] P_len_list=[] for sentence in train_sentcens: sentence = sentence.replace("\n", "") sentences = sentence.split("\t\t") # sentences=sentence.split(" ") Q_sentence = sentences[0] P_sentence=sentences[1] label=[int(e) for e in sentences[2].split("-")] Q_array,_=self.sent2vec(Q_sentence,self.Q_len) A_array,_=self.sent2vec(P_sentence,self.P_len) Q_list.append(list(Q_array)) P_list.append(list(A_array)) if len(str(Q_sentence).split(" "))>=self.Q_len: Q_len_list.append(self.Q_len) else: Q_len_list.append(len(str(Q_sentence).split(" "))) if len(str(P_sentence).split(" ")) >= self.P_len: P_len_list.append(self.P_len) else: P_len_list.append(len(str(P_sentence).split(" "))) label_list.append(label) train_file.close() result_Q=np.array(Q_list) result_P=np.array(P_list) result_Q_len_list=np.array(Q_len_list) result_P_len_list=np.array(P_len_list) result_label=np.array(label_list) result_Q,result_A,result_label=self.shuffle(result_Q,result_P,result_label) num_iter=int(file_size/self.batch_size) if self.index<num_iter: return_Q=result_Q[self.index*self.batch_size:(self.index+1)*self.batch_size] return_P=result_P[self.index*self.batch_size:(self.index+1)*self.batch_size] return_label=result_label[self.index*self.batch_size:(self.index+1)*self.batch_size] return_Q_len=result_Q_len_list[self.index*self.batch_size:(self.index+1)*self.batch_size] return_P_len=result_P_len_list[self.index*self.batch_size:(self.index+1)*self.batch_size] self.index+=1 else: self.index=0 return_Q=result_Q[0:self.batch_size] return_P=result_P[0:self.batch_size] return_Q_len = result_Q_len_list[0:self.batch_size] return_P_len = result_P_len_list[0:self.batch_size] return_label=result_label[0:self.batch_size] return return_Q,return_P,return_label,return_Q_len,return_P_len def get_dev(self): ''' 读取验证数据集 :return: ''' dev_file = open(self.dev_path, 'r') Q_list = [] A_list = [] label_list = [] train_sentcens = dev_file.readlines() for sentence in train_sentcens: sentences=sentence.split(" ") Q_sentence=sentences[0] A_sentence=sentences[1] label=sentences[2] Q_array=self.sent2array(Q_sentence,self.Q_len) A_array=self.sent2array(A_sentence,self.P_len) Q_list.append(list(Q_array)) A_list.append(list(A_array)) label_list.append(int(label)) dev_file.close() result_Q=np.array(Q_list) result_A=np.array(A_list) result_label=np.array(label_list) return result_Q,result_A,result_label def get_test(self): ''' 读取测试数据集 :return: ''' test_file = open(self.test_path, 'r') Q_list = [] A_list = [] label_list = [] train_sentcens = test_file.readlines() for sentence in train_sentcens: sentences=sentence.split(" ") Q_sentence=sentences[0] A_sentence=sentences[1] label=sentences[2] Q_array,_=self.sent2array(Q_sentence,self.Q_len) A_array,_=self.sent2array(A_sentence,self.P_len) Q_list.append(list(Q_array)) A_list.append(list(A_array)) label_list.append(int(label)) test_file.close() result_Q=np.array(Q_list) result_A=np.array(A_list) result_label=np.array(label_list) return result_Q,result_A,result_label def get_Q_array(self,Q_sentence): ''' 根据输入问句构建Q矩阵 :param Q_sentence: :return: ''' Q_len=len(str(Q_sentence).replace("\n","").split(" ")) if Q_len>=self.Q_len: Q_len=self.Q_len Q_array,_=self.sent2vec(Q_sentence,self.Q_len) return Q_array,np.array([Q_len],dtype=np.int32) def get_A_array(self,P_sentence): ''' 根据输入的答案句子构建A矩阵 :param A_sentence: :return: ''' P_sentence, _ = self.get_ev_ans(P_sentence) P_len = len(str(P_sentence).replace("\n", "").split(" ")) if P_len>=self.P_len: P_len=self.P_len P_vec,_=self.sent2vec(P_sentence, self.P_len) return P_vec,np.array([P_len],dtype=np.int32) def get_vocab_size(self): ''' 获取词典大小 :return: ''' return len(self.vocab) if __name__ == '__main__': dd = DataDealRNet(train_path="/SQUQA_train_1.txt", test_path="/test1.txt", dev_path="/dev1.txt", dim=100, batch_size=4 ,Q_len=30, P_len=100, flag="test") print(dd.get_vocab_size()) for i in range(100): print(i) return_Q, return_P, return_label, return_Q_len, return_P_len = dd.next_batch() print(return_P) print("\n")

UTF-8

Python

py

Data_deal_no_array.py

NTUE-Arduino-Lab/clec-inventory-backend

1683fb4f4c15599aa27b0fb25738c3464afc8165

fd0390330406f2d67efeca9dad978f6fef28bc1f

/app.py

02d366a197fb90d8c2e4e49cd1947116eeab538c

no_license

https://github.com/NTUE-Arduino-Lab/clec-inventory-backend

825df8f5ab31271da435cf828cefe3c0f855e1a1

61e6c2697498858b414c4220b60a97272ea4dcc9

refs/heads/main

import ftplib import re import sys import os import json import pymysql import socket import base64 import random import io from tenacity import * from uuid import uuid4 from flask import Flask, request, flash, redirect, url_for, send_file from werkzeug.datastructures import FileStorage from functools import wraps from flask_restful import reqparse, abort, Api, Resource from datetime import timedelta, datetime from pytz import timezone import time import logging from threading import Lock from ftplib import FTP app = Flask(__name__) api = Api(app) parser = reqparse.RequestParser() def NowTime(): return datetime.now(timezone('Asia/Taipei')).strftime("%Y-%m-%d %H:%M:%S") def after_request(response): response.headers['Access-Control-Allow-Origin'] = '*' response.headers['Access-Control-Allow-Methods'] = 'PUT,GET,PATCH,DELETE,POST' response.headers['Access-Control-Allow-Headers'] = 'Content-Type,Authorization,Referrer-Policy' return response app.after_request(after_request) app.config['PROPAGATE_EXCEPTIONS'] = True class ftpfunc(): def connect(self): ftp = FTP('gdlab-dev.local') ftp.login('GDLab','8TRcrZ') ftp.cwd('img') return ftp def upload(self, file, id): ftp = self.connect() ftp.storbinary("STOR "+id,file,1024) ftp.quit() def download(self, file, id): ftp = self.connect() try: ftp.retrbinary("RETR "+id+".jpg",file.write) except: ftp.quit() return False ftp.quit() class ConnectionManager(object): __instance = None __connection = None __lock = Lock() def __new__(cls): if ConnectionManager.__instance is None: ConnectionManager.__instance = object.__new__(cls) return ConnectionManager.__instance def __getConnection(self): if (self.__connection == None): db_settings = { "host": "gdlab-dev.local", "port": 3306, "user": "clec", "password": "jCLP4x", "db": "inventory", "charset": "utf8" } self.__connection = pymysql.connect(**db_settings) return self.__connection def __removeConnection(self): self.__connection = None @retry(stop=stop_after_attempt(3), wait=wait_fixed(10), retry=retry_if_exception_type(pymysql.Error), after=after_log(app.logger, logging.DEBUG)) def executeQueryJSON(self, procedure, payload=None): result = {} try: conn = self.__getConnection() cursor = conn.cursor() if payload: cursor.execute(f"CALL {procedure}(%s)", json.dumps(payload)) else: cursor.execute(f"CALL {procedure}()") raw = '' for r in cursor.fetchall(): for inr in r: raw = raw + inr result = json.loads(raw) print(result) except pymysql.Error as e: if isinstance(e, pymysql.ProgrammingError) or isinstance(e, pymysql.OperationalError): app.logger.error(f"{e.args[1]}") if e.args[0] == "08S01": self.__removeConnection() raise finally: conn.commit() cursor.close() self.__removeConnection() return result class Queryable(Resource): def executeQueryJson(self, verb, payload=None): entity = type(self).__name__.lower() procedure = f"web.`{verb}_{entity}`" print(procedure) result = ConnectionManager().executeQueryJSON(procedure, payload) return result @app.route('/') def index(): return "Hello" class Login(Queryable): def post(self): parser.add_argument('Account') parser.add_argument('Passwd') args = parser.parse_args() try: result = self.executeQueryJson("post", args) except: result = json.dumps({'message':'incorrect username or password'}) return result, 200 class Object(Queryable,ftpfunc): def get(self, multi): args = {} args['id'] = multi result = self.executeQueryJson("get", args) return result, 200 def post(self, multi = None): result = {} if multi == None: parser.add_argument('id') parser.add_argument('year') parser.add_argument('appellation') parser.add_argument('buydate') parser.add_argument('source') parser.add_argument('unit') parser.add_argument('keeper') parser.add_argument('note') args = parser.parse_args() args['status'] = 'in stock' if args['note'] == None: args['note'] = '' result = self.executeQueryJson("post", args) elif multi == 'multi': parser.add_argument('args',type = dict,action="append") args = parser.parse_args()['args'] result = [] for i in args: i['status'] = 'in stock' if i['note'] == None: i['note'] = '' result.append(self.executeQueryJson("post", i)) return result, 200 def delete(self): parser.add_argument('id') args = parser.parse_args() result = self.executeQueryJson("delete", args) return result, 200 def patch(self): parser.add_argument('args',type = dict) args = parser.parse_args()['args'] print(args) result = self.executeQueryJson("patch", args) return result, 200 class Borrow(Queryable): def get(self, id): args = {} args['id'] = id result = self.executeQueryJson("get", args) return result, 200 def post(self): parser.add_argument('args',type = dict,action="append") args = parser.parse_args()['args'] print(args) result = [] for i in args: result.append(self.executeQueryJson("post", i)) return result, 200 class Borrowing(Queryable): def get(self): result = self.executeQueryJson("get") return result, 200 class Return(Queryable): def get(self, id): args = {} args['id'] = id result = self.executeQueryJson("get", args) return result, 200 def post(self): parser.add_argument('args',type = dict,action="append") args = parser.parse_args()['args'] result = [] for i in args: result.append(self.executeQueryJson("post", i)) return result, 200 class Objects(Queryable): def get(self, type = None): result = {} if type == None: result = self.executeQueryJson("get") elif type == 'instock': result = self.executeQueryJson("get_instock") return result, 200 class Img(Queryable ,ftpfunc): def post(self, id): parser.add_argument('image', type=FileStorage, location='files') args = parser.parse_args() imgpath = id+'.jpg' img = args['image'].save(imgpath) del img file = open(imgpath,'rb') self.upload(file,imgpath) os.remove(imgpath) return send_file(io.BytesIO(file.read()),mimetype='image/jpeg',as_attachment=True,download_name='%s.jpg' % id) def get(self, id): file = open('file.jpg', 'wb') img = self.download(file, id) if img == False: return {"message":"no image"}, 200 else: file.close() file = open('file.jpg', 'rb') os.remove('file.jpg') return send_file(io.BytesIO(file.read()),mimetype='image/jpeg',as_attachment=True,download_name='%s.jpg' % id) class Search(Queryable): def get(self, keyword): key = keyword.split(',') print(key) result = [] for i in key: args = {} args['keyword'] = i temp = self.executeQueryJson("get", args) for t in temp: if not t in result: result.append(t) return result, 200 class History(Queryable): def get(self, id): args = {} args['id'] = id result = self.executeQueryJson("get", args) return result, 200 api.add_resource(Login, '/login') api.add_resource(Object, '/object','/object/<multi>') api.add_resource(Objects, '/objects','/objects/<type>') api.add_resource(Borrow, '/borrow', '/borrow/<id>') api.add_resource(Borrowing, '/borrowing') api.add_resource(Return, '/return') api.add_resource(Img, '/img/<id>') api.add_resource(Search, '/search/<keyword>') api.add_resource(History, '/history/<id>') if __name__ == '__main__': app.run(host='0.0.0.0', port=80)

UTF-8

Python

py

app.py

madhavGSU/Data_Programming

b2c83bd0089e37970f411814ca7df593f467bf40

8f7b390fcd5fab10d7bb377512c8b912a108fc88

/Assignment_1/chapter2_21.py

9ef20b8d9fa3d90534dc0869637249f09639da51

no_license

https://github.com/madhavGSU/Data_Programming

5f803f3f59a9e5c9c3e6381ce1e02a94e88bd2e2

a673ab8a2aa0d57700fd44abf10fa96280c2c904

refs/heads/master

# -*- coding: utf-8 -*- """ Created on Tue Jan 14 18:37:45 2020 @author: Administrator """ import math; principal=eval(input("Give the principal amount")); intRate=0.05/12; # interest=principal*((1+(0.05)/12))**6; interest=0; total=principal; for i in range(6): total=principal+interest; interest=total*(1+intRate); # print("account value after month", i, "is:", interest); print("After the sixth month, the account value is:", round(interest, 2));

UTF-8

Python

py

chapter2_21.py

vincentwyshan/tap

4603a6ce9e042d8cd9b0b41c766200d01588b7b3

d3c126f0fe2cd531e6c5041ff3e7751035c1fb89

/tap/security.py

a3f6aae9893d514443c2c3776b352777c5c329b8

no_license

https://github.com/vincentwyshan/tap

7e88e3c7042c428fa6bc008554ba134a8b856781

54a7c3bc0386db692abaf001dfa9bdfcacaab0ec

refs/heads/master

JavaScript

#coding=utf8 import datetime import os from hashlib import sha256 from hmac import HMAC from pyramid.security import Allow, Everyone, Authenticated from pyramid.security import unauthenticated_userid, authenticated_userid from pyramid.security import Allow, Deny import transaction from tap.service import exceptions from tap.models import ( DBSession, TapUser, TapPermission, TapUserPermission, TapProject, TapApi, ) def groupfinder(user_id, request): if user_id: user = bind_user(request, user_id) if user: return [user.name] raise exceptions.UserNotAvailable return [Everyone] class TempPermission(object): def __init__(self, user_permission): if not user_permission: self.a_view = False self.a_add = False self.a_edit = False self.a_delete = False else: self.a_view = user_permission.a_view self.a_add = user_permission.a_add self.a_edit = user_permission.a_edit self.a_delete = user_permission.a_delete class AuthControl(object): def __init__(self, request): self.request = request def route(self): """ 根据URL提供相应的权限查询 :return: """ need_permission = None if (self.request.path.startswith('/management/') and not self.request.path.startswith('/management/action')): need_permission = self.pages_permission(self.request.path) elif self.request.path.startswith('/management/action'): return [(Allow, Everyone, 'view'), (Allow, Everyone, 'edit'), (Allow, Everyone, 'delete'), (Allow, Everyone, 'add')] elif self.request.path == '/': need_permission = 'SYS_INDEX:view' else: raise Exception("No permission route") user_perm = self.get_userperm(need_permission) result = [] if user_perm: if user_perm.a_view: result.append((Allow, self.request.user.name, 'view')) if user_perm.a_add: result.append((Allow, self.request.user.name, 'add')) if user_perm.a_delete: result.append((Allow, self.request.user.name, 'delete')) if user_perm.a_edit: result.append((Allow, self.request.user.name, 'edit')) else: return [(Deny, Everyone, 'view'), (Deny, Everyone, 'edit'), (Deny, Everyone, 'delete'), (Deny, Everyone, 'add')] return result def get_userperm(self, perm_name): # import pdb; pdb.set_trace() with transaction.manager: need_permission, action = perm_name.split(':') permission = DBSession.query(TapPermission).filter_by( name=need_permission).first() user_perm = DBSession.query(TapUserPermission).filter_by( user_id=self.request.user.id, permission_id=permission.id).first() user_perm = TempPermission(user_perm) if not need_permission.startswith('SYS') and '.' in need_permission: # if parent-permission has the authorization, # also child-permission has it. permissions = need_permission.split('.') for i in range(len(permissions)): permission = '.'.join(permissions[:i+1]) permission = DBSession.query(TapPermission).filter_by( name=permission ).first() if not permission: continue _perm = DBSession.query(TapUserPermission).filter_by( user_id=self.request.user.id, permission_id=permission.id ).first() if _perm: user_perm.a_add = (user_perm.a_add or _perm.a_add) user_perm.a_edit = (user_perm.a_edit or _perm.a_edit) user_perm.a_delete = (user_perm.a_delete or _perm.a_delete) user_perm.a_view = (user_perm.a_view or _perm.a_view) return user_perm def project_name(self, project_id): project = DBSession.query(TapProject).get(project_id) return project.name def project_name_byapi(self, api_id): api = DBSession.query(TapApi).get(api_id) return api.project.name def api_name(self, api_id): api = DBSession.query(TapApi).get(api_id) return '%s.%s' % (api.project.name, api.name) def pages_permission(self, path): # Permission: result = None matchdict = self.request.matchdict params = self.request.params if path.startswith('/management/database'): result = 'SYS_DATABASE:view' elif path.startswith('/management/client'): result = 'SYS_CLIENT:view' elif path == '/management/user': result = 'SYS_USER:view' elif path.startswith('/management/user/edit/'): result = 'SYS_USER:edit' elif path == '/management/project': result = 'SYS_PROJECT:view' elif path.startswith('/management/project/'): result = '%s:view' % self.project_name(matchdict['project_id']) elif path.startswith('/management/api/'): result = '%s:view' % self.project_name_byapi(matchdict['api_id']) elif path.startswith('/management/api-test'): result = '%s:view' % self.project_name_byapi(params['id']) return result @property def __acl__(self): if not self.request.userid: return [(Deny, Everyone, 'view'), (Deny, Everyone, 'edit'), (Deny, Everyone, 'delete'), ] if self.request.user.is_admin: return [(Allow, self.request.user.name, 'view'), (Allow, self.request.user.name, 'edit'), (Allow, self.request.user.name, 'delete'), (Allow, self.request.user.name, 'access'), ] return self.route() class TmpContainer(object): @classmethod def load(cls, values): result = TmpContainer() for k, v in values.items(): setattr(result, k, v) return result def get_user(request): if not hasattr(request, 'inst_user'): user_id = authenticated_userid(request) if not user_id: user = unauthenticated_userid(request) request.inst_user = user else: bind_user(request, user_id) return request.inst_user def get_user_id(request): user_id = authenticated_userid(request) return user_id def bind_user(request, user_id): if hasattr(request, 'inst_user'): return request.inst_user if user_id is not None: with transaction.manager: user = DBSession.query(TapUser).get(user_id) if not user: return None user = [(k, getattr(user, k)) for k in user.__table__.columns.keys()] user = dict(user) user = TmpContainer.load(user) request.inst_user = user return request.inst_user def encrypt_password(password, salt=None): """Hash password on the fly.""" if salt is None: salt = os.urandom(8) # 64 bits. assert 8 == len(salt) assert isinstance(salt, str) if isinstance(password, unicode): password = password.encode('UTF-8') assert isinstance(password, str) result = password for i in xrange(10): result = HMAC(result, salt, sha256).digest() return salt + result def valid_password(user, password): salt = user.password[:8] return encrypt_password(password, salt) == user.password

UTF-8

Python

py

security.py

its-sachin/LeetCode

1051a67ee6bf24c3f7b030af1bb5a9e4ec642376

52ece7acc902c570a8981ed8e3104cad80f4c80d

/Random/q73.py

684cb1246be534f120a83fb25ebd06f44b50e3dd

no_license

https://github.com/its-sachin/LeetCode

fa1b4c30672bd82bd777001553f6e06c191ba6e3

44b79d3d55b383e74d4c9cd7a3da7d659f3f36d5

refs/heads/master

class Solution: def setZeroes(self, matrix): d={} for i in range(len(matrix)): for j in range(len(matrix[i])): if(matrix[i][j]==0): if(d.get(i)==None): d[i]=[j] else: d[i].append(j) done = {} for k in d: for i in range(len(matrix[k])): matrix[k][i]=0 for j in d[k]: if(done.get(j)==None): for i in range(len(matrix)): matrix[i][j]=0 done[j]=True o = Solution() matrix=[[0,1,2,0],[3,4,5,2],[1,3,1,5]] o.setZeroes(matrix) print(matrix)

UTF-8

Python

py

q73.py

kfconsultant/scale

e273b83a145ff56f86ce96288139068261a7d0b9

65306b41168a5afa6fc80904cc0fbf737939a01a

/scale/util/host.py

5c908d43ba68c7e1d9a8ec0e6b18bb843ad11a64

permissive

https://github.com/kfconsultant/scale

9e5df45cd36211d1bc5e946cf499a4584a2d71de

28618aee07ceed9e4a6eb7b8d0e6f05b31d8fd6b

refs/heads/master

Apache-2.0

"""Defines the named tuple for host locations""" from __future__ import unicode_literals from collections import namedtuple # Named tuple represents a host location HostAddress = namedtuple('HostAddress', ['hostname', 'port', 'protocol']) def host_address_from_mesos_url(url): """Extract URL components from a Mesos Master URL Must include the protocol prefix. Ports may be omitted. :param url: The URL to parse :type url: basestring :return: Parsed URL components :rtype: :class:`util.host.HostAddress` """ from urlparse import urlparse elements = urlparse(url) # infer port from scheme if not set: port = elements.port if not port: if elements.scheme == 'https': port = 443 elif elements.scheme == 'http': port = 80 return HostAddress(elements.hostname, port, elements.scheme)

UTF-8

Python

py

host.py

LightSys/kardia

acfcb76e2ed9fca44b7fe53e85c6a242c4f87a77

f8b5d28d02800761116049df9cbe85996ada6aa1

/kardia-dev-tools/zac_python_script.py

69d0ea60bd37335553f84a44e97b2ece18104e73

no_license

https://github.com/LightSys/kardia

5efee753765eb80fca36ee13fe996a278d374325

cc3b9b607dc60c23dabd7c3b950d9aad0054e562

refs/heads/master

Java

import csv from datetime import datetime counter = 0 modCount = 0 setID = set() setAmounts = set() setAmountErrorsId = [] setDatesErrorsPrev = [] setDatesErrorsNext = [] setErrorNTLOnce = [] setErrorNTLTwice = [] setErrorNTLMult = [] setErrorNTLMultBlank = [] setErrorNTLEqualFirst = [] setAmountZero = [] setIsExtraNull = [] with open('moreData.csv') as csvfile: rowReader = csv.reader(csvfile) prevRow = rowReader.__next__() for row in rowReader: firstDate = datetime.strptime(row[5], "%d %b %Y %H:%M") lastDate = datetime.strptime(row[6], "%d %b %Y %H:%M") if(row[18] == ""): setIsExtraNull.append(row[1]) setIsExtraNull.append(row[2]) setIsExtraNull.append(row[3]) if (int(row[8]) < 2): if(row[7] != ""): setErrorNTLOnce.append(row[1]) setErrorNTLOnce.append(row[2]) setErrorNTLOnce.append(row[3]) elif (int(row[8]) == 2): if(row[7] != row[5]): setErrorNTLTwice.append(row[1]) setErrorNTLTwice.append(row[2]) setErrorNTLTwice.append(row[3]) else: if(row[7] == ""): setErrorNTLMultBlank.append(row[1]) setErrorNTLMultBlank.append(row[2]) setErrorNTLMultBlank.append(row[3]) elif(row[7] == row[5]): setErrorNTLEqualFirst.append(row[1]) setErrorNTLEqualFirst.append(row[2]) setErrorNTLEqualFirst.append(row[3]) else: ntlDate = datetime.strptime(row[7], "%d %b %Y %H:%M") if(firstDate >= ntlDate or lastDate < ntlDate): setErrorNTLMult.append(row[1]) setErrorNTLMult.append(row[2]) setErrorNTLMult.append(row[3]) if(row[4][0] == "-"): setAmountZero.append(row[1]) setAmountZero.append(row[2]) setAmountZero.append(row[3]) if (row[2] == prevRow[2] and row[1] == prevRow[1]): if(row[6] != prevRow[20] and row[6] != "" and prevRow[20] != ""): setDatesErrorsPrev.append(row[1]) setDatesErrorsPrev.append(row[2]) setDatesErrorsPrev.append(row[3]) if(row[21] != prevRow[5] and row[21] != "" and row[5] != ""): setDatesErrorsNext.append(row[1]) setDatesErrorsNext.append(row[2]) setDatesErrorsNext.append(row[3]) if(row[4] == prevRow[4]): setAmountErrorsId.append(row[1]) setAmountErrorsId.append(row[2]) setAmountErrorsId.append(row[3]) prevRow = row print ("Amount Errors: ") print (setAmountErrorsId) print ("\nDate Errors Prev: ") print (setDatesErrorsPrev) print ("\nDate Errors Next: ") print (setDatesErrorsNext) print ("\n", setErrorNTLOnce) print ("\n", setErrorNTLTwice) print ("\n", setErrorNTLMult) print ("\n") f = open("thingsToEdit.txt", "w") f.write("Amount Errors: \n") f.write("Donor CostCenter Hist\n") for error in setAmountErrorsId: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nDate Errors Prev: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setDatesErrorsPrev: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nDate Errors Next: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setDatesErrorsNext: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nNTL Errors Count 1: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setErrorNTLOnce: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nNTL Errors Count 2: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setErrorNTLTwice: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nNTL Errors Count Mult: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setErrorNTLMult: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nNTL Errors Count Mult Blank: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setErrorNTLMultBlank: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nNTL Errors Count Mult Equal First: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setErrorNTLEqualFirst: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nSet Amounts Zero: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setAmountZero: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.write("\nIs Extra is Null: \n") f.write("Donor CostCenter Hist\n") modCount = 0 for error in setIsExtraNull: if(modCount % 3 == 2): f.write(error) f.write("\n") else: f.write(error) f.write("\t") modCount += 1 f.close() # for row in rowReader: # setID.add(row[1]) # setAmounts.add(row[4]) # setID.add(row[1]) # setAmounts.add(row[4]) # if(row[1] == "\"100400\""): # counter = counter + 1 # print row #print counter #print len(setID) #print "\"100400\"" in setID #print "$4.00" in setAmounts #print spamreader.next()[2] #print "\n"

UTF-8

Python

py

zac_python_script.py

NIDHISH99444/CodingNinjas

73f0ebfa647db2f6933841e21301cd10b8eb1520

a1678f80efe56423d08bea6a2843633b8a81dd34

/CN_DynamicProgramming1/AlphaCode.py

689a84cac1914bf5e3da9c12e9b24eba31886089

no_license

https://github.com/NIDHISH99444/CodingNinjas

af60aa93dbfcf050e727949d41201f72973b0608

b77b652cf0bf9b098ef9da4eff5eaecb7bfeaea5

refs/heads/master

def alphaCode(input,size,output): output[0]=1 output[1]=1 for i in range(2,size+1): if input[i-1]!=0: output[i]=output[i-1] if input[i-2]*10+input[i-1] <=26: output[i]+=output[i-2] return output[size] #output(i) -->checking i length ka input array ipt=int(input()) while ipt!=0: string=str(ipt) size=len(string) output=[0]*(size+1) arr=[int(string[i]) for i in range(len(string))] print(alphaCode(arr,size,output)) ipt=int(input())

UTF-8

Python

py

AlphaCode.py

quanloveshui/permission

74ee244bdb46f91951870672370f3962d5a2c6a3

526554a69524b4e2c9233985bd901578b9445b93

/src/repository/user_type.py

c48eb70a1c07d05a8baee2494eef161545a306fe

no_license

https://github.com/quanloveshui/permission

375e0b40d5dcf221f6a8776da2e4f85464c19dc9

9b8618f7ad888403f96385c738ac1d0194e694eb

refs/heads/master

#!/usr/bin/env python # -*- coding:utf-8 -*- from src.utils.db_connection import DbConnection class UserTypeRepository: def __init__(self): self.db_conn = DbConnection() def add(self, caption): """ 在user_type表中添加角色 :param caption: :return: """ cursor = self.db_conn.connect() sql = """ insert into user_type(caption) values(%s) """ cursor.execute(sql, [caption,]) self.db_conn.close() def del_role(self,role): """ 在user_type表中删除角色 :param role: :return: """ cursor = self.db_conn.connect() sql = "delete from user_type WHERE caption=%s" cursor.execute(sql, role) self.db_conn.close() def get_info(self): """ 获取user_type表中所有用户角色类型 :return: """ cursor = self.db_conn.connect() sql='select * from user_type' cursor.execute(sql) result = cursor.fetchall() self.db_conn.close() return result

UTF-8

Python

py

user_type.py

GuizaoBR/Django

4dbb343a34afecd5f25db08cf4b6a8551693171f

48f7e8b28b8b586e49905d78c4bf4bc19cb02293

/aplicativo2/aplicativo2/views.py

da85c459ee52b77f4662831c80feb4827a6ea2db

no_license

https://github.com/GuizaoBR/Django

14d8ddef8f0d47fb4d5baa149b0a23b04c3e2ebf

c8dbbfe96d4ed0cdfb562ba7f23aeff5dd8ff07f

refs/heads/master

from django.http import HttpResponse def ola(request): return HttpResponse("<h1>Guilherme<h1>")

UTF-8

Python

py

views.py

musikid/VitaPad

9adb28b50e9066cf94ac097215857f9d89a18056

c945d56d0d12b11b77677045ec06adeb79091a18

/client/client_test/client.py

10a7ce1777bc1822b5077bd7b242d70b41a8c2cb

no_license

https://github.com/musikid/VitaPad

09d563ba26cd75d5fd9166898c12540deded56f3

576e2279935a25e2456bd3c14e7868e39086b064

refs/heads/main

Rust

from socket import socket, AF_INET, SOCK_STREAM, SOCK_DGRAM from time import time import flatbuffers from NetProtocol.Handshake import Handshake, Endpoint from Pad import MainPacket, Vector3 sock = socket(AF_INET, SOCK_STREAM) sock.connect(("192.168.1.24", 5000)) print("Connected") udp_sock = socket(AF_INET, SOCK_DGRAM) udp_sock.bind(("0.0.0.0", 0)) _, port = udp_sock.getsockname() builder = flatbuffers.Builder() Handshake.HandshakeStart(builder) Handshake.HandshakeAddEndpoint(builder, Endpoint.Endpoint.Client) Handshake.HandshakeAddPort(builder, port) Handshake.HandshakeAddHeartbeatFreq(builder, 0) handshake = Handshake.HandshakeEnd(builder) builder.FinishSizePrefixed(handshake) sock.sendall(builder.Output()) response = sock.recv(1024) buf, prefix = flatbuffers.util.RemoveSizePrefix(response, 0) handshake = Handshake.Handshake.GetRootAs(buf[prefix:]) delay = handshake.HeartbeatFreq() magic = bytes([0x45, 0x4e, 0x44, 0x48, 0x42, 0x54]) udp_sock.sendto(magic, ("192.168.1.24", 5000)) last_sent = int(time()) while True: if int(time()) - last_sent >= delay - (delay // 2): sock.sendall(magic) last_sent = int(time()) data, addr = udp_sock.recvfrom(1024) buf, prefix = flatbuffers.util.RemoveSizePrefix(data, 0) main = MainPacket.MainPacket.GetRootAs(buf[prefix:]) vec = Vector3.Vector3() print(main.Motion().Accelerometer(vec).X())

UTF-8

Python

py

client.py

firanger/segundo-corte

2e92f69ce5c007b2629815f1b4bec4dc29ea72ca

4b361d63ac144914c59091047a4d7527f2a386b8

/meltdown-mitigation/conditionals.py

d15b8365347be1054ae811c22597e61a930bc715

no_license

https://github.com/firanger/segundo-corte

b30b52715731b20945f165a6c4244c7afce73d1a

baf494a0395d15f2b77e9fdfd987316e9fda5caf

refs/heads/main

def is_criticality_balanced(temperature, neutrons_emitted): return temperature < 800 and neutrons_emitted > 500 and temperature * neutrons_emitted < 500000 def reactor_efficiency(voltage, current, theoretical_max_power): efficiency = voltage * current / theoretical_max_power * 100 if efficiency >= 80: return "green" elif efficiency >= 60: return "orange" elif efficiency >= 30: return "red" else: return "black" def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) def fail_safe(temperature, neutrons_produced_per_second, threshold): volume = temperature * neutrons_produced_per_second if volume < int(threshold*0.4): return 'LOW' elif volume > int(threshold * 0.9) and volume < int(threshold * 1.1): return 'NORMAL' else: return 'DANGER'

UTF-8

Python

py

conditionals.py

poblouin/BudgetMe

05e59e48eabb0a187a0233d29a76c21f68e5c2f8

169cd04c0602735f0a648a7d44454d741fe25859

/budgetme/budgetme_api/filters.py

a4f539c7bb249f7a8b1d5c0a07818238315ef298

permissive

https://github.com/poblouin/BudgetMe

a1a2c939df5028018c9f7e27f8e7a20f3af70b56

b0b4b09042937e3f1d653078868bfaf3791dc03e

refs/heads/master

import django_filters from django_filters import rest_framework from budgetme.budgetme_api.models import Transaction, ScheduledTransaction class TransactionFilter(django_filters.rest_framework.FilterSet): from_date = django_filters.DateTimeFilter(name='date', lookup_expr='gte') to_date = django_filters.DateTimeFilter(name='date', lookup_expr='lte') transaction_category = django_filters.CharFilter(name='transaction_category__name', lookup_expr='iexact') transaction_type = django_filters.CharFilter(name='transaction_category__transaction_type__name', label='Transaction type name', lookup_expr='iexact') account_name = django_filters.CharFilter(name='account__name', lookup_expr='iexact') account_type = django_filters.CharFilter(name='account__account_type__name', label='Account type name', lookup_expr='iexact') class Meta: model = Transaction fields = ['date', 'from_date', 'to_date', 'transaction_category', 'transaction_type', 'account_name', 'account_type'] class ScheduledTransactionFilter(django_filters.rest_framework.FilterSet): start_date = django_filters.DateTimeFilter(name='start_date', lookup_expr='gte') transaction_category = django_filters.CharFilter(name='transaction_category__name', lookup_expr='iexact') transaction_type = django_filters.CharFilter(name='transaction_category__transaction_type__name', label='Transaction type name', lookup_expr='iexact') frequency = django_filters.CharFilter(name='frequency__name', lookup_expr='iexact') class Meta: model = ScheduledTransaction fields = ['start_date', 'transaction_category', 'transaction_type', 'frequency']

UTF-8

Python

py

filters.py

pioneers/PieCentral

40d131497c83471b52fe3604c6e3d0ed51d577ef

500362b86cfe4c692dd8a57a0caabe67e0efba79

/shepherd/runtimeclient.py

28f3304d72e84a98566542764c350dec1bfbc8eb

permissive

https://github.com/pioneers/PieCentral

e82c3c3f0ad37de2895dd15caafdad2965d9ff41

d6437d4ddfa386f44e6f70391c5211c723dde56d

refs/heads/master

Apache-2.0

Python

import msgpackrpc #pylint: disable=import-error class RuntimeClient: def __init__(self, host, port): self.host, self.port, self.client = host, port, None def connect(self): self.client = msgpackrpc.Client(msgpackrpc.Address(self.host, self.port)) def disconnect(self): self.client.close() self.client = None @property def connected(self): return self.client is not None def set_mode(self, mode=True): self.client.call('set_mode', mode) def set_alliance(self, alliance): self.client.call('set_alliance', alliance) def set_master(self, master): self.client.call('set_master', master) def set_starting_zone(self, zone): self.client.call('set_starting_zone', zone) def run_challenge(self, seed, timeout=1): self.client.notify('run_challenge', int(seed), timeout) def get_challenge_solution(self): return self.client.call('get_challenge_solution') class RuntimeClientManager: def __init__(self, blue_alliance, gold_alliance, b1_custom_ip=None, b2_custom_ip=None, g1_custom_ip=None, g2_custom_ip=None): custom_ips = (b1_custom_ip, b2_custom_ip, g1_custom_ip, g2_custom_ip) self.blue_alliance, self.gold_alliance = blue_alliance, gold_alliance self.clients = {} for i in range(len(self.blue_alliance+self.gold_alliance)): if custom_ips[i]: self.clients[(self.blue_alliance+self.gold_alliance)[i]] =\ (RuntimeClient(f'{custom_ips[i]}', 6020)) elif (self.blue_alliance + self.gold_alliance)[i] >= -100: self.clients[(self.blue_alliance+self.gold_alliance)[i]] =\ (RuntimeClient(f'192.168.128.{200 +(self.blue_alliance + self.gold_alliance)[i]}', 6020)) #pylint: disable=line-too-long else: self.clients[(self.blue_alliance+self.gold_alliance)[i]] = None for client in self.clients.values(): if client is not None: print(client.host) client.connect() for team in self.blue_alliance: client = self.clients[team] if client: client.set_alliance('blue') for team in self.gold_alliance: client = self.clients[team] if client: client.set_alliance('gold') def set_starting_zones(self, zones): for team, zone in zip(self.blue_alliance + self.gold_alliance, zones): if self.clients[team]: self.clients[team].set_starting_zone(zone) def set_mode(self, mode): for client in self.clients.values(): if client: print('Setting mode for client:', client.host) client.set_mode(mode) def get_challenge_solutions(self): return {team: (client.get_challenge_solution() if client else None) for team, client in self.clients.items()} def set_master_robots(self, blue_team, gold_team): """ blue_master = self.clients[blue_team] if blue_master: blue_master.set_master() gold_master = self.clients[gold_team] if gold_master: gold_master.set_master()""" """ client = RuntimeClient('192.168.128.107', 6020) client.connect() import time from Code import decode x = 1 client.run_challenge(x) time.sleep(0.2) print('Robot answer:', client.get_challenge_solution()) print('Authoritative answer:', decode(x)) """ #pylint: disable=pointless-string-statement # client = RuntimeClient('192.168.128.115', 6020) # client.connect() # client.set_mode('idle') # print('OK!') # client.set_alliance('blue') # client.set_starting_zone('left') # client.run_challenge(123) # time.sleep(0.2) # print(client.get_challenge_solution()) # time.sleep(1) # client.run_challenge(123) # print(client.get_challenge_solution())

UTF-8

Python

py

runtimeclient.py

cyan478/MFR

9c9731bbc61b267ee09a3aeb03a02d063b503d77

0a4cdb328c5808dc8b4ce9b32ca9d1942a7f45e5

/textReduction.py

ce769da687f47efad4b3382d1019516002ff5e95

no_license

https://github.com/cyan478/MFR

2ff029b12131541661a25bf6e224b9c9e53390db

3426ff899b3816833525c409c7088bf80e7a76b8

refs/heads/master

a = open("alice.txt", "r") text = a.read().split() a.close() words = [word.strip('()",.!*\'-') for word in text] def wordFreq(w): freq = filter(lambda a: a.lower() == w.lower(), words) return len(freq) #test print 'The frequency of the word "the" is ' + str(wordFreq('the')) print 'The frequency of the word "is" is ' + str(wordFreq('is')) print 'The frequency of the word "Alice" is ' + str(wordFreq('Alice')) def totalFreq(w): freqs = [wordFreq(x) for x in w] return reduce(lambda a, b: a + b, freqs) #test print 'The total frequency of the words "the", "is", and "Alice" is ' + str(totalFreq(['the', 'is', 'Alice'])) def mostFreq(): freqs = [[x, wordFreq(x)] for x in words] return reduce(lambda a, b: a if a[1] > b[1] else b, freqs)[0] #test -- works, but takes a long time when using list comprehensions/map/filter/reduce print 'The most frequently occurring word is ' + str(mostFreq())

UTF-8

Python

py

textReduction.py

AnonymousConferences/MCF

ed44f4bc2aeb4e4adffbc28304b310ea0e1788cb

ddd5b79e80d5b2a43a01a92dbd8fcefbc74fbe18

/KickstarterRec/parallel_mf_posneg_embedding_user.py

cd2a6b30bdc1fc081ce98c5300c06a014d61ab6f

no_license

https://github.com/AnonymousConferences/MCF

24b037dacd0ad4ee28a48dc3cde37f733dd5fd95

029a256ae91e6d8a7558385410cf6dc7e37af43f

refs/heads/master

import os import sys import time import threading import numpy as np from numpy import linalg as LA import ParallelSolver as ps import MultiProcessParallelSolver as mpps from joblib import Parallel, delayed from sklearn.base import BaseEstimator, TransformerMixin import rec_eval class ParallelMFPositiveNegativeUserEmbedding(BaseEstimator, TransformerMixin): def __init__(self, mu_u_p = 1, mu_u_n = 1, n_components=100, max_iter=10, batch_size=1000, init_std=0.01, dtype='float32', n_jobs=8, random_state=None, save_params=False, save_dir='.', early_stopping=False, verbose=False, **kwargs): ''' Parameters --------- n_components : int Number of latent factors max_iter : int Maximal number of iterations to perform batch_size: int Batch size to perform parallel update init_std: float The latent factors will be initialized as Normal(0, init_std**2) dtype: str or type Data-type for the parameters, default 'float32' (np.float32) n_jobs: int Number of parallel jobs to update latent factors random_state : int or RandomState Pseudo random number generator used for sampling save_params: bool Whether to save parameters after each iteration save_dir: str The directory to save the parameters early_stopping: bool Whether to early stop the training by monitoring performance on validation set verbose : bool Whether to show progress during model fitting **kwargs: dict Model hyperparameters ''' self.mu_u_p = mu_u_p self.mu_u_n = mu_u_n self.n_components = n_components self.max_iter = max_iter self.batch_size = batch_size self.init_std = init_std self.dtype = dtype self.n_jobs = n_jobs self.random_state = random_state self.save_params = save_params self.save_dir = save_dir self.early_stopping = early_stopping self.verbose = verbose if type(self.random_state) is int: np.random.seed(self.random_state) elif self.random_state is not None: np.random.setstate(self.random_state) self._parse_kwargs(**kwargs) def _parse_kwargs(self, **kwargs): ''' Model hyperparameters Parameters --------- lambda_alpha, lambda_beta, lambda_gamma: float Regularization parameter for user (lambda_alpha), item factors ( lambda_beta), and context factors (lambda_gamma). c0, c1: float Confidence for 0 and 1 in Hu et al., c0 must be less than c1 ''' self.lam_alpha = float(kwargs.get('lambda_alpha', 1e-5)) self.lam_beta = float(kwargs.get('lambda_beta', 1e-5)) self.lam_theta_p = float(kwargs.get('lambda_theta_p', 1e-5)) self.lam_theta_n = float(kwargs.get('lambda_theta_n', 1e-5)) self.c0 = float(kwargs.get('c0', 0.1)) self.c1 = float(kwargs.get('c1', 2.0)) assert self.c0 < self.c1, "c0 must be smaller than c1" def _init_params(self, n_users, n_projects): ''' Initialize all the latent factors and biases ''' self.alpha = self.init_std * np.random.randn(n_users, self.n_components).astype(self.dtype) self.beta = self.init_std * np.random.randn(n_projects, self.n_components).astype(self.dtype) self.theta_p = self.init_std * np.random.randn(n_users, self.n_components).astype(self.dtype) # bias for beta and gamma self.bias_b_p = np.zeros(n_users, dtype=self.dtype) self.bias_c_p = np.zeros(n_users, dtype=self.dtype) # global bias self.global_y_p = 0.0 self.theta_n = self.init_std * np.random.randn(n_users, self.n_components).astype(self.dtype) # bias for alpha and theta self.bias_b_n = np.zeros(n_users, dtype=self.dtype) self.bias_c_n = np.zeros(n_users, dtype=self.dtype) # global bias self.global_y_n = 0.0 # intercept of second factorization for user-user # Init got equivalent res # print 'Initial alpha: \n', self.alpha # print 'Initial beta: \n', self.beta # print 'Initial gamma: \n', self.gamma # # print 'Initial bias_d: \n', self.bias_d # print 'Initial bias_e: \n', self.bias_e # print 'Initial global_x: \n', self.global_x def fit(self, M, YP = None, YN = None, FYP=None, FYN = None, vad_data=None, **kwargs): '''Fit the model to the data in M. Parameters ---------- M : scipy.sparse.csr_matrix, shape (n_users, n_projects) Training click matrix. X : scipy.sparse.csr_matrix, shape (n_projects, n_projects) Training co-occurrence matrix. F : scipy.sparse.csr_matrix, shape (n_projects, n_projects) The weight for the co-occurrence matrix. If not provided, weight by default is 1. vad_data: scipy.sparse.csr_matrix, shape (n_users, n_projects) Validation click data. **kwargs: dict Additional keywords to evaluation function call on validation data Returns ------- self: object Returns the instance itself. ''' n_users, n_projects = M.shape assert YP.shape == (n_users, n_users) assert YN.shape == (n_users, n_users) self._init_params(n_users, n_projects) self._update(M, YP, YN, FYP, FYN, vad_data, **kwargs) return self def transform(self, M): pass def _update(self, M, YP, YN, FYP, FYN, vad_data, **kwargs): '''Model training and evaluation on validation set''' MT = M.T.tocsr() # pre-compute this YPT, YNT, FYPT, FYNT = None, None, None, None if YP != None: YPT = YP.T if YN != None: YNT = YN.T if FYP != None: FYPT = FYP.T if FYN != None: FYNT = FYN.T self.vad_ndcg = -np.inf for i in xrange(self.max_iter): if self.verbose: print('ITERATION #%d' % i) self._update_factors(M, MT, YP, YPT, YN, YNT, FYP, FYPT, FYN, FYNT) self._update_biases(YP, YPT, YN, YNT, FYP, FYPT, FYN, FYNT) if vad_data is not None: vad_ndcg = self._validate(M, vad_data, **kwargs) if self.early_stopping and self.vad_ndcg > vad_ndcg: break # we will not save the parameter for this iteration self.vad_ndcg = vad_ndcg if self.save_params: self._save_params(i) #print 'alpha:\n', self.alpha # print '\n' #print 'beta:\n', self.beta # print '\n' #print 'gamma:\n', self.gamma #print 'theta:\n', self.theta pass def _update_factors(self, M, MT, YP, YPT, YN, YNT, FYP, FYPT, FYN, FYNT): if self.verbose: start_t = _writeline_and_time('\tUpdating user factors...') self.alpha = update_alpha(self.beta, self.theta_p, self.theta_n, self.bias_b_p, self.bias_c_p, self.global_y_p, self.bias_b_n, self.bias_c_n, self.global_y_n, M, YP, FYP, YN, FYN, self.c0, self.c1, self.lam_alpha, n_jobs=self.n_jobs, batch_size=self.batch_size, mu_u_p=self.mu_u_p, mu_u_n=self.mu_u_n) # print('checking user factor isnan : %d'%(np.sum(np.isnan(self.alpha)))) if self.verbose: print('\r\tUpdating user factors: time=%.2f' % (time.time() - start_t)) start_t = _writeline_and_time('\tUpdating project factors...') self.beta = update_beta(self.alpha, MT, self.c0, self.c1, self.lam_beta, self.n_jobs, batch_size=self.batch_size) # print('checking project factor isnan : %d' % (np.sum(np.isnan(self.beta)))) if self.verbose: print('\r\tUpdating user embedding factors: time=%.2f' % (time.time() - start_t)) start_t = _writeline_and_time('\tUpdating positive user embedding factors...') # here it really should be X^T and FX^T, but both are symmetric self.theta_p = update_embedding_factor(self.alpha, self.bias_b_p, self.bias_c_p, self.global_y_p, YPT, FYPT, self.lam_theta_p, self.n_jobs, batch_size=self.batch_size, mu_p=self.mu_u_p) # print('checking theta_p isnan : %d' % (np.sum(np.isnan(self.theta_p)))) if self.verbose: print('\r\tUpdating positive user factors: time=%.2f' % (time.time() - start_t)) start_t = _writeline_and_time('\tUpdating negative user embedding factors...') self.theta_n = update_embedding_factor(self.alpha, self.bias_b_n, self.bias_c_n, self.global_y_n, YNT, FYNT, self.lam_theta_n, self.n_jobs, batch_size=self.batch_size, mu_p=self.mu_u_n) # print('checking theta_n isnan : %d' % (np.sum(np.isnan(self.theta_n)))) if self.verbose: print('\r\tUpdating negative user embedding factors: time=%.2f' % (time.time() - start_t)) pass def _update_biases(self, YP, YPT, YN, YNT, FYP, FYPT, FYN, FYNT): if self.verbose: start_t = _writeline_and_time('\tUpdating bias terms...') self.bias_b_p = update_bias(self.alpha, self.theta_p, self.bias_c_p, self.global_y_p, YP, FYP, self.n_jobs, batch_size=self.batch_size, mu = self.mu_u_p) # print('checking isnan bias_b_p : %d' % (np.sum(np.isnan(self.bias_b_p)))) if np.sum(np.isnan(self.bias_b_p)) > 0 : np.argwhere(np.isnan(self.bias_b_p)) # here it really should be X^T and FX^T, but both are symmetric self.bias_c_p = update_bias(self.theta_p, self.alpha, self.bias_b_p, self.global_y_p, YP, FYP, self.n_jobs, batch_size=self.batch_size, mu=self.mu_u_p) # print('checking isnan bias_c_p: %d' % (np.sum(np.isnan(self.bias_c_p)))) if np.sum(np.isnan(self.bias_c_p)) > 0 : np.argwhere(np.isnan(self.bias_c_p)) self.global_y_p = update_global(self.alpha, self.theta_p, self.bias_b_p, self.bias_c_p, YP, FYP, self.n_jobs, batch_size=self.batch_size, mu=self.mu_u_p) # print('checking isnan global_y_p: %d' % (np.sum(np.isnan(self.global_y_p)))) if np.sum(np.isnan(self.global_y_p)) > 0 : np.argwhere(np.isnan(self.global_y_p)) self.bias_b_n = update_bias(self.alpha, self.theta_n, self.bias_c_n, self.global_y_n, YN, FYN, self.n_jobs, batch_size=self.batch_size, mu=self.mu_u_n) # print('checking isnan : %d' % (np.sum(np.isnan(self.bias_b_n)))) # here it really should be X^T and FX^T, but both are symmetric self.bias_c_n = update_bias(self.theta_n, self.alpha, self.bias_b_n, self.global_y_n, YN, FYN, self.n_jobs, batch_size=self.batch_size, mu=self.mu_u_n) # print('checking isnan : %d' % (np.sum(np.isnan(self.bias_c_n)))) self.global_y_n = update_global(self.alpha, self.theta_n, self.bias_b_n, self.bias_c_n, YN, FYN, self.n_jobs, batch_size=self.batch_size, mu=self.mu_u_n) # print('checking isnan : %d' % (np.sum(np.isnan(self.global_y_n)))) if self.verbose: print('\r\tUpdating bias terms: time=%.2f' % (time.time() - start_t)) pass def _validate(self, M, vad_data, **kwargs): vad_ndcg = rec_eval.parallel_normalized_dcg_at_k(M, vad_data, self.alpha, self.beta, **kwargs) if self.verbose: print('\tValidation NDCG@k: %.5f' % vad_ndcg) return vad_ndcg def _save_params(self, iter): '''Save the parameters''' if not os.path.exists(self.save_dir): os.makedirs(self.save_dir) filename = 'CoFacto_K%d_iter%d.npz' % (self.n_components, iter) np.savez(os.path.join(self.save_dir, filename), U=self.alpha, V=self.beta) # Utility functions # def _writeline_and_time(s): sys.stdout.write(s) sys.stdout.flush() return time.time() def get_row(Y, i): '''Given a scipy.sparse.csr_matrix Y, get the values and indices of the non-zero values in i_th row''' lo, hi = Y.indptr[i], Y.indptr[i + 1] return Y.data[lo:hi], Y.indices[lo:hi] def update_alpha(beta, theta_p, theta_n, bias_b_p, bias_c_p, global_y_p, bias_b_n, bias_c_n, global_y_n, M, YP, FYP, YN, FYN, c0, c1, lam_alpha, n_jobs = 8, batch_size=1000, mu_u_p=1, mu_u_n=1): '''Update user latent factors''' m, n = M.shape # m: number of users, n: number of items f = beta.shape[1] # f: number of factors BTB = c0 * np.dot(beta.T, beta) # precompute this BTBpR = BTB + lam_alpha * np.eye(f, dtype=beta.dtype) return mpps.UpdateUserFactorParallel( beta, theta_p=theta_p, theta_n=theta_n, bias_b_p=bias_b_p, bias_c_p=bias_c_p, global_y_p=global_y_p, bias_b_n=bias_b_n, bias_c_n=bias_c_n, global_y_n=global_y_n, M=M, YP=YP, FYP=FYP, YN=YN, FYN=FYN, BTBpR=BTBpR, c0=c0, c1=c1, f=f, mu_u_p=mu_u_p, mu_u_n=mu_u_n, n_jobs=n_jobs, mode='hybrid' ).run() def update_beta(alpha, MT, c0, c1, lam_beta, n_jobs, batch_size=1000): '''Update item latent factors/embeddings''' n, m = MT.shape # m: number of users, n: number of projects f = alpha.shape[1] assert alpha.shape[0] == m TTT = c0 * np.dot(alpha.T, alpha) # precompute this TTTpR = TTT + lam_beta * np.eye(f, dtype=alpha.dtype) return mpps.UpdateProjectFactorParallel( alpha = alpha, MT = MT, TTTpR = TTTpR, c0=c0, c1=c1, f=f, n_jobs=n_jobs, mode=None ).run() def update_embedding_factor(alpha, bias_b, bias_c, global_y, YT, FYT, lam_theta, n_jobs, batch_size=1000, mu_p = 1): '''Update context latent factors''' m, f = alpha.shape # m: number of users, f: number of factors return mpps.UpdateEmbeddingFactorParallel( main_factor = alpha, bias_main=bias_b, bias_embedding=bias_c, intercept=global_y, XT=YT, FXT=FYT, f=f, lam_embedding=lam_theta, mu=mu_p, n_jobs=n_jobs ).run() def update_bias(alpha, theta, bias_c, global_y, Y, FY, n_jobs = 8, batch_size=1000, mu = 1): return mpps.UpdateBiasParallel( main_factor=alpha, embedding_factor=theta, bias=bias_c, intercept=global_y, X=Y, FX=FY, mu=mu, n_jobs=n_jobs ).run() def update_global(alpha, theta, bias_b, bias_c, Y, FY, n_jobs = 8, batch_size=1000, mu = 1): assert alpha.shape == theta.shape assert bias_b.shape == bias_c.shape return mpps.UpdateInterceptParallel( main_factor = alpha, embedding_factor = theta, bias_main = bias_b, bias_embedding = bias_c, X = Y, FX = FY, mu=mu, n_jobs=n_jobs ).run()

UTF-8

Python

py

parallel_mf_posneg_embedding_user.py

mtamer/spideyweb

ca5fc321e0df1d7eaf112cfe6f0bf577719f06b6

770277dd8ee8db9f998b7e5f9419941047fc2c5b

/spideyweb.py

9736ea1f18f13a04d588bd02d08cbd1771beb598

no_license

https://github.com/mtamer/spideyweb

7c6b5a73afef60bb6eae1f144253f8e55a9bb28b

938305482480d0b05f452cad2c24ea8d9776ac18

refs/heads/master

import requests from bs4 import BeautifulSoup #modules BeautifulSoup #WebCrawler for one page def spiderweb(max_pages): page = 25 while page <= max_pages: url = 'http://www.usedottawa.com/classifieds/all/' + str(page) + '?description=honda' source_code = requests.get(url) plain_text = source_code.text soup = BeautifulSoup(plain_text) for link in soup.findAll('a',{'itemprop':'name'}): href = "http://www.usedottawa.com/" + link.get('href') title = link.string print(href) print(title) get_single_item_data(href) page+=25 #Dynamic Web Crawler def get_single_item_data(item_url): source_code = requests.get(item_url) plain_text = source_code.text soup = BeautifulSoup(plain_text) for item_name in soup.findAll('div',{'class':'article'}): print(item_name.string) # for link in soup.findAll('p',{'class':'title'} ): # href = "http://www.usedottawa.com/"+link.get('href') # print(href) spiderweb(100) #of pages to crawl.

UTF-8

Python

py

spideyweb.py

mverteuil/SIMP

a6be4e265deb1164cb21589dc8de0fd6e466aa92

086ec6cb1de40f1c01ecb6d14a8830d32a1eb177

/simp/inventory/urls.py

3703fa4190616eb6d9ceb95ca44c84d0094114f9

no_license

https://github.com/mverteuil/SIMP

dbfd17c5b10a58c4a02b36d34446ae3feb3d1d02

cdd65f48d823b93176dc895cf5893937a9a3ad2e

refs/heads/master

from bootstrap import urls as bootstrap_urls from django.conf.urls import patterns, url from .forms import (AccountForm, InventoryItemForm, PurchaserForm, TransactionForm) from .models import (InventoryItem, Purchaser) from .views import (MarkupDetailView, CreateView, ItemListView, PurchaserListView) urlpatterns = bootstrap_urls.bootstrap_patterns(AccountForm) urlpatterns += bootstrap_urls.bootstrap_pattern(InventoryItemForm, list_view=None) urlpatterns += bootstrap_urls.bootstrap_pattern(TransactionForm, create_view=None) urlpatterns += bootstrap_urls.bootstrap_pattern(PurchaserForm, list_view=None) urlpatterns += patterns( '', url( r'^inventoryitem/$', ItemListView.as_view(model=InventoryItem), name="inventoryitem_list" ), url( r'^inventoryitem/(?P<pk>\d+)/json/$', MarkupDetailView.as_view(model=InventoryItem), name='inventoryitem_json', ), url( r'^purchaser/$', PurchaserListView.as_view(model=Purchaser), name="purchaser_list" ), url( r'^transaction/add/$', CreateView.as_view(form_class=TransactionForm), name="transaction_form" ), )

UTF-8

Python

py

urls.py

sfmajors373/city-scrapers

d5ae3b1b598bc43fb518f1c8994ac674689fda20

5850afa8458946781b15b8c3122f4e9b3c760b83

/city_scrapers/spiders/det_zoning_appeals.py

b9593f5598806fcc1ce6638c38db8744021e3e9e

permissive

https://github.com/sfmajors373/city-scrapers

8ad1b70ea5669c3b4063fbfcb90bdb928c35eca7

49ad24460bc6e7132fbace33d2ae8b10a38764d7

refs/heads/master

MIT

# -*- coding: utf-8 -*- from datetime import time from dateutil.parser import parse from city_scrapers.constants import BOARD from city_scrapers.spider import Spider class DetZoningAppealsSpider(Spider): name = 'det_zoning_appeals' agency_name = 'Detroit Zoning Division' timezone = 'America/Detroit' allowed_domains = ['www.detroitmi.gov'] start_urls = ['https://www.detroitmi.gov/Government/Boards/Board-of-Zoning-Appeals-Meeting'] def parse(self, response): """ `parse` should always `yield` a dict that follows the Event Schema <https://city-bureau.github.io/city-scrapers/06_event_schema.html>. Change the `_parse_id`, `_parse_name`, etc methods to fit your scraping needs. """ location = { 'name': '13th Floor in the Auditorium, Coleman A. Young Municipal Center', 'address': '2 Woodward Avenue, Detroit, MI 48226', 'neighborhood': '', } for item in response.css('.field td::text').extract(): if not item.strip(): continue start = self._parse_start(item) data = { '_type': 'event', 'name': 'Board of Zoning Appeals', 'event_description': '', 'classification': BOARD, 'start': start, 'end': { 'date': start['date'], 'time': None, 'note': '' }, 'all_day': False, 'location': location, 'documents': self._parse_documents(start['date'], response), 'sources': [{ 'url': response.url, 'note': '' }], } data['status'] = self._generate_status(data) data['id'] = self._generate_id(data) yield data def _parse_start(self, item): """ Parse start date and time. """ return { 'date': parse(item).date(), 'time': time(9, 0), 'note': '', } def _parse_documents(self, dt, response): """ Parse or generate documents. """ dt_str = '{}-{dt.day}-{dt.year}'.format(dt.strftime('%B').lower(), dt=dt) minutes_url = response.css('a[href*="{}"]::attr(href)'.format(dt_str)).extract_first() if minutes_url: return [{'url': response.urljoin(minutes_url), 'note': 'Minutes'}] return []

UTF-8

Python

py

det_zoning_appeals.py

HYEEWON/practice_for_coding_test

a6302a0e8b29ece3d8f55feb135a5c1bc7e7629d

ecba61c279d1c7a729e463841382f5486e9dc7d7

/leetcode/220324_68_TextJustification_Hard.py

c8828b7996402f09d1155fd0b14bb59d8506773b

no_license

https://github.com/HYEEWON/practice_for_coding_test

458479ef75f7430ace4ddafcb408f4c322492f84

d13f1b5a1cd6132ab8592c31359e3a55aa6ad7d5

refs/heads/main

# Simulation class Solution: def fullJustify(self, words, maxWidth): answer = [] cnt, tmp = 0, [] # 단어 길이 합, 단어 배열 for word in words: if cnt + len(word) + len(tmp) <= maxWidth: # 단어 사이의 공백 필수 cnt += len(word) tmp.append(word) continue if len(tmp) == 1: answer.append(tmp[0]+(' '*(maxWidth-len(tmp[0])))) else: space = [(maxWidth - cnt) // (len(tmp) - 1) for i in range(len(tmp)-1)] # 공백 개수 for i in range((maxWidth-cnt) % len(space)): space[i] += 1 # 왼쪽부터 공백 개수 증가 a = '' for i in range(len(space)): a += tmp[i] + space[i]*' ' a += tmp[-1] answer.append(a) cnt, tmp = len(word), [word] answer.append(' '.join(tmp) + ' '*(maxWidth-cnt-len(tmp)+1)) # 마지막은 왼쪽 정렬 return answer s = Solution() print(s.fullJustify(["What","must","be","acknowledgment","shall","be"], 16))

UTF-8

Python

py

220324_68_TextJustification_Hard.py

GRSEB9S/HSI_Classification-1

8cd2349ea7221e59892fdee4802b31223e13892f

70db19ef8603bf2209e85b865d6a1e42231a1f46

/svm/svm.py

4ee78f222a3af959b831f29d437469a4ddbb4e57

no_license

https://github.com/GRSEB9S/HSI_Classification-1

1c68a8822d64348f3fb46ce6479fa906fd9ba433

35d600cc695e695d50fc97f024f4e425a8b1f2ab

refs/heads/master

# -*-coding=utf-8 -*- from sklearn.neighbors import KNeighborsClassifier from sklearn import svm import numpy as np import scipy.io as sio import random import matplotlib.pyplot as plt import seaborn as sn import pandas as pd import time import os from datetime import datetime PaviaU = '../data/PaviaU.mat' PaviaU_gt = '../data/PaviaU_gt.mat' Indian_pines = '../data/Indian_pines.mat' Indian_pines_gt = '../data/Indian_pines_gt.mat' Salinas = '../data/Salinas_corrected.mat' Salinas_gt = '../data/Salinas_gt.mat' for NO_data in ['Salians']: if NO_data == 'PaviaU':#use paviaU data = sio.loadmat(PaviaU) data_gt = sio.loadmat(PaviaU_gt) im = data['paviaU'] imGIS = data_gt['paviaU_gt'] elif NO_data == 'Inidan_pines':#use indian_pines data = sio.loadmat(Indian_pines) data_gt = sio.loadmat(Indian_pines_gt) im = data['indian_pines'] imGIS = data_gt['indian_pines_gt'] iG = np.zeros([imGIS.shape[0], imGIS.shape[1]], dtype=int) C = np.max(imGIS) origin_num = np.zeros(shape=[C + 1], dtype=int) for i in range(imGIS.shape[0]): for j in range(imGIS.shape[1]): for k in range(1, C + 1): if imGIS[i][j] == k: origin_num[k] += 1 index = 0 data_num = np.zeros(shape=[9], dtype=int) data_label = np.zeros(shape=[9], dtype=int) for i in range(len(origin_num)): if origin_num[i] > 400: data_num[index] = origin_num[i] data_label[index] = i index += 1 # del too few classes for i in range(imGIS.shape[0]): for j in range(imGIS.shape[1]): if imGIS[i, j] in data_label: for k in range(len(data_label)): if imGIS[i][j] == data_label[k]: iG[i, j] = k + 1 continue imGIS = iG elif NO_data == 'Salians':#use Salinas data = sio.loadmat(Salinas) data_gt = sio.loadmat(Salinas_gt) im = data['salinas_corrected'] imGIS = data_gt['salinas_gt'] im = (im - float(np.min(im))) im = im/np.max(im) sample_num = 200 deepth = im.shape[2] classes = np.max(imGIS) data_ = {} train_data = {} test_data = {} for i in range(1,classes+1): data_[i]=[] train_data[i]=[] test_data[i] = [] for i in range(imGIS.shape[0]): for j in range(imGIS.shape[1]): for k in range(1,classes+1): if imGIS[i,j]==k: data_[k].append(im[i,j]) for i in range(1,classes+1): indexies = random.sample(range(len(data_[i])),sample_num) for k in range(len(data_[i])): if k not in indexies: test_data[i].append(data_[i][k]) else: train_data[i].append(data_[i][k]) train = [] train_label = [] test = [] test_label = [] for i in range(1,len(train_data)+1): for j in range(len(train_data[i])): train.append(train_data[i][j]) train_label.append(i) for i in range(1,len(test_data)+1): for j in range(len(test_data[i])): test.append(test_data[i][j]) test_label.append(i) if not os.path.exists('result'): os.makedirs('result') time = str(datetime.now().strftime('%Y%m%d_%H%M%S')) result = open('result/result_for_NO_data_'+str(NO_data)+'_'+'_samplenum_'+'_'+str(k)+'_'+time+'.txt','w') clf = svm.SVC(gamma=1.0,C=100) clf.fit(train,train_label) C = np.max(imGIS) matrix = np.zeros((C,C)) for i in range(len(test)): r = clf.predict(test[i].reshape(-1,len(test[i]))) matrix[r-1,test_label[i]-1] += 1 print(np.int_(matrix)) print(np.sum(np.trace(matrix))) print(np.sum(np.trace(matrix)) / float(len(test_label))) result.write(str(np.int_(matrix))) result.write('\n') result.write(str(np.sum(np.trace(matrix)))) result.write('\n') result.write(str(np.sum(np.trace(matrix)) / float(len(test_label)))) result.write('\n') for i in range(len(matrix)): ac = matrix[i,i]/sum(matrix[:,i]) print('accuracy ',i+1,':',ac) result.write(str(i+1)+":"+str(ac)) result.write('\n') kk = 0 for i in range(matrix.shape[0]): kk += np.sum(matrix[i])*np.sum(matrix[:,i]) pe = kk/(np.sum(matrix)*np.sum(matrix)) pa = np.trace(matrix)/np.sum(matrix) kappa = (pa-pe)/(1-pe) print('kappa:',kappa) result.write('kappa:'+str(kappa)) result.write('\n') alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' df_cm = pd.DataFrame(matrix, index = [i for i in alphabet[:matrix.shape[0]]], columns = [i for i in alphabet[:matrix.shape[1]]]) plt1 = plt plt1.figure(figsize = (matrix.shape[0],matrix.shape[1])) sn.heatmap(df_cm, annot=True,fmt="g") plt1.savefig('result/confusion_matrix1_'+'_samplenum_'+'_'+str(k)+'_'+time+'.png', format='png') plt1.close() iG = np.zeros((imGIS.shape[0],imGIS.shape[1])) for i in range(imGIS.shape[0]): for j in range(imGIS.shape[1]): if imGIS[i,j]==0: iG[i,j]=0 else: iG[i,j] = (clf.predict(im[i,j].reshape(-1,len(im[i,j])))) plt2 = plt plt.pcolor(iG) plt.savefig('result/decode_result_'+'_sample_num_'+'_'+str(k)+'_'+str(time)+'.png', format='png') plt.close() print('end')

UTF-8

Python

py

svm.py

eric100lin/Learn

22089c995682c6d27e954ccaae9bcd364ff374e5

b21f0a87ec5f0784f1678ccb1e5a871e05822ebc

/algorithm/Array/1169.py

9d8823fc24ed3b1f715f3f1fe41919dc74f10315

no_license

https://github.com/eric100lin/Learn

b8236413e6000982861f28b8e7a7f9e6c13e2d78

317c927a06b7964af80c62487450622132974eea

refs/heads/master

''' 1169. Invalid Transactions https://leetcode.com/contest/weekly-contest-151/problems/invalid-transactions/ A transaction is possibly invalid if: the amount exceeds $1000, or; if it occurs within (and including) 60 minutes of another transaction with the same name in a different city. Each transaction string transactions[i] consists of comma separated values representing the name, time (in minutes), amount, and city of the transaction. Given a list of transactions, return a list of transactions that are possibly invalid. You may return the answer in any order. Example 1: Input: transactions = ["alice,20,800,mtv","alice,50,100,beijing"] Output: ["alice,20,800,mtv","alice,50,100,beijing"] Explanation: The first transaction is invalid because the second transaction occurs within a difference of 60 minutes, have the same name and is in a different city. Similarly the second one is invalid too. Example 2: Input: transactions = ["alice,20,800,mtv","alice,50,1200,mtv"] Output: ["alice,50,1200,mtv"] Example 3: Input: transactions = ["alice,20,800,mtv","bob,50,1200,mtv"] Output: ["bob,50,1200,mtv"] Constraints: transactions.length <= 1000 Each transactions[i] takes the form "{name},{time},{amount},{city}" Each {name} and {city} consist of lowercase English letters, and have lengths between 1 and 10. Each {time} consist of digits, and represent an integer between 0 and 1000. Each {amount} consist of digits, and represent an integer between 0 and 2000. ''' from typing import * class Solution: def invalidTransactions(self, transactions: List[str]) -> List[str]: T = [] # parsing all transactions for tran in transactions: name, time, amount, city = tran.split(',') time, amount = int(time), int(amount) T.append((name, time, amount, city)) invalidT = [] # do comparison on all permutation for name, time, amount, city in T: invlid = amount > 1000 if not invlid: for nameX, timeX, amountX, cityX in T: if name == nameX and abs(time - timeX) <= 60 and city != cityX: invlid = True break if invlid: invalidT.append("{},{},{},{}".format(name, time, amount, city)) return invalidT class UglySolution: def invalidTransactions(self, transactions): dict = {} invalid = [] for tran in transactions: tranInfo = tran.split(',') if tranInfo[0] in dict: dict[tranInfo[0]].append((int(tranInfo[1]),tranInfo[3],int(tranInfo[2]),tran)) else: dict[tranInfo[0]] = [(int(tranInfo[1]),tranInfo[3],int(tranInfo[2]),tran)] for personTran in dict: personTranList = dict[personTran] for i in range(len(personTranList)): invalidi = False (timei,cityi,amounti,trani) = personTranList[i] if amounti > 1000: invalidi = True for j in range(i,len(personTranList)): (timej,cityj,amountj,tranj) = personTranList[j] if cityi!=cityj and abs(timei-timej)<=60: invalidi = True if amountj <= 1000 and tranj not in invalid: invalid.append(tranj) if invalidi and trani not in invalid: invalid.append(trani) return invalid import unittest class InvalidTranTest(unittest.TestCase): def testExample1(self): tran = ["alice,20,800,mtv","alice,50,100,beijing"] r = Solution().invalidTransactions(tran) self.assertEqual(r, ["alice,20,800,mtv","alice,50,100,beijing"]) def testExample2(self): tran = ["alice,20,800,mtv","alice,50,1200,mtv"] r = Solution().invalidTransactions(tran) self.assertEqual(r, ["alice,50,1200,mtv"]) def testExample3(self): tran = ["alice,20,800,mtv","bob,50,1200,mtv"] r = Solution().invalidTransactions(tran) self.assertEqual(r, ["bob,50,1200,mtv"]) def testLeetCodeTestCase1(self): tran = ["bob,689,1910,barcelona", "alex,696,122,bangkok", "bob,832,1726,barcelona", "bob,820,596,bangkok", "chalicefy,217,669,barcelona", "bob,175,221,amsterdam"] r = Solution().invalidTransactions(tran) self.assertEqual(r, ["bob,689,1910,barcelona","bob,832,1726,barcelona", "bob,820,596,bangkok"]) def testLeetCodeTestCase2(self): tran = ["bob,627,1973,amsterdam", "alex,387,885,bangkok", "alex,355,1029,barcelona", "alex,587,402,bangkok", "chalicefy,973,830,barcelona", "alex,932,86,bangkok", "bob,188,989,amsterdam"] r = Solution().invalidTransactions(tran) self.assertEqual(r, ["bob,627,1973,amsterdam","alex,387,885,bangkok", "alex,355,1029,barcelona"]) if __name__ == "__main__": unittest.main()

UTF-8

Python

py

1169.py

sadimauro/projecteuler

e0c0110adb34f30ab33b8e55d285d873ad4fc96c

4b30fa26f7c5dfb413950fc4dc9e4e89129944c4

/stevepe.py

8458c20d2ac29612c42ae3cae49e0faf000d4baa

no_license

https://github.com/sadimauro/projecteuler

ca6f6cc3e8eac83bdb667c88a9b892923a3df57c

a07b71ecdf7f0cc7b53fcd021c67c5a7518d8571

refs/heads/master

#!/usr/bin/python3 import math import itertools def isPrime(a): """ >>> isPrime(1) False >>> isPrime(2) True >>> isPrime(3) True >>> isPrime(4) False >>> isPrime(25) False >>> isPrime(27) False >>> isPrime(29) True """ if a <= 0: return False # 1 is not prime if a == 1: return False # 2 is prime if a == 2: return True # all other evens are not prime if a % 2 == 0: return False # check odd divisors between 3 and sqrt(a) #for i in range(3, math.floor(math.sqrt(a))+1, 2): for i in range(3, int(math.sqrt(a))+1, 2): if a % i == 0: return False return True def isComposite(n): return not isPrime(n) def getNextOddPrime(n): """ Return next odd prime greater than n. n is expected to be odd. """ n += 2 while True: if isPrime(n): return n n += 2 def getPrimesBelow(n): """ Return a list of the primes strictly below n. >>> getPrimesBelow(-3) [] >>> getPrimesBelow(2) [] >>> getPrimesBelow(3) [2] >>> getPrimesBelow(8) [2, 3, 5, 7] >>> getPrimesBelow(13) [2, 3, 5, 7, 11] """ return getPrimesBetween(1, n) def getPrimesBetween(m, n): """ Return the list of primes strictly greater than m and strictly less than n. >>> getPrimesBetween(64, -3) [] >>> getPrimesBetween(0, 2) [] >>> getPrimesBetween(1, 3) [2] >>> getPrimesBetween(1, 8) [2, 3, 5, 7] >>> getPrimesBetween(1, 13) [2, 3, 5, 7, 11] >>> getPrimesBetween(2, 13) [3, 5, 7, 11] >>> getPrimesBetween(8, 13) [11] """ ret = list() if m >= n: return ret if n <= 2: return ret if m <= 1: ret.append(2) if n <= 3: return ret if m % 2 == 0: m = m - 1 i = getNextOddPrime(m) while i < n: ret.append(i) i = getNextOddPrime(i) return ret def getPrimesBelowBySieve(n): """ Return a list of the primes strictly below n. Calculate by using the Sieve of Eratosthenes. This is a *lot* faster than getPrimesBelow(). >>> getPrimesBelowBySieve(-3) [] >>> getPrimesBelowBySieve(2) [] >>> getPrimesBelowBySieve(3) [2] >>> getPrimesBelowBySieve(8) [2, 3, 5, 7] >>> getPrimesBelowBySieve(13) [2, 3, 5, 7, 11] """ ret = list() if n <= 2: return ret boolList = list() for i in range(n): boolList.append(True) boolList[0] = False; boolList[1] = False for i in range(2,math.floor(math.sqrt(n))+1): if boolList[i]: for j in range(i**2, n, i): boolList[j] = False for i in range(len(boolList)): if boolList[i]: ret.append(i) return ret def getPrimesBetweenBySieve(m,n): """ Return the list of primes strictly greater than m and strictly less than n. >>> getPrimesBetweenBySieve(64, -3) [] >>> getPrimesBetweenBySieve(0, 2) [] >>> getPrimesBetweenBySieve(1, 3) [2] >>> getPrimesBetweenBySieve(1, 8) [2, 3, 5, 7] >>> getPrimesBetweenBySieve(1, 13) [2, 3, 5, 7, 11] >>> getPrimesBetweenBySieve(2, 13) [3, 5, 7, 11] >>> getPrimesBetweenBySieve(8, 13) [11] """ primes = getPrimesBelowBySieve(n) i = 0 while i < len(primes): if primes[i] > m: break i += 1 if i == len(primes): return [] return primes[i:] def getFactors(n): """ Return a list containing the factors of n, including 1 and n itself. """ list2d = [[1], getPrimeFactors(n), [n]] return list(itertools.chain.from_iterable(list2d)) def getPrimeFactors(n): """ Return a list containing the factors of n, not including 1 and n itself. >>> getPrimeFactors(-3) [] >>> getPrimeFactors(0) [] >>> getPrimeFactors(1) [] >>> getPrimeFactors(2) [] >>> getPrimeFactors(4) [2, 2] >>> getPrimeFactors(7) [] >>> getPrimeFactors(18) [2, 3, 3] >>> getPrimeFactors(64) [2, 2, 2, 2, 2, 2] >>> getPrimeFactors(238202) [2, 119101] """ ret = list() if n < 4: return ret primesToTry = getPrimesBelow(n) i = 0 while i < len(primesToTry) and primesToTry[i] <= n: if n % primesToTry[i] == 0: ret.append(primesToTry[i]) n = n // primesToTry[i] else: i += 1 return ret def isStrPandigital(n): nSet = set(n) return len(nSet) == len(n) and min(nSet) == '1' and max(nSet) == str(len(n)) def isIntPandigital(n): """Return True if n is pandigital, False otherwise. We shall say that an n-digit number is pandigital if it makes use of all the digits 1 to n exactly once; for example, the 5-digit number, 15234, is 1 through 5 pandigital. >>> isIntPandigital(12345) True >>> isIntPandigital(15243) True >>> isIntPandigital(1) True >>> isIntPandigital(14326) False >>> isIntPandigital(14445) False """ return isStrPandigital(str(n)) def tupleToInt(tupIn): res = 0 for i in range(len(tupIn)): res *= 10 res += int(tupIn[i]) return res def sortIntDigits(intIn): """ >>> sortIntDigits(1423) 1234 """ a = list(str(intIn)) a.sort() return tupleToInt(a) def isPentagonal(n): """ Return True is n is pentagonal, i.e. if n = k(3k-1)/2 for some value k. >>> isPentagonal(0) False >>> isPentagonal(1) True >>> isPentagonal(5) True >>> isPentagonal(117) True >>> isPentagonal(118) False >>> isPentagonal(-3) False """ if n <= 0: return False maybeNat = (math.sqrt((24*n)+1) + 1) / 6 return maybeNat == int(maybeNat) def isHexagonal(n): """ Return True is n is pentagonal, i.e. if n = k(3k-1)/2 for some value k. >>> isHexagonal(0) False >>> isHexagonal(1) True >>> isHexagonal(6) True >>> isHexagonal(231) True >>> isHexagonal(250) False >>> isHexagonal(-3) False """ if n <= 0: return False maybeNat = (math.sqrt((8*n)+1) + 1) / 4 return maybeNat == int(maybeNat) def isTriangular(n): """ Return True if n is triangular, i.e. if n = k(k+1)/2 for some integer k. >>> isTriangular(1) True >>> isTriangular(0) False >>> isTriangular(15) True >>> isTriangular(16) False >>> isTriangular(-3) False """ if n <= 0: return False maybeSq = math.sqrt((8*n) + 1) return maybeSq == int(maybeSq) def isSquare(n): """ Return n if n is a square > 0, False otherwise. """ if n < 0: return False temp = math.sqrt(n) return temp == int(temp) def getNextSquare(n): """ Return the next number greater than n that is a square. """ n += 1 while True: if isSquare(n): return n n += 1 def getIntPermutations(n): """ Return a set filled with all of the permutations of int n, including n itself. e.g. if n = 123, return is {123, 132, 213, 231, 312, 321}. >>> getIntPermutations(1) {1} >>> getIntPermutations(12) {12, 21} >>> getIntPermutations(123) {321, 132, 231, 213, 312, 123} >>> a = list(getIntPermutations(122)) >>> a.sort() >>> a [122, 212, 221] >>> getIntPermutations(144) {144, 441, 414} """ ret_list = [] ints = (int(x) for x in str(n)) iter = itertools.permutations(ints) ret_list = [tupleToInt(tup) for tup in iter] #for tup in iter: # ret.add(tupleToInt(tup)) return ret_list def getIntPermutationsDeduped(n): return set(getIntPermutations(n)) def genIntPermutations(n): """ Generate all of the permutations of int n, including n itself. e.g. if n = 123, return is {123, 132, 213, 231, 312, 321}. """ for tup in itertools.permutations(str(n)): yield tupleToInt(tup) return def getIntReverse(n): """ Return the reverse of int n. >>> getIntReverse(1234) 4321 >>> getIntReverse(0) 0 >>> getIntReverse(-7575) -5757 """ neg = False if n < 0: neg = True n *= -1 n_str = str(n) n_str_rev = '' for i in range(len(n_str)): n_str_rev += n_str[len(n_str)-i-1] if neg: n_str_rev = '-' + n_str_rev return int(n_str_rev) def isIntPalindrome(n): """ Return True if n is a palindrome. >>> isIntPalindrome(1234) False >>> isIntPalindrome(12321) True """ return getIntReverse(n) == n def getDigitalSum(n): """ Return the sum of the digits in n. >>> getDigitalSum(1234) 10 """ tot = 0 while n > 0: tot += n % 10 n = n // 10 return tot def numberOfDigits(n): """ Return the number of digits in n. >>> numberOfDigits(123) 3 >>> numberOfDigits(-4567) 4 >>> numberOfDigits(0) 1 """ if n == 0: return 1 count = 0 n = abs(n) while n > 0: count += 1 n = n // 10 return count if __name__ == "__main__": import doctest doctest.testmod()

UTF-8

Python

py

stevepe.py

carlosescorche/pythoneando

2a319a8cd54f9cb01ed54cac7c5cc13099c0b985

b9611ff290c88a4907020b2a2026d5ad4d713fc4

/teoria/12_excepciones/03.py

040133be04ff099ec643d72cc873a02e2490ed22

no_license

https://github.com/carlosescorche/pythoneando

fddd70a1e5b27e2d37fa6512fbfef5a63e89ed81

7e4a430d8a0a076dfb655f82b089f3c58efce21d

refs/heads/master

#!/usr/bin/python #coding=utf-8 #Por último es posible utilizar un bloque finally que se ejecute al final del código, ocurra o no ocurra un error: while(True): try: n = float(input("Introduce un número: ")) m = 4 print("{}/{}={}".format(n,m,n/m)) except: print("Ha ocurrido un error, introduce bien el número") else: print("Todo ha funcionado correctamente") break #Importante romper la iteración si todo ha salido bien finally: print("Fin de la iteración") # Siempre se ejecuta

UTF-8

Python

py

03.py

wongmi21/uoft-mail-merge

21764df53c0640faa721f0113d6e3e56947dcb65

e5e472c4e838788ace767ddbab3594fc47d619f2

/Step 1-scrape.py

f50e4abe09fafbdd12bc87863fdbf0163929977c

no_license

https://github.com/wongmi21/uoft-mail-merge

aa9f5a763ab06210c91b0033fbc0d61aefd85e62

c5c8bfa8f3b27b1b4af55581e43173e9f8c9121d

refs/heads/master

# -*- coding: utf-8 -*- """ Created on Thu Jul 25 02:21:06 2013 @author: mike """ from bs4 import BeautifulSoup import requests import csv from titlecase import titlecase url = raw_input("Enter a website to extract the URL's from: ") while url != 'done': jobid = url.index('JOB_ID=') + 7 endjobid = url.index('&abPage=') tr = url.index('tr=') + 3 endtr = url.index('&K1=') printurl = 'http://www.careers.utoronto.ca/st/jobview.aspx?print=Y&JOB_ID=' + url[jobid:endjobid] + '&tr=' + url[tr:endtr] print printurl r = requests.get(url) data = r.text soup = BeautifulSoup(data) try: email = soup.find('span', {'id' :'ctl00_MainContent_ctl03_lbl_apply'}) email = unicode(email)[unicode(email).index(u'<strong>Email:</strong>\u00a0') + len(u'<strong>Email:</strong>\u00a0'):] email = email[:email.index('<br')] print email except: email = 'email' print 'no email' try: employer = soup.find('span', {'id' :'ctl00_MainContent_ctl03_lbl_contact'}).text print employer except: employer = 'Hiring Manager' print 'no employer' try: company = titlecase(soup.find('span', {'id' :'ctl00_MainContent_ctl03_lbl_org'}).text) print company except: company = '' print 'no company' try: companyaddress = soup.find('span', {'id' :'ctl00_MainContent_ctl03_lbl_address'}) companyaddress = unicode(companyaddress) companyaddress = companyaddress[companyaddress.index('ctl03_lbl_address">') + len('ctl03_lbl_address">'):companyaddress.index('<br/></span>')] companyaddress = companyaddress.replace('<br/>', r'\\') print companyaddress except: companyaddress = '' print 'no companyaddress' try: position = soup.find('span', {'id' :'ctl00_MainContent_ctl03_lbl_position_title'}).text print position except: position = '' print 'no position' try: actuarial = soup.find('span', {'id' :'ctl00_MainContent_ctl03_lbl_discipline'}).text actuarial = actuarial[actuarial.index('Actuarial Science'):] actuarial = '1' print actuarial except: actuarial = '0' print 'no actuarial' try: industry = soup.find('span', {'id' :'ctl00_MainContent_ctl03_lbl_industry'}).text print industry except: industry = 'Finance/Insurance and Big Data' print 'industry' source = "University of Toronto's career website" resultFile = open("jobs.csv",'a') index = sum(1 for row in csv.reader(open("jobs.csv",'r'))) newrow = [str(index),employer,company,companyaddress,position,industry,source,actuarial,url,email] newentry = ','.join('"' + item.replace(unichr(160), " ").strip() + '"' for item in newrow) + '\n' resultFile.write(newentry) resultFile.close url = raw_input("Enter a website to extract the URL's from: ")

UTF-8

Python

py

Step 1-scrape.py