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

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from typing import List """ 给定一个未排序的整数数组，找出其中没有出现的最小的正整数。示例 1: 输入: [1,2,0] 输出: 3 示例 2: 输入: [3,4,-1,1] 输出: 2 示例 3: 输入: [7,8,9,11,12] 输出: 1 说明: 你的算法的时间复杂度应为O(n)，并且只能使用常数级别的空间。来源：力扣（LeetCode）链接：https://leetcode-cn.com/problems/first-missing-positive 著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。 """ class Solution: def firstMissingPositive(self, nums: List[int]) -> int: c = [0] * ((len(nums) // (4 * 8)) + 1) m = len(nums) for i in nums: if i > m or i < 1: continue else: ci = i // (4 * 8) di = i % (4 * 8) c[ci] = 1 << di | c[ci] for ci, cv in enumerate(c): if cv != b'1111111111111111': for i in range(0, 32): if (1 << i & cv) == 0: if i + (ci * 4 * 8) != 0: return (i + (ci * 4 * 8)) else: continue return len(c) * 4 * 8 if __name__ == '__main__': l = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127] print(Solution().firstMissingPositive(l))

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from rest_framework import serializers class VerticalSerializer(serializers.Serializer): name = serializers.CharField() identifier = serializers.CharField() audience = serializers.CharField() content_forms = serializers.ListField(child=serializers.IntegerField()) content_tones = serializers.ListField(child=serializers.IntegerField()) channels = serializers.ListField(child=serializers.CharField())

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# -*- coding: utf-8 -*- """ Created on Fri Mar 23 10:09:51 2018 """ import matplotlib.pyplot as plt from matplotlib.patches import Ellipse import numpy as np import pims import scipy.ndimage import pandas as pd from scipy.ndimage.morphology import binary_fill_holes import skimage.measure import skimage.filters import skimage.morphology from matplotlib import cm import trackpy as tp import skimage import cv2 from skimage.filters import threshold_local import fluids2d.backlight as backlight import fluids2d.geometry from datetime import datetime thresh=500 folder = r'E:\Stephane\171114\\' cine_name = r'balloon_breakup_pumps_fps10000_backlight_D800minch_d20mm' c = pims.open(folder+cine_name+'.cine') im = c[12000][400:800,20:600] dx = 0.000112656993755 g = fluids2d.geometry.GeometryScaler(dx=dx,im_shape=np.shape(im),origin_pos=(0,0),origin_units='m') bg = backlight.find_bg_image(c) plt.figure() plt.imshow(bg) stophere print('--- random walker (red) ---') start_rw = datetime.now() rw = backlight.random_walker_detection(im,thresh,g,viz=False) end_rw = datetime.now() dur_rw = end_rw - start_rw print(dur_rw) df = backlight.labeled_props(rw,g) vf = backlight.estimate_void_fraction(df) print(vf) print('--- watershed (green) ---') start_ws = datetime.now() ws = backlight.watershed_detection(im,thresh,g,RadiusDiskMean=1,viz=False) #[550:750,400:600] end_ws = datetime.now() dur_ws = end_ws - start_ws print(dur_ws) df_ws = backlight.labeled_props(ws,g) vf = backlight.estimate_void_fraction(df_ws) print(vf) print('--- standard (blue) ---') start_standard = datetime.now() filled = backlight.get_filled(im,thresh) df_standard = backlight.filled2regionpropsdf(filled,g=g) end_standard = datetime.now() dur_standard = end_standard - start_standard print(dur_standard) vf = backlight.estimate_void_fraction(df_standard) print(vf) # #df = backlight.labeled_props(rw,g) #vf = backlight.estimate_void_fraction(df) #print(vf) ax = backlight.show_and_annotate(im,g,df,ax=None,vmin=0,vmax=600) backlight.add_ellipses_to_ax(df_ws,ax,color=[0,1,0,0.5]) backlight.add_ellipses_to_ax(df_standard,ax,color=[0,0,1,0.5]) #df_all = backlight.run_bubble_detection(c,thresh,g,frames=frames,method='watershed')

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from django.shortcuts import render from .models import * # Create your views here. def monitor(request): monitors = Monitor.objects.all() context = {} context['monitors'] = monitors return render(request , 'monitor.html' , context)

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#! /usr/bin/env python def same_hand(str_arg): point(str_arg) print('time') def point(str_arg): print(str_arg) if __name__ == '__main__': same_hand('time')

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#p number=list(map(int,input())) sum=0 for i in range(len(number)): sum+=(number[i]**2) print(sum)

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from WikId.wiki.models import Article, Article_section from django.contrib import admin class Article_section_inline(admin.StackedInline): model = Article_section extra = 1 class Article_admin(admin.ModelAdmin): inlines = [Article_section_inline] admin.site.register(Article, Article_admin)

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import os import pytest import torch import torch.nn as nn import apex from pytorch_tools.metrics import Accuracy from pytorch_tools.fit_wrapper import Runner from pytorch_tools.losses import CrossEntropyLoss import pytorch_tools.fit_wrapper.callbacks as pt_clb HIDDEN_DIM = 16 NUM_CLASSES = 10 IMG_SHAPE = 16 LOADER_LEN = 20 BS = 2 class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, HIDDEN_DIM, kernel_size=3) self.bn1 = nn.BatchNorm2d(HIDDEN_DIM) self.conv2 = nn.Conv2d(HIDDEN_DIM, HIDDEN_DIM, kernel_size=3) self.pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Linear(HIDDEN_DIM, NUM_CLASSES) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.conv2(x) x = self.pool(x) x = torch.flatten(x, 1) x = self.fc(x) return x class Loader: def __init__(self): self.i = 1 pass def __iter__(self): return self def __len__(self): return LOADER_LEN def __next__(self): img = torch.randn(BS, 3, IMG_SHAPE, IMG_SHAPE) target = torch.randint(NUM_CLASSES, (BS,)) return img.cuda(), target.cuda() TestLoader = Loader() TestModel = Model().cuda() TestOptimizer = torch.optim.SGD(TestModel.parameters(), lr=1e-3) TestCriterion = CrossEntropyLoss().cuda() TestMetric = Accuracy() TestModel, TestOptimizer = apex.amp.initialize(TestModel, TestOptimizer, verbosity=0) def test_default(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=None ) runner.fit(TestLoader, epochs=2) def test_val_loader(): runner = Runner(model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric,) runner.fit(TestLoader, epochs=2, steps_per_epoch=100, val_loader=TestLoader, val_steps=200) # We only test that callbacks don't crash NOT that they do what they should do TMP_PATH = "/tmp/pt_tools2/" os.makedirs(TMP_PATH, exist_ok=True) # TODO: (emil 05.02.20) use pytest parametrize instead def test_Timer_callback(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=pt_clb.Timer(), ) runner.fit(TestLoader, epochs=2) def test_ReduceLROnPlateau_callback(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=pt_clb.ReduceLROnPlateau(), ) runner.fit(TestLoader, epochs=2) def test_CheckpointSaver_callback(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=pt_clb.CheckpointSaver(TMP_PATH, save_name="model.chpn"), ) runner.fit(TestLoader, epochs=2) def test_FileLogger_callback(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=pt_clb.FileLogger(TMP_PATH), ) runner.fit(TestLoader, epochs=2) def test_TensorBoard(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=pt_clb.TensorBoard(log_dir=TMP_PATH), ) runner.fit(TestLoader, epochs=2) def test_TensorBoardWithCM(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=pt_clb.TensorBoardWithCM(log_dir=TMP_PATH), ) runner.fit(TestLoader, epochs=2) def test_Cutmix(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=pt_clb.Cutmix(1.0, NUM_CLASSES), ) runner.fit(TestLoader, epochs=2) def test_Mixup(): runner = Runner( model=TestModel, optimizer=TestOptimizer, criterion=TestCriterion, metrics=TestMetric, callbacks=pt_clb.Mixup(0.2, NUM_CLASSES), ) runner.fit(TestLoader, epochs=2)

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bonlimezak@gmail.com

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/applications/tools/wav2lip.py

74f40f6082f4caa9f5b39a44f88567f8f7c14986

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permissive

moiseshorta/PaddleGAN

88680876e9a563cd24647ca4d04a3ff9ef8c2429

1d8cd182219e83653570ce03d9be8b5d5a955187

refs/heads/develop

2023-04-28T16:11:10.494290

2021-05-19T02:59:33

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import argparse import paddle from ppgan.apps.wav2lip_predictor import Wav2LipPredictor parser = argparse.ArgumentParser( description= 'Inference code to lip-sync videos in the wild using Wav2Lip models') parser.add_argument('--checkpoint_path', type=str, help='Name of saved checkpoint to load weights from', default=None) parser.add_argument( '--audio', type=str, help='Filepath of video/audio file to use as raw audio source', required=True) parser.add_argument('--face', type=str, help='Filepath of video/image that contains faces to use', required=True) parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.', default='results/result_voice.mp4') parser.add_argument( '--static', type=bool, help='If True, then use only first video frame for inference', default=False) parser.add_argument( '--fps', type=float, help='Can be specified only if input is a static image (default: 25)', default=25., required=False) parser.add_argument( '--pads', nargs='+', type=int, default=[0, 10, 0, 0], help= 'Padding (top, bottom, left, right). Please adjust to include chin at least' ) parser.add_argument('--face_det_batch_size', type=int, help='Batch size for face detection', default=16) parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128) parser.add_argument( '--resize_factor', default=1, type=int, help= 'Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p' ) parser.add_argument( '--crop', nargs='+', type=int, default=[0, -1, 0, -1], help= 'Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. ' 'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width' ) parser.add_argument( '--box', nargs='+', type=int, default=[-1, -1, -1, -1], help= 'Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.' 'Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).' ) parser.add_argument( '--rotate', default=False, action='store_true', help= 'Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.' 'Use if you get a flipped result, despite feeding a normal looking video') parser.add_argument( '--nosmooth', default=False, action='store_true', help='Prevent smoothing face detections over a short temporal window') parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.") parser.add_argument( "--face_detector", dest="face_detector", type=str, default='sfd', help="face detector to be used, can choose s3fd or blazeface") if __name__ == "__main__": args = parser.parse_args() if args.cpu: paddle.set_device('cpu') predictor = Wav2LipPredictor(args) predictor.run()

[ "noreply@github.com" ]

moiseshorta.noreply@github.com

9d5e174451a029b8858288c3bb342a6bafd87be3

a5fb604b0476edd61334f6b01085e24169b0269e

/predict.py

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[]

no_license

eperry4750/TensorFlow_Image_Classifier

2ff984ab02032921ea6f7c4bcdefbb6ce5b62ece

22c6dee36eecf94db9ce8fa17abbf47bb05ad5ef

refs/heads/main

2023-05-25T12:43:09.439699

2021-06-12T14:54:26

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import numpy as np import json import tensorflow as tf import tensorflow_hub as hub from argparse import ArgumentParser from PIL import Image import warnings warnings.filterwarnings('ignore') def process_image(image): IMAGE_SIZE = 224 image_tensor = tf.convert_to_tensor(image) resized_image = tf.image.resize(image_tensor, (IMAGE_SIZE, IMAGE_SIZE))/255.0 return resized_image.numpy() def predict(image_path, model_path, top_k): im = Image.open(image_path) image = np.asarray(im) processed_image = process_image(image) finished_image = np.expand_dims(processed_image, axis=0) predictions = model_path.predict(finished_image) top_predictions = tf.math.top_k(predictions, k = top_k) return top_predictions[0].numpy(), top_predictions[1].numpy() def build_argparser(): """ Parse command line arguments. :return: command line arguments """ parser = ArgumentParser() parser.add_argument("-m", "--model_path", required=True, type=str, help="Path to a trained model.") parser.add_argument("-i", "--image_path", required=True, type=str, help="Path to an image file") parser.add_argument("-k", "--top_k", required=False, type=int, default=1, help="Return top k probabiliites.") parser.add_argument("-l", "--label_map", required=False, type=str, default=None, help="A mapping of classes to real names from a json file") return parser def predict_main(args): model_path = args.model_path saved_model = tf.keras.models.load_model(model_path, custom_objects={'KerasLayer':hub.KerasLayer}) image_path = args.image_path top_k = args.top_k label_map = args.label_map probs, classes = predict(image_path, saved_model, top_k) if label_map == None: for prob, clas in zip(probs[0], classes[0]): print(prob, clas) else: with open(label_map, 'r') as f: class_names = json.load(f) for prob, clas in zip(probs[0], [class_names[str(clas + 1)] for clas in classes[0]]): print(prob, clas) def main(): """ Load the network and parse the output. :return: None """ # Grab command line args args = build_argparser().parse_args() predict_main(args) if __name__ == '__main__': main()

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eperry4750.noreply@github.com

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304a1fa65079d89a9a5a67e0d29d42f7dc9a89b8

/GUI-BankSystem.py

d843740bb9f7b7f5aee4da2d296a0f55e4e9ae8a

[]

no_license

hsa306/banking-system

1ff190e270bd48109a9e751340f839efb142311a

6e2897d6b64785721030a66065a29f731448615a

refs/heads/main

2023-01-10T00:48:13.397449

2020-11-16T13:12:01

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import tkinter as tk from tkinter import messagebox from time import gmtime, strftime def is_number(s): try: float(s) return 1 except ValueError: return 0 def check_acc_nmb(num): try: fpin=open(num+".txt",'r') except FileNotFoundError: messagebox.showinfo("Error","Invalid Credentials!\nTry Again!") return 0 fpin.close() return def home_return(master): master.destroy() Main_Menu() def write(master,name,oc,pin): if( (is_number(name)) or (is_number(oc)==0) or (is_number(pin)==0)or name==""): messagebox.showinfo("Error","Invalid Credentials\nPlease try again.") master.destroy() return f1=open("Accnt_Record.txt",'r') accnt_no=int(f1.readline()) accnt_no+=1 f1.close() f1=open("Accnt_Record.txt",'w') f1.write(str(accnt_no)) f1.close() fdet=open(str(accnt_no)+".txt","w") fdet.write(pin+"\n") fdet.write(oc+"\n") fdet.write(str(accnt_no)+"\n") fdet.write(name+"\n") fdet.close() frec=open(str(accnt_no)+"-rec.txt",'w') frec.write("Date Credit Debit Balance\n") frec.write(str(strftime("[%Y-%m-%d] [%H:%M:%S] ",gmtime()))+" "+oc+" "+oc+"\n") frec.close() messagebox.showinfo("Details","Your Account Number is:"+str(accnt_no)) master.destroy() return def crdt_write(master,amt,accnt,name): if(is_number(amt)==0): messagebox.showinfo("Error","Invalid Credentials\nPlease try again.") master.destroy() return fdet=open(accnt+".txt",'r') pin=fdet.readline() camt=int(fdet.readline()) fdet.close() amti=int(amt) cb=amti+camt fdet=open(accnt+".txt",'w') fdet.write(pin) fdet.write(str(cb)+"\n") fdet.write(accnt+"\n") fdet.write(name+"\n") fdet.close() frec=open(str(accnt)+"-rec.txt",'a+') frec.write(str(strftime("[%Y-%m-%d] [%H:%M:%S] ",gmtime()))+" "+str(amti)+" "+str(cb)+"\n") frec.close() messagebox.showinfo("Operation Successfull!!","Amount Credited Successfully!!") master.destroy() return def debit_write(master,amt,accnt,name): if(is_number(amt)==0): messagebox.showinfo("Error","Invalid Credentials\nPlease try again.") master.destroy() return fdet=open(accnt+".txt",'r') pin=fdet.readline() camt=int(fdet.readline()) fdet.close() if(int(amt)>camt): messagebox.showinfo("Error!!","You dont have that amount left in your account\nPlease try again.") else: amti=int(amt) cb=camt-amti fdet=open(accnt+".txt",'w') fdet.write(pin) fdet.write(str(cb)+"\n") fdet.write(accnt+"\n") fdet.write(name+"\n") fdet.close() frec=open(str(accnt)+"-rec.txt",'a+') frec.write(str(strftime("[%Y-%m-%d] [%H:%M:%S] ",gmtime()))+" "+" "+str(amti)+" "+str(cb)+"\n") frec.close() messagebox.showinfo("Operation Successfull!!","Amount Debited Successfully!!") master.destroy() return def Cr_Amt(accnt,name): creditwn=tk.Tk() creditwn.geometry("600x300") creditwn.title("Credit Amount") creditwn.configure(bg="orange") fr1=tk.Frame(creditwn,bg="blue") l_title=tk.Message(creditwn,text="UNITED BANK",relief="raised",width=2000,padx=600,pady=0,fg="white",bg="black",justify="center",anchor="center") l_title.config(font=("Courier","50","bold")) l_title.pack(side="top") l1=tk.Label(creditwn,relief="raised",text="Enter Amount to be credited: ") e1=tk.Entry(creditwn,relief="raised") l1.pack(side="top") e1.pack(side="top") b=tk.Button(creditwn,text="Credit",relief="raised",command=lambda:crdt_write(creditwn,e1.get(),accnt,name)) b.pack(side="top") creditwn.bind("<Return>",lambda x:crdt_write(creditwn,e1.get(),accnt,name)) def De_Amt(accnt,name): debitwn=tk.Tk() debitwn.geometry("600x300") debitwn.title("Debit Amount") debitwn.configure(bg="orange") fr1=tk.Frame(debitwn,bg="blue") l_title=tk.Message(debitwn,text="UNITED BANK",relief="raised",width=2000,padx=600,pady=0,fg="white",bg="black",justify="center",anchor="center") l_title.config(font=("Courier","50","bold")) l_title.pack(side="top") l1=tk.Label(debitwn,relief="raised",text="Enter Amount to be debited: ") e1=tk.Entry(debitwn,relief="raised") l1.pack(side="top") e1.pack(side="top") b=tk.Button(debitwn,text="Debit",relief="raised",command=lambda:debit_write(debitwn,e1.get(),accnt,name)) b.pack(side="top") debitwn.bind("<Return>",lambda x:debit_write(debitwn,e1.get(),accnt,name)) def disp_bal(accnt): fdet=open(accnt+".txt",'r') fdet.readline() bal=fdet.readline() fdet.close() messagebox.showinfo("Balance",bal) def disp_tr_hist(accnt): disp_wn=tk.Tk() disp_wn.geometry("900x600") disp_wn.title("Transaction History") disp_wn.configure(bg="orange") fr1=tk.Frame(disp_wn,bg="blue") l_title=tk.Message(disp_wn,text="UNITED BANK",relief="raised",width=2000,padx=600,pady=0,fg="white",bg="black",justify="center",anchor="center") l_title.config(font=("Courier","50","bold")) l_title.pack(side="top") fr1=tk.Frame(disp_wn) fr1.pack(side="top") l1=tk.Message(disp_wn,text="Your Transaction History:",padx=100,pady=20,width=1000,bg="blue",fg="orange",relief="raised") l1.pack(side="top") fr2=tk.Frame(disp_wn) fr2.pack(side="top") frec=open(accnt+"-rec.txt",'r') for line in frec: l=tk.Message(disp_wn,anchor="w",text=line,relief="raised",width=2000) l.pack(side="top") b=tk.Button(disp_wn,text="Quit",relief="raised",command=disp_wn.destroy) b.pack(side="top") frec.close() def logged_in_menu(accnt,name): rootwn=tk.Tk() rootwn.geometry("1600x500") rootwn.title("UNITED BANK-"+name) rootwn.configure(background='orange') fr1=tk.Frame(rootwn) fr1.pack(side="top") l_title=tk.Message(rootwn,text="SIMPLE BANKING\n SYSTEM",relief="raised",width=2000,padx=600,pady=0,fg="white",bg="black",justify="center",anchor="center") l_title.config(font=("Courier","50","bold")) l_title.pack(side="top") label=tk.Label(text="Logged in as: "+name,relief="raised",bg="black",fg="white",anchor="center",justify="center") label.pack(side="top") img2=tk.PhotoImage(file="credit.gif") myimg2=img2.subsample(2,2) img3=tk.PhotoImage(file="debit.gif") myimg3=img3.subsample(2,2) img4=tk.PhotoImage(file="balance1.gif") myimg4=img4.subsample(2,2) img5=tk.PhotoImage(file="transaction.gif") myimg5=img5.subsample(2,2) b2=tk.Button(image=myimg2,command=lambda: Cr_Amt(accnt,name)) b2.image=myimg2 b3=tk.Button(image=myimg3,command=lambda: De_Amt(accnt,name)) b3.image=myimg3 b4=tk.Button(image=myimg4,command=lambda: disp_bal(accnt)) b4.image=myimg4 b5=tk.Button(image=myimg5,command=lambda: disp_tr_hist(accnt)) b5.image=myimg5 img6=tk.PhotoImage(file="logout.gif") myimg6=img6.subsample(2,2) b6=tk.Button(image=myimg6,relief="raised",command=lambda: logout(rootwn)) b6.image=myimg6 b2.place(x=100,y=150) b3.place(x=100,y=220) b4.place(x=900,y=150) b5.place(x=900,y=220) b6.place(x=500,y=400) def logout(master): messagebox.showinfo("Logged Out","You Have Been Successfully Logged Out!!") master.destroy() Main_Menu() def check_log_in(master,name,acc_num,pin): if(check_acc_nmb(acc_num)==0): master.destroy() Main_Menu() return if( (is_number(name)) or (is_number(pin)==0) ): messagebox.showinfo("Error","Invalid Credentials\nPlease try again.") master.destroy() Main_Menu() else: master.destroy() logged_in_menu(acc_num,name) def log_in(master): master.destroy() loginwn=tk.Tk() loginwn.geometry("600x300") loginwn.title("Log in") loginwn.configure(bg="orange") fr1=tk.Frame(loginwn,bg="blue") l_title=tk.Message(loginwn,text="UNITED BANK",relief="raised",width=2000,padx=600,pady=0,fg="white",bg="black",justify="center",anchor="center") l_title.config(font=("Courier","50","bold")) l_title.pack(side="top") l1=tk.Label(loginwn,text="Enter Name:",relief="raised") l1.pack(side="top") e1=tk.Entry(loginwn) e1.pack(side="top") l2=tk.Label(loginwn,text="Enter account number:",relief="raised") l2.pack(side="top") e2=tk.Entry(loginwn) e2.pack(side="top") l3=tk.Label(loginwn,text="Enter your PIN:",relief="raised") l3.pack(side="top") e3=tk.Entry(loginwn,show="*") e3.pack(side="top") b=tk.Button(loginwn,text="Submit",command=lambda: check_log_in(loginwn,e1.get().strip(),e2.get().strip(),e3.get().strip())) b.pack(side="top") b1=tk.Button(text="HOME",relief="raised",bg="black",fg="white",command=lambda: home_return(loginwn)) b1.pack(side="top") loginwn.bind("<Return>",lambda x:check_log_in(loginwn,e1.get().strip(),e2.get().strip(),e3.get().strip())) def Create(): crwn=tk.Tk() crwn.geometry("600x300") crwn.title("Create Account") crwn.configure(bg="orange") fr1=tk.Frame(crwn,bg="blue") l_title=tk.Message(crwn,text="UNITED BANK",relief="raised",width=2000,padx=600,pady=0,fg="white",bg="black",justify="center",anchor="center") l_title.config(font=("Courier","50","bold")) l_title.pack(side="top") l1=tk.Label(crwn,text="Enter Name:",relief="raised") l1.pack(side="top") e1=tk.Entry(crwn) e1.pack(side="top") l2=tk.Label(crwn,text="Enter opening credit:",relief="raised") l2.pack(side="top") e2=tk.Entry(crwn) e2.pack(side="top") l3=tk.Label(crwn,text="Enter desired PIN:",relief="raised") l3.pack(side="top") e3=tk.Entry(crwn,show="*") e3.pack(side="top") b=tk.Button(crwn,text="Submit",command=lambda: write(crwn,e1.get().strip(),e2.get().strip(),e3.get().strip())) b.pack(side="top") crwn.bind("<Return>",lambda x:write(crwn,e1.get().strip(),e2.get().strip(),e3.get().strip())) return def Main_Menu(): rootwn=tk.Tk() rootwn.geometry("1600x500") rootwn.title("UNITED Bank") rootwn.configure(background='orange') fr1=tk.Frame(rootwn) fr1.pack(side="top") bg_image = tk.PhotoImage(file ="pile1.gif") x = tk.Label (image = bg_image) x.place(y=-400) l_title=tk.Message(text="SIMPLE BANKING\n SYSTEM",relief="raised",width=2000,padx=600,pady=0,fg="white",bg="black",justify="center",anchor="center") l_title.config(font=("Courier","50","bold")) l_title.pack(side="top") imgc1=tk.PhotoImage(file="new.gif") imglo=tk.PhotoImage(file="login.gif") imgc=imgc1.subsample(2,2) imglog=imglo.subsample(2,2) b1=tk.Button(image=imgc,command=Create) b1.image=imgc b2=tk.Button(image=imglog,command=lambda: log_in(rootwn)) b2.image=imglog img6=tk.PhotoImage(file="quit.gif") myimg6=img6.subsample(2,2) b6=tk.Button(image=myimg6,command=rootwn.destroy) b6.image=myimg6 b1.place(x=800,y=300) b2.place(x=800,y=200) b6.place(x=920,y=400) rootwn.mainloop() Main_Menu()

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hsa306.noreply@github.com

12401c84977b036427a9104776abcfb54dae2926

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/autorest/python/swagger/models/tagged_sensor.py

1f7a06e5448848378589dfcd960293e1d3a51f99

[]

no_license

silverspace/samsara-sdks

cefcd61458ed3c3753ac5e6bf767229dd8df9485

c054b91e488ab4266f3b3874e9b8e1c9e2d4d5fa

refs/heads/master

2020-04-25T13:16:59.137551

2019-03-01T05:49:05

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# coding=utf-8 # -------------------------------------------------------------------------- # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .tagged_sensor_base import TaggedSensorBase class TaggedSensor(TaggedSensorBase): """TaggedSensor. :param id: The ID of the Sensor being tagged. :type id: long :param name: Name of the Sensor being tagged. :type name: str """ _validation = { 'id': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'long'}, 'name': {'key': 'name', 'type': 'str'}, } def __init__(self, id, name=None): super(TaggedSensor, self).__init__(id=id) self.name = name

[ "greg@samsara.com" ]

greg@samsara.com

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/CISTER_image_processing/devel/lib/python2.7/dist-packages/image_processing/msg/_coords.py

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pmssantos/copaDrive

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refs/heads/master

2023-05-05T02:40:53.283905

2021-05-21T23:39:10

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4,715

py

# This Python file uses the following encoding: utf-8 """autogenerated by genpy from image_processing/coords.msg. Do not edit.""" import sys python3 = True if sys.hexversion > 0x03000000 else False import genpy import struct class coords(genpy.Message): _md5sum = "07271e516a6d3af46ff39e3801eabeb2" _type = "image_processing/coords" _has_header = False #flag to mark the presence of a Header object _full_text = """float32 X2 float32 X1 float32 dif_X float32 Y2 float32 Y1 float32 dif_Y float32 slope float32 length float32 intercept """ __slots__ = ['X2','X1','dif_X','Y2','Y1','dif_Y','slope','length','intercept'] _slot_types = ['float32','float32','float32','float32','float32','float32','float32','float32','float32'] def __init__(self, *args, **kwds): """ Constructor. Any message fields that are implicitly/explicitly set to None will be assigned a default value. The recommend use is keyword arguments as this is more robust to future message changes. You cannot mix in-order arguments and keyword arguments. The available fields are: X2,X1,dif_X,Y2,Y1,dif_Y,slope,length,intercept :param args: complete set of field values, in .msg order :param kwds: use keyword arguments corresponding to message field names to set specific fields. """ if args or kwds: super(coords, self).__init__(*args, **kwds) #message fields cannot be None, assign default values for those that are if self.X2 is None: self.X2 = 0. if self.X1 is None: self.X1 = 0. if self.dif_X is None: self.dif_X = 0. if self.Y2 is None: self.Y2 = 0. if self.Y1 is None: self.Y1 = 0. if self.dif_Y is None: self.dif_Y = 0. if self.slope is None: self.slope = 0. if self.length is None: self.length = 0. if self.intercept is None: self.intercept = 0. else: self.X2 = 0. self.X1 = 0. self.dif_X = 0. self.Y2 = 0. self.Y1 = 0. self.dif_Y = 0. self.slope = 0. self.length = 0. self.intercept = 0. def _get_types(self): """ internal API method """ return self._slot_types def serialize(self, buff): """ serialize message into buffer :param buff: buffer, ``StringIO`` """ try: _x = self buff.write(_get_struct_9f().pack(_x.X2, _x.X1, _x.dif_X, _x.Y2, _x.Y1, _x.dif_Y, _x.slope, _x.length, _x.intercept)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize(self, str): """ unpack serialized message in str into this message instance :param str: byte array of serialized message, ``str`` """ try: end = 0 _x = self start = end end += 36 (_x.X2, _x.X1, _x.dif_X, _x.Y2, _x.Y1, _x.dif_Y, _x.slope, _x.length, _x.intercept,) = _get_struct_9f().unpack(str[start:end]) return self except struct.error as e: raise genpy.DeserializationError(e) #most likely buffer underfill def serialize_numpy(self, buff, numpy): """ serialize message with numpy array types into buffer :param buff: buffer, ``StringIO`` :param numpy: numpy python module """ try: _x = self buff.write(_get_struct_9f().pack(_x.X2, _x.X1, _x.dif_X, _x.Y2, _x.Y1, _x.dif_Y, _x.slope, _x.length, _x.intercept)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize_numpy(self, str, numpy): """ unpack serialized message in str into this message instance using numpy for array types :param str: byte array of serialized message, ``str`` :param numpy: numpy python module """ try: end = 0 _x = self start = end end += 36 (_x.X2, _x.X1, _x.dif_X, _x.Y2, _x.Y1, _x.dif_Y, _x.slope, _x.length, _x.intercept,) = _get_struct_9f().unpack(str[start:end]) return self except struct.error as e: raise genpy.DeserializationError(e) #most likely buffer underfill _struct_I = genpy.struct_I def _get_struct_I(): global _struct_I return _struct_I _struct_9f = None def _get_struct_9f(): global _struct_9f if _struct_9f is None: _struct_9f = struct.Struct("<9f") return _struct_9f

[ "enpvf@isep.ipp.pt" ]

enpvf@isep.ipp.pt

36c83d1f224f722a5d5c6e306c21a837d0e6ab2b

71d7398290d3f88aab4a8afd75be0408ee677e31

/apps/goods/serializers.py

7931dc6d9341875e24a2dc7c8f7f51c11380c3f8

[]

no_license

LIpengcccc/MxShop

d8e6ae34d836845b6f3d7bd74e551dbe5ad74c88

2f16c942ab4ac02f7e4ea2f8f56e2c30f892c5bb

refs/heads/master

2022-12-14T05:21:20.353791

2019-11-09T14:40:04

220,652,926

0

null

2022-12-08T07:31:43

2019-11-09T14:20:21

JavaScript

UTF-8

Python

false

3,543

py

from django.db.models import Q from rest_framework import serializers from .models import Goods, GoodsCategory, GoodsImage, Banner, IndexAd, GoodsCategoryBrand, HotSearchWords class CategorySerializer3(serializers.ModelSerializer): class Meta: model = GoodsCategory # fields = ('name', 'click_num', 'market_price', 'add_time') fields = '__all__' class CategorySerializer2(serializers.ModelSerializer): sub_cat = CategorySerializer3(many=True) class Meta: model = GoodsCategory # fields = ('name', 'click_num', 'market_price', 'add_time') fields = '__all__' class CategorySerializer(serializers.ModelSerializer): sub_cat = CategorySerializer2(many=True) class Meta: model = GoodsCategory # fields = ('name', 'click_num', 'market_price', 'add_time') fields = '__all__' class GoodsImageSerializer(serializers.ModelSerializer): class Meta: model = GoodsImage fields = ("image",) class GoodsSerializer(serializers.ModelSerializer): category = CategorySerializer() # images是数据库中设置的related_name="images"，把轮播图嵌套进来 images = GoodsImageSerializer(many=True) class Meta: model = Goods # fields = ('name', 'click_num', 'market_price', 'add_time') fields = '__all__' class BannerSerializer(serializers.ModelSerializer): ''' 轮播图 ''' class Meta: model = Banner fields = "__all__" class BrandSerializer(serializers.ModelSerializer): ''' 大类下面的宣传商标 ''' class Meta: model = GoodsCategoryBrand fields = "__all__" class IndexCategorySerializer(serializers.ModelSerializer): # 某个大类的商标，可以有多个商标，一对多的关系 brands = BrandSerializer(many=True) # good有一个外键category，但这个外键指向的是三级类，直接反向通过外键category（三级类），取某个大类下面的商品是取不出来的 goods = serializers.SerializerMethodField() # 在parent_category字段中定义的related_name="sub_cat" # 取二级商品分类 sub_cat = CategorySerializer2(many=True) # 广告商品 ad_goods = serializers.SerializerMethodField() def get_ad_goods(self, obj): goods_json = {} ad_goods = IndexAd.objects.filter(category_id=obj.id, ) if ad_goods: # 取到这个商品Queryset[0] good_ins = ad_goods[0].goods # 在serializer里面调用serializer的话，就要添加一个参数context（上下文request）,嵌套serializer必须加 # serializer返回的时候一定要加 “.data” ，这样才是json数据 goods_json = GoodsSerializer(good_ins, many=False, context={'request': self.context['request']}).data return goods_json # 自定义获取方法 def get_goods(self, obj): # 将这个商品相关父类子类等都可以进行匹配 all_goods = Goods.objects.filter(Q(category_id=obj.id) | Q(category__parent_category_id=obj.id) | Q( category__parent_category__parent_category_id=obj.id)) goods_serializer = GoodsSerializer(all_goods, many=True, context={'request': self.context['request']}) return goods_serializer.data class Meta: model = GoodsCategory fields = "__all__" class HotWordsSerializer(serializers.ModelSerializer): class Meta: model = HotSearchWords fields = "__all__"

[ "1014741954@qq.com" ]

1014741954@qq.com

913f9d79cbda01e06d0e01bb1575e92a1e90b1cf

311334ddd79553353c493e8bb7d0228ee427715f

/CSV_Join.py

ceadeb414a90069cca27440e1a08773974c28124

[ "MIT" ]

permissive

it-fm/SRTM_to_STL

8e28f06f13ee2db40a3f147ebd283a23e4e3591d

2b29031ce6d4031118b79a83193d97e14b9cf10e

refs/heads/master

2021-09-15T14:03:35.542200

2018-06-03T21:07:43

null

0

null

UTF-8

Python

false

6,961

py

## Code for the CSV joiner ## Run from the command line as: ## python CSV_Join.py filename1.csv filename2.csv ## Joins two SRTM csv files of the same size into one ## The code will attempt to join non-adjacent files so long as they share lat or lon! import numpy import sys if __name__ == '__main__': try: print 'Joining CSV files' filename1 = '' filename2 = '' #filename1 = 'S35E018.csv' # Cape Town S #filename2 = 'S34E018.csv' # Cape Town N #filename1 = 'S35E019.csv' # #filename2 = 'S34E019.csv' # #filename1 = 'S35E018_join.csv' # #filename2 = 'S35E019_join.csv' # #filename1 = 'N54W004.csv' # UK Lake District W #filename2 = 'N54W003.csv' # UK Lake District E if filename1 == '' or filename2 == '': # Check if the csv filenames were passed in argv (e.g. "python CSV_Join.py N54W004.csv N54W003.csv") if len(sys.argv) > 2: filename1 = sys.argv[1] filename2 = sys.argv[2] if filename1 == '' or filename2 == '': # Ask for the two csv filenames filename1 = raw_input('Enter the 1st csv filename: ') filename2 = raw_input('Enter the 2nd csv filename: ') # Find the starting lats & lons from the filenames start_lon1 = float(filename1[4:7]) if filename1[3] == 'W': start_lon1 = -start_lon1 start_lat1 = float(filename1[1:3]) if filename1[0] == 'S': start_lat1 = -start_lat1 start_lon2 = float(filename2[4:7]) if filename2[3] == 'W': start_lon2 = -start_lon2 start_lat2 = float(filename2[1:3]) if filename2[0] == 'S': start_lat2 = -start_lat2 # Check if files need to be joined L-R or T-B if start_lat1 == start_lat2: print 'Files have equal latitude. Joining Left-Right.' LR = True if start_lon1 < start_lon2: print 'File order does not need to be changed.' else: print 'Swapping file order.' filename = filename1 # Swap filenames filename1 = filename2 filename2 = filename elif start_lon1 == start_lon2: print 'Files have equal longitude. Joining Top-Bottom.' LR = False if start_lat1 < start_lat2: print 'File order does not need to be changed.' else: print 'Swapping file order.' filename = filename1 # Swap filenames filename1 = filename2 filename2 = filename else: raise Exception('Files do not have same lat or lon!') outfile = str(filename1[:-4] + '_join' + filename1[-4:]) print 'Processing',filename1,'and',filename2 print 'Outputting data to',outfile try: # read data from file1 as float east1,north1,hgt1 = numpy.loadtxt(filename1,delimiter=',',unpack=True) except: raise Exception('Invalid file!') width1 = int(east1[0]) # Get the width of file1 height1 = int(north1[0]) # Get the height of file1 hgt_max1 = int(hgt1[0]) # Get the max_height of file1 print filename1,': Width',width1,'Height',height1,'Max_Height',hgt_max1 east1 = east1[1:] # Discard the width north1 = north1[1:] # Discard the height hgt1 = hgt1[1:] # Discard the max_height points1 = len(hgt1) # Check the number of points is correct if points1 != width1 * height1: raise Exception('Invalid file!') # Reshape file1 data into Y,X format east1 = numpy.reshape(east1,(height1,-1)) north1 = numpy.reshape(north1,(height1,-1)) hgt1 = numpy.reshape(hgt1,(height1,-1)) try: # read data from file2 as float east2,north2,hgt2 = numpy.loadtxt(filename2,delimiter=',',unpack=True) except: raise Exception('Invalid file!') width2 = int(east2[0]) # Get the width of file2 height2 = int(north2[0]) # Get the height of file2 hgt_max2 = int(hgt2[0]) # Get the max_height of file2 print filename2,': Width',width2,'Height',height2,'Max_Height',hgt_max2 east2 = east2[1:] # Discard the width north2 = north2[1:] # Discard the height hgt2 = hgt2[1:] # Discard the max_height points2 = len(hgt2) # Check the number of points is correct if points2 != width2 * height2: raise Exception('Invalid file!') # Reshape file2 data into Y,X format east2 = numpy.reshape(east2,(height2,-1)) north2 = numpy.reshape(north2,(height2,-1)) hgt2 = numpy.reshape(hgt2,(height2,-1)) # Check files share either width or height if ((width1 != width2) and (height1 != height2)): raise Exception('Files do not share width or height!') if LR: # remove duplicated column hgt1 = hgt1[:,:-1] east1 = east1[:,:-1] north1 = north1[:,:-1] width1 -= 1 points1 -= height1 # join the data hgt = numpy.concatenate((hgt1,hgt2),1) east = numpy.concatenate((east1,east2),1) north = numpy.concatenate((north1,north2),1) width = width1 + width2 height = height1 points = points1 + points2 hgt_max = hgt.max() else: # remove duplicated row hgt1 = hgt1[1:,:] east1 = east1[1:,:] north1 = north1[1:,:] height1 -= 1 points1 -= width1 # join the data hgt = numpy.concatenate((hgt2,hgt1),0) east = numpy.concatenate((east2,east1),0) north = numpy.concatenate((north2,north1),0) width = width1 height = height1 + height2 points = points1 + points2 hgt_max = hgt.max() # Convert back to a 1D array hgt = numpy.ravel(hgt) east = numpy.ravel(east) north = numpy.ravel(north) print 'Width',width print 'Height',height print 'Max Height',hgt_max print 'Top Left:',int(east[0]),int(north[0]) print 'Bottom Right:',int(east[points-1]),int(north[points-1]) # Save the joined data print 'Saving to',outfile fp = open(outfile,'w') outstr = str(width) + ',' + str(height) + ',' + str(int(hgt_max)) + '\n' fp.write(outstr) for l in range(points): outstr = str(int(east[l])) + ',' + str(int(north[l])) + ',' + str(int(hgt[l])) + '\n' fp.write(outstr) fp.close() print 'Complete!' except KeyboardInterrupt: print 'CTRL+C received...' finally: print 'Bye!'

[ "pc235603@gmail.com" ]

pc235603@gmail.com

3b2c176863a5e194f22e386c5b7f32f37633bd8a

c2806d4c1044d4e26b46127bb5d9bdf6e8e42d0a

/hardware/hameg.py

a65aa91f9ca4523543a634dbd45b6d6d0d4c8997

[]

no_license

Faridelnik/Pi3Diamond

f7876cc878793f13037b3007bd432fd9e62b0eb9

62e05ccb365d7f0b8c52aaa7be3723dd16d83689

refs/heads/master

2021-01-22T12:38:08.801473

2017-10-10T08:37:13

102,353,439

1

0

null

UTF-8

Python

false

10,393

py

''' Created on 20.04.2012 author: Helmut Fedder ''' import time import numpy as np # helper class to represent a visa instrument via a socket class SocketInstrument(): def __init__(self, device): import socket host,port = device.split(':') sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((host, port)) self.sock = sock def write(self, cmd): """Sends a command over the socket""" cmd_string = cmd + '\n' sent = self.sock.sendall(cmd_string) if sent != None: raise RuntimeError('Transmission failed') time.sleep(.1) #add a timeout for the transfer to take place. Should be replaced by a proper error handling at some point def ask(self,question): """sends the question and receives the answer""" self.write(question) answer = self.sock.recv(2048)#2000 return answer[:-2] def close(self): self.sock.close() class HMP2030(): def __init__(self,device, voltage_max=20.0, current_max=2.0, fuse_voltage_max=20.0, fuse_current_max=2.5): """ Provides communication with a HMP2030 power supply via USB (virtual COM) or LAN. Usage Examples: hmp = HMP2030('ASRL11::INSTR') hmp = HMP2030('192.168.0.11:50000') Parameters: device: string that describes the device (see examples above) Optional Parameters: voltage_max: maximum allowed voltage current_max: maximum allowed current fuse_voltage_max: maximum allowed fuse voltage fuse_current_max: maximum allowed fuse current """ if '::' in device: self._connect_serial(device) else: self._connect_lan(device) self.voltage_max=voltage_max self.current_max=current_max self.fuse_voltage_max=fuse_voltage_max self.fuse_current_max=fuse_current_max def _connect_serial(self, device): import visa instr=visa.instrument('ASRL11::INSTR') instr.term_chars='\n' instr.chunk_size=4096 instr.timeout=1.0 self.instr = instr def _connect_lan(self, device): """connects to the hameg powersupply""" self.instr=SocketInstrument(device) # convenience method def set_output(self,channel,current): """Set the current on the given channel. Turn output on or off depending on the specified current.""" self.set_ch(channel) if current<=0 or current is None: self.stop() else: self.set_current(current) self.run() #functions to perform different SCPI-commands def set_ch(self,ch): """sets the channel 1, 2 or 3""" if ch in [1,2,3]: self.instr.write('INST OUPT' + str(ch)) else: raise ValueError('Wrong channel number. Chose 1, 2 or 3.') def get_ch(self): """asks for the selected channel""" channel = int(self.instr.ask('INST:NSEL?')) return channel def status(self,ch): """gets the current status of the selected channel (CC or CV)""" state = self.instr.ask('STAT:QUES:INST:ISUM' + str(ch) + ':COND?') if state == 1: return 'CC' elif state == 2: return 'CV' else: print "Couldn't read the status of the selected channel." def set_voltage(self,volt): """sets the voltage to the desired value""" if volt < 0: print 'The selected voltage cannot be set.' elif volt > self.voltage_max : #the voltage_max will be set on the power supply if volt exceed voltage_max self.instr.write('VOLT %1.3f' %self.voltage_max) print 'The set voltage exceed the maximum voltage: %1.3f' %self.voltage_max else: self.instr.write('VOLT %1.3f' %volt) def set_voltage_step(self,vstep): """increases the voltage by a desired step""" vset = get_voltage() set_voltage(vset + vstep) def get_voltage(self): """measures the voltage""" voltage = float(self.instr.ask('MEAS:VOLT?')) return voltage def set_current(self,curr): """sets the current to the desired value""" if curr < 0: print 'The selected current cannot be set.' elif curr > self.current_max : #the voltage_max will be set on the power supply if volt exceed voltage_max self.instr.write('CURR %1.3f' %self.current_max) print 'The set current exceed the maximum current: %1.3f' %self.current_max else: self.instr.write('CURR %1.3f' %curr) def set_current_step(self,cstep): """increases the current by a desired step""" cset = get_current() set_current(cset + cstep) def get_current(self): """measures the current""" current = float(self.instr.ask('MEAS:CURR?')) return current def set_arbitrary(self,ch, seq, N): """performs a sequence of voltage and current values for a given time on one channel with a number of repetitions. ch: channel for output seq: sequence to be set in form of a nested list = [(voltage,current,time),(..),(..),...] N: number of repetitions [1..255]. 0 means infinite repetitions.""" seq_ary = np.array(seq) if max(seq_ary[:,0]) > self.voltage_max: print 'The set voltage exceed the maximum voltage: %1.3f' %self.voltage_max elif max(seq_ary[:,1]) > self.current_max: print 'The set current exceed the maximum current: %1.3f' %self.current_max elif min(seq_ary[:,2]) < .5: print 'The set time is shorter than 0.5s.' elif seq >= 0: print 'Negative value of voltage, current or time.' elif ch != [1,2,3]: print 'Wrong channel number. Chose 1, 2 or 3.' elif N != range(0,256): print 'The set repetitions are outside the range [0,255].' else: self.instr.write('ARB:DATA' + ' ' + str(seq).translate(None, '[()] ')) self.instr.write('ARB:REP' + ' ' + str(N)) self.instr.write('ARB:TRANS' + ' ' + str(ch)) self.instr.write('ARB:STAR' + ' ' + str(ch)) set_ch(ch) run() def stop_arbitrary(self,ch): """stops the arbitrary sequence of a specified channel ch, but leafs the output on.""" self.instr.write('ARB:STOP' + ' ' + str(ch)) def get_arbitrary(self,ch): """gets the number of performed repetitions of the arbitrary sequence""" set_ch(ch) num = int(self.instr.ask('ARB:REP?')) return num def get_all(self): """gets the measured values for all channels in the form [(ch,V,A),]""" l = [] for i in [1,2,3]: set_ch(i) vset = get_voltage() cset = get_current() l.append((i,vset,cset)) return l def run(self): """turns the output from the chosen channel on""" self.instr.write('OUTP ON') def run_all(self): """turns the output from all channels on""" set_ch(1) self.instr.write('OUTP:SEL ON') set_ch(2) self.instr.write('OUTP:SEL ON') set_ch(3) self.instr.write('OUTP:SEL ON') self.instr.write('OUTP:GEN ON') def stop(self): """turns the output from the chosen channel off""" self.instr.write('OUTP OFF') def stop_all(self): """stops the output of all channels""" set_ch(1) self.instr.write('OUTP:SEL OFF') set_ch(2) self.instr.write('OUTP:SEL OFF') set_ch(3) self.instr.write('OUTP:SEL OFF') self.instr.write('OUTP:GEN OFF') def start(self): """starts up the whole system""" self.instr.write('*RST') #resets the device self.instr.write('SYST:REM') #sets the instrument to remote control def close(self): """stops and disconnects the device""" stop_all() self.instr.close() def beep(self): """gives an acoustical signal from the device""" self.instr.write('SYST:BEEP') def error_list(self): """prints all errors from the error register.""" error = str(self.instr.ask('SYST:ERR?')) return error def OVP(self,fuse_voltage_max): """sets the Over-Voltage-Protection to the value fuse_voltage_max for a selected channel""" if fuse_voltage_max < 0: print 'The selected value for voltage protection cannot be set.' elif fuse_voltage_max > 32.0: #the maximal voltage which the HMP2030 supplies print 'The set voltage exceed the maximum voltage: 32V' else: self.instr.write('VOLT:PROT %1.3f' %fuse_voltage_max) def FUSE(self,fuse_current_max): """sets the fuse to the value fuse_current_max and the delay time to 0ms for a selected channel""" self.instr.write('FUSE ON') if fuse_current_max < 0: print 'The selected value for current fuse cannot be set.' elif fuse_current_max > 5.0: #the maximal current which the HMP2030 supplies print 'The set current exceed the maximum current: 5A' else: self.instr.write('CURR %1.3f' %fuse_current_max) self.instr.write('FUSE:DEL 0') class BipolarHMP2030(HMP2030): #funktions to reverse the polarity def set_polarity(self,ch,p): """sets the polarity p of a given channel""" pass def get_polarity(self,ch): """gets the polarity p of a given channel""" pass #------------------------------------------------------------------------------------------------------------------------- #define the sub-function including:set_channel, set_voltage, set_current and run #def set(ch): # write('SYST REM') # write('INST OUPT1') # write('OUTP OFF') # #write('SYST REM') #to remote control #write('INST OUPT1') #select channel 1 #stop()

[ "shagieva.farida@physics.msu.ru" ]

shagieva.farida@physics.msu.ru

978c93ab7716a070e4b5c045ab3c4fac5825d8e4

7bbe7bf766dcf2bdf6b129ece0b78b5406a93adc

/python/20.py

0642a43dc2916f147a36bcea87a04511465a5e1b

[]

no_license

ottiferous/euler

8261c17139374777e14c17748e6e5ac9e8968aa2

4eebf05941fdada5c4c6b605864d61ea386e7f1b

refs/heads/master

2021-01-18T21:33:16.732445

2016-05-24T23:55:48

2,253,811

1

null

UTF-8

Python

false

104

py

result = 0 for x in str(reduce(lambda x,y: x*y, xrange(1, 100), 1)): result += int(x) print result

[ "github@andrewmarrone.com" ]

github@andrewmarrone.com

01ea9b89c21c2662978b5f1fcd58afc9ebd35ada

e3365bc8fa7da2753c248c2b8a5c5e16aef84d9f

/indices/nnsingula.py

2760b64a1532a4d213c8fae350fa41d153d1278c

[]

no_license

psdh/WhatsintheVector

e8aabacc054a88b4cb25303548980af9a10c12a8

a24168d068d9c69dc7a0fd13f606c080ae82e2a6

refs/heads/master

2021-01-25T10:34:22.651619

2015-09-23T11:54:06

42,749,205

2

3

null

2015-09-23T11:54:07

2015-09-18T22:06:38

Python

UTF-8

Python

false

104

py

ii = [('WilkJMC2.py', 4), ('GilmCRS.py', 1), ('WestJIT2.py', 2), ('DibdTRL2.py', 1), ('MereHHB3.py', 1)]

[ "varunwachaspati@gmail.com" ]

varunwachaspati@gmail.com

06079a88e0315ff4a3ca5c78262cba05ed302bef

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/ch7_funtinal_api.py

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oakareahio/deeplearning_with_python

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refs/heads/master

2022-12-07T11:43:13.671594

2019-09-14T06:02:51

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from keras import Input, layers from keras.models import Model input_tensor = Input(shape=(64,)) x = layers.Dense(32, activation='relu')(input_tensor) x = layers.Dense(32, activation='relu')(x) output_tensor = layers.Dense(10, activation='softmax')(x) model = Model(input_tensor, output_tensor) model.compile(optimizer='rmsprop', loss='categorical_crossentropy') print(model.summary()) import numpy as np x_train = np.random.random((1000, 64)) y_train = np.random.random((1000, 10)) from IPython import embed; embed() model.fit(x_train, y_train, epochs=10, batch_size=128) score = model.evaluate(x_train, y_train)

[ "okehara_aoi@microad.co.jp" ]

okehara_aoi@microad.co.jp

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/args.py

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lv10wizard/ig-highlights-bot

ce061275acaca9108bd27da3017ec492832d3934

e551d68f51f222c58428029df1eda7838825101f

refs/heads/master

2021-05-16T05:38:38.934210

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from __future__ import print_function import argparse import os import re from six import iteritems from six.moves import input from constants import ( AUTHOR, DATA_ROOT_DIR, ) from src import ( config, reddit, ) from src.database import ( BadUsernamesDatabase, Database, get_class_from_name, InstagramDatabase, SUBCLASSES, SubredditsDatabase, UniqueConstraintFailed, ) from src.util import ( confirm, logger, mkdirs, ) DRY_RUN = 'dry-run' SHUTDOWN = 'shutdown' BACKUP = 'backup' LOAD_BACKUP = 'load-backup' ADD_SUBREDDIT = 'add-subreddit' RM_SUBREDDIT = 'rm-subreddit' ADD_BLACKLIST = 'add-blacklist' RM_BLACKLIST = 'rm-blacklist' ADD_BAD_USERNAME = 'add-bad-username' RM_BAD_USERNAME = 'rm-bad-username' DELETE_DATA = 'delete-data' DUMP = 'dump' IG_DB = 'ig-db' IG_DB_LIKES = 'ig-db-likes' IG_DB_COMMENTS = 'ig-db-comments' IG_DB_LINKS_RAW = 'ig-db-links-raw' IG_CHOICES = 'ig-db-choices' DATABASE_CHOICES = sorted(list(SUBCLASSES.keys())) try: # disallow InstagramDatabase from --dump choices since they are handled # separately DATABASE_CHOICES.remove('InstagramDatabase') except ValueError: # database class renamed? this shouldn't happen pass BACKUP_CHOICES = [ name for name in DATABASE_CHOICES # don't bother backing up the ratelimit database if name != 'InstagramRateLimitDatabase' ] igdb_path = Database.format_path(InstagramDatabase.PATH) resolved_igdb_path = config.resolve_path(igdb_path) try: IG_DB_CHOICES = [ name for name in os.listdir(resolved_igdb_path) if name.endswith('.db') ] except OSError: IG_DB_CHOICES = [] IG_DB_DISPLAY_CHOICES = [] else: IG_DB_DISPLAY_CHOICES = sorted([name[:-3] for name in IG_DB_CHOICES]) IG_DB_DISPLAY_CHOICES += ['*'] def to_opt_str(arg_str): return arg_str.replace('-', '_') def to_cmdline(arg_str): return arg_str.replace('_', '-') def shutdown(cfg, do_shutdown=True): import sys if sys.platform == 'win32': from signal import CTRL_C_EVENT as SIGINT else: from signal import SIGINT from src.mixins.proc import get_pid_file fail_msg = 'Could not determine bot\'s pid (is the bot running?)' bot_pid_file = get_pid_file('IgHighlightsBot') if not bot_pid_file: logger.info(fail_msg) return try: with open(bot_pid_file, 'r') as fd: main_pid = fd.read() except (IOError, OSError): logger.exception(fail_msg) return try: main_pid = int(main_pid) except (TypeError, ValueError): msg = [fail_msg, '{path} contents: \'{content}\''] logger.exception('\n'.join(msg), path=bot_pid_file, content=main_pid, ) return if confirm('Shutdown bot (pid={0})?'.format(main_pid)): logger.info('Shutting down bot ({color_pid}) ...', color_pid=main_pid) try: os.kill(main_pid, SIGINT) except OSError: logger.exception('Could not shutdown the bot ({color_pid})!', color_pid=main_pid, ) else: logger.info('Leaving the bot alive ({color_pid})', color_pid=main_pid) def backup(cfg, *databases): import sqlite3 if '*' in databases: databases = BACKUP_CHOICES for db_name in databases: db_class = get_class_from_name(db_name) if not db_class: continue path = Database.format_path(db_class.PATH) resolved_path = Database.resolve_path(path) if os.path.exists(resolved_path): backup_path = Database.format_backup_path( db_class.PATH ) resolved_backup = Database.resolve_path(backup_path) mkdirs(os.path.dirname(resolved_backup)) logger.info('Backing up \'{db_name}\' ({basename})' ' to \'{backup_path}\' ...', db_name=db_name, basename=os.path.basename(path), backup_path=backup_path, ) connection = sqlite3.connect(resolved_path) try: with open(resolved_backup, 'w') as fd: for line in connection.iterdump(): print(line, end=os.linesep, file=fd) except (IOError, OSError): logger.exception('Failed to backup \'{db_name}\' to SQL' ' text format!', db_name=db_name, ) finally: logger.info('Successfully backed up \'{db_name}\' to' ' \'{backup_path}\'!', db_name=db_name, backup_path=backup_path, ) connection.close() else: logger.info('Cannot backup \'{db_name}\': no database file' ' \'{path}\'', db_name=db_name, path=path, ) def _load_backup(db_path, sql_script): """ Loads the database @ db_path with the specified sql_script string. ** This will wipe the existing database ** db_path (str) - the path to the database to load sql_script (str) - the SQL script to populate the database with (see sqlite3#executescript) Returns True if the database is successfully loaded from the script """ import sqlite3 success = False do_load = True resolved_path = Database.resolve_path(db_path) # keep the database file in case something goes wrong # XXX: this isn't 100% safe because it may move the file while it is in # an incomplete state, potentially corrupting it. it is however better than # simply deleting the database file outright. if os.path.exists(resolved_path): tmp_path = '{0}.tmp'.format(resolved_path) logger.debug('Moving \'{old}\' -> \'{tmp}\'', old=db_path, tmp=tmp_path, ) try: os.rename(resolved_path, tmp_path) except OSError: logger.exception('Failed to move old database file \'{path}\'!', path=db_path, ) do_load = False if do_load: mkdirs(os.path.dirname(resolved_path)) connection = sqlite3.connect(resolved_path) try: with connection: connection.executescript(sql_script) except: logger.exception('Failed to load \'{db_path}\' from backup!', db_path=db_path, ) logger.info('Reverting ...') try: # XXX: remove first in case .rename doesn't clobber if os.path.exists(resolved_path): os.remove(resolved_path) os.rename(tmp_path, resolved_path) except OSError: logger.exception('Failed to revert \'{tmp}\' -> \'{old}\'!', tmp=tmp_path, old=db_path, ) else: logger.debug('Removing \'{tmp}\' ...', tmp=tmp_path) try: os.remove(tmp_path) except OSError: # littering: load success but the previous database file # wasn't cleaned up properly logger.warn('Failed to remove previous database file' ' \'{tmp}\'', tmp=tmp_path, exc_info=True, ) success = True return success def load_backup(cfg, *databases): import time if '*' in databases: databases = BACKUP_CHOICES logger.info('** Loading from backup will wipe any changes in the' ' database **\n') for db_name in databases: db_class = get_class_from_name(db_name) if not db_class: continue backup_path = Database.format_backup_path(db_class.PATH) resolved_backup = Database.resolve_path(backup_path) if os.path.exists(resolved_backup): try: backup_mtime = os.path.getmtime(resolved_backup) except (IOError, OSError): logger.debug('Failed to stat \'{path}\' (for mtime)', path=backup_path, ) backup_mtime = -1 confirm_msg = ['Load \'{0}\'?'] if backup_mtime > 0: confirm_msg.append('(backup last modified @ {1})') confirm_msg = ' '.join(confirm_msg).format( db_name, time.strftime( '%m/%d, %H:%M:%S', time.localtime(backup_mtime) ), ) if confirm(confirm_msg): try: with open(resolved_backup, 'r') as fd: sql = [line for line in fd if line] except (IOError, OSError): logger.exception('Failed to read \'{db_name}\' backup' ' ({path})!', db_name=db_name, path=backup_path, ) else: path = Database.format_path(db_class.PATH) if _load_backup(path, ''.join(sql)): logger.info('Successfully loaded \'{db_name}\'' ' ({basename}) from \'{backup_path}\'', db_name=db_name, basename=os.path.basename(path), backup_path=backup_path, ) else: logger.info('Cannot load \'{db_name}\': no backup found ({path})', db_name=db_name, path=backup_path, ) def add_subreddit(cfg, *subreddits): subreddits_db = SubredditsDatabase(do_seed=False) for sub in subreddits: _, sub_name = reddit.split_prefixed_name(sub) # in case the user passed something like '/u/' if sub_name: if sub_name not in subreddits_db: try: with subreddits_db: subreddits_db.insert(sub_name) except UniqueConstraintFailed: # this means there is a bug in __contains__ logger.warn('Failed to add \'{sub_name}\' (already added)!', sub_name=reddit.prefix_subreddit(sub_name), exc_info=True, ) else: logger.info('Successfully added \'{sub_name}\'!', sub_name=reddit.prefix_subreddit(sub_name), ) else: logger.info('Cannot add \'{sub_name}\': already added!', sub_name=sub_name, ) def rm_subreddit(cfg, *subreddits): subreddits_db = SubredditsDatabase(do_seed=False) for sub in subreddits: _, sub_name = reddit.split_prefixed_name(sub) # in case the user passed something like '/u/' if sub_name: if sub_name in subreddits_db: with subreddits_db: subreddits_db.delete(sub_name) logger.info('Successfully removed \'{sub_name}\'!', sub_name=reddit.prefix_subreddit(sub_name), ) else: logger.info('Cannot remove \'{sub_name}\': not in database!', sub_name=sub_name, ) def add_blacklist(cfg, *names): from src.blacklist import Blacklist blacklist = Blacklist(cfg) for name in names: blacklist.add(name) def rm_blacklist(cfg, *names): from src.blacklist import Blacklist blacklist = Blacklist(cfg) for name in names: blacklist.remove(name) def add_bad_username(cfg, text, fullname, score): # TODO? look up submission thing prefix dynamically submission_prefix = 't3_' if not fullname.startswith(submission_prefix): logger.info('Usage: --{add_bad_username_opt} TEXT FULLNAME SCORE', add_bad_username_opt=ADD_BAD_USERNAME, ) logger.info('FULLNAME must be a submission fullname (starting with' ' {prefix}, not {fullname}): not adding \'{text}\'', prefix=submission_prefix, fullname=fullname, text=text, ) return bad_usernames = BadUsernamesDatabase() logger.info('Adding \'{color_text}\' ({fullname}, {score}) as a' ' bad-username ...', color_text=text, fullname=fullname, score=score, ) try: with bad_usernames: bad_usernames.insert(text, fullname, score) except UniqueConstraintFailed: logger.info('\'{color_text}\' already considered a bad-username', color_text=text, ) else: logger.info('Successfully added \'{color_text}\' as a bad-username!', color_text=text, ) def rm_bad_username(cfg, *text): bad_usernames = BadUsernamesDatabase() for t in text: if t in bad_usernames: logger.info('Removing bad-username: \'{color_text}\' ...', color_text=t, ) with bad_usernames: bad_usernames.delete(t) else: logger.info('\'{color_text}\' was not considered a bad-username!', color_text=t, ) def delete_data(cfg, do_delete=True): import shutil import stat if not do_delete: return # assumption: all data is stored under a single directory base_path = DATA_ROOT_DIR resolved_path = config.resolve_path(base_path) if not os.path.exists(resolved_path): logger.info('No program data found in \'{0}\'', resolved_path) return if confirm('Delete all data in \'{0}\'?'.format(base_path)): logger.info('Deleting all data ...') def onerr(func, path, exc): """ https://docs.python.org/3/library/shutil.html#rmtree-example """ # qualify the func name so that we get a better sense of which # function was called funcname = [] try: funcname.append(func.__module__) except AttributeError: # can this happen? pass funcname.append(func.__name__) logger.debug('An error occured calling {funcname}({path}) !', funcname='.'.join(funcname), path=path, exc_info=True, ) if not os.access(path, os.W_OK): logger.debug('Attempting to clear readonly bit ...') try: os.chmod(path, stat.S_IWRITE) func(path) except (IOError, OSError): logger.warn('Could not remove \'{path}\'!', path=path, exc_info=True, ) shutil.rmtree(resolved_path, onerror=onerr) else: logger.info('Leaving data as is.') def do_print_database(path, query=''): import sqlite3 logger.info('Dumping \'{path}\' ...', path=path) connection = sqlite3.connect(path) connection.row_factory = sqlite3.Row try: # https://stackoverflow.com/a/305639 tables = connection.execute( 'SELECT name FROM sqlite_master WHERE type = \'table\'' ) except sqlite3.DatabaseError as e: # eg. not a database logger.exception('Could not lookup tables in \'{path}\'', path=path, ) else: print('{0}:'.format(os.path.basename(path)), end='\n\n') # https://stackoverflow.com/a/13335514 # [('foo',), ('bar',), ('baz',)] # -> ('foo', 'bar', 'baz') tables = [name[0] for name in tables] cursors = { name: connection.execute( 'SELECT * FROM {0} {1}'.format(name, query) ) for name in tables } sep = ' | ' horiz_sep = '-' * 72 end = 'number of rows:' for name in cursors: print(horiz_sep) print('table \'{0}\':'.format(name)) print(horiz_sep) cur = cursors[name] num = 0 keys = [] padding = {} for row in cur: if not keys: keys = row.keys() columns = [] for k in keys: # XXX: assume the first row is emblematic of the width # of each column to avoid reading the entire database # into memory (this may mean that some rows are not # formatted correctly) padding[k] = max(len(str(row[k])), len(k)) columns.append('{0:^{1}}'.format(k, padding[k])) # print out the columns print(*columns, sep=sep) print(horiz_sep) # print out each row row_str = [ '{0:<{1}}'.format(str(row[k]), padding[k]) for k in keys ] print(*row_str, sep=sep) num += 1 end_out_len = len(end) + len(str(num)) + 1 # +1 for space end_sep = ' ' + horiz_sep[end_out_len + 1:] # +1 for space print(end, num, end=end_sep + '\n\n') def print_database(cfg, *databases): if '*' in databases: # dump all databases databases = DATABASE_CHOICES for db_name in databases: if db_name == 'InstagramDatabase': logger.info('Please use --{opt} to dump individual instagram' ' databases', opt=IG_DB, ) continue db_class = get_class_from_name(db_name) if not db_class: continue path = Database.format_path(db_class.PATH) resolved_path = Database.resolve_path(path) if os.path.exists(resolved_path): do_print_database(resolved_path) else: logger.info('No database file: \'{path}\'', path=path) def print_instagram_database_wrapper(callback, order, *user_databases): if '*' in user_databases: # dump all instagram databases user_databases = IG_DB_CHOICES orig_order = order for user_db in user_databases: if not user_db.endswith('.db'): user_db = '{0}.db'.format(user_db) path = os.path.join(resolved_igdb_path, user_db) if os.path.exists(path): if not orig_order: # use the default order if none was specified igdb = InstagramDatabase(path) if igdb.size() == 0: igdb.close() logger.info('Removing \'{path}\': empty database ...', path=path, ) try: os.remove(path) except (IOError, OSError): logger.exception('Failed to remove \'{path}\'!', path=path, ) continue order = 'ORDER BY {0}'.format(igdb.order_string) logger.debug(order) callback(path, order) else: path_raw = os.path.join(InstagramDatabase.PATH, user_db) logger.info('No instagram data for user: \'{user}\'', user=re.sub(r'[.]db$', '', user_db), ) def print_instagram_database(cfg, order, *user_databases): print_instagram_database_wrapper(do_print_database, order, *user_databases) def print_instagram_database_links(cfg, order, *user_databases): def do_print_links(path, order): import sqlite3 from src.instagram.constants import MEDIA_LINK_FMT if os.path.exists(path): db = sqlite3.connect(path) db.row_factory = sqlite3.Row cursor = db.execute('SELECT code FROM cache {0}'.format(order)) for row in cursor: print(MEDIA_LINK_FMT.format(row['code'])) print_instagram_database_wrapper(do_print_links, order, *user_databases) def print_igdb_choices(cfg, do_print=True): if not do_print: return print('--{0} choices:'.format(IG_DB)) line = [] sep = ', ' first_char = None for c in IG_DB_DISPLAY_CHOICES: formatted_line = sep.join(line) c_first = c[0].lower() if first_char != c_first: # separate the choices by the first letter end = '' if formatted_line: end = sep end += '\n\n' print(formatted_line, end=end) first_char = c_first line = [c] # '..., <c>, ' => 2 * len(sep) elif len(formatted_line) + 2*len(sep) + len(c) >= 80: print(formatted_line, end=sep+'\n') line = [c] else: line.append(c) if line: # print the trailing database choices print(sep.join(line)) def handle(cfg, args): handlers = { SHUTDOWN: shutdown, BACKUP: backup, LOAD_BACKUP: load_backup, ADD_SUBREDDIT: add_subreddit, RM_SUBREDDIT: rm_subreddit, ADD_BLACKLIST: add_blacklist, RM_BLACKLIST: rm_blacklist, ADD_BAD_USERNAME: add_bad_username, RM_BAD_USERNAME: rm_bad_username, DELETE_DATA: delete_data, DUMP: print_database, IG_DB: print_instagram_database, IG_DB_LIKES: print_instagram_database, IG_DB_COMMENTS: print_instagram_database, IG_DB_LINKS_RAW: print_instagram_database_links, IG_CHOICES: print_igdb_choices, } order = { IG_DB: None, IG_DB_LIKES: 'ORDER BY num_likes DESC', IG_DB_COMMENTS: 'ORDER BY num_comments DESC', IG_DB_LINKS_RAW: None, } had_handleable_opt = False for opt, opt_val in iteritems(args): opt_key = to_cmdline(opt) # XXX: options should evaluate to true if they are to be handled if opt_key in handlers and bool(opt_val): had_handleable_opt = True try: handler_func = handlers[opt_key] except KeyError as e: logger.exception('No option handler defined for \'{opt}\'!', opt=opt, ) else: try: if opt_key in order: handler_func(cfg, order[opt_key], *opt_val) else: handler_func(cfg, *opt_val) except TypeError: # opt_val not iterable handler_func(cfg, opt_val) return had_handleable_opt def parse(): parser = argparse.ArgumentParser( description='Instagram Highlights Bot, a reddit bot that will reply' ' to comments linking to instagram accounts with their most popular' ' media.' ) parser.add_argument('-c', '--config', metavar='PATH', help='Custom config path; default: {0}'.format(config.Config.PATH), ) parser.add_argument('-d', '--{0}'.format(DRY_RUN), action='store_true', help='Runs the bot normally but disables it from replying to' ' comments and posts. This mode is intended as a sort of "live"' ' test.' ) parser.add_argument('-P', '--logging-path', metavar='PATH', help='Set the root directory to save logs to (this overrides the' ' config setting).', ) parser.add_argument('-L', '--logging-level', choices=[ logger.DEBUG, 'DEBUG', logger.INFO, 'INFO', logger.WARNING, 'WARNING', logger.ERROR, 'ERROR', logger.CRITICAL, 'CRITICAL', ], help='Set the logging level (this overrides the config setting).', ) parser.add_argument('-N', '--logging-no-color', action='store_true', help='Turn off logging colors (this overrides the config setting).', ) parser.add_argument('--{0}'.format(SHUTDOWN), action='store_true', help='Kills the bot process and all sub-processes.', ) parser.add_argument('--{0}'.format(ADD_SUBREDDIT), metavar='SUBREDDIT', nargs='+', help='Add subreddit(s) to the comment stream (these are subreddits' ' that the bot crawls).', ) parser.add_argument('--{0}'.format(RM_SUBREDDIT), metavar='SUBREDDIT', nargs='+', help='Remove subreddit(s) from the comment stream (the bot will' ' no longer crawl these subreddits but will still make replies if' ' summoned).', ) user_example = 'user(s) (eg. \'{0}{1}\')'.format(reddit.PREFIX_USER, AUTHOR) sub_example = 'subreddit(s) (eg. \'{0}{1}\')'.format( reddit.PREFIX_SUBREDDIT, 'history' ) note = ( 'Note: user profiles are specified as \'{0}u_<username>\'' ' (eg. \'{0}u_{1}\')'.format(reddit.PREFIX_SUBREDDIT, AUTHOR) ) parser.add_argument('--{0}'.format(ADD_BLACKLIST), metavar='NAME', nargs='+', help='Blacklist {user} or {sub} so that the bot no longer replies' ' to those user(s) or to comments/posts in those subreddit(s).' ' {note}.'.format( user=user_example, sub=sub_example, note=note, ), ) parser.add_argument('--{0}'.format(RM_BLACKLIST), metavar='NAME', nargs='+', help='Remove {user} or {sub} from the blacklist so that the bot can' ' reply to those user(s) or comments/posts in those subreddit(s).' ' {note}.'.format( user=user_example, sub=sub_example, note=note, ) ) parser.add_argument('--{0}'.format(ADD_BAD_USERNAME), metavar='STRING', nargs=3, help='Adds a string to the bad-usernames database so that it will' ' not be matched in the future as a potential username.' ' Usage: --{0} bad_username submission_fullname score. The first' ' argument should be the string that the bot should ignore in the' ' future. The second argument should be the fullname of the' ' submission containing the username. The third should be' ' the score of the deleted bot comment.'.format(ADD_BAD_USERNAME), ) parser.add_argument('--{0}'.format(RM_BAD_USERNAME), metavar='STRING', nargs='+', help='Removes the string(s) from the bad-usernames database so that' ' they can be matched in the future as potential instagram' ' usernames again.' ) parser.add_argument('--{0}'.format(DELETE_DATA), action='store_true', help='Remove all data saved by the program.' ' This will ask for confirmation.', ) database_choices = DATABASE_CHOICES + ['*'] backup_choices = BACKUP_CHOICES + ['*'] parser.add_argument('--{0}'.format(DUMP), metavar='NAME', nargs='+', choices=database_choices, help='Dump the specified database(s) to stdout.' ' Choices: {0}'.format(database_choices), ) parser.add_argument('--{0}'.format(BACKUP), metavar='NAME', nargs='+', choices=backup_choices, help='Backup the specified database(s) to an SQL text format.' ' Backups are stored in \'{0}\'. Choices: {1}.'.format( Database.BACKUPS_PATH_ROOT, backup_choices, ), ) parser.add_argument('--{0}'.format(LOAD_BACKUP), metavar='NAME', nargs='+', choices=backup_choices, help='Load the specified database(s) from' ' its last --{0} dump. **This will cause any changes since the' ' last --{0} to be lost**' ' (will ask for confirmation). See --{0} for choices.'.format( BACKUP, ), ) ig_actual_choices = IG_DB_CHOICES + IG_DB_DISPLAY_CHOICES parser.add_argument('--{0}'.format(IG_DB), metavar='NAME', nargs='+', choices=ig_actual_choices, help='Dump the specified instagram user databases to stdout.' ' See --{0} for choices.'.format(IG_CHOICES), ) parser.add_argument('--{0}'.format(IG_DB_LIKES), metavar='NAME', nargs='+', choices=ig_actual_choices, help='Dump the specified instagram user databases to stdout' ' sorted by most likes -> least likes.' ' See --{0} for choices.'.format(IG_CHOICES), ) parser.add_argument('--{0}'.format(IG_DB_COMMENTS), metavar='NAME', nargs='+', choices=ig_actual_choices, help='Dump the specified instagram user databases to stdout' ' sorted by most comments -> least comments.' ' See --{0} for choices.'.format(IG_CHOICES), ) parser.add_argument('--{0}'.format(IG_DB_LINKS_RAW), metavar='NAME', nargs='+', choices=ig_actual_choices, help='Dump the specified instagram user databases\' links to' ' stdout. See --{0} for choices.'.format(IG_CHOICES), ) parser.add_argument('-I', '--{0}'.format(IG_CHOICES), action='store_true', help='List valid --{0} choices.'.format(IG_DB), ) return vars(parser.parse_args()) __all__ = [ 'handle', 'parse', ]

[ "lv10wizard@gmail.com" ]

lv10wizard@gmail.com

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dnlbellfield/official_website

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2023-03-17T11:36:29.221759

2021-03-11T23:05:31

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#!/Users/johnbellfield/official_website/env/bin/python # -*- coding: utf-8 -*- import re import sys from gunicorn.app.wsgiapp import run if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(run())

[ "dnlbellfield@gmail.com" ]

dnlbellfield@gmail.com

d13f09077c30b481d74bd155cde1b173f07758c0

295f34f4411d984f0ff6026be6e96fe134dc1550

/home/pi/.local/share/Trash/files/lectoraTTL.py

3741207e9b221e2106d60bb92b8a05f4e64ecb34

[]

no_license

mcashjavier/disco-linux-raspy

1e3fed914b6040fa9972e7cfc7357ecb72070e8c

8c23103cf089059fbdadfad8cfb7059c1580da83

refs/heads/master

2022-12-20T17:06:39.967203

2019-03-12T12:09:22

2019-03-12T20:01:10

175,072,541

0

3

null

2022-12-18T06:59:27

2019-03-11T19:44:12

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UTF-8

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py

#Programa para lectora TTL #DATA IN por el pin 7-->GPIO4 #CLK IN por el pin 13-->GPIO27 #CARD PRESENT IN por el pin 15-->GPI22 import tkinter import RPi.GPIO as IO import time IO.setwarnings(True) IO.setmode(IO.BCM) #anda con los pines GPIOxx #IO.setmode(IO.BOARD) #anda con los pines de placa #IO.setup(4,IO.IN,pull_up_down=IO.PUD_UP) IO.setup(27,IO.IN,pull_up_down=IO.PUD_UP) #IO.setup(22,IO.IN,pull_up_down=IO.PUD_UP) #IO.setup(22,IO.IN) #IO.setup(27,IO.IN) Hab_CLK=True contador=0 x=0 #def inte_DATA(channel): # print ('canal %s activo GPIO4',channel) ## print ('Marcos Vetta') # os.system('clear') #def inte_CLK(channel): ## global Hab_CLK # global contador # if channel==27: # contador=contador+1 # print ('CLK ',channel,IO.input(27),contador) # #IO.cleanup() ## print('CONT',contador) # os.system('clear') # if channel==4: # print ('DATA %d %d',channel,IO.input(4)) # elif channel==22: # print('CARD present %d %d',channel,IO.input(22)) def inte_CARDPRESENT(channel): # global Hab_CLK # global contador # if channel==22: # contador=contador+1 # Hab_CLK=True print ('CARD PRESENTE:',channel,contador) # else: # print('otro pin') #IO.add_event_detect(4,IO.RISING,interrupcion_flanco_asc) #IO.add_event_detect(4,IO.BOTH,interrupcion_flanco_asc) #IO.add_event_detect(4,IO.FALLING,callback=inte_DATA) #IO.add_event_detect(27,IO.RISING,callback=inte_CLK) #IO.add_event_callback(27,inte_CLK) IO.add_event_detect(27,IO.FALLING,callback=inte_CARDPRESENT,bouncetime=1) #IO.add_event_detect(27,IO.FALLING,inte_CLK,bouncetime=500) for x in range (0,500): #entrada=IO.input(4) #print('.') #time.sleep(1) if IO.input(27)>0.5: print('input=',IO.input(27)) else: print('input=',I0.input(27)) # time.sleep(0.5)

[ "prog2@magneticash.com" ]

prog2@magneticash.com

4808fcb09c746d4045f3885a0b1760f7e8bdeb06

d82995f33a603b61e06deecd644f5a293823b746

/fully_convolutional_networks/transposed_convolutional.py

48d78190eca9ceba1e6e2adef3e7b2b4eb010ff1

[]

no_license

RyanCargan/udacity-robond-term1

79d065aab42e4e308a93e36fa869bb48477aebef

a92a6e665e72930f9a33eb3a82f64da206b64fcf

refs/heads/master

2021-09-07T08:39:07.181342

2018-02-20T12:27:29

null

0

null

UTF-8

Python

false

1,024

py

import tensorflow as tf import numpy as np from one_by_one import custom_init def upsample(x): """ Apply a 2 times upsample on x and return the result x - the input feature - 4 rank tensor return: TF operation """ shape = x.get_shape() input_shape = shape[1] kernel_size = (input_shape * 2,input_shape * 2) stride = 2 return tf.contrib.layers.conv2d_transpose(inputs=x, num_outputs=shape[3], kernel_size=kernel_size, stride=stride, padding='SAME') if __name__ == '__main__': x = tf.constant(np.random.randn(1, 4, 4, 3), dtype=tf.float32) conv = upsample(x) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) result = sess.run(conv) print('Input Shape: {}'.format(x.get_shape())) print('Output Shape: {}'.format(result.shape))

[ "j.tabacii@gmail.com" ]

j.tabacii@gmail.com

d558ea33f4ad605890eb70d3641f85bb38c410a8

71b934d407ce48148ae68370f5f5844b1e3a418a

/src/asker.py

4d1d1ccab26b5d60299de35466b2b2d023dc888e

[ "MIT" ]

permissive

davidteather/zoom-scheduler

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7f575979b508f50af65458ccb4ea73d842ea657e

refs/heads/main

2023-02-19T20:11:07.954339

2021-01-23T09:57:18

330,213,863

0

null

UTF-8

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py

import dateparser import json import os if not os.path.isdir('data'): os.mkdir('data') data_path = "data/meetings.json" images_path = "images/" def ask(): name = input("What is the name of your class? (e.g. CS 101): ") time = input("What time is your class? (e.g. 14:30): ") hour = time.split(":")[0] minute = time.split(":")[1] print("\nDay options: Sun, Mon, Tue, Wed, Thu, Fri, Sat") days = input("What days are your classes? (e.g. Mon,Wed,Fri): ") room_id = input("Zoom ID: (e.g. 01234567890): ") password = input("Zoom Password: (e.g. 123456): ") end = input("When do these meetings end? (e.g. 5/15/2021): ") end_epoch = dateparser.parse( end, settings={"PREFER_DATES_FROM": "future"}).timestamp() new_meeting = { "class_name": name, "crontab": f"{minute} {hour} * * {days}", "room_id": room_id, "password": password, "end_date": end_epoch } try: with open(data_path, "r") as i: current_data = json.loads(i.read())['meetings'] current_data.append(new_meeting) except (FileNotFoundError, ValueError): current_data = [new_meeting] with open(data_path, 'w+', encoding='utf-8') as f: json.dump({'meetings': current_data}, f, ensure_ascii=False, indent=4) inp = 'y' while inp.lower() != 'n': ask() inp = input("Add more classes y/n: ")

[ "noreply@github.com" ]

davidteather.noreply@github.com

f282d1785b0e2341e87ca9678267cf02af90232c

ebfcad44f8314f9efa758431eee180cbbddc8d53

/EplatServer/old/test.py

9879d5b7ff11f98037036a773bc08560b3bb0688

[]

no_license

rudi-code/e-plat

7baa22e72f9e7cb7e2a0d0ad2fc3dd380d1af97f

ecb0ec9eb82e7d9b974d7fe7a0c2e463e9c8b8dd

refs/heads/master

2023-06-19T20:40:29.846446

2021-07-22T12:21:02

388,450,515

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null

UTF-8

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py

import json import math def calculate_distance(lat1, lon1, lat2, lon2): earth_radius = 6378.137 # Radius of earth in KM dLat = lat2 * math.pi / 180 - lat1 * math.pi / 180 dLon = lon2 * math.pi / 180 - lon1 * math.pi / 180 a = math.sin(dLat/2) * math.sin(dLat/2) + \ math.cos(lat1 * math.pi / 180) * math.cos(lat2 * math.pi / 180) * \ math.sin(dLon/2) * math.sin(dLon/2) c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a)) d = earth_radius * c return d * 1000 # meters def calculate_angle(lat1, lon1, lat2, lon2): dLon = lon2 * math.pi / 180 - lon1 * math.pi / 180 y = math.sin(dLon) * math.cos(lat2 * math.pi / 180) x = math.cos(lat1 * math.pi / 180) * math.sin(lat2 * math.pi / 180) \ - math.sin(lat1 * math.pi / 180) * math.cos(lat2 * math.pi / 180) * math.cos(dLon) brng = math.atan2(y, x) brng = math.degrees(brng) brng = (brng + 360) % 360 return brng def convert_angle(degr): direction = -1 if degr > 315 and degr < 45: # north direction = 0 elif degr > 45 and degr < 135: # east direction = 1 elif degr > 135 and degr < 225: # south direction = 2 else: # west direction = 3 return direction if __name__ == '__main__': junction = [-7.762049, 110.369364] request = '{"device_id":"12345","direction":"north","lat":-7.762151897747425,"lon":110.37007759357256}' # request = '{"device_id":"12345","direction":"north","lat":-7.762151897746425,"lon":110.37007759357256}' # read previous data with open('data.json') as data_file: data_json = json.load(data_file) # get the json ambulance = json.loads(request) data = data_json if ambulance['lat'] == 0 and ambulance['lon'] == 0: # exit signal, emitted when the ambulance leaving the traffic light direction = 0 data['ambulance']['direction'] = 0 data['ambulance']['angle'] = 0 data['ambulance']['distance'] = -1 else: # convert from angle to direction and set appropriate action # for corresponding traffic light angle = calculate_angle(junction[0], junction[1],\ ambulance['lat'], ambulance['lon']) # calculate distance distance = calculate_distance(junction[0], junction[1],\ ambulance['lat'], ambulance['lon']) direction = convert_angle(angle) data['ambulance']['direction'] = direction data['ambulance']['angle'] = angle data['ambulance']['distance'] = distance print direction print angle # check the delta_distance to determine whether the ambulance is approaching # or leaving the traffic light if data['ambulance']['distance'] != -1 and distance >= data['ambulance']['distance']: # approaching; set appropriate traffic light to green data['traffic_light'][direction] = 1 elif data['ambulance']['distance'] != -1 and distance < data['ambulance']['distance']: print 'here2' # leaving set all traffic light to red data['traffic_light'] = [0, 0, 0, 0] else: print 'here' # exit signal, turn all lights to red # leaving set all traffic light to red data['traffic_light'] = [0, 0, 0, 0] # write to file data['ambulance']['info'] = ambulance with open('data.json', 'w') as data_file: data_file.write(json.dumps(data))

[ "rudi.purnomo@quadras.co.id" ]

rudi.purnomo@quadras.co.id

47ca79ad241ea2f2be47ad3ac495a37ae51d61fc

f0d713996eb095bcdc701f3fab0a8110b8541cbb

/TmasgxCm6iz3gTGHk_24.py

36dac173b508a5b700464e9c37d5bdb750607cd4

[]

no_license

daniel-reich/turbo-robot

feda6c0523bb83ab8954b6d06302bfec5b16ebdf

a7a25c63097674c0a81675eed7e6b763785f1c41

refs/heads/main

2023-03-26T01:55:14.210264

2021-03-23T16:08:01

350,773,815

0

null

UTF-8

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756

py

""" Write a function that returns the **length of the shortest contiguous sublist** whose sum of all elements **strictly exceeds** `n`. ### Examples min_length([5, 8, 2, -1, 3, 4], 9) ➞ 2 min_length([3, -1, 4, -2, -7, 2], 4) ➞ 3 # Shortest sublist whose sum exceeds 4 is: [3, -1, 4] min_length([1, 0, 0, 0, 1], 1) ➞ 5 min_length([0, 1, 1, 0], 2) ➞ -1 ### Notes * The sublist should be composed of **contiguous elements** from the original list. * If no such sublist exists, return `-1`. """ def min_length(lst, n): s = sum(lst) if lst: x = min_length(lst[1:], n) y = min_length(lst[:-1], n) return min((k for k in (x, y) if k >= 0), default = len(lst) if s > n else -1) return -1

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

daniel.reich@danielreichs-MacBook-Pro.local

b7d757571c521ec0b025a9ed314c2e638c8e5170

ffeae925479dfa94d0ec710fcd19061ac3a6e98a

/Fibonacci_recursive.py

ad067db15f7af75be20738034514ae661d2f50b1

[ "MIT" ]

permissive

NageshJ2014/TestRepor

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bea3c5feca4fda196f43797d12bf752d7c97959a

refs/heads/master

2020-04-02T00:42:05.923675

2018-10-19T17:28:45

153,817,603

0

null

UTF-8

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810

py

def fibonaaci(pos): if pos == 1: #print('0') return 0 if pos == 2: #print('1') return 1; n1 = fibonaaci(pos -1); n2 = fibonaaci (pos -2); print (n1) return n1 + n2; n5 = fibonaaci ( 5); print(n5); name = 'Andy' # define name def printBob(): global name; print('printing from the def: ', name) # print from function name = 'Bob' # define name in function print('printing from the def: ', name) # print from function # the main function print('printing from the main:{} Before Global Change '.format(name)) # print from the main printBob() # call the function to print print('printing from the main: ', name) # print from the main num = input('Enter a number : ') print('The zero-padded number is : ', str(num).rjust(10,'0'))

[ "nagesh.jayaram@gmail.com" ]

nagesh.jayaram@gmail.com

4cf5c113f26e735a332c73a5524e7f0e21574ed9

e140817f5831fdf7cf93111455a12788794da8eb

/testFiles/outdatedFiles/ZoneCollection.py

de4a428260c0d30b144b4ec282a45ee427f6680e

[]

no_license

Davidchristy/TVAC

0ef40fbf5ecb6ab369f69db9ef281c9b09ecd7e6

ab111fa67e7e1cecb4543887f5eaea28edcbc29e

refs/heads/master

2021-09-09T14:14:05.342538

2018-03-16T21:57:56

116,292,748

0

null

2018-02-02T04:09:16

2018-01-04T18:18:47

Python

UTF-8

Python

false

1,235

py

import uuid from Logging.Logging import Logging class ZoneCollection: def __init__(self, parent): Logging.debugPrint(2,"Creating ZoneCollection") # self.zone_dict = build_zone_collection() self.profile_uuid = uuid.uuid4() self.parent = parent # self.update_period = 10 # self.profile_name = None # self.thermal_start_time = None # self.expected_time_values = None # self.set_points_start_time = None def getZone(self,d): return self.zone_dict[d] def get_zones_data_json(self): ''' This returns a json string of the currently loaded profile ''' return ('{"profileuuid":"%s","updateperiod":%s,"profile":[ %s ]}' % (self.profile_uuid,self.parent.update_period,self.fill_zones())) def fill_zones(self): '''password This is a helper function for getJson() ''' message = [] zone_len = len(self.zone_dict) count = 0 for zone in self.zone_dict: message.append(self.zone_dict[zone].getJson()) if count < (zone_len - 1): message.append(',') count = count + 1 return ''.join(message)

[ "DavidChristy520@gmail.com" ]

DavidChristy520@gmail.com

24a3c675c28fa9825a10c00d02ac5b8e5e160fe3

337a67e82c446a27bd96770d6b02e889a426eb8d

/03-Python/1/Activities/12-Stu_NumberChain/Solved/NumberChain_Solved.py

0f299c03f8ad92022d5b6a63c1516139f2ee21bd

[]

no_license

ktatineni/GTechCode

6a317a3020c91ef259aba8c647341c6b6f59396e

01bcfca38f4f403825ff54ff1497ac78329d120e

refs/heads/master

2020-05-01T13:44:06.320959

2019-05-14T16:31:10

177,499,930

1

0

null

UTF-8

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false

461

py

# Initial variable to track game play user_play = "y" # While we are still playing... while user_play == "y": # Ask the user how many numbers to loop through user_number = input("How many numbers? ") # Loop through the numbers. (Be sure to cast the string into an integer.) for x in range(int(user_number)): # Print each number in the range print(x) # Once complete... user_play = input("Continue: (y)es or (n)o? ")

[ "krishna@krishnas-mbp.attlocal.net" ]

krishna@krishnas-mbp.attlocal.net

43f0bad355eb6dd19692fb8dfa7f34dd1d19199a

7a14fa5256977a08c033aea484dace528a8e6ae2

/formacao_primeit/exercises/swallows.py

56dfa7d20d9ed4486ce54119c73f00f490b29816

[]

no_license

vinicius-vph/python

cdf8496d12940d0ce2399b25d94da2aac0e4983b

503712c83cf3f712a825127620974125945e2601

refs/heads/main

2023-05-16T15:26:53.440422

2023-05-09T19:42:54

340,350,963

0

null

UTF-8

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false

253

py

# Vinicius Santos, Curso de python, Sessão 1 """ Coméntarios qualquer que está em várias linhas """ print("Hello, world!") print("Hello, world!") print() print("Suppose two swallows \"carry\" it together.") print('African or "European" swallows?')

[ "vinicius.vph@gmail.com" ]

vinicius.vph@gmail.com

226ea7fa9126703aafbe9372476da27dfecbdb89

7f54637e347e5773dfbfded7b46b58b50544cfe5

/step6/qdoba/qdoba/middlewares.py

a506e086c2996cefbf6a582ca4db11cd012ac34d

[]

no_license

simba999/all-scrapy

5cc26fd92b1d03366b74d4fff58c4a0641c85609

d48aeb3c00fa2474153fbc8d131cf58402976e1d

refs/heads/master

2021-01-25T14:24:04.715550

2018-03-03T13:43:13

123,695,640

1

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UTF-8

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py

# -*- coding: utf-8 -*- # Define here the models for your spider middleware # # See documentation in: # http://doc.scrapy.org/en/latest/topics/spider-middleware.html from scrapy import signals class QdobaSpiderMiddleware(object): # Not all methods need to be defined. If a method is not defined, # scrapy acts as if the spider middleware does not modify the # passed objects. @classmethod def from_crawler(cls, crawler): # This method is used by Scrapy to create your spiders. s = cls() crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) return s def process_spider_input(response, spider): # Called for each response that goes through the spider # middleware and into the spider. # Should return None or raise an exception. return None def process_spider_output(response, result, spider): # Called with the results returned from the Spider, after # it has processed the response. # Must return an iterable of Request, dict or Item objects. for i in result: yield i def process_spider_exception(response, exception, spider): # Called when a spider or process_spider_input() method # (from other spider middleware) raises an exception. # Should return either None or an iterable of Response, dict # or Item objects. pass def process_start_requests(start_requests, spider): # Called with the start requests of the spider, and works # similarly to the process_spider_output() method, except # that it doesn’t have a response associated. # Must return only requests (not items). for r in start_requests: yield r def spider_opened(self, spider): spider.logger.info('Spider opened: %s' % spider.name)

[ "oliverking8985@yahoo.com" ]

oliverking8985@yahoo.com

b0e3e29b8c8d3347a402574bd4137ff42303a1f4

23a5c5ccb7bedbfd9929c611a358aba0caf1ea1c

/CFRplus.py

e081dc1af3c2c49e9ee76c590ef1f53c661e99cd

[]

no_license

snarb/RPSnn

058db0483aa674f101242c0436b57686952e19ac

60fa010a870fb1edb7de095469f1585b3868ad5e

refs/heads/master

2021-01-18T18:01:48.233984

2017-06-12T14:01:48

86,837,612

2

1

null

UTF-8

Python

false

3,203

py

from KuhnPoker import * from treelib import Node, Tree from CfrNode import CfrNode from GameTree import GameTree from matplotlib import pyplot as plt import Utils import math from collections import Counter from math import sqrt import random import time import pandas as pd from NodeEstimator import Estimator class CFRtrainer: def __init__(self): self.playerOneTree = GameTree(CfrNode) self.playerTwoTree = GameTree(CfrNode) self.kuhn = KuhnPoker() self.stats = Counter() #self.alpha = alpha self.trainigXdata = [] self.trainigYdata = [] self.hists = [] self.avgStr = [] self.stratSum = 0 self.iter = 0 self.betRegrets = [] def CFR(self, p0, p1): curPlayer = self.kuhn.GetCurrentPlayer() if(self.kuhn.IsTerminateState()): return self.kuhn.GetPayoff(curPlayer) curPlayerProb = p0 if curPlayer == Players.one else p1 opProb = p1 if curPlayer == Players.one else p0 tree = self.playerOneTree if curPlayer == Players.one else self.playerTwoTree cfrNode = tree.GetOrCreateDataNode(self.kuhn, curPlayer) strategy = cfrNode.GetStrategy(curPlayerProb) util = [0.0] * NUM_ACTIONS nodeUtil = 0 infosetStr = self.kuhn.GetInfoset(curPlayer) infosetBackup = self.kuhn.SaveInfoSet() for action in range(NUM_ACTIONS): self.kuhn.MakeAction(action) if(curPlayer == Players.one): util[action] += -self.CFR(p0 * strategy[action], p1) else: util[action] += -self.CFR(p0, p1 * strategy[action]) nodeUtil += strategy[action] * util[action] self.kuhn.RestoreInfoSet(infosetBackup) for action in range(NUM_ACTIONS): regret = util[action] - nodeUtil cfrNode.regretSum[action] = cfrNode.regretSum[action] + opProb * regret if(('1 | uplayed;uplayed;uplayed' in infosetStr) and curPlayer == Players.one): self.trainigXdata.append(np.array(strategy)) self.trainigYdata.append(nodeUtil) self.betRegrets.append(cfrNode.regretSum[1]) self.stratSum += strategy[1] self.avgStr.append(self.stratSum / (len(self.avgStr) + 1)) self.iter += 1 return nodeUtil def running_mean(self, x, N): cumsum = np.cumsum(np.insert(x, 0, 0)) return (cumsum[N:] - cumsum[:-N]) / N def Train(self): util = 0 cnt = 0 start_time = time.time() results = [] # utils = [] for i in range(1, 500): self.kuhn.NewRound() curUtil = self.CFR(1, 1) util += curUtil if(cnt % 80 == 0): results.append(util / i) def CheckNash(self): if (self.kuhn.IsPlayerOneCloseToNash(self.playerOneTree)): print("Player one is in Nash") else: print("Player one is not in Nash") if(self.kuhn.IsPlayerTwoCloseToNash(self.playerTwoTree)): print("Player two is in Nash") else: print("Player two is not in Nash")

[ "thesnarb@gmail.com" ]

thesnarb@gmail.com

c5a8fe695a4c1a3e451a369a01d1885998462d0d

c72266d064fe2b52f9389e9352e01bb369d7ee52

/keyword_extraction/extra_analysis/word.py

694d9741c234169c35719c08c5d6378ef7045175

[ "MIT" ]

permissive

luckypython777/stackoverflow-search

5f0e5f8bea4b778f46e4dd4ac0bc20196f9ed1e3

990b339a1b87530894d1068eb99d0b03e6476268

refs/heads/master

2021-12-09T13:11:35.448758

2016-05-17T06:33:41

null

0

null

UTF-8

Python

false

1,171

py

######################################### # Plotting word clouds for common # # words in the queries of each # # language being analysed # ######################################### from os import path from PIL import Image import numpy as np import matplotlib.pyplot as plt import wordcloud from wordcloud import WordCloud, ImageColorGenerator # d = path.dirname(__file__) # Read the whole text. text = open('fullfilePython.txt').read() # read the mask / color image # taken from http://jirkavinse.deviantart.com/art/quot-Real-Life-quot-Alice-282261010 alice_coloring = np.array(Image.open( "pythonlogo.png")) wc = WordCloud(background_color="black", max_words=200, mask=alice_coloring, max_font_size=80, random_state=42) # generate word cloud wc.generate(text) # create coloring from image image_colors = ImageColorGenerator(alice_coloring) # show plt.imshow(wc) plt.axis("off") plt.figure() # recolor wordcloud and show # we could also give color_func=image_colors directly in the constructor plt.imshow(wc.recolor(color_func=image_colors)) plt.axis("off") plt.figure() plt.imshow(alice_coloring, cmap=plt.cm.gray) plt.axis("off") plt.show()

[ "hagarwa3@illinois.edu" ]

hagarwa3@illinois.edu

1f63fe94a4ed3323b05298eb9685d791fe4f3e89

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/services/migrations/0015_auto_20210314_1558.py

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[]

no_license

Code-Institute-Submissions/Solid-Properties-Project

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refs/heads/master

2023-04-02T16:21:20.437436

2021-04-05T10:25:05

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# Generated by Django 3.1.5 on 2021-03-14 15:58 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('services', '0014_auto_20210314_1556'), ] operations = [ migrations.AddField( model_name='category', name='icon', field=models.CharField(max_length=50, null=True), ), migrations.AlterField( model_name='category', name='name', field=models.CharField(max_length=254), ), ]

[ "astig.mandalian2014@gmail.com" ]

astig.mandalian2014@gmail.com

0d159cde821bf873eb32118c556e7c536b923354

f7ed06524bfd49b8a3840f477696acea924e9728

/django_project/users/views.py

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[]

no_license

alifarukyucel/django-web-app

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01bc26d189e21e20206f8fc0ecede13cc58b8348

refs/heads/main

2023-07-09T12:41:28.808039

2021-08-18T15:39:25

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from django.shortcuts import render, redirect from django.contrib import messages from django.contrib.auth.decorators import login_required from .forms import UserRegisterForm, UserUpdateForm, ProfileUpdateForm def register(request): if request.method == 'POST': form = UserRegisterForm(request.POST) if form.is_valid(): form.save() username = form.cleaned_data.get('username') messages.success(request, f'Account created for {username}! You are now able to log in') return redirect('login') else: form = UserRegisterForm() return render(request, 'users/register.html', {'form': form}) @login_required def profile(request): if request.method == 'POST': u_form = UserUpdateForm(request.POST, instance=request.user) p_form = ProfileUpdateForm(request.POST, request.FILES, instance=request.user.profile) if u_form.is_valid() and p_form.is_valid(): u_form.save() p_form.save() messages.success(request, 'Your account has been updated!') return redirect('profile') # redirect due to POST, GET, REDIRECT pattern. # This way, the user doesn't get 'Are you sure you want to resubmit the form?' question. That question comes # up when we make another POST request. By redirecting, we cause the browser to send a GET request after a # reload. else: u_form = UserUpdateForm(instance=request.user) p_form = ProfileUpdateForm(instance=request.user.profile) context = { 'u_form': u_form, 'p_form': p_form } return render(request, 'users/profile.html', context)

[ "alifarukyucel@gmail.com" ]

alifarukyucel@gmail.com

e9dfda36ce273fddb9ce89b2447f01d11b529d54

a15046d4bfd07025ed271a22820cdc43fb624cdc

/复读机.py

17964c542ef5d795f1f4a0211bc6fca29b788552

[]

no_license

vashti0622/WeChat-Auto-Reply

3d5909a7085eeb37c8f993bdce1e8399fa666676

fbea0c40a43c361411edc882d55aa9e85958aa24

refs/heads/master

2020-06-02T18:57:17.940154

2019-06-12T00:19:12

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py

import itchat,re @itchat.msg_register(itchat.content.TEXT) def text_reply(msg): reply_text = msg['Text'] if msg['Text'] == '？': reply_text = '自动回复：欢迎来到复读机模式。' return reply_text itchat.auto_login(hotReload=True) #不用每次都扫码 itchat.run()

[ "noreply@github.com" ]

vashti0622.noreply@github.com

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/6kyu/Replace Alphabet Position.py

108d0fe4c6aac63d0b5c0887e3d1d12eb861d251

[]

no_license

cyrustsiu/Codewars-Solution

141088cb2ad735182e90f65b635bd2ff390d1a40

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refs/heads/master

2022-12-24T09:54:17.379176

2020-10-04T13:44:34

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def alphabet_position(text): replace = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':6, 'g':7, 'h':8, 'i':9, 'j':10, 'k':11, 'l':12, 'm':13, 'n':14, 'o':15, 'p':16, 'q':17, 'r': 18, 's':19, 't':20, 'u':21, 'v':22, 'w':23, 'x':24, 'y':25, 'z':26} result = [] for word in text.lower(): if word in replace: result.append(replace[word]) else: pass return " ".join(map(str,result))

[ "noreply@github.com" ]

cyrustsiu.noreply@github.com

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/src/djanban/apps/members/migrations/0023_auto_20170519_1715.py

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[ "MIT" ]

permissive

my-favorite-repositories/djanban

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refs/heads/master

2021-03-01T14:23:19.745085

2018-05-15T17:12:01

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py

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2017-05-19 15:15 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('members', '0022_member_custom_avatar'), ] operations = [ migrations.AlterField( model_name='member', name='custom_avatar', field=models.ImageField(blank=True, default=None, null=True, upload_to=b'', verbose_name='Custom avatar'), ), ]

[ "diegojromerolopez@gmail.com" ]

diegojromerolopez@gmail.com

3d1151563451aabd27889d52b7c622cb5e6e639c

08e160550216d40025c37f6617b2c5b87350ee0c

/django_rest_api/django_rest_api/settings.py

855c75182761580ddcaa1e097989c1bad717f0a9

[]

no_license

Poornima7/Django_RESt_API

92b7c946a8afcc7304be71f518a24a86aae2588a

6197372a0307b23d19bc0f7f73df29c6489867ac

refs/heads/master

2020-04-15T04:26:34.194050

2019-01-07T08:02:22

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py

""" Django settings for django_rest_api project. Generated by 'django-admin startproject' using Django 2.1.5. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '@&4+6v#k4uckq5_k3dda2wxn_5_jgmmdsi8i(8(t1w(4k*nm5p' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'my_app.apps.MyAppConfig', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'django_rest_api.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'django_rest_api.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/'

[ "poornima.slv@outlook.com" ]

poornima.slv@outlook.com

a40adac7fa8a2c68d1dce489b4ffce81f7b70679

f885d8c09b9069817f1a25d0a11e889dbafdc4b4

/a1/test_res/scrap.py

998c748949cd3306a0315d40f8d49d18de2a726e

[]

no_license

icecreambean/COMP9517

39dcd0578e8b17bd957bdb79e40d1644395f18e9

9cae8ae147c55e8f714c8af12f22f2cb6e4e54d9

refs/heads/master

2022-08-21T14:50:23.155144

2020-06-02T01:32:27

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py

border = int(max(g.shape)/4) g_new = cv2.copyMakeBorder(g, border, border, border, border, cv2.BORDER_CONSTANT) res = cv2.matchTemplate(r,g_new,cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) r_top_left = max_loc r_bottom_right = (r_top_left[0] + r.shape[1], r_top_left[1] + r.shape[0]) res = cv2.matchTemplate(b,g_new,cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) b_top_left = max_loc b_bottom_right = (b_top_left[0] + b.shape[1], b_top_left[1] + b.shape[0]) r_new = np.zeros(g_new.shape) print(r_new.shape, r_top_left, r.shape) h,w = r.shape rstart, cstart = r_top_left rstop = rstart + w cstop = cstart + h print(r_new[rstart:rstop,cstart:cstop].shape) print(rstart, cstart, rstop, cstop) r_new[cstart:cstop,rstart:rstop] = r cv2.rectangle(r_new,r_top_left, r_bottom_right, 255, 2) #bottom_right = (top_left[0] + w, top_left[1] + h) #cv2.rectangle(b,top_left, bottom_right, 255, 2) #https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/ #color_img = cv2.merge((b,g,r)) #plt.imshow(color_img) plt.subplot(1,2,1) plt.imshow(r_new) plt.subplot(1,2,2) plt.imshow(g_new) r_top_left, b_top_left bn,gn,rn = alignComponents(b,g,r) plt.subplot(3,1,1) plt.imshow(bn) plt.subplot(3,1,2) plt.imshow(gn) plt.subplot(3,1,3) plt.imshow(rn) bn.shape,gn.shape,rn.shape plt.subplot(3,1,1) plt.imshow(b) plt.subplot(3,1,2) plt.imshow(g) plt.subplot(3,1,3) plt.imshow(r) ptype = type(g[0,0]) img_plane = np.array([np.iinfo(ptype).max] * template.size) img_plane = np.reshape(img_plane, template.shape) # comparison logic #def _matchTemplate(mt_img, mt_template, cv_method): # mt_result = cv2.matchTemplate(mt_img, mt_template, cv_method) # _, _, mt_locmin, mt_locmax = cv2.minMaxLoc(mt_result) # if cv_method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]: # mt_start = mt_locmin # else: # mt_start = mt_locmax # mt_stop = (mt_start[0]+width(mt_img), mt_start[1]+height(mt_img)) # return mt_start, mt_stop # (top left, bot right) each in (r,c) format # perform image alignment def alignComponents(b,g,r): # assume g == template (most accurately sliced) border = int(max(g.shape)/4) g_new = cv2.copyMakeBorder(g, border, border, border, border, cv2.BORDER_CONSTANT) pix_type = type(g[0,0]) # compare: r,b <--> g (template) r_start, _ = _matchTemplate(r, g_new) b_start, _ = _matchTemplate(b, g_new) # apply translation (to larger img plane) r_new = np.zeros(g_new.shape, dtype=pix_type) b_new = np.zeros(g_new.shape, dtype=pix_type) print(r_start, b_start) r_new[ r_start[1]:r_start[1]+height(r), r_start[1]:r_start[1]+width(r) ] = r b_new[ b_start[0]:b_start[0]+height(b), b_start[1]:b_start[1]+width(b) ] = b img_out = cv2.merge((b_new,g_new,r_new)) return img_out mt: off(54,54): imgsize=(106, 167) (maxsize=(160, 221)); res=1290.5042508521697 def downsize(img): r,c = img.shape nr = r/2 nc = c/2 if nr < 1 or nc < 1: # can't scale down any further return None return cv2.resize(img, ( int(r/2), int(c/2) ))

[ "vict.tse.business@gmail.com" ]

vict.tse.business@gmail.com

d5885eb6a99f09e649a1083ed122485adadb790c

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/asign/settings.py

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[]

no_license

akulacharan/asign

afe53b78197b6063c224853cad2ca67d6e98c584

c22d782d8accb8c5d5230f29939e4bdf7a5f1b61

refs/heads/master

2022-11-21T21:30:42.552848

2020-07-29T08:29:09

282,475,318

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null

2020-07-25T15:51:28

2020-07-25T15:50:34

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UTF-8

Python

false

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py

""" Django settings for asign project. Generated by 'django-admin startproject' using Django 3.0.6. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import django_heroku import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '#e$#bug(*s%n8$7)dxfwu9=p6kg^t*3j%12s_&kt9%x)_iyps4' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'my_app', 'crispy_forms', ] CRISPY_TEMPLATE_PACK ='bootstrap4' MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'asign.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] AUTH_USER_MODEL = 'my_app.User' WSGI_APPLICATION = 'asign.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': 'profile', 'USER': 'postgres', 'PASSWORD':'Cherry@143', 'HOST':'localhost' } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Asia/Kolkata' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' MEDIA_URL='/media/' MEDIA_ROOT=os.path.join(BASE_DIR,'media') # Activate Django-Heroku. django_heroku.settings(locals())

[ "akulacharanteja@gmail.com" ]

akulacharanteja@gmail.com

c039d48f55f8ecde519357b38ca442c0c18abd2c

0a68afb453c069bf16c6931a158d2f43a717b429

/nbc6.py

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[]

no_license

neriki/newbiecontest

7b53e05632aa9cc8f1358d82548c4a18de992244

d80a020a1f52c3b0ee3c81b9ea554e7e785b0d55

refs/heads/master

2021-01-10T07:01:28.198592

2015-10-19T16:22:15

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import urllib.request from http import cookiejar from urllib.parse import urlencode import datetime import math jar = cookiejar.CookieJar() credentials = {'user': 'neriki', 'passwrd': 'abcd1234!', 'cookielength' : '-1'} credenc = urlencode(credentials).encode('ascii') opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(jar)) urllib.request.install_opener(opener) req = opener.open('https://www.newbiecontest.org/forums/index.php?action=login2', credenc) reqq = opener.open('https://www.newbiecontest.org/epreuves/prog/prog6.php') q=str(reqq.read()) q=q.split(':')[1].replace('<br />', '') print(q) a=int(q.split('x')[0].replace(' ', '')) b=int(q.split('x')[3].replace('b2','').replace(' ', '')) q=q.split('x')[4].split('=') if q[0] !=' ': c=int(q[0].replace(' ', '')) else: c=0 y=int(q[1].replace('"','').replace(' ', '')) print(a) print(b) print(c) print(y) x1 = (-b + math.sqrt((b*b)- (4 *a *c))) / (2*a) x2 = (-b - math.sqrt((b*b)- (4 *a *c))) / (2*a) if x1>x2: x=x1 else: x=x2 print(x) print(str(x)[:len(str(x).split('.')[0])+3]) test2 = opener.open('https://www.newbiecontest.org/epreuves/prog/verifpr6.php?solution='+ str("%.2f" % x)) print(test2.read())

[ "neriki@free.fr" ]

neriki@free.fr

9080eb145a61a76607a018edafa5a61503c3baa2

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/mywebsite/videorequest/views.py

be34303b8398af594f98ecf3a08f3126fc350f54

[]

no_license

tusharcastic/django-deployment-example

3be0bc41775848d8fe60111be70c6996561cf9eb

ca355986eb7e598b2059547baae983b6b40bdc7c

refs/heads/master

2021-10-12T03:11:21.699756

2019-02-01T05:10:08

null

0

null

UTF-8

Python

false

700

py

from django.shortcuts import render,redirect from . models import Video # Create your views here. from .forms import VideoForm def index(request): videos=Video.objects.order_by('date_added') context={'videos':videos} return render(request,'videorequest/index.html',context) def vrform(request): if request.method =='POST': form=VideoForm(request.POST) if form.is_valid(): new_req=Video(videotitle=request.POST['videoname'],videodesc=request.POST['videodesc']) new_req.save() return redirect('index') else: form=VideoForm() context={'form': form} return render(request,'videorequest/vrform.html',context)

[ "tush.bhoge@gmail.com" ]

tush.bhoge@gmail.com

6716a5f542a8636259abde410ce1729c4fff1f86

1e019ee356416b73c4232351e250a90f2eefa8e7

/snippets/sqlite.py

59c6aed70a4cacf67d0494d56238764373d26f7f

[ "MIT" ]

permissive

Teamworksapp/pydbvolve

cb131a66c72f42097f604726989463c6131afbd5

e18bb8cf98997c52de0cf30a8e106c480ba0bb57

refs/heads/master

2021-08-26T00:12:23.924347

2017-08-11T14:18:00

87,454,422

5

2

MIT

2021-08-21T03:51:47

2017-04-06T17:03:20

Python

UTF-8

Python

false

967

py

import sqlite3 # Simpler is better, in this case. The main script works with dict types. def dict_factory(cur, row): return {col[0]: row[ix] for ix, col in enumerate(cur.description)} # End dict_factory # This is a subclass of sqlite3.Cursor that includes rudimentary __enter__, __exit__ # methods so it can be used in with context manager statements class CMCursor(sqlite3.Cursor): def __enter__(self): return self def __exit__(self, e_type, e_value, e_tb): self.close() # End class CMCursor # This is a subclass of the sqlite3.Connection class. It does nothing but force # dict_factory as the row factory and the CMCursor as the cursor factory class CMConnection(sqlite3.Connection): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.row_factory = dict_factory def cursor(self, *args, **kwargs): return super().cursor(factory=CMCursor) # End class CMConnection

[ "hproctor@lasllc.com" ]

hproctor@lasllc.com

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d883bda370722eb014ac4589602fbde725044777

/Final Project/decision_tree.py

a43c342d01fccbcfb5a67e71d85a7a4cdb9c840b

[]

no_license

vvv44/AIFinalProject

4e74307d51487369f7868a2092bb65dc11019b27

4d58b48747ec438782caa203ad8ac9f059462af9

refs/heads/master

2022-06-04T02:41:30.075382

2020-05-02T19:24:01

260,759,202

0

null

UTF-8

Python

false

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import numpy as np import csv import pandas as pd class DecisionTreeNode(object): # Constructor def __init__(self, att, thr, left, right): self.attribute = att self.threshold = thr # left and right are either binary classifications or references to # decision tree nodes self.left = left self.right = right def print_tree(self,indent=''): # If prints the right subtree, corresponding to the condition x[attribute] > threshold # above the condition stored in the node if self.right in [0,1]: print(indent+' ','class=',self.right) else: self.right.print_tree(indent+' ') print(indent,'if x['+str(self.attribute)+'] <=',self.threshold) if self.left in [0,1]: print(indent+' ','class=',self.left) else: self.left.print_tree(indent+' ') class DecisionTreeClassifier(object): # Constructor def __init__(self, max_depth=10, min_samples_split=10, min_accuracy =1): self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_accuracy = min_accuracy def fit(self,x,y): self.root = self._id3(x,y,depth=0) def predict(self,x_test): pred = np.zeros(len(x_test),dtype=int) for i in range(len(x_test)): pred[i] = self._classify(self.root,x_test[i]) return pred #this method checks several thresholds per attribute and chooses the one with highest information gain def _best_treshold_generate(self,x,y, orig_entropy): best_thrs = np.zeros(x.shape[1]) best_gains = np.zeros(x.shape[1]) for i in range(x.shape[1]): #We first generate several random values for an attribute thr = np.random.uniform(min(x[:,i]),max(x[:,i]),x.shape[1]) entropy_thr = np.zeros(thr.shape[0]) #We now check the entropy to get info gain of each attribute and threshold for j in range(thr.shape[0]): less = x[:,i] <= thr[j] more = ~ less entropy_thr[j] = self._entropy(y[less], y[more]) #Get the gains of the attribute and choose the threshold that yields the best gain gains = orig_entropy - entropy_thr best_gains[i] = np.max(gains) best_thrs[i] = thr[np.argmax(gains)] best_att = np.argmax(best_gains) #Return a tuple containing the index of the best attribute, and the best threshold (from that attribute) return (best_att, best_thrs[best_att]) def _id3(self,x,y,depth): if y.size==0: return mean_val = np.mean(y) #check for possible empty y array if depth >= self.max_depth or len(y) <= self.min_samples_split or max([mean_val,1-mean_val])>=self.min_accuracy: return int(round(mean_val)) orig_entropy = self._entropy(y, []) # thr = np.mean(x,axis=0) # entropy_attribute = np.zeros(len(thr)) # for i in range(x.shape[1]): # less = x[:,i] <= thr[i] # more = ~ less # entropy_attribute[i] = self._entropy(y[less], y[more]) # gain = orig_entropy - entropy_attribute # # #print('Gain:',gain) # best_att = np.argmax(gain) best_att_thr = self._best_treshold_generate(x,y,orig_entropy) #We extract the treshold from the second "row" and attribute from first row best_thr = best_att_thr[1] #We extract the best attribute index best_att = best_att_thr[0] #We proceed to divide values less = x[:,best_att] <= best_thr more = ~ less #Will have to divide x and y into xl, yl and xr, yr to give to the node, so that it gives a reference to a decision tree return DecisionTreeNode(best_att, best_thr, self._id3(x[less],y[less],depth+1), self._id3(x[more],y[more],depth+1)) def _entropy(self,l,m): ent = 0 for p in [l,m]: if len(p)>0: pp = sum(p)/len(p) pn = 1 -pp if pp<1 and pp>0: ent -= len(p)*(pp*np.log2(pp)+pn*np.log2(pn)) ent = ent/(len(l)+len(m)) return ent def _classify(self, dt_node, x): if dt_node in [0,1]: return dt_node if x[dt_node.attribute] <= dt_node.threshold: return self._classify(dt_node.left, x) else: return self._classify(dt_node.right, x) def display(self): print('Model:') self.root.print_tree() def processCSV(): with open('placement_data.csv') as file: read = csv.reader(file) lines = list(read) for row in lines: if(row[1]=='M'): row[1] = 1 else: row[1] = 0 del row[3] del row[4] del row[4] del row[5] if(row[5]=='No'): row[5] = 0 else: row[5] = 1 del row[7] del row[9] if(row[8]== 'Placed'): row[8] = 1 else: row[8] = 0 del lines[0] writer = csv.writer(open('placement_data_cleaned.csv', 'w')) writer.writerows(lines) def processIncomeCSV(): with open('income_evaluation.csv') as file: read = csv.reader(file) lines = list(read) for row in lines: if(row[9]==' Male' or row[9]=='Male'): row[9] = 1 else: row[9] = 0 del row[1] del row[1] del row[1] del row[2] del row[2] del row[2] del row[4] del row[4] del row[5] if(row[5] == " <=50K" or row[5] == "<=50K"): row[5] = 0 else: row[5] = 1 #we make white be one and other be 0 if(row[2] == " White" or row[2] == "White"): row[2] = 1 else: row[2] = 0 del lines[0] writer = csv.writer(open('income_evaluation_cleansed.csv', 'w',newline='')) writer.writerows(lines) #Code for the first dataset # with open('placement_data_cleaned.csv') as file: # read = csv.reader(file) # lines = list(read) # x = np.array(lines).astype(np.float) # y = x[:,8] # x = np.delete(x,-1,axis=1) # x = np.delete(x,0,axis = 1) #code for the second dataset with open('income_evaluation_cleansed.csv') as file: read = csv.reader(file) lines = list(read) x = np.array(lines).astype(np.float) y = x[:,5] x = np.delete(x,-1,axis = 1) #Split data into training and testing ind = np.random.permutation(len(y)) split_ind = int(len(y)*0.8) x_train = x[ind[:split_ind]] x_test = x[ind[split_ind:]] y_train = y[ind[:split_ind]] y_test = y[ind[split_ind:]] model = DecisionTreeClassifier() model.fit(x_train, y_train) train_pred = model.predict(x_train) test_pred = model.predict(x_test) train_acc = np.sum(train_pred==y_train)/len(train_pred) print('train accuracy:', train_acc) test_acc = np.sum(test_pred==y_test)/len(test_pred) print('test accuracy:', test_acc) #Prun the tree, by getting rid of unnecessary comparisons(nodes), collapsing those #which children classify to the same number. (Done for decision trees) def post_prunning_decision_tree(root): if isinstance(root,int): return root #Do left subtree then right subtree root.left = post_prunning_decision_tree(root.left) root.right = post_prunning_decision_tree(root.right) #if classifications are same if root.left==root.right: #Give value to substitute the root for new_root = root.left temp = root root = None del(temp) return new_root else: return root post_prunning_decision_tree(model.root) model.display()

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/Fitory/FitApp/forms.py

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dvdgatik/FitoryWeb

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refs/heads/master

2023-02-05T20:21:14.420245

2020-12-31T11:45:59

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from django import forms from django.forms import ModelForm, Textarea,SelectDateWidget from image_cropping import ImageCropWidget,ImageRatioField from .models import Estado, Ciudad, Servicio, Actividad, Club, ServicioClub, ActividadClub, ActividadHorario, Sucursal, Foto, EvaluacionSucursal, Cliente, EvaluacionCliente, Favorito, Horario, RegistroHorario, Sesion, Subscripcion, Visita, PagoSucursal, Usuario class cropFotoClub(forms.ModelForm): class Meta: model = Club fields = {'foto','fotocrop'} widgets = { 'foto': ImageCropWidget,'fotocrop':ImageRatioField('foto','200x200'), } class cropLogoSucursal(forms.ModelForm): class Meta: model = Sucursal fields = {'logo','logocrop'} widgets = { 'logo': ImageCropWidget,'logocrop':ImageRatioField('logo','200x200'), } class cropFotoCliente(forms.ModelForm): class Meta: model = Cliente fields = {'foto','fotocrop'} widgets = { 'foto': ImageCropWidget,'fotocrop':ImageRatioField('foto','200x200'), }

[ "davgatik@gmail.com" ]

davgatik@gmail.com

a4750e44ec2f69260818178da844f2c7d29155d3

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/projects/migrations/0007_auto_20190210_1457.py

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[]

no_license

degerli/wagtail-portfolio-1

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refs/heads/master

2020-04-25T00:50:24.169432

2019-02-19T19:44:03

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# Generated by Django 2.1.5 on 2019-02-10 14:57 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('projects', '0006_auto_20190210_1454'), ] operations = [ migrations.AlterField( model_name='individualprojectspage', name='technology_used', field=models.CharField(blank=True, choices=[('WP', 'WordPress'), ('PS', 'Prestashop'), ('DJ', 'Django')], max_length=255), ), ]

[ "raf@webconexus.nl" ]

raf@webconexus.nl

079dd4266aad26fae72f8b1508ad6b63f9b96300

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/tic_tac_toe_oop.py

bb70ea5b7065b4838ae47309e0de323cab60fbc4

[]

no_license

jpiversen/tic_tac_toe

7cd0ba9c55713fb6f7dde157cc67a87a0dca57d9

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refs/heads/master

2022-12-16T18:03:39.417270

2020-09-16T07:13:45

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from os import system, name import random class board: def __init__(self): # Define board self.placeholder_text = " " self.board = [] self.reset_board() def reset_board(self): self.board = [self.placeholder_text for i in range(9)] def print_board(self): str = """ | {} | {} | {} | --- --- --- | {} | {} | {} | --- --- --- | {} | {} | {} | """.format(*self.board) print(str) def place_symbol(self, index, symbol, verbose = True): if self.board[index] == self.placeholder_text: self.board[index] = symbol return True else: if verbose: print("This space is taken. Please enter another number. ") return False class game: def __init__(self, p1, p2, board, visible = True): self.visible = visible self.game_over = False self.is_winner = False self.winner = None self.board = board self.p1 = p1 self.p2 = p2 self.current_player = self.p1 self.games_played = 0 # Welcome message if self.visible: print("Welcome to a game of Tic Tac Toe! Press 'q' to quit or 'h' for help.") self.board.print_board() def clear(self): # for windows if name == 'nt': _ = system('cls') # for mac and linux(here, os.name is 'posix') else: _ = system('clear') def print_help(self): help_txt = """ You are now playing a game of Tic Tac Toe. The 3x3 grid is numerated like the numpad at a phone: 1 is at the top left and 9 is at the bottom right, like this: \n | 1 | 2 | 3 | --- --- --- | 4 | 5 | 6 | --- --- --- | 7 | 8 | 9 | \n Choose a number to place your symbol. The first player to get three in a row - either horizontally, vertically or diagonally - wins. If the board is full and nobody won yet, then it's a tie. """ self.clear() print(help_txt) input("Press 'enter' to go back to the game.") self.clear() self.board.print_board() def check_winner(self): current_board = self.board.board row1 = (current_board[0] == current_board[1] == current_board[2]) and self.board.placeholder_text not in current_board[0:2] row2 = (current_board[3] == current_board[4] == current_board[5]) and self.board.placeholder_text not in current_board[3:5] row3 = (current_board[6] == current_board[7] == current_board[8]) and self.board.placeholder_text not in current_board[6:8] col1 = (current_board[0] == current_board[3] == current_board[6]) and self.board.placeholder_text not in current_board[0:9:3] col2 = (current_board[1] == current_board[4] == current_board[7]) and self.board.placeholder_text not in current_board[1:9:3] col3 = (current_board[2] == current_board[5] == current_board[8]) and self.board.placeholder_text not in current_board[2:9:3] diagonal1 = (current_board[0] == current_board[4] == current_board[8]) and current_board[4] != self.board.placeholder_text diagonal2 = (current_board[2] == current_board[4] == current_board[6]) and current_board[4] != self.board.placeholder_text # Check if there's a winner self.is_winner = row1 or row2 or row3 or col1 or col2 or col3 or diagonal1 or diagonal2 self.winner = self.current_player if self.is_winner: self.game_over = True if self.visible: print(self.current_player.symbol + " won! Congratulations!") print() elif self.board.placeholder_text not in current_board: self.game_over = True if self.visible: print("Board is full. It's a tie.") print() else: # Change player's turn if self.current_player == self.p1: self.current_player = self.p2 else: self.current_player = self.p1 if self.game_over: self.give_reward() def give_reward(self): if self.winner == self.p1: self.p1.get_reward(10) self.p2.get_reward(-10) elif self.winner == self.p2: self.p1.get_reward(-10) self.p2.get_reward(10) else: # Draw is better for p2, as p1 has a higher prob of winning self.p1.get_reward(0) self.p2.get_reward(2) def quit_game(self): print("Goodbye!") self.game_over = True quit() def play(self): while not self.game_over: if self.visible: print(self.current_player.symbol + "'s turn!") move_finished = False while not move_finished: choice = self.current_player.choose_move(board = self.board.board, free_pos_txt = self.board.placeholder_text) # Quit if input is "q" if choice == "q": self.quit_game() # Provide help if input is "h" elif choice == "h": self.print_help() continue # Check that input is valid elif choice not in [str(i) for i in range(1,10)]: print("'" + choice + "'" + " is invalid. Please enter a number between 1 and 9.") continue # Input is a valid move, continue with game logic else: # Set the choice to the same indexing as the board choice_board_index = int(choice) -1 move_finished = self.board.place_symbol(choice_board_index, self.current_player.symbol, verbose = self.current_player.type == "human") if self.visible: self.clear() self.board.print_board() self.check_winner() class human_player: def __init__(self, symbol = "x"): self.symbol = symbol self.type = "human" def choose_move(self, board = None, free_pos_txt = None): # board and free_pos_txt are inputs for the AI player, but is not used for the human choice = input("Enter your move (1-9): ").strip().lower() return choice def get_reward(self, reward): pass class ai_player: def __init__(self, symbol = "y", er = 0.1, lr = 0.8, discount_factor = 0.95): self.symbol = symbol self.type = "machine" self.explore_rate = er self.states = [] self.states_value = {} self.lr = lr # Learning rate self.discount_factor = discount_factor def get_possible_moves(self, board, free_pos_txt): return [i+1 for i, x in enumerate(board) if x == free_pos_txt] def choose_move(self, board, free_pos_txt): current_board = board.copy() possible_moves = self.get_possible_moves(current_board, free_pos_txt) if random.uniform(0, 1) < self.explore_rate: choice = random.choice(possible_moves) # random choice from possible_moves else: value_max = -999 for move in possible_moves: next_board = current_board.copy() next_board[move-1] = self.symbol hash_next_board = "".join(next_board) value = random.uniform(-5, 5) if self.states_value.get(hash_next_board) is None else self.states_value.get(hash_next_board) if value > value_max: value_max = value choice = move # Hash next board next_board = current_board.copy() next_board[choice -1] = self.symbol hash_next_board = "".join(next_board) self.states.append(hash_next_board) choice = str(choice) return choice def get_reward(self, reward): for state in reversed(self.states): if self.states_value.get(state) is None: self.states_value[state] = 0 #random.uniform(-5, 5) self.states_value[state] += self.lr * (self.discount_factor * reward - self.states_value[state]) reward = self.states_value[state] # Reset states self.states = [] # Start game! iterations = 200000 p1 = ai_player(symbol = "x", er = 1) p2 = ai_player(er = 1) print("Training the AI:") for i in range(iterations): if i % 100 == 0: print(str(i).rjust(len(str(iterations))) + "/" + str(iterations)) er = 1 - (i / iterations) if er > 0.9: er = 1 if er < 0.1: er = 0.1 p1.explore_rate = er p2.explore_rate = er tic_tac_toe = game(p1, p2, board(), visible = False) tic_tac_toe.play() tic_tac_toe.clear() # Play the game! play_again = True while play_again: tic_tac_toe = game(human_player(), p2, board()) tic_tac_toe.play() play_again = input("Play again [y/n]: ") == "y"

[ "jan.p.iversen@gmail.com" ]

jan.p.iversen@gmail.com

cbe3caf0e708d976b345b5027044c01b3de3bea0

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/Intro-Python-CS1/Sprint-Challenge--Intro-Python/src/comp/comp.py

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nvisagan/Intro-Python-CSS1

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refs/heads/master

2021-05-25T21:43:08.074857

2020-04-07T23:04:55

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# The following list comprehension exercises will make use of the # defined Human class. class Human: def __init__(self, name, age): self.name = name self.age = age def __repr__(self): return f"<Human: {self.name}, {self.age}>" humans = [ Human("Alice", 29), Human("Bob", 32), Human("Charlie", 37), Human("Daphne", 30), Human("Eve", 26), Human("Frank", 18), Human("Glenn", 42), Human("Harrison", 12), Human("Igon", 41), Human("David", 31), ] # Write a list comprehension that creates a list of names of everyone # whose name starts with 'D': print("Starts with D:") a = [x.name for x in humans if x.name[0]== "D"] print(a) # Write a list comprehension that creates a list of names of everyone # whose name ends in "e". print("Ends with e:") b = [x.name for x in humans if x.name[-1]== "e"] print(b) # Write a list comprehension that creates a list of names of everyone # whose name starts with any letter between 'C' and 'G' inclusive. print("Starts between C and G, inclusive:") c = [x.name for x in humans if x.name[0]>= "C" and x.name[0]<="G"] print(c) # Write a list comprehension that creates a list of all the ages plus 10. print("Ages plus 10:") d = [x.age+10 for x in humans] print(d) # Write a list comprehension that creates a list of strings which are the name # joined to the age with a hyphen, for example "David-31", for all humans. print("Name hyphen age:") e = [f"{x.name}-{x.age}" for x in humans] print(e) # Write a list comprehension that creates a list of tuples containing name and # age, for example ("David", 31), for everyone between the ages of 27 and 32, # inclusive. print("Names and ages between 27 and 32:") f = [(x.name,x.age) for x in humans if x.age >=27 and x.age<=32] print(f) # Write a list comprehension that creates a list of new Humans like the old # list, except with all the names uppercase and the ages with 5 added to them. # The "humans" list should be unmodified. print("All names uppercase:") g = [Human(x.name.upper(), x.age+5) for x in humans] print(g) # Write a list comprehension that contains the square root of all the ages. print("Square root of ages:") import math h = [math.sqrt(x.age) for x in humans] print(h)

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nvisagan.noreply@github.com

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/forum/migrations/0012_rename_usermodel_profile.py

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[]

no_license

apurbadh/Orion

e09f000c8df33b24fddd0434494a4f6af7ab0c7e

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refs/heads/main

2023-09-05T12:31:04.911527

2021-10-16T16:11:13

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# Generated by Django 3.2.6 on 2021-08-23 00:20 from django.conf import settings from django.db import migrations class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('forum', '0011_auto_20210823_0011'), ] operations = [ migrations.RenameModel( old_name='UserModel', new_name='Profile', ), ]

[ "erikduke182@gmail.com" ]

erikduke182@gmail.com

be9ce7135e19b5c6f9bee27fe3eea1c9f56a2fd0

7615f326b4109fe4bfc30b2aaff5784d69610f40

/blackj4ckPY.py

b4671e0fa830625b2dd26eafbd150cbbd233d099

[]

no_license

attila5287/blackjack_app

bc8022048edc6c247ab12a0c6808f54eed503af1

baa2a1f73703b20de278c401aa272a31b5c79cf8

refs/heads/master

2023-02-08T08:36:45.149201

2019-10-10T01:06:23

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null

2023-02-02T06:35:23

2019-07-12T09:00:15

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from Card_Deck_Hand import Card, Deck, Hand # -------------- 1-ON-1 BLACKJACK ------------------------ class blackjack_1on1(Deck): """ CLASS INH FROM DECK THAT DEALS CARDS AND REPORTS THE PROBLTY """ def __init__(self, player_name = 'Attila'): self.player_name = player_name self.cards = [Card(suit, rank) for suit in range(len(Card.suit_l1st)) for rank in range(1, len(Card.rank_l1st))] * 6 print('self-cards') print(len(self.cards)) self.shuffle() self.player = Hand(play3r_name = self.player_name) self.de4ler = Hand('de4ler') self.player_blackjack = 0 self.player_busted = 0 self.playerStands_dealerWins = 0 self.dealer_blackjack = 0 self.dealer_busted = 0 self.dealerStands_playerWins = 0 self.game_push = 0 print('\n...play blackjack 1-on-1...\n') def display_with_style(self, display_message=''): print('\n...' + display_message + '...\n')reload def deal_first_cards(self): self.display_with_style('first cards') self.move_cards(self.player,1) self.move_cards(self.de4ler,1) print('>>>--- ' + str(self.player_name) + ' --- >') self.show_player_total() print(' < --- de4ler --- <<<') self.show_dealer_total() def show_dealer_total_cardClosed(self): open_card_list = self.de4ler.to_l1st()[0].__split2rows__() closed_card_list = [ '┌───┐' , '│♦♦♦│' , '│♦♦│' , '│♦♦♦│' , '└───┘' ] for openCard_split2row, closedCard2row in zip(open_card_list, closed_card_list): print(openCard_split2row,closedCard2row) def show_player_total(self): print(self.player) player_total = 0 for number in self.player.blackjack_sum(): player_total += number print('player total is: ' + str(player_total)) def deal_second_cards(self): self.display_with_style ('second cards') self.move_cards(self.player,1) print('>>>--- ' + str(self.player_name) + ' --- >') self.show_player_total() print(' < --- de4ler --- <<<') self.show_dealer_total_cardClosed() self.move_cards(self.de4ler,1) self.show_dealer1st_total() def show_dealer1st_total(self): dealer_total = self.de4ler.blackjack_sum()[0] print('dealer first card only total is: ' + str(dealer_total)) def show_dealer_total(self): dealer_total = 0 for numb3r in self.de4ler.blackjack_sum(): dealer_total += numb3r print('dealer total is: ' + str(dealer_total)) print(self.de4ler) def player_hits(self): print('>>>--- ' + str(self.player_name) + ' --- >') self.display_with_style('player hits') self.move_cards(self.player,1) self.show_player_total() def dealer_hits(self): self.display_with_style('dealer hits') self.move_cards(self.de4ler, 1) self.show_dealer_total() def player_calc_total(self): current_total = 0 for number in self.player.blackjack_sum(): current_total += number return current_total def dealer_calc_total(self): dealer_total = 0 for number in self.de4ler.blackjack_sum(): dealer_total += number return dealer_total def the_play(self, upper_limit = 15): pass self.display_with_style('simulation upper limit is {}'.format(upper_limit)) if self.player_calc_total() == 21: self.player_blackjack += 1 self.sim_totals() elif self.player_calc_total() >= upper_limit: self.display_with_style('player stands') self.dealer_plays(17) while self.player_calc_total() <= upper_limit: self.player_hits() if self.player_calc_total()>= 22: self.display_with_style('player busted, dealer wins') self.player_busted += 1 self.sim_totals() elif self.player_calc_total() >= upper_limit: self.display_with_style('player stands') self.dealer_plays(17) def dealer_plays(self, upper_limit = 17): pass print(' < --- de4ler --- <<<') print('...second card up....') self.show_dealer_total() if self.dealer_calc_total() == 21: self.dealer_blackjack +=1 elif self.dealer_calc_total() > self.player_calc_total(): self.display_with_style('\t\t\tdealer wins') self.playerStands_dealerWins += 1 self.sim_totals() elif self.dealer_calc_total() == self.player_calc_total(): self.display_with_style('\t\t\tboth sides push') self.game_push += 1 self.sim_totals() while self.dealer_calc_total() <= self.player_calc_total() & self.dealer_calc_total() <= 20 : self.dealer_hits() if self.dealer_calc_total() >= 22: self.display_with_style('\t\t\tdealer busted, player wins') self.dealer_busted += 1 self.sim_totals() elif self.dealer_calc_total() >= self.player_calc_total(): if self.dealer_calc_total() <=21: self.display_with_style('\t\t\tdealer wins') self.playerStands_dealerWins += 1 self.sim_totals() def sim_results(self): print('player wins, blackjack: {}'.format(self.player_blackjack)) print('player busted: {}'.format(self.player_busted)) print('player stands, dealer wins: {}'.format(self.playerStands_dealerWins)) print('player stands-wins, dealer busted: {}'.format(self.dealer_busted)) print('player stands, dealer push: {}'.format(self.game_push)) print('num of cards remaining in deck: {}'.format(len(self.cards))) def sim_totals(self): pass print('\t\t\t\t\tplayer total is: {}'.format(self.player_calc_total())) print('\t\t\t\t\tdealer total is: {}'.format(self.dealer_calc_total())) def simulation_mode(self, number_of_rounds = 10): pass for n in range(number_of_rounds): self.deal_first_cards() self.deal_second_cards() self.the_play(13) self.player.cards = [] self.de4ler.cards = [] self.sim_results() # -------------------------------------- if __name__ == "__main__": pass test_game = blackjack_1on1() test_game.simulation_mode()

[ "atiturkoz@hotmail.com" ]

atiturkoz@hotmail.com

4f5015ddbd668fd1c8c83c6f8a9d803f097b4891

43c8d27c72571d0a45a9ed5126fcee3b89f309df

/recommender.py

4c9d68dda8b238b056a00ed67105e9e21fa19f13

[ "Apache-2.0" ]

permissive

weaponsjtu/RecommederSystem

592463a1e57eec5dcb80aca3de9905f08774dc0f

0d148117da5299f76acaae93ff096ac98ba106e6

refs/heads/master

2021-01-25T06:40:04.839769

2015-01-08T09:09:54

27,113,958

0

null

UTF-8

Python

false

6,506

py

import math import time class UserBased(): def __init__(self, matrix, user_list, item_list, sim_mat = None, related_users = None, user_item_mat = None): # train matirx self.matrix = matrix self.user_list = user_list self.item_list = item_list # find the TOP-3 highest similarity users for each item , and overall def refer_user(self, ten_offering): # TOP-3 highest similarity users print "Overall\t" for i in range( len(self.user_list) ): uid = self.user_list[i] u_sim = self.sim_mat[i] u_sim_dic = {} for u in range( len(u_sim) ): u_sim_dic[u] = u_sim[u] u_sim = sorted(u_sim_dic.items(), key = lambda d: -d[1])[:3] u_refer = [] for u in u_sim: u_refer.append( str(self.user_list[u[0]]) ) print "%s\t%s"%(str(uid), ','.join(u_refer)) # TOP-3 highest similarity users for each item for item_index in ten_offering: print str(item_index) + '\t' for i in range( len(user_list) ): uid = self.user_list[i] u_sim = self.sim_mat[i] u_sim_dic = {} for u in range( len(u_sim) ): if self.matrix[u][ item_index - 1 ] > 0: u_sim_dic[u] = u_sim[u] u_sim = sorted(u_sim_dic.items(), key = lambda d: -d[1])[:3] u_refer = [] for u in u_sim: u_refer.append( str(self.user_list[u[0]]) ) print "%s\t%s"%(str(uid), ','.join(u_refer)) def user_similarity(self): print "function user_similarity" start = time.time() rows = len(self.matrix) cols = len(self.matrix[0]) mat = [] for i in range(rows): u_sim = [0] * rows if sum( self.matrix[i] ) < 0.1: mat.append(u_sim) continue for j in range(i + 1, rows): user_a = self.matrix[i] user_b = self.matrix[j] if sum( user_a ) < 0.1 and sum( user_b ) < 0.1: continue sim = 0 for k in range(cols): #if item_index != (k + 1): # sim = sim + user_a[k] * user_b[k] sim = sim + user_a[k] * user_b[k] if sim > 0: sim = sim * 1.0 / ( math.sqrt( sum(user_a) ) * math.sqrt( sum(user_b) ) ) u_sim[j] = sim for j in range(0, i): u_sim[j] = mat[j][i] mat.append(u_sim) end = time.time() print str( end - start ) + ' seconds' self.sim_mat = mat def related_users(self, K): print "function related_users" start = time.time() related = {} rows = len(user_sim) for i in range( rows ): u_sim = {} for x in range(len(self.sim_mat[i])): u_sim[x] = self.sim_mat[i][x] u_sim = sorted(u_sim.items(), key = lambda d: -d[1])[:K] related[i] = u_sim end = time.time() print str( end - start ) + ' seconds' self.related_users = related def predict_user_based(): print "function predict_user_based" start = time.time() rows = len(self.matrix) cols = len(self.matrix[0]) # user item matrix, probability user_item_mat = [] for i in range(rows): u_item = [0] * cols for j in range(cols): # based on user-similarity u_sim = self.related_users[i] sum_u_sim = 0 for sim in u_sim: u_item[j] = u_item[j] + sim[1] * self.matrix[ sim[0] ][j] sum_u_sim = sum_u_sim + sim[1] if sum_u_sim > 0: u_item[j] = u_item[j] * 1.0 / sum_u_sim user_item_mat.append(u_item) #for each item, we get a user ranking list end = time.time() print str( end - start ) + ' seconds' self.user_item_mat = user_item_mat def ranking_users( user_item_mat, item_index ): print "hello" # TODO class ItemBased(): def __init__(self, matrix, user_list, item_list, item_sim = None, related_items = None, user_item_mat = None): self.matrix = matrix self.user_list = user_list self.item_list = item_list def item_similarity(self): print "function item_similarity" rows = len(self.matrix) cols = len(self.matrix[0]) mat = [] for i in range(cols): i_sim = [0] * cols for j in range(i + 1, cols): user_a = [] user_b = [] sim = 0 for k in range(rows): user_a.append(self.matrix[k][i]) user_b.append(self.matrix[k][j]) sim = sim + self.matrix[k][i] * self.matrix[k][j] if sim > 0: sim = sim * 1.0 / ( math.sqrt( sum(user_a) ) * math.sqrt( sum(user_b) ) ) i_sim[j] = sim for j in range(0, i): i_sim[j] = mat[j][i] mat.append(i_sim) self.sim_mat = mat def related_items(self, K): related = {} rows = len( self.item_sim ) for i in range( rows ): i_sim = {} for x in range(len(self.item_sim[j])): i_sim[x] = self.item_sim[j][x] i_sim = sorted(i_sim.items(), key = lambda d: -d[1])[:K] related[i] = i_sim self.related_items = related def predict_item_based(self): print "function predict_item_based" rows = len(self.matrix) cols = len(self.matrix[0]) # user item matrix, probability user_item_mat = [] for i in range(rows): i_item = [0] * cols for j in range(cols): i_sim = self.related_items[i] sum_i_sim = 0 for sim in i_sim: i_item[j] = i_item[j] + sim[1] * self.matrix[i][ sim[0] ] sum_i_sim += sim[1] if sum_i_sim > 0: i_item[j] = i_item[j] * 1.0 / sum_i_sim user_item_mat.append(i_item) #for each item, we get a user ranking list self.user_item_mat = user_item_mat

[ "javawebzwp@gmail.com" ]

javawebzwp@gmail.com

1cb86d8bcc4f103101dcac632edba87aa1a4f73f

35f91893173471ee30bf2438a60a865307b3b08d

/gdp/urls.py

106776407bebcd3893f12bd26e9a7eb9a45dd2bc

[]

no_license

csetarun/django-basic

cfb9bd3178db3832162c77dfd19ded57a9e48a8e

4ff5ae11149f758f70f6ac28937d5de4f912cca8

refs/heads/master

2020-03-23T10:00:30.558304

2018-07-18T10:43:34

141,420,620

0

null

UTF-8

Python

false

167

py

from .views import ListCountries from django.conf.urls import include, url from django.contrib import admin urlpatterns = [ url(r'^$', ListCountries.as_view()), ]

[ "noreply@github.com" ]

csetarun.noreply@github.com

0b39fa028df21f2382d22e21b76c38dae502a0a8

19b7c6149efe5ad3da76479da7131c2d66293194

/pcap包dns解析.py

ad3db6eea779ebedb4ea1a5658a573bafb47969a

[]

no_license

jitianze/yanzixu

7aab20183b394cd08397ae67015bd60249098cbc

df961cb6ee41af5e335974d49dae458a40e3f1f8

refs/heads/master

2021-01-12T06:50:58.711991

2017-09-30T01:06:33

76,844,333

0

null

UTF-8

Python

false

5,005

py

#!/usr/bin/python #-*-coding:utf-8-*- import struct import dpkt import sys import socket import binascii import os #mulu =raw_input("Please enter the directory you want to parse：") DNS_A = 1 # RR types DNS_IN = 1 # RR classes def addr2str(addrobj): if len(addrobj) != 4: return "addr error!" else: return str(ord(addrobj[0]))+"."+str(ord(addrobj[1]))+"."+str(ord(addrobj[2]))+"."+str(ord(addrobj[3])) def dns_response_body_parse(body): # parse the response message's body identification = body[0:2] flag = body[2:4] num_ques = body[4:6] num_ans_RR = body[6:8] num_auth_RR = body[8:10] num_addi_RR = body[10:12] query_name = '' ans_ip = [] flag = 12 while(ord(body[flag])!=0x0): query_name = query_name + body[flag+1:flag+ord(body[flag])+1] flag = flag + ord(body[flag]) + 1 try: if ord(body[flag]) != 0x0: query_name = query_name+'.' except Exception, e: print "error when parse query domain name" #print query_name flag = flag + 1 query_type = ord(body[flag])*256 + ord(body[flag+1]) if query_type == 0x01: # use domain query IP addr flag = flag + 4 i = 1 answer_num = ord(num_ans_RR[0])*256 + ord(num_ans_RR[1]) while(i<=answer_num): if ord(body[flag]) == 0xc0: flag = flag + 2 else: while(ord(body[flag])!=0x0): flag = flag + ord(body[flag]) + 1 flag = flag + 1 if ( ord(body[flag])*256+ord(body[flag+1]) == DNS_A and ord(body[flag+2])*256+ord(body[flag+3]) == DNS_IN): flag = flag + 8 RR_data_len = ord(body[flag])*256 + ord(body[flag+1]) if RR_data_len == 4: ans_ip.append(addr2str(body[flag+2:flag+6])) flag = flag + ord(body[flag])*256 + ord(body[flag+1]) + 2 else: flag = flag + 8 flag = flag + ord(body[flag])*256 + ord(body[flag+1]) + 2 i = i + 1 else: print "query type is PTR not A" print ("Finished, the result was in file dns-message.txt ") return return "%s\t%s"%(query_name,ans_ip) def jiexi(): def GetFileList(dir, fileList): newDir = dir if os.path.isfile(dir): fileList.append(dir.decode('gbk')) elif os.path.isdir(dir): for s in os.listdir(dir): newDir=os.path.join(dir,s) GetFileList(newDir, fileList) return fileList mulu =raw_input("Please enter the directory you want to parse:") wenjianlist = GetFileList(mulu, []) #list 现在是一个文件路径的列表 nr_list=[] for i in wenjianlist: a=file(i,"rb") nr_list.append(a) f_xieru = open("C:\Users\jitianze\Desktop\dns_message.txt","a") #fw_list = map(file("for i in list","rb"),list) for fw_list in nr_list: pcap = dpkt.pcap.Reader(fw_list) for ts,buf in pcap: ethheader = buf[0:14] dstmac = ethheader[0:6] srcmac = ethheader[6:12] netlayer_type = ethheader[12:14] pktheader = buf[14:34] trans_type = pktheader[9] srcip = pktheader[12:16] dstip = pktheader[16:20] if (ord(trans_type) == 0x11): #UDP udpheader = buf[34:42] srcport = udpheader[0:2] dstport = udpheader[2:4] udplen = udpheader[4:6] bodylen = ord(udplen[0])*256+ord(udplen[1])-8 dnsbody = buf[42:(42+bodylen)] if (ord(dstport[0]) == 0x00 and ord(dstport[1]) == 0x35): print "this stream has a DNS Request" elif (ord(srcport[0]) == 0x00 and ord(srcport[1]) == 0x35): print "this stream has a DNS Response" # try: # print "dnsbody:", dnsbody jibangb = dns_response_body_parse(dnsbody) print 'this is result:', jibangb if jibangb != None: f_xieru.write(jibangb + '\n') else: print "ibangb is none ..", jibangb # f_xieru.writelines(dns_response_body_parse(dnsbody)) # wirte result to file # except: # print u"文件读取有误，捕获异常：" else: print "this stream has not dns" elif (ord(trans_type) == 0x06): #TCP tcpheader = buf[34:54] srcport = tcpheader[0:2] dstport = tcpheader[2:4] print ("Finished, the result was in file dns-message.txt ") f_xieru.close() if __name__ == "__main__": jiexi() # 推荐几个学习网站 www.baidu.com # 你差我3k

[ "978941570@qq.com" ]

978941570@qq.com

112ee0674724c5a9a38297f73f438a2f4fdcf921

f9ef62886912d547e05a25492d1c826d7c5b7ce4

/main.py

38159c098f450b62bae3af5c8987475757688976

[]

no_license

DariaKutkanych/telebot1601

f7fa3f7f2aa533eca969691afc2731edc562a7c1

525ff3787190d10d6c0c07c967ee3a1efb9a6af1

refs/heads/master

2020-12-14T10:29:03.986242

2020-01-18T10:38:21

234,712,122

0

null

UTF-8

Python

false

1,526

py

from telebot import TeleBot import requests from telebot import types import re from flask import Flask, request import git app = Flask(__name__) @app.route('/update_server', methods=['POST']) def webhook(): if request.method == 'POST': repo = git.Repo('https://github.com/DariaKutkanych/telebot1601.git') origin = repo.remotes.origin origin.pull() return 'Updated PythonAnywhere successfully', 200 else: return 'Wrong event type', 400 bot = TeleBot("1007314497:AAHvjuAFIIN0GpUOevA3HturouG_tyS2hQ0") @bot.message_handler() def handle_massage(message): result = requests.get(f"http://api.urbandictionary.com/v0/" f"define?term={message.text}").json() total = "\n".join(n["definition"] for n in result["list"]) # bot.send_message(message.chat.id, f'{total}') words = re.findall(r"[^[]*\[([^]]*)\]", total) markup = types.ReplyKeyboardMarkup() listy = [] for a in words: listy.append(types.KeyboardButton(a)) for a in range(len(listy)): if a % 3 == 0: try: markup.row(listy[a], listy[a + 1], listy[a + 2]) except IndexError: try: markup.row(listy[a], listy[a + 1]) except IndexError: markup.row(listy[a]) continue bot.send_message(message.chat.id, f"{total} \n Choose one word:", reply_markup=markup) if __name__ == "__main__": bot.polling()

[ "d.goubenko@gmail.com" ]

d.goubenko@gmail.com

ef0e6cf6ca9250cefe2fa77139f106ff0ef48947

a1bffcd8854e1843e56bb812d4d83b3161a5211e

/tests/unit/modules/network/eric_eccli/test_eccli_command.py

69d1f7282b1984162e12aa895f75537d400e9f7d

[]

no_license

goneri/ansible.community

1a71f9d98c164b77f8ed2ed7f558b4963005ff8f

f26f612dd0a3154050d90b51a75502018c95f6e4

refs/heads/master

2020-12-29T07:47:35.353515

2020-01-22T17:43:18

null

0

null

UTF-8

Python

false

5,111

py

# (c) 2019 Ericsson. # # This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # Make coding more python3-ish from __future__ import (absolute_import, division, print_function) __metaclass__ = type import json from ansible_collections.ansible.community.tests.unit.compat.mock import patch from ansible_collections.ansible.community.plugins.modules import eric_eccli_command from ansible_collections.ansible.community.tests.unit.modules.utils import set_module_args from ..eccli_module import TestEccliModule, load_fixture class TestEccliCommandModule(TestEccliModule): module = eric_eccli_command def setUp(self): super(TestEccliCommandModule, self).setUp() self.mock_run_commands = patch('ansible_collections.ansible.community.plugins.modules.eric_eccli_command.run_commands') self.run_commands = self.mock_run_commands.start() def tearDown(self): super(TestEccliCommandModule, self).tearDown() self.mock_run_commands.stop() def load_fixtures(self, commands=None): def load_from_file(*args, **kwargs): module, commands = args output = list() for item in commands: try: obj = json.loads(item['command']) command = obj['command'] except ValueError: command = item['command'] filename = str(command).replace(' ', '_') output.append(load_fixture(filename)) return output self.run_commands.side_effect = load_from_file def test_eric_eccli_command_simple(self): set_module_args(dict(commands=['show version'])) result = self.execute_module() self.assertEqual(len(result['stdout']), 1) self.assertTrue(result['stdout'][0].startswith('Ericsson IPOS Version')) def test_eric_eccli_command_multiple(self): set_module_args(dict(commands=['show version', 'show version'])) result = self.execute_module() self.assertEqual(len(result['stdout']), 2) self.assertTrue(result['stdout'][0].startswith('Ericsson IPOS Version')) def test_eric_eccli_command_wait_for(self): wait_for = 'result[0] contains "Ericsson IPOS"' set_module_args(dict(commands=['show version'], wait_for=wait_for)) self.execute_module() def test_eric_eccli_command_wait_for_fails(self): wait_for = 'result[0] contains "test string"' set_module_args(dict(commands=['show version'], wait_for=wait_for)) self.execute_module(failed=True) self.assertEqual(self.run_commands.call_count, 10) def test_eric_eccli_command_retries(self): wait_for = 'result[0] contains "test string"' set_module_args(dict(commands=['show version'], wait_for=wait_for, retries=2)) self.execute_module(failed=True) self.assertEqual(self.run_commands.call_count, 2) def test_eric_eccli_command_match_any(self): wait_for = ['result[0] contains "Ericsson IPOS"', 'result[0] contains "test string"'] set_module_args(dict(commands=['show version'], wait_for=wait_for, match='any')) self.execute_module() def test_eric_eccli_command_match_all(self): wait_for = ['result[0] contains "Ericsson IPOS"', 'result[0] contains "Version IPOS"'] set_module_args(dict(commands=['show version'], wait_for=wait_for, match='all')) self.execute_module() def test_eric_eccli_command_match_all_failure(self): wait_for = ['result[0] contains "Ericsson IPOS"', 'result[0] contains "test string"'] commands = ['show version', 'show version'] set_module_args(dict(commands=commands, wait_for=wait_for, match='all')) self.execute_module(failed=True) def test_eric_eccli_command_configure_check_warning(self): commands = ['configure terminal'] set_module_args({ 'commands': commands, '_ansible_check_mode': True, }) result = self.execute_module() self.assertEqual( result['warnings'], ['only non-config commands are supported when using check mode, not executing configure terminal'], ) def test_eric_eccli_command_configure_not_warning(self): commands = ['configure terminal'] set_module_args(dict(commands=commands)) result = self.execute_module() self.assertEqual(result['warnings'], [])

[ "ansible_migration@example.com" ]

ansible_migration@example.com

f9b94fd16549e4335a99f5b29e47f86f3db2b938

fba385a1beed395472bd05d99b8e994c3b88b450

/Get_Save_Airfoils_Selig.py

fe7ebfa67e5ebd2559dbd3a1847ea7f74b747ebe

[ "MIT" ]

permissive

irfan-gh/Panel_Methods

d9730871de57b4cc57ab5eac07683916e54a1900

a7d05d865e90bff36af06451b911bd4fa50df049

refs/heads/master

2023-08-08T21:56:17.675805

2020-09-27T15:05:50

null

0

null

UTF-8

Python

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1,662

py

# GET AIRFOIL .DAT FILES FROM UIUC AIRFOIL SITE # Written by: JoshTheEngineer # Permission: Dr. Michael Selig (01/16/19) # Started : 01/14/19 # Updated : 01/14/19 - Works as expected # # UIUC Airfoil Database: https://m-selig.ae.illinois.edu/ads/coord_database.html # Importing from bs4 import BeautifulSoup # Import the BeautifulSoup library import re # Import regular expressions try: # Import urllib import urllib.request as urllib2 except ImportError: import urllib2 # Base filepath for the UIUC airfoil website (used for accessing .dat files) baseFlpth = "https://m-selig.ae.illinois.edu/ads/coord_seligFmt/" # Base filepath for saving # Open the webpage and create the soup html_page = urllib2.urlopen(baseFlpth) # Open the URL soup = BeautifulSoup(html_page,'lxml') # Create the soup # Loop over all relevant files and save each one ind = 1 # Iteration counter #links = [] # Initialize list of links for appending for link in soup.find_all('a',attrs={'href': re.compile('\.dat', re.IGNORECASE)}): # Loop over all appropriate links on webpage #links.append(link.get('href')) # Append the link to the list urllib2.urlretrieve(baseFlpth+link.get('href'), link.get('href').rsplit('/',1)[-1]) # Get the data from the webpage, and save it to the save data file as the link name print("Saving file %i" %ind) # Indicate the link that we are currently saving ind = ind + 1 # Increment the counter

[ "noreply@github.com" ]

irfan-gh.noreply@github.com

ecdffd9ed404030f3f8c1e61cc3ed95a75635d09

9e988c0dfbea15cd23a3de860cb0c88c3dcdbd97

/sdBs/AllRun/kuv_08497+3856/sdB_KUV_08497+3856_coadd.py

2a1ac0d64e1ee8a22903d7ed6955d68bab2928e7

[]

no_license

tboudreaux/SummerSTScICode

73b2e5839b10c0bf733808f4316d34be91c5a3bd

4dd1ffbb09e0a599257d21872f9d62b5420028b0

refs/heads/master

2021-01-20T18:07:44.723496

2016-08-08T16:49:53

65,221,159

0

null

UTF-8

Python

false

441

py

from gPhoton.gMap import gMap def main(): gMap(band="NUV", skypos=[133.22575,38.747683], skyrange=[0.0333333333333,0.0333333333333], stepsz = 30., cntfile="/data2/fleming/GPHOTON_OUTPUT/LIGHTCURVES/sdBs/sdB_KUV_08497+3856/sdB_KUV_08497+3856_movie_count.fits", cntcoaddfile="/data2/fleming/GPHOTON_OUTPUT/LIGHTCURVES/sdB/sdB_KUV_08497+3856/sdB_KUV_08497+3856_count_coadd.fits", overwrite=True, verbose=3) if __name__ == "__main__": main()

[ "thomas@boudreauxmail.com" ]

thomas@boudreauxmail.com

ab810a2b2cc6921f5853a2df75b92560f5dd8270

cc241a46fc269fe69294575b9ce9cd48d3485955

/core/validator.py

10d526bfbee5accc8a69a149140fda8a701fc75b

[]

no_license

xiami303/xiami

322db504e1b7ba08023ee7dbfe70bb830b0a7f77

c56ae851f6d5555a9f61b142ca5f12a267ff9303

refs/heads/master

2021-03-12T23:34:11.153647

2015-03-12T13:15:51

20,083,445

0

null

UTF-8

Python

false

29

py

#this is a validator of html

[ "wuhongxia303@163.com" ]

wuhongxia303@163.com

24fe36866435adf25568f086ec78f306946c56a1

e34392bca8ad5d12a32fe9dc58ddcdf430283da4

/sequential.py

a08d589e80e4ecc4a75ab94891902365c96c7051

[]

no_license

sharnam19/Document-Classification-Using-RNN

cafda6cde3b2bc897a854e00e53dd074d0c24d92

4092363b5c827788dd77d05cea03d8b7fb7f1f1d

refs/heads/master

2021-05-07T16:38:03.125478

2020-09-12T09:54:21

108,593,763

1

0

null

2020-09-12T10:34:23

2017-10-27T20:42:28

Python

UTF-8

Python

false

1,578

py

import numpy as np def rnn_step(x, h_prev, Wx, Wh, b): """ Input: x : Input Sequence of shape (N,D) h_prev : Previous Hidden State (N,H) Wx : Input to Hidden Weight of shape (D,H) Wh : Hidden to Hidden Weight of shape (H,H) b : Bias of shape (H,) Output: h_next : Hidden State at Next Time Step of shape (N,H) cache : Cached Values for Backprop """ h_next = np.tanh(x.dot(Wx)+h_prev.dot(Wh) + b[np.newaxis,:]) cache = (x,h_prev,Wx,Wh,h_next) return h_next,cache def rnn_step_backward(dOut,cache): """ Input dOut: Upstream Gradients wrt h (N,H) cache : Cached Values useful for backprop Output: dx: Gradients wrt x dh_prev : Gradients wrt h_prev dWx : Gradients wrt Wx dWh : Gradients wrt Wh db : Gradients wrt b """ x,h_prev,Wx,Wh,h_next = cache dSq = (1-np.square(h_next))*dOut dx = dSq.dot(Wx.T) dWx = x.T.dot(dSq) dh_prev = dSq.dot(Wh.T) dWh = h_prev.T.dot(dSq) db = np.sum(dSq,axis=0) return dx,dh_prev,dWx,dWh,db def word_embedding_forward(x, W): out, cache = None, None N = x.shape[0] V,D = W.shape out = W[x] cache = (x,V) return out, cache def word_embedding_backward(dout, cache): dW = None x,V = cache N = x.shape[0] D = dout.shape[1] dW = np.zeros((V, D)) np.add.at(dW, x.reshape(N), dout.reshape(N, D)) return dW

[ "sharnam19.nc@gmail.com" ]

sharnam19.nc@gmail.com

4d0157e39ea590c852ee5ca71d560e90eebf2017

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/python_basic/PycharmProjects/learnpython/functions.py

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[]

no_license

NumanIbnMazid/Python

5db305554783f0b9bd1a741911b0dddabb9c96f5

dea5e36853e6c833df49f0d5abc69b33e366151d

refs/heads/master

2020-06-16T19:34:07.417135

2019-07-07T17:40:59

195,680,231

0

null

UTF-8

Python

false

218

py

#must be properly indented. Indention Rules def say_hi(name, age): print("Hellow " + name, ", you are " + str(age)) print("Top", "35") #calling function say_hi("Numan", 22) say_hi("Mike", 33) print("Bottom", 36)

[ "numanibnmazid@gmail.com" ]

numanibnmazid@gmail.com

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/modules/music_player.py

22fd1021c2228cfa16adffb8bc31a4a7f4f2a10b

[]

no_license

carrtesy/embedded_system_project

7ad7bb92b2f66b40ca39127c2e9f917b5ad3a962

baedcdb2594d37fd81a2ca32f5103caff0b44a73

refs/heads/master

2023-01-31T00:38:18.854120

2020-12-10T08:00:48

null

0

null

UTF-8

Python

false

923

py

import subprocess import signal import threading import time, os class MusicPlayer: def __init__(self): subprocess.call('pwd', shell=True) subprocess.call('ls ./assets', shell=True) self.process = None def play(self, song): if self.process is not None: os.killpg(os.getpgid(self.process.pid), signal.SIGTERM) song_loc = "./assets/" + song self.process = subprocess.Popen( args = ["omxplayer", "--vol", "-3000", song_loc], preexec_fn = os.setsid, ) def stop(self): print("killing proc {} pid {} os {}".format(self.process, self.process.pid, os.getpgid(self.process.pid))) os.killpg(os.getpgid(self.process.pid), signal.SIGTERM) self.process = None if __name__ == "__main__": player = MusicPlayer() player.play("calm1.mp3") time.sleep(5) player.stop()

[ "dongminkim0220@gmail.com" ]

dongminkim0220@gmail.com

b817f5069575a404f5eb5ca145bbed754f5c8792

40799b8e2cec6a481a2923183c79e35c7f654676

/int_test.py

bde76eb3dd27975c152b2e5b15361bc0a18416ab

[]

no_license

chengxinlun/se-finder

af5c145e6037a9687717a759d5463caae155b1a4

ffcd4f0450cbd7f5508271d0d001f892aab5c396

refs/heads/master

2021-01-10T13:37:18.213570

2016-02-22T12:53:24

47,450,241

0

null

UTF-8

Python

false

308

py

import numpy as np from fe_temp_int import FeII_template_int from scipy.integrate import quad import matplotlib.pyplot as plt int_func_class = FeII_template_int(0.0, 2000.0, 1.0, 0.0, 2000.0, 1.0) x = np.linspace(4000.0, 5500.0, 100000) plt.plot(x, list(map(int_func_class.func, x))) plt.show() plt.close()

[ "chengxinlun@gmail.com" ]

chengxinlun@gmail.com

3a1b5cd260232af67ce4f66ab9915fe16b5c4d6e

a46b981266a27217e8bb2f9345a8f13c60062986

/ex14.py

eb0c8d973e93668012eaff9f996b02ee96bb3b8e

[]

no_license

huxx-j/python-ex

788d674ff2d0e8494a222de77df187de10dd2b04

7f9f7fd30c8a0878cb9b8ca6ac3844529d34718a

refs/heads/master

2020-03-18T18:26:54.866320

2018-05-28T00:58:41

135,093,410

0

null

UTF-8

Python

false

133

py

s = '/usr/local/bin/python.exe' s1 = s[1:].split("/",4) s2 = ",".join(s1) print(s2) s3 = s.rsplit("/",1) s4 = ",".join(s3) print(s4)

[ "perfekta213@gmail.com" ]

perfekta213@gmail.com

513b637f309d707ae9864a831908331cb3bf9d69

d0db054bd2c437b741af9b8dc9fb05176a00912d

/manage.py

87402b0f5cbf25485cbddb6ce75f2e770be0583e

[]

no_license

daubers/HackDoorMenu

72ae16220c53f2fad9f25dccc8b115d888380eb5

6bf8732a082470b456bb29e93b1b447bed7509d5

refs/heads/master

2020-06-05T09:18:45.643756

2014-06-10T20:28:13

20,156,300

1

0

null

2014-06-10T17:43:36

2014-05-25T14:21:58

Python

UTF-8

Python

false

255

py

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "HackDoorMenu.settings") from django.core.management import execute_from_command_line execute_from_command_line(sys.argv)

[ "matt@daubers.co.uk" ]

matt@daubers.co.uk

be1b47f8e5db8863598580cf9d003925704e3d68

e9fd34f8d78ad69c3586557fd3d0ff2d66b23f02

/finishRoboFogExport.py

b09fc8689a75ba067ae33abe7b8dc5e2c26d6565

[]

no_license

LettError/robofogExport

878021681d7df090138563086318ca186bba2b26

1406dfbfb0aee0d3716da3d1fcdede849c585ff7

refs/heads/master

2021-01-01T03:44:46.116479

2016-04-17T17:05:21

56,446,460

3

1

null

UTF-8

Python

false

664

py

# finish the export from RoboFog. """ put the background glyphs in the right place """ def cleanupBackground(f): moved = [] for g in f: if g.name[0] == "#": # do we have a foreground companion? other = g.name.replace("#", "") if other not in f: print "background glyph %s has no foreground"%g.name continue bg = f[other].getLayer("background") bg.clear() moved.append(g.name) print bg bg.appendGlyph(g) for name in moved: f.removeGlyph(name) f = CurrentFont() cleanupBackground(f) print 'done'

[ "erik@letterror.com" ]

erik@letterror.com

3001443877633db13aaffaceac1c2e45cbcdaa2c

89c0ffc249cb9df44040841f5b639c4487aa0a74

/web/talks/management/commands/update_all_talks.py

4b03dfab115262bc02db6bcbe216ddd21fdbfdf0

[ "Apache-2.0" ]

permissive

vtalks/vtalks.net

161bb0b46199a4b7e81997560198500c940a0be5

80fb19ff9684e0854c6abe5f0eef73e80ec326a6

refs/heads/master

2018-09-01T13:18:08.236948

2018-07-25T02:34:10

112,273,942

2

0

Apache-2.0

2018-07-19T00:51:10

2017-11-28T02:18:29

Python

UTF-8

Python

false

785

py

from datetime import date from datetime import datetime from datetime import timedelta from django.utils import timezone from django.core import management from django.core.management.base import BaseCommand from talks.models import Talk class Command(BaseCommand): help = 'Update all talks on the database.' def handle(self, *args, **options): today = date.today() yesterday = today-timedelta(days=1) datetime_updated_at = datetime.strptime(str(yesterday), "%Y-%m-%d") datetime_updated_at = datetime_updated_at.replace(tzinfo=timezone.utc) talks = Talk.published_objects.filter(updated__lte=datetime_updated_at).order_by('-updated') for talk in talks: management.call_command("update_talk", talk.youtube_url)

[ "repejota@gmail.com" ]

repejota@gmail.com

193330416b0d6f460cc33d93bbe928afd007ae08

d2509e6d77789993530830e12b15d709831f6c06

/array1.py

796113e1ca7f141621ed98718b406133236bdfb0

[]

no_license

jawnhoang/LessonsWithDaHomies

08725537ff03600aabda9cee230d9b8eb0e88cd5

25ea7eaeb3e12ccb4bda56af0f57e5e516fd6e10

refs/heads/main

2023-06-16T12:34:53.181614

2021-07-13T19:40:12

383,579,430

0

null

UTF-8

Python

false

773

py

# # fill an array with 100 integers, of multiples of 3s # #declare array # exArr =[] # for i in range(100): #this should go from 0-99 # if i % 3 == 0: # exArr.append(i) # # print(exArr) # # #remove all elements that end with a 5 # for i in exArr: # if i % 5 == 0: # exArr.remove(i) # # print(exArr) # remove elements x = [] #fill an array for i in range(10): x.append(i) print(x) #remove first element inside list x.remove(0) print(x) #remove element using it's index del x[0] print(x) #remove last element del x[-1] print(x) #remove element in the middle with known index del x[3] #or del x[-4] <- starts from right to left print(x) for i in x: if i == 6: x.remove(i) print(x)

[ "noreply@github.com" ]

jawnhoang.noreply@github.com

f3b39a7c3cc0ea7050dcfead11919b8c2437c76b

2499d071c0c354d8d7ec800244ac964824756e4e

/ipcamera5.py

c317dc37883c6bb1b61f18185de310981cd19cec

[]

no_license

harshvardhan33/ImageProcessing

959d88aca920f817f814aa493e1e436d01af29e5

6d137d9873731cf8f03d0d758308c145a15ff7d7

refs/heads/master

2023-06-10T22:36:30.324033

2021-06-26T06:34:58

260,647,348

0

null

UTF-8

Python

false

899

py

# -*- coding: utf-8 -*- """ Created on Thu Nov 19 12:37:22 2020 @author: harshvardhan """ import cv2 video = cv2.VideoCapture(0) address = "http://192.168.29.112:8080/video" video.open(address) if (video.isOpened() == False): print("Error reading video file") frame_width = int(video.get(3)) frame_height = int(video.get(4)) size = (frame_width, frame_height) result = cv2.VideoWriter('ycrcb.avi',cv2.VideoWriter_fourcc(*'MJPG'), 10, size) while(True): ret, frame = video.read() frame = cv2.cvtColor(frame, cv2.COLOR_BGR2YCrCb) if ret == True: result.write(frame) cv2.imshow('Frame', frame) if cv2.waitKey(1) & 0xFF == ord('s'): break else: break video.release() result.release() # Closes all the frames cv2.destroyAllWindows() print("The video was successfully saved")

[ "noreply@github.com" ]

harshvardhan33.noreply@github.com

d7604731d4f62ad742ee171ac900078bcbe1aeb9

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/participantes/urls.py

32cd775bee002325151e6b86c642822389c979a8

[]

no_license

pachecomuoz-pedro/Hackaton_2019

c77b721f537035047fbbb2dbae76a08eeadd76ca

0dd29167a9b390cf577caf7aad65023834a5f07e

refs/heads/master

2020-04-09T21:37:30.372003

2018-12-06T02:50:01

160,607,935

0

null

UTF-8

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false

679

py

from django.urls import path from participantes.views import entrar,lista_participantes,registro_participante,editar_participante,eliminar_participante,mostrar_mapa,carga_municipio urlpatterns = [ path('', entrar, name='participantes'), path('lista', lista_participantes, name='lista_participantes'), path('mapa', mostrar_mapa, name='mostrar_mapa'), path('registrar', registro_participante, name='registro_participante'), path('editar/<int:id>', editar_participante, name='editar_participante'), path('eliminar/<int:id>', eliminar_participante, name='eliminar_participante'), path('carga_mun', carga_municipio, name='carga_municipio'), ]

[ "pachecomuoz_pedro@yahoo.com" ]

pachecomuoz_pedro@yahoo.com

3d842aa4e62c82f865264b01e0f6d25b58ab2546

d4cdc6c9e2580b2011d63f6d62f70ab9e13cd317

/sld-dashboard/app/helpers/converter.py

a2bdb7cace1f5fe277b4e417f8993dae10948627

[ "MIT" ]

permissive

timezombi/Stack-Lifecycle-Deployment

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d84241099fb44762476b4201a2fc195e76975e26

refs/heads/master

2023-07-13T11:11:35.001371

2021-08-20T13:35:14

null

0

null

UTF-8

Python

false

444

py

import ast def convert_to_dict(data): result = [] for key, val in data.items(): try: check = ast.literal_eval(val) except: continue if isinstance(check, dict): data[key] = check result.append(data) elif isinstance(check, list): data[key] = check result.append(data) if not len(result): return data return result[0]

[ "{ID}+{username}@users.noreply.github.com" ]

{ID}+{username}@users.noreply.github.com

21a8b679743967c19a4d6d958380256532a3929d

fa8036fd416aecab3f1ca617acf0989f032f02ce

/abc162/C.py

568b921d6090c303822258c9f02c9ac29a107550

[]

no_license

MitsuruFujiwara/Atcoder

e2e2e82014e33e3422ea40eca812c6fdd8bcaaaa

bc73c4cd35a80c106d0e9b14cee34a064d89d343

refs/heads/master

2022-12-14T23:50:56.843336

2020-09-17T22:25:57

252,980,448

0

null

UTF-8

Python

false

177

py

from math import gcd K = int(input()) ans = 0 for a in range(1,K+1): for b in range(1,K+1): for c in range(1,K+1): ans += gcd(gcd(a,b),c) print(ans)

[ "fujiwara52jp@gmail.com" ]

fujiwara52jp@gmail.com

3856aa50b182d6597a5a7fb4aeed9d94f6ab3a0f

a6add24cdf8a3428d69d647feae92175cf66b40c

/test/test_puts_demo.py

aaca9f2f37e99e3b813fd0c07963e932cdc2415c

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

permissive

jriehl/llvmpy

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d2364404c7d8b6d376b4185ddd982d0d9748038d

refs/heads/master

2021-01-18T17:59:12.119304

2012-11-15T17:53:24

null

0

null

UTF-8

Python

false

2,037

py

#! /usr/bin/env python # ______________________________________________________________________ '''The goal of this test is to build and demonstrate the following LLVM assembly code using the API: @msg = internal constant [15 x i8] c"Hello, world.\\0A\\00" declare i32 @puts(i8 *) define i32 @not_really_main() { %cst = getelementptr [15 x i8]* @msg, i32 0, i32 0 call i32 @puts(i8 * %cst) ret i32 0 } ''' import llvm.core as lc import llvm.ee as le # ______________________________________________________________________ def main (*args, **kws): m = lc.Module.new(b'demo_module') i8 = lc.Type.int(8) i32 = lc.Type.int(32) i8ptr = lc.Type.pointer(i8) puts_ty = lc.Type.function(i32, [i8ptr]) puts_decl = m.add_function(puts_ty, b'puts') hello_fn_ty = lc.Type.function(i32, []) hello_fn = m.add_function(hello_fn_ty, b'hello_fn') bb = hello_fn.append_basic_block(b'entry') builder = lc.Builder.new(bb) # Was having a devil of time using stringz(), since it returns a # value of type [15 x i8], as opposed to [15 x i8]*. The weird # part is that global variables seem to become pointer types when # used inside functions. # See: http://comments.gmane.org/gmane.comp.compilers.llvm.devel/28601 hello_str = lc.Constant.stringz(b'Hello, world.\n') hello_var = m.add_global_variable(hello_str.type, b'msg') # Required a patch to get this to work. # XXX Need to extend patch to other constant constructors in core.py. hello_var._set_initializer(hello_str) zero = lc.Constant.int(i32, 0) cst = builder.gep(hello_var, [zero, zero], b'cst') builder.call(puts_decl, [cst]) builder.ret(zero) print(str(m.__str__())) ee = le.ExecutionEngine.new(m) ee.run_function(hello_fn, []) # ______________________________________________________________________ if __name__ == "__main__": import sys main(*sys.argv[1:]) # ______________________________________________________________________ # End of test_puts_demo.py

[ "jriehl@spaceship.com" ]

jriehl@spaceship.com

4632a80711b229a9ae8b9947f18494f68b594ed6

892785b084dde0a996e3d31246e1336335e41815

/deployment/pkgs/bin/sample

1d7489dc5a96c33c2339b0ad17ec2536791dd811

[]

no_license

RyanBacastow/splitter

5598090be1065687f54ee8a4d80836e533612e11

013869a40286f99facb09461fa5c8d3449c5c8b6

refs/heads/master

2022-12-11T08:43:02.046776

2020-08-31T14:40:25

289,096,974

0

null

UTF-8

Python

false

212

#!/var/lang/bin/python3.7 # -*- coding: utf-8 -*- import re import sys from sample import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "ryan.bacastow@tmsw.com" ]

ryan.bacastow@tmsw.com

d4803c4b5e71f53cb79dc8d47e3a297374733c42

5cfe09ff0079e8e39fc3bcfb7a4a7ba0803150de

/Python/grafica.py

a4b1acd8fbb5c04d6a81a25cea9033ecaeab55d6

[]

no_license

MauricioD13/Proyecto1_Codigos

bf89b27cab3c91de3271382c896c211c825ec7d8

378076bcf9bdc36348cee9faa683be00048547ba

refs/heads/master

2023-01-19T10:07:06.039576

2020-11-24T16:22:31

298,728,858

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py

import matplotlib.pyplot as plt import numpy as np import sys import math import statistics import mplcursors def guardar(archivo,time,voltage0,voltage1,voltage2): labels=[] labels=archivo.readline().split("\t") #labels[1]=labels[1].replace("\n","") i=0 for line in archivo: linea=line.split("\t") temp=linea[0].split(",") time.append(float(temp[0]+"."+temp[1])) temp=linea[1].split(",") voltage0.append(float(temp[0]+"."+temp[1])) temp=linea[3].split(",") voltage1.append(float(temp[0]+"."+temp[1])) temp=linea[5].split(",") voltage2.append(float(temp[0]+"."+temp[1])) i+=1 if(i>=20000): break return labels option=input("Opcion?: ") if(option=="ganancia"): archivo0=open("./datos/Ganancia_1mV_80Hz.txt","r"); #~/Documents/Python/Proyecto/ #parameter=sys.argv[1].split(".") #file_write=open(parameter[0]+"_fixed.txt","w") time0=[] voltage0=[] time1=[] voltage1=[] time2=[] voltage2=[] labels0=guardar(archivo0,time0,voltage0,voltage1,voltage2) archivo0.close() time_array0=np.array(time0[1000:5000]) voltage_array0=np.array(voltage0[1000:5000]) time_array1=np.array(time1[1000:5000]) voltage_array1=np.array(voltage1[1000:5000]) time_array2=np.array(time2[1000:5000]) voltage_array2=np.array(voltage2[1000:5000]) fig, ax=plt.subplots(3,1) fig.subplots_adjust(hspace=0.5) print("MUESTRAS:",len(time0)) ax[0].plot(time_array0,voltage_array0,label="Primera Etapa: Entrada diferencial") # Plot some data on the axes. ax[0].grid(True) ax[0].set_ylabel("Voltage") ax[0].set_xlabel("Tiempo") ax[0].set(ylim=(1,3)) ax[0].set_title("Primera Etapa") ax[1].plot(time_array0,voltage_array1,'g',label="Segunda Etapa: Filtro") # Plot some data on the axes. ax[1].grid(True) ax[1].set_ylabel("Voltage") ax[1].set_xlabel("Tiempo") ax[1].set(ylim=(1,3)) ax[1].set_title("Segunda Etapa") ax[2].plot(time_array0,voltage_array2,'r',label="Tercera Etapa: Filtro") ax[2].grid(True) ax[2].set_ylabel("Voltage") ax[2].set_xlabel("Tiempo") ax[2].set(ylim=(1,3)) ax[2].set_title("Tercera Etapa") mplcursors.cursor(multiple=True).connect( "add", lambda sel: sel.annotation.draggable(False)) plt.show() elif(option=="manejo"): archivo0=open("./datos/Manejo_80Hz_30mV.txt","r"); #~/Documents/Python/Proyecto/ #parameter=sys.argv[1].split(".") #file_write=open(parameter[0]+"_fixed.txt","w") time0=[] voltage0=[] time1=[] voltage1=[] time2=[] voltage2=[] labels0=guardar(archivo0,time0,voltage0,voltage1,voltage2) archivo0.close() time_array0=np.array(time0[0:500]) voltage_array0=np.array(voltage0[0:500]) voltage_array1=np.array(voltage1[0:500]) voltage_array2=np.array(voltage2[0:500]) fig, ax=plt.subplots(1,3) print("MUESTRAS:",len(time0)) ax[0].plot(time_array0,voltage_array0,'r',label="Primera Etapa: Entrada diferencial") # Plot some data on the axes. ax[0].set_ylabel("Voltage") ax[0].set_xlabel("Tiempo") ax[0].set_title("Primera Etapa") ax[0].grid(True) ax[0].set(ylim=(0,5.5)) ax[1].plot(time_array0,voltage_array1,'g',label="Segunda Etapa: Filtro") # Plot some data on the axes. ax[1].set_title("Segunda Etapa") ax[1].grid(True) ax[1].set(ylim=(0,5.5)) ax[1].set_ylabel("Voltage") ax[1].set_xlabel("Tiempo") ax[2].set_title("Tercera Etapa") ax[2].plot(time_array0,voltage_array2,label="Tercera Etapa: Filtro") ax[2].grid(True) ax[2].set(ylim=(0,5.5)) ax[2].set_ylabel("Voltage") ax[2].set_xlabel("Tiempo") #plt.grid(True) #ax[1].set_ylabel(labels1[1]) #ax[1].set_xlabel(labels1[0]) mplcursors.cursor() plt.show() elif(option=="comun"): archivo0=open("./datos/Ganancia_comun_1mV_80Hz.txt","r"); time_common=[] voltage_common=[] labels=[] labels=archivo0.readline().split("\t") for line in archivo0: linea=line.split("\t") temp=linea[0].split(",") time_common.append(float(temp[0]+"."+temp[1])) temp=linea[1].split(",") voltage_common.append(float(temp[0]+"."+temp[1])) archivo0.close() time_array0=np.array(time_common[0:5000]) voltage_array0=np.array(voltage_common[0:5000]) average=0 for i in time_common: average=i+average average=average/len(time_common) fig, ax=plt.subplots() common_gain=(average/0.001)-200 print(f"MUESTRAS:{len(time_common)}, Promedio: {average}: Ganancia comun: {common_gain} CMRR:{20*math.log10(1000/common_gain)}") ax.plot(time_array0,voltage_array0,label="Entrada modo comun") # Plot some data on the axes. plt.grid(True) ax.set_ylabel("Voltage") ax.set_xlabel("Tiempo") plt.legend(loc='upper left') plt.show() elif(option=="ruido"): archivo0=open("./datos/Distorsión_1m_80Hz.txt","r"); time_common=[] voltage_common=[] labels=[] labels=archivo0.readline().split("\t") for line in archivo0: linea=line.split("\t") temp=linea[0].split(",") time_common.append(float(temp[0]+"."+temp[1])) temp=linea[1].split(",") voltage_common.append(float(temp[0]+"."+temp[1])) archivo0.close() time_array0=np.array(time_common[0:1000]) voltage_array0=np.array(voltage_common[0:1000]) average=0 for i in time_common: average=i+average average=average/len(time_common) fig, ax=plt.subplots() common_gain=(average/0.001)-200 print(f"MUESTRAS:{len(time_common)}, Promedio: {average}: Ganancia comun: {common_gain} CMRR:{20*math.log10(1000/common_gain)}") ax.plot(time_array0,voltage_array0) # Plot some data on the axes. plt.grid(True) ax.set_ylabel("Potencia [dB]") ax.set_title("Power Spectrum") ax.set_xlabel("Frecuencia [Hz]") mplcursors.cursor(multiple=True).connect( "add", lambda sel: sel.annotation.draggable(False)) plt.show()

[ "mdavid_cuello@javeriana.edu.co" ]

mdavid_cuello@javeriana.edu.co

d067020ff739438aeb20ffea7e8a9e17bfabef8d

4f96e9ebc0ee5bd99b1e01cedcb6ce3eb5a83874

/Gzuis.py

6e8760977958ff91a26e992084e37d20ef192e95

[]

no_license

hbisneto/Gzuis

e764c4057ce45406a552ef1632385d1ebe8e56c6

a12d9d525300392b445eb77109f5bc2e1ec12d49

refs/heads/main

2023-03-03T00:52:12.621609

2021-02-08T21:03:14

337,183,020

0

null

UTF-8

Python

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1,621

py

# Bibliotecas e outras dependencias import Palavra import LoveCard from datetime import date AnoAtual = date.today().year SoftwareName = "GZUIS" Version = "1.0" CopyrightName = "Heitor Bisneto." Space = " " print("="*80) print(f'[{SoftwareName}] - Em Execução...') print("="*80) print("Nome:", SoftwareName) print("Versão:", Version) print("Criado por:", CopyrightName) if AnoAtual == 2021: print("Copyright ©", AnoAtual, "|", CopyrightName, "All rights reserved.") else: print("Copyright © 2021 -", AnoAtual, "|", CopyrightName, "All rights reserved.") print("") MatrizOpc = ['LOVECARD', 'DWORD'] Count = int() AboutItem = str() Space = " " UserInput = int() # Funções de comandos def CartaAmor(): LoveCard.LCFunc() def PalavraDia(): print("="*80) print(f'{Space} >> Palavra do dia <<') print("="*80) Palavra.Process() def MyApp(): # Input do usuário: Opc = int(input(f'<{SoftwareName}/Opção:> <{SoftwareName}/')) UserInput = Opc if UserInput == 1: CartaAmor() elif UserInput == 2: PalavraDia() # Lista de comandos print("="*80) print(f'{Space} >> Digite o número da opção desejada <<') print("="*80) print(" COMANDO FUNÇÃO DO COMANDO") print("="*80) for i in MatrizOpc: Count = Count + 1 if Count == 1: AboutItem = f'{Space} Abrir função "Carta de amor"' elif Count == 2: AboutItem = f'{Space} Abrir função "Palavra do Dia"' print(f'<{SoftwareName}/{Count}> {i}{AboutItem}') print("="*80) MyApp()

[ "noreply@github.com" ]

hbisneto.noreply@github.com

0dd18912dbc944902d7ec38e966c65cba6b73979

f9d564f1aa83eca45872dab7fbaa26dd48210d08

/huaweicloud-sdk-iotedge/huaweicloudsdkiotedge/v2/model/batch_confirm_configs_new_request.py

371de812400930aa867c1cfd0c328c4bb1858b17

[ "Apache-2.0" ]

permissive

huaweicloud/huaweicloud-sdk-python-v3

cde6d849ce5b1de05ac5ebfd6153f27803837d84

f69344c1dadb79067746ddf9bfde4bddc18d5ecf

refs/heads/master

2023-09-01T19:29:43.013318

2023-08-31T08:28:59

262,207,814

103

44

NOASSERTION

2023-06-22T14:50:48

2020-05-08T02:28:43

Python

UTF-8

Python

false

4,737

py

# coding: utf-8 import six from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization class BatchConfirmConfigsNewRequest: """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ sensitive_list = [] openapi_types = { 'node_id': 'str', 'ia_id': 'str', 'body': 'ConfirmIaConfigsRequestBody' } attribute_map = { 'node_id': 'node_id', 'ia_id': 'ia_id', 'body': 'body' } def __init__(self, node_id=None, ia_id=None, body=None): """BatchConfirmConfigsNewRequest The model defined in huaweicloud sdk :param node_id: 边缘节点ID :type node_id: str :param ia_id: 边侧第三方应用的模块ID :type ia_id: str :param body: Body of the BatchConfirmConfigsNewRequest :type body: :class:`huaweicloudsdkiotedge.v2.ConfirmIaConfigsRequestBody` """ self._node_id = None self._ia_id = None self._body = None self.discriminator = None self.node_id = node_id self.ia_id = ia_id if body is not None: self.body = body @property def node_id(self): """Gets the node_id of this BatchConfirmConfigsNewRequest. 边缘节点ID :return: The node_id of this BatchConfirmConfigsNewRequest. :rtype: str """ return self._node_id @node_id.setter def node_id(self, node_id): """Sets the node_id of this BatchConfirmConfigsNewRequest. 边缘节点ID :param node_id: The node_id of this BatchConfirmConfigsNewRequest. :type node_id: str """ self._node_id = node_id @property def ia_id(self): """Gets the ia_id of this BatchConfirmConfigsNewRequest. 边侧第三方应用的模块ID :return: The ia_id of this BatchConfirmConfigsNewRequest. :rtype: str """ return self._ia_id @ia_id.setter def ia_id(self, ia_id): """Sets the ia_id of this BatchConfirmConfigsNewRequest. 边侧第三方应用的模块ID :param ia_id: The ia_id of this BatchConfirmConfigsNewRequest. :type ia_id: str """ self._ia_id = ia_id @property def body(self): """Gets the body of this BatchConfirmConfigsNewRequest. :return: The body of this BatchConfirmConfigsNewRequest. :rtype: :class:`huaweicloudsdkiotedge.v2.ConfirmIaConfigsRequestBody` """ return self._body @body.setter def body(self, body): """Sets the body of this BatchConfirmConfigsNewRequest. :param body: The body of this BatchConfirmConfigsNewRequest. :type body: :class:`huaweicloudsdkiotedge.v2.ConfirmIaConfigsRequestBody` """ self._body = body def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" import simplejson as json if six.PY2: import sys reload(sys) sys.setdefaultencoding("utf-8") return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) def __repr__(self): """For `print`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, BatchConfirmConfigsNewRequest): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

[ "hwcloudsdk@huawei.com" ]

hwcloudsdk@huawei.com

23d043ad9da2ca665ea22dd1ccb6f5fdcd1127e0

5b711af0e64686c218bf25fa776d6fb461a70235

/00-06/00-06_main.py

561b11af37988dd96939cf122089f9d87558e4e7

[]

no_license

slowhand123/region_OpenTK

2f835205e4c935a1e5a656aab890ac87fdaa2ede

53db37a12c7036ee9672617832a210de5586d574

refs/heads/master

2021-05-28T20:05:26.855525

2015-03-30T12:03:08

2015-03-30T12:05:22

null

0

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py

# # Reference Path : OpenTK # import clr clr.AddReference("OpenTK") from System import * from OpenTK import * from OpenTK.Graphics import * from OpenTK.Graphics.OpenGL import * from OpenTK.Input import * class Game(GameWindow): #800x600のウィンドウを作る。タイトルは「0-6:with IronPython」 def __init__(self): super(Game, self).__init__() self.Width = 800 self.Height = 600 self.Title = "0-6:with IronPython" self.VSync = VSyncMode.On #ウィンドウの起動時に実行される def OnLoad(self, e): super(Game, self).OnLoad(e) GL.ClearColor(Color4.Black) GL.Enable(EnableCap.DepthTest) #ウィンドウのサイズが変更された場合に実行される def OnResize(self, e): super(Game, self).OnResize(e) GL.Viewport(self.ClientRectangle.X, self.ClientRectangle.Y, self.ClientRectangle.Width, self.ClientRectangle.Height) GL.MatrixMode(MatrixMode.Projection) projection = Matrix4.CreatePerspectiveFieldOfView(float(Math.PI / 4), float(self.Width) / float(self.Height), 1.0, 64.0) GL.LoadMatrix(projection) #画面更新で実行される def OnUpdateFrame(self, e): super(Game, self).OnUpdateFrame(e) #Escapeキーで終了 if(self.Keyboard[Key.Escape]): self.Exit() #画面描画で実行される def OnRenderFrame(self, e): super(Game, self).OnRenderFrame(e) GL.Clear(ClearBufferMask.ColorBufferBit | ClearBufferMask.DepthBufferBit) GL.MatrixMode(MatrixMode.Modelview) modelview = Matrix4.LookAt(Vector3.Zero, Vector3.UnitZ, Vector3.UnitY) GL.LoadMatrix(modelview) GL.Begin(BeginMode.Quads) GL.Color4(Color4.White) #色名で指定 GL.Vertex3(-1.0, 1.0, 4.0) GL.Color4(Array[float]([1.0, 0.0, 0.0, 1.0])) #配列で指定 GL.Vertex3(-1.0, -1.0, 4.0) GL.Color4(0.0, 1.0, 0.0, 1.0) #4つの引数にfloat型で指定 GL.Vertex3(1.0, -1.0, 4.0) GL.Color4(Byte(0), Byte(0), Byte(255), Byte(255)) #byte型で指定 GL.Vertex3(1.0, 1.0, 4.0) GL.End() self.SwapBuffers() if __name__ == '__main__': window = Game() window.Run() window.Dispose()

[ "occar@hotmail.co.jp" ]

occar@hotmail.co.jp

281fde97849774e3210a17505fd4660c09c7f133

93ba28a7808ed5a406753748fedbdbaea5f3c8b2

/drawVarsData_KS_CheckFlat_sync.py

d4ec1d1e613ae79f95cf4ec181d5c49e8173d5cf

[]

no_license

zaixingmao/samples-plots

7a55005abab1e7644296d1eb2e76f603d160a37b

bb2371c7f664a84c454189ec648bb55630cb7565

refs/heads/master

2020-05-21T23:27:34.390427

2017-07-14T14:59:52

24,139,867

0

null

2015-10-23T16:00:22

2014-09-17T10:17:08

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py

#!/usr/bin/env python import ROOT as r import tool from operator import itemgetter import os from cfg import enVars import varsList import optparse import math from array import array import numpy import random from cfg import draw_sync as draw_cfg def opts(): parser = optparse.OptionParser() parser.add_option("--variable", dest="varName", default = 'test', help="") parser.add_option("--signal", dest="signal", default = '', help="") parser.add_option("--logY", dest="logy", default = True, help="") parser.add_option("--sigBoost", dest="sigBoost", default = 1.0, help="") parser.add_option("--nbins", dest="nbins", default = 0, help="") parser.add_option("--useData", dest="useData", default = 'True', help="") parser.add_option("--setMax", dest="max", default = 50, help="") parser.add_option("--setRangeMin", dest="rangeMin", default = 0, help="") parser.add_option("--setRangeMax", dest="rangeMax", default = 100, help="") parser.add_option("--location", dest="location", default = '.', help="") parser.add_option("--bTag", dest="bTag", default = 'True', help="") parser.add_option("--predict", dest="predict", default = 'False', help="") parser.add_option("--predictPtBin", dest="predictPtBin", default = 'False', help="") parser.add_option("--region", dest="region", default = 'LL', help="") parser.add_option("--thirdLeptonVeto", dest="thirdLeptonVeto", default = 'False', help="") parser.add_option("--weight", dest="weight", default = "0.05", help="") parser.add_option("--yMax", dest="yMax", default = 300, help="") parser.add_option("--relaxedRegionOption", dest="relaxedRegionOption", default = 'relaxed', help="") parser.add_option("--usePUWeight", dest="usePUWeight", default = False, action="store_true", help="use pu reweight") options, args = parser.parse_args() return options def conditions(selection, region): if selection == 1: return 'OS', region elif selection == 2: return 'SS', region elif selection == 3: return 'OS', 'relaxed' elif selection == 4: return 'SS', 'relaxed' elif selection == 5: return 'OS', 'cut-off with iso3' elif selection == 6: return 'SS', 'cut-off with iso3' def getNewLegend(legendHisto, fakeHist): MCTotal = 0.0 for iHist, iLegend in legendHisto: if ("H" not in iLegend) and ("observed" not in iLegend): MCTotal += iHist.Integral(0, iHist.GetNbinsX()+1) newLegend = [] indexForSignal = 0 for i in range(len(legendHisto)): if 'H' not in legendHisto[i][1]: newLegend.append(legendHisto[i]) else: indexForSignal = i newLegend.append(legendHisto[indexForSignal]) newLegend.append((fakeHist, 'bkg (%.2f)' %MCTotal)) return newLegend def getCombinedError(x, x_e, y, y_e): if x != 0: x_part = math.pow(x_e/x,2) else: x_part = 0 if x_e == 0: return 0 if y != 0: y_part = math.pow(y_e/y,2) else: y_part = 0 return math.sqrt(x_part + y_part) def bTagSelection(tree, bTag): passCut = 0 if bTag == 'True' and tree.CSVJ1 >= 0.679 and tree.CSVJ2 >= 0.244: passCut = 1 if bTag == 'False': passCut = 1 if bTag == 'None' and (tree.CSVJ1 < 0.679 and tree.CSVJ2 < 0.679): passCut = 1 if bTag == '2M' and (tree.CSVJ1 >= 0.679 and tree.CSVJ2 >= 0.679): passCut = 1 if bTag == '1M1NonM' and (tree.CSVJ1 >= 0.679 and tree.CSVJ2 < 0.679): passCut = 1 if bTag == '1M' and (tree.CSVJ1 > 0.679): passCut = 1 return passCut def getIntegralFromString(info): integral = info[info.find('(')+1:info.find(')')] return float(integral) def passCut(tree, bTag, region, thirdLeptonVeto, relaxedRegionOption = "relaxed"): isoCut = 3.0 iso_count = 3 isoMax = 10.0 isoTight = 1.5 if "INFN" in relaxedRegionOption: isoMax = 4.0 isoCut = 1.0 isoTight = 1.0 if "very" in relaxedRegionOption: isoCut = 1.5 if thirdLeptonVeto == 'True': if tree.nElectrons > 0 or tree.nMuons>0: return 0 if bTagSelection(tree, bTag) and abs(tree.eta1.at(0))<2.1 and abs(tree.eta2.at(0))<2.1: sign_count = 0 if tree.iso1.at(0) > isoMax or tree.iso2.at(0) > isoMax: return 0 elif tree.iso1.at(0)>isoCut and tree.iso2.at(0)>isoCut: iso_count = 1 elif tree.iso1.at(0) < isoTight: if 'tight' in region and tree.iso2.at(0) < isoTight: iso_count = 0 if 'semiTight' in region and tree.iso2.at(0) > isoCut: iso_count = 0 elif tree.iso2.at(0) < isoTight: if 'semiTight' in region and tree.iso1.at(0) > isoCut: iso_count = 0 if tree.charge1.at(0) - tree.charge2.at(0) == 0: sign_count = 1 return (iso_count<<1) + sign_count + 1 else: return 0 def findBin(x, nBins, xMin, xMax): bin = int(nBins*(x-xMin)/(xMax-xMin)) if bin >= nBins: return nBins-1 else: return bin def dontUnblind(tree): if 90 < tree.svMass.at(0) < 150: return True # if 70 < tree.mJJ < 150: # return True # if tree.BDT > 0: # return True return False def getAccuDist(hist, xMin, xMax, name): nBins = hist.GetNbinsX() total = hist.Integral() accuDist = r.TH1F(name, '', nBins, xMin, xMax) for i in range(nBins): accuDist.SetBinContent(i+1, hist.Integral(1, i+1)/total) return accuDist def getHistos(varName, signalSelection, logY, sigBoost, nbins, useData, max, rangeMin, rangeMax, location, bTag, predict, predictPtBin, region, thirdLeptonVeto, SF, yMax, relaxedRegionOption, usePU): r.gStyle.SetOptStat(0) SF = float(SF) fileList = draw_cfg.MCFileList histList = [] histList_4QCD = [] QCDHistList = [] QCDHistList_4KS = [] QCDHistList_withScale = [] varRange = [nbins, rangeMin, rangeMax] nBins = 10000 Lumi = 19.7 legendHistos = [] var_background = [] scaleMCPt = 1.0 tmpFile = [] tmpTree = [] var_data_4KS = [] var_data = [] var_data_4QCD = [] histList_4KS = [] MC_Counts_0 = 0.0 MC_Counts_1 = 0.0 MC_Counts_2 = 0.0 for i in range(6): var_data.append(r.TH1F('data_%i' %(i),"", varRange[0], varRange[1], varRange[2])) var_data_4KS.append(r.TH1F('data_4KS_%i' %(i),"", nBins, varRange[1], varRange[2])) if i < 5: var_data_4QCD.append(r.TH1F('data_4QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2])) dataName = draw_cfg.dataFile fData = r.TFile(dataName) treeData = fData.Get('eventTree') print 'Adding events from: %s ...' %dataName for iEntry in range(treeData.GetEntries()): treeData.GetEntry(iEntry) select = passCut(treeData, bTag, region, thirdLeptonVeto, relaxedRegionOption) if (select == 0) or (select > 6): continue if (select == 1) and dontUnblind(treeData): continue var_data[select-1].Fill(varsList.findVar(treeData, varName)) if (select != 1): var_data_4KS[select-2].Fill(varsList.findVar(treeData, varName)) if select == 2: var_data_4QCD[0].Fill(varsList.findVar(treeData, varName), 1.0) elif select == 3: var_data_4QCD[1].Fill(varsList.findVar(treeData, varName), SF) elif select == 4: var_data_4QCD[2].Fill(varsList.findVar(treeData, varName), 1.0) var_data_4QCD[3].Fill(varsList.findVar(treeData, varName), SF) legendHistos.append([]) for j in range(6): var_data[j].SetMarkerStyle(8) var_data[j].SetMarkerSize(0.9) legendHistos.append([]) integral = 'observed' if j != 0 or (region != 'tight') or (bTag == 'None'): integral = 'observed (%.0f)' %var_data[j].Integral(0, varRange[0]+1) legendHistos[j].append((var_data[j], integral)) for i in range(len(fileList)): for j in range(6): histList_4KS.append(r.TH1F('%s_%i_KS' %(fileList[i][0],j),fileList[i][0], nBins, varRange[1], varRange[2])) histList.append(r.TH1F('%s_%i' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2])) histList_4QCD.append(r.TH1F('%s_%i_2' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2])) print 'Adding events from: %s ...' %(fileList[i][1]) tmpFile.append(r.TFile(fileList[i][1])) tmpTree.append(tmpFile[i].Get('eventTree')) for iEntry in range(tmpTree[i].GetEntries()): tmpTree[i].GetEntry(iEntry) select = passCut(tmpTree[i], bTag, region, thirdLeptonVeto, relaxedRegionOption) if (not select) or (select > 6): continue if usePU: allWeights = tmpTree[i].triggerEff*tmpTree[i].PUWeight else: allWeights = tmpTree[i].triggerEff histList[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName), allWeights*tmpTree[i].xs/(tmpTree[i].initEvents)) histList_4KS[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName), allWeights*tmpTree[i].xs/(tmpTree[i].initEvents)) if select == 2: histList_4QCD[6*i].Fill(varsList.findVar(tmpTree[i], varName), allWeights*1.0*tmpTree[i].xs/(tmpTree[i].initEvents)) elif select == 3: histList_4QCD[6*i+1].Fill(varsList.findVar(tmpTree[i], varName), allWeights*SF*tmpTree[i].xs/(tmpTree[i].initEvents)) elif select == 4: histList_4QCD[6*i+2].Fill(varsList.findVar(tmpTree[i], varName), allWeights*1.0*tmpTree[i].xs/(tmpTree[i].initEvents)) histList_4QCD[6*i+3].Fill(varsList.findVar(tmpTree[i], varName), allWeights*SF*tmpTree[i].xs/(tmpTree[i].initEvents)) for j in range(6): var_background.append(r.THStack()) histList[6*i+j].SetFillColor(fileList[i][2]) histList[6*i+j].Scale(Lumi) histList_4QCD[6*i+j].Scale(Lumi) histList_4KS[6*i+j].Scale(Lumi) var_background[j].Add(histList[6*i+j]) legendHistos[j].append((histList[6*i+j], '%s (%.2f)' %(fileList[i][0], histList[6*i+j].Integral(0, varRange[0]+1)))) data_i = [] MC_i = [] data_r = [] MC_r = [] e = [] MC_List = [] for i in range(3): QCDHistList.append(r.TH1F('QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2])) QCDHistList_4KS.append(r.TH1F('QCD_%i_KS' %(i),"", nBins, varRange[1], varRange[2])) MC_List.append(r.TH1F('MC_total_%i' %(i),"", varRange[0], varRange[1], varRange[2])) for j in range(varRange[0]+2): dataValue = var_data[i+1].GetBinContent(j) dataError = var_data[i+1].GetBinError(j) MCValue = 0 for k in range(len(fileList)): MCValue += histList[6*k+1+i].GetBinContent(j) if i == 0: data_i.append(dataValue) e.append(dataError) MC_i.append(MCValue) if i == 2: data_r.append(dataValue) MC_r.append(MCValue) MC_List[i].SetBinContent(j, MCValue) if dataValue - MCValue > 0: QCDHistList[i].SetBinContent(j, dataValue - MCValue) QCDHistList[i].SetBinError(j, dataError) MC_List[i].Sumw2() for j in range(nBins+2): dataValue4KS = var_data_4KS[i].GetBinContent(j) MCValue4KS = 0 for k in range(len(fileList)): MCValue4KS += histList_4KS[6*k+1+i].GetBinContent(j) if dataValue4KS - MCValue4KS > 0: QCDHistList_4KS[i].SetBinContent(j, dataValue4KS - MCValue4KS) ss_t = QCDHistList[0].Integral(0, varRange[0]+1) ss_l = QCDHistList[2].Integral(0, varRange[0]+1) os_l = QCDHistList[1].Integral(0, varRange[0]+1) os_l_data = var_data[2].Integral(0, varRange[0]+1) print "QCD in SS_T: %.4f" %ss_t print "QCD in SS_L: %.4f" %ss_l print "QCD in OS_L: %.4f" %os_l print "Data in OS_L:%.4f" %os_l_data print "SF: %.4f" %(ss_t/ss_l) print "SF qcd/data: %.4f" %(os_l/os_l_data) for i in range(4): QCDHistList_withScale.append(r.TH1F('QCD_withScale_%i' %(i),"", varRange[0], varRange[1], varRange[2])) for j in range(varRange[0]+2): dataValue = var_data_4QCD[i].GetBinContent(j) dataError = var_data_4QCD[i].GetBinError(j) MCValue = 0 for k in range(len(fileList)): MCValue += histList_4QCD[6*k+i].GetBinContent(j) if dataValue - MCValue > 0: QCDHistList_withScale[i].SetBinContent(j, dataValue - MCValue) QCDDiff = r.TH1F('QCD_diff',"", varRange[0], varRange[1], varRange[2]) QCDDiff2 = r.TH1F('QCD_diff2',"", varRange[0], varRange[1], varRange[2]) QCDDiff_R2T = r.TH1F('QCDDiff_R2T',"", varRange[0], varRange[1], varRange[2]) # QCDDiff2.Sumw2() fit1 = r.TF1("fit1","[0]", varRange[1],varRange[2]) fit1.SetParName(0,'scale') fit1.FixParameter(0,1.0) QCDDiff.Fit('fit1', '0EM') fit1.SetLineStyle(2) fit1.SetLineColor(r.kRed) fit2 = r.TF1("fit2","[0]", varRange[1],varRange[2]) fit2.SetParName(0,'scale') fit2.FixParameter(0,1.0) QCDDiff2.Fit('fit2', '0EM') fit2.SetLineStyle(2) fit2.SetLineColor(r.kRed) DrawSignal = False if signalSelection != '': var_signal = [] var_signal_4KS = [] for i in range(6): var_signal.append(r.TH1F('%s_%i' %(signalSelection,i),"", varRange[0], varRange[1], varRange[2])) var_signal_4KS.append(r.TH1F('%s_%i_4KS' %(signalSelection,i),"", nBins, varRange[1], varRange[2])) signalDict = draw_cfg.signalDict if signalSelection in signalDict: fSignal = r.TFile(signalDict[signalSelection]) treeSignal = fSignal.Get('eventTree') print 'Adding events from: %s ...' %(signalDict[signalSelection]) for iEntry in range(treeSignal.GetEntries()): treeSignal.GetEntry(iEntry) select = passCut(treeSignal, bTag, region, thirdLeptonVeto, relaxedRegionOption) if (not select) or (select > 6): continue if usePU: allWeights = treeSignal.triggerEff*treeSignal.PUWeight else: allWeights = treeSignal.triggerEff var_signal[select-1].Fill(varsList.findVar(treeSignal, varName), allWeights*treeSignal.xs/(treeSignal.initEvents)) var_signal_4KS[select-1].Fill(varsList.findVar(treeSignal, varName), allWeights*treeSignal.xs/(treeSignal.initEvents)) initNEventsSignal = fSignal.Get('preselection') for i in range(6): var_signal[i].SetLineStyle(7) var_signal[i].SetLineWidth(4) var_signal[i].Scale(sigBoost*Lumi) if sigBoost != 1: sum = var_signal[i].Integral(0, var_signal[i].GetNbinsX()+1) legendHistos[i].append((var_signal[i], '%sx%0.f (%.2f)' %(signalSelection, sigBoost, var_signal[i].Integral(0, varRange[0]+1)))) else: legendHistos[i].append((var_signal[i], '%s (%.2f)' %(signalSelection, var_signal[i].Integral(0, varRange[0]+1)))) DrawSignal = True else: print '%s not supported, please use H260, H300 or H350' %signalSelection scale_SS2OS = fit1.GetParameter(0) scale_er_SS2OS = fit1.GetParError(0) scale_relaxed2Tight = fit2.GetParameter(0) scale_er_relaxed2Tight = fit2.GetParError(0) QCDHistList_withScale[0].Scale(scale_SS2OS) QCDHistList_withScale[1].Scale(scale_relaxed2Tight) QCDHistList_withScale[2].Scale(scale_SS2OS) QCDHistList_withScale[3].Scale(scale_relaxed2Tight) QCDHistList_withScale[3].SetFillColor(r.TColor.GetColor(250,202,255)) QCDHistList_withScale[2].SetFillColor(r.TColor.GetColor(250,202,255)) QCDHistList_withScale[0].SetLineColor(r.TColor.GetColor(250,202,255)) QCDHistList_withScale[0].SetLineWidth(2) QCDHistList_withScale[1].SetLineStyle(2) QCDHistList_withScale[1].SetLineColor(r.TColor.GetColor(250,202,255)) QCDHistList_withScale[1].SetLineWidth(2) var_background[1].Add(QCDHistList_withScale[3]) var_background[2].Add(QCDHistList_withScale[2]) legendHistos[1].append((QCDHistList_withScale[3], 'QCD (%.2f)' %QCDHistList_withScale[3].Integral(0, varRange[0]+1))) legendHistos[2].append((QCDHistList_withScale[2], 'QCD (%.2f)' %QCDHistList_withScale[2].Integral(0, varRange[0]+1))) allStacked = var_background[0].Clone() QCDPredict = QCDHistList_withScale[1].Clone() QCDPredict.SetLineStyle(1) QCDPredict.SetLineWidth(1) QCDPredict.SetLineColor(r.kBlack) legendHistos[0].append((QCDPredict, 'QCD (%.0f, SF = %.3f)' %(QCDPredict.Integral(0, varRange[0]+1), SF))) QCDPredict.SetFillColor(r.TColor.GetColor(250,202,255)) allStacked.Add(QCDPredict) QCDHistList_withScale[1] = tool.addFakeTHStack(QCDHistList_withScale[1],var_background[0]) QCDHistList_withScale[0] = tool.addFakeTHStack(QCDHistList_withScale[0],var_background[0]) # var_data[1].Sumw2() # MC_List[1].Sumw2() for i in range(varRange[0]): oldValue = var_data[2].GetBinContent(i+1) mcValue = MC_List[1].GetBinContent(i+1) if oldValue - mcValue > 0: QCDDiff2.SetBinContent(i+1, (oldValue - mcValue)/oldValue) QCDDiff2.SetBinError(i+1, MC_List[1].GetBinError(i+1)/oldValue) print QCDDiff2.Integral(0, varRange[0]+1) QCDDiff = var_data[2].Clone() QCDDiff_sub = QCDHistList_withScale[2].Clone() + MC_List[1].Clone() QCDDiff.Divide(QCDDiff_sub) QCDDiff_R2T = var_data[1].Clone() QCDDiff_R2T_sub = QCDHistList_withScale[3].Clone() + MC_List[0].Clone() QCDDiff_R2T.Divide(QCDDiff_R2T_sub) legendPosition = (0.6, 0.7, 0.90, 0.88) l = [] r.gROOT.SetBatch(True) # to suppress canvas pop-outs if bTag == 'True': titleName = '1 Medium 1 Loose b-tag' fileName = 'bTag' elif bTag == 'False': titleName = '' fileName = 'all' elif bTag == '2M': titleName = '2 Medium b-tags' fileName = '2MbTag' elif bTag == '1M': titleName = '1 Medium b-tag' fileName = '1MbTag' elif bTag == '1M1NonM': titleName = '1 Medium 1 Anti-Medium b-tag' fileName = '1M1NonMbTag' elif bTag == 'None': titleName = '0 b-tag' fileName = '0bTag' KS1 = QCDHistList_4KS[0].KolmogorovTest(QCDHistList_4KS[2]) KS2 = QCDHistList_4KS[1].KolmogorovTest(QCDHistList_4KS[2]) ks_values = [] tmpHists = [] ks_values2 = [] tmpHists2 = [] nTimes = 10000 # for i in range(nTimes): # tool.printProcessStatus(i, nTimes, processName = 'Making Sample Histograms') # tmpHists.append(r.TH1F('tmpHist_%i' %(i),"", nBins, varRange[1], varRange[2])) # tmpHists[i].FillRandom(QCDHistList_4KS[2], 100) # ks_values.append(QCDHistList_4KS[0].KolmogorovTest(tmpHists[i])) # tmpHists2.append(r.TH1F('tmpHist2_%i' %(i),"", nBins, varRange[1], varRange[2])) # tmpHists2[i].FillRandom(QCDHistList_4KS[2], 100) # ks_values2.append(QCDHistList_4KS[2].KolmogorovTest(tmpHists[i])) print '' print 'KS Test 1: %.3f' %KS1 print 'KS Test 2: %.3f' %KS2 fakeHist = r.TH1F() fakeHist.SetLineColor(0) usePUWeightName = '' if usePU: usePUWeightName = '_usePU' psfile = '%s_%s_%s_%s_%s%s.pdf' %(varName, fileName, signalSelection, region, relaxedRegionOption, usePUWeightName) c = r.TCanvas("c","Test", 800, 900) #ps = r.TPDF(psfile,112) c.Divide(2,3) drawOpt = '' QCDDiff.SetTitle('Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background' %(titleName, Lumi,varName)) QCDDiff.SetMarkerStyle(8) QCDDiff.SetMarkerSize(0.9) QCDDiff.SetMaximum(4) QCDDiff.SetMinimum(0) QCDDiff_R2T.SetTitle('Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background' %(titleName, Lumi,varName)) QCDDiff_R2T.SetMarkerStyle(8) QCDDiff_R2T.SetMarkerSize(0.9) QCDDiff_R2T.SetMaximum(4) QCDDiff_R2T.SetMinimum(0) pl_1 = r.TPad("pl_1","pl_1",0.,1,0.5,0.65) pl_1_delta = r.TPad("pl_1_delta","pl_1_delta",0.,0.65,0.5,0.45) pl_2 = r.TPad("pl_2","pl_2",0.,0.45,0.5,0.0) pl_1.SetMargin(1, 1, 0, 1) pl_1_delta.SetMargin(1, 1, 0.2, 0.05) pr_1 = r.TPad("pr_1","pr_1",0.5,1,1.,0.65) pr_1_delta = r.TPad("pr_1_delta","pr_1_delta",0.5,0.65,1.0,0.45) pr_2 = r.TPad("pr_2","pr_2",0.5,0.45,1.0,0.0) pr_1.SetMargin(1, 1, 0, 1) pr_1_delta.SetMargin(1, 1, 0.2, 0.05) pl_1.Draw() pl_1_delta.Draw() pl_2.Draw() pr_1.Draw() pr_1_delta.Draw() pr_2.Draw() pl_1.cd() r.gPad.SetTicky() signSelection, iso = conditions(1, region) allStacked.SetMaximum(int(yMax)) allStacked.SetTitle('CMS Preliminary %.1f fb^{-1} at 8 TeV; %s; events / bin' %(Lumi,varName)) allStacked.Draw() var_data[0].Draw('PE same') legendPosition = (0.43, 0.9 - 0.06*len(legendHistos[0]), 0.87, 0.9) l.append(tool.setMyLegend(lPosition=legendPosition, lHistList=getNewLegend(legendHistos[0], fakeHist))) l[0].Draw('same') var_signal[0].Draw('same') pl_1_delta.cd() r.gPad.SetTicky() r.gPad.SetTickx() pl_1_delta.SetGridy(1) bkgEst = QCDHistList_withScale[1].Clone() delta = var_data[0].Clone() delta.Sumw2() bkgEst.Sumw2() delta.Divide(bkgEst) delta.SetMinimum(0.5) delta.SetMaximum(1.5) delta.GetXaxis().SetTitle(varName) delta.GetXaxis().SetLabelSize(0.07) delta.GetXaxis().SetTitleSize(0.07) delta.GetYaxis().SetLabelSize(0.07) delta.GetYaxis().SetNdivisions(5,5,0) delta.Draw() pList = [pr_1, pl_2, pr_2] for k in range(1, len(pList)+1): pList[k-1].cd() r.gPad.SetTicky() if k > 1 and logY == 'True': r.gPad.SetLogy() signSelection, iso = conditions(k+1, region) var_background[k].SetTitle('%s %s Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName)) if k < 2: var_background[k].SetMaximum(int(yMax)) else: var_background[k].SetMaximum(max) var_background[k].SetMinimum(0.01) var_background[k].Draw() if useData == 'True': var_data[k].Draw('PE same') legendPosition = (0.47, 0.9 - 0.06*len(legendHistos[k]), 0.87, 0.9) l.append(tool.setMyLegend(lPosition=legendPosition, lHistList=getNewLegend(legendHistos[k], fakeHist))) if k == 1: ksLegend1 = tool.setMyLegend((0.2, 0.8, 0.5, 0.9), [(QCDHistList_withScale[3], 'KS Test: %.3f' %KS1)]) ksLegend1.Draw('same') if k == 2: ksLegend2 = tool.setMyLegend((0.2, 0.8, 0.5, 0.9), [(QCDHistList_withScale[2], 'KS Test: %.3f' %KS2)]) ksLegend2.Draw('same') l[k].Draw('same') var_signal[k].Draw('same') pr_1_delta.cd() r.gPad.SetTicky() r.gPad.SetTickx() pr_1_delta.SetGridy(1) bkgEst2 = QCDHistList_withScale[3].Clone() delta2 = var_data[1].Clone() delta2.Sumw2() bkgEst2.Sumw2() delta2.Divide(bkgEst2) delta2.SetMinimum(0.5) delta2.SetMaximum(1.5) delta2.GetXaxis().SetTitle(varName) delta2.GetXaxis().SetLabelSize(0.07) delta2.GetXaxis().SetTitleSize(0.07) delta2.GetYaxis().SetLabelSize(0.07) delta2.GetYaxis().SetNdivisions(5,5,0) delta2.Draw() c.Update() c.Print('%s(' %psfile) c.Clear() p1 = r.TPad("p1","p1",0.,1,1.,0.4) p1_r = r.TPad("p1_r","p1_r",0.,0.39,1.,0.06) p1.SetMargin(1, 1, 0, 1) p1_r.SetMargin(1, 1, 0.2, 1) p1.Draw() p1_r.Draw() p1.cd() r.gPad.SetTicky() allStacked.Draw() var_data[0].Draw('PE same') l[0].Draw('same') var_signal[0].Draw('same') p1_r.cd() r.gPad.SetTicky() r.gPad.SetTickx() p1_r.SetGridy(1) delta.Draw() c.Print('%s)' %psfile) print "Plot saved at %s" %(psfile) c.Close() op = opts() if op.varName != 'test': getHistos(op.varName, op.signal, op.logy, float(op.sigBoost), int(op.nbins), op.useData, float(op.max), float(op.rangeMin), float(op.rangeMax), op.location, op.bTag, op.predict, 'False', op.region, op.thirdLeptonVeto, op.weight, op.yMax, op.relaxedRegionOption, op.usePUWeight)

[ "zaixing.mao@cern.ch" ]

zaixing.mao@cern.ch

b3c2d2be4040aef38a58cee25d669a3bf9fde47c

40f9a3c7a1d90f9ff6ce230ff1bc9ffba2689c92

/setup.py

9049b95342383d1c4dc0933b16a039d4a6dd2bcb

[]

no_license

daemianmack/python-markdown-video

f79c52fb85203b242f846fe6614901597cd4d8db

551d5f27402b073154b99c1aa9ec2e8aeaeff884

refs/heads/master

2021-01-18T14:11:44.620641

2015-11-02T13:23:31

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1

null

2015-11-02T13:23:31

2010-09-25T20:33:04

Python

UTF-8

Python

false

361

py

# -*- coding: utf-8 -*- from distutils.core import setup from mdx_video import version setup( name='mdx_video', version=version, description="Markdown 2.0 extension for easy video embedding", author="Tyler Lesmann", author_email="redhatcat@gmail.com", url="http://code.tylerlesmann.com/mdx_video2", py_modules = ["mdx_video"], )

[ "maturburu@gmail.com" ]

maturburu@gmail.com

8ec7942f489530fd78fc17adda10403d07703211

8fa7967bec0bf9aefd2d170113e323bffd61a0fa

/shop/migrations/0005_product_discounted_price.py

27bbbbc489c4d13faa511e2c2877df5e0ce8a2dd

[ "MIT" ]

permissive

RitvikDayal/The-Stone-Shop

327395a2ddba86cd60c9483ffd1936b4e6aa197c

fed85d5ebb993fb1ce235f5e8a8ebc06a76fb956

refs/heads/master

2022-12-06T09:05:55.129652

2020-08-27T07:24:01

290,305,600

2

0

null

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Python

false

431

py

# Generated by Django 3.0.8 on 2020-07-28 12:46 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('shop', '0004_product_product_image'), ] operations = [ migrations.AddField( model_name='product', name='discounted_price', field=models.FloatField(default=None), preserve_default=False, ), ]

[ "ritvikr1605@gmail.com" ]

ritvikr1605@gmail.com

12ffbea99224b0bf7e1abef29cd18bad1ccd3b21

6d44c144381f2c231f9f999aeefbea61bbeee4dc

/ShadingRatioModeling/ShadeModel.py

b1e379844d7e14eec3e2a432977e5dc03917d6b0

[]

no_license

daigo0927/pyworks

8d93327e2c8da45380d5acf8497cddfcb94814fe

97c3af7ebc31c4857492e6b06c141ecaa7e6fa27

refs/heads/master

2021-05-01T11:50:22.796770

2017-05-13T17:20:38

72,403,162

1

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import sys, os sys.path.append(os.pardir) import numpy as np import matplotlib.pyplot as plt import seaborn as sns from common.gradient import numerical_gradient from PIL import Image from scipy.stats import multivariate_normal from mpl_toolkits.mplot3d.axes3d import Axes3D from misc import * import pdb # pdb.set_trace() class uGMModel: def __init__(self, xy_lim = np.array([30, 30]), mixture_size = 20, frame_num = 5, logistic_coefficient = np.array([50, 0])): self.dimension = xy_lim.shape[0] self.mix = mixture_size self.xgrid = np.arange(xy_lim[0]) self.ygrid = np.arange(xy_lim[1]) self.frame = np.arange(frame_num) # ! set basis frame : (frame_num-1)/2 : center frame self.std_frame = (frame_num-1)/2 self.params = {} # ! set basis frame : (frame_num-1)/2 : center frame self.params['mus'] = np.random.rand(self.mix, self.dimension)\ * xy_lim self.params['covs'] = np.array([np.identity(self.dimension) * xy_lim/5. for i in range(self.mix)]) self.params['pi'] = np.random.dirichlet([3]*self.mix) self.params['move'] = (np.random.rand(self.mix, self.dimension) - 0.5) \ * xy_lim / 10 self.logistic_coefficient = logistic_coefficient self.Norms = None self.q_each = None # each component density value self.q = None # component mixture value self.g = None # infered shade ratio self.lossvalue = None def predict(self): mus_plus = np.array([self.params['mus'] \ + self.params['move'] * (f - self.std_frame) \ for f in self.frame]) self.Norms = [Norm2Dmix(mus = mus_p, covs = self.params['covs'], pi = self.params['pi']) for mus_p in mus_plus] self.q_each = np.array([[[self.Norms[f].pdf_each(x = np.array([x, y])) for x in self.xgrid] for y in self.ygrid] for f in self.frame]) self.q = np.sum(self.q_each \ * self.params['pi'].reshape(1,1,1,self.mix), axis = 3) a, b = self.logistic_coefficient z = a * self.q + b self.g = sigmoid(z) def GenerateFrame(self, f): mus_p = self.params['mus'] \ + self.params['move'] * (f - self.std_frame) Norm = Norm2Dmix(mus = mus_p, covs = self.params['covs'], pi = self.params['pi']) q = np.array([[Norm.pdf(x = np.array([x, y])) for x in self.xgrid] for y in self.ygrid]) a, b = self.logistic_coefficient z = a * q + b g = sigmoid(z) # pdb.set_trace() return g def loss(self, f): # f : data value (not frame) self.predict() a ,b = self.logistic_coefficient z = a * self.q + b U_q = 1/a * np.log(1 + np.exp(z)) # U function : integrated logistic sigmoid self.lossvalue = U_q - f * self.q def gradient_move(self, f): self.loss(f = f) mus_plus = np.array([self.params['mus'] \ + self.params['move'] * (frm - self.std_frame) \ for frm in self.frame]) # shape(frame, ygrid, xgrid, mix, 2) grad_move = np.array([[[ self.params['pi'].reshape(self.mix, 1) \ * (self.g[frm, y, x] - f[frm, y, x]) \ * self.q_each[frm, y, x, :].reshape(self.mix, 1) \ * (frm - self.std_frame) \ * np.linalg.solve(self.params['covs'], (np.array([x, y]) - mus_plus[frm])) \ for x in self.xgrid] for y in self.ygrid] for frm in self.frame]) return grad_move def modelplot(self, update = False): if(update == True): self.predict() plt.figure(figsize = (10, 3.5*(1+self.frame.size))) for frm in self.frame: plt.subplot(self.frame.size+1, 2, frm*2+1) plt.title('approxed shade ratio') sns.heatmap(self.g[frm], annot = False, cmap = 'YlGnBu_r', vmin = 0, vmax = 1) plt.subplot(self.frame.size+1, 2, frm*2+2) plt.title('approxed probability density') sns.heatmap(self.q[frm], annot = False, cmap = 'YlGnBu_r') sns.plt.show() class uEpaMixModel(object): def __init__(self, xy_lim = np.array([30, 30]), mixture_size = 20, frame_num = 5, logistic_coefficient = np.array([50, 0])): self.dimension = xy_lim.shape[0] self.mix = mixture_size self.xgrid = np.arange(start = 0, stop = xy_lim[0], step = 1) self.ygrid = np.arange(start = 0, stop = xy_lim[1], step = 1) self.frame = np.arange(frame_num) self.params = {} self.params['mus'] = \ np.random.rand(self.mix, self.dimension)\ *(xy_lim) tmp = np.array([np.identity(self.dimension)*xy_lim/0.1 \ for i in range(self.mix)]) self.params['covs'] = tmp self.params['pi'] = np.random.dirichlet([3]*self.mix) move = np.random.rand(self.mix, self.dimension)-0.5 self.params['move'] = move * xy_lim / 10 self.mus_plus = None self.logistic_coefficient = logistic_coefficient self.q = None self.q_specific = None # self.Epas[frame].Epas[mix] self.Epas = None self.Epas_specific_frame = None def predict_specific_frame(self, obj_frame = 0): self.Epas_specific_frame = Epanechnikov2Dmix(mus = self.params['mus'] \ + self.params['move'] * obj_frame, covs = self.params['covs'], pi = self.params['pi']) q_and_mask = np.array([[self.Epas_specific_frame.pdf_and_mask(x = np.array([x, y])) for x in self.xgrid] for y in self.ygrid]) self.q_specific = q_and_mask[:,:,0] mask = q_and_mask[:,:,1:] # pdb.set_trace() # return specific frame predict value, and component mask # q.shape : (ygird ,xgrid) # mask.shape : (ygrid, xgrid, mix) return self.q_specific, mask def predict(self): self.mus_plus = np.array([self.params['mus'] + self.params['move'] * f \ for f in self.frame]) self.Epas = [Epanechnikov2Dmix(mus = mus_p, covs = self.params['covs'], pi = self.params['pi']) for mus_p in self.mus_plus] # compute from given frames q_and_mask = np.array([[[self.Epas[f].pdf_and_mask(x = np.array([x, y])) for x in self.xgrid] for y in self.ygrid] for f in self.frame]) # pdb.set_trace() self.q = q_and_mask[:,:,:,0] mask = q_and_mask[:,:,:,1:] # return predict value, and component mask # q.shape : (frame, ygird ,xgrid) # mask.shape : (frame, ygrid, xgrid, mix) return self.q, mask def loss_specific_frame(self, f, obj_frame = 0): q, mask = self.predict_specific_frame(obj_frame = obj_frame) a, b = self.logistic_coefficient z = a * q + b U_q = 1/a * np.log(1 + np.exp(z)) loss = U_q - f[obj_frame] * q return loss, mask def loss(self, f): # f : data value q, mask = self.predict() a, b = self.logistic_coefficient z = a * q + b U_q = 1/a * np.log(1 + np.exp(z)) loss = U_q - f * q return loss, mask def gradient_specific_frame(self, f, obj_frame = 0): # it will get better for estimate mus, covs, and pi at first (without move) # return gradient of mus, covs, pi loss, mask = self.loss_specific_frame(f = f, obj_frame = obj_frame) self.Epas_specific_frame mus = self.params['mus'] + self.params['move'] * obj_frame covs = self.params['covs'] a, b = self.logistic_coefficient z = a * self.q_specific + b g = sigmoid(z) f = f[obj_frame] dpi = np.array([[ (g[y,x] - f[y,x]) \ * self.Epas_specific_frame.pdf_each(x = np.array([x, y])) for x in self.xgrid] for y in self.ygrid]) dmus = np.array([[ self.params['pi'] * (g[y,x] - f[y,x]) * 2 \ * np.linalg.solve(covs, (np.array([x, y]) - mus) ).T \ * mask[y, x] for x in self.xgrid] for y in self.ygrid]) # dcovs = np.array([[ ]]) pdb.set_trace() def gradient(self, f): loss, mask = self.loss(f = f) a, b = self.logistic_coefficient z = a * self.q + b g = sigmoid(z) def ModelPlot(self, frame = range(5), axtype='contourf'): if(self.predict_value== None): self.predict_value = self.predict() x = self.xy[:, 0] y = self.xy[:, 1] Z = self.predict_value xgrid = x.reshape(self.grid[0], self.grid[1]) ygrid = y.reshape(self.grid[0], self.grid[1]) for f in frame: fig = plt.figure() ax = Axes3D(fig) z = Z[f] zgrid = z.reshape(self.grid[0], self.grid[1]) if(axtype == 'wireframe'): ax.plot_wireframe(x, y, z) elif(axtype == 'contour'): ax.contour3D(xgrid, ygrid, zgrid) elif(axtype == 'contourf'): ax.contourf3D(xgrid, ygrid, zgrid) plt.show()

[ "tappy.hello-planet@ezweb.ne.jp" ]

tappy.hello-planet@ezweb.ne.jp

771d8fdd6297663bfb24be64ff621e13d7eb2298

a86d1744f3b77d4dbdfc01949d24f707205dfba1

/Euler19.py

d60650fe8fe9e4a3bb9ffe6806889183d67823a1

[]

no_license

ThomasR75/python_work

6da1737d5d54db654f3b022c3ea43659f90ec87d

0ae9d93dc2a7934b93062433e3ce136e8c8fdcea

refs/heads/master

2023-09-02T01:44:27.585714

2023-08-28T12:06:44

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#Euler 19 import datetime year = 1901 month = 1 day = 1 counter = 0 while year < 2001: while month < 13: date = datetime.datetime(year, month, day) x = datetime.datetime.weekday(date) if x == 6: counter = counter + 1 month = month + 1 year = year + 1 month = 1 print(counter) #date = datetime.datetime(year, month, day) #x = datetime.datetime.weekday(date) #print(x)

[ "ThomasR75@gmail.com" ]

ThomasR75@gmail.com

d7f644a7dba5f32a8525416d4e6ae82d13e7e038

c7654051c33c72cf2da830a522495bf0b6ba6d2b

/get_article_info.py

3695824556aec90858639ebda8dd066d18ee9c81

[]

no_license

SamuelYJKim/checkbait

74044ea29b24bb107e183eb2d7235c76154886e5

05db8968535e7342ae1e6190d3492b08b79a7645

refs/heads/main

2022-12-30T18:56:57.819089

2020-10-19T05:39:20

305,045,341

1

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2020-10-18T07:20:28

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# Retrieve news article url and returns info about article. # Title, Summary, Keywords from newsfetch.news import newspaper from summa import summarizer #article = "https://www.cnn.com/2020/10/17/health/us-coronavirus-saturday/index.html" # Returns title of article with URL def get_title(url): return newspaper(url).headline # Returns list of keywords with URL def get_keywords(url, queue): news = newspaper(url) queue.put(news.keywords) # return news.keywords # Returns summary of article (100 CHAR) with article text. def summarize(url, queue): news = newspaper(url) queue.put(summarizer.summarize(news.article, words=100)) # return summarizer.summarize(news.article, words=100)

[ "atalefard@gmail.com" ]

atalefard@gmail.com

7e2e5b6da245f3631bc35adfab5d6b5c034f2293

4fa68c72849d96cc2e8c04649f18573b605e6e8c

/prymer/converter.py

43c186561ba5ba768413327283209efa765ab63c

[]

no_license

jrjhealey/prymer

3bb1d6cd83272dcdaff810dace3fd68bfa7a6576

c82068cc5b560d602f4eb99af42dacd904ecc4be

refs/heads/master

2020-04-08T00:15:11.201293

2019-02-27T14:47:36

158,843,944

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# A converter function to allow multiple input sequence types # Big thanks to Peter van Heusden for refactoring the genbank code to yeild generators! import os from Bio import SeqIO from Bio.SeqRecord import SeqRecord class InputFormatException(Exception): """The exception to return when an input format cannot be identified or coerced (informative name)""" def __init__(self, message): super().__init__(message) def yield_from_genbank(infile): for rec in SeqIO.parse(infile, 'genbank'): cds_features = (f for f in rec.features if f.type == 'CDS') for feat in cds_features: try: header = feat.qualifiers['gene'][0] + '_' + rec.id except KeyError: header = feat.qualifiers['locus_tag'][0] + '_' + rec.id header.replace(' ', '_') yield SeqRecord(id=header, seq=feat.location.extract(rec).seq) def convert_seqs(infile): with open(infile, 'r') as ifh: firstline = ifh.readline() if os.path.splitext(infile)[1] in (".fasta", ".fa", ".fas", ".fna"): try: assert firstline[0] == '>' except AssertionError: raise InputFormatException("File extension implies fasta but the first line doesn't look like a header.") return SeqIO.parse(infile, 'fasta') elif os.path.splitext(infile)[1] in (".gbk", ".gb", ".genbank", ".gbff"): try: assert firstline.startswith("LOCUS") except AssertionError: raise InputFormatException("File extension implies genbank, but the first line doesn't look like a header.") return yield_from_genbank(infile)

[ "jrj.healey@gmail.com" ]

jrj.healey@gmail.com

0c13a0e21cc134454428127ba79f39c2fe89e3f7

84f08baaab25012fe46d16a6b7c3180fb4bb1097

/shop/views.py

bd5ab57a66a1d8ffc807f879f1db859bde2457ac

[]

no_license

caravan4eg/myshop

24fdccd4fd78720a0fbafa1565668f5229044d10

c00c12ebf09b84a7713ecf06ba5c128297b4b069

refs/heads/master

2022-10-19T20:16:54.372608

2020-06-11T16:26:50

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from django.shortcuts import render, get_object_or_404 from .models import Category, Product from cart.forms import CartAddProductForm def product_list(request, category_slug=None): category = None categories = Category.objects.all() products = Product.objects.filter(available=True) if category_slug: category = get_object_or_404(Category, slug=category_slug) products = products.filter(category=category) context = {'category': category, 'categories': categories, 'products': products} return render(request, 'shop/product/list.html', context) def product_detail(request, id, slug): product = get_object_or_404(Product, id=id, slug=slug, available=True) cart_product_form = CartAddProductForm() return render(request, 'shop/product/detail.html', {'product': product, 'cart_product_form': cart_product_form})

[ "alex.apanasik@gmail.com" ]

alex.apanasik@gmail.com

e25c7e7229cb9c605258c662fe0855bcd3294a98

c655f659c7dcd2657aeeef0177731f41790ad780

/retrain.py

a8d9ccda035d55a7945802e155af48459015f2b1

[]

no_license

AbdesslemOuerghemmi/Mvision

693b33923c8239c9e301db3f7f3af2d376ce728c

41529923b39a6db8230f77957be8da68b1412350

refs/heads/main

2023-01-19T03:05:01.226348

2020-11-20T11:54:00

314,537,091

0

null

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# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== r"""Simple transfer learning with Inception v3 or Mobilenet models. With support for TensorBoard. This example shows how to take a Inception v3 or Mobilenet model trained on ImageNet images, and train a new top layer that can recognize other classes of images. The top layer receives as input a 2048-dimensional vector (1001-dimensional for Mobilenet) for each image. We train a softmax layer on top of this representation. Assuming the softmax layer contains N labels, this corresponds to learning N + 2048*N (or 1001*N) model parameters corresponding to the learned biases and weights. Here's an example, which assumes you have a folder containing class-named subfolders, each full of images for each label. The example folder flower_photos should have a structure like this: ~/flower_photos/daisy/photo1.jpg ~/flower_photos/daisy/photo2.jpg ... ~/flower_photos/rose/anotherphoto77.jpg ... ~/flower_photos/sunflower/somepicture.jpg The subfolder names are important, since they define what label is applied to each image, but the filenames themselves don't matter. Once your images are prepared, you can run the training with a command like this: ```bash bazel build tensorflow/examples/image_retraining:retrain && \ bazel-bin/tensorflow/examples/image_retraining/retrain \ --image_dir ~/flower_photos ``` Or, if you have a pip installation of tensorflow, `retrain.py` can be run without bazel: ```bash python tensorflow/examples/image_retraining/retrain.py \ --image_dir ~/flower_photos ``` You can replace the image_dir argument with any folder containing subfolders of images. The label for each image is taken from the name of the subfolder it's in. This produces a new model file that can be loaded and run by any TensorFlow program, for example the label_image sample code. By default this script will use the high accuracy, but comparatively large and slow Inception v3 model architecture. It's recommended that you start with this to validate that you have gathered good training data, but if you want to deploy on resource-limited platforms, you can try the `--architecture` flag with a Mobilenet model. For example: ```bash python tensorflow/examples/image_retraining/retrain.py \ --image_dir ~/flower_photos --architecture mobilenet_1.0_224 ``` There are 32 different Mobilenet models to choose from, with a variety of file size and latency options. The first number can be '1.0', '0.75', '0.50', or '0.25' to control the size, and the second controls the input image size, either '224', '192', '160', or '128', with smaller sizes running faster. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html for more information on Mobilenet. To use with TensorBoard: By default, this script will log summaries to /tmp/retrain_logs directory Visualize the summaries with this command: tensorboard --logdir /tmp/retrain_logs """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import collections from datetime import datetime import hashlib import os.path import random import re import sys import tarfile import numpy as np from six.moves import urllib import tensorflow as tf from tensorflow.python.framework import graph_util from tensorflow.python.framework import tensor_shape from tensorflow.python.platform import gfile from tensorflow.python.util import compat FLAGS = None # These are all parameters that are tied to the particular model architecture # we're using for Inception v3. These include things like tensor names and their # sizes. If you want to adapt this script to work with another model, you will # need to update these to reflect the values in the network you're using. MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1 # ~134M def create_image_lists(image_dir, testing_percentage, validation_percentage): """Builds a list of training images from the file system. Analyzes the sub folders in the image directory, splits them into stable training, testing, and validation sets, and returns a data structure describing the lists of images for each label and their paths. Args: image_dir: String path to a folder containing subfolders of images. testing_percentage: Integer percentage of the images to reserve for tests. validation_percentage: Integer percentage of images reserved for validation. Returns: A dictionary containing an entry for each label subfolder, with images split into training, testing, and validation sets within each label. """ if not gfile.Exists(image_dir): tf.logging.error("Image directory '" + image_dir + "' not found.") return None result = collections.OrderedDict() sub_dirs = [ os.path.join(image_dir,item) for item in gfile.ListDirectory(image_dir)] sub_dirs = sorted(item for item in sub_dirs if gfile.IsDirectory(item)) for sub_dir in sub_dirs: extensions = ['jpg', 'jpeg', 'JPG', 'JPEG'] file_list = [] dir_name = os.path.basename(sub_dir) if dir_name == image_dir: continue tf.logging.info("Looking for images in '" + dir_name + "'") for extension in extensions: file_glob = os.path.join(image_dir, dir_name, '*.' + extension) file_list.extend(gfile.Glob(file_glob)) if not file_list: tf.logging.warning('No files found') continue if len(file_list) < 20: tf.logging.warning( 'WARNING: Folder has less than 20 images, which may cause issues.') elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS: tf.logging.warning( 'WARNING: Folder {} has more than {} images. Some images will ' 'never be selected.'.format(dir_name, MAX_NUM_IMAGES_PER_CLASS)) label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower()) training_images = [] testing_images = [] validation_images = [] for file_name in file_list: base_name = os.path.basename(file_name) # We want to ignore anything after '_nohash_' in the file name when # deciding which set to put an image in, the data set creator has a way of # grouping photos that are close variations of each other. For example # this is used in the plant disease data set to group multiple pictures of # the same leaf. hash_name = re.sub(r'_nohash_.*$', '', file_name) # This looks a bit magical, but we need to decide whether this file should # go into the training, testing, or validation sets, and we want to keep # existing files in the same set even if more files are subsequently # added. # To do that, we need a stable way of deciding based on just the file name # itself, so we do a hash of that and then use that to generate a # probability value that we use to assign it. hash_name_hashed = hashlib.sha1(compat.as_bytes(hash_name)).hexdigest() percentage_hash = ((int(hash_name_hashed, 16) % (MAX_NUM_IMAGES_PER_CLASS + 1)) * (100.0 / MAX_NUM_IMAGES_PER_CLASS)) if len(validation_images) == 0: validation_images.append(base_name) elif percentage_hash < validation_percentage: testing_images.append(base_name) else: training_images.append(base_name) result[label_name] = { 'dir': dir_name, 'training': training_images, 'testing': testing_images, 'validation': validation_images, } return result def get_image_path(image_lists, label_name, index, image_dir, category): """"Returns a path to an image for a label at the given index. Args: image_lists: Dictionary of training images for each label. label_name: Label string we want to get an image for. index: Int offset of the image we want. This will be moduloed by the available number of images for the label, so it can be arbitrarily large. image_dir: Root folder string of the subfolders containing the training images. category: Name string of set to pull images from - training, testing, or validation. Returns: File system path string to an image that meets the requested parameters. """ if label_name not in image_lists: tf.logging.fatal('Label does not exist %s.', label_name) label_lists = image_lists[label_name] if category not in label_lists: tf.logging.fatal('Category does not exist %s.', category) category_list = label_lists[category] if not category_list: tf.logging.fatal('Label %s has no images in the category %s.', label_name, category) mod_index = index % len(category_list) base_name = category_list[mod_index] sub_dir = label_lists['dir'] full_path = os.path.join(image_dir, sub_dir, base_name) return full_path def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir, category, architecture): """"Returns a path to a bottleneck file for a label at the given index. Args: image_lists: Dictionary of training images for each label. label_name: Label string we want to get an image for. index: Integer offset of the image we want. This will be moduloed by the available number of images for the label, so it can be arbitrarily large. bottleneck_dir: Folder string holding cached files of bottleneck values. category: Name string of set to pull images from - training, testing, or validation. architecture: The name of the model architecture. Returns: File system path string to an image that meets the requested parameters. """ return get_image_path(image_lists, label_name, index, bottleneck_dir, category) + '_' + architecture + '.txt' def create_model_graph(model_info): """"Creates a graph from saved GraphDef file and returns a Graph object. Args: model_info: Dictionary containing information about the model architecture. Returns: Graph holding the trained Inception network, and various tensors we'll be manipulating. """ with tf.Graph().as_default() as graph: model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name']) with gfile.FastGFile(model_path, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) bottleneck_tensor, resized_input_tensor = (tf.import_graph_def( graph_def, name='', return_elements=[ model_info['bottleneck_tensor_name'], model_info['resized_input_tensor_name'], ])) return graph, bottleneck_tensor, resized_input_tensor def run_bottleneck_on_image(sess, image_data, image_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor): """Runs inference on an image to extract the 'bottleneck' summary layer. Args: sess: Current active TensorFlow Session. image_data: String of raw JPEG data. image_data_tensor: Input data layer in the graph. decoded_image_tensor: Output of initial image resizing and preprocessing. resized_input_tensor: The input node of the recognition graph. bottleneck_tensor: Layer before the final softmax. Returns: Numpy array of bottleneck values. """ # First decode the JPEG image, resize it, and rescale the pixel values. resized_input_values = sess.run(decoded_image_tensor, {image_data_tensor: image_data}) # Then run it through the recognition network. bottleneck_values = sess.run(bottleneck_tensor, {resized_input_tensor: resized_input_values}) bottleneck_values = np.squeeze(bottleneck_values) return bottleneck_values def maybe_download_and_extract(data_url): """Download and extract model tar file. If the pretrained model we're using doesn't already exist, this function downloads it from the TensorFlow.org website and unpacks it into a directory. Args: data_url: Web location of the tar file containing the pretrained model. """ dest_directory = FLAGS.model_dir if not os.path.exists(dest_directory): os.makedirs(dest_directory) filename = data_url.split('/')[-1] filepath = os.path.join(dest_directory, filename) if not os.path.exists(filepath): def _progress(count, block_size, total_size): sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename, float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress) print() statinfo = os.stat(filepath) tf.logging.info('Successfully downloaded', filename, statinfo.st_size, 'bytes.') tarfile.open(filepath, 'r:gz').extractall(dest_directory) def ensure_dir_exists(dir_name): """Makes sure the folder exists on disk. Args: dir_name: Path string to the folder we want to create. """ if not os.path.exists(dir_name): os.makedirs(dir_name) bottleneck_path_2_bottleneck_values = {} def create_bottleneck_file(bottleneck_path, image_lists, label_name, index, image_dir, category, sess, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor): """Create a single bottleneck file.""" tf.logging.info('Creating bottleneck at ' + bottleneck_path) image_path = get_image_path(image_lists, label_name, index, image_dir, category) if not gfile.Exists(image_path): tf.logging.fatal('File does not exist %s', image_path) image_data = gfile.FastGFile(image_path, 'rb').read() try: bottleneck_values = run_bottleneck_on_image( sess, image_data, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor) except Exception as e: raise RuntimeError('Error during processing file %s (%s)' % (image_path, str(e))) bottleneck_string = ','.join(str(x) for x in bottleneck_values) with open(bottleneck_path, 'w') as bottleneck_file: bottleneck_file.write(bottleneck_string) def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir, category, bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, architecture): """Retrieves or calculates bottleneck values for an image. If a cached version of the bottleneck data exists on-disk, return that, otherwise calculate the data and save it to disk for future use. Args: sess: The current active TensorFlow Session. image_lists: Dictionary of training images for each label. label_name: Label string we want to get an image for. index: Integer offset of the image we want. This will be modulo-ed by the available number of images for the label, so it can be arbitrarily large. image_dir: Root folder string of the subfolders containing the training images. category: Name string of which set to pull images from - training, testing, or validation. bottleneck_dir: Folder string holding cached files of bottleneck values. jpeg_data_tensor: The tensor to feed loaded jpeg data into. decoded_image_tensor: The output of decoding and resizing the image. resized_input_tensor: The input node of the recognition graph. bottleneck_tensor: The output tensor for the bottleneck values. architecture: The name of the model architecture. Returns: Numpy array of values produced by the bottleneck layer for the image. """ label_lists = image_lists[label_name] sub_dir = label_lists['dir'] sub_dir_path = os.path.join(bottleneck_dir, sub_dir) ensure_dir_exists(sub_dir_path) bottleneck_path = get_bottleneck_path(image_lists, label_name, index, bottleneck_dir, category, architecture) if not os.path.exists(bottleneck_path): create_bottleneck_file(bottleneck_path, image_lists, label_name, index, image_dir, category, sess, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor) with open(bottleneck_path, 'r') as bottleneck_file: bottleneck_string = bottleneck_file.read() did_hit_error = False try: bottleneck_values = [float(x) for x in bottleneck_string.split(',')] except ValueError: tf.logging.warning('Invalid float found, recreating bottleneck') did_hit_error = True if did_hit_error: create_bottleneck_file(bottleneck_path, image_lists, label_name, index, image_dir, category, sess, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor) with open(bottleneck_path, 'r') as bottleneck_file: bottleneck_string = bottleneck_file.read() # Allow exceptions to propagate here, since they shouldn't happen after a # fresh creation bottleneck_values = [float(x) for x in bottleneck_string.split(',')] return bottleneck_values def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, architecture): """Ensures all the training, testing, and validation bottlenecks are cached. Because we're likely to read the same image multiple times (if there are no distortions applied during training) it can speed things up a lot if we calculate the bottleneck layer values once for each image during preprocessing, and then just read those cached values repeatedly during training. Here we go through all the images we've found, calculate those values, and save them off. Args: sess: The current active TensorFlow Session. image_lists: Dictionary of training images for each label. image_dir: Root folder string of the subfolders containing the training images. bottleneck_dir: Folder string holding cached files of bottleneck values. jpeg_data_tensor: Input tensor for jpeg data from file. decoded_image_tensor: The output of decoding and resizing the image. resized_input_tensor: The input node of the recognition graph. bottleneck_tensor: The penultimate output layer of the graph. architecture: The name of the model architecture. Returns: Nothing. """ how_many_bottlenecks = 0 ensure_dir_exists(bottleneck_dir) for label_name, label_lists in image_lists.items(): for category in ['training', 'testing', 'validation']: category_list = label_lists[category] for index, unused_base_name in enumerate(category_list): get_or_create_bottleneck( sess, image_lists, label_name, index, image_dir, category, bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, architecture) how_many_bottlenecks += 1 if how_many_bottlenecks % 100 == 0: tf.logging.info( str(how_many_bottlenecks) + ' bottleneck files created.') def get_random_cached_bottlenecks(sess, image_lists, how_many, category, bottleneck_dir, image_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, architecture): """Retrieves bottleneck values for cached images. If no distortions are being applied, this function can retrieve the cached bottleneck values directly from disk for images. It picks a random set of images from the specified category. Args: sess: Current TensorFlow Session. image_lists: Dictionary of training images for each label. how_many: If positive, a random sample of this size will be chosen. If negative, all bottlenecks will be retrieved. category: Name string of which set to pull from - training, testing, or validation. bottleneck_dir: Folder string holding cached files of bottleneck values. image_dir: Root folder string of the subfolders containing the training images. jpeg_data_tensor: The layer to feed jpeg image data into. decoded_image_tensor: The output of decoding and resizing the image. resized_input_tensor: The input node of the recognition graph. bottleneck_tensor: The bottleneck output layer of the CNN graph. architecture: The name of the model architecture. Returns: List of bottleneck arrays, their corresponding ground truths, and the relevant filenames. """ class_count = len(image_lists.keys()) bottlenecks = [] ground_truths = [] filenames = [] if how_many >= 0: # Retrieve a random sample of bottlenecks. for unused_i in range(how_many): label_index = random.randrange(class_count) label_name = list(image_lists.keys())[label_index] image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1) image_name = get_image_path(image_lists, label_name, image_index, image_dir, category) bottleneck = get_or_create_bottleneck( sess, image_lists, label_name, image_index, image_dir, category, bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, architecture) ground_truth = np.zeros(class_count, dtype=np.float32) ground_truth[label_index] = 1.0 bottlenecks.append(bottleneck) ground_truths.append(ground_truth) filenames.append(image_name) else: # Retrieve all bottlenecks. for label_index, label_name in enumerate(image_lists.keys()): for image_index, image_name in enumerate( image_lists[label_name][category]): image_name = get_image_path(image_lists, label_name, image_index, image_dir, category) bottleneck = get_or_create_bottleneck( sess, image_lists, label_name, image_index, image_dir, category, bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, architecture) ground_truth = np.zeros(class_count, dtype=np.float32) ground_truth[label_index] = 1.0 bottlenecks.append(bottleneck) ground_truths.append(ground_truth) filenames.append(image_name) return bottlenecks, ground_truths, filenames def get_random_distorted_bottlenecks( sess, image_lists, how_many, category, image_dir, input_jpeg_tensor, distorted_image, resized_input_tensor, bottleneck_tensor): """Retrieves bottleneck values for training images, after distortions. If we're training with distortions like crops, scales, or flips, we have to recalculate the full model for every image, and so we can't use cached bottleneck values. Instead we find random images for the requested category, run them through the distortion graph, and then the full graph to get the bottleneck results for each. Args: sess: Current TensorFlow Session. image_lists: Dictionary of training images for each label. how_many: The integer number of bottleneck values to return. category: Name string of which set of images to fetch - training, testing, or validation. image_dir: Root folder string of the subfolders containing the training images. input_jpeg_tensor: The input layer we feed the image data to. distorted_image: The output node of the distortion graph. resized_input_tensor: The input node of the recognition graph. bottleneck_tensor: The bottleneck output layer of the CNN graph. Returns: List of bottleneck arrays and their corresponding ground truths. """ class_count = len(image_lists.keys()) bottlenecks = [] ground_truths = [] for unused_i in range(how_many): label_index = random.randrange(class_count) label_name = list(image_lists.keys())[label_index] image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1) image_path = get_image_path(image_lists, label_name, image_index, image_dir, category) if not gfile.Exists(image_path): tf.logging.fatal('File does not exist %s', image_path) jpeg_data = gfile.FastGFile(image_path, 'rb').read() # Note that we materialize the distorted_image_data as a numpy array before # sending running inference on the image. This involves 2 memory copies and # might be optimized in other implementations. distorted_image_data = sess.run(distorted_image, {input_jpeg_tensor: jpeg_data}) bottleneck_values = sess.run(bottleneck_tensor, {resized_input_tensor: distorted_image_data}) bottleneck_values = np.squeeze(bottleneck_values) ground_truth = np.zeros(class_count, dtype=np.float32) ground_truth[label_index] = 1.0 bottlenecks.append(bottleneck_values) ground_truths.append(ground_truth) return bottlenecks, ground_truths def should_distort_images(flip_left_right, random_crop, random_scale, random_brightness): """Whether any distortions are enabled, from the input flags. Args: flip_left_right: Boolean whether to randomly mirror images horizontally. random_crop: Integer percentage setting the total margin used around the crop box. random_scale: Integer percentage of how much to vary the scale by. random_brightness: Integer range to randomly multiply the pixel values by. Returns: Boolean value indicating whether any distortions should be applied. """ return (flip_left_right or (random_crop != 0) or (random_scale != 0) or (random_brightness != 0)) def add_input_distortions(flip_left_right, random_crop, random_scale, random_brightness, input_width, input_height, input_depth, input_mean, input_std): """Creates the operations to apply the specified distortions. During training it can help to improve the results if we run the images through simple distortions like crops, scales, and flips. These reflect the kind of variations we expect in the real world, and so can help train the model to cope with natural data more effectively. Here we take the supplied parameters and construct a network of operations to apply them to an image. Cropping ~~~~~~~~ Cropping is done by placing a bounding box at a random position in the full image. The cropping parameter controls the size of that box relative to the input image. If it's zero, then the box is the same size as the input and no cropping is performed. If the value is 50%, then the crop box will be half the width and height of the input. In a diagram it looks like this: < width > +---------------------+ | | | width - crop% | | < > | | +------+ | | | | | | | | | | | | | | +------+ | | | | | +---------------------+ Scaling ~~~~~~~ Scaling is a lot like cropping, except that the bounding box is always centered and its size varies randomly within the given range. For example if the scale percentage is zero, then the bounding box is the same size as the input and no scaling is applied. If it's 50%, then the bounding box will be in a random range between half the width and height and full size. Args: flip_left_right: Boolean whether to randomly mirror images horizontally. random_crop: Integer percentage setting the total margin used around the crop box. random_scale: Integer percentage of how much to vary the scale by. random_brightness: Integer range to randomly multiply the pixel values by. graph. input_width: Horizontal size of expected input image to model. input_height: Vertical size of expected input image to model. input_depth: How many channels the expected input image should have. input_mean: Pixel value that should be zero in the image for the graph. input_std: How much to divide the pixel values by before recognition. Returns: The jpeg input layer and the distorted result tensor. """ jpeg_data = tf.placeholder(tf.string, name='DistortJPGInput') decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth) decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32) decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0) margin_scale = 1.0 + (random_crop / 100.0) resize_scale = 1.0 + (random_scale / 100.0) margin_scale_value = tf.constant(margin_scale) resize_scale_value = tf.random_uniform(tensor_shape.scalar(), minval=1.0, maxval=resize_scale) scale_value = tf.multiply(margin_scale_value, resize_scale_value) precrop_width = tf.multiply(scale_value, input_width) precrop_height = tf.multiply(scale_value, input_height) precrop_shape = tf.stack([precrop_height, precrop_width]) precrop_shape_as_int = tf.cast(precrop_shape, dtype=tf.int32) precropped_image = tf.image.resize_bilinear(decoded_image_4d, precrop_shape_as_int) precropped_image_3d = tf.squeeze(precropped_image, squeeze_dims=[0]) cropped_image = tf.random_crop(precropped_image_3d, [input_height, input_width, input_depth]) if flip_left_right: flipped_image = tf.image.random_flip_left_right(cropped_image) else: flipped_image = cropped_image brightness_min = 1.0 - (random_brightness / 100.0) brightness_max = 1.0 + (random_brightness / 100.0) brightness_value = tf.random_uniform(tensor_shape.scalar(), minval=brightness_min, maxval=brightness_max) brightened_image = tf.multiply(flipped_image, brightness_value) offset_image = tf.subtract(brightened_image, input_mean) mul_image = tf.multiply(offset_image, 1.0 / input_std) distort_result = tf.expand_dims(mul_image, 0, name='DistortResult') return jpeg_data, distort_result def variable_summaries(var): """Attach a lot of summaries to a Tensor (for TensorBoard visualization).""" with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) tf.summary.scalar('stddev', stddev) tf.summary.scalar('max', tf.reduce_max(var)) tf.summary.scalar('min', tf.reduce_min(var)) tf.summary.histogram('histogram', var) def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, bottleneck_tensor_size): """Adds a new softmax and fully-connected layer for training. We need to retrain the top layer to identify our new classes, so this function adds the right operations to the graph, along with some variables to hold the weights, and then sets up all the gradients for the backward pass. The set up for the softmax and fully-connected layers is based on: https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html Args: class_count: Integer of how many categories of things we're trying to recognize. final_tensor_name: Name string for the new final node that produces results. bottleneck_tensor: The output of the main CNN graph. bottleneck_tensor_size: How many entries in the bottleneck vector. Returns: The tensors for the training and cross entropy results, and tensors for the bottleneck input and ground truth input. """ with tf.name_scope('input'): bottleneck_input = tf.placeholder_with_default( bottleneck_tensor, shape=[None, bottleneck_tensor_size], name='BottleneckInputPlaceholder') ground_truth_input = tf.placeholder(tf.float32, [None, class_count], name='GroundTruthInput') # Organizing the following ops as `final_training_ops` so they're easier # to see in TensorBoard layer_name = 'final_training_ops' with tf.name_scope(layer_name): with tf.name_scope('weights'): initial_value = tf.truncated_normal( [bottleneck_tensor_size, class_count], stddev=0.001) layer_weights = tf.Variable(initial_value, name='final_weights') variable_summaries(layer_weights) with tf.name_scope('biases'): layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases') variable_summaries(layer_biases) with tf.name_scope('Wx_plus_b'): logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases tf.summary.histogram('pre_activations', logits) final_tensor = tf.nn.softmax(logits, name=final_tensor_name) tf.summary.histogram('activations', final_tensor) with tf.name_scope('cross_entropy'): cross_entropy = tf.nn.softmax_cross_entropy_with_logits( labels=ground_truth_input, logits=logits) with tf.name_scope('total'): cross_entropy_mean = tf.reduce_mean(cross_entropy) tf.summary.scalar('cross_entropy', cross_entropy_mean) with tf.name_scope('train'): optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate) train_step = optimizer.minimize(cross_entropy_mean) return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input, final_tensor) def add_evaluation_step(result_tensor, ground_truth_tensor): """Inserts the operations we need to evaluate the accuracy of our results. Args: result_tensor: The new final node that produces results. ground_truth_tensor: The node we feed ground truth data into. Returns: Tuple of (evaluation step, prediction). """ with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): prediction = tf.argmax(result_tensor, 1) correct_prediction = tf.equal( prediction, tf.argmax(ground_truth_tensor, 1)) with tf.name_scope('accuracy'): evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.summary.scalar('accuracy', evaluation_step) return evaluation_step, prediction def save_graph_to_file(sess, graph, graph_file_name): output_graph_def = graph_util.convert_variables_to_constants( sess, graph.as_graph_def(), [FLAGS.final_tensor_name]) with gfile.FastGFile(graph_file_name, 'wb') as f: f.write(output_graph_def.SerializeToString()) return def prepare_file_system(): # Setup the directory we'll write summaries to for TensorBoard if tf.gfile.Exists(FLAGS.summaries_dir): tf.gfile.DeleteRecursively(FLAGS.summaries_dir) tf.gfile.MakeDirs(FLAGS.summaries_dir) if FLAGS.intermediate_store_frequency > 0: ensure_dir_exists(FLAGS.intermediate_output_graphs_dir) return def create_model_info(architecture): """Given the name of a model architecture, returns information about it. There are different base image recognition pretrained models that can be retrained using transfer learning, and this function translates from the name of a model to the attributes that are needed to download and train with it. Args: architecture: Name of a model architecture. Returns: Dictionary of information about the model, or None if the name isn't recognized Raises: ValueError: If architecture name is unknown. """ architecture = architecture.lower() if architecture == 'inception_v3': # pylint: disable=line-too-long data_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' # pylint: enable=line-too-long bottleneck_tensor_name = 'pool_3/_reshape:0' bottleneck_tensor_size = 2048 input_width = 299 input_height = 299 input_depth = 3 resized_input_tensor_name = 'Mul:0' model_file_name = 'classify_image_graph_def.pb' input_mean = 128 input_std = 128 elif architecture.startswith('mobilenet_'): parts = architecture.split('_') if len(parts) != 3 and len(parts) != 4: tf.logging.error("Couldn't understand architecture name '%s'", architecture) return None version_string = parts[1] if (version_string != '1.0' and version_string != '0.75' and version_string != '0.50' and version_string != '0.25'): tf.logging.error( """"The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25', but found '%s' for architecture '%s'""", version_string, architecture) return None size_string = parts[2] if (size_string != '224' and size_string != '192' and size_string != '160' and size_string != '128'): tf.logging.error( """The Mobilenet input size should be '224', '192', '160', or '128', but found '%s' for architecture '%s'""", size_string, architecture) return None if len(parts) == 3: is_quantized = False else: if parts[3] != 'quantized': tf.logging.error( "Couldn't understand architecture suffix '%s' for '%s'", parts[3], architecture) return None is_quantized = True data_url = 'http://download.tensorflow.org/models/mobilenet_v1_' data_url += version_string + '_' + size_string + '_frozen.tgz' bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' bottleneck_tensor_size = 1001 input_width = int(size_string) input_height = int(size_string) input_depth = 3 resized_input_tensor_name = 'input:0' if is_quantized: model_base_name = 'quantized_graph.pb' else: model_base_name = 'frozen_graph.pb' model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string model_file_name = os.path.join(model_dir_name, model_base_name) input_mean = 127.5 input_std = 127.5 else: tf.logging.error("Couldn't understand architecture name '%s'", architecture) raise ValueError('Unknown architecture', architecture) return { 'data_url': data_url, 'bottleneck_tensor_name': bottleneck_tensor_name, 'bottleneck_tensor_size': bottleneck_tensor_size, 'input_width': input_width, 'input_height': input_height, 'input_depth': input_depth, 'resized_input_tensor_name': resized_input_tensor_name, 'model_file_name': model_file_name, 'input_mean': input_mean, 'input_std': input_std, } def add_jpeg_decoding(input_width, input_height, input_depth, input_mean, input_std): """Adds operations that perform JPEG decoding and resizing to the graph.. Args: input_width: Desired width of the image fed into the recognizer graph. input_height: Desired width of the image fed into the recognizer graph. input_depth: Desired channels of the image fed into the recognizer graph. input_mean: Pixel value that should be zero in the image for the graph. input_std: How much to divide the pixel values by before recognition. Returns: Tensors for the node to feed JPEG data into, and the output of the preprocessing steps. """ jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput') decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth) decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32) decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0) resize_shape = tf.stack([input_height, input_width]) resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32) resized_image = tf.image.resize_bilinear(decoded_image_4d, resize_shape_as_int) offset_image = tf.subtract(resized_image, input_mean) mul_image = tf.multiply(offset_image, 1.0 / input_std) return jpeg_data, mul_image def main(_): # Needed to make sure the logging output is visible. # See https://github.com/tensorflow/tensorflow/issues/3047 tf.logging.set_verbosity(tf.logging.INFO) # Prepare necessary directories that can be used during training prepare_file_system() # Gather information about the model architecture we'll be using. model_info = create_model_info(FLAGS.architecture) if not model_info: tf.logging.error('Did not recognize architecture flag') return -1 # Set up the pre-trained graph. maybe_download_and_extract(model_info['data_url']) graph, bottleneck_tensor, resized_image_tensor = ( create_model_graph(model_info)) # Look at the folder structure, and create lists of all the images. image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage, FLAGS.validation_percentage) class_count = len(image_lists.keys()) if class_count == 0: tf.logging.error('No valid folders of images found at ' + FLAGS.image_dir) return -1 if class_count == 1: tf.logging.error('Only one valid folder of images found at ' + FLAGS.image_dir + ' - multiple classes are needed for classification.') return -1 # See if the command-line flags mean we're applying any distortions. do_distort_images = should_distort_images( FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale, FLAGS.random_brightness) with tf.Session(graph=graph) as sess: # Set up the image decoding sub-graph. jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding( model_info['input_width'], model_info['input_height'], model_info['input_depth'], model_info['input_mean'], model_info['input_std']) if do_distort_images: # We will be applying distortions, so setup the operations we'll need. (distorted_jpeg_data_tensor, distorted_image_tensor) = add_input_distortions( FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale, FLAGS.random_brightness, model_info['input_width'], model_info['input_height'], model_info['input_depth'], model_info['input_mean'], model_info['input_std']) else: # We'll make sure we've calculated the 'bottleneck' image summaries and # cached them on disk. cache_bottlenecks(sess, image_lists, FLAGS.image_dir, FLAGS.bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_image_tensor, bottleneck_tensor, FLAGS.architecture) # Add the new layer that we'll be training. (train_step, cross_entropy, bottleneck_input, ground_truth_input, final_tensor) = add_final_training_ops( len(image_lists.keys()), FLAGS.final_tensor_name, bottleneck_tensor, model_info['bottleneck_tensor_size']) # Create the operations we need to evaluate the accuracy of our new layer. evaluation_step, prediction = add_evaluation_step( final_tensor, ground_truth_input) # Merge all the summaries and write them out to the summaries_dir merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train', sess.graph) validation_writer = tf.summary.FileWriter( FLAGS.summaries_dir + '/validation') # Set up all our weights to their initial default values. init = tf.global_variables_initializer() sess.run(init) # Run the training for as many cycles as requested on the command line. for i in range(FLAGS.how_many_training_steps): # Get a batch of input bottleneck values, either calculated fresh every # time with distortions applied, or from the cache stored on disk. if do_distort_images: (train_bottlenecks, train_ground_truth) = get_random_distorted_bottlenecks( sess, image_lists, FLAGS.train_batch_size, 'training', FLAGS.image_dir, distorted_jpeg_data_tensor, distorted_image_tensor, resized_image_tensor, bottleneck_tensor) else: (train_bottlenecks, train_ground_truth, _) = get_random_cached_bottlenecks( sess, image_lists, FLAGS.train_batch_size, 'training', FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor, decoded_image_tensor, resized_image_tensor, bottleneck_tensor, FLAGS.architecture) # Feed the bottlenecks and ground truth into the graph, and run a training # step. Capture training summaries for TensorBoard with the `merged` op. train_summary, _ = sess.run( [merged, train_step], feed_dict={bottleneck_input: train_bottlenecks, ground_truth_input: train_ground_truth}) train_writer.add_summary(train_summary, i) # Every so often, print out how well the graph is training. is_last_step = (i + 1 == FLAGS.how_many_training_steps) if (i % FLAGS.eval_step_interval) == 0 or is_last_step: train_accuracy, cross_entropy_value = sess.run( [evaluation_step, cross_entropy], feed_dict={bottleneck_input: train_bottlenecks, ground_truth_input: train_ground_truth}) tf.logging.info('%s: Step %d: Train accuracy = %.1f%%' % (datetime.now(), i, train_accuracy * 100)) tf.logging.info('%s: Step %d: Cross entropy = %f' % (datetime.now(), i, cross_entropy_value)) validation_bottlenecks, validation_ground_truth, _ = ( get_random_cached_bottlenecks( sess, image_lists, FLAGS.validation_batch_size, 'validation', FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor, decoded_image_tensor, resized_image_tensor, bottleneck_tensor, FLAGS.architecture)) # Run a validation step and capture training summaries for TensorBoard # with the `merged` op. validation_summary, validation_accuracy = sess.run( [merged, evaluation_step], feed_dict={bottleneck_input: validation_bottlenecks, ground_truth_input: validation_ground_truth}) validation_writer.add_summary(validation_summary, i) tf.logging.info('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' % (datetime.now(), i, validation_accuracy * 100, len(validation_bottlenecks))) # Store intermediate results intermediate_frequency = FLAGS.intermediate_store_frequency if (intermediate_frequency > 0 and (i % intermediate_frequency == 0) and i > 0): intermediate_file_name = (FLAGS.intermediate_output_graphs_dir + 'intermediate_' + str(i) + '.pb') tf.logging.info('Save intermediate result to : ' + intermediate_file_name) save_graph_to_file(sess, graph, intermediate_file_name) # We've completed all our training, so run a final test evaluation on # some new images we haven't used before. test_bottlenecks, test_ground_truth, test_filenames = ( get_random_cached_bottlenecks( sess, image_lists, FLAGS.test_batch_size, 'testing', FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor, decoded_image_tensor, resized_image_tensor, bottleneck_tensor, FLAGS.architecture)) test_accuracy, predictions = sess.run( [evaluation_step, prediction], feed_dict={bottleneck_input: test_bottlenecks, ground_truth_input: test_ground_truth}) tf.logging.info('Final test accuracy = %.1f%% (N=%d)' % (test_accuracy * 100, len(test_bottlenecks))) if FLAGS.print_misclassified_test_images: tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===') for i, test_filename in enumerate(test_filenames): if predictions[i] != test_ground_truth[i].argmax(): tf.logging.info('%70s %s' % (test_filename, list(image_lists.keys())[predictions[i]])) # Write out the trained graph and labels with the weights stored as # constants. save_graph_to_file(sess, graph, FLAGS.output_graph) with gfile.FastGFile(FLAGS.output_labels, 'w') as f: f.write('\n'.join(image_lists.keys()) + '\n') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--image_dir', type=str, default='', help='Path to folders of labeled images.' ) parser.add_argument( '--output_graph', type=str, default='/tmp/output_graph.pb', help='Where to save the trained graph.' ) parser.add_argument( '--intermediate_output_graphs_dir', type=str, default='/tmp/intermediate_graph/', help='Where to save the intermediate graphs.' ) parser.add_argument( '--intermediate_store_frequency', type=int, default=0, help="""\ How many steps to store intermediate graph. If "0" then will not store.\ """ ) parser.add_argument( '--output_labels', type=str, default='/tmp/output_labels.txt', help='Where to save the trained graph\'s labels.' ) parser.add_argument( '--summaries_dir', type=str, default='/tmp/retrain_logs', help='Where to save summary logs for TensorBoard.' ) parser.add_argument( '--how_many_training_steps', type=int, default=6000, help='How many training steps to run before ending.' ) parser.add_argument( '--learning_rate', type=float, default=0.01, help='How large a learning rate to use when training.' ) parser.add_argument( '--testing_percentage', type=int, default=10, help='What percentage of images to use as a test set.' ) parser.add_argument( '--validation_percentage', type=int, default=10, help='What percentage of images to use as a validation set.' ) parser.add_argument( '--eval_step_interval', type=int, default=10, help='How often to evaluate the training results.' ) parser.add_argument( '--train_batch_size', type=int, default=100, help='How many images to train on at a time.' ) parser.add_argument( '--test_batch_size', type=int, default=-1, help="""\ How many images to test on. This test set is only used once, to evaluate the final accuracy of the model after training completes. A value of -1 causes the entire test set to be used, which leads to more stable results across runs.\ """ ) parser.add_argument( '--validation_batch_size', type=int, default=100, help="""\ How many images to use in an evaluation batch. This validation set is used much more often than the test set, and is an early indicator of how accurate the model is during training. A value of -1 causes the entire validation set to be used, which leads to more stable results across training iterations, but may be slower on large training sets.\ """ ) parser.add_argument( '--print_misclassified_test_images', default=False, help="""\ Whether to print out a list of all misclassified test images.\ """, action='store_true' ) parser.add_argument( '--model_dir', type=str, default='/tmp/imagenet', help="""\ Path to classify_image_graph_def.pb, imagenet_synset_to_human_label_map.txt, and imagenet_2012_challenge_label_map_proto.pbtxt.\ """ ) parser.add_argument( '--bottleneck_dir', type=str, default='/tmp/bottleneck', help='Path to cache bottleneck layer values as files.' ) parser.add_argument( '--final_tensor_name', type=str, default='final_result', help="""\ The name of the output classification layer in the retrained graph.\ """ ) parser.add_argument( '--flip_left_right', default=False, help="""\ Whether to randomly flip half of the training images horizontally.\ """, action='store_true' ) parser.add_argument( '--random_crop', type=int, default=0, help="""\ A percentage determining how much of a margin to randomly crop off the training images.\ """ ) parser.add_argument( '--random_scale', type=int, default=0, help="""\ A percentage determining how much to randomly scale up the size of the training images by.\ """ ) parser.add_argument( '--random_brightness', type=int, default=0, help="""\ A percentage determining how much to randomly multiply the training image input pixels up or down by.\ """ ) parser.add_argument( '--architecture', type=str, default='inception_v3', help="""\ Which model architecture to use. 'inception_v3' is the most accurate, but also the slowest. For faster or smaller models, chose a MobileNet with the form 'mobilenet_<parameter size>_<input_size>[_quantized]'. For example, 'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224 pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much less accurate, but smaller and faster network that's 920 KB on disk and takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html for more information on Mobilenet.\ """) FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

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from collections import defaultdict dict1 = defaultdict(int) dict2 = defaultdict(set) dict3 = defaultdict(str) dict4 = defaultdict(list) dict1[2] ='two' print(dict1[1]) print(dict2[1]) print(dict3[1]) print(dict4[1]) dict1[0]=1 print(dict1[3]) print(dict1.items()) print(dict1.popitem())

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('ims', '0004_auto_20150912_0647'), ] operations = [ migrations.CreateModel( name='ProductCategory', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('category', models.CharField(default=b'', max_length=100)), ], options={ }, bases=(models.Model,), ), migrations.AddField( model_name='productinformation', name='category', field=models.OneToOneField(null=True, blank=True, to='ims.ProductCategory', help_text=b'Category'), preserve_default=True, ), ]

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import django from django.apps import AppConfig from django.utils.translation import ugettext_lazy as _ from django_jsonfield_backport import features, models class JSONFieldConfig(AppConfig): name = "django_jsonfield_backport" verbose_name = _("JSONField backport from Django 3.1") def ready(self): if django.VERSION >= (3, 1): return features.connect_signal_receivers() models.register_lookups()

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boat = """00000 01100 01010 00100 00000""" beacon = """000000 011000 011000 000110 000110 000000""" blinker = """00000 00000 01110 00000 00000""" glider = """000000 010100 001100 001000 000000"""

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#!/usr/bin/python3 import unittest from city_functions import city_country class cityTestCase(unittest.TestCase): def test_cities(self): _location_ = city_country('santiago','chile') self.assertEqual(_location_, 'Santiago, Chile') def test_population(self): location_population = city_country('beijing', 'china', 100000) self.assertEqual(location_population, 'Beijing, China: 100000') unittest.main()

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import gymnasium as gym from typing import Optional from ray.util.annotations import DeveloperAPI @DeveloperAPI def check_old_gym_env( env: Optional[gym.Env] = None, *, step_results=None, reset_results=None ): # Check `reset()` results. if reset_results is not None: if ( # Result is NOT a tuple? not isinstance(reset_results, tuple) # Result is a tuple of len!=2? or len(reset_results) != 2 # The second item is a NOT dict (infos)? or not isinstance(reset_results[1], dict) # Result is a tuple of len=2 and the second item is a dict (infos) and # our env does NOT have obs space 2-Tuple with the second space being a # dict? or ( env and isinstance(env.observation_space, gym.spaces.Tuple) and len(env.observation_space.spaces) >= 2 and isinstance(env.observation_space.spaces[1], gym.spaces.Dict) ) ): raise ValueError # Check `step()` results. elif step_results is not None: if len(step_results) == 4: return ValueError elif len(step_results) == 5: return False else: raise ValueError( "The number of values returned from `gym.Env.step([action])` must be " "5 (new gym.Env API including `truncated` flags)! Make sure your " "`step()` method returns: [obs], [reward], [terminated], " "[truncated], and [infos]!" ) else: raise AttributeError( "Either `step_results` or `reset_results` most be provided to " "`check_old_gym_env()`!" ) return False @DeveloperAPI def convert_old_gym_space_to_gymnasium_space(space) -> gym.Space: """Converts an old gym (NOT gymnasium) Space into a gymnasium.Space. Args: space: The gym.Space to convert to gymnasium.Space. Returns: The converted gymnasium.space object. """ from ray.rllib.utils.serialization import gym_space_from_dict, gym_space_to_dict return gym_space_from_dict(gym_space_to_dict(space)) @DeveloperAPI def try_import_gymnasium_and_gym(): try: import gymnasium as gym except (ImportError, ModuleNotFoundError): raise ImportError( "The `gymnasium` package seems to be not installed! As of Ray 2.2, it is " "required for RLlib. Try running `pip install gymnasium` from the " "command line to fix this problem." ) old_gym = None try: import gym as old_gym except (ImportError, ModuleNotFoundError): pass return gym, old_gym

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from data import * from utils.augmentations import SSDAugmentation from layers.modules import MultiBoxLoss from ssd import build_ssd import os import sys import time import torch from torch.autograd import Variable import torch.nn as nn import torch.optim as optim import torch.backends.cudnn as cudnn import torch.nn.init as init import torch.utils.data as data import numpy as np import argparse def str2bool(v): return v.lower() in ("yes", "true", "t", "1") parser = argparse.ArgumentParser( description='Single Shot MultiBox Detector Training With Pytorch') train_set = parser.add_mutually_exclusive_group() parser.add_argument('--dataset', default='VOC', choices=['VOC', 'COCO'], type=str, help='VOC or COCO') parser.add_argument('--dataset_root', default=VOC_ROOT, help='Dataset root directory path') parser.add_argument('--basenet', default='vgg16_reducedfc.pth', help='Pretrained base model') #parser.add_argument('--batch_size', default=32, type=int, parser.add_argument('--batch_size', default=1, type=int, help='Batch size for training') parser.add_argument('--resume', default=None, type=str, help='Checkpoint state_dict file to resume training from') parser.add_argument('--start_iter', default=30001, type=int, help='Resume training at this iter') parser.add_argument('--num_workers', default=4, type=int, help='Number of workers used in dataloading') parser.add_argument('--cuda', default=True, type=str2bool, help='Use CUDA to train model') #parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, # help='initial learning rate') parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float, help='initial learning rate') parser.add_argument('--momentum', default=0.9, type=float, help='Momentum value for optim') parser.add_argument('--weight_decay', default=5e-4, type=float, help='Weight decay for SGD') parser.add_argument('--gamma', default=0.1, type=float, help='Gamma update for SGD') parser.add_argument('--visdom', default=False, type=str2bool, help='Use visdom for loss visualization') parser.add_argument('--save_folder', default='weights/', help='Directory for saving checkpoint models') args = parser.parse_args() if torch.cuda.is_available(): if args.cuda: torch.set_default_tensor_type('torch.cuda.FloatTensor') if not args.cuda: print("WARNING: It looks like you have a CUDA device, but aren't " + "using CUDA.\nRun with --cuda for optimal training speed.") torch.set_default_tensor_type('torch.FloatTensor') else: torch.set_default_tensor_type('torch.FloatTensor') if not os.path.exists(args.save_folder): os.mkdir(args.save_folder) def train(): if args.dataset == 'COCO': if args.dataset_root == VOC_ROOT: if not os.path.exists(COCO_ROOT): parser.error('Must specify dataset_root if specifying dataset') print("WARNING: Using default COCO dataset_root because " + "--dataset_root was not specified.") args.dataset_root = COCO_ROOT cfg = coco dataset = COCODetection(root=args.dataset_root, transform=SSDAugmentation(cfg['min_dim'], MEANS)) elif args.dataset == 'VOC': if args.dataset_root == COCO_ROOT: parser.error('Must specify dataset if specifying dataset_root') cfg = voc dataset = VOCDetection(root=args.dataset_root, transform=SSDAugmentation(cfg['min_dim'], MEANS)) if args.visdom: import visdom global viz viz = visdom.Visdom() ssd_net = build_ssd('train', cfg['min_dim'], cfg['num_classes']) net = ssd_net if args.cuda: net = torch.nn.DataParallel(ssd_net) cudnn.benchmark = True if args.resume: print('Resuming training, loading {}...'.format(args.resume)) ssd_net.load_weights(args.resume) else: vgg_weights = torch.load(args.save_folder + args.basenet) print('Loading base network...') ssd_net.vgg.load_state_dict(vgg_weights) if args.cuda: net = net.cuda() if not args.resume: print('Initializing weights...') # initialize newly added layers' weights with xavier method ssd_net.extras.apply(weights_init) ssd_net.loc.apply(weights_init) ssd_net.conf.apply(weights_init) optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) criterion = MultiBoxLoss(cfg['num_classes'], 0.5, True, 0, True, 3, 0.5, False, args.cuda) net.train() # loss counters loc_loss = 0 conf_loss = 0 epoch = 0 print('Loading the dataset...') epoch_size = len(dataset) // args.batch_size print('Training SSD on:', dataset.name) print('Using the specified args:') print(args) step_index = 0 if args.visdom: vis_title = 'SSD.PyTorch on ' + dataset.name vis_legend = ['Loc Loss', 'Conf Loss', 'Total Loss'] iter_plot = create_vis_plot('Iteration', 'Loss', vis_title, vis_legend) epoch_plot = create_vis_plot('Epoch', 'Loss', vis_title, vis_legend) data_loader = data.DataLoader(dataset, args.batch_size, num_workers=args.num_workers, shuffle=True, collate_fn=detection_collate, pin_memory=True) # create batch iterator batch_iterator = iter(data_loader) for iteration in range(args.start_iter, cfg['max_iter']): if args.visdom and iteration != 0 and (iteration % epoch_size == 0): update_vis_plot(epoch, loc_loss, conf_loss, epoch_plot, None, 'append', epoch_size) # reset epoch loss counters loc_loss = 0 conf_loss = 0 epoch += 1 if iteration in cfg['lr_steps']: step_index += 1 adjust_learning_rate(optimizer, args.gamma, step_index) # load train data # images, targets = next(batch_iterator) try: images, targets = next(batch_iterator) except StopIteration: batch_iterator = iter(data_loader) images, targets = next(batch_iterator) if args.cuda: images = Variable(images.cuda()) targets = [Variable(ann.cuda(), volatile=True) for ann in targets] else: images = Variable(images) targets = [Variable(ann, volatile=True) for ann in targets] # forward t0 = time.time() out = net(images) # backprop optimizer.zero_grad() loss_l, loss_c = criterion(out, targets) loss = loss_l + loss_c loss.backward() optimizer.step() t1 = time.time() #loc_loss += loss_l.data[0] #conf_loss += loss_c.data[0] loc_loss += loss_l.item() conf_loss += loss_c.item() if iteration % 10 == 0: print('timer: %.4f sec.' % (t1 - t0)) #print('iter ' + repr(iteration) + ' || Loss: %.4f ||' % (loss.data[0]), end=' ') print('iter ' + repr(iteration) + ' || Loss: %.4f ||' % (loss.item()), end=' ') if args.visdom: # update_vis_plot(iteration, loss_l.data[0], loss_c.data[0], update_vis_plot(iteration, loss_l.item(), loss_c.item(), iter_plot, epoch_plot, 'append') if iteration != 0 and iteration % 5000 == 0: print('Saving state, iter:', iteration) torch.save(ssd_net.state_dict(), 'weights/ssd300_COCO_' + repr(iteration) + '.pth') torch.save(ssd_net.state_dict(), args.save_folder + '' + args.dataset + '.pth') def adjust_learning_rate(optimizer, gamma, step): """Sets the learning rate to the initial LR decayed by 10 at every specified step # Adapted from PyTorch Imagenet example: # https://github.com/pytorch/examples/blob/master/imagenet/main.py """ lr = args.lr * (gamma ** (step)) for param_group in optimizer.param_groups: param_group['lr'] = lr def xavier(param): init.xavier_uniform(param) def weights_init(m): if isinstance(m, nn.Conv2d): xavier(m.weight.data) m.bias.data.zero_() def create_vis_plot(_xlabel, _ylabel, _title, _legend): return viz.line( X=torch.zeros((1,)).cpu(), Y=torch.zeros((1, 3)).cpu(), opts=dict( xlabel=_xlabel, ylabel=_ylabel, title=_title, legend=_legend ) ) def update_vis_plot(iteration, loc, conf, window1, window2, update_type, epoch_size=1): viz.line( X=torch.ones((1, 3)).cpu() * iteration, Y=torch.Tensor([loc, conf, loc + conf]).unsqueeze(0).cpu() / epoch_size, win=window1, update=update_type ) # initialize epoch plot on first iteration if iteration == 0: viz.line( X=torch.zeros((1, 3)).cpu(), Y=torch.Tensor([loc, conf, loc + conf]).unsqueeze(0).cpu(), win=window2, update=True ) if __name__ == '__main__': train()

[ "161230578@qq.com" ]

161230578@qq.com

cb98d277a37629212ee122fcf3ef6d0dd7231947

53896f6339d20b806424bfa1a51f6fc518287cfc

/app/__init__.py

088271f233d139d506f0be527e1bcaa541864b60

[]

no_license

Jayin/python-microblog

82dcf91dfc253434acf0dafa74724f3b2078bd20

3bd09ff79d2fbf8b4a864769ae5050c391fc9739

refs/heads/master

2020-12-24T14:09:27.391971

2014-04-09T13:36:35

null

0

null

UTF-8

Python

false

190

py

#encoding:utf-8 from flask import Flask from flask.ext.sqlalchemy import SQLAlchemy app = Flask(__name__) app.config.from_object('config') db = SQLAlchemy(app) from app import views,models

[ "tonjayin@gmail.com" ]

tonjayin@gmail.com

fdd81c4ba3c9985c22ee4b986bf4b92364bd87bd

8ee717196b3c1b8674de84e3bcfc081661d3dc7d

/resync/test/test_resource_list.py

0e0c665f590a1f325335331651512beba880691c

[]

no_license

semantalytics/resync

61ecd04685dc33f2c54f682936e44def4e4a377f

ec4ad4b76d6985d8cc955ae24e512d8074460dd7

refs/heads/master

2021-01-24T02:00:00.097911

2013-03-26T16:59:29

null

0

null

UTF-8

Python

false

7,636

py

import unittest import StringIO import re from resync.resource import Resource from resync.resource_list import ResourceList, ResourceListDupeError from resync.sitemap import SitemapParseError class TestResourceList(unittest.TestCase): def test01_same(self): src = ResourceList() src.add( Resource('a',timestamp=1) ) src.add( Resource('b',timestamp=2) ) dst = ResourceList() dst.add( Resource('a',timestamp=1) ) dst.add( Resource('b',timestamp=2) ) ( same, changed, deleted, added ) = dst.compare(src) self.assertEqual( len(same), 2, "2 things unchanged" ) i = iter(same) self.assertEqual( i.next().uri, 'a', "first was a" ) self.assertEqual( i.next().uri, 'b', "second was b" ) self.assertEqual( len(changed), 0, "nothing changed" ) self.assertEqual( len(deleted), 0, "nothing deleted" ) self.assertEqual( len(added), 0, "nothing added" ) def test02_changed(self): src = ResourceList() src.add( Resource('a',timestamp=1) ) src.add( Resource('b',timestamp=2) ) dst = ResourceList() dst.add( Resource('a',timestamp=3) ) dst.add( Resource('b',timestamp=4) ) ( same, changed, deleted, added ) = dst.compare(src) self.assertEqual( len(same), 0, "0 things unchanged" ) self.assertEqual( len(changed), 2, "2 things changed" ) i = iter(changed) self.assertEqual( i.next().uri, 'a', "first was a" ) self.assertEqual( i.next().uri, 'b', "second was b" ) self.assertEqual( len(deleted), 0, "nothing deleted" ) self.assertEqual( len(added), 0, "nothing added" ) def test03_deleted(self): src = ResourceList() src.add( Resource('a',timestamp=1) ) src.add( Resource('b',timestamp=2) ) dst = ResourceList() dst.add( Resource('a',timestamp=1) ) dst.add( Resource('b',timestamp=2) ) dst.add( Resource('c',timestamp=3) ) dst.add( Resource('d',timestamp=4) ) ( same, changed, deleted, added ) = dst.compare(src) self.assertEqual( len(same), 2, "2 things unchanged" ) self.assertEqual( len(changed), 0, "nothing changed" ) self.assertEqual( len(deleted), 2, "c and d deleted" ) i = iter(deleted) self.assertEqual( i.next().uri, 'c', "first was c" ) self.assertEqual( i.next().uri, 'd', "second was d" ) self.assertEqual( len(added), 0, "nothing added" ) def test04_added(self): src = ResourceList() src.add( Resource('a',timestamp=1) ) src.add( Resource('b',timestamp=2) ) src.add( Resource('c',timestamp=3) ) src.add( Resource('d',timestamp=4) ) dst = ResourceList() dst.add( Resource('a',timestamp=1) ) dst.add( Resource('c',timestamp=3) ) ( same, changed, deleted, added ) = dst.compare(src) self.assertEqual( len(same), 2, "2 things unchanged" ) self.assertEqual( len(changed), 0, "nothing changed" ) self.assertEqual( len(deleted), 0, "nothing deleted" ) self.assertEqual( len(added), 2, "b and d added" ) i = iter(added) self.assertEqual( i.next().uri, 'b', "first was b" ) self.assertEqual( i.next().uri, 'd', "second was d" ) def test05_add(self): r1 = Resource(uri='a',length=1) r2 = Resource(uri='b',length=2) i = ResourceList() i.add(r1) self.assertRaises( ResourceListDupeError, i.add, r1) i.add(r2) self.assertRaises( ResourceListDupeError, i.add, r2) # allow dupes r1d = Resource(uri='a',length=10) i.add(r1d,replace=True) self.assertEqual( len(i), 2 ) self.assertEqual( i.resources['a'].length, 10 ) def test06_add_iterable(self): r1 = Resource(uri='a',length=1) r2 = Resource(uri='b',length=2) i = ResourceList() i.add( [r1,r2] ) self.assertRaises( ResourceListDupeError, i.add, r1) self.assertRaises( ResourceListDupeError, i.add, r2) # allow dupes r1d = Resource(uri='a',length=10) i.add( [r1d] ,replace=True) self.assertEqual( len(i), 2 ) self.assertEqual( i.resources['a'].length, 10 ) def test07_has_md5(self): r1 = Resource(uri='a') r2 = Resource(uri='b') i = ResourceList() self.assertFalse( i.has_md5() ) i.add(r1) i.add(r2) self.assertFalse( i.has_md5() ) r1.md5="aabbcc" self.assertTrue( i.has_md5() ) def test08_iter(self): i = ResourceList() i.add( Resource('a',timestamp=1) ) i.add( Resource('b',timestamp=2) ) i.add( Resource('c',timestamp=3) ) i.add( Resource('d',timestamp=4) ) resources=[] for r in i: resources.append(r) self.assertEqual(len(resources), 4) self.assertEqual( resources[0].uri, 'a') self.assertEqual( resources[3].uri, 'd') def test20_as_xml(self): rl = ResourceList() rl.add( Resource('a',timestamp=1) ) rl.add( Resource('b',timestamp=2) ) xml = rl.as_xml() print xml self.assertTrue( re.search(r'<rs:md .*capability="resourcelist"', xml), 'XML has capability' ) self.assertTrue( re.search(r'<rs:md .*modified="\d\d\d\d\-\d\d\-\d\dT\d\d:\d\d:\d\dZ"', xml), 'XML has modified to seconds precision (and not more)' ) self.assertTrue( re.search(r'<url><loc>a</loc><lastmod>1970-01-01T00:00:01Z</lastmod></url>', xml), 'XML has resource a' ) def test30_parse(self): xml='<?xml version=\'1.0\' encoding=\'UTF-8\'?>\n\ <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:rs="http://www.openarchives.org/rs/terms/">\ <rs:md capability="resourcelist" modified="2013-01-01"/>\ <url><loc>/tmp/rs_test/src/file_a</loc><lastmod>2012-03-14T18:37:36Z</lastmod><rs:md change="updated" length="12" /></url>\ <url><loc>/tmp/rs_test/src/file_b</loc><lastmod>2012-03-14T18:37:36Z</lastmod><rs:md length="32" /></url>\ </urlset>' rl=ResourceList() rl.parse(fh=StringIO.StringIO(xml)) self.assertEqual( len(rl.resources), 2, 'got 2 resources') self.assertEqual( rl.md['capability'], 'resourcelist', 'capability set' ) self.assertEqual( rl.md['modified'], '2013-01-01' ) def test31_parse_no_capability(self): xml='<?xml version=\'1.0\' encoding=\'UTF-8\'?>\n\ <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">\ <url><loc>http://example.com/res1</loc><lastmod>2012-03-14T18:37:36Z</lastmod></url>\ </urlset>' rl=ResourceList() rl.parse(fh=StringIO.StringIO(xml)) self.assertEqual( len(rl.resources), 1, 'got 1 resource') self.assertEqual( rl.md['capability'], 'resourcelist', 'capability set by reading routine' ) self.assertFalse( 'modified' in rl.md ) def test32_parse_bad_capability(self): # the <rs:md capability="bad_capability".. should give error xml='<?xml version=\'1.0\' encoding=\'UTF-8\'?>\n\ <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:rs="http://www.openarchives.org/rs/terms/">\ <rs:md capability="bad_capability" modified="2013-01-01"/>\ <url><loc>http://example.com/bad_res_1</loc><lastmod>2012-03-14T18:37:36Z</lastmod></url>\ </urlset>' rl=ResourceList() self.assertRaises( SitemapParseError, rl.parse, fh=StringIO.StringIO(xml) ) if __name__ == '__main__': suite = unittest.defaultTestLoader.loadTestsFromTestCase(TestResourceList) unittest.TextTestRunner().run(suite)

[ "simeon.warner@cornell.edu" ]

simeon.warner@cornell.edu

6c7a891f3da07aaeb08f6dc637af604162da73ee

91c946b62413b1092e344c0bc05ab5808ef9deb8

/regex_patent_classification.py

7e7b0061faae45e64d9f80f08b24f7794450907f

[]

no_license

aaag97/PatentAnalysis

b540c0fea30e7b18966ee3940bc6f94715663ddc

1096861a67c3aa33ed15a6f4e35919ba05a04cba

refs/heads/main

2023-04-05T11:01:02.110539

2021-04-05T16:34:41

303,387,384

0

null

UTF-8

Python

false

2,753

py

import os import re import argparse from PIL import Image from ocrutils import OCR_Pages def has_drawing_from_path(text_path): """ function to evaluate whether a patent has a drawing using regex Args: text_path - path towards text file Returns: boolean indicating whether the patent has a drawing or not according to the regex method """ with open(text_path, "r") as text_file: text_file_str = text_file.read() return re.search('[Ff][Ii][Gg]\.[0-9]+', text_file_str) or re.search('[Ff][Ii][Gg]\. [0-9]+', text_file_str) or re.search('drawing', text_file_str) def has_drawing(text_file_str): """ function to evaluate whether a patent has a drawing using regex Args: text - string of patent Returns: boolean indicating whether the patent has a drawing or not according to the regex method """ return re.search('[Ff][Ii][Gg]\.[0-9]+', text_file_str) or re.search('[Ff][Ii][Gg]\. [0-9]+', text_file_str) or re.search('drawing', text_file_str) def get_ordered_images(folder): """ function to get all images from a given folder Args: folder - the folder which contains the images Returns: images - the images in cv2 format img_names - the filenames of the images """ imgs = [] filenames = sorted(os.listdir(folder)) img_names = [] for filename_i in range(len(filenames)): filename = filenames[filename_i] img = Image.open(os.path.join(folder,filename)) if img is not None: imgs.append(img) img_names.append(filename.split('.')[0]) return imgs, img_names def parse_args(): """ function to parse command line arguments Returns: args - an argspace object containing the arguments """ parser=argparse.ArgumentParser(description='Segement and visualize patent pages.') parser.add_argument('-i', '--input', type=str, help='the path to a folder containing the images', required=True) parser.add_argument('-o', '--output', type=str, help='path to the output text file', required=True) args = parser.parse_args() return args def main(pages, output_path): text = OCR_Pages(pages) if has_drawing(text): print('This patent has one or more figures!') with open(output_path, 'w') as f: f.write('This patent has one or more figures!') else: print('This patent does not have any figures!') with open(output_path, 'w') as f: f.write('This patent does not have any figures!') if __name__ == "__main__": args = parse_args() input_path = args.input output_path = args.output pages, page_names = get_ordered_images(folder=input_path) main(pages, output_path=output_path)

[ "andre.ghattas@epfl.ch" ]

andre.ghattas@epfl.ch

077791066522a7a2b06a16218258937703b43249

0378aa649119767970f4cccbe35afa732346c706

/manage.py

7ef7772b966f5323343d961a63cfe728363ef583

[]

no_license

h4cktivist/job-agency

4098041cf318e7740bc0ecae740b1bee195c0771

0fa82d42d61deb5e392ccf4856abf88ee8f2f175

refs/heads/main

2023-05-27T03:03:06.467090

2021-06-04T07:50:38

366,696,287

0

null

2021-06-01T09:05:59

2021-05-12T11:44:37

HTML

UTF-8

Python

false

665

py

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

[ "popovvadim0605@gmail.com" ]

popovvadim0605@gmail.com