From be5464619098b5adb120f19c1df312b00398231c Mon Sep 17 00:00:00 2001 From: FEIJINTI <83849113+FEIJINTI@users.noreply.github.com> Date: Thu, 21 Jul 2022 14:12:12 +0800 Subject: [PATCH] =?UTF-8?q?=E9=87=87=E4=BA=86=E6=96=B0=E6=95=B0=E6=8D=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 01_dataset.ipynb | 262 +++++++++++++++++++--------------------- 02_classification.ipynb | 33 +++-- 03_data_update.ipynb | 84 ++++++++++--- main_test.py | 16 ++- 4 files changed, 225 insertions(+), 170 deletions(-) diff --git a/01_dataset.ipynb b/01_dataset.ipynb index a7d76cc..f148618 100644 --- a/01_dataset.ipynb +++ b/01_dataset.ipynb @@ -2,63 +2,64 @@ "cells": [ { "cell_type": "markdown", - "source": [ - "# 彩色图像读取与分析" - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "# 彩色图像读取与分析" + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 25, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "import cv2\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "## 单张图片分析" - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "## 单张图片分析" + ] }, { "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "img_path = r\"data/dataset/img/beijing.bmp\"\n", - "label_path = r\"data/dataset/label/beijing.bmp\"" - ], + "execution_count": 26, "metadata": { - "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "img_path = r\"data/dataset_old/img/yangeng.bmp\"\n", + "label_path = r\"data/dataset_old/label/yangeng.bmp\"" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# 读取图片和色彩空间转换\n", @@ -69,38 +70,38 @@ "alpha, beta = np.array([100 / 255, 1, 1], dtype=float), np.array([0, -128, -128], dtype=float)\n", "img = img * alpha + beta\n", "img = np.asarray(np.round(img, 0), dtype=int)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# 构建数据集\n", "color_dict = {(0, 0, 255): 'tobacco', (255, 0, 0): 'background'}\n", "dataset = {label: img[np.all(label_img == color, axis=2)] for color, label in color_dict.items()}" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "
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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -120,75 +121,72 @@ "axs.set_ylabel('b')\n", "plt.legend()\n", "plt.show()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "单张图片仅能够观察到单一类别的色彩分布情况, 但已经可以看出背景的颜色分布情况多集中在较为暗的区域" - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "单张图片仅能够观察到单一类别的色彩分布情况, 但已经可以看出背景的颜色分布情况多集中在较为暗的区域" + ] }, { "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## 多张图片分析\n", "为了能够有效分析各类杂质的色彩分布情况, 构建多个类别的读取函数与图形绘制函数" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "from utils import read_labeled_img,lab_scatter" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ - "dataset = read_labeled_img(\"data/dataset\", color_dict={(0, 0, 255): \"yangeng\", (255, 0, 0): \"bejing\", (0, 255, 0): \"hongdianxian\", (255, 0, 255): \"chengsebangbangtang\",(0, 255, 255): \"lvdianxian\"})" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + "dataset = read_labeled_img(\"data/dataset_old\", color_dict={(0, 0, 255): \"yangeng\", (255, 0, 0): \"bejing\", (0, 255, 0): \"hongdianxian\", (255, 0, 255): \"chengsebangbangtang\",(0, 255, 255): \"lvdianxian\"})" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "
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HD4d8/RSiUPo8lM5Rb6KItZj7xnHtSngGGPfu3UtCQgI6nY7FixdHrG5AFjKtllY0kgbhEkhColf0YhTG4T88BDwnWJeWlnLgwAG6urrUCsfvfve7XHbZZer7L7roIuCr9mqNRsOmTZu4+eabSUhIYP78+RQVFeFwOPjkk09Yvny5Ov7u0ksv9UkO5eXl3HXXXXR3d9PX18eqVavUv11wwQVoNBrmzJmjTt0GePTRR5k3bx4nnXQSl112GXa7fcA2u7u7ufrqq9m/fz+SJKmZBegXmVH6NmbPns2hQ4eYPHmy32sUzAPr3Tmq3BuenaOerpgQAovFoqaVTzTX47glBs/ahO7ubsxmM9OnTx+0gg0HxZXwdyO4hRun24nk6hdpNelMmB1m5DD1GLynTnd1dQX0fs/2as8HRynUMhqNmEz9KlOK2rTD4QAY1JZ97bXXsm7dOubNm8dzzz3Hxx9/7PP4PPdz5MgRNBoNzc3NPsfw3X333Zx66qmsW7eO2tpaTj/9dL/nPFybOIT+gAbSYq40fSlEcSLFKI5LV0IJQLndbg4ePMi+ffuIj48PmhQC2pfTjdPhRG/Uo9PpcMkuNGjQhNld6Y2UlBTS0tLUeMA///lPTjnllCE/s2zZMl577TUAKisr1casxYsX88knn9DX14der+ett97qP5ejD4ZSCt7b28u4ceNwOp3861//GvYYXS4X1113HS+88AKzZ8/mkUceGbSqd3d3M3HiRKA/rjBW4N1iPlTnKBz/rsdxRQxKgFG5sbdv347b7WbRokURLxBSCpaELMhNz0UgsDqtuGU3WfFZEScGgL///e/8+te/Zv78+ezatWtYabcbb7yR1tZW5s+fz0MPPURxcTEpKSmMHz+eu+66i+XLl7Nq1SpKS0sB1DoLg8GAVqvlzjvvVPUi8/Pzh73pH3jgAZYtW8Ypp5zCH/7wB/7+97+zZ8+eAavprbfeyq9+9StOOeUUNRAYKqJZz6HUSATSYq4ci6eqVWdn5wA36VjDcdNEpZjFsizT1tZGdXU1M2fOJDMzE4AvvviCk046KSCCOHDggDpmXilM8vycr4IlWci4hRutpEVCwmKxDNmUNBJNVIoFYDKZOHDgAKtWraKqqmpAEVUg21X8cWVl9FRyGi5WY7PZ0Ov1UekF8fXdRAqyLKsjBf39Xbkm3uXskiRRW1vLpEmTBjXS+WqikiTpH8B5QIsQoujoa+nAWiAPqAUuFUJ0Hv3b7cC1gBv4sRDiP5E7834c8zEGz9oEWZbZv38/fX19LFy4cIDPqhTDhHITeT7EShu2kg5Tty995T5E25QMdKW0WCysXLlSLSl+/PHHfZLCcPDlj3vrLhxvpdvDXWN/Vap2u53GxkbsdjtdXV2kpaUNuA/94FngceB5j9duAz4QQvxOkqTbjv7+S0mS5gCXAYXABOB9SZIKhBDhmV9eOKaJwTPrYLVaKSsrIycnh5kzZw76UhViCGdfgc6gHCtISkriyy+/jPh2AyndVlbPaJLkWCoN97Sepk6dSnV1NUII6urqmDp16pC9N0KITyRJyvN6+QLg1KP/fw7YCPzy6OsvCyHswEFJkvYDi4EviCCOWWJQypqFEDQ1NVFbW8ucOXNITU31+f5QiUGJTismcSgrrj+MpRs7HPjrkrRarWocQXEnjpXzDee7UTJZ48aNC+d8c4QQjUePpVGSpOyjr08ENnm8r/7oaxHFMUcM3mXNe/bsQZZlFi9ePCQrh0oMyk3u7ToMhWPl5o8GvJufrFYrOp3Ob6lyONdqLEf/o3hsvi5YxHd2TBGDZ21CX18f5eXl5ObmMnHixGFvsGCJQXEdZFnGaDQGHTwb6sZQGn+8W4kDeUiORdJRqkCNRqNK7Ep7eTCBTF+I1vUYA9ZcsyRJ449aC+OBlqOv1wOeVV+TgIZI7/yYIQalrFl5WBsaGiguLiYxMTGgzwdDDIpGofcsx0hAUU02mUxoNJqoNEKNZShEoFh3SorveBOricB98xZwFfC7o/++6fH6vyRJepj+4GM+sDmcHfnCmL/ynhLkdrud2tpazGYzixcvDpgUIHBiaGhoUFN8BoMh4CaqQKDkvhMSEtTV0bvxx3MSlNJKLMvygPmUYwWByM4Pd+18SdJDvzq32Wxm2rRpNDY2hhU4Hg7PPfccDQ0DF91wLIZgi5wkSXqJ/uDhTEmS6iVJupZ+QjhDkqRq4IyjvyOEqABeASqBd4EfRTojAWPcYvB0Hbq6ujhw4IA6kSlYDEcMSrzC6XSSmJgY0by7Unev0+mIj49XX/OGv7Sg3W5X04PHolBLMMfqnfFQiNlXxiNSeO655ygsLFTneEL4xBCMtSOEuNzPn0739aIQ4j7gvhAOLWCMWYtBUVZSJNf2799PQUGB34KT4aCY7b5gNpvZvHkzSUlJzJs3b8ANEejNIWQZ4XQivMjH7XZjNpsxGo2B5LNVvPDCC6qOwQ033IBOp+OLL77gjDPOoKCggBdffFEVavnDH/7AkiVLmD9/PmvWrAH6xV6KiooGtW4DbNmyhfnz57Ns2TJ+8YtfMG/ePAAqKipYsmQJCxYsYP78+VRXVwPw4osvqq/feOONA67jrbfeyqJFizjjjDNobW0FBrZrX3nllcO2a8uyzE033URxcTGrV6/mvPPOG2AdPfbYY6xYsYLTTz+dQ4cOIcsy69evZ8WKFZSWlnLGGWfQ1NQE9LeNX3fddaxYsYL8/Hz+/Oc/q9v57W9/S2FhIWeeeSbf/va3+eMf/8i6devYtm0b3/3ud1mwYAFWq5V7772XU045hQULFnDDDTeoJL5ixQpuu+02lixZwuzZs/n000+B/nqRyy67TG1vP/nkk0OeEjZmoKxAfn5GHLIsC4fDISwWi+js7BSffvqpKCsrExaLRTQ3N4utW7cKm80W9E9ZWZmoq6sb9Hptba348MMPRXNzs/paRUWFcLvdwu12C6vVKqxWq/q7rx+n2SysNTXCun+/sNbUCKfZLLq7u4XVahU9PT3C6XQO+ozL5RJOp1O4XK5BP7t27RIFBQWiqalJuFwu0dLSIr773e+Kb37zm6Krq0vs2rVLTJ8+XdhsNvHmm2+Kq666SnR3d4ve3l5x9tlniw8//FDs379faLVasXXrVuFyucTFF18snnvuOeFyuURhYaH45JNPhMvlEj//+c9FYWGh6O7uFj/60Y/E888/L1wul7BYLKK3t1eUlZWJc889V1itVuFyucQNN9wg/vGPfwiXyyUA9f133323+OEPfyhcLpdobm5Wz+XWW28VjzzyiHC5XOo5OBwOsXv3bjF9+nThcrnE2rVrxVlnnSUcDoeor68XqampYu3atcLlcokpU6aI3/zmN8Llcol//OMf4pxzzhEul0u0traKrq4uYbPZxOOPPy5uuukm0dPTI+644w6xZMkS0dfXJ5qamkR6erqwWq1i06ZNYt68eaK3t1d0dnaKGTNmiAcffFC4XC7xta99TWzatEk95paWFtHX1ydsNpv49re/LV5//XX1fbfccotwuVzirbfeEitWrBAul0s8+OCD4vrrrxcul0vs3LlTaLVasX79ep/3d2Vlpa+Xh3sOR/xnTLkSnmXNra2t7N+/n1mzZpGRkQGEV6SkdBN67mvPnj3Y7XYWLVoUsnaCkGVczc1Iej2STodwuXA2NeNOTUHW6UKa1fDRRx/xjW98Qy3nTk9PBwa3Out0OjZu3MjGjRvVhqq+vj4qKirIzs4mLy+P4uJioL91u7a2lq6uLnp7ezn55JMBuPzyy3nnnXcAWLJkCQ888AD19fVcdNFF5Ofn8+GHH7J9+3ZVgclqtZKVlQX0fx+XXnopAN/+9re55JJLgIHt2r29vcO2a3/22WdcfPHFaDQaxo0bx6mnnjrgeijt5Zdddhn/8z//A0B9fT0/+9nPaG1txeFwkJeXp4rirFq1CrfbTXx8PFlZWTQ0NPDZZ59x/vnnqxbnueee6/f6b9y4kd///vdYrVY6OzuZM2cO559/PjC4vV05/ptvvhmAoqIiioqKjilXzxfGBDEIr7Lm6upqLBYLixYtGuBvarXakBtvPF0Ji8XC7t27GT9+PLNnzw7vS3T3j6OTjvq8QqPBYu5Dm5I85MCZoSD8+Le+Wp2FEPzyl7/k+9///oD3HjhwAKPRqPrmwiNe4w+XX345ixcv5p133uGcc87hr3/9K0IIvvOd73D//fcPe9zKMXu2az/11FMDZmz6O4dAtuv5/5/85CfceOONXHLJJWzcuJHf/OY3auNTQkICCQkJyLKMVqvFarUOiNMMFZ+w2WzcdNNNfPzxx0ybNo3f/va3asWr5/H7a29Xfj+WMyowBmIMQnwluWaxWNi6dStxcXGUlpYOqjIMx2JQPtvU1MSOHTuYPXs2U6ZMGZYUhs1KaLX9062Pin5YensxJSQghRG8XLFiBa+99hrt7e0AdHR0+H3vqlWr+Mc//qHOvzhy5AgtLS0D9CEV4VQhhNpW/Omnn+JyudRp15IkUVNTw7Rp07j55ps5//zzKSsrY8WKFaxfv56Wlhb1WJSVUpZlNRbw0ksvsWzZMgB6e3sZP348TqeTV155ZdhrvGzZMtavX48syzQ3Nw/QfQB45ZVX1H8Vy6W7u5vx48cD+BWvVVK+JpOJ0047jXfffRebzUZ7ezsbNmxQhVoSExPp7e0FUEkgIyMDs9msTgUf7vg929srKyuPeWIYVYvBs6y5oaGBuro6CgsL/abBwrEYoD8VqdfrIyr+Kmk06HJyMB8+jNvlIi4uHv24HNwBiIz4Q2FhIbfffjsrVqxAq9WqSk6+sGrVKvbs2aMqOyUkJPD8888PyKooK6lWqyU+Pp6nn36aG264gfj4eJYtW0ZiYiKyLLN27Vpeeukl9Ho9OTk53HnnnaSnp/Ob3/yGs88+G1mW0ev1PPbYY0yZMoWEhAQqKipYvHgxycnJvPTSS0B/APDkk08mNzeXWbNmDVhxfeEb3/gGH374IfPmzSM/P1/dngK73c7SpUsRQvDPf/4TgLvuuovvfe97TJw4kZNOOomDBw8OuY9FixZx/vnns2TJEnJzc1m4cCFpaWnIsszll1/OjTfeSFxcHJ988gnXXnstJ510ElOnTmXhwoVDbhf629uvvvpq5s+fT0lJCXPmzAl4gvhYxai0XQuP5ie3201VVRUAc+bMGdLMk2WZL7/8kqVLlwa1P8USSUhIoLS0dNgVzLPtWnFv/GUUlCpGrUaDUacDrRZJo6Gvr2/IOgvFvA/WjVHkxsJxfzyP7cEHH6SxsZHf/va3ahwmkt2SgbaJK8fU3t7O0qVL+eSTT4YU1hFH9TCU9G8gUPZhsVg47bTTePLJJ1UtCsWd9fzR6/UBVaV6t7evXLmSTz/9lClTpgx6b2x2pR94+rq9vb1UVFSQl5c3IIfsD6EUGzU3N7N//35VNSjYB2qofSopVZPJFNG8erTxzjvv8OCDD+JyucjNzeVvf/ubKkoCqCXLviZARSuotnr1arq7u3E4HPzqV78aVm0r2PsA4IYbbqCqqgqbzcZ3vvMdlRRgcEWm2WxW4wjDVaV6t7f/7ne/CzmtPlYwonezy+WipaWFjo4OdDodzc3NzJs3L+DIfTA3pSzL7Nu3Tw1idnV10d3dHeqhD4ISzFL895FEKJaGJy699FI1m6BsT6lxgMHDZ/2VbUeyCOzDDz8M+jPBXgPFDQkUQ2lCKq6ZVqsd1N7e0tISizEEAk/XweVyceTIEXJycli8eHFULqDVamX37t1kZ2er2gzh6jEoUB4iJfp9vMNzJVWaoBRrwmazHTe9DcNBEdMdTqxGyX4FU8zmCUmSfgpcR78bXwZcDcTjR80pWog6MXjWJnR2dqoy7j78rIigpaWF6upq5syZQ1pamvp6OHoMCgKdQen9OX9/D8UcHm0MVbatpAc9Jc6ihdG+dv7Eapqbm+ns7FQtsJSUlGDa9ScCPwbmCCGskiS9Qr9a0xx8qDlF9owGImrE4F2bcPDgQbq6uigqKqKuri7i+1PqH/r6+gbVP0B4qU4lpepwOIJyHcI1+X1hrBXOeD4gwkukRQmwulyuqMQnxtK1UAhxwoQJanGVMrM0SJdLB8RJkuSk31JoAG7Ht5pT1BAVYlAeJEXuq7y8nIyMDBYuXKjKuoe7fc+bwmazsWvXLrKysvxmHcIhBuV4IzGDUoHisx5LqkbDwVukRQiB2WyOmPbCsQJZlklMTAwqYwIghDgiSdIfgDrACrwnhHhPkiR/ak5RQ8SdQkVd1+1209LSwo4dO8jPz1cnSYdbi+Btgre2trJt2zby8/OZNm2a34csFGJQUpFA2ClCTyhj0vyNS4PAWpo9sWLFCnUu5XnnnTfsgJpgcffdd/P+++8H9RlFk0ApqjKZTKoEu9lsxmq1DpBgDwbRsMYiBSU4GSwkSUqjX9NxKv1aCwmSJF0Z4cMLCBGzGDwDjEpGQOlDiFRZs/fn9+/fT09Pj0/XwRvBEoOSijQajRGdD+CZzYCvNCW9/XQI/eZ/++23I3a8Cu65556wt+EdwPMlIhvttGggCJd0PL/DILESOCiEaAWQJGk9cDL+1ZyihohaDEqL8ZYtW0hMTKSkpMSnrx9O4Eir1aoFS1qtlgULFgQk0OrdROUPCsHZ7fYBgirDfk4WuN0yQu4/N+8bSxzVZBBCkJCQMGBFUfz0uLg44uPj1Vy6xWLh0ksv5c0331TJ8Ac/+AHr16/HarVyxRVXqK2+nunG6dOn09bWBvRXFS5evJji4mKefvpp9T0pKSnceeedlJaWsmzZMrXk+aKLLlJLjJ966im+853vAAwQirn33ntZsmQJ8+bNC6gt2R8UAVmlbFvRhlREahTLc7QDjcEi1DEF9LsQSyRJipf6b6DTgSq+UnOCgWpOUUPEiEGSJBoaGigrK6OoqIjc3NyosL7T6WT37t1Mnz5ddU8CQSAWg8PhUCdCKxLxgWzf5XDT02alt81GT5sVl+OrUeswUCpuuMYqxQ+H/vLmyy+/nPXr1+NwOOjs7OTjjz/mjDPO4MknnyQ+Pp4dO3Zw++23s337dp/be+aZZ9i8eTNffvkljz/+uNp/YTabOemkk9i+fTvLly/n2WefBeB///d/+e1vf8unn37KI488wqOPPjpomz/60Y/YtGkTu3btwmq1DrBQXC4XmzZt4o9//OOAadzDQTlvf2pWvqZTj1W9xzDml3wJvAZspz9VqQGewo+aUzQRUYtBq9WyePFikpKSIrlZoP/Lqq6uxmq1MmfOHLUVO1AMJdQC/U05W7ZsYfLkyYNSkUOtWEIWmLvsaLQSeqMWjVbC3GVXLQcljmAymULqzzj77LP5+OOP0Wg0fPrpp5x88smYTCY+/vhjvvGNb2Cz2ZgzZw5z5871+fk///nPqlVw+PBhVXzFYDBw3nnnAf0t2UqmKCcnhzVr1rBy5Up+//vfqy3fnti4cSNLly6lpKSEjz76aMCkbF9tyaFASYt6zpKEryTflOD2sWZNDAchxN1CiFlCiCIhxHeEEHYhRLsQ4nQhRP7Rf/131UUIEc1KTJgwISrafHa7nd27d5OWlkZmZmZI5cf+LAYhBPX19dTX1zN//nzi4+PVTrtAIAuBEKDR9nOsRqvB7XKrpd9utzusATUmk4mvf/3rvPfee7zyyitcfPHF6oyGuLi4/kG6R+M6NptNnVwN/Q/wBx98wP/93/8RHx/PihUr1IYmRegWBk+WLisrIyMjg8bGxkHHo7Qlf/nll0yePJl77rln2LbkSMA7LapcWyUt6pntCNeSGMuBzZHCqJWqBcr07e3tbN26lWnTpjFjxgzVDw0Wvr5ot9tNeXk5XV1dLF682Gd6aVhZeklCkkB295OO+u/RyceRmFp16aWX8uyzz/J///d/rFy5EoBTTjmFl156CZ1Ox/79+6moqFAHryrxjLa2NlJTU4mLi2PPnj0BTaXavHkz//nPf9i6dSsPP/zwoK5FhQQyMzPp6+sbti05Gg+Y4uLp9Xo1PqEQkaeIbrhp8XCP71hGRIkh0IsRSGZCCMH+/fupqalh4cKFqusQblZDgcViYfPmzaSmplJUVDRkkHEoEpM0EgmpRmS3wGl343LJYHCh1WmHDYoGer1WrVrFp59+yumnn65u84YbbqCvr4/58+fzhz/8gUWLFqkPi6LDcM455+B0OikpKeFXv/oVixcvVgnLF+x2OzfccANPP/00EyZM4KGHHuK6664b8P7U1FSuvfZaSkpK+MY3vhFQW3K0ocQnIpUWDcdiOF5cm4i2XStt1MNh69atzJ071289ueI6pKamMmPGjAFf0v79+0lKSiInJyeYQwPg888/5+STT1bLpouKinzWC3i2XQPDtlBDf6xBkbmPT4jH4XCo4+T9fiaEmyjUCc9KtkVJEXr2OABDTnYOFaG0RgcKu90+IPU5FJRzdrlcAaVFlesUSr+DIl7sef94ItZ2PQSGWvU7OjqoqqoaMMI+0M8OByEE+/bto7e3N6Dah2DgcPabrolJiWPSjPTX4+Ap/Rbp0uVor57BWKieszWHG/ITjsUQRqpyTCGixBCOKyGEoKamhvb2dhYsWOA3rRdqabPdbsdisaDRaAISawkUnt2WnivjWCQHTyjBPIPBoD4k0WitHmvXYahuUc+KxVAf7hgxhAFvYnA4HOzevZvk5GQWLlw45IUNxWLo6upSg3MzZswI+bi9odQnBNJtGUlEeiVWgmXK7AvlYfFuKY6EolOkEKnMgS9LSqlOVSyoYLpFY8QQBjwfbsV1KCgoUGXJh4JS9BIIhBDU1dXR2NhIaWkpO3bsCPiGGu59SjxhqCnYx2ogyvth8S5djmRqcKxBKdvW6XTo9Xo1NhFoWlRpjPOFY+l+GDVXwuVyUVNTQ2tr65Cug6/PBmIxuFwuKioq0Ol0qiBMoP6j0Wiko6OD9PR0n+9VButGIhV5LMCXj+7dMXm8CbV41nh4q1kN1S3qz2IQQtDe3j7kfS5JUirwDFBEf+D/GmAvIyzSAqNkMUiSxP79+8nMzGTRokVB3VCBEENfXx9lZWXk5uaqWo/wVXxiuP2NGzeOpqYmdeSa3W5XTWyn06muqEPB6XSqkX9/CGUFcTqdERdBUVyHUNwhRYNBSYMqallarTas7Q6HaFwHBS6XS7UchoL3uXsShiLn7wmTycSkSZOG2uSjwLtCiIslSTLQr8dwBxESaZEkqU8IEdAk6BEnhs7OTg4fPkxOTg4zZ84M+vPDBR+bmpqoqamhqKhogAR5IJ9VoNPpBnyBn3/+OXPnzmX37t3k5+eTnT18O3x1dTUpKSl+36tU7wV7Y1dVVZGdnR3RsnOz2UxtbS3Tp08PazuKwG9HRwetra0qgRcUFJCcnBxRi6KsrIwZM2ZERXR17969jBs3LqjWdyEEBw4c4E9/+hPbt2/HZDLxm9/8Rp1gNRwkSUoGvgZ87+j2HIBDkqQLGGGRFhhBV0IIQW1tLS0tLeTl5YXM9P4sBm/xV1+rVKAdlt5wOp2UlZUxb968YesZFAwl3aYUbzU2NpKSkkJGRgZpaWkjGsD0Pp5IrLwajYaUlBRSUlKYOnWqOpymqamJffv2ERcXR3p6Ounp6WE/0NEM8g0VJ/AHSZKYMWMGpaWlLF++nGuvvXZAx2sAmAa0Av+QJGkesA34CTDiIi0wQhaDw+GgvLyc+Ph4Fi1aREtLC2azOaRt+Vr1bTYbu3fvJjMzUxV/9ffZYDIaCtk4nU6WLl0aVN2DP2JwuVyUlZURFxfHokWL6Ovro729nbq6OjQaDenp6WRkZJCY6L8e4lgJYikDbmbOnKmWaXd0dKhaHampqaSnp5Oamhp0/4ssy1GL74RDOhaLhZycHDUVHAR0QClwsxDiS0mSHqXfbRgVRJ0YlFThjBkz1GrFcIqUvD+rZDU8h9/6QzA1EA6Hg127dpGenk5iYmLQN4ov3QmLxcKuXbvIzc1lwoQJOBwOdYWdNm0aDoeDjo4O6urq6OvrIykpSV1hlZvsWAp2eloikiSpMyUnT56MLMt0dXXR0dFBbW1twKToue2xZDEoCHTAjg/UA/VHW6+hv/36NkZBpAWi6EoIIVQzUulaVBAJYvB0TQLNagRKDN3d3ZSXl6sp1I6OjqBXaW+Lob29nT179lBYWEhqaqrP7RkMBsaNG8e4ceMQQtDb20t7eztlZWUApKWlqSP9IolodRMOtV2FCJS2brvdPoAUExMT1b/7Kk2OpisRzrbNZnPA7qYnhBBNkiQdliRpphBiL/0iLZVHf66iX4NhRERaIEoWg9PppLy8HJPJ5HN2RCSmVu/cuROTyRRUViMQYjhy5Ah1dXWUlJSozK/IrwUD5TNCCA4fPkxjY2NQaVlJkkhOTiY5OZmpU6fidDrp6OigpaWFyspKkpOT1RU2kqXdkUQwhGM0Ghk/fjzjx49HCEFfXx8dHR1UVlbicrlIS0sjPT1dlWOPZmt0uBZDKMRwFDcDLx7NSNTQP1NCA7wiSdK19Cs8XRLqxoNBxImhp6eHsrIypk+f7nfMWDjEYLFY6OnpoaioSJ12HCiGIgZZltmzZw8Oh4NFixYN8HlDkaOTJAm3201FRQWyLLNw4cKwyouVQbNdXV3k5OSg1Wppb2+nvLwcWZZJS0sjIyMjpOh/tGIWoT68kiSRlJREUlISU6ZMweVy0dXVRVtbG/v378doNOJwOLBYLBFV7g73uCEsVwIhxE7AV7vq6SFtcDDiJUmq9/j9YSHEw77eGFFiUMx7b9dh0E5D1FRoaGigtrYWk8kUNCmAf2Kw2+2q/Pzs2bMH3RShWAxut5vDhw+Tm5vLlClTInrzej44eXl5uFwuOjs7aWpqYu/evcTHx5ORkUFGRkbAHYLRWH0jZe7rdDoyMzPVpjqr1cr27dupqanBarWSlJQU8cxOOMQQDQWzSEAIEfCXEfEYw7x584ZdgYK1GDxX88WLF7N58+aQjs8XMSjBUX/dnP4+NxR6eno4dOgQWVlZ5OXlhXSs/uAr26HT6cjKyiIrK0ud5eBthmdkZJCSkjKi1YnRMvfj4uIwGAzMnTtXrZ1ob2/n8OHDAOr5JiUljXg1ZpiuxJhBxF2JofL3CoKR/VLmUObk5PhczYOB9wNeX1/P4cOHh7VwAjknBY2NjRw8eJDc3NxRKRGWJInExEQSExPJzc1VzXBFg8JkMpGRkTGglmA0go+RgmftBPTHtzo7O2loaKC3tzeitROBYCxbDMFgVEqiA/XZ29ra2Lt376A5lOHsVylhraqqwuVysXjx4mF9/0AsBkWstre3l8WLF9PS0oLdbg/7mMOFpxmutIi3t7ezd+9enE4nqampmEymqMQZRkM7Ua/Xk52dTXZ2dsRrJwKBw+EIeaDtWMKoEMNwULQZOjo6WLhwYcQutEajwW63s2XLFnJycgL2/YcjMpfLxe7du0lMTFS1HsKdn+EPwVgvvj6raCROnjwZt9tNV1cXjY2NdHZ2YrfbVWvC04JyyQKbw02X1YkQkGjUkhKvRxNArcFo1l1410643W66u7vp6Ojg4MGD6HQ61e1QaieOleKxaCMqrkQ4UOZGJCYmsmDBAr/meCg3ndVqpaGhgeLi4qDk54cKPprNZnbt2sXUqVMHBUTH+k2m1WrJyMhQOycnT55Me3s7+/fvx2az9VsTiSnYNHG0mV1otZCdaKTbJpCBjISh06TRdFFCgVarHbZ2Ii0tLSy9x2OpAG0ojCmLQUl1elZJ+oJi2gc8JepoLUFzczMTJ04MaSaFL2JQXB1f2pHhDNEdLcTFxTFp0iQmTZqELMu0d3RSdbiFrq5uLLKWjNQU3M5k8rKSMdvdZAyTlRvrsQtftRMtLS1YLBa2bNmi1k6kpqaO6oTz0cCoEoPnRVQCgZ6FRf4QDDG43W51IEp+fj4WiyXo4/Q2MZWqzubmZr+uTjTN0mhUPnpDo9FgTExGn6ohJ3UcHX02dLKZ+vojtNcfJCU5mTh3NmlpaX599WiVLUej6lFJAet0OiwWC7Nnzx5UO6FYG/5qJ0IdZjsWMWrE4LmiVlVVIctyQIFA+CrdOVzO2mq1smvXLiZMmMDkyZNpa2sLaRX3PFZZlqmoqECSpCGrLqNFDNFajby363TLtPU50Eug04Ber8MpJzE5N53sJAPx2Onp6eLQoUOqiZ6RkTFAvCZaq+dIlEP7qp3o6OhQayeUylPP2gmLxRJycROAJElaYCtwRAhxniRJ6YyCSAuMYoxBq9XS19dHVVWV+uCGIybrDaW5yjOjEWx3pQLlIbfZbOzatYtx48YNO5vzWA9kOd0CDTAuxUSb2UGKSYvNqWHWuEQSTTr02kTI7nfJFF+9trYWs9lMcnIyGRkZURNpGY0Gqri4OCZOnMjEiRORZZmenh46OjrU2onExEQqKirClcr/Cf1DbBUhkduIkEhLsBg1i8HtdrN7927mzp1LampqUJ8dyn/3NPO9exNC9fs1Gg19fX3qvIlAYhTDEUNXVxf79u0jOTmZzMxMkpOTAybGkXAlJAlkwKDTMC7JgMUpY9RKpPkIOHr66p4FR42NjWi1Wmw2W8Bdk4FgtFuuNRoNqamp6n3rdDo5cOAAL7/8Mlu3buXiiy/mqquuClikBUCSpEnAucB9wM+OvjwqIi0wCsSgiJRYrVbmzZsXNCmAf4tB6U3QarU+zfxQiaG3t5fOzk4WLVoU8IowFDE0NDRw6NAhCgoKsFgsNDY2snfvXhISEtR0ob/GqJFyJUw6DYlGHd1WJ219DoSAcclGbE43Jr1/d8+z4EiRqNdoNINayTMyMkK2JsaaSIter2fWrFnccccdPP3009x+++10d3cHu+s/Ab8APKujRkWkBUbYlVBk4lNSUsjKygr5y/X1gCtaB5MmTWLy5MkBf24oCCHYu3cvvb29TJkyJSgz0RcxKEVQfX19lJaW4na7MZlMavpM8WPLy8sRQqgPUFJS0ohHuiVJIjNBT6/NRVaigQSjDiEEjd02spOM6LUaDLrhNRENBgOZmZmDWsl3794NoAb0grGYxmrLtZLy9DFpakhIknQe0CKE2CZJ0qkh7TzCGDGLQelJUDQO9uzZEzGxFiVtqGgd+EMwxOB0Otm1axepqank5uaG1HbtSQyKclN8fDzFxcXqqDRFeVgIofqxytTwzs5O6uvr6e3tVf32oWZPyrLA3GXDbpFBkolPMmJK0KmTuP3BX5BQIKHVSMTpNLhtVqz1Rzjc1IU5JYG4vCmkpiUOWcvgbfL7ayU/cuQIe/bsUTUYhmslH2sWgwKLxRJqn8QyYLUkSecAJiBZkqR/MkoiLTACxKDUEDQ0NAzoSQhXrEV5QGpra2ltbQ2oQjJQYujr62P37t1MmzaNcePG0dDQEPSxeu7LZrOxc+dOJk+eTE5OjvogKj/KTa6ck9vt7l+xMzPJyMhAkiS1MaqlpYWOjg7MZvOALEBfp42G/T10N5qRBZgS9SRnmkgbn0ByhnFYcvB5DhJohIz9cCOd77xL3b5a7LJEQroRQ0EBHWecSfykLOL8uBbDZSWUVnLlmigyd8O1ko9ViyEMkZbbgdsBjloMtwohrpQk6SFGQaQFouxKKD6/RqNh0aJFA5g4XLEWZUU3GAzDTq/y/NxwxNDa2sq+ffsoLi5Wm2FCFWoRQqiW0uzZs0lKShoyoq687jmjwO12qzMsEhIScLvdJCQkqKRosViIMyYhWUx01TvpbDLT2+nCFAdpWYkIQGfQkJgafFm5JEkk2PvY8dqbdG7ZTa8hnlRbL92HrWjrDqPLyMaddSrofTcnBZOuDKaVPNoiLaH2UISjxeAHv2MURFogihaD2Wxm9+7dTJ482aeWfjjE4Ha7qaurY8aMGQPmRgyH4bIZBw8epL29fdDA21CFWiwWC1VVVcybNw+j0ahaCMEcr6c1odT55+TkEBcXp0rTNxxspWLnEdrKv/psXy/0tfShMWiIS9ATl6hH6ycm4O9B67E6qN+1D+sXW3AZ4ki2d2OSZVwaHdaWdjSbN6NdcTIQPjF4Y6hWcpvNhl6vp7OzM+Kt5KFOuYbIdFYKITbSn31ACNFO5ERagkJUiKG5uZkDBw74nO2gIFRiaG1tpb6+nnHjxgVFCuCfGNxuN+Xl5ej1ep/9GcFaDErK1Gq1smzZMnUKVjirXFtbGwcPHmT+/Pnqjet2u3E6XPQ0O2mr9nkkHNrRQ+I4LWkT4tDqApeA67Q42H6oi8YdlRxJm0yiw4pRF4+w96FFxqh1MrGuesgAZKRWdu9W8sbGRtWt8tdKHirCdSVCERAai4g4MbhcLpqbm/3OdlB3rNPhcDgC3q5nx+X06dMDnl/pCX/S8zt37mTixIkRyWa43W7KysrQ6XTEx8cPiCeEAoVkOjo6KC0tHXBNNRoN1m43rYes4LPDu3+fDXs7sWk7SEozqea4Z32HtzXUZXFQ2dBLQ0MbLUfa6dEl0GNIIN3WR7zTQrq1h9y+ZowTC2CIBzFaJr/iduTm5vptJVeEaYINJI5GjGEsIuLEoNfrKS4uHvZ9wVgMnrMYFixYQGtra0haB943aWdnJ5WVlcPqPQRaxehJMjk5ORw4cIBt27ZhNBrV8tpgVjRFuQqgpKRk0A0rhKCvw0ZXw9D9Hy6rjqKifLTG/nPes2cPTqeTtLQ0VadBkiQcLpkui4Oq5h7aexzUNXezR0oj2dCDUXYgkHFo9KTZ+jDKbqRTlqMdwh+PZkm0sl1/reSePQ6+Wsn9YRSFYMcUouJKRFLFSckQ5OXlMWHCBPWzocYnFChNW6WlpcM+rIFYDIrk/KxZs1T3qaCgAOivT2htbaWqqgqn00l6ejqZmZlD+sfK9Kv09HS/uhFCFrQ19OG0D30t7BYXOr0Og1HLuHHj1M7Vrq4umpqa6OjowIWGZqtEg1VLfZeD2o5empqd2PUGLFImCW4b43q7kLUa3FoNbiRSly4dcr/R7K70d92UVnKlOlWxJjxbyZUeB18EECOGfoxaSXQgD7fiQ86dO3dArCIcYhBCUFVVhd1uD7hpa7jgozIvs7i4GJPJNMh1iIuLIzc3l9zcXNxut1ourOTuFWtCcRMUObu8vLwh28+tvU5a67pxDmM8abSg1+oxGHRqOlRJB6ampmJKSGJ/YydWs5WdB1rpskJND3RbQDalkWHpwik01CdlMbPxCG5JS3taNlnDEGo0LYZAMwfereRdXV20t7dz8OBB9Hr9AGtCiSWF40pEOCsxahiTxKCUTXd3dw/KEEDopc2K5Pi4ceOYNWtWUKk0X/tT4h6dnZ2Ulpai1WqHjSdotdoB0mO9vb20tbWxc+dOoL8Zp729naKiomHLxXs6LLTX9yEPE24xxeuw25zEJRvUY4D+1bGho4/K2ibcxiR6hQkRL+Nw2ui0mjFjRaPR0yMZMQknObYuUp1W4mQn1oK5aOKGNs3HWnel95Abm81Ge3u72jGZkpKC1WoNuRfleNF7hFF2JXwRg7eCk68bKxSLobe3l7KyMgwGQ9BTnX1ZDEomw2AwMG/ePICgg4yelYDTpk3jyJEj1NTUkJiYSFVVFampqWRmZpKenu7TsulqsWA1i/5upyEQn6j3+Z4+m4Md5VXkTZqAJi6JLQfbsTj7JdysbnADSBosxni6JTcJjl4skkSCVouYMxvtMM1kY40YvGEymQZ0THZ3d9Pe3k5lZSU6nc5nK/lQCKPyccxhTFkM3hWH/hCsxaCkT4uLi9Ua/WDgbTHY7XZ27tzJ+PHjmTBhQkQzD0uWLEGv16tmb1tbGwcOHFADmFlZWWpGweWQkQMYqJySE0ecV4GTxWJhx45dTJo0meycTLotDsanx3Ow3c5uaxcSYASE24UsaTC4nGg1WiyGJOzGBKSsNLbs2EpKcgpZmVmkp6cPMu/HuoKTJzQaDWlpaRgMBkpLS9Vybe9W8qFmV8RiDBGANzEofvrcuXOHNccCtRg8TX3P9GmwN5YnESnyczNnziQlJSVsbQAl8yCEGJB58DZ7LRYLbW1tVFZW4nQ6ycjIwO6gf4DZUBypgXEzUjGavvqqe3p6qKioYG7hbDpcOuo7bThdLnpsMjkpBhIMWjolNzohgZDRCpnxtm5mdh1BJ2Sc8W5ax7fQldCK3qFnQtME4mriSDQmqoE/JVV7rCg4KVDuDX+t5ENNJXc6nUGPCzw6lOgjYBz93+RTQohHR1OkBaLoSgwH5WHzlF0frvZBgdIrMRRcLhfl5eUYjUZKS0sHPHDB6EXCV66RYnnMnTuXuLi4sIuWFHXptLQ08vLyhtxWfHy8GsB0uVx0dHTQJA6BLFDqFQYedP9Pco6e5MyvAmJKGq+kpIS4uDj62s0091jptbnQaKDLYscgaUg2uXFZZWRk4p1mStv2keju5VCqnp1zLTS1b8TV029JZcdnc9aUs5iVPQtnr1P12ZUZD5mZmWGN5/NGNIkBBt+/nq3k/qaS79mzJ6RS6qOf+R8hxHZJkpKAbZIk/Rf4HqMk0gKjaDEoD9u2bdtISUlRZdcDwXBKTFarlZ07d5KbmzuoOjJUYjCbzdTV1QUcZBwOgWYefEGn05GdnU3CSSnUbdqB3cc6IunAYIS0nDgMhv6HqKGhgSNHjlBaWorBYEAIQXufA7cMySYdh9vNHGyz4BYyJoOGHqsMbohzOIgXXZSN07NpuoM9eU4kd+3RIAR0ODowaA2YdCaWjFtCVlYWANu3b6erq4u6ujoMBoNqTYxmdWIk4Gsq+UsvvURdXR2nnHIKF154IbfeemtA2zoqRLsdQAjRK0lSFTCRURRpgVEkhp6eHsxm87CK0L4wVPpQkXTz14IdbHxCMfXdbjclJSVhxxOgv+ZBKazyVpcOBgaTjrhkHfZOF2hRH1QAIcAuCeS4HtpaO2hqt6o6EAopOt0CtxD02px09dmpabdQ32nDbHfjcoPO7iLJ1k2Su5dP83Loy2yhNteCpBtIqr300mBpoM3ehlNyYtQasdlsyLJMXl4eWq0Wu91OZ2cn+/btw+FwhDU2b7SJwRNKAPk3v/kNGzdu5K233qKioiLUbeUB84EvGUWRFhglV0IZ4xYXFxc0KQyFuro6Ghoahhw3HwwxKEHG7Oxsurq66OnpCUpQxBdaWlo4ePCgasqHA41GIn18Epa2TpwyCCUQqYWkTC1xKQYysxOorT0EBgcpKSk0NjaSmZmpXh+jToPZ7uJwl5XqFjNtPW5c9Du7OlmDUafDrbPTaUzgUFYcOq3dZ89Uj6OHPlcfGq1GVeYuKChAr9ergi05OTlq41d3d7dapxIXFxfUEN6xKNHucrlUUdxTTjkl6M9LkpQIrANuEUL0jPb5jajFIMsy+/btw2q1qsNpI/ElKyPn3G73oPZubwRKDL29vezevZuCggLS0tIwGo0cPnyYvr4+VYHKXxrRF4bqeQgVWp2GKbNTaanpwdH9lblgTOgvaHL0uGlvb2Py7GzyZ01XA5gVFRW4XC7S09NxEI/F6aa1x0prrx0bIACBFdAgSW4EDg6nS6DTMcg0OQq72068Lh7ZJrOzciezZs0aYLF5ak3IsjxAM9Fms6nl6W63Ww3s+SPhaFkM4WhphlPcJEmSnn5SeFEIsf7oy6Mm0gIjSAwOh4Ndu3aRnp7OzJkzVYGSYP19X9vduXOnOll6OJIJhBhaWlrYv3+/GmQEVEERJd/d2tqqphGV9mB/q50sy+zduxdZln32PISD7Lw0Jhb00FzbRXezG40eTAkGkGQsVgf5uRnkz5rer61wVNNhypQpuFyu/lLhA0eoPdjNkS6wOL9KcGiQQbgxGw30JUnYE1swav0/OGbMJIkkKssrKZ5bPCizNJTWhMlkGpAB6OrqoqGhQa0MVawJhUyjRQyjJNIC8DegSgjxsMef3mKURFpghIhB6SNQZN0UKGnHUIlBSR16b3coDKfJUFtbS1tbm98go5LvVpquzGYzra2tlJWVIcuyWmugpLGCyTyEAuEWJGXEk5Rh5HBFJ7Y+J25ZxiG5mFSUzNT8wTL3TreMU5ZISc8kT44jrqkWR1c7XxVQWpGtGjTChV7qos94BJ2hA0ljhzjfQV8DBhoON3D6KacH1KzkrTWhWBOA2sug0Wgwm810dnYO0Ih0OBxRcSXC7ZMIxWL47LPPAL4DlEmStPPoy3cwiiItMAIxhiNHjlBXV+dzwlQ4PQ9Op5Py8vKAJld5wh8xyLKsTqwqKSkBAqtkVFbhvLw8nE6nqpugrCDd3d1Mnz49an36Gq2GuAQDIMiemkzb4T66+vqYPieTzAlpmBIGfsU2p5umbjtCEggZ9jR2UdnUTceABs040JjRIWOIO4RIbEJn7EHS2PF3ObRoOXneySHNVRjKmlA6J5XqxI6ODmw2Gzt27FCLjnwVV4WCcIhBEYINFsuXL0cI4e8mGxWRFoiixaBE8x0OB4sWLfL5xYVCDEofhdPp5KSTTvIbZPQHXzUQijuSnZ2tpjdDyTzo9XrVJO7s7KS8vJyUlBR1zkVWVhaZmZkRHZOuN2rRGSTMvW7ckgOzbCa/KIfcWZmkjktA65VBaO1zYNBJ6LRa2nptfHGwix6zi0HKGEYN8UgY47vQJ7aBNLT/vShjEQVZBRE5J29rQvkBVGn90tJS+vr66OzsVIuOFI1MfyPkhkOsgeorRIUY7HY7W7duJSsri9mzZ/v9koIlBsUsT0hIGCQQGii8LQalDHvGjBmkp6dHJB2pZB4WLlxIXFwcQggsFovqcgghyMjIGOByhAqtTiIx1URHZwM95k4WfG0ayWmJpGabBgnA9pvrMkajDodL5mC7BRBofO4+DgMO0o122iSBGCKBokPHt2Z9C6M2coSnwJMk3G43Bw4cUOX0PVWdlGIqz4YopYQ5UCsgnHhXjBgCgFarZfr06cNObAqGGJS5EVOmTGHChAls27Yt7DmUra2tVFdXU1hYGJESXiEEdXV1aoxCCZZ5Bv7y8vJwOBxq66/ZbFYFU9LT00PI6QuaWhtw6fo46dRidDodTrsbXwF2SZKIN+qw2F04ZRkQdFodXm7EV8hMTSFv8hyanE100+f3GKZKU+mp6aGis0JdtSNh2ntCqZAVQlBUVKT2ryguh6dGJPTHnzo7Owe0VyvWhD+EM5T2eOqTgCgRg/JFDLtznS4gYmhvb2fPnj0Dxs2HGp/QaDS4XC5qa2tpaWlh/vz56HS6sK0Ez8zD/Pnzh7zBDAbDgCh8Z2cnbW1tVFdXEx8fr7ocw9Xdy7LMnr1VuGRBwYyZ/dfEJSNpJDS+zQBS43X02BwcardwqM1CU5vZpypcih7GjdMzdcpcOtoaKe8qx8rgjq1UUvnewu9x6pRT6enpobW1VR10q+hMhLuSKhoaOp2O/Px89XvyF8CUZVntWs3Ly1OLq6qrq7Hb7Wpxlfd4+5hIy1cYtcpHGP7h9pxD6T03IlRNBkmSqK+vJz4+nvnz56uvjVTPgzc0Gs0AWXQly7Fr1y4ANcvh3fqraEumpKSQO3MKtj4nLoeMRq8hIUmP5IMYHG6ZQx1WmrvsWBxuKhp76bD6mFsJTE43MjHFRFpCHynWFDItmbQ6WrFjR9D/mTjiWD19NUsnLkWSJLWfAPprExSys9lsqlWkZBsChTJdPD4+nmnTpvm9tv4CmEpxlaKBIUnSAOk3RUhWGeYTjsWgNLwdDxi1JioYWt5tuHHzoVgMDoeDw4cPq2PEIhFPCKfnwRuefvPUqVNxOBxq27XFYiEtLU0libKyMnVqFUBCAHMjWnvsdJkd6LQSLreg1+7E7EPkJc0IC3LTWT4jjfhEic8aHDhkBwKBAQM6dGQaMjl7ytlcXnQ5Jt3gALDJZFKVk9xuN52dnerMjvj4eNWaGCoQK8syZWVl6uSqYDCUNZGamkpKSgqSJKnWRFVVFVarFZPJRFpaWtAxLLPZ7FdM+FjEmLQYlFLkocbNBzvSXgkyZmRkqNaGIuseKpT25dmzZ4c0nHc4GAwGJkyYMGBkXWNjI83NzarUncPhCKjVt8/upL7LSpfF0S8NX9vBnnrzoGyEESjISeCsomxykuPR6CUKUgtw97qxmWy4JBdJpiTOn3Y+SyYs8UkK3vB0KxSrqK2tTa39UAKxnjM6lWnoGRkZ5ObmBnvpBmCodKjRaFQbohobGzGbzeqQG2XI8HAj8yDmSgSMQFWcvCXkPUVVh4pTBNJ6rUCZbTl37lwkSaKmpobNmzeTnJxMdnZ2UKXNCiLZ8xAINBoNer2e3t5edfJWW1ub6nIocQlfakP9XZRO0uN1NHfb2FrbwUd7OvHhRZBkghnZSZgMetIS9LT2ObC2Wjlt+mkYE43YnDYcsoOF4xcGRAre8LSKlNoPRedAmdGZnp7OkSNHGD9+fNCzQwKBL2vC5XLR1dVFdna2OhbQYrEEPGT4eFJvgjFmMSjj4T1nXAb6WX+oq6ujsbGR0tJSNchYVFSEEEJt5Dlw4AAmk0mNag+1OvjLPEQb3joKAElJSarLoZRoW61W1eVQgmuC/m5LvVZLl9VB5ZFun6SgA3LTTRSNT2RyWhwuu43qimpm5M5AY9CgERrcwk1BagEJusik5vR6/YAW5vb2dqqqqpAkiaamJtxuN5mZmSEVTgUCpVN3//79JCUlMW7cOLVuQhkyrBRXeQ4ZTkpKUour9Hp9RNKVkiSdBTxKf0PKM0KI30XiHEPBmCAGIQT79u3DYrH4LYbyxnDBR6XAyuVyUVpaCgwMMkqSNKCRxzPoJ0nSgKCf5zYDzTxEEt46Ct4wGAzqDaz4883Nzao5nJWVhc6QyKEOG/vbzLT3+b5uqXEwPzeNJdMzcdksVFRUsLB4IbJRprqzGpvbxpTkKUxL9h8EDAdOZ7/IS0FBATk5OWoAc+/evWo2wZPwIgEhBJWVlRiNRqZP7+8p8XQ5PIurFLdCkiT6+vro6Ojg8OHDrFu3jkOHDtHQ0BByU6AkSVrgL8AZQD2wRZKkt4QQlRE50WCPZxhzP+R2M6fTOayp39XVxeHDh3E4HCQnJzNjxoyAL2pdXR2SJPkM+CgDb9PS0pgyZUrQQUa73U5bWxutra3YbDZ1ZaitrSU9PT0qPQ++oMzT7OnpYe7cuUG7O8oE6ZaWFvbUNbGlyUFZO+zpGPzFGoGVczL43slTGG9yq4N9o7VSe0OJK02bNs1n34tnALOrq4uEhAQ1bhGsnJoCX6TgD4rLocQnFEiSRENDA9///vfJyMigrq6OTz/9NFi1aEmSpKXAGiHEmUdfuP3oMT4Q0smFiVG1GBwOBy0tLRQWFg4p/uoLSj2CN8xmM7t27WLatGkDpiwF8yAbjcYBK3BTUxNlZWVotVpMJhNtbW0hxSWCgWLxSJJEcXFxSCukdHSUm8YYR5s2jRRnE1Jbs0+2L5kYz1mFOSTIFvbvPzxgRma0oUzwKigo8Jvy8xXAVCw8IYT6N1/+vy8EQwowMICpiPUq1m5WVha1tbWsXbuW8ePHh3pfTAQOe/xeD5wUyoYigVEjBiV1pfh1wcJX4NKzECpSYqSKpFtJSQkpKSkhxSWChWeNQiSsE1kWdPQ5kSQ9sqQnDqdaqiQBCcCsVCcmSwvNPZYRjZ1YrVZ27do1SL9hKHindZXmtUOHDql6GUolqS+3NFhS8AUlgOlyufjRj37ElVdeGQ4pgE/hztAt9nAR1ayELyitza2trZSUlFBVVRXS9r2Dj4cPH6ahoYH58+ej1+vDrk8A35kHf3EJQCWJcIJQim6FZ41CuDDoNEgSOF0uJK2O5AQXRodAr4M4vZav5WdxUo4TbD2g07F79241xhJNV8JsNrN7924KCwv9TkUPBJ7Na4pehtLlqtfrVWtCWSzCJQUFbrebG2+8kcLCQu6+++5w77d6wNMvngQ0hLPBcDCiFoPb7aaiogKdTsfChQsH9OAHCyX4qAQEHQ4H8+fPVwkhnC8p0MyDd/+D0nuhxCWysrLUQppAYLFY1IauzMzMkI9/AGQXOo2WZIOGuk4rFqcLg04iwaAn3iCRlWgkXWMhXqNn4dKlaDSaQUG/9PR09VwiFfTr6+ujrKyMoqKiiE5v8tbLsFqtA85FKZeeOnVq2KTw4x//mNzc3EiQAsAWIF+SpKnAEeAy4IpwNxoqohZ8dLlcAx56xY+cMGGCWrAihOCLL77g5JNPDnr7nZ2dHDlyBLvdrprckahk9Mw8zJ49O6QHQZlP2draqupEDlcvoRRLhbt6qug+DAc/Qbb2sZ9JbHLOoMEsU9NqodPi6O/KNOqZarLxtWlJLC+d6/O6ud1uOjo6aG1tpbu7m6SkJLVRKlR3QznX4uLiEetIFEKo9Qg6nY7u7u6QA5iyLPOzn/2MpKQkHnrooUiQpQQgSdI5wJ/oT1f+XQhxX7gbDvmAokUMbrdbDQ52dXWpFYLewaXPP/88JGJobm6mvLycOXPmkJWVFRFScLlclJWVkZqaGrHMg1Iv0draSnt7u8+4hFKjMG/evMgUSzVWwGtX4LK0U+/O4L+aM2jKWkxaXgmSTkd7r53sRD2JzjYWTcmgID+wbJAQgp6eHtra2mhvb0er1arnEuhxd3V1sWfPnsidawBQ3AeTyaT2WygZm7a2Ntra2gBUkhiqFV6WZW677TYAHnvssUhZUGNL2ZYRIAZl3Ly/CsFQiKGjo4OKigri4uLUuZHhfkE2m01t6w4lGBoolLhEa2sr0J8BsVgsfmsUgoa1G/48CwlolNNocaexyT0TK4nY519NclJSvwvX28oZRROYOS0v5F3ZbDZaW1tpa2vD4XAMcDl8PVgdHR3s27ePkpKSoAV2QoUvUvAFpRW+tbUVs9nsU/BXlmXuuusu+vr6+N///d9I1rGcOMTgdDqpqKjAbrdTVFTkt2gpWGKor6+nvr6emTNnsnv3biZPnkx2dnZYq0+0ex58QdEXUKwIu90eUlxiEF79ORz8Jwg4JMbRI+JodKZQzlScMy/BnZiDtbuV8+dPYfHsvIidj7f7lJSURFZWlqrNoDSDlZSUjFga1JMUghlk7BnAVCyjjRs30tbWRm9vL3/7298inao+cYihsbGRzs7OIVkaAicGIQR79+7FZrMxZ84cJElSuw9bWlpwuVxkZGSQnZ0dlCqSknmYO3fuiBXzeNYozJw5U20I845LKA9WUDfh76cAjqMWQzqtcjIO9HS5E9g/41o6pAzOXJBPyYyJaKJUpKW4HIr7JITA6XQyb968yMRPAjyGUEjBFzo6OvjpT3/K5s2bycrK4tprr+XGG2+M0JECY5AYopaVyMnJCag/XVHiGcosc7lc7Nq1i+TkZIqKipBlGUmSBrT2Ks04iipSeno62dnZpKam+iSJ0ep58FejoNVqVc0Az7hETU1NkPUSDvUuy5C6sUt6moURo9bJZNHMqq9/nakTAlPUDhWe2gwJCQnU1dUxefJkVaszIyODzMzM8CyjIRBJUhBC8NxzzyFJEgcOHMBut3PkyJEIHenYRdQsBlmWcTp9NPt7YcuWLZSUlPh9MD0l3ZSHZrggoyzL6urb3d09yF+MROYhFIRao+Adl/BbL+F2wR8nD1h+hACH0CIjYbvsHVLz5kbgTALDkSNHaGpqYt68eaorqcyzaGtrG2AZRUrpWQihxp8iQQpPPPEEn3/+OWvXro1oEZsXxpzFMOrEsH37dmbPnu0zRqBMJ5ozZw5JR4NmwT7Enl2UHR0dmEwmbDYbmZmZYRe4BINI1Sgo9RKefRxqXEJ2DyIG6P8SHZrpGH7yX9CPTCagrq6O9vZ2iouL/bpCnpZRR0cHer1eJb1QgpORJoVnnnmG//73v6xbty7acZEThxiEEINKln1h165dTJ8+fVAvuzKPori4GIPBEJFKRqvVyo4dO0hISMBut6sCotnZ2VH94iNeo3AUg+ISSUlM3Xw/8W0fDbjTZEC65D+QNxe/gyEiiNraWrq7u5k7d25QRG61WtUsh+JyZGVlBTQvNJKkAPDss8/y1ltv8cYbb4xEBiVGDN4oLy9n8uTJqlagEq3v6+tT1YAjQQq+Mg/KjdjS0qI2w4Rb0uyNiNco+IGy+rYdrCb5izWk921HAO6EGZjOfwImF4IUXZdJCEFNTQ0Wi4XCwsKwXDTF5WhtbaW3t1ftf/AVjI00Kbz44ousXbuWt956a6QC0jFi8EZVVZUaqFQKjBISEpg2bVpEipagP/NQU1MzZBuxZ4ZDcTUCXa38QdFRmDdvXjT900GoPVhDe9NhMtJSaevqQ2gNUSE9TyiE7nK5hpwlEuq2FQHX9vZ2dV6oohkZSVJ45ZVXePbZZ9mwYcNIzok4cYgB+nvsh8O+fftIS0sjMTGRnTt3kpubS05OTkRIwTPzUFxcHHDmwe12q3oMvb29pKWlqRmOQFbBcHUUQoWiRGS325kzZ456rEPGJSJU3emZfo123EYZ3qPUFSQkJDBz5syAW6794Y033uDJJ5/k7bffVi3YEcKJRQwOh2NY3ccDBw4A0NTUNCDIGImeh3379uF2u8PKPCiSXq2trXR2dpKUlKTqAvp64H3VKIwElNkLWq2WgoICv9dO6X1oaWkJr17CY7+VlZUYDIaghHbCheI+GI1GkpKSaG1tVVuuvSsWA8GGDRt45JFH2LBhg9qANYKIEYM3du/eTWdnJwsWLMBoNEbEdYhGzwMMLNxpa2vDZDKRnZ2tNuFEWkchULjdbsrLy1UNyED3G0gfx1CQZZny8nJVF2GkScHbfVAqFpUsh+JyZGVlDRlcfu+993jggQd45513AhqUFAXEiEHd8FGzt6WlhfHjxzN58uSIkILNZmP37t3k5uZGtecB+usLWlpa1CYcu93OpEmTyMvLi+p+PaEUf2VnZ4c91yDgegn6H0JlyM6UKVPC2m8wCCbQqMjUt7W14Xa7fcrUf/jhh9xzzz1s2LCB7OzskTgFXzixiMGf7qMyMyAuLo60tDSqq6uZMGHCMdnzAP0+786dO0lPT8diseByucjMzCQ7O9unnHukoEzpjgYJescllErSlJQUZFlm165dZGVljeiQlXCyD4rKU1tbG319fWzatAm3283rr7/Ov//976gvIsMgRgyKLsOkSZNUyXDlJmxpacHtdqu1BcFEhUej5wF81ygoN2FLSwtWq3XAQxUpklCuY0RFXfzAMy7R3d2N0+kkJyeH/Pz8EQ2seo6qCweyLPP000/z17/+VZ2H+fzzz0dUMCZInNjEoAyTmT17NsnJyT6DjE6nk9bWVpqbm7Hb7SpJ+GuMEkJw+PBhWltbg8o8RAKB1Ch4B/tSUlJU0ZZw5iSWlZUFpZMYCTidTnbs2KHOeQwlLhEKIkkK0F+G/5Of/IS33nqLyZMnU11dPWBYbqC45pprePvtt8nOzqa8vBzob7j61re+RW1tLXl5ebzyyitqMPOBBx5QOzMfe+wxzjzzTGVTJy4xNDY2UltbG1Qlo8vlUldes9msdk8qK2+kMg+hIJQaBSUfr5RnJyQkqMHLQPsEuru7qaysZO7cuSM6+UhxW/Ly8gb44sHEJUJBpElhx44d/PCHP+SNN94Ieh6mNz755BMSExP57ne/qxLDL37xC9LT07ntttv43e9+R2dnJw8++CCVlZVcfvnlbN68mYaGBlauXMm+ffsUi+vEIgaXy4XL5eLAgQN0d3dTVFSkzosMlp2V8t+Wlha1Eq63t5esrKwRj4hHokbBc+ZDW1sber2e7OzsISPoitDJSKofwVczH2bMmDFk1H6ouEQo30+kSaGsrIzrr7+edevWkZ+fH/b2oL/8+7zzzlOJYebMmWzcuJHx48fT2NjIqaeeyt69e3nggf7xELfffjsAZ555JmvWrGHp0qUwBokhqmKwbrebXbt2YTQamTdvXlj1CZ5tyRaLhR07dmAymWhubsZqtaq1BdG0GiIx60GBMvMhKSmJ6dOnY7VaaWlpoaysTC3Pzs7OVuMlzc3N6vi+kRI6ga/k3WfOnDlsft97IlZHRwdHjhyhqqoq6HoJRaNRqYINF5WVlVx//fWsXbs2YqTgC83NzYwfPx6A8ePH09LSAvT3/ixZskR936RJk8Z0+3ZUiaG2tpaMjAx1WnMkex4KCwtJTU0dYJ7v37+fxMRE1TyPZGAs2jUKcXFxTJkyhSlTpqgrr6J+bTAYsNvtkZN/CxBKR+js2bODrgT01IP01pcwGo3qd+SL5CJNCvv27eOaa67hxRdfZPbs2WFvLxT4ssxHysoNBVElhry8PFwuV8RIQbmx5s2bp66kkiSpcuFCCHp7e9UMRVxcnHoDhhOUjMash6HgufLW1NTQ0tJCfHw827ZtC7o8O1REUt7dc05ofn6+GpdQrCPP1G6kSaGmpobvfve7PPfcc8ydG30tipycHBobG1VXQonHTJo0icOHvxo0VV9fPyL3UqiIaozhiiuuoLOzkwsuuIDzzjsvIEUnnwcRQuZBGWPW0tJCa2ur6sNnZ2cHtepGZdZDAPDV96CUZ7e0tNDV1TVseXao6O3tpby8fEQCnN5xCVmWSUlJUeX7wsGhQ4e47LLLeOaZZ1i0aFGEjnggvGMMP//5z8nIyFCDjx0dHfz+97+noqKCK664Qg0+nn766VRXV5+YwUelfv+1115TG1MuuOACzj//fDIzMwP64iOVebBYLCpJSJKkksRQvfbR0lEYDrIsU1VVhU6n89v3oJRnt7S00N7eTlxcnGq6h2MddXd3U1VVNaIDbaH/fMrKypAkCY1GE3Yfx5EjR7jkkkt48sknlQBfxHH55ZerIrE5OTncc889XHjhhVx66aXU1dWRm5vLq6++qi6I9913H3//+9/R6XT86U9/4uyzz1Y2dWIRw4ANHV0B161bx1tvvYXRaGT16tVccMEF5OTk+Lz5o9XzoMiet7S0IMvyoEAfjJyOgjdC7Xvo6+tTV17Fvx+O+LzR2dnJ3r17R/ycfbkP3n0cw8UlPNHY2MjFF1/Mo48+yte+9rWROIVwceISw4CNCsGhQ4dYt24db7zxBgDnn38+F154IRMnTkSSJKxWK2VlZVHvefCsunQ4HKol09bWRklJyYgG+yLV9+BJfEolqVJb4I9o2tvbVSIcqZkP0G8dVVRUDBtT8K6X8IxLeKK5uZlvfvOb/OEPf2DFihVRPfYIIkYMg3YgBA0NDaxbt47XX38dm83G4sWL2bp1K2vXrg05LhEKnE4nlZWVdHV1YTAY1JsvHLGWQBGtvgfv8mxfWgytra3q8N6RJEKFFJTuzEDhLaqTnp6ukvqll17Kfffd51lVeCwgRgxD7kwI/vnPf3LnnXcyc+ZMurq6OOecc7jwwgtDKlkNBt46CkKIAQVVaWlp5OTk+JWjDwdKrUC0A5zeGpGpqano9Xo6OjrUKeEjhVBJwRtKvcS9997LW2+9RXFxMTfddBPnnnvuiJ5PmBhzxDCi066HgyRJtLe3s3PnTtLS0mhra+PNN9/kjjvuoKWlhTPPPJOLLroo4tJh/moUlAClLMt0dHTQ2NjInj17ItLvoEAZBT8SHaGeRWKyLHPgwAEaGhrQ6XTs2bNHzXBEQsZ9KESKFKD/nAwGA5WVlTz55JNMnDiRt99+m3POOSfs48zLyyMpKQmtVotOp2Pr1q1D9kIcTxhTFsNQ6Orq4q233mLdunXU1dVxxhlncNFFFwWtROyNYGsUlIKq5ubmgBSdhsJo9T1Afx69paWFefPmodFo6O3tVQVoDAaDGryMtGsRSVKA/tTqxRdfzM0338yll14agSP8Cnl5eWzdunWAFeevFyJMjDmL4ZghBk/09PSwYcMG1q1bR3V1NaeffjoXXHABCxYsCIokwq1R8JUyVPodhlt129vbqa6uHvEMAPTn9zs7O/32enindpXgZbjpy0iTgtls5pJLLuG6667jyiuvDHt73vBFDP56IcJEjBgiDYvFwjvvvMP69espKyvj1FNP5YILLuCkk04acgWPdI2CUlDV3NysrroKSXivukrfw7x580a07wH6KwEVaf5ASNRut6vZACXAF+x8UIg8KVitVi699FK+/e1vc80114S9PV+YOnUqaWlpSJLED37wA77//e+TmppKV1eX+p60tDQ6OzvD3VWMGKIJm83Ge++9x2uvvcb27dtZtmwZF110ESeffPKAFXwkahQ8V12NRqOSRFtbmzq2bSSDY0IIDhw4gM1mo7CwMKQYjdIGrwivKmPvlYfHHxRtSKU2I1zYbDYuv/xyvvGNb/D9738/akHphoYGJkyYQEtLC2eccQZ//vOfWb16dYwYOMaIwRMOh4MPPviAdevW8cUXX7BkyRIuvPBCqqqqmDRpEuecc86IpeZsNhstLS3U1dXhdDrJy8sjJydnxCoLhRDs27cPWZaZNWtWRB4kJSCrqDolJyerAVlPSy3SpGC32/nOd77DmWeeyU033TRijUhr1qwhMTGRp59+OuZKcAwTgyecTicff/yxmt045ZRTuOiiizjttNNGxJRXhrE4HA5mzJih5uCdTucAGbtoTX4ORFY+3H14zgdVYi3p6ens3bs3YqTgdDq56qqrOOWUU/jZz34WVVIwm83IskxSUhJms5kzzjiDu+66iw8++MBnL0SYiBHDaOHtt99mw4YN/OlPf2LTpk289tprbNy4kaKiIi688EJWrlwZFbdiqL4H7+IjxX8Pd3CK5749x8GPxOrqGWs5fPgwOp2OKVOmhDyoVoHL5eKaa65hwYIF3HbbbVE/l5qaGi666CJ131dccQW/+tWvaG9v99sLEQZixDBaUM7T84aSZVkliffff5+CggIuvPBCVq1aFZH0odL3kJycPGyvhzL9qqWlRfXflfbqUB6CSJvwoe5bEStpbW0NWejX7XZzww03kJ+fz9133z2mdQxCxJg7oROGGIaDLMts376d1157jXfffZe8vDxWr17N2WefHdK4MqXvIScnh0mTJgV9LB0dHTQ3N6sVitnZ2aSlpQWUSVDk+TMyMsjNzQ362MPBUITkXcqsaHgOVXLudru5+eabmTBhAvfdd9/xSAoQI4ZjA8rN/eqrr/LOO++Qk5OjakoEUuWm9D1MmTKFnJycsI9FUagKpKBKkdPLzs4OmpDCRTBWireGpy/yk2WZn/70pyQnJ/PQQw+NqNjvCCNGDMcaPDUlNmzYoGpKnHfeeWRlZQ16v9L3kJ+fH/FxZ55Bvvb29kEq0y6Xi507dzJx4kRVd3CkEI7r4k1+breb/fv3s2vXLvR6PY899tjxTAoQI4ZjG4qmxGuvvcb/+3//D5PJxOrVq1m9ejU5OTnU19dTX18/In0PvlSmLRYLU6dOZeLEiVHdtzcUUlBiKeFAqbf4+c9/zq5du1i4cCE33HAD5513XmQOdmwiRgzHC4QQ1NbWqpoSNpuNtrY2XnzxRUpKSkbUF3Y4HGzdupXk5GQsFsuAZqlop2MjSQrQf13vvfdempubeeaZZ6ipqaG9vX2AwnIk8O677/KTn/wEt9vNddddx2233RbR7QeJGDEcj/jggw/46U9/yje+8Q02btyIw+HgvPPO44ILLoj61GtlVJ2n62K1WlWhFiGEShKRTsdGgxQefPBBampqeO6556I2/s7tdlNQUMB///tfJk2axKJFi3jppZeYM2dOVPYXAMYcMYyI4/bqq69SWFiIRqNh69atA/72wAMPMGPGDGbOnMl//vMf9fVt27Yxd+5cZsyYwY9//GO/U7PHAtLS0nj//fdZs2YNH330EevXryctLY1bbrmFFStW8NBDD7Fv376In4PVamXnzp3MmjVrQDwjLi6O3NxcFi5cSHFxMVqtlqqqKjZv3szBgwcxm81h7zsapPDII4+wZ88enn322ajOxNy8eTMzZsxg2rRpGAwGLrvsMt58882o7e9YxIgQQ1FREevXrx+kv1dZWcnLL79MRUUF7777Lj/84Q9xu90A3HjjjTz11FNUV1dTXV3Nu+++OxKHGhJKS0tVmXBJkhg3bhw33ngj//3vf9mwYQMTJ07k9ttv5+tf/zoPPPAAlZWVYZOE2Wxm165dw8YzjEYjkyZNorS0VB1WU11dzaZNm9i/fz89PT1BH0s0SOEvf/kL27Zt45///GfU9SCOHDkyQDpvrA9/GQ2MiFCLvyEfb775JpdddhlGo5GpU6cyY8YMNm/eTF5eHj09Paq673e/+13eeOMNT1XdYwaZmZlcd911XHfddXR2dvLWW29x7733UldXx6pVq7jooosC7nRUEOrMB71ez4QJE5gwYQIul4v29nZqa2t9zgX1h2iQwtNPP80nn3zCunXrRqR/5Vgb/jIaGFUFJ39ju/R6/YAc/PHC6GlpaVx11VVcddVV9PT08Pbbb/OHP/yB6upqVq5cyQUXXEBpaemQJKG0ixcXF4c1OFan05GTk0NOTs6gcXKpqamqjJ3nsUSDFJ599lneffdd3njjjRFrQT/Whr+MBiJGDCtXrqSpqWnQ6/fddx8XXHCBz8/4Y+4TgdGTk5O54ooruOKKK1RNiSeeeIKKigpOO+00LrjgAhYvXjzA1+7q6mLPnj0DJnFFAp7j5JShNs3Nzezdu1ftmkxLS6OysjJipADw4osv8vrrr/PWW2+NqDL1okWLqK6u5uDBg0ycOJGXX36Zf/3rXyO2/2MBESOG999/P+jP+GPuSZMmUV9fP+j14xXx8fFcfPHFXHzxxaqmxLPPPsstt9zCsmXLuPDCC+nq6qK7u5tLLrkkqg+RRqMhIyODjIwMtaCqubmZ8vJyTCYTOTk5uFyusOMAr7zyCv/617/YsGHDiA62gX5r6fHHH+fMM8/E7XZzzTXXUFhYOKLHMNYxqq7E6tWrueKKK/jZz35GQ0MD1dXV6iqZlJTEpk2bOOmkk3j++ee5+eabR/NQRwyeRVOKpsQjjzzCjh07OPfcc5k4cSKnnHLKiIi8SJJEcnIydXV15OXlkZGRoc4FNZlMqvhMsMfy+uuv8/e//50NGzaE5Q6Fg3POOScigrHHK0aEGF5//XVuvvlmWltbOffccykpKeE///kPhYWFXHrppcyZMwedTsdf/vIX1XR+8skn+d73vofVauXss88+JgOP4cJgMFBQUEBfXx+7d++mvLyc1157jdtuu40FCxZw4YUXcuqpp0bNN/cVU0hOTmbGjBnq5KsdO3ag0+lUkhjuWN5++23+8pe/sGHDhrCH5cYQPRw3BU5r1qzh6aefVvsX7r//fnVFeOCBB/jb3/6GVqvlscceO9aGkWC32wc8cG63m08//ZR169axceNG5s6dy4UXXsjpp58esSImWZYHSOoPBavVSktLCy0tLUPOBf3Pf/7Dgw8+yIYNGyLeRxJteA+vhf4YmSRJrFmzhjVr1qi/h4AxF0A7roghMTGRW2+9dcDrlZWVXH755eqU4ZUrV7Jv376oFtCMJNxuN5s2bWLdunW8//77zJw5U9WUCNVMD4YUvGG321WScLvdZGRkYLFYaG1t5Z577uGdd97x2Xw21uGLGHbs2ME//vEPAFasWMHmzZu5//77Q9n8mCOGMTVwJhrwVysRrQnIIw2tVsuyZctYtmyZqinx6quv8tBDDzF16lRVUyJQJexwSAH6C6omT57M5MmTcTqd7N+/nx//+MdUV1dz9dVX09TUdEwSA/RrbFx11VXs2LGDgoICnn/+eX74wx+ydOlSnE4nTz755GgfYsRwXPWyPv744xQXF3PNNdeoyr0nUpWbRqNh4cKFPPjgg2zfvp01a9Zw4MABzj33XC655BL++c9/DqlorJCCMl08XOj1ejo7O3E4HHzxxRcsXryYf//732Fv1xtr1qxh4sSJlJSUUFJSwjvvvKP+zV/JfSjYu3cv3//+99m9ezfJyck88cQTPPHEE1x55ZWceeaZ3HnnneGeytiBEGKonzGF008/XRQWFg76eeONN0RTU5NwuVzC7XaLO+64Q1x99dVCCCF++MMfihdeeEHdxjXXXCNee+210TqFUYEsy6KiokLcc889YtGiRWLVqlXiL3/5izh06JAwm83CbDaL3t5e8fnnn4vKykr1tXB/Nm7cKObNmycOHToU1fO7++67xUMPPTTo9YqKClFcXCxsNpuoqakR06ZNEy6XK6R9HDx4UEyePFn9/YMPPhCrV69W9y9E/3UOEcM9hyP+c0y5EoHWSlx//fVq/36syq0/7Thnzhzuuusufv3rX6uaEpdddhkmk4nzzjuPDz74gNtuu40pU6ZEZJ87duzgxz/+MW+88caIy8spiLQb6R1YVKpC16xZ4/PvxzKOG1eisbFR/f/rr79OUVER0F8r8fLLL2O32zl48KBaK3GiQpIk8vPzuf322/nss8/43//9X5599lkaGhq48847eeKJJzhy5EhYTV5lZWX88Ic/5LXXXhsxIdqRcCPr6ur44osvAHjppZdYvnx5eAc9hnHcEMMvfvEL5s6dS3FxMR999BGPPPIIwIBaibPOOmtArcSJDkmSqKqq4qqrrmL79u28+OKL6PV6rr/+elatWsWjjz5KbW1tUCRRWVnJ9ddfz9q1a8nPz4/Ysa5cuZKioqJBP2+++SY33ngjBw4cYOfOnYwfP57/+Z//ASLfLDV79myee+45iouL6ejo4MYbbwx5W2Mew/gaMRzFv//9b1FQUCCmT58uHnjggdE+nKhClmXR2Ngo/vKXv4jTTz9dLF68WNxzzz1i586doq+vz29MYfv27WLu3Lli9+7do3bsBw8eFIWFhUIIIe6//35x//33q39btWqV+Pzzz0fr0IbCqMcUvH9ixBAAXC6XmDZtmjhw4ICw2+2iuLhYVFRUjPZhjRhaWlrEU089Jc466yyxcOFC8etf/1ps2bJlAEmUlZWJuXPnih07doz48TU0NKj/f/jhh8W3vvUtIYQQ5eXlA4KPU6dODTn4GGWMOhF4/8SIIQB8/vnnYtWqVerv3ivRiYSOjg7x7LPPivPPP1/Mnz9f/PKXvxSvv/66KC4uFlu2bBmVY7ryyitFUVGRmDt3rjj//PMHEMVvf/tbMW3aNFFQUCDeeeedUTm+ADDqROD9c9xUPkYTyhCaZ555BoAXXniBL7/8kscff3yUj2x0oWhK3Hffffz2t79VR7rFEDTGXDrjmEpXjhZ8kefxlJoKFZ6aEjEcXzhushLRRKwWIoYTDTFiCACeij8Oh4OXX36Z1atXj/ZhxRBD1BBzJQJANBR/8vLySEpKQqvVotPp2Lp1Kx0dHXzrW9+itraWvLw8XnnllYBmZcYQQ8QxTHQyhihhypQporW1dcBrP//5z9UaiQceeED84he/GI1DG3G88sorYs6cOUKSpEGZjfvvv19Mnz5dFBQUiHfffVd9fevWraKoqEhMnz5d3HzzzeH0KYwFjHoWwvsnRgyjBF/EUFBQoKbaGhoaREFBwWgc2oijsrJS7NmzR3z9618fQAxDNUEtWrRIfP7550KWZXHWWWeN5VRkIBh1IvD+icUYRgmSJLFq1SoWLFjAU089BUBzc7M6pXr8+PG0tLSM5iGOGGbPns3MmTMHve6vCaqxsVGdOyJJkjp3JIbIIRZjGCV89tlnTJgwgZaWFs444wxmzZo12oc05nCizR0ZS4gRwyhBSXdmZ2dz0UUXsXnzZnJycmhsbGT8+PE0NjaqY++OB8TmjhxbiBHDKMBsNiPLMklJSZjNZt577z3uuusuVq9ezXPPPcdtt93Gc8895/eBORYRmztybCEWY/CAshIpwhu+VqZIoLm5meXLlzNv3jwWL17Mueeey1lnncVtt93Gf//7X/Lz8/nvf//LbbfdFpX9Hyvwp6Uxfvx4de6IEILnn3/+uCLRMYFhopMnFN59911xxx13iFtvvVU8/fTT4uGHHx7tQwoYV199tcjKylJbjoUQor29XaxcuVLMmDFDrFy5UnR0dKh/85cGHA2sX79eTJw4URgMBpGdnT2gYc1fE9SWLVtEYWGhmDZtmvjRj34US1fG0pWRwwUXXCBKS0vFnDlzxF//+lchhBAff/yxMBgM4ne/+90oH11w+Pjjj8W2bdsGEIO/uohIaiHGEBGMOhF4/wzXXXlcQ5KkdCFEhyRJccAW4F6gGDAAe4EEIcSjo3mMwUCSpDzgbSFE0dHf9wKnCiEaJUkaD2wUQsyUJOl2ACHEA0ff9x9gjRDii1E69BjGGE704OOPJUlSeoUnA3VCiLWSJK0RQjwjHfuh7hwhRCPAUXJQ0hwTgU0e76s/+loMMQAnMDFIknQqsBJYKoSwSJK0ETACCCHWHP33eDWnfBHe8XquMYSAEzkrkQJ0HiWFWcCS4T5wDKL5qAvB0X+VUsp6+i0kBZOAhhE+thjGME5kYngX0EmStJv+2MKmYd5/LOIt4Kqj/78KeNPj9cskSTJKkjQVyAc2j8LxxTBGcUIHH48nSJL0EnAqkAk0A3cDbwCvALlAHXCJEKLj6Pt/BVwDuIBbhBCRnx0XwzGLGDHEEEMMg3AiuxIxxBCDH8SIIYYYYhiEGDHEEEMMgxAjhhhiiGEQYsQQQwwxDEKMGGKIIYZBiBFDDDHEMAgxYoghhhgG4f8Dq5RMy4mJnqsAAAAASUVORK5CYII=\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -198,34 +196,34 @@ ], "source": [ "lab_scatter(dataset, class_max_num=200, is_3d=True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "上图所示是LAB色彩空间内的分布情况,每个类别只取了200个样本，这样子看来各个物体的颜色重叠是不太严重的" - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "上图所示是LAB色彩空间内的分布情况,每个类别只取了200个样本，这样子看来各个物体的颜色重叠是不太严重的" + ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 33, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -235,34 +233,34 @@ ], "source": [ "lab_scatter(dataset, class_max_num=10000, is_3d=True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "但是如果用很多样本来看，情况就不容乐观了，重叠还是很严重的，尤其是和背景以及橙色棒棒糖之间重叠特别厉害" - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "但是如果用很多样本来看，情况就不容乐观了，重叠还是很严重的，尤其是和背景以及橙色棒棒糖之间重叠特别厉害" + ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 34, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -272,58 +270,50 @@ ], "source": [ "lab_scatter(dataset, is_3d=False)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "如果我们抛弃亮度通道，只看颜色，就像上图这样，从这个角度看，烟梗的颜色和其他颜色之间的重叠度超级高呢。所以必须同时考虑亮度和其他类型的颜色。" - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "如果我们抛弃亮度通道，只看颜色，就像上图这样，从这个角度看，烟梗的颜色和其他颜色之间的重叠度超级高呢。所以必须同时考虑亮度和其他类型的颜色。" + ] }, { "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [], + "execution_count": 34, "metadata": { - "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.10.0" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } \ No newline at end of file diff --git a/02_classification.ipynb b/02_classification.ipynb index e6a54c9..a820e2e 100644 --- a/02_classification.ipynb +++ b/02_classification.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" @@ -107,12 +107,7 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 3, "outputs": [ { "name": "stdout", @@ -133,11 +128,29 @@ "source": [ "# model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,\n", "# is_save_dataset=True, model_selection='dt')\n", - "data = scipy.io.loadmat('data/dataset/dataset_2022-07-19_17-06.mat')\n", + "data = scipy.io.loadmat('data/dataset/dataset_2022-07-20_10-04.mat')\n", "x, y = data['x'], data['y'].ravel()\n", "model.fit(x, y=y, is_generate_negative=False, model_selection='dt')\n", "model.save()" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } } ], "metadata": { diff --git a/03_data_update.ipynb b/03_data_update.ipynb index fb02582..6b09105 100644 --- a/03_data_update.ipynb +++ b/03_data_update.ipynb @@ -11,9 +11,10 @@ "import numpy as np\n", "import pickle\n", "from sklearn.tree import DecisionTreeClassifier\n", - "\n", + "%matplotlib notebook\n", "from main_test import virtual_main\n", - "from models import AnonymousColorDetector" + "from models import AnonymousColorDetector\n", + "from utils import lab_scatter" ], "metadata": { "collapsed": false, @@ -40,7 +41,7 @@ "outputs": [], "source": [ "model_path = 'models/dt_2022-07-19_16-03.model'\n", - "img_path = 'data/dataset/img/yangeng.bmp'" + "img_path = r'C:\\Users\\FEIJINTI\\Desktop\\720\\binning1\\tobacco\\Image_2022_0720_1354_46_215-003050.bmp'" ], "metadata": { "collapsed": false, @@ -55,22 +56,34 @@ "outputs": [ { "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" + "text/plain": "", + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" }, + "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "
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" }, - "metadata": { - "needs_background": "light" + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "
" + }, + "metadata": {}, "output_type": "display_data" } ], @@ -89,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "outputs": [], "source": [ "dataset_path = 'data/dataset/dataset_2022-07-19_16-03.mat'" @@ -103,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "outputs": [], "source": [ "new_dataset_path = datetime.datetime.now().strftime(\"data/dataset/dataset_%Y-%m-%d_%H-%M.mat\")" @@ -117,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "outputs": [], "source": [ "data = scipy.io.loadmat(dataset_path)\n", @@ -132,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "outputs": [], "source": [ "nimg = img[result == 0]" @@ -146,7 +159,40 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib notebook\n", + "draw_dataset = {\"original\":x[y.ravel()==1,:] ,\"new\":nimg}\n", + "lab_scatter(draw_dataset, is_3d=True, is_ps_color_space=False,class_max_num=5000)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 26, "outputs": [], "source": [ "ny = np.ones(shape=(1, nimg.shape[0]))" @@ -160,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 27, "outputs": [], "source": [ "x = np.concatenate((x,nimg),axis=0)\n", @@ -175,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, "outputs": [], "source": [ "scipy.io.savemat(new_dataset_path, {'x': x, 'y': y})" diff --git a/main_test.py b/main_test.py index 9e64d24..1165ae5 100644 --- a/main_test.py +++ b/main_test.py @@ -31,7 +31,7 @@ def virtual_main(detector: Detector, test_img=None, test_img_dir=None, test_mode else: raise TypeError("test img should be np.ndarray or str") t1 = time.time() - # img = cv2.resize(img, (1024, 256)) + img = cv2.resize(img, (1024, 256)) t2 = time.time() result = 1 - detector.predict(img) t3 = time.time() @@ -55,7 +55,13 @@ def virtual_main(detector: Detector, test_img=None, test_img_dir=None, test_mode if __name__ == '__main__': - detector = AnonymousColorDetector(file_path='dt_2022-07-19_17-07.model') - virtual_main(detector, test_img=r'data/dataset/img/yangeng.bmp', test_model=True) - virtual_main(detector, test_img=r'data/dataset/img/yangeng.bmp', test_model=True) - virtual_main(detector, test_img=r'data/dataset/img/yangeng.bmp', test_model=True) + detector = AnonymousColorDetector(file_path='dt_2022-07-20_14-40.model') + virtual_main(detector, + test_img=r'C:\Users\FEIJINTI\Desktop\720\binning1\tobacco\Image_2022_0720_1354_46_472-003051.bmp', + test_model=True) + virtual_main(detector, + test_img=r'C:\Users\FEIJINTI\Desktop\720\binning1\tobacco\Image_2022_0720_1354_46_472-003051.bmp', + test_model=True) + virtual_main(detector, + test_img=r'C:\Users\FEIJINTI\Desktop\720\binning1\tobacco\Image_2022_0720_1354_46_472-003051.bmp', + test_model=True)