play.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython, graphviz, re\n",
    "from io import StringIO\n",
    "from IPython.display import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from sklearn import tree\n",
    "from sklearn.datasets import load_boston, load_iris, load_wine, load_breast_cancer\n",
    "from collections import defaultdict\n",
    "import string\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inspired by Mark Needham's blog\n",
    "# https://markhneedham.com/blog/2017/09/23/python-3-create-sparklines-using-matplotlib/\n",
    "\n",
    "#def sparkline(data, filename, fill=False, figsize=(4, 0.25), **kwags):\n",
    "\"\"\"\n",
    "Save a sparkline image\n",
    "\"\"\"\n",
    "\n",
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = boston.data\n",
    "feature_names = boston.feature_names\n",
    "target = boston.target\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10,2))\n",
    "ax.scatter(data[:,0], target, s=2, alpha=.5)\n",
    "# ax.scatter(data[:,1], target, s=2)\n",
    "# ax.scatter(data[:,2], target, s=2)\n",
    "for k,v in ax.spines.items():\n",
    "    v.set_visible(False)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10,2))\n",
    "ax.scatter(data[:,1], target, s=2)\n",
    "for k,v in ax.spines.items():\n",
    "    v.set_visible(False)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.array([16.2, 18., 14.3, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14., 14.4, 13.4, 15.6\n",
    ", 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 19.4, 17., 15.6, 13.1, 17.8, 16.8, 13.8, 13.8\n",
    ", 13.9, 13.3, 13.1, 10.2, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1\n",
    ", 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5., 6.3, 5.6, 7.2, 12.1, 8.3, 8.5\n",
    ", 5., 11.9, 7., 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 13.4, 8.3, 10.9, 11.\n",
    ", 9.5, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8\n",
    ", 14.9, 12.6, 14.1, 13., 13.4, 15.2, 16.1, 14.9, 14.1, 12.7, 13.5, 14.9, 20., 16.4\n",
    ", 17.7, 19., 16.7, 12., 14.6, 15.2, 7., 8.1, 13.6])\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(.8,5))\n",
    "ax.xaxis.set_visible(False)\n",
    "ax.set_xticks([])\n",
    "#ax.set_xlim(0,.2)\n",
    "\n",
    "y_range = (3,50)\n",
    "ax.set_ylim(y_range)\n",
    "ax.tick_params(axis='both', which='major', labelsize=18)\n",
    "meanprops = {'linewidth':1.2,'linestyle':'-','color':'black'}\n",
    "bp =ax.boxplot(y, notch=False, medianprops={'linewidth':0}, meanprops=meanprops,\n",
    "           widths=[.9], showmeans=True, meanline=True, sym='', patch_artist=True)\n",
    "BLUE = \"#D9E6F5\"\n",
    "YELLOW = \"#fefecd\"\n",
    "for patch in bp['boxes']:\n",
    "    patch.set(facecolor=BLUE)       \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFkAAAEoCAYAAAAzAl1/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAACpRJREFUeJztnW2MHWUZhq9bPoKtZVthrcq3EbsEKehujBEtQZsg+kMTDPGHAglSCZbwobUlGEJQBESo/CAhbRQDjSKCQGIwShUIxKRki5KAQkQtRNSyBFq2AiW1jz9mDizHOefM2T377J7d+0om0/POM3PeXjv7zjuTPfdRRGCml7fNdAfmA5acgCUnYMkJWHIClpxAbcmSHpEUFcsdE2qOkvQLSS+Wyy2SBqen6/2D6syTJQl4GdgM3Nm0+ZmIeEjSQcBWYH/gBmBfYA2wDfhIRLzew373FxHRcQGOAgI4q03NlcAe4JgJbSvL/c6p8z5zdak7XBxbrv/cpuaLwAMR8UZNRGwGniq3zVsmJVnSwokbJS0B3kcxXDTzKDAy2Q7OBepK/iAwDlwvaRzYJemvkhpn6CHl+rmKff8FHChpYGpd7V/2rVl3LLAIWAycUa4vAH4qaT/g6bLulYp9Xy3XC4Gdk+9q/1JX8gZgn4i4sdEg6TbgceBa4LSyud1UZW9zg6RVwCqAhQsXDg8NDdXszuxg69atL0RE5ynqVK6awOUUYo8v16sraq4rty1sd6zh4eHoN4DR6OHsohXPl+sF5fo9FTXvBXZExH+m+F59S0fJkg6R9ISkyyo2N36//14uH66o+RAwOvku9j8dJUfEc8AAcI6kAxvtkg4DzgLuj4h/U9wJrpQ0NKFmJbAMuK3H/e4r6l74VgN3Ab+XtJFiprGa4g7va2XN9yhmHr+VdB1wAPBNirnzpl52uu+oM3AXYzyfA7YArwEvUUgfaqpZBtwL7KIYr38MDNY5/ly+8NU9k4mIe4B7OtQ8BXym2x/0XMfPkxOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5ga4lS1ou6XVJlze1O92wBbU/wA4gaV+KT/7v19R+EHA/RbrhNbyZbrhc0vxON6RLycAlvJkRN5GLgUOB46IM35O0BbgPOBPYOJVO9jvdBKIeB3wL+HbFZqcbtqGW5HKYuJkiEHVT0zanG3ag7nCxFjga+HzFPrXSDSNiXgbvQb00rWOBy4BvRMQ/KkoWletO6YZVx14laVTS6NjYWJ3+9iVtJUvah2KYeDgiWl28GsfoKt0QICI2RMRIRIwMDs7d2V6n4WINRXrhxyUdXLYtKdcLyrbx8vXbK/ZvtI1XbJs3dBouPk0x930EGCuXR8tta8rXjWM43bAFnc7kr/PmmdtgKcUM41bgFuBJnG7YlraSI+L/pmWSjiz/+bdyLoykO4ELJQ1FxJNlWyPd8Npedrgf6faOrxVON2xDT57CRcQYsAJ4DLgCuBC4Gzg1Inb34j36ma7P5IjYBqii3emGLfDz5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOoJvIsk9KeljSuKTnJP1A0juaapxyWEGtD7BLOpkiSG8rsA44DLgAGJG0IiL2OuWwNXVTAr4PPAucFBGvAkh6FrgROAX4FU45bEmdyLIDKLKGNjYElzxYrpeXa6cctqDjmRwRr1EEPjVzQrl+dkLK4R0VdY8Cn510D+cAXYeKSDoCOBm4DngcuAt4f7nZKYcVdBvt+05gW/nyFeD8iHhNUt2Uw7dIlrQKWAVw+OGHd9OVvqLbeXJQjK9nAH8CNkv6woTjdJVyOF8SDruSHBEvRcTPIuJWimCnZ4DrccphWyZ9x1fONH5JMWf+Z9nslMMK6kzhhiRtk3RexeZFFEPEbpxy2JI6Z/LTwABwrqT9G43lLOM04MGIGAfuBFZKGppQ00g5vK2nve4z6syT90g6nyKb80FJm4CDgNUUZ/H5ZalTDlsREbUW4HQKYbuBF4GfAx9oqlkG3AvsAp6n+CaHwTrHHx4ejn4DGI0a/7fa8+SIuB24vUONUw4r8PPkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS06gm4TDUyQ9JOkVSbskbZb00aYaJxxWUDfh8CSKgL0ngEvL/c6jiGb4REQ84oTDNtSJEgD+QJE3tGBC21KKSIb7ytdXAnuAYybUrKTIxDin03vM5SiGOvE4S4Djgdsj4o1IsojYTpFy+LGyyQmHLagzXLxMERZSFdR0MLDHCYftqROP81/gL83tkpYDJwK/Bg4pm51wWEHXMZIAZW7yLeXLqylStWAGEg7Xr1/P9u3bJ7Vvg6VLl3LRRRdN6RhtqTNwT1yABcDvKC5o3y3bTixfn11R/51y27vbHXc6L3xr166dluPSqwvfRCQtBn5DEYj6I4rpHDjhsC21hwtJ76IYf08ANgDnlj9NKAKswQmHldQ6k8tU2Ybg9RHx1QmCiYgdOOGwJXWHixspBN8QERe3qHHCYQs6DheSjgG+TDEz+KOkLzXXRMQmnHDYkjpj8knlegC4uUXNpogYk7QCWA9cQTGduxtYExG7p9zTPqbOzchNwE11DhZOOKzEz5MTsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5AQm9WdaWVx91ZXs2Nmbv4lZt27dlPZfPLCIdZdc2rmwglktecfOcS45e9lMdwOAq3741KT39XCRgCUnYMkJWHIClpyAJSdgyQlYcgKWnMCsvuODqd1pzRZmvWTfVptaWHIClpyAJSdgyQlYcgKWnIAlJ2DJCVhyApacgCUnMCnJkjZKeqCi3QmHFXT9FE7S2cBXKDLhJrY74bAF3cTj7EOROXR5i5KLgUOB46IM4JO0BbgPOBPYOKWe9jF1szoPALYAyymiyj5VUVaZcCipkXDYteTFA4tmzUP7xQOLOhe1ok7kFrCYImPo9PL1Ngqhje1LKGLJrqnY9yfAzk7vMZcjy+oOFy8DR0fEnhbbnXDYhlqzi4jY20Yw1E84fAuSVkkalTQ6NjZWpyt9Sa/myY3jRJuavc0NEbEhIkYiYmRwcO7O9Hol2QmHbeiVZCcctqEnksMJh23p5bMLJxy2oJd/3OKEwxb07EyOiDFgBfAYRcLhhRQJh6eGEw67JyKObNHuhMMK/Dw5AUtOwJITsOQELDkBS07AkhOw5AQsOQFLTsCSE7DkBCw5AUtOwJITsOQELDkBS07AkhOw5ARmfd5FJ+p+pXK7rM7p/krlvpc8rd833SM8XCRgyQlYcgKWnIAlJ2DJCVhyApacgCUnoOKDlTOPpDHgmWk6/MHAC9Nw3CMiouNn42aN5OlE0mhEjMzU+3u4SMCSE5gvkjfM5JvPizF5ppkvZ/KMYskJWHIClpyAJSfwPyNkOkCwmNZKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 57.6x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(.8,5))\n",
    "sns.set(style=\"white\")\n",
    "ax.set_ylim(3,50)\n",
    "#ax.set_ylim('', fontsize=28, fontname=\"Arial)\n",
    "#plt.xticks(fontsize=38)\n",
    "plt.tick_params(axis='both', which='major', labelsize=18)\n",
    "ax = sns.boxplot(y=y, color='#fee090', ax=ax, linewidth=1, sym='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 273.6x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.array([16.2, 18., 14.3, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14., 14.4, 13.4, 15.6\n",
    ", 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 19.4, 17., 15.6, 13.1, 17.8, 16.8, 13.8, 13.8\n",
    ", 13.9, 13.3, 13.1, 10.2, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1\n",
    ", 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5., 6.3, 5.6, 7.2, 12.1, 8.3, 8.5\n",
    ", 5., 11.9, 7., 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 13.4, 8.3, 10.9, 11.\n",
    ", 9.5, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8\n",
    ", 14.9, 12.6, 14.1, 13., 13.4, 15.2, 16.1, 14.9, 14.1, 12.7, 13.5, 14.9, 20., 16.4\n",
    ", 17.7, 19., 16.7, 12., 14.6, 15.2, 7., 8.1, 13.6])\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(3.8,5))\n",
    "plt.subplots_adjust(wspace=.2)\n",
    "ax0 = axes[0]\n",
    "#ax0.xaxis.set_visible(False)\n",
    "#ax0.set_xlabel(\"price\", fontsize=18)\n",
    "ax0.set_xticklabels(['ss','abc'])\n",
    "#ax0.set_xticks([3], minor=True)\n",
    "\n",
    "y_range = (3,50)\n",
    "ax0.set_ylim(y_range)\n",
    "ax0.tick_params(axis='both', which='major', labelsize=18)\n",
    "meanprops = {'linewidth':1.2,'linestyle':'-','color':'black'}\n",
    "bp =ax0.boxplot(y, notch=False,\n",
    "                medianprops={'linewidth':0}, \n",
    "                meanprops=meanprops,\n",
    "                widths=[.9],\n",
    "                showmeans=True,\n",
    "                meanline=True,\n",
    "                sym='',\n",
    "                labels=[\"price\"],\n",
    "                patch_artist=True)\n",
    "\n",
    "BLUE = \"#D9E6F5\"\n",
    "YELLOW = \"#fefecd\"\n",
    "for patch in bp['boxes']:\n",
    "    patch.set(facecolor='#a6bddb')       \n",
    "    \n",
    "ax1 = axes[1]\n",
    "#ax1.xaxis.set_visible(False)\n",
    "ax1.set_xticklabels(['foo'])\n",
    "# ax1.set_xticklabels(['ff'], minor=False)\n",
    "#ax1.set_xlabel(\"n\", fontsize=18)\n",
    "ax1.yaxis.tick_right()\n",
    "ax1.set_ylim(0,350)\n",
    "ax1.tick_params(axis='both', which='major', labelsize=18)\n",
    "ax1.bar(0,223,color='yellow',tick_label='foo', linewidth=0)\n",
    "ax1.axhline(223, color=\"grey\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dtreeviz.trees import *\n",
    "\n",
    "precision = 1\n",
    "GREY = '#444443'\n",
    "\n",
    "iris = load_iris()\n",
    "data = pd.DataFrame(iris.data)\n",
    "data.columns = iris.feature_names\n",
    "\n",
    "clf = tree.DecisionTreeClassifier(max_depth=2, random_state=666)\n",
    "clf = clf.fit(data, iris.target)\n",
    "\n",
    "YELLOW = \"#fefecd\" # \"#fbfbd0\" # \"#FBFEB0\"\n",
    "BLUE = \"#D9E6F5\"\n",
    "GREEN = \"#cfe2d4\"\n",
    "DARKBLUE = '#313695'\n",
    "DARKGREEN = '#006400'\n",
    "LIGHTORANGE = '#fee090'\n",
    "LIGHTBLUE = '#a6bddb'\n",
    "GREY = '#444443'\n",
    "dark_colors = [DARKBLUE, DARKGREEN, '#a50026', '#fdae61', '#c51b7d', '#fee090']\n",
    "mycolors = [YELLOW,BLUE,GREEN]\n",
    "\n",
    "class_names=[\"setosa\", \"versicolor\", \"virginica\"]\n",
    "shadow_tree = ShadowDecTree(clf, data, iris.target,\n",
    "                            feature_names=iris.feature_names, \n",
    "                            class_names=class_names)\n",
    "\n",
    "node = shadow_tree.root\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize=(15,7))\n",
    "ax.set_xlabel(f\"variety\", fontsize=18, fontname=\"Arial\",\n",
    "              color='grey')\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "n_classes = 3\n",
    "class_names = ['A','B','C']\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "X = X[:,node.feature()]\n",
    "X, y = X[node.samples()], y[node.samples()]\n",
    "\n",
    "X_hist = [X[y==cl] for cl in range(n_classes)]\n",
    "\n",
    "binwidth = 30\n",
    "hist,bins,barcontainers = ax.hist(X_hist,\n",
    "              color=mycolors,\n",
    "               #bins=np.arange(min(X), max(X) + binwidth , binwidth),\n",
    "               label=class_names,\n",
    "               linewidth=3,\n",
    "              alpha=.85)\n",
    "#ax.legend(prop={'size': 18})\n",
    "\n",
    "for area in [100, 300, 500]:\n",
    "    plt.scatter([], [], c='k', alpha=0.3, s=area,\n",
    "                label=str(area) + ' km$^2$')\n",
    "plt.legend(scatterpoints=1, labelspacing=1)\n",
    "\n",
    "for patch in barcontainers:\n",
    "    for rect in patch.patches:\n",
    "#         rect.linestyle='solid'\n",
    "        rect.set_linewidth(1.2)\n",
    "        rect.set_edgecolor(GREY)\n",
    "    \n",
    "#    print(patch)\n",
    "#    patch.set(facecolor='#a6bddb')   \n",
    "    \n",
    "ax.set_xticks([round(node.split(),precision)])\n",
    "ax.set_ylabel(\"n\", fontsize=24)\n",
    "ax.tick_params(axis='x', direction='out', length=15, width=10, color=GREY,\n",
    "               labelsize=24)\n",
    "ax.tick_params(axis='y', color=GREY, labelsize=24)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as patches\n",
    "from matplotlib.patches import FancyBboxPatch\n",
    "from matplotlib.transforms import Bbox\n",
    "from matplotlib.figure import figaspect\n",
    "import matplotlib.font_manager as font_manager\n",
    "\n",
    "GREY = '#444443'\n",
    "\n",
    "def draw_legend():\n",
    "    box1 = patches.Rectangle((0, 0), 20, 10, linewidth=.4, edgecolor=GREY,\n",
    "                             facecolor=\"#D9E6F5\", label='malignant')\n",
    "    #ax.add_patch(box1)\n",
    "    box2 = patches.Rectangle((0, 0), 0, 0, linewidth=.4, edgecolor=GREY,\n",
    "                             facecolor='#fee090', label='benign')\n",
    "\n",
    "\n",
    "    # font = font_manager.FontProperties(family='Arial',\n",
    "    #                                    style='normal',\n",
    "    #                                    size=16)\n",
    "\n",
    "    fs = figaspect(1/3)\n",
    "    fs = (1,1)\n",
    "    fig, ax = plt.subplots(1, 1, figsize=fs)\n",
    "    leg = ax.legend(handles=[box1,box2],\n",
    "                    frameon=True,\n",
    "                    loc='center',\n",
    "                    title='Price',\n",
    "                    handletextpad=.35,\n",
    "                    borderpad=.8,\n",
    "                    #          bbox_to_anchor=(0.0, 0.0),\n",
    "                    edgecolor=GREY,\n",
    "                    )\n",
    "\n",
    "    leg.get_title().set_color(GREY)\n",
    "    leg.get_title().set_fontsize(12)\n",
    "    leg.get_title().set_fontweight('bold')\n",
    "    for text in leg.get_texts():\n",
    "        text.set_color(GREY)\n",
    "        text.set_fontsize(12)\n",
    "\n",
    "    ax.set_xlim(0,20)\n",
    "    ax.set_ylim(0,10)\n",
    "    ax.axis('off')\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.yaxis.set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(\"/tmp/legend.svg\", bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "draw_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAADwAAAAfCAYAAAC7xK7qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAI5JREFUWIXt2DEOgCAQRNHBWK7X9BpUNt6Ac7qJDQmeQEwMYjLMa7eZX1BoKKUUDGT6e0BvCmanYHbDBc+1475tOHPutaWZGOPtrRp85ow1JZh781FfcTPgbTAAmDuW42g66k/DvWEFs1MwOwWzUzA7BbNTMLvHryU367GjGTfDUrkH/Zcmp2B2CmY3XPAF1O0czma90HgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 72x43.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as patches\n",
    "from matplotlib.patches import FancyBboxPatch\n",
    "from matplotlib.transforms import Bbox\n",
    "from matplotlib.figure import figaspect\n",
    "\n",
    "fs = (1,1)\n",
    "fig, ax = plt.subplots(1, 1, figsize=(1,.6))\n",
    "box1 = patches.Rectangle((0, 0), 2, 1, linewidth=1.2, edgecolor='grey',\n",
    "                         facecolor='red', label='one')\n",
    "ax.add_patch(box1)\n",
    "\n",
    "ax.set_xlim(0,2)\n",
    "ax.set_ylim(0,1)\n",
    "ax.axis('off')\n",
    "ax.xaxis.set_visible(False)\n",
    "ax.yaxis.set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"/tmp/legend.svg\", bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1,\n",
       "        0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2,\n",
       "        0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2,\n",
       "        0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2]),\n",
       " array([1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1. , 1.3, 1.4, 1. , 1.5, 1. ,\n",
       "        1.4, 1.3, 1.4, 1.5, 1. , 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4,\n",
       "        1.4, 1.7, 1.5, 1. , 1.1, 1. , 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3,\n",
       "        1.3, 1.2, 1.4, 1.2, 1. , 1.3, 1.2, 1.3, 1.3, 1.1, 1.3]),\n",
       " array([2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2. , 1.9, 2.1,\n",
       "        2. , 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2. , 2. , 1.8, 2.1, 1.8,\n",
       "        1.8, 1.8, 2.1, 1.6, 1.9, 2. , 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8,\n",
       "        2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2. , 2.3, 1.8])]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0.1, 0.6), (1.0, 1.8), (1.4, 2.5)]\n",
      "(0.1, 0.6)\n",
      "50\n",
      "(1.0, 1.8)\n",
      "50\n",
      "(1.4, 2.5)\n",
      "50\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'bbox' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-c05e1b8c6e62>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mxaxis_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;31m#tria = np.array([[x,xaxis_y],[x-10,xaxis_y-10],[x+10,xaxis_y-10]])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'bbox' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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Zwf8tX652FCGEEEKIaqvcMcBpaWmMGTOGuXPn0rp16zJtvr6+JCUlkZGRgZXVnTVqx44dq9Ow2rZ//358G4Sh0WjUjlJpjVv35OuvX2f2iy9Wq9xC6EJycjLbtm3j0OHDpFxLwcTEhDq13Wgc3ph27drh4eGhdkQhhBBVULlXgBcvXkxmZiaLFi0iJiaGmJgY1q5dy4oVKzA1NWXmzJmMHTuWYcOGMXjwYGrXrq2v3Fqxd+8+6nqHqB3jgXg3aER2dg7Hjh1TO4oQqtm/fz89e/ckKCSID79axIX8y5j52KCpZ8aftxJ574sPaRgeRmijhrz33ntkZGSoHVkIIUQVolH0uLDs5cuXiYiIYOvWrbi7qz/soHmLVrTqNYYGoS3UjvJAflnxEaHeTrz55htqRxFCrzIzMxk/cQKbN2+mx5h+tOrVFjOLey9hWFxUzNkjf7J37U4S9h1l+PAoZs96Ua4KCyGEgSiv7jTYjTAKCgqIjz+Gl2/1ugIMd4ZBfPvdd5SUlKgdRQi9OX78OGHhYVy9ncqcFQvoMLDLfYtfAGMTYxo0C2b0K+OZ838LuJiXQmhYQ8Y/NZ4rV67oMbkQQoiqxmAL4KNHj1KnnhcWltZqR3lgdT39MLe0Yc+ePWpHEUIvdu7cScfOnYgY3Zthz43Ewsrige5v7+LAwIlDmbP8NS5kXyE4JJhJkydx/fp1HSUWQghRlRlsAbx//348fRuqHeOhNWrZg6++/kbtGELo3G+//Ub/gf2JfWkcLXu2faRz2TnZM+iZYcz+v39z8vpZAgIDmPnCTNLT07WUVgghRHVgsAXw7t17cfepfsMf/takXU9WrVpFXl6e2lGE0JnDhw8zaMhgRr86geBW2nvBau/iyOPPRjPzq3n8fuYQvv5+zJs3j8zMTK31IYQQouoy2AJ43/791PevvleAnVzqUN8vlNWrZWc4UTNduXKFnr178fj0EQQ2182LVSc3F4Y9P5JpS19ky8Hf8Pb1ZuHChWXWOBdCCFHzGGQBfP36dW7evEmtuvXVjvJImnV4jE8Wf6p2DCG0Lj8/n/4D+9N6QAeaRuh+lZZaHm7EzP0Xz3z0PD9uXUt9H28WvrGwWu5uKYQQomIGWQDv378fv8AwjIyq97cf2qwjJ0+e5PTp02pHEUKrnpk8CcXamJ6j+lV8Yy2q61OPMf9+ignvTuXHrWvxrO/FK6++wq1bt/SaQwghhG5V7wrwIe3du4+69YPVjvHITExMada+N5999rnaUYTQmp9//pl1v64jdu6/VHuR6u7vyZj5TzF50fNs2L8FL+/6zJo9SybLCSFEDWGQBfDuPXvxqsbjf+/WomN/ln35FYWFhWpHEeKRZWZm8sT4Jxg+cxSWNlZqx6GOdz1Gvfwk0z+bza74fXj7ejNtxjRZPk0IIao5gyuAi4uLOXL4EPX9akYB7Obug6ubB2vWrFE7ihCPbOqMZ/FrHqizSW8Pq5aHGzEv/ouZX73CofPH8AvwZ9KUSaSmpqodTQghxEMwuAI4ISEBJ5faWNnYqR1Fa1p3Hcrb77yndgwhHsmOHTtY89MaBj0zTO0o9+Vcx4Xhz4/ixW/nc+LqaQKDg/hi2RfocUd5IYQQWmBwBfDu3btrzPCHv4W16Ezi+QscOnRI7ShCPJTbt28zcswoIqfFYGVb9XdndKzlxNBpMUx4Zwqvvb2Azl27cPXqVbVjCSGEqCSDK4B/27ELL/9wtWNolbGxCW26DuGdd+UqsKieZs15ERef2oR3aqZ2lAfiGejNtKVzsPN1Iiy8EZs3b1Y7khBCiEqoVAF89OhRYmJi/nF82bJl9OnTh5iYGGJiYkhMTNR6QG3bvXsXPoE1qwAGaN1lIGvXruXatWtqRxHigRw6dIgvln3BsGmxakd5KMYmxvQdN4gRc8cyLHo4n3zyidqRhBBCVMCkohssXbqUtWvXYmlp+Y+2hIQEFi5cSGhoqE7CadvFixe5fTsfVzdPtaNonbWtA+Etu7Jo0SfMm/ey2nGEqJSioiJGjIph4MSh2Dnbqx3nkQQ2D+HZJbOYP/XfnL9wnoWvL0Sj0agdSwghxD1UeAXY09OTDz/88J5tCQkJfPrppwwfPpwlS5ZoPZy27dq1C//gJjX2j1KHXlF89NHHso2rqDYWvLEAI2sTWvVpr3YUrXB1r820pbP58defmDRlskyOE0KIKqrCArhHjx6YmNz7QnGfPn14+eWX+eqrr4iLi2P79u1aD6hNO3buwsMvTO0YOuPm7oNPYGM+/VS2RxZV3+nTp3nrrbeJmjm6Rr0otXGwZeL70/h1y3qenfasFMFCCFEFPfQkOEVRGDlyJE5OTpiZmdGxY0dOnDihzWxat2PHTnwCG6sdQ6c69xvJG2++RUFBgdpRhLivkpISYkbF0mvMY7jUdVU7jtZZ2Vrz9PvTWfPrTyxcuFDtOHqnKApnz55l+/bt7Ny5k4SEBIqKitSOJYQQpR66AM7OzqZv377k5OSgKAoHDhyo0mOB09PTuXz5EvW8AtSOolOeviHUquvN119/rXYUIe7rk8WfcDM3g05DuqkdRWes7W0Y/9YU3vngXVasXKF2HL04c+YMEydMoLaLC53atuWFp59mxvgJPNajB/a2tvToEsEXX3xBbm6u2lGFEAauwklw/2vdunXk5uYSGRnJ1KlTiY2NxczMjNatW9OxY0ddZNSKvXv34hcYhrHxA3/L1U7nfqOZ/+/XGD16NMbGxmrHEaKMS5cuMXvObCYvegEj45q9EqNjbWeefHMyE56agI+3D82bN1c7kk7k5eUx76WX+OzTTxnWph3LJ0/B06Xslf2svDx2nkjgu48+YuaMGUx46imenT4de/vqPflRCFE9aRQ9DlC7fPkyERERbN26FXd3d311C8CMGc9x6lI2PQY/odd+1aAoCoteHccLMyYRG1s9l5YSNZOiKPTs3RMLLzv6jB2gdhy9ObT9D37+aBVHDx/F2dlZ7ThadenSJR7r04c65hbMHjSYWpUoaM9dvcrSbVvZefIEc156ifETJmBqaqqHtEIIQ1Je3VmzL7/c5bcdO6nfoOat/3svGo2GHkMmMHvOXBkLLKqU7777jj8Tz9Ajtq/aUfSqSefmhHQMJ3L4MEpKStSOozUnTpygZbNm9PDz5/1RoytV/AL4urnxelQ0nz85gZVLl9IwKJhNmzbpOK0QQvyXwRTABQUFhITW7Alwd/MLboqjqzuff/652lGEACA1NZXJU6cQNWsUJqY1fyjS/xrw1ONcuZHM/Pnz1Y6iFadOnaJr585M69WHf3Xt9lAreQS5u/PF+KeY2rUrT4waxYB+/arFhkpCiOrPYArgo0cOMW5wM3q08qKui7XacfSix5AJvDzvVfLy8tSOIgRPPf0UzXq0on6wr9pRVGFsYsKoeU/y7gfvcfDgQbXjPJK0tDR6duvGpO496d+ixSOdS6PREBHWiF+efwE/E1OaN23KnDlzZKKcEEKnDKYABjA20uDv4cigzv5Edm1AsLcTJjV4Eo6nbwievqF8+NFHakcRBm716tXs++MAfZ8YpHYUVTnWdmbI1CiGRQ2vtgVeYWEhQwYOpEdIKENat9baec1NTRnfrTtrps/g2LbtNPD3Z+XKlbKOshBCJ2pu9VcBV0dLujTzZFSfYNqE1cXO2kztSDrRffCTvP76G2RkZKgdRRiotLQ0Jjw1gegXR2NmUTOfZw+ieffWuPq6Mf256WpHeSgzpk1Dk5nFs3376eT8dRydeDt6BAuHDmPeCy/QuWNHjh8/rpO+hBCGy2AL4L9ZmJvQpEEtRvQMok9bbzxq26odSavqePgS0qQDr7z6qtpRhIGa8NQEmnRviW9YzV6D+0EMnR7Dqh9Ws3XrVrWjPJCNGzey8v/+j7diYjA20u2fjxb+/qye8iyd63nQpUMHoocP59ixYzrtUwhhOAy+AP6bkZEG77r29O/gS1SPQBr6umBqUjP+e3oNncAXXyzj3LlzakcRBub7779n3x/7DX7ow/+ytrMmckYMY/41ptoMhUhPT2fMyJEsGB6FvZV+5lGYGBsT3b49G1+cQ93b+XTv0oUuHTvy7bffkpOTo5cMQoiaqWZUeFrmZGdBxybujO4bQofG9XCwNVc70iOxc3ChY+8RPDuter7lKqqnCxcuMH7CeGJeGidDH+6hYbvG1A30ZPbcOWpHqZSJEybQLSSUtoFBeu/bzsqKJ7t1Z+ucl3jM148v3n6HenXqMKBfP5YsWcLZs2dlrLAQ4oEYzEYYj0JRFC5dy+L42TQuXM2qlr9oCwpus3D6EL5f8R0dOnRQO46o4QoLC2nTri3erQPoGt1L7ThVVmb6LV6Lns2WTVto0qSJ2nHu67fffmPE0KGsnzUbS7Oq8WLmRlYmu06cZM/Z0xw4fZqc/HzCGzakYaNGePv64uXlhZeXFx4eHri6usqumEIYoPLqTsNbjPMhaDQaPN3s8HSz41Z2PvGJNzhx/gb5BcVqR6s0MzMLeg2dyDOTpnD40EGMdDx+Txi2WS/OosishC7De6gdpUqzc7Kn/8THiR01ksNxh6rkbmjFxcVMfuoppvftV2WKXwBnWzsGtGzJgJYtAbiRlcWJy5c4m5JCwpmzbMm8xZUb6aSk3+BWVhauzs7UqV2bunXqUM/dg3qeHrh7elKvXj3q1q2Lr68vFhYWKn9XQgh9kQL4AdnbmNM2rC4tgt04ffEmx8+lkZZRPdbZbdq2J3s3reDLL79kzJgxascRNdSKFSv4+ttveG7ZS/JCqxJa9WnPwU0HeOvtt3hh5gtqx/mHz5cuxayoiD5Nm6kdpVzOtra0DwqmfVDwP9oKiopIy8zkWkYG125lcC0jg0u7LxKXuZ7r2dlczbhJcmoaPvW9aNy4MS3btiUiIoKgoKCH2uBDCFH1yRCIR6QoCilpORw7m0bilVuUVPHhEUlnE1j2zrOcOX0KR0dHteOIGubgwYN069Gdpz+YjkeAl9pxqo3UK9d5c/Q8DsUdwsfHR+04pTIzM/H38WHx2HE09KrZj2d+YSFnUpI5cekSRy5eZN/p0xSWFBMREcHgoUPp2bMnVlZWascsIzs7m71793Ls2DFOnzzJzRs3KCwswsHRAS8fHxo3aULHjh3ld70wWOXVnVIAa1F2XiHx59JISLxBXn6R2nHua9XnrxHo7cLiTxapHUXUIImJibRp15aBUyJp3Lm52nGqnY1f/0zGqets2bSlylx1nDZpEkl/HOT1mFi1o6jiUloau06eYHN8PEfPJ9ItIoLI6Gj69u2rWjGclZXFiuXL+XrZMg4dOUKotzeBtd2o7+qKo40NxkZGZOXlcSktjRNXU4g7c4YWzZoxcfJk+vfvL2OhhUF55AL46NGjvPXWW3zzzTdljm/bto2PP/4YExMTBg8ezNChQx86SE1SXFzC2csZHDubxrX0qrfEUU5WBgtnPM6WzRur9MQbUX1cunSJtu3a0mF4VzoMjlA7TrVUXFTEG6NfYf6cV4iOjlY7DmfPnqV5kyb88sKL1LK3VzuO6tKzs9l67Cjrjx/jaGIivXv3ZkRsLN26dcNMx2OjFUVh9+7dLP3kE35at46W/v4MbN6CdkHBFY7LzisoYMuxo3y7Zzc3cvP498LXGT58eJV5kSWELj1SAbx06VLWrl2LpaUlK1euLD1eWFhI7969WbVqFZaWlgwfPpzFixfj6ur6UEFqqmvpuRw/m8qZSxkUl1Sd4RH7tv3I6bgNHNi/T8ZpikeSlJRE54guNO/XhoionmrHqdbOx5/l85kfc+rkKZycnFTN0q97DxpYWjK+hzym/ystM5P1hw/x6/HjJF5NYfCQIURFRdGhQwet/j69cuUKX335JZ8vXYpxcTGDm7ekf4sWuNjZPdT5Dpw5zcK1P2Hh4MAXX31FaGio1rIKURWVV3dW+Ez19PTkww8//Mfxc+fO4enpib29PWZmZjRt2pSDBw9qL3UNUdvJiq4tvBjZJ5hWoXWwsawas7xbdupPRtZtli1bpnYUUY0dPnyYVq1b0aJ/Wyl+tcA71I9GnZsyfcYMVXNs2byZo0eOMLqLXM2/Fxc7O2I6duL/nn6GH6ZOwzH9Jk+PG4eHuzvPPvssBw8efOgyzoTpAAAgAElEQVTlMnNycvj222/p1qULIYFBxG/azMLHI/ll5izGdu360MUvQEv/AFZNncaAwGA6tWvH66+9RnFx9VnNSAhtqnAViB49enD58uV/HM/OzsbW9r/bBltbW5Odna3ddDWIlYUpzYJq06RBLRKTb3H8bBpXUtX7/zIyMmLgqOd57vkpDBw4UPWrTaJ6URSFL774gunPzSDyuRiadGmhdqQao++Tg/h31Gx27dpF+/bt9d5/UVERkydO5Ln+/TGvgsuyVTX1nJ0Z1yWCcV0iOJOSwobjxxg6cCBGJiY8HhlJ23btaN68ObVr177n/fPz80lISGD37t2s//lndu/dS3P/AB5r3Jh3Hhug9aXnjIyMGNq2LW0DA3nh22/55eef+f6HH3Bzc9NqP0JUdQ+9DJqNjU2ZrShzcnLKFMTi3oyMNPi5O+Dn7sCNW3kcO5vG6aSbFBaX6D2Lp08wYc27MOO55/n8s6V6719UTxcuXGDSlEkcO3GcyYtmUtenntqRahRLGysGTR3O2H+N5fix45ib63cnyk8XL8bWyIge4Y312m9N4F+nDv516vB0t+4cv5jE1mPHeXPDBo4lJqIA9erUxcHBHmNjY/Ly8rh+PZXUG2nUr1OXhh4e9A4IYH73HnrZarqeszPLJkzk4w3raRoezsrVq2nbtq3O+xWiqnjoAtjX15ekpCQyMjKwsrLi4MGDjB07VpvZajxne0s6N/WgdcM6nLqQzrGzaWTmFOg1Q6/Iibzx3OOMUulqk6g+EhMTeff99/jmm2/oHNmd6dPmyhbHOtK4UzPi1u9n4cKFzJ07V2/9ZmRkMO+ll1j6xHiZJPUINBoNYV71CfOqD9x5xyQjJ4eUmzfJvn2bEqUEcxNTnO1sqW3voNqVdmMjIyb17kOYpxcD+vXjlfnzmfDUU6pkEULfHrgAXrduHbm5uURGRjJz5kzGjh2LoigMHjz4vm/xiPJZmJkQHlCLRv6uJF3N4tjZVC5ezdJL31bWtgyIncHoMWOJP35MdkKqYRRFITk5mcuXL3P9+nWKi4tRFAVra2scHR1xcHDAwcEBW1tbzM3NS4uewsJCrl+/Tnx8PAcPHuSndT9x5swZWvfrwAvfvIJjLRkyo0sajYbBzw5n4aiXGTZsGAEBAXrp9+XZs+kcHEKwh4de+jMUGo0GRxsbHG1s1I5yT51CQ1k+eSrjX1vAqZMneee992S5NFHjyTrAVdTNrNvEn7vByQvpFBTqfpLCl+9Op2eX1syf/6rO+xK6lZmZyY8//siKVSvZv3cfaMC5jit2jnYYGRuDBgpuF5CbmUNuVg7ZmVnk5eZRVFiEmbkZGo0RhQUF2Dva4+7riZtvXQJbhhLQJBBTc7niq0/bl28i+dAFftv2m86vyP7555+0adGCX2e9iLPtw0+0EtXXrdwcJi1bhqOnB8u//x6bKlqwC1FZshFGNVZQWMyfF29y/Gwa6Zm3ddZPRvp13n5hOLt37SQkJERn/QjdSU1N5Y0332DpZ0vxDQsgPKIZAY0DcaztXKn7l5SUUFRQRElJCeaW5vIWeBVQXFTMO+P+zewZsxg5cqRO++rdvTthNrb8q2s3nfYjqrbC4mJeXrmCUzfT+WXDBvlbLaq1R1oGTajLzNSYhr4uDO/egP4dfPGpZ6+TwsTBqRY9h0xg9OixlJTof0KeeHglJSUsXryYBkGBxJ0/ysyv5vHkm5Np2bNtpYtfuDM73MzCDAsrCyl+qwhjE2OGPhfDtBnTSUtL01k/GzZs4GR8PLGdOuusD1E9mBobM3/YcHoFNKBV8+bExcWpHUkInZACuJrQaDR41LaldxtvYnoF0aRBLSzMHnoO4z21jhhEVl4RixbJFsnVRVpaGt16dOftRe/y9AfTGfbcSJzcXNSOJbTIK8ibZt1b8eyzz+rk/IWFhUx55hlmPtYfMxPt/k4R1ZNGo+FfXbsxq19/enTtyqpVq9SOJITWSQFcDdlZm9EmrC6j+gYT0cwTVwdLrZzXyMiIwWNmMXvOS1y8eFEr5xS6c+zYMcKbhGNWx4pnl7yIu7+n2pGEjvQeN4BNWzezbds2rZ/7k0WLcLWwpEvDMK2fW1Rv3cPD+fzJCUx5aiLzX331oTf3EKIqkgK4GjMxNiLI24mhXQMY3Nkffw9HjB7xrWs3dx869o4mJmakDIWownbu3EmnLp3p9eQABj4TibGJzNiuySysLBg8LYp/PTGO27e1NxcgNTWVV+fN44X+/WXYi7inEE9PVkyZyqqvvmZEVJRWf/6EUJMUwDWARqOhjos1PVrd2XK5RYgbVhYPv65kl36xpKRm8PHHH2sxpdCWDRs2MGDQAEbOe4Lm3VurHUfoSaP2TXDxrs2cuXO0ds5ZM2fSr2kzAurKZibi/mo7OPDN089wK/E8ndq359q1a2pHEuKRSQFcw1hbmtIi2I2RvYPo3tKLOs4PvqOQsbEJw558mTlzX+b06dM6SCke1s6dOxkePZx/vf4MQS1C1Y4j9Gzo9BEs++pLdu3a9cjn+uOPP1i7Zg3P9OylhWSiprM0M+Pd2JG0dKtDi6ZNOXTokNqRhHgkUgDXUMbGRgR4OjK4iz9DuwYQVN8JY6PKv8VZq64X3QeNI3pEDEVFRTpMKirr0KFDDBg0kJHznsSvkX42RhBVi52TPcNnjiQqOopbt2499HlKSkqYOH48z/bui62lduYQiJpPo9HwTK/eTO/Zi+4RESxZvFjGBYtqSwpgA1DL0YqI5p6M6htCm4Z1sbWq3GYG7boPJbdAw8KFb+g4oahIcnIyvfr0ZuhzMQS3bKh2HKGihu0aE9AqhAkTH37L2mXLllGclc3Ali21mEwYil5NmvLdpMm8u2ABsSNiyM3NVTuSEA9MCmADYmluQpPAWsT0CqJ3G2/ca9mWe3sjIyMin5jLW2+/wx9//KGnlOJ/5efn02/AY7Qe2IEmnZurHUdUAQOfGcquvbv47rvvHvi+V69e5YXnnmPuoMEYGcmfAPFwfGq7sXLKs2SfP0+Lpk05deqU2pGEeCDy288AGRlp8Klnz4COvkT1CKShrwumJvf+UXByrcvg0TMZ8vhQMjIy9JxUAIyfOB6NrTG9Rj2mdhRRRZhZmDPq1fE8PekZTpw48UD3nTh+PINbtCTEU5bNE4/GytycN6JHMLxJU9q2asUnixbJkAhRbUgBbOCc7Czo2MSdUX1DaB9eDwcb83/cJrxVV3xDWjF6zFj55aZnixZ/wubtW4mdM06WqRJleAR40X/iEB4b0J/MzMxK3WfVqlUcjYvjaZn4JrREo9EQ2bYd302awidvv02/Pn1ITU1VO5YQFZICWABgbmpMI39XonsG0q+9D/Xr2JUpuPpFTeHo8ZMsWvSJiikNy969e5n14iyeXDgJC2uZqCT+qXXfDniEeRM7KrbCF6dpaWk889RTvBY5DHPTh18mUYh78XVzY/nkqbgr0Cg0lA0bNqgdSYhySQEsytBoNHi52dG3nQ8jegYSHuCKuZkxpmbmjHhmAbPnzGX//v1qx6zxkpOTGTh4IDGzx1Lbq47acUQVNmTKcE6cPclLL79039soisLIESPo17gJTXx89ZhOGBIzExOm93uMN4dHMzY2lmeeeoq8vDy1YwlxTxUWwCUlJcydO5fIyEhiYmJISkoq0z5//nwGDRpETEwMMTExZGVl6Sys0C97G3PaNarHqD7BdG7qQVCDBgwdN4cBAwZx5coVtePVWAUFBTw28DFa9e9Aw3aN1Y4jqjhTczOeeGMyS5d9xrJly+55m3fefpuUxESm9u2n53TCELUMCGDtc89zIe4QTRs14vDhw2pHEuIfTCq6wZYtWygoKGDFihUcOXKE119/nU8++e/b4AkJCXz22Wc4OTnpNKhQj6mJMSE+zgR7O9GhiTvF2Sn07fcYe/fsxlLWENW6CRMnoFgb0Wu0THoTlWPnbM/4t6YwbeJ0PDw86Nq1a2nb3r17ef3f/2bl1GmYGsuW2UI/7K2seSd2JGv/+INuXbowbfp0nps5E2P5GRRVRIVXgOPi4mjfvj0A4eHhxMfHl7aVlJSQlJTE3LlzGTZsGKtWrdJdUqE6jUZDPVcbln60gOCgQAYPjSI7N1/tWDXKkiVL2LhtM7FzxskSVeKB1PGux5j5TzF0WCR79+4F4MKFCwwaMIAFw6PxcHFROaEwNBqNhv4tWvDDtBn89J9v6di2LRcuXFA7lhBAJQrg7OxsbGxsSr82NjYu3RksNzeXESNG8Oabb/LZZ5/x3XffyVqABkCj0fDZZ5+RnnadAZFj2bT/AtfSZSH0R7V//36enzWTJxY+g6WNldpxRDUU0CSQEXPH0u+xfmzZsoU+PXvyRKcudAqVbbOFeuo6OfHVxKdpW6cezRo35qsvv5QVhYTqKiyAbWxsyMnJKf26pKQEE5M7IycsLS2JjY3F0tISGxsbWrVqJQWwgbC0tGTD+l9IOX+MD997k++3nub7rac5lZROcXGJ2vGqnZSUFAYMGkj0rNG4edVVO46oxkJbN2LI9BH07NUTT2sbYjp2VDuSEBgbGTGuWzeWTZjIa3Pm8vjAgaSnp6sdSxiwCgvgJk2asHPnTgCOHDlCQEBAaduFCxeIioqiuLiYwsJCDh06REhIiO7SiirFwcGBrVs2cfzAen775T9cS89ly+8X+fKXE+yPTyE7t0DtiNVCfn4+AwYNoEXftjTq0FTtOKKau52Tx7ZvfyXctz77Tp1ko0xAElVIkLs7P0yfgW1WNmHBwWzatEntSMJAVTgJrlu3buzZs4dhw4ahKAqvvfYay5Ytw9PTk4iICPr168fQoUMxNTWlf//++Pv76yO3qCLc3NzYueM3OnTsTEHBbboP/Bd5+UUcPHmNQ6eu413PnjA/F+q52lR8MgOkKArjJ4xHsTKi99j+ascR1Vz6tRssmvwmIS4ujO/ZmfNXU5m3fDmX028wNqKrbKYiqgRzU1NeHDyETiEhjIqOZvDjQ3nj7bdkUrXQK42ix4E4ly9fJiIigq1bt+Lu7q6vboUepKSk0KlzBN7Bregd+fQ/JnC5OFjS0NeFAE/H+267bIg++OAD3vnoXZ5dOhsLKwu144hq7NQfCXw5dxH9m4YzsFWT0mL3ekYmC7//GZ9abiwYEYutFBmiCsnIyeHl71eSmJHBdytXEB4ernYkUYOUV3dKJSK0ok6dOuzZvZP0Kyf5+oOZ5N8uu/h5WkYe2+Mu8eUvCew+eoVb2bJ6xNatW5k3fx7j3pgkxa94aHnZuax862u+fPEjpvSKYFDrpmWu9NZysGPB6KFojBUGLHyNw4mJKqYVoiwHa2veHTmKsW3a0LVzZ956801KSmQeidA9KYCF1ri4uLBzx3YCvGrx8avjSE9N/sdt8guKOXI6lf9sOMXPuxNJupppkLOBT506ReTwYYyaNx7XerXUjiOqods5eWz5bj0vDZoOiSl8OG4EjX3r3/O2ZiYmPNU3gmEdWzJ+ySe8tup78gpkjL6oGu4sl9aS76dOY/nnn9MtIoLk5H/+/RBCm6QAFlplYWHBt99+w4Rxo3h3dixxe+69H7yiKFxIyWTdrkS+3XiKo2dSKSgs1nNadSQnJ9Otezf6ThhEg2bBascR1UhRYREnf4/nu9c+58XHpnBlWxwvPd6PyX26YWdV8dCGdiENeH/8CM7fSKH7vJdYc+CAXG0TVYaHiwv/mfgMYXb2NG7UiB9//FHtSKIGkzHAQmfi4uIY8ngkbp6BPDbiWWzty98t0NTEiEAvJxr6ueBkVzOHBGRkZNCmXVuCOzeie2wfteOIR5R54xZXzl3iRkoaN6+mkXH1BjkZWeTn3qYgL5/CgkI0Rho0GiOMjIwwMTXBxNwUU3MzTCxMMTE3u/O5mSmmZqaYmpuW+VyjMSLzRgYZKWkkn73E5fNX8KxTi7b+PrQLCaC2g/1DZz9x8Qpfbt6FBiMm9e5Ll4YNZfMVUWUcPp/IjG//Q7devXjvgw+wtrZWO5KohsqrO6UAFjqVnZ3N7Dlz+eqrr+n5+ARadxmAkVHFW2G617IlzM+F+nXsMDKqGTPXs7Ky6N6zOzZejgyZEiUz8quZ3Kwczh09zZm4k1xKSORy4mVKiovxdHPFzd4OF2trXOxssbOywNLcDAszM0yNjVFQKClRKFEUioqLKSwqpqCo6L//FpeQrxRTUFxCQXExhcV/HS8opLi4BGcrS5xtbXB3dsS3bm0szcy09j0pisK+k2f4YU8cRcUl/Ktrd/o0bYqVubnW+hDiYWXfvs38H3/gWHIy/7dyBU2byjKR4sFIASxUd+zYMZ54cgLJV1PpPvhJGrXoUqkC0M7ajFAfF4K9nbAwr3DVviorMzOTrt27Yu3uQOSMGLnSVg0oisLFUxc4vPV3EnYf5nrydfw96tKwXl3869Wmfi0XnGxtasQLGUVROHr+Ir/8foSTF5Pp0bgJj7dpQ3h97xrx/Ynq7Ze4OOb/uJppM2Yw47nnMDau+CKKECAFsKgiFEVh/fr1PD9zFrm3i+jQewThLSMwNjGt8L4mxkb4ezgQ5ueKq2P1Wsbpxo0b9OjVA4f6LgyZFi3FbxVWUlJC4vGzHNm4j8O/HcQEaNPAjxYBPvjXc8PUAP7w3sjKZvvRE2w7coLiEoXujcLpHt6Ypr6+GMvPrlBJcno6z3/3LdhY89myZbLplqgUKYBFlaIoCmvXruWNN9/m9OkztO46hJadHsPe0bVS96/jbE1DPxd83R0wruLDI06fPk2PXj0Jat+Q/k89LlfTqqDiomJOHzrJkU37ObIjDlsLC9o28KV1sD/1a7kY7GOmKApJ19PYf+ocv/95jhuZ2bQJDKJ9UDBtg4Jwc3BQO6IwMCUlJSzfs5sPNqxnwsSJvDh7NhYWNXO+iNAOKYBFlXXkyBHee/8DfvjhB7wDGtK4TW/CmnfCzLziq7zWFqaE+DoT6uOMlUXFV5H1bevWrUQOH0bvcf1pN6Cz2nHEXQoLCjn1ezxHNu3n6J4j1LK3pU2AH62D/HB3KX+ypqG6npHJkcQkjiZe4uj5JFxs7WgXFEybBg1o5ueHvZVMUhL6cTUjg3+v+YHE9HTe/+gjevToYbAvVEX5pAAWVV5ubi5r1qzhs8+X8ccffxAc3pqgxh0IadIOSyvbcu9rZKTBt54DYX4u1HFR/49wYWEhc+fOZekXnxH70jgCm8tbdVVBft5tEvYd48jmAxzfdwzPWs60DfCldZA/tRzs1I5XrRSXlJB49TpHzl0kIekypy4l4+HqQiv/BrQKuFMQO8isfaFjW48f4411a/EOCODtd9+lUaNGakcSVYwUwKJauX79OuvWrWPF96vZs3sXfkHhNAhrR2CjVri6eZb7St/V0ZIwX1f8PR0wMdb/eMU//viDcU+OQ2NlTPTssdg5P/wyVeLRZWdkcXz3YY5u/YNTh07i5+5GW39fWgX64mRro3a8GqOwuJizyddISLpMQtIVTl5Kxt3ZmZb+AbQKaEBzPz8cbeT/W2hfYXExK/bs5uNNG+narRsvzplDaGio2rFEFSEFsKi2srKy2LhxIz+uWcuWLZtBY0yDhq3wD21JQMMWWNvcu8C0NDchqP6dNYVtrbS3bNT9XLx4kXmvzGPN2p94bOLjtOrVVt6SU4GiKKRevsaxXYc5tu0PLp65SJivF639vGnm712pzSLEoysqLuZsyjUSkq5wIukKJy5eoa6TEy39A2gZEEBzP3+cbct/Z0eIB5GVl8d3u3fx9c4dNG/RgmdnzKBTp04y6djASQEsagRFUThx4gSbNm3i5183cGDfXmrV8cDTL4z6AY3wadAIR5c6ZQpPI42G+nXtaOjrgkdt7f/BjY+P5/0P3uf777+n7YBOdB3RG2s7eetXn7JuZnLq4AlO7T/GqQPxFOcX0sS/Pq39fGjk44m5adUbH25oiktKOJdyjfi7CmJnW1vCvb1p7O1DuLcPDerWxcQAVtkQunW7oIDV+/ezfP9eCoCx48YRFR1N/fr11Y4mVPBIBXBJSQkvv/wyf/75J2ZmZsyfPx8vL6/S9pUrV7J8+XJMTEyYMGECnTvff7KPFMBCmwoKCjh06BC7d+9m+45d7N+3DyMjY7wDwqjl7kc9L3/qeQWUFsVOdhY09HMh0MsRU5OH/0ObmJjIzz//zNf/+Zqkixdp1bcdnSO7Y+MgV7R0rbCgkJTEy5yPP8f5Y6e5cPwcGekZhPh40tjDnUY+nni4OMnV9yquuKSEy2np/Hk5hTNXrvHn5RSuZdwiyN2d8PreNKhXjwZ16+Hr5oaFFjf+EIZDURSOJSWx+vcDbD56hDp16zJw8GC6dutG8+bNZfUIA/FIBfCmTZvYtm0br7/+OkeOHGHJkiV88sknAKSmpjJmzBhWr15Nfn4+UVFRrF69GrP7/MKSAljokqIoJCYmcuDAAQ4dOkzcoSPExx/j9u3beNQPwNG1Ho4udXGr50nzxiF0btMIP2/3ct8iy8rK4vTp0xw8eJADvx9g565dpN9Mp2GbcMI6NSa4VRjGj1BMi38qKiwi43o66ddukH71BlcvJHP17CVSzl/hxvV0ars40qBeHRq41aJBvTp41nKW9WlrgJzb+ZxJvsqZ5Gtcup5O0vU0rtxIp66TI35udfByrYWHiwvuzs64O7tQ18lJq7viiZqruKSEw4mJbI2P548LiZy5dIlGDRvSsk0bQhs2JDg4mKCgIOzt7eXFcw1TXt1Z4dZacXFxtG/fHoDw8HDi4+NL244dO0bjxo0xMzPDzMwMT09PTp06RVhYmJa/BSEqptFo8PX1xdfXl6ioqNLj169fJyEhgcTERE6fOcOfpw/y0dYVvHDpEllZt+jWrRtdunThxo0bpN1I43paKhcvXiTpwgVyc/Ko41kHz0Bv6jbwIHL2SDwaeFXbcWWKonDzejpKiYJSUoJy5yCKoqAof32OAgrlHLvzwV/td15D33Xbv29XUkJBfgGFBYUU3v7r34JCCvMLKMwvIDcrl9ybWeRmZpN7K5uczBxupWeSnZWDo70tzg62uNra4OHoQEO3eng2bEgdZ0eD2IzCEFlbmBPu40W4z3/fYSwsLib5xk0upd7g6s1bHDh3knUHM7macYvrGbcwNTHG0cYGZxtbnG1tcbC2wcbCAhsLC6wtLLA2N8fK3BxLM3NMjY0x+evj7s/vfG2ERmPE37WPBg0azZ1/4c7vFs2dT/j7nzu3+evrv2+j49rJzMRUxk4/BGMjI5r5+dHMzw+AnNu3OXLhPAlJF/k17hAfXrvK2cuX0Wg01HFzw71ePerWq4ejszP2Dg44ODhgb2+PnZ0d5ubmpTXP3R+mpqYYGRn99XPwYB9AjS+8nZ2dsbKyUjtGGRUWwNnZ2djcNXvX2NiYoqIiTExMyM7OxvauJ6O1tTXZ2dm6SSrEQ6pVqxa1atW65/CcpKQkXn75ZS5duYydgz1BoUG0cWxDPXd36nvXp1btWmg0Gio9UP5/3lCp7P2UytzyHjep1P3uut3v+3/n1Qkz7/1LWKMp84f9Xu1GRncd4+427jp253MjIyMszC0wNzfDwtwcczNTLMzMsDI1xcLUAltbJxzq2WFvb4e9rS0O9nY4OzpSy8W5zFanup6kUNn/Q30/tpX1sNM4KpOr8mf+5y0rdd9KZA++T2+KopCTm8uN9AzSb97kxs0MMjJukZObS25eHhm5t0m5fZu89Ezy8m5TVFRMUXHRnX+Liko/Cv/6t+SvLGVe5JX5+u++79euAPcoYipZ11TmZrbW1uz+4UdMZVz7I/MGBv7PsczsbJKvXiX5+jVSrl3nVlYmmbcyuZCSQlZODlk5ORQWFVFQWEhhYSEFf30UFRVRUFBASUnJnZ8Fyv5clPcBD/8crk5GjBjBggUL1I5RRoUFsI2NDTk5OaVfl5SUYGJics+2nJycMgWxEFWdl5cXy5YtUzuG3vTu0pPLly6rHUMIIaocO0AGZxqOCt/HbdKkCTt37gTu7NoVEBBQ2hYWFkZcXBz5+flkZWVx7ty5Mu1CCCGEEEJUNRVeAe7WrRt79uxh2LBhKIrCa6+9xrJly/D09CQiIoKYmBiioqJQFIWpU6dibm6uj9xCCCGEEEI8lAoLYCMjI1555ZUyx3x9fUs/Hzp0KEOHDtV+MiGEEEIIIXSgek5lF0IIIYQQ4iFJASyEEEIIIQxKhUMgtKm4uBiAq1ev6rNbIYQQQghhYP6uN/+uP++m1wI4NTUVgOjoaH12K4QQQgghDFRqaipeXl5ljlW4FbI23b59m/j4eFxdXcssdC+EEEIIIYQ2FRcXk5qaSmhoKBYWFmXa9FoACyGEEEIIoTaZBCeEEEIIIQyKFMBCCCGEEMKgSAEshBBCCCEMihTAQgghhBDCoFTJArikpIS5c+cSGRlJTEwMSUlJZdpXrlzJoEGDGDp0KNu3b1cppeGo6PGYP38+gwYNIiYmhpiYGLKyslRKajiOHj1KTEzMP45v27aNwYMHExkZycqVK1VIZpju93gsW7aMPn36lD43EhMTVUhnOAoLC5kxYwZRUVEMGTKErVu3lmmX54d+VfR4yPNDv4qLi3nhhRcYNmwY0dHRXLx4sUy7wT0/lCpo48aNyvPPP68oiqIcPnxYGT9+fGnb9evXlb59+yr5+flKZmZm6edCd8p7PBRFUYYNG6bcuHFDjWgG6dNPP1X69u2rPP7442WOFxQUKF27dlUyMjKU/Px8ZdCgQcr169dVSmk47vd4KIqiTJs2TTl+/LgKqQzTqlWrlPnz5yuKoijp6elKx44dS9vk+aF/5T0eiiLPD33bvHmzMnPmTEVRFGX//v1l/pYb4vOjSl4BjouLo3379gCEh4cTHx9f2nbs2DEaN26MmZkZtra2eHp6curUKbWiGoTyHo+SkhKSkpKYO3cuw4YNY9WqVWrFNBienp58+OGH/zh+7t1VISQAAAM2SURBVNw5PD09sbe3x8zMjKZNm3Lw4EEVEhqW+z0eAAkJCXz66acMHz6cJUuW6DmZ4enZsyeTJ08u/fru9ebl+aF/5T0eIM8PfevatSuvvvoqAMnJybi4uJS2GeLzQ687wVVWdnY2NjY2pV8bGxtTVFSEiYkJ2dnZ2NralrZZW1uTnZ2tRkyDUd7jkZuby4gRIxg9ejTFxcXExsYSGhpKYGCgiolrth49enD58uV/HJfnhjru93gA9OnTh6ioKGxsbHj66afZvn07nTt31nNCw2FtbQ3ceS5MmjSJKVOmlLbJ80P/yns8QJ4fajAxMeH5559n8+bNfPDBB6XHDfH5USWvANvY2JCTk1P6dUlJCSYmJvdsy8nJKfOgCe0r7/GwtLQkNjYWS0tLbGxsaNWqlVyRV4k8N6oWRVEYOXIkTk5OmJmZ0bFjR06cOKF2rBovJSWF2NhY+vfvT79+/UqPy/NDHfd7POT5oZ6FCxeyceNG5syZQ25uLmCYz48qWQA3adKEnTt3AnDkyBECAgJK28LCwoiLiyM/P5+srCzOnTtXpl1oX3mPx4ULF4iKiqK4uJjCwkIOHTpESEiIWlENmq+vL0lJSWRkZFBQUMDBgwdp3Lix2rEMVnZ2Nn379v3/du0XVYEoiuP4b4KKK7DZbLMBk2HQJgb/ooxBVzBBUBfgEgabWxBsdoN7mSSoRTg2ea8805sr3u+n3vKDw+H+wtH1epWZ6Xw+KwxD17G+WpZlms1mWiwW6vV6v97Yj/z9NQ/2I3/7/f51alIulxUEwessxcf9+MgTiGazqdPppNFoJDPTZrPRbrdTtVpVFEWK41jj8VhmpiRJVCqVXEf+au/m0W63NRgMVCgU1Ol0VKvVXEf2yuFw0O1203A41HK51Hw+l5mp2+2qUqm4juedn/NIkkTT6VTFYlH1el2NRsN1vK+23W51uVyUpqnSNJUk9ft93e939sOBd/NgP/LVarW0Wq00mUz0eDy0Xq91PB69/T8CMzPXIQAAAIC8fOQJBAAAAPBfKMAAAADwCgUYAAAAXqEAAwAAwCsUYAAAAHiFAgwAAACvUIABAADgFQowAAAAvPIEEh8Z4lKlAS8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "from sklearn.neighbors import KernelDensity\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12,3))\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "n_cols = X.shape[1]\n",
    "X_hist = [X[y==cl] for cl in range(n_classes)]\n",
    "\n",
    "i_splitvar = 3\n",
    "\n",
    "ranges = [(np.min(X_hist[cl][:,i_splitvar]),np.max(X_hist[cl][:,i_splitvar])) for cl in range(n_classes)]\n",
    "print(ranges)\n",
    "\n",
    "for cl in range(n_classes):\n",
    "    r=ranges[cl]\n",
    "    print(r)\n",
    "    X = X_hist[cl][:,i_splitvar]\n",
    "    x_grid = np.linspace(r[0]-r[0]*.25, r[1]*1.25, 1000)\n",
    "    if False:\n",
    "        kernel = stats.gaussian_kde(X)\n",
    "        heights = kernel.evaluate(x_grid)\n",
    "    n = len(X)\n",
    "    d = 1\n",
    "    print(n)\n",
    "    bw = n**(-1./(3))\n",
    "    bw = (n * (d + 2) / 4.)**(-1. / (d + 4))\n",
    "    bw /= 6\n",
    "    kde = KernelDensity(kernel='gaussian', bandwidth=bw).fit(X.reshape(-1, 1))\n",
    "    log_dens = kde.score_samples(x_grid.reshape(-1, 1))\n",
    "    heights = np.exp(log_dens)\n",
    "#     print(heights)\n",
    "    plt.plot(x_grid, heights, linewidth=1, c='black')\n",
    "    plt.fill(x_grid, heights, alpha=.6)\n",
    "#    plt.ylim(0.0,3)\n",
    "\n",
    "\n",
    "x = 2.5\n",
    "xaxis_y = bbox.y0\n",
    "\n",
    "#tria = np.array([[x,xaxis_y],[x-10,xaxis_y-10],[x+10,xaxis_y-10]])\n",
    "tria = np.array([[x,0],[x-.2,-.3],[x+.2,-0.3]])\n",
    "t = patches.Polygon(tria, linewidth=1.2, edgecolor='orange',\n",
    "                      facecolor='orange', label='foo')\n",
    "t.set_clip_on(False)\n",
    "ax.set_xlabel(f'petal length')\n",
    "#ax.add_patch(t)\n",
    "#ax.set_xticks([])\n",
    "#ax.yaxis.set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from sklearn.neighbors import KernelDensity\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12,3))\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "n_cols = X.shape[1]\n",
    "X_hist = [X[y==cl] for cl in range(n_classes)]\n",
    "\n",
    "i_splitvar = 3\n",
    "\n",
    "ranges = [(np.min(X_hist[cl][:,i_splitvar]),np.max(X_hist[cl][:,i_splitvar])) for cl in range(n_classes)]\n",
    "\n",
    "\n",
    "colors = [\"#fefecd\",\"#D9E6F5\",'#a1dab4']\n",
    "\n",
    "for cl in range(n_classes):\n",
    "    X = X_hist[cl][:,i_splitvar]\n",
    "    hist, bin_edges = np.histogram(X, density=False)\n",
    "#     print(hist, bin_edges)\n",
    "    print(X)\n",
    "#     plt.scatter(X, [0]*len(X), s=50, alpha=.1, c=colors[cl], linewidth=.5, edgecolor='grey')\n",
    "    plt.scatter(bin_edges, [0]*len(bin_edges), s=hist**2*20, alpha=.7, c=colors[cl], linewidth=1.2, edgecolor='grey')\n",
    "\n",
    "x = 2.5\n",
    "xaxis_y = bbox.y0\n",
    "ax.set_ylim(-10,10)\n",
    "#tria = np.array([[x,xaxis_y],[x-10,xaxis_y-10],[x+10,xaxis_y-10]])\n",
    "tria = np.array([[x,0],[x-.2,-.3],[x+.2,-0.3]])\n",
    "t = patches.Polygon(tria, linewidth=1.2, edgecolor='orange',\n",
    "                      facecolor='orange', label='foo')\n",
    "t.set_clip_on(False)\n",
    "ax.set_xlabel(f'petal length')\n",
    "#ax.add_patch(t)\n",
    "#ax.set_xticks([])\n",
    "#ax.yaxis.set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.patches as patches\n",
    "from matplotlib.patches import FancyBboxPatch\n",
    "from matplotlib.transforms import Bbox\n",
    "from matplotlib.figure import figaspect\n",
    "\n",
    "size = 1\n",
    "fig, ax = plt.subplots(1, 1, figsize=(size,size), subplot_kw=dict(aspect=\"equal\"))\n",
    "\n",
    "ratios = [50, 4, 0, 19]\n",
    "#ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "patches, _ = ax.pie(ratios, radius=1, shadow=False)\n",
    "for patch in patches:\n",
    "    print(patch)\n",
    "    patch.set_linewidth(.5)\n",
    "    patch.set_edgecolor('black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = boston.data\n",
    "feature_names = boston.feature_names\n",
    "target = boston.target\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6,2))\n",
    "mu = 0\n",
    "sigma=.2\n",
    "x = np.random.normal(mu, sigma, size=len(target))\n",
    "ax.scatter(x, target, s=5, alpha=.7)\n",
    "# ax.scatter(data[:,1], target, s=2)\n",
    "# ax.scatter(data[:,2], target, s=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(boston.data)\n",
    "data.columns = boston.feature_names\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate table"
   ]
  }
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