visualize_ddm_model_with_rt_hist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import seaborn as sns\n",
    "from string import ascii_letters\n",
    "import pandas as pd\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import cddm_data_simulation as cds\n",
    "import boundary_functions as bf\n",
    "\n",
    "# Set styles\n",
    "sns.set(style = \"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.4\n"
     ]
    }
   ],
   "source": [
    "from platform import python_version\n",
    "\n",
    "print(python_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot bound\n",
    "# Mean posterior predictives\n",
    "\n",
    "# ORNSTEIN DOESNT WORK FOR NOW....\n",
    "def bgn_boundary_posterior_plot(params = [],\n",
    "                                cols = 3,\n",
    "                                model = 'weibull_cdf',\n",
    "                                max_t = 10,\n",
    "                                show = True,\n",
    "                                save = False,\n",
    "                                y_limh = 2.2,\n",
    "                                y_liml = -2.2):\n",
    "    \n",
    "    sns.set(style = \"white\", \n",
    "            palette = \"muted\", \n",
    "            color_codes = True,\n",
    "            font_scale = 2)\n",
    "\n",
    "    fig, ax = plt.subplots(1, 1, \n",
    "                           figsize = (8, 8), \n",
    "                           sharex = False, \n",
    "                           sharey = False)\n",
    "   # my_suptitle = fig.suptitle(title + model, fontsize = 20)\n",
    "    sns.despine(right = True)\n",
    "    \n",
    "    t_s = np.arange(0, max_t, 0.01)\n",
    "    ax.set_xlim(0, max_t)\n",
    "#     y_limh = 3\n",
    "#     y_liml = -3\n",
    "    ax.set_ylim(y_liml, y_limh)\n",
    "    # Run simulations and add histograms\n",
    "    # True params\n",
    "    if model == 'angle':\n",
    "        ndt = params[3]\n",
    "        out = cds.ddm_flexbound(v = params[0],\n",
    "                                a = params[1],\n",
    "                                w = params[2],\n",
    "                                ndt = ndt,\n",
    "                                s = 1,\n",
    "                                delta_t = 0.001, \n",
    "                                max_t = 20,\n",
    "                                n_samples = 20000,\n",
    "                                print_info = False,\n",
    "                                boundary_fun = bf.angle,\n",
    "                                boundary_multiplicative = False,\n",
    "                                boundary_params = {'theta': params[4]})\n",
    "\n",
    "    if model == 'weibull_cdf':\n",
    "        ndt = params[3]\n",
    "        out = cds.ddm_flexbound(v = params[0],\n",
    "                                a = params[1],\n",
    "                                w = params[2],\n",
    "                                ndt = ndt,\n",
    "                                s = 1,\n",
    "                                delta_t = 0.001, \n",
    "                                max_t = 20,\n",
    "                                n_samples = 20000,\n",
    "                                print_info = False,\n",
    "                                boundary_fun = bf.weibull_cdf,\n",
    "                                boundary_multiplicative = True,\n",
    "                                boundary_params = {'alpha': params[4],\n",
    "                                                   'beta': params[5]})\n",
    "        \n",
    "    if model == 'ddm':\n",
    "        ndt = params[3]\n",
    "        out = cds.ddm_flexbound(v = params[0],\n",
    "                                a = params[1],\n",
    "                                w = params[2],\n",
    "                                ndt = ndt,\n",
    "                                s = 1,\n",
    "                                delta_t = 0.001,\n",
    "                                max_t = 20,\n",
    "                                n_samples = 20000,\n",
    "                                print_info = False,\n",
    "                                boundary_fun = bf.constant,\n",
    "                                boundary_multiplicative = True,\n",
    "                                boundary_params = {})\n",
    "    \n",
    "    if model == 'ornstein':\n",
    "        ndt = params[4]\n",
    "        out = cds.ornstein_uhlenbeck(v = params[0],\n",
    "                                     a = params[1],\n",
    "                                     w = params[2],\n",
    "                                     g = params[3],\n",
    "                                     ndt = ndt,\n",
    "                                     s = 1,\n",
    "                                     delta_t = 0.001,\n",
    "                                     max_t = 20,\n",
    "                                     n_samples = 20000,\n",
    "                                     print_info = False,\n",
    "                                     boundary_fun = bf.constant,\n",
    "                                     boundary_multiplicative = True,\n",
    "                                     boundary_params = {}\n",
    "                                     )\n",
    "    \n",
    "    tmp_true = np.concatenate([out[0], out[1]], axis = 1)\n",
    "    choice_p_up = np.sum(tmp_true[:, 1] == 1) / 20000\n",
    "    print('p up: ', choice_p_up)\n",
    "    \n",
    "    #ax.set_ylim(-4, 2)\n",
    "    ax_tmp = ax.twinx()\n",
    "    ax_tmp.set_ylim(y_liml - params[1] -0.1, y_limh - params[1] - 0.1)\n",
    "    ax_tmp.set_yticks([])\n",
    "    \n",
    "    counts, bins = np.histogram(tmp_true[tmp_true[:, 1] == 1, 0],\n",
    "                                bins = np.linspace(0, 10, 100))\n",
    "    \n",
    "    counts_2, bins = np.histogram(tmp_true[tmp_true[:, 1] == 1, 0],\n",
    "                                  bins = np.linspace(0, 10, 100),\n",
    "                                  density = True)\n",
    "    print(counts_2)\n",
    "    ax_tmp.hist(bins[:-1], \n",
    "                bins, \n",
    "                weights = choice_p_up * counts_2,\n",
    "                alpha = 0.2, \n",
    "                color = 'black',\n",
    "                edgecolor = 'none',\n",
    "                zorder = -1)\n",
    "    \n",
    "    ax_tmp = ax.twinx()\n",
    "    ax_tmp.set_ylim(y_limh - params[1] - 0.1,y_liml - params[1] - 0.1)\n",
    "    ax_tmp.set_yticks([])\n",
    "    \n",
    "    counts, bins = np.histogram(tmp_true[tmp_true[:, 1] == -1, 0],\n",
    "                                bins = np.linspace(0, 10, 100))\n",
    "    \n",
    "    counts_2, bins = np.histogram(tmp_true[tmp_true[:, 1] == -1, 0],\n",
    "                                  bins = np.linspace(0, 10, 100),\n",
    "                                  density = True)\n",
    "    ax_tmp.hist(bins[:-1], \n",
    "                bins, \n",
    "                weights = (1 - choice_p_up) * counts_2,\n",
    "                alpha = 0.2, \n",
    "                color = 'black',\n",
    "                edgecolor = 'none',\n",
    "                zorder = -1)\n",
    "    \n",
    "    # Plot true data  \n",
    "    if model == 'weibull_cdf':\n",
    "        b = params[1] * bf.weibull_cdf(t = t_s, \n",
    "                                       alpha = params[4],\n",
    "                                       beta = params[5])\n",
    "    if model == 'angle':\n",
    "        b = np.maximum(params[1] + bf.angle(t = t_s, theta = params[4]), 0)\n",
    "        \n",
    "    if model == 'ddm' or model == 'ornstein':\n",
    "        b = np.zeros(len(t_s))\n",
    "        \n",
    "        cnt = 0\n",
    "        for t_i in t_s:\n",
    "            b[cnt] = params[1] * bf.constant(t = t_i)\n",
    "            cnt += 1\n",
    "        print(b)\n",
    "\n",
    "    start_point_tmp = - params[1] + \\\n",
    "                      (2 * params[1] * params[2])\n",
    "    slope_tmp = params[0]\n",
    "    \n",
    "    for i in range(len(t_s)):\n",
    "        if model != 'ornstein':\n",
    "            #print('passed')\n",
    "            if (start_point_tmp + (slope_tmp * t_s[i])) > b[i]:\n",
    "                maxid = i\n",
    "                print('maxid: ', maxid)\n",
    "                break\n",
    "        else:\n",
    "            if (start_point_tmp + (slope_tmp * t_s[i])) > b[i]:\n",
    "                maxid = i\n",
    "                print('maxid: ', maxid)\n",
    "                break\n",
    "\n",
    "    ax.plot(t_s + ndt, b, 'black', \n",
    "            t_s + ndt, -b, 'black', \n",
    "            alpha = 1,\n",
    "            linewidth = 3,\n",
    "            zorder = 1000)\n",
    "    \n",
    "    if model == 'ornstein':\n",
    "        # start_point_tmp * np.exp( - params[3] * t_s[:maxid]) + slope_tmp * (1 - np.exp( - params[3] * t_s[:maxid]))\n",
    "        ax.plot(t_s[:maxid] + ndt, \n",
    "                start_point_tmp + slope_tmp * (1 - np.exp( - params[3] * t_s[:maxid])), \n",
    "                'black', \n",
    "                alpha = 1, \n",
    "                linewidth = 3, \n",
    "                zorder = 1000)\n",
    "    else:\n",
    "        ax.plot(t_s[:maxid] + ndt, \n",
    "                start_point_tmp + (slope_tmp * t_s[:maxid]),\n",
    "                'black',\n",
    "                alpha = 1,\n",
    "                linewidth = 3,\n",
    "                zorder = 1000)\n",
    "        \n",
    "    ax.set_zorder(ax_tmp.get_zorder() + 1)\n",
    "    ax.patch.set_visible(False)\n",
    "    print('passed through')\n",
    "\n",
    "    #ax[row_tmp, col_tmp].legend(labels = [model, 'bg_stn'], fontsize = 20)\n",
    "    ax.set_xlabel('rt', \n",
    "                  fontsize = 20);\n",
    "    ax.set_ylabel('', \n",
    "                  fontsize = 20);\n",
    "    ax.set_title(model,\n",
    "                 fontsize = 20)\n",
    "    ax.tick_params(axis = 'y', \n",
    "                   size = 20)\n",
    "    ax.tick_params(axis = 'x', \n",
    "                   size = 20)\n",
    "    \n",
    "    plt.tight_layout(rect = [0, 0.03, 1, 0.9])\n",
    "    if save:\n",
    "        plt.savefig(os.getcwd() + '/figures/' + figure_name + model,\n",
    "                    dpi = 150, \n",
    "                    transparent = False,\n",
    "                    bbox_inches = 'tight',\n",
    "                    bbox_extra_artists = [my_suptitle])\n",
    "    if show:\n",
    "        return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p up:  0.73065\n",
      "[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 6.77478957e-04 1.30075960e-01 4.87107370e-01\n",
      " 7.05255594e-01 8.21104496e-01 8.46848696e-01 8.28556765e-01\n",
      " 8.08232396e-01 7.71648532e-01 7.09320468e-01 6.42250051e-01\n",
      " 5.79244508e-01 5.26401150e-01 4.42393759e-01 4.15294601e-01\n",
      " 3.27222336e-01 2.66926709e-01 2.16793266e-01 1.53110244e-01\n",
      " 9.48470540e-02 6.77478957e-02 3.86163006e-02 1.42270581e-02\n",
      " 2.70991583e-03 2.70991583e-03 6.77478957e-04 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
      "maxid:  147\n",
      "passed through\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgn_boundary_posterior_plot(params = [0.5, 1.25, 0.5, 0.5, 2, 2],\n",
    "                            model = 'weibull_cdf',\n",
    "                            max_t = 10,\n",
    "                            show = True,\n",
    "                            save = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p up:  0.74045\n",
      "[0.         0.         0.         0.         0.         0.00200554\n",
      " 0.14840975 0.50004727 0.70327504 0.84098859 0.83296644 0.79486123\n",
      " 0.71129718 0.66450132 0.61168884 0.55152272 0.50138429 0.42851644\n",
      " 0.37102438 0.32155446 0.31954892 0.25604025 0.2212776  0.19787967\n",
      " 0.16445405 0.13905058 0.11698967 0.09827132 0.0862381  0.06284016\n",
      " 0.06551421 0.04077926 0.02941455 0.03543116 0.02406645 0.01938686\n",
      " 0.01002769 0.00601661 0.00869066 0.00334256 0.00467959 0.00200554\n",
      " 0.00133702 0.00133702 0.00133702 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.        ]\n",
      "maxid:  178\n",
      "passed through\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgn_boundary_posterior_plot(params = [0.5, 1.25, 0.5, 0.5, 0.2],\n",
    "                            model = 'angle',\n",
    "                            max_t = 10,\n",
    "                            show = True,\n",
    "                            save = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p up:  0.78435\n",
      "[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 1.26251355e-03 1.16782503e-01 3.99585538e-01\n",
      " 5.65606070e-01 6.28100491e-01 6.41988140e-01 6.36306829e-01\n",
      " 5.95275139e-01 5.22680610e-01 5.04374163e-01 4.53873621e-01\n",
      " 4.22942039e-01 3.90747944e-01 3.65497673e-01 3.30147293e-01\n",
      " 2.90378116e-01 2.64496589e-01 2.40508831e-01 2.23464898e-01\n",
      " 2.12733533e-01 1.73595613e-01 1.74858127e-01 1.43926545e-01\n",
      " 1.35720207e-01 1.32563923e-01 1.33195179e-01 1.07944908e-01\n",
      " 1.01001084e-01 1.00369827e-01 8.14321240e-02 6.81757317e-02\n",
      " 6.69132181e-02 5.30255691e-02 4.86067717e-02 4.92380284e-02\n",
      " 4.22942039e-02 4.67130013e-02 3.59816362e-02 3.09315820e-02\n",
      " 3.09315820e-02 3.03003252e-02 2.77752981e-02 2.71440413e-02\n",
      " 2.14627303e-02 1.38876490e-02 2.02002168e-02 1.45189058e-02\n",
      " 1.07313652e-02 1.51501626e-02 1.51501626e-02 1.07313652e-02\n",
      " 1.57814194e-02 1.07313652e-02 7.57508130e-03 8.20633807e-03\n",
      " 6.94382452e-03 6.31256775e-03 7.57508130e-03 4.41879742e-03\n",
      " 3.15628387e-03 6.94382452e-03 5.68131097e-03 5.68131097e-03\n",
      " 3.78754065e-03 5.05005420e-03 4.41879742e-03 3.15628387e-03\n",
      " 3.15628387e-03 3.15628387e-03 0.00000000e+00 3.15628387e-03\n",
      " 1.89377032e-03 1.26251355e-03 1.26251355e-03 0.00000000e+00\n",
      " 6.31256775e-04 0.00000000e+00 6.31256775e-04 1.26251355e-03\n",
      " 1.89377032e-03 6.31256775e-04 0.00000000e+00 0.00000000e+00\n",
      " 1.26251355e-03 0.00000000e+00 6.31256775e-04 0.00000000e+00\n",
      " 1.26251355e-03 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 6.31256775e-04 6.31256775e-04 0.00000000e+00]\n",
      "[1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25]\n",
      "maxid:  251\n",
      "passed through\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgn_boundary_posterior_plot(params = [0.5, 1.25, 0.5, 0.5],\n",
    "                            model = 'ddm',\n",
    "                            max_t = 10,\n",
    "                            show = True,\n",
    "                            save = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p up:  0.7489\n",
      "[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 2.64387769e-03 1.77800774e-01 5.55875284e-01\n",
      " 7.58792896e-01 8.24228869e-01 7.89197490e-01 7.44912538e-01\n",
      " 7.01949526e-01 6.00821204e-01 5.23487782e-01 4.54085993e-01\n",
      " 3.89310989e-01 3.45026038e-01 3.31145680e-01 2.82894913e-01\n",
      " 2.65048738e-01 2.28695420e-01 1.97629857e-01 1.81766591e-01\n",
      " 1.82427560e-01 1.44091334e-01 1.16991588e-01 1.20957404e-01\n",
      " 1.02450260e-01 9.78234744e-02 6.87408199e-02 6.21311257e-02\n",
      " 7.00627587e-02 6.54359728e-02 4.23020430e-02 6.08091868e-02\n",
      " 4.09801042e-02 4.29630124e-02 3.43704099e-02 3.96581653e-02\n",
      " 3.30484711e-02 2.90826546e-02 1.65242355e-02 2.04900521e-02\n",
      " 2.11510215e-02 9.25357191e-03 1.32193884e-02 1.85071438e-02\n",
      " 1.18974496e-02 8.59260248e-03 7.93163306e-03 7.93163306e-03\n",
      " 2.64387769e-03 7.27066364e-03 7.27066364e-03 2.64387769e-03\n",
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      " 1.98290827e-03 1.32193884e-03 1.32193884e-03 1.98290827e-03\n",
      " 6.60969422e-04 6.60969422e-04 1.32193884e-03 6.60969422e-04\n",
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      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
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      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
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      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
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      " 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25\n",
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      "maxid:  251\n",
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      " -1.16005846 -1.16837954 -1.17674233 -1.18514703 -1.19359387 -1.20208304\n",
      " -1.21061477 -1.21918926 -1.22780673 -1.2364674  -1.24517148]\n",
      "passed through\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgn_boundary_posterior_plot(params = [0.5, 1.25, 0.5, -0.5, 0.5],\n",
    "                            model = 'ornstein',\n",
    "                            max_t = 10,\n",
    "                            show = True,\n",
    "                            save = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " hist_arr, bins_arr = np.histogram(tmp_true[tmp_true[:, 1] == 1, 0], \n",
    "                            bins = np.linspace(0, 10, 100),\n",
    "                            density = True)\n",
    "    ax_tmp.hist(hist_arr / 10, \n",
    "                bins = bins_arr, \n",
    "                density = False, \n",
    "                alpha = 0.2,\n",
    "                color = 'black',\n",
    "                edgecolor='none',\n",
    "                zorder = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = np.histogram(np.random.uniform(size = 1000, high = 10, low = 0),\n",
    "                    np.linspace(0, 10, 100),\n",
    "                    density = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = np.histogram(np.random.uniform(size = 1000, high = 10, low = 0),\n",
    "                    density = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.594 , 1.1979, 1.089 , 0.8316, 0.693 , 0.9009, 1.0296, 0.2376,\n",
       "        0.594 , 0.3168, 1.3068, 0.8019, 0.9702, 0.4851, 0.8712, 0.8316,\n",
       "        0.4752, 1.0692, 1.1979, 0.7128, 0.5544, 0.9801, 0.792 , 1.089 ,\n",
       "        0.7128, 0.7128, 1.089 , 1.3365, 1.287 , 0.4158, 0.3564, 1.386 ,\n",
       "        0.4752, 1.1583, 0.6237, 0.693 , 1.287 , 0.6336, 1.089 , 2.0196,\n",
       "        1.5048, 1.8018, 1.4157, 0.9504, 1.287 , 0.4455, 1.8018, 1.683 ,\n",
       "        0.2376, 1.3464, 0.792 , 0.99  , 0.3564, 1.2474, 2.079 , 2.1879,\n",
       "        0.6534, 0.3564, 1.9305, 1.3068, 0.7128, 0.7623, 1.386 , 1.1583,\n",
       "        1.584 , 1.188 , 0.594 , 0.8019, 2.1384, 1.683 , 1.0692, 0.4455,\n",
       "        0.7623, 1.089 , 0.4158, 1.6335, 1.188 , 0.7128, 1.089 , 0.792 ,\n",
       "        1.386 , 0.693 , 1.0395, 1.1088, 0.3564, 1.3068, 1.188 , 1.1979,\n",
       "        1.1583, 1.0296, 1.6731, 0.9504, 1.0395, 0.594 , 0.9801, 1.0692,\n",
       "        1.9602, 0.6336, 0.792 ]),\n",
       " array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "         0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "         1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "         1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "         2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "         2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "         3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "         3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "         4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "         4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "         5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "         5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "         6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "         6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "         7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "         7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "         8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "         8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "         9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "         9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ]),\n",
       " <a list of 99 Patch objects>)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts, bins = np.histogram(np.random.uniform(size = 1000, \n",
    "                                              high = 10, \n",
    "                                              low = 0), \n",
    "                            bins  = np.linspace(0, 10, 100))\n",
    "counts_2, bins = np.histogram(np.random.uniform(size = 1000, \n",
    "                                              high = 10, \n",
    "                                              low = 0), \n",
    "                                bins  = np.linspace(0, 10, 100),\n",
    "                            density = True)\n",
    "plt.hist(bins[:-1], bins, weights = counts * counts_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0891, 0.0495, 0.1287, 0.0792, 0.1089, 0.1089, 0.1188, 0.1584,\n",
       "       0.0693, 0.0693, 0.0594, 0.099 , 0.0495, 0.0891, 0.1188, 0.0693,\n",
       "       0.1287, 0.0594, 0.0891, 0.0792, 0.1485, 0.0495, 0.0891, 0.1287,\n",
       "       0.099 , 0.0891, 0.0891, 0.0594, 0.1089, 0.1386, 0.0594, 0.0891,\n",
       "       0.099 , 0.1386, 0.0495, 0.0693, 0.1485, 0.1287, 0.1683, 0.1386,\n",
       "       0.0792, 0.099 , 0.1485, 0.1188, 0.0891, 0.1089, 0.1485, 0.0891,\n",
       "       0.1089, 0.1584, 0.1188, 0.1287, 0.1584, 0.1287, 0.1287, 0.0891,\n",
       "       0.0891, 0.0396, 0.0693, 0.1188, 0.1683, 0.099 , 0.0297, 0.0495,\n",
       "       0.099 , 0.0297, 0.0792, 0.1287, 0.0594, 0.0792, 0.1188, 0.1188,\n",
       "       0.1386, 0.1089, 0.0792, 0.0693, 0.099 , 0.0792, 0.1188, 0.0495,\n",
       "       0.1386, 0.0594, 0.099 , 0.1188, 0.1188, 0.0891, 0.099 , 0.1683,\n",
       "       0.0594, 0.0792, 0.1089, 0.099 , 0.1188, 0.1287, 0.0891, 0.0594,\n",
       "       0.0891, 0.1188, 0.1287])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 10, 100)"
   ]
  }
 ],
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