test_script_sanity_checks_ddm_cddm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cddm_data_simulation as cds\n",
    "import ddm_data_simulation as ds\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import boundary_functions as bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DDM\n",
    "#cdat = cds.ddm(a = 3, v = 1, n_samples = 100000)\n",
    "#cdat = cds.ddm_flexbound(a = 1, w = 0.7, v = 0.5, boundary_fun = bf.constant, n_samples = 100000)\n",
    "# cdat = cds.full_ddm(v = 0.5,\n",
    "#                     w = 0.7, \n",
    "#                     a = 2, \n",
    "#                     dw = 0.05, \n",
    "#                     sdv = 0.1,\n",
    "#                     boundary_fun = bf.constant, \n",
    "#                     boundary_params = {}, \n",
    "#                     n_samples = 100000)\n",
    "# cdat = cds.ornstein_uhlenbeck(a = 0.6, \n",
    "#                               v = 1,\n",
    "#                               g = -3, \n",
    "#                               n_samples = 5000, \n",
    "#                               boundary_fun = bf.constant, \n",
    "#                               boundary_params = {})\n",
    "# cdat = cds.race_model(v = np.array([0.3, 0.3, 0], dtype = np.float32),\n",
    "#                       w = np.array([0, 0.5, 0.7], dtype = np.float32),\n",
    "#                       a = 4.0, \n",
    "#                       boundary_fun = bf.constant,\n",
    "#                       boundary_params = {},\n",
    "#                       n_samples = 20000)\n",
    "# cdat = cds.lca(v = np.array([0, 1, 0], dtype = np.float32),\n",
    "#                w = np.array([0.5, 0.7, 0.5], dtype = np.float32),\n",
    "#                a = 2,\n",
    "#                g = -1,\n",
    "#                b = 2,\n",
    "#                boundary_fun = bf.constant,\n",
    "#                boundary_params = {},\n",
    "#                n_samples = 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([47., 85., 87., 39., 34., 24., 22., 10., 10.,  9.,  4.,  5.,  2.,\n",
       "         1.,  0.,  0.,  2.,  1.,  0.,  1.]),\n",
       " array([0.09600002, 0.37033892, 0.6446778 , 0.9190167 , 1.1933556 ,\n",
       "        1.4676945 , 1.7420334 , 2.0163722 , 2.2907112 , 2.5650501 ,\n",
       "        2.839389  , 3.1137278 , 3.3880668 , 3.6624057 , 3.9367447 ,\n",
       "        4.2110834 , 4.4854226 , 4.7597613 , 5.0341    , 5.3084393 ,\n",
       "        5.582778  ], dtype=float32),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cdat[0][cdat[1] == 0], alpha = 0.3, bins = 20)\n",
    "plt.hist(cdat[0][cdat[1] == 1], alpha = 0.3, bins = 20)\n",
    "plt.hist(cdat[0][cdat[1] == 2], alpha = 0.3, bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(cdat[0] * cdat[1], bins = np.arange(-15, 15, 0.5))\n",
    "plt.xlim(- 15, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(dat[0] * dat[1], bins = np.arange(-15, 15, 0.5))\n",
    "plt.xlim(- 15, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DDM Flexbound\n",
    "cdat = cds.ddm_flexbound(a = 1, boundary_fun = bf.constant, n_samples = 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = ds.ddm_flexbound_simulate(a = 1, boundary_fun = bf.constant, boundary_params = {}, n_samples = 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(cdat[0] * cdat[1], bins = np.arange(-15, 15, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(dat[0] * dat[1], bins = np.arange(-15, 15, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Full ddm\n",
    "dat = ds.full_ddm(boundary_fun = bf.constant,\n",
    "                  boundary_params = {}, n_samples = 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdat = cds.full_ddm(boundary_fun = bf.constant, \n",
    "                    boundary_params = {}, n_samples = 5000,\n",
    "                    w = 0.7, v = 0.5, a = 2, dw = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(dat[0] * dat[1], bins = np.arange(-5, 5, 0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(cdat[0] * cdat[1], bins = np.arange(-5, 4, 0.25))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}