dataloaders.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important links\n",
    "\n",
    "1. https://moonbooks.org/Articles/How-to-put-the-origin-in-the-center-of-the-figure-with-matplotlib-/\n",
    "2. https://stackoverflow.com/questions/19677963/matplotlib-keep-grid-lines-behind-the-graph-but-the-y-and-x-axis-above\n",
    "3. https://stackoverflow.com/questions/34025306/how-to-use-matplotlib-quiver-scale\n",
    "4. https://stackoverflow.com/questions/42281966/how-to-plot-vectors-in-python-using-matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataloaders.ipynb      lorentz_attractor.mp4  \u001b[1m\u001b[36msrc\u001b[m\u001b[m/\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src import data_io, hyperparams, main, model, ops, utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mdata_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataGenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_interval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'square'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      <no docstring>\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Args:\n",
       "    num_interval : number of interval steps to take\n",
       "    min : minimum length of the playground\n",
       "    max : maximum length of the playground\n",
       "    shape : shape of the playground\n",
       "Returns:\n",
       "    ....\n",
       "\u001b[0;31mFile:\u001b[0m           ~/Google Drive/MS@NCState/research@NCState/research@tianfu_wu/GridTr/AOGTransformer/GridTr/src/data_io.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_io.DataGenerator?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_velocity_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minterval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "\u001b[0;32mdef\u001b[0m \u001b[0mgenerate_velocity_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minterval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"## VERIFIED 🎯🎯\u001b[0m\n",
       "\u001b[0;34m    Args:\u001b[0m\n",
       "\u001b[0;34m        max_velocity : maximum velocity in the experiment\u001b[0m\n",
       "\u001b[0;34m        min_velocity : minimum velocity in the experiment\u001b[0m\n",
       "\u001b[0;34m        interval : step of unit motion\u001b[0m\n",
       "\u001b[0;34m        verbose : If True, then plot all the velocities in the generated velocity list.\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m    Returns:\u001b[0m\n",
       "\u001b[0;34m        [a list of possible motion velocities]\u001b[0m\n",
       "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvelocity_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m## converting max velocity to integer\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_velocity_int\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_velocity\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m## sampling the unit-motion in x-direction\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_velocity_int\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m## sampling the unit-motion in y-direction\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_velocity_int\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mspeed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mspeed\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mmax_velocity\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mspeed\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;31m## stores movement along (-x) axis\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mvelocity_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mif\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;31m## stores movement along (-y) axis\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mvelocity_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;31m## stores diagonal movement along (-x) & (-y) axes\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mvelocity_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvelocity_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0morigin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquiver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"xy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"blue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axisbelow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"equal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"left\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"bottom\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"right\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"top\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mvelocity_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Google Drive/MS@NCState/research@NCState/research@tianfu_wu/GridTr/AOGTransformer/GridTr/src/utils.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.generate_velocity_list??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(28, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = utils.generate_velocity_list(3, 0, 1)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(48, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = utils.generate_velocity_list(5.4, 2.5, 1.3)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'xy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Return coordinate matrices from coordinate vectors.\n",
       "\n",
       "Make N-D coordinate arrays for vectorized evaluations of\n",
       "N-D scalar/vector fields over N-D grids, given\n",
       "one-dimensional coordinate arrays x1, x2,..., xn.\n",
       "\n",
       ".. versionchanged:: 1.9\n",
       "   1-D and 0-D cases are allowed.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "x1, x2,..., xn : array_like\n",
       "    1-D arrays representing the coordinates of a grid.\n",
       "indexing : {'xy', 'ij'}, optional\n",
       "    Cartesian ('xy', default) or matrix ('ij') indexing of output.\n",
       "    See Notes for more details.\n",
       "\n",
       "    .. versionadded:: 1.7.0\n",
       "sparse : bool, optional\n",
       "    If True a sparse grid is returned in order to conserve memory.\n",
       "    Default is False.\n",
       "\n",
       "    .. versionadded:: 1.7.0\n",
       "copy : bool, optional\n",
       "    If False, a view into the original arrays are returned in order to\n",
       "    conserve memory.  Default is True.  Please note that\n",
       "    ``sparse=False, copy=False`` will likely return non-contiguous\n",
       "    arrays.  Furthermore, more than one element of a broadcast array\n",
       "    may refer to a single memory location.  If you need to write to the\n",
       "    arrays, make copies first.\n",
       "\n",
       "    .. versionadded:: 1.7.0\n",
       "\n",
       "Returns\n",
       "-------\n",
       "X1, X2,..., XN : ndarray\n",
       "    For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,\n",
       "    return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'\n",
       "    or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'\n",
       "    with the elements of `xi` repeated to fill the matrix along\n",
       "    the first dimension for `x1`, the second for `x2` and so on.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "This function supports both indexing conventions through the indexing\n",
       "keyword argument.  Giving the string 'ij' returns a meshgrid with\n",
       "matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing.\n",
       "In the 2-D case with inputs of length M and N, the outputs are of shape\n",
       "(N, M) for 'xy' indexing and (M, N) for 'ij' indexing.  In the 3-D case\n",
       "with inputs of length M, N and P, outputs are of shape (N, M, P) for\n",
       "'xy' indexing and (M, N, P) for 'ij' indexing.  The difference is\n",
       "illustrated by the following code snippet::\n",
       "\n",
       "    xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')\n",
       "    for i in range(nx):\n",
       "        for j in range(ny):\n",
       "            # treat xv[i,j], yv[i,j]\n",
       "\n",
       "    xv, yv = np.meshgrid(x, y, sparse=False, indexing='xy')\n",
       "    for i in range(nx):\n",
       "        for j in range(ny):\n",
       "            # treat xv[j,i], yv[j,i]\n",
       "\n",
       "In the 1-D and 0-D case, the indexing and sparse keywords have no effect.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "index_tricks.mgrid : Construct a multi-dimensional \"meshgrid\"\n",
       "                 using indexing notation.\n",
       "index_tricks.ogrid : Construct an open multi-dimensional \"meshgrid\"\n",
       "                 using indexing notation.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> nx, ny = (3, 2)\n",
       ">>> x = np.linspace(0, 1, nx)\n",
       ">>> y = np.linspace(0, 1, ny)\n",
       ">>> xv, yv = np.meshgrid(x, y)\n",
       ">>> xv\n",
       "array([[0. , 0.5, 1. ],\n",
       "       [0. , 0.5, 1. ]])\n",
       ">>> yv\n",
       "array([[0.,  0.,  0.],\n",
       "       [1.,  1.,  1.]])\n",
       ">>> xv, yv = np.meshgrid(x, y, sparse=True)  # make sparse output arrays\n",
       ">>> xv\n",
       "array([[0. ,  0.5,  1. ]])\n",
       ">>> yv\n",
       "array([[0.],\n",
       "       [1.]])\n",
       "\n",
       "`meshgrid` is very useful to evaluate functions on a grid.\n",
       "\n",
       ">>> import matplotlib.pyplot as plt\n",
       ">>> x = np.arange(-5, 5, 0.1)\n",
       ">>> y = np.arange(-5, 5, 0.1)\n",
       ">>> xx, yy = np.meshgrid(x, y, sparse=True)\n",
       ">>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)\n",
       ">>> h = plt.contourf(x,y,z)\n",
       ">>> plt.show()\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.meshgrid?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m80\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'square'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "## VERIFIED 🎯🎯\n",
       "Args:\n",
       "    size : size of the playground\n",
       "    shape : dimensions of the playground (eg.: square, rect, circle, triangle....etc)\n",
       "    verbose : whether to show the plot of the mask or not\n",
       "\n",
       "Returns:\n",
       "    [a numpy.ndarray of bools]\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Google Drive/MS@NCState/research@NCState/research@tianfu_wu/GridTr/AOGTransformer/GridTr/src/utils.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.shape_mask?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = utils.shape_mask(size=200, shape=\"circle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200, 200), numpy.ndarray)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape, type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('bool')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0+x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ffe9d1a6c10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.imshow(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mdata_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataGenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_interval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'square'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      <no docstring>\n",
       "\u001b[0;31mSource:\u001b[0m        \n",
       "\u001b[0;32mclass\u001b[0m \u001b[0mDataGenerator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_interval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmin\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmax\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"square\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"\u001b[0m\n",
       "\u001b[0;34m        Args:\u001b[0m\n",
       "\u001b[0;34m            num_interval : number of interval steps to take\u001b[0m\n",
       "\u001b[0;34m            min : minimum length of the playground\u001b[0m\n",
       "\u001b[0;34m            max : maximum length of the playground\u001b[0m\n",
       "\u001b[0;34m            shape : shape of the playground\u001b[0m\n",
       "\u001b[0;34m        Returns:\u001b[0m\n",
       "\u001b[0;34m            ....\u001b[0m\n",
       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmax_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmin_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_steps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mtest\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mvisualize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmotion_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"continuous\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"\u001b[0m\n",
       "\u001b[0;34m        Args:\u001b[0m\n",
       "\u001b[0;34m            num_data : number of data samples\u001b[0m\n",
       "\u001b[0;34m            max_velocity : maximum allowed velocity (i.e. steps)\u001b[0m\n",
       "\u001b[0;34m            min_velocity : minimum allowed velocity\u001b[0m\n",
       "\u001b[0;34m            num_steps : number of allowed steps to be taken at a time instant\u001b[0m\n",
       "\u001b[0;34m            dtype : type of dataset needed\u001b[0m\n",
       "\u001b[0;34m            test : Is it in_training? or in_testing? phase\u001b[0m\n",
       "\u001b[0;34m            visualize :\u001b[0m\n",
       "\u001b[0;34m            motion_type : Is the motion \"continuous\" or \"discrete\"?\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m        Raises: [NotImplementedError]\u001b[0m\n",
       "\u001b[0;34m        Returns: place_pair ::\u001b[0m\n",
       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mplace_pair\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen_2D_multi_dtype1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mplace_pair\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen_2D_multi_dtype2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmax_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_velocity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmin_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_velocity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvisualize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvisualize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmotion_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmotion_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mplace_pair\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mgen_2D_multi_dtype1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"## VERIFIED 🎯🎯\u001b[0m\n",
       "\u001b[0;34m        Args:\u001b[0m\n",
       "\u001b[0;34m            num_data : number of positions and velocity data points\u001b[0m\n",
       "\u001b[0;34m        Raise: NotImplementedError\u001b[0m\n",
       "\u001b[0;34m        Returns:\u001b[0m\n",
       "\u001b[0;34m            place_pair : a dict of before & after positions (mu) and velocities\u001b[0m\n",
       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m# 🎯 Generating random before and after positions (mu) of motion\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m##   used to calculate velocity & direction\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"square\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## 🎯  Square Playground\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_before\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_after\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"circle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## 🎯  Circular Playground\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## \"factor\" determines how much datapoints are thrown out of the playable area\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mfactor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mradius\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m  \u001b[0;31m## diameter of the playground is 1; so radius is 0.5\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0minside_circle_condition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minside_circle_condition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m]\u001b[0m  \u001b[0;31m## selecting twice the samples; half for mu_before and half for mu_after\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_before\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_after\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"triangle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## 🎯  Triangular Playground\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mfactor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4.2\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0minside_triangle_condition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minside_triangle_condition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_before\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_after\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m## 🎯 calculate velocity\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m## multiplying with len_interval to balance mu_interval magnification\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m## while generating mu_before and mu_after\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_after\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu_before\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_before\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_after\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mplace_pair\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"mu_before\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmu_before\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"mu_after\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmu_after\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"velocity\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mplace_pair\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mgen_2D_multi_dtype2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmax_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmin_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_steps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mtest\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mvisualize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmotion_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"## VERIFIED 🎯🎯\u001b[0m\n",
       "\u001b[0;34m        Samples discretized motions and their corresponding place pairs\u001b[0m\n",
       "\u001b[0;34m        Args:\u001b[0m\n",
       "\u001b[0;34m            num_data :\u001b[0m\n",
       "\u001b[0;34m            max_velocity :\u001b[0m\n",
       "\u001b[0;34m            min_velocity :\u001b[0m\n",
       "\u001b[0;34m            num_steps :\u001b[0m\n",
       "\u001b[0;34m            test :\u001b[0m\n",
       "\u001b[0;34m            visualize :\u001b[0m\n",
       "\u001b[0;34m            motion_type :\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m        Raises: NotImplementedError\u001b[0m\n",
       "\u001b[0;34m        Returns:\u001b[0m\n",
       "\u001b[0;34m            place_pair : a dict of before and after positions (mu) and velcities & its indices\u001b[0m\n",
       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmotion_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"discrete\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_velocity_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmax_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_velocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_velocity\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mnum_velocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mnum_velocity\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0mnum_steps\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## We have more velocities than required.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## So, sample it to make (size = [num_data, num_steps])\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnum_velocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## We don't have enough velocity data;\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## So we generate it by iterative multiplication\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## size=[num_velocity**num_steps, num_steps]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_velocity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepeat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_velocity_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_list\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m## num_velocity ** num_steps\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mquotient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_data\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mnum_velocity_list\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mremainder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_data\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mnum_velocity_list\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                        \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquotient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                        \u001b[0mvelocity_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                            \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_velocity_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mremainder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                        \u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## indexing the velocities. same as -> velocity[velocity_idx, ...]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## velocity grid contains the various step motions (i.e. velocities)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## for every data point (num_data). Its shape is [num_data, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## here 2 represents the velocities in x & y direction\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"velocity.shape = {velocity.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"velocity_idx.shape = {velocity_idx.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"velocity_grid.shape = {velocity_grid.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## magnifying the velocity's speed by the interval length\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## i.e. magnifying by the step size of each motion step\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## calculating the cummulative displacement in x & y direction (axis=1)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## for each data point. shape=[num_data, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## NOTE: velocity in unit-time in x-axis gives displacement in x-axis ;\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m##       similar rule for y-axis\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity_grid_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## finding the max & min displacement along x & y axes for every data point\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_max & mu_min has the same shape as velocity_grid_cumsum (i.e. [num_data,2])\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## sampling the start displacements by randomly scaling\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## the calculated min & max displacements in each x & y-axis.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_start has shape=[num_data, 1, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmu_max\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu_min\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m## == num_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## assert mu_start.shape == velocity_grid_cumsum.shape  ## == [num_data, 1, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## adding aggregated velocities into starting positions to get the\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## next positions for every step.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_steps.shape = [num_data, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## creating a sequence of positions including the starting position\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_sequence.shape = [num_data, num_steps+1, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmotion_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"continuous\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"square\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_data_sample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"circle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## area_of_square / area_of_incircle = 1.27 ~= 1.5\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## (we choose a bit higher value for safety)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_data_sample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"triangle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## area_of_square / are_of_equilateral_traingle_inside_square = 4\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_data_sample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## sampling theta in range [-π, +π)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mtheta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data_sample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## sampling random distance for each step of each data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## and scaling it by the max_velocity limit and\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## shift it by min_velocity limit\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mdistance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data_sample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_velocity\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m+\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## calculating the velocity components from the random distances\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## vel_x = r*cos(theta)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## vel_y = r*sin(theta)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvel_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvel_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## concatenation the vel_x & vel_y. Shape=[num_data_sample, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvel_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvel_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity_sequence_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_sequence_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_sequence_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data_sample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmu_max\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu_min\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m+\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_sequence\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m## == num_data_sample\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_steps is the displacement along x & y-axes for every step of each data point\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## shape = [num_data_sample, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvelocity_sequence\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## concatenate the start position and future positions along the \"num_steps\" axis\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## this creates a sequence of motion for every step (including the starting position)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## shape = [num_data_sample, num_steps+1, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## magnifying the speed of each data for every step\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## in x & y axis by the length of allowed step size\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## shape=[num_data_sample, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_sequence\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"circle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## modification for circular playground\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mradius\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence_magnified\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_magnified\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_magnified\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## creates a mask for every step of all data points\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## True  = if a particular step for a data point lies outside the circle\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## False = if a particular step for a data point lies inside the circle\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## only select those data points \"nonw of whose steps are outside\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## the circle boundary.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## (that's why we sum across axis=-1 i.e. the num_steps dimension)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## pick the data points whose v=every step-motion is inside the circle\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## potentially we could have more valid samples; so we pick only \"num_data\" data-points\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## NOTE: Earlier we had defined \"num_data_sample\" and \"num_data\". REMEMBER!!\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"triangle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence_magnified\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_magnified\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_magnified\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnum_steps\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## testing mode\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_velocity_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmax_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_velocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_velocity\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mnum_velocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mvisualize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## length of playground is 80x80; so the center is (40,40)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m40\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## sampling 10X more data points\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mvelocity_valid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu_end\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmu_sequence\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_sequence_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_sequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_idx_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence_reshaped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mmu_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnum_steps\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_reshaped\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## This is the code for testing phase\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnum_velocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_max\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselect_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmu_max\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmu_min\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_sequence\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mplace_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"mu_sequence\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"velocity\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"velocity_idx\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mplace_sequence\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m           ~/Google Drive/MS@NCState/research@NCState/research@tianfu_wu/GridTr/AOGTransformer/GridTr/src/data_io.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_io.DataGenerator??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=data_io.DataGenerator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = d.gen_2D_multi_dtype1(num_data=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mu_before': array([[480.13519101, 280.977366  ],\n",
       "        [592.45486703, 637.5518623 ],\n",
       "        [745.28471341, 394.72157665],\n",
       "        [915.71914033, 537.45824418],\n",
       "        [945.25548369,  99.649555  ],\n",
       "        [239.05265334, 635.50942224],\n",
       "        [586.77241366, 591.82045424],\n",
       "        [328.31525567,  34.93030244],\n",
       "        [359.82805829,  26.52212151],\n",
       "        [711.34447412,  72.80656686]]),\n",
       " 'mu_after': array([[261.65009358,  56.61596165],\n",
       "        [529.04852502,  85.12269595],\n",
       "        [423.45733423, 883.58135965],\n",
       "        [166.16484952, 594.71200537],\n",
       "        [453.72551535, 592.55533383],\n",
       "        [173.77569421, 316.80599493],\n",
       "        [ 38.68721263, 336.99740623],\n",
       "        [607.57873978, 989.45936657],\n",
       "        [407.40091719, 822.92795408],\n",
       "        [359.40118518, 412.59588394]]),\n",
       " 'velocity': array([0.31348081, 0.55661268, 0.58586935, 0.75249023, 0.69679867,\n",
       "        0.32564542, 0.60503218, 0.99553756, 0.79862406, 0.48969401])}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = y[\"mu_after\"]\n",
    "b = y[\"mu_before\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[261.65009358,  56.61596165],\n",
       "       [529.04852502,  85.12269595],\n",
       "       [423.45733423, 883.58135965],\n",
       "       [166.16484952, 594.71200537],\n",
       "       [453.72551535, 592.55533383],\n",
       "       [173.77569421, 316.80599493],\n",
       "       [ 38.68721263, 336.99740623],\n",
       "       [607.57873978, 989.45936657],\n",
       "       [407.40091719, 822.92795408],\n",
       "       [359.40118518, 412.59588394]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[480.13519101, 280.977366  ],\n",
       "       [592.45486703, 637.5518623 ],\n",
       "       [745.28471341, 394.72157665],\n",
       "       [915.71914033, 537.45824418],\n",
       "       [945.25548369,  99.649555  ],\n",
       "       [239.05265334, 635.50942224],\n",
       "       [586.77241366, 591.82045424],\n",
       "       [328.31525567,  34.93030244],\n",
       "       [359.82805829,  26.52212151],\n",
       "       [711.34447412,  72.80656686]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([313.16733156, 556.05606555, 585.28347783, 751.73773887,\n",
       "       696.10187227, 325.31977495, 604.42714481, 994.54201913,\n",
       "       797.82543645, 489.20431174])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.sqrt(np.sum(np.square(a-b), axis=1))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen_2D_multi_dtype2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_steps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtest\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvisualize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmotion_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "    \u001b[0;32mdef\u001b[0m \u001b[0mgen_2D_multi_dtype2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmax_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmin_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnum_steps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mtest\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mvisualize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmotion_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"## VERIFIED 🎯🎯\u001b[0m\n",
       "\u001b[0;34m        Samples discretized motions and their corresponding place pairs\u001b[0m\n",
       "\u001b[0;34m        Args:\u001b[0m\n",
       "\u001b[0;34m            num_data :\u001b[0m\n",
       "\u001b[0;34m            max_velocity :\u001b[0m\n",
       "\u001b[0;34m            min_velocity :\u001b[0m\n",
       "\u001b[0;34m            num_steps :\u001b[0m\n",
       "\u001b[0;34m            test :\u001b[0m\n",
       "\u001b[0;34m            visualize :\u001b[0m\n",
       "\u001b[0;34m            motion_type :\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m        Raises: NotImplementedError\u001b[0m\n",
       "\u001b[0;34m        Returns:\u001b[0m\n",
       "\u001b[0;34m            place_pair : a dict of before and after positions (mu) and velcities & its indices\u001b[0m\n",
       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmotion_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"discrete\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_velocity_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmax_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_velocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_velocity\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mnum_velocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mnum_velocity\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0mnum_steps\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## We have more velocities than required.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## So, sample it to make (size = [num_data, num_steps])\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnum_velocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## We don't have enough velocity data;\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## So we generate it by iterative multiplication\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## size=[num_velocity**num_steps, num_steps]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_velocity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepeat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_velocity_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_list\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m## num_velocity ** num_steps\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mquotient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_data\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mnum_velocity_list\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mremainder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_data\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mnum_velocity_list\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                        \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquotient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                        \u001b[0mvelocity_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                            \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_velocity_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mremainder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                        \u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## indexing the velocities. same as -> velocity[velocity_idx, ...]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## velocity grid contains the various step motions (i.e. velocities)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## for every data point (num_data). Its shape is [num_data, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## here 2 represents the velocities in x & y direction\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"velocity.shape = {velocity.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"velocity_idx.shape = {velocity_idx.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"velocity_grid.shape = {velocity_grid.shape}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## magnifying the velocity's speed by the interval length\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## i.e. magnifying by the step size of each motion step\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## calculating the cummulative displacement in x & y direction (axis=1)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## for each data point. shape=[num_data, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## NOTE: velocity in unit-time in x-axis gives displacement in x-axis ;\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m##       similar rule for y-axis\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity_grid_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## finding the max & min displacement along x & y axes for every data point\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_max & mu_min has the same shape as velocity_grid_cumsum (i.e. [num_data,2])\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## sampling the start displacements by randomly scaling\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## the calculated min & max displacements in each x & y-axis.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_start has shape=[num_data, 1, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmu_max\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu_min\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m## == num_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## assert mu_start.shape == velocity_grid_cumsum.shape  ## == [num_data, 1, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## adding aggregated velocities into starting positions to get the\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## next positions for every step.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_steps.shape = [num_data, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## creating a sequence of positions including the starting position\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_sequence.shape = [num_data, num_steps+1, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmotion_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"continuous\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"square\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_data_sample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"circle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## area_of_square / area_of_incircle = 1.27 ~= 1.5\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## (we choose a bit higher value for safety)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_data_sample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"triangle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## area_of_square / are_of_equilateral_traingle_inside_square = 4\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnum_data_sample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## sampling theta in range [-π, +π)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mtheta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data_sample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## sampling random distance for each step of each data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## and scaling it by the max_velocity limit and\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## shift it by min_velocity limit\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mdistance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data_sample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_velocity\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m+\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## calculating the velocity components from the random distances\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## vel_x = r*cos(theta)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## vel_y = r*sin(theta)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvel_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvel_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## concatenation the vel_x & vel_y. Shape=[num_data_sample, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvel_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvel_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity_sequence_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_sequence_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_sequence_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data_sample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmu_max\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu_min\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m+\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_sequence\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m## == num_data_sample\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## mu_steps is the displacement along x & y-axes for every step of each data point\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## shape = [num_data_sample, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvelocity_sequence\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## concatenate the start position and future positions along the \"num_steps\" axis\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## this creates a sequence of motion for every step (including the starting position)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## shape = [num_data_sample, num_steps+1, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## magnifying the speed of each data for every step\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## in x & y axis by the length of allowed step size\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## shape=[num_data_sample, num_steps, 2]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_sequence\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"circle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## modification for circular playground\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mradius\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence_magnified\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_magnified\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_magnified\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## creates a mask for every step of all data points\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## True  = if a particular step for a data point lies outside the circle\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## False = if a particular step for a data point lies inside the circle\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## only select those data points \"nonw of whose steps are outside\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## the circle boundary.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## (that's why we sum across axis=-1 i.e. the num_steps dimension)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## pick the data points whose v=every step-motion is inside the circle\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## potentially we could have more valid samples; so we pick only \"num_data\" data-points\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## NOTE: Earlier we had defined \"num_data_sample\" and \"num_data\". REMEMBER!!\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"triangle\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence_magnified\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_magnified\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_magnified\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnum_steps\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m## testing mode\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_velocity_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmax_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_velocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_velocity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_velocity\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mnum_velocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mvisualize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## length of playground is 80x80; so the center is (40,40)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m40\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## sampling 10X more data points\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mvelocity_valid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu_end\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmu_sequence\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_sequence_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_sequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_idx_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence_reshaped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mmu_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnum_steps\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence_reshaped\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m## This is the code for testing phase\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnum_velocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_interval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mselect_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_max\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m:\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid_cumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselect_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmu_max\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu_min\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmu_min\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mmu_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34m(\u001b[0m\u001b[0mmu_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvelocity_grid_cumsum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mvelocity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvelocity_grid\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen_interval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu_sequence\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnum_data\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mplace_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"mu_sequence\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmu_sequence\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"velocity\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"velocity_idx\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvelocity_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mplace_sequence\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Google Drive/MS@NCState/research@NCState/research@tianfu_wu/GridTr/AOGTransformer/GridTr/src/data_io.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.gen_2D_multi_dtype2??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/cdhj2tvbY9y4cbFixYqYNWvWIY/p7u4uCKhdXV3R0tIS0+KyGJqrOex7+/53AIDiONL3wn+Q9cSaeCw6Oztj5MiRfR5b9GtAP/Lmm2/G6tWr45FHHunzuKamphg3blxs3br1sMfU1tZGbW1tsYcIAEAZlKwFv2zZshg9enRcfPHFfR63Y8eO2LZtWzQ1NZVqKAAAVJCSBND9+/fHsmXLYs6cOTF06P8XWXfv3h0LFy6M//qv/4o33ngj1qxZEzNnzoyGhoa44oorSjEUAAAqTEla8KtXr4633norrrvuuoLtQ4YMiU2bNsUDDzwQO3fujKamppg+fXo8/PDDUVdXV4qhAABQYUoSQGfMmBGHWts0fPjweOqpp0rxTwIAUCV8FzwAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJFWyr+IsF9//DgBQfAdmrCN9L/yRqIACAJCUAAoAQFICKAAASQmgAAAkJYACAJCUAAoAQFICKAAASQmgAAAkJYACAJCUAAoAQFICKAAASQmgAAAkJYACAJCUAAoAQFICKAAASQmgAAAkJYACAJCUAAoAQFJDyz2AYnjqnY3lHgIAwKBxYPa6sPlT/X69CigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQlAAKAEBS/Q6gzz77bMycOTOam5sjl8vFypUrC/ZnWRaLFy+O5ubmGD58eEybNi22bNlScEx3d3fcdNNN0dDQECNGjIhLL7003n777eP6IAAAVId+B9A9e/bEpEmTYunSpYfc/8Mf/jDuvPPOWLp0aaxbty4aGxvjggsuiF27duWPmT9/fjz66KOxYsWKeP7552P37t1xySWXxL59+479kwAAUBWG9vcFra2t0draesh9WZbFT37yk/jud78bs2bNioiI+++/P8aMGRMPPfRQ/P3f/310dnbGvffeG7/4xS/iS1/6UkREPPjgg9HS0hKrV6+OCy+88Dg+DgAAla6o14C2tbVFR0dHzJgxI7+ttrY2zjvvvHjhhRciImL9+vXR09NTcExzc3NMmDAhf8zBuru7o6urq+ABAEB1KmoA7ejoiIiIMWPGFGwfM2ZMfl9HR0cMGzYsPvGJTxz2mIMtWbIk6uvr84+WlpZiDhsAgIRKsgo+l8sVPM+yrNe2g/V1zKJFi6KzszP/2LZtW9HGCgBAWkUNoI2NjRERvSqZ27dvz1dFGxsbY+/evfHuu+8e9piD1dbWxsiRIwseAABUp6IG0PHjx0djY2OsWrUqv23v3r2xdu3amDp1akRETJkyJWpqagqOaW9vj82bN+ePAQBg4Or3Kvjdu3fH66+/nn/e1tYWGzdujFGjRsXYsWNj/vz5cfvtt8dpp50Wp512Wtx+++1x4oknxjXXXBMREfX19XH99dfHzTffHCeddFKMGjUqFi5cGBMnTsyvigcAYODqdwB96aWXYvr06fnnCxYsiIiIOXPmxH333Rff+ta34v33348bbrgh3n333Tj77LPjV7/6VdTV1eVf8+Mf/ziGDh0aV111Vbz//vvxxS9+Me67774YMmRIET4SAACVLJdlWVbuQfRXV1dX1NfXx7S4LIbmauKpdzaWe0gAAIPShc2fioiID7KeWBOPRWdn5xHX6/gueAAAkhJAAQBISgAFACApARQAgKQEUAAAkhJAAQBISgAFACApARQAgKQEUAAAkhJAAQBISgAFACApARQAgKQEUAAAkhJAAQBISgAFACApARQAgKQEUAAAkhJAAQBISgAFACApARQAgKQEUAAAkhJAAQBISgAFACApARQAgKQEUAAAkhJAAQBISgAFACApARQAgKQEUAAAkhJAAQBISgAFACApARQAgKQEUAAAkhJAAQBIami5B1AMFzZ/Kv/zU+9sLNs4AAAGgwOz17FQAQUAICkBFACApARQAACSEkABAEhKAAUAICkBFACApARQAACSEkABAEhKAAUAICkBFACApARQAACSEkABAEhKAAUAICkBFACApARQAACSEkABAEhKAAUAICkBFACApIaWewDFdmHzp/I/P/XOxrKNAwBgIDkwYx0vFVAAAJISQAEASEoABQAgKQEUAICkBFAAAJISQAEASEoABQAgKQEUAICkBFAAAJISQAEASEoABQAgqQH3XfAH8r3wAADHrpjf/36gfldAn3322Zg5c2Y0NzdHLpeLlStX5vf19PTEt7/97Zg4cWKMGDEimpub42tf+1q88847Be8xbdq0yOVyBY/Zs2cf94cBAKDy9TuA7tmzJyZNmhRLly7tte+9996LDRs2xPe+973YsGFDPPLII/Hb3/42Lr300l7Hzps3L9rb2/OPe+6559g+AQAAVaXfLfjW1tZobW095L76+vpYtWpVwbaf/exn8dnPfjbeeuutGDt2bH77iSeeGI2Njf395wEAqHIlX4TU2dkZuVwuPv7xjxdsX758eTQ0NMSZZ54ZCxcujF27dh32Pbq7u6Orq6vgAQBAdSrpIqQ//elPccstt8Q111wTI0eOzG+/9tprY/z48dHY2BibN2+ORYsWxSuvvNKrevqRJUuWxG233VbKoQIAkEjJAmhPT0/Mnj079u/fH3fddVfBvnnz5uV/njBhQpx22mlx1llnxYYNG2Ly5Mm93mvRokWxYMGC/POurq5oaWkp1dABACihkgTQnp6euOqqq6KtrS2efvrpgurnoUyePDlqampi69athwygtbW1UVtbW4qhAgCQWNED6Efhc+vWrfHMM8/ESSeddMTXbNmyJXp6eqKpqanYwwEAoML0O4Du3r07Xn/99fzztra22LhxY4waNSqam5vjb/7mb2LDhg3xn//5n7Fv377o6OiIiIhRo0bFsGHD4ne/+10sX748vvzlL0dDQ0O8+uqrcfPNN8enP/3p+NznPle8TwYAQEXqdwB96aWXYvr06fnnH12bOWfOnFi8eHE8/vjjERHxqU99quB1zzzzTEybNi2GDRsWv/71r+OnP/1p7N69O1paWuLiiy+OW2+9NYYMGXIcHwUAgGrQ7wA6bdq0yLLssPv72hcR0dLSEmvXru3vPwsAwAAxoL8L/kC+Fx4A4MhK9f3vByr5jegBAOBAAigAAEkJoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJCWAAgCQ1KC5Ef2B3JQeAOD/pbj5/IFUQAEASEoABQAgKQEUAICkBFAAAJISQAEASEoABQAgKQEUAICkBFAAAJISQAEASGpQfhPSgQ6+879vRgIABrrU33x0MBVQAACSEkABAEhKAAUAICkBFACApARQAACSEkABAEhKAAUAICkBFACApAb9jegPduCNWd2UHgAYKMp98/kDqYACAJCUAAoAQFICKAAASQmgAAAkJYACAJCUVfB9sCIeAKhmlbTy/UAqoAAAJCWAAgCQlAAKAEBSAigAAEkJoAAAJGUV/FGyIh4AqAaVuvL9QCqgAAAkJYACAJCUAAoAQFICKAAASQmgAAAkZRX8MbAiHgCoJNWw8v1AKqAAACQlgAIAkJQACgBAUgIoAABJCaAAACQlgAIAkJTbMB0nt2QCAMqh2m69dCAVUAAAkhJAAQBISgAFACApARQAgKQEUAAAkrIKvoisiAcASqmaV74fSAUUAICkBFAAAJISQAEASEoABQAgKQEUAICkrIIvESviAYBiGCgr3w/U7wros88+GzNnzozm5ubI5XKxcuXKgv1z586NXC5X8DjnnHMKjunu7o6bbropGhoaYsSIEXHppZfG22+/fVwfBACA6tDvALpnz56YNGlSLF269LDHXHTRRdHe3p5/PPnkkwX758+fH48++misWLEinn/++di9e3dccsklsW/fvv5/AgAAqkq/W/Ctra3R2tra5zG1tbXR2Nh4yH2dnZ1x7733xi9+8Yv40pe+FBERDz74YLS0tMTq1avjwgsv7O+QAACoIiVZhLRmzZoYPXp0nH766TFv3rzYvn17ft/69eujp6cnZsyYkd/W3NwcEyZMiBdeeOGQ79fd3R1dXV0FDwAAqlPRA2hra2ssX748nn766bjjjjti3bp1cf7550d3d3dERHR0dMSwYcPiE5/4RMHrxowZEx0dHYd8zyVLlkR9fX3+0dLSUuxhAwCQSNFXwV999dX5nydMmBBnnXVWjBs3Lp544omYNWvWYV+XZVnkcrlD7lu0aFEsWLAg/7yrq6uqQqgV8QBAfwzEle8HKvl9QJuammLcuHGxdevWiIhobGyMvXv3xrvvvltw3Pbt22PMmDGHfI/a2toYOXJkwQMAgOpU8gC6Y8eO2LZtWzQ1NUVExJQpU6KmpiZWrVqVP6a9vT02b94cU6dOLfVwAAAos3634Hfv3h2vv/56/nlbW1ts3LgxRo0aFaNGjYrFixfHlVdeGU1NTfHGG2/Ed77znWhoaIgrrrgiIiLq6+vj+uuvj5tvvjlOOumkGDVqVCxcuDAmTpyYXxUPAMDA1e8A+tJLL8X06dPzzz+6NnPOnDlx9913x6ZNm+KBBx6InTt3RlNTU0yfPj0efvjhqKury7/mxz/+cQwdOjSuuuqqeP/99+OLX/xi3HfffTFkyJAifCQAACpZLsuyrNyD6K+urq6or6+PaXFZDM3VlHs4/WIREgBwJNW4COmDrCfWxGPR2dl5xPU6vgs+MSviAYBDqcbQeaxKvggJAAAOJIACAJCUFnwZaccDwOA2mNruB1IBBQAgKQEUAICkBFAAAJISQAEASEoABQAgKavgK4QV8QAwOAzWle8HUgEFACApARQAgKQEUAAAknINaAU6+NoQ14QCQPVyzWdvKqAAACQlgAIAkJQWfBVwiyYAqC7a7n1TAQUAICkBFACApARQAACSEkABAEhKAAUAICmr4KuMFfEAUJmsfD96KqAAACQlgAIAkJQWfBXTjgeA8tJ2PzYqoAAAJCWAAgCQlBb8AKEdDwBpaLsfPxVQAACSEkABAEhKAAUAICkBFACApARQAACSsgp+ALIiHgCKy8r34lIBBQAgKQEUAICktOAHOO14ADg22u6lowIKAEBSAigAAElpwQ8i2vEA0Ddt9zRUQAEASEoABQAgKS34QUo7HgA+pO2engooAABJCaAAACSlBY92PACDjrZ7eamAAgCQlAAKAEBSAigAAEm5BpQCrgcFYKBy3WflUAEFACApARQAgKS04Dks7XgAqp22e2VSAQUAICkBFACApLTgOSra8QBUC233yqcCCgBAUgIoAABJacHTbwe3NrTkASgnLffqowIKAEBSAigAAElpwXPcrJAHIDVt9+qmAgoAQFICKAAASWnBU1Ta8QCUirb7wNHvCuizzz4bM2fOjObm5sjlcrFy5cqC/blc7pCPH/3oR/ljpk2b1mv/7Nmzj/vDAABQ+fodQPfs2ROTJk2KpUuXHnJ/e3t7wePnP/955HK5uPLKKwuOmzdvXsFx99xzz7F9AgAAqkq/W/Ctra3R2tp62P2NjY0Fzx977LGYPn16/OVf/mXB9hNPPLHXsQws2vEAHC9t94GppIuQ/vCHP8QTTzwR119/fa99y5cvj4aGhjjzzDNj4cKFsWvXrsO+T3d3d3R1dRU8AACoTiVdhHT//fdHXV1dzJo1q2D7tddeG+PHj4/GxsbYvHlzLFq0KF555ZVYtWrVId9nyZIlcdttt5VyqAAAJJLLsiw75hfncvHoo4/G5Zdffsj9Z5xxRlxwwQXxs5/9rM/3Wb9+fZx11lmxfv36mDx5cq/93d3d0d3dnX/e1dUVLS0tMS0ui6G5mmMdPmWiHQ9AX7Tdq9MHWU+siceis7MzRo4c2eexJauAPvfcc/Haa6/Fww8/fMRjJ0+eHDU1NbF169ZDBtDa2tqora0txTABAEisZNeA3nvvvTFlypSYNGnSEY/dsmVL9PT0RFNTU6mGAwBAheh3BXT37t3x+uuv55+3tbXFxo0bY9SoUTF27NiI+LBF/u///u9xxx139Hr97373u1i+fHl8+ctfjoaGhnj11Vfj5ptvjk9/+tPxuc997jg+CtXicK0VrXmAwUWrffDqdwB96aWXYvr06fnnCxYsiIiIOXPmxH333RcREStWrIgsy+KrX/1qr9cPGzYsfv3rX8dPf/rT2L17d7S0tMTFF18ct956awwZMuQYPwYAANXiuBYhlUtXV1fU19dbhDTAqIACDC4qoANLRSxCgv5y43qAgU/oJKLEN6IHAICDCaAAACSlBU9F0o4HGDi03TmYCigAAEkJoAAAJKUFT8Vz43qA6qDVztFSAQUAICkBFACApLTgqVpWygOUn7Y7x0IFFACApARQAACSEkABAEjKNaAMCAdfg+SaUIDScM0nxaACCgBAUgIoAABJacEzIPn2JIDjo9VOKamAAgCQlAAKAEBSWvAMKr49CeDwtN1JRQUUAICkBFAAAJLSgmfQslIeGKy02ik3FVAAAJISQAEASEoLHg6iNQ8MFFrtVCoVUAAAkhJAAQBISgsejpLWPFCptNqpNiqgAAAkJYACAJCUFjwcJ615IBWtdgYKFVAAAJISQAEASEoLHkqkr1aZ9jxwONrsDAYqoAAAJCWAAgCQlBY8lIGV84BWO4OZCigAAEkJoAAAJKUFDxVEax4GHq126E0FFACApARQAACS0oKHKqA1D5VPqx2OngooAABJCaAAACSlBQ9V7Ghaftr0cPy016G4VEABAEhKAAUAICkBFACApFwDCgPc0V675lpRBiPXdkJ5qIACAJCUAAoAQFJa8EBEuKUTA4/2OlQuFVAAAJISQAEASEoLHjhq/W1patlTTFrqMHCogAIAkJQACgBAUlrwQMlo2dMXLXUYvFRAAQBISgAFACApLXigYhSzJaudXxra5kAxqIACAJCUAAoAQFJa8MCApFUMULlUQAEASKoqK6BZlkVExAfRE5GVeTAAAHyYy+L/c1pfqjKA7tq1KyIino8nyzwSAAAOtGvXrqivr+/zmFx2NDG1wuzfvz/eeeedyLIsxo4dG9u2bYuRI0eWe1hVoaurK1paWpyzfnDO+s856z/nrP+cs/5zzvrPOTt6WZbFrl27orm5OU44oe+rPKuyAnrCCSfEKaecEl1dXRERMXLkSJOin5yz/nPO+s856z/nrP+cs/5zzvrPOTs6R6p8fsQiJAAAkhJAAQBIqqoDaG1tbdx6661RW1tb7qFUDees/5yz/nPO+s856z/nrP+cs/5zzkqjKhchAQBQvaq6AgoAQPURQAEASEoABQAgKQEUAICkBFAAAJKq2gB61113xfjx4+NjH/tYTJkyJZ577rlyD6liLFmyJD7zmc9EXV1djB49Oi6//PJ47bXXCo6ZO3du5HK5gsc555xTphGX3+LFi3udj8bGxvz+LMti8eLF0dzcHMOHD49p06bFli1byjji8vvkJz/Z65zlcrm48cYbI8Ici4h49tlnY+bMmdHc3By5XC5WrlxZsP9o5lV3d3fcdNNN0dDQECNGjIhLL7003n777YSfIq2+zllPT098+9vfjokTJ8aIESOiubk5vva1r8U777xT8B7Tpk3rNfdmz56d+JOkc6R5djS/i+bZyoL9h/rblsvl4kc/+lH+mME2z4qtKgPoww8/HPPnz4/vfve78fLLL8df//VfR2tra7z11lvlHlpFWLt2bdx4443x4osvxqpVq+KDDz6IGTNmxJ49ewqOu+iii6K9vT3/ePLJJ8s04spw5plnFpyPTZs25ff98Ic/jDvvvDOWLl0a69ati8bGxrjgggti165dZRxxea1bt67gfK1atSoiIr7yla/kjxnsc2zPnj0xadKkWLp06SH3H828mj9/fjz66KOxYsWKeP7552P37t1xySWXxL59+1J9jKT6OmfvvfdebNiwIb73ve/Fhg0b4pFHHonf/va3cemll/Y6dt68eQVz75577kkx/LI40jyLOPLvonlW6MBz1d7eHj//+c8jl8vFlVdeWXDcYJpnRZdVoc9+9rPZ17/+9YJtZ5xxRnbLLbeUaUSVbfv27VlEZGvXrs1vmzNnTnbZZZeVb1AV5tZbb80mTZp0yH379+/PGhsbsx/84Af5bX/605+y+vr67F/+5V8SjbDyffOb38xOPfXUbP/+/VmWmWMHi4js0UcfzT8/mnm1c+fOrKamJluxYkX+mP/93//NTjjhhOyXv/xlsrGXy8Hn7FD++7//O4uI7M0338xvO++887JvfvObpR1chTrUOTvS76J5duR5dtlll2Xnn39+wbbBPM+KoeoqoHv37o3169fHjBkzCrbPmDEjXnjhhTKNqrJ1dnZGRMSoUaMKtq9ZsyZGjx4dp59+esybNy+2b99ejuFVjK1bt0Zzc3OMHz8+Zs+eHb///e8jIqKtrS06OjoK5lxtbW2cd9555tyf7d27Nx588MG47rrrIpfL5bebY4d3NPNq/fr10dPTU3BMc3NzTJgwwdz7s87OzsjlcvHxj3+8YPvy5cujoaEhzjzzzFi4cOGg7lZE9P27aJ717Q9/+EM88cQTcf311/faZ54du6HlHkB//fGPf4x9+/bFmDFjCraPGTMmOjo6yjSqypVlWSxYsCA+//nPx4QJE/LbW1tb4ytf+UqMGzcu2tra4nvf+16cf/75sX79+kH5dWNnn312PPDAA3H66afHH/7wh/j+978fU6dOjS1btuTn1aHm3JtvvlmO4VaclStXxs6dO2Pu3Ln5beZY345mXnV0dMSwYcPiE5/4RK9j/L2L+NOf/hS33HJLXHPNNTFy5Mj89muvvTbGjx8fjY2NsXnz5li0aFG88sor+ctEBpsj/S6aZ327//77o66uLmbNmlWw3Tw7PlUXQD9yYJUl4sOgdfA2Ir7xjW/E//zP/8Tzzz9fsP3qq6/O/zxhwoQ466yzYty4cfHEE0/0+iUbDFpbW/M/T5w4Mc4999w49dRT4/77789frG/OHd69994bra2t0dzcnN9mjh2dY5lX5t6HC5Jmz54d+/fvj7vuuqtg37x58/I/T5gwIU477bQ466yzYsOGDTF58uTUQy27Y/1dNM8+9POf/zyuvfba+NjHPlaw3Tw7PlXXgm9oaIghQ4b0+n9l27dv71VJGOxuuummePzxx+OZZ56JU045pc9jm5qaYty4cbF169ZEo6tsI0aMiIkTJ8bWrVvzq+HNuUN78803Y/Xq1fF3f/d3fR5njhU6mnnV2NgYe/fujXffffewxwxGPT09cdVVV0VbW1usWrWqoPp5KJMnT46amhpz788O/l00zw7vueeei9dee+2If98izLP+qroAOmzYsJgyZUqvEveqVati6tSpZRpVZcmyLL7xjW/EI488Ek8//XSMHz/+iK/ZsWNHbNu2LZqamhKMsPJ1d3fHb37zm2hqasq3WA6cc3v37o21a9eacxGxbNmyGD16dFx88cV9HmeOFTqaeTVlypSoqakpOKa9vT02b948aOfeR+Fz69atsXr16jjppJOO+JotW7ZET0+PufdnB/8ummeHd++998aUKVNi0qRJRzzWPOunMi6AOmYrVqzIampqsnvvvTd79dVXs/nz52cjRozI3njjjXIPrSL8wz/8Q1ZfX5+tWbMma29vzz/ee++9LMuybNeuXdnNN9+cvfDCC1lbW1v2zDPPZOeee272F3/xF1lXV1eZR18eN998c7ZmzZrs97//ffbiiy9ml1xySVZXV5efUz/4wQ+y+vr67JFHHsk2bdqUffWrX82ampoG7fn6yL59+7KxY8dm3/72twu2m2Mf2rVrV/byyy9nL7/8chYR2Z133pm9/PLL+RXbRzOvvv71r2ennHJKtnr16mzDhg3Z+eefn02aNCn74IMPyvWxSqqvc9bT05Ndeuml2SmnnJJt3Lix4O9bd3d3lmVZ9vrrr2e33XZbtm7duqytrS174oknsjPOOCP79Kc/PSjP2dH+Lppnhb+bWZZlnZ2d2YknnpjdfffdvV4/GOdZsVVlAM2yLPvnf/7nbNy4cdmwYcOyyZMnF9xiaLCLiEM+li1blmVZlr333nvZjBkzspNPPjmrqanJxo4dm82ZMyd76623yjvwMrr66quzpqamrKamJmtubs5mzZqVbdmyJb9///792a233po1NjZmtbW12Re+8IVs06ZNZRxxZXjqqaeyiMhee+21gu3m2IeeeeaZQ/4uzpkzJ8uyo5tX77//fvaNb3wjGzVqVDZ8+PDskksuGdDnsa9z1tbWdti/b88880yWZVn21ltvZV/4wheyUaNGZcOGDctOPfXU7B//8R+zHTt2lPeDlVBf5+xofxfNs8LfzSzLsnvuuScbPnx4tnPnzl6vH4zzrNhyWZZlJS2xAgDAAaruGlAAAKqbAAoAQFICKAAASQmgAAAkJYACAJCUAAoAQFICKAAASQmgAAAkJYACAJCUAAoAQFICKAAASf0fHOz84FsOkdkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "velocity.shape = (28, 2)\n",
      "velocity_idx.shape = (30, 3)\n",
      "velocity_grid.shape = (30, 3, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mu_sequence': array([[[192.47527012, 585.36468335],\n",
       "         [191.47527012, 587.36468335],\n",
       "         [191.47527012, 588.36468335],\n",
       "         [193.47527012, 589.36468335]],\n",
       " \n",
       "        [[546.20131479, 796.29199484],\n",
       "         [548.20131479, 797.29199484],\n",
       "         [549.20131479, 796.29199484],\n",
       "         [547.20131479, 794.29199484]],\n",
       " \n",
       "        [[902.46210046, 903.19894402],\n",
       "         [904.46210046, 901.19894402],\n",
       "         [902.46210046, 902.19894402],\n",
       "         [900.46210046, 903.19894402]],\n",
       " \n",
       "        [[864.48369192, 145.05729909],\n",
       "         [866.48369192, 146.05729909],\n",
       "         [864.48369192, 145.05729909],\n",
       "         [866.48369192, 145.05729909]],\n",
       " \n",
       "        [[629.16424918, 430.54304744],\n",
       "         [629.16424918, 428.54304744],\n",
       "         [629.16424918, 426.54304744],\n",
       "         [630.16424918, 427.54304744]],\n",
       " \n",
       "        [[ 96.86070712, 684.30132152],\n",
       "         [ 96.86070712, 687.30132152],\n",
       "         [ 94.86070712, 687.30132152],\n",
       "         [ 96.86070712, 688.30132152]],\n",
       " \n",
       "        [[483.15370969, 716.67087009],\n",
       "         [482.15370969, 718.67087009],\n",
       "         [483.15370969, 720.67087009],\n",
       "         [482.15370969, 721.67087009]],\n",
       " \n",
       "        [[869.10900369, 168.54487266],\n",
       "         [872.10900369, 168.54487266],\n",
       "         [871.10900369, 167.54487266],\n",
       "         [873.10900369, 167.54487266]],\n",
       " \n",
       "        [[749.62759691, 646.95714088],\n",
       "         [750.62759691, 646.95714088],\n",
       "         [752.62759691, 644.95714088],\n",
       "         [750.62759691, 646.95714088]],\n",
       " \n",
       "        [[604.7161915 , 819.19113368],\n",
       "         [603.7161915 , 817.19113368],\n",
       "         [603.7161915 , 819.19113368],\n",
       "         [605.7161915 , 820.19113368]],\n",
       " \n",
       "        [[334.74337794,  82.66041894],\n",
       "         [332.74337794,  81.66041894],\n",
       "         [331.74337794,  82.66041894],\n",
       "         [333.74337794,  84.66041894]],\n",
       " \n",
       "        [[766.56593223, 839.88062594],\n",
       "         [769.56593223, 839.88062594],\n",
       "         [772.56593223, 839.88062594],\n",
       "         [769.56593223, 839.88062594]],\n",
       " \n",
       "        [[812.80711519, 931.1455286 ],\n",
       "         [810.80711519, 933.1455286 ],\n",
       "         [811.80711519, 934.1455286 ],\n",
       "         [811.80711519, 935.1455286 ]],\n",
       " \n",
       "        [[ 34.13071941, 342.01545602],\n",
       "         [ 34.13071941, 343.01545602],\n",
       "         [ 32.13071941, 344.01545602],\n",
       "         [ 30.13071941, 343.01545602]],\n",
       " \n",
       "        [[ 77.01266078, 715.36652149],\n",
       "         [ 75.01266078, 715.36652149],\n",
       "         [ 74.01266078, 715.36652149],\n",
       "         [ 75.01266078, 714.36652149]],\n",
       " \n",
       "        [[902.12350057, 901.94610976],\n",
       "         [902.12350057, 904.94610976],\n",
       "         [900.12350057, 902.94610976],\n",
       "         [899.12350057, 902.94610976]],\n",
       " \n",
       "        [[843.1085258 , 172.04498402],\n",
       "         [844.1085258 , 170.04498402],\n",
       "         [846.1085258 , 171.04498402],\n",
       "         [844.1085258 , 173.04498402]],\n",
       " \n",
       "        [[231.35190808, 370.0368238 ],\n",
       "         [229.35190808, 372.0368238 ],\n",
       "         [231.35190808, 370.0368238 ],\n",
       "         [231.35190808, 367.0368238 ]],\n",
       " \n",
       "        [[ 65.04366491, 943.84350424],\n",
       "         [ 63.04366491, 943.84350424],\n",
       "         [ 64.04366491, 941.84350424],\n",
       "         [ 62.04366491, 939.84350424]],\n",
       " \n",
       "        [[761.96086902, 912.81850677],\n",
       "         [761.96086902, 909.81850677],\n",
       "         [761.96086902, 906.81850677],\n",
       "         [762.96086902, 906.81850677]],\n",
       " \n",
       "        [[596.08912172, 335.4811565 ],\n",
       "         [594.08912172, 333.4811565 ],\n",
       "         [594.08912172, 330.4811565 ],\n",
       "         [593.08912172, 328.4811565 ]],\n",
       " \n",
       "        [[102.00808622, 163.57463811],\n",
       "         [101.00808622, 164.57463811],\n",
       "         [ 99.00808622, 163.57463811],\n",
       "         [100.00808622, 161.57463811]],\n",
       " \n",
       "        [[481.67451214, 391.24848893],\n",
       "         [483.67451214, 391.24848893],\n",
       "         [483.67451214, 393.24848893],\n",
       "         [484.67451214, 394.24848893]],\n",
       " \n",
       "        [[625.06542624, 740.5711049 ],\n",
       "         [627.06542624, 741.5711049 ],\n",
       "         [629.06542624, 740.5711049 ],\n",
       "         [630.06542624, 742.5711049 ]],\n",
       " \n",
       "        [[497.49772171, 835.12902882],\n",
       "         [495.49772171, 834.12902882],\n",
       "         [495.49772171, 833.12902882],\n",
       "         [497.49772171, 831.12902882]],\n",
       " \n",
       "        [[385.98208472, 140.51700511],\n",
       "         [387.98208472, 141.51700511],\n",
       "         [389.98208472, 142.51700511],\n",
       "         [389.98208472, 139.51700511]],\n",
       " \n",
       "        [[315.60749445, 360.69715426],\n",
       "         [314.60749445, 361.69715426],\n",
       "         [313.60749445, 360.69715426],\n",
       "         [312.60749445, 361.69715426]],\n",
       " \n",
       "        [[190.07838758, 598.20472522],\n",
       "         [187.07838758, 598.20472522],\n",
       "         [188.07838758, 600.20472522],\n",
       "         [185.07838758, 600.20472522]],\n",
       " \n",
       "        [[985.83476436, 415.07210002],\n",
       "         [988.83476436, 415.07210002],\n",
       "         [990.83476436, 414.07210002],\n",
       "         [989.83476436, 413.07210002]],\n",
       " \n",
       "        [[123.16351933, 975.11958759],\n",
       "         [123.16351933, 978.11958759],\n",
       "         [124.16351933, 978.11958759],\n",
       "         [126.16351933, 977.11958759]]]),\n",
       " 'velocity': array([[[-0.001001,  0.002002],\n",
       "         [ 0.      ,  0.001001],\n",
       "         [ 0.002002,  0.001001]],\n",
       " \n",
       "        [[ 0.002002,  0.001001],\n",
       "         [ 0.001001, -0.001001],\n",
       "         [-0.002002, -0.002002]],\n",
       " \n",
       "        [[ 0.002002, -0.002002],\n",
       "         [-0.002002,  0.001001],\n",
       "         [-0.002002,  0.001001]],\n",
       " \n",
       "        [[ 0.002002,  0.001001],\n",
       "         [-0.002002, -0.001001],\n",
       "         [ 0.002002,  0.      ]],\n",
       " \n",
       "        [[ 0.      , -0.002002],\n",
       "         [ 0.      , -0.002002],\n",
       "         [ 0.001001,  0.001001]],\n",
       " \n",
       "        [[ 0.      ,  0.003003],\n",
       "         [-0.002002,  0.      ],\n",
       "         [ 0.002002,  0.001001]],\n",
       " \n",
       "        [[-0.001001,  0.002002],\n",
       "         [ 0.001001,  0.002002],\n",
       "         [-0.001001,  0.001001]],\n",
       " \n",
       "        [[ 0.003003,  0.      ],\n",
       "         [-0.001001, -0.001001],\n",
       "         [ 0.002002,  0.      ]],\n",
       " \n",
       "        [[ 0.001001,  0.      ],\n",
       "         [ 0.002002, -0.002002],\n",
       "         [-0.002002,  0.002002]],\n",
       " \n",
       "        [[-0.001001, -0.002002],\n",
       "         [ 0.      ,  0.002002],\n",
       "         [ 0.002002,  0.001001]],\n",
       " \n",
       "        [[-0.002002, -0.001001],\n",
       "         [-0.001001,  0.001001],\n",
       "         [ 0.002002,  0.002002]],\n",
       " \n",
       "        [[ 0.003003,  0.      ],\n",
       "         [ 0.003003,  0.      ],\n",
       "         [-0.003003,  0.      ]],\n",
       " \n",
       "        [[-0.002002,  0.002002],\n",
       "         [ 0.001001,  0.001001],\n",
       "         [ 0.      ,  0.001001]],\n",
       " \n",
       "        [[ 0.      ,  0.001001],\n",
       "         [-0.002002,  0.001001],\n",
       "         [-0.002002, -0.001001]],\n",
       " \n",
       "        [[-0.002002,  0.      ],\n",
       "         [-0.001001,  0.      ],\n",
       "         [ 0.001001, -0.001001]],\n",
       " \n",
       "        [[ 0.      ,  0.003003],\n",
       "         [-0.002002, -0.002002],\n",
       "         [-0.001001,  0.      ]],\n",
       " \n",
       "        [[ 0.001001, -0.002002],\n",
       "         [ 0.002002,  0.001001],\n",
       "         [-0.002002,  0.002002]],\n",
       " \n",
       "        [[-0.002002,  0.002002],\n",
       "         [ 0.002002, -0.002002],\n",
       "         [ 0.      , -0.003003]],\n",
       " \n",
       "        [[-0.002002,  0.      ],\n",
       "         [ 0.001001, -0.002002],\n",
       "         [-0.002002, -0.002002]],\n",
       " \n",
       "        [[ 0.      , -0.003003],\n",
       "         [ 0.      , -0.003003],\n",
       "         [ 0.001001,  0.      ]],\n",
       " \n",
       "        [[-0.002002, -0.002002],\n",
       "         [ 0.      , -0.003003],\n",
       "         [-0.001001, -0.002002]],\n",
       " \n",
       "        [[-0.001001,  0.001001],\n",
       "         [-0.002002, -0.001001],\n",
       "         [ 0.001001, -0.002002]],\n",
       " \n",
       "        [[ 0.002002,  0.      ],\n",
       "         [ 0.      ,  0.002002],\n",
       "         [ 0.001001,  0.001001]],\n",
       " \n",
       "        [[ 0.002002,  0.001001],\n",
       "         [ 0.002002, -0.001001],\n",
       "         [ 0.001001,  0.002002]],\n",
       " \n",
       "        [[-0.002002, -0.001001],\n",
       "         [ 0.      , -0.001001],\n",
       "         [ 0.002002, -0.002002]],\n",
       " \n",
       "        [[ 0.002002,  0.001001],\n",
       "         [ 0.002002,  0.001001],\n",
       "         [ 0.      , -0.003003]],\n",
       " \n",
       "        [[-0.001001,  0.001001],\n",
       "         [-0.001001, -0.001001],\n",
       "         [-0.001001,  0.001001]],\n",
       " \n",
       "        [[-0.003003,  0.      ],\n",
       "         [ 0.001001,  0.002002],\n",
       "         [-0.003003,  0.      ]],\n",
       " \n",
       "        [[ 0.003003,  0.      ],\n",
       "         [ 0.002002, -0.001001],\n",
       "         [-0.001001, -0.001001]],\n",
       " \n",
       "        [[ 0.      ,  0.003003],\n",
       "         [ 0.001001,  0.      ],\n",
       "         [ 0.002002, -0.001001]]]),\n",
       " 'velocity_idx': array([[13,  0, 18],\n",
       "        [18, 10, 25],\n",
       "        [24, 19, 19],\n",
       "        [18, 21, 16],\n",
       "        [ 3,  3,  8],\n",
       "        [ 4, 17, 18],\n",
       "        [13, 12,  9],\n",
       "        [26, 11, 16],\n",
       "        [ 6, 24, 23],\n",
       "        [15,  2, 18],\n",
       "        [21,  9, 22],\n",
       "        [26, 26, 27],\n",
       "        [23,  8,  0],\n",
       "        [ 0, 19, 21],\n",
       "        [17,  7, 10],\n",
       "        [ 4, 25,  7],\n",
       "        [14, 18, 23],\n",
       "        [23, 24,  5],\n",
       "        [17, 14, 25],\n",
       "        [ 5,  5,  6],\n",
       "        [25,  5, 15],\n",
       "        [ 9, 21, 14],\n",
       "        [16,  2,  8],\n",
       "        [18, 20, 12],\n",
       "        [21,  1, 24],\n",
       "        [18, 18,  5],\n",
       "        [ 9, 11,  9],\n",
       "        [27, 12, 27],\n",
       "        [26, 20, 11],\n",
       "        [ 4,  6, 20]])}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = d.gen_2D_multi_dtype2(30, 3, 0, 3, False, False, \"discrete\")\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-64ac1eb93376>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "y[2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Return the cumulative sum of the elements along a given axis.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "a : array_like\n",
       "    Input array.\n",
       "axis : int, optional\n",
       "    Axis along which the cumulative sum is computed. The default\n",
       "    (None) is to compute the cumsum over the flattened array.\n",
       "dtype : dtype, optional\n",
       "    Type of the returned array and of the accumulator in which the\n",
       "    elements are summed.  If `dtype` is not specified, it defaults\n",
       "    to the dtype of `a`, unless `a` has an integer dtype with a\n",
       "    precision less than that of the default platform integer.  In\n",
       "    that case, the default platform integer is used.\n",
       "out : ndarray, optional\n",
       "    Alternative output array in which to place the result. It must\n",
       "    have the same shape and buffer length as the expected output\n",
       "    but the type will be cast if necessary. See `ufuncs-output-type` for\n",
       "    more details.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "cumsum_along_axis : ndarray.\n",
       "    A new array holding the result is returned unless `out` is\n",
       "    specified, in which case a reference to `out` is returned. The\n",
       "    result has the same size as `a`, and the same shape as `a` if\n",
       "    `axis` is not None or `a` is a 1-d array.\n",
       "\n",
       "\n",
       "See Also\n",
       "--------\n",
       "sum : Sum array elements.\n",
       "\n",
       "trapz : Integration of array values using the composite trapezoidal rule.\n",
       "\n",
       "diff :  Calculate the n-th discrete difference along given axis.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "Arithmetic is modular when using integer types, and no error is\n",
       "raised on overflow.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> a = np.array([[1,2,3], [4,5,6]])\n",
       ">>> a\n",
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])\n",
       ">>> np.cumsum(a)\n",
       "array([ 1,  3,  6, 10, 15, 21])\n",
       ">>> np.cumsum(a, dtype=float)     # specifies type of output value(s)\n",
       "array([  1.,   3.,   6.,  10.,  15.,  21.])\n",
       "\n",
       ">>> np.cumsum(a,axis=0)      # sum over rows for each of the 3 columns\n",
       "array([[1, 2, 3],\n",
       "       [5, 7, 9]])\n",
       ">>> np.cumsum(a,axis=1)      # sum over columns for each of the 2 rows\n",
       "array([[ 1,  3,  6],\n",
       "       [ 4,  9, 15]])\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.cumsum?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2,3], [4,5,6]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  6, 10, 15, 21])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [5, 7, 9]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(a, axis=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-e49e54c6ca33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "y[2][:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-05a9cfb58b7a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "np.cumsum(y[2][:4], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-d8e428069b94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "np.min(y[2][:4], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-1aefb7a2e58d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m np.fmin(-1.5, \n\u001b[0;32m----> 2\u001b[0;31m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m        )\n",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "np.fmin(-1.5, \n",
    "        np.min(y[2][:4], axis=1),\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Expand the shape of an array.\n",
       "\n",
       "Insert a new axis that will appear at the `axis` position in the expanded\n",
       "array shape.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "a : array_like\n",
       "    Input array.\n",
       "axis : int or tuple of ints\n",
       "    Position in the expanded axes where the new axis (or axes) is placed.\n",
       "\n",
       "    .. deprecated:: 1.13.0\n",
       "        Passing an axis where ``axis > a.ndim`` will be treated as\n",
       "        ``axis == a.ndim``, and passing ``axis < -a.ndim - 1`` will\n",
       "        be treated as ``axis == 0``. This behavior is deprecated.\n",
       "\n",
       "    .. versionchanged:: 1.18.0\n",
       "        A tuple of axes is now supported.  Out of range axes as\n",
       "        described above are now forbidden and raise an `AxisError`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "result : ndarray\n",
       "    View of `a` with the number of dimensions increased.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "squeeze : The inverse operation, removing singleton dimensions\n",
       "reshape : Insert, remove, and combine dimensions, and resize existing ones\n",
       "doc.indexing, atleast_1d, atleast_2d, atleast_3d\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> x = np.array([1, 2])\n",
       ">>> x.shape\n",
       "(2,)\n",
       "\n",
       "The following is equivalent to ``x[np.newaxis, :]`` or ``x[np.newaxis]``:\n",
       "\n",
       ">>> y = np.expand_dims(x, axis=0)\n",
       ">>> y\n",
       "array([[1, 2]])\n",
       ">>> y.shape\n",
       "(1, 2)\n",
       "\n",
       "The following is equivalent to ``x[:, np.newaxis]``:\n",
       "\n",
       ">>> y = np.expand_dims(x, axis=1)\n",
       ">>> y\n",
       "array([[1],\n",
       "       [2]])\n",
       ">>> y.shape\n",
       "(2, 1)\n",
       "\n",
       "``axis`` may also be a tuple:\n",
       "\n",
       ">>> y = np.expand_dims(x, axis=(0, 1))\n",
       ">>> y\n",
       "array([[[1, 2]]])\n",
       "\n",
       ">>> y = np.expand_dims(x, axis=(2, 0))\n",
       ">>> y\n",
       "array([[[1],\n",
       "        [2]]])\n",
       "\n",
       "Note that some examples may use ``None`` instead of ``np.newaxis``.  These\n",
       "are the same objects:\n",
       "\n",
       ">>> np.newaxis is None\n",
       "True\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.7/site-packages/numpy/lib/shape_base.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.expand_dims?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[1],\n",
       "         [2],\n",
       "         [3]],\n",
       "\n",
       "        [[4],\n",
       "         [5],\n",
       "         [6]]]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.expand_dims(a, axis=(0,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-e49e54c6ca33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "y[2][:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-64ac0268fdad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "np.concatenate((y[2][:2], y[2][2:4]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mCall signature:\u001b[0m  \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mType:\u001b[0m            ufunc\n",
       "\u001b[0;31mString form:\u001b[0m     <ufunc 'fmin'>\n",
       "\u001b[0;31mFile:\u001b[0m            ~/anaconda3/lib/python3.7/site-packages/numpy/__init__.py\n",
       "\u001b[0;31mDocstring:\u001b[0m      \n",
       "fmin(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n",
       "\n",
       "Element-wise minimum of array elements.\n",
       "\n",
       "Compare two arrays and returns a new array containing the element-wise\n",
       "minima. If one of the elements being compared is a NaN, then the\n",
       "non-nan element is returned. If both elements are NaNs then the first\n",
       "is returned.  The latter distinction is important for complex NaNs,\n",
       "which are defined as at least one of the real or imaginary parts being\n",
       "a NaN. The net effect is that NaNs are ignored when possible.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "x1, x2 : array_like\n",
       "    The arrays holding the elements to be compared.\n",
       "    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n",
       "    shape (which becomes the shape of the output).\n",
       "out : ndarray, None, or tuple of ndarray and None, optional\n",
       "    A location into which the result is stored. If provided, it must have\n",
       "    a shape that the inputs broadcast to. If not provided or None,\n",
       "    a freshly-allocated array is returned. A tuple (possible only as a\n",
       "    keyword argument) must have length equal to the number of outputs.\n",
       "where : array_like, optional\n",
       "    This condition is broadcast over the input. At locations where the\n",
       "    condition is True, the `out` array will be set to the ufunc result.\n",
       "    Elsewhere, the `out` array will retain its original value.\n",
       "    Note that if an uninitialized `out` array is created via the default\n",
       "    ``out=None``, locations within it where the condition is False will\n",
       "    remain uninitialized.\n",
       "**kwargs\n",
       "    For other keyword-only arguments, see the\n",
       "    :ref:`ufunc docs <ufuncs.kwargs>`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "y : ndarray or scalar\n",
       "    The minimum of `x1` and `x2`, element-wise.\n",
       "    This is a scalar if both `x1` and `x2` are scalars.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "fmax :\n",
       "    Element-wise maximum of two arrays, ignores NaNs.\n",
       "minimum :\n",
       "    Element-wise minimum of two arrays, propagates NaNs.\n",
       "amin :\n",
       "    The minimum value of an array along a given axis, propagates NaNs.\n",
       "nanmin :\n",
       "    The minimum value of an array along a given axis, ignores NaNs.\n",
       "\n",
       "maximum, amax, nanmax\n",
       "\n",
       "Notes\n",
       "-----\n",
       ".. versionadded:: 1.3.0\n",
       "\n",
       "The fmin is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither\n",
       "x1 nor x2 are NaNs, but it is faster and does proper broadcasting.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> np.fmin([2, 3, 4], [1, 5, 2])\n",
       "array([1, 3, 2])\n",
       "\n",
       ">>> np.fmin(np.eye(2), [0.5, 2])\n",
       "array([[ 0.5,  0. ],\n",
       "       [ 0. ,  1. ]])\n",
       "\n",
       ">>> np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])\n",
       "array([ 0.,  0., nan])\n",
       "\u001b[0;31mClass docstring:\u001b[0m\n",
       "Functions that operate element by element on whole arrays.\n",
       "\n",
       "To see the documentation for a specific ufunc, use `info`.  For\n",
       "example, ``np.info(np.sin)``.  Because ufuncs are written in C\n",
       "(for speed) and linked into Python with NumPy's ufunc facility,\n",
       "Python's help() function finds this page whenever help() is called\n",
       "on a ufunc.\n",
       "\n",
       "A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.\n",
       "\n",
       "Calling ufuncs:\n",
       "===============\n",
       "\n",
       "op(*x[, out], where=True, **kwargs)\n",
       "Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.\n",
       "\n",
       "The broadcasting rules are:\n",
       "\n",
       "* Dimensions of length 1 may be prepended to either array.\n",
       "* Arrays may be repeated along dimensions of length 1.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "*x : array_like\n",
       "    Input arrays.\n",
       "out : ndarray, None, or tuple of ndarray and None, optional\n",
       "    Alternate array object(s) in which to put the result; if provided, it\n",
       "    must have a shape that the inputs broadcast to. A tuple of arrays\n",
       "    (possible only as a keyword argument) must have length equal to the\n",
       "    number of outputs; use None for uninitialized outputs to be\n",
       "    allocated by the ufunc.\n",
       "where : array_like, optional\n",
       "    This condition is broadcast over the input. At locations where the\n",
       "    condition is True, the `out` array will be set to the ufunc result.\n",
       "    Elsewhere, the `out` array will retain its original value.\n",
       "    Note that if an uninitialized `out` array is created via the default\n",
       "    ``out=None``, locations within it where the condition is False will\n",
       "    remain uninitialized.\n",
       "**kwargs\n",
       "    For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "r : ndarray or tuple of ndarray\n",
       "    `r` will have the shape that the arrays in `x` broadcast to; if `out` is\n",
       "    provided, it will be returned. If not, `r` will be allocated and\n",
       "    may contain uninitialized values. If the function has more than one\n",
       "    output, then the result will be a tuple of arrays.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.fmin?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "choice(a, size=None, replace=True, p=None)\n",
       "\n",
       "Generates a random sample from a given 1-D array\n",
       "\n",
       ".. versionadded:: 1.7.0\n",
       "\n",
       ".. note::\n",
       "    New code should use the ``choice`` method of a ``default_rng()``\n",
       "    instance instead; please see the :ref:`random-quick-start`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "a : 1-D array-like or int\n",
       "    If an ndarray, a random sample is generated from its elements.\n",
       "    If an int, the random sample is generated as if a were np.arange(a)\n",
       "size : int or tuple of ints, optional\n",
       "    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
       "    ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
       "    single value is returned.\n",
       "replace : boolean, optional\n",
       "    Whether the sample is with or without replacement\n",
       "p : 1-D array-like, optional\n",
       "    The probabilities associated with each entry in a.\n",
       "    If not given the sample assumes a uniform distribution over all\n",
       "    entries in a.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "samples : single item or ndarray\n",
       "    The generated random samples\n",
       "\n",
       "Raises\n",
       "------\n",
       "ValueError\n",
       "    If a is an int and less than zero, if a or p are not 1-dimensional,\n",
       "    if a is an array-like of size 0, if p is not a vector of\n",
       "    probabilities, if a and p have different lengths, or if\n",
       "    replace=False and the sample size is greater than the population\n",
       "    size\n",
       "\n",
       "See Also\n",
       "--------\n",
       "randint, shuffle, permutation\n",
       "Generator.choice: which should be used in new code\n",
       "\n",
       "Notes\n",
       "-----\n",
       "Sampling random rows from a 2-D array is not possible with this function,\n",
       "but is possible with `Generator.choice` through its ``axis`` keyword.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "Generate a uniform random sample from np.arange(5) of size 3:\n",
       "\n",
       ">>> np.random.choice(5, 3)\n",
       "array([0, 3, 4]) # random\n",
       ">>> #This is equivalent to np.random.randint(0,5,3)\n",
       "\n",
       "Generate a non-uniform random sample from np.arange(5) of size 3:\n",
       "\n",
       ">>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n",
       "array([3, 3, 0]) # random\n",
       "\n",
       "Generate a uniform random sample from np.arange(5) of size 3 without\n",
       "replacement:\n",
       "\n",
       ">>> np.random.choice(5, 3, replace=False)\n",
       "array([3,1,0]) # random\n",
       ">>> #This is equivalent to np.random.permutation(np.arange(5))[:3]\n",
       "\n",
       "Generate a non-uniform random sample from np.arange(5) of size\n",
       "3 without replacement:\n",
       "\n",
       ">>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n",
       "array([2, 3, 0]) # random\n",
       "\n",
       "Any of the above can be repeated with an arbitrary array-like\n",
       "instead of just integers. For instance:\n",
       "\n",
       ">>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n",
       ">>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n",
       "array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random\n",
       "      dtype='<U11')\n",
       "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.choice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0, 0],\n",
       "       [0, 0, 0, 4, 0, 0],\n",
       "       [0, 0, 0, 0, 5, 0],\n",
       "       [0, 0, 0, 0, 0, 6]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.diag((1,2,3,4,5,6))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  0,  0,  0,  0,  0],\n",
       "       [ 0, 20,  0,  0,  0,  0],\n",
       "       [ 0,  0, 30,  0,  0,  0],\n",
       "       [ 0,  0,  0, 40,  0,  0],\n",
       "       [ 0,  0,  0,  0, 50,  0],\n",
       "       [ 0,  0,  0,  0,  0, 60]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "choice(a, size=None, replace=True, p=None)\n",
       "\n",
       "Generates a random sample from a given 1-D array\n",
       "\n",
       ".. versionadded:: 1.7.0\n",
       "\n",
       ".. note::\n",
       "    New code should use the ``choice`` method of a ``default_rng()``\n",
       "    instance instead; please see the :ref:`random-quick-start`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "a : 1-D array-like or int\n",
       "    If an ndarray, a random sample is generated from its elements.\n",
       "    If an int, the random sample is generated as if a were np.arange(a)\n",
       "size : int or tuple of ints, optional\n",
       "    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
       "    ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
       "    single value is returned.\n",
       "replace : boolean, optional\n",
       "    Whether the sample is with or without replacement\n",
       "p : 1-D array-like, optional\n",
       "    The probabilities associated with each entry in a.\n",
       "    If not given the sample assumes a uniform distribution over all\n",
       "    entries in a.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "samples : single item or ndarray\n",
       "    The generated random samples\n",
       "\n",
       "Raises\n",
       "------\n",
       "ValueError\n",
       "    If a is an int and less than zero, if a or p are not 1-dimensional,\n",
       "    if a is an array-like of size 0, if p is not a vector of\n",
       "    probabilities, if a and p have different lengths, or if\n",
       "    replace=False and the sample size is greater than the population\n",
       "    size\n",
       "\n",
       "See Also\n",
       "--------\n",
       "randint, shuffle, permutation\n",
       "Generator.choice: which should be used in new code\n",
       "\n",
       "Notes\n",
       "-----\n",
       "Sampling random rows from a 2-D array is not possible with this function,\n",
       "but is possible with `Generator.choice` through its ``axis`` keyword.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "Generate a uniform random sample from np.arange(5) of size 3:\n",
       "\n",
       ">>> np.random.choice(5, 3)\n",
       "array([0, 3, 4]) # random\n",
       ">>> #This is equivalent to np.random.randint(0,5,3)\n",
       "\n",
       "Generate a non-uniform random sample from np.arange(5) of size 3:\n",
       "\n",
       ">>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n",
       "array([3, 3, 0]) # random\n",
       "\n",
       "Generate a uniform random sample from np.arange(5) of size 3 without\n",
       "replacement:\n",
       "\n",
       ">>> np.random.choice(5, 3, replace=False)\n",
       "array([3,1,0]) # random\n",
       ">>> #This is equivalent to np.random.permutation(np.arange(5))[:3]\n",
       "\n",
       "Generate a non-uniform random sample from np.arange(5) of size\n",
       "3 without replacement:\n",
       "\n",
       ">>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n",
       "array([2, 3, 0]) # random\n",
       "\n",
       "Any of the above can be repeated with an arbitrary array-like\n",
       "instead of just integers. For instance:\n",
       "\n",
       ">>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n",
       ">>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n",
       "array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random\n",
       "      dtype='<U11')\n",
       "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.choice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0, 0],\n",
       "       [0, 0, 0, 4, 0, 0],\n",
       "       [0, 0, 0, 0, 5, 0],\n",
       "       [0, 0, 0, 0, 0, 6]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m/\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "product(*iterables, repeat=1) --> product object\n",
       "\n",
       "Cartesian product of input iterables.  Equivalent to nested for-loops.\n",
       "\n",
       "For example, product(A, B) returns the same as:  ((x,y) for x in A for y in B).\n",
       "The leftmost iterators are in the outermost for-loop, so the output tuples\n",
       "cycle in a manner similar to an odometer (with the rightmost element changing\n",
       "on every iteration).\n",
       "\n",
       "To compute the product of an iterable with itself, specify the number\n",
       "of repetitions with the optional repeat keyword argument. For example,\n",
       "product(A, repeat=4) means the same as product(A, A, A, A).\n",
       "\n",
       "product('ab', range(3)) --> ('a',0) ('a',1) ('a',2) ('b',0) ('b',1) ('b',2)\n",
       "product((0,1), (0,1), (0,1)) --> (0,0,0) (0,0,1) (0,1,0) (0,1,1) (1,0,0) ...\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itertools.product?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(itertools.product(range(4), repeat=10))) == 4**10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1048576"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4**10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.random.choice(6, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 2, 4])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0, 0],\n",
       "       [0, 0, 0, 4, 0, 0],\n",
       "       [0, 0, 0, 0, 5, 0],\n",
       "       [0, 0, 0, 0, 0, 6]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 6],\n",
       "       [0, 0, 3, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 5, 0]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Construct an array by repeating A the number of times given by reps.\n",
       "\n",
       "If `reps` has length ``d``, the result will have dimension of\n",
       "``max(d, A.ndim)``.\n",
       "\n",
       "If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new\n",
       "axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,\n",
       "or shape (1, 1, 3) for 3-D replication. If this is not the desired\n",
       "behavior, promote `A` to d-dimensions manually before calling this\n",
       "function.\n",
       "\n",
       "If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.\n",
       "Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as\n",
       "(1, 1, 2, 2).\n",
       "\n",
       "Note : Although tile may be used for broadcasting, it is strongly\n",
       "recommended to use numpy's broadcasting operations and functions.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "A : array_like\n",
       "    The input array.\n",
       "reps : array_like\n",
       "    The number of repetitions of `A` along each axis.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "c : ndarray\n",
       "    The tiled output array.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "repeat : Repeat elements of an array.\n",
       "broadcast_to : Broadcast an array to a new shape\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> a = np.array([0, 1, 2])\n",
       ">>> np.tile(a, 2)\n",
       "array([0, 1, 2, 0, 1, 2])\n",
       ">>> np.tile(a, (2, 2))\n",
       "array([[0, 1, 2, 0, 1, 2],\n",
       "       [0, 1, 2, 0, 1, 2]])\n",
       ">>> np.tile(a, (2, 1, 2))\n",
       "array([[[0, 1, 2, 0, 1, 2]],\n",
       "       [[0, 1, 2, 0, 1, 2]]])\n",
       "\n",
       ">>> b = np.array([[1, 2], [3, 4]])\n",
       ">>> np.tile(b, 2)\n",
       "array([[1, 2, 1, 2],\n",
       "       [3, 4, 3, 4]])\n",
       ">>> np.tile(b, (2, 1))\n",
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [1, 2],\n",
       "       [3, 4]])\n",
       "\n",
       ">>> c = np.array([1,2,3,4])\n",
       ">>> np.tile(c,(4,1))\n",
       "array([[1, 2, 3, 4],\n",
       "       [1, 2, 3, 4],\n",
       "       [1, 2, 3, 4],\n",
       "       [1, 2, 3, 4]])\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.7/site-packages/numpy/lib/shape_base.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.tile?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [50, 60, 70, 80],\n",
       "       [ 1,  2,  3,  4],\n",
       "       [50, 60, 70, 80],\n",
       "       [ 1,  2,  3,  4],\n",
       "       [50, 60, 70, 80],\n",
       "       [ 1,  2,  3,  4],\n",
       "       [50, 60, 70, 80],\n",
       "       [ 1,  2,  3,  4],\n",
       "       [50, 60, 70, 80]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array([[1,2,3,4],[50,60,70,80]])\n",
    "print(c.shape)\n",
    "np.tile(c, (5,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array([1,2,3,4])\n",
    "print(c.shape)\n",
    "np.tile(c[:,None], (4,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[:,None].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[None].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Stack arrays in sequence vertically (row wise).\n",
       "\n",
       "This is equivalent to concatenation along the first axis after 1-D arrays\n",
       "of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by\n",
       "`vsplit`.\n",
       "\n",
       "This function makes most sense for arrays with up to 3 dimensions. For\n",
       "instance, for pixel-data with a height (first axis), width (second axis),\n",
       "and r/g/b channels (third axis). The functions `concatenate`, `stack` and\n",
       "`block` provide more general stacking and concatenation operations.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "tup : sequence of ndarrays\n",
       "    The arrays must have the same shape along all but the first axis.\n",
       "    1-D arrays must have the same length.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "stacked : ndarray\n",
       "    The array formed by stacking the given arrays, will be at least 2-D.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "concatenate : Join a sequence of arrays along an existing axis.\n",
       "stack : Join a sequence of arrays along a new axis.\n",
       "block : Assemble an nd-array from nested lists of blocks.\n",
       "hstack : Stack arrays in sequence horizontally (column wise).\n",
       "dstack : Stack arrays in sequence depth wise (along third axis).\n",
       "column_stack : Stack 1-D arrays as columns into a 2-D array.\n",
       "vsplit : Split an array into multiple sub-arrays vertically (row-wise).\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> a = np.array([1, 2, 3])\n",
       ">>> b = np.array([2, 3, 4])\n",
       ">>> np.vstack((a,b))\n",
       "array([[1, 2, 3],\n",
       "       [2, 3, 4]])\n",
       "\n",
       ">>> a = np.array([[1], [2], [3]])\n",
       ">>> b = np.array([[2], [3], [4]])\n",
       ">>> np.vstack((a,b))\n",
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.7/site-packages/numpy/core/shape_base.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.vstack?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 3, 4)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros(shape=(2,3,4))\n",
    "b = np.ones(shape=(5,3,4))\n",
    "\n",
    "c = np.vstack((a,b))\n",
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Take elements from an array along an axis.\n",
       "\n",
       "When axis is not None, this function does the same thing as \"fancy\"\n",
       "indexing (indexing arrays using arrays); however, it can be easier to use\n",
       "if you need elements along a given axis. A call such as\n",
       "``np.take(arr, indices, axis=3)`` is equivalent to\n",
       "``arr[:,:,:,indices,...]``.\n",
       "\n",
       "Explained without fancy indexing, this is equivalent to the following use\n",
       "of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of\n",
       "indices::\n",
       "\n",
       "    Ni, Nk = a.shape[:axis], a.shape[axis+1:]\n",
       "    Nj = indices.shape\n",
       "    for ii in ndindex(Ni):\n",
       "        for jj in ndindex(Nj):\n",
       "            for kk in ndindex(Nk):\n",
       "                out[ii + jj + kk] = a[ii + (indices[jj],) + kk]\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "a : array_like (Ni..., M, Nk...)\n",
       "    The source array.\n",
       "indices : array_like (Nj...)\n",
       "    The indices of the values to extract.\n",
       "\n",
       "    .. versionadded:: 1.8.0\n",
       "\n",
       "    Also allow scalars for indices.\n",
       "axis : int, optional\n",
       "    The axis over which to select values. By default, the flattened\n",
       "    input array is used.\n",
       "out : ndarray, optional (Ni..., Nj..., Nk...)\n",
       "    If provided, the result will be placed in this array. It should\n",
       "    be of the appropriate shape and dtype. Note that `out` is always\n",
       "    buffered if `mode='raise'`; use other modes for better performance.\n",
       "mode : {'raise', 'wrap', 'clip'}, optional\n",
       "    Specifies how out-of-bounds indices will behave.\n",
       "\n",
       "    * 'raise' -- raise an error (default)\n",
       "    * 'wrap' -- wrap around\n",
       "    * 'clip' -- clip to the range\n",
       "\n",
       "    'clip' mode means that all indices that are too large are replaced\n",
       "    by the index that addresses the last element along that axis. Note\n",
       "    that this disables indexing with negative numbers.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "out : ndarray (Ni..., Nj..., Nk...)\n",
       "    The returned array has the same type as `a`.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "compress : Take elements using a boolean mask\n",
       "ndarray.take : equivalent method\n",
       "take_along_axis : Take elements by matching the array and the index arrays\n",
       "\n",
       "Notes\n",
       "-----\n",
       "\n",
       "By eliminating the inner loop in the description above, and using `s_` to\n",
       "build simple slice objects, `take` can be expressed  in terms of applying\n",
       "fancy indexing to each 1-d slice::\n",
       "\n",
       "    Ni, Nk = a.shape[:axis], a.shape[axis+1:]\n",
       "    for ii in ndindex(Ni):\n",
       "        for kk in ndindex(Nj):\n",
       "            out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]\n",
       "\n",
       "For this reason, it is equivalent to (but faster than) the following use\n",
       "of `apply_along_axis`::\n",
       "\n",
       "    out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> a = [4, 3, 5, 7, 6, 8]\n",
       ">>> indices = [0, 1, 4]\n",
       ">>> np.take(a, indices)\n",
       "array([4, 3, 6])\n",
       "\n",
       "In this example if `a` is an ndarray, \"fancy\" indexing can be used.\n",
       "\n",
       ">>> a = np.array(a)\n",
       ">>> a[indices]\n",
       "array([4, 3, 6])\n",
       "\n",
       "If `indices` is not one dimensional, the output also has these dimensions.\n",
       "\n",
       ">>> np.take(a, [[0, 1], [2, 3]])\n",
       "array([[4, 3],\n",
       "       [5, 7]])\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.take?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [4, 3, 5, 7, 6, 8]\n",
    "idxs = [0,1,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 6])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.take(a, idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 6])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.take(a, idxs, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 6])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs = [[0,1],[2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/imflash217/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-75-8c973d1c3fb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "a[idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 3],\n",
       "       [5, 7]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.take(a, idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 3],\n",
       "       [5, 7]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.take(a, idxs, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 5, 7, 6, 8])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.random(size=(5,10))\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.95908288, 0.17326521, 0.85681131, 0.81670285, 0.91035415,\n",
       "        0.18300537, 0.96297978, 0.58579499, 0.78904032, 0.39650531],\n",
       "       [0.91388137, 0.19738397, 0.68229702, 0.86462577, 0.44662846,\n",
       "        0.20869013, 0.3096414 , 0.4581872 , 0.17981049, 0.81541285],\n",
       "       [0.05688169, 0.25262494, 0.59536979, 0.98367656, 0.75305176,\n",
       "        0.62575387, 0.57008328, 0.52329606, 0.32615293, 0.6734343 ],\n",
       "       [0.48596203, 0.90190305, 0.66021509, 0.66303805, 0.78310713,\n",
       "        0.32908209, 0.72778974, 0.20303929, 0.67813262, 0.57080083],\n",
       "       [0.87743776, 0.77300492, 0.91758685, 0.02594944, 0.07678043,\n",
       "        0.9154619 , 0.38312645, 0.31043583, 0.52389251, 0.56344435]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs = [[0,1,3,3],[2,2,2,4]]\n",
    "idxs = np.array(idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs.ndim == a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 3, 3],\n",
       "       [2, 2, 2, 4]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.95908288, 0.17326521, 0.85681131, 0.81670285, 0.91035415,\n",
       "         0.18300537, 0.96297978, 0.58579499, 0.78904032, 0.39650531],\n",
       "        [0.91388137, 0.19738397, 0.68229702, 0.86462577, 0.44662846,\n",
       "         0.20869013, 0.3096414 , 0.4581872 , 0.17981049, 0.81541285],\n",
       "        [0.48596203, 0.90190305, 0.66021509, 0.66303805, 0.78310713,\n",
       "         0.32908209, 0.72778974, 0.20303929, 0.67813262, 0.57080083],\n",
       "        [0.48596203, 0.90190305, 0.66021509, 0.66303805, 0.78310713,\n",
       "         0.32908209, 0.72778974, 0.20303929, 0.67813262, 0.57080083]],\n",
       "\n",
       "       [[0.05688169, 0.25262494, 0.59536979, 0.98367656, 0.75305176,\n",
       "         0.62575387, 0.57008328, 0.52329606, 0.32615293, 0.6734343 ],\n",
       "        [0.05688169, 0.25262494, 0.59536979, 0.98367656, 0.75305176,\n",
       "         0.62575387, 0.57008328, 0.52329606, 0.32615293, 0.6734343 ],\n",
       "        [0.05688169, 0.25262494, 0.59536979, 0.98367656, 0.75305176,\n",
       "         0.62575387, 0.57008328, 0.52329606, 0.32615293, 0.6734343 ],\n",
       "        [0.87743776, 0.77300492, 0.91758685, 0.02594944, 0.07678043,\n",
       "         0.9154619 , 0.38312645, 0.31043583, 0.52389251, 0.56344435]]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4, 10)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idxs].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.95908288, 0.17326521, 0.81670285, 0.81670285],\n",
       "       [0.85681131, 0.85681131, 0.85681131, 0.91035415]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.take(a, idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.95908288, 0.17326521, 0.85681131, 0.81670285, 0.91035415,\n",
       "        0.18300537, 0.96297978, 0.58579499, 0.78904032, 0.39650531],\n",
       "       [0.91388137, 0.19738397, 0.68229702, 0.86462577, 0.44662846,\n",
       "        0.20869013, 0.3096414 , 0.4581872 , 0.17981049, 0.81541285],\n",
       "       [0.05688169, 0.25262494, 0.59536979, 0.98367656, 0.75305176,\n",
       "        0.62575387, 0.57008328, 0.52329606, 0.32615293, 0.6734343 ],\n",
       "       [0.48596203, 0.90190305, 0.66021509, 0.66303805, 0.78310713,\n",
       "        0.32908209, 0.72778974, 0.20303929, 0.67813262, 0.57080083],\n",
       "       [0.87743776, 0.77300492, 0.91758685, 0.02594944, 0.07678043,\n",
       "        0.9154619 , 0.38312645, 0.31043583, 0.52389251, 0.56344435]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs = [[9,6,7,7],[2,2,2,9]]\n",
    "idxs = np.array(idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 6, 7, 7],\n",
       "       [2, 2, 2, 9]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.39650531, 0.96297978, 0.58579499, 0.58579499],\n",
       "        [0.85681131, 0.85681131, 0.85681131, 0.39650531]],\n",
       "\n",
       "       [[0.81541285, 0.3096414 , 0.4581872 , 0.4581872 ],\n",
       "        [0.68229702, 0.68229702, 0.68229702, 0.81541285]],\n",
       "\n",
       "       [[0.6734343 , 0.57008328, 0.52329606, 0.52329606],\n",
       "        [0.59536979, 0.59536979, 0.59536979, 0.6734343 ]],\n",
       "\n",
       "       [[0.57080083, 0.72778974, 0.20303929, 0.20303929],\n",
       "        [0.66021509, 0.66021509, 0.66021509, 0.57080083]],\n",
       "\n",
       "       [[0.56344435, 0.38312645, 0.31043583, 0.31043583],\n",
       "        [0.91758685, 0.91758685, 0.91758685, 0.56344435]]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.39650531, 0.96297978, 0.58579499, 0.58579499],\n",
       "        [0.85681131, 0.85681131, 0.85681131, 0.39650531]],\n",
       "\n",
       "       [[0.81541285, 0.3096414 , 0.4581872 , 0.4581872 ],\n",
       "        [0.68229702, 0.68229702, 0.68229702, 0.81541285]],\n",
       "\n",
       "       [[0.6734343 , 0.57008328, 0.52329606, 0.52329606],\n",
       "        [0.59536979, 0.59536979, 0.59536979, 0.6734343 ]],\n",
       "\n",
       "       [[0.57080083, 0.72778974, 0.20303929, 0.20303929],\n",
       "        [0.66021509, 0.66021509, 0.66021509, 0.57080083]],\n",
       "\n",
       "       [[0.56344435, 0.38312645, 0.31043583, 0.31043583],\n",
       "        [0.91758685, 0.91758685, 0.91758685, 0.56344435]]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.take(a, idxs, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 2, 4)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.take(a, idxs, axis=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_data = 10\n",
    "num_steps = 5\n",
    "max_v = 3.\n",
    "min_v = 0.\n",
    "distance = np.sqrt(np.random.random(size=(num_data, num_steps))) * (max_v - min_v) + min_v\n",
    "distance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta = np.random.random(size=(num_data, num_steps)) * 2 * np.pi - np.pi\n",
    "theta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = distance * np.cos(theta)\n",
    "x.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5, 1)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.expand_dims(x, axis=-1)\n",
    "x1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5, 1)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = distance * np.sin(theta)\n",
    "y1 = np.expand_dims(y, axis=-1)\n",
    "y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-2.32338519],\n",
       "        [-0.8724784 ],\n",
       "        [ 2.74080415],\n",
       "        [-1.16655256],\n",
       "        [-2.20222748]],\n",
       "\n",
       "       [[-2.16707711],\n",
       "        [-2.46139744],\n",
       "        [ 2.24355116],\n",
       "        [-0.64550707],\n",
       "        [ 1.47110304]],\n",
       "\n",
       "       [[-0.56038381],\n",
       "        [ 1.53857743],\n",
       "        [-1.12376254],\n",
       "        [-0.19328586],\n",
       "        [ 1.28848132]],\n",
       "\n",
       "       [[-0.44645104],\n",
       "        [-2.40737466],\n",
       "        [ 0.37177756],\n",
       "        [-0.430728  ],\n",
       "        [ 0.82861008]],\n",
       "\n",
       "       [[-0.08088935],\n",
       "        [-1.77310009],\n",
       "        [ 0.66287152],\n",
       "        [-1.06482184],\n",
       "        [-1.47498431]],\n",
       "\n",
       "       [[ 1.72556774],\n",
       "        [ 0.52555176],\n",
       "        [ 1.34645968],\n",
       "        [-1.14869476],\n",
       "        [ 2.57476196]],\n",
       "\n",
       "       [[-0.92028555],\n",
       "        [ 0.84663968],\n",
       "        [-1.16097222],\n",
       "        [-0.40410378],\n",
       "        [ 2.62039716]],\n",
       "\n",
       "       [[ 1.56751183],\n",
       "        [-1.87749696],\n",
       "        [-1.49364279],\n",
       "        [ 1.30464056],\n",
       "        [ 0.44502697]],\n",
       "\n",
       "       [[-1.10945115],\n",
       "        [ 1.96737707],\n",
       "        [-1.0400669 ],\n",
       "        [-0.67763409],\n",
       "        [-2.24983622]],\n",
       "\n",
       "       [[-0.8768132 ],\n",
       "        [-0.74698127],\n",
       "        [-0.46258914],\n",
       "        [-0.7046802 ],\n",
       "        [-0.14721651]]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5, 2)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = np.concatenate((x1, y1), axis=-1)\n",
    "v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-2.32338519, -0.74734715],\n",
       "        [-0.8724784 ,  1.02168506],\n",
       "        [ 2.74080415, -0.97797964],\n",
       "        [-1.16655256, -0.35168655],\n",
       "        [-2.20222748, -0.54588423]],\n",
       "\n",
       "       [[-2.16707711,  1.16782539],\n",
       "        [-2.46139744,  0.93885386],\n",
       "        [ 2.24355116,  1.20793699],\n",
       "        [-0.64550707,  0.44272826],\n",
       "        [ 1.47110304, -2.52857552]],\n",
       "\n",
       "       [[-0.56038381,  2.73333211],\n",
       "        [ 1.53857743,  1.43402168],\n",
       "        [-1.12376254, -0.73319334],\n",
       "        [-0.19328586,  0.86227963],\n",
       "        [ 1.28848132,  1.87292151]],\n",
       "\n",
       "       [[-0.44645104,  1.10637511],\n",
       "        [-2.40737466,  0.87964274],\n",
       "        [ 0.37177756,  0.3221282 ],\n",
       "        [-0.430728  , -1.45527141],\n",
       "        [ 0.82861008,  2.34413761]],\n",
       "\n",
       "       [[-0.08088935,  1.21497984],\n",
       "        [-1.77310009,  0.36592458],\n",
       "        [ 0.66287152, -2.47347695],\n",
       "        [-1.06482184, -2.06075399],\n",
       "        [-1.47498431, -1.52610906]],\n",
       "\n",
       "       [[ 1.72556774,  2.11042183],\n",
       "        [ 0.52555176,  0.40624771],\n",
       "        [ 1.34645968,  2.20349044],\n",
       "        [-1.14869476,  0.29907351],\n",
       "        [ 2.57476196, -0.78440748]],\n",
       "\n",
       "       [[-0.92028555,  1.53446407],\n",
       "        [ 0.84663968,  2.64813608],\n",
       "        [-1.16097222,  0.83651063],\n",
       "        [-0.40410378, -1.98227305],\n",
       "        [ 2.62039716,  1.1444537 ]],\n",
       "\n",
       "       [[ 1.56751183,  0.17896546],\n",
       "        [-1.87749696, -1.72048603],\n",
       "        [-1.49364279,  0.11482887],\n",
       "        [ 1.30464056,  2.08260367],\n",
       "        [ 0.44502697, -1.91759202]],\n",
       "\n",
       "       [[-1.10945115, -1.1421404 ],\n",
       "        [ 1.96737707,  0.96891472],\n",
       "        [-1.0400669 ,  0.95472539],\n",
       "        [-0.67763409, -0.43797994],\n",
       "        [-2.24983622, -1.17797103]],\n",
       "\n",
       "       [[-0.8768132 ,  1.99005221],\n",
       "        [-0.74698127,  1.83975704],\n",
       "        [-0.46258914, -1.60464352],\n",
       "        [-0.7046802 ,  2.26839833],\n",
       "        [-0.14721651,  1.52924817]]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5, 2)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_cum = np.cumsum(v, axis=-2)\n",
    "v_cum.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-2.32338519, -0.74734715],\n",
       "        [-3.1958636 ,  0.27433791],\n",
       "        [-0.45505944, -0.70364173],\n",
       "        [-1.62161201, -1.05532828],\n",
       "        [-3.82383949, -1.60121251]],\n",
       "\n",
       "       [[-2.16707711,  1.16782539],\n",
       "        [-4.62847455,  2.10667925],\n",
       "        [-2.38492339,  3.31461624],\n",
       "        [-3.03043046,  3.7573445 ],\n",
       "        [-1.55932741,  1.22876897]],\n",
       "\n",
       "       [[-0.56038381,  2.73333211],\n",
       "        [ 0.97819362,  4.1673538 ],\n",
       "        [-0.14556891,  3.43416046],\n",
       "        [-0.33885477,  4.29644008],\n",
       "        [ 0.94962655,  6.16936159]],\n",
       "\n",
       "       [[-0.44645104,  1.10637511],\n",
       "        [-2.85382569,  1.98601785],\n",
       "        [-2.48204813,  2.30814605],\n",
       "        [-2.91277612,  0.85287464],\n",
       "        [-2.08416604,  3.19701225]],\n",
       "\n",
       "       [[-0.08088935,  1.21497984],\n",
       "        [-1.85398944,  1.58090441],\n",
       "        [-1.19111791, -0.89257253],\n",
       "        [-2.25593975, -2.95332652],\n",
       "        [-3.73092406, -4.47943558]],\n",
       "\n",
       "       [[ 1.72556774,  2.11042183],\n",
       "        [ 2.2511195 ,  2.51666954],\n",
       "        [ 3.59757918,  4.72015998],\n",
       "        [ 2.44888442,  5.01923349],\n",
       "        [ 5.02364639,  4.23482601]],\n",
       "\n",
       "       [[-0.92028555,  1.53446407],\n",
       "        [-0.07364588,  4.18260015],\n",
       "        [-1.2346181 ,  5.01911079],\n",
       "        [-1.63872188,  3.03683773],\n",
       "        [ 0.98167528,  4.18129144]],\n",
       "\n",
       "       [[ 1.56751183,  0.17896546],\n",
       "        [-0.30998514, -1.54152057],\n",
       "        [-1.80362793, -1.4266917 ],\n",
       "        [-0.49898736,  0.65591197],\n",
       "        [-0.05396039, -1.26168006]],\n",
       "\n",
       "       [[-1.10945115, -1.1421404 ],\n",
       "        [ 0.85792592, -0.17322569],\n",
       "        [-0.18214098,  0.78149971],\n",
       "        [-0.85977507,  0.34351976],\n",
       "        [-3.10961129, -0.83445127]],\n",
       "\n",
       "       [[-0.8768132 ,  1.99005221],\n",
       "        [-1.62379447,  3.82980926],\n",
       "        [-2.08638361,  2.22516574],\n",
       "        [-2.7910638 ,  4.49356407],\n",
       "        [-2.93828032,  6.02281224]]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_cum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5, 2)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_cum.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = v_cum[..., 0]\n",
    "y = v_cum[..., 1]\n",
    "rad = 0.5\n",
    "mask = np.sqrt((x-rad)**2 + (y-rad)**2) > rad\n",
    "# mask.shape\n",
    "\n",
    "mask_sum = np.sum(mask, axis=-1)\n",
    "select_idx = np.where(mask_sum>4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_sum.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'd', 'k', 'r']"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(set(\"abrakardabra\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b', 'd', 'k', 'r'}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(\"abrakardabra\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<src.data_io.DataGenerator at 0x7ffe9d1acd50>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen_2D_multi_dtype1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "## VERIFIED 🎯🎯\n",
       "Args:\n",
       "    num_data : number of positions and velocity data points\n",
       "Raise: NotImplementedError\n",
       "Returns:\n",
       "    place_pair : a dict of before & after positions (mu) and velocities\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Google Drive/MS@NCState/research@NCState/research@tianfu_wu/GridTr/AOGTransformer/GridTr/src/data_io.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.gen_2D_multi_dtype1?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_velocity_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmin_velocity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minterval\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "## VERIFIED 🎯🎯\n",
       "Args:\n",
       "    max_velocity : maximum velocity in the experiment\n",
       "    min_velocity : minimum velocity in the experiment\n",
       "    interval : step of unit motion\n",
       "    verbose : If True, then plot all the velocities in the generated velocity list.\n",
       "\n",
       "Returns:\n",
       "    [a list of possible motion velocities]\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Google Drive/MS@NCState/research@NCState/research@tianfu_wu/GridTr/AOGTransformer/GridTr/src/utils.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.generate_velocity_list?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "velocity.shape = (28, 2)\n",
      "velocity_idx.shape = (30, 5)\n",
      "velocity_grid.shape = (30, 5, 2)\n"
     ]
    }
   ],
   "source": [
    "y = d.gen_2D_multi_dtype2(30, 3, 0, 5, False, False, \"discrete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mu_sequence', 'velocity', 'velocity_idx'])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 6, 2)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[\"mu_sequence\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[690.54609208, 907.84311467],\n",
       "        [688.54609208, 905.84311467],\n",
       "        [688.54609208, 902.84311467],\n",
       "        [688.54609208, 904.84311467],\n",
       "        [690.54609208, 905.84311467],\n",
       "        [691.54609208, 903.84311467]],\n",
       "\n",
       "       [[320.65721981, 952.05853945],\n",
       "        [319.65721981, 951.05853945],\n",
       "        [320.65721981, 949.05853945],\n",
       "        [318.65721981, 949.05853945],\n",
       "        [317.65721981, 948.05853945],\n",
       "        [318.65721981, 948.05853945]],\n",
       "\n",
       "       [[633.51591792, 944.2719287 ],\n",
       "        [635.51591792, 944.2719287 ],\n",
       "        [635.51591792, 946.2719287 ],\n",
       "        [636.51591792, 944.2719287 ],\n",
       "        [638.51591792, 943.2719287 ],\n",
       "        [636.51591792, 945.2719287 ]],\n",
       "\n",
       "       [[466.63219755, 869.4013432 ],\n",
       "        [465.63219755, 869.4013432 ],\n",
       "        [464.63219755, 871.4013432 ],\n",
       "        [464.63219755, 873.4013432 ],\n",
       "        [462.63219755, 875.4013432 ],\n",
       "        [461.63219755, 877.4013432 ]],\n",
       "\n",
       "       [[798.3238646 , 612.28279997],\n",
       "        [796.3238646 , 612.28279997],\n",
       "        [797.3238646 , 614.28279997],\n",
       "        [795.3238646 , 612.28279997],\n",
       "        [796.3238646 , 612.28279997],\n",
       "        [795.3238646 , 610.28279997]],\n",
       "\n",
       "       [[426.97403756, 957.13380836],\n",
       "        [426.97403756, 956.13380836],\n",
       "        [424.97403756, 955.13380836],\n",
       "        [423.97403756, 957.13380836],\n",
       "        [425.97403756, 957.13380836],\n",
       "        [425.97403756, 960.13380836]],\n",
       "\n",
       "       [[770.55697609, 889.80035214],\n",
       "        [772.55697609, 887.80035214],\n",
       "        [774.55697609, 885.80035214],\n",
       "        [772.55697609, 886.80035214],\n",
       "        [772.55697609, 884.80035214],\n",
       "        [772.55697609, 887.80035214]],\n",
       "\n",
       "       [[824.83404941, 933.04647813],\n",
       "        [824.83404941, 931.04647813],\n",
       "        [821.83404941, 931.04647813],\n",
       "        [823.83404941, 930.04647813],\n",
       "        [825.83404941, 929.04647813],\n",
       "        [824.83404941, 930.04647813]],\n",
       "\n",
       "       [[191.2833483 , 698.14267055],\n",
       "        [191.2833483 , 701.14267055],\n",
       "        [189.2833483 , 701.14267055],\n",
       "        [188.2833483 , 703.14267055],\n",
       "        [188.2833483 , 701.14267055],\n",
       "        [187.2833483 , 699.14267055]],\n",
       "\n",
       "       [[343.8182224 , 145.82228175],\n",
       "        [344.8182224 , 146.82228175],\n",
       "        [346.8182224 , 146.82228175],\n",
       "        [345.8182224 , 146.82228175],\n",
       "        [343.8182224 , 145.82228175],\n",
       "        [342.8182224 , 144.82228175]],\n",
       "\n",
       "       [[617.2522729 , 703.36566611],\n",
       "        [615.2522729 , 702.36566611],\n",
       "        [617.2522729 , 702.36566611],\n",
       "        [617.2522729 , 703.36566611],\n",
       "        [617.2522729 , 701.36566611],\n",
       "        [619.2522729 , 699.36566611]],\n",
       "\n",
       "       [[542.14086678, 124.01077512],\n",
       "        [540.14086678, 124.01077512],\n",
       "        [541.14086678, 124.01077512],\n",
       "        [541.14086678, 127.01077512],\n",
       "        [539.14086678, 126.01077512],\n",
       "        [541.14086678, 124.01077512]],\n",
       "\n",
       "       [[574.85736598, 676.22366975],\n",
       "        [574.85736598, 679.22366975],\n",
       "        [576.85736598, 679.22366975],\n",
       "        [578.85736598, 678.22366975],\n",
       "        [580.85736598, 679.22366975],\n",
       "        [582.85736598, 678.22366975]],\n",
       "\n",
       "       [[ 40.90657434, 420.14767063],\n",
       "        [ 42.90657434, 421.14767063],\n",
       "        [ 40.90657434, 423.14767063],\n",
       "        [ 39.90657434, 422.14767063],\n",
       "        [ 38.90657434, 422.14767063],\n",
       "        [ 39.90657434, 424.14767063]],\n",
       "\n",
       "       [[568.45670021,  87.7182915 ],\n",
       "        [567.45670021,  87.7182915 ],\n",
       "        [567.45670021,  89.7182915 ],\n",
       "        [565.45670021,  89.7182915 ],\n",
       "        [566.45670021,  90.7182915 ],\n",
       "        [566.45670021,  93.7182915 ]],\n",
       "\n",
       "       [[962.35475432,  55.80367141],\n",
       "        [963.35475432,  54.80367141],\n",
       "        [965.35475432,  55.80367141],\n",
       "        [964.35475432,  53.80367141],\n",
       "        [965.35475432,  52.80367141],\n",
       "        [962.35475432,  52.80367141]],\n",
       "\n",
       "       [[271.61862305, 363.75458516],\n",
       "        [271.61862305, 361.75458516],\n",
       "        [268.61862305, 361.75458516],\n",
       "        [268.61862305, 358.75458516],\n",
       "        [266.61862305, 357.75458516],\n",
       "        [265.61862305, 356.75458516]],\n",
       "\n",
       "       [[ 68.84832665,  87.15973683],\n",
       "        [ 66.84832665,  87.15973683],\n",
       "        [ 64.84832665,  88.15973683],\n",
       "        [ 64.84832665,  90.15973683],\n",
       "        [ 62.84832665,  92.15973683],\n",
       "        [ 63.84832665,  91.15973683]],\n",
       "\n",
       "       [[986.89160791, 397.45006344],\n",
       "        [986.89160791, 395.45006344],\n",
       "        [986.89160791, 398.45006344],\n",
       "        [985.89160791, 398.45006344],\n",
       "        [986.89160791, 396.45006344],\n",
       "        [984.89160791, 397.45006344]],\n",
       "\n",
       "       [[882.05721153, 790.77855595],\n",
       "        [881.05721153, 790.77855595],\n",
       "        [881.05721153, 789.77855595],\n",
       "        [883.05721153, 787.77855595],\n",
       "        [881.05721153, 789.77855595],\n",
       "        [881.05721153, 792.77855595]],\n",
       "\n",
       "       [[ 12.21322914, 455.27466913],\n",
       "        [ 12.21322914, 454.27466913],\n",
       "        [ 13.21322914, 452.27466913],\n",
       "        [ 12.21322914, 453.27466913],\n",
       "        [ 15.21322914, 453.27466913],\n",
       "        [ 16.21322914, 451.27466913]],\n",
       "\n",
       "       [[151.34537259,  82.81942185],\n",
       "        [153.34537259,  82.81942185],\n",
       "        [153.34537259,  80.81942185],\n",
       "        [156.34537259,  80.81942185],\n",
       "        [156.34537259,  78.81942185],\n",
       "        [154.34537259,  78.81942185]],\n",
       "\n",
       "       [[190.86144708, 424.04109084],\n",
       "        [189.86144708, 422.04109084],\n",
       "        [186.86144708, 422.04109084],\n",
       "        [186.86144708, 425.04109084],\n",
       "        [184.86144708, 423.04109084],\n",
       "        [184.86144708, 426.04109084]],\n",
       "\n",
       "       [[192.35992723, 607.22072878],\n",
       "        [193.35992723, 606.22072878],\n",
       "        [194.35992723, 607.22072878],\n",
       "        [193.35992723, 605.22072878],\n",
       "        [192.35992723, 605.22072878],\n",
       "        [192.35992723, 603.22072878]],\n",
       "\n",
       "       [[293.18876455, 762.02701221],\n",
       "        [293.18876455, 761.02701221],\n",
       "        [291.18876455, 759.02701221],\n",
       "        [290.18876455, 761.02701221],\n",
       "        [289.18876455, 761.02701221],\n",
       "        [287.18876455, 763.02701221]],\n",
       "\n",
       "       [[247.7848084 , 326.46349706],\n",
       "        [245.7848084 , 326.46349706],\n",
       "        [245.7848084 , 329.46349706],\n",
       "        [242.7848084 , 329.46349706],\n",
       "        [244.7848084 , 330.46349706],\n",
       "        [246.7848084 , 330.46349706]],\n",
       "\n",
       "       [[339.62404332, 100.77604248],\n",
       "        [339.62404332,  99.77604248],\n",
       "        [341.62404332, 101.77604248],\n",
       "        [343.62404332, 101.77604248],\n",
       "        [342.62404332, 102.77604248],\n",
       "        [343.62404332, 101.77604248]],\n",
       "\n",
       "       [[690.5378118 , 734.91572628],\n",
       "        [689.5378118 , 735.91572628],\n",
       "        [687.5378118 , 735.91572628],\n",
       "        [685.5378118 , 737.91572628],\n",
       "        [685.5378118 , 734.91572628],\n",
       "        [685.5378118 , 731.91572628]],\n",
       "\n",
       "       [[476.84110701, 732.15511034],\n",
       "        [479.84110701, 732.15511034],\n",
       "        [478.84110701, 733.15511034],\n",
       "        [480.84110701, 732.15511034],\n",
       "        [482.84110701, 734.15511034],\n",
       "        [481.84110701, 734.15511034]],\n",
       "\n",
       "       [[553.56128517, 628.26099621],\n",
       "        [555.56128517, 627.26099621],\n",
       "        [553.56128517, 628.26099621],\n",
       "        [551.56128517, 628.26099621],\n",
       "        [549.56128517, 630.26099621],\n",
       "        [548.56128517, 632.26099621]]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[\"mu_sequence\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import integrate\n",
    "\n",
    "# Note: t0 is required for the odeint function, though it's not used here.\n",
    "def lorentz_deriv(X, t0, sigma=10., beta=8./3, rho=28.0):\n",
    "    \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
    "    x, y, z = X\n",
    "    return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
    "\n",
    "x0 = [1, 1, 1]  # starting vector\n",
    "t = np.linspace(0, 3, 1000)  # one thousand time steps\n",
    "x_t = integrate.odeint(lorentz_deriv, x0, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "ename": "CalledProcessError",
     "evalue": "Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '640x480', '-pix_fmt', 'rgba', '-r', '15', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-vcodec', 'libx264', '-y', 'lorentz_attractor.mp4']' returned non-zero exit status 255.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1156\u001b[0m                     \u001b[0;31m# TODO: See if turning off blit is really necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1157\u001b[0;31m                     \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_next_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1158\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mprogress_callback\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_next_frame\u001b[0;34m(self, framedata, blit)\u001b[0m\n\u001b[1;32m   1191\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pre_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1192\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1193\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1741\u001b[0m         \u001b[0;31m# func needs to return a sequence of any artists that were modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1742\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1743\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-123-a639b564fedd>\u001b[0m in \u001b[0;36manimate\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.3\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mpts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    406\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1863\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1864\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0;31m# Then rest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    410\u001b[0m                 **kwargs)\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2747\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/mpl_toolkits/mplot3d/art3d.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0mxs3d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys3d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzs3d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_verts3d\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m         \u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproj3d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs3d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys3d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzs3d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/mpl_toolkits/mplot3d/proj3d.py\u001b[0m in \u001b[0;36mproj_transform\u001b[0;34m(xs, ys, zs, M)\u001b[0m\n\u001b[1;32m    142\u001b[0m     \"\"\"\n\u001b[0;32m--> 143\u001b[0;31m     \u001b[0mvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_vec_pad_ones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_proj_transform_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/mpl_toolkits/mplot3d/proj3d.py\u001b[0m in \u001b[0;36m_vec_pad_ones\u001b[0;34m(xs, ys, zs)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_vec_pad_ones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mones_like\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36mones_like\u001b[0;34m(a, dtype, order, subok, shape)\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mempty_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m     \u001b[0mmultiarray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopyto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'unsafe'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcopyto\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-123-a639b564fedd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;31m# Save as mp4. This requires mplayer or ffmpeg to be installed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'lorentz_attractor.mp4'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'-vcodec'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'libx264'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1159\u001b[0m                         \u001b[0mprogress_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_number\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_frames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1160\u001b[0m                         \u001b[0mframe_number\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m                 \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m    128\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m                 \u001b[0;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    251\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mfinish\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m         \u001b[0;34m\"\"\"Finish any processing for writing the movie.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mcleanup\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m             raise subprocess.CalledProcessError(\n\u001b[0;32m--> 391\u001b[0;31m                 self._proc.returncode, self._proc.args, out, err)\n\u001b[0m\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCalledProcessError\u001b[0m: Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '640x480', '-pix_fmt', 'rgba', '-r', '15', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-vcodec', 'libx264', '-y', 'lorentz_attractor.mp4']' returned non-zero exit status 255."
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import integrate\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.colors import cnames\n",
    "from matplotlib import animation\n",
    "\n",
    "N_trajectories = 20\n",
    "\n",
    "\n",
    "def lorentz_deriv(X, t0, sigma=10., beta=8./3, rho=28.0):\n",
    "    \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
    "    x, y, z = X\n",
    "    return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
    "\n",
    "# Choose random starting points, uniformly distributed from -15 to 15\n",
    "np.random.seed(1)\n",
    "x0 = -15 + 30 * np.random.random((N_trajectories, 3))\n",
    "\n",
    "# Solve for the trajectories\n",
    "t = np.linspace(0, 4, 1000)\n",
    "x_t = np.asarray([integrate.odeint(lorentz_deriv, x0i, t)\n",
    "                  for x0i in x0])\n",
    "\n",
    "# Set up figure & 3D axis for animation\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n",
    "ax.axis('off')\n",
    "\n",
    "# choose a different color for each trajectory\n",
    "colors = plt.cm.jet(np.linspace(0, 1, N_trajectories))\n",
    "\n",
    "# set up lines and points\n",
    "lines = sum([ax.plot([], [], [], '-', c=c)\n",
    "             for c in colors], [])\n",
    "pts = sum([ax.plot([], [], [], 'o', c=c)\n",
    "           for c in colors], [])\n",
    "\n",
    "# prepare the axes limits\n",
    "ax.set_xlim((-25, 25))\n",
    "ax.set_ylim((-35, 35))\n",
    "ax.set_zlim((5, 55))\n",
    "\n",
    "# set point-of-view: specified by (altitude degrees, azimuth degrees)\n",
    "ax.view_init(30, 0)\n",
    "\n",
    "# initialization function: plot the background of each frame\n",
    "def init():\n",
    "    for line, pt in zip(lines, pts):\n",
    "        line.set_data(np.array([]), np.array([]))\n",
    "        line.set_3d_properties(np.array([]))\n",
    "\n",
    "        pt.set_data(np.array([]), np.array([]))\n",
    "        pt.set_3d_properties(np.array([]))\n",
    "    return lines + pts\n",
    "\n",
    "# animation function.  This will be called sequentially with the frame number\n",
    "def animate(i):\n",
    "    # we'll step two time-steps per frame.  This leads to nice results.\n",
    "    i = (2 * i) % x_t.shape[1]\n",
    "\n",
    "    for line, pt, xi in zip(lines, pts, x_t):\n",
    "        x, y, z = xi[:i].T\n",
    "        line.set_data(x, y)\n",
    "        line.set_3d_properties(z)\n",
    "\n",
    "        pt.set_data(x[-1:], y[-1:])\n",
    "        pt.set_3d_properties(z[-1:])\n",
    "\n",
    "    ax.view_init(30, 0.3 * i)\n",
    "    fig.canvas.draw()\n",
    "    return lines + pts\n",
    "\n",
    "# instantiate the animator.\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
    "                               frames=500, interval=30, blit=True)\n",
    "\n",
    "# Save as mp4. This requires mplayer or ffmpeg to be installed\n",
    "anim.save('lorentz_attractor.mp4', fps=15, extra_args=['-vcodec', 'libx264'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.choice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.take?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y[\"velocity\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.zeros_like(y[\"velocity\"])\n",
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z.unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.sample?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random_sample?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.random_sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "velocity.shape = (28, 2)\n",
      "velocity_idx.shape = (30, 5)\n",
      "velocity_grid.shape = (30, 5, 2)\n"
     ]
    }
   ],
   "source": [
    "d = data_io.DataGenerator()\n",
    "y = d.gen_2D_multi_dtype2(30, 3, 0, 5, False, False, \"discrete\")\n",
    "y0 = y1 = y2 = None\n",
    "for i, k in enumerate(y.keys()):\n",
    "    if i==0: y0 = y[k]\n",
    "    if i==1: y1 = y[k]\n",
    "    if i==2: y2 = y[k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mu_sequence', 'velocity', 'velocity_idx'])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30, 6, 2), (30, 5, 2))"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0.shape, y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(987.112423394037, 1.0092322762446777)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0.max(), y0.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.003003003003003003, -0.003003003003003003)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1.max(), y1.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.003003,  0.      ],\n",
       "       [-0.002002, -0.001001],\n",
       "       [ 0.002002,  0.001001],\n",
       "       [ 0.001001, -0.002002],\n",
       "       [ 0.001001,  0.002002]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[83.04506785, 46.1394831 ],\n",
       "       [86.04506785, 46.1394831 ],\n",
       "       [84.04506785, 45.1394831 ],\n",
       "       [86.04506785, 46.1394831 ],\n",
       "       [87.04506785, 44.1394831 ],\n",
       "       [88.04506785, 46.1394831 ]])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08304507, 0.08604507, 0.08404507, 0.08604507, 0.08704507,\n",
       "        0.08804507],\n",
       "       [0.08304507, 0.04613948, 0.04513948, 0.04613948, 0.04413948,\n",
       "        0.04613948]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "origin = np.zeros_like(y0[0].T)\n",
    "\n",
    "X = y0[0][:, 0]/1_000                                                                      \n",
    "Y = y0[0][:, 1]/1_000\n",
    "origin[0] = np.insert(X[1:], 0, X[0])\n",
    "origin[1] = np.insert(Y[1:], 0, X[0])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))                                                       \n",
    "ax.quiver(*origin, X, Y, scale=0.1, units=\"xy\", width=0.00005)                                    \n",
    "ax.scatter(X, Y, c=\"blue\")                                                                   \n",
    "ax.grid(True)                                                                                \n",
    "ax.set_axisbelow(True)                                                                       \n",
    "# ax.set_aspect(\"equal\")                                                                       \n",
    "# ax.spines[\"left\"].set_position(\"zero\")                                                       \n",
    "# ax.spines[\"bottom\"].set_position(\"zero\")                                                     \n",
    "# ax.spines[\"right\"].set_position(\"zero\")                                                      \n",
    "# ax.spines[\"top\"].set_position(\"zero\")                                                        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "soa = np.array([[3, 2, 0, 0], [3, 2, 1-3, 1-2], [1,1, 9-1, 9-1]])\n",
    "X, Y, U, V = zip(*soa)\n",
    "plt.figure()\n",
    "ax = plt.gca()\n",
    "ax.quiver(X, Y, U, V, angles='xy', scale_units='xy', scale=1)\n",
    "ax.set_xlim([-1, 10])\n",
    "ax.set_ylim([-1, 10])\n",
    "plt.draw()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 3, 1)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2, 1)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1, 9)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1, 9)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "expected Tensor as element 0 in argument 0, but got tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-176-79a008f01c80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_block_diagonal_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Google Drive/MS@NCState/research@NCState/research@tianfu_wu/GridTr/AOGTransformer/GridTr/src/ops.py\u001b[0m in \u001b[0;36mcreate_block_diagonal_weights\u001b[0;34m(num_channels, num_blocks, block_size)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0msample_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_blocks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_channels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     )\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblock_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/functional.py\u001b[0m in \u001b[0;36mblock_diag\u001b[0;34m(*tensors)\u001b[0m\n\u001b[1;32m   1001\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mTensor\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhas_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1002\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock_diag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_VariableFunctions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblock_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: expected Tensor as element 0 in argument 0, but got tuple"
     ]
    }
   ],
   "source": [
    "ops.create_block_diagonal_weights(2,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "x = torch.distributions.normal.Normal(0,0.01)\n",
    "x = x.sample((2,3,4,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 3, 4, 5])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[-1.8568e-03, -1.9164e-04,  3.0507e-03,  2.1565e-03, -1.7850e-02],\n",
       "          [-7.1848e-04,  1.5543e-02,  5.5077e-03,  1.8281e-03, -2.6098e-04],\n",
       "          [ 4.0320e-03,  1.8454e-02, -1.4729e-02, -2.0263e-02,  1.3141e-02],\n",
       "          [-1.5536e-03,  7.4260e-03, -1.0671e-02, -1.1682e-04,  1.5903e-02]],\n",
       "\n",
       "         [[ 1.5896e-02, -1.4607e-02,  1.0196e-03, -1.5209e-02,  7.9353e-04],\n",
       "          [-1.2036e-02, -7.4486e-03, -8.3842e-03, -9.9309e-03, -1.5641e-02],\n",
       "          [-1.4816e-02,  8.1237e-03, -6.0345e-03,  5.6840e-03, -1.3187e-02],\n",
       "          [-1.0789e-02,  1.4729e-03, -1.3207e-02,  2.7065e-02, -5.2587e-03]],\n",
       "\n",
       "         [[ 1.1059e-02, -8.2831e-03,  7.1388e-03,  1.3462e-02, -2.6166e-03],\n",
       "          [ 1.8101e-03, -5.2856e-03, -1.4261e-03, -1.1592e-02, -2.2861e-03],\n",
       "          [ 2.2928e-02, -1.2374e-02, -2.3242e-02,  3.8956e-03, -1.0456e-02],\n",
       "          [-7.4616e-04, -3.7184e-03, -1.1301e-02, -1.1117e-02, -7.0239e-03]]],\n",
       "\n",
       "\n",
       "        [[[-1.2346e-02,  2.6968e-03, -1.2720e-02, -3.3789e-03,  1.2631e-02],\n",
       "          [ 1.1743e-02, -2.4894e-03,  1.6059e-02, -1.5668e-04,  1.9573e-02],\n",
       "          [ 1.3177e-02, -1.2858e-02,  5.8333e-03,  6.4180e-03,  1.5370e-02],\n",
       "          [ 1.8014e-03, -1.3870e-03, -2.0878e-03,  7.4779e-04,  1.3788e-02]],\n",
       "\n",
       "         [[ 1.9584e-02,  8.6577e-03,  2.7666e-03, -1.0666e-02,  2.6541e-02],\n",
       "          [-2.3031e-02,  1.3054e-02,  1.7562e-02,  1.0844e-02,  1.8551e-03],\n",
       "          [ 6.0377e-03,  1.7706e-02, -3.2278e-03,  7.5265e-04,  3.4710e-06],\n",
       "          [ 7.5218e-03,  7.1163e-03, -5.1386e-03,  9.0428e-03,  3.9954e-03]],\n",
       "\n",
       "         [[ 7.0714e-03, -7.3462e-03,  4.7119e-03, -1.2271e-02,  1.1831e-02],\n",
       "          [ 1.4302e-02, -2.4990e-04, -1.1336e-02, -4.3829e-03,  4.8787e-04],\n",
       "          [-3.4850e-03,  1.9833e-03,  3.1607e-03, -1.3362e-02, -7.2603e-03],\n",
       "          [-6.2786e-03, -7.4443e-03,  3.1554e-03,  1.0465e-02, -2.6551e-03]]]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = torch.unbind(x, dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[[-0.0019, -0.0002,  0.0031,  0.0022, -0.0178],\n",
       "          [-0.0007,  0.0155,  0.0055,  0.0018, -0.0003],\n",
       "          [ 0.0040,  0.0185, -0.0147, -0.0203,  0.0131],\n",
       "          [-0.0016,  0.0074, -0.0107, -0.0001,  0.0159]],\n",
       " \n",
       "         [[ 0.0159, -0.0146,  0.0010, -0.0152,  0.0008],\n",
       "          [-0.0120, -0.0074, -0.0084, -0.0099, -0.0156],\n",
       "          [-0.0148,  0.0081, -0.0060,  0.0057, -0.0132],\n",
       "          [-0.0108,  0.0015, -0.0132,  0.0271, -0.0053]],\n",
       " \n",
       "         [[ 0.0111, -0.0083,  0.0071,  0.0135, -0.0026],\n",
       "          [ 0.0018, -0.0053, -0.0014, -0.0116, -0.0023],\n",
       "          [ 0.0229, -0.0124, -0.0232,  0.0039, -0.0105],\n",
       "          [-0.0007, -0.0037, -0.0113, -0.0111, -0.0070]]]),\n",
       " tensor([[[-1.2346e-02,  2.6968e-03, -1.2720e-02, -3.3789e-03,  1.2631e-02],\n",
       "          [ 1.1743e-02, -2.4894e-03,  1.6059e-02, -1.5668e-04,  1.9573e-02],\n",
       "          [ 1.3177e-02, -1.2858e-02,  5.8333e-03,  6.4180e-03,  1.5370e-02],\n",
       "          [ 1.8014e-03, -1.3870e-03, -2.0878e-03,  7.4779e-04,  1.3788e-02]],\n",
       " \n",
       "         [[ 1.9584e-02,  8.6577e-03,  2.7666e-03, -1.0666e-02,  2.6541e-02],\n",
       "          [-2.3031e-02,  1.3054e-02,  1.7562e-02,  1.0844e-02,  1.8551e-03],\n",
       "          [ 6.0377e-03,  1.7706e-02, -3.2278e-03,  7.5265e-04,  3.4710e-06],\n",
       "          [ 7.5218e-03,  7.1163e-03, -5.1386e-03,  9.0428e-03,  3.9954e-03]],\n",
       " \n",
       "         [[ 7.0714e-03, -7.3462e-03,  4.7119e-03, -1.2271e-02,  1.1831e-02],\n",
       "          [ 1.4302e-02, -2.4990e-04, -1.1336e-02, -4.3829e-03,  4.8787e-04],\n",
       "          [-3.4850e-03,  1.9833e-03,  3.1607e-03, -1.3362e-02, -7.2603e-03],\n",
       "          [-6.2786e-03, -7.4443e-03,  3.1554e-03,  1.0465e-02, -2.6551e-03]]]))"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "torch.block_diag: Input tensors must have 2 or fewer dimensions. Input 0 has 3 dimensions",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-196-cb8a9b41cc12>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblock_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/functional.py\u001b[0m in \u001b[0;36mblock_diag\u001b[0;34m(*tensors)\u001b[0m\n\u001b[1;32m   1001\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mTensor\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhas_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1002\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock_diag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_VariableFunctions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblock_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: torch.block_diag: Input tensors must have 2 or fewer dimensions. Input 0 has 3 dimensions"
     ]
    }
   ],
   "source": [
    "torch.block_diag(*y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[-0.0019, -0.0002,  0.0031,  0.0022, -0.0178],\n",
      "         [-0.0007,  0.0155,  0.0055,  0.0018, -0.0003],\n",
      "         [ 0.0040,  0.0185, -0.0147, -0.0203,  0.0131],\n",
      "         [-0.0016,  0.0074, -0.0107, -0.0001,  0.0159]],\n",
      "\n",
      "        [[ 0.0159, -0.0146,  0.0010, -0.0152,  0.0008],\n",
      "         [-0.0120, -0.0074, -0.0084, -0.0099, -0.0156],\n",
      "         [-0.0148,  0.0081, -0.0060,  0.0057, -0.0132],\n",
      "         [-0.0108,  0.0015, -0.0132,  0.0271, -0.0053]],\n",
      "\n",
      "        [[ 0.0111, -0.0083,  0.0071,  0.0135, -0.0026],\n",
      "         [ 0.0018, -0.0053, -0.0014, -0.0116, -0.0023],\n",
      "         [ 0.0229, -0.0124, -0.0232,  0.0039, -0.0105],\n",
      "         [-0.0007, -0.0037, -0.0113, -0.0111, -0.0070]]]) tensor([[[-1.2346e-02,  2.6968e-03, -1.2720e-02, -3.3789e-03,  1.2631e-02],\n",
      "         [ 1.1743e-02, -2.4894e-03,  1.6059e-02, -1.5668e-04,  1.9573e-02],\n",
      "         [ 1.3177e-02, -1.2858e-02,  5.8333e-03,  6.4180e-03,  1.5370e-02],\n",
      "         [ 1.8014e-03, -1.3870e-03, -2.0878e-03,  7.4779e-04,  1.3788e-02]],\n",
      "\n",
      "        [[ 1.9584e-02,  8.6577e-03,  2.7666e-03, -1.0666e-02,  2.6541e-02],\n",
      "         [-2.3031e-02,  1.3054e-02,  1.7562e-02,  1.0844e-02,  1.8551e-03],\n",
      "         [ 6.0377e-03,  1.7706e-02, -3.2278e-03,  7.5265e-04,  3.4710e-06],\n",
      "         [ 7.5218e-03,  7.1163e-03, -5.1386e-03,  9.0428e-03,  3.9954e-03]],\n",
      "\n",
      "        [[ 7.0714e-03, -7.3462e-03,  4.7119e-03, -1.2271e-02,  1.1831e-02],\n",
      "         [ 1.4302e-02, -2.4990e-04, -1.1336e-02, -4.3829e-03,  4.8787e-04],\n",
      "         [-3.4850e-03,  1.9833e-03,  3.1607e-03, -1.3362e-02, -7.2603e-03],\n",
      "         [-6.2786e-03, -7.4443e-03,  3.1554e-03,  1.0465e-02, -2.6551e-03]]])\n"
     ]
    }
   ],
   "source": [
    "print(*y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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