NES Tetris RNG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_rng():\n",
    "    # Tetris (U), routine at 00:AB47. typical args: X=0x17, Y=0x02\n",
    "    hi, lo = 0x89, 0x88\n",
    "    def nes_rng():\n",
    "        nonlocal hi, lo\n",
    "        carry_hi = (hi & 2) ^ (lo & 2) != 0\n",
    "        carry_lo = hi & 1\n",
    "        hi = (carry_hi << 7) | (hi >> 1)  # ROR\n",
    "        lo = (carry_lo << 7) | (lo >> 1)  # ROR\n",
    "        return hi, lo\n",
    "    return nes_rng\n",
    "\n",
    "rng = make_rng()\n",
    "samples = np.array([rng() for i in range(0x10008)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hi, lo = samples.T\n",
    "np.all(hi[:0x8000] == hi[0x7FFF:0xFFFF]), np.all(lo[8:0x8008] == hi[:0x8000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "limit = 0x7FFF\n",
    "plt.hist(hi[:limit], bins=256, range=(0, 255), density=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.diff(hi[:limit + 1]) % 256, bins=256, range=(0, 255), density=True);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

btw this is just a linear feedback shift register, which makes this whole notebook a little pointless. oh well.