notebook/memories-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter notebook to analyze the different effects used in the demo\n",
    "\n",
    "This **Jupyter** notebook will be useful to understand some of the ideas behind each of the different effects that appear in the demo. We'll start by looking at the values of the `DX` registers that is used in all effects. Then we'll go over each individual effect and try to explore the math that goes behind it.\n",
    "\n",
    "## Analyzing the contents of DX\n",
    "\n",
    "In the program, all the effects use the `DX` register which is pre-loaded when the loop starts with the high word of the result of multiplying the `DI` register with the *magic number* `0xcccd`. To understand why the author uses this number we need to dig a bit into this bit of code:\n",
    "\n",
    "```\n",
    "    mov    ax, 0xcccd\n",
    "    mul    di\n",
    "```\n",
    "\n",
    "Remember that `DI` is basically a counter. When you multiply `DI` (a 16 bit number) with `0xcccd` (another 16 bit numbre) you get a 32 bit number. The high part is stored in `DX` and the lower part in `AX`. For the code of each effect the value of `AX` is not relevant so lets focus on `DX`. Lets look at how it behaves as a function of `DI`. For this let's do couple of simple graphs. Lets import the Matplotlib and NumPy libraries :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and set up two arrays to hold the top (`DH`) and bottom registers (`DL`) part of `DX`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dh = []\n",
    "result_dl = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets loop through the first **n** bytes of DI and calculate `DX`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 0xffff;\n",
    "for di in range(0, n, 1):\n",
    "\n",
    "    # mov ax, 0xcccd\n",
    "    # mul di\n",
    "    m = (di+160) * 0xcccd\n",
    "    dx = (m & 0xffff0000) >> 16\n",
    "    dx = dx & 0xffff\n",
    "    ax = (m & 0xffff)\n",
    "    \n",
    "    result_dl.append(dx & 0xff)\n",
    "    result_dh.append((dx & 0xff00) >> 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets plot the low and high bytes of `DX` as a function of `DI`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "offset = 0\n",
    "plt.figure(figsize=(14,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(result_dl[offset:offset+700])\n",
    "plt.title(\"DL register as a functoin of DI\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(result_dl[offset:offset+16])\n",
    "plt.title(\"DL register as a functoin of DI (detail)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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.plot(result_dh[offset:offset+640*2])\n",
    "plt.title(\"DH register as a function of DI\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we see the following important properties:\n",
    "\n",
    "1. Both functions have a period of **320** which is the width of the screen.\n",
    "2. In the case of `DH` we see that its an increasing function which basically adds 1 after 320 bytes. So, in effect this is basically the *y* coordinate of the pixel at `DI`.\n",
    "3. In the case of `DL` we see its a sawtooth with period 320. The values go from 0 to 255. Since there are only 256 possible values and the function reaches its maximum when `DI` is 320 then some values must repeat. If you look closely this can be seen. You can think of this as the *x* coordinate except that instead of going up to 319 it goes up to 255.\n",
    "\n",
    "After HellMood published his article this is what he calls the [\"Rrrola trick\"](http://www.sizecoding.org/wiki/General_Coding_Tricks#Obtaining_X_and_Y_without_DIV_.28The_Rrrola_Trick.29). In this way you can get an estimate to *y*,*x* without the need to divide and move the results around which require more bytes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Board of Chessboards effect\n",
    "\n",
    "The first efffect that appear in the demo is generated by this code:\n",
    "\n",
    "```\n",
    "140 fx2: ; board of chessboards\n",
    "141     xchg dx,ax\n",
    "142     sub ax,bp\n",
    "143     xor al,ah\n",
    "144     or al,0xDB\n",
    "145     add al,13h\n",
    "146 ret\n",
    "```\n",
    "\n",
    "If you read the main text you'll learn that the most important thing happening here is the line `xor al,ah`. To explore this let's look at what the `XOR` of two 8 bit numbers will look like.\n",
    "\n",
    "\n",
    "### Calculating the XOR of two bytes\n",
    "\n",
    "Lets calculate the table of all possible XOR between two nibbles (4 bit numbers). We can calculate this easily like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]\n",
      " [ 1  0  3  2  5  4  7  6  9  8 11 10 13 12 15 14]\n",
      " [ 2  3  0  1  6  7  4  5 10 11  8  9 14 15 12 13]\n",
      " [ 3  2  1  0  7  6  5  4 11 10  9  8 15 14 13 12]\n",
      " [ 4  5  6  7  0  1  2  3 12 13 14 15  8  9 10 11]\n",
      " [ 5  4  7  6  1  0  3  2 13 12 15 14  9  8 11 10]\n",
      " [ 6  7  4  5  2  3  0  1 14 15 12 13 10 11  8  9]\n",
      " [ 7  6  5  4  3  2  1  0 15 14 13 12 11 10  9  8]\n",
      " [ 8  9 10 11 12 13 14 15  0  1  2  3  4  5  6  7]\n",
      " [ 9  8 11 10 13 12 15 14  1  0  3  2  5  4  7  6]\n",
      " [10 11  8  9 14 15 12 13  2  3  0  1  6  7  4  5]\n",
      " [11 10  9  8 15 14 13 12  3  2  1  0  7  6  5  4]\n",
      " [12 13 14 15  8  9 10 11  4  5  6  7  0  1  2  3]\n",
      " [13 12 15 14  9  8 11 10  5  4  7  6  1  0  3  2]\n",
      " [14 15 12 13 10 11  8  9  6  7  4  5  2  3  0  1]\n",
      " [15 14 13 12 11 10  9  8  7  6  5  4  3  2  1  0]]\n"
     ]
    }
   ],
   "source": [
    "matrix = []\n",
    "for row in range(0, 16):\n",
    "    matrix.append( map(lambda col: (row ^ col), range(0, 16)) )\n",
    "    \n",
    "print np.array(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each element in this matrix is also a nible so we can look at its binary representation. So, if we print this same matrix but each element has the binary representation we can create 4 new matrices with a `0` or `1` in it corresponding to the first, second, etc bits. Let's look at how this 4 matrices look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "cmap = matplotlib.colors.ListedColormap([\"white\", [.12,.1,.17,1]])\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "i = 1\n",
    "for mask in [0b1000, 0b0100, 0b0010, 0b0001]:\n",
    "    bits = copy.deepcopy(matrix)\n",
    "    for row in range(0, 16):\n",
    "        for col in range(0, 16):\n",
    "            bits[row][col] = matrix[row][col] & mask\n",
    "\n",
    "    plt.subplot(2, 2, i)\n",
    "    plt.imshow(bits, interpolation='none', cmap=cmap, origin='upper' )\n",
    "    plt.xticks([0,15],['0', '0xf'])\n",
    "    plt.yticks([0,15],['0', '0xf'])\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"Values of matrix with bit %d\" % (i))\n",
    "    i = i + 1\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These should start to look familiar. \n",
    "\n",
    "### Simulating the effect in Python code\n",
    "\n",
    "To study a bit more we can write the effect as Python code. I've named all my variables with the name of the corresponding register. You'll notice that often I need to do an AND (&) operation with `0xFF` or `0xFFFF` to make sure the variable doesn't exceed the corresponding register size. I've commented each section of code with the corrresponding part of the orginal assembler effect.\n",
    "\n",
    "We'll need to read the VGA palette values to use from here on. Let's read the palette froma Git repo I have with a CSV of all the colors and display the first 64 colors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read VGA palette: the row number is the index into the array and the first 3 columns are the RGB values\n",
    "vga_palette = np.genfromtxt('https://gist.githubusercontent.com/cesarmiquel/1780ab6078b9735371d1f10a9d60d233/raw/fb1ab3d46c81f1d43a11838f82cde8df767568d7/vga-palette.csv', delimiter=',')\n",
    "\n",
    "rgb = []\n",
    "numrows = 16\n",
    "for color in range(0, 16 * numrows):\n",
    "    rgb_pixel = vga_palette[ color ]\n",
    "    rgb.append(int(rgb_pixel[0]))\n",
    "    rgb.append(int(rgb_pixel[1]))\n",
    "    rgb.append(int(rgb_pixel[2]))\n",
    "\n",
    "rgb_m = np.array(rgb)\n",
    "rgb_m.shape = (numrows, 16, 3)\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.imshow(rgb_m, interpolation='none',origin='upper' )\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---------------------------------------------------------\n",
    "# This code simulates the effect\n",
    "# ---------------------------------------------------------\n",
    "vga_memory = []\n",
    "\n",
    "bp = 0x13                # you can change bp manually to see what happens\n",
    "\n",
    "for di in range(0, 0xffff + 1):\n",
    "\n",
    "    # mov ax, 0xcccd\n",
    "    # mul di\n",
    "    m =  di * 0xcccd\n",
    "    dx = (m & 0xffff0000) >> 16\n",
    "    dx = dx & 0xffff\n",
    "    \n",
    "    # Start of effect code ------------------------------------\n",
    "    # xchg  ax, dx\n",
    "    ax = dx\n",
    "    \n",
    "    # sub ax, bp\n",
    "    ax = ax - bp\n",
    "    \n",
    "    # xor al, ah     ; calculate x XOR y\n",
    "    al = 0xff & ax\n",
    "    ah = (0xff00 & ax) >> 8\n",
    "    al = al ^ ah \n",
    "    \n",
    "    # or al, 0xDB\n",
    "    al = al | 0xDB\n",
    "    \n",
    "    # add al, 13h\n",
    "    al = al + 0x13\n",
    "    al = al & 0xff # convert to 8bit\n",
    "    \n",
    "    # End of effect code ---------------------------------------\n",
    "    \n",
    "    # Save byte to Video RAM\n",
    "    vga_memory.append(al)\n",
    "\n",
    "    \n",
    "# Plot the contents of VGA RAM with correct colors\n",
    "bits = np.mat(vga_memory[0:320*200])\n",
    "bits = bits.reshape(200, 320)\n",
    "\n",
    "rgb = []\n",
    "for row in range(0, 200):\n",
    "    for col in range(0, 320):\n",
    "        rgb_pixel = vga_palette[ bits[row, col] ]\n",
    "        rgb.append(int(rgb_pixel[0]))\n",
    "        rgb.append(int(rgb_pixel[1]))\n",
    "        rgb.append(int(rgb_pixel[2]))\n",
    "\n",
    "rgb_m = np.array(rgb)\n",
    "rgb_m.shape = (200, 320, 3)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(rgb_m, interpolation='none',origin='upper' )\n",
    "plt.xticks([0,320],['0', '320'])\n",
    "plt.yticks([0,200],['0', '200'])\n",
    "#plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zooming circles effect\n",
    "\n",
    "```\n",
    "\tmov al,dh\t\t; get Y in AL\n",
    "\tsub al,100\t\t; align Y vertically\n",
    "\timul al\t\t\t; AL = Y²\n",
    "\txchg dx,ax\t\t; Y²/256 in DH, X in AL\n",
    "\timul al\t\t\t; AL = X²\n",
    "\tadd dh,ah\t\t; DH = (X² + Y²)/256\n",
    "\tmov al,dh\t\t; AL = (X² + Y²)/256\n",
    "\tadd ax,bp\t\t; offset color by time\n",
    "\tand al,8+16\t\t; select special rings\n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---------------------------------------------------------\n",
    "# This code simulates the effect\n",
    "# ---------------------------------------------------------\n",
    "\n",
    "# Simulta imul for two signed bytes as input\n",
    "def imul8(a, b):\n",
    "    if a > 0x80:\n",
    "        a = a - 0x100\n",
    "    if b > 0x80:\n",
    "        b = b - 0x100\n",
    "    result = a * b\n",
    "    return result & 0xffff\n",
    "\n",
    "vga_memory = []\n",
    "\n",
    "bp = 0                # you can change bp manually to see what happens\n",
    "\n",
    "for di in range(0, 0xffff + 1):\n",
    "    \n",
    "    # mov ax, 0xcccd\n",
    "    # mul di\n",
    "    m = di * 0xcccd\n",
    "    dx = (m & 0xffff0000) >> 16\n",
    "    ax = (m & 0xffff)\n",
    "\n",
    "    dl = dx & 0xff\n",
    "    dh = (dx & 0xff00) >> 8\n",
    "    al = ax & 0xff\n",
    "    ah = (ax & 0xff00) >> 8\n",
    "    \n",
    "    # DEBUG\n",
    "    #dl = 0\n",
    "    #dx = dh << 16 + dl\n",
    "    #dl = 0\n",
    "    #dh = 100\n",
    "    \n",
    "    # Start of effect code ------------------------------------\n",
    "    # mov al, dh\n",
    "    al = dh\n",
    "\n",
    "    # sub al, 100\n",
    "    al = (al - 100)\n",
    "\n",
    "    # imul al\n",
    "    ax = imul8(al, al)\n",
    "    \n",
    "    # xchg dx, ax\n",
    "    temp = dx\n",
    "    dx = ax\n",
    "    ax = temp\n",
    "    \n",
    "    dl = dx & 0xff\n",
    "    dh = (dx & 0xff00) >> 8\n",
    "    al = ax & 0xff\n",
    "    ah = (ax & 0xff00) >> 8\n",
    "\n",
    "    # imul al\n",
    "    ax = imul8(al, al)\n",
    "    al = ax & 0xff\n",
    "    ah = (ax & 0xff00) >> 8\n",
    "    \n",
    "    # add dh, ah\n",
    "    dh = (dh + ah) & 0xff\n",
    "    \n",
    "    # mov al,dh     ; AL = (X² + Y²)/256\n",
    "    al = dh\n",
    "    \n",
    "    # add ax,bp     ; offset color by time\n",
    "    ax = (ah << 8) + al + bp\n",
    "    ax = ax & 0xffff\n",
    "    al = (ax & 0xff)\n",
    "    \n",
    "    # and al,8+16   ; select special rings\n",
    "    al = al & 0b00011000\n",
    "\n",
    "    # End of effect code ---------------------------------------\n",
    "    \n",
    "    # Save byte to Video RAM\n",
    "    vga_memory.append(al)\n",
    "    \n",
    "# Plot the contents of VGA RAM with correct colors\n",
    "bits = np.mat(vga_memory[160:320*200+160])\n",
    "bits = bits.reshape(200, 320)\n",
    "\n",
    "rgb = []\n",
    "for row in range(0, 200):\n",
    "    for col in range(0, 320):\n",
    "        rgb_pixel = vga_palette[ bits[row, col] ]\n",
    "        rgb.append(int(rgb_pixel[0]))\n",
    "        rgb.append(int(rgb_pixel[1]))\n",
    "        rgb.append(int(rgb_pixel[2]))\n",
    "\n",
    "rgb_m = np.array(rgb)\n",
    "rgb_m.shape = (200, 320, 3)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(rgb_m, interpolation='none',origin='upper' )\n",
    "plt.xticks([0,320],['0', '320'])\n",
    "plt.yticks([0,200],['0', '200'])\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}