musicmouse/espmusicmouse/host_driver/effect_debug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5d8a856-d466-4102-8bfc-2a9014ab0767",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import importlib.util\n",
    "from ipywidgets import interactive\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4fc1c81-3f08-4cd8-bb38-3566b033318a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39d0f384-6533-41e0-8093-b1b2a9d59608",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_test(path):\n",
    "    spec = importlib.util.spec_from_file_location(\"test\", path)\n",
    "    module = importlib.util.module_from_spec(spec)\n",
    "    spec.loader.exec_module(module)\n",
    "    for sym in ['r', 'g', 'b', 'h', 's', 'v']:\n",
    "        a = np.array(getattr(module, sym))\n",
    "        setattr(module, sym, a)\n",
    "    return module\n",
    "\n",
    "path = \"/home/martin/code/musicmouse/espmusicmouse/.pio/build/native/swipe.py\"\n",
    "path = \"/home/martin/code/musicmouse/espmusicmouse/.pio/build/native/reverse_swipe.py\"\n",
    "res = load_test(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb3bc44d-2523-43d5-87eb-2cded8d0d8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09617018b02c449d912ef07757fcc8c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=99, description='t', max=199), Output(layout=Layout(height='350px'))), _…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_v(t):\n",
    "    plt.figure(2)\n",
    "    plt.ylim(-0.1, 1.1)    \n",
    "    plt.plot(res.v[t], label=f\"v at {t}\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "interactive_plot = interactive(plot_v, t=(0, len(res.v)-1))\n",
    "output = interactive_plot.children[-1]\n",
    "output.layout.height = '350px'\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ef27efb-b021-42b7-8cb5-c84add88ab17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2,\n",
       "       2, 1, 1, 1, 1, 1, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.r[70]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "817d4771-f210-48e9-a342-0b0d238d0e11",
   "metadata": {},
   "source": [
    "## Bell Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "763b5ffe-4224-4dec-a147-40be2aaa0d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bell_curve_approximation_old(x, inverse_width):\n",
    "    if x < 0:\n",
    "        x = -x\n",
    "    \n",
    "    nx = x * inverse_width  * 4\n",
    "    if nx > 2:\n",
    "        return 0\n",
    "        \n",
    "    x2 = nx * nx\n",
    "    x3 = x2 * nx\n",
    "    res = 1 + 0.2760695894 * x3 - 0.8021391 * x2\n",
    "    \n",
    "    return res if res > 0 else 0\n",
    "\n",
    "\n",
    "def bell_curve_approximation(x, inverse_width):  \n",
    "    nx = x * inverse_width  * 4\n",
    "    x2 = nx * nx\n",
    "    x3 = x2 * nx\n",
    "    res = 1 + 0.2760695894 * x3 - 0.8021391 * x2\n",
    "    \n",
    "    return res if res > 0 else 0\n",
    "\n",
    "# // Function start at 0 (x=0) and goes smoothly up to 1 and arrives 1 at x=width\n",
    "# // inverse width has to be 1 / width (should be cached outside)\n",
    "def step_function(x, width, inverse_width):\n",
    "    if x < 0:\n",
    "        return 0\n",
    "    if x >= width:\n",
    "        return 1\n",
    "    bell_inv_width = inverse_width * 0.5\n",
    "    nx = (-x + width) * bell_inv_width  * 4\n",
    "    x2 = nx * nx\n",
    "    x3 = x2 * nx\n",
    "    res = 1 + 0.2760695894 * x3 - 0.8021391 * x2\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be193428-3fec-4a2f-9903-4dbfa4ad19e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de8b9ee1-6232-4cab-8a1a-b62ec723b4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-1, 10, 20)\n",
    "y = np.array([step_function(x_i, 8,  1 / 8) for x_i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "db9bddcc-ae89-462c-b8f4-56384a36a1df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa511346af0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad451aa5-8645-4432-a0e8-821eb4826f19",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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