lasp/examples/test_SLM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from lasp import SLM\n",
    "import numpy as np\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-03 17:21:56+0200 DebugTrace-cpp 2.0.0a2 (g++ 12.2.0)\n",
      "2022-09-03 17:21:56+0200 \n",
      "2022-09-03 17:21:56+0200 Enter SeriesBiquad (lasp_biquadbank.cpp: 12)\n",
      "2022-09-03 17:21:56+0200 Leave SeriesBiquad (lasp_biquadbank.cpp)\n",
      "2022-09-03 17:21:56+0200 \n",
      "2022-09-03 17:21:56+0200 Enter SeriesBiquad (lasp_biquadbank.cpp: 12)\n",
      "2022-09-03 17:21:56+0200 Leave SeriesBiquad (lasp_biquadbank.cpp)\n"
     ]
    }
   ],
   "source": [
    "fs = 44100.\n",
    "Lref = 1\n",
    "ds = 1\n",
    "tau = 1/8\n",
    "\n",
    "bp_coefs = np.zeros((6,2), order='F')\n",
    "bp_coefs[0,:] = 1\n",
    "bp_coefs[3,:] = 1\n",
    "\n",
    "slm = SLM.fromBiquads(\n",
    "    fs, \n",
    "    Lref,\n",
    "    ds, tau, bp_coefs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [0., 0.],\n",
       "       [0., 0.],\n",
       "       [1., 1.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bp_coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.zeros(20100, dtype=float)\n",
    "x[:] = 1\n",
    "\n",
    "res = slm.run(x)\n",
    "print(res.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3823125cf0>,\n",
       " <matplotlib.lines.Line2D at 0x7f3823125d50>]"
      ]
     },
     "execution_count": 12,
     "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": [
    "plot(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [1.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slm.Pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.11480531, -0.11480531])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}