lasp/examples/Test SLM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-16 21:20:04+0200 \n",
      "2022-08-16 21:20:04+0200 Enter fromBiquads (lasp_slm.cpp: 82)\n",
      "2022-08-16 21:20:04+0200 | Enter createBandPass (lasp_slm.cpp: 62)\n",
      "2022-08-16 21:20:04+0200 | | Enter SeriesBiquad (lasp_biquadbank.cpp: 12)\n",
      "2022-08-16 21:20:04+0200 | | Leave SeriesBiquad (lasp_biquadbank.cpp)\n",
      "2022-08-16 21:20:04+0200 | | \n",
      "2022-08-16 21:20:04+0200 | | Enter SeriesBiquad (lasp_biquadbank.cpp: 12)\n",
      "2022-08-16 21:20:04+0200 | | Leave SeriesBiquad (lasp_biquadbank.cpp)\n",
      "2022-08-16 21:20:04+0200 | Leave createBandPass (lasp_slm.cpp)\n",
      "2022-08-16 21:20:04+0200 | \n",
      "2022-08-16 21:20:04+0200 | Enter SLM (lasp_slm.cpp: 37)\n",
      "2022-08-16 21:20:04+0200 | Leave SLM (lasp_slm.cpp)\n",
      "2022-08-16 21:20:04+0200 Leave fromBiquads (lasp_slm.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": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bp_coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20100, 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-16 21:20:04+0200 \n",
      "2022-08-16 21:20:04+0200 Enter run (lasp_slm.cpp: 151)\n",
      "2022-08-16 21:20:04+0200 | Enter filter (lasp_biquadbank.cpp: 44)\n",
      "2022-08-16 21:20:04+0200 | | Enter filter (lasp_biquadbank.cpp: 44)\n",
      "2022-08-16 21:20:04+0200 | | Leave filter (lasp_biquadbank.cpp)\n",
      "2022-08-16 21:20:04+0200 | Leave filter (lasp_biquadbank.cpp)\n",
      "2022-08-16 21:20:04+0200 | N = 0ul\n",
      "2022-08-16 21:20:04+0200 | N = 0ul\n",
      "2022-08-16 21:20:04+0200 Leave run (lasp_slm.cpp)\n"
     ]
    }
   ],
   "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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\n",
      "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}