{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modifying a Dataset\n",
    "===================\n",
    "\n",
    "In some cases, you may want to modify a dataset. For example, you may want to use a **different microphone geometry** or want a **different environment**. \n",
    "Let's say you want to use the **miniDSP UMA 16** channel microphone array, a popular MEMS microphone array. The geometric layout is available in the `Acoular` package.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import acoular\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "uma_file = Path(acoular.__file__).parent / 'xml' / 'minidsp_uma16.xml'\n",
    "mg = acoular.MicGeom(from_file=uma_file)\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(mg.mpos[0], mg.mpos[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Changing the underlying dataset configuration\n",
    "---------------------------------------------\n",
    "\n",
    "\n",
    "Each dataset in the `acoupipe.datasets` module has its own default configuration object, which, after instantiation of the dataset class, is available as the `config` attribute.\n",
    "The configuration object holds all necessary objects needed to generate the dataset. For example, the microphone geometry is stored in the `mics` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from acoupipe.datasets.synthetic import DatasetSynthetic\n",
    "\n",
    "dataset = DatasetSynthetic()\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(\n",
    "    dataset.config.mics.mpos[0], \n",
    "    dataset.config.mics.mpos[1]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Theoretically, one could change the microphone geometry by directly modifying the `mics` attribute. However, this is not recommended, as the configuration object is not aware of the change. \n",
    "A better way is to subclass a new configuration object and to overwrite the method responsible for creating the microphone geometry. In this case, the method is called `create_mics`. \n",
    " We could also change the speed of sound by changing the environment created in the `create_env` method.\n",
    "Both methods are called during the instantiation of the dataset, so we need to overwrite it before we create the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from acoupipe.datasets.synthetic import DatasetSyntheticConfig\n",
    "\n",
    "class ConfigUMA(DatasetSyntheticConfig):\n",
    "    \n",
    "    def create_mics(self):\n",
    "        uma_file = Path(acoular.__file__).parent / 'xml' / 'minidsp_uma16.xml'\n",
    "        return acoular.MicGeom(from_file=uma_file)\n",
    "\n",
    "    def create_env(self):\n",
    "        return acoular.Environment(c=345.0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "With the new configuration object, we can create a new dataset that uses the UMA 16 microphone array.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "config = ConfigUMA()\n",
    "dataset_uma = DatasetSynthetic(config=config)\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(\n",
    "    dataset_uma.config.mics.mpos[0], \n",
    "    dataset_uma.config.mics.mpos[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "It's worth noting that the grid has changed as well, as it is scaled by the microphone geometries aperture size (see the `create_grid` method for details).\n",
    "\n",
    "Now one can extract the data from the dataset as usual, e.g., the source mapping.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data_generator = dataset_uma.generate(\n",
    "    split='training', features=['sourcemap'], \n",
    "    f=4000, size=1000, progress_bar=False)\n",
    "\n",
    "data = next(data_generator)\n",
    "\n",
    "Lm = acoular.L_p(data['sourcemap'].T) # convert to dB\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(Lm, origin='lower', extent=dataset_uma.config.grid.extend(),\n",
    "            cmap='viridis', vmin=Lm.max() - 20, vmax=Lm.max())\n",
    "plt.colorbar(label='L_p [dB]')\n",
    "plt.xlabel('x [m]')\n",
    "plt.ylabel('y [m]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py39",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}