[Control,HDF5] check HDF5 contents.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path \n",
    "\n",
    "plot = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PATH TO TO DATA DIR\n",
    "\n",
    "path_to_data = Path(\"./deliverable/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signals in this dataset: ['rir']\n",
      "Rooms in this dataset: ['000000', '000001', '000010', '000100', '001000', '010000', '011000', '011100', '011110', '011111', '020002']\n",
      "Sources in this dataset: ['0', '1', '2', '3', '4', '5', '6', '7', '8']\n",
      "RIR matrix shape (48000, 31)\n",
      "31 mics = 30 mics + 1 control signals!\n"
     ]
    }
   ],
   "source": [
    "# LOAD RIRs\n",
    "\n",
    "path_to_rirs_hdf = path_to_data / Path('dEchorate_rir.h5')\n",
    "rir_dset = h5py.File(path_to_rirs_hdf, mode='r')\n",
    "\n",
    "signals = list(rir_dset.keys())\n",
    "print('Signals in this dataset:', signals)\n",
    "\n",
    "rooms = list(rir_dset[signals[0]].keys())\n",
    "print('Rooms in this dataset:', rooms)\n",
    "\n",
    "sources = list(rir_dset[signals[0]][rooms[0]].keys())\n",
    "print('Sources in this dataset:', sources)\n",
    "\n",
    "print('RIR matrix shape', rir_dset[signals[0]][rooms[0]][sources[0]].shape)\n",
    "print('31 mics = 30 mics + 1 control signals!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_dim_names : ['n_samples' 'n_mics' '(n_utts)']\n",
      "n_mics : 31\n",
      "n_samples : 48000\n",
      "n_srcs : 9\n",
      "n_utts : 1\n",
      "sampling_rate : 48000\n",
      "signal : rirs\n",
      "Sampling rate: 48000\n"
     ]
    }
   ],
   "source": [
    "# PRINT DSET ATTRIBUTES\n",
    "\n",
    "for attr in rir_dset.attrs:\n",
    "    print(f'{attr} :', rir_dset.attrs[attr])\n",
    "\n",
    "\n",
    "fs = rir_dset.attrs['sampling_rate']\n",
    "print('Sampling rate:', fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arrays_direction : (3, 6)\n",
      "arrays_position : (3, 6)\n",
      "echo_amp : (7, 30, 6)\n",
      "echo_toa : (7, 30, 6)\n",
      "echo_wall : (7, 30, 6)\n",
      "microphones : (3, 30)\n",
      "room_size : (3,)\n",
      "sources_directional_direction : (3, 6)\n",
      "sources_directional_position : (3, 6)\n",
      "sources_noise_direction : (3, 4)\n",
      "sources_noise_position : (3, 4)\n",
      "sources_omnidirection_position : (3, 3)\n"
     ]
    }
   ],
   "source": [
    "# LOAD ANNOTATIONs\n",
    "\n",
    "path_to_echo_h5 = path_to_data / Path('dEchorate_annotations.h5')\n",
    "note_dset = h5py.File(path_to_echo_h5, 'r')\n",
    "# let s check the content of this annotation dataset\n",
    "for key in note_dset:\n",
    "    print(f'{key} :', note_dset[key][()].shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DRAW GEOMETRY in 2D\n",
    "\n",
    "room_size = note_dset['room_size']\n",
    "mics = note_dset['microphones']\n",
    "arrs = note_dset['arrays_position']\n",
    "arrs_view = note_dset['arrays_direction']\n",
    "srcs_dir = note_dset['sources_directional_position']\n",
    "srcs_dir_view = note_dset['sources_directional_direction']\n",
    "srcs_nse = note_dset['sources_noise_position']\n",
    "srcs_nse_view = note_dset['sources_noise_direction']\n",
    "srcs_omn = note_dset['sources_omnidirection_position']\n",
    "\n",
    "m = { # marker type\n",
    "    'arrs' : 'x',\n",
    "    'mics' : 'X',\n",
    "    'srcs_dir' : 'v',\n",
    "    'srcs_omn' : 'o',\n",
    "    'srcs_nse' : 'D',\n",
    "}\n",
    "s = { # marker size\n",
    "    'arrs' : 120,\n",
    "    'mics' : 80,\n",
    "    'srcs_dir' : 100,\n",
    "    'srcs_omn' : 120,\n",
    "    'srcs_nse' : 100,\n",
    "}\n",
    "c = { # colors\n",
    "    'arrs' : 'k',\n",
    "    'mics' : 'C0',\n",
    "    'srcs_dir' : 'C1',\n",
    "    'srcs_omn' : 'C2',\n",
    "    'srcs_nse' : 'C3',\n",
    "}\n",
    "l = { # labels\n",
    "    'arrs' : 'array barycenters',\n",
    "    'mics' : 'microphones',\n",
    "    'srcs_dir' : 'directional sources',\n",
    "    'srcs_omn' : 'omnidirectional sources',\n",
    "    'srcs_nse' : 'diffuse noise',\n",
    "}\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "# Plot ROOM\n",
    "plt.gca().add_patch(\n",
    "    plt.Rectangle((0, 0),\n",
    "                room_size[0], room_size[1], fill=False,\n",
    "                edgecolor='g', linewidth=4)\n",
    ")\n",
    "plt.text(room_size[0]/2, -0.15, 'South wall')\n",
    "plt.text(room_size[0]/2,   room_size[1] + 0.05, 'North wall')\n",
    "plt.text(-0.15, room_size[1]/2, 'West wall', rotation=90)\n",
    "plt.text(room_size[0], room_size[1]/2, 'East wall', rotation=-90)\n",
    "\n",
    "\n",
    "plt.scatter(mics[0, :], mics[1, :], marker=m['mics'], s=s['mics'], c=c['mics'], label=l['mics'])\n",
    "plt.scatter(arrs[0, :], arrs[1, :], marker=m['arrs'], s=s['arrs'], c=c['arrs'], label=l['arrs'])\n",
    "\n",
    "plt.text(arrs[0, 0]+0.1, arrs[1, 0]-0.15, r'$arr_%d$' %0)\n",
    "plt.text(arrs[0, 1]+0.1, arrs[1, 1]-0.15, r'$arr_%d$' %1)\n",
    "plt.text(arrs[0, 2]+0.1, arrs[1, 2]+0.10, r'$arr_%d$' %2)\n",
    "plt.text(arrs[0, 3]+0.1, arrs[1, 3]-0.15, r'$arr_%d$' %3)\n",
    "plt.text(arrs[0, 4]+0.1, arrs[1, 4]-0.1,  r'$arr_%d$' %4)\n",
    "plt.text(arrs[0, 5]+0.1, arrs[1, 5]+0.1,  r'$arr_%d$' %5)\n",
    "\n",
    "for a in range(arrs.shape[1]):\n",
    "    x = arrs[0,a]\n",
    "    y = arrs[1,a]\n",
    "    dx = arrs_view[0,a] / 3\n",
    "    dy = arrs_view[1,a] / 3\n",
    "    plt.annotate(\"\", xy=(x+dx,y+dy), xytext=(x, y), arrowprops=dict(arrowstyle=\"->\", alpha=0.5, color=c['mics']))\n",
    "\n",
    "# DIR\n",
    "plt.scatter(srcs_dir[0, 0], srcs_dir[1, 0], marker='v', s=s['srcs_dir'], c=c['srcs_dir'], label=l['srcs_dir'])\n",
    "plt.text(srcs_dir[0, 0]+0.1, srcs_dir[1, 0]-0.1, r'$dir_%d$' %0)\n",
    "plt.text(srcs_dir[0, 1]+0.1, srcs_dir[1, 1]+0.1, r'$dir_%d$' %1)\n",
    "plt.text(srcs_dir[0, 2]-0.2, srcs_dir[1, 2]-0.2, r'$dir_%d$' %2)\n",
    "plt.text(srcs_dir[0, 3]-0.2, srcs_dir[1, 3]-0.2, r'$dir_%d$' %3)\n",
    "plt.text(srcs_dir[0, 4]+0.1, srcs_dir[1, 4]-0.1, r'$dir_%d$' %4)\n",
    "plt.text(srcs_dir[0, 5]+0.1, srcs_dir[1, 5]-0.2, r'$dir_%d$' %5)\n",
    "\n",
    "for j in range(srcs_dir_view.shape[1]):\n",
    "    x = srcs_dir[0,j]\n",
    "    y = srcs_dir[1,j]\n",
    "    dx = srcs_dir_view[0,j] / 3\n",
    "    dy = srcs_dir_view[1,j] / 3\n",
    "    plt.annotate(\"\", xy=(x+dx,y+dy), xytext=(x, y), arrowprops=dict(arrowstyle=\"->\", alpha=0.5, color=c['srcs_dir']))\n",
    "\n",
    "# DIR\n",
    "plt.scatter(srcs_dir[0, 1], srcs_dir[1, 1], marker='^', s=s['srcs_dir'], c=c['srcs_dir'])\n",
    "plt.scatter(srcs_dir[0, 2], srcs_dir[1, 2], marker='<', s=s['srcs_dir'], c=c['srcs_dir'])\n",
    "plt.scatter(srcs_dir[0, 3], srcs_dir[1, 3], marker='<', s=s['srcs_dir'], c=c['srcs_dir'])\n",
    "plt.scatter(srcs_dir[0, 4], srcs_dir[1, 4], marker='v', s=s['srcs_dir'], c=c['srcs_dir'])\n",
    "plt.scatter(srcs_dir[0, 5], srcs_dir[1, 5], marker='>', s=s['srcs_dir'], c=c['srcs_dir'])\n",
    "\n",
    "# OMNI\n",
    "plt.scatter(srcs_omn[0, :], srcs_omn[1, :], marker=m['srcs_omn'], s=s['srcs_omn'], c=c['srcs_omn'], label=l['srcs_omn'])\n",
    "plt.text(srcs_omn[0, 0]-0.2, srcs_omn[1, 0]-0.2, r'$omni_%d$' %0)\n",
    "plt.text(srcs_omn[0, 1]-0.2, srcs_omn[1, 1]-0.2, r'$omni_%d$' %1)\n",
    "plt.text(srcs_omn[0, 2]-0.2, srcs_omn[1, 2]-0.2, r'$omni_%d$' %2)\n",
    "\n",
    "\n",
    "# NOISE\n",
    "plt.scatter(srcs_nse[0, :], srcs_nse[1, :], marker=m['srcs_nse'], s=s['srcs_nse'], c=c['srcs_nse'], label=l['srcs_nse'])\n",
    "plt.text(srcs_nse[0, 0]-0.2, srcs_nse[1, 0]-0.25, r'$noise_%d$' %0)\n",
    "plt.text(srcs_nse[0, 1]-0.2, srcs_nse[1, 1]-0.25, r'$noise_%d$' %1)\n",
    "plt.text(srcs_nse[0, 2]-0.2, srcs_nse[1, 2]-0.25, r'$noise_%d$' %2)\n",
    "plt.text(srcs_nse[0, 3]-0.2, srcs_nse[1, 3]-0.25, r'$noise_%d$' %3)\n",
    "\n",
    "for j in range(srcs_nse_view.shape[1]):\n",
    "    x = srcs_nse[0,j]\n",
    "    y = srcs_nse[1,j]\n",
    "    dx = srcs_nse_view[0,j] / 3\n",
    "    dy = srcs_nse_view[1,j] / 3\n",
    "    plt.annotate(\"\", xy=(x+dx,y+dy), xytext=(x, y), arrowprops=dict(arrowstyle=\"->\", alpha=0.5, color=c['srcs_nse']))\n",
    "\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DRAW GEOMTRY IN 3D\n",
    "\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter3D(mics[0,:], mics[1,:], mics[2,:], marker='.')\n",
    "ax.scatter3D(srcs_dir[0,:], srcs_dir[1,:], srcs_dir[2,:], marker='*')\n",
    "ax.set_xlim([0, room_size[0]])\n",
    "ax.set_ylim([0, room_size[1]])\n",
    "ax.set_zlim([0, room_size[2]])\n",
    "ax.set_xlabel(\"x (m)\")\n",
    "ax.set_ylabel(\"y (m)\")\n",
    "ax.set_zlabel(\"z (m)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'RIR [amplitude]')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a RIR\n",
    "signal = 'rir'\n",
    "room = '011111'\n",
    "src = 1\n",
    "mic = 3\n",
    "rir = rir_dset['rir']['011111'][str(src)][()][:,mic]\n",
    "\n",
    "L = rir.shape[0]\n",
    "time = np.arange(L)/fs\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title(f'RIR of room:{room}, src:{src}, mic:{mic}')\n",
    "plt.plot(time, rir)\n",
    "plt.xlabel('Time [seconds]')\n",
    "plt.ylabel('RIR [amplitude]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLOT RIR SKYLINEs\n",
    "\n",
    "echo_toa = note_dset['echo_toa']\n",
    "echo_wall = note_dset['echo_wall']\n",
    "walls_code_to_name = {\n",
    "    'd' : 'Direct path',\n",
    "    'c' : 'Ceiling',\n",
    "    'e' : 'East',\n",
    "    'w' : 'West',\n",
    "    's' : 'South',\n",
    "    'n' : 'North',\n",
    "    'f' : 'Floor'\n",
    "}\n",
    "\n",
    "for room in rooms:\n",
    "    rirs = np.stack([rir_dset['rir'][room][s][()] for s in sources], axis=1)\n",
    "    rirs = rirs[:,:,:-1] # do not use the loopback signal\n",
    "    n_smpl, n_srcs, n_mics = rirs.shape\n",
    "\n",
    "    fs = rir_dset.attrs['sampling_rate']\n",
    "    L = 1500 # visualize on the first L samples\n",
    "\n",
    "    to_plot = rirs.transpose([0,1,2]).reshape([n_smpl,n_srcs*n_mics])\n",
    "    to_plot = to_plot / np.max(np.abs(to_plot), axis=0, keepdims=True)\n",
    "    to_plot = np.abs(to_plot)[:L,:]\n",
    "\n",
    "\n",
    "    plt.figure(figsize=(16,9))\n",
    "    plt.title(f'RIR Skyline for room {room}')\n",
    "    plt.imshow(to_plot, extent=[0, n_mics*n_srcs, L, 0], aspect='auto', interpolation='none', origin='upper')\n",
    "\n",
    "    # plot srcs boundaries\n",
    "    for j in range(n_srcs):\n",
    "        plt.axvline(j*n_mics, color='C7')\n",
    "        \n",
    "    # plot peak annotation\n",
    "    for k in range(echo_toa.shape[0]):\n",
    "        wall_name = walls_code_to_name[echo_wall[k,0,0].decode('UTF-8')]\n",
    "        plt.scatter(np.arange(n_mics*6)+0.5, echo_toa[k, :, :].T.flatten()*fs, \n",
    "                    c='C%d' % (k+2), marker='x', label=wall_name)\n",
    "\n",
    "    plt.xlim([0, n_mics * n_srcs])\n",
    "    plt.ylabel(\"Samples\")\n",
    "    plt.xlabel(\"Mics x Srcs\")\n",
    "    plt.legend(loc='lower right', title='Walls')\n",
    "    plt.savefig(f'figures/room_{room}_rir_skyline.png')\n",
    "    # plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes in the dataset:\n",
      "data_dim_names : ['n_samples' 'n_mics' '(n_utts)']\n",
      "n_mics : 31\n",
      "n_samples : 104000\n",
      "n_srcs : 9\n",
      "n_utts : 3\n",
      "sampling_rate : 16000\n",
      "signal : speech\n",
      "Plot signals\n",
      "(104000, 9, 31, 3)\n",
      "000000 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "000001 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "000010 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "000100 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "001000 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "010000 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "011000 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "011100 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "011110 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "011111 speech (104000, 9, 31, 3) 16000\n",
      "(104000, 9, 31, 3)\n",
      "020002 speech (104000, 9, 31, 3) 16000\n"
     ]
    }
   ],
   "source": [
    "signal = 'speech'\n",
    "path_h5 = path_to_data / Path(f'dEchorate_{signal}.h5')\n",
    "dset = h5py.File(path_h5, mode='r')\n",
    "\n",
    "print('Attributes in the dataset:')\n",
    "for attr in dset.attrs:\n",
    "    print(f'{attr} :', dset.attrs[attr])\n",
    "\n",
    "def normalize(x):\n",
    "    return x / np.max(np.abs(x), axis=0, keepdims=True)\n",
    "\n",
    "print('Plot signals')\n",
    "for room in rooms:\n",
    "    data = np.stack([dset[signal][room][src][()] for src in dset[signal][room].keys()], axis=1)\n",
    "    print(data.shape)\n",
    "    \n",
    "    fs = dset.attrs['sampling_rate']\n",
    "    print(room, signal, data.shape, fs)\n",
    "    time = np.arange(data.shape[0]) / fs\n",
    "    \n",
    "    L, J, I, R = data.shape\n",
    "\n",
    "    time = np.arange(data.shape[0]) / fs\n",
    "    \n",
    "    if plot:\n",
    "        fig, axarr = plt.subplots(J, R, figsize=(16,8))\n",
    "        for j in range(J):\n",
    "            for r in range(R):\n",
    "                axarr[j,r].plot(time, normalize(data[:,j,:-1,r])**2, label=\"Recorded\")\n",
    "                axarr[j,r].set_ylabel(f\"src:{j}\")\n",
    "                axarr[j,r].grid(axis = 'x')\n",
    "                axarr[j,r].xaxis.set_ticks(np.arange(0,6.5,1))\n",
    "                axarr[j,r].set_ylim(0,1)\n",
    "                axarr[j,r].set_xlim(0,6.5)\n",
    "                if j == 0:\n",
    "                    axarr[j,r].set_title(f'Utterance #{r+1}')\n",
    "                \n",
    "        plt.suptitle(f\"Room: {room}\")\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'figures/room_{room}_{signal}_all_mics_srcs.png')\n",
    "        # plt.show()\n",
    "        plt.close()\n",
    "dset.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes in the dataset:\n",
      "data_dim_names : ['n_samples' 'n_mics' '(n_utts)']\n",
      "n_mics : 31\n",
      "n_samples : 104000\n",
      "n_srcs : 9\n",
      "n_utts : 2\n",
      "sampling_rate : 16000\n",
      "signal : noise\n",
      "(104000, 9, 31, 2)\n",
      "000000 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "000001 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "000010 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "000100 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "001000 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "010000 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "011000 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "011100 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "011110 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "011111 noise (104000, 9, 31, 2) 16000\n",
      "(104000, 9, 31, 2)\n",
      "020002 noise (104000, 9, 31, 2) 16000\n"
     ]
    }
   ],
   "source": [
    "signal = 'noise'\n",
    "path_h5 = path_to_data / Path(f'dEchorate_{signal}.h5')\n",
    "dset = h5py.File(path_h5, mode='r')\n",
    "\n",
    "print('Attributes in the dataset:')\n",
    "for attr in dset.attrs:\n",
    "    print(f'{attr} :', dset.attrs[attr])\n",
    "\n",
    "def normalize(x):\n",
    "    return x / np.max(np.abs(x), axis=0, keepdims=True)\n",
    "\n",
    "for room in rooms:\n",
    "    data = np.stack([dset[signal][room][src][()] for src in dset[signal][room].keys()], axis=1)\n",
    "    print(data.shape)\n",
    "    \n",
    "    fs = dset.attrs['sampling_rate']\n",
    "    print(room, signal, data.shape, fs)\n",
    "    time = np.arange(data.shape[0]) / fs\n",
    "    \n",
    "    L, J, I, R = data.shape\n",
    "\n",
    "    time = np.arange(data.shape[0]) / fs\n",
    "    \n",
    "    if plot:\n",
    "        fig, axarr = plt.subplots(J, R, figsize=(16,8))\n",
    "        for j in range(J):\n",
    "            for r in range(R):\n",
    "                axarr[j,r].plot(time, normalize(data[:,j,:-1,r])**2, label=\"Recorded\")\n",
    "                axarr[j,r].set_ylabel(f\"src:{j}\")\n",
    "                axarr[j,r].grid(axis = 'x')\n",
    "                axarr[j,r].xaxis.set_ticks(np.arange(0,6.5,1))\n",
    "                axarr[j,r].set_ylim(0,1)\n",
    "                axarr[j,r].set_xlim(0,6.5)\n",
    "                \n",
    "        plt.suptitle(f\"Room: {room}\")\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'figures/room_{room}_{signal}_all_mics_srcs.png')\n",
    "        # plt.show()\n",
    "        plt.close()\n",
    "dset.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes in the dataset:\n",
      "data_dim_names : ['n_samples' 'n_mics' '(n_utts)']\n",
      "n_mics : 31\n",
      "n_samples : 104000\n",
      "n_srcs : 4\n",
      "n_utts : 2\n",
      "sampling_rate : 16000\n",
      "signal : babble\n",
      "(104000, 4, 31, 2)\n",
      "000000 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "000001 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "000010 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "000100 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "001000 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "010000 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "011000 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "011100 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "011110 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "011111 babble (104000, 4, 31, 2) 16000\n",
      "(104000, 4, 31, 2)\n",
      "020002 babble (104000, 4, 31, 2) 16000\n"
     ]
    }
   ],
   "source": [
    "signal = 'babble'\n",
    "path_h5 = path_to_data / Path(f'dEchorate_{signal}.h5')\n",
    "dset = h5py.File(path_h5, mode='r')\n",
    "\n",
    "print('Attributes in the dataset:')\n",
    "for attr in dset.attrs:\n",
    "    print(f'{attr} :', dset.attrs[attr])\n",
    "\n",
    "def normalize(x):\n",
    "    return x / np.max(np.abs(x), axis=0, keepdims=True)\n",
    "\n",
    "for room in rooms:\n",
    "    data = np.stack([dset[signal][room][src][()] for src in dset[signal][room].keys()], axis=1)\n",
    "    print(data.shape)\n",
    "    \n",
    "    fs = dset.attrs['sampling_rate']\n",
    "    print(room, signal, data.shape, fs)\n",
    "    time = np.arange(data.shape[0]) / fs\n",
    "    \n",
    "    L, J, I, R = data.shape\n",
    "\n",
    "    time = np.arange(data.shape[0]) / fs\n",
    "\n",
    "    if plot:    \n",
    "        fig, axarr = plt.subplots(J, R, figsize=(16,8))\n",
    "        for j in range(J):\n",
    "            for r in range(R):\n",
    "                axarr[j,r].plot(time, normalize(data[:,j,:-1,r])**2, label=\"Recorded\")\n",
    "                axarr[j,r].set_ylabel(f\"src:{j}\")\n",
    "                axarr[j,r].grid(axis = 'x')\n",
    "                axarr[j,r].xaxis.set_ticks(np.arange(0,6.5,1))\n",
    "                axarr[j,r].set_ylim(0,1)\n",
    "                axarr[j,r].set_xlim(0,6.5)\n",
    "                \n",
    "        plt.suptitle(f\"Room: {room}\")\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'figures/room_{room}_{signal}_all_mics_srcs.png')\n",
    "        # plt.show()\n",
    "        plt.close()\n",
    "dset.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes in the dataset:\n",
      "data_dim_names : ['n_samples' 'n_mics' '(n_utts)']\n",
      "n_mics : 31\n",
      "n_samples : 104000\n",
      "n_srcs : 1\n",
      "n_utts : 2\n",
      "sampling_rate : 16000\n",
      "signal : silence\n",
      "(104000, 1, 31, 1)\n",
      "000000 silence (104000, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "000001 silence (104000, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "000010 silence (104000, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "000100 silence (104000, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "001000 silence (104000, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "010000 silence (104000, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "011000 silence (104000, 1, 31, 1) 16000\n",
      "(79693, 1, 31, 1)\n",
      "011100 silence (79693, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "011110 silence (104000, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "011111 silence (104000, 1, 31, 1) 16000\n",
      "(104000, 1, 31, 1)\n",
      "020002 silence (104000, 1, 31, 1) 16000\n"
     ]
    }
   ],
   "source": [
    "signal = 'silence'\n",
    "path_h5 = path_to_data / Path(f'dEchorate_{signal}.h5')\n",
    "dset = h5py.File(path_h5, mode='r')\n",
    "\n",
    "print('Attributes in the dataset:')\n",
    "for attr in dset.attrs:\n",
    "    print(f'{attr} :', dset.attrs[attr])\n",
    "\n",
    "def normalize(x):\n",
    "    return x / np.max(np.abs(x), axis=0, keepdims=True)\n",
    "\n",
    "for room in rooms:\n",
    "    data = np.stack([dset[signal][room][src][()] for src in dset[signal][room].keys()], axis=1)\n",
    "    print(data.shape)\n",
    "    \n",
    "    fs = dset.attrs['sampling_rate']\n",
    "    print(room, signal, data.shape, fs)\n",
    "    time = np.arange(data.shape[0]) / fs\n",
    "    \n",
    "    L, J, I, R = data.shape\n",
    "\n",
    "    time = np.arange(data.shape[0]) / fs\n",
    "\n",
    "    if plot:    \n",
    "        fig, axarr = plt.subplots(J, R, figsize=(16,8), squeeze=False)\n",
    "        for j in range(J):\n",
    "            for r in range(R):\n",
    "                axarr[j,r].plot(time, normalize(data[:,j,:-1,r])**2, label=\"Recorded\")\n",
    "                axarr[j,r].set_ylabel(f\"src:{j}\")\n",
    "                axarr[j,r].grid(axis = 'x')\n",
    "                axarr[j,r].xaxis.set_ticks(np.arange(0,6.5,1))\n",
    "                axarr[j,r].set_ylim(0,1)\n",
    "                axarr[j,r].set_xlim(0,6.5)\n",
    "                \n",
    "        plt.suptitle(f\"Room: {room}\")\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'figures/room_{room}_{signal}_all_mics_srcs.png')\n",
    "        # plt.show()\n",
    "        plt.close()\n",
    "    \n",
    "dset.close()"
   ]
  }
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