VAE2Chords-Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatialized exploration of the MILES stellar library\n",
    "\n",
    "Data download: http://svocats.cab.inta-csic.es/miles/index.php?action=credits\n",
    "#### ACKNOWLEDGMENT\n",
    "This work is based on data from the MILES service developed by the Spanish Virtual Observatory in the framework of the IAU Comission G5 Working Group : Spectral Stellar Libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import time\n",
    "from pythonosc import udp_client\n",
    "\n",
    "import os\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Variational Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-09-07 19:15:32.526797: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "vae = tf.keras.models.load_model('Miles_VAE.tf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "encoder = tf.keras.models.load_model('Miles_VAE_Encoder.tf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "decoder = tf.keras.models.load_model('Miles_VAE_Decoder.tf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7fb189889d00>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vae.load_weights('Miles_VAE_Weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)\n",
    "vae.compile(optimizer, loss=tf.keras.losses.MeanSquaredError())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = '## YOUR FOLDER PATH TO THE DOWNLOADED SPECTRA ##'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launching the sonification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1 Spectrum represented: miles_spec_fits_s0597\n",
      "[-1276.96454525]\n",
      "[-244.36088279]\n",
      "[-3023.50342274]\n",
      "[505.76936454]\n",
      "[404.74582464]\n",
      "[4145.79123259]\n",
      "[1969.01291609]\n",
      "[-1102.25327313]\n",
      "[-1742.7867651]\n",
      "[-2434.33013558]\n",
      "[-2120.01413107]\n",
      "[1547.33613133]\n",
      " 2 Spectrum represented: miles_spec_fits_s0981\n",
      "[-1217.15798974]\n",
      "[-1153.64916623]\n",
      "[6.4342469]\n",
      "[451.25506818]\n",
      "[1311.3373518]\n",
      "[2846.34411335]\n",
      "[-1281.55976534]\n",
      "[1127.81181931]\n",
      "[-1119.05142665]\n",
      "[-3488.20209503]\n",
      "[-516.75051451]\n",
      "[148.65033329]\n",
      " 3 Spectrum represented: miles_spec_fits_s0578\n",
      "[-971.93740308]\n",
      "[-382.67131895]\n",
      "[426.46721005]\n",
      "[-1107.45385289]\n",
      "[-566.95349514]\n",
      "[-4065.23734331]\n",
      "[-1176.09657347]\n",
      "[363.93549293]\n",
      "[-690.29927254]\n",
      "[360.13014615]\n",
      "[-2542.28383303]\n",
      "[350.15918314]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28mprint\u001b[39m(x[\u001b[38;5;241m11\u001b[39m]) \n\u001b[1;32m     96\u001b[0m plt\u001b[38;5;241m.\u001b[39mclose(fig)\n\u001b[0;32m---> 97\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     99\u001b[0m client_s\u001b[38;5;241m.\u001b[39msend_message(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/s\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "curves=0\n",
    "for path, subdirs, files in os.walk(root):\n",
    "    for name in files:\n",
    "        curves=curves+1\n",
    "        file = [os.path.join(path, name)]\n",
    "        str = \" \" \n",
    "        Ffile = (str.join(file))\n",
    "        route = Path(name)\n",
    "        Fname = route.with_suffix('')\n",
    "        Fpng = route.with_suffix('.png')\n",
    "        \n",
    "        \n",
    "        sp = fits.open(Ffile)\n",
    "        flux2 = np.array(sp[0].data)\n",
    "        flux_norm = flux2/(np.nanmax(flux2))\n",
    "        wave2 = np.ones(sp[0].header['NAXIS1'], dtype=float)\n",
    "        for i in range(sp[0].header['NAXIS1']):\n",
    "            wave2[i] = sp[0].header['CRVAL1'] + i*sp[0].header['CDELT1']\n",
    "        sp.close()\n",
    "        \n",
    "        flux_print = np.squeeze(flux_norm)\n",
    "        \n",
    "        encoded_imgs = encoder(flux_norm).numpy()\n",
    "        decoded_imgs = vae(flux_norm).numpy()        \n",
    "        \n",
    "        fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
    "        fig.suptitle(Fname, size=14)\n",
    "        fig.subplots_adjust(hspace=0.35, wspace=0.15, left=0.07, right=0.97)\n",
    "        \n",
    "        ax[0].plot(wave2, flux_print)\n",
    "        ax[0].set_xlabel('Original Spectra')   \n",
    "        ax[1].plot(wave2, decoded_imgs[0])\n",
    "        ax[1].set_xlabel('Decoded Spectra')\n",
    "        plt.savefig('Autoencoder.png')\n",
    "        \n",
    "        encoded_imgs = np.squeeze(encoded_imgs)\n",
    "        x = np.zeros((len(encoded_imgs), 1))\n",
    "        for i in range(len(encoded_imgs)):\n",
    "            x[i] = encoded_imgs[i]*10000\n",
    "              \n",
    "        duration = 4\n",
    "        interval = 1\n",
    "        \n",
    "        print (\"\", curves, \"Spectrum represented:\",Fname)\n",
    "\n",
    "        client_s = udp_client.SimpleUDPClient(\"127.0.0.1\", 9989)\n",
    "        client_s.send_message(\"/s\", 1)\n",
    "\n",
    "        client0 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9990)   \n",
    "        client0.send_message(\"/0\", x[0])\n",
    "        print(x[0])\n",
    "        client1 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9991) \n",
    "        client1.send_message(\"/1\", x[1])\n",
    "        print(x[1])\n",
    "        client2 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9992) \n",
    "        client2.send_message(\"/2\", x[2])\n",
    "        print(x[2])\n",
    "        client3 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9993) \n",
    "        client3.send_message(\"/3\", x[3])\n",
    "        print(x[3])\n",
    "        client4 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9994) \n",
    "        client4.send_message(\"/4\", x[4])\n",
    "        print(x[4])\n",
    "        client5 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9995)\n",
    "        client5.send_message(\"/5\", x[5])\n",
    "        print(x[5])\n",
    "        client6 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9996)\n",
    "        client6.send_message(\"/6\", x[6])\n",
    "        print(x[6])\n",
    "        client7 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9997)\n",
    "        client7.send_message(\"/7\", x[7])\n",
    "        print(x[7])\n",
    "        client8 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9998)\n",
    "        client8.send_message(\"/8\", x[8])\n",
    "        print(x[8])\n",
    "        client9 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9999)\n",
    "        client9.send_message(\"/9\", x[9])\n",
    "        print(x[9])\n",
    "        client10 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9980)\n",
    "        client10.send_message(\"/10\", x[10])\n",
    "        print(x[10])\n",
    "        client11 = udp_client.SimpleUDPClient(\"127.0.0.1\", 9981)\n",
    "        client11.send_message(\"/11\", x[11])\n",
    "        print(x[11]) \n",
    "        plt.close(fig)\n",
    "        time.sleep(duration+1)\n",
    "        \n",
    "        client_s.send_message(\"/s\", 0)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<details>\n",
       "<summary>Click to view session information</summary>\n",
       "<pre>\n",
       "-----\n",
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       "session_info        1.0.0\n",
       "tensorflow          2.9.1\n",
       "-----\n",
       "</pre>\n",
       "<details>\n",
       "<summary>Click to view modules imported as dependencies</summary>\n",
       "<pre>\n",
       "CoreFoundation                              NA\n",
       "Foundation                                  NA\n",
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       "</details> <!-- seems like this ends pre, so might as well be explicit -->\n",
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       "notebook            6.4.11\n",
       "-----\n",
       "Python 3.8.5 (default, Sep  4 2020, 02:22:02) [Clang 10.0.0 ]\n",
       "macOS-10.15.7-x86_64-i386-64bit\n",
       "-----\n",
       "Session information updated at 2023-09-07 19:27\n",
       "</pre>\n",
       "</details>"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import session_info\n",
    "session_info.show()"
   ]
  }
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