{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert `.mat`\n",
    "Converts Camera Signal and Radiant Temperature of `.mat` files to `.p` filetype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imageio\n",
    "import numpy as np\n",
    "import pickle\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = \"powder_plate_7_bare_pad_195_w_800_mm_s\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(501, 127, 360)\n",
      "16367\n"
     ]
    }
   ],
   "source": [
    "filename = \"camera_signal\"\n",
    "\n",
    "mat = scipy.io.loadmat(f\"data/{folder}/{filename}.mat\")\n",
    "video = mat[\"CameraSignal\"]\n",
    "\n",
    "# Reshapes from (y, x, f) to (f, x, y)\n",
    "video_reshaped = np.transpose(video, (2, 0, 1))\n",
    "print(video_reshaped.shape)\n",
    "print(np.max(video_reshaped))\n",
    "\n",
    "with open(f\"data/{folder}/{filename}.pkl\", \"wb\") as file:\n",
    "    pickle.dump(video_reshaped, file)\n",
    "\n",
    "# Normalizes the video for visual output\n",
    "video_normalized = np.interp(\n",
    "    video_reshaped,\n",
    "    (video_reshaped.min(), video_reshaped.max()),\n",
    "    (0, 255)\n",
    ")\n",
    "\n",
    "frames = []\n",
    "for frame in video_normalized:\n",
    "    frames.append(frame)\n",
    "\n",
    "imageio.mimsave(f\"data/{folder}/{filename}.gif\", frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(501, 127, 360)\n",
      "1073.1382220207333\n"
     ]
    }
   ],
   "source": [
    "filename = \"radiant_temperature\"\n",
    "\n",
    "mat = scipy.io.loadmat(f\"data/{folder}/{filename}.mat\")\n",
    "video = mat[\"RadiantTemperature\"]\n",
    "\n",
    "# Reshapes from (x, y, f) to (f, x, y)\n",
    "video_reshaped = np.transpose(video, (2, 0, 1))\n",
    "print(video_reshaped.shape)\n",
    "print(np.max(video_reshaped))\n",
    "\n",
    "with open(f\"data/{folder}/{filename}.pkl\", \"wb\") as file:\n",
    "    pickle.dump(video_reshaped, file)\n",
    "\n",
    "# Normalizes the video for visual output\n",
    "video_normalized = np.interp(\n",
    "    video_reshaped,\n",
    "    (video_reshaped.min(), video_reshaped.max()),\n",
    "    (0, 255)\n",
    ")\n",
    "\n",
    "frames = []\n",
    "for frame in video_normalized:\n",
    "    frames.append(frame)\n",
    "\n",
    "imageio.mimsave(f\"data/{folder}/{filename}.gif\", frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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