{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a232aef",
   "metadata": {},
   "source": [
    "# Tail Angle Preprocessing\n",
    "\n",
    "**The following notebook illustrate the `TailPreprocessing` class how to run the different preprocessing steps.**\n",
    "- **Several preprocessing steps are used for the tail angle**:\n",
    "  - Interpolating missing values\n",
    "  - PCA denoising of posture using 'eigen-fish'\n",
    "  - Savgol filter for time series smoothing\n",
    "  - Baseline correction\n",
    "\n",
    "- **The tail vigor is also computed and will be useful for detecting tail bouts**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Loading dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from megabouts.tracking_data import TrackingConfig, FullTrackingData, load_example_data\n",
    "from megabouts.config import TailPreprocessingConfig\n",
    "from megabouts.preprocessing import TailPreprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* TrackingConfig and TrackingData similar to [tutorial_Loading_Data](./Loading_Data.ipynb), here we use the dataset from the poorly trained SLEAP model to underlie the effect of smoothing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data and set tracking configuration\n",
    "df_recording, fps, mm_per_unit = load_example_data(\"SLEAP_fulltracking\")\n",
    "tracking_cfg = TrackingConfig(fps=fps, tracking=\"full_tracking\")\n",
    "\n",
    "# List of keypoints\n",
    "keypoints = [\"left_eye\", \"right_eye\", \"tail0\", \"tail1\", \"tail2\", \"tail3\", \"tail4\"]\n",
    "\n",
    "# Place NaN where the score is below a threshold\n",
    "thresh_score = 0.0\n",
    "for kps in keypoints:\n",
    "    score_below_thresh = df_recording[\"instance.score\"] < thresh_score\n",
    "    df_recording.loc[\n",
    "        score_below_thresh | (df_recording[f\"{kps}.score\"] < thresh_score),\n",
    "        [f\"{kps}.x\", f\"{kps}.y\"],\n",
    "    ] = np.nan\n",
    "\n",
    "# Compute head and tail coordinates and convert to mm\n",
    "head_x = ((df_recording[\"left_eye.x\"] + df_recording[\"right_eye.x\"]) / 2) * mm_per_unit\n",
    "head_y = ((df_recording[\"left_eye.y\"] + df_recording[\"right_eye.y\"]) / 2) * mm_per_unit\n",
    "tail_x = df_recording[[f\"tail{i}.x\" for i in range(5)]].values * mm_per_unit\n",
    "tail_y = df_recording[[f\"tail{i}.y\" for i in range(5)]].values * mm_per_unit\n",
    "\n",
    "# Create FullTrackingData object\n",
    "tracking_data = FullTrackingData.from_keypoints(\n",
    "    head_x=head_x.values, head_y=head_y.values, tail_x=tail_x, tail_y=tail_y\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Define preprocessing config using default:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TailPreprocessingConfig(fps=350, limit_na_ms=100, num_pcs=4, savgol_window_ms=15, baseline_method='median', baseline_params={'fps': 350, 'half_window': 175}, tail_speed_filter_ms=100, tail_speed_boxcar_filter_ms=14)\n"
     ]
    }
   ],
   "source": [
    "tail_preprocessing_cfg = TailPreprocessingConfig(fps=tracking_cfg.fps)\n",
    "print(tail_preprocessing_cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Alternatively the tail preprocessing parameters can be set to custom values using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tail_preprocessing_cfg = TailPreprocessingConfig(\n",
    "    fps=tracking_cfg.fps,\n",
    "    num_pcs=3,\n",
    "    savgol_window_ms=30,\n",
    "    tail_speed_filter_ms=100,\n",
    "    tail_speed_boxcar_filter_ms=14,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we change the median filter for computing the tail baseline to 200 frame width:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tail_preprocessing_cfg.baseline_params[\"half_window\"] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Let's run the preprocessing pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tail_df_input = tracking_data.tail_df\n",
    "tail = TailPreprocessing(tail_preprocessing_cfg).preprocess_tail_df(tail_df_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* tail.df contains information about the raw angle, the baseline, the smooth angle as well as the vigor of the tail oscillations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      angle                                                              \\\n",
       "   segments                                                               \n",
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       "\n",
       "                                 ... angle_smooth                      \\\n",
       "                                 ...     segments                       \n",
       "          7         8         9  ...            2         3         4   \n",
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       "\n",
       "                                                    no_tracking vigor  \n",
       "                                                                       \n",
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail.df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can visualize the result of preprocessing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cycler import cycler\n",
    "\n",
    "blue_cycler = cycler(color=plt.cm.Blues(np.linspace(0.2, 0.9, 10)))\n",
    "\n",
    "t = np.arange(tracking_data.T) / tracking_cfg.fps\n",
    "IdSt = 140612  # np.random.randint(tracking_data.T)\n",
    "Duration = 150\n",
    "t_win = t[IdSt : IdSt + Duration] - t[IdSt]\n",
    "\n",
    "# Prepare the data, titles, and subtitles\n",
    "angle_data = [tail.angle, tail.angle_smooth]\n",
    "subtitles = [\"Raw Data\", \"Smoothed Data\"]\n",
    "\n",
    "# Create subplots\n",
    "fig, ax = plt.subplots(2, 1, figsize=(10, 7), sharex=True)\n",
    "\n",
    "# Set a main title for the figure\n",
    "fig.suptitle(\"Tail Preprocessing\", fontsize=16)\n",
    "\n",
    "# Loop over the axes, data, and subtitles\n",
    "for axis, data, subtitle in zip(ax, angle_data, subtitles):\n",
    "    axis.set_prop_cycle(blue_cycler)\n",
    "    axis.plot(t_win, data[IdSt : IdSt + Duration, :7])\n",
    "    axis.set(ylabel=\"Angle (rad)\", ylim=(-4, 4))\n",
    "    axis.set_title(subtitle, fontsize=12)\n",
    "\n",
    "# Set x-label for the last subplot\n",
    "ax[-1].set_xlabel(\"Time (s)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### 📝 Note\n",
    "> Smoothing the tail tracking data is optional for classifying tail bouts. The transformer model was trained on raw tracking data, so it can handle unsmoothed input just as well."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "megabouts_dev",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}