{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a232aef",
   "metadata": {},
   "source": [
    "# Tail Segmentation \n",
    "\n",
    "**The following notebook illustrate the `TailSegmentation` class.**"
   ]
  },
  {
   "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, load_example_data, FullTrackingData\n",
    "from megabouts.config import TailPreprocessingConfig, TailSegmentationConfig\n",
    "from megabouts.preprocessing import TailPreprocessing\n",
    "from megabouts.segmentation import Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data and Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* TrackingConfig and TrackingData similar to [tutorial_Tail_Preprocessing](./tutorial_Tail_Preprocessing.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recording, fps, mm_per_unit = load_example_data(\"fulltracking_posture\")\n",
    "\n",
    "tracking_cfg = TrackingConfig(fps=fps, tracking=\"full_tracking\")\n",
    "\n",
    "head_x = df_recording[\"head_x\"].values * mm_per_unit\n",
    "head_y = df_recording[\"head_y\"].values * mm_per_unit\n",
    "head_yaw = df_recording[\"head_angle\"].values\n",
    "tail_angle = df_recording.filter(like=\"tail_angle\").values\n",
    "\n",
    "tracking_data = FullTrackingData.from_posture(\n",
    "    head_x=head_x, head_y=head_y, head_yaw=head_yaw, tail_angle=tail_angle\n",
    ")\n",
    "t = np.arange(tracking_data.T) / tracking_cfg.fps\n",
    "\n",
    "tail_preprocessing_cfg = TailPreprocessingConfig(fps=tracking_cfg.fps)\n",
    "\n",
    "tail_preprocessing_cfg = TailPreprocessingConfig(\n",
    "    fps=tracking_cfg.fps,\n",
    "    num_pcs=4,\n",
    "    savgol_window_ms=15,\n",
    "    tail_speed_filter_ms=100,\n",
    "    tail_speed_boxcar_filter_ms=25,\n",
    ")\n",
    "tail_df_input = tracking_data.tail_df\n",
    "tail = TailPreprocessing(tail_preprocessing_cfg).preprocess_tail_df(tail_df_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">angle</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"8\" halign=\"left\">angle_smooth</th>\n",
       "      <th>no_tracking</th>\n",
       "      <th>vigor</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>-0.101865</td>\n",
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       "      <td>-0.110575</td>\n",
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       "      <td>-0.145887</td>\n",
       "      <td>-0.130414</td>\n",
       "      <td>-0.058892</td>\n",
       "      <td>-0.128705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051057</td>\n",
       "      <td>-0.055168</td>\n",
       "      <td>-0.055898</td>\n",
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       "      <td>-0.061091</td>\n",
       "      <td>-0.069420</td>\n",
       "      <td>-0.115014</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-0.087957</td>\n",
       "      <td>-0.096951</td>\n",
       "      <td>-0.092459</td>\n",
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       "      <td>-0.099788</td>\n",
       "      <td>-0.101741</td>\n",
       "      <td>-0.171555</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046070</td>\n",
       "      <td>-0.052906</td>\n",
       "      <td>-0.056578</td>\n",
       "      <td>-0.063577</td>\n",
       "      <td>-0.062286</td>\n",
       "      <td>-0.064780</td>\n",
       "      <td>-0.091617</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.093377</td>\n",
       "      <td>-0.095235</td>\n",
       "      <td>-0.094292</td>\n",
       "      <td>-0.105936</td>\n",
       "      <td>-0.073785</td>\n",
       "      <td>-0.084193</td>\n",
       "      <td>-0.144378</td>\n",
       "      <td>-0.112398</td>\n",
       "      <td>-0.042585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041909</td>\n",
       "      <td>-0.050905</td>\n",
       "      <td>-0.056916</td>\n",
       "      <td>-0.064832</td>\n",
       "      <td>-0.062943</td>\n",
       "      <td>-0.060658</td>\n",
       "      <td>-0.072246</td>\n",
       "      <td>0.000430</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.092590</td>\n",
       "      <td>-0.083650</td>\n",
       "      <td>-0.100938</td>\n",
       "      <td>-0.088223</td>\n",
       "      <td>-0.097370</td>\n",
       "      <td>-0.099559</td>\n",
       "      <td>-0.101538</td>\n",
       "      <td>-0.091272</td>\n",
       "      <td>-0.021459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038574</td>\n",
       "      <td>-0.049166</td>\n",
       "      <td>-0.056913</td>\n",
       "      <td>-0.065467</td>\n",
       "      <td>-0.063063</td>\n",
       "      <td>-0.057053</td>\n",
       "      <td>-0.056900</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.086849</td>\n",
       "      <td>-0.081982</td>\n",
       "      <td>-0.096705</td>\n",
       "      <td>-0.118475</td>\n",
       "      <td>-0.046264</td>\n",
       "      <td>-0.136459</td>\n",
       "      <td>-0.115412</td>\n",
       "      <td>-0.085300</td>\n",
       "      <td>-0.015487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036064</td>\n",
       "      <td>-0.047688</td>\n",
       "      <td>-0.056569</td>\n",
       "      <td>-0.065483</td>\n",
       "      <td>-0.062646</td>\n",
       "      <td>-0.053965</td>\n",
       "      <td>-0.045579</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 32 columns</p>\n",
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      ],
      "text/plain": [
       "      angle                                                              \\\n",
       "   segments                                                               \n",
       "          0         1         2         3         4         5         6   \n",
       "0 -0.101865 -0.092813 -0.107645 -0.110575 -0.047699 -0.145887 -0.130414   \n",
       "1 -0.082618 -0.087957 -0.096951 -0.092459 -0.119418 -0.043354 -0.099788   \n",
       "2 -0.093377 -0.095235 -0.094292 -0.105936 -0.073785 -0.084193 -0.144378   \n",
       "3 -0.092590 -0.083650 -0.100938 -0.088223 -0.097370 -0.099559 -0.101538   \n",
       "4 -0.086849 -0.081982 -0.096705 -0.118475 -0.046264 -0.136459 -0.115412   \n",
       "\n",
       "                           ... angle_smooth                                \\\n",
       "                           ...     segments                                 \n",
       "          7         8   9  ...            2         3         4         5   \n",
       "0 -0.058892 -0.128705 NaN  ...    -0.051057 -0.055168 -0.055898 -0.061701   \n",
       "1 -0.101741 -0.171555 NaN  ...    -0.046070 -0.052906 -0.056578 -0.063577   \n",
       "2 -0.112398 -0.042585 NaN  ...    -0.041909 -0.050905 -0.056916 -0.064832   \n",
       "3 -0.091272 -0.021459 NaN  ...    -0.038574 -0.049166 -0.056913 -0.065467   \n",
       "4 -0.085300 -0.015487 NaN  ...    -0.036064 -0.047688 -0.056569 -0.065483   \n",
       "\n",
       "                                          no_tracking vigor  \n",
       "                                                             \n",
       "          6         7         8         9                    \n",
       "0 -0.061091 -0.069420 -0.115014  0.000183        True   NaN  \n",
       "1 -0.062286 -0.064780 -0.091617  0.000321        True   NaN  \n",
       "2 -0.062943 -0.060658 -0.072246  0.000430        True   NaN  \n",
       "3 -0.063063 -0.057053 -0.056900  0.000512        True   NaN  \n",
       "4 -0.062646 -0.053965 -0.045579  0.000565        True   NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail.df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segmentation using tail vigor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Set the threshold to 20:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tail_segmentation_cfg = TailSegmentationConfig(fps=tracking_cfg.fps, threshold=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Apply segmentation to `tail.vigor`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "segmentation_function = Segmentation.from_config(tail_segmentation_cfg)\n",
    "segments = segmentation_function.segment(tail.vigor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can visualize the results of the segmentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_bouts = np.zeros(tracking_data.T, dtype=bool)\n",
    "# Set to True for the indices within the bouts\n",
    "for on_, off_ in zip(segments.onset, segments.offset):\n",
    "    is_bouts[on_:off_] = True\n",
    "\n",
    "IdSt = 139000\n",
    "Duration = 5 * tracking_cfg.fps\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n",
    "fig.suptitle(\"Tail Segmentation\", fontsize=16)\n",
    "\n",
    "x = np.copy(tracking_data._tail_angle[:, 7])\n",
    "x_bouts = np.where(is_bouts, x, np.nan)\n",
    "x_nobouts = np.where(~is_bouts, x, np.nan)\n",
    "\n",
    "ax[0].plot(t, x_nobouts, \"tab:gray\", lw=1)\n",
    "ax[0].plot(t, x_bouts, \"tab:red\", lw=1)\n",
    "ax[0].plot(t[segments.onset], x[segments.onset], \"x\", color=\"tab:green\", label=\"onset\")\n",
    "ax[0].plot(t[segments.offset], x[segments.offset], \"x\", color=\"red\", label=\"offset\")\n",
    "ax[0].set(ylabel=\"angle (rad)\", ylim=(-4, 4))\n",
    "ax[0].legend()\n",
    "\n",
    "x = np.copy(tail.vigor)\n",
    "x_bouts = np.where(is_bouts, x, np.nan)\n",
    "x_nobouts = np.where(~is_bouts, x, np.nan)\n",
    "\n",
    "ax[1].plot(t, x_nobouts, \"tab:gray\", lw=1)\n",
    "ax[1].plot(t, x_bouts, \"tab:red\", lw=1)\n",
    "ax[1].plot(t[segments.onset], x[segments.onset], \"x\", color=\"tab:green\", label=\"onset\")\n",
    "ax[1].plot(t[segments.offset], x[segments.offset], \"x\", color=\"red\", label=\"offset\")\n",
    "ax[1].set(ylabel=\"vigor (A.U)\")\n",
    "ax[1].legend()\n",
    "\n",
    "t = np.arange(tracking_data.T) / tracking_cfg.fps\n",
    "\n",
    "ax[1].set_xlim(t[IdSt], t[IdSt + Duration])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To find the ideal segmentation threshold for your dataset, it is useful to compute the number of bouts detected as a function of the threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of bouts as function of threshold:\n",
    "thresh_list = np.linspace(4, 400, 500)\n",
    "num_bouts = np.zeros_like(thresh_list)\n",
    "\n",
    "for i, thresh in enumerate(thresh_list):\n",
    "    tail_segmentation_cfg = TailSegmentationConfig(\n",
    "        fps=tracking_cfg.fps, threshold=thresh\n",
    "    )\n",
    "    segmentation_function = Segmentation.from_config(tail_segmentation_cfg)\n",
    "    segments = segmentation_function.segment(tail.vigor)\n",
    "    num_bouts[i] = len(segments.onset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* For very small threshold values, too many bouts are detected, while for large threshold values, no bouts are detected. There is an optimal range on the plateau between these extremes where a suitable threshold can be found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.plot(thresh_list, num_bouts)\n",
    "\n",
    "ax.fill_between(\n",
    "    thresh_list,\n",
    "    0,\n",
    "    1000,\n",
    "    where=(num_bouts < 767) & (num_bouts > 755),\n",
    "    color=\"green\",\n",
    "    alpha=0.5,\n",
    "    label=\"good range for choice of threshold\",\n",
    ")\n",
    "ax.legend()\n",
    "ax.set_ylabel(\"number of swim bouts detected\", fontsize=11)\n",
    "ax.set_xlabel(\"segmentation threshold\", fontsize=11)\n",
    "plt.show()"
   ]
  }
 ],
 "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
}