{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "178597e4054ba0a6",
   "metadata": {},
   "source": [
    "# B Point Plotting Example\n",
    "\n",
    "This notebook demonstrates running a B-point extraction (Pale et al., 2021) on the example dataset and several\n",
    "plotting styles: low-level plotting, split/zoomed axes, showing reference points, detected points, and heartbeat borders."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7caa710a8f6b4214",
   "metadata": {},
   "source": [
    "## Setup and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3632144a56f5392d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-01T16:26:04.245053Z",
     "start_time": "2026-02-01T16:26:02.956217Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from fau_colors import cmaps\n",
    "\n",
    "from pepbench.algorithms.icg import BPointExtractionPale2021, CPointExtractionScipyFindPeaks\n",
    "from pepbench.example_data import get_example_dataset\n",
    "from pepbench.plotting.algorithms import plot_b_point_extraction_pale2021\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac6b05c6275d34ee",
   "metadata": {},
   "source": [
    "## Plotting style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c11b0c5633e3b61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-01T16:26:04.265481Z",
     "start_time": "2026-02-01T16:26:04.251001Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.close(\"all\")\n",
    "palette = sns.color_palette(cmaps.faculties)\n",
    "sns.set_theme(context=\"notebook\", style=\"ticks\", font=\"sans-serif\", palette=palette)\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c93a99454736b04",
   "metadata": {},
   "source": [
    "## Load example dataset using get_example_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4fbc5bf4e76bbc17",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-01T16:26:04.294575Z",
     "start_time": "2026-02-01T16:26:04.265963Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4 style=\"margin-bottom: 0.1em;\">ExampleDataset [2 groups/rows]</h3>\n",
       "<div style=\"margin-top: 0em\">\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table style=\"margin-left: 3em;\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"text-align: center;\"></th>\n",
       "      <th style=\"text-align: center;\">participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th style=\"text-align: center;\">0</th>\n",
       "      <td style=\"text-align: center; padding-left: 2em; padding-right: 2em;\">VP_001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"text-align: center;\">1</th>\n",
       "      <td style=\"text-align: center; padding-left: 2em; padding-right: 2em;\">VP_002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ExampleDataset [2 groups/rows]\n",
       "\n",
       "     participant\n",
       "   0      VP_001\n",
       "   1      VP_002"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will download the example zip automatically if it's not present locally\n",
    "dataset = get_example_dataset(return_clean=True)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "338e3841dccd71bb",
   "metadata": {},
   "source": [
    "### Pick the first available participant/phase from the example dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd9cf7803c43f958",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-01T16:26:04.358414Z",
     "start_time": "2026-02-01T16:26:04.311262Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset index (first rows):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VP_001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VP_002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  participant\n",
       "0      VP_001\n",
       "1      VP_002"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using: VP_001 None None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h4 style=\"margin-bottom: 0.1em;\">ExampleDataset [1 groups/rows]</h3>\n",
       "<div style=\"margin-top: 0em\">\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table style=\"margin-left: 3em;\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"text-align: center;\"></th>\n",
       "      <th style=\"text-align: center;\">participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th style=\"text-align: center;\">0</th>\n",
       "      <td style=\"text-align: center; padding-left: 2em; padding-right: 2em;\">VP_001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ExampleDataset [1 groups/rows]\n",
       "\n",
       "     participant\n",
       "   0      VP_001"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Dataset index (first rows):\")\n",
    "display(dataset.index.head(10))\n",
    "\n",
    "row = dataset.index.iloc[0]\n",
    "participant, condition, phase = row[\"participant\"], row.get(\"condition\", None), row.get(\"phase\", None)\n",
    "print(\"Using:\", participant, condition, phase)\n",
    "\n",
    "# create a datapoint subset for this entry\n",
    "datapoint = dataset.get_subset(participant=participant)\n",
    "datapoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d930377bf78376",
   "metadata": {},
   "source": [
    "## Run the algorithms (C-point then B-point) on the datapoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6cada1a54898a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-01T16:26:04.812050Z",
     "start_time": "2026-02-01T16:26:04.380786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b_point_sample</th>\n",
       "      <th>nan_reason</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heartbeat_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>179</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>533</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1466</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1873</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              b_point_sample nan_reason\n",
       "heartbeat_id                           \n",
       "0                        179        NaN\n",
       "1                        533        NaN\n",
       "2                       1009        NaN\n",
       "3                       1466        NaN\n",
       "4                       1873        NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# C-point extraction (used by Pale et al. B-point algorithm)\n",
    "c_algo = CPointExtractionScipyFindPeaks()\n",
    "c_algo.extract(icg=datapoint.icg, heartbeats=datapoint.heartbeats, sampling_rate_hz=datapoint.sampling_rate_icg)\n",
    "\n",
    "# B-point extraction (Pale et al. 2021)\n",
    "b_algo = BPointExtractionPale2021()\n",
    "b_algo.extract(\n",
    "    icg=datapoint.icg,\n",
    "    heartbeats=datapoint.heartbeats,\n",
    "    c_points=c_algo.points_,\n",
    "    sampling_rate_hz=datapoint.sampling_rate_icg,\n",
    ")\n",
    "\n",
    "\n",
    "# show detected points table (head)\n",
    "display(b_algo.points_.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f9c4ef5d5b7e75",
   "metadata": {},
   "source": [
    "## 1) High-level helper plot (recommended)\n",
    "Use the provided plotting helper `plot_b_point_extraction_pale2021` to visualise multiple heartbeats with\n",
    "reference labels, detected C/B points and heartbeat borders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6592ef9d08aa7811",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-01T16:28:53.537866Z",
     "start_time": "2026-02-01T16:28:53.421093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datapoint.heartbeats.index.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b1359ddd019a8bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-02-01T16:26:05.736709Z",
     "start_time": "2026-02-01T16:26:04.949624Z"
    }
   },
   "outputs": [],
   "source": [
    "hb_ids = datapoint.heartbeats.index.get_level_values(\"heartbeat_id\").unique()[:5].tolist()\n",
    "print(\"Plotting heartbeat ids:\", hb_ids)\n",
    "fig, ax = plot_b_point_extraction_pale2021(datapoint, heartbeat_subset=hb_ids, normalize_time=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5912d0ef67fc7ebe",
   "metadata": {},
   "source": [
    "## 2) Low-level plotting: raw ICG with detected & reference markers and heartbeat borders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "259f5054bcc865c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot raw ICG and overlay reference/detected B-points and heartbeat borders\n",
    "fig, ax = plt.subplots(figsize=(20, 5))\n",
    "icg = datapoint.icg.squeeze()\n",
    "ax.plot(icg.index, icg.values, label=\"ICG (preprocessed)\")\n",
    "\n",
    "# detected b points (from algorithm result)\n",
    "b_samples = b_algo.points_[\"b_point_sample\"].dropna().astype(int)\n",
    "ax.scatter(icg.index[b_samples], icg.values[b_samples], color=\"C1\", marker=\"o\", label=\"Detected B\")\n",
    "\n",
    "# reference b points if available\n",
    "try:\n",
    "    ref_b = datapoint.reference_labels_icg.reindex([\"ICG\"], level=\"channel\")[\"sample_relative\"]\n",
    "    ref_b = ref_b.droplevel([\"channel\"]).astype(int)\n",
    "    ax.scatter(icg.index[ref_b], icg.values[ref_b], color=\"C2\", marker=\"X\", label=\"Reference B\")\n",
    "except Exception:\n",
    "    pass\n",
    "\n",
    "# heartbeat borders (start samples)\n",
    "hb_borders = datapoint.heartbeats\n",
    "ax.vlines(\n",
    "    icg.index[hb_borders[\"start_sample\"]],\n",
    "    ymin=icg.min(),\n",
    "    ymax=icg.max(),\n",
    "    colors=\"C3\",\n",
    "    linestyles=\"--\",\n",
    "    alpha=0.6,\n",
    "    label=\"Heartbeat borders\",\n",
    ")\n",
    "\n",
    "ax.legend(loc=\"upper right\")\n",
    "ax.set_title(\"Raw ICG with detected and reference B-points\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e1545bc04e017fb",
   "metadata": {},
   "source": [
    "## 4) Show heartbeat borders and annotations using plotting utilities\n",
    "Use the internal plotting utilities to add heartbeat borders and legend handling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a759603b1e1966a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pepbench.plotting._utils import _add_heartbeat_borders, _add_icg_b_points, _add_pep_from_reference\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 3))\n",
    "ax.plot(icg.index, icg.values, label=\"ICG\")\n",
    "# heartbeat borders (use utility)\n",
    "_add_heartbeat_borders(datapoint.heartbeats, ax)\n",
    "# add detected B points using internal helper\n",
    "_add_icg_b_points(datapoint.icg.squeeze(), b_samples, ax)\n",
    "# add reference PEP spans if available\n",
    "try:\n",
    "    _add_pep_from_reference(\n",
    "        datapoint.ecg,\n",
    "        datapoint.icg,\n",
    "        datapoint.reference_labels_ecg.join(datapoint.reference_labels_icg),\n",
    "        ax,\n",
    "        pep_hatch=None,\n",
    "    )\n",
    "except Exception:\n",
    "    pass\n",
    "ax.set_title(\"ICG with heartbeat borders and annotations\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9cb9a9d2288679",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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