{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b9811655",
   "metadata": {},
   "source": [
    "# Intervals\n",
    "\n",
    "Many analyses look at stretches of data rather than single time points:\n",
    "the second after a stimulus, the time between trial start and response,\n",
    "blinks, or periods with bad data. In `pypillometry`, such stretches are\n",
    "stored as `Intervals`, a list of (start, end) pairs with their units and,\n",
    "if they belong to events, the event labels. Windows around events are\n",
    "`Epochs`: the event onsets with one window.\n",
    "\n",
    "This tutorial shows where intervals come from, how to change and combine\n",
    "them, and how to use them in an analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfc85730",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:28.103251Z",
     "iopub.status.busy": "2026-11-10T13:34:28.103082Z",
     "iopub.status.idle": "2026-11-10T13:34:28.902002Z",
     "shell.execute_reply": "2026-11-10T13:34:28.901354Z"
    }
   },
   "outputs": [],
   "source": [
    "import pypillometry as pp\n",
    "from pypillometry import u\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pp.logging_set_level(\"WARNING\")\n",
    "\n",
    "d = pp.get_example_data(\"rlmw_002_short\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "895a0ffd",
   "metadata": {},
   "source": [
    "## Epochs: windows around events\n",
    "\n",
    "`get_epochs()` creates one window for each selected event. The first\n",
    "argument selects the events (a string contained in the label, a function,\n",
    "or an `Events` object; see the [Events tutorial](events.ipynb)). The\n",
    "second gives the window relative to the event onset, here from 200 ms\n",
    "before until 1000 ms after each stimulus. The result is an `Epochs`\n",
    "object (onsets and window in ms); it can be used wherever intervals are\n",
    "expected, and `stim.to_intervals()` gives its spans as `Intervals`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1e6cabd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:28.905558Z",
     "iopub.status.busy": "2026-11-10T13:34:28.905325Z",
     "iopub.status.idle": "2026-11-10T13:34:28.910952Z",
     "shell.execute_reply": "2026-11-10T13:34:28.910550Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Epochs 'S_' | 10 epochs, window (-200, 1000) ms"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim = d.get_epochs(\"S_\", (-200, 1000) * u.ms)\n",
    "stim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec0140a",
   "metadata": {},
   "source": [
    "`as_pandas()` shows the epochs as a table, together with the events\n",
    "they belong to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19b6f367",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:28.914085Z",
     "iopub.status.busy": "2026-11-10T13:34:28.913916Z",
     "iopub.status.idle": "2026-11-10T13:34:28.925216Z",
     "shell.execute_reply": "2026-11-10T13:34:28.924636Z"
    }
   },
   "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>onset</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5677.0</td>\n",
       "      <td>5477.0</td>\n",
       "      <td>6677.0</td>\n",
       "      <td>S_1_1_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9575.0</td>\n",
       "      <td>9375.0</td>\n",
       "      <td>10575.0</td>\n",
       "      <td>S_2_6_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12574.0</td>\n",
       "      <td>12374.0</td>\n",
       "      <td>13574.0</td>\n",
       "      <td>S_3_2_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16473.0</td>\n",
       "      <td>16273.0</td>\n",
       "      <td>17473.0</td>\n",
       "      <td>S_4_4_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20388.0</td>\n",
       "      <td>20188.0</td>\n",
       "      <td>21388.0</td>\n",
       "      <td>S_5_3_5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     onset    start      end    label\n",
       "0   5677.0   5477.0   6677.0  S_1_1_5\n",
       "1   9575.0   9375.0  10575.0  S_2_6_2\n",
       "2  12574.0  12374.0  13574.0  S_3_2_1\n",
       "3  16473.0  16273.0  17473.0  S_4_4_1\n",
       "4  20388.0  20188.0  21388.0  S_5_3_5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim.as_pandas().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07df4558",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "**Note:** Plain numbers without units are **ms**, like everywhere in\n",
    "pypillometry. Sample indices need `units=\"samples\"`: at 500 Hz,\n",
    "`(-200, 1000)` with `units=\"samples\"` means 400 ms before until 2000 ms\n",
    "after the event.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca69a553",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:28.927668Z",
     "iopub.status.busy": "2026-11-10T13:34:28.927408Z",
     "iopub.status.idle": "2026-11-10T13:34:28.932502Z",
     "shell.execute_reply": "2026-11-10T13:34:28.931604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Epochs 'S_' | 10 epochs, window (-200, 1000) ms"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.get_epochs(\"S_\", (-200, 1000))   # plain numbers are ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3483bfe0",
   "metadata": {},
   "source": [
    "## Intervals between two events\n",
    "\n",
    "`get_intervals()` takes two selectors, `start` and `end`; each interval\n",
    "spans from a `start` event to the next `end` event. Here, from the start\n",
    "of each trial (`F` events)\n",
    "to the feedback (`R` events). By default, an interval spans exactly from\n",
    "the first to the second event; a `window` extends or shortens it (e.g.,\n",
    "`window=(-200, 500) * u.ms` starts 200 ms earlier and ends 500 ms later):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73c34431",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:28.934617Z",
     "iopub.status.busy": "2026-11-10T13:34:28.934350Z",
     "iopub.status.idle": "2026-11-10T13:34:28.946632Z",
     "shell.execute_reply": "2026-11-10T13:34:28.945764Z"
    }
   },
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>units</th>\n",
       "      <th>label</th>\n",
       "      <th>onset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5177.0</td>\n",
       "      <td>7010.0</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>ms</td>\n",
       "      <td>F_1 to R_1_15</td>\n",
       "      <td>5177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8776.0</td>\n",
       "      <td>10808.0</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>ms</td>\n",
       "      <td>F_2 to R_2_20</td>\n",
       "      <td>8776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12374.0</td>\n",
       "      <td>13957.0</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>ms</td>\n",
       "      <td>F_3 to R_3_18</td>\n",
       "      <td>12374.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     start      end  duration units          label    onset\n",
       "0   5177.0   7010.0    1833.0    ms  F_1 to R_1_15   5177.0\n",
       "1   8776.0  10808.0    2032.0    ms  F_2 to R_2_20   8776.0\n",
       "2  12374.0  13957.0    1583.0    ms  F_3 to R_3_18  12374.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials = d.get_intervals(start=\"F_\", end=\"R_\", units=\"ms\")\n",
    "trials.as_pandas().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd35e4e",
   "metadata": {},
   "source": [
    "`stats()` summarizes the durations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1b27248",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:28.949218Z",
     "iopub.status.busy": "2026-11-10T13:34:28.948922Z",
     "iopub.status.idle": "2026-11-10T13:34:28.953752Z",
     "shell.execute_reply": "2026-11-10T13:34:28.952894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n': 10,\n",
       " 'mean': 1791.1,\n",
       " 'sd': 219.8792623236671,\n",
       " 'min': 1333.0,\n",
       " 'max': 2033.0,\n",
       " 'total_duration': 17911.0,\n",
       " 'units': 'ms'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials.stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715fd6bf",
   "metadata": {},
   "source": [
    "## Other sources of intervals\n",
    "\n",
    "Detection methods store their results as mask layers; getters return\n",
    "them as intervals. For example, after `pupil.blinks_detect()`,\n",
    "`get_blinks()` returns one `Intervals` object per eye and variable (in\n",
    "ms). Detecting on a copy (`d | pp.pupil.blinks_detect()`) leaves the data unchanged, so\n",
    "you can check the blinks first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90b12443",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:28.956218Z",
     "iopub.status.busy": "2026-11-10T13:34:28.955958Z",
     "iopub.status.idle": "2026-11-10T13:34:29.645904Z",
     "shell.execute_reply": "2026-11-10T13:34:29.644817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intervals 'left_pupil_blinks' | 14 intervals (total duration: 3040.00 ms), 217.14 ms +/- 236.47 ms, [48.00 ms, 984.00 ms]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blinks = (d | pp.pupil.blinks_detect()).get_blinks()\n",
    "blinks[\"left_pupil\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62843cc",
   "metadata": {},
   "source": [
    "Blinks stored in the object can be retrieved with `get_blinks()`, and\n",
    "masked samples with `mask_as_intervals()`. You can also create intervals\n",
    "directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be6ade01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.648716Z",
     "iopub.status.busy": "2026-11-10T13:34:29.648423Z",
     "iopub.status.idle": "2026-11-10T13:34:29.652426Z",
     "shell.execute_reply": "2026-11-10T13:34:29.651870Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intervals 'bad data' | 2 intervals (total duration: 3.50 sec), 1.75 sec +/- 0.75 sec, [1.00 sec, 2.50 sec]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.Intervals([(1, 2), (5, 7.5)] * u.sec, label=\"bad data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a46e1c",
   "metadata": {},
   "source": [
    "## Units\n",
    "\n",
    "`to_units()` converts intervals to other time units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "240959f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.655220Z",
     "iopub.status.busy": "2026-11-10T13:34:29.655001Z",
     "iopub.status.idle": "2026-11-10T13:34:29.662502Z",
     "shell.execute_reply": "2026-11-10T13:34:29.662079Z"
    }
   },
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>units</th>\n",
       "      <th>label</th>\n",
       "      <th>onset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.177</td>\n",
       "      <td>7.010</td>\n",
       "      <td>1.833</td>\n",
       "      <td>sec</td>\n",
       "      <td>F_1 to R_1_15</td>\n",
       "      <td>5.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.776</td>\n",
       "      <td>10.808</td>\n",
       "      <td>2.032</td>\n",
       "      <td>sec</td>\n",
       "      <td>F_2 to R_2_20</td>\n",
       "      <td>8.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.374</td>\n",
       "      <td>13.957</td>\n",
       "      <td>1.583</td>\n",
       "      <td>sec</td>\n",
       "      <td>F_3 to R_3_18</td>\n",
       "      <td>12.374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    start     end  duration units          label   onset\n",
       "0   5.177   7.010     1.833   sec  F_1 to R_1_15   5.177\n",
       "1   8.776  10.808     2.032   sec  F_2 to R_2_20   8.776\n",
       "2  12.374  13.957     1.583   sec  F_3 to R_3_18  12.374"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials.to_units(\"sec\").as_pandas().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef9666b8",
   "metadata": {},
   "source": [
    "Intervals in sample indices (`units=\"samples\"`) are only\n",
    "valid for the data object they came from: after slicing or downsampling,\n",
    "or in another recording, the same indices point to other samples.\n",
    "Converting between sample indices and time therefore needs the data\n",
    "object, whose time vector gives the time of each sample (a time is\n",
    "converted to the first sample at or after it). Here, the blinks as sample\n",
    "indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f61a475",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.664890Z",
     "iopub.status.busy": "2026-11-10T13:34:29.664737Z",
     "iopub.status.idle": "2026-11-10T13:34:29.670560Z",
     "shell.execute_reply": "2026-11-10T13:34:29.669840Z"
    }
   },
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2704</td>\n",
       "      <td>2748</td>\n",
       "      <td>44</td>\n",
       "      <td>samples</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3781</td>\n",
       "      <td>3832</td>\n",
       "      <td>51</td>\n",
       "      <td>samples</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3932</td>\n",
       "      <td>3956</td>\n",
       "      <td>24</td>\n",
       "      <td>samples</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start   end  duration    units\n",
       "0   2704  2748        44  samples\n",
       "1   3781  3832        51  samples\n",
       "2   3932  3956        24  samples"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blinks[\"left_pupil\"].to_units(\"samples\", d).as_pandas().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36980d7",
   "metadata": {},
   "source": [
    "## Changing intervals\n",
    "\n",
    "The following methods return new `Intervals` and leave the original\n",
    "unchanged.\n",
    "\n",
    "`filter()` keeps intervals by duration, time range or a function of\n",
    "`(start, end)`. Here, the trials that took longer than 1.8 seconds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71488ebf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.673264Z",
     "iopub.status.busy": "2026-11-10T13:34:29.673083Z",
     "iopub.status.idle": "2026-11-10T13:34:29.676799Z",
     "shell.execute_reply": "2026-11-10T13:34:29.676141Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intervals 'F_ to R_' | 7 intervals (total duration: 13445.00 ms), 1920.71 ms +/- 88.30 ms, [1800.00 ms, 2033.00 ms]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials.filter(min_duration=1.8 * u.sec)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d31001",
   "metadata": {},
   "source": [
    "`pad()` extends each interval. For blinks, this is useful because the\n",
    "samples just before and after a blink are often distorted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f42a04fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.678410Z",
     "iopub.status.busy": "2026-11-10T13:34:29.678221Z",
     "iopub.status.idle": "2026-11-10T13:34:29.684861Z",
     "shell.execute_reply": "2026-11-10T13:34:29.684217Z"
    }
   },
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5358.0</td>\n",
       "      <td>5596.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7512.0</td>\n",
       "      <td>7764.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7814.0</td>\n",
       "      <td>8012.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    start     end  duration units\n",
       "0  5358.0  5596.0     238.0    ms\n",
       "1  7512.0  7764.0     252.0    ms\n",
       "2  7814.0  8012.0     198.0    ms"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = blinks[\"left_pupil\"]\n",
    "left.pad(50 * u.ms, 100 * u.ms).as_pandas().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39fdcba7",
   "metadata": {},
   "source": [
    "`merge()` combines intervals that overlap (or are closer than\n",
    "`distance`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1546cbd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.686527Z",
     "iopub.status.busy": "2026-11-10T13:34:29.686341Z",
     "iopub.status.idle": "2026-11-10T13:34:29.689748Z",
     "shell.execute_reply": "2026-11-10T13:34:29.689245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 -> 12\n"
     ]
    }
   ],
   "source": [
    "print(len(left), \"->\", len(left.merge(distance=200 * u.ms)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92d3aa5",
   "metadata": {},
   "source": [
    "## Combining intervals\n",
    "\n",
    "`+` (or `pp.concat_intervals()` for many at once, or a list or dict) puts\n",
    "the intervals of two objects together. For instance, the blinks of both\n",
    "eyes. Overlapping intervals are kept separate until you call `merge()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "90405d42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.691567Z",
     "iopub.status.busy": "2026-11-10T13:34:29.691384Z",
     "iopub.status.idle": "2026-11-10T13:34:29.695259Z",
     "shell.execute_reply": "2026-11-10T13:34:29.694693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intervals 'combined' | 15 intervals (total duration: 3480.00 ms), 232.00 ms +/- 232.02 ms, [48.00 ms, 984.00 ms]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "both_eyes = pp.concat_intervals(blinks[\"left_pupil\"], blinks[\"right_pupil\"])\n",
    "both_eyes = both_eyes.merge()\n",
    "both_eyes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1d06578",
   "metadata": {},
   "source": [
    "`-` removes the parts of one set of intervals that overlap with another.\n",
    "Here, the parts of each trial without blinks in either eye:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f48b1ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.697063Z",
     "iopub.status.busy": "2026-11-10T13:34:29.696882Z",
     "iopub.status.idle": "2026-11-10T13:34:29.703700Z",
     "shell.execute_reply": "2026-11-10T13:34:29.703060Z"
    }
   },
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5177.0</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5496.0</td>\n",
       "      <td>7010.0</td>\n",
       "      <td>1514.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8776.0</td>\n",
       "      <td>10484.0</td>\n",
       "      <td>1708.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12374.0</td>\n",
       "      <td>13957.0</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15973.0</td>\n",
       "      <td>17822.0</td>\n",
       "      <td>1849.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     start      end  duration units\n",
       "0   5177.0   5408.0     231.0    ms\n",
       "1   5496.0   7010.0    1514.0    ms\n",
       "2   8776.0  10484.0    1708.0    ms\n",
       "3  12374.0  13957.0    1583.0    ms\n",
       "4  15973.0  17822.0    1849.0    ms"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_trials = trials - both_eyes\n",
    "clean_trials.as_pandas().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff34c81",
   "metadata": {},
   "source": [
    "Trials with a blink are split into several pieces.\n",
    "\n",
    "## Plotting intervals\n",
    "\n",
    "`plot()` draws the intervals on a time line. The plots of the data shade\n",
    "intervals with `highlight=` (converted to the units of the plot); with a\n",
    "list of Intervals, `highlight_color=` gives one color for each. Here, trials\n",
    "are green and blinks red:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abf1da8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.705609Z",
     "iopub.status.busy": "2026-11-10T13:34:29.705417Z",
     "iopub.status.idle": "2026-11-10T13:34:29.973529Z",
     "shell.execute_reply": "2026-11-10T13:34:29.972579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "d.plot.plot_pupil(plot_range=(0, 20) * u.sec, show_blinks=False,\n",
    "                  show_events=False, highlight=[trials, both_eyes],\n",
    "                  highlight_color=[\"green\", \"red\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64d6c8a",
   "metadata": {},
   "source": [
    "`d.plot.plot_intervals()` shows the data in each interval in a small\n",
    "plot of its own, e.g., to check every blink (for blinks, `plot_blinks()`\n",
    "does the same):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3d68848",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:29.976597Z",
     "iopub.status.busy": "2026-11-10T13:34:29.976270Z",
     "iopub.status.idle": "2026-11-10T13:34:30.657478Z",
     "shell.execute_reply": "2026-11-10T13:34:30.656679Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.plot.plot_intervals(stim, eyes=\"left\",\n",
    "                      variables=\"pupil\", nrow=2, ncol=5, figsize=(14, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34a7fdf",
   "metadata": {},
   "source": [
    "## Using intervals\n",
    "\n",
    "Intervals are the input for many methods of the data objects.\n",
    "\n",
    "**Masking and interpolating**: `mask_intervals()` marks the samples in\n",
    "the intervals as invalid, `interpolate_intervals()` replaces them by\n",
    "interpolated values. Here we mask the padded blinks in the pupil data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "687973b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:30.660800Z",
     "iopub.status.busy": "2026-11-10T13:34:30.660602Z",
     "iopub.status.idle": "2026-11-10T13:34:30.667756Z",
     "shell.execute_reply": "2026-11-10T13:34:30.667079Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2791"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2 = d.mask_intervals(both_eyes.pad(50 * u.ms, 100 * u.ms),\n",
    "                      variables=\"pupil\", inplace=False)\n",
    "d2.data.count_masked()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d2aa51",
   "metadata": {},
   "source": [
    "**One value per interval**: `stat_per_event()` applies a function to the\n",
    "data in each interval, e.g., the mean pupil size after each stimulus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8253fe08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:30.670027Z",
     "iopub.status.busy": "2026-11-10T13:34:30.669837Z",
     "iopub.status.idle": "2026-11-10T13:34:30.680170Z",
     "shell.execute_reply": "2026-11-10T13:34:30.679341Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'left_pupil': array([1003.03142329, 1188.35538752, 1281.70833333, 1160.34333333,\n",
       "        1100.505     , 1095.62833333,  993.23      , 1113.09907121,\n",
       "         963.89      , 1065.74666667]),\n",
       " 'right_pupil': array([ 830.89833641,  977.61625709, 1095.90166667,  969.57833333,\n",
       "         916.80833333,  920.22833333,  843.105     ,  932.53560372,\n",
       "         808.16333333,  889.60166667])}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2.stat_per_event(intervals=stim, variables=\"pupil\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83aa941",
   "metadata": {},
   "source": [
    "**Segments**: `get_segments()` cuts the data in each epoch into a\n",
    "`SegmentedEyeData` object (time axis from the window; Intervals only if\n",
    "they all have the same length), the basis for event-related pupil dilations\n",
    "(see the [ERPD tutorial](erpds.ipynb)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a9145a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:30.682502Z",
     "iopub.status.busy": "2026-11-10T13:34:30.682221Z",
     "iopub.status.idle": "2026-11-10T13:34:30.729260Z",
     "shell.execute_reply": "2026-11-10T13:34:30.728052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"font-family:system-ui, sans-serif; border:1px solid #ddd; border-radius:6px; padding:12px; max-width: min(600px, 50vw); min-width: 300px;\">\n",
       "  <h3 style=\"margin-top:0;\">SegmentedEyeData — <span style=\"color:#555;\">S_</span></h3>\n",
       "\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(140px,1fr)); gap:8px; margin-bottom:10px;\">\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Epochs</div>\n",
       "      <div style=\"font-weight:600;\">S_</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Segments</div>\n",
       "      <div style=\"font-weight:600;\">10</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Time points</div>\n",
       "      <div style=\"font-weight:600;\">600</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Window</div>\n",
       "      <div style=\"font-weight:600;\">-200 to 998 ms</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Missing</div>\n",
       "      <div style=\"font-weight:600;\">6.8%</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Sampling rate</div>\n",
       "      <div style=\"font-weight:600;\">500 Hz</div>\n",
       "    </div>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    <details open style=\"margin-bottom:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Channels (mean over segments; red line = event onset)</summary>\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(180px,1fr)); gap:8px; margin-top:8px;\">\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_pupil (6.8% missing)</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_pupil (6.8% missing) sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "    </div>\n",
       "  </details>\n",
       "\n",
       "\n",
       "  <details style=\"margin-top:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Epochs</summary>\n",
       "    <pre style=\"margin-top:8px; background:#f9f9f9; padding:8px; border-radius:4px;\">Epochs &#39;S_&#39; | 10 epochs, window (-200, 1000) ms</pre>\n",
       "  </details>\n",
       "</div>"
      ],
      "text/plain": [
       "SegmentedEyeData(S_): 10 segments x 600 time points (-200 to 998 ms, 500 Hz)\n",
       "  channels: left_pupil (6.8% missing)\n",
       "  epochs: Epochs 'S_' | 10 epochs, window (-200, 1000) ms"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2.get_segments(stim, \"left_pupil\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2891009",
   "metadata": {},
   "source": [
    "## Masks and intervals\n",
    "\n",
    "Intervals can be converted to a boolean mask with one entry per sample\n",
    "of a data object, and back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "38aab478",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:30.731638Z",
     "iopub.status.busy": "2026-11-10T13:34:30.731328Z",
     "iopub.status.idle": "2026-11-10T13:34:30.736752Z",
     "shell.execute_reply": "2026-11-10T13:34:30.736039Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20465,) 1740 samples in blinks\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Intervals | 15 intervals (total duration: 3480.00 ms), 232.00 ms +/- 232.02 ms, [48.00 ms, 984.00 ms]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = both_eyes.to_mask(d)\n",
    "print(mask.shape, mask.sum(), \"samples in blinks\")\n",
    "\n",
    "pp.Intervals.from_mask(mask, d, units=\"ms\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.15.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}