{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6458d390",
   "metadata": {},
   "source": [
    "# Activities, peaks, and exposure statistics\n",
    "\n",
    "This example shows how to:\n",
    "\n",
    "- Load a 1D time series (CPC total number concentration)\n",
    "- Inspect main and extra data\n",
    "- Define simple time segments (activities)\n",
    "- Detect peaks using `Peak_finder`\n",
    "- Plot total concentration with activity shading\n",
    "- Summarize activities and compute exposure metrics with `summarize_exposure`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6577419b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import aerosoltools as at"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bf1344",
   "metadata": {},
   "source": [
    "## Load CPC sample data\n",
    "\n",
    "Here we load a sample CPC file from the test data folder.\n",
    "Adjust the path if you keep example data elsewhere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8dc0d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = r\"..\\..\\tests\\data\\Sample_CPC_AIM.txt\"\n",
    "cpc = at.Load_CPC_file(filename,extra_data=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0a70b2",
   "metadata": {},
   "source": [
    "## Inspect metadata and extra data\n",
    "\n",
    "The loader returns an `Aerosol1D` instance with:\n",
    "\n",
    "- `.data` – main time series + activity masks\n",
    "- `.extra_data` – optional additional channels (environmental, meta, etc.)\n",
    "- `.metadata` – instrument, unit, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9269a5b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metadata:\n",
      " {'instrument': 'CPC', 'serial_number': '06160001 ', 'unit': 'cm$^{-3}$'}\n",
      "\n",
      "Main columns: ['Total_conc', 'All data']\n",
      "Extra-data columns: ['Sample Length (s)', 'Averaging Interval (s)', 'Title', 'Instrument ID', 'Instrument Errors', 'Mean(#/cm³)', 'Min(#/cm³)', 'Max(#/cm³)', 'Std. Dev.(#/cm³)', 'Comments']\n"
     ]
    }
   ],
   "source": [
    "print(\"Metadata:\\n\", cpc.metadata)\n",
    "print(\"\\nMain columns:\", list(cpc.data.columns))\n",
    "print(\"Extra-data columns:\", list(cpc.extra_data.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3259df41",
   "metadata": {},
   "source": [
    "You can access data in the main DataFrame via the `.data` property of the defined cpc variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b5d51aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_conc</th>\n",
       "      <th>All data</th>\n",
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       "    <tr>\n",
       "      <th>Datetime</th>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2023-08-14 11:20:49</th>\n",
       "      <td>1293</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-14 11:20:50</th>\n",
       "      <td>1307</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-14 11:20:51</th>\n",
       "      <td>1311</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-14 11:20:52</th>\n",
       "      <td>1295</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-14 11:20:53</th>\n",
       "      <td>1311</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Total_conc  All data\n",
       "Datetime                                 \n",
       "2023-08-14 11:14:29        1468      True\n",
       "2023-08-14 11:14:30        1459      True\n",
       "2023-08-14 11:14:31        1437      True\n",
       "2023-08-14 11:14:32        1462      True\n",
       "2023-08-14 11:14:33        1481      True\n",
       "...                         ...       ...\n",
       "2023-08-14 11:20:49        1293      True\n",
       "2023-08-14 11:20:50        1307      True\n",
       "2023-08-14 11:20:51        1311      True\n",
       "2023-08-14 11:20:52        1295      True\n",
       "2023-08-14 11:20:53        1311      True\n",
       "\n",
       "[384 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpc.data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ac78d4",
   "metadata": {},
   "source": [
    "Similarly, you can access any additional data columns extracted from the raw data via the `.extra_data` property:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1ea3e0c1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>00:01</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>3007-06160001 3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1311</td>\n",
       "      <td>1311</td>\n",
       "      <td>1311</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2023-08-14 11:20:52</th>\n",
       "      <td>00:01</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>3007-06160001 3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1295</td>\n",
       "      <td>1295</td>\n",
       "      <td>1295</td>\n",
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       "      <td>00:01</td>\n",
       "      <td>1</td>\n",
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       "      <td>3007-06160001 3.1</td>\n",
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       "      <td>1311</td>\n",
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       "      <td>NaN</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                    Sample Length (s)  Averaging Interval (s) Title  \\\n",
       "Datetime                                                              \n",
       "2023-08-14 11:14:29             00:01                       1         \n",
       "2023-08-14 11:14:30             00:01                       1         \n",
       "2023-08-14 11:14:31             00:01                       1         \n",
       "2023-08-14 11:14:32             00:01                       1         \n",
       "2023-08-14 11:14:33             00:01                       1         \n",
       "...                               ...                     ...   ...   \n",
       "2023-08-14 11:20:49             00:01                       1         \n",
       "2023-08-14 11:20:50             00:01                       1         \n",
       "2023-08-14 11:20:51             00:01                       1         \n",
       "2023-08-14 11:20:52             00:01                       1         \n",
       "2023-08-14 11:20:53             00:01                       1         \n",
       "\n",
       "                         Instrument ID  Instrument Errors  Mean(#/cm³)  \\\n",
       "Datetime                                                                 \n",
       "2023-08-14 11:14:29  3007-06160001 3.1                NaN         1468   \n",
       "2023-08-14 11:14:30  3007-06160001 3.1                NaN         1459   \n",
       "2023-08-14 11:14:31  3007-06160001 3.1                NaN         1437   \n",
       "2023-08-14 11:14:32  3007-06160001 3.1                NaN         1462   \n",
       "2023-08-14 11:14:33  3007-06160001 3.1                NaN         1481   \n",
       "...                                ...                ...          ...   \n",
       "2023-08-14 11:20:49  3007-06160001 3.1                NaN         1293   \n",
       "2023-08-14 11:20:50  3007-06160001 3.1                NaN         1307   \n",
       "2023-08-14 11:20:51  3007-06160001 3.1                NaN         1311   \n",
       "2023-08-14 11:20:52  3007-06160001 3.1                NaN         1295   \n",
       "2023-08-14 11:20:53  3007-06160001 3.1                NaN         1311   \n",
       "\n",
       "                     Min(#/cm³)  Max(#/cm³)  Std. Dev.(#/cm³)  Comments  \n",
       "Datetime                                                                 \n",
       "2023-08-14 11:14:29        1468        1468                 0       NaN  \n",
       "2023-08-14 11:14:30        1459        1459                 0       NaN  \n",
       "2023-08-14 11:14:31        1437        1437                 0       NaN  \n",
       "2023-08-14 11:14:32        1462        1462                 0       NaN  \n",
       "2023-08-14 11:14:33        1481        1481                 0       NaN  \n",
       "...                         ...         ...               ...       ...  \n",
       "2023-08-14 11:20:49        1293        1293                 0       NaN  \n",
       "2023-08-14 11:20:50        1307        1307                 0       NaN  \n",
       "2023-08-14 11:20:51        1311        1311                 0       NaN  \n",
       "2023-08-14 11:20:52        1295        1295                 0       NaN  \n",
       "2023-08-14 11:20:53        1311        1311                 0       NaN  \n",
       "\n",
       "[384 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cpc.extra_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed2e9a3",
   "metadata": {},
   "source": [
    "## Plot total concentration\n",
    "\n",
    "Use `plot_total_conc()` to quickly visualize the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a134d016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1000x500 with 1 Axes>,\n",
       " <Axes: xlabel='Time', ylabel='dN, cm$^{-3}$'>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpc.plot_total_conc()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00df16d6",
   "metadata": {},
   "source": [
    "## Detect peaks with `Peak_finder`\n",
    "\n",
    "`Peak_finder` flags time steps where the signal exceeds a rolling baseline\n",
    "by more than `ratio * rolling_std`. The resulting mask is stored as the\n",
    "`\"Peak\"` activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1660574d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['All data', 'Peak']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpc.Peak_finder(window=15, ratio=2.5, method=\"median\")\n",
    "cpc.activities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2884f243",
   "metadata": {},
   "source": [
    "We can also mark specific activities based on timestamps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c83c0346",
   "metadata": {},
   "outputs": [],
   "source": [
    "activity_periods= {\n",
    "    \"initial_phase\": [\n",
    "        (\"2023-08-14 11:14:00\", \"2023-08-14 11:17:00\"),\n",
    "        (\"2023-08-14 11:17:30\", \"2023-08-14 11:18:00\")],\n",
    "    \"second_phase\": [\n",
    "        (\"2023-08-14 11:19:00\", \"2023-08-14 11:20:00\")]\n",
    "}\n",
    "cpc.mark_activities(activity_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8a3f12",
   "metadata": {},
   "source": [
    "## Plot with activity shading\n",
    "\n",
    "We can overlay activities (including the automatically created `\"Peak\"` mask)\n",
    "on top of the total concentration time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c9acd29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1000x500 with 1 Axes>,\n",
       " <Axes: xlabel='Time', ylabel='dN, cm$^{-3}$'>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpc.plot_total_conc(mark_activities=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8552706",
   "metadata": {},
   "source": [
    "## Summarize activities\n",
    "\n",
    "`summarize_activities()` reports basic descriptive statistics per activity,\n",
    "including duration, mean, median, and number of samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e19e1594",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary of total concentration per activity:\n",
      "\n",
      "+--------------------+----------+--------+---------------+--------------+\n",
      "|    Metric/Field    | All data |  Peak  | initial_phase | second_phase |\n",
      "+--------------------+----------+--------+---------------+--------------+\n",
      "|   Duration (min)   |  6.417   | 0.017  |     3.05      |    1.017     |\n",
      "|  Duration (HH:MM)  |  00:06   | 00:00  |     00:03     |    00:01     |\n",
      "|  Min (cm$^{-3}$)   |  1214.0  | 2127.0 |    1267.0     |    1240.0    |\n",
      "|  Max (cm$^{-3}$)   |  2127.0  | 2127.0 |    1559.0     |    1399.0    |\n",
      "|  Mean (cm$^{-3}$)  | 1379.339 | 2127.0 |   1417.346    |   1319.869   |\n",
      "| Median (cm$^{-3}$) |  1364.0  | 2127.0 |    1425.0     |    1320.0    |\n",
      "|  Std (cm$^{-3}$)   |  94.659  |  nan   |    62.829     |    33.21     |\n",
      "|     N samples      |   384    |   1    |      182      |      61      |\n",
      "+--------------------+----------+--------+---------------+--------------+\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>Segment</th>\n",
       "      <th>Duration (min)</th>\n",
       "      <th>Duration (HH:MM)</th>\n",
       "      <th>Min (cm$^{-3}$)</th>\n",
       "      <th>Max (cm$^{-3}$)</th>\n",
       "      <th>Mean (cm$^{-3}$)</th>\n",
       "      <th>Median (cm$^{-3}$)</th>\n",
       "      <th>Std (cm$^{-3}$)</th>\n",
       "      <th>N samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All data</td>\n",
       "      <td>6.417</td>\n",
       "      <td>00:06</td>\n",
       "      <td>1214.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>1379.339</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>94.659</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peak</td>\n",
       "      <td>0.017</td>\n",
       "      <td>00:00</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>2127.000</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>initial_phase</td>\n",
       "      <td>3.050</td>\n",
       "      <td>00:03</td>\n",
       "      <td>1267.0</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>1417.346</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>62.829</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>second_phase</td>\n",
       "      <td>1.017</td>\n",
       "      <td>00:01</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>1319.869</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>33.210</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Segment  Duration (min) Duration (HH:MM)  Min (cm$^{-3}$)  \\\n",
       "0       All data           6.417            00:06           1214.0   \n",
       "1           Peak           0.017            00:00           2127.0   \n",
       "2  initial_phase           3.050            00:03           1267.0   \n",
       "3   second_phase           1.017            00:01           1240.0   \n",
       "\n",
       "   Max (cm$^{-3}$)  Mean (cm$^{-3}$)  Median (cm$^{-3}$)  Std (cm$^{-3}$)  \\\n",
       "0           2127.0          1379.339              1364.0           94.659   \n",
       "1           2127.0          2127.000              2127.0              NaN   \n",
       "2           1559.0          1417.346              1425.0           62.829   \n",
       "3           1399.0          1319.869              1320.0           33.210   \n",
       "\n",
       "   N samples  \n",
       "0        384  \n",
       "1          1  \n",
       "2        182  \n",
       "3         61  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpc.summarize_activities()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fea94d1",
   "metadata": {},
   "source": [
    "## Exposure summary for PNC\n",
    "\n",
    "Finally, we use `summarize_exposure` on the `Aerosol1D` object to compute\n",
    "exposure metrics for total number concentration (PNC):\n",
    "\n",
    "- duration, mean, percentiles\n",
    "- time above a long-term limit\n",
    "- short-term (window-based) exceedances\n",
    "- an 8-hour TWA (by default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e2a94395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Exposure summary for activity 'All data' and metric 'PNC' (cm$^{-3}$):\n",
      "\n",
      "+--------------------------------------------+-----------+\n",
      "|                   Field                    |   Value   |\n",
      "+--------------------------------------------+-----------+\n",
      "|                   Metric                   |    PNC    |\n",
      "|                    Unit                    | cm$^{-3}$ |\n",
      "|                  Samples                   |    384    |\n",
      "|                Duration_min                |   6.42    |\n",
      "|               Duration_HH:MM               |   00:06   |\n",
      "|                    Mean                    | 1379.018  |\n",
      "|                    Std                     |  94.659   |\n",
      "|                   Median                   |  1364.0   |\n",
      "|                    Max                     |  2127.0   |\n",
      "|                  Seg_TWA                   | 1379.018  |\n",
      "|                 Window_TWA                 | 1379.018  |\n",
      "|             Window_TWA_window              |    8h     |\n",
      "|                Short_limit                 |    1.0    |\n",
      "|                Short_window                |   15min   |\n",
      "|         Short_limit_exceedance_min         |   6.42    |\n",
      "|      Short_limit_exceedance_fraction       |    1.0    |\n",
      "|   Short_limit_fullwindow_exceedance_min    |   0.02    |\n",
      "| Short_limit_fullwindow_exceedance_episodes |     1     |\n",
      "|                 Long_limit                 |    1.0    |\n",
      "|            Long_limit_exceeded             |   True    |\n",
      "|         Long_limit_exceedance_min          |   6.42    |\n",
      "|                    C95                     |  1503.95  |\n",
      "|                    C99                     |  1605.24  |\n",
      "|                    IQR                     |  107.25   |\n",
      "|                   Peaks                    |     1     |\n",
      "+--------------------------------------------+-----------+\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Segment</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Samples</th>\n",
       "      <th>Duration_min</th>\n",
       "      <th>Duration_HH:MM</th>\n",
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       "      <th>Short_limit_exceedance_fraction</th>\n",
       "      <th>Short_limit_fullwindow_exceedance_min</th>\n",
       "      <th>Short_limit_fullwindow_exceedance_episodes</th>\n",
       "      <th>Long_limit</th>\n",
       "      <th>Long_limit_exceeded</th>\n",
       "      <th>Long_limit_exceedance_min</th>\n",
       "      <th>C95</th>\n",
       "      <th>C99</th>\n",
       "      <th>IQR</th>\n",
       "      <th>Peaks</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All data</td>\n",
       "      <td>PNC</td>\n",
       "      <td>cm$^{-3}$</td>\n",
       "      <td>384</td>\n",
       "      <td>6.42</td>\n",
       "      <td>00:06</td>\n",
       "      <td>1379.018</td>\n",
       "      <td>94.659</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>6.42</td>\n",
       "      <td>1503.95</td>\n",
       "      <td>1605.24</td>\n",
       "      <td>107.25</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "    Segment Metric       Unit  Samples  Duration_min Duration_HH:MM      Mean  \\\n",
       "0  All data    PNC  cm$^{-3}$      384          6.42          00:06  1379.018   \n",
       "\n",
       "      Std  Median     Max  ...  Short_limit_exceedance_fraction  \\\n",
       "0  94.659  1364.0  2127.0  ...                              1.0   \n",
       "\n",
       "   Short_limit_fullwindow_exceedance_min  \\\n",
       "0                                   0.02   \n",
       "\n",
       "  Short_limit_fullwindow_exceedance_episodes  Long_limit Long_limit_exceeded  \\\n",
       "0                                          1         1.0                True   \n",
       "\n",
       "   Long_limit_exceedance_min      C95      C99     IQR  Peaks  \n",
       "0                       6.42  1503.95  1605.24  107.25      1  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp = cpc.summarize_exposure(\n",
    "    metric=\"PNC\",           # use total_concentration\n",
    "    activity=\"All data\",    # summarize whole record\n",
    "    background=None,        # assume zero background outside the record\n",
    "    exposure_hours=8.0,     # Set how long a worker was exposed to the measured concentration\n",
    "    short_limit=1.0,        # Short term exposure limit for the given metric (if available)\n",
    "    long_limit=1.0,         # long term or 8hr exposure limit for the given metric\n",
    "    short_window=\"15min\",   # Length of short term exposures to identify\n",
    ")\n",
    "exp"
   ]
  }
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