{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e912ecb",
   "metadata": {},
   "source": [
    "# Dtypes, PM plots and exposure\n",
    "\n",
    "This example demonstrates how to:\n",
    "\n",
    "- Load size-resolved ELPI data (`Aerosol2D`)\n",
    "- Inspect dtype, units, and particle density\n",
    "- Convert between different distribution types via `dtype_converter`\n",
    "- Compute cumulative Pₓ (e.g. PM₂.₅, PM₄.₂) with `PM_calc`\n",
    "- Plot PMₓ time series with `plot_pm_timeseries`\n",
    "- Adjust assumed particle density with `set_density`\n",
    "- Use `summarize_activities` and `summarize_exposure` for PM-based metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8d67b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import aerosoltools as at"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aba54786",
   "metadata": {},
   "source": [
    "## Load ELPI sample data\n",
    "\n",
    "Initially load an ELPI test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca6b8d83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################# Warning! #################\n",
      "          Density ≠ 1.0 assumed           \n",
      "  Bin edges estimated via geometric means \n",
      "###########################################\n"
     ]
    }
   ],
   "source": [
    "filename = r\"..\\..\\tests\\data\\Sample_ELPI.txt\"\n",
    "elpi = at.Load_ELPI_file(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d987004",
   "metadata": {},
   "source": [
    "A warning is given, seeing that the density in the given raw data is not set equal to 1.0. The density of the raw data is read, loaded and stored within the generated elpi variable, but the user is still alerted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ba28e7",
   "metadata": {},
   "source": [
    "## Check dtype, unit, and density\n",
    "\n",
    "The loader sets the initial distribution type (e.g. `dN`) and unit.\n",
    "Assumed particle density is stored in `elpi.density` (g/cm³)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "76b61c28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial dtype: dN\n",
      "Initial unit: cm⁻³\n",
      "Initial density (g/cm³): 0.14\n"
     ]
    }
   ],
   "source": [
    "print(\"Initial dtype:\", elpi.dtype)\n",
    "print(\"Initial unit:\", elpi.unit)\n",
    "print(\"Initial density (g/cm³):\", elpi.density)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aeb606f",
   "metadata": {},
   "source": [
    "## Change density and convert to mass distribution\n",
    "\n",
    "We can update the assumed particle density with `set_density()` and then\n",
    "convert to a mass-based size distribution using `dtype_converter(\"dM\")`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca837b41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated dtype: dM\n",
      "Updated density (g/cm³): 1.5\n",
      "Updated unit: ug/m³\n"
     ]
    }
   ],
   "source": [
    "# For illustration, set density to 1.5 g/cm³\n",
    "elpi.set_density(1.5)\n",
    "elpi.dtype_converter(\"dM\")\n",
    "print(\"Updated dtype:\", elpi.dtype)\n",
    "print(\"Updated density (g/cm³):\", elpi.density)\n",
    "print(\"Updated unit:\", elpi.unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b35131a",
   "metadata": {},
   "source": [
    "## Compute PM fractions with `PM_calc`\n",
    "\n",
    "We now calculate mass-based Pₓ metrics such as PM₂.₅ and PM₄.₂.\n",
    "The results are stored in `elpi.extra_data` as additional columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca5d5884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PM2.5', 'PM4.2']\n"
     ]
    }
   ],
   "source": [
    "# Compute cumulative PM2.5 and PM4.2 (µm cut points)\n",
    "elpi.PM_calc(dtype=\"dM\", PM=2.5)\n",
    "elpi.PM_calc(dtype=\"dM\", PM=4.2)\n",
    "print([c for c in elpi.extra_data.columns if c.startswith(\"PM\")])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa18d9e",
   "metadata": {},
   "source": [
    "## Plot PM time series\n",
    "\n",
    "Use `plot_pm_timeseries` to visualize the evolution of PMₓ over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d167339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1900x1000 with 1 Axes>, <Axes: ylabel='PM, ug/m³'>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1900x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elpi.plot_PM_timeseries(PM_values=[2.5, 4.2, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88950cb4",
   "metadata": {},
   "source": [
    "## Define activities and summarize\n",
    "\n",
    "As with `Aerosol1D`, we can mark time segments and compute activity-based\n",
    "statistics using `summarize_activities()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8051244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary of aerosol properties (transposed):\n",
      "\n",
      "+------------------------+----------+------------+----------+---------+\n",
      "|                        | All data | Background | Emission |  Decay  |\n",
      "+------------------------+----------+------------+----------+---------+\n",
      "|    Duration (HH:MM)    |  00:04   |   00:01    |  00:00   |  00:01  |\n",
      "|       PNC [cm⁻³]       |  197.44  |   222.84   |  203.63  | 179.39  |\n",
      "|     PNC [cm⁻³] std     |   48.9   |   61.26    |  42.67   |  39.3   |\n",
      "|      PM1 [µg/m³]       |  13.79   |   14.97    |  14.06   |  12.85  |\n",
      "|    PM1 [µg/m³] std     |   1.15   |    0.89    |   0.26   |  0.38   |\n",
      "|     PM2.5 [µg/m³]      |  36.51   |   37.94    |  37.83   |  34.95  |\n",
      "|   PM2.5 [µg/m³] std    |   1.95   |    1.6     |   1.09   |  1.13   |\n",
      "|      PM4 [µg/m³]       |  61.78   |   60.51    |  66.44   |  61.04  |\n",
      "|    PM4 [µg/m³] std     |   6.28   |   10.08    |   2.77   |  3.12   |\n",
      "|      PM10 [µg/m³]      |  325.0   |   320.29   |  374.75  | 318.69  |\n",
      "|    PM10 [µg/m³] std    |  99.82   |   169.28   |  22.18   |  42.5   |\n",
      "|   Total Mass [µg/m³]   | 1335.13  |  1421.95   | 1615.86  | 1225.18 |\n",
      "| Total Mass [µg/m³] std |  549.75  |   896.41   |  216.46  | 248.88  |\n",
      "|      Mode Dp [nm]      |  125.2   |   113.6    |  125.4   |  132.9  |\n",
      "|    Mode Dp [nm] std    |  131.1   |   125.6    |  128.9   |  135.4  |\n",
      "|     Median Dp [nm]     |  224.2   |   210.6    |  226.2   |  228.1  |\n",
      "|   Median Dp [nm] std   |   68.6   |    76.9    |   66.2   |  67.3   |\n",
      "|        GMD [nm]        |  203.3   |   193.0    |  201.4   |  212.6  |\n",
      "|      GMD [nm] std      |   79.5   |    79.2    |   77.8   |  84.1   |\n",
      "+------------------------+----------+------------+----------+---------+\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Segment</th>\n",
       "      <th>Duration (HH:MM)</th>\n",
       "      <th>PNC [cm⁻³]</th>\n",
       "      <th>PNC [cm⁻³] std</th>\n",
       "      <th>PM1 [µg/m³]</th>\n",
       "      <th>PM1 [µg/m³] std</th>\n",
       "      <th>PM2.5 [µg/m³]</th>\n",
       "      <th>PM2.5 [µg/m³] std</th>\n",
       "      <th>PM4 [µg/m³]</th>\n",
       "      <th>PM4 [µg/m³] std</th>\n",
       "      <th>PM10 [µg/m³]</th>\n",
       "      <th>PM10 [µg/m³] std</th>\n",
       "      <th>Total Mass [µg/m³]</th>\n",
       "      <th>Total Mass [µg/m³] std</th>\n",
       "      <th>Mode Dp [nm]</th>\n",
       "      <th>Mode Dp [nm] std</th>\n",
       "      <th>Median Dp [nm]</th>\n",
       "      <th>Median Dp [nm] std</th>\n",
       "      <th>GMD [nm]</th>\n",
       "      <th>GMD [nm] std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All data</td>\n",
       "      <td>00:04</td>\n",
       "      <td>197.44</td>\n",
       "      <td>48.90</td>\n",
       "      <td>13.79</td>\n",
       "      <td>1.15</td>\n",
       "      <td>36.51</td>\n",
       "      <td>1.95</td>\n",
       "      <td>61.78</td>\n",
       "      <td>6.28</td>\n",
       "      <td>325.00</td>\n",
       "      <td>99.82</td>\n",
       "      <td>1335.13</td>\n",
       "      <td>549.75</td>\n",
       "      <td>125.2</td>\n",
       "      <td>131.1</td>\n",
       "      <td>224.2</td>\n",
       "      <td>68.6</td>\n",
       "      <td>203.3</td>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Background</td>\n",
       "      <td>00:01</td>\n",
       "      <td>222.84</td>\n",
       "      <td>61.26</td>\n",
       "      <td>14.97</td>\n",
       "      <td>0.89</td>\n",
       "      <td>37.94</td>\n",
       "      <td>1.60</td>\n",
       "      <td>60.51</td>\n",
       "      <td>10.08</td>\n",
       "      <td>320.29</td>\n",
       "      <td>169.28</td>\n",
       "      <td>1421.95</td>\n",
       "      <td>896.41</td>\n",
       "      <td>113.6</td>\n",
       "      <td>125.6</td>\n",
       "      <td>210.6</td>\n",
       "      <td>76.9</td>\n",
       "      <td>193.0</td>\n",
       "      <td>79.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emission</td>\n",
       "      <td>00:00</td>\n",
       "      <td>203.63</td>\n",
       "      <td>42.67</td>\n",
       "      <td>14.06</td>\n",
       "      <td>0.26</td>\n",
       "      <td>37.83</td>\n",
       "      <td>1.09</td>\n",
       "      <td>66.44</td>\n",
       "      <td>2.77</td>\n",
       "      <td>374.75</td>\n",
       "      <td>22.18</td>\n",
       "      <td>1615.86</td>\n",
       "      <td>216.46</td>\n",
       "      <td>125.4</td>\n",
       "      <td>128.9</td>\n",
       "      <td>226.2</td>\n",
       "      <td>66.2</td>\n",
       "      <td>201.4</td>\n",
       "      <td>77.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decay</td>\n",
       "      <td>00:01</td>\n",
       "      <td>179.39</td>\n",
       "      <td>39.30</td>\n",
       "      <td>12.85</td>\n",
       "      <td>0.38</td>\n",
       "      <td>34.95</td>\n",
       "      <td>1.13</td>\n",
       "      <td>61.04</td>\n",
       "      <td>3.12</td>\n",
       "      <td>318.69</td>\n",
       "      <td>42.50</td>\n",
       "      <td>1225.18</td>\n",
       "      <td>248.88</td>\n",
       "      <td>132.9</td>\n",
       "      <td>135.4</td>\n",
       "      <td>228.1</td>\n",
       "      <td>67.3</td>\n",
       "      <td>212.6</td>\n",
       "      <td>84.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Segment Duration (HH:MM)  PNC [cm⁻³]  PNC [cm⁻³] std  PM1 [µg/m³]  \\\n",
       "0    All data            00:04      197.44           48.90        13.79   \n",
       "1  Background            00:01      222.84           61.26        14.97   \n",
       "2    Emission            00:00      203.63           42.67        14.06   \n",
       "3       Decay            00:01      179.39           39.30        12.85   \n",
       "\n",
       "   PM1 [µg/m³] std  PM2.5 [µg/m³]  PM2.5 [µg/m³] std  PM4 [µg/m³]  \\\n",
       "0             1.15          36.51               1.95        61.78   \n",
       "1             0.89          37.94               1.60        60.51   \n",
       "2             0.26          37.83               1.09        66.44   \n",
       "3             0.38          34.95               1.13        61.04   \n",
       "\n",
       "   PM4 [µg/m³] std  PM10 [µg/m³]  PM10 [µg/m³] std  Total Mass [µg/m³]  \\\n",
       "0             6.28        325.00             99.82             1335.13   \n",
       "1            10.08        320.29            169.28             1421.95   \n",
       "2             2.77        374.75             22.18             1615.86   \n",
       "3             3.12        318.69             42.50             1225.18   \n",
       "\n",
       "   Total Mass [µg/m³] std  Mode Dp [nm]  Mode Dp [nm] std  Median Dp [nm]  \\\n",
       "0                  549.75         125.2             131.1           224.2   \n",
       "1                  896.41         113.6             125.6           210.6   \n",
       "2                  216.46         125.4             128.9           226.2   \n",
       "3                  248.88         132.9             135.4           228.1   \n",
       "\n",
       "   Median Dp [nm] std  GMD [nm]  GMD [nm] std  \n",
       "0                68.6     203.3          79.5  \n",
       "1                76.9     193.0          79.2  \n",
       "2                66.2     201.4          77.8  \n",
       "3                67.3     212.6          84.1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity_periods = {\n",
    "    \"Background\": [(\"2023/09/07 09:06:50\", \"2023/09/07 09:07:50\")],\n",
    "    \"Emission\":   [(\"2023/09/07 09:07:55\", \"2023/09/07 09:08:30\")],\n",
    "    \"Decay\":      [(\"2023/09/07 09:09:00\", \"2023/09/07 09:10:50\")],\n",
    "}\n",
    "elpi.mark_activities(activity_periods)\n",
    "summary = elpi.summarize_activities()\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5955c1cc",
   "metadata": {},
   "source": [
    "## Exposure summary for respirable dust (PM4.2)\n",
    "\n",
    "Finally, we use `summarize_exposure` on the `Aerosol2D` object\n",
    "to obtain exposure metrics for PM₄.₂ during the *Emission* task,\n",
    "using *Background* as the background activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0aebb06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Exposure summary for segment 'Emission' and metric 'PM4.2' (µg/m³):\n",
      "\n",
      "+--------------------------------------------+----------+\n",
      "|                   Metric                   | Emission |\n",
      "+--------------------------------------------+----------+\n",
      "|                   Metric                   |  PM4.2   |\n",
      "|                    Unit                    |  µg/m³   |\n",
      "|                Duration_min                |   0.6    |\n",
      "|               Duration_HH:MM               |  00:00   |\n",
      "|                    Mean                    |  71.797  |\n",
      "|                    Std                     |  3.127   |\n",
      "|                   Median                   |  71.21   |\n",
      "|                    Max                     |  78.57   |\n",
      "|                  Seg_TWA                   |  71.797  |\n",
      "|                 Window_TWA                 |  64.657  |\n",
      "|             Window_TWA_window              |    8h    |\n",
      "|                Short_limit                 |   1.0    |\n",
      "|                Short_window                |  15min   |\n",
      "|         Short_limit_exceedance_min         |   0.6    |\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          |   0.6    |\n",
      "|                    C95                     |  76.709  |\n",
      "|                    C99                     |  78.018  |\n",
      "|                    IQR                     |  4.235   |\n",
      "|                   Peaks                    |    0     |\n",
      "+--------------------------------------------+----------+\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emission</td>\n",
       "      <td>PM4.2</td>\n",
       "      <td>µg/m³</td>\n",
       "      <td>0.6</td>\n",
       "      <td>00:00</td>\n",
       "      <td>71.797</td>\n",
       "      <td>3.127</td>\n",
       "      <td>71.21</td>\n",
       "      <td>78.57</td>\n",
       "      <td>71.797</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>0.6</td>\n",
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       "      <td>78.018</td>\n",
       "      <td>4.235</td>\n",
       "      <td>0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "    Segment Metric   Unit  Duration_min Duration_HH:MM    Mean    Std  Median  \\\n",
       "0  Emission  PM4.2  µg/m³           0.6          00:00  71.797  3.127   71.21   \n",
       "\n",
       "     Max  Seg_TWA  ...  Short_limit_exceedance_fraction  \\\n",
       "0  78.57   71.797  ...                              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                        0.6  76.709  78.018  4.235      0  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp = elpi.summarize_exposure(\n",
    "    metric=\"PM4.2\",\n",
    "    activity=\"Emission\",\n",
    "    background=\"Background\",  # use mean Background as background level\n",
    "    exposure_hours=None,       # assume exposure equals task duration\n",
    "    short_limit=1.0,\n",
    "    long_limit=1.0,\n",
    "    short_window=\"15min\",\n",
    "    twa_window=\"8h\",\n",
    ")\n",
    "exp"
   ]
  }
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