example_for_nc_file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d4139d7",
   "metadata": {},
   "source": [
    "# Example on how to use the python class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d8b67a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Get file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9438daeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = !ls -lh <file_path>/*.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e21798",
   "metadata": {},
   "source": [
    "## Load class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "398b831e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from package import calculate_rf as rf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93d1acc",
   "metadata": {},
   "source": [
    "## Calculate radiative forcing and emissions for each emission_inventory-file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00581443",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "emission_inventory = rf.EmissionInventory(files[0].split()[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71c59507",
   "metadata": {},
   "outputs": [],
   "source": [
    "emission_inventory.drop_vertical_levels()\n",
    "\n",
    "net_rf = emission_inventory.total_rf()\n",
    "\n",
    "net_emis = emission_inventory.total_emis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18044456",
   "metadata": {},
   "source": [
    "`calculate_rf(file.split()[-1])` creates a class instance with the emission_inventory data file.\n",
    "`load_nc_as_dataframe()` extracts the relevant data from the file within `__init__()`.\n",
    "`drop_vertical_levels()` limits the vertical emission to emission above the tropopause, which is the default. Other vertical limits can be given in hectopascal with e.g. `drop_vertical_levels(100)` to limit emissions to above 100 hPa.\n",
    "`total_rf()` returns the stratospheric adjusted radiative forcing of the emission_inventory.\n",
    "`total_emis()` returns the accumulated emissions for the chosen vertical levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f97c9e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17c2f93e",
   "metadata": {},
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Altitude [km]</th>\n",
       "      <th>tp_WMO [Pa]</th>\n",
       "      <th>Altitude [ft]</th>\n",
       "      <th>Altitude [Pa]</th>\n",
       "      <th>H2O [kg]</th>\n",
       "      <th>H2 [kg]</th>\n",
       "      <th>NO [kg]</th>\n",
       "      <th>H2O RF from H2O [mW m-2]</th>\n",
       "      <th>O3 RF from H2O [mW m-2]</th>\n",
       "      <th>O3 RF from H2 [mW m-2]</th>\n",
       "      <th>O3 RF from NO [mW m-2]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>52.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>10.8204</td>\n",
       "      <td>23319.923828</td>\n",
       "      <td>35500.0</td>\n",
       "      <td>23280.389545</td>\n",
       "      <td>2.774616e+08</td>\n",
       "      <td>2.719424e+06</td>\n",
       "      <td>48461.515627</td>\n",
       "      <td>0.30992</td>\n",
       "      <td>-0.046599</td>\n",
       "      <td>-0.001492</td>\n",
       "      <td>0.006201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Latitude  Longitude  Altitude [km]   tp_WMO [Pa]  Altitude [ft]   \n",
       "75      52.5        3.5        10.8204  23319.923828        35500.0  \\\n",
       "\n",
       "    Altitude [Pa]      H2O [kg]       H2 [kg]       NO [kg]   \n",
       "75   23280.389545  2.774616e+08  2.719424e+06  48461.515627  \\\n",
       "\n",
       "   H2O RF from H2O [mW m-2] O3 RF from H2O [mW m-2] O3 RF from H2 [mW m-2]   \n",
       "75                  0.30992               -0.046599              -0.001492  \\\n",
       "\n",
       "   O3 RF from NO [mW m-2]  \n",
       "75               0.006201  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emission_inventory.data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7771ad1",
   "metadata": {},
   "source": [
    "total_emis() returns three values with H$_2$O, H$_2$ and NO emission above the tropopause in tons in this order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9455e5e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[84627041.49, 293423.68, 650378.39]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_emis # t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1380682",
   "metadata": {},
   "source": [
    "total_rf() returns one value with radiative forcing from water vapour changes due to water vapour emisson and ozone radiative forcing from H$_2$O, H$_2$ and NO emission in mWm-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5875c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174.7348825517643"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_rf # mWm-2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421e0e14",
   "metadata": {},
   "source": [
    "## Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "df313419",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 36, 20) # Altitude bins\n",
    "bin_idxs = np.digitize(emission_inventory.data['Altitude [km]'], bins)\n",
    "data = emission_inventory.data.groupby(bin_idxs).sum()\n",
    "data.index=bins[data.index]\n",
    "\n",
    "fig, axs = plt.subplots(1,3, figsize=(14,3), sharey=True)\n",
    "\n",
    "axs[0].barh(data.index,data['H2O [kg]'],height=3.5), axs[0].set_ylabel('Altitude [km]'), axs[0].set_xlabel('H$_2$O [kg]')\n",
    "axs[1].barh(data.index,data['NO [kg]'],height=3.5), axs[1].set_xlabel('NO [kg]')\n",
    "axs[2].barh(data.index,data['H2 [kg]'],height=3.5), axs[2].set_xlabel('H$_2$ [kg]')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ba397c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,3))\n",
    "\n",
    "columns = ['O3 RF from H2O [mW m-2]','O3 RF from H2 [mW m-2]','O3 RF from NO [mW m-2]','H2O RF from H2O [mW m-2]']\n",
    "\n",
    "data = emission_inventory.data[columns].sum()\n",
    "data.plot.bar()\n",
    "\n",
    "plt.ylabel('Radiative forcing [mW$\\,$m-2]')\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d881b5a-02ec-4a14-9868-05549fb672ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "1 Python 3 (based on the module python3/2023.01)",
   "language": "python",
   "name": "python3_2023_01"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}