{
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Example usage\n",
                "\n",
                "To use `malta` in a project:"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "A simple run of constant emissons of 10 Gg of CFC-11 with zero initial conditions from 2010-2020 inclusive could be:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "import numpy as np\n",
                "from malta import model\n",
                "\n",
                "start_year = 2010\n",
                "end_year = 2021\n",
                "species = \"CFC11\"\n",
                "\n",
                "# Set up and run model\n",
                "years = np.array([str(yr) for yr in range(start_year,end_year)])\n",
                "emistot = np.repeat(10, len(years))\n",
                "emissions = model.create_emissions(species, emistot)\n",
                "sink = model.create_sink(species)\n",
                "ds_out = model.run_model(years, emissions, sink) "
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "It is then simple to plot the outputs using the simple plotting tools for xarray's datasets:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "ds_out.burden.plot()"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Or, for example, the global mean mole fraction at the surface could be plot using the 'weighted' functionality:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "ds_out[species][:,0,:].weighted(np.cos(np.deg2rad(ds_out.lat))).mean(\"lat\").plot()"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "To change how the emissions are distributed in space, we can allocate this by, for example:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "emissions = model.create_emissions(species, emistot, distribute=\"gdp\")"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "which distributes the 10 Gg of emissions by the latitudinal dependence on gdp."
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Initial conditions (of dimensions altitude x latitude) can be passed to the model using the `ics` keyword, for example using the final mole fraction from the previous run:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "ics = ds_out[f\"{species}_end\"][-1,:,:]\n",
                "ds_out = model.run_model(years, emissions, sink, ics=ics) \n",
                "ds_out[species][:,0,:].weighted(np.cos(np.deg2rad(ds_out.lat))).mean(\"lat\").plot()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "By far the slowest part of transport is the convection scheme as it involves solving a matrix. \\\n",
                "If you want to speed up the run, there is little loss of accurace for many long-lived substances (lifetime of a year or more) by turning off the convective scheme by setting `ics=False`:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "ds_out = model.run_model(years, emissions, sink, ics=ics, convection=False) \n",
                "ds_out[species][:,0,:].weighted(np.cos(np.deg2rad(ds_out.lat))).mean(\"lat\").plot()"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.9.16"
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    "nbformat": 4,
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