.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/modules/plot_emissions_animal_scaling.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_modules_plot_emissions_animal_scaling.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_modules_plot_emissions_animal_scaling.py:


===================================================
Plot emissions from different item groups and years
===================================================
This example demonstrates how to manipulate a Food Balance Sheet array, add
items and years to it and combine it with impact data to plot total GHG
emissions dissagregated by a selected coordinate. 

Two datasets are imported from the agrifoodpy_data package:

- FAOSTAT: United Nations Food and Agriculture Organization Food Balance Sheets
- PN18: Poore & Nemecek (2018) LCA data, scaled to match the FAOSTAT item base

.. GENERATED FROM PYTHON SOURCE LINES 14-44

.. code-block:: Python


    import numpy as np
    import xarray as xr

    from agrifoodpy_data.impact import PN18_FAOSTAT as PN18
    from agrifoodpy_data.food import FAOSTAT

    import agrifoodpy.food
    from agrifoodpy.impact.model import fbs_impacts

    from matplotlib import pyplot as plt

    # Select food items and production values for the UK and the US
    # Values are in [1000 Tonnes]
    UK_FAO_CODE = 229
    US_FAO_CODE = 231

    country_codes = [UK_FAO_CODE, US_FAO_CODE]
    food = FAOSTAT.sel(Region=country_codes)["production"]

    # Convert emissions from [g CO2e] to [Gt CO2e]
    ghg_emissions = PN18["GHG Emissions (IPCC 2013)"] / 1e6
    food_emissions = fbs_impacts(food, ghg_emissions)

    ax = food_emissions.fbs.plot_years(show="Region", labels=["UK", "USA"])
    ax.set_xlabel("Year")
    ax.set_ylabel("GHG emissions Gt CO2e")

    plt.show()




.. image-sg:: /examples/modules/images/sphx_glr_plot_emissions_animal_scaling_001.png
   :alt: plot emissions animal scaling
   :srcset: /examples/modules/images/sphx_glr_plot_emissions_animal_scaling_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 45-46

We can also plot by item origin by using the group_sum accessor function

.. GENERATED FROM PYTHON SOURCE LINES 46-52

.. code-block:: Python

    food_emissions_origin = food_emissions.fbs.group_sum("Item_origin")
    ax = food_emissions_origin.fbs.plot_years(show="Item_origin", labels="show")
    ax.set_xlabel("Year")
    ax.set_ylabel("GHG emissions [Gt CO2e]")
    plt.show()




.. image-sg:: /examples/modules/images/sphx_glr_plot_emissions_animal_scaling_002.png
   :alt: plot emissions animal scaling
   :srcset: /examples/modules/images/sphx_glr_plot_emissions_animal_scaling_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 53-55

We can add an item to the emissions DataArray and a new list of years,
scaling the values linearly

.. GENERATED FROM PYTHON SOURCE LINES 55-69

.. code-block:: Python


    emissions_proj = food_emissions.fbs.add_items(items=5000)
    emissions_proj.loc[{"Item":5000}] = 2*food_emissions.sel(Item=2731)

    proj = np.linspace(1, 1.5, num=10)
    emissions_proj = emissions_proj.fbs.add_years(years=np.arange(2021,2031), projection="constant")
    emissions_proj.loc[{"Year":np.arange(2021,2031)}] *= xr.DataArray(np.linspace(1, 1.1, num=10), coords={"Year":np.arange(2021,2031)})
    emissions_by_origin_proj = emissions_proj.fbs.group_sum("Item_origin")
    ax = emissions_by_origin_proj.fbs.plot_years(show="Item_origin", labels="show")

    ax.set_xlabel("Year")
    ax.set_ylabel("GHG emissions [Gt CO2e]")
    ax.axvline(2020, linewidth=0.5, alpha=0.5, color="k")

    plt.show()


.. image-sg:: /examples/modules/images/sphx_glr_plot_emissions_animal_scaling_003.png
   :alt: plot emissions animal scaling
   :srcset: /examples/modules/images/sphx_glr_plot_emissions_animal_scaling_003.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.853 seconds)


.. _sphx_glr_download_examples_modules_plot_emissions_animal_scaling.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_emissions_animal_scaling.ipynb <plot_emissions_animal_scaling.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_emissions_animal_scaling.py <plot_emissions_animal_scaling.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_emissions_animal_scaling.zip <plot_emissions_animal_scaling.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_