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

.. only:: html

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

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

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

.. _sphx_glr_source_auto_examples_workflows_plot_weighted_average.py:


Calculate and plot weighted means
---------------------------------

This is an example of how to calculate a
weighted average from the MET TBRG, ORG
and PWD.  This also calculates the
accumulated precipitation and displays it

.. GENERATED FROM PYTHON SOURCE LINES 11-108



.. image-sg:: /source/auto_examples/workflows/images/sphx_glr_plot_weighted_average_001.png
   :alt: weighted weighted_mean_accumulated on 20190101
   :srcset: /source/auto_examples/workflows/images/sphx_glr_plot_weighted_average_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    from arm_test_data import DATASETS
    import matplotlib.pyplot as plt
    import xarray as xr

    import act

    # Specify dictionary of datastreams, variables, and weights
    # Note, all weights should add up to 1.
    cf_ds = {
        'sgpmetE13.b1': {
            'variable': [
                'tbrg_precip_total',
                'org_precip_rate_mean',
                'pwd_precip_rate_mean_1min',
            ],
            'weight': [0.8, 0.15, 0.05],
        }
    }

    # Other way to define cf_ds
    # cf_ds = {'sgpmetE13.b1': {'variable': ['tbrg_precip_total'], 'weight': [0.5]},
    #         'sgpmetE13.b1': {'variable': ['org_precip_rate_mean'], 'weight': [0.25]},
    #         'sgpmetE13.b1': {'variable': ['pwd_precip_rate_mean_1min'], 'weight': [0.25]}
    #        }

    # Get a list of filenames to use
    met_wildcard_list = [
        'sgpmetE13.b1.20190101.000000.cdf',
        'sgpmetE13.b1.20190102.000000.cdf',
        'sgpmetE13.b1.20190103.000000.cdf',
        'sgpmetE13.b1.20190104.000000.cdf',
        'sgpmetE13.b1.20190105.000000.cdf',
        'sgpmetE13.b1.20190106.000000.cdf',
        'sgpmetE13.b1.20190107.000000.cdf',
    ]

    ds = {}
    new = {}
    out_units = 'mm/hr'
    for d in cf_ds:
        met_filenames = [DATASETS.fetch(file) for file in met_wildcard_list]
        ds = act.io.arm.read_arm_netcdf(met_filenames)
        # Loop through each variable and add to data list
        new_da = []
        for v in cf_ds[d]['variable']:
            da = ds[v]
            # Accumulate precip variables in new dataset
            ds = act.utils.data_utils.accumulate_precip(ds, v)

            # Convert units and add to dataarray list
            units = da.attrs['units']
            if units == 'mm':
                da.attrs['units'] = 'mm/min'
            da.values = act.utils.data_utils.convert_units(da.values, da.attrs['units'], out_units)
            da = da.resample(time='1min').mean()
            new_da.append(da)

        # Depending on number of variables for each datastream, merge or create dataset
        if len(new_da) > 1:
            new_da = xr.merge(new_da)
        else:
            new_da = new_da[0].to_dataset()

        # Add to dictionary for the weighting
        cf_ds[d]['ds'] = new_da

        # Add dataset to dictionary for plotting
        new[d] = ds

    # Calculate weighted averages using the dict defined above
    data = act.utils.data_utils.ts_weighted_average(cf_ds)

    # Add weighted mean to plotting object and calculate accumulation
    new['weighted'] = data.to_dataset(name='weighted_mean')
    new['weighted']['weighted_mean'].attrs['units'] = 'mm/hr'
    new['weighted'] = act.utils.data_utils.accumulate_precip(new['weighted'], 'weighted_mean')

    # Plot the accumulations
    display = act.plotting.TimeSeriesDisplay(new, figsize=(12, 8), subplot_shape=(1,))
    display.plot('tbrg_precip_total_accumulated', dsname='sgpmetE13.b1', color='b', label='TBRG, 0.8')
    display.plot(
        'org_precip_rate_mean_accumulated',
        dsname='sgpmetE13.b1',
        color='g',
        label='ORG 0.15',
    )
    display.plot(
        'pwd_precip_rate_mean_1min_accumulated',
        dsname='sgpmetE13.b1',
        color='y',
        label='PWD 0.05',
    )
    display.plot('weighted_mean_accumulated', dsname='weighted', color='k', label='Weighted Avg')
    display.day_night_background('sgpmetE13.b1')
    display.axes[0].legend()
    plt.show()


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

   **Total running time of the script:** (0 minutes 4.848 seconds)


.. _sphx_glr_download_source_auto_examples_workflows_plot_weighted_average.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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