.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "source/auto_examples/workflows/plot_multiple_dataset.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_multiple_dataset.py>`
        to download the full example code.

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

.. _sphx_glr_source_auto_examples_workflows_plot_multiple_dataset.py:


Plot multiple datasets
----------------------

This is an example of how to download and
plot multiple datasets at a time.

.. GENERATED FROM PYTHON SOURCE LINES 9-63



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /source/auto_examples/workflows/images/sphx_glr_plot_multiple_dataset_001.png
         :alt: sgpceilC1.b1 backscatter on 20220101, sgpmetE13.b1 temp_mean on 20220101
         :srcset: /source/auto_examples/workflows/images/sphx_glr_plot_multiple_dataset_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /source/auto_examples/workflows/images/sphx_glr_plot_multiple_dataset_002.png
         :alt: ceiliometer backscatter on 20220101, met temp_mean on 20220101
         :srcset: /source/auto_examples/workflows/images/sphx_glr_plot_multiple_dataset_002.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [DOWNLOADING] sgpceilC1.b1.20220101.000014.nc
    [DOWNLOADING] sgpceilC1.b1.20220106.000003.nc
    [DOWNLOADING] sgpceilC1.b1.20220102.000011.nc
    [DOWNLOADING] sgpceilC1.b1.20220103.000009.nc
    [DOWNLOADING] sgpceilC1.b1.20220105.000006.nc
    [DOWNLOADING] sgpceilC1.b1.20220107.000000.nc
    [DOWNLOADING] sgpceilC1.b1.20220104.000008.nc

    If you use these data to prepare a publication, please cite:

    Zhang, D., Ermold, B., & Morris, V. Ceilometer (CEIL). Atmospheric Radiation
    Measurement (ARM) User Facility. https://doi.org/10.5439/1181954

    [DOWNLOADING] sgpmetE13.b1.20220107.000000.cdf
    [DOWNLOADING] sgpmetE13.b1.20220101.000000.cdf
    [DOWNLOADING] sgpmetE13.b1.20220106.000000.cdf
    [DOWNLOADING] sgpmetE13.b1.20220104.000000.cdf
    [DOWNLOADING] sgpmetE13.b1.20220102.000000.cdf
    [DOWNLOADING] sgpmetE13.b1.20220105.000000.cdf
    [DOWNLOADING] sgpmetE13.b1.20220103.000000.cdf

    If you use these data to prepare a publication, please cite:

    Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation
    (MET). Atmospheric Radiation Measurement (ARM) User Facility.
    https://doi.org/10.5439/1786358







|

.. code-block:: Python


    import os

    from arm_test_data import DATASETS
    import matplotlib.pyplot as plt

    import act

    # Place your username and token here
    username = os.getenv('ARM_USERNAME')
    token = os.getenv('ARM_PASSWORD')

    # Get data from the web service if username and token are available
    # if not, use test data
    if username is None or token is None or len(username) == 0 or len(token) == 0:
        filename_ceil = DATASETS.fetch('sgpceilC1.b1.20190101.000000.nc')
        ceil_ds = act.io.arm.read_arm_netcdf(filename_ceil)
        filename_met = DATASETS.fetch('sgpmetE13.b1.20190101.000000.cdf')
        met_ds = act.io.arm.read_arm_netcdf(filename_met)
    else:
        # Download and read data
        results = act.discovery.download_arm_data(
            username, token, 'sgpceilC1.b1', '2022-01-01', '2022-01-07'
        )
        ceil_ds = act.io.arm.read_arm_netcdf(results)
        results = act.discovery.download_arm_data(
            username, token, 'sgpmetE13.b1', '2022-01-01', '2022-01-07'
        )
        met_ds = act.io.arm.read_arm_netcdf(results)

    # Read in CEIL data and correct it
    ceil_ds = act.corrections.ceil.correct_ceil(ceil_ds, -9999.0)


    # You can use tuples if the datasets in the tuple contain a
    # datastream attribute. This is required in all ARM datasets.
    display = act.plotting.TimeSeriesDisplay((ceil_ds, met_ds), subplot_shape=(2,), figsize=(15, 10))
    display.plot('backscatter', 'sgpceilC1.b1', subplot_index=(0,))
    display.plot('temp_mean', 'sgpmetE13.b1', subplot_index=(1,))
    display.day_night_background('sgpmetE13.b1', subplot_index=(1,))
    plt.show()

    # You can also use a dictionary so that you can customize
    # your datastream names to something that may be more useful.
    display = act.plotting.TimeSeriesDisplay(
        {'ceiliometer': ceil_ds, 'met': met_ds}, subplot_shape=(2,), figsize=(15, 10)
    )
    display.plot('backscatter', 'ceiliometer', subplot_index=(0,))
    display.plot('temp_mean', 'met', subplot_index=(1,))
    display.day_night_background('met', subplot_index=(1,))
    plt.show()

    ceil_ds.close()
    met_ds.close()


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

   **Total running time of the script:** (0 minutes 38.326 seconds)


.. _sphx_glr_download_source_auto_examples_workflows_plot_multiple_dataset.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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