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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_generated_gallery_clearsky-detection_clearsky-detection.py>`
        to download the full example code

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

.. _sphx_glr_generated_gallery_clearsky-detection_clearsky-detection.py:


Clear-Sky Detection
===================

Identifying periods of clear-sky conditions using measured irradiance.

.. GENERATED FROM PYTHON SOURCE LINES 9-14

Identifying and filtering for clear-sky conditions is a useful way to
reduce noise when analyzing measured data.  This example shows how to
use :py:func:`pvanalytics.features.clearsky.reno` to identify clear-sky
conditions using measured GHI data.  For this example we'll use
GHI measurements from NREL in Golden CO.

.. GENERATED FROM PYTHON SOURCE LINES 14-22

.. code-block:: default


    import pvanalytics
    from pvanalytics.features.clearsky import reno
    import pvlib
    import matplotlib.pyplot as plt
    import pandas as pd
    import pathlib








.. GENERATED FROM PYTHON SOURCE LINES 23-26

First, read in the GHI measurements.  For this example we'll use an example
file included in pvanalytics covering a single day, but the same process
applies to data of any length.

.. GENERATED FROM PYTHON SOURCE LINES 26-37

.. code-block:: default


    pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
    ghi_file = pvanalytics_dir / 'data' / 'midc_bms_ghi_20220120.csv'
    data = pd.read_csv(ghi_file, index_col=0, parse_dates=True)

    # or you can fetch the data straight from the source using pvlib:
    # date = pd.to_datetime('2022-01-20')
    # data = pvlib.iotools.read_midc_raw_data_from_nrel('BMS', date, date)

    measured_ghi = data['Global CMP22 (vent/cor) [W/m^2]']








.. GENERATED FROM PYTHON SOURCE LINES 38-40

Now model clear-sky irradiance for the location and times of the
measured data:

.. GENERATED FROM PYTHON SOURCE LINES 40-45

.. code-block:: default


    location = pvlib.location.Location(39.742, -105.18)
    clearsky = location.get_clearsky(data.index)
    clearsky_ghi = clearsky['ghi']








.. GENERATED FROM PYTHON SOURCE LINES 46-48

Finally, use :py:func:`pvanalytics.features.clearsky.reno` to identify
measurements during clear-sky conditions:

.. GENERATED FROM PYTHON SOURCE LINES 48-56

.. code-block:: default


    is_clearsky = reno(measured_ghi, clearsky_ghi)

    # clear-sky times indicated in black
    measured_ghi.plot()
    measured_ghi[is_clearsky].plot(ls='', marker='o', ms=2, c='k')
    plt.ylabel('Global Horizontal Irradiance [W/m2]')
    plt.show()



.. image-sg:: /generated/gallery/clearsky-detection/images/sphx_glr_clearsky-detection_001.png
   :alt: clearsky detection
   :srcset: /generated/gallery/clearsky-detection/images/sphx_glr_clearsky-detection_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.279 seconds)


.. _sphx_glr_download_generated_gallery_clearsky-detection_clearsky-detection.py:

.. only:: html

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


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

      :download:`Download Python source code: clearsky-detection.py <clearsky-detection.py>`

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

      :download:`Download Jupyter notebook: clearsky-detection.ipynb <clearsky-detection.ipynb>`


.. only:: html

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

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