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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_generated_gallery_orientation_fixed-nrel.py>`
        to download the full example code

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

.. _sphx_glr_generated_gallery_orientation_fixed-nrel.py:


Flag Sunny Days for a Fixed-Tilt System
=======================================

Flag sunny days for a fixed-tilt PV system.

.. GENERATED FROM PYTHON SOURCE LINES 9-17

Identifying and masking sunny days for a fixed-tilt system is important
when performing future analyses that require filtered sunny day data.
For this example we will use data from the fixed-tilt NREL SERF East system
located on the NREL campus in Colorado, USA, and generate a sunny day mask.
This data set is publicly available via the PVDAQ database in the
DOE Open Energy Data Initiative (OEDI)
(https://data.openei.org/submissions/4568), as system ID 50.
This data is timezone-localized.

.. GENERATED FROM PYTHON SOURCE LINES 17-25

.. code-block:: default


    import pvanalytics
    from pvanalytics.features import daytime as day
    from pvanalytics.features.orientation import fixed_nrel
    import matplotlib.pyplot as plt
    import pandas as pd
    import pathlib








.. GENERATED FROM PYTHON SOURCE LINES 26-28

First, read in data from the NREL SERF East fixed-tilt system. This data
set contains 15-minute interval AC power data.

.. GENERATED FROM PYTHON SOURCE LINES 28-34

.. code-block:: default


    pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
    file = pvanalytics_dir / 'data' / 'serf_east_15min_ac_power.csv'
    data = pd.read_csv(file, index_col=0, parse_dates=True)









.. GENERATED FROM PYTHON SOURCE LINES 35-39

Mask day-night periods using the
:py:func:`pvanalytics.features.daytime.power_or_irradiance` function.
Then apply :py:func:`pvanalytics.features.orientation.fixed_nrel`
to the AC power stream and mask the sunny days in the time series.

.. GENERATED FROM PYTHON SOURCE LINES 39-45

.. code-block:: default


    daytime_mask = day.power_or_irradiance(data['ac_power'])

    fixed_sunny_days = fixed_nrel(data['ac_power'],
                                  daytime_mask)








.. GENERATED FROM PYTHON SOURCE LINES 46-47

Plot the AC power stream with the sunny day mask applied to it.

.. GENERATED FROM PYTHON SOURCE LINES 47-56

.. code-block:: default


    data['ac_power'].plot()
    data.loc[fixed_sunny_days, 'ac_power'].plot(ls='', marker='.')
    plt.legend(labels=["AC Power", "Sunny Day"],
               loc="upper left")
    plt.xlabel("Date")
    plt.ylabel("AC Power (kW)")
    plt.tight_layout()
    plt.show()



.. image-sg:: /generated/gallery/orientation/images/sphx_glr_fixed-nrel_001.png
   :alt: fixed nrel
   :srcset: /generated/gallery/orientation/images/sphx_glr_fixed-nrel_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  1.369 seconds)


.. _sphx_glr_download_generated_gallery_orientation_fixed-nrel.py:

.. only:: html

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


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

      :download:`Download Python source code: fixed-nrel.py <fixed-nrel.py>`

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

      :download:`Download Jupyter notebook: fixed-nrel.ipynb <fixed-nrel.ipynb>`


.. only:: html

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

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