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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_generated_gallery_irradiance-quality_clearsky-limits-irradiance.py>`
        to download the full example code

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

.. _sphx_glr_generated_gallery_irradiance-quality_clearsky-limits-irradiance.py:


Clearsky Limits for Irradiance Data
===================================

Checking the clearsky limits of irradiance data.

.. GENERATED FROM PYTHON SOURCE LINES 9-15

Identifying and filtering out invalid irradiance data is a
useful way to reduce noise during analysis. In this example,
we use :py:func:`pvanalytics.quality.irradiance.clearsky_limits`
to identify irradiance values that do not exceed
a limit based on a clear-sky model. For this example we will
use GHI data from the RMIS weather system located on the NREL campus in CO.

.. GENERATED FROM PYTHON SOURCE LINES 15-24

.. code-block:: default


    import pvanalytics
    from pvanalytics.quality.irradiance import clearsky_limits
    from pvanalytics.features.daytime import power_or_irradiance
    import pvlib
    import matplotlib.pyplot as plt
    import pandas as pd
    import pathlib








.. GENERATED FROM PYTHON SOURCE LINES 25-28

First, read in data from the RMIS NREL system. This data set contains
5-minute right-aligned POA, GHI, DNI, DHI, and GNI measurements,
but only the GHI is relevant here.

.. GENERATED FROM PYTHON SOURCE LINES 28-37

.. code-block:: default


    pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
    rmis_file = pvanalytics_dir / 'data' / 'irradiance_RMIS_NREL.csv'
    data = pd.read_csv(rmis_file, index_col=0, parse_dates=True)
    freq = '5T'
    # Make the datetime index tz-aware.
    data.index = data.index.tz_localize("Etc/GMT+7")









.. GENERATED FROM PYTHON SOURCE LINES 38-42

Now model clear-sky irradiance for the location and times of the
measured data. You can do this using
:py:meth:`pvlib.location.Location.get_clearsky`, using the lat-long
coordinates associated the RMIS NREL system.

.. GENERATED FROM PYTHON SOURCE LINES 42-46

.. code-block:: default


    location = pvlib.location.Location(39.7407, -105.1686)
    clearsky = location.get_clearsky(data.index)








.. GENERATED FROM PYTHON SOURCE LINES 47-54

Use :py:func:`pvanalytics.quality.irradiance.clearsky_limits`.
Here, we check GHI data in field 'irradiance_ghi__7981'.
:py:func:`pvanalytics.quality.irradiance.clearsky_limits`
returns a mask that identifies data that falls between
lower and upper limits. The defaults (used here)
are upper bound of 110% of clear-sky GHI, and
no lower bound.

.. GENERATED FROM PYTHON SOURCE LINES 54-59

.. code-block:: default


    clearsky_limit_mask = clearsky_limits(data['irradiance_ghi__7981'],
                                          clearsky['ghi'])









.. GENERATED FROM PYTHON SOURCE LINES 60-63

Mask nighttime values in the GHI time series using the
:py:func:`pvanalytics.features.daytime.power_or_irradiance` function.
We will then remove nighttime values from the GHI time series.

.. GENERATED FROM PYTHON SOURCE LINES 63-67

.. code-block:: default


    day_night_mask = power_or_irradiance(series=data['irradiance_ghi__7981'],
                                         freq=freq)








.. GENERATED FROM PYTHON SOURCE LINES 68-74

Plot the 'irradiance_ghi__7981' data stream and its associated clearsky GHI
data stream. Mask the GHI time series by its clearsky_limit_mask for daytime
periods.
Please note that a simple Ineichen model with static monthly turbidities
isn't always accurate, as in this case. Other models that may provide better
clear-sky estimates include McClear or PSM3.

.. GENERATED FROM PYTHON SOURCE LINES 74-85

.. code-block:: default

    data['irradiance_ghi__7981'].plot()
    clearsky['ghi'].plot()
    data.loc[clearsky_limit_mask & day_night_mask][
        'irradiance_ghi__7981'].plot(ls='', marker='.')
    plt.legend(labels=["RMIS GHI", "Clearsky GHI",
                       "Under Clearsky Limit"],
               loc="upper left")
    plt.xlabel("Date")
    plt.ylabel("GHI (W/m^2)")
    plt.tight_layout()
    plt.show()



.. image-sg:: /generated/gallery/irradiance-quality/images/sphx_glr_clearsky-limits-irradiance_001.png
   :alt: clearsky limits irradiance
   :srcset: /generated/gallery/irradiance-quality/images/sphx_glr_clearsky-limits-irradiance_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.548 seconds)


.. _sphx_glr_download_generated_gallery_irradiance-quality_clearsky-limits-irradiance.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-limits-irradiance.py <clearsky-limits-irradiance.py>`

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

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


.. only:: html

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

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