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

.. only:: html

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

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

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

.. _sphx_glr__auto_examples_field_evolution.py:


Field evolution and zonal stats
=================================================================

Using Agriculture cloud mask from EarthDaily, and data from L2A, zonal stats for evolution

.. GENERATED FROM PYTHON SOURCE LINES 8-10

Import librairies
-------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 10-15

.. code-block:: Python


    from matplotlib import pyplot as plt

    import earthdaily as ed








.. GENERATED FROM PYTHON SOURCE LINES 16-18

Load plot
-------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 18-22

.. code-block:: Python


    # load geojson
    pivot = ed.datasets.load_pivot()








.. GENERATED FROM PYTHON SOURCE LINES 23-25

Init earthdatastore with environment variables or default credentials
----------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 25-28

.. code-block:: Python


    eds = ed.EarthDataStore()



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

.. code-block:: pytb

    Traceback (most recent call last):
      File "/private/var/folders/_t/czv_cfrd613_6_gd_p8qpqd80000gp/T/tmpmap_d3kq/7261d0bf6bc6b22a0278281791f6623d19ddf89a/examples/field_evolution.py", line 26, in <module>
        eds = ed.EarthDataStore()
              ^^^^^^^^^^^^^^^^^^^
      File "/private/var/folders/_t/czv_cfrd613_6_gd_p8qpqd80000gp/T/tmpmap_d3kq/7261d0bf6bc6b22a0278281791f6623d19ddf89a/earthdaily/__init__.py", line 51, in EarthDataStore
        return earthdatastore.Auth.from_credentials(
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/private/var/folders/_t/czv_cfrd613_6_gd_p8qpqd80000gp/T/tmpmap_d3kq/7261d0bf6bc6b22a0278281791f6623d19ddf89a/earthdaily/earthdatastore/__init__.py", line 464, in from_credentials
        config = cls.read_credentials(
                 ^^^^^^^^^^^^^^^^^^^^^
      File "/private/var/folders/_t/czv_cfrd613_6_gd_p8qpqd80000gp/T/tmpmap_d3kq/7261d0bf6bc6b22a0278281791f6623d19ddf89a/earthdaily/earthdatastore/__init__.py", line 529, in read_credentials
        raise NotImplementedError("Credentials weren't found.")
    NotImplementedError: Credentials weren't found.




.. GENERATED FROM PYTHON SOURCE LINES 29-31

Search for collection items for June 2022.
where at least 50% of the field is clear according to the native cloudmask.

.. GENERATED FROM PYTHON SOURCE LINES 31-42

.. code-block:: Python


    datacube = eds.datacube(
        "sentinel-2-l2a",
        intersects=pivot,
        datetime=["2022-06", "2022-07"],
        assets=["red", "green", "blue", "nir"],
        mask_with="native",
        clear_cover=50,
    )
    datacube.clear_percent.plot.scatter(x="time")


.. GENERATED FROM PYTHON SOURCE LINES 43-45

Add spectral indices using spyndex from earthdaily accessor
------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: Python


    datacube = datacube.ed.add_indices(["NDVI"])


.. GENERATED FROM PYTHON SOURCE LINES 49-51

Plots cube with SCL with at least 50% of clear data
----------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 51-56

.. code-block:: Python

    datacube = datacube.load()
    datacube.ed.plot_rgb(col_wrap=4, vmin=0, vmax=0.3)
    plt.title("Pivot evolution masked with native cloudmasks")
    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 57-59

Compute zonal stats for the pivot
----------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 59-65

.. code-block:: Python


    zonal_stats = datacube.ed.zonal_stats(pivot, ["mean", "max", "min"])
    zonal_stats.isel(feature=0).to_array(dim="band").plot.line(
        x="time", col="band", hue="zonal_statistics", col_wrap=3
    )
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.005 seconds)


.. _sphx_glr_download__auto_examples_field_evolution.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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