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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_gallery_lightning-creek-magnetic.py>`
        to download the full example code

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

.. _sphx_glr_gallery_lightning-creek-magnetic.py:


Magnetic anomaly grid of the Lightning Creek Sill Complex, Australia
--------------------------------------------------------------------

This is a section of an airborne survey acquired in 1990 by the Queensland
Government, Australia. The grid has 50 m resolution (UTM coordinates) and
is at a uniform orthometric height of 500 m.

**Original source:**
`Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa
Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia,
Canberra <http://pid.geoscience.gov.au/dataset/ga/142419>`__

**Pre-processing:** `Source code for preparation of the original dataset for
redistribution in Ensaio
<https://github.com/fatiando-data/lightning-creek-magnetic-grid>`__

.. GENERATED FROM PYTHON SOURCE LINES 25-30

.. code-block:: default

    import pygmt
    import xarray as xr

    import ensaio








.. GENERATED FROM PYTHON SOURCE LINES 31-32

Download and cache the data and return the path to it on disk

.. GENERATED FROM PYTHON SOURCE LINES 32-35

.. code-block:: default

    fname = ensaio.fetch_lightning_creek_magnetic(version=1)
    print(fname)





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

 Out:

 .. code-block:: none

    /home/runner/work/_temp/cache/ensaio/v1/lightning-creek-magnetic-grid.nc




.. GENERATED FROM PYTHON SOURCE LINES 36-37

Load the netCDF grid with xarray

.. GENERATED FROM PYTHON SOURCE LINES 37-40

.. code-block:: default

    data = xr.load_dataarray(fname)
    data






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    </style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;total_field_anomaly&#x27; (northing: 370, easting: 346)&gt;
    array([[  35.        ,   36.19995   ,   36.69995   , ..., -101.099976  ,
            -100.400024  ,  -99.599976  ],
           [  36.5       ,   37.599976  ,   38.        , ..., -102.20007   ,
            -101.5       , -100.70007   ],
           [  37.099976  ,   38.19995   ,   38.599976  , ..., -103.30005   ,
            -102.599976  , -101.900024  ],
           ...,
           [ 182.79993   ,  172.40002   ,  160.79993   , ...,    0.79992676,
             -24.200073  ,  -41.80005   ],
           [ 182.09998   ,  172.59998   ,  161.40002   , ...,    6.        ,
             -21.5       ,  -41.        ],
           [ 178.79993   ,  170.40002   ,  160.29993   , ...,   11.400024  ,
             -16.        ,  -35.80005   ]], dtype=float32)
    Coordinates:
      * easting   (easting) float64 4.655e+05 4.656e+05 ... 4.827e+05 4.828e+05
      * northing  (northing) float64 7.576e+06 7.576e+06 ... 7.595e+06 7.595e+06
        height    (northing, easting) float64 500.0 500.0 500.0 ... 500.0 500.0
    Attributes:
        Conventions:   CF-1.8
        title:         Magnetic total-field anomaly of the Lightning Creek sill c...
        crs:           proj=utm zone=54 south datum=WGS84 units=m no_defs ellps=W...
        source:        Interpolated from airborne magnetic line data using gradie...
        license:       Creative Commons Attribution 4.0 International Licence
        references:    Geophysical Acquisition &amp; Processing Section 2019. MIM Dat...
        long_name:     total-field magnetic anomaly
        units:         nT
        actual_range:  [-1785.  3798.]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'total_field_anomaly'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>northing</span>: 370</li><li><span class='xr-has-index'>easting</span>: 346</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-82ed3b73-6931-4032-8251-3fd7a3e30b02' class='xr-array-in' type='checkbox' checked><label for='section-82ed3b73-6931-4032-8251-3fd7a3e30b02' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>35.0 36.2 36.7 36.6 36.3 35.6 ... 113.1 78.6 43.7 11.4 -16.0 -35.8</span></div><div class='xr-array-data'><pre>array([[  35.        ,   36.19995   ,   36.69995   , ..., -101.099976  ,
            -100.400024  ,  -99.599976  ],
           [  36.5       ,   37.599976  ,   38.        , ..., -102.20007   ,
            -101.5       , -100.70007   ],
           [  37.099976  ,   38.19995   ,   38.599976  , ..., -103.30005   ,
            -102.599976  , -101.900024  ],
           ...,
           [ 182.79993   ,  172.40002   ,  160.79993   , ...,    0.79992676,
             -24.200073  ,  -41.80005   ],
           [ 182.09998   ,  172.59998   ,  161.40002   , ...,    6.        ,
             -21.5       ,  -41.        ],
           [ 178.79993   ,  170.40002   ,  160.29993   , ...,   11.400024  ,
             -16.        ,  -35.80005   ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-b69fe4f8-c807-4ff0-a8cb-45bf33346722' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b69fe4f8-c807-4ff0-a8cb-45bf33346722' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>easting</span></div><div class='xr-var-dims'>(easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.655e+05 4.656e+05 ... 4.828e+05</div><input id='attrs-db4226df-c187-4402-8a6f-c76bd19af48c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db4226df-c187-4402-8a6f-c76bd19af48c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e46da91-fff1-4704-8363-5b1441dfd429' class='xr-var-data-in' type='checkbox'><label for='data-7e46da91-fff1-4704-8363-5b1441dfd429' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>UTM easting</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>actual_range :</span></dt><dd>[465533.31767977 482783.31767977]</dd></dl></div><div class='xr-var-data'><pre>array([465533.31768, 465583.31768, 465633.31768, ..., 482683.31768,
           482733.31768, 482783.31768])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>northing</span></div><div class='xr-var-dims'>(northing)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.576e+06 7.576e+06 ... 7.595e+06</div><input id='attrs-3494264a-f2ad-43e7-b3a3-7c8995cd84b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3494264a-f2ad-43e7-b3a3-7c8995cd84b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be113309-bb37-4a9f-8eda-37c151d325f9' class='xr-var-data-in' type='checkbox'><label for='data-be113309-bb37-4a9f-8eda-37c151d325f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>UTM northing</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>actual_range :</span></dt><dd>[7576368.82029097 7594818.82029097]</dd></dl></div><div class='xr-var-data'><pre>array([7576368.820291, 7576418.820291, 7576468.820291, ..., 7594718.820291,
           7594768.820291, 7594818.820291])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>500.0 500.0 500.0 ... 500.0 500.0</div><input id='attrs-57ff380f-394a-4cc5-a11e-8e105b91b7bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57ff380f-394a-4cc5-a11e-8e105b91b7bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7cd6341-8e32-48ba-8a68-fa4f9c7cc7c5' class='xr-var-data-in' type='checkbox'><label for='data-b7cd6341-8e32-48ba-8a68-fa4f9c7cc7c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>orthometric height</dd><dt><span>standard_name :</span></dt><dd>height_above_geopotential_datum</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>actual_range :</span></dt><dd>[500. 500.]</dd></dl></div><div class='xr-var-data'><pre>array([[500., 500., 500., ..., 500., 500., 500.],
           [500., 500., 500., ..., 500., 500., 500.],
           [500., 500., 500., ..., 500., 500., 500.],
           ...,
           [500., 500., 500., ..., 500., 500., 500.],
           [500., 500., 500., ..., 500., 500., 500.],
           [500., 500., 500., ..., 500., 500., 500.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f64ce3d-38e7-4724-94f7-a61196d51d4d' class='xr-section-summary-in' type='checkbox'  ><label for='section-4f64ce3d-38e7-4724-94f7-a61196d51d4d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>easting</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-932a1301-b9dc-4824-bf51-631b91d770dc' class='xr-index-data-in' type='checkbox'/><label for='index-932a1301-b9dc-4824-bf51-631b91d770dc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([465533.31767976715, 465583.31767976715, 465633.31767976715,
                  465683.31767976715, 465733.31767976715, 465783.31767976715,
                  465833.31767976715, 465883.31767976715, 465933.31767976715,
                  465983.31767976715,
                  ...
                  482333.31767976715, 482383.31767976715, 482433.31767976715,
                  482483.31767976715, 482533.31767976715, 482583.31767976715,
                  482633.31767976715, 482683.31767976715, 482733.31767976715,
                  482783.31767976715],
                 dtype=&#x27;float64&#x27;, name=&#x27;easting&#x27;, length=346))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>northing</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-833e3ebd-3e12-4db2-a618-fa7295b92982' class='xr-index-data-in' type='checkbox'/><label for='index-833e3ebd-3e12-4db2-a618-fa7295b92982' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([7576368.82029097, 7576418.82029097, 7576468.82029097,
                  7576518.82029097, 7576568.82029097, 7576618.82029097,
                  7576668.82029097, 7576718.82029097, 7576768.82029097,
                  7576818.82029097,
                  ...
                  7594368.82029097, 7594418.82029097, 7594468.82029097,
                  7594518.82029097, 7594568.82029097, 7594618.82029097,
                  7594668.82029097, 7594718.82029097, 7594768.82029097,
                  7594818.82029097],
                 dtype=&#x27;float64&#x27;, name=&#x27;northing&#x27;, length=370))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7306415-9444-4ee1-85a9-8d06349b2648' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7306415-9444-4ee1-85a9-8d06349b2648' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8</dd><dt><span>title :</span></dt><dd>Magnetic total-field anomaly of the Lightning Creek sill complex, Australia</dd><dt><span>crs :</span></dt><dd>proj=utm zone=54 south datum=WGS84 units=m no_defs ellps=WGS84 towgs84=0,0,0</dd><dt><span>source :</span></dt><dd>Interpolated from airborne magnetic line data using gradient-boosted equivalent sources</dd><dt><span>license :</span></dt><dd>Creative Commons Attribution 4.0 International Licence</dd><dt><span>references :</span></dt><dd>Geophysical Acquisition &amp; Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. http://pid.geoscience.gov.au/dataset/ga/142419</dd><dt><span>long_name :</span></dt><dd>total-field magnetic anomaly</dd><dt><span>units :</span></dt><dd>nT</dd><dt><span>actual_range :</span></dt><dd>[-1785.  3798.]</dd></dl></div></li></ul></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 41-42

Make a PyGMT pseudo-color map of the total field magnetic anomaly.

.. GENERATED FROM PYTHON SOURCE LINES 42-58

.. code-block:: default

    fig = pygmt.Figure()
    scale = 2500
    pygmt.makecpt(cmap="polar+h", series=[-scale, scale], background=True)
    fig.grdimage(
        grid=data,
        cmap=True,
        shading="+a45+nt0.1",
        projection="X15c/17c",
        frame="af",
    )
    fig.colorbar(
        frame='af+l"total field magnetic anomaly [nT]"',
        position="JBC+h+o0/1c+e",
    )
    fig.show()




.. image-sg:: /gallery/images/sphx_glr_lightning-creek-magnetic_001.png
   :alt: lightning creek magnetic
   :srcset: /gallery/images/sphx_glr_lightning-creek-magnetic_001.png
   :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    <IPython.core.display.Image object>




.. GENERATED FROM PYTHON SOURCE LINES 59-60

The anomaly at the top right is the  Lightning Creek sill complex.


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

   **Total running time of the script:** ( 0 minutes  5.157 seconds)


.. _sphx_glr_download_gallery_lightning-creek-magnetic.py:


.. only :: html

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



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

     :download:`Download Python source code: lightning-creek-magnetic.py <lightning-creek-magnetic.py>`



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

     :download:`Download Jupyter notebook: lightning-creek-magnetic.ipynb <lightning-creek-magnetic.ipynb>`


.. only:: html

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

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