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

.. only:: html

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

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

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

.. _sphx_glr_examples_cross_section.py:


======================
Cross Section Analysis
======================

The MetPy function `metpy.interpolate.cross_section` can obtain a cross-sectional slice through
gridded data.

.. GENERATED FROM PYTHON SOURCE LINES 12-23

.. code-block:: Python


    import cartopy.crs as ccrs
    import cartopy.feature as cfeature
    import matplotlib.pyplot as plt
    import numpy as np
    import xarray as xr

    import metpy.calc as mpcalc
    from metpy.cbook import get_test_data
    from metpy.interpolate import cross_section








.. GENERATED FROM PYTHON SOURCE LINES 24-32

**Getting the data**

This example uses [NARR reanalysis data](
https://www.ncei.noaa.gov/products/weather-climate-models/north-american-regional)
for 18 UTC 04 April 1987 from NCEI.

We use MetPy's CF parsing to get the data ready for use, and squeeze down the size-one time
dimension.

.. GENERATED FROM PYTHON SOURCE LINES 32-37

.. code-block:: Python


    data = xr.open_dataset(get_test_data('narr_example.nc', False))
    data = data.metpy.parse_cf().squeeze()
    print(data)





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

 .. code-block:: none

    <xarray.Dataset> Size: 21MB
    Dimensions:              (isobaric: 29, y: 118, x: 292)
    Coordinates:
        time                 datetime64[ns] 8B 1987-04-04T18:00:00
      * isobaric             (isobaric) float64 232B 1e+03 975.0 ... 125.0 100.0
      * y                    (y) float64 944B -3.087e+06 -3.054e+06 ... 7.114e+05
      * x                    (x) float64 2kB -3.977e+06 -3.945e+06 ... 5.47e+06
        metpy_crs            object 8B Projection: lambert_conformal_conic
    Data variables:
        Temperature          (isobaric, y, x) float32 4MB ...
        Lambert_Conformal    |S1 1B ...
        lat                  (y, x) float64 276kB ...
        lon                  (y, x) float64 276kB ...
        u_wind               (isobaric, y, x) float32 4MB ...
        v_wind               (isobaric, y, x) float32 4MB ...
        Geopotential_height  (isobaric, y, x) float32 4MB ...
        Specific_humidity    (isobaric, y, x) float32 4MB ...
    Attributes: (12/14)
        Conventions:              CF-1.0
        Originating_center:       US National Weather Service - NCEP(WMC) (7)
        Originating_subcenter:    The North American Regional Reanalysis (NARR) P...
        Generating_Model:         North American Regional Reanalysis (NARR)
        Product_Type:             Forecast/Uninitialized Analysis/Image Product
        title:                    US National Weather Service - NCEP(WMC) North A...
        ...                       ...
        history:                  Direct read of GRIB-1 into NetCDF-Java 4 API
        CF:feature_type:          GRID
        file_format:              GRIB-1
        location:                 /nomads3_data/raid2/noaaport/merged/narr/198704...
        _CoordinateModelRunDate:  1987-04-04T18:00:00Z
        History:                  Translated to CF-1.0 Conventions by Netcdf-Java...




.. GENERATED FROM PYTHON SOURCE LINES 38-39

Define start and end points:

.. GENERATED FROM PYTHON SOURCE LINES 39-43

.. code-block:: Python


    start = (37.0, -105.0)
    end = (35.5, -65.0)








.. GENERATED FROM PYTHON SOURCE LINES 44-45

Get the cross section, and convert lat/lon to supplementary coordinates:

.. GENERATED FROM PYTHON SOURCE LINES 45-49

.. code-block:: Python


    cross = cross_section(data, start, end).set_coords(('lat', 'lon'))
    print(cross)





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

 .. code-block:: none

    <xarray.Dataset> Size: 120kB
    Dimensions:              (isobaric: 29, index: 100)
    Coordinates:
        time                 datetime64[ns] 8B 1987-04-04T18:00:00
      * isobaric             (isobaric) float64 232B 1e+03 975.0 ... 125.0 100.0
        metpy_crs            object 8B Projection: lambert_conformal_conic
        x                    (index) float64 800B 1.818e+05 2.18e+05 ... 3.712e+06
        y                    (index) float64 800B -1.454e+06 ... -5.573e+05
      * index                (index) int64 800B 0 1 2 3 4 5 6 ... 94 95 96 97 98 99
        lat                  (index) float64 800B 37.0 37.05 37.11 ... 35.58 35.5
        lon                  (index) float64 800B -105.0 -104.6 ... -65.39 -65.0
    Data variables:
        Temperature          (isobaric, index) float64 23kB 287.7 286.9 ... 211.4
        Lambert_Conformal    |S1 1B ...
        u_wind               (isobaric, index) float64 23kB -2.729 0.4776 ... 23.68
        v_wind               (isobaric, index) float64 23kB 8.473 5.723 ... -1.082
        Geopotential_height  (isobaric, index) float64 23kB 118.6 ... 1.636e+04
        Specific_humidity    (isobaric, index) float64 23kB 0.006367 ... 4.223e-06
    Attributes: (12/14)
        Conventions:              CF-1.0
        Originating_center:       US National Weather Service - NCEP(WMC) (7)
        Originating_subcenter:    The North American Regional Reanalysis (NARR) P...
        Generating_Model:         North American Regional Reanalysis (NARR)
        Product_Type:             Forecast/Uninitialized Analysis/Image Product
        title:                    US National Weather Service - NCEP(WMC) North A...
        ...                       ...
        history:                  Direct read of GRIB-1 into NetCDF-Java 4 API
        CF:feature_type:          GRID
        file_format:              GRIB-1
        location:                 /nomads3_data/raid2/noaaport/merged/narr/198704...
        _CoordinateModelRunDate:  1987-04-04T18:00:00Z
        History:                  Translated to CF-1.0 Conventions by Netcdf-Java...




.. GENERATED FROM PYTHON SOURCE LINES 50-52

For this example, we will be plotting potential temperature, relative humidity, and
tangential/normal winds. And so, we need to calculate those, and add them to the dataset:

.. GENERATED FROM PYTHON SOURCE LINES 52-71

.. code-block:: Python


    cross['Potential_temperature'] = mpcalc.potential_temperature(
        cross['isobaric'],
        cross['Temperature']
    )
    cross['Relative_humidity'] = mpcalc.relative_humidity_from_specific_humidity(
        cross['isobaric'],
        cross['Temperature'],
        cross['Specific_humidity']
    )
    cross['u_wind'] = cross['u_wind'].metpy.convert_units('knots')
    cross['v_wind'] = cross['v_wind'].metpy.convert_units('knots')
    cross['t_wind'], cross['n_wind'] = mpcalc.cross_section_components(
        cross['u_wind'],
        cross['v_wind']
    )

    print(cross)





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

 .. code-block:: none

    <xarray.Dataset> Size: 213kB
    Dimensions:                (isobaric: 29, index: 100)
    Coordinates:
        time                   datetime64[ns] 8B 1987-04-04T18:00:00
      * isobaric               (isobaric) float64 232B 1e+03 975.0 ... 125.0 100.0
        metpy_crs              object 8B Projection: lambert_conformal_conic
        x                      (index) float64 800B 1.818e+05 2.18e+05 ... 3.712e+06
        y                      (index) float64 800B -1.454e+06 ... -5.573e+05
      * index                  (index) int64 800B 0 1 2 3 4 5 ... 94 95 96 97 98 99
        lat                    (index) float64 800B 37.0 37.05 37.11 ... 35.58 35.5
        lon                    (index) float64 800B -105.0 -104.6 ... -65.39 -65.0
    Data variables:
        Temperature            (isobaric, index) float64 23kB 287.7 286.9 ... 211.4
        Lambert_Conformal      |S1 1B ...
        u_wind                 (isobaric, index) float64 23kB <Quantity([[ -5.304...
        v_wind                 (isobaric, index) float64 23kB <Quantity([[16.4704...
        Geopotential_height    (isobaric, index) float64 23kB 118.6 ... 1.636e+04
        Specific_humidity      (isobaric, index) float64 23kB 0.006367 ... 4.223e-06
        Potential_temperature  (isobaric, index) float64 23kB <Quantity([[287.717...
        Relative_humidity      (isobaric, index) float64 23kB <Quantity([[0.61534...
        t_wind                 (isobaric, index) float64 23kB <Quantity([[-2.0266...
        n_wind                 (isobaric, index) float64 23kB <Quantity([[ 17.184...
    Attributes: (12/14)
        Conventions:              CF-1.0
        Originating_center:       US National Weather Service - NCEP(WMC) (7)
        Originating_subcenter:    The North American Regional Reanalysis (NARR) P...
        Generating_Model:         North American Regional Reanalysis (NARR)
        Product_Type:             Forecast/Uninitialized Analysis/Image Product
        title:                    US National Weather Service - NCEP(WMC) North A...
        ...                       ...
        history:                  Direct read of GRIB-1 into NetCDF-Java 4 API
        CF:feature_type:          GRID
        file_format:              GRIB-1
        location:                 /nomads3_data/raid2/noaaport/merged/narr/198704...
        _CoordinateModelRunDate:  1987-04-04T18:00:00Z
        History:                  Translated to CF-1.0 Conventions by Netcdf-Java...




.. GENERATED FROM PYTHON SOURCE LINES 72-73

Now, we can make the plot.

.. GENERATED FROM PYTHON SOURCE LINES 73-133

.. code-block:: Python


    # Define the figure object and primary axes
    fig = plt.figure(1, figsize=(16., 9.))
    ax = plt.axes()

    # Plot RH using contourf
    rh_contour = ax.contourf(cross['lon'], cross['isobaric'], cross['Relative_humidity'],
                             levels=np.arange(0, 1.05, .05), cmap='YlGnBu')
    rh_colorbar = fig.colorbar(rh_contour)

    # Plot potential temperature using contour, with some custom labeling
    theta_contour = ax.contour(cross['lon'], cross['isobaric'], cross['Potential_temperature'],
                               levels=np.arange(250, 450, 5), colors='k', linewidths=2)
    theta_contour.clabel(theta_contour.levels[1::2], fontsize=8, colors='k', inline=1,
                         inline_spacing=8, fmt='%i', rightside_up=True, use_clabeltext=True)

    # Plot winds using the axes interface directly, with some custom indexing to make the barbs
    # less crowded
    wind_slc_vert = list(range(0, 19, 2)) + list(range(19, 29))
    wind_slc_horz = slice(5, 100, 5)
    ax.barbs(cross['lon'][wind_slc_horz], cross['isobaric'][wind_slc_vert],
             cross['t_wind'][wind_slc_vert, wind_slc_horz],
             cross['n_wind'][wind_slc_vert, wind_slc_horz], color='k')

    # Adjust the y-axis to be logarithmic
    ax.set_yscale('symlog')
    ax.set_yticklabels(np.arange(1000, 50, -100))
    ax.set_ylim(cross['isobaric'].max(), cross['isobaric'].min())
    ax.set_yticks(np.arange(1000, 50, -100))

    # Define the CRS and inset axes
    data_crs = data['Geopotential_height'].metpy.cartopy_crs
    ax_inset = fig.add_axes([0.125, 0.665, 0.25, 0.25], projection=data_crs)

    # Plot geopotential height at 500 hPa using xarray's contour wrapper
    ax_inset.contour(data['x'], data['y'], data['Geopotential_height'].sel(isobaric=500.),
                     levels=np.arange(5100, 6000, 60), cmap='inferno')

    # Plot the path of the cross section
    endpoints = data_crs.transform_points(ccrs.Geodetic(),
                                          *np.vstack([start, end]).transpose()[::-1])
    ax_inset.scatter(endpoints[:, 0], endpoints[:, 1], c='k', zorder=2)
    ax_inset.plot(cross['x'], cross['y'], c='k', zorder=2)

    # Add geographic features
    ax_inset.coastlines()
    ax_inset.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='k', alpha=0.2, zorder=0)

    # Set the titles and axes labels
    ax_inset.set_title('')
    ax.set_title(f'NARR Cross-Section \u2013 {start} to {end} \u2013 '
                 f'Valid: {cross["time"].dt.strftime("%Y-%m-%d %H:%MZ").item()}\n'
                 'Potential Temperature (K), Tangential/Normal Winds (knots), Relative Humidity '
                 '(dimensionless)\nInset: Cross-Section Path and 500 hPa Geopotential Height')
    ax.set_ylabel('Pressure (hPa)')
    ax.set_xlabel('Longitude (degrees east)')
    rh_colorbar.set_label('Relative Humidity (dimensionless)')

    plt.show()




.. image-sg:: /examples/images/sphx_glr_cross_section_001.png
   :alt: NARR Cross-Section – (37.0, -105.0) to (35.5, -65.0) – Valid: 1987-04-04 18:00Z Potential Temperature (K), Tangential/Normal Winds (knots), Relative Humidity (dimensionless) Inset: Cross-Section Path and 500 hPa Geopotential Height
   :srcset: /examples/images/sphx_glr_cross_section_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/runner/work/MetPy/MetPy/examples/cross_section.py:99: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
      ax.set_yticklabels(np.arange(1000, 50, -100))




.. GENERATED FROM PYTHON SOURCE LINES 134-137

Note: The x-axis can display any variable that is the same length as the
plotted variables, including latitude. Additionally, arguments can be provided
to ``ax.set_xticklabels`` to label lat/lon pairs, similar to the default NCL output.


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

   **Total running time of the script:** (0 minutes 5.035 seconds)


.. _sphx_glr_download_examples_cross_section.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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