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

.. only:: html

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

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

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

.. _sphx_glr_examples_plots_Mesonet_Stationplot.py:


====================
Mesonet Station Plot
====================

Make a surface station plot with Oklahoma Mesonet data.

The station plot itself is pretty straightforward, but there is a bit of code to perform the
data-wrangling.

.. GENERATED FROM PYTHON SOURCE LINES 14-24

.. code-block:: Python

    import cartopy.crs as ccrs
    import cartopy.feature as cfeature
    import matplotlib.pyplot as plt
    import pandas as pd

    import metpy.calc as mpcalc
    from metpy.cbook import get_test_data
    from metpy.plots import add_metpy_logo, StationPlot
    from metpy.units import units








.. GENERATED FROM PYTHON SOURCE LINES 25-31

Read in the data and wrangle it
-------------------------------

First read in the data. We use pandas because it simplifies a lot of tasks, like dealing
with strings. We'll also convert any blank cells to NaNs, and then drop rows with NaNs
in variables that we want to plot

.. GENERATED FROM PYTHON SOURCE LINES 31-39

.. code-block:: Python


    # Current observations can be downloaded here:
    # https://www.mesonet.org/index.php/weather/category/past_data_files
    data = pd.read_csv(get_test_data('mesonet_sample.txt'), na_values=' ')

    # Drop stations with missing values of data we want
    data = data.dropna(how='any', subset=['PRES', 'TAIR', 'TDEW', 'WDIR', 'WSPD'])








.. GENERATED FROM PYTHON SOURCE LINES 40-42

The mesonet has so many stations that it would clutter the plot if we used them all.
The number of stations plotted will be reduced using `reduce_point_density`.

.. GENERATED FROM PYTHON SOURCE LINES 42-48

.. code-block:: Python


    # Reduce the density of observations so the plot is readable
    proj = ccrs.LambertConformal(central_longitude=-98)
    point_locs = proj.transform_points(ccrs.PlateCarree(), data['LON'].values, data['LAT'].values)
    data = data[mpcalc.reduce_point_density(point_locs, 50 * units.km)]








.. GENERATED FROM PYTHON SOURCE LINES 49-54

Now that we have the data we want, we need to perform some conversions:

- First, assign units to the data, as applicable
- Convert cardinal wind direction to degrees
- Get wind components from speed and direction

.. GENERATED FROM PYTHON SOURCE LINES 54-69

.. code-block:: Python


    # Read in the data and assign units as defined by the Mesonet
    temperature = data['TAIR'].values * units.degF
    dewpoint = data['TDEW'].values * units.degF
    pressure = data['PRES'].values * units.hPa
    wind_speed = data['WSPD'].values * units.mph
    wind_direction = data['WDIR']
    latitude = data['LAT']
    longitude = data['LON']
    station_id = data['STID']

    # Take cardinal direction and convert to degrees, then convert to components
    wind_direction = mpcalc.parse_angle(list(wind_direction))
    u, v = mpcalc.wind_components(wind_speed.to('knots'), wind_direction)








.. GENERATED FROM PYTHON SOURCE LINES 70-72

Create the figure
-----------------

.. GENERATED FROM PYTHON SOURCE LINES 72-109

.. code-block:: Python


    # Create the figure and an axes set to the projection.
    fig = plt.figure(figsize=(20, 8))
    add_metpy_logo(fig, 70, 30, size='large')
    ax = fig.add_subplot(1, 1, 1, projection=proj)

    # Add some various map elements to the plot to make it recognizable.
    ax.add_feature(cfeature.LAND)
    ax.add_feature(cfeature.STATES.with_scale('50m'))

    # Set plot bounds
    ax.set_extent((-104, -93, 33.4, 37.2))

    stationplot = StationPlot(ax, longitude.values, latitude.values, clip_on=True,
                              transform=ccrs.PlateCarree(), fontsize=12)

    # Plot the temperature and dew point to the upper and lower left, respectively, of
    # the center point. Each one uses a different color.
    stationplot.plot_parameter('NW', temperature, color='red')
    stationplot.plot_parameter('SW', dewpoint, color='darkgreen')

    # A more complex example uses a custom formatter to control how the sea-level pressure
    # values are plotted. This uses the standard trailing 3-digits of the pressure value
    # in tenths of millibars.
    stationplot.plot_parameter('NE', pressure.m, formatter=lambda v: format(10 * v, '.0f')[-3:])

    # Add wind barbs
    stationplot.plot_barb(u, v)

    # Also plot the actual text of the station id. Instead of cardinal directions,
    # plot further out by specifying a location of 2 increments in x and -1 in y.
    stationplot.plot_text((2, -1), station_id)

    # Add title and display figure
    plt.title('Oklahoma Mesonet Observations', fontsize=16, loc='left')
    plt.title('Time: 2100 UTC 09 September 2019', fontsize=16, loc='right')
    plt.show()



.. image-sg:: /examples/plots/images/sphx_glr_Mesonet_Stationplot_001.png
   :alt: Oklahoma Mesonet Observations, Time: 2100 UTC 09 September 2019
   :srcset: /examples/plots/images/sphx_glr_Mesonet_Stationplot_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 13.496 seconds)


.. _sphx_glr_download_examples_plots_Mesonet_Stationplot.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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