.. _sphx_glr_examples_plots_Simple_Sounding.py:


Simple Sounding
===============

Use MetPy as straightforward as possible to make a Skew-T LogP plot.



.. code-block:: python

    from datetime import datetime

    import matplotlib.pyplot as plt
    import numpy as np

    from metpy.calc import resample_nn_1d
    from metpy.io import get_upper_air_data
    from metpy.io.upperair import UseSampleData
    from metpy.plots import SkewT
    from metpy.units import units








.. code-block:: python


    # Change default to be better for skew-T
    plt.rcParams['figure.figsize'] = (9, 9)

    with UseSampleData():  # Only needed to use our local sample data
        # Download and parse the data
        dataset = get_upper_air_data(datetime(2013, 1, 20, 12), 'OUN')

    p = dataset.variables['pressure'][:]
    T = dataset.variables['temperature'][:]
    Td = dataset.variables['dewpoint'][:]
    u = dataset.variables['u_wind'][:]
    v = dataset.variables['v_wind'][:]








.. code-block:: python

    skew = SkewT()

    # Plot the data using normal plotting functions, in this case using
    # log scaling in Y, as dictated by the typical meteorological plot
    skew.plot(p, T, 'r')
    skew.plot(p, Td, 'g')
    skew.plot_barbs(p, u, v)

    # Add the relevant special lines
    skew.plot_dry_adiabats()
    skew.plot_moist_adiabats()
    skew.plot_mixing_lines()
    skew.ax.set_ylim(1000, 100)




.. image:: /examples/plots/images/sphx_glr_Simple_Sounding_001.png
    :align: center





.. code-block:: python


    # Example of defining your own vertical barb spacing
    skew = SkewT()

    # Plot the data using normal plotting functions, in this case using
    # log scaling in Y, as dictated by the typical meteorological plot
    skew.plot(p, T, 'r')
    skew.plot(p, Td, 'g')

    # Set spacing interval--Every 50 mb from 1000 to 100 mb
    my_interval = np.arange(100, 1000, 50) * units('mbar')

    # Get indexes of values closest to defined interval
    ix = resample_nn_1d(p, my_interval)

    # Plot only values nearest to defined interval values
    skew.plot_barbs(p[ix], u[ix], v[ix])

    # Add the relevant special lines
    skew.plot_dry_adiabats()
    skew.plot_moist_adiabats()
    skew.plot_mixing_lines()
    skew.ax.set_ylim(1000, 100)

    # Show the plot
    plt.show()



.. image:: /examples/plots/images/sphx_glr_Simple_Sounding_002.png
    :align: center




**Total running time of the script:** ( 0 minutes  0.707 seconds)



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



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

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