.. _sphx_glr_examples_plots_Skew-T_Layout.py:


Skew-T with Complex Layout
==========================

Combine a Skew-T and a hodograph using Matplotlib's `GridSpec` layout capability.



.. code-block:: python


    import matplotlib.gridspec as gridspec
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    import metpy.calc as mpcalc
    from metpy.cbook import get_test_data
    from metpy.plots import add_metpy_logo, Hodograph, SkewT
    from metpy.units import units







Upper air data can be obtained using the siphon package, but for this example we will use
some of MetPy's sample data.



.. code-block:: python


    col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

    df = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),
                     skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

    df['u_wind'], df['v_wind'] = mpcalc.get_wind_components(df['speed'],
                                                            np.deg2rad(df['direction']))

    # Drop any rows with all NaN values for T, Td, winds
    df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                           'u_wind', 'v_wind'), how='all').reset_index(drop=True)







We will pull the data out of the example dataset into individual variables and
assign units.



.. code-block:: python


    p = df['pressure'].values * units.hPa
    T = df['temperature'].values * units.degC
    Td = df['dewpoint'].values * units.degC
    wind_speed = df['speed'].values * units.knots
    wind_dir = df['direction'].values * units.degrees
    u, v = mpcalc.get_wind_components(wind_speed, wind_dir)








.. code-block:: python


    # Create a new figure. The dimensions here give a good aspect ratio
    fig = plt.figure(figsize=(9, 9))
    add_metpy_logo(fig, 630, 80, size='large')

    # Grid for plots
    gs = gridspec.GridSpec(3, 3)
    skew = SkewT(fig, rotation=45, subplot=gs[:, :2])

    # 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)
    skew.ax.set_ylim(1000, 100)

    # Add the relevant special lines
    skew.plot_dry_adiabats()
    skew.plot_moist_adiabats()
    skew.plot_mixing_lines()

    # Good bounds for aspect ratio
    skew.ax.set_xlim(-30, 40)

    # Create a hodograph
    ax = fig.add_subplot(gs[0, -1])
    h = Hodograph(ax, component_range=60.)
    h.add_grid(increment=20)
    h.plot(u, v)

    # Show the plot
    plt.show()



.. image:: /examples/plots/images/sphx_glr_Skew-T_Layout_001.png
    :align: center




**Total running time of the script:** ( 0 minutes  0.579 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: Skew-T_Layout.py <Skew-T_Layout.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: Skew-T_Layout.ipynb <Skew-T_Layout.ipynb>`


.. only:: html

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

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