RCAIDE.Library.Plots.Topography.plot_elevation_contours

plot_elevation_contours#

plot_elevation_contours(topography_file, number_of_latitudinal_points=100, number_of_longitudinal_points=100, use_lat_long_coordinates=True, save_figure=False, show_legend=True, save_filename='Elevation_Contours', file_type='.png', width=11, height=7)[source]#

Creates a contour plot visualization of terrain elevation data.

Parameters:

  • topography_file (str) –

    Path to file containing topographical data in format:

    • Column 1: Longitude [degrees]

    • Column 2: Latitude [degrees]

    • Column 3: Elevation [meters]

  • number_of_latitudinal_points (int, optional) – Number of interpolation points in latitude direction (default: 100)

  • number_of_longitudinal_points (int, optional) – Number of interpolation points in longitude direction (default: 100)

  • use_lat_long_coordinates (bool, optional) – If True, plot in lat/long coordinates If False, plot in distance coordinates (default: True)

  • save_figure (bool, optional) – Flag for saving the figure (default: False)

  • show_legend (bool, optional) – Flag to display elevation legend (default: True)

  • save_filename (str, optional) – Name of file for saved figure (default: “Elevation_Contours”)

  • file_type (str, optional) – File extension for saved figure (default: “.png”)

  • width (float, optional) – Figure width in inches (default: 11)

  • height (float, optional) – Figure height in inches (default: 7)

Returns:

fig

Return type:

matplotlib.figure.Figure

Notes

Creates visualization showing terrain elevation contours, color-coded elevation levels, geographic or distance-based coordinates, and a custom terrain-specific colormap.

When use_lat_long_coordinates is True the X-axis is Longitude [degrees] and the Y-axis is Latitude [degrees]. When use_lat_long_coordinates is False the X-axis is Longitudinal Distance [nmi] and the Y-axis is Latitudinal Distance [nmi].

Major Assumptions

  • Earth is approximated as spherical for distance calculations

  • Linear interpolation between data points

  • Sea level reference at 0 elevation

  • Positive elevations above sea level

  • Negative elevations below sea level

Definitions

‘Elevation’

Height above sea level

‘Contour’

Line of constant elevation

‘Great Circle Distance’

Shortest distance between points on sphere

‘Terrain’

Surface topography of land

See also

RCAIDE.Framework.Analyses.Geodesics.Geodesics.Calculate_Distance

Distance calculation

class FixPointNormalize(vmin=None, vmax=None, sealevel=0, col_val=0.21875, clip=False)[source]#

Bases: Normalize

Inspired by https://stackoverflow.com/questions/20144529/shifted-colorbar-matplotlib Subclassing Normalize to obtain a colormap with a fixpoint somewhere in the middle of the colormap. This may be useful for a terrain map, to set the “sea level” to a color in the blue/turquise range.

__init__(vmin=None, vmax=None, sealevel=0, col_val=0.21875, clip=False)[source]#
Parameters:

  • vmin (float or None) – Values within the range [vmin, vmax] from the input data will be linearly mapped to [0, 1]. If either vmin or vmax is not provided, they default to the minimum and maximum values of the input, respectively.

  • vmax (float or None) – Values within the range [vmin, vmax] from the input data will be linearly mapped to [0, 1]. If either vmin or vmax is not provided, they default to the minimum and maximum values of the input, respectively.

  • clip (bool, default: False) –

    Determines the behavior for mapping values outside the range [vmin, vmax].

    If clipping is off, values outside the range [vmin, vmax] are also transformed, resulting in values outside [0, 1]. This behavior is usually desirable, as colormaps can mark these under and over values with specific colors.

    If clipping is on, values below vmin are mapped to 0 and values above vmax are mapped to 1. Such values become indistinguishable from regular boundary values, which may cause misinterpretation of the data.

Notes

If vmin == vmax, input data will be mapped to 0.