pyhum.map_texture module

Create plots of the texture lengthscale maps made in PyHum.texture module using the algorithm detailed by Buscombe et al. (forthcoming) This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical representation that integrates over many attributes of bed texture, of which grain size is the most important. The technique is a physically based means to identify regions of texture within a sidescan echogram, and could provide a basis for objective, automated riverbed sediment classification.

Syntax

You call the function like this:

[] = PyHum.map_texture(humfile, sonpath, cs2cs_args, res, mode, nn, numstdevs)

Parameters

humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, optional [Default=”epsg:26949”]
arguments to create coordinates in a projected coordinate system this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates into any projection supported by the proj.4 libraries
res : float, optional [Default=0.5]
grid resolution of output gridded texture map
mode: int, optional [Default=3]
gridding mode. 1 = nearest neighbour
2 = inverse weighted nearest neighbour 3 = Gaussian weighted nearest neighbour
nn: int, optional [Default=64]
number of nearest neighbours for gridding (used if mode > 1)
numstdevs: int, optional [Default = 4]
Threshold number of standard deviations in texture lengthscale per grid cell up to which to accept

Returns

sonpath+’x_y_class’+str(p)+’.asc’ : text file
contains the point cloud of easting, northing, and texture lengthscales of the pth chunk
sonpath+’class_GroundOverlay’+str(p)+’.kml’: kml file
contains gridded (or point cloud) texture lengthscale map for importing into google earth of the pth chunk
sonpath+’class_map’+str(p)+’.png’ :
image overlay associated with the kml file
sonpath+’class_map_imagery’+str(p)+’.png’ : png image file
gridded (or point cloud) texture lengthscale map overlain onto an image pulled from esri image server

References

[1] Buscombe, D., Grams, P.E., and Smith, S.M.C., Automated riverbed sediment classification using low-cost sidescan sonar. Journal of Hydraulic Engineering, 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
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