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.![]()