lidar_review_tools

Notebooks and tools for reviewing and comparing bathy lidar data. 
import lidar_review_tools.lidar_tools  as lt
from lidar_review_tools import lidar_tools

Install

Clone the repository to your computer and then:


pip install lidar_review_tools

or

pip install -e .

How to use

Generate histograms for 30m depth data from a SONAR, CZMIL, and Bulldog.

hx4_a = lt.TEST_AREA()
hx4_a.area_name      = "Area A"
hx4_a.area_width     = 2
hx4_a.area_length    = 45
hx4_a.area           = hx4_a.area_width * hx4_a.area_length

hx4_a.bin_size       = 0.02
hx4_a.density        = True
hx4_a.data_path      = "../data/ch4x/"
width = 3
lt.data( hx4_a, offset=26.0, fn = hx4_a.data_path + "A-30m-sonar.txt", color = "red",   width = width, name = "SONAR", ref=True    )
lt.data( hx4_a, offset=26.0, fn = hx4_a.data_path + "A-30m-czmil.txt", color = "green", width = width, name = "CZMIL"              )
lt.data( hx4_a, offset=26.0, fn = hx4_a.data_path + "A-30m-hx4.txt",   color = "blue",  width = width, name = "Hawkeye 4x"         )
lt.gen_all_stats( hx4_a )
hx4_a.title          = (f"{hx4_a.area_name} "
                        f"{ abs(hx4_a.data_list[0].mean):3.1f} meter depth."
                        f"{hx4_a.bin_size*100:4.1f}cm bins.")
lt.plot_hists( hx4_a )

The test area is 90.0 square meters. (2 by 45 meters)

                        Ref            Std                                 Total
    Data Source  Ref    Dif  Mean(m)  Dev(m)   Min(m)    Max(m)   P2P(m)  Points  Points/m  Scale  Offset(m)
          SONAR <--   0.000 -29.928    0.087  -30.161   -29.739    0.422     684     7.600  1.0000 26.000
          CZMIL       0.070 -29.998    0.206  -30.621   -29.470    1.151     168     1.867  1.0000 26.000
     Hawkeye 4x      -0.499 -29.429    0.097  -29.700   -29.190    0.510     294     3.267  1.0000 26.000
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