import lidar_review_tools.lidar_tools as lt
lidar_review_tools
Notebooks and tools for reviewing and comparing bathy lidar data.
from lidar_review_tools import lidar_tools
- Project Website: https://lidar532.github.io/lidar_review_tools/
- Github Repository: https://github.com/lidar532/lidar_review_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.
= lt.TEST_AREA()
hx4_a = "Area A"
hx4_a.area_name = 2
hx4_a.area_width = 45
hx4_a.area_length = hx4_a.area_width * hx4_a.area_length
hx4_a.area
= 0.02
hx4_a.bin_size = True
hx4_a.density = "../data/ch4x/"
hx4_a.data_path = 3
width =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.data( hx4_a, offset
lt.gen_all_stats( hx4_a )= (f"{hx4_a.area_name} "
hx4_a.title 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