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A las spatial indexing implementation in Python.
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README.md

Relax with laxpy. Let it handle your spatial queries for you!

laxpy is a way to read and write .lax files generated from lasindex in Python. I have ambitions to merge this into the laspy codebase, but for now this standalone package can handle spatial queries in conjuction with laspy and presence of .lax files that match the name of the .las file you are interested in querying.

Why Index?

Indexing LiDAR data is only relevant if the entire file is not needed. This most frequently happens when LiDAR files need to be clipped using some geometry smaller than the original file extent. Indexing allows loading smaller chunks of the file into memory, rather than the whole thing, at the cost of some initial overhead.

Whether or not indexing ultimately increases computational efficiency depends on the clipping geometry, and the structure and size of the LiDAR file.

Release Status

Current Release: 0.1.9

Status: laxpy is in a stable condition.

Installation

# via pip
pip install laxpy

# via GitHub
git clone https://github.com/brycefrank/laxpy.git
cd laxpy
pip install .

# via conda
conda install laxpy -n <your environment> -c conda-forge

Example

Clipping a Polygon

laxpy clips a shapely polygon by adjusting the memory map (an internal mechanism in laspy) of the original reference object. Long story short, this is done in place in the following manner:

from laxpy import IndexedLAS

my_las = IndexedLAS('my_las.las')
my_las.map_polygon(my_shapely_polygon)

# Print the points within the polygon
print(my_las.points)

Note that an IndexedLAS object inherits directly from laspy.file.File, and all dimensions are available for querying, including .x, .y, etc. etc.

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