PDAL

PDAL Python support allows you to process data with PDAL into Numpy arrays. It supports embedding Python in PDAL pipelines with the readers.numpy and filters.python stages, and it provides a PDAL extension module to control Python interaction with PDAL.

Additionally, you can use it to fetch schema and metadata from PDAL operations.

Installation

PyPI

PDAL Python support is installable via PyPI:

pip install PDAL

GitHub

The repository for PDAL's Python extension is available at https://github.com/PDAL/python

Python support released independently from PDAL itself as of PDAL 1.7.

Usage

Simple

Given the following pipeline, which simply reads an ASPRS LAS file and sorts it by the X dimension:

json = """
{
  "pipeline": [
    "1.2-with-color.las",
    {
        "type": "filters.sort",
        "dimension": "X"
    }
  ]
}"""

import pdal
pipeline = pdal.Pipeline(json)
count = pipeline.execute()
arrays = pipeline.arrays
metadata = pipeline.metadata
log = pipeline.log

Reading using Numpy Arrays

The following more complex scenario demonstrates the full cycling between PDAL and Python:

• Read a small testfile from GitHub into a Numpy array
• Filters those arrays with Numpy for Intensity
• Pass the filtered array to PDAL to be filtered again
• Write the filtered array to an LAS file.

data = "https://github.com/PDAL/PDAL/blob/master/test/data/las/1.2-with-color.las?raw=true"


json = """
    {
      "pipeline": [
        {
            "type": "readers.las",
            "filename": "%s"
        }
      ]
    }"""

import pdal
import numpy as np
pipeline = pdal.Pipeline(json % data)
count = pipeline.execute()

# get the data from the first array
# [array([(637012.24, 849028.31, 431.66, 143, 1,
# 1, 1, 0, 1,  -9., 132, 7326, 245380.78254963,  68,  77,  88),
# dtype=[('X', '<f8'), ('Y', '<f8'), ('Z', '<f8'), ('Intensity', '<u2'),
# ('ReturnNumber', 'u1'), ('NumberOfReturns', 'u1'), ('ScanDirectionFlag', 'u1'),
# ('EdgeOfFlightLine', 'u1'), ('Classification', 'u1'), ('ScanAngleRank', '<f4'),
# ('UserData', 'u1'), ('PointSourceId', '<u2'),
# ('GpsTime', '<f8'), ('Red', '<u2'), ('Green', '<u2'), ('Blue', '<u2')])

arr = pipeline.arrays[0]
print (len(arr)) # 1065 points


# Filter out entries that have intensity < 50
intensity = arr[arr['Intensity'] > 30]
print (len(intensity)) # 704 points


# Now use pdal to clamp points that have intensity
# 100 <= v < 300, and there are 387
clamp =u"""{
  "pipeline":[
    {
      "type":"filters.range",
      "limits":"Intensity[100:300)"
    }
  ]
}"""

p = pdal.Pipeline(clamp, [intensity])
count = p.execute()
clamped = p.arrays[0]
print (count)

# Write our intensity data to an LAS file
output =u"""{
  "pipeline":[
    {
      "type":"writers.las",
      "filename":"clamped.las",
      "offset_x":"auto",
      "offset_y":"auto",
      "offset_z":"auto",
      "scale_x":0.01,
      "scale_y":0.01,
      "scale_z":0.01
    }
  ]
}"""

p = pdal.Pipeline(output, [clamped])
count = p.execute()
print (count)

Requirements

• PDAL 2.2+
• Python >=3.6
• Cython (eg pip install cython)
• Packaging (eg pip install packaging)
• scikit-build (eg pip install scikit-build)