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This guide covers the fundamentals of using PDAL Python to process point cloud data with simple pipeline operations.

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Simple pipeline from JSON

The most straightforward way to use PDAL Python is to define a pipeline as a JSON string. Here’s an example that 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

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Execute and access results

1

Create the pipeline

Instantiate a pdal.Pipeline object with your JSON string:
pipeline = pdal.Pipeline(json)
2

Execute the pipeline

Call the execute() method to run the pipeline. It returns the number of points processed:
count = pipeline.execute()
3

Access the results

After execution, you can access:
  • pipeline.arrays - NumPy arrays containing the point data
  • pipeline.metadata - Metadata from the pipeline stages
  • pipeline.log - Log output from the pipeline execution
The execute() method returns the total number of points that were processed by the pipeline.

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Using filters

Filters transform point cloud data. The example above uses filters.sort to sort points by a specific dimension. You can chain multiple filters in your pipeline: 
json = """
{
  "pipeline": [
    "input.las",
    {
        "type": "filters.sort",
        "dimension": "X"
    }
  ]
}"""
The pipeline processes stages sequentially from top to bottom.

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