The Reader class represents PDAL reader stages that load point cloud data from files or other sources. Readers are the first stage in most PDAL pipelines.
Constructor
Reader(filename: Optional[str] = None, **options: Any)
filename
Optional[str]
default:"None"
Path to the input file. Can be passed as the first positional argument or as a keyword argument.
Additional reader-specific options as keyword arguments.
Example
import pdal
# Specify filename positionally
reader = pdal.Reader("input.las")
# Specify filename as keyword argument
reader = pdal.Reader(filename="input.las")
# With additional options
reader = pdal.Reader("input.las", type="readers.las", spatialreference="EPSG:4326")
Type Inference
The Reader class automatically infers the appropriate PDAL reader driver from the file extension when type is not explicitly specified.
# Type automatically inferred as "readers.las"
reader = pdal.Reader("data.las")
# Type automatically inferred as "readers.bpf"
reader = pdal.Reader("data.bpf")
# Explicitly specify type (overrides inference)
reader = pdal.Reader("data.xyz", type="readers.text")
Properties
Inherits all properties from Stage:
type - The reader type (e.g., "readers.las")streamable - Whether the reader supports streamingtag - Optional tag identifierinputs - List of input stages (typically empty for readers)options - Dictionary of all reader options
Methods
Inherits all methods from Stage:
pipeline(*arrays, loglevel) - Create a Pipeline from this reader__or__(other) - Pipe operator for composing with other stages
Driver-Specific Static Methods
The Reader class provides static methods for each available PDAL reader driver. These are automatically generated and provide convenient shortcuts.
Common readers
las
Reader.las(filename: str, **kwargs) -> Reader
reader = pdal.Reader.las("input.las")
# With options
reader = pdal.Reader.las(
filename="input.laz",
spatialreference="EPSG:4326",
use_eb_vlr=True
)
bpf
Reader.bpf(filename: str, **kwargs) -> Reader
reader = pdal.Reader.bpf("input.bpf")
ept
Reader.ept(filename: str, **kwargs) -> Reader
reader = pdal.Reader.ept(
filename="https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_LosAngeles_2016_LAS_2018/ept.json",
bounds="([0, 100], [0, 100])"
)
text
Reader.text(filename: str, **kwargs) -> Reader
reader = pdal.Reader.text(
filename="points.csv",
separator=",",
header="X,Y,Z,Intensity"
)
gdal
Reader.gdal(filename: str, **kwargs) -> Reader
reader = pdal.Reader.gdal(
filename="elevation.tif",
header="X,Y,Z"
)
tiledb
Reader.tiledb(array_name: str, **kwargs) -> Reader
reader = pdal.Reader.tiledb(array_name="my_point_cloud")
The specific static methods available depend on the PDAL drivers installed in your environment. Use help(pdal.Reader.driver_name) to see documentation for a specific driver.
Usage Examples
Basic file reading
import pdal
# Read a LAS file
pipeline = pdal.Reader.las("input.las").pipeline()
count = pipeline.execute()
points = pipeline.arrays[0]
print(f"Read {count} points")
print(f"X range: {points['X'].min()} to {points['X'].max()}")
Reading with filters
import pdal
# Read and filter in one pipeline
pipeline = (
pdal.Reader.las("input.las")
| pdal.Filter.range(limits="Classification[2:2]") # Ground points only
| pdal.Filter.sort(dimension="X")
)
count = pipeline.execute()
ground_points = pipeline.arrays[0]
Reading remote data
import pdal
# Read from URL
url = "https://github.com/PDAL/PDAL/raw/master/test/data/las/1.2-with-color.las"
pipeline = pdal.Reader.las(filename=url).pipeline()
count = pipeline.execute()
Reading multiple files
import pdal
# Read and merge multiple files
reader1 = pdal.Reader.las("file1.las", tag="input1")
reader2 = pdal.Reader.las("file2.las", tag="input2")
merge = pdal.Filter.merge(inputs=["input1", "input2"])
pipeline = pdal.Pipeline([reader1, reader2, merge])
count = pipeline.execute()
Streaming large files
import pdal
# Process large file in chunks
pipeline = pdal.Reader.las("large_file.las") | pdal.Filter.range(limits="Intensity[100:200]")
for chunk in pipeline.iterator(chunk_size=100000):
print(f"Processing {len(chunk)} points")
# Process chunk...