Basic Manipulation

Opening & Reading

Reading

Reading is done using laspy.read() function. This function will read everything in the file (Header, vlrs, point records, …) and return an object that you can use to access to the data.

import laspy

las = laspy.read('somefile.las')
print(np.unique(las.classification))

Opening

laspy can also laspy.open() files reading just the header and vlrs but not the points, this is useful if you are interested in metadata that are contained in the header and do not need to read the points.

import s3fs
import laspy

fs = s3fs.S3FileSystem()
with fs.open('my-bucket/some_file.las', 'rb') as f:
     if f.header.point_count < 100_000_000:
         las = laspy.read(f)

Chunked reading

Sometimes files are big, too big to be read entirely and fit into your RAM. The object returned by the laspy.open() function, LasReader can also be used to read points chunk by chunk by using LasReader.chunk_iterator(), which will allow you to do some processing on large files (splitting, filtering, etc)

import laspy

with laspy.open("some_big_file.laz") as f:
    for points in f.chunk_iterator(1_000_000):
        do_something_with(points)

Writing

To be able to write a las file you will need a LasData. You obtain this type of object by using one of the function described in the section above use its method LasData.write() to write to a file or a stream.

import laspy

las = laspy.read("some_file.laz")
las.points = las.points[las.classification == 2]
las.write("ground.laz")

Chunked Writing

Similar to LasReader there exists a way to write a file chunk by chunk.

import laspy

with laspy.open("some_big_file.laz") as f:
    with laspy.open("grounds.laz", mode="w", header=f.header) as writer:
        for points in f.chunk_iterator(1_234_567):
            writer.write_points(points[points.classification == 2]

Creating

Creating a new Las from scratch is hopefully simple:

Use laspy.create().

Or the LasData constructor which requires a LasHeader.

You can get a header from a file or creating a new one.

import laspy
import numpy as np

las = laspy.read("some_file.laz")

new_las = laspy.LasData(las.header)
new_las.points[las.classification == 2].copy()
new_las.write("ground.laz")

new_hdr = laspy.LasHeader(version="1.4", point_format=6)
# You can set the scales and offsets to values tha suits your data
new_hdr.scales = np.array([1.0, 0.5, 0.1])
new_las = laspy.LasData(new_hdr)

Converting

laspy also offers the ability to convert a file between the different version and point format available (as long as they are compatible).

To convert, use the laspy.convert()

Accessing the file header

You can access the header of a las file you read or opened by retrieving the ‘header’ attribute:

>>> import laspy
>>> las = laspy.read('tests/data/simple.las')
>>> las.header
<LasHeader(1.2, <PointFormat(3, 0 bytes of extra dims)>)>
>>> las.header.point_count
1065

>>> with laspy.open('tests/data/simple.las') as f:
...     f.header.point_count
1065

you can see the accessible fields in LasHeader.

Accessing Points Records

To access point records using the dimension name, you have 2 options:

regular attribute access using the las.dimension_name syntax
dict-like attribute access las[dimension_name].

>>> import numpy as np
>>> las = laspy.read('tests/data/simple.las')
>>> bool(np.all(las.user_data == las['user_data']))
True

Point Format

The dimensions available in a file are dictated by the point format. The tables in the introduction section contains the list of dimensions for each of the point format. To get the point format of a file you have to access it through the las object:

>>> point_format = las.point_format
>>> point_format
<PointFormat(3, 0 bytes of extra dims)>
>>> point_format.id
3

If you don’t want to remember the dimensions for each point format, you can access the list of available dimensions in the file you read just like that:

>>> list(point_format.dimension_names)
['X', 'Y', 'Z', 'intensity', 'return_number', 'number_of_returns', 'scan_direction_flag', 'edge_of_flight_line', 'classification', 'synthetic', 'key_point', 'withheld', 'scan_angle_rank', 'user_data', 'point_source_id', 'gps_time', 'red', 'green', 'blue']

This gives you all the dimension names, including extra dimensions if any. If you wish to get only the extra dimension names the point format can give them to you:

>>> list(point_format.standard_dimension_names)
['X', 'Y', 'Z', 'intensity', 'return_number', 'number_of_returns', 'scan_direction_flag', 'edge_of_flight_line', 'classification', 'synthetic', 'key_point', 'withheld', 'scan_angle_rank', 'user_data', 'point_source_id', 'gps_time', 'red', 'green', 'blue']
>>> list(point_format.extra_dimension_names)
[]
>>> las = laspy.read('tests/data/extrabytes.las')
>>> list(las.point_format.extra_dimension_names)
['Colors', 'Reserved', 'Flags', 'Intensity', 'Time']

You can also have more information:

>>> point_format[3].name
'intensity'
>>> point_format[3].num_bits
16
>>> point_format[3].kind
<DimensionKind.UnsignedInteger: 1>
>>> point_format[3].max
65535

Manipulating VLRs

To access the VLRs stored in a file, simply access the vlr member of the las object.

>>> las = laspy.read('tests/data/extrabytes.las')
>>> las.vlrs
[<ExtraBytesVlr(extra bytes structs: 5)>]

>>> with laspy.open('tests/data/extrabytes.las') as f:
...     f.header.vlrs
[<ExtraBytesVlr(extra bytes structs: 5)>]

To retrieve a particular vlr from the list there are 2 ways: VLRList.get() and VLRList.get_by_id()