# 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.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:

1. regular attribute access using the las.dimension_name syntax

2. dict-like attribute access las[dimension_name].

    >>> import numpy as np
    >>> las = laspy.read('tests/data/simple.las')
    >>> 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()`