# Less Basic Things

## Extra Dimensions

The LAS Specification version 1.4 defines a standard way to add extra dimensions to a LAS file.

In laspy you can add extra dimensions using the `LasData.add_extra_dim()` function

The Allowed base types for an extra dimensions are:

laspy name | size (bits) | type
--- | --- | ---
u1 or uint8 | 8 | unsigned
i1 or int8 | 8 | signed
u2 or uint16 | 16 | unsigned
i2 or int16 | 16 | signed
u4 or uint32 | 32 | unsigned
i4 or int32 | 32 | signed
u8 or uint64 | 64 | unsigned
i8 or int64 | 64 | signed
f4 or float32 | 32 | floating
f8 or float64 | 64 | floating

You can prepend the number ‘2’ or ‘3’ to one of the above base type to define an extra dimension that is array of 2 or 3 elements per points. Example: 3u2 -> each points will have an extra dimension that is an array of 3 * 16 bits

Here we are adding a new dimension called “codification” where each value is stored on a 64 bit unsigned integer and an array field of 3 doubles for each points.

    import laspy
    import numpy as np

    las = laspy.read("somefile.las")

    las.add_extra_dim(laspy.ExtraBytesParams(
        name="codification",
        type=np.uint64,
        description="More classes available"
    ))

    las.add_extra_dim(laspy.ExtraBytesParams(name="mysterious", type="3f8"))

Note

Although the specification of the ExtraBytesVlr appeared in the 1.4 LAS Spec, laspy allows to add new dimensions to file with version < 1.4

Note

If you are adding multiple extra dimensions use `LasData.add_extra_dims()` as it is more efficient (it allows to allocate all the dimensions at once instead of re-allocating each time a new dimension is added.

## Custom VLRs

Provided you have a valid user_id and record_id (meaning that they are not taken by a VLR described in the LAS specification) You can add you own VLRs to a file

### Fast & Easy way

The fastest and easiest way to add your custom VLR to a file is to create a `VLR`, set its record_data (which must be bytes) and add it to the VLR list.

    >>> import laspy
    >>> vlr = laspy.vlrs.VLR(user_id='A UserId', record_id=1, description='Example VLR')
    >>> vlr
    <VLR(user_id: 'A UserId', record_id: '1', data len: 0)>
    >>> vlr.record_data = b'somebytes'
    >>> vlr
    <VLR(user_id: 'A UserId', record_id: '1', data len: 9)>
    >>> las = laspy.create()
    >>> las.vlrs
    []
    >>> las.vlrs.append(vlr)
    >>> las.vlrs
    [<VLR(user_id: 'A UserId', record_id: '1', data len: 9)>]

### Complete & Harder way

While the way shown above is quick & easy it might not be perfect for complex VLRs. Also when reading a file that has custom VLR, laspy won’t be able to automatically parse its data into a better structure, so you will have to find the VLR in the vlrs list and parse it yourself one laspy is done.

One way to automate this task is to create your own Custom VLR Class that extends `BaseKnownVLR` by implementing the missing methods laspy will be able to automatically parse the VLR when reading the file & write it when saving the file.

    >>> class CustomVLR(laspy.vlrs.BaseKnownVLR):
    ...     def __init__(self):
    ...         super().__init__()
    ...         self.numbers = []
    ...
    ...     @staticmethod
    ...     def official_user_id():
    ...         return "CustomId"
    ...
    ...     @staticmethod
    ...     def official_record_ids():
    ...         return 1,
    ...
    ...     def record_data_bytes(self):
    ...         return bytes(self.numbers)
    ...
    ...     def parse_record_data(self, record_data):
    ...         self.numbers = [b for b in record_data]
    ...
    ...     def __repr__(self):
    ...         return "<MyCustomVLR>"

    >>> import numpy as np
    >>> cvlr = CustomVLR()
    >>> cvlr.numbers
    []
    >>> cvlr.numbers = [1,2, 3]
    >>> las = laspy.create()
    >>> las.vlrs.append(cvlr)
    >>> las.vlrs
    [<MyCustomVLR>]
    >>> las.x = np.array([1.0, 2.0])
    >>> las = laspy.lib.write_then_read_again(las)
    >>> las.vlrs
    [<MyCustomVLR>]
    >>> las.vlrs[0].numbers
    [1, 2, 3]