PKG-INFO

Metadata-Version: 1.1
Name: PDAL
Version: 2.3.0
Summary: Point cloud data processing
Home-page: https://pdal.io
Author: Howard Butler
Author-email: howard@hobu.co
License: BSD
Description: ================================================================================
    PDAL
    ================================================================================

    PDAL Python support allows you to process data with PDAL into `Numpy`_
    arrays. It supports embedding Python in PDAL pipelines with the `readers.numpy <https://pdal.io/stages/readers.numpy.html>`__
    and `filters.python <https://pdal.io/stages/filters.python.html>`__ stages, and it provides a PDAL extension module to control
    Python interaction with PDAL.

    Additionally, you can use it to fetch `schema`_ and `metadata`_ from
    PDAL operations.

    Installation
    --------------------------------------------------------------------------------

    PyPI
    ................................................................................

    PDAL Python support is installable via PyPI:

    .. code-block::

        pip install PDAL

    GitHub
    ................................................................................

    The repository for PDAL's Python extension is available at https://github.com/PDAL/python

    Python support released independently from PDAL itself as of PDAL 1.7.

    Usage
    --------------------------------------------------------------------------------

    Simple
    ................................................................................

    Given the following pipeline, which simply reads an `ASPRS LAS`_ file and
    sorts it by the ``X`` dimension:

    .. _`ASPRS LAS`: https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html

    .. code-block:: python


        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

    Reading  using Numpy Arrays
    ................................................................................

    The following more complex scenario demonstrates the full cycling between
    PDAL and Python:

     * Read a small testfile from GitHub into a Numpy array
     * Filters those arrays with Numpy for Intensity
     * Pass the filtered array to PDAL to be filtered again
     * Write the filtered array to an LAS file.

    .. code-block:: python

        data = "https://github.com/PDAL/PDAL/blob/master/test/data/las/1.2-with-color.las?raw=true"


        json = """
            {
              "pipeline": [
                {
                    "type": "readers.las",
                    "filename": "%s"
                }
              ]
            }"""

        import pdal
        import numpy as np
        pipeline = pdal.Pipeline(json % data)
        count = pipeline.execute()

        # get the data from the first array
        # [array([(637012.24, 849028.31, 431.66, 143, 1, 1, 1, 0, 1,  -9., 132, 7326, 245380.78254963,  68,  77,  88),
        # dtype=[('X', '<f8'), ('Y', '<f8'), ('Z', '<f8'), ('Intensity', '<u2'), ('ReturnNumber', 'u1'), ('NumberOfReturns', 'u1'), ('ScanDirectionFlag', 'u1'), ('EdgeOfFlightLine', 'u1'), ('Classification', 'u1'), ('ScanAngleRank', '<f4'), ('UserData', 'u1'), ('PointSourceId', '<u2'), ('GpsTime', '<f8'), ('Red', '<u2'), ('Green', '<u2'), ('Blue', '<u2')])

        arr = pipeline.arrays[0]
        print (len(arr)) # 1065 points


        # Filter out entries that have intensity < 50
        intensity = arr[arr['Intensity'] > 30]
        print (len(intensity)) # 704 points


        # Now use pdal to clamp points that have intensity
        # 100 <= v < 300, and there are 387
        clamp =u"""{
          "pipeline":[
            {
              "type":"filters.range",
              "limits":"Intensity[100:300)"
            }
          ]
        }"""

        p = pdal.Pipeline(clamp, [intensity])
        count = p.execute()
        clamped = p.arrays[0]
        print (count)

        # Write our intensity data to an LAS file
        output =u"""{
          "pipeline":[
            {
              "type":"writers.las",
              "filename":"clamped.las",
              "offset_x":"auto",
              "offset_y":"auto",
              "offset_z":"auto",
              "scale_x":0.01,
              "scale_y":0.01,
              "scale_z":0.01
            }
          ]
        }"""

        p = pdal.Pipeline(output, [clamped])
        count = p.execute()
        print (count)

    Accessing Mesh Data
    ................................................................................

    Some PDAL stages (for instance ``filters.delaunay``) create TIN type mesh data. 

    This data can be accessed in Python using the ``Pipeline.meshes`` property, which returns ``numpy.ndarray`` 
    of shape (1,n) where n is the number of Triangles in the mesh. If the PointView contains no mesh data, then n = 0.

    Each Triangle is a tuple ``(A,B,C)`` where A, B and C are indicees into the PointView for the point that is the vertex for the Triangle.

    Meshio Integration
    ................................................................................

    The meshes property provides the face data but is not easy to use as a mesh. Therefore, we have provided optional Integration
    into the `Meshio <https://github.com/nschloe/meshio>`__ library.

    The ``pdal.Pipeline`` class provides the ``get_meshio(idx: int) -> meshio.Mesh`` method. This 
    method creates a `Mesh` object from the `PointView` array and mesh properties.

    .. note:: The meshio integration requires that meshio is installed (e.g. ``pip install meshio``). If it
    is not, then the method fails with an informative RuntimeError.

    Simple use of the functionality could be as follows:

    -- code-block:: python
      import pdal

      ...
      pl = pdal.Pipeline(pipeline)
      pl.execute()

      mesh = pl.get_meshio(0)
      mesh.write('test.obj')


        .. _`Numpy`: http://www.numpy.org/
        .. _`schema`: http://www.pdal.io/dimensions.html
        .. _`metadata`: http://www.pdal.io/development/metadata.html


        .. image:: https://ci.appveyor.com/api/projects/status/of4kecyahpo8892d
          :target: https://ci.appveyor.com/project/hobu/python/

    Requirements
    ================================================================================

    * PDAL 2.1+
    * Python >=3.6
    * Cython (eg :code:`pip install cython`)
    * Packaging (eg :code:`pip install packaging`)




Keywords: point cloud spatial
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: GIS
Requires: Python (>=3.6)