| Name | pdal JSON |
| Version |
3.3.0
JSON |
| download |
| home_page | https://pdal.io |
| Summary | Point cloud data processing |
| upload_time | 2024-01-03 20:17:54 |
| maintainer | Howard Butler |
| docs_url | None |
| author | Howard Butler |
| requires_python | |
| license | BSD |
| keywords |
point
cloud
spatial
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
================================================================================
PDAL
================================================================================
PDAL Python support allows you to process data with PDAL into `Numpy`_ arrays.
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
Programmatic Pipeline Construction
................................................................................
The previous example specified the pipeline as a JSON string. Alternatively, a
pipeline can be constructed by creating ``Stage`` instances and piping them
together. For example, the previous pipeline can be specified as:
.. code-block:: python
pipeline = pdal.Reader("1.2-with-color.las") | pdal.Filter.sort(dimension="X")
Stage Objects
=============
- A stage is an instance of ``pdal.Reader``, ``pdal.Filter`` or ``pdal.Writer``.
- A stage can be instantiated by passing as keyword arguments the options
applicable to the respective PDAL stage. For more on PDAL stages and their
options, check the PDAL documentation on `Stage Objects <https://pdal.io/pipeline.html#stage-objects>`__.
- The ``filename`` option of ``Readers`` and ``Writers`` as well as the ``type``
option of ``Filters`` can be passed positionally as the first argument.
- The ``inputs`` option specifies a sequence of stages to be set as input to the
current stage. Each input can be either the string tag of another stage, or
the ``Stage`` instance itself.
- The ``Reader``, ``Filter`` and ``Writer`` classes come with static methods for
all the respective PDAL drivers. For example, ``pdal.Filter.head()`` is a
shortcut for ``pdal.Filter(type="filters.head")``. These methods are
auto-generated by introspecting ``pdal`` and the available options are
included in each method's docstring:
.. code-block::
>>> help(pdal.Filter.head)
Help on function head in module pdal.pipeline:
head(**kwargs)
Return N points from beginning of the point cloud.
user_data: User JSON
log: Debug output filename
option_file: File from which to read additional options
where: Expression describing points to be passed to this filter
where_merge='auto': If 'where' option is set, describes how skipped points should be merged with kept points in standard mode.
count='10': Number of points to return from beginning. If 'invert' is true, number of points to drop from the beginning.
invert='false': If true, 'count' specifies the number of points to skip from the beginning.
Pipeline Objects
================
A ``pdal.Pipeline`` instance can be created from:
- a JSON string: ``Pipeline(json_string)``
- a sequence of ``Stage`` instances: ``Pipeline([stage1, stage2])``
- a single ``Stage`` with the ``Stage.pipeline`` method: ``stage.pipeline()``
- nothing: ``Pipeline()`` creates a pipeline with no stages.
- joining ``Stage`` and/or other ``Pipeline`` instances together with the pipe
operator (``|``):
- ``stage1 | stage2``
- ``stage1 | pipeline1``
- ``pipeline1 | stage1``
- ``pipeline1 | pipeline2``
Every application of the pipe operator creates a new ``Pipeline`` instance. To
update an existing ``Pipeline`` use the respective in-place pipe operator (``|=``):
.. code-block:: python
# update pipeline in-place
pipeline = pdal.Pipeline()
pipeline |= stage
pipeline |= pipeline2
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 the array with Numpy for Intensity
* Pass the filtered array to PDAL to be filtered again
* Write the final filtered array to a LAS file and a TileDB_ array
via the `TileDB-PDAL integration`_ using the `TileDB writer plugin`_
.. code-block:: python
import pdal
data = "https://github.com/PDAL/PDAL/blob/master/test/data/las/1.2-with-color.las?raw=true"
pipeline = pdal.Reader.las(filename=data).pipeline()
print(pipeline.execute()) # 1065 points
# 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]
# 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
pipeline = pdal.Filter.range(limits="Intensity[100:300)").pipeline(intensity)
print(pipeline.execute()) # 387 points
clamped = pipeline.arrays[0]
# Write our intensity data to a LAS file and a TileDB array. For TileDB it is
# recommended to use Hilbert ordering by default with geospatial point cloud data,
# which requires specifying a domain extent. This can be determined automatically
# from a stats filter that computes statistics about each dimension (min, max, etc.).
pipeline = pdal.Writer.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,
).pipeline(clamped)
pipeline |= pdal.Filter.stats() | pdal.Writer.tiledb(array_name="clamped")
print(pipeline.execute()) # 387 points
# Dump the TileDB array schema
import tiledb
with tiledb.open("clamped") as a:
print(a.schema)
Executing Streamable Pipelines
................................................................................
Streamable pipelines (pipelines that consist exclusively of streamable PDAL
stages) can be executed in streaming mode via ``Pipeline.iterator()``. This
returns an iterator object that yields Numpy arrays of up to ``chunk_size`` size
(default=10000) at a time.
.. code-block:: python
import pdal
pipeline = pdal.Reader("test/data/autzen-utm.las") | pdal.Filter.range(limits="Intensity[80:120)")
for array in pipeline.iterator(chunk_size=500):
print(len(array))
# or to concatenate all arrays into one
# full_array = np.concatenate(list(pipeline))
``Pipeline.iterator()`` also takes an optional ``prefetch`` parameter (default=0)
to allow prefetching up to to this number of arrays in parallel and buffering
them until they are yielded to the caller.
If you just want to execute a streamable pipeline in streaming mode and don't
need to access the data points (typically when the pipeline has Writer stage(s)),
you can use the ``Pipeline.execute_streaming(chunk_size)`` method instead. This
is functionally equivalent to ``sum(map(len, pipeline.iterator(chunk_size)))``
but more efficient as it avoids allocating and filling any arrays in memory.
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 a ``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 indices into the PointView identifying 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')
Advanced Mesh Use Case
................................................................................
USE-CASE : Take a LiDAR map, create a mesh from the ground points, split into tiles and store the tiles in PostGIS.
.. note:: Like ``Pipeline.arrays``, ``Pipeline.meshes`` returns a list of ``numpy.ndarray`` to provide for the case where the output from a Pipeline is multiple PointViews
(example using 1.2-with-color.las and not doing the ground classification for clarity)
.. code-block:: python
import pdal
import psycopg2
import io
pl = (
pdal.Reader(".../python/test/data/1.2-with-color.las")
| pdal.Filter.splitter(length=1000)
| pdal.Filter.delaunay()
)
pl.execute()
conn = psycopg(%CONNNECTION_STRING%)
buffer = io.StringIO
for idx in range(len(pl.meshes)):
m = pl.get_meshio(idx)
if m:
m.write(buffer, file_format = "wkt")
with conn.cursor() as curr:
curr.execute(
"INSERT INTO %table-name% (mesh) VALUES (ST_GeomFromEWKT(%(ewkt)s)",
{ "ewkt": buffer.getvalue()}
)
conn.commit()
conn.close()
buffer.close()
.. _`Numpy`: http://www.numpy.org/
.. _`schema`: http://www.pdal.io/dimensions.html
.. _`metadata`: http://www.pdal.io/development/metadata.html
.. _`TileDB`: https://tiledb.com/
.. _`TileDB-PDAL integration`: https://docs.tiledb.com/geospatial/pdal
.. _`TileDB writer plugin`: https://pdal.io/stages/writers.tiledb.html
.. image:: https://github.com/PDAL/python/workflows/Build/badge.svg
:target: https://github.com/PDAL/python/actions?query=workflow%3ABuild
Requirements
================================================================================
* PDAL 2.5+
* Python >=3.9
* Pybind11 (eg :code:`pip install pybind11[global]`)
* Numpy (eg :code:`pip install numpy`)
* scikit-build (eg :code:`pip install scikit-build`)
Changes
--------------------------------------------------------------------------------
3.2.3
................................................................................
Do not build and include wheels in distro
3.2.2
................................................................................
* Implement move ctor to satisfy MSVC 2019 https://github.com/PDAL/python/commit/667f56bd0ee465f55a14636986e80b0a9cefcf14
3.2.1
................................................................................
* implement #129, add pandas DataFrame i/o for convenience by @hobu in
https://github.com/PDAL/python/pull/130
* harden getMetadata and related calls from getting non-utf-8 'json' by @hobu
in https://github.com/PDAL/python/pull/140
* ignore DataFrame test if not GeoPandas, give up on Python 3.7 builds by @hobu
in https://github.com/PDAL/python/pull/137
3.2.0
................................................................................
* PDAL base library 2.4.0+ is required
* CMake project name updated to pdal-python
* `srswkt2` property added to allow fetching of SRS info
* pip builds require cmake >= 3.11
* CMAKE_CXX_STANDARD set to c++17 to match PDAL 2.4.x
* Driver and options *actually* uses the library instead of
shelling out to `pdal` application :)
* _get_json renamed to toJSON and made public
* Fix #119, 'json' optional kwarg put back for now
* DEVELOPMENT_COMPONENT in CMake FindPython skipped on OSX
* Make sure 'type' gets set when serializing to JSON
3.1.0
................................................................................
* **Breaking change** – pipeline.metadata now returns a dictionary from
json.loads instead of a string.
* pipeline.quickinfo will fetch the PDAL preview() information for a data source.
You can use this to fetch header or other information without reading data.
https://github.com/PDAL/python/pull/109
* PDAL driver and option collection now uses the PDAL library directly rather
than shelling out to the pdal command https://github.com/PDAL/python/pull/107
* Pipelines now support pickling for use with things like Dask
https://github.com/PDAL/python/pull/110
3.0.0
................................................................................
* Pythonic pipeline creation https://github.com/PDAL/python/pull/91
* Support streaming pipeline execution https://github.com/PDAL/python/pull/94
* Replace Cython with PyBind11 https://github.com/PDAL/python/pull/102
* Remove pdal.pio module https://github.com/PDAL/python/pull/101
* Move readers.numpy and filters.python to separate repository https://github.com/PDAL/python/pull/104
* Miscellaneous refactorings and cleanups
2.3.5
................................................................................
* Fix memory leak https://github.com/PDAL/python/pull/74
* Handle metadata with invalid unicode by erroring https://github.com/PDAL/python/pull/74
2.3.0
................................................................................
* PDAL Python support 2.3.0 requires PDAL 2.1+. Older PDAL base libraries
likely will not work.
* Python support built using scikit-build
* readers.numpy and filters.python are installed along with the extension.
* Pipeline can take in a list of arrays that are passed to readers.numpy
* readers.numpy now supports functions that return arrays. See
https://pdal.io/stages/readers.numpy.html for more detail.
2.0.0
................................................................................
* PDAL Python extension is now in its own repository on its own release
schedule at https://github.com/PDAL/python
* Extension now builds and works under PDAL OSGeo4W64 on Windows.
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"description": "================================================================================\nPDAL\n================================================================================\n\nPDAL Python support allows you to process data with PDAL into `Numpy`_ arrays.\nIt provides a PDAL extension module to control Python interaction with PDAL.\nAdditionally, you can use it to fetch `schema`_ and `metadata`_ from PDAL operations.\n\nInstallation\n--------------------------------------------------------------------------------\n\nPyPI\n................................................................................\n\nPDAL Python support is installable via PyPI:\n\n.. code-block::\n\n pip install PDAL\n\nGitHub\n................................................................................\n\nThe repository for PDAL's Python extension is available at https://github.com/PDAL/python\n\nPython support released independently from PDAL itself as of PDAL 1.7.\n\nUsage\n--------------------------------------------------------------------------------\n\nSimple\n................................................................................\n\nGiven the following pipeline, which simply reads an `ASPRS LAS`_ file and\nsorts it by the ``X`` dimension:\n\n.. _`ASPRS LAS`: https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html\n\n.. code-block:: python\n\n\n json = \"\"\"\n {\n \"pipeline\": [\n \"1.2-with-color.las\",\n {\n \"type\": \"filters.sort\",\n \"dimension\": \"X\"\n }\n ]\n }\"\"\"\n\n import pdal\n pipeline = pdal.Pipeline(json)\n count = pipeline.execute()\n arrays = pipeline.arrays\n metadata = pipeline.metadata\n log = pipeline.log\n\nProgrammatic Pipeline Construction\n................................................................................\n\nThe previous example specified the pipeline as a JSON string. Alternatively, a\npipeline can be constructed by creating ``Stage`` instances and piping them\ntogether. For example, the previous pipeline can be specified as:\n\n.. code-block:: python\n\n pipeline = pdal.Reader(\"1.2-with-color.las\") | pdal.Filter.sort(dimension=\"X\")\n\nStage Objects\n=============\n\n- A stage is an instance of ``pdal.Reader``, ``pdal.Filter`` or ``pdal.Writer``.\n- A stage can be instantiated by passing as keyword arguments the options\n applicable to the respective PDAL stage. For more on PDAL stages and their\n options, check the PDAL documentation on `Stage Objects <https://pdal.io/pipeline.html#stage-objects>`__.\n\n - The ``filename`` option of ``Readers`` and ``Writers`` as well as the ``type``\n option of ``Filters`` can be passed positionally as the first argument.\n - The ``inputs`` option specifies a sequence of stages to be set as input to the\n current stage. Each input can be either the string tag of another stage, or\n the ``Stage`` instance itself.\n- The ``Reader``, ``Filter`` and ``Writer`` classes come with static methods for\n all the respective PDAL drivers. For example, ``pdal.Filter.head()`` is a\n shortcut for ``pdal.Filter(type=\"filters.head\")``. These methods are\n auto-generated by introspecting ``pdal`` and the available options are\n included in each method's docstring:\n\n.. code-block::\n\n >>> help(pdal.Filter.head)\n Help on function head in module pdal.pipeline:\n\n head(**kwargs)\n Return N points from beginning of the point cloud.\n\n user_data: User JSON\n log: Debug output filename\n option_file: File from which to read additional options\n where: Expression describing points to be passed to this filter\n where_merge='auto': If 'where' option is set, describes how skipped points should be merged with kept points in standard mode.\n count='10': Number of points to return from beginning. If 'invert' is true, number of points to drop from the beginning.\n invert='false': If true, 'count' specifies the number of points to skip from the beginning.\n\nPipeline Objects\n================\n\nA ``pdal.Pipeline`` instance can be created from:\n\n- a JSON string: ``Pipeline(json_string)``\n- a sequence of ``Stage`` instances: ``Pipeline([stage1, stage2])``\n- a single ``Stage`` with the ``Stage.pipeline`` method: ``stage.pipeline()``\n- nothing: ``Pipeline()`` creates a pipeline with no stages.\n- joining ``Stage`` and/or other ``Pipeline`` instances together with the pipe\n operator (``|``):\n\n - ``stage1 | stage2``\n - ``stage1 | pipeline1``\n - ``pipeline1 | stage1``\n - ``pipeline1 | pipeline2``\n\nEvery application of the pipe operator creates a new ``Pipeline`` instance. To\nupdate an existing ``Pipeline`` use the respective in-place pipe operator (``|=``):\n\n.. code-block:: python\n\n # update pipeline in-place\n pipeline = pdal.Pipeline()\n pipeline |= stage\n pipeline |= pipeline2\n\nReading using Numpy Arrays\n................................................................................\n\nThe following more complex scenario demonstrates the full cycling between\nPDAL and Python:\n\n* Read a small testfile from GitHub into a Numpy array\n* Filters the array with Numpy for Intensity\n* Pass the filtered array to PDAL to be filtered again\n* Write the final filtered array to a LAS file and a TileDB_ array\n via the `TileDB-PDAL integration`_ using the `TileDB writer plugin`_\n\n.. code-block:: python\n\n import pdal\n\n data = \"https://github.com/PDAL/PDAL/blob/master/test/data/las/1.2-with-color.las?raw=true\"\n\n pipeline = pdal.Reader.las(filename=data).pipeline()\n print(pipeline.execute()) # 1065 points\n\n # Get the data from the first array\n # [array([(637012.24, 849028.31, 431.66, 143, 1,\n # 1, 1, 0, 1, -9., 132, 7326, 245380.78254963, 68, 77, 88),\n # dtype=[('X', '<f8'), ('Y', '<f8'), ('Z', '<f8'), ('Intensity', '<u2'),\n # ('ReturnNumber', 'u1'), ('NumberOfReturns', 'u1'), ('ScanDirectionFlag', 'u1'),\n # ('EdgeOfFlightLine', 'u1'), ('Classification', 'u1'), ('ScanAngleRank', '<f4'),\n # ('UserData', 'u1'), ('PointSourceId', '<u2'),\n # ('GpsTime', '<f8'), ('Red', '<u2'), ('Green', '<u2'), ('Blue', '<u2')])\n arr = pipeline.arrays[0]\n\n # Filter out entries that have intensity < 50\n intensity = arr[arr[\"Intensity\"] > 30]\n print(len(intensity)) # 704 points\n\n # Now use pdal to clamp points that have intensity 100 <= v < 300\n pipeline = pdal.Filter.range(limits=\"Intensity[100:300)\").pipeline(intensity)\n print(pipeline.execute()) # 387 points\n clamped = pipeline.arrays[0]\n\n # Write our intensity data to a LAS file and a TileDB array. For TileDB it is\n # recommended to use Hilbert ordering by default with geospatial point cloud data,\n # which requires specifying a domain extent. This can be determined automatically\n # from a stats filter that computes statistics about each dimension (min, max, etc.).\n pipeline = pdal.Writer.las(\n filename=\"clamped.las\",\n offset_x=\"auto\",\n offset_y=\"auto\",\n offset_z=\"auto\",\n scale_x=0.01,\n scale_y=0.01,\n scale_z=0.01,\n ).pipeline(clamped)\n pipeline |= pdal.Filter.stats() | pdal.Writer.tiledb(array_name=\"clamped\")\n print(pipeline.execute()) # 387 points\n\n # Dump the TileDB array schema\n import tiledb\n with tiledb.open(\"clamped\") as a:\n print(a.schema)\n\nExecuting Streamable Pipelines\n................................................................................\nStreamable pipelines (pipelines that consist exclusively of streamable PDAL\nstages) can be executed in streaming mode via ``Pipeline.iterator()``. This\nreturns an iterator object that yields Numpy arrays of up to ``chunk_size`` size\n(default=10000) at a time.\n\n.. code-block:: python\n\n import pdal\n pipeline = pdal.Reader(\"test/data/autzen-utm.las\") | pdal.Filter.range(limits=\"Intensity[80:120)\")\n for array in pipeline.iterator(chunk_size=500):\n print(len(array))\n # or to concatenate all arrays into one\n # full_array = np.concatenate(list(pipeline))\n\n``Pipeline.iterator()`` also takes an optional ``prefetch`` parameter (default=0)\nto allow prefetching up to to this number of arrays in parallel and buffering\nthem until they are yielded to the caller.\n\nIf you just want to execute a streamable pipeline in streaming mode and don't\nneed to access the data points (typically when the pipeline has Writer stage(s)),\nyou can use the ``Pipeline.execute_streaming(chunk_size)`` method instead. This\nis functionally equivalent to ``sum(map(len, pipeline.iterator(chunk_size)))``\nbut more efficient as it avoids allocating and filling any arrays in memory.\n\nAccessing Mesh Data\n................................................................................\n\nSome PDAL stages (for instance ``filters.delaunay``) create TIN type mesh data.\n\nThis data can be accessed in Python using the ``Pipeline.meshes`` property, which returns a ``numpy.ndarray``\nof shape (1,n) where n is the number of Triangles in the mesh.\n\nIf the PointView contains no mesh data, then n = 0.\n\nEach Triangle is a tuple ``(A,B,C)`` where A, B and C are indices into the PointView identifying the point that is the vertex for the Triangle.\n\nMeshio Integration\n................................................................................\n\nThe meshes property provides the face data but is not easy to use as a mesh. Therefore, we have provided optional Integration\ninto the `Meshio <https://github.com/nschloe/meshio>`__ library.\n\nThe ``pdal.Pipeline`` class provides the ``get_meshio(idx: int) -> meshio.Mesh`` method. This\nmethod creates a `Mesh` object from the `PointView` array and mesh properties.\n\n.. 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.\n\nSimple use of the functionality could be as follows:\n\n.. code-block:: python\n\n import pdal\n\n ...\n pl = pdal.Pipeline(pipeline)\n pl.execute()\n\n mesh = pl.get_meshio(0)\n mesh.write('test.obj')\n\nAdvanced Mesh Use Case\n................................................................................\n\nUSE-CASE : Take a LiDAR map, create a mesh from the ground points, split into tiles and store the tiles in PostGIS.\n\n.. note:: Like ``Pipeline.arrays``, ``Pipeline.meshes`` returns a list of ``numpy.ndarray`` to provide for the case where the output from a Pipeline is multiple PointViews\n\n(example using 1.2-with-color.las and not doing the ground classification for clarity)\n\n.. code-block:: python\n\n import pdal\n import psycopg2\n import io\n\n pl = (\n pdal.Reader(\".../python/test/data/1.2-with-color.las\")\n | pdal.Filter.splitter(length=1000)\n | pdal.Filter.delaunay()\n )\n pl.execute()\n\n conn = psycopg(%CONNNECTION_STRING%)\n buffer = io.StringIO\n\n for idx in range(len(pl.meshes)):\n m = pl.get_meshio(idx)\n if m:\n m.write(buffer, file_format = \"wkt\")\n with conn.cursor() as curr:\n curr.execute(\n \"INSERT INTO %table-name% (mesh) VALUES (ST_GeomFromEWKT(%(ewkt)s)\",\n { \"ewkt\": buffer.getvalue()}\n )\n\n conn.commit()\n conn.close()\n buffer.close()\n\n\n\n.. _`Numpy`: http://www.numpy.org/\n.. _`schema`: http://www.pdal.io/dimensions.html\n.. _`metadata`: http://www.pdal.io/development/metadata.html\n.. _`TileDB`: https://tiledb.com/\n.. _`TileDB-PDAL integration`: https://docs.tiledb.com/geospatial/pdal\n.. _`TileDB writer plugin`: https://pdal.io/stages/writers.tiledb.html\n\n.. image:: https://github.com/PDAL/python/workflows/Build/badge.svg\n :target: https://github.com/PDAL/python/actions?query=workflow%3ABuild\n\nRequirements\n================================================================================\n\n* PDAL 2.5+\n* Python >=3.9\n* Pybind11 (eg :code:`pip install pybind11[global]`)\n* Numpy (eg :code:`pip install numpy`)\n* scikit-build (eg :code:`pip install scikit-build`)\n\n\nChanges\n--------------------------------------------------------------------------------\n\n\n3.2.3\n................................................................................\n\nDo not build and include wheels in distro\n\n\n3.2.2\n................................................................................\n\n* Implement move ctor to satisfy MSVC 2019 https://github.com/PDAL/python/commit/667f56bd0ee465f55a14636986e80b0a9cefcf14\n\n\n3.2.1\n................................................................................\n\n* implement #129, add pandas DataFrame i/o for convenience by @hobu in\n https://github.com/PDAL/python/pull/130\n* harden getMetadata and related calls from getting non-utf-8 'json' by @hobu\n in https://github.com/PDAL/python/pull/140\n* ignore DataFrame test if not GeoPandas, give up on Python 3.7 builds by @hobu\n in https://github.com/PDAL/python/pull/137\n\n3.2.0\n................................................................................\n\n* PDAL base library 2.4.0+ is required\n\n* CMake project name updated to pdal-python\n\n* `srswkt2` property added to allow fetching of SRS info\n\n* pip builds require cmake >= 3.11\n\n* CMAKE_CXX_STANDARD set to c++17 to match PDAL 2.4.x\n\n* Driver and options *actually* uses the library instead of\n shelling out to `pdal` application :)\n\n* _get_json renamed to toJSON and made public\n\n* Fix #119, 'json' optional kwarg put back for now\n\n* DEVELOPMENT_COMPONENT in CMake FindPython skipped on OSX\n\n* Make sure 'type' gets set when serializing to JSON\n\n3.1.0\n................................................................................\n\n* **Breaking change** \u2013 pipeline.metadata now returns a dictionary from\n json.loads instead of a string.\n\n* pipeline.quickinfo will fetch the PDAL preview() information for a data source.\n You can use this to fetch header or other information without reading data.\n https://github.com/PDAL/python/pull/109\n\n* PDAL driver and option collection now uses the PDAL library directly rather\n than shelling out to the pdal command https://github.com/PDAL/python/pull/107\n\n* Pipelines now support pickling for use with things like Dask\n https://github.com/PDAL/python/pull/110\n\n\n\n3.0.0\n................................................................................\n\n* Pythonic pipeline creation https://github.com/PDAL/python/pull/91\n\n* Support streaming pipeline execution https://github.com/PDAL/python/pull/94\n\n* Replace Cython with PyBind11 https://github.com/PDAL/python/pull/102\n\n* Remove pdal.pio module https://github.com/PDAL/python/pull/101\n\n* Move readers.numpy and filters.python to separate repository https://github.com/PDAL/python/pull/104\n\n* Miscellaneous refactorings and cleanups\n\n2.3.5\n................................................................................\n\n* Fix memory leak https://github.com/PDAL/python/pull/74\n\n* Handle metadata with invalid unicode by erroring https://github.com/PDAL/python/pull/74\n\n2.3.0\n................................................................................\n\n* PDAL Python support 2.3.0 requires PDAL 2.1+. Older PDAL base libraries\n likely will not work.\n\n* Python support built using scikit-build\n\n* readers.numpy and filters.python are installed along with the extension.\n\n* Pipeline can take in a list of arrays that are passed to readers.numpy\n\n* readers.numpy now supports functions that return arrays. See\n https://pdal.io/stages/readers.numpy.html for more detail.\n\n2.0.0\n................................................................................\n\n* PDAL Python extension is now in its own repository on its own release\n schedule at https://github.com/PDAL/python\n\n* Extension now builds and works under PDAL OSGeo4W64 on Windows.\n",
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