V 2.1.0

• Changing the code structure of the project and also the point clouds processing approach.

  A point cloud is represented by a structured numpy array with different fields for each point : X coordinate, Y coordinate, Z coordinate, Blue, Green, Red... The PDAL pipeline written by the user is decomposed in stages and each stage is executed on a numpy array representing a cloud (or a tile in case of single cloud processing).

• Taking numpy array for each point cloud instead of an entire file.

• The unmanaged memory generated by a task is now suppressed when the task is finished.

This new approach allows the user to process the number of tasks they want, regardless the number of workers.

V 2.0.3

• New merge_tiles option to merge all your tiles at the end of an execution. It takes into account compression, minor_version and dataformat_id specified in your tiles pipelines
• New remove_tiles option to delete all your tiles after merged

V 2.0.2

• Create temp and output folders if they don't exist
• Add a merge_tiles option to merge all the resulting chunks into one file
• Trigger warnings
  • if the tile_size value is not changed
  • if the number of workers exceed the existing number of CPU avalaible
• If the tile_size value exceeds the dimensions of the input cloud, it will now be adapted to the actual boundaries of the cloud.

V 2.0.1

Fix import bug. The API is now functional

V 2.0.0

New API for pdal-parallelizer :

• The process_pipelines function can now be used directly via Python

V 1.10.19

• fixing memory leaks

V 1.10.18

• Fixing a little bug in file_manager

V 1.10.17

• Fix little bugs in the split function

V 1.10.16

• dependencies : dask[distributed] -> distributed

V 1.10.15

• trigger garbage collector at the end of the execution
• adding the ClassFlags dimension if there is not
• buffer has now the wittheld flag