dedalus.tools.post
==================

.. py:module:: dedalus.tools.post

.. autoapi-nested-parse::

   Post-processing helpers.









Module Contents
---------------

.. py:class:: DedalusXarrayBackend



   Xarray backend targeting Dedalus HDF5 outputs.


   .. py:method:: open_dataset(filename_or_obj, *, drop_variables=None)


.. py:function:: dedalus_h5_to_xarray(dset)

   Convert Dedalus HDF5 dataset to an Xarray DataArray.


.. py:function:: get_all_writes(set_paths)

   Get write numbers from a list of analysis sets.

   :Parameters: **set_paths** (*list of str or pathlib.Path*) -- List of set paths


.. py:function:: get_assigned_sets(base_path, distributed=False)

   Divide analysis sets from a FileHandler between MPI processes.

   :Parameters: * **base_path** (*str or pathlib.Path*) -- Base path of FileHandler output
                * **distributed** (*bool, optional*) -- Divide distributed sets instead of merged sets (default: False)


.. py:function:: get_assigned_writes(set_paths)

   Divide writes from a list of analysis sets between MPI processes.

   :Parameters: **set_paths** (*list of str or pathlib.Path*) -- List of set paths


.. py:function:: load_tasks_to_xarray(filename, tasks=None, squeeze_constant=True)

   Load task from Dedalus HDF5 output to an Xarray DataArray.


.. py:function:: merge_analysis(base_path, cleanup=False)

   Merge distributed analysis sets from a FileHandler.

   :Parameters: * **base_path** (*str or pathlib.Path*) -- Base path of FileHandler output
                * **cleanup** (*bool, optional*) -- Delete distributed files after merging (default: False)

   .. rubric:: Notes

   This function is parallelized over sets, and so can be effectively
   parallelized up to the number of distributed sets.


.. py:function:: merge_data(joint_file, proc_path)

   Merge data from part of a distributed analysis set into a joint file.

   :Parameters: * **joint_file** (*HDF5 file*) -- Joint file
                * **proc_path** (*str or pathlib.Path*) -- Path to part of a distributed analysis set


.. py:function:: merge_distributed_set(set_path, cleanup=False)

   Merge a distributed analysis set from a FileHandler.

   :Parameters: * **set_path** (*str of pathlib.Path*) -- Path to distributed analysis set folder
                * **cleanup** (*bool, optional*) -- Delete distributed files after merging (default: False)


.. py:function:: merge_setup(joint_file, proc_paths, virtual=False)

   Merge HDF5 setup from part of a distributed analysis set into a joint file.

   :Parameters: * **joint_file** (*HDF5 file*) -- Joint file
                * **proc_paths** (*list of [str or pathlib.Path]*) -- List of files in a distributed analysis set
                * **virtual** (*bool, optional*) -- If True, merging a virtual file into a single file rather than distributed set


.. py:function:: merge_virtual_analysis(base_path, cleanup=False)

   Merge virtual files from a FileHandler into single files.

   :Parameters: * **base_path** (*str or pathlib.Path*) -- Base path of FileHandler output
                * **cleanup** (*bool, optional*) -- Delete virtual and distributed files after merging (default: False)

   .. rubric:: Notes

   This function is parallelized over sets, and so can be effectively
   parallelized up to the number of virtual files.

   Merged files are output into a 'merged_X' directory, where X is the
   stem name of the input FileHandler.


.. py:function:: merge_virtual_file(virtual_file_path, cleanup=False)

   Merge a virtual file from a FileHandler.

   :Parameters: * **virtual_file_path** (*str of pathlib.Path*) -- Path to a virtual .h5 file
                * **cleanup** (*bool, optional*) -- Delete distributed files and virtual file after merging (default: False)


.. py:function:: visit_writes(set_paths, function, **kw)

   Apply function to writes from a list of analysis sets.

   :Parameters: * **set_paths** (*list of str or pathlib.Path*) -- List of set paths
                * **function** (*function(set_path, start, count, **kw)*) -- A function on an HDF5 file, start index, and count.
                * **Other keyword arguments are passed on to `function`**

   .. rubric:: Notes

   This function is parallelized over writes, and so can be effectively
   parallelized up to the number of writes from all specified sets.


.. py:data:: MPI_RANK

.. py:data:: MPI_SIZE

.. py:data:: logger

.. py:data:: merge_virtual