dragonfly_sim.utils.checkpoint
==============================

.. py:module:: dragonfly_sim.utils.checkpoint

.. autoapi-nested-parse::

   Partition-independent checkpoints for time-dependent runs.

   A checkpoint is one .npz written on rank 0. Every DG field is stored per cell,
   keyed by the cell's index in the INPUT mesh (topology.original_cell_index)
   and the cell-local dof order, so a run may be restarted on any number of
   ranks. The dof coordinates are stored as well and checked on load.

   ..
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Functions
---------

.. autoapisummary::

   dragonfly_sim.utils.checkpoint._owned_cell_dofs
   dragonfly_sim.utils.checkpoint.load_checkpoint
   dragonfly_sim.utils.checkpoint.save_checkpoint


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

.. py:function:: _owned_cell_dofs(V)

   
   (owned cells' input-mesh indices, their dof blocks, the dof coordinates).
















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.. py:function:: load_checkpoint(path, functions, coord_tol=1e-10, optional=False)

   
   Fill `functions` from a save_checkpoint file, on any rank count, and
   return its meta dict. With optional=True a field missing from the file is
   left untouched instead of raising (e.g. far-field history when restarting
   a riemann2 run from a plain riemann checkpoint).
















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.. py:function:: save_checkpoint(path, functions, meta)

   
   Write `functions` (DG Functions on one mesh, distinct names) and the JSON
   serializable dict `meta` to `path`. The dof coordinates are stored as
   "coords" for the first function's space and "<name>__coords" for any
   function on another space. Collective.
















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