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Merged
olivhoenen merged 4 commits into from
Mar 3, 2026
Merged

Feature/list filled paths #104

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6fca4fd
NetCDF implementation for DBEntry.list_filled_paths
maarten-ic Feb 13, 2026
32f3559
Implement list_fill_paths for IMAS-Core (HDF5 backend)
maarten-ic Feb 13, 2026
34ec6be
Update documentation
maarten-ic Feb 13, 2026
70694a3
Update docstring
maarten-ic Feb 16, 2026
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Update documentation
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@maarten-ic
maarten-ic committed Feb 13, 2026
commit 34ec6be61edfd3504d9fde5382655c116ed8830e
40 changes: 25 additions & 15 deletions docs/source/intro.rst
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Expand Up @@ -86,17 +86,6 @@ get an error message if this is not possible:
Load and store an IDS to disk with IMAS-Core
''''''''''''''''''''''''''''''''''''''''''''

.. note::

- This functionality requires the IMAS-Core, until this library is openly available
on GitHub you may need to fetch it from `git.iter.org <https://git.iter.org/>`_
(requires to have an ITER account). Using IMAS-Core also enable slicing methods
:py:meth:`~imas.db_entry.DBEntry.get_slice`,
:py:meth:`~imas.db_entry.DBEntry.put_slice` and
:py:meth:`~imas.db_entry.DBEntry.get_sample` (with IMAS-Core>=5.4).
- If you can't have access to it, you can save IDS to disk with the built-in
netCDF backend :ref:`Load and store an IDS to disk with netCDF`

To store an IDS to disk, we need to indicate the following URI to the
IMAS-Core: ``imas:<backend>?path=<path_to_folder>`` or using the legacy query keys
``imas:<backend>?user=<user>;database=<database>;version=<version>;pulse=<pulse>;run=<run>``
Expand All @@ -115,11 +104,9 @@ In IMAS-Python you do this as follows:
>>> # now store the core_profiles IDS we just populated
>>> dbentry.put(core_profiles)

.. image:: imas_structure.png

To load an IDS from disk, you need to specify the same information as
when storing the IDS (see above). Once the data entry is opened, you
can use ``<IDS>.get()`` to load IDS data from disk:
can use ``dbentry.get()`` to load IDS data from disk:

.. code-block:: python

Expand All @@ -146,11 +133,34 @@ In IMAS-Python you do this as follows:

To load an IDS from disk, you need to specify the same file information as
when storing the IDS. Once the data entry is opened, you
can use ``<IDS>.get()`` to load IDS data from disk:
can use ``dbentry.get()`` to load IDS data from disk:

.. code-block:: python

>>> # Now load the core_profiles IDS back from disk
>>> dbentry2 = imas.DBEntry("mypulsefile.nc","r")
>>> core_profiles2 = dbentry2.get("core_profiles")
>>> print(core_profiles2.ids_properties.comment.value)


Data Entry API overview
'''''''''''''''''''''''

See the documentation of :py:class:`imas.DBEntry <imas.db_entry.DBEntry>` for more
details on reading and writing IDSs to disk. Useful functions include:

- :py:meth:`~imas.db_entry.DBEntry.put` and :py:meth:`~imas.db_entry.DBEntry.put_slice`
to write a full IDS or write append a time slice to existing data.
- :py:meth:`~imas.db_entry.DBEntry.get`, :py:meth:`~imas.db_entry.DBEntry.get_slice` and
:py:meth:`~imas.db_entry.DBEntry.get_sample` to read all time slices, a single time
slice, or a sample of time slices from disk. ``get_slice()`` and ``get_sample()`` can
also interpolate data to a requested point in time.

All three ``get()`` methods have a ``lazy`` mode, which will only load data from disk
when you need it. This can greatly speed up data access in some scenarios. See
:ref:`Lazy loading` for more details.
- :py:meth:`~imas.db_entry.DBEntry.list_all_occurrences` to query whether there are any
occurrences of a certain IDS stored on disk.
- :py:meth:`~imas.db_entry.DBEntry.list_filled_paths` to query which Data Dictionary
paths have data filled inside a specific IDS.

6 changes: 5 additions & 1 deletion imas/db_entry.py
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Expand Up @@ -813,10 +813,14 @@ def list_filled_paths(
ids_name: Name of the IDS to request filled data for.
occurrence: Occurrence number of the IDS to request filled data for.

Keyword Args:
autoconvert: If enabled (default), this method will take NBC renames into
account in the returned list of filled paths.

Returns:
List of paths which have some data filled in the backend. For example, when
``profiles_1d/ion/temperature`` is in this list, it means that there is at
least on ``ion`` in one ``profiles_1d`` entry for which the temperature is
least one ``ion`` in one ``profiles_1d`` entry for which the temperature is
filled.

The paths in this list may be ordered arbitrarily.
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