******************************************************
MIVOT (``pyvo.mivot``): Annotation Viewer - Public API
******************************************************


Introduction
============
.. pull-quote::

    Model Instances in VOTables (MIVOT) defines a syntax to map VOTable
    data to any model serialized in VO-DML. The annotation operates as a
    bridge between the data and the model. It associates the column/param
    metadata from the VOTable to the data model elements (class, attributes,
    types, etc.) [...].
    The data model elements are grouped in an independent annotation block
    complying with the MIVOT XML syntax. This annotation block is added
    as an extra resource element at the top of the VOTable result resource. The
    MIVOT syntax allows to describe a data structure as a hierarchy of classes.
    It is also able to represent relations and composition between them. It can
    also build up data model objects by aggregating instances from different
    tables of the VOTable (get more in  :doc:`index`).

Using the API
=============

 .. attention::
	The module based on XPath queries and allowing to browse the XML
	annotations (``viewer.XmlViewer``)  has been removed from version 1.8

Integrated Readout
------------------
The ``ModelViewer`` module manages access to data mapped to a model through dynamically
generated objects (``MivotInstance`` class).

``MivotInstance`` is a generic class that does not refer to any specific model.
The mapped class of a particular instance is stored in its ``dmtype`` attribute.

These objects can be used as standard Python objects, with their fields representing
elements of the model instances.

The first step is to instanciate a viewer that will provide the API for browsing annotations.
The viewer can be built from a VOTable file path, a parsed VOtable (``VOTableFile`` object),
or a ``DALResults`` instance.

 .. attention::

    The code below only works with ``astropy 6+``


.. doctest-skip::

    >>> import astropy.units as u
    >>> from astropy.coordinates import SkyCoord
    >>> from pyvo.dal.scs import SCSService
    >>> from pyvo.utils.prototype import activate_features
    >>> from pyvo.mivot.viewer.mivot_viewer import MivotViewer
    >>>
    >>> scs_srv = SCSService("https://vizier.cds.unistra.fr/viz-bin/conesearch/V1.5/I/239/hip_main")
    >>> m_viewer = MivotViewer(
    ...     scs_srv.search(
    ...         pos=SkyCoord(ra=52.26708 * u.degree, dec=59.94027 * u.degree,
    ...                      frame='icrs'),
    ...         radius=0.1
    ...         ),
    ...     resolve_ref=True
    ... )

In this example, the query result is mapped to the ``mango:EpochPosition`` class,
but users do not need to know this in advance, since the API provides tools
to discover the mapped models.

.. doctest-skip::

   >>> if m_viewer.get_models().get("mango"):
   >>>     print("data is mapped to the MANGO data model")
   data is mapped to the MANGO data model

We can also check which datamodel classes the data is mapped to.

.. doctest-skip::

   >>> mivot_instances = m_viewer.dm_instances
   >>> print(f"data is mapped to {len(mivot_instances)} model class(es)")
   data is mapped to 1 model class(es)

.. doctest-skip::

   >>> mivot_instance = m_viewer.dm_instances[0]
   >>> print(f"data is mapped to the {mivot_instance.dmtype} class")
   data is mapped to the mango:EpochPosition class

At this point, we know that the data has been mapped to the ``MANGO`` model,
and that the data rows can be interpreted as instances of the ``mango:EpochPosition``.

.. doctest-skip::

   >>> print(mivot_instance.spaceSys.frame.spaceRefFrame.value)
   ICRS

.. doctest-skip::

   >>> while m_viewer.next_row_view():
   >>>     print(f"position: {mivot_instance.latitude.value} {mivot_instance.longitude.value}")
   position: 59.94033461 52.26722684
   ....

.. important::

   Coordinate systems are usually mapped in the GLOBALS MIVOT block.
   This allows them to be referenced from any other MIVOT element.
   The viewer resolves such references when the constructor flag ``resolve_ref`` is set to ``True``.
   In this case the coordinate system instances are copied into their host elements.

The code below shows how to access GLOBALS instances (one in this example) independently of the mapped data.

.. doctest-skip::

   >>> for globals_instance in m_viewer.dm_globals_instances:
   >>>    print(globals_instance)
   <MivotInstance: dmtype="coords:SpaceSys">

We can also provide a complete instance representation that includes all fields in the entire hierarchy.

.. doctest-skip::

   >>> print(repr(globals_instance))
   {
      "dmtype": "coords:SpaceSys",
      "dmid": "SpaceFrame_ICRS",
      "frame": {
        "dmrole": "coords:PhysicalCoordSys.frame",
        "dmtype": "coords:SpaceFrame",
        "spaceRefFrame": {
          "dmtype": "ivoa:string",
          "value": "ICRS"
        }
      }
    }


As you can see from the previous examples, model leaves (class attributes) are complex types.
This is because they contain additional metadata as well as values:

- ``value`` : attribute value
- ``dmtype`` : attribute type such as defined in the Mivot annotations
- ``unit`` (if any) : attribute unit such as defined in the Mivot annotations

Per-Row Readout
---------------

This annotation schema can also be applied to table rows read using the `astropy.io.votable` API
outside of the ``MivotViewer`` context.

.. doctest-skip::

    >>> votable = parse(path_to_votable)
    >>> table = votable.resources[0].tables[0]
    >>> # init the viewer on the first resource of the votable (default)
    >>> mivot_viewer = MivotViewer(votable)
    >>> mivot_object = mivot_viewer.dm_instance
    >>> # and feed it with the numpy table row
    >>> for rec in table.array:
    >>>     # apply the mapping to current row
    >>>     mivot_object.update(rec)
    >>>     # show that the model retrieve the correct values
    >>>     # ... or do whatever you want
    >>>     assert rec["RAICRS"] == mivot_object.longitude.value
    >>>     assert rec["DEICRS"] == mivot_object.latitude.value

Mivot/Mango as a Direct Gateway from Data to Astropy SkyCoord
-------------------------------------------------------------

A simple way to get the most out of annotations is to use them
to directly create Astropy objects, without having to parse the metadata,
whether it comes from the annotation or the VOTable.

.. doctest-skip::

    >>> from pyvo.mivot.features.sky_coord_builder import SkyCoordBuilder
    >>>
    >>> m_viewer.rewind()
    >>> while m_viewer.next_row_view():
    >>>     sky_coord_builder = SkyCoordBuilder(mivot_instance)
    >>>     sky_coord = sky_coord_builder.build_sky_coord()
    >>>     print(sky_coord)
    <SkyCoord (ICRS): (ra, dec, distance) in (deg, deg, pc)
        (52.26722684, 59.94033461, 1315.7894902)
     (pm_ra_cosdec, pm_dec) in mas / yr
        (-0.82, -1.85)>

In the above example, we assume that the mapped model can be used as a ``SkyCoord`` precursor.
If this is not the case, an error is raised.

.. important::

   In the current implementation, the only functioning gateway connects
   ``Mango:EpochPosition`` objects with the ``SkyCoord`` class.
   The ultimate objective is to generalize this mechanism to any property modeled by Mango,
   and eventually to other IVOA models, thereby realizing the full potential
   of a comprehensive and interoperable mapping framework.

Class Generation in a Nutshell
------------------------------

MIVOT reconstructs model structures with 3 elements:

- ``INSTANCE`` for the objects
- ``ATTRIBUTE`` for the attributes
- ``COLLECTION`` for the elements with a cardinality greater than 1

The role played by each of these elements in the model hierarchy is defined
by its ``@dmrole`` XML attribute. Types of both ``INSTANCE`` and ``ATTRIBUTE`` are defined by
their ``@dmtype`` XML attributes.

``MivotInstance`` classes are built by following MIVOT annotation structure:

- ``INSTANCE`` are represented by Python classes
- ``ATTRIBUTE`` are represented by Python class fields
- ``COLLECTION`` are represented by Python lists ([])

``@dmrole`` and ``@dmtype`` cannot be used as Python keywords as such, because they are built from VO-DML
identifiers, which have the following structure: ``model:a.b``.

- Only the last part of the path is kept for attribute names.
- For class names, forbidden characters (``:`` or ``.``) are replaced with ``_``.
- Original ``@dmtype`` are kept as attributes of generated Python objects.
- If the end-user is unaware of the class mapped by the actual ``MivotInstance``,
  if can can explore it by using its class dictionary ``MivotInstance.__dict__``
  (see the Python `data model <https://docs.python.org/3/reference/datamodel.html>`_).

.. doctest-skip::

   >>> mivot_instance = mivot_viewer.dm_instance
   >>> print(mivot_instance.__dict__.keys())
   dict_keys(['dmtype', 'longitude', 'latitude', 'pmLongitude', 'pmLatitude', 'epoch', 'Coordinate_coordSys'])

.. doctest-skip::

   >>> print(mivot_instance.Coordinate_coordSys.__dict__.keys())
   dict_keys(['dmtype', 'dmid', 'dmrole', 'spaceRefFrame'])

.. doctest-skip::

   >>> print(mivot_instance.Coordinate_coordSys.spaceRefFrame.__dict__.keys())
   dict_keys(['dmtype', 'value', 'unit', 'ref'])


*More examples can be found* :ref:`here <mivot-examples>`.

Reference/API
=============

.. automodapi:: pyvo.mivot.viewer
