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# Source code for silx.io.nxdata.parse

    # /*##########################################################################
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    # Copyright (c) 2017-2022 European Synchrotron Radiation Facility
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    """This package provides a collection of functions to work with h5py-like
    groups following the NeXus *NXdata* specification.

    See http://download.nexusformat.org/sphinx/classes/base_classes/NXdata.html

    The main class is :class:`NXdata`.
    You can also fetch the default NXdata in a NXroot or a NXentry with function
    :func:`get_default`.

    Other public functions:

     - :func:`is_valid_nxdata`
     - :func:`is_NXroot_with_default_NXdata`
     - :func:`is_NXentry_with_default_NXdata`
     - :func:`is_group_with_default_NXdata`

    """

    import json
    from typing import Optional

    import numpy

    from silx.io.utils import is_group, is_file, is_dataset, h5py_read_dataset

    from ._utils import (
        get_attr_as_unicode,
        INTERPDIM,
        nxdata_logger,
        get_uncertainties_names,
        get_signal_name,
        get_auxiliary_signals_names,
        validate_auxiliary_signals,
        validate_number_of_axes,
    )

    __authors__ = ["P. Knobel"]
    __license__ = "MIT"
    __date__ = "24/03/2020"

    class InvalidNXdataError(Exception):
        pass

    class _SilxStyle(object):
        """NXdata@SILX_style parser.

        :param NXdata nxdata:
            NXdata description for which to extract silx_style information.
        """

        def __init__(self, nxdata):
            naxes = len(nxdata.axes)
            self._axes_scale_types = [None] * naxes
            self._signal_scale_type = None

            stylestr = get_attr_as_unicode(nxdata.group, "SILX_style")
            if stylestr is None:
                return

            try:
                style = json.loads(stylestr)
            except json.JSONDecodeError:
                nxdata_logger.error("Ignoring SILX_style, cannot parse: %s", stylestr)
                return

            if not isinstance(style, dict):
                nxdata_logger.error("Ignoring SILX_style, cannot parse: %s", stylestr)

            if "axes_scale_types" in style:
                axes_scale_types = style["axes_scale_types"]

                if isinstance(axes_scale_types, str):
                    # Convert single argument to list
                    axes_scale_types = [axes_scale_types]

                if not isinstance(axes_scale_types, list):
                    nxdata_logger.error("Ignoring SILX_style:axes_scale_types, not a list")
                else:
                    for scale_type in axes_scale_types:
                        if scale_type not in ("linear", "log"):
                            nxdata_logger.error(
                                "Ignoring SILX_style:axes_scale_types, invalid value: %s",
                                str(scale_type),
                            )
                            break
                    else:  # All values are valid
                        if len(axes_scale_types) > naxes:
                            nxdata_logger.error(
                                "Clipping SILX_style:axes_scale_types, too many values"
                            )
                            axes_scale_types = axes_scale_types[:naxes]
                        elif len(axes_scale_types) < naxes:
                            # Extend axes_scale_types with None to match number of axes
                            axes_scale_types = [None] * (
                                naxes - len(axes_scale_types)
                            ) + axes_scale_types
                        self._axes_scale_types = tuple(axes_scale_types)

            if "signal_scale_type" in style:
                scale_type = style["signal_scale_type"]
                if scale_type not in ("linear", "log"):
                    nxdata_logger.error(
                        "Ignoring SILX_style:signal_scale_type, invalid value: %s",
                        str(scale_type),
                    )
                else:
                    self._signal_scale_type = scale_type

        axes_scale_types = property(
            lambda self: self._axes_scale_types,
            doc="Tuple of NXdata axes scale types (None, 'linear' or 'log'). List[str]",
        )

        signal_scale_type = property(
            lambda self: self._signal_scale_type,
            doc="NXdata signal scale type (None, 'linear' or 'log'). str",
        )

    class NXdata(object):
        """NXdata parser.

        .. note::

            Before attempting to access any attribute or property,
            you should check that :attr:`is_valid` is *True*.

        :param group: h5py-like group following the NeXus *NXdata* specification.
        :param boolean validate: Set this parameter to *False* to skip the initial
            validation. This option is provided for optimisation purposes, for cases
            where :meth:`silx.io.nxdata.is_valid_nxdata` has already been called
            prior to instantiating this :class:`NXdata`.
        """

        def __init__(self, group, validate=True):
            super(NXdata, self).__init__()
            self._plot_style = None

            self.group = group
            """h5py-like group object with @NX_class=NXdata.
            """

            self.issues = []
            """List of error messages for malformed NXdata."""

            if validate:
                self._validate()
            self.is_valid = not self.issues
            """Validity status for this NXdata.
            If False, all properties and attributes will be None.
            """

            self._is_scatter = None
            self._axes = None

            self.signal = None
            """Main signal dataset in this NXdata group.
            In case more than one signal is present in this group,
            the other ones can be found in :attr:`auxiliary_signals`.
            """

            self.signal_name = None
            """Signal long name, as specified in the @long_name attribute of the
            signal dataset. If not specified, the dataset name is used."""

            self.signal_ndim = None
            self.signal_is_0d = None
            self.signal_is_1d = None
            self.signal_is_2d = None
            self.signal_is_3d = None

            self.axes_names = None
            """List of axes names in a NXdata group.

            This attribute is similar to :attr:`axes_dataset_names` except that
            if an axis dataset has a "@long_name" attribute, it will be used
            instead of the dataset name.
            """

            if not self.is_valid:
                nxdata_logger.debug("%s", self.issues)
            else:
                self.signal = self.group[self.signal_dataset_name]
                self.signal_name = get_attr_as_unicode(self.signal, "long_name")

                if self.signal_name is None:
                    self.signal_name = self.signal_dataset_name

                # ndim will be available in very recent h5py versions only
                self.signal_ndim = getattr(self.signal, "ndim", len(self.signal.shape))

                self.signal_is_0d = self.signal_ndim == 0
                self.signal_is_1d = self.signal_ndim == 1
                self.signal_is_2d = self.signal_ndim == 2
                self.signal_is_3d = self.signal_ndim == 3

                self.axes_names = []
                # check if axis dataset defines @long_name
                for _, dsname in enumerate(self.axes_dataset_names):
                    if dsname is not None and "long_name" in self.group[dsname].attrs:
                        self.axes_names.append(
                            get_attr_as_unicode(self.group[dsname], "long_name")
                        )
                    else:
                        self.axes_names.append(dsname)

                # excludes scatters
                self.signal_is_1d = (
                    self.signal_is_1d and len(self.axes) <= 1
                )  # excludes n-D scatters

                self._plot_style = _SilxStyle(self)

        def _validate(self):
            """Fill :attr:`issues` with error messages for each error found."""
            if not is_group(self.group):
                raise TypeError("group must be a h5py-like group")
            if get_attr_as_unicode(self.group, "NX_class") != "NXdata":
                self.issues.append("Group has no attribute @NX_class='NXdata'")
                return

            signal_name = get_signal_name(self.group)
            if signal_name is None:
                self.issues.append(
                    "No @signal attribute on the NXdata group, "
                    "and no dataset with a @signal=1 attr found"
                )
                # very difficult to do more consistency tests without signal
                return

            elif signal_name not in self.group or not is_dataset(self.group[signal_name]):
                self.issues.append("Cannot find signal dataset '%s'" % signal_name)
                return

            auxiliary_signals_names = get_auxiliary_signals_names(self.group)
            self.issues += validate_auxiliary_signals(
                self.group, signal_name, auxiliary_signals_names
            )

            axes_names = get_attr_as_unicode(self.group, "axes")
            if axes_names is None:
                # try @axes on signal dataset (older NXdata specification)
                axes_names = get_attr_as_unicode(self.group[signal_name], "axes")
                if axes_names is not None:
                    # we expect a comma separated string
                    if hasattr(axes_names, "split"):
                        axes_names = axes_names.split(":")

            if isinstance(axes_names, (str, bytes)):
                axes_names = [axes_names]

            if axes_names:
                self.issues += validate_number_of_axes(
                    self.group, signal_name, num_axes=len(axes_names)
                )

                # Test consistency of @uncertainties
                uncertainties_names = get_uncertainties_names(self.group, signal_name)
                if uncertainties_names is not None:
                    if len(uncertainties_names) != len(axes_names):
                        if len(uncertainties_names) < len(axes_names):
                            # ignore the field to avoid index error in the axes loop
                            uncertainties_names = None
                            self.issues.append(
                                "@uncertainties does not define the same "
                                + "number of fields than @axes. Field ignored"
                            )
                        else:
                            self.issues.append(
                                "@uncertainties does not define the same "
                                + "number of fields than @axes"
                            )

                # Test individual axes
                is_scatter = True  # true if all axes have the same size as the signal
                signal_size = 1
                for dim in self.group[signal_name].shape:
                    signal_size *= dim
                polynomial_axes_names = []
                for i, axis_name in enumerate(axes_names):
                    if axis_name == ".":
                        continue
                    if axis_name not in self.group or not is_dataset(self.group[axis_name]):
                        self.issues.append("Could not find axis dataset '%s'" % axis_name)
                        continue

                    axis_size = 1
                    for dim in self.group[axis_name].shape:
                        axis_size *= dim

                    if len(self.group[axis_name].shape) != 1:
                        # I don't know how to interpret n-D axes
                        self.issues.append("Axis %s is not 1D" % axis_name)
                        continue
                    else:
                        # for a  1-d axis,
                        fg_idx = self.group[axis_name].attrs.get("first_good", 0)
                        lg_idx = self.group[axis_name].attrs.get(
                            "last_good", len(self.group[axis_name]) - 1
                        )
                        axis_len = lg_idx + 1 - fg_idx

                    if axis_len != signal_size:
                        if axis_len not in self.group[signal_name].shape + (1, 2):
                            self.issues.append(
                                "Axis %s number of elements does not " % axis_name
                                + "correspond to the length of any signal dimension,"
                                " it does not appear to be a constant or a linear calibration,"
                                + " and this does not seem to be a scatter plot."
                            )
                            continue
                        elif axis_len in (1, 2):
                            polynomial_axes_names.append(axis_name)
                        is_scatter = False

                    # Test individual uncertainties
                    errors_name = axis_name + "_errors"
                    if errors_name not in self.group and uncertainties_names is not None:
                        errors_name = uncertainties_names[i]
                        if (
                            errors_name in self.group
                            and axis_name not in polynomial_axes_names
                        ):
                            if self.group[errors_name].shape != self.group[axis_name].shape:
                                self.issues.append(
                                    "Errors '%s' does not have the same " % errors_name
                                    + "dimensions as axis '%s'." % axis_name
                                )

            # test dimensions of errors associated with signal

            signal_errors = signal_name + "_errors"
            if "errors" in self.group and is_dataset(self.group["errors"]):
                errors = "errors"
            elif signal_errors in self.group and is_dataset(self.group[signal_errors]):
                errors = signal_errors
            else:
                errors = None
            if errors:
                if self.group[errors].shape != self.group[signal_name].shape:
                    # In principle just the same size should be enough but
                    # NeXus documentation imposes to have the same shape
                    self.issues.append(
                        "Dataset containing standard deviations must "
                        + "have the same dimensions as the signal."
                    )

        @property
        def signal_dataset_name(self):
            """Name of the main signal dataset."""
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")
            signal_dataset_name = get_attr_as_unicode(self.group, "signal")
            if signal_dataset_name is None:
                # find a dataset with @signal == 1
                for dsname in self.group:
                    signal_attr = self.group[dsname].attrs.get("signal")
                    if signal_attr in [1, b"1", "1"]:
                        # This is the main (default) signal
                        signal_dataset_name = dsname
                        break
            assert signal_dataset_name is not None
            return signal_dataset_name

        @property
        def auxiliary_signals_dataset_names(self):
            """Sorted list of names of the auxiliary signals datasets.

            These are the names provided by the *@auxiliary_signals* attribute
            on the NXdata group.

            In case the NXdata group does not specify a *@signal* attribute
            but has a dataset with an attribute *@signal=1*,
            we look for datasets with attributes *@signal=2, @signal=3...*
            (deprecated NXdata specification)."""
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")
            signal_dataset_name = get_attr_as_unicode(self.group, "signal")
            if signal_dataset_name is not None:
                auxiliary_signals_names = get_attr_as_unicode(
                    self.group, "auxiliary_signals"
                )
                if auxiliary_signals_names is not None:
                    if not isinstance(
                        auxiliary_signals_names, (tuple, list, numpy.ndarray)
                    ):
                        # tolerate a single string, but coerce into a list
                        return [auxiliary_signals_names]
                    return list(auxiliary_signals_names)
                return []

            # try old spec, @signal=1 (2, 3...) on dataset
            numbered_names = []
            for dsname in self.group:
                if dsname == self.signal_dataset_name:
                    # main signal, not auxiliary
                    continue
                ds = self.group[dsname]
                signal_attr = ds.attrs.get("signal")
                if signal_attr is not None and not is_dataset(ds):
                    nxdata_logger.warning(
                        "Item %s with @signal=%s is not a dataset (%s)",
                        dsname,
                        signal_attr,
                        type(ds),
                    )
                    continue
                if signal_attr is not None:
                    try:
                        signal_number = int(signal_attr)
                    except (ValueError, TypeError):
                        nxdata_logger.warning(
                            "Could not parse attr @signal=%s on " "dataset %s as an int",
                            signal_attr,
                            dsname,
                        )
                        continue
                    numbered_names.append((signal_number, dsname))
            return [a[1] for a in sorted(numbered_names)]

        @property
        def auxiliary_signals_names(self):
            """List of names of the auxiliary signals.

            Similar to :attr:`auxiliary_signals_dataset_names`, but the @long_name
            is used when this attribute is present, instead of the dataset name.
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            signal_names = []
            for asdn in self.auxiliary_signals_dataset_names:
                if "long_name" in self.group[asdn].attrs:
                    signal_names.append(self.group[asdn].attrs["long_name"])
                else:
                    signal_names.append(asdn)
            return signal_names

        @property
        def auxiliary_signals(self):
            """List of all auxiliary signal datasets."""
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            return [self.group[dsname] for dsname in self.auxiliary_signals_dataset_names]

        @property
        def interpretation(self):
            """*@interpretation* attribute associated with the *signal*
            dataset of the NXdata group. ``None`` if no interpretation
            attribute is present.

            The *interpretation* attribute provides information about the last
            dimensions of the signal. The allowed values are:

                 - *"scalar"*: 0-D data to be plotted
                 - *"spectrum"*: 1-D data to be plotted
                 - *"image"*: 2-D data to be plotted
                 - *"vertex"*: 3-D data to be plotted

            For example, a 3-D signal with interpretation *"spectrum"* should be
            considered to be a 2-D array of 1-D data. A 3-D signal with
            interpretation *"image"* should be interpreted as a 1-D array (a list)
            of 2-D images. An n-D array with interpretation *"image"* should be
            interpreted as an (n-2)-D array of images.

            A warning message is logged if the returned interpretation is not one
            of the allowed values, but no error is raised and the unknown
            interpretation is returned anyway.
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            allowed_interpretations = [
                None,
                "scaler",
                "scalar",
                "spectrum",
                "image",
                "rgba-image",  # "hsla-image", "cmyk-image"
                "vertex",
            ]

            interpretation = get_attr_as_unicode(self.signal, "interpretation")
            if interpretation is None:
                interpretation = get_attr_as_unicode(self.group, "interpretation")

            if interpretation not in allowed_interpretations:
                nxdata_logger.warning(
                    "Interpretation %s is not valid." % interpretation
                    + " Valid values: "
                    + ", ".join(str(s) for s in allowed_interpretations)
                )
            return interpretation

        @property
        def axes(self):
            """List of the axes datasets.

            The list typically has as many elements as there are dimensions in the
            signal dataset, the exception being scatter plots which use a 1D
            signal and multiple 1D axes of the same size.

            If an axis dataset applies to several dimensions of the signal, it
            will be repeated in the list.

            If a dimension of the signal has no dimension scale, `None` is
            inserted in its position in the list.

            .. note::

                The *@axes* attribute should define as many entries as there
                are dimensions in the signal, to avoid  any ambiguity.
                If this is not the case, this implementation relies on the existence
                of an *@interpretation* (*spectrum* or *image*) attribute in the
                *signal* dataset.

            .. note::

                If an axis dataset defines attributes @first_good or @last_good,
                the output will be a numpy array resulting from slicing that
                axis (*axis[first_good:last_good + 1]*).

            :rtype: List[Dataset or 1D array or None]
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            if self._axes is not None:
                # use cache
                return self._axes
            axes = []
            for axis_name in self.axes_dataset_names:
                if axis_name is None:
                    axes.append(None)
                else:
                    axes.append(self.group[axis_name])

            # keep only good range of axis data
            for i, axis in enumerate(axes):
                if axis is None:
                    continue
                if "first_good" not in axis.attrs and "last_good" not in axis.attrs:
                    continue
                fg_idx = axis.attrs.get("first_good", 0)
                lg_idx = axis.attrs.get("last_good", len(axis) - 1)
                axes[i] = axis[fg_idx : lg_idx + 1]

            self._axes = axes
            return self._axes

        @property
        def axes_dataset_names(self):
            """List of axes dataset names.

            If an axis dataset applies to several dimensions of the signal, its
            name will be repeated in the list.

            If a dimension of the signal has no dimension scale (i.e. there is a
            "." in that position in the *@axes* array), `None` is inserted in the
            output list in its position.
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            numbered_names = []  # used in case of @axis=0 (old spec)
            axes_dataset_names = get_attr_as_unicode(self.group, "axes")
            if axes_dataset_names is None:
                # try @axes on signal dataset (older NXdata specification)
                axes_dataset_names = get_attr_as_unicode(self.signal, "axes")
                if axes_dataset_names is not None:
                    # we expect a comma separated string
                    if hasattr(axes_dataset_names, "split"):
                        axes_dataset_names = axes_dataset_names.split(":")
                else:
                    # try @axis on the individual datasets (oldest NXdata specification)
                    for dsname in self.group:
                        if not is_dataset(self.group[dsname]):
                            continue
                        axis_attr = self.group[dsname].attrs.get("axis")
                        if axis_attr is not None:
                            try:
                                axis_num = int(axis_attr)
                            except (ValueError, TypeError):
                                nxdata_logger.warning(
                                    "Could not interpret attr @axis as" "int on dataset %s",
                                    dsname,
                                )
                                continue
                            numbered_names.append((axis_num, dsname))

            ndims = len(self.signal.shape)
            if axes_dataset_names is None:
                if numbered_names:
                    axes_dataset_names = []
                    numbers = [a[0] for a in numbered_names]
                    names = [a[1] for a in numbered_names]
                    for i in range(ndims):
                        if i in numbers:
                            axes_dataset_names.append(names[numbers.index(i)])
                        else:
                            axes_dataset_names.append(None)
                    return axes_dataset_names
                else:
                    return [None] * ndims

            if isinstance(axes_dataset_names, (str, bytes)):
                axes_dataset_names = [axes_dataset_names]

            for i, axis_name in enumerate(axes_dataset_names):
                if hasattr(axis_name, "decode"):
                    axis_name = axis_name.decode()
                if axis_name == ".":
                    axes_dataset_names[i] = None

            if len(axes_dataset_names) != ndims:
                if self.is_scatter and ndims == 1:
                    # case of a 1D signal with arbitrary number of axes
                    return list(axes_dataset_names)
                if self.interpretation != "rgba-image":
                    # @axes may only define 1 or 2 axes if @interpretation=spectrum/image.
                    # Use the existing names for the last few dims, and prepend with Nones.
                    assert len(axes_dataset_names) == INTERPDIM[self.interpretation]
                    all_dimensions_names = [None] * (ndims - INTERPDIM[self.interpretation])
                    for axis_name in axes_dataset_names:
                        all_dimensions_names.append(axis_name)
                else:
                    # 2 axes applying to the first two dimensions.
                    # The 3rd signal dimension is expected to contain 3(4) RGB(A) values.
                    assert len(axes_dataset_names) == 2
                    all_dimensions_names = [axn for axn in axes_dataset_names]
                    all_dimensions_names.append(None)
                return all_dimensions_names

            return list(axes_dataset_names)

        @property
        def title(self):
            """Plot title. If not found, returns an empty string.

            This attribute does not appear in the NXdata specification, but it is
            implemented in *nexpy* as a dataset named "title" inside the NXdata
            group. This dataset is expected to contain text.

            Because the *nexpy* approach could cause a conflict if the signal
            dataset or an axis dataset happened to be called "title", we also
            support providing the title as an attribute of the NXdata group.
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            title = self.group.get("title")
            data_dataset_names = [self.signal_name] + self.axes_dataset_names
            if (
                title is not None
                and is_dataset(title)
                and "title" not in data_dataset_names
            ):
                return str(h5py_read_dataset(title))

            title = self.group.attrs.get("title")
            if title is None:
                return ""
            return str(title)

        def get_axis_errors(self, axis_name):
            """Return errors (uncertainties) associated with an axis.

            If the axis has attributes @first_good or @last_good, the output
            is trimmed accordingly (a numpy array will be returned rather than a
            dataset).

            :param str axis_name: Name of axis dataset. This dataset **must exist**.
            :return: Dataset with axis errors, or None
            :raise KeyError: if this group does not contain a dataset named axis_name
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            # ensure axis_name is decoded, before comparing it with decoded attributes
            if hasattr(axis_name, "decode"):
                axis_name = axis_name.decode("utf-8")
            if axis_name not in self.group:
                # tolerate axis_name given as @long_name
                for item in self.group:
                    long_name = get_attr_as_unicode(self.group[item], "long_name")
                    if long_name is not None and long_name == axis_name:
                        axis_name = item
                        break

            if axis_name not in self.group:
                raise KeyError("group does not contain a dataset named '%s'" % axis_name)

            len_axis = len(self.group[axis_name])

            fg_idx = self.group[axis_name].attrs.get("first_good", 0)
            lg_idx = self.group[axis_name].attrs.get("last_good", len_axis - 1)

            # case of axisname_errors dataset present
            errors_name = axis_name + "_errors"
            if errors_name in self.group and is_dataset(self.group[errors_name]):
                if fg_idx != 0 or lg_idx != (len_axis - 1):
                    return self.group[errors_name][fg_idx : lg_idx + 1]
                else:
                    return self.group[errors_name]
            # case of uncertainties dataset name provided in @uncertainties
            uncertainties_names = get_attr_as_unicode(self.group, "uncertainties")
            if isinstance(uncertainties_names, str):
                uncertainties_names = [uncertainties_names]
            if uncertainties_names is not None:
                # take the uncertainty with the same index as the axis in @axes
                axes_ds_names = get_attr_as_unicode(self.group, "axes")
                if axes_ds_names is None:
                    axes_ds_names = get_attr_as_unicode(self.signal, "axes")
                if isinstance(axes_ds_names, str):
                    axes_ds_names = [axes_ds_names]
                elif isinstance(axes_ds_names, numpy.ndarray):
                    # transform numpy.ndarray into list
                    axes_ds_names = list(axes_ds_names)
                assert isinstance(axes_ds_names, list)
                if hasattr(axes_ds_names[0], "decode"):
                    axes_ds_names = [ax_name.decode("utf-8") for ax_name in axes_ds_names]
                if axis_name not in axes_ds_names:
                    raise KeyError(
                        "group attr @axes does not mention a dataset "
                        + "named '%s'" % axis_name
                    )
                errors = self.group[
                    uncertainties_names[list(axes_ds_names).index(axis_name)]
                ]
                if fg_idx == 0 and lg_idx == (len_axis - 1):
                    return errors  # dataset
                else:
                    return errors[fg_idx : lg_idx + 1]  # numpy array
            return None

        @property
        def errors(self):
            """Return errors (uncertainties) associated with the signal values.

            :return: Dataset with errors, or None
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            dataset_names = [
                # From NXData:
                "errors",
                # Not Nexus (VARIABLE_errors is only for axes), but supported anyway
                self.signal_dataset_name + "_errors",
            ]
            for name in dataset_names:
                entity = self.group.get(name)
                if entity is not None and is_dataset(entity):
                    return entity

            return None

        @property
        def plot_style(self):
            """Information extracted from the optional SILX_style attribute

            :raises: InvalidNXdataError
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            return self._plot_style

        @property
        def is_scatter(self):
            """True if the signal is 1D and all the axes have the
            same size as the signal."""
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            if self._is_scatter is not None:
                return self._is_scatter
            if not self.signal_is_1d:
                self._is_scatter = False
            else:
                self._is_scatter = True
                sigsize = 1
                for dim in self.signal.shape:
                    sigsize *= dim
                for axis in self.axes:
                    if axis is None:
                        continue
                    axis_size = 1
                    for dim in axis.shape:
                        axis_size *= dim
                    self._is_scatter = self._is_scatter and (axis_size == sigsize)
            return self._is_scatter

        @property
        def is_x_y_value_scatter(self):
            """True if this is a scatter with a signal and two axes."""
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            return self.is_scatter and len(self.axes) == 2

        # we currently have no widget capable of plotting 4D data
        @property
        def is_unsupported_scatter(self):
            """True if this is a scatter with a signal and more than 2 axes."""
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            return self.is_scatter and len(self.axes) > 2

        @property
        def is_curve(self):
            """This property is True if the signal is 1D or :attr:`interpretation` is
            *"spectrum"*, and there is at most one axis with a consistent length.
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            if self.signal_is_0d or self.interpretation not in [None, "spectrum"]:
                return False
            # the axis, if any, must be of the same length as the last dimension
            # of the signal, or of length 2 (a + b *x scale)
            if self.axes[-1] is not None and len(self.axes[-1]) not in [
                self.signal.shape[-1],
                2,
            ]:
                return False
            if self.interpretation is None:
                # We no longer test whether x values are monotonic
                # (in the past, in that case, we used to consider it a scatter)
                return self.signal_is_1d
            # everything looks good
            return True

        @property
        def is_image(self):
            """True if the signal is 2D, or 3D with last dimension of length 3 or 4
            and interpretation *rgba-image*, or >2D with interpretation *image*.
            The axes (if any) length must also be consistent with the signal shape.
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            if self.interpretation in ["scalar", "spectrum", "scaler"]:
                return False
            if self.signal_is_0d or self.signal_is_1d:
                return False
            if not self.signal_is_2d and self.interpretation not in ["image", "rgba-image"]:
                return False
            if self.signal_is_3d and self.interpretation == "rgba-image":
                if self.signal.shape[-1] not in [3, 4]:
                    return False
                img_axes = self.axes[0:2]
                img_shape = self.signal.shape[0:2]
            else:
                img_axes = self.axes[-2:]
                img_shape = self.signal.shape[-2:]
            for i, axis in enumerate(img_axes):
                if axis is not None and len(axis) not in [img_shape[i], 2]:
                    return False

            return True

        @property
        def is_stack(self):
            """True in the signal is at least 3D and interpretation is not
            "scalar", "spectrum", "image" or "rgba-image".
            The axes length must also be consistent with the last 3 dimensions
            of the signal.
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            if self.signal_ndim < 3 or self.interpretation in [
                "scalar",
                "scaler",
                "spectrum",
                "image",
                "rgba-image",
            ]:
                return False
            stack_shape = self.signal.shape[-3:]
            for i, axis in enumerate(self.axes[-3:]):
                if axis is not None and len(axis) not in [stack_shape[i], 2]:
                    return False
            return True

        @property
        def is_volume(self):
            """True in the signal is exactly 3D and interpretation
                "scalar", or nothing.

            The axes length must also be consistent with the 3 dimensions
            of the signal.
            """
            if not self.is_valid:
                raise InvalidNXdataError("Unable to parse invalid NXdata")

            if self.signal_ndim != 3:
                return False
            if self.interpretation not in [None, "scalar", "scaler"]:
                # 'scaler' and 'scalar' for a three dimensional array indicate a scalar field in 3D
                return False
            volume_shape = self.signal.shape[-3:]
            for i, axis in enumerate(self.axes[-3:]):
                if axis is not None and len(axis) not in [volume_shape[i], 2]:
                    return False
            return True

    def is_valid_nxdata(group):  # noqa
        """Check if a h5py group is a **valid** NX_data group.

        :param group: h5py-like group
        :return: True if this NXdata group is valid.
        :raise TypeError: if group is not a h5py group, a spech5 group,
            or a fabioh5 group
        """
        nxd = NXdata(group)
        return nxd.is_valid

    def is_group_with_default_NXdata(group, validate=True):
        """Return True if group defines a valid default
        NXdata.

        .. note::

            See https://github.com/silx-kit/silx/issues/2215

        :param group: h5py-like object.
        :param bool validate: Set this to skip the NXdata validation, and only
            check the existence of the group.
            Parameter provided for optimisation purposes, to avoid double
            validation if the validation is already performed separately."""
        default_nxdata_name = group.attrs.get("default")
        if default_nxdata_name is None or default_nxdata_name not in group:
            return False

        default_nxdata_group = group.get(default_nxdata_name)

        if not is_group(default_nxdata_group):
            return False

        if not validate:
            return True
        else:
            return is_valid_nxdata(default_nxdata_group)

    def is_NXentry_with_default_NXdata(group, validate=True):
        """Return True if group is a valid NXentry defining a valid default
        NXdata.

        :param group: h5py-like object.
        :param bool validate: Set this to skip the NXdata validation, and only
            check the existence of the group.
            Parameter provided for optimisation purposes, to avoid double
            validation if the validation is already performed separately."""
        if not is_group(group):
            return False

        if get_attr_as_unicode(group, "NX_class") != "NXentry":
            return False

        return is_group_with_default_NXdata(group, validate)

    def is_NXroot_with_default_NXdata(group, validate=True):
        """Return True if group is a valid NXroot defining a default NXentry
        defining a valid default NXdata.

        .. note::

            A NXroot group cannot directly define a default NXdata. If a
            *@default* argument is present, it must point to a NXentry group.
            This NXentry must define a valid NXdata for this function to return
            True.

        :param group: h5py-like object.
        :param bool validate: Set this to False if you are sure that the target group
            is valid NXdata (i.e. :func:`silx.io.nxdata.is_valid_nxdata(target_group)`
            returns True). Parameter provided for optimisation purposes.
        """
        if not is_group(group):
            return False

        # A NXroot is supposed to be at the root of a data file, and @NX_class
        # is therefore optional. We accept groups that are not located at the root
        # if they have @NX_class=NXroot (use case: several nexus files archived
        # in a single HDF5 file)
        if get_attr_as_unicode(group, "NX_class") != "NXroot" and not is_file(group):
            return False

        default_nxentry_name = group.attrs.get("default")
        if default_nxentry_name is None or default_nxentry_name not in group:
            return False

        default_nxentry_group = group.get(default_nxentry_name)
        return is_NXentry_with_default_NXdata(default_nxentry_group, validate=validate)

    def _get_default(
        group,
        validate: bool,
        traversed: list,
    ) -> Optional[NXdata]:
        if not is_group(group):
            raise TypeError("Provided parameter is not a h5py-like group")

        if get_attr_as_unicode(group, "NX_class") == "NXdata":
            nxdata = NXdata(group, validate=validate)
            return nxdata if nxdata.is_valid else None

        default_name = get_attr_as_unicode(group, "default")
        if default_name is None:
            return None

        default_entity = group.get(default_name)
        if default_entity is None or default_entity in traversed:
            return None

        try:
            return _get_default(default_entity, validate, traversed + [default_entity])
        except TypeError:
            return None

    def get_default(group, validate: bool = True) -> Optional[NXdata]:
        """Find the default :class:`NXdata` group in given group.

        `@default` attributes are recursively followed until finding a group with
        NX_class="NXdata".
        Return None if no valid NXdata group could be found.

        :param group: h5py-like group to look for @default NXdata.
            In cas it is a NXdata group, it is returned.
        :param validate: False to disable checking the returned NXdata group.
        :raise TypeError: if group is not a h5py-like group
        """
        return _get_default(group, validate, [])