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nexus_scan

NeXus Scan Classes

NexusScan - NeXus Scan class, lazy loader of scan files NexusDataHolder - Loads scan data and meta data into attributes

NexusDataHolder

Bases: DataHolder, NexusScan

Nexus data holder class - Automatically reads scannable and metadata from file - acts like the old .dat DataHolder class - has additional functions to read data from NeXus file

Example: scan = NexusDataHolder('12345.nxs') scan.eta -> returns array scan.metadata.metadata -> returns value scan('signal') -> evaluate expression

Parameters:

Name Type Description Default
filename str | None

path to Nexus file

 required
hdf_map NexusMap | None

NexusMap object or None to generate

 None
flatten_scannables bool

if True, flattens all scannable arrays to 1D

 True
Source code in mmg_toolbox/nexus/nexus_scan.py 
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class NexusDataHolder(DataHolder, NexusScan):
    """
    Nexus data holder class
     - Automatically reads scannable and metadata from file
     - acts like the old .dat DataHolder class
     - has additional functions to read data from NeXus file

    Example:
        scan = NexusDataHolder('12345.nxs')
        scan.eta -> returns array
        scan.metadata.metadata -> returns value
        scan('signal') -> evaluate expression

    :param filename: path to Nexus file
    :param hdf_map: NexusMap object or None to generate
    :param flatten_scannables: if True, flattens all scannable arrays to 1D
    """
    filename: str
    map: NexusMap
    metadata: DataHolder

    def __init__(self, filename: str | None, hdf_map: NexusMap | None = None, flatten_scannables: bool = True,
                 config: dict | None = None):
        NexusScan.__init__(self, filename, hdf_map, config)

        with load_hdf(filename) as hdf:
            metadata = self.map.get_metadata(hdf)
            scannables = self.map.get_scannables(hdf, flatten=flatten_scannables)
        DataHolder.__init__(self, **scannables)
        self.metadata = DataHolder(**metadata)

    def __repr__(self):
        return f"NexusDataHolder('{self.filename}')"

NexusScan

Bases: NexusLoader

Light-weight NeXus file reader

Example: >>> scan = NexusScan('scan.nxs') >>> scan('scan_command') 'scan x ...' >>> x, y = scan('axes, signal / monitor') >>> scan.plot() # default plot >>> scan.plot.image() # other plot options >>> result = scan.fit.multi_peak_fit() >>> print(scan) # print scan default metadata >>> print(scan.info()) # print scan namespace >>> scan.map.add_roi('name', ...) # add ROI to namespace >>> scan.image(0) # return first detector image as array >>> scan.volume() # return image stack >>> data = scan.get_plot_data() # return dict of plot data

Parameters:

Name Type Description Default
nxs_filename str

path to nexus file

 required
hdf_map NexusMap | None

NexusMap object or None

 None
config dict | None

configuration dict

 None
Source code in mmg_toolbox/nexus/nexus_scan.py 
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class NexusScan(NexusLoader):
    """
    Light-weight NeXus file reader

    Example:
        >>> scan = NexusScan('scan.nxs')
        >>> scan('scan_command')
         'scan x ...'
        >>> x, y = scan('axes, signal / monitor')
        >>> scan.plot()  # default plot
        >>> scan.plot.image()  # other plot options
        >>> result = scan.fit.multi_peak_fit()
        >>> print(scan)  # print scan default metadata
        >>> print(scan.info())  # print scan namespace
        >>> scan.map.add_roi('name', ...)  # add ROI to namespace
        >>> scan.image(0)  # return first detector image as array
        >>> scan.volume()  # return image stack
        >>> data = scan.get_plot_data()  # return dict of plot data

    :param nxs_filename: path to nexus file
    :param hdf_map: NexusMap object or None
    :param config: configuration dict
    """
    MAX_STR_LEN: int = 100

    def __init__(self, nxs_filename: str, hdf_map: NexusMap | None = None, config: dict | None = None):
        super().__init__(nxs_filename, hdf_map)
        self.config: dict = config or beamline_config()
        self.beamline = self.config.get(C.beamline, None)

        # add scan number to eval namespace
        self.add_local(scan_number=self.scan_number(), beamline=self.beamline)
        # add alternate names
        self.map.add_named_expression(**self.config.get(C.replace_names, {}))
        # add ROIs
        for (name, cen_i, cen_j, wid_i, wid_j, det_name) in self.config.get(C.roi, []):
            self.map.add_roi(name, cen_i, cen_j, wid_i, wid_j, det_name)

        from mmg_toolbox.fitting import ScanFitManager, poisson_errors
        self.fit = ScanFitManager(self)
        self._error_function = poisson_errors
        from mmg_toolbox.plotting.scan_plot_manager import ScanPlotManager
        self.plot = ScanPlotManager(self)

    def __repr__(self):
        if self.beamline:
            return f"NexusScan<{self.beamline}>({self.scan_number()}: '{self.filename}')"
        return f"NexusScan('{self.filename}')"

    def __str__(self):
        try:
            return self.metadata_str()
        except Exception as ex:
            return f"{repr(self)}\n  Metadata failed with: \n{ex}\n"

    def metadata_str(self, expression: str | None = None):
        """Generate metadata string from beamline config"""
        if expression is None:
            expression = self.config.get(C.metadata_string, '')
        return self.format(expression)

    def info(self, arrays=False, values=False, combined=False,
             metadata=False, scannables=True, image_data=True,
             local=False, alternate=True) -> str:
        """Return string of namespace information"""
        local_data = (
                "Local Data:\n" +
                "\n".join(
                    f"  {name}: {value}" for name, value in self._local_data.items()
                ) +
                "\n"
        ) if local else ""
        alternate_data = (
            "Alternate Names:\n  Name: Expression" +
            "\n".join(
                f"  {name}: '{expr}'" for name, expr in self.map._alternate_names.items()
            ) +
            "\n"
        ) if alternate else ""
        map_data = self.map.info_names(arrays=arrays, values=values, combined=combined,
                                       metadata=metadata, scannables=scannables, image_data=image_data)
        return local_data + alternate_data + map_data

    def scan_number(self) -> int:
        return get_scan_number(self.filename)

    def title(self) -> str:
        return f"#{self.scan_number()}"

    def label(self) -> str:
        return f"#{self.scan_number()}"

    def load_hdf(self) -> h5py.File:
        """Load the Hdf file"""
        return load_hdf(self.filename)

    def hdf_tree_string(self, group: str = '/', all_links: bool = True, attributes: bool = True) -> str:
        """
        Generate string of the hdf file structure, similar to h5ls. Uses h5py.visititems

        :param all_links: bool, if True, also show links
        :param group: only display tree structure of this group (default root)
        :param attributes: if True, display the attributes of groups and datasets
        :return: str
        """
        return hdfmap.hdf_tree_string(self.filename, all_links=all_links, group=group, attributes=attributes)

    def hdf_find(self, *field_or_class: str | list[str], find_all: bool = False) -> (h5py.Dataset | h5py.Group) | list[h5py.Dataset | h5py.Group]:
        """
        Find datasets and groups within hdf file

            >>> HdfDataset = scan.hdf_find('NXentry', ['NXdata', 'measurement'], 'signal')

        Warning: HDF file stays in open state while Dataset or Group objects exist.

        :param field_or_class: names to search for, in hierarchical order. Lists treated as OR
        :param find_all: whether to return all datasets or only the first match
        :returns: matching Dataset or Group, or list of matching Datasets or Groups
        """
        with self.load_hdf() as hdf:
            if find_all:
                return nx_find_all(hdf, *field_or_class)
            return nx_find(hdf, *field_or_class)

    def datasets(self, *args) -> list[h5py.Dataset]:
        """Return HDF5 datasets from NeXus file (leaves file in open state)"""
        with self.load_hdf() as hdf:
            return [hdf[self.map.combined[name]] for name in args]

    def arrays(self, *args, units: str = '', default: np.ndarray = np.array([np.nan])) -> list[np.ndarray]:
        """Return Numpy arrays"""
        with self.load_hdf() as hdf:
            return [
                get_dataset_value(self.map.combined[name], hdf, units=units, default=default)
                for name in args
            ]

    def values(self, *args, value_func=np.mean,
               units: str = '', default: np.ndarray = np.array(np.nan)) -> list[np.floating]:
        """Return float values"""
        with self.load_hdf() as hdf:
            return [
                value_func(get_dataset_value(self.map.combined[name], hdf, units=units, default=default))
                for name in args
            ]

    def times(self, *args) -> list[datetime.datetime]:
        """Return datetime object"""
        with self.load_hdf() as hdf:
            data = [dataset2data(hdf[self.map.combined[name]]) for name in args]
            dt = [
                obj if isinstance(obj, datetime.datetime)
                else datetime.datetime.fromisoformat(obj) if isinstance(obj, str)
                else datetime.datetime.fromtimestamp(float(obj))
                for obj in data
            ]
        return dt

    def strings(self, *args, units=False) -> list[str]:
        """Return string value"""
        with self.load_hdf() as hdf:
            return [dataset2str(hdf[self.map.combined[name]], units=units) for name in args]

    def image(self, index: int | tuple | slice | str | None = None) -> np.ndarray:
        """Return image or selection from default detector"""
        if not self.map.image_data:
            raise ValueError(f'{repr(self)} contains no image data')
        with self.load_hdf() as hdf:
            if index == 'sum':
                volume = self.map.get_image(hdf, ())
                image = volume.sum(axis=tuple(range(volume.ndim-2)))  # this will fail for filenames
            else:
                image = self.map.get_image(hdf, index)

            if issubclass(type(image), str):
                # TIFF image, NXdetector/image_data -> array('file.tif')
                file_directory = os.path.dirname(self.filename)
                image_filename = os.path.join(file_directory, image)
                if not os.path.isfile(image_filename):
                    raise FileNotFoundError(f"File not found: {image_filename}")
                image = read_tiff(image_filename)
            elif image.ndim == 0:
                # image is file path number, NXdetector/path -> arange(n_points)
                scan_number = get_scan_number(self.filename)
                file_directory = os.path.dirname(self.filename)
                detector_names = list(self.map.image_data.keys())
                for detector_name in detector_names:
                    image_filename = os.path.join(file_directory, f"{scan_number}-{detector_name}-files/{image:05.0f}.tif")
                    if os.path.isfile(image_filename):
                        break
                if not os.path.isfile(image_filename):
                    raise FileNotFoundError(f"File not found: {image_filename}")
                image = read_tiff(image_filename)
            elif image.ndim != 2:
                raise Exception(f"detector image[{index}] is the wrong shape: {image.shape}")
            return image

    def volume(self) -> np.ndarray:
        """Return complete stack of images"""
        return self.map.get_image(self.load_hdf(), ())

    def image_background(self, index: int | tuple | slice | str | None = (), n_bins: int  = 100) -> np.ndarray:
        """
        Return the modal value of the detector image,
        which usually gives the background value.

        The modal value is determined by histograming the image (or image stack) and taking
        the value of the largest bin.

        :param index: index of image to return, use () for full image stack.
        :param n_bins: number of histogram bins
        :return: modal value or values per image
        """
        image = self.map.get_image(self.load_hdf(), index)  # hdf data only
        n, bins = np.histogram(np.log10(image[image>0].flatten()), bins=n_bins)
        return 10 ** bins[np.argmax(n)]

    def table(self, delimiter=', ', string_spec='', format_spec='f', default_decimals=8) -> str:
        """Return data table"""
        with self.load_hdf() as hdf:
            return self.map.create_scannables_table(hdf, delimiter, string_spec, format_spec, default_decimals)

    def _get_plot_axis(self, hdf: h5py.File | h5py.Group, axis_name: str,
                       reduce_shape: bool = True, flatten: bool = False) -> tuple[np.ndarray, str]:
        """
        Return plot axis data and label for given axis name

        E.G.
            >>> data, label = scan.get_plot_axis('axes', flatten=True)

        :param hdf: h5py.File or h5py.Group
        :param axis_name: axis name as given in self.map
        :param reduce_shape: reduces shape (summing additional axes) of >2D arrays to self.map.scannables_shape
        :param flatten: flatten data array if True
        :return: (data, label) tuple
        """
        # Default scannables if not generated by hdfmap
        if ('axes' in axis_name or 'signal' in axis_name) and axis_name not in self.map:
            axes_names, signal_names = self.map.nexus_default_names()
            if re.match(r"axes\d?", axis_name):
                index = int(axis_name.strip('axes') or 0)
                axis_name = list(axes_names)[index]
            elif re.match(r"signal\d?", axis_name):
                index = int(axis_name.strip('signal') or 0)
                axis_name = list(signal_names)[index]
        label, = self.map.generate_ids(axis_name, modify_missing=False)
        data = self.map.eval(hdf, axis_name)
        if np.ndim(data) > 1 and reduce_shape:
            # reduce high dimensional arrays to the default scannable shape
            shape = self.map.scannables_shape()
            if np.ndim(data) == len(shape) + 2:
                # Image data
                data = np.sum(data, axis=(-1, -2))
            if np.shape(data) != shape:
                raise ValueError(f"2+D Arrays must have same shape: {axis_name}{np.shape(data)} != {shape}")
        if flatten:
            data = np.reshape(data, -1)
        return data, label

    def get_plot_axis(self, axis_name: str, reduce_shape: bool = True, flatten: bool = False) -> tuple[np.ndarray, str]:
        """
        Return plot axis data and label for given axis name

            >>> data, label = scan.get_plot_axis('axes', flatten=True)

        :param axis_name: axis name as given in self.map
        :param reduce_shape: reduces shape (summing additional axes) of >2D arrays to self.map.scannables_shape
        :param flatten: flattens output if True
        :return: (data, label) tuple
        """
        with self.load_hdf() as hdf:
            return self._get_plot_axis(hdf, axis_name, reduce_shape=reduce_shape, flatten=flatten)

    def get_plot_data(self, x_axis: str | None = None, *y_axis: str | None, z_axis: str | None = None) -> dict:
        """
        Return dict of plottable data

            >>> data = scan.get_plot_data('axes', 'signal')
            >>> plt.plot(data['x'], data['y'])
            >>> plt.xlabel(data['xlabel'])
            >>> plt.ylabel(data['ylabel'])
            >>> plt.title(data['title'])
            >>> plt.legend(data['legend'])

        :param x_axis: axis name or expression as given in self.map
        :param y_axis: axis name or expression as given in self.map
        :param z_axis: axis name or expression as given in self.map
        :returns: {
            'xlabel': str label of first axes
            'ylabel': str label of first signal
            'xdata': flattened array of first axes
            'ydata': flattened array of first signal
            'axes_names': list of axes names,
            'signal_names': list of signal + auxiliary signal names,
            'axes_data': list of ND arrays of data for axes,
            'signal_data': list of ND array of data for signal + auxiliary signals,
            'axes_labels': list of axes labels as 'name [units]',
            'signal_labels': list of signal labels,
            'data': dict of all scannables axes,
            'title': str title as 'filename\nNXtitle'
        if dataset is a 2D grid scan, additional rows:
            'grid_xlabel': str label of grid x-axis
            'grid_ylabel': str label of grid y-axis
            'grid_label': str label of height or colour
            'grid_xdata': 2D array of x-coordinates
            'grid_ydata': 2D array of y-coordinates
            'grid_data': 2D array of height or colour
        }
        """
        with self.load_hdf() as hdf:
            data = self.map.get_plot_data(hdf)
            cmd = self.map.eval(hdf, Md.cmd)
            if len(cmd) > self.MAX_STR_LEN:
                cmd = shorten_string(cmd)
            x_data, x_lab = self._get_plot_axis(hdf, x_axis or 'axes', reduce_shape=True, flatten=True)

            y_data, y_labs = [], []
            y_axis = y_axis or [None]
            for n, _y_axis in enumerate(y_axis):
                _y_data, _y_lab = self._get_plot_axis(hdf, _y_axis or f"signal{n}", reduce_shape=True, flatten=True)
                y_data.append(_y_data)
                y_labs.append(_y_lab)
            y_data = np.array(y_data)
            y_error = self._error_function(y_data)

            additional = {
                'x': x_data,
                'y': y_data[0],
                'xdata': x_data,
                'ydata': y_data.T,
                'yerror': y_error.T,
                'xlabel': x_lab,
                'ylabel': y_labs[0],
                'title': f"#{self.scan_number()}\n{cmd}",
                'legend': y_labs
            }
            if z_axis is not None:
                z_data, z_lab = self._get_plot_axis(hdf, z_axis, reduce_shape=True, flatten=True)
                additional['zdata'] = z_data
                additional['zlabel'] = z_lab
            # 2+D data
            shape = self.map.scannables_shape()
            if len(shape) >= 2:
                x_data = x_data.reshape(shape)
                y_data, y_lab = self._get_plot_axis(hdf, y_axis[0] or 'axes1')
                z_data, z_lab = self._get_plot_axis(hdf,
                    y_axis[1] if len(y_axis) > 1 else z_axis or 'signal',
                )
                # reduce dimensions to 2
                #TODO: taking the first dimension might not always be right
                x_data = x_data[(..., ) + (0, ) * (x_data.ndim - 2)]
                y_data = y_data[(..., ) + (0, ) * (y_data.ndim - 2)]
                z_data = z_data.sum(axis=tuple(range(2, z_data.ndim)))

                if y_data.shape != x_data.shape:
                    raise ValueError(f"Shape of '{y_lab}' {y_data.shape} != '{x_lab}' {x_data.shape}")
                if z_data.shape != x_data.shape:
                    raise ValueError(f"Shape of '{z_lab}' {z_data.shape} != '{x_lab}' {x_data.shape}")

                additional['grid_xlabel'] = x_lab
                additional['grid_ylabel'] = y_lab
                additional['grid_label'] = z_lab
                additional['grid_xdata'] = x_data
                additional['grid_ydata'] = y_data
                additional['grid_data'] = z_data
            data.update(additional)
            return data

    def xas_spectra(self, sample_name: str | None = None, element_edge: str | None = None, mode: str | list[str] = 'all',
                    dls_loader: bool = False) -> SpectraContainer:
        """
        Load XAS Spectra from the scan file

            >>> spectra = scan.xas_spectra()
            >>> spectra = spectra.remove_background('slope')
            >>> spectra.plot()

        :param sample_name: sample name, e.g. 'sample1' or None to load from NeXus file
        :param element_edge: element edge, e.g. 'FeL3' or None to determine from energy range
        :param mode: detector values to load, 'all', 'default' or e.g. 'tey', 'tfy' as specified in file
        :param dls_loader: bool, if True uses explicit loading of metadata from DLS MMG beamlines
        :return: SpectraContainer
        """
        return load_xas_scans(self.filename, sample_name=sample_name, element_edge=element_edge,
                              mode=mode, dls_loader=dls_loader)[0]

    def instrument_model(self) -> NXInstrumentModel:
        """Build and instrument model from NeXus file"""
        with self.load_hdf() as hdf:
            return NXInstrumentModel(hdf)

arrays(*args, units='', default=np.array([np.nan]))

Return Numpy arrays

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def arrays(self, *args, units: str = '', default: np.ndarray = np.array([np.nan])) -> list[np.ndarray]:
    """Return Numpy arrays"""
    with self.load_hdf() as hdf:
        return [
            get_dataset_value(self.map.combined[name], hdf, units=units, default=default)
            for name in args
        ]

datasets(*args)

Return HDF5 datasets from NeXus file (leaves file in open state)

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def datasets(self, *args) -> list[h5py.Dataset]:
    """Return HDF5 datasets from NeXus file (leaves file in open state)"""
    with self.load_hdf() as hdf:
        return [hdf[self.map.combined[name]] for name in args]

get_plot_axis(axis_name, reduce_shape=True, flatten=False)

Return plot axis data and label for given axis name

>>> data, label = scan.get_plot_axis('axes', flatten=True)

Parameters:

Name Type Description Default
axis_name str

axis name as given in self.map

 required
reduce_shape bool

reduces shape (summing additional axes) of >2D arrays to self.map.scannables_shape

 True
flatten bool

flattens output if True

 False

Returns:

Type Description
tuple[ndarray, str]

(data, label) tuple
Source code in mmg_toolbox/nexus/nexus_scan.py 
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def get_plot_axis(self, axis_name: str, reduce_shape: bool = True, flatten: bool = False) -> tuple[np.ndarray, str]:
    """
    Return plot axis data and label for given axis name

        >>> data, label = scan.get_plot_axis('axes', flatten=True)

    :param axis_name: axis name as given in self.map
    :param reduce_shape: reduces shape (summing additional axes) of >2D arrays to self.map.scannables_shape
    :param flatten: flattens output if True
    :return: (data, label) tuple
    """
    with self.load_hdf() as hdf:
        return self._get_plot_axis(hdf, axis_name, reduce_shape=reduce_shape, flatten=flatten)

get_plot_data(x_axis=None, *y_axis, z_axis=None) 

    Return dict of plottable data

        >>> data = scan.get_plot_data('axes', 'signal')
        >>> plt.plot(data['x'], data['y'])
        >>> plt.xlabel(data['xlabel'])
        >>> plt.ylabel(data['ylabel'])
        >>> plt.title(data['title'])
        >>> plt.legend(data['legend'])

    :param x_axis: axis name or expression as given in self.map
    :param y_axis: axis name or expression as given in self.map
    :param z_axis: axis name or expression as given in self.map
    :returns: {
        'xlabel': str label of first axes
        'ylabel': str label of first signal
        'xdata': flattened array of first axes
        'ydata': flattened array of first signal
        'axes_names': list of axes names,
        'signal_names': list of signal + auxiliary signal names,
        'axes_data': list of ND arrays of data for axes,
        'signal_data': list of ND array of data for signal + auxiliary signals,
        'axes_labels': list of axes labels as 'name [units]',
        'signal_labels': list of signal labels,
        'data': dict of all scannables axes,
        'title': str title as 'filename

NXtitle' if dataset is a 2D grid scan, additional rows: 'grid_xlabel': str label of grid x-axis 'grid_ylabel': str label of grid y-axis 'grid_label': str label of height or colour 'grid_xdata': 2D array of x-coordinates 'grid_ydata': 2D array of y-coordinates 'grid_data': 2D array of height or colour }

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def get_plot_data(self, x_axis: str | None = None, *y_axis: str | None, z_axis: str | None = None) -> dict:
    """
    Return dict of plottable data

        >>> data = scan.get_plot_data('axes', 'signal')
        >>> plt.plot(data['x'], data['y'])
        >>> plt.xlabel(data['xlabel'])
        >>> plt.ylabel(data['ylabel'])
        >>> plt.title(data['title'])
        >>> plt.legend(data['legend'])

    :param x_axis: axis name or expression as given in self.map
    :param y_axis: axis name or expression as given in self.map
    :param z_axis: axis name or expression as given in self.map
    :returns: {
        'xlabel': str label of first axes
        'ylabel': str label of first signal
        'xdata': flattened array of first axes
        'ydata': flattened array of first signal
        'axes_names': list of axes names,
        'signal_names': list of signal + auxiliary signal names,
        'axes_data': list of ND arrays of data for axes,
        'signal_data': list of ND array of data for signal + auxiliary signals,
        'axes_labels': list of axes labels as 'name [units]',
        'signal_labels': list of signal labels,
        'data': dict of all scannables axes,
        'title': str title as 'filename\nNXtitle'
    if dataset is a 2D grid scan, additional rows:
        'grid_xlabel': str label of grid x-axis
        'grid_ylabel': str label of grid y-axis
        'grid_label': str label of height or colour
        'grid_xdata': 2D array of x-coordinates
        'grid_ydata': 2D array of y-coordinates
        'grid_data': 2D array of height or colour
    }
    """
    with self.load_hdf() as hdf:
        data = self.map.get_plot_data(hdf)
        cmd = self.map.eval(hdf, Md.cmd)
        if len(cmd) > self.MAX_STR_LEN:
            cmd = shorten_string(cmd)
        x_data, x_lab = self._get_plot_axis(hdf, x_axis or 'axes', reduce_shape=True, flatten=True)

        y_data, y_labs = [], []
        y_axis = y_axis or [None]
        for n, _y_axis in enumerate(y_axis):
            _y_data, _y_lab = self._get_plot_axis(hdf, _y_axis or f"signal{n}", reduce_shape=True, flatten=True)
            y_data.append(_y_data)
            y_labs.append(_y_lab)
        y_data = np.array(y_data)
        y_error = self._error_function(y_data)

        additional = {
            'x': x_data,
            'y': y_data[0],
            'xdata': x_data,
            'ydata': y_data.T,
            'yerror': y_error.T,
            'xlabel': x_lab,
            'ylabel': y_labs[0],
            'title': f"#{self.scan_number()}\n{cmd}",
            'legend': y_labs
        }
        if z_axis is not None:
            z_data, z_lab = self._get_plot_axis(hdf, z_axis, reduce_shape=True, flatten=True)
            additional['zdata'] = z_data
            additional['zlabel'] = z_lab
        # 2+D data
        shape = self.map.scannables_shape()
        if len(shape) >= 2:
            x_data = x_data.reshape(shape)
            y_data, y_lab = self._get_plot_axis(hdf, y_axis[0] or 'axes1')
            z_data, z_lab = self._get_plot_axis(hdf,
                y_axis[1] if len(y_axis) > 1 else z_axis or 'signal',
            )
            # reduce dimensions to 2
            #TODO: taking the first dimension might not always be right
            x_data = x_data[(..., ) + (0, ) * (x_data.ndim - 2)]
            y_data = y_data[(..., ) + (0, ) * (y_data.ndim - 2)]
            z_data = z_data.sum(axis=tuple(range(2, z_data.ndim)))

            if y_data.shape != x_data.shape:
                raise ValueError(f"Shape of '{y_lab}' {y_data.shape} != '{x_lab}' {x_data.shape}")
            if z_data.shape != x_data.shape:
                raise ValueError(f"Shape of '{z_lab}' {z_data.shape} != '{x_lab}' {x_data.shape}")

            additional['grid_xlabel'] = x_lab
            additional['grid_ylabel'] = y_lab
            additional['grid_label'] = z_lab
            additional['grid_xdata'] = x_data
            additional['grid_ydata'] = y_data
            additional['grid_data'] = z_data
        data.update(additional)
        return data

hdf_find(*field_or_class, find_all=False)

Find datasets and groups within hdf file

>>> HdfDataset = scan.hdf_find('NXentry', ['NXdata', 'measurement'], 'signal')

Warning: HDF file stays in open state while Dataset or Group objects exist.

Parameters:

Name Type Description Default
field_or_class str | list[str]

names to search for, in hierarchical order. Lists treated as OR

 ()
find_all bool

whether to return all datasets or only the first match

 False

Returns:

Type Description
Dataset | Group | list[Dataset | Group]

matching Dataset or Group, or list of matching Datasets or Groups
Source code in mmg_toolbox/nexus/nexus_scan.py 
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def hdf_find(self, *field_or_class: str | list[str], find_all: bool = False) -> (h5py.Dataset | h5py.Group) | list[h5py.Dataset | h5py.Group]:
    """
    Find datasets and groups within hdf file

        >>> HdfDataset = scan.hdf_find('NXentry', ['NXdata', 'measurement'], 'signal')

    Warning: HDF file stays in open state while Dataset or Group objects exist.

    :param field_or_class: names to search for, in hierarchical order. Lists treated as OR
    :param find_all: whether to return all datasets or only the first match
    :returns: matching Dataset or Group, or list of matching Datasets or Groups
    """
    with self.load_hdf() as hdf:
        if find_all:
            return nx_find_all(hdf, *field_or_class)
        return nx_find(hdf, *field_or_class)

hdf_tree_string(group='/', all_links=True, attributes=True)

Generate string of the hdf file structure, similar to h5ls. Uses h5py.visititems

Parameters:

Name Type Description Default
all_links bool

bool, if True, also show links

 True
group str

only display tree structure of this group (default root)

 '/'
attributes bool

if True, display the attributes of groups and datasets

 True

Returns:

Type Description
str

str
Source code in mmg_toolbox/nexus/nexus_scan.py 
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def hdf_tree_string(self, group: str = '/', all_links: bool = True, attributes: bool = True) -> str:
    """
    Generate string of the hdf file structure, similar to h5ls. Uses h5py.visititems

    :param all_links: bool, if True, also show links
    :param group: only display tree structure of this group (default root)
    :param attributes: if True, display the attributes of groups and datasets
    :return: str
    """
    return hdfmap.hdf_tree_string(self.filename, all_links=all_links, group=group, attributes=attributes)

image(index=None)

Return image or selection from default detector

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def image(self, index: int | tuple | slice | str | None = None) -> np.ndarray:
    """Return image or selection from default detector"""
    if not self.map.image_data:
        raise ValueError(f'{repr(self)} contains no image data')
    with self.load_hdf() as hdf:
        if index == 'sum':
            volume = self.map.get_image(hdf, ())
            image = volume.sum(axis=tuple(range(volume.ndim-2)))  # this will fail for filenames
        else:
            image = self.map.get_image(hdf, index)

        if issubclass(type(image), str):
            # TIFF image, NXdetector/image_data -> array('file.tif')
            file_directory = os.path.dirname(self.filename)
            image_filename = os.path.join(file_directory, image)
            if not os.path.isfile(image_filename):
                raise FileNotFoundError(f"File not found: {image_filename}")
            image = read_tiff(image_filename)
        elif image.ndim == 0:
            # image is file path number, NXdetector/path -> arange(n_points)
            scan_number = get_scan_number(self.filename)
            file_directory = os.path.dirname(self.filename)
            detector_names = list(self.map.image_data.keys())
            for detector_name in detector_names:
                image_filename = os.path.join(file_directory, f"{scan_number}-{detector_name}-files/{image:05.0f}.tif")
                if os.path.isfile(image_filename):
                    break
            if not os.path.isfile(image_filename):
                raise FileNotFoundError(f"File not found: {image_filename}")
            image = read_tiff(image_filename)
        elif image.ndim != 2:
            raise Exception(f"detector image[{index}] is the wrong shape: {image.shape}")
        return image

image_background(index=(), n_bins=100)

Return the modal value of the detector image, which usually gives the background value.

The modal value is determined by histograming the image (or image stack) and taking the value of the largest bin.

Parameters:

Name Type Description Default
index int | tuple | slice | str | None

index of image to return, use () for full image stack.

 ()
n_bins int

number of histogram bins

 100

Returns:

Type Description
ndarray

modal value or values per image
Source code in mmg_toolbox/nexus/nexus_scan.py 
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def image_background(self, index: int | tuple | slice | str | None = (), n_bins: int  = 100) -> np.ndarray:
    """
    Return the modal value of the detector image,
    which usually gives the background value.

    The modal value is determined by histograming the image (or image stack) and taking
    the value of the largest bin.

    :param index: index of image to return, use () for full image stack.
    :param n_bins: number of histogram bins
    :return: modal value or values per image
    """
    image = self.map.get_image(self.load_hdf(), index)  # hdf data only
    n, bins = np.histogram(np.log10(image[image>0].flatten()), bins=n_bins)
    return 10 ** bins[np.argmax(n)]

info(arrays=False, values=False, combined=False, metadata=False, scannables=True, image_data=True, local=False, alternate=True)

Return string of namespace information

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def info(self, arrays=False, values=False, combined=False,
         metadata=False, scannables=True, image_data=True,
         local=False, alternate=True) -> str:
    """Return string of namespace information"""
    local_data = (
            "Local Data:\n" +
            "\n".join(
                f"  {name}: {value}" for name, value in self._local_data.items()
            ) +
            "\n"
    ) if local else ""
    alternate_data = (
        "Alternate Names:\n  Name: Expression" +
        "\n".join(
            f"  {name}: '{expr}'" for name, expr in self.map._alternate_names.items()
        ) +
        "\n"
    ) if alternate else ""
    map_data = self.map.info_names(arrays=arrays, values=values, combined=combined,
                                   metadata=metadata, scannables=scannables, image_data=image_data)
    return local_data + alternate_data + map_data

instrument_model()

Build and instrument model from NeXus file

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def instrument_model(self) -> NXInstrumentModel:
    """Build and instrument model from NeXus file"""
    with self.load_hdf() as hdf:
        return NXInstrumentModel(hdf)

load_hdf()

Load the Hdf file

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def load_hdf(self) -> h5py.File:
    """Load the Hdf file"""
    return load_hdf(self.filename)

metadata_str(expression=None)

Generate metadata string from beamline config

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def metadata_str(self, expression: str | None = None):
    """Generate metadata string from beamline config"""
    if expression is None:
        expression = self.config.get(C.metadata_string, '')
    return self.format(expression)

strings(*args, units=False)

Return string value

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def strings(self, *args, units=False) -> list[str]:
    """Return string value"""
    with self.load_hdf() as hdf:
        return [dataset2str(hdf[self.map.combined[name]], units=units) for name in args]

table(delimiter=', ', string_spec='', format_spec='f', default_decimals=8)

Return data table

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def table(self, delimiter=', ', string_spec='', format_spec='f', default_decimals=8) -> str:
    """Return data table"""
    with self.load_hdf() as hdf:
        return self.map.create_scannables_table(hdf, delimiter, string_spec, format_spec, default_decimals)

times(*args)

Return datetime object

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def times(self, *args) -> list[datetime.datetime]:
    """Return datetime object"""
    with self.load_hdf() as hdf:
        data = [dataset2data(hdf[self.map.combined[name]]) for name in args]
        dt = [
            obj if isinstance(obj, datetime.datetime)
            else datetime.datetime.fromisoformat(obj) if isinstance(obj, str)
            else datetime.datetime.fromtimestamp(float(obj))
            for obj in data
        ]
    return dt

values(*args, value_func=np.mean, units='', default=np.array(np.nan))

Return float values

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def values(self, *args, value_func=np.mean,
           units: str = '', default: np.ndarray = np.array(np.nan)) -> list[np.floating]:
    """Return float values"""
    with self.load_hdf() as hdf:
        return [
            value_func(get_dataset_value(self.map.combined[name], hdf, units=units, default=default))
            for name in args
        ]

volume()

Return complete stack of images

Source code in mmg_toolbox/nexus/nexus_scan.py 
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def volume(self) -> np.ndarray:
    """Return complete stack of images"""
    return self.map.get_image(self.load_hdf(), ())

xas_spectra(sample_name=None, element_edge=None, mode='all', dls_loader=False)

Load XAS Spectra from the scan file

>>> spectra = scan.xas_spectra()
>>> spectra = spectra.remove_background('slope')
>>> spectra.plot()

Parameters:

Name Type Description Default
sample_name str | None

sample name, e.g. 'sample1' or None to load from NeXus file

 None
element_edge str | None

element edge, e.g. 'FeL3' or None to determine from energy range

 None
mode str | list[str]

detector values to load, 'all', 'default' or e.g. 'tey', 'tfy' as specified in file

 'all'
dls_loader bool

bool, if True uses explicit loading of metadata from DLS MMG beamlines

 False

Returns:

Type Description
SpectraContainer

SpectraContainer
Source code in mmg_toolbox/nexus/nexus_scan.py 
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def xas_spectra(self, sample_name: str | None = None, element_edge: str | None = None, mode: str | list[str] = 'all',
                dls_loader: bool = False) -> SpectraContainer:
    """
    Load XAS Spectra from the scan file

        >>> spectra = scan.xas_spectra()
        >>> spectra = spectra.remove_background('slope')
        >>> spectra.plot()

    :param sample_name: sample name, e.g. 'sample1' or None to load from NeXus file
    :param element_edge: element edge, e.g. 'FeL3' or None to determine from energy range
    :param mode: detector values to load, 'all', 'default' or e.g. 'tey', 'tfy' as specified in file
    :param dls_loader: bool, if True uses explicit loading of metadata from DLS MMG beamlines
    :return: SpectraContainer
    """
    return load_xas_scans(self.filename, sample_name=sample_name, element_edge=element_edge,
                          mode=mode, dls_loader=dls_loader)[0]