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MMG_Toolbox

Magnetic Materials Group Toolbox

Experiment

Experiment class Monitors data folders for scan files and provies a convenient way to load scan data using only a scan number.

E.G. from mmg_toolbox import Experiment exp = Experiment('path/to/folder1', 'path/to/folder2', instrument='i06') print(scan) scan = exp.scan(-1) # latest scan scan_list = exp.scans(range(12345, 12355)) data = exp.join_scan_data(range(-100, 0), data_fields=['cmd', 'Ta']) # returns dict of arrays

Parameters:

Name Type Description Default
folder_paths str

file directories containing .nxs files

 ()
instrument str | None

instrument name for configuration.

 None
Source code in mmg_toolbox/utils/experiment.py 
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class Experiment:
    """
    Experiment class
    Monitors data folders for scan files and provies a
    convenient way to load scan data using only a scan number.

    E.G.
        from mmg_toolbox import Experiment
        exp = Experiment('path/to/folder1', 'path/to/folder2', instrument='i06')
        print(scan)
        scan = exp.scan(-1)  # latest scan
        scan_list = exp.scans(*range(12345, 12355))
        data = exp.join_scan_data(*range(-100, 0), data_fields=['cmd', 'Ta'])  # returns dict of arrays

    :param folder_paths: file directories containing .nxs files
    :param instrument: instrument name for configuration.
    """

    def __init__(self, *folder_paths: str, instrument: str | None = None):
        self.folder_paths = [os.path.dirname(f) if os.path.isfile(f) else f for f in folder_paths]
        self.scan_list = {}
        self._scan_list_update = None
        self.instrument = instrument or get_beamline_from_directory(folder_paths[0], None)
        self.config = beamline_config(self.instrument)
        from ..plotting.exp_plot_manager import ExperimentPlotManager
        self.plot = ExperimentPlotManager(self)

    def __repr__(self):
        paths = ', '.join(f"'{p}'" for p in self.folder_paths)
        return f"Experiment({paths}, instrument={self.instrument})"

    def __str__(self):
        self._update_scan_list()
        scan_numbers = self._scan_numbers()
        lines = ['Instrument: ' + self.instrument]
        lines.extend(self.folder_paths)
        if scan_numbers:
            lines.extend([
                f"    Files: {len(scan_numbers)}",
                f"    Scans: {scan_numbers[0]}-{scan_numbers[-1]}",
            ])
        else:
            lines.extend(["  No NeXus files found."])
        return '\n'.join(lines)

    def __getitem__(self, item: int | slice) -> NexusScan | list[NexusScan]:
        if isinstance(item, slice):
            scan_numbers = self.all_scan_numbers()[item]
            return self.scans(*scan_numbers)
        else:
            scan_numbers = self.all_scan_numbers()[item]
            return self.scans(scan_numbers)[0]

    def __len__(self) -> int:
        return len(self.all_scan_numbers())

    def _update_scan_list(self):
        mod_times = [last_folder_update(folder) for folder in self.folder_paths]
        folders = [
            folder for folder, time in zip(self.folder_paths, mod_times)
            if self._scan_list_update is None or time > self._scan_list_update
        ]
        self._scan_list_update = max(mod_times)  # datetime.now()?
        self.scan_list.update(scan_number_mapping(*folders))

    def _scan_numbers(self) -> list[int]:
        self._update_scan_list()
        return list(self.scan_list.keys())

    def add_data_paths(self, *folder_paths: str):
        """Add additional paths"""
        new_paths = list(
            path for path in folder_paths
            if path not in self.folder_paths and os.path.isdir(path)
        )
        self.folder_paths = self.folder_paths + new_paths
        self._update_scan_list()

    def all_scans(self) -> dict[int, str]:
        self._update_scan_list()
        return self.scan_list.copy()

    def all_scan_numbers(self) -> list[int]:
        self._update_scan_list()
        return list(self.scan_list.keys())

    def all_scan_files(self) -> list[str]:
        self._update_scan_list()
        return list(self.scan_list.values())

    def get_scan_filename(self, scan_file: int | str = -1) -> str:
        """Return the full filename of a scan number"""
        if isinstance(scan_file, int) or scan_file.isdigit():
            scan_file = int(scan_file)
            if scan_file < 1:
                scan_numbers = self._scan_numbers()
                return self.scan_list[scan_numbers[scan_file]]
            self._update_scan_list()
            if scan_file in self.scan_list:
                return self.scan_list[scan_file]
            scan_numbers = self._scan_numbers()
            return self.scan_list[scan_numbers[scan_file]]

        if os.path.isfile(scan_file):
            return os.path.abspath(scan_file)
        raise FileNotFoundError(f"scan file {scan_file} not found")

    def scan(self, scan_file: int | str = -1) -> NexusDataHolder:
        """read Nexus file as NexusDataHolder"""
        return NexusDataHolder(self.get_scan_filename(scan_file), config=self.config)

    def scans(self, *scan_files: int | str, hdf_map: hdfmap.NexusMap | None = None) -> list[NexusScan]:
        """Read Nexus files as lazy NexusScan. All files use the same HdfMap, based on the first scan"""
        filenames = [self.get_scan_filename(scan_file) for scan_file in scan_files]
        if not filenames:
            filenames = list(self.all_scans().values())
        if filenames and hdf_map is None:
            hdf_map = hdfmap.create_nexus_map(filenames[0])
        return [NexusScan(file, hdf_map, config=self.config) for file in filenames]

    def find_scans(self, *scan_files: int | str,  hdf_map: hdfmap.NexusMap | None = None, first_only: bool = False,
                   **matches: str | float | tuple[float, float]) -> list[NexusScan]:
        """
        Find scans files with matching metadata

            matches = {
                'name1': 'scan', # matches if 'scan' in file['name1']
                'name2': value, # matches if file['name2'] ~= value
                'name3': (value, tol), # matches if abs(file['name3'] - value) < tol
            }
            match_files = find_scans(*filenames, **matches)

        :param scan_files: set of scan numbers or filenames, if not given will search all scans in folder.
        :param hdf_map: if given, uses this hdfmap rather than generating one.
        :param first_only: if true, returns on the first result
        :param matches: keyword arguments for matching parameters
        :returns: list of scan files that match all requirements
        """
        filenames = [self.get_scan_filename(scan_file) for scan_file in scan_files]
        if not filenames:
            filenames = list(self.all_scans().values())
        if hdf_map is None:
            hdf_map = hdfmap.create_nexus_map(filenames[0])
        matches = find_scans(*filenames, hdf_map=hdf_map, first_only=first_only, **matches)
        return self.scans(*matches, hdf_map=hdf_map)

    def join_scan_data(self, *scan_files: int | str, hdf_map: hdfmap.NexusMap | None = None,
                       data_fields: list[str] | None = None, default: np.ndarray = np.array([0.0])) -> dict[str, list]:
        """
        Join data from scans
        """
        scans = self.scans(*scan_files, hdf_map=hdf_map)
        data_fields = [self.config[C.scan_description]] if data_fields is None else data_fields
        data = {name: [] for name in data_fields}
        for scan in scans:
            with scan.load_hdf() as hdf:
                for name in data_fields:
                    data[name].append(scan.map.eval(hdf, name, default=default))
        return data

    def get_all_data(self, *fields: str, default: np.ndarray = np.array([0.0])) -> dict[str, list]:
        """
        Return dict of data for all files
        """
        return self.join_scan_data(data_fields=list(fields), default=default)

    def generate_mesh(self, *scan_files: int | str, hdf_map: hdfmap.NexusMap | None = None,
                      axes: str | tuple[str, str] = 'axes', signal: str = 'axes',
                      values: str | None = None) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Generate 2D mesh from scan or scans

            x, y, z = generate_mesh(*range(-10, 0), axes='eta', signal='roi2_sum', values='Tsample')
            # or, if scan 12345 is a 2D grid scan
            x, y, z = generate_mesh(12345, axes=('sx', 'sy'), signal='roi2_sum')

        :param scan_files: multiple files or single 2D grid scan
        :param hdf_map: hdfmap.NeXus map, or None to generate
        :param axes: x-axis name, or for grid scans the names of ('xaxis', 'yaxis')
        :param signal: signal name
        :param values: name of the value that changes between multiple files
        :returns: X, Y, IMAGE rank 2 arrays
        """
        scans = self.scans(*scan_files, hdf_map=hdf_map)
        if len(scan_files) == 1:
            # Single 2D Grid scan
            scan = scans[0]
            try:
                x_axis, y_axis = axes
            except ValueError:
                raise Exception("axes should be specified as axes=('axes0', 'axes1')")
            with scan.load_hdf() as hdf:
                x_data = scan.map.eval(hdf, x_axis, default=0.0).squeeze()
                y_data = scan.map.eval(hdf, y_axis, default=0.0).squeeze()
                z_data = scan.map.eval(hdf, signal, default=0.0).squeeze()
            if x_data.size != y_data.size:
                raise Exception(f"arrays '{x_axis}'[{x_data.size}] and '{y_axis}'[{y_data.size}] have different sizes")
            if x_data.ndim == 1 and y_data.ndim == 1:
                y_data, x_data = np.meshgrid(y_data, x_data)
            if z_data.shape != x_data.shape or z_data.shape != y_data.shape:
                raise Exception(
                    f"{repr(scan)} '{x_axis}', '{y_axis}' and '{signal}' shapes are not consistent: " +
                    f"x: {x_data.shape}, y: {y_data.shape}, im: {z_data.shape}"
                )
            return x_data, y_data, z_data
        else:
            x_data, y_data, z_data = [], [], []
            for n, scan in enumerate(scans):
                with scan.load_hdf() as hdf:
                    x = np.reshape(scan.map.eval(hdf, axes, default=0.0), -1)
                    y = np.reshape(scan.map.eval(hdf, signal, default=0.0), -1)
                    val = np.reshape(scan.map.eval(hdf, values, default=0.0), -1) if values is not None else np.array([n])
                if val.size == 1:
                    val = np.tile(val, x.size)
                if y.size != x.size or val.size != x.size:
                    raise Exception(
                        f"{repr(scan)} '{axes}', '{signal}' and '{values}' shapes are not consistent: " +
                        f"x, y, val = ({x.size}, {y.size}, {val.size})"
                    )
                x_data.append(x)
                y_data.append(val)
                z_data.append(y)
            # create a regular sized array
            min_len = min(len(x) for x in x_data)
            # array size [len(scan_files), min_len]
            x_array = np.array([x[:min_len] for x in x_data])
            y_array = np.array([y[:min_len] for y in y_data])
            z_array = np.array([z[:min_len] for z in z_data])
            return x_array, y_array, z_array

    def scan_str(self, scan_file: int | str = 0, metadata_str: str | None = None,
                 hdf_map: hdfmap.NexusMap | None = None) -> str:
        """Read scan file and return metadata string"""
        if metadata_str is None:
            from ..tkguis.misc.beamline_metadata import META_STRING, BEAMLINE_META
            if self.instrument in BEAMLINE_META:
                metadata_str = BEAMLINE_META[self.instrument]
            else:
                metadata_str = META_STRING

        scan_file = self.get_scan_filename(scan_file)

        if hdf_map is None:
            hdf_map = hdfmap.create_nexus_map(scan_file)

        with hdfmap.load_hdf(self.get_scan_filename(scan_file)) as hdf:
            return hdf_map.format_hdf(hdf, metadata_str, raise_errors=True)

    def scans_str(self, *scan_files: int | str, metadata_str: str | None = None,
                  hdf_map: hdfmap.NexusMap | None = None) -> list[str]:
        """Return list of string descriptions for multiple files"""
        if metadata_str is None:
            metadata_str = " : {str(start_time):30} : " + self.config[C.scan_description]
        filenames = [self.get_scan_filename(scan_file) for scan_file in scan_files]
        if hdf_map is None:
            hdf_map = hdfmap.create_nexus_map(filenames[0])
        folder_file = ['/'.join(filename.split(os.sep)[-2:]) for filename in filenames]
        return [
            name + self.scan_str(file, metadata_str, hdf_map)
            for file, name in zip(filenames, folder_file)
        ]

    def all_scans_str(self, metadata_str: str | None = None, hdf_map: hdfmap.NexusMap | None = None) -> str:
        """Return string descriptions for all files"""
        scan_files = self.all_scan_files()
        return '\n'.join(self.scans_str(*scan_files, metadata_str=metadata_str, hdf_map=hdf_map))

    def _generate_scans_title(self, *scans: NexusScan, metadata_str: str | None = None) -> str:
        """Generate title from multiple scan files"""
        if metadata_str is None:
            metadata_str = "\n" + self.config[C.scan_description]
        first_scan = scans[0]
        folder = first_scan.filename.split(os.sep)[-2]
        meta = first_scan.format(metadata_str)
        scan_numbers = [scan.scan_number() for scan in scans]
        number_range = numbers2string(scan_numbers)
        return f"{folder} {number_range} {meta}"

    def generate_scans_title(self, *scan_files: int | str, metadata_str: str | None = None,
                             hdf_map: hdfmap.NexusMap | None = None) -> str:
        """Generate title from multiple scan files"""
        scans = self.scans(*scan_files, hdf_map=hdf_map)
        return self._generate_scans_title(*scans, metadata_str=metadata_str)

    def load_xas(self, *scan_files: int | str, sample_name: str | None = '', element_edge: str | None = None,
                 mode: str | list[str] = 'all', dls_loader: bool = False, match_metadata: bool = True,
                 temp_tol: float = 1., field_tol: float = 0.1) -> list[SpectraContainer]:
        """
        Read XAS spectra - see xas.SpectraContainer

            spectra_list = exp.load_xas(12345, 12346, mode='TEY', match_metadata=True)

        :param scan_files: List of filenames or scan numbers in folder
        :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
        :param match_metadata: bool, if True uses metadata to determine from sample name
        :param temp_tol: Tolerance for temperature comparison (default: 0.1 K)
        :param field_tol: Tolerance for field comparison (default: 0.1 T)
        :return: List of SpectraContainer objects containing XAS spectra
        """
        filenames = [self.get_scan_filename(file) for file in scan_files]
        kwargs = dict(sample_name=sample_name, element_edge=element_edge, mode=mode, dls_loader=dls_loader)
        if match_metadata and len(scan_files) > 1:
            return find_similar_measurements(*filenames, temp_tol=temp_tol, field_tol=field_tol, **kwargs)
        return load_xas_scans(*filenames, **kwargs)

    def xas_polarised_spectra(self, *scan_files: int | str, sample_name: str | None = '',
                              element_edge: str | None = None, mode: str | list[str] = 'all',
                              dls_loader: bool = False, match_metadata: bool = True,
                              temp_tol: float = 1., field_tol: float = 0.1) -> tuple[SpectraContainer, SpectraContainer]:
        """
        Read XAS spectra - see xas.SpectraContainer

            pol1, pol2 = exp.xas_polarised_spectra(12345, 12346, mode='TEY', match_metadata=True)

        :param scan_files: List of filenames or scan numbers in folder
        :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
        :param match_metadata: bool, if True uses metadata to determine from sample name
        :param temp_tol: Tolerance for temperature comparison (default: 0.1 K)
        :param field_tol: Tolerance for field comparison (default: 0.1 T)
        :return: pol1, pol2 SpectraContainer objects containing averaged XAS spectra
        """
        spectra = self.load_xas(*scan_files, sample_name=sample_name, element_edge=element_edge,
                                mode=mode, dls_loader=dls_loader, match_metadata=match_metadata,
                                temp_tol=temp_tol, field_tol=field_tol)
        return average_polarised_scans(*spectra)

    def add_roi(self, name: str, cen_i: int | str, cen_j: int | str,
                wid_i: int = 30, wid_j: int = 30, image_name: str = 'IMAGE'):
        """
        Add an image ROI (region of interest) to scans
        The ROI operates on the default IMAGE dataset, loading only the required region from the file.
        The following expressions will be added, for use in self.eval etc.
            *name* -> returns the whole ROI array as a HDF5 dataset
            *name*_total -> returns the sum of each image in the ROI array
            *name*_max -> returns the max of each image in the ROI array
            *name*_min -> returns the min of each image in the ROI array
            *name*_mean -> returns the mean of each image in the ROI array
            *name*_bkg -> returns the background ROI array (area around ROI)
            *name*_rmbkg -> returns the total with background subtracted
            *name*_box -> returns the pixel positions of the ROI corners
            *name*_bkg_box -> returns the pixel positions of the background ROI

        :param name: string name of the ROI
        :param cen_i: central pixel index along first dimension, can be callable string
        :param cen_j: central pixel index along second dimension, can be callable string
        :param wid_i: full width along first dimension, in pixels
        :param wid_j: full width along second dimension, in pixels
        :param image_name: string name of the image
        """
        add_roi(self.config, name, cen_i, cen_j, wid_i, wid_j, image_name)

add_data_paths(*folder_paths)

Add additional paths

Source code in mmg_toolbox/utils/experiment.py 
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def add_data_paths(self, *folder_paths: str):
    """Add additional paths"""
    new_paths = list(
        path for path in folder_paths
        if path not in self.folder_paths and os.path.isdir(path)
    )
    self.folder_paths = self.folder_paths + new_paths
    self._update_scan_list()

add_roi(name, cen_i, cen_j, wid_i=30, wid_j=30, image_name='IMAGE')

Add an image ROI (region of interest) to scans The ROI operates on the default IMAGE dataset, loading only the required region from the file. The following expressions will be added, for use in self.eval etc. name -> returns the whole ROI array as a HDF5 dataset name_total -> returns the sum of each image in the ROI array name_max -> returns the max of each image in the ROI array name_min -> returns the min of each image in the ROI array name_mean -> returns the mean of each image in the ROI array name_bkg -> returns the background ROI array (area around ROI) name_rmbkg -> returns the total with background subtracted name_box -> returns the pixel positions of the ROI corners name_bkg_box -> returns the pixel positions of the background ROI

Parameters:

Name Type Description Default
name str

string name of the ROI

 required
cen_i int | str

central pixel index along first dimension, can be callable string

 required
cen_j int | str

central pixel index along second dimension, can be callable string

 required
wid_i int

full width along first dimension, in pixels

 30
wid_j int

full width along second dimension, in pixels

 30
image_name str

string name of the image

 'IMAGE'
Source code in mmg_toolbox/utils/experiment.py 
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def add_roi(self, name: str, cen_i: int | str, cen_j: int | str,
            wid_i: int = 30, wid_j: int = 30, image_name: str = 'IMAGE'):
    """
    Add an image ROI (region of interest) to scans
    The ROI operates on the default IMAGE dataset, loading only the required region from the file.
    The following expressions will be added, for use in self.eval etc.
        *name* -> returns the whole ROI array as a HDF5 dataset
        *name*_total -> returns the sum of each image in the ROI array
        *name*_max -> returns the max of each image in the ROI array
        *name*_min -> returns the min of each image in the ROI array
        *name*_mean -> returns the mean of each image in the ROI array
        *name*_bkg -> returns the background ROI array (area around ROI)
        *name*_rmbkg -> returns the total with background subtracted
        *name*_box -> returns the pixel positions of the ROI corners
        *name*_bkg_box -> returns the pixel positions of the background ROI

    :param name: string name of the ROI
    :param cen_i: central pixel index along first dimension, can be callable string
    :param cen_j: central pixel index along second dimension, can be callable string
    :param wid_i: full width along first dimension, in pixels
    :param wid_j: full width along second dimension, in pixels
    :param image_name: string name of the image
    """
    add_roi(self.config, name, cen_i, cen_j, wid_i, wid_j, image_name)

all_scans_str(metadata_str=None, hdf_map=None)

Return string descriptions for all files

Source code in mmg_toolbox/utils/experiment.py 
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def all_scans_str(self, metadata_str: str | None = None, hdf_map: hdfmap.NexusMap | None = None) -> str:
    """Return string descriptions for all files"""
    scan_files = self.all_scan_files()
    return '\n'.join(self.scans_str(*scan_files, metadata_str=metadata_str, hdf_map=hdf_map))

find_scans(*scan_files, hdf_map=None, first_only=False, **matches)

Find scans files with matching metadata

matches = {
    'name1': 'scan', # matches if 'scan' in file['name1']
    'name2': value, # matches if file['name2'] ~= value
    'name3': (value, tol), # matches if abs(file['name3'] - value) < tol
}
match_files = find_scans(*filenames, **matches)

Parameters:

Name Type Description Default
scan_files int | str

set of scan numbers or filenames, if not given will search all scans in folder.

 ()
hdf_map NexusMap | None

if given, uses this hdfmap rather than generating one.

 None
first_only bool

if true, returns on the first result

 False
matches str | float | tuple[float, float]

keyword arguments for matching parameters

 {}

Returns:

Type Description
list[NexusScan]

list of scan files that match all requirements
Source code in mmg_toolbox/utils/experiment.py 
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def find_scans(self, *scan_files: int | str,  hdf_map: hdfmap.NexusMap | None = None, first_only: bool = False,
               **matches: str | float | tuple[float, float]) -> list[NexusScan]:
    """
    Find scans files with matching metadata

        matches = {
            'name1': 'scan', # matches if 'scan' in file['name1']
            'name2': value, # matches if file['name2'] ~= value
            'name3': (value, tol), # matches if abs(file['name3'] - value) < tol
        }
        match_files = find_scans(*filenames, **matches)

    :param scan_files: set of scan numbers or filenames, if not given will search all scans in folder.
    :param hdf_map: if given, uses this hdfmap rather than generating one.
    :param first_only: if true, returns on the first result
    :param matches: keyword arguments for matching parameters
    :returns: list of scan files that match all requirements
    """
    filenames = [self.get_scan_filename(scan_file) for scan_file in scan_files]
    if not filenames:
        filenames = list(self.all_scans().values())
    if hdf_map is None:
        hdf_map = hdfmap.create_nexus_map(filenames[0])
    matches = find_scans(*filenames, hdf_map=hdf_map, first_only=first_only, **matches)
    return self.scans(*matches, hdf_map=hdf_map)

generate_mesh(*scan_files, hdf_map=None, axes='axes', signal='axes', values=None)

Generate 2D mesh from scan or scans

x, y, z = generate_mesh(*range(-10, 0), axes='eta', signal='roi2_sum', values='Tsample')
# or, if scan 12345 is a 2D grid scan
x, y, z = generate_mesh(12345, axes=('sx', 'sy'), signal='roi2_sum')

Parameters:

Name Type Description Default
scan_files int | str

multiple files or single 2D grid scan

 ()
hdf_map NexusMap | None

hdfmap.NeXus map, or None to generate

 None
axes str | tuple[str, str]

x-axis name, or for grid scans the names of ('xaxis', 'yaxis')

 'axes'
signal str

signal name

 'axes'
values str | None

name of the value that changes between multiple files

 None

Returns:

Type Description
tuple[ndarray, ndarray, ndarray]

X, Y, IMAGE rank 2 arrays
Source code in mmg_toolbox/utils/experiment.py 
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def generate_mesh(self, *scan_files: int | str, hdf_map: hdfmap.NexusMap | None = None,
                  axes: str | tuple[str, str] = 'axes', signal: str = 'axes',
                  values: str | None = None) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Generate 2D mesh from scan or scans

        x, y, z = generate_mesh(*range(-10, 0), axes='eta', signal='roi2_sum', values='Tsample')
        # or, if scan 12345 is a 2D grid scan
        x, y, z = generate_mesh(12345, axes=('sx', 'sy'), signal='roi2_sum')

    :param scan_files: multiple files or single 2D grid scan
    :param hdf_map: hdfmap.NeXus map, or None to generate
    :param axes: x-axis name, or for grid scans the names of ('xaxis', 'yaxis')
    :param signal: signal name
    :param values: name of the value that changes between multiple files
    :returns: X, Y, IMAGE rank 2 arrays
    """
    scans = self.scans(*scan_files, hdf_map=hdf_map)
    if len(scan_files) == 1:
        # Single 2D Grid scan
        scan = scans[0]
        try:
            x_axis, y_axis = axes
        except ValueError:
            raise Exception("axes should be specified as axes=('axes0', 'axes1')")
        with scan.load_hdf() as hdf:
            x_data = scan.map.eval(hdf, x_axis, default=0.0).squeeze()
            y_data = scan.map.eval(hdf, y_axis, default=0.0).squeeze()
            z_data = scan.map.eval(hdf, signal, default=0.0).squeeze()
        if x_data.size != y_data.size:
            raise Exception(f"arrays '{x_axis}'[{x_data.size}] and '{y_axis}'[{y_data.size}] have different sizes")
        if x_data.ndim == 1 and y_data.ndim == 1:
            y_data, x_data = np.meshgrid(y_data, x_data)
        if z_data.shape != x_data.shape or z_data.shape != y_data.shape:
            raise Exception(
                f"{repr(scan)} '{x_axis}', '{y_axis}' and '{signal}' shapes are not consistent: " +
                f"x: {x_data.shape}, y: {y_data.shape}, im: {z_data.shape}"
            )
        return x_data, y_data, z_data
    else:
        x_data, y_data, z_data = [], [], []
        for n, scan in enumerate(scans):
            with scan.load_hdf() as hdf:
                x = np.reshape(scan.map.eval(hdf, axes, default=0.0), -1)
                y = np.reshape(scan.map.eval(hdf, signal, default=0.0), -1)
                val = np.reshape(scan.map.eval(hdf, values, default=0.0), -1) if values is not None else np.array([n])
            if val.size == 1:
                val = np.tile(val, x.size)
            if y.size != x.size or val.size != x.size:
                raise Exception(
                    f"{repr(scan)} '{axes}', '{signal}' and '{values}' shapes are not consistent: " +
                    f"x, y, val = ({x.size}, {y.size}, {val.size})"
                )
            x_data.append(x)
            y_data.append(val)
            z_data.append(y)
        # create a regular sized array
        min_len = min(len(x) for x in x_data)
        # array size [len(scan_files), min_len]
        x_array = np.array([x[:min_len] for x in x_data])
        y_array = np.array([y[:min_len] for y in y_data])
        z_array = np.array([z[:min_len] for z in z_data])
        return x_array, y_array, z_array

generate_scans_title(*scan_files, metadata_str=None, hdf_map=None)

Generate title from multiple scan files

Source code in mmg_toolbox/utils/experiment.py 
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def generate_scans_title(self, *scan_files: int | str, metadata_str: str | None = None,
                         hdf_map: hdfmap.NexusMap | None = None) -> str:
    """Generate title from multiple scan files"""
    scans = self.scans(*scan_files, hdf_map=hdf_map)
    return self._generate_scans_title(*scans, metadata_str=metadata_str)

get_all_data(*fields, default=np.array([0.0]))

Return dict of data for all files

Source code in mmg_toolbox/utils/experiment.py 
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def get_all_data(self, *fields: str, default: np.ndarray = np.array([0.0])) -> dict[str, list]:
    """
    Return dict of data for all files
    """
    return self.join_scan_data(data_fields=list(fields), default=default)

get_scan_filename(scan_file=-1)

Return the full filename of a scan number

Source code in mmg_toolbox/utils/experiment.py 
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def get_scan_filename(self, scan_file: int | str = -1) -> str:
    """Return the full filename of a scan number"""
    if isinstance(scan_file, int) or scan_file.isdigit():
        scan_file = int(scan_file)
        if scan_file < 1:
            scan_numbers = self._scan_numbers()
            return self.scan_list[scan_numbers[scan_file]]
        self._update_scan_list()
        if scan_file in self.scan_list:
            return self.scan_list[scan_file]
        scan_numbers = self._scan_numbers()
        return self.scan_list[scan_numbers[scan_file]]

    if os.path.isfile(scan_file):
        return os.path.abspath(scan_file)
    raise FileNotFoundError(f"scan file {scan_file} not found")

join_scan_data(*scan_files, hdf_map=None, data_fields=None, default=np.array([0.0]))

Join data from scans

Source code in mmg_toolbox/utils/experiment.py 
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def join_scan_data(self, *scan_files: int | str, hdf_map: hdfmap.NexusMap | None = None,
                   data_fields: list[str] | None = None, default: np.ndarray = np.array([0.0])) -> dict[str, list]:
    """
    Join data from scans
    """
    scans = self.scans(*scan_files, hdf_map=hdf_map)
    data_fields = [self.config[C.scan_description]] if data_fields is None else data_fields
    data = {name: [] for name in data_fields}
    for scan in scans:
        with scan.load_hdf() as hdf:
            for name in data_fields:
                data[name].append(scan.map.eval(hdf, name, default=default))
    return data

load_xas(*scan_files, sample_name='', element_edge=None, mode='all', dls_loader=False, match_metadata=True, temp_tol=1.0, field_tol=0.1)

Read XAS spectra - see xas.SpectraContainer

spectra_list = exp.load_xas(12345, 12346, mode='TEY', match_metadata=True)

Parameters:

Name Type Description Default
scan_files int | str

List of filenames or scan numbers in folder

 ()
sample_name str | None

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

 ''
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
match_metadata bool

bool, if True uses metadata to determine from sample name

 True
temp_tol float

Tolerance for temperature comparison (default: 0.1 K)

 1.0
field_tol float

Tolerance for field comparison (default: 0.1 T)

 0.1

Returns:

Type Description
list[SpectraContainer]

List of SpectraContainer objects containing XAS spectra
Source code in mmg_toolbox/utils/experiment.py 
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def load_xas(self, *scan_files: int | str, sample_name: str | None = '', element_edge: str | None = None,
             mode: str | list[str] = 'all', dls_loader: bool = False, match_metadata: bool = True,
             temp_tol: float = 1., field_tol: float = 0.1) -> list[SpectraContainer]:
    """
    Read XAS spectra - see xas.SpectraContainer

        spectra_list = exp.load_xas(12345, 12346, mode='TEY', match_metadata=True)

    :param scan_files: List of filenames or scan numbers in folder
    :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
    :param match_metadata: bool, if True uses metadata to determine from sample name
    :param temp_tol: Tolerance for temperature comparison (default: 0.1 K)
    :param field_tol: Tolerance for field comparison (default: 0.1 T)
    :return: List of SpectraContainer objects containing XAS spectra
    """
    filenames = [self.get_scan_filename(file) for file in scan_files]
    kwargs = dict(sample_name=sample_name, element_edge=element_edge, mode=mode, dls_loader=dls_loader)
    if match_metadata and len(scan_files) > 1:
        return find_similar_measurements(*filenames, temp_tol=temp_tol, field_tol=field_tol, **kwargs)
    return load_xas_scans(*filenames, **kwargs)

scan(scan_file=-1)

read Nexus file as NexusDataHolder

Source code in mmg_toolbox/utils/experiment.py 
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def scan(self, scan_file: int | str = -1) -> NexusDataHolder:
    """read Nexus file as NexusDataHolder"""
    return NexusDataHolder(self.get_scan_filename(scan_file), config=self.config)

scan_str(scan_file=0, metadata_str=None, hdf_map=None)

Read scan file and return metadata string

Source code in mmg_toolbox/utils/experiment.py 
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def scan_str(self, scan_file: int | str = 0, metadata_str: str | None = None,
             hdf_map: hdfmap.NexusMap | None = None) -> str:
    """Read scan file and return metadata string"""
    if metadata_str is None:
        from ..tkguis.misc.beamline_metadata import META_STRING, BEAMLINE_META
        if self.instrument in BEAMLINE_META:
            metadata_str = BEAMLINE_META[self.instrument]
        else:
            metadata_str = META_STRING

    scan_file = self.get_scan_filename(scan_file)

    if hdf_map is None:
        hdf_map = hdfmap.create_nexus_map(scan_file)

    with hdfmap.load_hdf(self.get_scan_filename(scan_file)) as hdf:
        return hdf_map.format_hdf(hdf, metadata_str, raise_errors=True)

scans(*scan_files, hdf_map=None)

Read Nexus files as lazy NexusScan. All files use the same HdfMap, based on the first scan

Source code in mmg_toolbox/utils/experiment.py 
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def scans(self, *scan_files: int | str, hdf_map: hdfmap.NexusMap | None = None) -> list[NexusScan]:
    """Read Nexus files as lazy NexusScan. All files use the same HdfMap, based on the first scan"""
    filenames = [self.get_scan_filename(scan_file) for scan_file in scan_files]
    if not filenames:
        filenames = list(self.all_scans().values())
    if filenames and hdf_map is None:
        hdf_map = hdfmap.create_nexus_map(filenames[0])
    return [NexusScan(file, hdf_map, config=self.config) for file in filenames]

scans_str(*scan_files, metadata_str=None, hdf_map=None)

Return list of string descriptions for multiple files

Source code in mmg_toolbox/utils/experiment.py 
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def scans_str(self, *scan_files: int | str, metadata_str: str | None = None,
              hdf_map: hdfmap.NexusMap | None = None) -> list[str]:
    """Return list of string descriptions for multiple files"""
    if metadata_str is None:
        metadata_str = " : {str(start_time):30} : " + self.config[C.scan_description]
    filenames = [self.get_scan_filename(scan_file) for scan_file in scan_files]
    if hdf_map is None:
        hdf_map = hdfmap.create_nexus_map(filenames[0])
    folder_file = ['/'.join(filename.split(os.sep)[-2:]) for filename in filenames]
    return [
        name + self.scan_str(file, metadata_str, hdf_map)
        for file, name in zip(filenames, folder_file)
    ]

xas_polarised_spectra(*scan_files, sample_name='', element_edge=None, mode='all', dls_loader=False, match_metadata=True, temp_tol=1.0, field_tol=0.1)

Read XAS spectra - see xas.SpectraContainer

pol1, pol2 = exp.xas_polarised_spectra(12345, 12346, mode='TEY', match_metadata=True)

Parameters:

Name Type Description Default
scan_files int | str

List of filenames or scan numbers in folder

 ()
sample_name str | None

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

 ''
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
match_metadata bool

bool, if True uses metadata to determine from sample name

 True
temp_tol float

Tolerance for temperature comparison (default: 0.1 K)

 1.0
field_tol float

Tolerance for field comparison (default: 0.1 T)

 0.1

Returns:

Type Description
tuple[SpectraContainer, SpectraContainer]

pol1, pol2 SpectraContainer objects containing averaged XAS spectra
Source code in mmg_toolbox/utils/experiment.py 
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def xas_polarised_spectra(self, *scan_files: int | str, sample_name: str | None = '',
                          element_edge: str | None = None, mode: str | list[str] = 'all',
                          dls_loader: bool = False, match_metadata: bool = True,
                          temp_tol: float = 1., field_tol: float = 0.1) -> tuple[SpectraContainer, SpectraContainer]:
    """
    Read XAS spectra - see xas.SpectraContainer

        pol1, pol2 = exp.xas_polarised_spectra(12345, 12346, mode='TEY', match_metadata=True)

    :param scan_files: List of filenames or scan numbers in folder
    :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
    :param match_metadata: bool, if True uses metadata to determine from sample name
    :param temp_tol: Tolerance for temperature comparison (default: 0.1 K)
    :param field_tol: Tolerance for field comparison (default: 0.1 T)
    :return: pol1, pol2 SpectraContainer objects containing averaged XAS spectra
    """
    spectra = self.load_xas(*scan_files, sample_name=sample_name, element_edge=element_edge,
                            mode=mode, dls_loader=dls_loader, match_metadata=match_metadata,
                            temp_tol=temp_tol, field_tol=field_tol)
    return average_polarised_scans(*spectra)

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]

create_notebooks(directory=None, beamline=None, start_jupyter=None, prefix='', quiet=False)

Create notebooks at start of experiment

Parameters:

Name Type Description Default
directory str | None

directory where to create notebooks, or visit ID for current beamline

 None
beamline str | None

Beamline name, e.g. i06-1

 None
start_jupyter bool | None

start jupyter lab

 None
prefix str

prefix for notebook/ script filenames

 ''
quiet bool

quiet mode (no user inputs)

 False
Source code in mmg_toolbox/scripts/experiment_startup.py 
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def create_notebooks(directory: str | None = None, beamline: str | None = None,
                     start_jupyter: bool | None = None, prefix: str = '', quiet: bool = False):
    """
    Create notebooks at start of experiment

    :param directory: directory where to create notebooks, or visit ID for current beamline
    :param beamline: Beamline name, e.g. i06-1
    :param start_jupyter: start jupyter lab
    :param prefix: prefix for notebook/ script filenames
    :param quiet: quiet mode (no user inputs)
    """
    print("Creating experiment notebooks for mmg_toolbox")
    print(f"Directory: {directory}, Beamline: {beamline}")
    # Beamline
    if beamline is None:
        if directory is None and not quiet:
            beamline = input("Which beamline? e.g. i06-1:")
        else:
            beamline = get_beamline_from_directory(directory or '', default='')

    # Visits (from beamline)
    visits = get_dls_visits(beamline)
    current_visit = next(iter(visits.keys()), os.getcwd())
    if directory is None and not quiet:
        if visits:
            print(f"Visits for beamline {beamline}:\n  " + "\n  ".join(visits))
        directory = input(f"Which visit or directory? [default: {current_visit}]:")
    if not directory:
        directory = current_visit
    if directory in visits:
        directory = visits[directory]

    # Directory (from visits)
    directory = os.path.abspath(directory)
    if not os.path.isdir(directory):
        raise FileNotFoundError(f"Directory {directory} does not exist")
    processing_directory = get_processing_directory(directory)
    if not quiet and os.path.isdir(get_processing_directory(directory)):
        use_processing = input("Use the processing directory? [Y/n]:")
        if use_processing.lower() == 'y':
            directory = processing_directory

    # Beamline (again, from directory)
    if not beamline:
        beamline = get_beamline_from_directory(directory, default='')

    print(f"Creating notebooks for {beamline} in {directory}")
    title = f"{beamline} {os.path.basename(directory)} mmg_toolbox Example Script"
    create_beamline_scripts(beamline, directory, prefix=prefix, title=title)

    # Start Jupyter
    if not quiet and start_jupyter is None:
        start_jupyter = input("Would you like to start Jupyter? [Y/n]:")
        start_jupyter = start_jupyter.lower() == 'y'
    if start_jupyter:
        open_jupyter_lab(directory)

data_file_reader(filename, beamline=None)

Read Nexus or dat file as DataHolder

Source code in mmg_toolbox/utils/file_reader.py 
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def data_file_reader(filename: str, beamline: str | None = None) -> NexusDataHolder | DataHolder:
    """
    Read Nexus or dat file as DataHolder
    """
    beamline = beamline or get_beamline(None, filename=filename)
    if filename.endswith('.dat'):
        return read_dat_file(filename)
    return read_nexus_file(filename, beamline=beamline)

find_scan_files(scan_file, directory=None, instrument=None, extension='.nxs')

Recursively search a directory for scan files, returns a generator

fn = find_scan_files(12345, '/dls/i16/data')
filepath = next(fn, None)  # returns first filepath with '12345' in the title
all_paths = list(fn)

Parameters:

Name Type Description Default
scan_file str | int

scan number or part of filename

 required
directory str | None

directory to search for scan files, or None to use instrument

 None
instrument str | None

if directory is None, use instrument to use DLS data directory (None to use current beamline)

 None
extension str

extension to search for scan files

 '.nxs'

Returns:

Type Description
Iterator[str]

generator object for search
Source code in mmg_toolbox/utils/env_functions.py 
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def find_scan_files(scan_file: str | int, directory: str | None = None,
                    instrument: str | None = None, extension: str = '.nxs') -> Iterator[str]:
    """
    Recursively search a directory for scan files, returns a generator

        fn = find_scan_files(12345, '/dls/i16/data')
        filepath = next(fn, None)  # returns first filepath with '12345' in the title
        all_paths = list(fn)

    :param scan_file: scan number or part of filename
    :param directory: directory to search for scan files, or None to use instrument
    :param instrument: if directory is None, use instrument to use DLS data directory (None to use current beamline)
    :param extension: extension to search for scan files
    :return: generator object for search
    """
    scan_file = str(scan_file)
    if directory is None:
        if instrument is None:
            instrument = get_beamline()
        directory = os.path.join(DLS, instrument.lower(), 'data')

    def scantree(path: str):
        for entry in os.scandir(path):
            if os.access(entry.path, os.R_OK) and not entry.name.startswith('.'):
                if entry.is_file() and entry.name.endswith(extension) and scan_file in entry.name:
                    yield entry.path
                elif entry.is_dir():
                    yield from scantree(entry.path)
    return scantree(directory)

get_dls_visits(instrument=None, year=None, max_visits=None, omit_empty=False)

Return dict of {visit: path} for each visible visit in the beamline directory

visits = get_dls_visits('i16')

Parameters:

Name Type Description Default
instrument str | None

displays visits for this instrument, or None for current beamline.

 None
year str | int | None

displays visits in this year, or None for current year.

 None
max_visits int | None

maximum number of visits to display, nor None for all visits

 None
omit_empty bool

if True, omits visits containing no scans

 False
Source code in mmg_toolbox/utils/env_functions.py 
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def get_dls_visits(instrument: str | None = None, year: str | int | None = None,
                   max_visits: int | None = None, omit_empty: bool = False) -> dict[str, str]:
    """
    Return dict of {visit: path} for each visible visit in the beamline directory

        visits = get_dls_visits('i16')

    :param instrument: displays visits for this instrument, or None for current beamline.
    :param year: displays visits in this year, or None for current year.
    :param max_visits: maximum number of visits to display, nor None for all visits
    :param omit_empty: if True, omits visits containing no scans
    """
    if instrument is None:
        instrument = get_beamline()
    if year is None:
        year = datetime.now().year

    if max_visits:
        visits = get_dls_visits(instrument, year, max_visits=None, omit_empty=omit_empty)
        return {visit: visits[visit] for visit in list(visits)[:max_visits]}

    dls_dir = os.path.join(DLS, instrument.lower(), 'data', str(year))
    if os.path.isdir(dls_dir):
        return {
            os.path.basename(path): path
            for path in sorted(
                (
                    file.path for file in os.scandir(dls_dir)
                    if file.is_dir() and
                       os.access(file.path, os.R_OK) and
                       (contains_filetype(file.path, '.nxs') if omit_empty else True)
                 ),
                key=lambda x: os.path.getmtime(x), reverse=True
            )
        }
    return {}

get_dls_visits_str(instrument=None, year=None, max_visits=3, omit_empty=True)

Return str of visits for each visible visit int he beamline directory

print(get_dls_visits_str('i16', 2025))

Parameters:

Name Type Description Default
instrument str | None

displays visits for this instrument, or None for current beamline.

 None
year str | int | None

displays visits in this year, or None for current year.

 None
max_visits int | None

maximum number of visits to display, nor None for all visits

 3
omit_empty bool

if True, omits visits containing no scans

 True

Returns:

Type Description
str

multiline string
Source code in mmg_toolbox/utils/env_functions.py 
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def get_dls_visits_str(instrument: str | None = None, year: str | int | None = None,
                       max_visits: int | None = 3, omit_empty: bool = True) -> str:
    """
    Return str of visits for each visible visit int he beamline directory

        print(get_dls_visits_str('i16', 2025))

    :param instrument: displays visits for this instrument, or None for current beamline.
    :param year: displays visits in this year, or None for current year.
    :param max_visits: maximum number of visits to display, nor None for all visits
    :param omit_empty: if True, omits visits containing no scans
    :return: multiline string
    """
    instrument = instrument or get_beamline()
    year = year or datetime.now().year
    visits = get_dls_visits(instrument, year)
    if not visits:
        return f"No visits for instrument '{instrument}'"

    strings = []
    for visit, path in visits.items():
        scans = scan_number_mapping(path)
        if len(scans) > 0:
            last_scan_number = list(scans)[-1]
            last_scan_path = list(scans.values())[-1]
            last_scan_time = datetime.fromtimestamp(os.path.getmtime(last_scan_path), tz=timezone.utc)
            last_scan = f", #{last_scan_number:<8} ({last_scan_time:%Y-%m-%d %H:%M})"
        else:
            last_scan = ""
            if omit_empty:
                continue
        strings.append(f"{visit:10} Scans: {len(scans):4}{last_scan}")
        if len(strings) == max_visits:
            break
    return f"{instrument} visits in {year}\n" + '\n'.join(strings)

scan_number_mapping(*folders, extension='.nxs')

Build mapping of scan number to scan file

Source code in mmg_toolbox/utils/env_functions.py 
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def scan_number_mapping(*folders: str, extension: str = '.nxs') -> dict[int, str]:
    """Build mapping of scan number to scan file"""
    mapping = {
        number: filename
        for folder in folders
        for filename in list_files(folder, extension=extension)
        if (number := get_scan_number(filename)) > 0
    }
    return dict(sorted(mapping.items()))