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experiment

Experiment

Bases: NewBase

Holds data from an experiment as a sc.DataArray along with metadata.

This is a minimal implementation that will be extended in the future.

Source code in src/easydynamics/experiment/experiment.py 
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class Experiment(NewBase):
    """Holds data from an experiment as a sc.DataArray along with
    metadata.

    This is a minimal implementation that will be extended in the
    future.
    """

    def __init__(
        self,
        display_name: str | None = 'MyExperiment',
        unique_name: str | None = None,
        data: sc.DataArray | str | None = None,
    ) -> None:
        """Initialize the Experiment object.

        Args:
            display_name (str | None, default="MyExperiment"): Display name of the experiment.
            unique_name (str | None, default=None): Unique name of the experiment. If
                None, a unique name will be generated.
            data (sc.DataArray | str | None, default=None): Dataset associated with
                the experiment. Can be a sc.DataArray or a filename
                string to load from. If None, no data is loaded.

        Raises:
            TypeError: If data is not a sc.DataArray, a string, or None.
        """
        super().__init__(
            display_name=display_name,
            unique_name=unique_name,
        )

        if data is None:
            self._data = None
        elif isinstance(data, str):
            self.load_hdf5(filename=data)
        elif isinstance(data, sc.DataArray):
            self._validate_coordinates(data)
            self._data = data
        else:
            raise TypeError(
                f'Data must be a sc.DataArray or a filename string, not {type(data).__name__}'
            )

        self._binned_data = (
            self._convert_to_bin_centers(self._data) if self._data is not None else None
        )

    ###########
    # Properties
    ###########

    @property
    def data(self) -> sc.DataArray | None:
        """Get the dataset associated with this experiment.

        Returns:
            sc.DataArray | None: The dataset associated with this
                experiment, or None if no data is loaded.
        """
        return self._data

    @data.setter
    def data(self, value: sc.DataArray) -> None:
        """Set the dataset associated with this experiment.

        Args:
            value (sc.DataArray): The new dataset to associate with this
                experiment.

        Raises:
            TypeError: If the value is not a sc.DataArray.
        """
        if not isinstance(value, sc.DataArray):
            raise TypeError(f'Data must be a sc.DataArray, not {type(value).__name__}')
        self._validate_coordinates(value)
        self._data = value
        self._binned_data = (
            self._convert_to_bin_centers(self._data) if self._data is not None else None
        )

    @property
    def binned_data(self) -> sc.DataArray | None:
        """Get the binned dataset associated with this experiment.

        Returns:
            sc.DataArray | None: The binned dataset associated with this
                experiment, or None if no data is loaded.
        """
        return self._binned_data

    @binned_data.setter
    def binned_data(self, value: sc.DataArray) -> None:
        """Set the binned dataset associated with this experiment. Read-
        only property. Use rebin() to rebin the data instead.

        Args:
            value (sc.DataArray): The new binned dataset to associate
                with this experiment (ignored)

        Raises:
            AttributeError: Always, since binned_data is read-only.
        """
        raise AttributeError('binned_data is a read-only property. Use rebin() to rebin the data')

    @property
    def Q(self) -> sc.Variable | None:
        """Get the Q values from the dataset.

        Returns:
            sc.Variable | None: The Q values from the dataset, or None
                if no data is loaded.
        """
        if self._binned_data is None:
            return None
        return self._binned_data.coords['Q']

    @Q.setter
    def Q(self, value: sc.Variable) -> None:
        """Set the Q values for the dataset. Q is a read-only property
        derived from the data, so this setter raises an error.

        Args:
            value (sc.Variable): The new Q values to set (ignored)

        Raises:
            AttributeError: Always, since Q is read-only.
        """
        raise AttributeError('Q is a read-only property derived from the data.')

    @property
    def energy(self) -> sc.Variable | None:
        """Get the energy values from the dataset.

        Returns:
            sc.Variable | None: The energy values from the dataset, or
                None if no data is loaded.
        """
        if self._binned_data is None:
            return None
        return self._binned_data.coords['energy']

    @energy.setter
    def energy(self, value: sc.Variable) -> None:
        """Set the energy values for the dataset. Energy is a read-only
        property derived from the data, so this setter raises an error.

        Args:
            value (sc.Variable): The new energy values to set (ignored)

        Raises:
            AttributeError: Always, since energy is read-only.
        """
        raise AttributeError('energy is a read-only property derived from the data.')

    def get_masked_energy(self, Q_index: int) -> sc.Variable | None:
        """Get the energy values from the dataset, removing points where
        the y values or variances are NaN or Inf for the given Q index.

        Args:
            Q_index (int): The Q index to get the masked energy values
                for.

        Returns:
            sc.Variable | None: The masked energy values from the
                dataset, or None if no data is loaded.

        Raises:
            IndexError: If Q_index is not a valid index for the Q values.
        """
        if self._binned_data is None:
            return None

        if (
            not isinstance(Q_index, int)
            or Q_index < 0
            or (self.Q is not None and Q_index >= len(self.Q))
        ):
            raise IndexError('Q_index must be a valid index for the Q values.')

        energy = self._binned_data.coords['energy']
        _, _, _, mask = self._extract_x_y_weights_only_finite(Q_index=Q_index)

        mask_var = sc.array(dims=['energy'], values=mask)
        masked_energy = energy[mask_var]
        return masked_energy

    ###########
    # Handle data
    ###########

    def load_hdf5(self, filename: str, display_name: str | None = None) -> None:
        """Load data from an HDF5 file.

        Args:
            filename (str ): Path to the HDF5 file.
            display_name (str | None, default=None): Optional display name for the
                experiment.

        Raises:
            TypeError: If filename is not a string or if display_name is
                not a string or None or if the loaded data is not a sc.DataArray.
        """
        if not isinstance(filename, str):
            raise TypeError(f'Filename must be a string, not {type(filename).__name__}')

        if display_name is not None:
            if not isinstance(display_name, str):
                raise TypeError(
                    f'Display name must be a string, not {type(display_name).__name__}'
                )
            self.display_name = display_name

        loaded_data = sc_load_hdf5(filename)
        if not isinstance(loaded_data, sc.DataArray):
            raise TypeError(
                f'Loaded data must be a sc.DataArray, not {type(loaded_data).__name__}'
            )
        self._validate_coordinates(loaded_data)
        self.data = loaded_data

    def save_hdf5(self, filename: str | None = None) -> None:
        """Save the dataset to HDF5.

        Args:
            filename (str | None, default=None): Path to the output HDF5 file.
                If None, the file will be named after the unique_name of
                the experiment with a .h5 extension.

        Raises:
            TypeError: If filename is not a string or None.
            ValueError: If there is no data to save.
        """

        if filename is None:
            filename = f'{self.unique_name}.h5'

        if not isinstance(filename, str):
            raise TypeError(f'Filename must be a string, not {type(filename).__name__}')

        if self._data is None:
            raise ValueError('No data to save.')

        dir_name = os.path.dirname(filename)
        if dir_name:
            os.makedirs(dir_name, exist_ok=True)

        sc_save_hdf5(self._data, filename)

    def remove_data(self) -> None:
        """Remove the dataset from the experiment."""
        self._data = None
        self._binned_data = None

    def rebin(self, dimensions: dict[str, int | sc.Variable]) -> None:
        """Rebin the dataset along specified dimensions.

        Args:
            dimensions (dict[str, int | sc.Variable]): A dictionary
                mapping dimension names to number of bins (int) or bin
                edges (sc.Variable).

        Raises:
            TypeError: If dimensions is not a dictionary or if
                keys/values are of incorrect types.
            ValueError: If there is no data to rebin.
            KeyError: If a specified dimension is not in the dataset.
        """

        if not isinstance(dimensions, dict):
            raise TypeError(
                'dimensions must be a dictionary mapping dimension names '
                'to number of bins or bin values as sc.Variable.'
            )
        if self._data is None:
            raise ValueError('No data to rebin. Please load data first.')
        binned_data = self._data.copy()
        dim_copy = dimensions.copy()
        for dim, value in dim_copy.items():
            if not isinstance(dim, str):
                raise TypeError(
                    f'Dimension keys must be strings. Got {type(dim)} for {dim} instead.'
                )
            if dim not in self._data.dims:
                raise KeyError(
                    f"Dimension '{dim}' not a valid dimension for rebinning. "
                    f'Should be one of {self._data.dims}.'
                )
            if isinstance(value, float) and value.is_integer():  # I allow eg. 2.0 as well as 2
                value = int(value)
                # This line can be removed when scipp resize support
                # resizing with coordinates
                dimensions[dim] = value
            if not (isinstance(value, int) or isinstance(value, sc.Variable)):
                raise TypeError(
                    f'Dimension values must be integers or sc.Variable. '
                    f"Got {type(value)} for dimension '{dim}' instead."
                )
            binned_data = binned_data.bin({dim: value})

        binned_data = binned_data.bins.mean()
        binned_data = self._convert_to_bin_centers(binned_data)
        self._binned_data = binned_data

    ###########
    # other methods
    ###########

    def plot_data(self, slicer: bool = False, **kwargs: dict) -> None:
        """Plot the dataset using plopp: https://scipp.github.io/plopp/

        Args:
            slicer (bool, default=False): If True, use plopp's slicer instead of plot.
            **kwargs (dict): Additional keyword arguments to pass to plopp.

        Raises:
            ValueError: If there is no data to plot.
            RuntimeError: If not in a Jupyter notebook environment.
        """

        if self._binned_data is None:
            raise ValueError('No data to plot. Please load data first.')

        if not _in_notebook():
            raise RuntimeError('plot_data() can only be used in a Jupyter notebook environment.')

        plot_kwargs_defaults = {
            'title': self.display_name,
        }
        # Overwrite defaults with any user-provided kwargs
        plot_kwargs_defaults.update(kwargs)
        if slicer:
            fig = pp.slicer(
                self._binned_data,
                **plot_kwargs_defaults,
            )
            for widget in fig.bottom_bar[0].controls.values():
                widget.slider_toggler.value = '-o-'

        else:
            fig = pp.plot(
                self._binned_data.transpose(dims=['energy', 'Q']),
                **plot_kwargs_defaults,
            )
        return fig

    ###########
    # private methods
    ###########

    @staticmethod
    def _validate_coordinates(data: sc.DataArray) -> None:
        """Validate that required coordinates are present in the data.

        Args:
            data (sc.DataArray): The data to validate.

        Raises:
            TypeError: If data is not a sc.DataArray.
            ValueError: If required coordinates are missing.
        """
        if not isinstance(data, sc.DataArray):
            raise TypeError('Data must be a sc.DataArray.')

        required_coords = ['Q', 'energy']
        for coord in required_coords:
            if coord not in data.coords:
                raise ValueError(f"Data is missing required coordinate: '{coord}'")

    def _convert_to_bin_centers(self, data: sc.DataArray) -> sc.DataArray:
        """Convert the coordinates of the data to bin centers.

        Args:
            data (sc.DataArray): The data to convert.

        Returns:
            sc.DataArray: The data with coordinates at bin centers.
        """
        for dim in data.dims:
            coord = data.coords[dim]
            if coord.ndim == 1 and coord.size == data.sizes[dim] + 1:
                # Coordinate is at bin edges, convert to bin centers
                data = data.assign_coords({dim: sc.midpoints(coord)})
        return data

    def _extract_x_y_var(self, Q_index: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        """Extract the x, y, and weights arrays from the experiment for
        the given Q index.

        Args:
            Q_index (int): The Q index to extract the data for.

        Returns:
            tuple[np.ndarray, np.ndarray, np.ndarray]: The x, y, and
                variances arrays extracted from the experiment for the
                given Q index.
        """
        data = self.binned_data['Q', Q_index]
        x = data.coords['energy'].values
        y = data.values
        var = data.variances
        return x, y, var

    def _extract_x_y_weights_only_finite(
        self, Q_index: int
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        """Extract the x, y, and weights arrays from the experiment for
        the given Q index, removing any NaN and Inf values.

        Args:
            Q_index (int): The Q index to extract the data for.

        Returns:
            tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: The
                x, y, weights, and mask arrays extracted from the
                experiment for the given Q index, with NaNs and Infs
                removed.

        Raises:
            ValueError: If any variances are zero after removing NaNs
                and Infs, since this would lead to infinite weights.
        """
        x, y, var = self._extract_x_y_var(Q_index)

        if var is None:
            mask = np.isfinite(y) & np.isfinite(x)
            # If variances are not provided, set them to 1 to make all
            # weights be 1
            var = np.ones_like(y)
        else:
            mask = np.isfinite(y) & np.isfinite(var) & np.isfinite(x)

        x = x[mask]
        y = y[mask]
        var = var[mask]

        if np.any(var == 0):
            raise ValueError('Cannot compute weights: some variances are zero.')

        weights = 1.0 / var**0.5

        return x, y, weights, mask

    ########
    # dunder methods
    ###########

    def __repr__(self) -> str:
        """Return a string representation of the Experiment object.

        Returns:
            str: A string representation of the Experiment object.
        """

        return f'Experiment `{self.unique_name}` with data: {self._data}'

    def __copy__(self) -> 'Experiment':
        """Return a copy of the object.

        Returns:
            Experiment: A copy of the Experiment object.
        """
        temp = self.to_dict(skip=['unique_name'])
        new_obj = self.__class__.from_dict(temp)
        new_obj.data = self.data.copy() if self.data is not None else None
        return new_obj

Q property writable

Get the Q values from the dataset.

Returns:

Type Description
Variable | None

sc.Variable | None: The Q values from the dataset, or None if no data is loaded.

__copy__()

Return a copy of the object.

Returns:

Name Type Description
Experiment Experiment

A copy of the Experiment object.
Source code in src/easydynamics/experiment/experiment.py 
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def __copy__(self) -> 'Experiment':
    """Return a copy of the object.

    Returns:
        Experiment: A copy of the Experiment object.
    """
    temp = self.to_dict(skip=['unique_name'])
    new_obj = self.__class__.from_dict(temp)
    new_obj.data = self.data.copy() if self.data is not None else None
    return new_obj

__init__(display_name='MyExperiment', unique_name=None, data=None)

Initialize the Experiment object.

Parameters:

Name Type Description Default
display_name str | None, default="MyExperiment"

Display name of the experiment.

 'MyExperiment'
unique_name str | None, default=None

Unique name of the experiment. If None, a unique name will be generated.

 None
data sc.DataArray | str | None, default=None

Dataset associated with the experiment. Can be a sc.DataArray or a filename string to load from. If None, no data is loaded.

 None

Raises:

Type Description
TypeError

If data is not a sc.DataArray, a string, or None.
Source code in src/easydynamics/experiment/experiment.py 
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def __init__(
    self,
    display_name: str | None = 'MyExperiment',
    unique_name: str | None = None,
    data: sc.DataArray | str | None = None,
) -> None:
    """Initialize the Experiment object.

    Args:
        display_name (str | None, default="MyExperiment"): Display name of the experiment.
        unique_name (str | None, default=None): Unique name of the experiment. If
            None, a unique name will be generated.
        data (sc.DataArray | str | None, default=None): Dataset associated with
            the experiment. Can be a sc.DataArray or a filename
            string to load from. If None, no data is loaded.

    Raises:
        TypeError: If data is not a sc.DataArray, a string, or None.
    """
    super().__init__(
        display_name=display_name,
        unique_name=unique_name,
    )

    if data is None:
        self._data = None
    elif isinstance(data, str):
        self.load_hdf5(filename=data)
    elif isinstance(data, sc.DataArray):
        self._validate_coordinates(data)
        self._data = data
    else:
        raise TypeError(
            f'Data must be a sc.DataArray or a filename string, not {type(data).__name__}'
        )

    self._binned_data = (
        self._convert_to_bin_centers(self._data) if self._data is not None else None
    )

__repr__()

Return a string representation of the Experiment object.

Returns:

Name Type Description
str str

A string representation of the Experiment object.
Source code in src/easydynamics/experiment/experiment.py 
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def __repr__(self) -> str:
    """Return a string representation of the Experiment object.

    Returns:
        str: A string representation of the Experiment object.
    """

    return f'Experiment `{self.unique_name}` with data: {self._data}'

binned_data property writable

Get the binned dataset associated with this experiment.

Returns:

Type Description
DataArray | None

sc.DataArray | None: The binned dataset associated with this experiment, or None if no data is loaded.

data property writable

Get the dataset associated with this experiment.

Returns:

Type Description
DataArray | None

sc.DataArray | None: The dataset associated with this experiment, or None if no data is loaded.

energy property writable

Get the energy values from the dataset.

Returns:

Type Description
Variable | None

sc.Variable | None: The energy values from the dataset, or None if no data is loaded.

get_masked_energy(Q_index)

Get the energy values from the dataset, removing points where the y values or variances are NaN or Inf for the given Q index.

Parameters:

Name Type Description Default
Q_index int

The Q index to get the masked energy values for.

 required

Returns:

Type Description
Variable | None

sc.Variable | None: The masked energy values from the dataset, or None if no data is loaded.

Raises:

Type Description
IndexError

If Q_index is not a valid index for the Q values.
Source code in src/easydynamics/experiment/experiment.py 
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def get_masked_energy(self, Q_index: int) -> sc.Variable | None:
    """Get the energy values from the dataset, removing points where
    the y values or variances are NaN or Inf for the given Q index.

    Args:
        Q_index (int): The Q index to get the masked energy values
            for.

    Returns:
        sc.Variable | None: The masked energy values from the
            dataset, or None if no data is loaded.

    Raises:
        IndexError: If Q_index is not a valid index for the Q values.
    """
    if self._binned_data is None:
        return None

    if (
        not isinstance(Q_index, int)
        or Q_index < 0
        or (self.Q is not None and Q_index >= len(self.Q))
    ):
        raise IndexError('Q_index must be a valid index for the Q values.')

    energy = self._binned_data.coords['energy']
    _, _, _, mask = self._extract_x_y_weights_only_finite(Q_index=Q_index)

    mask_var = sc.array(dims=['energy'], values=mask)
    masked_energy = energy[mask_var]
    return masked_energy

load_hdf5(filename, display_name=None)

Load data from an HDF5 file.

Parameters:

Name Type Description Default
filename str

Path to the HDF5 file.

 required
display_name str | None, default=None

Optional display name for the experiment.

 None

Raises:

Type Description
TypeError

If filename is not a string or if display_name is not a string or None or if the loaded data is not a sc.DataArray.
Source code in src/easydynamics/experiment/experiment.py 
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def load_hdf5(self, filename: str, display_name: str | None = None) -> None:
    """Load data from an HDF5 file.

    Args:
        filename (str ): Path to the HDF5 file.
        display_name (str | None, default=None): Optional display name for the
            experiment.

    Raises:
        TypeError: If filename is not a string or if display_name is
            not a string or None or if the loaded data is not a sc.DataArray.
    """
    if not isinstance(filename, str):
        raise TypeError(f'Filename must be a string, not {type(filename).__name__}')

    if display_name is not None:
        if not isinstance(display_name, str):
            raise TypeError(
                f'Display name must be a string, not {type(display_name).__name__}'
            )
        self.display_name = display_name

    loaded_data = sc_load_hdf5(filename)
    if not isinstance(loaded_data, sc.DataArray):
        raise TypeError(
            f'Loaded data must be a sc.DataArray, not {type(loaded_data).__name__}'
        )
    self._validate_coordinates(loaded_data)
    self.data = loaded_data

plot_data(slicer=False, **kwargs)

Plot the dataset using plopp: https://scipp.github.io/plopp/

Parameters:

Name Type Description Default
slicer bool, default=False

If True, use plopp's slicer instead of plot.

 False
**kwargs dict

Additional keyword arguments to pass to plopp.

 {}

Raises:

Type Description
ValueError

If there is no data to plot.
RuntimeError

If not in a Jupyter notebook environment.
Source code in src/easydynamics/experiment/experiment.py 
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def plot_data(self, slicer: bool = False, **kwargs: dict) -> None:
    """Plot the dataset using plopp: https://scipp.github.io/plopp/

    Args:
        slicer (bool, default=False): If True, use plopp's slicer instead of plot.
        **kwargs (dict): Additional keyword arguments to pass to plopp.

    Raises:
        ValueError: If there is no data to plot.
        RuntimeError: If not in a Jupyter notebook environment.
    """

    if self._binned_data is None:
        raise ValueError('No data to plot. Please load data first.')

    if not _in_notebook():
        raise RuntimeError('plot_data() can only be used in a Jupyter notebook environment.')

    plot_kwargs_defaults = {
        'title': self.display_name,
    }
    # Overwrite defaults with any user-provided kwargs
    plot_kwargs_defaults.update(kwargs)
    if slicer:
        fig = pp.slicer(
            self._binned_data,
            **plot_kwargs_defaults,
        )
        for widget in fig.bottom_bar[0].controls.values():
            widget.slider_toggler.value = '-o-'

    else:
        fig = pp.plot(
            self._binned_data.transpose(dims=['energy', 'Q']),
            **plot_kwargs_defaults,
        )
    return fig

rebin(dimensions)

Rebin the dataset along specified dimensions.

Parameters:

Name Type Description Default
dimensions dict[str, int | Variable]

A dictionary mapping dimension names to number of bins (int) or bin edges (sc.Variable).

 required

Raises:

Type Description
TypeError

If dimensions is not a dictionary or if keys/values are of incorrect types.
ValueError

If there is no data to rebin.
KeyError

If a specified dimension is not in the dataset.
Source code in src/easydynamics/experiment/experiment.py 
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def rebin(self, dimensions: dict[str, int | sc.Variable]) -> None:
    """Rebin the dataset along specified dimensions.

    Args:
        dimensions (dict[str, int | sc.Variable]): A dictionary
            mapping dimension names to number of bins (int) or bin
            edges (sc.Variable).

    Raises:
        TypeError: If dimensions is not a dictionary or if
            keys/values are of incorrect types.
        ValueError: If there is no data to rebin.
        KeyError: If a specified dimension is not in the dataset.
    """

    if not isinstance(dimensions, dict):
        raise TypeError(
            'dimensions must be a dictionary mapping dimension names '
            'to number of bins or bin values as sc.Variable.'
        )
    if self._data is None:
        raise ValueError('No data to rebin. Please load data first.')
    binned_data = self._data.copy()
    dim_copy = dimensions.copy()
    for dim, value in dim_copy.items():
        if not isinstance(dim, str):
            raise TypeError(
                f'Dimension keys must be strings. Got {type(dim)} for {dim} instead.'
            )
        if dim not in self._data.dims:
            raise KeyError(
                f"Dimension '{dim}' not a valid dimension for rebinning. "
                f'Should be one of {self._data.dims}.'
            )
        if isinstance(value, float) and value.is_integer():  # I allow eg. 2.0 as well as 2
            value = int(value)
            # This line can be removed when scipp resize support
            # resizing with coordinates
            dimensions[dim] = value
        if not (isinstance(value, int) or isinstance(value, sc.Variable)):
            raise TypeError(
                f'Dimension values must be integers or sc.Variable. '
                f"Got {type(value)} for dimension '{dim}' instead."
            )
        binned_data = binned_data.bin({dim: value})

    binned_data = binned_data.bins.mean()
    binned_data = self._convert_to_bin_centers(binned_data)
    self._binned_data = binned_data

remove_data()

Remove the dataset from the experiment.

Source code in src/easydynamics/experiment/experiment.py 
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def remove_data(self) -> None:
    """Remove the dataset from the experiment."""
    self._data = None
    self._binned_data = None

save_hdf5(filename=None)

Save the dataset to HDF5.

Parameters:

Name Type Description Default
filename str | None, default=None

Path to the output HDF5 file. If None, the file will be named after the unique_name of the experiment with a .h5 extension.

 None

Raises:

Type Description
TypeError

If filename is not a string or None.
ValueError

If there is no data to save.
Source code in src/easydynamics/experiment/experiment.py 
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def save_hdf5(self, filename: str | None = None) -> None:
    """Save the dataset to HDF5.

    Args:
        filename (str | None, default=None): Path to the output HDF5 file.
            If None, the file will be named after the unique_name of
            the experiment with a .h5 extension.

    Raises:
        TypeError: If filename is not a string or None.
        ValueError: If there is no data to save.
    """

    if filename is None:
        filename = f'{self.unique_name}.h5'

    if not isinstance(filename, str):
        raise TypeError(f'Filename must be a string, not {type(filename).__name__}')

    if self._data is None:
        raise ValueError('No data to save.')

    dir_name = os.path.dirname(filename)
    if dir_name:
        os.makedirs(dir_name, exist_ok=True)

    sc_save_hdf5(self._data, filename)

experiment

Experiment

Bases: NewBase

Holds data from an experiment as a sc.DataArray along with metadata.

This is a minimal implementation that will be extended in the future.

Source code in src/easydynamics/experiment/experiment.py 
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class Experiment(NewBase):
    """Holds data from an experiment as a sc.DataArray along with
    metadata.

    This is a minimal implementation that will be extended in the
    future.
    """

    def __init__(
        self,
        display_name: str | None = 'MyExperiment',
        unique_name: str | None = None,
        data: sc.DataArray | str | None = None,
    ) -> None:
        """Initialize the Experiment object.

        Args:
            display_name (str | None, default="MyExperiment"): Display name of the experiment.
            unique_name (str | None, default=None): Unique name of the experiment. If
                None, a unique name will be generated.
            data (sc.DataArray | str | None, default=None): Dataset associated with
                the experiment. Can be a sc.DataArray or a filename
                string to load from. If None, no data is loaded.

        Raises:
            TypeError: If data is not a sc.DataArray, a string, or None.
        """
        super().__init__(
            display_name=display_name,
            unique_name=unique_name,
        )

        if data is None:
            self._data = None
        elif isinstance(data, str):
            self.load_hdf5(filename=data)
        elif isinstance(data, sc.DataArray):
            self._validate_coordinates(data)
            self._data = data
        else:
            raise TypeError(
                f'Data must be a sc.DataArray or a filename string, not {type(data).__name__}'
            )

        self._binned_data = (
            self._convert_to_bin_centers(self._data) if self._data is not None else None
        )

    ###########
    # Properties
    ###########

    @property
    def data(self) -> sc.DataArray | None:
        """Get the dataset associated with this experiment.

        Returns:
            sc.DataArray | None: The dataset associated with this
                experiment, or None if no data is loaded.
        """
        return self._data

    @data.setter
    def data(self, value: sc.DataArray) -> None:
        """Set the dataset associated with this experiment.

        Args:
            value (sc.DataArray): The new dataset to associate with this
                experiment.

        Raises:
            TypeError: If the value is not a sc.DataArray.
        """
        if not isinstance(value, sc.DataArray):
            raise TypeError(f'Data must be a sc.DataArray, not {type(value).__name__}')
        self._validate_coordinates(value)
        self._data = value
        self._binned_data = (
            self._convert_to_bin_centers(self._data) if self._data is not None else None
        )

    @property
    def binned_data(self) -> sc.DataArray | None:
        """Get the binned dataset associated with this experiment.

        Returns:
            sc.DataArray | None: The binned dataset associated with this
                experiment, or None if no data is loaded.
        """
        return self._binned_data

    @binned_data.setter
    def binned_data(self, value: sc.DataArray) -> None:
        """Set the binned dataset associated with this experiment. Read-
        only property. Use rebin() to rebin the data instead.

        Args:
            value (sc.DataArray): The new binned dataset to associate
                with this experiment (ignored)

        Raises:
            AttributeError: Always, since binned_data is read-only.
        """
        raise AttributeError('binned_data is a read-only property. Use rebin() to rebin the data')

    @property
    def Q(self) -> sc.Variable | None:
        """Get the Q values from the dataset.

        Returns:
            sc.Variable | None: The Q values from the dataset, or None
                if no data is loaded.
        """
        if self._binned_data is None:
            return None
        return self._binned_data.coords['Q']

    @Q.setter
    def Q(self, value: sc.Variable) -> None:
        """Set the Q values for the dataset. Q is a read-only property
        derived from the data, so this setter raises an error.

        Args:
            value (sc.Variable): The new Q values to set (ignored)

        Raises:
            AttributeError: Always, since Q is read-only.
        """
        raise AttributeError('Q is a read-only property derived from the data.')

    @property
    def energy(self) -> sc.Variable | None:
        """Get the energy values from the dataset.

        Returns:
            sc.Variable | None: The energy values from the dataset, or
                None if no data is loaded.
        """
        if self._binned_data is None:
            return None
        return self._binned_data.coords['energy']

    @energy.setter
    def energy(self, value: sc.Variable) -> None:
        """Set the energy values for the dataset. Energy is a read-only
        property derived from the data, so this setter raises an error.

        Args:
            value (sc.Variable): The new energy values to set (ignored)

        Raises:
            AttributeError: Always, since energy is read-only.
        """
        raise AttributeError('energy is a read-only property derived from the data.')

    def get_masked_energy(self, Q_index: int) -> sc.Variable | None:
        """Get the energy values from the dataset, removing points where
        the y values or variances are NaN or Inf for the given Q index.

        Args:
            Q_index (int): The Q index to get the masked energy values
                for.

        Returns:
            sc.Variable | None: The masked energy values from the
                dataset, or None if no data is loaded.

        Raises:
            IndexError: If Q_index is not a valid index for the Q values.
        """
        if self._binned_data is None:
            return None

        if (
            not isinstance(Q_index, int)
            or Q_index < 0
            or (self.Q is not None and Q_index >= len(self.Q))
        ):
            raise IndexError('Q_index must be a valid index for the Q values.')

        energy = self._binned_data.coords['energy']
        _, _, _, mask = self._extract_x_y_weights_only_finite(Q_index=Q_index)

        mask_var = sc.array(dims=['energy'], values=mask)
        masked_energy = energy[mask_var]
        return masked_energy

    ###########
    # Handle data
    ###########

    def load_hdf5(self, filename: str, display_name: str | None = None) -> None:
        """Load data from an HDF5 file.

        Args:
            filename (str ): Path to the HDF5 file.
            display_name (str | None, default=None): Optional display name for the
                experiment.

        Raises:
            TypeError: If filename is not a string or if display_name is
                not a string or None or if the loaded data is not a sc.DataArray.
        """
        if not isinstance(filename, str):
            raise TypeError(f'Filename must be a string, not {type(filename).__name__}')

        if display_name is not None:
            if not isinstance(display_name, str):
                raise TypeError(
                    f'Display name must be a string, not {type(display_name).__name__}'
                )
            self.display_name = display_name

        loaded_data = sc_load_hdf5(filename)
        if not isinstance(loaded_data, sc.DataArray):
            raise TypeError(
                f'Loaded data must be a sc.DataArray, not {type(loaded_data).__name__}'
            )
        self._validate_coordinates(loaded_data)
        self.data = loaded_data

    def save_hdf5(self, filename: str | None = None) -> None:
        """Save the dataset to HDF5.

        Args:
            filename (str | None, default=None): Path to the output HDF5 file.
                If None, the file will be named after the unique_name of
                the experiment with a .h5 extension.

        Raises:
            TypeError: If filename is not a string or None.
            ValueError: If there is no data to save.
        """

        if filename is None:
            filename = f'{self.unique_name}.h5'

        if not isinstance(filename, str):
            raise TypeError(f'Filename must be a string, not {type(filename).__name__}')

        if self._data is None:
            raise ValueError('No data to save.')

        dir_name = os.path.dirname(filename)
        if dir_name:
            os.makedirs(dir_name, exist_ok=True)

        sc_save_hdf5(self._data, filename)

    def remove_data(self) -> None:
        """Remove the dataset from the experiment."""
        self._data = None
        self._binned_data = None

    def rebin(self, dimensions: dict[str, int | sc.Variable]) -> None:
        """Rebin the dataset along specified dimensions.

        Args:
            dimensions (dict[str, int | sc.Variable]): A dictionary
                mapping dimension names to number of bins (int) or bin
                edges (sc.Variable).

        Raises:
            TypeError: If dimensions is not a dictionary or if
                keys/values are of incorrect types.
            ValueError: If there is no data to rebin.
            KeyError: If a specified dimension is not in the dataset.
        """

        if not isinstance(dimensions, dict):
            raise TypeError(
                'dimensions must be a dictionary mapping dimension names '
                'to number of bins or bin values as sc.Variable.'
            )
        if self._data is None:
            raise ValueError('No data to rebin. Please load data first.')
        binned_data = self._data.copy()
        dim_copy = dimensions.copy()
        for dim, value in dim_copy.items():
            if not isinstance(dim, str):
                raise TypeError(
                    f'Dimension keys must be strings. Got {type(dim)} for {dim} instead.'
                )
            if dim not in self._data.dims:
                raise KeyError(
                    f"Dimension '{dim}' not a valid dimension for rebinning. "
                    f'Should be one of {self._data.dims}.'
                )
            if isinstance(value, float) and value.is_integer():  # I allow eg. 2.0 as well as 2
                value = int(value)
                # This line can be removed when scipp resize support
                # resizing with coordinates
                dimensions[dim] = value
            if not (isinstance(value, int) or isinstance(value, sc.Variable)):
                raise TypeError(
                    f'Dimension values must be integers or sc.Variable. '
                    f"Got {type(value)} for dimension '{dim}' instead."
                )
            binned_data = binned_data.bin({dim: value})

        binned_data = binned_data.bins.mean()
        binned_data = self._convert_to_bin_centers(binned_data)
        self._binned_data = binned_data

    ###########
    # other methods
    ###########

    def plot_data(self, slicer: bool = False, **kwargs: dict) -> None:
        """Plot the dataset using plopp: https://scipp.github.io/plopp/

        Args:
            slicer (bool, default=False): If True, use plopp's slicer instead of plot.
            **kwargs (dict): Additional keyword arguments to pass to plopp.

        Raises:
            ValueError: If there is no data to plot.
            RuntimeError: If not in a Jupyter notebook environment.
        """

        if self._binned_data is None:
            raise ValueError('No data to plot. Please load data first.')

        if not _in_notebook():
            raise RuntimeError('plot_data() can only be used in a Jupyter notebook environment.')

        plot_kwargs_defaults = {
            'title': self.display_name,
        }
        # Overwrite defaults with any user-provided kwargs
        plot_kwargs_defaults.update(kwargs)
        if slicer:
            fig = pp.slicer(
                self._binned_data,
                **plot_kwargs_defaults,
            )
            for widget in fig.bottom_bar[0].controls.values():
                widget.slider_toggler.value = '-o-'

        else:
            fig = pp.plot(
                self._binned_data.transpose(dims=['energy', 'Q']),
                **plot_kwargs_defaults,
            )
        return fig

    ###########
    # private methods
    ###########

    @staticmethod
    def _validate_coordinates(data: sc.DataArray) -> None:
        """Validate that required coordinates are present in the data.

        Args:
            data (sc.DataArray): The data to validate.

        Raises:
            TypeError: If data is not a sc.DataArray.
            ValueError: If required coordinates are missing.
        """
        if not isinstance(data, sc.DataArray):
            raise TypeError('Data must be a sc.DataArray.')

        required_coords = ['Q', 'energy']
        for coord in required_coords:
            if coord not in data.coords:
                raise ValueError(f"Data is missing required coordinate: '{coord}'")

    def _convert_to_bin_centers(self, data: sc.DataArray) -> sc.DataArray:
        """Convert the coordinates of the data to bin centers.

        Args:
            data (sc.DataArray): The data to convert.

        Returns:
            sc.DataArray: The data with coordinates at bin centers.
        """
        for dim in data.dims:
            coord = data.coords[dim]
            if coord.ndim == 1 and coord.size == data.sizes[dim] + 1:
                # Coordinate is at bin edges, convert to bin centers
                data = data.assign_coords({dim: sc.midpoints(coord)})
        return data

    def _extract_x_y_var(self, Q_index: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        """Extract the x, y, and weights arrays from the experiment for
        the given Q index.

        Args:
            Q_index (int): The Q index to extract the data for.

        Returns:
            tuple[np.ndarray, np.ndarray, np.ndarray]: The x, y, and
                variances arrays extracted from the experiment for the
                given Q index.
        """
        data = self.binned_data['Q', Q_index]
        x = data.coords['energy'].values
        y = data.values
        var = data.variances
        return x, y, var

    def _extract_x_y_weights_only_finite(
        self, Q_index: int
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        """Extract the x, y, and weights arrays from the experiment for
        the given Q index, removing any NaN and Inf values.

        Args:
            Q_index (int): The Q index to extract the data for.

        Returns:
            tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: The
                x, y, weights, and mask arrays extracted from the
                experiment for the given Q index, with NaNs and Infs
                removed.

        Raises:
            ValueError: If any variances are zero after removing NaNs
                and Infs, since this would lead to infinite weights.
        """
        x, y, var = self._extract_x_y_var(Q_index)

        if var is None:
            mask = np.isfinite(y) & np.isfinite(x)
            # If variances are not provided, set them to 1 to make all
            # weights be 1
            var = np.ones_like(y)
        else:
            mask = np.isfinite(y) & np.isfinite(var) & np.isfinite(x)

        x = x[mask]
        y = y[mask]
        var = var[mask]

        if np.any(var == 0):
            raise ValueError('Cannot compute weights: some variances are zero.')

        weights = 1.0 / var**0.5

        return x, y, weights, mask

    ########
    # dunder methods
    ###########

    def __repr__(self) -> str:
        """Return a string representation of the Experiment object.

        Returns:
            str: A string representation of the Experiment object.
        """

        return f'Experiment `{self.unique_name}` with data: {self._data}'

    def __copy__(self) -> 'Experiment':
        """Return a copy of the object.

        Returns:
            Experiment: A copy of the Experiment object.
        """
        temp = self.to_dict(skip=['unique_name'])
        new_obj = self.__class__.from_dict(temp)
        new_obj.data = self.data.copy() if self.data is not None else None
        return new_obj

Q property writable

Get the Q values from the dataset.

Returns:

Type Description
Variable | None

sc.Variable | None: The Q values from the dataset, or None if no data is loaded.

__copy__()

Return a copy of the object.

Returns:

Name Type Description
Experiment Experiment

A copy of the Experiment object.
Source code in src/easydynamics/experiment/experiment.py 
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def __copy__(self) -> 'Experiment':
    """Return a copy of the object.

    Returns:
        Experiment: A copy of the Experiment object.
    """
    temp = self.to_dict(skip=['unique_name'])
    new_obj = self.__class__.from_dict(temp)
    new_obj.data = self.data.copy() if self.data is not None else None
    return new_obj

__init__(display_name='MyExperiment', unique_name=None, data=None)

Initialize the Experiment object.

Parameters:

Name Type Description Default
display_name str | None, default="MyExperiment"

Display name of the experiment.

 'MyExperiment'
unique_name str | None, default=None

Unique name of the experiment. If None, a unique name will be generated.

 None
data sc.DataArray | str | None, default=None

Dataset associated with the experiment. Can be a sc.DataArray or a filename string to load from. If None, no data is loaded.

 None

Raises:

Type Description
TypeError

If data is not a sc.DataArray, a string, or None.
Source code in src/easydynamics/experiment/experiment.py 
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def __init__(
    self,
    display_name: str | None = 'MyExperiment',
    unique_name: str | None = None,
    data: sc.DataArray | str | None = None,
) -> None:
    """Initialize the Experiment object.

    Args:
        display_name (str | None, default="MyExperiment"): Display name of the experiment.
        unique_name (str | None, default=None): Unique name of the experiment. If
            None, a unique name will be generated.
        data (sc.DataArray | str | None, default=None): Dataset associated with
            the experiment. Can be a sc.DataArray or a filename
            string to load from. If None, no data is loaded.

    Raises:
        TypeError: If data is not a sc.DataArray, a string, or None.
    """
    super().__init__(
        display_name=display_name,
        unique_name=unique_name,
    )

    if data is None:
        self._data = None
    elif isinstance(data, str):
        self.load_hdf5(filename=data)
    elif isinstance(data, sc.DataArray):
        self._validate_coordinates(data)
        self._data = data
    else:
        raise TypeError(
            f'Data must be a sc.DataArray or a filename string, not {type(data).__name__}'
        )

    self._binned_data = (
        self._convert_to_bin_centers(self._data) if self._data is not None else None
    )

__repr__()

Return a string representation of the Experiment object.

Returns:

Name Type Description
str str

A string representation of the Experiment object.
Source code in src/easydynamics/experiment/experiment.py 
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def __repr__(self) -> str:
    """Return a string representation of the Experiment object.

    Returns:
        str: A string representation of the Experiment object.
    """

    return f'Experiment `{self.unique_name}` with data: {self._data}'

binned_data property writable

Get the binned dataset associated with this experiment.

Returns:

Type Description
DataArray | None

sc.DataArray | None: The binned dataset associated with this experiment, or None if no data is loaded.

data property writable

Get the dataset associated with this experiment.

Returns:

Type Description
DataArray | None

sc.DataArray | None: The dataset associated with this experiment, or None if no data is loaded.

energy property writable

Get the energy values from the dataset.

Returns:

Type Description
Variable | None

sc.Variable | None: The energy values from the dataset, or None if no data is loaded.

get_masked_energy(Q_index)

Get the energy values from the dataset, removing points where the y values or variances are NaN or Inf for the given Q index.

Parameters:

Name Type Description Default
Q_index int

The Q index to get the masked energy values for.

 required

Returns:

Type Description
Variable | None

sc.Variable | None: The masked energy values from the dataset, or None if no data is loaded.

Raises:

Type Description
IndexError

If Q_index is not a valid index for the Q values.
Source code in src/easydynamics/experiment/experiment.py 
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def get_masked_energy(self, Q_index: int) -> sc.Variable | None:
    """Get the energy values from the dataset, removing points where
    the y values or variances are NaN or Inf for the given Q index.

    Args:
        Q_index (int): The Q index to get the masked energy values
            for.

    Returns:
        sc.Variable | None: The masked energy values from the
            dataset, or None if no data is loaded.

    Raises:
        IndexError: If Q_index is not a valid index for the Q values.
    """
    if self._binned_data is None:
        return None

    if (
        not isinstance(Q_index, int)
        or Q_index < 0
        or (self.Q is not None and Q_index >= len(self.Q))
    ):
        raise IndexError('Q_index must be a valid index for the Q values.')

    energy = self._binned_data.coords['energy']
    _, _, _, mask = self._extract_x_y_weights_only_finite(Q_index=Q_index)

    mask_var = sc.array(dims=['energy'], values=mask)
    masked_energy = energy[mask_var]
    return masked_energy

load_hdf5(filename, display_name=None)

Load data from an HDF5 file.

Parameters:

Name Type Description Default
filename str

Path to the HDF5 file.

 required
display_name str | None, default=None

Optional display name for the experiment.

 None

Raises:

Type Description
TypeError

If filename is not a string or if display_name is not a string or None or if the loaded data is not a sc.DataArray.
Source code in src/easydynamics/experiment/experiment.py 
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def load_hdf5(self, filename: str, display_name: str | None = None) -> None:
    """Load data from an HDF5 file.

    Args:
        filename (str ): Path to the HDF5 file.
        display_name (str | None, default=None): Optional display name for the
            experiment.

    Raises:
        TypeError: If filename is not a string or if display_name is
            not a string or None or if the loaded data is not a sc.DataArray.
    """
    if not isinstance(filename, str):
        raise TypeError(f'Filename must be a string, not {type(filename).__name__}')

    if display_name is not None:
        if not isinstance(display_name, str):
            raise TypeError(
                f'Display name must be a string, not {type(display_name).__name__}'
            )
        self.display_name = display_name

    loaded_data = sc_load_hdf5(filename)
    if not isinstance(loaded_data, sc.DataArray):
        raise TypeError(
            f'Loaded data must be a sc.DataArray, not {type(loaded_data).__name__}'
        )
    self._validate_coordinates(loaded_data)
    self.data = loaded_data

plot_data(slicer=False, **kwargs)

Plot the dataset using plopp: https://scipp.github.io/plopp/

Parameters:

Name Type Description Default
slicer bool, default=False

If True, use plopp's slicer instead of plot.

 False
**kwargs dict

Additional keyword arguments to pass to plopp.

 {}

Raises:

Type Description
ValueError

If there is no data to plot.
RuntimeError

If not in a Jupyter notebook environment.
Source code in src/easydynamics/experiment/experiment.py 
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def plot_data(self, slicer: bool = False, **kwargs: dict) -> None:
    """Plot the dataset using plopp: https://scipp.github.io/plopp/

    Args:
        slicer (bool, default=False): If True, use plopp's slicer instead of plot.
        **kwargs (dict): Additional keyword arguments to pass to plopp.

    Raises:
        ValueError: If there is no data to plot.
        RuntimeError: If not in a Jupyter notebook environment.
    """

    if self._binned_data is None:
        raise ValueError('No data to plot. Please load data first.')

    if not _in_notebook():
        raise RuntimeError('plot_data() can only be used in a Jupyter notebook environment.')

    plot_kwargs_defaults = {
        'title': self.display_name,
    }
    # Overwrite defaults with any user-provided kwargs
    plot_kwargs_defaults.update(kwargs)
    if slicer:
        fig = pp.slicer(
            self._binned_data,
            **plot_kwargs_defaults,
        )
        for widget in fig.bottom_bar[0].controls.values():
            widget.slider_toggler.value = '-o-'

    else:
        fig = pp.plot(
            self._binned_data.transpose(dims=['energy', 'Q']),
            **plot_kwargs_defaults,
        )
    return fig

rebin(dimensions)

Rebin the dataset along specified dimensions.

Parameters:

Name Type Description Default
dimensions dict[str, int | Variable]

A dictionary mapping dimension names to number of bins (int) or bin edges (sc.Variable).

 required

Raises:

Type Description
TypeError

If dimensions is not a dictionary or if keys/values are of incorrect types.
ValueError

If there is no data to rebin.
KeyError

If a specified dimension is not in the dataset.
Source code in src/easydynamics/experiment/experiment.py 
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def rebin(self, dimensions: dict[str, int | sc.Variable]) -> None:
    """Rebin the dataset along specified dimensions.

    Args:
        dimensions (dict[str, int | sc.Variable]): A dictionary
            mapping dimension names to number of bins (int) or bin
            edges (sc.Variable).

    Raises:
        TypeError: If dimensions is not a dictionary or if
            keys/values are of incorrect types.
        ValueError: If there is no data to rebin.
        KeyError: If a specified dimension is not in the dataset.
    """

    if not isinstance(dimensions, dict):
        raise TypeError(
            'dimensions must be a dictionary mapping dimension names '
            'to number of bins or bin values as sc.Variable.'
        )
    if self._data is None:
        raise ValueError('No data to rebin. Please load data first.')
    binned_data = self._data.copy()
    dim_copy = dimensions.copy()
    for dim, value in dim_copy.items():
        if not isinstance(dim, str):
            raise TypeError(
                f'Dimension keys must be strings. Got {type(dim)} for {dim} instead.'
            )
        if dim not in self._data.dims:
            raise KeyError(
                f"Dimension '{dim}' not a valid dimension for rebinning. "
                f'Should be one of {self._data.dims}.'
            )
        if isinstance(value, float) and value.is_integer():  # I allow eg. 2.0 as well as 2
            value = int(value)
            # This line can be removed when scipp resize support
            # resizing with coordinates
            dimensions[dim] = value
        if not (isinstance(value, int) or isinstance(value, sc.Variable)):
            raise TypeError(
                f'Dimension values must be integers or sc.Variable. '
                f"Got {type(value)} for dimension '{dim}' instead."
            )
        binned_data = binned_data.bin({dim: value})

    binned_data = binned_data.bins.mean()
    binned_data = self._convert_to_bin_centers(binned_data)
    self._binned_data = binned_data

remove_data()

Remove the dataset from the experiment.

Source code in src/easydynamics/experiment/experiment.py 
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def remove_data(self) -> None:
    """Remove the dataset from the experiment."""
    self._data = None
    self._binned_data = None

save_hdf5(filename=None)

Save the dataset to HDF5.

Parameters:

Name Type Description Default
filename str | None, default=None

Path to the output HDF5 file. If None, the file will be named after the unique_name of the experiment with a .h5 extension.

 None

Raises:

Type Description
TypeError

If filename is not a string or None.
ValueError

If there is no data to save.
Source code in src/easydynamics/experiment/experiment.py 
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def save_hdf5(self, filename: str | None = None) -> None:
    """Save the dataset to HDF5.

    Args:
        filename (str | None, default=None): Path to the output HDF5 file.
            If None, the file will be named after the unique_name of
            the experiment with a .h5 extension.

    Raises:
        TypeError: If filename is not a string or None.
        ValueError: If there is no data to save.
    """

    if filename is None:
        filename = f'{self.unique_name}.h5'

    if not isinstance(filename, str):
        raise TypeError(f'Filename must be a string, not {type(filename).__name__}')

    if self._data is None:
        raise ValueError('No data to save.')

    dir_name = os.path.dirname(filename)
    if dir_name:
        os.makedirs(dir_name, exist_ok=True)

    sc_save_hdf5(self._data, filename)