scipp.DataArray#

class scipp.DataArray#

Named variable with associated coords, masks, and attributes.

__init__(self, data: Variable, coords: Dict[str, Variable] = {}, masks: Dict[str, Variable] = {}, attrs: Dict[str, Variable] = {}, name: str = '') None#

DataArray initializer.

Parameters
  • data (Variable) – Data and optionally variances.

  • coords (Dict[str, Variable]) – Coordinates referenced by dimension.

  • masks (Dict[str, Variable]) – Masks referenced by name.

  • attrs (Dict[str, Variable]) – Attributes referenced by dimension.

  • name (str) – Name of DataArray.

Methods

__init__(self, data[, coords, masks, attrs, ...])

DataArray initializer.

all([dim, out])

Element-wise AND over the specified dimension or all dimensions if not provided.

any([dim, out])

Element-wise OR over the specified dimension or all dimensions if not provided.

astype(self, type, *[, copy])

Converts a Variable or DataArray to a different dtype.

broadcast([dims, shape, sizes])

Broadcast a Variable or a DataArray.

ceil(*[, out])

Round up to the nearest integer of all values passed in x.

copy(self[, deep])

Return a (by default deep) copy.

flatten([dims, to])

Flatten multiple dimensions of a variable or data array into a single dimension.

floor(*[, out])

Round down to the nearest integer of all values passed in x.

fold(dim[, sizes, dims, shape])

Fold a single dimension of a variable or data array into multiple dims.

groupby(group, *[, bins])

Group dataset or data array based on values of specified labels.

mean([dim, out])

Element-wise mean over the specified dimension.

nanmean([dim, out])

Element-wise mean over the specified dimension ignoring NaNs.

nansum([dim, out])

Element-wise sum over the specified dimension; NaNs ignored.

plot(**kwargs)

Plot a Scipp object.

rename([dims_dict])

Rename the dimensions, coordinates and attributes of a Dataset.

rename_dims(self, dims_dict, /)

Rename dimensions.

round(*[, out])

Round to the nearest integer of all values passed in x.

squeeze([dim])

Remove dimensions of length 1.

sum([dim, out])

Element-wise sum over the specified dimension.

to(*[, unit, dtype, copy])

Converts a Variable or DataArray to a different dtype and/or a different unit.

to_hdf5(filename)

Writes object out to file in hdf5 format.

transform_coords(targets, graph, *[, ...])

Compute new coords based on transformations of input coords.

transpose([dims])

Transpose dimensions of a variable, a data array, or a dataset.

underlying_size

Attributes

attrs

Dict of attrs.

bins

Returns helper scipp.Bins allowing bin-wise operations to be performed or None if not binned data.

coords

Dict of aligned coords.

data

Underlying data item.

dim

The only dimension label for 1-dimensional data, raising an exception if the data is not 1-dimensional.

dims

Dimension labels of the data (read-only).

dtype

Data type contained in the variable.

masks

Dict of masks.

meta

Dict of coords and attrs.

name

The name of the held data.

ndim

Number of dimensions of the data (read-only).

shape

Shape of the data (read-only).

sizes

Makes a dictionary of dimensions labels to dimension sizes

unit

Physical unit of the data.

value

The only value for 0-dimensional data, raising an exception if the data is not 0-dimensional.

values

Array of values of the data.

variance

The only variance for 0-dimensional data, raising an exception if the data is not 0-dimensional.

variances

Array of variances of the data.

all(dim=None, *, out=None)#

Element-wise AND over the specified dimension or all dimensions if not provided.

Seealso

Details in scipp.all()

Return type

Variable

any(dim=None, *, out=None)#

Element-wise OR over the specified dimension or all dimensions if not provided.

Seealso

Details in scipp.any()

Return type

Variable

astype(self: scipp._scipp.core.DataArray, type: object, *, copy: bool = True) scipp._scipp.core.DataArray#

Converts a Variable or DataArray to a different dtype.

If the dtype is unchanged and copy is False, the object is returned without making a deep copy.

Parameters
  • type – Target dtype.

  • copy – If False, return the input object if possible. If True, the function always returns a new object.

Raises

If the data cannot be converted to the requested dtype.

Returns

New variable or data array with specified dtype.

Return type

Union[scipp.Variable, scipp.DataArray]

property attrs#

Dict of attrs.

property bins#

Returns helper scipp.Bins allowing bin-wise operations to be performed or None if not binned data.

broadcast(dims=None, shape=None, sizes=None)#

Broadcast a Variable or a DataArray. If the input is a DataArray, coordinates and attributes are shallow-copied and masks are deep copied.

Seealso

Details in scipp.broadcast()

Return type

Variable

ceil(*, out=None)#

Round up to the nearest integer of all values passed in x.

Seealso

Details in scipp.ceil()

Return type

Union[Variable, DataArray, Dataset]

property coords#

Dict of aligned coords.

copy(self: scipp._scipp.core.DataArray, deep: bool = True) scipp._scipp.core.DataArray#

Return a (by default deep) copy.

If deep=True (the default), a deep copy is made. Otherwise, a shallow copy is made, and the returned data (and meta data) values are new views of the data and meta data values of this object.

property data#

Underlying data item.

property dim#

The only dimension label for 1-dimensional data, raising an exception if the data is not 1-dimensional.

property dims#

Dimension labels of the data (read-only).

property dtype#

Data type contained in the variable.

flatten(dims=None, to=None)#

Flatten multiple dimensions of a variable or data array into a single dimension. If dims is omitted, then we flatten all of the inputs dimensions into a single dim.

Seealso

Details in scipp.flatten()

Return type

Union[Variable, DataArray, Dataset]

floor(*, out=None)#

Round down to the nearest integer of all values passed in x.

Seealso

Details in scipp.floor()

Return type

Union[Variable, DataArray, Dataset]

fold(dim, sizes=None, dims=None, shape=None)#

Fold a single dimension of a variable or data array into multiple dims.

Seealso

Details in scipp.fold()

Return type

Union[Variable, DataArray, Dataset]

groupby(group, *, bins=None)#

Group dataset or data array based on values of specified labels.

Seealso

Details in scipp.groupby()

Return type

Union[GroupByDataArray, GroupByDataset]

property masks#

Dict of masks.

mean(dim=None, *, out=None)#

Element-wise mean over the specified dimension.

Seealso

Details in scipp.mean()

Return type

Union[Variable, DataArray, Dataset]

property meta#

Dict of coords and attrs.

property name#

The name of the held data.

nanmean(dim=None, *, out=None)#

Element-wise mean over the specified dimension ignoring NaNs.

Seealso

Details in scipp.nanmean()

Return type

Union[Variable, DataArray, Dataset]

nansum(dim=None, *, out=None)#

Element-wise sum over the specified dimension; NaNs ignored.

Seealso

Details in scipp.nansum()

Return type

Union[Variable, DataArray, Dataset]

property ndim#

Number of dimensions of the data (read-only).

plot(**kwargs)#

Plot a Scipp object.

Possible inputs are: - Variable - DataArray - Dataset - dict of Variables - dict of DataArrays

For more details, see https://scipp.github.io/visualization/plotting-overview.html

Parameters
  • aspect (str, optional) – Specify the aspect ratio for 2d images. Possible values are “auto” or “equal”. Defaults to “auto”.

  • ax (matplotlib.axes.Axes, optional) – Attach returned plot to supplied Matplotlib axes (1d and 2d only). Defaults to None.

  • labels (dict, optional) – Dict specifying which coordinate should be used to label the tics for a dimension. If not specifified the dimension coordinate is used. labels={“time”: “time-labels”}. Defaults to None.

  • camera (dict, optional) – Dict configuring the camera. Valid entries are ‘position’ and ‘look_at’. This option is valid only for 3-D scatter plots. The ‘position’ entry defines the position of the camera and the ‘look_at’ entry defines the point the camera is looking at. Both must be variables containing a single vector with the correct unit, i.e., a unit compatible with the unit of the scatter point positions. Defaults to None, in which case the camera looks at the center of the cloud of plotted points.

  • cax (matplotlib.axes.Axes, optional) – Attach colorbar to supplied Matplotlib axes. Defaults to None.

  • cmap (str, optional) – Matplotlib colormap (2d and 3d only). See https://matplotlib.org/tutorials/colors/colormaps.html. Defaults to None.

  • color (str, optional) – Matplotlib line color (1d only). See https://matplotlib.org/tutorials/colors/colors.html. Defaults to None.

  • errorbars (str or dict, optional) – Show errorbars if True, hide them if False (1d only). Defaults to True. This can also be a dict of bool where the keys correspond to data entries.

  • figsize (tuple, optional) – The size of the figure in inches (1d and 2d only). See https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html. Defaults to None.

  • filename (str, optional) – If specified, the figure will be saved to disk. Possible file extensions are .jpg, .png and .pdf. The default directory for writing the file is the same as the directory where the script or notebook is running. Defaults to None.

  • grid (bool, optional) – Show grid on axes if True. Defaults to False.

  • linestyle (str, optional) – Matplotlib linestyle (1d only). See https://matplotlib.org/gallery/lines_bars_and_markers/linestyles.html. Defaults to “none”.

  • marker (str, optional) – Matplotlib line marker (1d only). See https://matplotlib.org/api/markers_api.html. Defaults to ‘o’.

  • masks (dict, optional) – A dict to hold display parameters for masks such as a color or a cmap. Defaults to None.

  • norm (str, optional) – Normalization of the data. Possible choices are “linear” and “log”. Defaults to “linear”.

  • pax (matplotlib.axes.Axes, optional) – Attach profile plot to supplied Matplotlib axes. Defaults to None.

  • pixel_size (float, optional) – Specify the size of the pixels to be used for the point cloud (3d only). If none is supplied, the size is guessed based on the extents of the data in the 3d space. Defaults to None.

  • positions (Variable, optional) – Specify an array of position vectors to be used as scatter points positions (3d only). Defaults to None.

  • projection (str, optional) – Specify the projection to be used. Possible choices are “1d”, “2d”, or “3d”. Defaults to “2d” if the number of dimensions of the input is >= 2.

  • resampling_mode (str, optional) – Resampling mode. Possible choices are “sum” and “mean”. This applies only to binned event data and non-1d data. Defaults to “mean” unless the unit is ‘counts’ or ‘dimensionless’.

  • scale (dict, optional) – Specify the scale (“linear” or “log”) for a displayed dimension axis. E.g. scale={“tof”: “log”}. Defaults to None.

  • vmin (float, optional) – Minimum value for the y-axis (1d) or colorscale (2d and 3d). Defaults to None.

  • vmax (float, optional) – Maximum value for the y-axis (1d) or colorscale (2d and 3d). Defaults to None.

rename(dims_dict=None, /, **names)#

Rename the dimensions, coordinates and attributes of a Dataset. The renaming can be defined:

  • using a dict mapping the old to new names, e.g. rename({‘x’: ‘a’, ‘y’: ‘b’})

  • using keyword arguments, e.g. rename(x=’a’, y=’b’)

Return type

DataArray

rename_dims(self: scipp._scipp.core.DataArray, dims_dict: Dict[scipp._scipp.core.Dim, scipp._scipp.core.Dim], /) scipp._scipp.core.DataArray#

Rename dimensions.

round(*, out=None)#

Round to the nearest integer of all values passed in x.

Seealso

Details in scipp.round()

Return type

Union[Variable, DataArray, Dataset]

property shape#

Shape of the data (read-only).

property sizes#

Makes a dictionary of dimensions labels to dimension sizes

squeeze(dim=None)#

Remove dimensions of length 1.

Seealso

Details in scipp.squeeze()

Return type

Union[Variable, DataArray, Dataset]

sum(dim=None, *, out=None)#

Element-wise sum over the specified dimension.

Seealso

Details in scipp.sum()

Return type

Union[Variable, DataArray, Dataset]

to(*, unit=None, dtype=None, copy=True)#

Converts a Variable or DataArray to a different dtype and/or a different unit.

Seealso

Details in scipp.to()

to_hdf5(filename)#

Writes object out to file in hdf5 format.

transform_coords(targets, graph, *, rename_dims=True, keep_aliases=True, keep_intermediate=True, keep_inputs=True, quiet=False)#

Compute new coords based on transformations of input coords.

Seealso

Details in scipp.transform_coords()

Return type

Union[DataArray, Dataset]

transpose(dims=None)#

Transpose dimensions of a variable, a data array, or a dataset.

Seealso

Details in scipp.transpose()

Return type

Union[Variable, DataArray, Dataset]

property unit#

Physical unit of the data.

property value#

The only value for 0-dimensional data, raising an exception if the data is not 0-dimensional.

property values#

Array of values of the data.

property variance#

The only variance for 0-dimensional data, raising an exception if the data is not 0-dimensional.

property variances#

Array of variances of the data.