scipp.Dataset#

class scipp.Dataset#

Dict of data arrays with aligned dimensions.

__init__(self, data: Union[Mapping[str, Union[Variable, DataArray]], Iterable[Tuple[str, Union[Variable, DataArray]]]] = {}, coords: Union[Mapping[str, Variable], Iterable[Tuple[str, Variable]]] = {}) → None#

Dataset initializer.

Parameters

  • data – Dictionary of name and data pairs.

  • coords – Dictionary of name and coord pairs.

Methods

__init__(self[, data, coords])

Dataset initializer.

all([dim])

Logical AND over input values.

any([dim])

Logical OR over input values.

clear(self)

Removes all data, preserving coordinates.

copy(self[, deep])

Return a (by default deep) copy.

get(key[, default])

Get the value associated with the provided key or the default value.

groupby(group, *[, bins])

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

hist([arg_dict])

Compute a histogram.

items(self)

view on self's items

keys(self)

view on self's keys

max([dim])

Maximum of elements in the input.

mean([dim])

Arithmetic mean of elements in the input.

min([dim])

Minimum of elements in the input.

nanmax([dim])

Maximum of elements in the input ignoring NaN's.

nanmean([dim])

Arithmetic mean of elements in the input ignoring NaN's.

nanmin([dim])

Minimum of elements in the input ignoring NaN's.

nansum([dim])

Sum of elements in the input ignoring NaN's.

plot([aspect, cbar, crop, errorbars, grid, ...])

Plot a Scipp object.

pop(key[, default])

Remove and return an element.

rebin([arg_dict, deprecated])

Rebin a data array or dataset.

rename([dims_dict])

Rename the dimensions, coordinates and attributes of all the items.

rename_dims([dims_dict])

Rename dimensions.

squeeze([dim])

Remove dimensions of length 1.

sum([dim])

Sum of elements in the input.

to_hdf5(filename)

Writes object out to file in hdf5 format.

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

Compute new coords based on transformations of input coords.

underlying_size(self)

Return the size of the object in bytes.

update(self[, other])

Update items from dict-like or iterable.

values(self)

view on self's values

Attributes

bins

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

coords

Dict of coordinates.

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).

meta

Dict of coordinates.

ndim

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

shape

Shape of the data (read-only).

sizes

dict mapping dimension labels to dimension sizes (read-only).
__getitem__(*args, **kwargs)#

Overloaded function.

  1. __getitem__(self: scipp._scipp.core.Dataset, arg0: str) -> scipp._scipp.core.DataArray

  2. __getitem__(self: scipp._scipp.core.Dataset, arg0: int) -> scipp._scipp.core.Dataset

  3. __getitem__(self: scipp._scipp.core.Dataset, arg0: slice) -> scipp._scipp.core.Dataset

  4. __getitem__(self: scipp._scipp.core.Dataset, arg0: scipp._scipp.core.Variable) -> scipp._scipp.core.Dataset

  5. __getitem__(self: scipp._scipp.core.Dataset, arg0: Tuple[str, scipp._scipp.core.Variable]) -> scipp._scipp.core.Dataset

  6. __getitem__(self: scipp._scipp.core.Dataset, arg0: Tuple[str, int]) -> scipp._scipp.core.Dataset

  7. __getitem__(self: scipp._scipp.core.Dataset, arg0: Tuple[str, slice]) -> scipp._scipp.core.Dataset

  8. __getitem__(self: scipp._scipp.core.Dataset, arg0: ellipsis) -> scipp._scipp.core.Dataset

  9. __getitem__(self: scipp._scipp.core.Dataset, arg0: List[int]) -> scipp._scipp.core.Dataset

  10. __getitem__(self: scipp._scipp.core.Dataset, arg0: Tuple[str, List[int]]) -> scipp._scipp.core.Dataset

all(dim=None)#

Logical AND over input values.

Seealso

Details in scipp.all()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

any(dim=None)#

Logical OR over input values.

Seealso

Details in scipp.any()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

property bins#

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

clear(self: scipp._scipp.core.Dataset) → None#

Removes all data, preserving coordinates.

property coords#

Dict of coordinates.

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

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 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).

get(key, default=None)#

Get the value associated with the provided key or the default value.

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]

hist(arg_dict=None, /, **kwargs)#

Compute a histogram.

Seealso

Details in scipp.hist()

Return type

Union[DataArray, Dataset]

items(self: scipp._scipp.core.Dataset) → scipp._scipp.core.Dataset_items_view#

view on self’s items

keys(self: scipp._scipp.core.Dataset) → scipp._scipp.core.Dataset_keys_view#

view on self’s keys

max(dim=None)#

Maximum of elements in the input.

Seealso

Details in scipp.max()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

mean(dim=None)#

Arithmetic mean of elements in the input.

Seealso

Details in scipp.mean()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

property meta#

Dict of coordinates.

min(dim=None)#

Minimum of elements in the input.

Seealso

Details in scipp.min()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

nanmax(dim=None)#

Maximum of elements in the input ignoring NaN’s.

Seealso

Details in scipp.nanmax()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

nanmean(dim=None)#

Arithmetic mean of elements in the input ignoring NaN’s.

Seealso

Details in scipp.nanmean()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

nanmin(dim=None)#

Minimum of elements in the input ignoring NaN’s.

Seealso

Details in scipp.nanmin()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

nansum(dim=None)#

Sum of elements in the input ignoring NaN’s.

Seealso

Details in scipp.nansum()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

property ndim#

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

plot(aspect='auto', cbar=True, crop=None, errorbars=True, grid=False, ignore_size=False, mask_color='black', norm='linear', scale=None, title=None, vmin=None, vmax=None, **kwargs)#

Plot a Scipp object.

Parameters

  • obj (Union[Variable, DataArray, Dataset, ndarray, Dict[str, Union[Variable, DataArray, Dataset, ndarray]]]) – The object to be plotted.

  • aspect (Literal[‘auto’, ‘equal’], default: 'auto') – Aspect ratio for the axes.

  • cbar (bool, default: True) – Show colorbar in 2d plots if True.

  • crop (Optional[Dict[str, Dict[str, Variable]]], default: None) – Set the axis limits. Limits should be given as a dict with one entry per dimension to be cropped. Each entry should be a nested dict containing scalar values for 'min' and/or 'max'. Example: da.plot(crop={'time': {'min': 2 * sc.Unit('s'), 'max': 40 * sc.Unit('s')}})

  • errorbars (bool, default: True) – Show errorbars in 1d plots if True.

  • grid (bool, default: False) – Show grid if True.

  • ignore_size (bool, default: False) – If True, skip the check that prevents the rendering of very large data objects.

  • mask_color (str, default: 'black') – Color of masks in 1d plots.

  • norm (Literal[‘linear’, ‘log’], default: 'linear') – Set to 'log' for a logarithmic y-axis (1d plots) or logarithmic colorscale (2d plots).

  • scale (Optional[Dict[str, str]], default: None) – Change axis scaling between log and linear. For example, specify scale={'tof': 'log'} if you want log-scale for the tof dimension.

  • title (Optional[str], default: None) – The figure title.

  • vmin (Optional[Variable], default: None) – Lower bound for data to be displayed (y-axis for 1d plots, colorscale for 2d plots).

  • vmax (Optional[Variable], default: None) – Upper bound for data to be displayed (y-axis for 1d plots, colorscale for 2d plots).

  • **kwargs – All other kwargs are directly forwarded to Matplotlib, the underlying plotting library. The underlying functions called are the following: - 1d data with a non bin-edge coordinate: plot - 1d data with a bin-edge coordinate: step - 2d data: pcolormesh

Returns

A figure.

pop(key, default=NotSpecified)#

Remove and return an element.

If key is not found, default is returned if given, otherwise KeyError is raised.

rebin(arg_dict=None, deprecated=None, /, **kwargs)#

Rebin a data array or dataset.

Seealso

Details in scipp.rebin()

Return type

Union[DataArray, Dataset]

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

Rename the dimensions, coordinates and attributes of all the items.

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')

In both cases, x is renamed to a and y to b.

Names not specified in either input are unchanged.

Parameters

  • dims_dict (Optional[Dict[str, str]], default: None) – Dictionary mapping old to new names.

  • names (str) – Mapping of old to new names as keyword arguments.

Returns

Dataset – A new dataset with renamed dimensions, coordinates, and attributes. Buffers are shared with the input.

See also

scipp.Dataset.rename_dims

Only rename dimensions, not coordinates and attributes.

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

Rename dimensions.

The renaming can be defined:

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

  • using keyword arguments, e.g. rename_dims(x='a', y='b')

In both cases, x is renamed to a and y to b.

Dimensions not specified in either input are unchanged.

This function only renames dimensions. See the rename method to also rename coordinates and attributes.

Parameters

  • dims_dict (Optional[Dict[str, str]], default: None) – Dictionary mapping old to new names.

  • names (str) – Mapping of old to new names as keyword arguments.

Returns

TypeVar(VariableLikeType, Variable, DataArray, Dataset) – A new object with renamed dimensions.

property shape#

Shape of the data (read-only).

property sizes#

dict mapping dimension labels to dimension sizes (read-only).

squeeze(dim=None)#

Remove dimensions of length 1.

Seealso

Details in scipp.squeeze()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

sum(dim=None)#

Sum of elements in the input.

Seealso

Details in scipp.sum()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

to_hdf5(filename)#

Writes object out to file in hdf5 format.

transform_coords(targets=None, /, graph=None, *, rename_dims=True, keep_aliases=True, keep_intermediate=True, keep_inputs=True, quiet=False, **kwargs)#

Compute new coords based on transformations of input coords.

Seealso

Details in scipp.transform_coords()

Return type

Union[DataArray, Dataset]

underlying_size(self: scipp._scipp.core.Dataset) → int#

Return the size of the object in bytes.

The size includes the object itself and all arrays contained in it. But arrays may be counted multiple times if components share buffers, e.g. multiple coordinates referencing the same memory. Conversely, the size may be underestimated. Especially, but not only, with dtype=PyObject.

This function includes all memory of the underlying buffers. Use __sizeof__ to get the size of the current slice only.

update(self: scipp._scipp.core.Dataset, other: object = None, /, **kwargs) → None#

Update items from dict-like or iterable.

If other has a .keys() method, then update does: for k in other.keys(): self[k] = other[k].

If other is given but does not have a .keys() method, then update does: for k, v in other: self[k] = v.

In either case, this is followed by: for k in kwargs: self[k] = kwargs[k].

See also

dict.update

values(self: scipp._scipp.core.Dataset) → scipp._scipp.core.Dataset_values_view#

view on self’s values