scipp.DataArray#

class scipp.DataArray#

Named variable with associated coords, masks, and attributes.

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

DataArray initializer.

Parameters
  • data – Data and optionally variances.

  • coords – Coordinates referenced by dimension.

  • masks – Masks referenced by name.

  • attrs – Attributes referenced by dimension.

  • name – Name of the data array.

Methods

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

DataArray initializer.

all([dim])

Logical AND over input values.

any([dim])

Logical OR over input values.

astype(self, type, *[, copy])

Converts a Variable or DataArray to a different dtype.

bin([arg_dict])

Create binned data by binning input along all dimensions given by edges.

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 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.

group(*args)

Create binned data by grouping input by one or more coordinates.

groupby(group, *[, bins])

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

hist([arg_dict])

Compute a histogram.

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.

nanhist([arg_dict])

Compute a histogram, skipping NaN values.

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(**kwargs)

rebin([arg_dict, deprecated])

Rebin a data array or dataset.

rename([dims_dict])

Rename the dimensions, coordinates, and attributes.

rename_dims([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])

Sum of elements in the input.

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 the input.

underlying_size(self)

Return the size of the object in bytes.

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

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

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.

__getitem__(*args, **kwargs)#

Overloaded function.

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

  2. __getitem__(self: scipp._scipp.core.DataArray, arg0: slice) -> scipp._scipp.core.DataArray

  3. __getitem__(self: scipp._scipp.core.DataArray, arg0: scipp._scipp.core.Variable) -> scipp._scipp.core.DataArray

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

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

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

  7. __getitem__(self: scipp._scipp.core.DataArray, arg0: ellipsis) -> scipp._scipp.core.DataArray

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

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

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)

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.

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

Create binned data by binning input along all dimensions given by edges.

Seealso

Details in scipp.bin()

Return type

Union[DataArray, Dataset]

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.

Seealso

Details in scipp.broadcast()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

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 into a single dimension.

Seealso

Details in scipp.flatten()

Return type

TypeVar(VariableLikeType, 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

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

group(*args)#

Create binned data by grouping input by one or more coordinates.

Seealso

Details in scipp.group()

Return type

Union[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]

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

Compute a histogram.

Seealso

Details in scipp.hist()

Return type

Union[DataArray, Dataset]

property masks#

Dict of masks.

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 coords and attrs.

min(dim=None)#

Minimum of elements in the input.

Seealso

Details in scipp.min()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

property name#

The name of the held data.

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

Compute a histogram, skipping NaN values.

Seealso

Details in scipp.nanhist()

Return type

Union[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).

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.

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

DataArray – A new data array with renamed dimensions, coordinates, and attributes. Buffers are shared with the input.

See also

scipp.DataArray.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.

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#

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

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

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

This method will choose whether to do the dtype or units translation first, by using the following rules in order:

  • If either the input or output dtype is float64, the unit translation will be done on the float64 type

  • If either the input or output dtype is float32, the unit translation will be done on the float32 type

  • If both the input and output dtypes are integer types, the unit translation will be done on the larger type

  • In other cases, the dtype is converted first and then the unit translation is done

Parameters
  • unit (Union[Unit, str, None], default: None) – Target unit. If None, the unit is unchanged.

  • dtype (Optional[Any], default: None) – Target dtype. If None, the dtype is unchanged.

  • copy (bool, default: True) – If False, return the input object if possible. If True, the function always returns a new object.

Returns

Same as input – New object with specified dtype and unit.

Raises
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]

transpose(dims=None)#

Transpose dimensions of the input.

Seealso

Details in scipp.transpose()

Return type

TypeVar(VariableLikeType, Variable, DataArray, Dataset)

underlying_size(self: scipp._scipp.core.DataArray) 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.

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.