scipp.Dataset#
- class scipp.Dataset#
Dict of data arrays with aligned dimensions.
- __init__(self, data: Mapping[str, Variable | DataArray] | Iterable[tuple[str, Variable | DataArray]] = {}, coords: 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.
assign_coords
([coords])Return new object with updated or inserted coordinate.
clear
(self)Removes all data, preserving coordinates.
copy
(self[, deep])Return a (by default deep) copy.
drop_coords
(*args, **kwargs)Overloaded function.
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, dim])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.
median
([dim])Compute the median of the input values.
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.
nanmedian
([dim])Compute the median of the input values ignoring NaN's.
nanmin
([dim])Minimum of elements in the input ignoring NaN's.
nanstd
([dim])Compute the standard deviation of the input values ignoring NaN's.
nansum
([dim])Sum of elements in the input ignoring NaN's.
nanvar
([dim])Compute the variance of the input values ignoring NaN's.
plot
(**kwargs)Wrapper function to plot data.
Remove and return an element.
rebin
([arg_dict])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.
save_hdf5
(filename)Write an object out to file in HDF5 format.
squeeze
([dim])Remove dimensions of length 1.
std
([dim])Compute the standard deviation of the input values.
sum
([dim])Sum of elements in the input.
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
var
([dim])Compute the variance of the input values.
Attributes
Returns helper
scipp.Bins
allowing bin-wise operations to be performed or None if not binned data.Dict of coordinates.
The only dimension label for 1-dimensional data, raising an exception if the data is not 1-dimensional.
Dimension labels of the data (read-only).
Dict of coordinates.
Number of dimensions of the data (read-only).
Shape of the data (read-only).
dict mapping dimension labels to dimension sizes (read-only).
- __getitem__(*args, **kwargs)#
Overloaded function.
__getitem__(self: scipp._scipp.core.Dataset, arg0: str) -> scipp._scipp.core.DataArray
__getitem__(self: scipp._scipp.core.Dataset, arg0: int) -> scipp._scipp.core.Dataset
__getitem__(self: scipp._scipp.core.Dataset, arg0: slice) -> scipp._scipp.core.Dataset
__getitem__(self: scipp._scipp.core.Dataset, arg0: scipp._scipp.core.Variable) -> scipp._scipp.core.Dataset
__getitem__(self: scipp._scipp.core.Dataset, arg0: tuple[str, scipp._scipp.core.Variable]) -> scipp._scipp.core.Dataset
__getitem__(self: scipp._scipp.core.Dataset, arg0: tuple[str, int]) -> scipp._scipp.core.Dataset
__getitem__(self: scipp._scipp.core.Dataset, arg0: tuple[str, slice]) -> scipp._scipp.core.Dataset
__getitem__(self: scipp._scipp.core.Dataset, arg0: ellipsis) -> scipp._scipp.core.Dataset
__getitem__(self: scipp._scipp.core.Dataset, arg0: list[int]) -> scipp._scipp.core.Dataset
__getitem__(self: scipp._scipp.core.Dataset, arg0: tuple[str, list[int]]) -> scipp._scipp.core.Dataset
- all(dim=None)#
Logical AND over input values.
- any(dim=None)#
Logical OR over input values.
- assign_coords(coords=None, /, **coords_kwargs)#
Return new object with updated or inserted coordinate.
- property bins: Bins | None#
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).
- drop_coords(*args, **kwargs)#
Overloaded function.
drop_coords(self: scipp._scipp.core.Dataset, arg0: str) -> scipp._scipp.core.Dataset
drop_coords(self: scipp._scipp.core.Dataset, arg0: list[str]) -> scipp._scipp.core.Dataset
- get()#
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:
- hist(arg_dict=None, /, *, dim=None, **kwargs)#
Compute a histogram.
- Seealso:
Details in
scipp.hist()
- 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.
- mean(dim=None)#
Arithmetic mean of elements in the input.
- median(dim=None)#
Compute the median of the input values.
- property meta#
Dict of coordinates.
- min(dim=None)#
Minimum of elements in the input.
- nanmax(dim=None)#
Maximum of elements in the input ignoring NaN’s.
- nanmean(dim=None)#
Arithmetic mean of elements in the input ignoring NaN’s.
- nanmedian(dim=None)#
Compute the median of the input values ignoring NaN’s.
- nanmin(dim=None)#
Minimum of elements in the input ignoring NaN’s.
- nanstd(dim=None, *, ddof)#
Compute the standard deviation of the input values ignoring NaN’s.
- nansum(dim=None)#
Sum of elements in the input ignoring NaN’s.
- nanvar(dim=None, *, ddof)#
Compute the variance of the input values ignoring NaN’s.
- property ndim#
Number of dimensions of the data (read-only).
- plot(**kwargs)#
Wrapper function to plot data. See https://scipp.github.io/plopp/ for details.
- Return type:
- pop()#
Remove and return an element.
If key is not found, default is returned if given, otherwise KeyError is raised.
- rebin(arg_dict=None, /, **kwargs)#
Rebin a data array or dataset.
- Seealso:
Details in
scipp.rebin()
- Return type:
TypeVar
(_T
)
- 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:
- 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.
- 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.
- std(dim=None, *, ddof)#
Compute the standard deviation of the input values.
- sum(dim=None)#
Sum of elements in the input.
- 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:
- 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
- values(self: scipp._scipp.core.Dataset) scipp._scipp.core.Dataset_values_view #
view on self’s values