ess.reduce.nexus.types.EmptyMonitor#

class ess.reduce.nexus.types.EmptyMonitor(x: SUPER)[source]#

Monitor without neutron data.

__init__(self, data: Variable, coords: Mapping[str, Variable] | Iterable[tuple[str, Variable]] = {}, masks: 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.

  • name – Name of the data array.

Methods

__init__(self, data[, coords, masks, name])

DataArray initializer.

all([dim])

Logical AND over input values.

any([dim])

Logical OR over input values.

assign(data)

Return new data array with updated data.

assign_coords([coords])

Return new object with updated or inserted coordinate.

assign_masks([masks])

Return new object with updated or inserted masks.

astype(self, type, *[, copy])

Converts a Variable or DataArray to a different dtype.

bin([arg_dict, dim])

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.

drop_coords(*args, **kwargs)

Overloaded function.

drop_masks(*args, **kwargs)

Overloaded function.

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, *[, dims, shape, sizes])

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

group(*args[, dim])

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, dim])

Compute a histogram.

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.

nanhist([arg_dict, dim])

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.

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.

rebin([arg_dict])

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.

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.

to(*[, unit, dtype, copy])

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

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.

var([dim])

Compute the variance of the input values.

Attributes

bins

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

coords

Dict of 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.

name

The name of the held data.

ndim

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

shape

Shape of the data (read-only).

size

Number of elements in 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.