ess.reduce.nexus.types.Analyzers

class ess.reduce.nexus.types.Analyzers(*args, **kwargs)

All analyzers in a NeXus file.

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
all([dim])
any([dim])
apply(func, *args, **kwargs)	Call func on all values and return new DataGroup containing the results.
astype(type, *[, copy])
bin([arg_dict])
broadcast(*[, dims, shape, sizes])
ceil()
clear()
copy([deep])
flatten([dims, to])
floor()
fold(dim, *[, dims, shape, sizes])
get(k[,d])
group(*args)
groupby(group, *[, bins])
hist([arg_dict])
items()
keys()
max([dim])
mean([dim])
median([dim])
min([dim])
nanhist([arg_dict])
nanmax([dim])
nanmean([dim])
nanmedian([dim])
nanmin([dim])
nanstd([dim])
nansum([dim])
nanvar([dim])
plot(*args, **kwargs)
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
popitem()	as a 2-tuple; but raise KeyError if D is empty.
rebin([arg_dict])
rename([dims_dict])
rename_dims([dims_dict])
round()
save_hdf5(filename)	Write an object out to file in HDF5 format.
setdefault(k[,d])
squeeze([dim])
std([dim])
sum([dim])
to(*[, unit, dtype, copy])
transform_coords([targets, graph, ...])
transpose([dims])
underlying_size()
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values()
var([dim])

Attributes

bins
dims	Union of dims of all items.
ndim	Number of dimensions, i.e., len(self.dims).
shape	Union of shape of all items.
sizes	Dict combining dims and shape, i.e., mapping dim labels to their size.