uwacan.background.Background

class Background(data, snr_requirement=3, **kwargs)

Bases: FrequencyData

A class for simple measured background noise.

Parameters:

dataFrequencyData

The measured background noise, as a a power spectral density.

snr_requirementfloat

The required SnR for a measurement to be valid. The compensation will output NaN for invalid data points.

Methods

__call__(sensor_power)

Compensate a recorded power spectral density.

Inherited methods

apply(func, *args, **kwargs)	Apply some function to the contained data.
estimate_bandwidth()	Estimate the bandwidth of the frequency vector.
from_dataset(dataset)	Instantiate the class from a dataset.
groupby(group)
isel([indexers, drop, missing_dims, drop_allnan])	Select a subset of the data from the coordinate indices.
load(path[, lookup_class])	Load data from a Zarr file and optionally restore the original class.
make_figure(**kwargs)	Create a plotly figure, styled for this data.
max([dim])	Maximum of this data, along some dimension.
mean([dim])	Average of this data, along some dimension.
min([dim])	Minimum of this data, along some dimension.
plot(**kwargs)	Make a scatter trace of this data.
reduce(func, dim, **kwargs)	Apply a reduction function along some dimension in this data.
save(path[, append_dim])	Save the data to a Zarr file at the specified path.
sel([indexers, method, tolerance, drop, ...])	Select a subset of the data from the coordinate labels.
std([dim])	Standard deviation of this data, along some dimension.
sum([dim])	Sum of this data, along some dimension.
where(cond[, other, drop])	Filter elements from this object according to a condition.

Attributes

attrs	Attributes stored in the data.
bandwidth	Compute bandwidth from frequency band edges.
coords	The coordinate (dimension) arrays for this data.
data	The contained data.
dims	The dimensions of this data.
frequency	The frequencies for the data.
sizes	Mapping from dimension names to lengths.