hera_sim.eor.NoiselikeEoR¶

class hera_sim.eor.NoiselikeEoR(eor_amp: float = 1e-05, min_delay: float | None = None, max_delay: float | None = None, fringe_filter_type: str = 'tophat', fringe_filter_kwargs: dict | None = None, rng: Generator | None = None)[source]¶

Generate a noiselike, fringe-filtered EoR visibility.

Parameters:

eor_amp – The amplitude of the EoR power spectrum.
min_delay – Minimum delay to allow through the delay filter. Default is -inf.
max_delay – Maximum delay to allow through the delay filter. Default is +inf
fringe_filter_type – The kind of filter to apply in fringe-space.
fringe_filter_kwargs – Arguments to pass to the fringe filter. See utils.rough_fringe_filter() for possible arguments.

Notes

This algorithm produces visibilities as a function of time/frequency that have white noise structure, filtered over the delay and fringe-rate axes. The fringe-rate filter makes the data look more like EoR by constraining it to moving with the sky (given the baseline vector).

Methods

`__call__`(lsts, freqs, bl_vec, **kwargs)	Compute the noise-like EoR model.
`get_aliases`()	Get all the aliases by which this model can be identified.
`get_model`(mdl)	Get a model with a particular name (including aliases).
`get_models`([with_aliases])	Get a dictionary of models associated with this component.

Attributes

`attrs_to_pull`	Mapping between parameter names and Simulator attributes
`is_multiplicative`	Whether this systematic multiplies existing visibilities
`is_randomized`	Whether this systematic contains a randomized component
`is_smooth_in_freq`
`return_type`	Whether the returned value is per-antenna, per-baseline, or the full array