gw.inference.gw_samplers.check_frequency_updates

gw.inference.gw_samplers.check_frequency_updates(
    model_metadata,
    f_min=None,
    f_max=None,
)

Validate and apply optional minimum and maximum frequency constraints for a model’s frequency domain.

This function checks that any provided per-detector minimum (f_min) or maximum (f_max) frequencies—either as a single float applied to all detectors or as a dict mapping each detector to its own value—: - Match exactly the set of detectors in the model metadata. - Respect the hard bounds defined by the domain (domain.f_min / domain.f_max). - Comply with optional random-strain-cropping settings (probability, independent vs. joint detectors, and per-detector caps/floors).

Parameters

Name	Type	Description	Default
model_metadata	dict	Dictionary containing the model’s training settings and data. Must include: - `["train_settings"]["data"]["detectors"]`: list of detector names. - `["train_settings"]["data"]["random_strain_cropping"]`: optional dict of cropping parameters.	required
f_min	dict[str, float], float, or None	Single float or per-detector dict of minimum frequencies to enforce. If a float is provided, it is applied to all detectors. Each value must be ≥ `domain.f_min`. If `None`, no minimum-frequency validation is performed.	`None`
f_max	dict[str, float], float, or None	Single float or per-detector dict of maximum frequencies to enforce. If a float is provided, it is applied to all detectors. Each value must be ≤ `domain.f_max`. If `None`, no maximum-frequency validation is performed.	`None`

Raises

Name	Type	Description
	ValueError	- If `model_metadata` does not describe a `UniformFrequencyDomain` or `MultibandedFrequencyDomain`. - If `f_min`/`f_max` keys don’t exactly match the detector list. - If any requested frequency lies outside the hard domain bounds. - If cropping is disabled but a change in frequency is requested. - If per-detector constraints (independent vs. joint) or cropping caps/floors are violated.

Returns

Name	Type	Description
	None