edges_analysis.filters.lst_model.GHAModelFilter¶
- class edges_analysis.filters.lst_model.GHAModelFilter(metric_model: Model, std_model: Model, meta: dict = _Nothing.NOTHING, n_sigma: float = 3.0)[source]¶
A filter that is able to act upon data that has been aggregated along frequency.
- Parameters:
metric_model (edges_cal.modelling.Model) – A linear model that provides a good guess of the typical aggregated metric as it evolves over GHA.
std_model (edges_cal.modelling.Model) – A linear model that provides a good guess of the typical standard deviation of the metric as it evolves over GHA. This may be gotten by fitting a model to the absolute residuals, for example.
meta (dict) – A dictionary holding meta-information about how the models were obtained.
n_sigma (float) – The number of sigma to threshold at.
Methods
__init__(metric_model, std_model[, meta, ...])Method generated by attrs for class GHAModelFilter.
apply_filter(gha, metric)Apply the filter to a set of metric data.
from_data(info, metric_model, std_model[, ...])Create the object from input metric data and models.
from_file(fname)Create the class from a h5 file.
write(fname)Write a h5 file.
Attributes