MAINT: Separate adding SNRs per IFO to a sample into a new function

Moves code snippet which adds IFO snrs to a sample into a separate function within the Likelihood. Thus different likelihoods may define this differently if they have different SNRs which must be computed.

bilby/gw/likelihood/base.py

@@ -9,12 +9,10 @@ from scipy.special import logsumexp
 from ...core.likelihood import Likelihood
 from ...core.utils import logger, UnsortedInterp2d, create_time_series
 from ...core.prior import Interped, Prior, Uniform, DeltaFunction
-from ..detector import InterferometerList, get_empty_interferometer, calibration, Interferometer
+from ..detector import InterferometerList, get_empty_interferometer, calibration
 from ..prior import BBHPriorDict, Cosmological
 from ..utils import noise_weighted_inner_product, zenith_azimuth_to_ra_dec, ln_i0
 
-from typing import Any
-
 
 class GravitationalWaveTransient(Likelihood):
     """ A gravitational-wave transient likelihood object
@@ -1144,34 +1142,3 @@ class GravitationalWaveTransient(Likelihood):
             reference_frame=self._reference_frame_str,
             lal_version=self.lal_version,
             lalsimulation_version=self.lalsimulation_version)
-
-    def add_interferometer_snrs_to_sample(
-        ifo : Interferometer,
-        ifo_index : int,
-        new_samples : list,
-        sample : Any
-    ) -> None:
-        """For use in gw.conversion.compute_snrs().
-        Attached here so alternate likelihoods may implement their own method.
-
-        Parameters
-        ==========
-        ifo : bilby.gw.interferometer.Interferometer
-            The interferometer object for which to compute SNRs
-        ifo_index : int
-            The index of the IFO within likelihood.interferometers, for indexing of new_samples
-        new_samples : list
-            The output of _compute_snrs which contains SNRs
-        sample : Any
-            The sample from the posterior for which SNRs are being computed
-        """
-        matched_filter_snrs = list()
-        optimal_snrs = list()
-        mf_key = f'{ifo.name}_matched_filter_snr'
-        optimal_key = f'{ifo.name}_optimal_snr'
-        for new_sample in new_samples:
-            matched_filter_snrs.append(new_sample[ifo_index].complex_matched_filter_snr)
-            optimal_snrs.append(new_sample[ifo_index].optimal_snr_squared.real ** 0.5)
-
-        sample[mf_key] = matched_filter_snrs
-        sample[optimal_key] = optimal_snrs