Using "pre-masked" arrays and "pre-masked-windowed" psd-array in nwip.

Library/likelihood_gradient.py

@@ -377,7 +377,7 @@ class GWTransientLikelihoodGradient(LikelihoodGradient):
             # # [DEBUG]: old way of computing inner products which does not use the strain_data.window_factor
             # ifo.s_inner_h_at_point = nwip(ifo.fd_strain, ifo.template_at_point, ifo.psd_array, ifo.strain_data.duration)
             # ifo.h_inner_h_at_point = np.real(nwip(ifo.template_at_point, ifo.template_at_point, ifo.psd_array, ifo.strain_data.duration))
-            ifo.s_inner_h_at_point = ifo.inner_product(ifo.template_at_point)
+            ifo.s_inner_h_at_point = ifo.inner_product_mask_fixed(ifo.template_at_point)
             ifo.h_inner_h_at_point = np.real(ifo.optimal_snr_squared(ifo.template_at_point))
             ifo.matched_filter_snr_at_point = np.real(ifo.s_inner_h_at_point) / ifo.h_inner_h_at_point**0.5
             self.s_inner_h_at_point += ifo.s_inner_h_at_point
@@ -509,8 +509,8 @@ class GWTransientLikelihoodGradient(LikelihoodGradient):
         # ifo.s_inner_h_plus = nwip(ifo.fd_strain, template_plus, ifo.psd_array, ifo.strain_data.duration)
         # ifo.s_inner_h_minus = nwip(ifo.fd_strain, template_minus, ifo.psd_array, ifo.strain_data.duration)
         # ifo.h_inner_dh = nwip(ifo.template_at_point, dh, ifo.psd_array, ifo.strain_data.duration)
-        ifo.s_inner_h_plus = ifo.inner_product(template_plus)
-        ifo.s_inner_h_minus = ifo.inner_product(template_minus)
+        ifo.s_inner_h_plus = ifo.inner_product_mask_fixed(template_plus)
+        ifo.s_inner_h_minus = ifo.inner_product_mask_fixed(template_minus)
         ifo.h_inner_dh = ifo.noise_weighted_inner_product(ifo.template_at_point, dh)
 
         return dh
@@ -560,7 +560,7 @@ class GWTransientLikelihoodGradient(LikelihoodGradient):
         # # [DEBUG]: old way of computing inner products which does not use the strain_data.window_factor
         # ifo.s_inner_h_plus = nwip(ifo.fd_strain, template_plus, ifo.psd_array, ifo.strain_data.duration)
         # ifo.h_inner_dh = nwip(ifo.template_at_point, dh, ifo.psd_array, ifo.strain_data.duration)
-        ifo.s_inner_h_plus = ifo.inner_product(template_plus)
+        ifo.s_inner_h_plus = ifo.inner_product_mask_fixed(template_plus)
         ifo.h_inner_dh = ifo.noise_weighted_inner_product(ifo.template_at_point, dh)
 
         return dh
@@ -608,7 +608,7 @@ class GWTransientLikelihoodGradient(LikelihoodGradient):
         # # [DEBUG]: old way of computing inner products which does not use the strain_data.window_factor
         # ifo.s_inner_h_minus = nwip(ifo.fd_strain, template_minus, ifo.psd_array, ifo.strain_data.duration)
         # ifo.h_inner_dh = nwip(ifo.template_at_point, dh, ifo.psd_array, ifo.strain_data.duration)
-        ifo.s_inner_h_minus = ifo.inner_product(template_minus)
+        ifo.s_inner_h_minus = ifo.inner_product_mask_fixed(template_minus)
         ifo.h_inner_dh = ifo.noise_weighted_inner_product(ifo.template_at_point, dh)
 
         return dh

gw/detector/interferometer.py

@@ -17,6 +17,8 @@ class Interferometer(BilbyInterferometer):
     """
     def __init__(self, name, power_spectral_density, minimum_frequency, maximum_frequency, length, latitude, longitude, elevation, xarm_azimuth, yarm_azimuth, xarm_tilt=0., yarm_tilt=0., calibration_model=None):
         BilbyInterferometer.__init__(self, name, power_spectral_density, minimum_frequency, maximum_frequency, length, latitude, longitude, elevation, xarm_azimuth, yarm_azimuth, xarm_tilt, yarm_tilt, calibration_model)
+        self._power_spectral_density_array_masked_windowed = None
+        self.frequency_domain_strain_masked = None
 
     def get_detector_response_geocent(self, waveform_polarizations, parameters):
         """ Get the detector response for a particular waveform before timeshifting it from geocent, ie it is the response of and identically oriented detector but located at the center of the earth.
@@ -100,5 +102,35 @@ class Interferometer(BilbyInterferometer):
         return gwutilsnwip(
             aa=aa[self.strain_data.frequency_mask],
             bb=bb[self.strain_data.frequency_mask],
-            power_spectral_density=self.power_spectral_density_array[self.strain_data.frequency_mask],
+            power_spectral_density=self.power_spectral_density_array_masked_windowed,
+            duration=self.strain_data.duration)
+
+    @property
+    def power_spectral_density_array_masked_windowed(self):
+        """
+        Pre-compute the masked and windowed psd array and cache it in this property to avoid unnecessary computations in the noise-weighted-inner-products.
+        """
+        if self._power_spectral_density_array_masked_windowed is None:
+            self._power_spectral_density_array_masked_windowed = self.power_spectral_density_array[self.strain_data.frequency_mask]
+        return self._power_spectral_density_array_masked_windowed
+
+    def inner_product_mask_fixed(self, signal):
+        """
+        Same as Bilby's original `interferometer.inner_product()` method but using "pre-masked" arrays and "pre-masked-windowed" psd-array to save some computation time.
+        Parameters
+        ----------
+        signal: array_like
+            Array containing the signal
+
+        Returns
+        -------
+        float: The optimal signal to noise ratio possible squared
+        """
+        if self.frequency_domain_strain_masked is None:
+            self.frequency_domain_strain_masked = self.strain_data.frequency_domain_strain[self.strain_data.frequency_mask]
+
+        return gwutilsnwip(
+            aa=signal[self.strain_data.frequency_mask],
+            bb=self.frequency_domain_strain_masked,
+            power_spectral_density=self.power_spectral_density_array_masked_windowed,
             duration=self.strain_data.duration)