Minor adjust to the ROQ weight generation

1. Convert the light travel time explicitly calculated value: uses 1.1 * R_earth / c (i.e. 10% more than the expected maximum)
2. Use a single fixed time_space: previously, the time resolution was set, then recalculated from the difference in the first two elements of time_samples. This could lead to buggy behaviour since that is no longer an evenly spaced array after the subtraction from the start time is made.
3. Add info statements
4. Adds a check if the seglen is shorter than the duration
5. Apply a safety factor of 5 to ensure the time step is short enough. This will increase the size of the ROQ array basis by ~ a factor of 5, but in testing I found it helped remove bias from examples just searching over RA, DEC, and geocent_time.
6. Add explicit clean up of memory

Closes #372 (closed)

bilby/gw/likelihood.py

 from __future__ import division
 
+import gc
 import os
 import json
 import copy
@@ -17,7 +18,7 @@ from scipy.special import i0e
 from ..core import likelihood
 from ..core.utils import (
     logger, UnsortedInterp2d, BilbyJsonEncoder, decode_bilby_json,
-    create_frequency_series, create_time_series)
+    create_time_series, speed_of_light, radius_of_earth)
 from ..core.prior import Interped, Prior, Uniform
 from .detector import InterferometerList
 from .prior import BBHPriorDict
@@ -842,7 +843,7 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
 
         dt = interferometer.time_delay_from_geocenter(
             self.parameters['ra'], self.parameters['dec'],
-            interferometer.strain_data.start_time)
+            self.parameters['geocent_time'])
         ifo_time = self.parameters['geocent_time'] + dt - \
             interferometer.strain_data.start_time
 
@@ -874,7 +875,7 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
 
         d_inner_h = interp1d(
             self.weights['time_samples'][indices],
-            d_inner_h_tc_array, kind='cubic')(ifo_time)
+            d_inner_h_tc_array, kind='cubic', assume_sorted=True)(ifo_time)
 
         optimal_snr_squared = \
             np.vdot(np.abs(h_plus_quadratic + h_cross_quadratic)**2,
@@ -913,29 +914,38 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
         return indices, in_bounds
 
     def _set_weights(self, linear_matrix, quadratic_matrix):
-        """
-        Setup the time-dependent ROQ weights.
-        This follows FIXME: Smith et al.
+        """ Setup the time-dependent ROQ weights.
+
+        Parameters
+        ----------
+        linear_matrix, quadratic_matrix: array_like
+            Arrays of the linear and quadratic basis
 
-        The times are chosen to allow all the merger times allows in the time
-        prior.
         """
 
-        self.weights['time_samples'] = np.arange(
-            self.priors['geocent_time'].minimum - 0.045,
-            self.priors['geocent_time'].maximum + 0.045,
-            self._get_time_resolution()) - self.interferometers.start_time
+        earth_light_crossing_time = radius_of_earth / speed_of_light
+        time_space = self._get_time_resolution()
+        delta_times = np.arange(
+            self.priors['geocent_time'].minimum - earth_light_crossing_time,
+            self.priors['geocent_time'].maximum + earth_light_crossing_time,
+            time_space)
+        time_samples = delta_times - self.interferometers.start_time
+        self.weights['time_samples'] = time_samples
+        logger.info("Using {} ROQ time samples".format(len(time_samples)))
 
         for ifo in self.interferometers:
             if self.roq_params is not None:
-                frequencies = create_frequency_series(
-                    sampling_frequency=self.roq_params['fhigh'] * 2,
-                    duration=self.roq_params['seglen'])
-                roq_mask = [frequencies >= self.roq_params['flow']]
-                frequencies = frequencies[roq_mask]
+                if ifo.maximum_frequency > self.roq_params['fhigh']:
+                    raise ValueError("Requested maximum frequency larger than ROQ basis fhigh")
+                # Generate frequencies for the ROQ
+                roq_frequencies = np.arange(
+                    self.roq_params['flow'],
+                    self.roq_params['fhigh'] + 1,
+                    1 / self.roq_params['seglen'])
                 overlap_frequencies, ifo_idxs, roq_idxs = np.intersect1d(
-                    ifo.frequency_array[ifo.frequency_mask], frequencies,
+                    ifo.frequency_array[ifo.frequency_mask], roq_frequencies,
                     return_indices=True)
+
             else:
                 overlap_frequencies = ifo.frequency_array[ifo.frequency_mask]
                 roq_idxs = np.arange(linear_matrix.shape[0], dtype=int)
@@ -950,26 +960,9 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
                     ifo.name, len(overlap_frequencies),
                     min(overlap_frequencies), max(overlap_frequencies)))
 
-            # array of relative time shifts to be applied to the data
-            # 0.045s comes from time for GW to traverse the Earth
-            time_space = (self.weights['time_samples'][1] -
-                          self.weights['time_samples'][0])
-
-            # array to be filled with data, shifted by discrete time_samples
-            tc_shifted_data = np.zeros([
-                len(self.weights['time_samples']), len(overlap_frequencies)],
-                dtype=complex)
-
-            # shift data to beginning of the prior increment by the time step
-            shifted_data =\
-                ifo.frequency_domain_strain[ifo.frequency_mask][ifo_idxs] * \
-                np.exp(2j * np.pi * overlap_frequencies *
-                       self.weights['time_samples'][0])
-            single_time_shift = np.exp(
-                2j * np.pi * overlap_frequencies * time_space)
-            for j in range(len(self.weights['time_samples'])):
-                tc_shifted_data[j] = shifted_data
-                shifted_data *= single_time_shift
+            data = ifo.frequency_domain_strain[ifo.frequency_mask][ifo_idxs]
+            tc_shifted_data = data * np.exp(
+                2j * np.pi * overlap_frequencies * time_samples[:, np.newaxis])
 
             # to not kill all computers this minimises the memory usage of the
             # required inner products
@@ -982,7 +975,8 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
                 linear_matrix[roq_idxs],
                 max_elements) * 4 / ifo.strain_data.duration
 
-            del tc_shifted_data
+            del tc_shifted_data, overlap_frequencies
+            gc.collect()
 
             self.weights[ifo.name + '_quadratic'] = build_roq_weights(
                 1 /
@@ -990,6 +984,8 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
                 quadratic_matrix[roq_idxs].real,
                 1 / ifo.strain_data.duration)
 
+            logger.info("Finished building weights for {}".format(ifo.name))
+
     def save_weights(self, filename):
         with open(filename, 'w') as file:
             json.dump(self.weights, file, indent=2, cls=BilbyJsonEncoder)
@@ -1055,6 +1051,10 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
 
         delta_t = fhigh**-1
 
+        # Apply a safety factor to ensure the time step is short enough
+        delta_t = delta_t / 5
+
+        logger.info("ROQ time-step = {}".format(delta_t))
         return delta_t
 
     def _rescale_signal(self, signal, new_distance):