Merge branch 'fix-mode-array' into 'master'

Fix mode array option

See merge request lscsoft/bilby!765

bilby/gw/source.py

@@ -67,18 +67,18 @@ def lal_binary_black_hole(
             mode_array:
                 Activate a specific mode array and evaluate the model using those
                 modes only.  e.g. waveform_arguments =
-                dict(waveform_approximant='IMRPhenomHM', modearray=[[2,2],[2,-2])
+                dict(waveform_approximant='IMRPhenomHM', mode_array=[[2,2],[2,-2])
                 returns the 22 and 2-2 modes only of IMRPhenomHM.  You can only
                 specify modes that are included in that particular model.  e.g.
                 waveform_arguments = dict(waveform_approximant='IMRPhenomHM',
-                modearray=[[2,2],[2,-2],[5,5],[5,-5]]) is not allowed because the
+                mode_array=[[2,2],[2,-2],[5,5],[5,-5]]) is not allowed because the
                 55 modes are not included in this model.  Be aware that some models
                 only take positive modes and return the positive and the negative
                 mode together, while others need to call both.  e.g.
                 waveform_arguments = dict(waveform_approximant='IMRPhenomHM',
-                modearray=[[2,2],[4,-4]]) returns the 22 and 2-2 of IMRPhenomHM.
+                mode_array=[[2,2],[4,-4]]) returns the 22 and 2-2 of IMRPhenomHM.
                 However, waveform_arguments =
-                dict(waveform_approximant='IMRPhenomXHM', modearray=[[2,2],[4,-4]])
+                dict(waveform_approximant='IMRPhenomXHM', mode_array=[[2,2],[4,-4]])
                 returns the 22 and 4-4 of IMRPhenomXHM.
 
     Returns
@@ -150,18 +150,18 @@ def lal_binary_neutron_star(
             mode_array:
                 Activate a specific mode array and evaluate the model using those
                 modes only.  e.g. waveform_arguments =
-                dict(waveform_approximant='IMRPhenomHM', modearray=[[2,2],[2,-2])
+                dict(waveform_approximant='IMRPhenomHM', mode_array=[[2,2],[2,-2])
                 returns the 22 and 2-2 modes only of IMRPhenomHM.  You can only
                 specify modes that are included in that particular model.  e.g.
                 waveform_arguments = dict(waveform_approximant='IMRPhenomHM',
-                modearray=[[2,2],[2,-2],[5,5],[5,-5]]) is not allowed because the
+                mode_array=[[2,2],[2,-2],[5,5],[5,-5]]) is not allowed because the
                 55 modes are not included in this model.  Be aware that some models
                 only take positive modes and return the positive and the negative
                 mode together, while others need to call both.  e.g.
                 waveform_arguments = dict(waveform_approximant='IMRPhenomHM',
-                modearray=[[2,2],[4,-4]]) returns the 22 a\nd 2-2 of IMRPhenomHM.
+                mode_array=[[2,2],[4,-4]]) returns the 22 a\nd 2-2 of IMRPhenomHM.
                 However, waveform_arguments =
-                dict(waveform_approximant='IMRPhenomXHM', modearray=[[2,2],[4,-4]])
+                dict(waveform_approximant='IMRPhenomXHM', mode_array=[[2,2],[4,-4]])
                 returns the 22 and 4-4 of IMRPhenomXHM.
 
     Returns
@@ -217,18 +217,18 @@ def lal_eccentric_binary_black_hole_no_spins(
             mode_array:
                 Activate a specific mode array and evaluate the model using those
                 modes only.  e.g. waveform_arguments =
-                dict(waveform_approximant='IMRPhenomHM', modearray=[[2,2],[2,-2])
+                dict(waveform_approximant='IMRPhenomHM', mode_array=[[2,2],[2,-2])
                 returns the 22 and 2-2 modes only of IMRPhenomHM.  You can only
                 specify modes that are included in that particular model.  e.g.
                 waveform_arguments = dict(waveform_approximant='IMRPhenomHM',
-                modearray=[[2,2],[2,-2],[5,5],[5,-5]]) is not allowed because the
+                mode_array=[[2,2],[2,-2],[5,5],[5,-5]]) is not allowed because the
                 55 modes are not included in this model.  Be aware that some models
                 only take positive modes and return the positive and the negative
                 mode together, while others need to call both.  e.g.
                 waveform_arguments = dict(waveform_approximant='IMRPhenomHM',
-                modearray=[[2,2],[4,-4]]) returns the 22 and 2-2 of IMRPhenomHM.
+                mode_array=[[2,2],[4,-4]]) returns the 22 and 2-2 of IMRPhenomHM.
                 However, waveform_arguments =
-                dict(waveform_approximant='IMRPhenomXHM', modearray=[[2,2],[4,-4]])
+                dict(waveform_approximant='IMRPhenomXHM', mode_array=[[2,2],[4,-4]])
                 returns the 22 and 4-4 of IMRPhenomXHM.
 
     Returns
@@ -343,12 +343,12 @@ def _base_lal_cbc_fd_waveform(
     lalsim_SimInspiralWaveformParamsInsertTidalLambda2(
         waveform_dictionary, lambda_2)
 
-    if 'modearray' in waveform_kwargs:
-        modearray = waveform_kwargs['modearray']
-        mode_array = lalsim.SimInspiralCreateModeArray()
-        for mode in modearray:
-            lalsim.SimInspiralModeArrayActivateMode(mode_array, mode[0], mode[1])
-        lalsim.SimInspiralWaveformParamsInsertModeArray(waveform_dictionary, mode_array)
+    if ('mode_array' in waveform_kwargs) and waveform_kwargs['mode_array'] is not None:
+        mode_array = waveform_kwargs['mode_array']
+        mode_array_lal = lalsim.SimInspiralCreateModeArray()
+        for mode in mode_array:
+            lalsim.SimInspiralModeArrayActivateMode(mode_array_lal, mode[0], mode[1])
+        lalsim.SimInspiralWaveformParamsInsertModeArray(waveform_dictionary, mode_array_lal)
 
     if lalsim.SimInspiralImplementedFDApproximants(approximant):
         wf_func = lalsim_SimInspiralChooseFDWaveform