tweak get_model

Author: TomDonoghue

specparam/models/event.py

@@ -272,13 +272,14 @@ def get_model(self, event_ind=None, window_ind=None, regenerate=True):
         model = super().get_model()
 
         # Add data for specified single power spectrum, if available
-        if self.data.has_data:
+        if event_ind is not None and window_ind is not None and self.data.has_data:
             model.data.power_spectrum = self.data.spectrograms[event_ind][:, window_ind]
 
         # Add results for specified power spectrum, regenerating full fit if requested
-        model.results.add_results(self.results.event_group_results[event_ind][window_ind])
-        if regenerate:
-            model.results._regenerate_model(self.data.freqs)
+        if event_ind is not None and window_ind is not None:
+            model.results.add_results(self.results.event_group_results[event_ind][window_ind])
+            if regenerate:
+                model.results._regenerate_model(self.data.freqs)
 
         return model
 

specparam/tests/models/test_event.py

@@ -118,13 +118,26 @@ def test_event_load():
 
 def test_event_get_model(tfe):
 
+    # Check getting null model
+    tfm_null = tfe.get_model()
+    assert tfm_null
+    # Check that settings are copied over properly, but data and results are empty
+    for setting in tfe.algorithm.settings.names:
+        assert getattr(tfe.algorithm, setting) == getattr(tfm_null.algorithm, setting)
+    assert not tfm_null.data.has_data
+    assert not tfm_null.results.has_model
+
     # Check without regenerating
     tfm0 = tfe.get_model(0, 0, False)
     assert tfm0
+    assert tfm0.data.has_data
+    assert tfm0.results.has_model
 
     # Check with regenerating
     tfm1 = tfe.get_model(1, 1, True)
     assert tfm1
+    assert tfm1.data.has_data
+    assert tfm1.results.has_model
     assert np.all(tfm1.results.modeled_spectrum_)
 
 def test_event_get_params(tfe):

specparam/tests/models/test_group.py

@@ -332,14 +332,13 @@ def test_get_model(tfg):
     """Check return of an individual model fit from a group object."""
 
     # Test with no ind (no data / results)
-    tfm = tfg.get_model()
-    assert tfm
+    tfm_null = tfg.get_model()
+    assert tfm_null
     # Check that settings are copied over properly, but data and results are empty
     for setting in tfg.algorithm.settings.names:
-        assert getattr(tfg.algorithm, setting) == getattr(tfm.algorithm, setting)
-    for result in tfg.results._fields:
-        assert np.all(np.isnan(getattr(tfm.results, result)))
-    assert not tfm.data.power_spectrum
+        assert getattr(tfg.algorithm, setting) == getattr(tfm_null.algorithm, setting)
+    assert not tfm_null.data.has_data
+    assert not tfm_null.results.has_model
 
     # Check without regenerating
     tfm0 = tfg.get_model(0, False)