small clarifications

jonescompneurolab · Mar 8, 2023 · b9a09d8 · b9a09d8
1 parent 01a64dc
commit b9a09d8
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Showing 3 changed files with 13 additions and 14 deletions.
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -369,4 +369,4 @@ People who contributed to this release (in alphabetical order):
 .. _Sarah Pugliese: https://bcs.mit.edu/directory/sarah-pugliese
 .. _Stephanie R. Jones: https://github.com/stephanie-r-jones
 .. _Steven Brandt: https://github.com/spbrandt
-.. _Kaisu Lankinen: http://klankinen.github.io/
+.. _Kaisu Lankinen: https://github.com/klankinen
diff --git a/hnn_core/optimization.py b/hnn_core/optimization.py
@@ -479,16 +479,17 @@ def optimize_evoked(net, tstop, n_trials, target_dpl, initial_dpl, maxiter=50,
         Evoked drives to optimize. If 'all', will opimize all evoked drives.
         If a subset list of evoked drives, will optimize only the evoked drives in the list.
     return_rmse : bool
-        Returns list of unweighted RMSEs between data in dpl and exp_dpl 
-        for each optimization step
+        Returns list of unweighted RMSEs between the simulated and experimental dipole 
+        waveforms for each optimization step
 
     Returns
     -------
     net : Network instance
         An instance of the Network object with the optimized configuration of
         attached drives.
     iter_avg_rmse : list of float
-        Unweighted RMSE between data in dpl and exp_dpl for each iteration
+        Unweighted RMSE between data in dpl and exp_dpl for each iteration. Returned only
+        if return_rmse is True
 
     Notes
     -----
@@ -545,7 +546,7 @@ def optimize_evoked(net, tstop, n_trials, target_dpl, initial_dpl, maxiter=50,
     opt_params = dict()
 
     if return_rmse is True:
-        opt_params['iter_avg_rmse'] = []
+        opt_params['iter_avg_rmse'] = list()
 
     for step in range(len(param_chunks)):
         opt_params['cur_step'] = step

diff --git a/hnn_core/tests/test_optimization.py b/hnn_core/tests/test_optimization.py
@@ -116,20 +116,18 @@ def test_optimize_evoked():
         net_empty = net_offset.copy()
         del net_empty.external_drives['evprox1']
         del net_empty.external_drives['evdist1']
-        net_opt, rmse = optimize_evoked(net_empty, tstop=tstop,
-                                        n_trials=n_trials, target_dpl=dpl_orig,
-                                        initial_dpl=dpl_offset, maxiter=10,
-                                        return_rmse=True)
+        net_opt = optimize_evoked(net_empty, tstop=tstop,
+                                  n_trials=n_trials, target_dpl=dpl_orig,
+                                  initial_dpl=dpl_offset, maxiter=10)
 
     with pytest.raises(ValueError, match='The drives selected to be optimized '
                        'are not evoked drives'):
         net_test_bursty = net_offset.copy()
         which_drives = ['bursty1']
-        net_opt, rmse = optimize_evoked(net_test_bursty, tstop=tstop,
-                                        n_trials=n_trials, target_dpl=dpl_orig,
-                                        initial_dpl=dpl_offset,
-                                        which_drives=which_drives, maxiter=10,
-                                        return_rmse=True)
+        net_opt = optimize_evoked(net_test_bursty, tstop=tstop,
+                                  n_trials=n_trials, target_dpl=dpl_orig,
+                                  initial_dpl=dpl_offset,
+                                  which_drives=which_drives, maxiter=10)
 
     which_drives = ['evprox1']  # drive selected to optimize
     maxiter = 10