Address comments and fix test script

hnn_core/opt_toy_example/general.py

@@ -61,7 +61,14 @@ def __init__(self, net, constraints, solver, obj_fun):
 >>>>>>> 1a7e98b (Address comments for more generalized routine)
 =======
                  tstop, scale_factor=1., smooth_window_len=None):
+<<<<<<< HEAD
 >>>>>>> 46f1268 (Add tests and address comments)
+=======
+        if net.external_drives:
+            raise ValueError("The current Network instance has external " +
+                             "drives, provide a Network object with no " +
+                             "drives.")
+>>>>>>> 51112fc (Address comments and fix test script)
         self.net = net
         self.constraints = constraints
         self._set_params = set_params
@@ -73,9 +80,16 @@ def __init__(self, net, constraints, solver, obj_fun):
             self._assemble_constraints = _assemble_constraints_cobyla
             self._run_opt = _run_opt_cobyla
 <<<<<<< HEAD
+<<<<<<< HEAD
+=======
+        else:
+            raise ValueError("solver must be 'bayesian' or 'cobyla'")
+>>>>>>> 51112fc (Address comments and fix test script)
         # Response to be optimized
         if obj_fun == 'evoked':
             self.obj_fun = _rmse_evoked
+        else:
+            raise ValueError("obj_fun must be 'evoked'")
         self.scale_factor = scale_factor
         self.smooth_window_len = smooth_window_len
         self.tstop = tstop
@@ -440,8 +454,8 @@ def _get_initial_params(constraints):
 
     initial_params = dict()
     for cons_key in constraints:
-        initial_params.update({cons_key: (constraints[cons_key][0] +
-                                          constraints[cons_key][1])/2})
+        initial_params.update({cons_key: ((constraints[cons_key][0] +
+                                          constraints[cons_key][1]))/2})
 
     return initial_params
 

hnn_core/opt_toy_example/optimize_evoked.py

@@ -7,9 +7,10 @@
 of the model simulation to match an experimental dipole waveform.
 """
 
-# Authors: Blake Caldwell <[email protected]>
+# Authors: Carolina Fernandez <[email protected]>
+#          Nick Tolley <[email protected]>
+#          Ryan Thorpe <[email protected]>
 #          Mainak Jas <[email protected]>
-#          Carolina Fernandez <[email protected]>
 
 import os.path as op
 

hnn_core/opt_toy_example/test_optimize_evoked.py

@@ -1,5 +1,4 @@
-# Authors: Mainak Jas <[email protected]>
-#          Carolina Fernandez <[email protected]>
+# Authors: Carolina Fernandez <[email protected]>
 
 from hnn_core import jones_2009_model, simulate_dipole
 from general import Optimizer  # change path***
@@ -8,12 +7,14 @@
 def _optimize_evoked(solver):
     """Test running the full routine in a reduced network."""
 
-    tstop = 5.
+    tstop = 10.
     n_trials = 1
 
     # simulate a dipole to establish ground-truth drive parameters
     net_orig = jones_2009_model()
-    mu_orig = 6.
+    net_orig._N_pyr_x = 3
+    net_orig._N_pyr_y = 3
+    mu_orig = 2.
     weights_ampa = {'L2_basket': 0.5,
                     'L2_pyramidal': 0.5,
                     'L5_basket': 0.5,
@@ -31,6 +32,8 @@ def _optimize_evoked(solver):
 
     # define set_params function and constraints
     net_offset = jones_2009_model()
+    net_offset._N_pyr_x = 3
+    net_offset._N_pyr_y = 3
 
     def set_params(net_offset, param_dict):
         weights_ampa = {'L2_basket': 0.5,
@@ -48,8 +51,7 @@ def set_params(net_offset, param_dict):
                                     synaptic_delays=synaptic_delays)
 
     # define constraints
-    mu_offset = 4.  # initial time-shifted drive
-    mu_range = (2, 8)
+    mu_range = (1, 6)
     constraints = dict()
     constraints.update({'mu_offset': mu_range})
 
@@ -58,9 +60,9 @@ def set_params(net_offset, param_dict):
                       obj_fun='evoked', tstop=tstop)
     optim.fit(dpl_orig.data['agg'])
 
-    opt_param = optim.opt_params
+    opt_param = optim.opt_params[0]
     # the optimized parameter is in the range
-    assert opt_param[0] in range(mu_range[0], mu_range[1]), "Optimized parameter is not in user-defined range"
+    assert mu_range[0] <= opt_param <= mu_range[1], "Optimized parameter is not in user-defined range"
 
     obj = optim.obj
     # the number of returned rmse values should be the same as max_iter