aln input_vars (#243)

* add "ext_inh_current" and "ext_inh_rate" to input_vars of aln

* Update test_autochunk.py

neurolib/models/aln/model.py

@@ -50,7 +50,7 @@ class ALNModel(Model):
     ]
     output_vars = ["rates_exc", "rates_inh", "IA"]
     default_output = "rates_exc"
-    input_vars = ["ext_exc_current", "ext_exc_rate"]
+    input_vars = ["ext_exc_current", "ext_inh_current", "ext_exc_rate", "ext_inh_rate"]
     default_input = "ext_exc_rate"
 
     def __init__(self, params=None, Cmat=None, Dmat=None, lookupTableFileName=None, seed=None):

tests/test_autochunk.py

@@ -86,15 +86,17 @@ def test_onstep_input_autochunk(self):
         ous = np.zeros((model.params["N"], duration_dt))
 
         # prepare input
-        inp_x = np.zeros((model.params["N"], duration_dt))
-        inp_y = np.zeros((model.params["N"], duration_dt))
+        inp_exc_current = np.zeros((model.params["N"], duration_dt))
+        inp_inh_current = np.zeros((model.params["N"], duration_dt))
+        inp_exc_rate = np.zeros((model.params["N"], duration_dt))
+        inp_inh_rate = np.zeros((model.params["N"], duration_dt))
 
         for n in range(model.params["N"]):
             fr = 1
-            inp_x[n, :] = np.sin(np.linspace(0, fr * 2 * np.pi, duration_dt)) * 0.1
+            inp_exc_current[n, :] = np.sin(np.linspace(0, fr * 2 * np.pi, duration_dt)) * 0.1
 
         for i in range(duration_dt):
-            inputs = [inp_x[:, i], inp_y[:, i]]
+            inputs = [inp_exc_current[:, i], inp_inh_current[:, i], inp_exc_rate[:, i], inp_inh_rate[:, i]]
             model.autochunk(inputs=inputs, append_outputs=True)