changes

punit_responses.py

@@ -3,7 +3,7 @@ import nixio as nix
 import os
 from numpy.core.fromnumeric import repeat
 from traitlets.traitlets import Instance
-from chirp_ams import get_signals
+from chirp_ams import get_signals, other_freq, other_signal, self_freq
 from model import simulate, load_models
 from IPython import embed
 import matplotlib.pyplot as plt
@@ -21,8 +21,7 @@ def append_settings(section, sec_name, sec_type, settings):
                 section[k] = settings[k]
 
     
-def save(filename, name, stimulus_settings, model_settings, stimulus, responses, overwrite=False):
-    print("saving! ", filename, name)
+def save(filename, name, stimulus_settings, model_settings, self_signal, other_signal, self_freq, other_freq, complete_stimulus, responses, overwrite=False):
     if os.path.exists(filename) and not overwrite:
         nf = nix.File.open(filename, nix.FileMode.ReadWrite)
     else:
@@ -40,8 +39,8 @@ def save(filename, name, stimulus_settings, model_settings, stimulus, responses,
     b.metadata = mdata
     
     # save stimulus
-    stim_da = b.create_data_array("stimulus", "nix.timeseries.sampled", dtype=nix.DataType.Float,
-                                  data=stimulus)
+    stim_da = b.create_data_array("complete_stimulus", "nix.timeseries.sampled", dtype=nix.DataType.Float,
+                                  data=complete_stimulus)
     stim_da.label = "voltage"
     stim_da.label = "mv/cm"
     dim = stim_da.append_sampled_dimension(model_settings["deltat"])
@@ -56,22 +55,18 @@ def save(filename, name, stimulus_settings, model_settings, stimulus, responses,
         da.unit = "s"
         dim = da.append_range_dimension()
         dim.link_data_array(da, [-1])
+        
+    
     nf.close()
-    pass
 
 
-def plot_responses():
-    pass
-
-
-def simulate_responses(stimulus_params, model_params, repeats=10):
+def simulate_responses(stimulus_params, model_params, repeats=10, deltaf=20):
     cell_params = model_params.copy()
     cell = cell_params["cell"]
     del cell_params["cell"]
     del cell_params["EODf"]
     for c in stimulus_params["contrasts"]:
-        print("creating stimuli\n\tcontrast: ", str(c), "\t condition: ", 
-              stimulus_params["condition"])
+        print("\t\tcreating stimuli ... ")
         params = stimulus_params.copy()
         del params["contrasts"]
         del params["chirp_frequency"]
@@ -79,20 +74,27 @@ def simulate_responses(stimulus_params, model_params, repeats=10):
         time, self_signal, self_freq, other_signal, other_freq = get_signals(**params)
         signal = (self_signal + other_signal)
         signal /= np.max(signal)
-        print("create p-unit responses for cell: ", cell)
+        print("\t\tcreating p-unit responses ...")
         
         spikes = []
+        no_other_spikes = []
         for r in range(repeats):
             spikes.append(simulate(signal, **cell_params))
-        save("test.nix", "contrast_%.3f_condition_%s" %(c, stimulus_params["condition"]), params,
-             cell_params, signal, spikes)
+            no_other_spikes.append(simulate, self_signal))
+        print("\t\tsaving ...")
+        save("cell_%s.nix" % cell, "contrast_%.3f_condition_%s_deltaf_%i" %(c, stimulus_params["condition"], deltaf), params,
+             cell_params, self_signal, other_signal, self_freq, other_freq, signal, spikes)
 
 
 def main():
-    cell_id = 20
     models = load_models("models.csv")
-    deltaf = 20.  # Hz, difference frequency between self and other
+    deltafs = [-200, -100, -20, 20, 100, 200]  # Hz, difference frequency between self and other
+    
+    for cell_id in range(len(models)):
         model_params = models[cell_id]
+        print("Cell: %s" % model_params["cell"])
+        for deltaf in deltafs:
+            print("\t Deltaf: %i" % deltaf)
             stimulus_params = {"eodfs": {"self": model_params["EODf"],
                                         "other": model_params["EODf"] + deltaf},
                             "contrasts": [20, 10, 5, 2.5, 1.25, 0.625, 0.3125],
@@ -110,10 +112,10 @@ def main():
             conditions = ["other", "self"]
         
             for c in conditions:
+                print("\t\tcondition: %s" % c)
                 stimulus_params["condition"] = c
-        simulate_responses(stimulus_params, model_params, repeats=25)
-    pass
-
+                simulate_responses(stimulus_params, model_params, repeats=25,
+                                   deltaf=deltaf)
     
 if __name__ == "__main__":
     main()

response_discriminability.py

@@ -0,0 +1,9 @@
+import nixio as nix
+import matplotlib.pyplot as plt
+
+
+def main():
+    pass
+
+if __name__ == "__main__":
+    main()