Merge pull request #96 from MarvinT/double_staircase

Double staircase

pyoperant/behavior/two_alt_choice.py

@@ -104,6 +104,9 @@ def __init__(self, *args, **kwargs):
         if 'session_schedule' not in self.parameters:
             self.parameters['session_schedule'] = self.parameters['light_schedule']
 
+        if 'no_response_correction_trials' not in self.parameters:
+            self.parameters['no_response_correction_trials'] = False
+
     def make_data_csv(self):
         """ Create the csv file to save trial data
 
@@ -352,7 +355,7 @@ def trial_post(self):
                     self.do_correction = False
                 elif self.this_trial.response == 'none':
                     if self.this_trial.type_ == 'normal':
-                        self.do_correction = False
+                        self.do_correction = self.parameters['no_response_correction_trials']
             else:
                 self.do_correction = False
         else:

pyoperant/queues.py

@@ -62,6 +62,7 @@ class AdaptiveBase(object):
     """
     def __init__(self, **kwargs):
         self.updated = True # for first trial, no update needed
+        self.update_error_str = "queue hasn't been updated since last trial"
 
     def __iter__(self):
         return self
@@ -73,12 +74,20 @@ def update(self, correct, no_resp):
 
     def next(self):
         if not self.updated: #hasn't been updated since last trial
-            raise Exception("queue hasn't been updated since last trial")
+            raise Exception(self.update_error_str)
         self.updated = False
 
     def no_response(self):
         pass
 
+    def on_load(self):
+        try:
+            super(AdaptiveBase, self).on_load()
+        except AttributeError:
+            pass
+        self.updated = True
+        self.no_response()
+
 class PersistentBase(object):
     """
     A mixin that allows for the creation of an obj through a load command that
@@ -95,10 +104,17 @@ def load(cls, filename, *args, **kwargs):
         try:
             with open(filename, 'rb') as handle:
                 ab = pickle.load(handle)
+                ab.on_load()
             return ab
         except IOError:
             return cls(*args, filename=filename, **kwargs)
 
+    def on_load(self):
+        try:
+            super(PersistentBase, self).on_load()
+        except AttributeError:
+            pass
+
     def save(self):
         with open(self.filename, 'wb') as handle:
             pickle.dump(self, handle)
@@ -186,6 +202,7 @@ def __init__(self, stims, rate_constant=.05, **kwargs):
         self.low_idx = 0
         self.high_idx = len(self.stims) - 1
         self.trial = {}
+        self.update_error_str = "double staircase queue %s hasn't been updated since last trial" % (self.stims[0])
 
     def update(self, correct, no_resp):
         super(DoubleStaircase, self).update(correct, no_resp)
@@ -227,12 +244,14 @@ class DoubleStaircaseReinforced(AdaptiveBase):
     rate_constant: the step size is the rate_constant*(high_idx-low_idx)
     probe_rate: proportion of trials that are between [0, low_idx] or [high_idx, length(stims)]
     """
-    def __init__(self, stims, rate_constant=.05, probe_rate=.1, **kwargs):
+    def __init__(self, stims, rate_constant=.05, probe_rate=.1, sample_log=False, **kwargs):
         super(DoubleStaircaseReinforced, self).__init__(**kwargs)
         self.dblstaircase = DoubleStaircase(stims, rate_constant)
         self.stims = stims
         self.probe_rate = probe_rate
+        self.sample_log = sample_log
         self.last_probe = False
+        self.update_error_str = "reinforced double staircase queue %s hasn't been updated since last trial" % (self.stims[0])
 
     def update(self, correct, no_resp):
         super(DoubleStaircaseReinforced, self).update(correct, no_resp)
@@ -255,16 +274,26 @@ def next(self):
         else:
             self.last_probe = False
             if random.random() < .5: # probe left
-                val = int((1 - rand_from_log_shape_dist()) * self.dblstaircase.low_idx)
+                if self.sample_log:
+                    val = int((1 - rand_from_log_shape_dist()) * self.dblstaircase.low_idx)
+                else:
+                    val = random.randrange(self.dblstaircase.low_idx)
                 return {'class': 'L',  'stim_name': self.stims[val]}
             else: # probe right
-                val = self.dblstaircase.high_idx - int(rand_from_log_shape_dist() * (len(self.stims) - 1 - self.dblstaircase.high_idx)) 
+                if self.sample_log:
+                    val = self.dblstaircase.high_idx + int(rand_from_log_shape_dist() * (len(self.stims) - self.dblstaircase.high_idx)) 
+                else:
+                    val = self.dblstaircase.high_idx + random.randrange(len(self.stims) - self.dblstaircase.high_idx)
                 return {'class': 'R',  'stim_name': self.stims[val]}
 
     def no_response(self):
         super(DoubleStaircaseReinforced, self).no_response()
         self.last_probe = False
 
+    def on_load(self):
+        super(DoubleStaircaseReinforced, self).on_load()
+        self.dblstaircase.on_load()
+
 
 class MixedAdaptiveQueue(PersistentBase, AdaptiveBase):
     """
@@ -285,6 +314,7 @@ def __init__(self, sub_queues, probabilities=None, **kwargs):
         self.sub_queues = sub_queues
         self.probabilities = probabilities
         self.sub_queue_idx = -1
+        self.update_error_str = "MixedAdaptiveQueue hasn't been updated since last trial"
         self.save()
 
     def update(self, correct, no_resp):
@@ -305,5 +335,14 @@ def next(self):
             #TODO: support variable probabilities for each sub_queue
             raise NotImplementedError
 
+    def on_load(self):
+        super(MixedAdaptiveQueue, self).on_load()
+        for sub_queue in self.sub_queues:
+            try:
+                sub_queue.on_load()
+            except AttributeError:
+                pass
+
+