Changeset 432

Timestamp:

08/26/09 15:38:17 (3 years ago)

Author:

pierre

Message:

Should be ok, all tests are passed. The Interval implementation is alsmost finished, up to date, and ensure backward compatibility. It will be tested for some time locally in order to see if bugs could still be found, and then it should be merged in the trunk.

Location:

branches/interval/src/signals

Files:

• analogs.py (modified) (17 diffs)
• intervals.py (modified) (1 diff)

branches/interval/src/signals/analogs.py

@@ -31 +31 @@
 """
 
-import os, re, numpy, copy
+import os, re, numpy, copy, logging
 from NeuroTools import check_dependency, check_numpy_version, analysis
 from NeuroTools.io import *
@@ -349 +349 @@
         t_min_l  = numpy.floor(t_min/self.dt)
         t_max_l  = numpy.floor(t_max/self.dt)
-        result   = numpy.zeros((t_min_l+t_max_l), numpy.float32)
+        
         events_interval = copy.copy(self.interval)
         events_interval.offset_start(t_min, colapse=True)
@@ -355 +355 @@
         events = events_interval.slice_times(events)
         assert len(events) > 0, "the PSTH windows [event-t_min, event+t_max] should be included within the spike train interval"
+        
+        if average:
+            result = numpy.zeros((t_min_l+t_max_l), numpy.float32)
+        else:
+            result = []
+        
         for ev in events:
             ev = numpy.floor((ev - self.interval.t_start())/self.dt)
             if average:
-                result += self.signal[(spike-t_min_l):(spike+t_max_l)]
+                result += self.signal[(ev-t_min_l):(ev+t_max_l)]
             else:
-                result.append(self.signal[(spike-t_min_l):(spike+t_max_l)])
+                result.append(self.signal[(ev-t_min_l):(ev+t_max_l)])
             Nspikes += 1
         if average:
@@ -449 +455 @@
         self.analog_signals = {}
         
-        signals = numpy.array(signals)        
+        signals = numpy.array(signals)
         for id in id_list:
             signal = signals[[signals[:,0] == id]][:,1]
@@ -455 +461 @@
             if len(signal) > 0:
                 self.analog_signals[id] = AnalogSignal(signal, self.dt)
-        self.interval = self.create_intervals(t_start, t_stop, interval)
+
+        self.interval = self.create_intervals(t_start, t_stop, interval)
         self.init_times()
+        
         if len(self) > 0:
             for id in self.id_list():
@@ -462 +470 @@
                 self.analog_signals[id].init_times()
         self.init_times()
-
-        #if len(self.id_list()) == 0 :
-            #logging.warning("id_list is empty")
-            #self.signal_length = 0
+        self.times  = self.interval.time_axis()
                     
     def create_intervals(self, t_start=None, t_stop=None, interval=None):
@@ -483 +488 @@
         else:
             if t_start is None and t_stop is None :
-                print "AnalogSignal interval derived from dt"
+                logging.info("AnalogSignal interval derived from dt")
                 interval_out = Interval([[0,max([len(signal),1])*self.dt]])
             elif t_start is None and t_stop is not None:
@@ -489 +494 @@
             elif t_start is not None and t_stop is not None:
                 if (t_stop - t_start)/self.dt != len(signal):
-                    print 't_start/t_stop and dt are not consistent with len(signal)'
+                    logging.info("t_start/t_stop and dt are not consistent with len(signal)")
                 interval_out = Interval([[t_start, t_stop]])
             elif t_start is not None and t_stop is None:
@@ -520 +525 @@
         """
         # Maybe not optimal, should be optimized
-        aslist = AnalogSignalList([], [], self.dt, self.t_start, self.t_stop, self.dimensions)
-        for id in self.id_list():
-            aslist.append(id, self.analog_signals[id])
-        return aslist
+        return copy.deepcopy(self)
     
     def __getitem__(self, id):
@@ -542 +544 @@
             self.interval = self.create_intervals(t_start=None, t_stop=None, interval=val.interval)
             self.t_start = val.t_start
-            self.t_stop = val.t_stop
+            self.t_stop  = val.t_stop
         self.analog_signals[i] = val
 
@@ -585 +587 @@
         self.init_times()
         time_pavement = numpy.arange(self.t_start, self.t_stop, self.dt)
-        return SpikeTrain(time_pavement, interval=self.interval).spike_times
+        return self.interval.slice_times(time_pavement)
 
     def id_offset(self, offset):
@@ -623 +625 @@
         """
         id_list = self.__sub_id_list(id_list)
-        new_AnalogSignalList = AnalogSignalList([], [], self.dt, self.t_start, self.t_stop, self.dimensions)
+        new_AnalogSignalList = AnalogSignalList([], [], self.dt, self.t_start, self.t_stop, self.interval, self.dimensions)
 
         for id in id_list:
@@ -773 +775 @@
             std
         """
-        result = []
-        for i in range(len(self.interval)) :
-            result.append(numpy.zeros(int((self.interval[i][1] - self.interval[i][0])/self.dt),float))
-            for id in self.id_list():
-                result[i] += self.analog_signals[id].signal[i]
-        return numpy.array(result)/len(self)
+        result = numpy.zeros(len(self.times), float)
+        for id in self.id_list():
+            result += self.analog_signals[id].signal
+        result /= len(self)
+        return AnalogSignal(numpy.array(result), dt=self.dt, interval=self.interval)
     
     def std(self):
@@ -792 +793 @@
             mean
         """
-        result = []
-        for i in range(len(self.interval)) :
-            result.append(numpy.zeros((int((self.interval[i][1] - self.interval[i][0])/self.dt),len(self.id_list())),float))
-          
-            for j in range(len(self.id_list())):
-                result[i][j,:] = self.analog_signals[j].signal[i]
-        return [numpy.std(result[i], axis=0) for i in range(len(self.interval))]
+        mu     = self.mean()
+        result = numpy.zeros(len(self.times), float)
+        for id in self.id_list():
+            result += (self.analog_signals[id].signal - mu.signal)**2
+        result = numpy.sqrt(result)/len(self)
+        return AnalogSignal(numpy.array(result), dt=self.dt, interval=self.interval)
+
 
     def event_triggered_average(self, eventdict, events_ids = None, analogsignal_ids = None, average = True, t_min = 0, t_max = 100, ylim = None, display = False, mode = 'same', kwargs={}):
@@ -985 +986 @@
 
 
-def load_conductancelist(user_file, id_list=None, dt=None, t_start=None, t_stop=None, dims=None):
+def load_conductancelist(user_file, id_list=None, dt=None, t_start=None, t_stop=None, interval=None, dims=None):
     """
     Returns TWO ConductanceList objects from a file. One for the excitatory and the other for
@@ -1016 +1017 @@
 
 
-def load_vmlist(user_file, id_list=None, dt=None, t_start=0, t_stop=None, dims=None):
+def load_vmlist(user_file, id_list=None, dt=None, t_start=0, t_stop=None, interval=None, dims=None):
     """
     Returns a VmList object from a file. If the file has been generated by PyNN, 
@@ -1042 +1043 @@
 
 
-def load_currentlist(user_file, id_list=None, dt=None, t_start=None, t_stop=None, dims=None):
+def load_currentlist(user_file, id_list=None, dt=None, t_start=None, t_stop=None, interval=None, dims=None):
     """
     Returns a CurrentList object from a file. If the file has been generated by PyNN, 

branches/interval/src/signals/intervals.py

@@ -46 +46 @@
 
     def __len__(self):
-        return shape(self.interval_data)[0]
+        return numpy.shape(self.interval_data)[0]
     
     def __iter__(self):