Changeset 210

Timestamp:

09/24/08 10:19:05 (4 months ago)

Author:

pierre

Message:

Should slow down all the commits, sorry for over commiting. Implement Andrew's idea of removing the id_list attribute for SpikeList and AnalogSignalList? objects. Add also a copy() method to all those objects, add_offset(),.... We should really find a clear definition for the key objects, to avoid refactoring the code too often. Now, the cleanup branch is roughly operationnal, if you admit that we don't have yet any unit tests :-)

Files:

• branches/cleanup/src/signals.py (modified) (47 diffs)

branches/cleanup/src/signals.py

@@ -127 +127 @@
-        return self.spike_times[i:j]
-    
+    def copy(self):
+        """
+        Return a copy of the SpikeTrain object
+        """
+        return SpikeTrain(self.spike_times, self.dt, self.t_start, self.t_stop)
+    
-    def __getdisplay__(self,display):
-        """
@@ -345 +351 @@
-                pylab.draw()
-
+    def add_offset(self, offset):
+        """
+        Add an offset to the SpikeTrain object. t_start and t_stop are
+        shifted from offset, so does all the spike times.
+         
+        Inputs:
+            offset - the time offset, in ms
+        
+        Examples:
+            >>>spktrain = SpikeTrain(arange(0,100,10))
+            >>>spktrain.add_offset(50)
+            >>>spklist.spike_times
+            >>>[  50.,   60.,   70.,   80.,   90.,  100.,  110.,  
+                120.,  130.,  140.]
+        """
+        self.t_start += offset
+        self.t_stop  += offset
+        self.spike_times += offset
-
-    def time_slice(self, t_start, t_stop):
@@ -611 +635 @@
-            SpikeList, loadSpikeList
-        """
-
-
-
-
+
+
+
-        self.dimensions  = dims
-        self.N           = len(id_list)
-        self.spiketrains = {}
-
@@ -622 +644 @@
-            self.spiketrains[id] = []
-        for id,time in spikes:
-
+()
-                self.spiketrains[id].append(time)
-
@@ -632 +654 @@
-            self.__calc_startstop()
-
+    def id_list(self):
+        """ 
+        Return the list of all the cells ids contained in the
+        SpikeList object
+        """
+        return numpy.array(self.spiketrains.keys())
+
+    def copy(self):
+        """
+        Return a copy of the SpikeList object
+        """
+        # Maybe not optimal, should be optimized
+        spklist = SpikeList([], [], self.dt, self.t_start, self.t_stop, self.dimensions)
+        for id in self.id_list():
+            spklist.append(id, self.spiketrains[id])
+        return spklist
+
-    def __calc_startstop(self):
-        """
@@ -640 +679 @@
-        if len(self) > 0:
-            if self.t_start is None:
-
+()
-                self.t_start = numpy.min(start_times)
-                logging.debug("Warning, t_start is infered from the data : %f" %self.t_start)
@@ -646 +685 @@
-                    self.spiketrains[id].t_start = self.t_start
-            if self.t_stop is None:
-
+()
-                self.t_stop  = numpy.max(stop_times)
-                logging.debug("Warning, t_stop  is infered from the data : %f" %self.t_stop)
@@ -657 +696 @@
-        return self.spiketrains[id]
-    
-
+
+
+
+
+
+
+
-    #def __setslice__(self, i, j):
-
@@ -663 +708 @@
-        assert isinstance(spktrain, SpikeTrain), "A SpikeList object can only contain SpikeTrain objects"
-        self.spiketrains[id] = spktrain
-        if not id in self.id_list:
-            self.id_list.append(id)
-        self.__calc_startstop()
-
@@ -673 +716 @@
-        return len(self.spiketrains)
-
+    def __sub_id_list(self, sub_list=None):
+        if sub_list == None:
+            return self.id_list()
+        if type(sub_list) == int:
+            return numpy.random.permutation(self.id_list())[0:sub_list]
+        if type(sub_list) == list:
+            return sub_list
+    
-    def __getdisplay__(self,display):
-        """
@@ -725 +776 @@
-        """
-        assert isinstance(spktrain, SpikeTrain), "A SpikeList object can only contain SpikeTrain objects"
-
+()
-            raise Exception("id %d already present in SpikeList. Use __setitem__ (spk[id]=...) instead()" %id)
-        else:
-            self.spiketrains[id] = spktrain.time_slice(self.t_start, self.t_stop)
-
-
-
+
-    def get_time_parameters(self):
-        """
@@ -770 +819 @@
-                raise Exception("Spike Lists should have similar time_axis")
-        for sl in spklists:
-
+()
-                self.append(id, sl.spiketrains[id])
-
@@ -781 +830 @@
-        """
-        for id, spiketrain in spikelist.spiketrains.items():
-
+()
-                self.spiketrains[id].merge(spiketrain, relative_times)
-            else:
@@ -810 +859 @@
-        """
-        new_SpkList = SpikeList([], [], self.dt, self.t_start, self.t_stop, self.dimensions)
-
-
+
-        for id in id_list:
-            try:
@@ -831 +879 @@
-        """
-        new_SpkList = SpikeList([], [], self.dt, t_start, t_stop, self.dimensions)
-
+()
-            new_SpkList.append(id, self.spiketrains[id].time_slice(t_start, t_stop))
-        new_SpkList.__calc_startstop()
-        return new_SpkList
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-    
-    def save(self, filename, method="text"):
@@ -870 +937 @@
-        """
-        isis = []
-
+()
-            isis.append(self.spiketrains[id].isi())
-        return isis
@@ -915 +982 @@
-        """
-        cvs_isi = []
-x,id in enumerate(self.id_list
+ in self.id_list(
-            isi = self.spiketrains[id].isi()
-            if len(isi) > 1:
@@ -1008 +1075 @@
-        """
-        rates = []
-
+()
-            rates.append(self.spiketrains[id].mean_rate(t_start, t_stop))
-        return rates
@@ -1059 +1126 @@
-        """
-        nbins      = self.time_axis(time_bin)
-
+
+
-        subplot    = self.__getdisplay__(display)
-
+()
-            spike_hist[idx,:] = self.spiketrains[id].time_histogram(time_bin, normalized)
-        if not subplot or not ENABLE_PLOTS:
@@ -1072 +1140 @@
-            xlabel = "Time (ms)"
-            self.__labels__(subplot, xlabel, ylabel)
-self.
+
-            pylab.draw()
-            
@@ -1123 +1191 @@
-        """
-        fano_factors = []
-
+()
-            try:
-                fano_factors.append(self.spiketrains[id].fano_factor_isi())
@@ -1200 +1268 @@
-            activity_map = numpy.zeros(self.dimensions, float)
-            rates        = spklist.mean_rates()
-
+()
-                position = spklist.id2position(id)
-                activity_map[position] = rates[id]
@@ -1283 +1351 @@
-            pylab.draw()
-
+
+    def CrossCorrZero(self, pairs, spklist=None, time_bin=1., mode="auto"):
+        #Just the Aertsen Value at 0 delay
+        t_start = float(time_parameters[0])
+        t_stop  = float(time_parameters[1])
+        
+        spk1 = CutSpikeList(f1, time_parameters)
+        spk2 = CutSpikeList(f2, time_parameters)
+    
+        if ((size(spk1)==0) or (size(spk2)==0)):
+            return 0
+        
+        if mode == "auto":
+            if (selected_set==None):
+                selected_set = GetCells(spk1)
+            if isinstance(selected_set,int):
+                all_positions = GetCells(spk1)
+                selected_set  = numpy.random.permutation(all_positions)[0:selected_set]
+            spk1 = CutSpikeList2(spk1,selected_set)
+            spk2 = CutSpikeList2(spk2,selected_set)
+            cells_id = sort(selected_set)
+            for idx in xrange(len(spk1[0])):
+                spk1[1][idx] = numpy.where(cells_id == spk1[1][idx])[0]
+            for idx in xrange(len(spk2[0])):
+                spk2[1][idx] = numpy.where(cells_id == spk2[1][idx])[0]
+            nb_neur   = len(selected_set)
+        
+        if mode == "cross":
+            # If nothing is entered, we perform the cross correlation between the two
+            # whole spike trains
+            if (selected_set==None):
+                cell_1 = GetCells(spk1)
+                cell_2 = GetCells(spk2)
+                N      = min(len(cell_1),len(cell_2))
+                cell_1 = cell_1[0:N]
+                cell_2 = cell_2[0:N]
+            
+            # If we want to select only X pairs of cells to performs the auto
+            # correlation, we select those random pairs.
+            if isinstance(selected_set,int):
+                N = selected_set
+                all_positions = GetCells(spk1)
+                shuffle       = numpy.random.permutation(all_positions)
+                cell_1        = shuffle[0:N]
+                # If we perform a cross correlation within the same spike file,
+                # we prevent the same neuron to be in the two signals, to have a real
+                # non overlapping cross correlation
+                if not(any(equal(spk1,spk2) == False)):
+                    shuffle = numpy.random.permutation(all_positions)
+                    cell_2  = shuffle[0:N]
+                else:
+                    # Otherwise we don't care...
+                    all_positions = GetCells(spk2)
+                    shuffle = numpy.random.permutation(all_positions)
+                    cell_2  = shuffle[0:N]
+            spk1 = CutSpikeList2(spk1,cell_1)
+            spk2 = CutSpikeList2(spk2,cell_2)
+            cells_id_1 = sort(cell_1)
+            cells_id_2 = sort(cell_2)
+            for count, cell in enumerate(cells_id_1):
+                spk1[1][numpy.where(cell == spk1[1])] = count
+            for count, cell in enumerate(cells_id_2):
+                spk2[1][numpy.where(cell == spk2[1])] = count
+            nb_neur = len(cells_id_1)
+        
+        
+        num_bins  = int((t_stop-t_start)/time_bin)+1
+        mat_neur1 = numpy.zeros((num_bins,nb_neur))
+        mat_neur2 = numpy.zeros((num_bins,nb_neur))
+        
+        timespk1 = numpy.array(((spk1[0] - t_start)/time_bin),'int')
+        timespk2 = numpy.array(((spk2[0] - t_start)/time_bin),'int')
+        
+        mat_neur1[timespk1,spk1[1]] = 1
+        mat_neur2[timespk2,spk2[1]] = 1
+        Z = float(sum(sum(mat_neur1*mat_neur2)))
+        
+        N = float(num_bins*nb_neur)
+        X = float(len(spk1[0]))
+        Y = float(len(spk2[0]))
+        
+        X=X/N
+        Y=Y/N
+        Z=Z/N
+        
+        return (Z-X*Y)/sqrt((X*(1-X))*(Y*(1-Y)))
-    
-    def VictorPurpuraDistance(self, id_list, spklist=None, cost=0.5):
@@ -1407 +1561 @@
-        subplot = self.__getdisplay__(display)
-        if id_list == None: 
-
+()
-            spk = self
-if id_list != self.id_list
+se
-            spk = self.id_slice(id_list)
-
@@ -1423 +1577 @@
-            idx = numpy.where((spike_times >= t_start) & (spike_times <= t_stop))[0]
-            if len(spike_times) > 0:
-                logging.debug("Plotting %d points for %s" % (len(spike_times), self.label))
-                subplot.scatter(spike_times, ids, **kwargs)
-            xlabel = "Time (ms)"
@@ -1596 +1749 @@
-            time and id.
-       
-
-
-
-
-
-
+
+
+
+
+
+
+
-                first one being the array of all the spikes, the second the array of all the
-                corresponding ids
-
+>>>
-        
-        See also
@@ -1697 +1851 @@
-        return len(self.signal)
-
+    def copy(self):
+        """
+        Return a copy of the AnalogSignal object
+        """
+        return AnalogSignal(self.signal, self.dt, self.t_start, self.t_stop)
+
-    def time_axis(self):
-        """
@@ -1719 +1879 @@
-class AnalogSignalList(object):
-    """
-
+, dims=None
-    
-    Return a AnalogSignalList object which will be a list of AnalogSignal objects.
@@ -1738 +1898 @@
-    """
-    def __init__(self, signals, id_list, dt=None, t_start=None, t_stop=None, dims=None):
-        self.id_list = id_list
-        self.N       = len(id_list)
-        self.t_start = t_start
-        self.t_stop  = t_stop
@@ -1759 +1917 @@
-            self.__calc_startstop()
-
+    def id_list(self):
+        """ 
+        Return the list of all the cells ids contained in the
+        SpikeList object
+        """
+        return numpy.array(self.analog_signals.keys())
+
+    def copy(self):
+        """
+        Return a copy of the AnalogSignalList object
+        """
+        # 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
-
-    def __calc_startstop(self):
@@ -1768 +1942 @@
-        if len(self) > 0:
-            if self.t_start is None:
-
+()
-                self.t_start = numpy.min(start_times)
-                logging.debug("Warning, t_start is infered from the data : %f" %self.t_start)
@@ -1774 +1948 @@
-                    self.analog_signals[id].t_start = self.t_start
-            if self.t_stop is None:
-
+()
-                self.t_stop = numpy.max(stop_times)
-                logging.debug("Warning, t_stop  is infered from the data : %f" %self.t_stop)
@@ -1825 +1999 @@
-        return len(self.analog_signals)
-
-get
+sub
-        if sub_list == None:
-
+()
-        if type(sub_list) == int:
-
+()
-        if type(sub_list) == list:
-            return sub_list
@@ -1848 +2022 @@
-        """
-        assert isinstance(signal, AnalogSignal), "An AnalogSignalList object can only contain AnalogSignal objects"
-
+()
-            raise Exception("Id already present in AnalogSignalList. Use setitem instead()")
-        else:
-            self.analog_signals[id] = signal
-            self.id_list.append(id)
-            self.N += 1
-        self.__calc_startstop()
-
@@ -1876 +2048 @@
-        """
-        new_AnalogSignalList = AnalogSignalList([], [], self.dt, self.t_start, self.t_stop, self.dimensions)
-
-
+
-        for id in id_list:
-            try:
@@ -1897 +2068 @@
-        """
-        new_AnalogSignalList = AnalogSignalList([], [], self.dt, t_start, t_stop, self.dimensions)
-
+()
-            new_AnalogSignalList.append(id, self.analog_signals[id].time_slice(t_start, t_stop))
-        new_AnalogSignalList.__calc_startstop()
@@ -1942 +2113 @@
-        """
-        subplot   = self.__getdisplay__(display)
-get
+sub
-        time_axis = self.time_axis()  
-        if not subplot or not ENABLE_PLOTS:
@@ -1977 +2148 @@
-        """
-        subplot   = self.__getdisplay__(display)
-get
+sub
-        time_axis = self.time_axis()  
-        if not subplot or not ENABLE_PLOTS:
@@ -2008 +2179 @@
-        """
-        subplot   = self.__getdisplay__(display)
-get
+sub
-        time_axis = self.time_axis()  
-        if not subplot or not ENABLE_PLOTS:
@@ -2039 +2210 @@
-    Inputs:
-        filename - the name of the spike file
-the range (0,..,N)), 
-
+
+the range (0,..,N)), 
-        dims     - if the cells were aranged on a 2/3D grid, a tuple with the dimensions
-        dt       - the discretization step, in ms
@@ -2046 +2217 @@
-        t_stop   - end of the simulation, in ms
-
-
-
+
+
+
+
-    """
-    spike_loader = io.SpikeListIO(filename)
@@ -2064 +2237 @@
-    Inputs:
-        filename - the name of the spike file
-the range (0,..,N)), 
-
+
+the range (0,..,N)), 
-        dims     - if the cells were aranged on a 2/3D grid, a tuple with the dimensions
-        dt       - the discretization step, in ms
@@ -2071 +2244 @@
-        t_stop   - end of the simulation, in ms
-
-
-
+
+
+
+
-    """
-    analogsignal_loader = io.AnalogSignalIO(filename)
@@ -2088 +2263 @@
-    Inputs:
-        filename - the name of the spike file
-the range (0,..,N)), 
-
+
+the range (0,..,N)), 
-        dims     - if the cells were aranged on a 2/3D grid, a tuple with the dimensions
-        dt       - the discretization step, in ms
@@ -2095 +2270 @@
-        t_stop   - end of the simulation, in ms
-
-
-
+
+
+
+
-    """
-    analogsignal_loader = io.AnalogSignalIO(filename)
@@ -2113 +2290 @@
-    Inputs:
-        filename - the name of the spike file
-the range (0,..,N)), 
-
+
+the range (0,..,N)), 
-        dims     - if the cells were aranged on a 2/3D grid, a tuple with the dimensions
-        dt       - the discretization step, in ms
@@ -2120 +2297 @@
-        t_stop   - end of the simulation, in ms
-
-
-
+
+
+
+
-    """
-    analogsignal_loader = io.AnalogSignalIO(filename)