- Timestamp:
- 08/25/09 11:09:38 (3 years ago)
- Files:
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- 1 modified
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branches/interval/src/signals/spikes.py (modified) (11 diffs)
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branches/interval/src/signals/spikes.py
r425 r426 97 97 raise ValueError("spikes time stamps should be *positive* *numbers*") 98 98 99 self.interval = self.extract_intervals_from_SpikeTrain_arguments(t_start, t_stop, interval) 100 if not (self.interval.t_start() >= 0) and (self.interval.t_stop() >= 0) : 99 self.interval = self.extract_intervals_from_SpikeTrain_arguments(t_start, t_stop, interval) 100 self.update_t_start_and_t_stop() 101 102 if not (self.t_start >= 0) and (self.t_stop >= 0) : 101 103 raise ValueError("t_start and t_stop should be greater than 0") 102 self.update_t_start_and_t_stop()103 104 104 105 self.spike_times = self.interval.slice_times(numpy.array(self.spike_times, numpy.float32)) … … 119 120 if t_start == None and t_stop == None : 120 121 try: 121 t_start = min(self.spike_times)122 t_start = numpy.min(self.spike_times) 122 123 except Exception: 123 124 print "Error in guessing t_start (first spike), spikes may be empty !" 124 125 t_start = 0 125 126 try: 126 t_stop = max(self.spike_times)127 t_stop = numpy.max(self.spike_times) 127 128 except Exception: 128 129 print "Error in guessing t_stop (last spike), spikes may be empty !" … … 132 133 if t_start == None : 133 134 try: 134 t_start = min(self.spike_times)135 t_start = numpy.min(self.spike_times) 135 136 except Exception: 136 137 print "Error in guessing t_stop (last spike), spikes may be empty !" … … 140 141 if t_start.__class__.__name__ == 'float' or t_start.__class__.__name__ == 'int' : 141 142 try: 142 t_stop = max(self.spike_times)143 t_stop = numpy.max(self.spike_times) 143 144 except Exception: 144 145 print "Error in guessing t_stop (last spike), spikes may be empty !" … … 381 382 382 383 383 def fano_factor_isi(self , t_start=None, t_stop=None, interval=None):384 def fano_factor_isi(self): 384 385 """ 385 386 Return the fano factor of this spike trains ISI. … … 848 849 if id in id_list: 849 850 self.spiketrains[id] = SpikeTrain(spikes[break_points[idx]:break_points[idx+1], 1]) 851 850 852 self.interval = self.extract_intervals_from_SpikeList_arguments(t_start, t_stop, interval) 853 851 854 if len(self) > 0: 852 855 for id in self.spiketrains.keys(): 853 856 self.spiketrains[id].interval = self.interval 857 self.spiketrains[id].update_t_start_and_t_stop() 858 854 859 self.update_t_start_and_t_stop() 855 860 856 861 def id_list(self): 857 862 """ … … 863 868 [0,1,2,3,....,9999] 864 869 """ 865 print self.spiketrains.keys()866 870 return numpy.array(self.spiketrains.keys(), int) 867 871 … … 886 890 if t_start == None and t_stop == None : 887 891 try: 888 start_times = numpy.array([self.spiketrains[idx]. interval.t_start() for idx in self.id_list()], numpy.float64)889 t_start = numpy.min(start_times)892 start_times = numpy.array([self.spiketrains[idx].t_start for idx in self.id_list()], numpy.float32) 893 t_start = numpy.min(start_times) 890 894 logging.debug("Warning, t_start is infered from the data : %f" %t_start) 891 895 except Exception: … … 893 897 t_start = 0 894 898 try: 895 stop_times = numpy.array([self.spiketrains[idx]. interval.t_stop() for idx in self.id_list()], numpy.float64)899 stop_times = numpy.array([self.spiketrains[idx].t_stop for idx in self.id_list()], numpy.float32) 896 900 t_stop = numpy.max(stop_times) 897 901 logging.debug("Warning, t_stop is infered from the data : %f" %t_stop) … … 903 907 if t_start == None : 904 908 try: 905 start_times = numpy.array([self.spiketrains[idx]. interval.t_start() for idx in self.id_list()], numpy.float64)909 start_times = numpy.array([self.spiketrains[idx].t_start for idx in self.id_list()], numpy.float32) 906 910 t_start = numpy.min(start_times) 907 911 logging.debug("Warning, t_start is infered from the data : %f" %t_start) … … 913 917 if t_start.__class__.__name__ == 'float' or t_start.__class__.__name__ == 'int' : 914 918 try: 915 stop_times = numpy.array([self.spiketrains[idx]. interval.t_stop()for idx in self.id_list()], numpy.float64)919 stop_times = numpy.array([self.spiketrains[idx].t_stop for idx in self.id_list()], numpy.float64) 916 920 t_stop = numpy.max(stop_times) 917 921 logging.debug("Warning, t_stop is infered from the data : %f" %t_stop)
