Index: fact/tools/pyscripts/pyfact/generator.py
===================================================================
--- fact/tools/pyscripts/pyfact/generator.py	(revision 13419)
+++ fact/tools/pyscripts/pyfact/generator.py	(revision 13420)
@@ -98,4 +98,118 @@
 
 
+class SignalGeneratorCSV(object):
+    
+    def __init__(self, file_name, option_str = 'len 100 noise 3', name = 'SignalGenerator'):
+        time, maxprob, mean, median = np.loadtxt( file_name, delimiter=',', unpack=True)
+        csv_data = maxprob
+        
+        # csv data was downshifted, I shift it up here
+        self.csv_data = csv_data - csv_data.min()
+        
+
+        self.__module__ = 'CSV generator'
+        self.option_str = option_str.lower()
+        self.options = make_options_from_str(option_str)
+        self.parse_options()
+        self.name = name
+        
+    def parse_options(self):
+        o = self.options #shortcut
+        if 'len' in o:
+            self.npoints = int(o['len'][0])
+        else: 
+            self.npoints = 100
+        if 'noise' in o:
+            self.sigma = float(o['noise'][0])
+        else:
+            self.sigma = 1
+        if 'bsl' in o:
+            self.bsl = float(o['bsl'][0])
+        else:
+            self.bsl = -0.5
+        
+        if 'step' in o:
+            self.step_height = float(o['step'][0])
+            self.step_start = int(o['step'][1])
+            self.step_stop = int(o['step'][2])
+
+        if 'triangle' in o:
+            self.pulses = []
+            # append 1st pulse to list of pulses
+            self.pulses.append( ( float(o['triangle'][0]) , float(o['triangle'][1]), int(o['triangle'][2]), int(o['triangle'][3]) ) )
+            number_of_pulses_after_1st = (len(o['triangle'])-4)/2
+            for i in range(number_of_pulses_after_1st):
+                self.pulses.append( ( float(o['triangle'][2*i+4]) , float(o['triangle'][2*i+5]), int(o['triangle'][2]), int(o['triangle'][3]) ) )
+
+        if 'spike' in o:
+            self.spikes = []
+            for i in range(len(o['spike'])/2):
+                self.spikes.append( ( int(o['spike'][2*i]), float(o['spike'][2*i+1]) ) )
+        
+        if 'csv' in o:
+            self.csvs = []
+            for i in range(len(o['csv'])/2):
+                time = int( o['csv'][2*i] )
+                amplitude = float( o['csv'][2*i+1] )
+                self.csvs.append( (time, amplitude) )
+
+    def __call__(self, option_str = ''):
+        if option_str:
+            self.option_str = option_str.lower()
+            self.options = make_options_from_str(self.option_str)
+            self.parse_options()
+
+        signal = np.zeros(self.npoints)
+        signal += self.bsl
+        
+        if 'step' in self.options:
+            signal[self.step_start:self.step_stop] += self.step_height
+        if 'triangle' in self.options:
+            for pulse in self.pulses:
+                pos = pulse[0]
+                height = pulse[1]
+                rise = pulse[2]
+                fall = pulse[3]
+                start = pos - rise
+                stop = pos + fall
+                signal[start:pos] += np.linspace(0., height, rise)
+                signal[pos:stop] += np.linspace(height, 0. , fall)
+        if 'spike' in self.options:
+            for spike in self.spikes:
+                signal[spike[0]] += spike[1]
+        if 'csv' in self.options:
+            for csv in self.csvs:
+                amplitude = csv[1]
+                time = csv[0]
+                csv_data = self.csv_data.copy()
+                #scale
+                csv_data *= amplitude
+                # add shifted
+                print 'bumm', len(csv_data)
+                print csv_data.shape
+                print signal[time:time+len(csv_data)].shape
+                signal[time:time+len(csv_data)] += csv_data
+                
+        # add noise
+        signal += + np.random.normal(0.0,self.sigma, signal.shape)
+        return signal 
+
+    def __str__(self):
+        s = self.name + '\n'
+        s += 'possible options and parameters\n'
+        s += ' * len:      number of samples (100)\n'
+        s += ' * noise:    sigma (1)\n'
+        s += ' * bsl:      level (-0.5)\n'
+        s += ' * step:     height, start, end\n'
+        s += ' * triangle: pos height risingedge, fallingedge [pos height ...]\n'
+        s += ' * spike:    pos height [pos height ...]\n'
+        s += ' * csv:      pos height [pos height ...]\n'
+        
+        s += 'current options are:\n'
+        for key in self.options.keys():
+            s += key + ':' + str(self.options[key]) + '\n'
+        return s
+
+
 # Helper function to parse signalname and create a dictionary
 # dictionary layout :
@@ -115,18 +229,20 @@
     return options
     
-def _plotter(signal, text):
-    x=range(len(signal))
-    ax = plt.plot(x, signal, 'b.', label='signal')
-    plt.title('test of SignalGenerator with option string:\n' + text)
-    plt.xlabel('sample')
-    plt.legend()
-    plt.grid(True)
-    plt.show()
-    
 if __name__ == '__main__':
-    import matplotlib.pyplot as plt
-    """ test the class """
-    myGenerator = SignalGenerator('len 400 noise 0.3 bsl -2.5 triangle 50 10.2 10 100 65 10 150 20 180 10 250 10 spike 100 50. 20 50 21 49')
+    from plotters import Plotter
+    myGenerator = SignalGenerator('len 400 noise 0.3 bsl -2.5 triangle 50 10.2 10 100 65 10 150 20 spike 100 50. 20 50 21 49')
     sig = myGenerator()
     print myGenerator
-    _plotter(sig, myGenerator.option_str)    
+    
+    p = Plotter('generator test')
+    p(sig)
+    
+    anothergen = SignalGeneratorCSV('PulseTemplate_PointSet_0.csv', 
+                'len 1000 noise 0.4 bsl -2.0 csv 300 1 60 2 650 1 spike 110 50')
+    sig2 = anothergen()
+    print anothergen
+    
+    pp = Plotter('CSV Gen Test')
+    pp(sig2)
+    
+    raw_input('any key to quit')