Changeset 13415 for fact/tools/pyscripts/sandbox/dneise

Timestamp:

04/23/12 13:28:06 (13 years ago)

Author:

neise

Message:

exchanged naming of cols, such that it fits to the way TB does it.

Location:

fact/tools/pyscripts/sandbox/dneise

Files:

• cleaning (copied) (copied from fact/tools/pyscripts/tools/cleaning )
• timecal.py (modified) (8 diffs)

fact/tools/pyscripts/sandbox/dneise/timecal.py

@@ -11 +11 @@
 from pyfact     import RawData
 from drs_spikes import DRSSpikes
-from extractors import ZeroXing
+from extractor import ZeroXing
 class CalculateDrsTimeCalibrationConstants( object ):
     """ basic analysis class for the calculation of DRS time aperture jitter calibration constants
@@ -34 +34 @@
         self.fsampling = 2.         # sampling frequency
         self.freq = 250.            # testfrequency
-        self.P_nom = 1000./freq     # nominal Period due to testfrequency
+        self.P_nom = 1000./self.freq     # nominal Period due to testfrequency
         self.DAMPING = 0.02         # Damping applied to correction
         
@@ -48 +48 @@
         """        
         # loop over events
+        counter = 0
         for event in self.run:
+            print 'counter', counter
+            counter +=1
             # in the next() method, which is called during looping,
             # the data is automatically amplitude calibrated.
@@ -60 +63 @@
             start_cell = event['start_cells'][niners_ids,:]
             
-            data = despike( data )
+            data = self.despike( data )
             
             # shift data down by the mean           # possible in one line
@@ -111 +114 @@
             
             # first we make the sublists of the lists to be numpy.arrays 
-            for i in len(float_slice_of_all_crossings): #both list have equal length
+            for i in range(len(float_slice_of_all_crossings)): #both list have equal length
                 float_slice_of_all_crossings[i] = np.array(float_slice_of_all_crossings[i])
                 time_of_all_crossings[i] = np.array(time_of_all_crossings[i])
@@ -120 +123 @@
                 all_measured_periods.append(np.diff(chip_times))
             
-            for chid,chip_periods in enumerate(all_measured_periods):
+            for chip,chip_periods in enumerate(all_measured_periods):
                 
                 for i,period in enumerate(chip_periods):
@@ -144 +147 @@
                     total_negative_correction = rest * (interval * (pos_fraction/rest))
                     # so we can call the product following 'rest' the 'neg_fraction
-                    neg_fraction = -1 * interval/rest * important
-                    assert total_positive_correction - total_negative_correction == 0
+                    neg_fraction = -1 * interval/rest * pos_fraction
+                    #print '1. should be zero', total_positive_correction - total_negative_correction
+                    #assert total_positive_correction - total_negative_correction == 0
                     
                     correction = np.zeros(1024)
@@ -151 +155 @@
                     correction[:start+1]      = neg_fraction
                     correction[end+1:]        = neg_fraction
-                    assert correction.sum() == 0
+                    #print '2.   should be zero', correction.sum()
+                    #assert correction.sum() == 0
                     
                     # now we have to add these correction values to self.time_calib