removed old code for kaiser window and use numpy method directly

src/data/DamarisFFT.py

@@ -1,7 +1,5 @@
 import numpy
-import sys
 from . import autophase
-from functools import reduce
 
 
 class DamarisFFT:
@@ -26,21 +24,19 @@ class DamarisFFT:
             start, stop = stop, start
 
         # do nothing if one uses clip as a "placeholder"
-        if start == None and stop == None:
+        if start is None and stop is None:
             return self
 
-        if start == None:
+        if start is None:
             start = self.x[ 0 ]
-        if stop == None:
+        if stop is None:
             stop = self.x[ -1 ]
 
         # check if data is fft which changes the start/stop units
         if self.xlabel == "Frequency / Hz":
-            isfft = True
             start = self.x.size * (0.5 + start / self.sampling_rate)
             stop = self.x.size * (0.5 + stop / self.sampling_rate)
         else:
-            isfft = False
             # get the corresponding indices
             start *= self.sampling_rate
             stop *= self.sampling_rate
@@ -147,7 +143,6 @@ class DamarisFFT:
         """
         Symmetric centered window (bartlett)
         """
-
         apod = numpy.bartlett( self.x.size )
         for i in range( 2 ):
             self.y[ i ] = self.y[ i ] * apod
@@ -157,26 +152,7 @@ class DamarisFFT:
         """
         Symmetric centered window (kaiser)
         """
-
-        if use_scipy == None:
-            # modified Bessel function of zero kind order from somewhere
-            def I_0( x ):
-                i0 = 0
-                fac = lambda n: reduce( lambda a, b: a * (b + 1), list(range( n)), 1 )
-                for n in range( 20 ):
-                    i0 += ((x / 2.0) ** n / (fac( n ))) ** 2
-                return i0
-
-            t = numpy.arange( self.x.size, type=numpy.Float ) - self.x.size / 2.0
-            T = self.x.size
-            # this is the window function array
-            apod = I_0( beta * numpy.sqrt( 1 - (2 * t / T) ** 2 ) ) / I_0( beta )
-        else:
-            # alternative method using scipy
-            import scipy
-
-            apod = scipy.kaiser( self.x.size, beta )
-
+        apod = numpy.kaiser( self.x.size, beta )
         for i in range( 2 ):
             self.y[ i ] = self.y[ i ] * apod
         return self