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window.py
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window.py
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#Windowing for NAFF transformation
import numpy as np
from matplotlib import *
import matplotlib.pyplot as plt
from pylab import *
from naff_transformation import naff_transformation
from fourier import fourier
from print_info import print_info
from tune_plots import tune_plots
#start is the initial point of the interval in which the transformation is going to be implemented
#stop is the final point of this interval
#width defines the window size
#step defines how much the window is moving
rc('text', usetex=True)
def window(start, stop, step, width, signal, turn, color='b', plot_all = False, chroma = 1):
#if not plot_all:
#plt.figure(figsize=(10,10))
amp = []
frequency = []
while True:
naff_list = []
#windowing
for min_turn in range(start,stop,step):
max_turn = min_turn+width
#print "Turns: ",min_turn,max_turn
amplitude, counter, naff_list = naff_transformation(width, signal[min_turn:max_turn], naff_list)
#print 'naff', naff_list
amp.append(amplitude[counter-1])
#print amp
mean = print_info(naff_list)
#use again NAFF to find tune oscillation frequency
a,c, frequency = naff_transformation(len(naff_list), np.array(naff_list), frequency)
for cnt in xrange (len(frequency)):
print 'Tune frequency', frequency[cnt] #in this case, the prominent frequency is only one, so this array has only one element!
#plot the tunes over the number of turns to see tune oscillation
tune_plots(turn, start, stop, step, naff_list, mean, color, plot_all)
if max_turn>=len(turn)-width:
break
return mean, frequency[0]