50 lines
1.7 KiB
Python
Executable File
50 lines
1.7 KiB
Python
Executable File
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from mathutils import *
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"""
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a = [1,2,3,4,7,10,50,4,3,2,1,2,1,1,2,3,4,7,10,50,4,3,2,1,1,2,1,2,3,4,7,10,50,4,3,2,1,2,1,2,3,4,7,10,50,4,3,2,1,1,2,1,2,3,4,7,10,50,4,3,2,1,2]
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ft = fft(a)
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it = ffti(ft)
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plot([a,get_real(ft),get_imag(ft) ,get_real(it),get_imag(it)],("a", "real","img", "i real","i img"))
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data = [2,3,4,10,20,5,3,2,1]
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print estimate_peak_indexes(data)
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print estimate_peak_indexes(data, threshold =21.0)
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print estimate_peak_indexes(data, positive=False)
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print estimate_peak_indexes(data, threshold =1.0, positive=False)
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print estimate_peak_indexes(data, threshold = None, min_peak_distance = 10.0 , positive=False)
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xdata = [1,3,7,23,34,45,56,58, 61]
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print estimate_peak_indexes(data, xdata, threshold = None, min_peak_distance = 10.0 , positive=False)
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"""
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import random
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#A sin wave is: A.sin(2.pi.f.t)
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#The signal is composed by 3 sinusoids (100Hz, 200Hz, 400Hz) and a background noise
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def signal_generator(t):
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return math.sin(100*2*math.pi*t) + 0.5 * math.sin(200*2*math.pi*t) + 0.25 * math.sin(400*2*math.pi*t) + 0.5* random.random()
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sampling_frequency = 1024.0
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number_of_samples = 1024
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time_vector = [x / sampling_frequency for x in frange(0, number_of_samples, 1)]
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signal = [signal_generator(x) for x in time_vector]
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tranform = fft(signal)
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two_side_spectrum = [x / number_of_samples for x in get_modulus(tranform)]
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spectrum = [two_side_spectrum[0],] + [x * 2 for x in two_side_spectrum[1:len(two_side_spectrum)/2 + 1] ]
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number_of_samples = len(tranform) # Signal may have been padded to next power of two
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freq_vector = [x * sampling_frequency / float(number_of_samples) for x in frange(0, len(spectrum) , 1)]
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#print f
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plot([signal,spectrum], ["signal", "spectrum"],[time_vector, freq_vector])
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#plot(p1, xdata = f) |