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input is like

<class 'numpy.ndarray'> [102 106 95 92 59 89 92 107 113 109 49 88 96 53 57 107 112 109
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58 96 108 111 114 112 114 105 59 45 43 103 54 57 87 112 106 106
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106 119 103 100 103 106 117 103 114 40 80 46 42 109 92 102 40 111
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102 95 109 111 55 97 56 56 49 56 54 47 41 84 107 100 106 104
95 55 46 57 112 116 111 111 105 47 107 103 43 48 51 44 89 88
102 91 109 54 43 98 96 109 104 114 106 95 85 54 109 98 110 40
102 100 104 113 43 44 109 104 111 106 108 54 56 58 49 98 50 58
78 40 104 100 101 112 106 31 92 100 102 116 111 98 99 43 93 108
48 109 111 47 46 52 105 45 106 45 54 46 96 103 104 104 107 113
49 42 107 113 111 109 46 53 58 41 104 105 44 118 115 113 108 105
96 94 118 96 47 103 52 109 91 110 106 105 108 41 115 113 103 59
42 51 110 112 110 113 103 41 97 89 107 46 40 48 110 105 91 107
40 106 45 43 50 104 45 39 42 98 108 113 89 40 49 48 41 108
104 118 117 114 71 99 57 55 108 116 108 111 52 50 42 48 111 109
108 101 55 106 104 110 103 112 103 101 96 104 114 106 111 107 105 40
42 44 42 89 40 108 104 109 109 99 80 93 107 117 107 113 115 85
102 58 108 113 100 112 111 118 111 109 47 55 42 46 59 106 105 97
54 48 93 107 114 104 97 107 108 117 114 108 111 112 113 105 114 111
111 101 109 100 95 98 106 59 104 92 108 99 51 92 106 115 95 94]

Hi, Nikos!

This is the my first time to use NAFF as a tool to analyze TBT data, and at this moment I can't run the code with a small sample routine anywhere.

I simulate the TBT data using a decayed sinusoidal, which records one data each turn, so the obtained data is one-dimensional.

Below is the sample code. B.T.W., I copy the PyNAFF.py to my current directory and imported it as a module.

import numpy as np
import scipy as sp
import matplotlib.pylab as plt
import PyNAFF as pnf
#import PyNAFF.Cython.PyNAFF as cpnf

q = 2.1#tune
f0 = 1e6#revolution frequency
N = 2**11#total sample number

sample_rate=10e6
sample_time = N/sample_rate

tau = sample_time/5#decay const.

t = np.linspace(0, sample_time, N)
y = np.sin(2*np.pi*q*f0*t)*(np.exp(-t/tau))
#plt.plot(y,"b.")

turns=len(y)
result = pnf.naff(data=y, turns=turns, nterm=1, icol=1, icx=1, icy=0, saveFiles=False, doAll=False, neps=100000000., loglevel=20, logfile=None, debug=False, batch=True,
         icplx=1, iw=+1, nulin=0,  iprnaf=0, iftes=0, ifsav=0, ifcomp=0, ifres=0, ifstab=1,imodt=0, inputParamFile=None, solutionFile='track.sol',
         parametersOutLog='nafpar.par', outBuffFile='naf.out', tabFile='naf.tab')

The Exception says

Traceback (most recent call last):

  File "<ipython-input-3-47671462eba5>", line 4, in <module>
    parametersOutLog='nafpar.par', outBuffFile='naf.out', tabFile='naf.tab')

  File "E:\CSRm_BPM_data\BPM_signal_2011\naff_test\PyNAFF.py", line 1540, in naff
    raise ValueError("# PyNAFF.naff() : Number of turns [{}] exceeds the minimum required data length [{}]".format(turns, len(data+1)))

ValueError: # PyNAFF.naff() : Number of turns [2048] exceeds the minimum required data length [2048]

Regards!
Yongchun

This line in pnf.naff() raises an AttributeError with numpy 1.24.1 because using the alias np.complex was deprecated in 1.20.0. Using python's complex() method fixes the problem.