Removed deprecated DaFFT module. This module is replaced by DamarisFFT,
which extends the ADC_Result and Accumulation class.
This commit is contained in:
Markus Rosenstihl
parent
31d809a618
commit
e457a5071f
@ -1,211 +0,0 @@
|
|||||||
import warnings
|
|
||||||
# enable warnings in Python 2.7
|
|
||||||
warnings.simplefilter('default')
|
|
||||||
warnings.warn("use of DaFFT is deprecated, please use the methods of Accumulation and ADC_data classes provided by DamarisFFT.", DeprecationWarning)
|
|
||||||
import numpy as N
|
|
||||||
import numpy.fft as F
|
|
||||||
|
|
||||||
class FFT:
|
|
||||||
def __init__(self, one_result):
|
|
||||||
# create copy of one_result and work only on the copy
|
|
||||||
# also extract some informations
|
|
||||||
self.the_result = one_result + 0
|
|
||||||
self.timepoints = N.array(one_result.x)
|
|
||||||
self.sampling_rate = one_result.get_sampling_rate()
|
|
||||||
self.data_points = one_result.get_ydata(0).size
|
|
||||||
self.aquisition_time = self.data_points / float(self.sampling_rate)
|
|
||||||
self.the_result.set_xlabel('Frequency [Hz]')
|
|
||||||
|
|
||||||
def write_n(self, afile):
|
|
||||||
filename = open(afile,'w')
|
|
||||||
filename = open(afile,'a')
|
|
||||||
#print self.the_result.get_description_dictionary()
|
|
||||||
#filename.write('%s'%self.get_description_dictionary())
|
|
||||||
for i in range(self.data_points):
|
|
||||||
filename.write('%e\t%e\t%e\n'%(self.the_result.x[i], self.the_result.y[0][i], self.the_result.y[1][i]))
|
|
||||||
filename.close()
|
|
||||||
return self
|
|
||||||
|
|
||||||
def base_corr(self, cutoff=0.3, show=0):
|
|
||||||
"""
|
|
||||||
Subtracts the mean of the last cutoff % of the timsignal
|
|
||||||
to get rid of the DC part in the FFT and returns the
|
|
||||||
new data.
|
|
||||||
If cutoff is not given, the mean of the last 30% will be
|
|
||||||
subtracted.
|
|
||||||
If show=1 the result is return and not the instance. This allows to plot the baseline corrected signal
|
|
||||||
Example:
|
|
||||||
base_corr(cutoff=0.2, show=1)
|
|
||||||
"""
|
|
||||||
last_points = int(cutoff*self.data_points)
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i] - self.the_result.y[i][-last_points:].mean()
|
|
||||||
if show == 1 :
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def abs_fft(self, points=None, zoom=None,write = 'off'):
|
|
||||||
"""
|
|
||||||
Fourier transforms the timesignal;
|
|
||||||
points is the number of points to transform, if more points given than data points
|
|
||||||
the rest is zero padded
|
|
||||||
|
|
||||||
absfft(points=4096)
|
|
||||||
"""
|
|
||||||
realdata = N.array(self.the_result.y[0])
|
|
||||||
imdata = N.array(self.the_result.y[1])
|
|
||||||
data = realdata + 1j*imdata
|
|
||||||
fftdata = F.fftshift(F.fft(data, points))
|
|
||||||
absfft = N.sqrt(fftdata.real**2 + fftdata.imag**2)
|
|
||||||
# create our x axis
|
|
||||||
n = fftdata.size
|
|
||||||
self.the_result.x = F.fftshift(F.fftfreq(n, 1.0/self.sampling_rate))
|
|
||||||
self.the_result.y[0] = absfft
|
|
||||||
self.the_result.y[1] = N.zeros(n)
|
|
||||||
if write == 'on':
|
|
||||||
return self
|
|
||||||
else:
|
|
||||||
if zoom is None:
|
|
||||||
return self.the_result
|
|
||||||
else:
|
|
||||||
center, width = zoom
|
|
||||||
return self.zoom(self.the_result, center, width)
|
|
||||||
|
|
||||||
|
|
||||||
def fft(self, points=None, zoom=None, write='off'):
|
|
||||||
realdata = N.array(self.the_result.y[0])
|
|
||||||
imdata = N.array(self.the_result.y[1])
|
|
||||||
data = realdata + 1j*imdata
|
|
||||||
fftdata = F.fftshift(F.fft(data, points))
|
|
||||||
# create our x axis
|
|
||||||
n = fftdata.size
|
|
||||||
self.the_result.x = F.fftshift(F.fftfreq(n, 1.0/self.sampling_rate))
|
|
||||||
self.the_result.y[0] = fftdata.real
|
|
||||||
self.the_result.y[1] = fftdata.imag
|
|
||||||
if write == 'on':
|
|
||||||
return self
|
|
||||||
else:
|
|
||||||
if zoom is None:
|
|
||||||
return self.the_result
|
|
||||||
else:
|
|
||||||
center, width = zoom
|
|
||||||
return self.zoom(self.the_result, center, width)
|
|
||||||
|
|
||||||
def zoom(self,some_result, center="auto", width=1000):
|
|
||||||
if center == "auto":
|
|
||||||
i_center = int(self.the_result.y[0].argmax())
|
|
||||||
maximum = self.the_result.y[0][i_center]
|
|
||||||
print "Maximum at Frequency:", self.the_result.x[i_center]
|
|
||||||
else:
|
|
||||||
i_center = int(self.data_points/2.0+self.data_points*center/self.sampling_rate)
|
|
||||||
#print "TODO: set width automagically"
|
|
||||||
#if width == "auto":
|
|
||||||
# i_width = int(self.data_points*width)
|
|
||||||
i_width = int(self.data_points*width/self.sampling_rate)
|
|
||||||
some_result.x=some_result.x[i_center-i_width/2:i_center+i_width/2]
|
|
||||||
some_result.y[0]=some_result.y[0][i_center-i_width/2:i_center+i_width/2]
|
|
||||||
some_result.y[1]=some_result.y[1][i_center-i_width/2:i_center+i_width/2]
|
|
||||||
return some_result
|
|
||||||
|
|
||||||
"""
|
|
||||||
Apodization functions:
|
|
||||||
* exp_window and gauss_window are S/N enhancing,
|
|
||||||
* dexp_window and traf_window are resolution enhancing
|
|
||||||
* standard windows [hamming, hanning, bartlett, blackman, kaiser-bessel] are also available
|
|
||||||
self.timepoints = time points
|
|
||||||
self.aquisition_time = aquisition time (no. samples / sampling_rate)
|
|
||||||
line_broadening = line broadening factor (standard = 10 Hz)
|
|
||||||
gaussian_multiplicator = Gaussian Multiplication Factor for
|
|
||||||
the double exponential apodization
|
|
||||||
function (standard = 0.3)
|
|
||||||
"""
|
|
||||||
def exp_window(self, line_broadening=10, show=0):
|
|
||||||
apod = N.exp(-self.timepoints*line_broadening)
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1 :
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def gauss_window(self, line_broadening=10, show=0):
|
|
||||||
apod = N.exp(-(self.timepoints*line_broadening)**2)
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1 :
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def dexp_window(self, line_broadening=10, gaussian_multiplicator=0.3, show=0):
|
|
||||||
apod = N.exp(-(self.timepoints*line_broadening - gaussian_multiplicator*self.aquisition_time)**2)
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1:
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def traf_window(self, line_broadening=10, show=0):
|
|
||||||
apod = (N.exp(-self.timepoints*line_broadening))**2 / ( (N.exp(-self.timepoints*line_broadening))**3
|
|
||||||
+ (N.exp(-self.aquisition_time*line_broadening))**3 )
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1:
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def hanning_window(self, show=0):
|
|
||||||
apod = N.hanning(self.data_points)
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1:
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def hamming_window(self, show=0):
|
|
||||||
apod = N.hamming(self.data_points)
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1:
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def blackman_window(self, show=0):
|
|
||||||
apod = N.blackman(self.data_points)
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1:
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def bartlett_window(self, show=0):
|
|
||||||
apod = N.bartlett(self.data_points)
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1:
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
def kaiser_window(self, beta=4, show=0, use_scipy=None):
|
|
||||||
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),range(n),1)
|
|
||||||
for n in range(20):
|
|
||||||
i0 += ((x/2.0)**n/(fac(n)))**2
|
|
||||||
return i0
|
|
||||||
|
|
||||||
t = N.arange(self.data_points, type=N.Float) - self.data_points/2.0
|
|
||||||
T = self.data_points
|
|
||||||
# this is the window function array
|
|
||||||
apod = I_0(beta*N.sqrt(1-(2*t/T)**2))/I_0(beta)
|
|
||||||
else:
|
|
||||||
# alternative method using scipy
|
|
||||||
import scipy
|
|
||||||
apod=scipy.kaiser(self.data_points, beta)
|
|
||||||
|
|
||||||
for i in range(2):
|
|
||||||
self.the_result.y[i] = self.the_result.y[i]*apod
|
|
||||||
if show == 1:
|
|
||||||
return self.the_result
|
|
||||||
return self
|
|
||||||
|
|
||||||
Loading…
Reference in New Issue
Block a user