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