def b_conv(v1, v2):
w1 = dual.fft(v1[rand_perm[0]])
w2 = dual.fft(v2[rand_perm[1]])
return np.real_if_close(dual.ifft(w1 * w2))
def f(vector_list):
if len(vector_list) == 0:
return np.zeros(self.dimension)
elif len(vector_list) == 1:
return vector_list[0]
else:
v1 = dual.fft(left_permutation(f(vector_list[:-1])))
v2 = dual.fft(right_permutation(vector_list[len(vector_list)-1]))
return dual.ifft(v1 * v2)
def LBfft(self, LB=0, zf=0, phase=None, logfile=None, ph1=0,
DCoffset=None, altDATA=None):
if altDATA is None:
DATA = self.DATA
else:
DATA = altDATA
LBdw = -LB * self.dwell[0] * np.pi # Multiply by pi to match TNMR
npts = DATA.shape[0]
npts_ft = npts * (2 ** zf)
if DCoffset is None:
# Taking the last eighth of the points seems to give OK (but not
# perfect) agreement with the TNMR DC offset correction.
# This hasn't been tested with enough different values of npts
# to be sure that this is the right formula.
DCoffset = np.mean(DATA[int(npts / -8):, :, :, :],
axis=0, keepdims=True)
if logfile is not None:
logfile.write("average DC offset is %g\n" % np.mean(DCoffset))
lbweight = np.exp(LBdw * np.arange(npts, dtype=float))
DATAlb = (DATA - DCoffset) * lbweight[:, np.newaxis, np.newaxis, np.newaxis]
DATAfft = npfast.fft(DATAlb, n=npts_ft, axis=0)
DATAfft = fftshift(DATAfft, axes=[0])
DATAfft /= np.sqrt(npts_ft) # To match TNMR behaviour
if phase is None: # Phase automatically
DATAfft *= np.exp(-1j * np.angle(np.sum(DATAfft)))
else:
DATAfft *= np.exp(1j * (phase + ph1 * np.linspace(-0.5, 0.5, npts_ft))
)[:, np.newaxis, np.newaxis, np.newaxis]
return DATAfft
def LBfft(self, LB=0, zf=0, phase=None, logfile=None, ph1=0,
DCoffset=None, altDATA=None):
if altDATA is None:
DATA = self.DATA
else:
DATA = altDATA
LBdw = -LB * self.dwell[0] * np.pi # Multiply by pi to match TNMR
npts = DATA.shape[0]
npts_ft = npts * (2 ** zf)
if DCoffset is None:
# Taking the last eighth of the points seems to give OK (but not
# perfect) agreement with the TNMR DC offset correction.
# This hasn't been tested with enough different values of npts
# to be sure that this is the right formula.
DCoffset = np.mean(DATA[int(npts / -8):, :, :, :],
axis=0, keepdims=True)
if logfile is not None:
logfile.write("average DC offset is %g\n" % np.mean(DCoffset))
lbweight = np.exp(LBdw * np.arange(npts, dtype=float))
DATAlb = (DATA - DCoffset) * lbweight[:, np.newaxis, np.newaxis, np.newaxis]
DATAfft = npfast.fft(DATAlb, n=npts_ft, axis=0)
DATAfft = fftshift(DATAfft, axes=[0])
DATAfft /= np.sqrt(npts_ft) # To match TNMR behaviour
if phase is None: # Phase automatically
DATAfft *= np.exp(-1j * np.angle(np.sum(DATAfft)))
else:
DATAfft *= np.exp(1j * (phase + ph1 * np.linspace(-0.5, 0.5, npts_ft))
)[:, np.newaxis, np.newaxis, np.newaxis]
return DATAfft
def __call__(self, realdata):
N = realdata.shape[0]
## Not sure which of these ways of calculating tweak_fid is best
## TODO: test them out more thoroughly
tweak_fid = np.empty((N, ), dtype=float)
tweak_fid[0] = 1
tweak_fid[1:] = np.linspace(2, 0, N - 1, endpoint=True)
#tweak_fid = np.linspace(2, 0, N, endpoint=False)
#tweak_fid[0] = 1
fid_guess = ifft(fftshift(realdata))
return fftshift(fft(fid_guess * tweak_fid))
def __call__(self, realdata):
N = realdata.shape[0]
## Not sure which of these ways of calculating tweak_fid is best
## TODO: test them out more thoroughly
tweak_fid = np.empty((N,), dtype=float)
tweak_fid[0] = 1
tweak_fid[1:] = np.linspace(2, 0, N - 1, endpoint=True)
#tweak_fid = np.linspace(2, 0, N, endpoint=False)
#tweak_fid[0] = 1
fid_guess = ifft(fftshift(realdata))
return fftshift(fft(fid_guess * tweak_fid))
def get_power_spectrum_of_sample_range(self, electrode_index, start_index, end_index):
if electrode_index < 0 or electrode_index > self.get_number_of_electrodes()-1:
raise RuntimeError("invalid electrode index")
if start_index < 0 or start_index > self.get_number_of_samples()-1:
raise RuntimeError("invalid start sample index")
if end_index > self.get_number_of_samples()-1:
raise RuntimeError("invalid end sample index")
if start_index > end_index:
raise RuntimeError("end sample index must be greater than start sample index")
# return power spectrum of cutout
return np.abs( npdual.fft( self.__data[electrode_index, start_index:end_index+1] ) )**2
def rx_fdomain(samples, prototype_freq, osr, oqam='SIOHAN', drop_in=None, drop_in2=None):
"""SMT receiver using a frequency domain filtering
"""
K = (len(prototype_freq) + 1) // 2
num_symbols = (len(samples) - (osr * K )) // (osr // 2) + 1
R_hat = zeros((num_symbols, osr * K), dtype=samples.dtype)
for i, offset in zip(range(num_symbols), range(0, len(samples), osr // 2)):
R_hat[i,:] = fft(samples[offset:offset + K * osr]) #/ np.sqrt(osr * K)
G_hat = _spreading_matrix(prototype_freq, osr)
d_hat = np.dot(G_hat, R_hat.T)
return d_hat
def project_H(self, H, copy=False):
N = H.shape[1]
if copy:
H = np.copy(H)
H.imag = 0 # Keep only the real part
H.real[H.real <= 0] = 0
# Do the Kramers-Kronig convolution
Hft = ifft(H, axis=1)
KKfactor = np.linspace(2, 0, N, endpoint=False)
KKfactor[0] = 1
Hft *= KKfactor[None, :]
H = fft(Hft, axis=1)
# Zero out small negative numbers caused by rounding errors
H.real[H.real <= 0] = 0
return H
def project_H(self, H, copy=False):
N = H.shape[1]
if copy:
H = np.copy(H)
H.imag = 0 # Keep only the real part
H.real[H.real <= 0] = 0
# Do the Kramers-Kronig convolution
Hft = ifft(H, axis=1)
KKfactor = np.linspace(2, 0, N, endpoint=False)
KKfactor[0] = 1
Hft *= KKfactor[None, :]
H = fft(Hft, axis=1)
# Zero out small negative numbers caused by rounding errors
H.real[H.real <= 0] = 0
return H
def project_H(self, H, copy=False, phase=False):
Hft = fft(H, axis=1)
if phase:
Hsum = np.sum(Hft, axis=1)
Hphase = Hsum / np.abs(Hsum)
Hft /= Hphase[:, None]
Hft.imag = 0 # Keep only the real part
Hft[Hft.real <= 0] = 0
if copy:
H = ifft(Hft, axis=1)
else:
H[:, :] = ifft(Hft, axis=1)
N = H.shape[1]
KKfactor = np.linspace(2, 0, N, endpoint=False)
KKfactor[0] = 1
H *= KKfactor[None, :]
if phase:
return H, Hphase
else:
return H
def project_H(self, H, copy=False, phase=False):
Hft = fft(H, axis=1)
if phase:
Hsum = np.sum(Hft, axis=1)
Hphase = Hsum / np.abs(Hsum)
Hft /= Hphase[:, None]
Hft.imag = 0 # Keep only the real part
Hft[Hft.real <= 0] = 0
if copy:
H = ifft(Hft, axis=1)
else:
H[:, :] = ifft(Hft, axis=1)
N = H.shape[1]
KKfactor = np.linspace(2, 0, N, endpoint=False)
KKfactor[0] = 1
H *= KKfactor[None, :]
if phase:
return H, Hphase
else:
return H
def NMR_svd_initialise(X, n_components, constraint):
"""Calculate a starting point for the generalised NMF fit of NMR
Does a standard NNDSVD (as used in standard NMF) on the real part of the
(approximately phased) NMR spectrum. If the constraint is a FIDconstraint,
Fourier transforms the X array so the NNDSVD is done on the spectrum."""
if isinstance(constraint, FIDConstraint):
FT = True
else:
FT = False
if FT:
X = fft(X, axis=1)
W, H = realpart_svd_initialise(X, n_components)
if FT:
H = ifft(H, axis=1)
H = constraint.project_H(H) # Need to add back in the imaginary part
W, H = constraint.normalise(W, H)
return W, H
def NMR_svd_initialise(X, n_components, constraint):
"""Calculate a starting point for the generalised NMF fit of NMR
Does a standard NNDSVD (as used in standard NMF) on the real part of the
(approximately phased) NMR spectrum. If the constraint is a FIDconstraint,
Fourier transforms the X array so the NNDSVD is done on the spectrum."""
if isinstance(constraint, FIDConstraint):
FT = True
else:
FT = False
if FT:
X = fft(X, axis=1)
W, H = realpart_svd_initialise(X, n_components)
if FT:
H = ifft(H, axis=1)
H = constraint.project_H(H) # Need to add back in the imaginary part
W, H = constraint.normalise(W, H)
return W, H
def imag(self, realdata):
N = realdata.shape[0]
tweak_fid = np.linspace(-1j, 1j, N, endpoint=False)
tweak_fid[0] = 0
fid_guess = ifft(fftshift(realdata))
return fftshift(fft(fid_guess * tweak_fid)).real
def b_conv(v1, v2):
w1 = dual.fft(v1[rand_perm[0]])
w2 = dual.fft(v2[rand_perm[1]])
return np.real_if_close(dual.ifft(w1 * w2))
def imag(self, realdata):
N = realdata.shape[0]
tweak_fid = np.linspace(-1j, 1j, N, endpoint=False)
tweak_fid[0] = 0
fid_guess = ifft(fftshift(realdata))
return fftshift(fft(fid_guess * tweak_fid)).real
# coding=gbk # coding£ºutf-8 ''' @Created on 2018Äê1ÔÂ26ÈÕ ÏÂÎç2:43:08 @author: Administrator ''' from myFunc import mfft, mifft from math import * from numpy import * # import numpy as np from matplotlib import * import matplotlib.pyplot as plt from numpy.dual import fft from numpy.fft.helper import fftshift from numpy.core.function_base import linspace x = linspace(-pi, pi, 100) print(type(x)) print(shape(x)) y = random.random(size=(1, 10)) print(type(y)) print(type(shape(y))) print(type(shape(y))) f = sin(x) F = fftshift(fft(f)) #F = mfft(f) plt.subplot(121) plt.plot(abs(F)) plt.show()
def mfft(f):
if size(f,0)==1 or size(f,1)==1:
F = fftshift(fft(ifftshift(f)))
else:
F = fftshift(fft2(ifftshift(f)))
return F