def analysis(self, x):
"""
Perform frequency analysis of a real input using DFT.
Parameters
----------
x : numpy array
Real signal in time domain.
Returns
-------
numpy array
DFT of input.
"""
# check for valid input
if x.shape != self.x.shape:
raise ValueError(
'Invalid input dimensions! Got (%d, %d), expecting (%d, %d).' %
(x.shape[0], x.shape[1], self.x.shape[0], self.x.shape[1]))
# apply window if needed
if self.analysis_window is not None:
np.multiply(self.analysis_window, x, x)
# apply DFT
if self.transform == 'fftw':
self.a[:, ] = x
self.X[:, ] = self._forward()
elif self.transform == 'mkl':
self.X[:, ] = mkl_fft.rfft_numpy(x, self.nfft, axis=self.axis)
else:
self.X[:, ] = rfft(x, self.nfft, axis=self.axis)
return self.X
a = pyfftw.empty_aligned([nfft, D], dtype="float32")
b = pyfftw.empty_aligned([nfft // 2 + 1, D], dtype="complex64")
c = pyfftw.empty_aligned([nfft, D], dtype="float32")
forward = pyfftw.FFTW(a, b, axes=(0, ))
backward = pyfftw.FFTW(b, c, axes=(0, ), direction="FFTW_BACKWARD")
start_time = time.time()
for k in range(n_trials):
forward()
analysis_time = (time.time() - start_time) / n_trials * 1e6
start_time = time.time()
for k in range(n_trials):
backward()
synthesis_time = (time.time() - start_time) / n_trials * 1e6
print(
"avg fftw w/o class : %f [1e-6 sec], (analysis, synthesis)=(%f, %f) [1e-6 sec]"
% (analysis_time + synthesis_time, analysis_time, synthesis_time))
if mkl_available:
start_time = time.time()
for k in range(n_trials):
X = mkl_fft.rfft_numpy(x)
analysis_time = (time.time() - start_time) / n_trials * 1e6
start_time = time.time()
for k in range(n_trials):
x_r = mkl_fft.irfft_numpy(X)
synthesis_time = (time.time() - start_time) / n_trials * 1e6
print(
"avg mkl w/o class : %f [1e-6 sec], (analysis, synthesis)=(%f, %f) [1e-6 sec]"
% (analysis_time + synthesis_time, analysis_time, synthesis_time))