def fourier(B, dx, dt):
from scipy.fft import rfft2, rfftfreq
Bwk = rfft2(B)
w = rfftfreq(B.shape[1], d=dt)
k = rfftfreq(B.shape[0], d=dx)
k = k * 2 * np.pi
w = w * 2 * np.pi
return Bwk, w, k
def inverse_psf_rfft(psf, shape=None, l=20, mode='laplacian'):
"""
Computes the real FFT of a regularized inversed 2D PSF (or projected 3D)
This follows the convention of fft.rfft: only half the spectrum is computed.
Parameters
----------
psf : array [ZXY] or [XY]
The 2D PSF (if 3D, will be projected on Z axis).
shape : tuple (int, int), optional
Shape of the full-sized desired PSF
(if None, will be the same as the PSF), by default None.
l : int, optional
Regularization lambda, by default 20
mode : str, optional
The regularizer used, by default laplacian.
One of: ['laplacian', 'constant']
Returns
-------
array [XY]
The real FFT of the inverse PSF.
Raises
------
ValueError
If the PSF has incorrect number of dimensions.
If the regularizer is unknown.
"""
if psf.ndim == 3:
psf = psf.sum(0)
elif psf.ndim != 2:
raise ValueError("Invalid dimensions for PSF: {}".format(psf.ndim))
if shape is None:
shape = psf.shape
psf_fft = fft.rfft2(psf, s=shape)
# We need to shift the PSF so that the center is located at the (0, 0) pixel
# otherwise deconvolving will shift every pixel
freq = fft.rfftfreq(shape[1])
phase_shift = freq * 2 * np.pi * ((psf.shape[1] - 1) // 2)
psf_fft *= np.exp(1j * phase_shift[None, :])
freq = fft.fftfreq(shape[0])
phase_shift = freq * 2 * np.pi * ((psf.shape[0] - 1) // 2)
psf_fft *= np.exp(1j * phase_shift[:, None])
if mode == 'laplacian':
# Laplacian regularization filter to avoid NaN
filt = [[0, -0.25, 0], [-0.25, 1, -0.25], [0, -0.25, 0]]
reg = np.abs(fft.rfft2(filt, s=shape))**2
elif mode == 'constant':
reg = 1
else:
raise ValueError('Unknown regularizer: {}'.format(mode))
return psf_fft.conjugate() / (np.abs(psf_fft)**2 + l * reg)
def time_fourier(
x,
*ys,
sampleFactor=1,
interpKind='linear',
minFreq='auto',
maxFreq='auto',
minAmps=None,
maxAmps=None,
):
if minAmps is None: minAmps = [None for y in ys]
if maxAmps is None: maxAmps = [None for y in ys]
x, *ys = time_smooth(x, *ys, sampleFactor=sampleFactor, kind=interpKind)
N = len(x)
T = np.diff(x).mean()
freqs = rfftfreq(N, T)[:N // 2]
if minFreq is None: minFreq = min(freqs)
elif minFreq == 'auto': minFreq = (1 / np.ptp(x)) * 10
if maxFreq is None: maxFreq = max(freqs)
elif maxFreq == 'auto': maxFreq = (1 / T) / 10
mask = np.logical_and(freqs >= minFreq, freqs <= maxFreq)
freqs = freqs[mask]
w = blackman(N)
amps = []
for y, minAmp, maxAmp in zip(ys, minAmps, maxAmps):
y = y - y.mean()
amp = np.abs(rfft(y * w))[:N // 2]
if not minAmp is None: amp = np.where(amp < minAmp, 0, amp)
if not maxAmp is None: amp = np.where(amp > maxAmp, maxAmp, amp)
amps.append(amp[mask])
return (freqs, *amps)
def _set_freqs(self):
# Set freqs indexes
if self._sides == "onesided":
self._freqs = rfftfreq(self._nfft, 1 / self._fs)
else:
self._freqs = fftfreq(self._nfft, 1 / self._fs)
def spectral_plots(signal, sample_rate, pitch=None):
N = len(signal)
X = rfft(signal, N)
Xmag = np.abs(X)
freq = rfftfreq(N, 1 / sample_rate)
fig, ax = plt.subplots(3, 1, sharex=True)
fig.set_figheight(5)
if pitch:
for ax_ in ax:
ax_.axvline(pitch, linestyle='--', color='black')
ax[0].plot(freq, Xmag)
ax[0].yaxis.set_ticklabels([])
ax[0].set_ylabel('spectrum')
ax[1].plot(freq, np.abs(func['ACF'](Xmag)))
ax[1].yaxis.set_ticklabels([])
ax[1].set_ylabel('ACF')
Nh = 10 # for example
hps = 20 * np.log10(
[np.sum(Xmag[k:Nh * k:k]) for k in range(1, len(freq))])
hss = np.array(
[20 * np.sum(np.log(Xmag[k:Nh * k:k])) for k in range(1, len(freq))])
ax[2].plot(freq[1:], normalize(hps), label='product')
ax[2].plot(freq[1:], normalize(hss), label='sum')
ax[2].legend()
ax[2].yaxis.set_ticklabels([])
ax[2].set_ylabel('harmonic sum/product')
ax[-1].set_xlim(0, 5000)
ax[-1].set_xlabel('frequency (Hz)')
plt.show()
def fft_plot(yf: np.ndarray, fname: str):
'''
Plots the FFT
params:
---
yf (np.ndarray): input data to plot
fname (str): save file name
'''
N = int((config.SAMPLE_RATE / config.RESAMPLE_RATE) * config.DURATION)
xf = rfftfreq(N - 1, 1 / int(config.SAMPLE_RATE / config.RESAMPLE_RATE))
_, axs = plt.subplots(nrows=1, figsize=(11, 9))
plt.rcParams['font.size'] = '14'
for label in (axs.get_xticklabels() + axs.get_yticklabels()):
label.set_fontsize(14)
plt.plot(xf, yf)
axs.set_title('Frequency spectra')
axs.set_ylabel('Signal strength', fontsize=14)
axs.set_xlabel('Frequency (Hz)', fontsize=14)
file_location = eda_config.OUTPUT_DATA_DIR / Path(f'{fname}.png')
plt.savefig(file_location)
def actualizarGraficoLectura(self):
#preparar datos a plotear
self.ploteando = True
tamano = len(self.tarea.datosLeidos[0])
if tamano > self.maximoPuntos:
liminferior = tamano - self.maximoPuntos - 1
elif tamano:
liminferior = 0
else:
self.ploteando = False
return
datos = []
for c in enumerate(self.tarea.datosLeidos):
datos.append(self.tarea.datosLeidos[c[0]][liminferior:])
for p in enumerate(self.lineas):
# print(p)
p[1].setData(datos[0], datos[self.indicesPlot[p[0]] + 1])
yf = rfft(datos[self.indicesPlot[p[0]] + 1])
xf = rfftfreq(len(datos[0]), 1 / self.tarea.freq)
self.lineasFreq[p[0]].setData(xf, np.abs(yf))
#plotear ffts
self.ploteando = False
def detect_coughs(
file='sounds/samples/vi95kMQ65UeU7K1wae12D1GUeXd2/sample-1613658921823.m4a'
):
# Replace the below random code with something meaningful which
# generates a one-column dataframe with a column named "peak_start"
y, sr = librosa.load(file) # sr is the sampling rate
N = y.shape[0] # N is number of samples
yf = rfft(y)
xf = rfftfreq(N, 1 / sr) # Going to frequency domain
points_per_freq = len(xf1) / (sr / 2)
target_idx_100 = int(points_per_freq * 100)
target_idx_2000 = int(points_per_freq * 2000)
# Removing all frequencies except 100-2000 Hz, as this is the typical range of frequencies for a cough from an adult.
# Reference: https://pubmed.ncbi.nlm.nih.gov/12666872/
yf[:target_idx_100 + 1] = 0
yf[target_idx_2000 - 1:] = 0
new_sig = irfft(yf) # Going back to time domain with filtered signal
peaks_array = find_peaks(
new_sig, prominence=0.02
)[0] #Finding start of peaks in filtered signal, should correspond to coughs
peaks = peaks_array / sr # The time instant of starting of peak is arrived at by dividing by sampling rate
out = pd.DataFrame({'peak_start': peaks})
return (out)
def stftfreq(wsize, sfreq=None): # noqa: D401
"""Compute frequencies of stft transformation.
Parameters
----------
wsize : int
Size of stft window.
sfreq : float
Sampling frequency. If None the frequencies are given between 0 and pi
otherwise it's given in Hz.
Returns
-------
freqs : array
The positive frequencies returned by stft.
See Also
--------
stft
istft
"""
from scipy.fft import rfftfreq
freqs = rfftfreq(wsize)
if sfreq is not None:
freqs *= float(sfreq)
return freqs
def _preprocess_spectrum(self, spectrum, low_cutoff_res, high_cutoff_res):
"""Cuts off low and high frequencies and then subtracts the background by
calculating a lower envelope to the min peaks (ctf zero crossings).
Also finds an upper envelope to be used as a damping function for computing
forward models.
"""
low_cutoff_freq = 1 / low_cutoff_res
high_cutoff_freq = 1 / high_cutoff_res
freqencies = fft.rfftfreq(n=self.full_spectrum.shape[0],
d=self.pixelsize)
# dft indices of low and high cutoffs
self._low_idx = np.argmax(freqencies > low_cutoff_freq)
self._high_idx = np.argmax(freqencies > high_cutoff_freq)
# get envelopes
bottom_envelope, self._upper_envelope = self._get_envelopes(
spectrum, self._low_idx, self._high_idx)
# cut off high frequencies
spectrum = spectrum[:, :self._high_idx]
spectrum = np.vstack(
(spectrum[:self._high_idx, :], spectrum[-self._high_idx:, :]))
spectrum = fft.fftshift(spectrum, axes=0)
# subtract bottom envelope
x = range(spectrum.shape[1])
y = range(-spectrum.shape[0] // 2, spectrum.shape[0] // 2)
X, Y = np.meshgrid(x, y)
r = np.sqrt(X**2 + Y**2)
spectrum -= bottom_envelope(r - self._low_idx)
# zero out distances not between low_idx and high_idx
w = np.where((r < self._low_idx) | (r > self._high_idx))
spectrum[w[0], w[1]] = 0
return spectrum
def _ctf_array(self, z1, z2, major_semiaxis_angle, phase_shift):
"""Returns a 2D array of CTF values in the same shape as the reduced spectrum"""
# calculate defocus matrix
minor_semiaxis_angle = 90 + major_semiaxis_angle
minor_semiaxis = np.array([
np.cos(minor_semiaxis_angle * np.pi / 180),
np.sin(minor_semiaxis_angle * np.pi / 180),
]).reshape(2, 1)
semiaxes, _ = np.linalg.qr(np.hstack((minor_semiaxis, np.identity(2))))
defocus_matrix = semiaxes @ [[z1, 0], [0, z2]] @ semiaxes.transpose()
# vectorize ctf calculation
kx = fft.rfftfreq(n=self.full_spectrum.shape[0], d=self.pixelsize)
kx = kx[:self._high_idx]
ky = fft.fftfreq(n=self.full_spectrum.shape[0], d=self.pixelsize)
ky = np.hstack((ky[:self._high_idx], ky[-self._high_idx:]))
ky = fft.fftshift(ky)
KX, KY = np.meshgrid(kx, ky)
chi_args = (
defocus_matrix,
KX,
KY,
self.electron_wavelength,
self.spherical_abberation,
phase_shift,
)
return ctf(self.amplitude_contrast, chi_args)
def binned_fft(self,
data: np.ndarray,
target_sample_rate: int,
original_sample_rate: int = 48000) -> np.ndarray:
# # Get the "normalized" FFT based on the sample rates
# " The height is a reflection of power density, so if you double the sampling frequency,
# and hence half the width of each frequency bin, you'll double the amplitude of the FFT result."
# >> https://wiki.analytica.com/FFT
#
# 2^(new_sample_rate/original_sample_rate) = power gain
# Divide by that but offset by 1 since if new fs = old fs we multiply by 1/2^0 = 1
#
try:
raw_fft = self.fft(data) * (2**math.log2(
original_sample_rate / target_sample_rate))
except TypeError as e:
print(e)
sys.exit()
# raw_fft = self.fft(data)
# # TODO: wont work for old fs and new fs equal
# if original_sample_rate != target_sample_rate:
# raw_fft *= (math.log10(original_sample_rate / target_sample_rate) / math.log10(2))
# # Get the frequencies that FFT represent based on the sample rate
# Get the frequencies that the indexes determine
fftf = rfftfreq(raw_fft.shape[0], 1 / target_sample_rate)
# # Return pairs of [[freq], [fft]] array
return np.array([fftf, raw_fft], dtype=object)
def spectrum_contour(self,
comp,
axis_vals,
axis_mode='half_channel',
fig=None,
ax=None,
pcolor_kw=None,
**kwargs):
if not comp in ('x', 'z'):
raise ValueError(" Variable `comp' must eiher be 'x' or 'z'\n")
axis_vals = misc_utils.check_list_vals(axis_vals)
y_index_axis_vals = indexing.y_coord_index_norm(
self._avg_data, axis_vals, None, axis_mode)
Ruu_all = self.autocorrDF[comp] #[:,axis_vals,:]
shape = Ruu_all.shape
Ruu = np.zeros((shape[0], len(axis_vals), shape[2]))
for i, vals in zip(range(shape[2]), y_index_axis_vals):
Ruu[:, :, i] = Ruu_all[:, vals, i]
kwargs = cplt.update_subplots_kw(kwargs,
figsize=[10, 4 * len(axis_vals)])
fig, ax = cplt.create_fig_ax_without_squeeze(len(axis_vals),
fig=fig,
ax=ax,
**kwargs)
coord = self._meta_data.CoordDF[comp].copy()[:shape[0]]
x_axis = self._avg_data._return_xaxis()
pcolor_kw = cplt.update_pcolor_kw(pcolor_kw)
for i in range(len(axis_vals)):
wavenumber_spectra = np.zeros((int(0.5 * shape[0]) + 1, shape[2]),
dtype=np.complex128)
for j in range(shape[2]):
wavenumber_spectra[:, j] = fft.rfft(Ruu[:, i, j])
comp_size = shape[0]
wavenumber_comp = 2 * np.pi * fft.rfftfreq(comp_size,
coord[1] - coord[0])
X, Y = np.meshgrid(x_axis, wavenumber_comp)
ax[i] = ax[i].pcolormesh(X, Y, np.abs(wavenumber_spectra),
**pcolor_kw)
ax[i].axes.set_ylabel(r"$\kappa_%s$" % comp)
title = r"$%s=%.3g$"%("y" if axis_mode=='half_channel' \
else r"\delta_u" if axis_mode=='disp_thickness' \
else r"\theta" if axis_mode=='mom_thickness' else "y^+", axis_vals[i] )
ax[i].axes.set_ylim(
[np.amin(wavenumber_comp[1:]),
np.amax(wavenumber_comp)])
ax[i].axes.set_title(title) # ,fontsize=15,loc='left')
fig.colorbar(ax[i], ax=ax[i].axes)
return fig, ax
def calculate_peak(waves, chunksize, sampling_rate, start, cycles):
yf = rfft(waves)
xf = rfftfreq(waves.size, 1 / sampling_rate)
peak = np.where(np.abs(yf) == np.abs(yf).max())[0][0]
peak = round((peak / ((chunksize) / sampling_rate)), 2)
return peak
def fourier_transform(self,df, name_column, show = False):
myarray = np.asarray((df[name_column]))
N = len(myarray)
yf = rfft(myarray)
xf = rfftfreq(N, 1 / analyzer.simple_rate)
if (show == True):
plt.plot(xf, np.abs(yf))
plt.show()
return yf[1]
def FourierTransformPower(Powers, Times):
# Do FFT (real) on the input powers
# Returns the frequency values and the corresponding amplitude
FFTPowers = fft.rfft(Powers)
nPoints = len(Times)
SampleSpacing = (Times.max() - Times.min()) / nPoints
FFTFreqs = fft.rfftfreq(nPoints, d=SampleSpacing)
FFTPowers = numpy.abs(FFTPowers)
return FFTFreqs, FFTPowers
def fourier_transform(arr, name):
arr = arr - np.mean(arr)
N = len(arr)
yf = rfft(arr)
xf = rfftfreq(N, 1)
plt.plot(xf, np.abs(yf), label='{}'.format(name))
return yf, xf
def makeFilter(shape: List[int], alpha: float, dtype=float) -> np.ndarray:
assert 1 <= len(shape) <= 2, "shape must be one or two elements"
if len(shape) == 1:
shape = [shape[0], shape[0]]
# Generate filter in the frequency domain
fy = sf.fftfreq(shape[0])[:, np.newaxis].astype(dtype)
fx = sf.rfftfreq(shape[1])[np.newaxis, :].astype(dtype)
H2 = (fx**2 + fy**2)**(alpha / 2.0)
return sf.fftshift(sf.irfft2(H2))
def plot_fourier(frame_rate, np_frames, output_file):
"""Plot Fourier Transformation of audio sample."""
plt_fourier = pyplot
n = len(np_frames)
#norm_frames = np.int16((byte_frames / byte_frames.max()) * 32767)
yf = rfft(np_frames)
xf = rfftfreq(n, 1 / frame_rate)
fig = plt_fourier.figure(num=None, figsize=(12, 7.5), dpi=100)
plt_fourier.plot(xf, np.abs(yf))
plt_fourier.savefig(output_file)
def get_1d_average(self, spectrum, horizontal_res=50, sigma_ratio=1 / 60):
"""Returns a 1D signal of the vertical spectrum inside the cutoff range"""
freqencies = fft.rfftfreq(n=self.full_spectrum.shape[0],
d=self.pixelsize)
width = np.argmax(freqencies > 1 / horizontal_res)
vertical_strip = spectrum[self._low_idx:self._high_idx, :width]
vertical_1d = np.average(vertical_strip, axis=1)
sigma = sigma_ratio * len(vertical_1d)
vertical_1d = ndimage.gaussian_filter1d(vertical_1d,
sigma=sigma,
mode="nearest")
return vertical_1d
def calculate():
self.sample_rate, self.audio = wavfile.read(self.audiofile)
self.audio = np.transpose(self.audio)
self.duration = len(self.audio[:][0]) / float(self.sample_rate)
self.audio_fft = [None] * self.audio.ndim
self.audio_fft[0] = rfft(self.audio[0])
self.audio_fft[1] = rfft(self.audio[1])
self.freq = rfftfreq(self.audio[0].size, 1. / self.sample_rate)
print(
f"Sample Rate: {self.sample_rate} with {self.audio.ndim} channels"
)
def fft(x, fs):
"""
FFT function
:param x: input data for fft
:param fs: sample frequency [Hz]
:return: frequency [Hz], magnitude
"""
n = len(x)
y = np.abs(rfft(x))
f = rfftfreq(n, 1/fs)
return f, y
def _get_approx_defocus(self, first_zero_crossing_idx):
# get approximate defocus from first zero crossing
freqencies = fft.rfftfreq(n=self.full_spectrum.shape[0],
d=self.pixelsize)
k_first_zero_crossing = freqencies[self._low_idx +
first_zero_crossing_idx]
chi_first_zero_crossing = -0.5
approximate_defocus = (
chi_first_zero_crossing - 10 *
(1 / 4) * self.spherical_abberation * self.electron_wavelength**3 *
k_first_zero_crossing**4) * (
-2 /
(100 * self.electron_wavelength * k_first_zero_crossing**2))
return approximate_defocus
def spectrum(a, delta=1, **kwargs):
"""
Return frequencies, amplitude and phase of FFT.
"""
nfreq = kwargs.get('nfreq')
if nfreq is None:
nfreq = next_fast_len(a.size)
spectrum = rfft(a, nfreq)
freqs = rfftfreq(n=nfreq, d=delta)
am = np.abs(spectrum)
ph = np.angle(spectrum)
return freqs, am, ph
def analyze_audio(audio):
# Just extract one column (mono) since it's stereo
audio = audio[:,0]
yf = rfft(audio)
# Return in abs because we want the module from a complex number, despite having only real part (because of the rrft)
yf_s = Series(abs(yf))
# The band where we want to detect our signal 17-21kHz
yf_s = yf_s[17000:21000]
print(yf_s.nlargest(20))
# Show on graph the correspondant point
xf = rfftfreq(POINTS, 1 / FS)
plt.plot(xf, np.abs(yf))
plt.show()
def fourier_tf(data, fs=128, viz=False, tmp_signal=None, tmp_channel=None):
if isinstance(data, pd.DataFrame):
tmp = np.absolute(
rfftn(data.values.reshape(data.shape[0], 14, 512), axes=2))
else:
tmp = np.absolute(rfftn(data.reshape(data.shape[0], 14, 512), axes=2))
fft_freq = rfftfreq(512, 1.0 / fs)
if viz == True:
plt.plot(fft_freq, t[tmp_signal, tmp_channel, :])
return tmp
def Peak_amplitude(a, cutoff): ################################################################ # calculates peak amplitude from a signal and a frequency band # # # # a : a signal (time series) # # cutoff : frequency band # ################################################################ fs = 20 #Hertz yf = rfft(a) #power spectra ps = 2*(np.abs(yf)**2.0) M = len(ps) xf = rfftfreq(M, 1/fs) idx = np.logical_and(xf >= cutoff[0], xf <= cutoff[1]) cutoff_idx = np.where(idx)[0] #peak amplitude pa = max(ps[cutoff_idx]/max(ps)) return pa
def _mt_spectra(x, dpss, sfreq, n_fft=None):
"""Compute tapered spectra.
Parameters
----------
x : array, shape=(..., n_times)
Input signal
dpss : array, shape=(n_tapers, n_times)
The tapers
sfreq : float
The sampling frequency
n_fft : int | None
Length of the FFT. If None, the number of samples in the input signal
will be used.
Returns
-------
x_mt : array, shape=(..., n_tapers, n_times)
The tapered spectra
freqs : array
The frequency points in Hz of the spectra
"""
from scipy.fft import rfft, rfftfreq
if n_fft is None:
n_fft = x.shape[-1]
# remove mean (do not use in-place subtraction as it may modify input x)
x = x - np.mean(x, axis=-1, keepdims=True)
# only keep positive frequencies
freqs = rfftfreq(n_fft, 1. / sfreq)
# The following is equivalent to this, but uses less memory:
# x_mt = fftpack.fft(x[:, np.newaxis, :] * dpss, n=n_fft)
n_tapers = dpss.shape[0] if dpss.ndim > 1 else 1
x_mt = np.zeros(x.shape[:-1] + (n_tapers, len(freqs)),
dtype=np.complex128)
for idx, sig in enumerate(x):
x_mt[idx] = rfft(sig[..., np.newaxis, :] * dpss, n=n_fft)
# Adjust DC and maybe Nyquist, depending on one-sided transform
x_mt[..., 0] /= np.sqrt(2.)
if x.shape[1] % 2 == 0:
x_mt[..., -1] /= np.sqrt(2.)
return x_mt, freqs
def run(self, win_data: WindowedData) -> SpectraData:
"""
Perform the FFT
Data is padded to the next fast length before performing the FFT to
speed up processing. Therefore, the output length may not be as
expected.
Parameters
----------
win_data : WindowedData
The input windowed data
Returns
-------
SpectraData
The Fourier transformed output
"""
from scipy.fft import next_fast_len, rfftfreq
metadata_dict = win_data.metadata.dict()
data = {}
spectra_levels_metadata = []
messages = []
logger.info("Performing fourier transforms of windowed decimated data")
for ilevel in range(win_data.metadata.n_levels):
logger.info(f"Transforming level {ilevel}")
level_metadata = win_data.metadata.levels_metadata[ilevel]
win_size = level_metadata.win_size
n_transform = next_fast_len(win_size, real=True)
logger.debug(
f"Padding size {win_size} to next fast len {n_transform}")
freqs = rfftfreq(n=n_transform, d=1.0 / level_metadata.fs).tolist()
data[ilevel] = self._get_level_data(level_metadata,
win_data.get_level(ilevel),
n_transform)
spectra_levels_metadata.append(
self._get_level_metadata(level_metadata, freqs))
messages.append(f"Calculated spectra for level {ilevel}")
metadata = self._get_metadata(metadata_dict, spectra_levels_metadata)
metadata.history.add_record(self._get_record(messages))
logger.info("Fourier transforms completed")
return SpectraData(metadata, data)
def texshadeFFT(x: np.ndarray, alpha: float) -> np.ndarray:
"""FFT-based texture shading elevation
Given an array `x` of elevation data and an `alpha` > 0, apply the
texture-shading algorithm using the full (real-only) FFT: the entire `x` array
will be FFT'd.
`alpha` is the shading detail factor, i.e., the power of the
fractional-Laplacian operator. `alpha=0` means no detail (output is the
input). `alpha=2.0` is the full (non-fractional) Laplacian operator and is
probably too high. `alpha <= 1.0` seem aesthetically pleasing.
Returns an array the same dimensions as `x` that contains the texture-shaded
version of the input array.
If `x` is memory-mapped and/or your system doesn't have 5x `x`'s memory
available, consider using `texshade.texshadeSpatial`, which implements a
low-memory version of the algorithm by approximating the frequency response of
the fractional-Laplacian filter with a finite impulse response filter applied
in the spatial-domain.
Implementation note: this function uses Scipy's FFTPACK routines (in
`scipy.fftpack`) instead of Numpy's FFT (`numpy.fft`) because the former can
return single-precision float32. In newer versions of Numpy/Scipy, this
advantage may have evaporated [1], [2].
[1] https://github.com/numpy/numpy/issues/6012
[2] https://github.com/scipy/scipy/issues/2487
"""
Nyx = [nextprod([2, 3, 5, 7], x) for x in x.shape]
# Generate filter in the frequency domain
fy = sf.fftfreq(Nyx[0])[:, np.newaxis].astype(x.dtype)
fx = sf.rfftfreq(Nyx[1])[np.newaxis, :].astype(x.dtype)
H2 = (fx**2 + fy**2)**(alpha / 2.0)
# Compute the FFT of the input and apply the filter
xr = sf.rfft2(x, s=Nyx) * H2
H2 = None # potentially trigger GC here to reclaim H2's memory
xr = sf.irfft2(xr)
# Return the same size as input
return xr[:x.shape[0], :x.shape[1]]
