def processing_ndarray(data, median_disks, closing_disks,
histogram_clip=[0.0, 99.], func=np.sqrt):
"""
:param data: 3d ndarray of the form (ny,nx,nt)
:param median_disks: disks used to do median noise cleaning
:param closing_disks: disks used to morphological closing
:param histogram_clip: clip the data
:param func: function used to scale the data nicely
:return: an AWARE processed
"""
nt = data.shape[2]
rdpi = datacube_tools.running_difference(datacube_tools.persistence(data))
mc_data = func(rdpi)
mc_data[mc_data < 0] = 0.0
clip_limit = np.nanpercentile(mc_data, histogram_clip)
# Rescale the data using the input function, and subtract the lower
# clip limit so it begins at zero.
f_data = mc_data - clip_limit[0] / (clip_limit[1]-clip_limit[0])
# Replace the nans with zeros - the reason for doing this rather than
# something more sophisticated is that nans will not contribute
# greatly to the final answer. The nans are put back in at the end
# and get carried through in the maps.
nans_here = np.logical_not(np.isfinite(f_data))
nans_replaced = deepcopy(f_data)
nans_replaced[nans_here] = 0.0
# Final datacube
processed_datacube = np.zeros_like(data)
for t in range(0, nt):
this = f_data[:, :, t]
for d in range(0, len(median_disks)):
# Get rid of noise by applying the median filter. Although the
# input is a byte array make sure that the output is a floating
# point array for use with the morphological closing operation.
new_d = 1.0*median(this, median_disks[d])
# Apply the morphological closing operation to rejoin separated
# parts of the wave front.
new_d = closing(new_d, closing_disks[d])
# Sum all the final results
processed_datacube[:, :, t] += new_d*1.0
return processed_datacube, nans_here
# Linear amplitude
lin_amp = 4.0
# Noise level
noise_amp = 1.5
osc = 1.0 * np.sin(2 * np.pi * t / osc_per)
linear = lin_amp * t / np.max(t)
noise = noise_amp * np.random.normal(size=nt)
data = osc + linear + noise
npdata = np.zeros((1, 1, nt))
npdata[0, 0, :] = data[:]
persistence = datacube_tools.persistence(npdata)
# Make the figure
plt.plot(t, data, label='simulated data $f(t)$')
plt.plot(t, persistence[0,0,:], label='persistence transform $P(t)$')
plt.legend(loc=4, framealpha=0.5)
plt.xlabel('time (seconds)')
plt.ylabel('data (arbitrary units)')
plt.savefig(os.path.expanduser('~/projects/eitwave-paper/persistence_explanation.eps'))
