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Diffusion LPBF Dynamics of Acoustic Emission on CM247LC (master) Browse

R13251/HHT -1/torchHHT/visualization.py
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visualization.py
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import torch
import matplotlib.pyplot as plt
def plot_IMFs(x, imfs, fs, index = None, align_yticks = True, time_scale = 1, time_range = None, save_fig = None, title = None):
'''
Visualize the IMFs extracted from the original signal by empirical model decomposition.
Parameters:
------------
x (Tensor) :
Signal data. The last dimension of `x` is considered as time.
imfs (Tensor) :
IMFs obtained from `emd`.
fs (real) :
Sampling frequencies in Hz.
index (list of integers, Optional) :
The indices of the IMF component to be shown.
align_yticks (bool, optional) :
Whether to align the y-axises of all IMFs.
Default: True.
time_scale (int, optional) :
The scale for the time axis for ploting.
time_range (an (L, R)-tuple, Optional) :
The range of time axis to be shown.
save_fig (list of string or string, Optional) :
Save the image as `save_fig` if specified.
title (str, optional) :
Specifying the title of the figure.
Returns: None
'''
assert(type(time_scale) is int)
assert( (save_fig is None) or (type(save_fig) in [list, str]) )
all_x = torch.as_tensor(x).view(-1, x.shape[-1])
all_imfs = torch.as_tensor(imfs).view(-1, imfs.shape[-2], imfs.shape[-1])
for batch_idx in range(all_x.shape[0]):
imfs, x = all_imfs[batch_idx], all_x[batch_idx]
# scaling the time axis
if (time_scale > 1):
N = x.shape[0]
fs /= time_scale
M = N // time_scale
x = x[:M*time_scale].view(M, time_scale).mean(dim = 1)
imfs = imfs[:, :M*time_scale].view(imfs.shape[0], M, time_scale).mean(dim = 2)
# the IMF indexes to be shown
num_imfs = len(imfs)
if (index is None):
index = list(range(num_imfs))
else:
num_imfs = len(index)
# the time range of interest
t = torch.arange(x.shape[0], dtype = torch.double) / fs
if (time_range):
L, R = time_range
L, R = min(int(L * fs), len(t)-1), min(int(R * fs)+1, len(t))
t = t[L:R]
# initialize pyplot
plt.figure(figsize=(10, num_imfs+2))
plt.subplots_adjust(hspace = 0.3, left = 0.3)
# plot signals
ax = plt.subplot(num_imfs+2, 1, 1)
scale = max(torch.abs(imfs[:, L:R] if time_range else imfs).max(), (x[L:R] if time_range else x).max())
scale = scale.cpu()
ax.plot(t, x[L:R].cpu() if time_range else x.cpu(), linewidth = 1)
ax.set_title("Empirical Mode Decomposition" if title is None else title)
ax.set_ylabel("signal")
ax.set_ylim(-scale, scale)
ax.set_xticks([])
# plot IMFs
for i_ in range(len(index)):
i = index[i_]
imf = imfs[i]
if not align_yticks:
scale = torch.abs(imf[L:R] if time_range else imf).max().cpu()
ax = plt.subplot(num_imfs+2, 1, i_+2)
ax.plot(t, imf[L:R].cpu() if time_range else imf.cpu(), linewidth = 1)
ax.set_ylim(-scale, scale)
ax.set_ylabel("IMF %d" % (i))
ax.set_xticks([])
# plot the residual
x = x - imfs.sum(axis = 0)
if not align_yticks:
scale = torch.abs(x[L:R] if time_range else x).max().cpu()
ax = plt.subplot(num_imfs+2, 1, num_imfs+2)
ax.plot(t, x[L:R].cpu() if time_range else x.cpu(), linewidth = 1)
ax.set_ylabel("residual")
ax.set_ylim(-scale, scale)
ax.set_xlabel("time")
if (save_fig is not None):
plt.savefig(save_fig if type(save_fig) is str else save_fig[batch_idx])
plt.show()
def plot_HilbertSpectrum(spectrum, time_axis, freq_axis, energy_scale = "log", save_spectrum = None,
save_marginal = None, title = None):
'''
Visualize the Hilbert spectrum, by plotting all the IMFs on a time-frequency plane.
Parameters:
------------
spectrum : Tensor, of shape ( ..., # time_bins, # freq_bins ).
A pytorch tensor, representing the Hilbert spectrum H(t, f).
The tensor will be on the same device as `imfs_env` and `imfs_freq`.
time_axis : Tensor, 1D, of shape ( # time_bins )
The label for the time axis of the spectrum.
freq_axis : Tensor, 1D, of shape ( # freq_bins )
The label for the frequency axis (in `freq_unit`) of the spectrum.
energy_scale : string ('linear' or 'log')
Specifying whether to visualize the energy in linear/log scale.
save_spectrum : string or list of string, Optional.
If specified, the Hilbert spectrum will be saved as a file named `save_spectrum`.
save_maeginal : string or list of string, Optional.
If specified, the Hilbert marginal spectrum will be saved as a file named `save_marginal`.
title : string, optional.
Specifying the title of the figure.
'''
spectrum = spectrum.view(-1, spectrum.shape[-2], spectrum.shape[-1])
time_axis = time_axis.cpu()
freq_axis = freq_axis.cpu()
for batch_idx in range(spectrum.shape[0]):
if (energy_scale == "log"):
eps = spectrum[batch_idx, :, :].max() * (1e-5)
coutour = plt.pcolormesh(time_axis.cpu(), freq_axis.cpu(),
10*torch.log10(spectrum[batch_idx, :, :].T + eps).cpu(),
shading='auto',
cmap = plt.cm.jet)
plt.colorbar(coutour, label = "energy (dB)")
else:
coutour = plt.pcolormesh(time_axis.cpu(), freq_axis.cpu(),
spectrum[batch_idx, :, :].T.cpu(),
shading='auto',
cmap = plt.cm.jet)
plt.colorbar(coutour, label = "energy")
plt.xlabel("time")
plt.ylabel("frequency (Hz)")
plt.title("Hilbert spectrum" if title is None else title)
if (save_spectrum is not None):
plt.savefig(save_spectrum if type(save_spectrum) is str else save_spectrum[batch_idx], dpi = 600)
plt.show()
if (save_marginal is None) :
return
marginal = spectrum.sum(dim = -2)
for batch_idx in range(spectrum.shape[0]):
if (energy_scale == "log"):
eps = marginal[batch_idx, :].max() * (1e-5)
plt.plot(freq_axis.cpu(), 10*torch.log10(marginal[batch_idx, :].cpu() + eps) )
plt.ylabel("energy (dB)")
else:
plt.plot(freq_axis.cpu(), marginal[batch_idx, :].cpu())
plt.ylabel("energy")
plt.xlabel("frequency (Hz)")
plt.title("Marginal spectrum" if title is None else title + " (marginal)")
plt.savefig(save_marginal if type(save_marginal) is str else save_marginal[batch_idx], dpi = 600)
plt.show()
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