def draw_3D_scalogram(signal, name, draw_contours=False):
# Perform a wavelet transform on the input signal.
print "Transforming '%s' using a Morlet wavelet..." % name
transformed = Morlet(
signal,
largestscale=4,
notes=16,
scaling='log')
power_matrix = transformed.getpower()
# Downsample the image.
matrix_dimensions = numpy.shape(power_matrix)
mat_size = (matrix_dimensions[0], 1000)
power = scipy.misc.imresize(power_matrix, mat_size)
# Display the power matrix generated during the wavelet transform.
fig = pyplot.figure(name)
pyplot.title(name)
axis = fig.gca(projection='3d')
# Setup each axis' data.
f = numpy.arange(mat_size[0])
t = numpy.arange(mat_size[1])
f_major, t_major = numpy.meshgrid(f, t)
# Plot everything.
axis.plot_surface(t_major, f_major, power.transpose(), rstride=8, cstride=8, alpha=0.3)
if draw_contours:
axis.contour(t_major, f_major, power.transpose(), zdir='z', offset=0, cmap=cm.coolwarm)
axis.contour(t_major, f_major, power.transpose(), zdir='x', offset=0, cmap=cm.coolwarm)
axis.contour(t_major, f_major, power.transpose(), zdir='y', offset=0, cmap=cm.coolwarm)
axis.set_xlabel('t')
axis.set_ylabel('f')
axis.set_zlabel('amplitude')
def draw_scalogram(signal, name):
# Perform a wavelet transform on the input signal.
print "Transforming '%s' using a Morlet wavelet..." % name
transformed = Morlet(
signal,
largestscale=4,
notes=16,
scaling='log')
power_matrix = transformed.getpower()
# Display the power matrix generated during the wavelet transform.
pylab.imshow(
power_matrix,
cmap=pylab.cm.jet,
aspect='auto')
