def test_visuals():
os.environ['SSQ_GPU'] = '0'
x = np.random.randn(10)
visuals.hist(x, show=1, stats=1)
y = x * (1 + 1j)
visuals.plot(y,
complex=1,
c_annot=1,
vlines=1,
ax_equal=1,
xticks=np.arange(len(y)),
yticks=y)
visuals.plot(y, abs=1, vert=1, dx1=1, ticks=0)
visuals.scat(x, vlines=1, hlines=1)
visuals.scat(y, complex=1, ticks=0)
visuals.plotscat(y, show=1, xlims=(-1, 1), dx1=1, ylabel="5")
visuals.plots([y, y], tight=1, show=1)
visuals.plots([y, y], nrows=2)
visuals.plots([y, y], ncols=2)
g = np.random.randn(4, 4)
visuals.imshow(g * (1 + 2j), complex=1)
visuals.imshow(g, ridge=1, ticks=0)
pass_on_error(visuals.plot, None, None)
pass_on_error(visuals.wavelet_tf, 'morlet', notext=True)
def viz(x,
Tf,
ridge_idxs,
yticks=None,
ssq=False,
transform='cwt',
show_x=True):
if show_x:
plot(x,
title="x(t)",
show=1,
xlabel="Time [samples]",
ylabel="Signal Amplitude [A.U.]")
ylabel = ("Frequency scales [1/Hz]" if
(transform == 'cwt' and not ssq) else "Frequencies [Hz]")
title = "abs({}{}) w/ ridge_idxs".format("SSQ_" if ssq else "",
transform.upper())
ikw = dict(abs=1, cmap='turbo', yticks=yticks, title=title)
pkw = dict(linestyle='--',
color='k',
xlabel="Time [samples]",
ylabel=ylabel,
xlims=(0, Tf.shape[1]))
imshow(Tf, **ikw, show=0)
plot(ridge_idxs, **pkw, show=1)
def test_visuals():
x = np.random.randn(10)
hist(x, show=1, stats=1)
y = x * (1 + 1j)
plot(y,
complex=1,
c_annot=1,
vlines=1,
ax_equal=1,
xticks=np.arange(len(y)),
yticks=y)
plot(y, abs=1, vert=1, dx1=1, ticks=0)
scat(x, vlines=1, hlines=1)
scat(y, complex=1, ticks=0)
plotscat(y, show=1, xlims=(-1, 1), dx1=1, ylabel="5")
plots([y, y], tight=1, show=1)
plots([y, y], nrows=2)
plots([y, y], ncols=2)
g = np.random.randn(4, 4)
imshow(g * (1 + 2j), complex=1)
imshow(g, ridge=1, ticks=0)
_pass_on_error(plot, None, None)
def _maybe_viz(Wx, xo, xrec, title, err):
if not VIZ:
return
mx = np.abs(Wx).max()
if 'SSQ' in title:
Wx = np.pad(np.flipud(Wx), [[5], [0]])
mx = .1*mx
else:
mx = .9*mx
imshow(Wx, abs=1, norm=(0, mx), cmap='jet', show=1, title=title)
plot(xo, title="Original vs reconstructed | MAD/RMS=%.4f" % err)
plot(xrec, show=1)
np.cos(2 * np.pi * f * (_t(i, i + 1, N) + phi))
for i, f in enumerate(freqs)
])
#%%## Configure, compute, plot ###############################################
wavelet = ('morlet', {'mu': 5})
f, N = 12, 512
x = cos_f([f], N=N)
Wx, scales, *_ = cwt(x, wavelet, fs=N)
#%%# Show, print max row
imshow(Wx,
abs=1,
yticks=scales,
title="f=%d, N=%d" % (f, N),
show=1,
cmap='bone')
mxidx = np.where(np.abs(Wx) == np.abs(Wx).max())[0][0]
print("Max row idx:", mxidx, flush=True)
#%%# Plot aroundI max row
idxs = slice(mxidx - 30, mxidx + 20)
Wxz = Wx[idxs]
imshow(Wxz, abs=1, title="abs(CWT), zoomed", show=0, cmap='bone')
plt.axhline(30, color='r')
plt.show()
#%%## Animate rows ###########################################################
def row_anim(Wxz, idxs, scales, superposed=False):
# downsampling factor for higher scales (used only if `scaletype='log-piecewise'`)
downsample = 4
# show this many of lowest-frequency wavelets
show_last = 20
#%%## Make scales ############################################################
# `cwt` uses `p2up`'d N internally
M = p2up(N)[0]
wavelet = Wavelet(wavelet, N=M)
min_scale, max_scale = cwt_scalebounds(wavelet, N=len(x), preset=preset)
scales = make_scales(N,
min_scale,
max_scale,
nv=nv,
scaletype=scaletype,
wavelet=wavelet,
downsample=downsample)
#%%# Visualize scales ########################################################
viz(wavelet, scales, scaletype, show_last, nv)
wavelet.viz('filterbank', scales=scales)
#%%# Show applied ############################################################
Tx, Wx, ssq_freqs, scales, *_ = ssq_cwt(x,
wavelet,
scales=scales,
padtype=padtype)
imshow(Wx, abs=1, title="abs(CWT)")
imshow(Tx, abs=1, title="abs(SSQ_CWT)")
plot(xo)
scat(xo, s=8, show=1)
plot(x)
scat(x, s=8, show=1)
plot(axf, show=1)
#%%# Synchrosqueeze ##########################################################
kw = dict(wavelet=('morlet', {'mu': 4.5}), nv=32, scales='log')
Tx, *_ = ssq_cwt(x, t=ts, **kw)
Wx, *_ = cwt(x, t=ts, **kw)
#%%# Visualize ###############################################################
pkw = dict(abs=1, cmap='bone')
_Tx = np.pad(Tx, [[4, 4]]) # improve display of top- & bottom-most freqs
imshow(Wx, **pkw)
imshow(np.flipud(_Tx), norm=(0, 4e-1), **pkw)
#%%# Estimate inversion ridge ###############################################
bw, slope, offset = .035, .45, .45
Cs, freqband = lin_band(Tx, slope, offset, bw, norm=(0, 4e-1))
#%%###########################################################################
xrec = issq_cwt(Tx, kw['wavelet'], Cs, freqband)[0]
plot(xo)
plot(xrec, show=1)
axof = np.abs(rfft(xo))
axrecf = np.abs(rfft(xrec))
plot(axof)
plot(axrecf, show=1)
# print(Wx)
print(len(Wx))
# print(Wx[1])
# print(len(Wx[1]))
# for wx in Wx:
# print(wx)
# print("________________")
# fig, ax = plt.subplots()
# ax.plot(t, wx)
# ax.set(xlabel='time (s)', ylabel='Frequency',
# title='About as simple as it gets, folks')
# ax.grid()
# plt.show()
imshow(Wx, abs=2, yticks=scales, title="f=%d, N=%d" % (f, N), show=1)
# mxidx = np.where(np.abs(Wx) == np.abs(Wx).max())[0][0]
# print("Max row idx:", mxidx, flush=True)
#%%# Plot aroundI max row
# idxs = slice(mxidx - 30, mxidx + 20)
# Wxz = Wx[idxs]
# imshow(Wxz, abs=1, title="abs(CWT), zoomed", show=0)
# plt.axhline(30, color='r')
# plt.show()
#%%## Animate rows ###########################################################
# def row_anim(Wxz, idxs, scales, superposed=False):
# mx = np.max(np.abs(Wxz))
# for scale, row in zip(scales[idxs], Wxz):
# if row.max() == Wxz.max():
# -*- coding: utf-8 -*-
"""Experimental feature example."""
if __name__ != '__main__':
raise Exception("ran example file as non-main")
import numpy as np
from ssqueezepy import TestSignals, ssq_cwt, Wavelet
from ssqueezepy.visuals import imshow
from ssqueezepy.experimental import phase_ssqueeze
#%%
x = TestSignals(N=2048).par_lchirp()[0]
x += x[::-1]
wavelet = Wavelet()
Tx0, Wx, _, scales, *_ = ssq_cwt(x, wavelet, get_dWx=1)
Tx1, *_ = phase_ssqueeze(Wx, wavelet=wavelet, scales=scales, flipud=1)
adiff = np.abs(Tx0 - Tx1)
print(adiff.mean(), adiff.max(), adiff.sum())
#%%
# main difference near boundaries; see `help(trigdiff)` w/ `rpadded=False`
imshow(Tx1, abs=1)
def cos_f(freqs, N=128, phi=0):
return np.concatenate([
np.cos(2 * np.pi * f * (_t(i, i + 1, N) + phi))
for i, f in enumerate(freqs)
])
#%%## Configure, compute, plot ###############################################
wavelet = ('morlet', {'mu': 5})
f, N = 12, 512
x = cos_f([f], N=N)
Wx, scales, *_ = cwt(x, wavelet, fs=N)
#%%# Show, print max row
imshow(Wx, abs=1, yticks=scales, title="f=%d, N=%d" % (f, N), show=1)
mxidx = np.where(np.abs(Wx) == np.abs(Wx).max())[0][0]
print("Max row idx:", mxidx, flush=True)
#%%# Plot aroundI max row
idxs = slice(mxidx - 30, mxidx + 20)
Wxz = Wx[idxs]
imshow(Wxz, abs=1, title="abs(CWT), zoomed", show=0)
plt.axhline(30, color='r')
plt.show()
#%%## Animate rows ###########################################################
def row_anim(Wxz, idxs, scales, superimposed=False):
mx = np.max(np.abs(Wxz))
for scale, row in zip(scales[idxs], Wxz):
plot(xo)
scat(xo, s=8, show=1)
plot(x)
scat(x, s=8, show=1)
plot(axf, show=1)
#%%# Synchrosqueeze ##########################################################
kw = dict(wavelet=('morlet', {'mu': 4.5}), nv=32, scales='log')
Tx, *_ = ssq_cwt(x, t=ts, **kw)
Wx, *_ = cwt(x, t=ts, **kw)
#%%# Visualize ###############################################################
pkw = dict(abs=1, cmap='bone')
_Tx = np.pad(Tx, [[4, 4]]) # improve display of top- & bottom-most freqs
imshow(Wx, **pkw)
imshow(_Tx, norm=(0, 4e-1), **pkw)
#%%# Estimate inversion ridge ###############################################
bw, slope, offset = .035, -.46, -1.8
Cs, freqband = lin_band(Tx, slope, offset, bw, norm=(0, 4e-1))
#%%###########################################################################
xrec = issq_cwt(Tx, kw['wavelet'], Cs, freqband)[0]
plot(xo)
plot(xrec, show=1)
axof = np.abs(rfft(xo))
axrecf = np.abs(rfft(xrec))
plot(axof)
plot(axrecf, show=1)
