def plotGlobalPower(self, subplot):
"""
Third sub-plot, the global wavelet and Fourier power spectra and
theoretical noise spectra.
Note that period scale is logarithmic.
"""
var = self.wavelet_spectra.feature.amplitude**2
glbl_power = self.wavelet_spectra.get_global_power()
glbl_signif = self.wavelet_spectra.get_global_significance(
self.alpha, self.threshold)
period = self.wavelet_spectra.get_periods()
fft_theor = self.wavelet_spectra.get_theoretical_fft(
self.alpha, self.threshold)
fft_power = self.fourier_spectra.get_power()
fft_periods = self.fourier_spectra.get_periods()
Yticks = 2**np.arange(np.ceil(np.log2(period.min())),
np.ceil(np.log2(period.max())))
return subplot.add(
data=None,
x=var * fft_power,
y=np.log2(fft_periods),
plot=plot_action.line(linestyle='-', color='#cccccc',
linewidth=1.),
xlabel="Power",
xlim=[0, fft_power.max() * var],
ylim=np.log2([period.min(), period.max()]),
tick=dict(labelleft=False, left=False, labelright=True,
right=True),
ytick={
"locations": np.log2(Yticks),
"labels": Yticks
},
title='c) Global Wavelet Spectrum',
).add(data=None,
x=var * glbl_power,
y=np.log2(period),
plot=plot_action.line(
linestyle='-', color="#1146b3", linewidth=1.5)).add(
data=None,
x=glbl_signif,
y=np.log2(period),
plot=plot_action.line(
linestyle='--', color="#2196f3")).add(
data=None,
x=var * fft_theor,
y=np.log2(period),
plot=plot_action.line(linestyle='--',
color='#cccccc'),
xscale="log",
)
def plotMotherWavelet(self, subplot):
t = np.arange(-5, 5, 0.01)
return subplot.add(
data=None,
x=t,
y=[self.wavelet_spectra.feature.mother.psi(x) for x in t],
plot=plot_action.line(color="k"),
tick=dict(bottom=False,
left=False,
labelbottom=False,
labelleft=False),
title="Mother wavelet")
def plotAverageScale(self, subplot):
return subplot.add(
data=None,
ypos=self.wavelet_spectra.get_averaged_scale_significance(
self.period_filter, self.alpha, self.threshold),
plot=plot_action.xband(color='k', linestyle='--', linewidth=1.),
xlabel='Time [' + self.wavelet_spectra.feature.time_unit + ']',
ylabel='Average variance []',
title='d) scale-averaged power',
).add(data=None,
x=self.t,
y=self.wavelet_spectra.get_averaged_scale(self.period_filter),
plot=plot_action.line(color="k", linewidth=1.5))
def plotInversedSignal(self, subplot, style={}):
"""
plot inversed signal
"""
st = {
"color": [0.5, 0.5, 0.5],
"linestyle": "-",
"linewidth": 2,
"alpha": 1,
**style
}
return subplot.add(data=None,
x=self.t,
y=self.wavelet_spectra.inverse(),
plot=plot_action.line(**st))
def plot_zoning_segment(
xs,
ys,
slope_center_xs,
context={}):
lasso_res = fusedlasso(ys)
fitted_ys = lasso_res["beta"]
print(f"lambda: {lasso_res['lambda.1se']}")
print(f"MSE: {get_MSE(ys,fitted_ys)}")
base = Subplot(context.get("axes_style", {}))
data_points = dict(
data={"x": xs, "y": ys},
x="x",
y="y",
plot=plot_action.scatter(context["artists"].get("data_point"))
)
fitted_line = dict(
data={"x": xs, "y": fitted_ys},
x="x",
y="y",
plot=plot_action.line(context["artists"].get("estimated_line"))
)
segment_boundary = dict(
data={"x": slope_center_xs},
xpos="x",
plot=plot_action.vband(context["artists"].get("segment_boundary"))
)
return base.add(
**data_points
).add(
**fitted_line
).add(
**segment_boundary
)
def plotSignal(self, subplot, style={}):
"""
Plot original signal and inversed signal.
"""
st = {
"color": '#2196f3',
"linestyle": "-",
"linewidth": 1.5,
"alpha": 1,
**style
}
return subplot.add(
data=None,
x=self.t,
y=CWT.detrend(self.signal),
plot=plot_action.line(**st),
xlim=[self.t.min(), self.t.max()],
title='a) Signal',
)
def plot(mcmc: MCMCResult, burn_in: int, style={}, label_map={}, lim_map={}):
data = mcmc.get_sampled()
params = mcmc.get_parameter_group()
n_set = mcmc.len_parameter_set()
(fig_style, axes_style, plot_style) = classify_plot_style(style)
figure = Figure()
transition_line = plot_action.line(color="black")
fill_burn_in_stage = plot_action.vband(
xpos=[[0, burn_in]], alpha=0.3, color="gray")
is_last_row = check_last_of(len(params))
for row_i, param in enumerate(params):
for column_i in range(n_set):
subplot = Subplot(axes_style).add(
data=data,
x="index",
y=f"{param}{column_i}",
ylim=lim_map.get(param, [None, None]),
plot=[
transition_line,
fill_burn_in_stage
],
ylabel=f"{label_map.get(param, param)}" if is_first(
column_i) else None,
tick={
"labelbottom": True if is_last_row(row_i) else False,
"labelleft": True if is_first(column_i) else False,
"right": True
},
title=f"Stage {column_i + 1}" if is_first(row_i) else None,
)
figure.add_subplot(subplot)
return figure.show(**fig_style, column=n_set)