def get_steps(self, sigs, weights, cutoff_hz=1.0, order=2, window=4.0,
x_threshold=2.0, figname=None, min_step_window=2.0):
"""
Parameters
----------
sigs : list(ndarray(n), ndarray(n))
List of signals (1D-arrays)
"""
x = sigs[0]
y = sigs[1]
xf, xf_ds, xf_ds_dt, x_regress = self.conditioning(x, cutoff_hz=cutoff_hz,
order=order, window=window)
xf_ds_regress = x_regress[:,0]
yf, yf_ds, yf_ds_dt, y_regress = self.conditioning(y, cutoff_hz=cutoff_hz,
order=order, window=window)
yf_ds_regress = y_regress[:,0]
# select on the product of yf_ds_regress * xf_ds_regress,
# both need to be steady!
xw = weights[0]
yw = weights[1]
xy_ds = np.abs(xf_ds_regress*xw) + np.abs(yf_ds_regress*yw)
xf_sel_mask, xf_sel_arg = self.select(xy_ds, x_threshold)
step_ds_mask, steps_ds = self.steady_steps(xf_sel_mask,
step_lenght=min_step_window)
# save steps in high-res sampling of the original signal
steps = np.round(steps_ds * self.sps / self.freq_ds, 0).astype(np.int)
np.savetxt(figname.replace('.png', '_steps.txt'), steps)
# steps_ds_times = self.t_ds[steps_ds.flatten()]
# steps = np.ndarray(steps_ds.shape) * np.nan
# for k in range(steps.shape[0]):
# t0 = self.t_ds[steps[k,0]]
# t1 = self.t_ds[steps[k,1]]
# steps[k,0] = np.abs(self.time - t0).argmin()
# steps[k,0] = np.abs(self.time - t1).argmin()
if figname is not None:
print('start plotting...')
fig, axes = plotting.subplots(nrows=3, ncols=1, figsize=(8,9),
dpi=120)
ax = axes[0,0]
ax.set_title('original and filtered signals')
ax.plot(self.time, x, 'r-', alpha=0.3)
ax.plot(self.time, xf, 'r-')
ax.grid()
axr = ax.twinx()
axr.plot(self.time, y, 'g-', alpha=0.3)
axr.plot(self.time, yf, 'g-')
ax = axes[1,0]
ax.set_title('lin regr window: %1.02f sec' % window)
t_mask = self.t_ds.copy()
t_mask[~xf_sel_mask] = np.nan
x_mask = xf_ds.copy()
x_mask[~xf_sel_mask] = np.nan
ax.plot(self.t_ds, xf_ds, 'r-', alpha=1.0, label='xf ds')
ax.plot(self.t_ds, x_mask, 'k-+', alpha=0.7, label='xf select')
ax.grid()
axr = ax.twinx()
axr.plot(self.t_ds, yf_ds, 'g-', alpha=0.8, label='yx ds')
y_mask = yf_ds.copy()
y_mask[~xf_sel_mask] = np.nan
axr.plot(self.t_ds, y_mask, 'k-+', alpha=0.7, label='yf select')
xmin = axr.get_ylim()[0]
xmax = axr.get_ylim()[1]
collection = region.span_where(self.t_ds, ymin=xmin, ymax=xmax,
where=xf_sel_mask, facecolor='grey',
alpha=0.4)
axr.add_collection(collection)
leg = plotting.one_legend(ax, axr, loc='best')
leg.get_frame().set_alpha(0.5)
ax = axes[2,0]
rpl = (x_threshold, min_step_window)
ax.set_title('threshold: %1.02f, min step window: %1.2f sec' % rpl)
ax.plot(self.t_ds, np.abs(xf_ds_regress), 'r-',
label='xf lin regress', alpha=0.9)
ax.plot(self.t_ds, np.abs(yf_ds_regress), 'g-',
label='yf lin regress', alpha=0.9)
ax.plot(self.t_ds, np.abs(xy_ds), 'k-', label='xy*w', alpha=0.7)
ax.axhline(y=x_threshold, linewidth=1, color='k', linestyle='--',
aa=False)
ax.set_ylim([0,5])
xmin = ax.get_ylim()[0]
xmax = ax.get_ylim()[1]
collection = region.span_where(self.t_ds, ymin=xmin, ymax=xmax,
where=step_ds_mask, facecolor='grey',
alpha=0.4)
ax.add_collection(collection)
# axr = ax.twinx()
# axr.plot(self.t_ds, np.abs(yf_ds_regress), 'g-',
# label='yf lin regress', alpha=0.9)
# ax, axr = plotting.match_yticks(ax, axr)
# axr.set_ylim([0,5])
# leg = plotting.one_legend(ax, axr, loc='best')
# leg.get_frame().set_alpha(0.5)
ax.grid()
leg = ax.legend(loc='best')
leg.get_frame().set_alpha(0.5)
fig.tight_layout()
fig.savefig(figname)
print(figname)
return steps
