# =============================================================================
# read 38 kHz calibrated data
# =============================================================================
print('Reading 38 kHz calibrated data...')
raw38 = ek60.get_raw_data(channel_number=1)
Sv38toml = np.transpose(raw38.get_Sv(calibration=params).data)
t38toml = raw38.get_Sv(calibration=params).ping_time
r38toml = raw38.get_Sv(calibration=params).range
# =============================================================================
# plot non-calibrated and calibrated data
# =============================================================================
print('Displaying results...')
plt.figure(figsize=(8, 6))
c = cmaps()
plt.subplot(211).invert_yaxis()
plt.pcolormesh(t38, r38, Sv38, vmin=-80, vmax=-50, cmap=c.ek500)
plt.colorbar().set_label('Sv 38 kHz (dB)')
plt.title('Calibration NOT applied')
plt.ylabel('Depth (m)')
plt.tick_params(labelbottom=False)
plt.subplot(212).invert_yaxis()
plt.pcolormesh(t38toml, r38toml, Sv38toml, vmin=-80, vmax=-50, cmap=c.ek500)
plt.colorbar().set_label('Sv 38 kHz (dB)')
plt.title('Calibration applied')
plt.ylabel('Depth (m)')
plt.xlabel('Time (dd HH:MM)')
m120sh = mSH.weill(Sv120cvv,
thr=-70,
maxvgap=15,
maxhgap=0,
minhlen=3,
minvlen=15)[0]
#------------------------------------------------------------------------------
# Figures
plt.figure(figsize=(8, 5))
plt.subplots_adjust(left=0.08, right=0.91, bottom=0.08, top=0.95, wspace=0.08)
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, .05])
# Sv original
plt.subplot(gs[0]).invert_yaxis()
im = plt.pcolormesh(t120, r120, Sv120, vmin=-80, vmax=-50, cmap=cmaps().ek500)
plt.ylabel('Depth (m)')
plt.xlabel('Time (dd HH:MM)')
plt.title('Sv')
# Swarms mask
plt.subplot(gs[1]).invert_yaxis()
plt.pcolormesh(t120, r120, m120sh * 1, cmap='Greys')
plt.tick_params(labelleft=False)
plt.xlabel('Time (dd HH:MM)')
plt.title('Swarms')
# colorbar
ax = plt.subplot(gs[2])
plt.colorbar(im, cax=ax).set_label('dB re m$^{-1}$')
#------------------------------------------------------------------------------
# Clean Sv from impulse noise with Wang's algorithm
Sv120wang, m120wang_ = mIN.wang(Sv120)
# Note that Wang's algorithm does not return a mask with impulse noise detected
# but Sv without impulse noise and other Sv modifications. It also removes
# Sv signal below and above the target of interest. In this case, krill swarms.
#------------------------------------------------------------------------------
# Figures
plt.figure(figsize=(8,4))
# Sv original
plt.subplot(131).invert_yaxis()
plt.pcolormesh(p120, r120, Sv120, vmin=-80, vmax=-50, cmap=cmaps().ek500)
plt.ylabel('Depth (m)')
plt.title('IN on')
# IN removed with Ryan's algorithm
plt.subplot(132).invert_yaxis()
plt.pcolormesh(p120, r120, Sv120ryan, vmin=-80, vmax=-50, cmap=cmaps().ek500)
plt.tick_params(labelleft=False)
plt.xlabel('Number of pings')
plt.title('IN off (Ryan)')
# IN removed (and further signal) with Wang's algorithm
plt.subplot(133).invert_yaxis()
plt.pcolormesh(p120, r120, Sv120wang, vmin=-80, vmax=-50, cmap=cmaps().ek500)
plt.tick_params(labelleft=False)
plt.title('IN off (Wang)')
#------------------------------------------------------------------------------
# integrate Nautical Area Scattering Coefficient (NASC), from 20 to 250 metres
NASC, NASCper = tf.Sv2NASC(Sv38inoff, r38, 20, 250, method='sum')
# note the sum method is used.
# the second output is the percentange vertical samples integrated behind every
# computation of NASC.
#------------------------------------------------------------------------------
# Figures
plt.figure(figsize=(8, 6))
# Sv with impulse noise removed
plt.subplot(311).invert_yaxis()
plt.pcolormesh(t38, r38, Sv38inoff, vmin=-80, vmax=-50, cmap=cmaps().ek500)
plt.tick_params(labelbottom=False)
plt.ylabel('Depth (m)')
plt.title('Sv with impulse noise removed')
# integrated sa
ax = plt.subplot(312)
ax = [ax, ax.twinx()]
ax[0].plot(t38, sa, '-r')
ax[0].set_xlim(t38[0], t38[-1])
ax[0].tick_params(axis='y', colors='r')
ax[0].yaxis.tick_left()
ax[0].yaxis.set_label_position("left")
ax[0].set_ylabel('s$_a$ (m$^2$ m$^{-2}$)', color='r')
ax[0].tick_params(labelbottom=False)
ax[1].plot(t38, saper, '-b')