def plot_full_figure(time_sec=None):
#loc_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\results\mobile_array_copper\localizations_1m_5cm.nc'
loc_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\results\mobile_array_copper\localizations_2cm_3m.nc'
loc_file_matlab = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\results\mobile_array_copper\localizations_matlab_with_CI.csv'
audio_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\data\mobile_array\2019-09-14_HornbyIsland_Trident\671404070.190918222812.wav'
video_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\data\large_array\2019-09-15_HornbyIsland_AMAR_07-HI\3420_FishCam01_20190920T163627.613206Z_1600x1200_awb-auto_exp-night_fr-10_q-20_sh-0_b-50_c-0_i-400_sat-0.mp4'
hp_config_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\data\mobile_array\2019-09-14_HornbyIsland_Trident\hydrophones_config_HI-201909.csv'
localization_config_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\config_files\localization_config_mobile_array.yaml'
t1_sec = 214 #216
t2_sec = 224 #223
filter_x = [-5, 5]
filter_y = [-5, 5]
filter_z = [-2, 5]
filter_x_std = 6
filter_y_std = 9
filter_z_std = 6
params = pd.DataFrame({
'loc_color': ['black'],
'loc_marker': ['o'],
'loc_alpha': [1],
'loc_size': [5],
'uncertainty_color': ['black'],
'uncertainty_style': ['-'],
'uncertainty_alpha': [1], #0.7
'uncertainty_width': [0.2], #0.2
'x_min': [-1.5],
'x_max': [1.5],
'y_min': [-0.5],
'y_max': [3],
'z_min': [-1.5],
'z_max': [1.5],
})
## ###########################################################################
localization_config = read_yaml(localization_config_file)
hydrophones_config = pd.read_csv(hp_config_file)
sound_speed_mps = localization_config['ENVIRONMENT']['sound_speed_mps']
ref_channel = localization_config['TDOA']['ref_channel']
hydrophone_pairs = defineReceiverPairs(len(hydrophones_config),
ref_receiver=ref_channel)
## load localization results
loc = Measurement()
loc.from_netcdf(loc_file)
loc_data = loc.data
# used matlab CI
loc_data = pd.read_csv(loc_file_matlab)
# ## recalculate data errors
# diff=[]
# idx = 0
# for idx in range(len(loc_data)):
# m = loc_data.loc[[idx],['x','y','z']]
# tdoa_m = predict_tdoa(m, sound_speed_mps, hydrophones_config, hydrophone_pairs)
# tdoa_measured = loc_data.loc[[idx],['tdoa_sec_1','tdoa_sec_2','tdoa_sec_3']].to_numpy()
# #diff_temp = (tdoa_m-tdoa_measured.T)**2
# if idx==0:
# diff = (tdoa_m-tdoa_measured.T)**2
# else:
# diff = np.vstack((diff,(tdoa_m-tdoa_measured.T)**2))
# Q = len(loc_data)
# #M = m.size # number of dimensions of the model (here: X, Y, and Z)
# #N = len(tdoa_sec) # number of measurements
# #error_std = np.sqrt((1/(Q*(N-M))) * (sum((tdoa_sec-tdoa_m)**2)))
# tdoa_errors_std = np.sqrt( (1/Q)*(sum(diff)))
# #tdoa_errors_std = calc_data_error(tdoa_sec, m, sound_speed_mps,hydrophones_config, hydrophone_pairs)
# for idx in range(len(loc_data)):
# loc_errors_std = calc_loc_errors(tdoa_errors_std, loc_data.loc[[idx],['x','y','z']] , sound_speed_mps, hydrophones_config, hydrophone_pairs)
# print('m')
# Filter
loc_data = loc_data.dropna(subset=['x', 'y', 'z']) # remove NaN
loc_data = loc_data.loc[(loc_data['x'] >= min(filter_x))
& (loc_data['x'] <= max(filter_x)) &
(loc_data['y'] >= min(filter_y)) &
(loc_data['y'] <= max(filter_y)) &
(loc_data['z'] >= min(filter_z)) &
(loc_data['z'] <= max(filter_z)) &
(loc_data['x_std'] <= filter_x_std) &
(loc_data['y_std'] <= filter_y_std) &
(loc_data['z_std'] <= filter_z_std)]
# Adjust detection times
loc_data['time_min_offset'] = loc_data['time_min_offset'] - t1_sec
loc_data['time_max_offset'] = loc_data['time_max_offset'] - t1_sec
if time_sec != None:
loc_data = loc_data.loc[(loc_data['time_max_offset'] <= time_sec)]
else:
print('Static')
# update loc object
loc.data = loc_data
# plots
# fig, ax = plt.subplots(figsize=(6, 1))
# fig.subplots_adjust(bottom=0.5)
# n_colors = t2_sec-t1_sec
# cmap = mpl.cm.get_cmap('CMRmap', n_colors*2)
# norm = mpl.colors.Normalize(vmin=0, vmax=n_colors)
# ax_cmap = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
# norm=norm,
# orientation='horizontal')
# ax_cmap.set_label('Time (s)')
# Plot spectrogram
fig_final, ax_spectro = plot_spectrogram(audio_file,
loc,
t1_sec,
t2_sec,
geometry=(5, 1, 1))
ax_spectro.set_title("")
ax_spectro.get_xaxis().set_visible(False)
n_colors = t2_sec - t1_sec
cmap = mpl.cm.get_cmap('viridis', n_colors * 4)
norm = mpl.colors.Normalize(vmin=0, vmax=n_colors)
divider = make_axes_locatable(ax_spectro)
cax = divider.append_axes('bottom', 0.1, pad=0.03)
ax_cmap = mpl.colorbar.ColorbarBase(cax,
cmap=cmap,
norm=norm,
orientation='horizontal')
ax_cmap.set_label('Time (s)')
if time_sec:
SFreq_min, SFreq_max = ax_spectro.get_ylim()
ax_spectro.plot([time_sec, time_sec], [SFreq_min, SFreq_max], 'r')
# plot detection points on top of spectrogram
#gs0 = fig_final.add_gridspec(60,1)
ax_detec = fig_final.add_subplot(20, 1, 1)
det_y = np.asarray(np.ones((1, len(loc_data['time_min_offset']))))[0]
det_x = np.asarray(loc_data['time_min_offset'])
ax_detec.scatter(det_x,
det_y,
c=loc_data['time_min_offset'],
cmap=cmap,
norm=norm,
s=12)
ax_detec.set_xlim(ax_spectro.get_xlim())
ax_detec.get_xaxis().set_visible(False)
ax_detec.get_yaxis().set_visible(False)
ax_detec.axis('off')
# #pos =[left, bottom, width, height]
# box = ax_detec.get_position()
# box.y0 = box.y0 + 0.6
# box.y1 = box.y1 + 0.6
# ax_detec.set_position(box)
#size = fig_final.get_size_inches()
plt.subplots_adjust(left=0.08,
bottom=0.1,
right=0.95,
top=0.95,
wspace=0,
hspace=0)
# divider2 = make_axes_locatable(ax_spectro)
# cax2 = divider2.append_axes('top', size=0.2, pad=10.0)
# det_y = np.asarray(np.ones((1,len(loc_data['time_min_offset']))))[0]
# det_x = np.asarray(loc_data['time_min_offset'])
# cax2.plot(det_x,det_y,'.r')
# cax2.set_xlim(ax_spectro.get_xlim())
# ax_cmap = mpl.colorbar.ColorbarBase(cax, cmap=cmap,
# norm=norm,
# orientation='horizontal')
gs = fig_final.add_gridspec(3, 2)
# plot localization top
ax_toploc = fig_final.add_subplot(gs[1:, 1])
plot_top_view(hydrophones_config, loc_data, params, cmap, norm, ax_toploc)
ax_toploc.set_anchor('E')
# plot localization side
#ax_sideloc = fig_final.add_subplot(3,3,7,sharex = ax_toploc)
ax_sideloc = fig_final.add_subplot(gs[1:, 0])
plot_side_view(hydrophones_config, loc_data, params, cmap, norm,
ax_sideloc)
ax_sideloc.set_anchor('W')
# set the spacing between subplots
plt.subplots_adjust(wspace=0, hspace=0)
# # plot video frame 1
# fig_video1, ax_video1 = plt.subplots(1,1)
# frame1_sec = 152.8 # second detection -> 16:38:59.8
# #ax_video1 = fig_final.add_subplot(3,3,5)
# plot_video_frame(video_file,frame1_sec, ax_video1)
# ax_video1.get_xaxis().set_visible(False)
# ax_video1.get_yaxis().set_visible(False)
# # plot video frame 2
# fig_video2, ax_video2 = plt.subplots(1,1)
# frame2_sec = 160 # 4th detection -> 16:39:07
# #ax_video2 = fig_final.add_subplot(3,3,6)
# plot_video_frame(video_file,frame2_sec, ax_video2)
# ax_video2.get_xaxis().set_visible(False)
# ax_video2.get_yaxis().set_visible(False)
fig_final.set_size_inches(9.08, 6.72)
box = ax_spectro.get_position()
box.y0 = box.y0 - 0.03
box.y1 = box.y1 - 0.03
ax_spectro.set_position(box)
return fig_final
# detec_idx_forced = 9
# # Config files mini array - Mill Bay - Copper RockFish on top of fishcam
# deployment_info_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\data\mini_array\deployment_info.csv'
# hydrophones_config_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\data\mini_array\hydrophones_config_05-MILL.csv'
# detection_config_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\config_files\detection_config_mini_array_copper.yaml'
# localization_config_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\config_files\localization_config_mini_array.yaml'
# infile = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\data\mini_array\671404070.190801232502.wav'
# t1 = 696
# t2 = 713
# #detec_idx_forced = 2
# load configuration parameters
hydrophones_config= pd.read_csv(hydrophones_config_file, skipinitialspace=True, dtype={'name': str, 'file_name_root': str}) # load hydrophone coordinates (meters)
detection_config = read_yaml(detection_config_file)
localization_config = read_yaml(localization_config_file)
if localization_config['METHOD']['linearized_inversion']:
localization_method_name = 'Linearized inversion'
if localization_config['METHOD']['grid_search']:
raise ValueError('Only 1 localization method allowed.')
else:
localization_method_name = 'Grid search'
if localization_config['METHOD']['grid_search'] == False:
raise ValueError('At least 1 localization method needs to be defined.')
# Look up data files for all channels
audio_files = find_audio_files(infile, hydrophones_config)
# run detector on selected channel
print('DETECTION')