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
# 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,
def plot_full_figure(time_sec=None):
loc_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\results\large-array_quillback\AMAR173.4.20190920T161248Z.nc'
audio_file = r'C:\Users\xavier.mouy\Documents\Reports_&_Papers\Papers\10-XAVarray_2020\data\large_array\2019-09-15_HornbyIsland_AMAR_07-HI\AMAR173.1.20190920T161248Z.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\large_array\2019-09-15_HornbyIsland_AMAR_07-HI\hydrophones_config_07-HI.csv'
t1_sec = 1570
t2_sec = 1587 #1590
filter_x = [-1.5, 1.5]
filter_y = [-1.5, 1.5]
filter_z = [-1.5, 1.5]
filter_x_std = 0.5
filter_y_std = 0.5
filter_z_std = 0.5
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': [-1.26],
'x_max': [1.26],
'y_min': [-1.26],
'y_max': [1.26],
'z_min': [-1.5],
'z_max': [2.1],
})
## ###########################################################################
## load localization results
loc = Measurement()
loc.from_netcdf(loc_file)
loc_data = loc.data
## load hydrophone locations
hydrophones_config = pd.read_csv(hp_config_file)
# 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(8.6, 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
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.loc['y'] = m['y'].values[0]
detec.loc['z'] = m['z'].values[0]
detec.loc['x_std'] = loc_errors_std['x_std'].values[0]
detec.loc['y_std'] = loc_errors_std['y_std'].values[0]
detec.loc['z_std'] = loc_errors_std['z_std'].values[0]
detec.loc['tdoa_errors_std'] = tdoa_errors_std[0]
if len(tdoa_sec) >= 3:
detec.loc['tdoa_sec_1'] = tdoa_sec[0][0]
detec.loc['tdoa_sec_2'] = tdoa_sec[1][0]
detec.loc['tdoa_sec_3'] = tdoa_sec[2][0]
if len(tdoa_sec) > 3:
detec.loc['tdoa_sec_4'] = tdoa_sec[3][0]
detec.loc['tdoa_sec_5'] = tdoa_sec[4][0]
# stack to results into localization object
localizations.data = localizations.data.append(detec, ignore_index=True)
# Plot hydrophones
fig1 = plt.figure()
ax = fig1.add_subplot(111, projection='3d')
colors = matplotlib.cm.tab10(hydrophones_config.index.values)
# Sources
for index, hp in hydrophones_config.iterrows():
point = ax.scatter(hp['x'],hp['y'],hp['z'],
s=20,
color=colors[index],
label=hp['name'],
)
localization = ax.scatter(localizations.data['x'],
localizations.data['y'],
def run_localization(infile, deployment_info_file, detection_config,
hydrophones_config, localization_config):
t1 = 0
t2 = 70
# Look up data files for all channels
audio_files = find_audio_files(infile, hydrophones_config)
# run detector on selected channel
print('DETECTION')
detections = run_detector(
audio_files['path'][detection_config['AUDIO']['channel']],
audio_files['channel'][detection_config['AUDIO']['channel']],
detection_config,
chunk=[t1, t2],
deployment_file=deployment_info_file)
#detections.insert_values(frequency_min=20)
print(str(len(detections)) + ' detections')
# # plot spectrogram/waveforms of all channels and detections
# plot_data(audio_files,
# detection_config['SPECTROGRAM']['frame_sec'],
# detection_config['SPECTROGRAM']['window_type'],
# detection_config['SPECTROGRAM']['nfft_sec'],
# detection_config['SPECTROGRAM']['step_sec'],
# detection_config['SPECTROGRAM']['fmin_hz'],
# detection_config['SPECTROGRAM']['fmax_hz'],
# chunk = [t1, t2],
# detections=detections,
# detections_channel=detection_config['AUDIO']['channel'])
# localization
sound_speed_mps = localization_config['ENVIRONMENT']['sound_speed_mps']
ref_channel = localization_config['TDOA']['ref_channel']
# define search window based on hydrophone separation and sound speed
hydrophones_dist_matrix = calc_hydrophones_distances(hydrophones_config)
TDOA_max_sec = np.max(hydrophones_dist_matrix) / sound_speed_mps
# define hydrophone pairs
hydrophone_pairs = defineReceiverPairs(len(hydrophones_config),
ref_receiver=ref_channel)
# pre-compute grid search if needed
if localization_config['METHOD']['grid_search']:
sources = defineSphereVolumeGrid(
localization_config['GRIDSEARCH']['spacing_m'],
localization_config['GRIDSEARCH']['radius_m'],
origin=localization_config['GRIDSEARCH']['origin'])
#sources = defineCubeVolumeGrid(0.2, 2, origin=[0, 0, 0])
sources_tdoa = np.zeros(shape=(len(hydrophone_pairs), len(sources)))
for source_idx, source in sources.iterrows():
sources_tdoa[:, source_idx] = predict_tdoa(source, sound_speed_mps,
hydrophones_config,
hydrophone_pairs).T
theta = np.arctan2(sources['y'].to_numpy(),
sources['x'].to_numpy()) * (180 / np.pi) # azimuth
phi = np.arctan2(
sources['y'].to_numpy()**2 + sources['x'].to_numpy()**2,
sources['z'].to_numpy()) * (180 / np.pi)
sources['theta'] = theta
sources['phi'] = phi
# Define Measurement object for the localization results
if localization_config['METHOD']['linearized_inversion']:
localizations = Measurement()
localizations.metadata['measurer_name'] = localization_method_name
localizations.metadata['measurer_version'] = '0.1'
localizations.metadata['measurements_name'] = [[
'x', 'y', 'z', 'x_std', 'y_std', 'z_std', 'tdoa_errors_std'
]]
# need to define what output is for grid search
# pick single detection (will use loop after)
print('LOCALIZATION')
for detec_idx, detec in detections.data.iterrows():
if 'detec_idx_forced' in locals():
print('Warning: forced to only process detection #',
str(detec_idx_forced))
detec = detections.data.iloc[detec_idx_forced]
print(str(detec_idx + 1) + '/' + str(len(detections)))
# load data from all channels for that detection
waveform_stack = stack_waveforms(audio_files, detec, TDOA_max_sec)
# readjust signal boundaries to only focus on section with most energy
percentage_max_energy = 90
chunk = ecosound.core.tools.tighten_signal_limits_peak(
waveform_stack[detection_config['AUDIO']['channel']],
percentage_max_energy)
waveform_stack = [x[chunk[0]:chunk[1]] for x in waveform_stack]
# calculate TDOAs
tdoa_sec, corr_val = calc_tdoa(
waveform_stack,
hydrophone_pairs,
detec['audio_sampling_frequency'],
TDOA_max_sec=TDOA_max_sec,
upsample_res_sec=localization_config['TDOA']['upsample_res_sec'],
normalize=localization_config['TDOA']['normalize'],
doplot=False,
)
if localization_config['METHOD']['grid_search']:
delta_tdoa = sources_tdoa - tdoa_sec
delta_tdoa_norm = np.linalg.norm(delta_tdoa, axis=0)
sources['delta_tdoa'] = delta_tdoa_norm
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
colors = matplotlib.cm.tab10(hydrophones_config.index.values)
#alphas = delta_tdoa_norm - min(delta_tdoa_norm)
#alphas = alphas/max(alphas)
#alphas = alphas - 1
#alphas = abs(alphas)
#alphas = np.array(alphas)
alphas = 0.5
for index, hp in hydrophones_config.iterrows():
point = ax.scatter(
hp['x'],
hp['y'],
hp['z'],
s=40,
color=colors[index],
label=hp['name'],
)
ax.scatter(
sources['x'],
sources['y'],
sources['z'],
c=sources['delta_tdoa'],
s=2,
alpha=alphas,
)
# Axes labels
ax.set_xlabel('X (m)', labelpad=10)
ax.set_ylabel('Y (m)', labelpad=10)
ax.set_zlabel('Z (m)', labelpad=10)
# legend
ax.legend(bbox_to_anchor=(1.07, 0.7, 0.3, 0.2), loc='upper left')
plt.tight_layout()
plt.show()
plt.figure()
sources.plot.hexbin(x="theta",
y="phi",
C="delta_tdoa",
reduce_C_function=np.mean,
gridsize=40,
cmap="viridis")
# Lineralized inversion
if localization_config['METHOD']['linearized_inversion']:
[m, iterations_logs
] = linearized_inversion(tdoa_sec,
hydrophones_config,
hydrophone_pairs,
localization_config['INVERSION'],
sound_speed_mps,
doplot=False)
# Estimate uncertainty
tdoa_errors_std = calc_data_error(tdoa_sec, m, sound_speed_mps,
hydrophones_config,
hydrophone_pairs)
loc_errors_std = calc_loc_errors(tdoa_errors_std, m,
sound_speed_mps,
hydrophones_config,
hydrophone_pairs)
# Bring all detection and localization informations together
detec.loc['x'] = m['x'].values[0]
detec.loc['y'] = m['y'].values[0]
detec.loc['z'] = m['z'].values[0]
detec.loc['x_std'] = loc_errors_std['x_std'].values[0]
detec.loc['y_std'] = loc_errors_std['y_std'].values[0]
detec.loc['z_std'] = loc_errors_std['z_std'].values[0]
detec.loc['tdoa_errors_std'] = tdoa_errors_std[0]
# stack to results into localization object
localizations.data = localizations.data.append(detec,
ignore_index=True)
return localizations
min_threshold = 0.7
noise_label = 'NN'
# load names of file and start/stop times where false alarms have been manually
# identified
df = pd.read_excel(xls_file, header=None)
for idx in range(0, len(df)):
# file name to load
wav_file_name = df[0][idx]
tmin_sec = df[1][idx]
tmax_sec = df[2][idx]
print(wav_file_name, tmin_sec, tmax_sec)
detec_file_path = os.path.join(in_dir, wav_file_name + '.nc')
# load detection/measurement file
meas = Measurement()
meas.from_netcdf(detec_file_path)
data_df = meas.data
# Only keep fish detections above the given confidence threshold and times
data_df_filt = data_df[(data_df.label_class == fish_label)
& (data_df.confidence >= min_threshold)
& (data_df.time_min_offset >= tmin_sec)
& (data_df.time_max_offset <= tmax_sec)]
data_df_filt.reset_index(inplace=True, drop=True)
meas.data = data_df_filt
# Change fish labels to noise labels
meas.insert_values(label_class=noise_label)
# Save to new nc file
meas.to_netcdf(os.path.join(out_dir, wav_file_name + str(idx)))
print('done')
data.dropna(subset=features,
axis=0,
how='any',
thresh=None,
inplace=True)
n2 = len(data)
print('Deleted observations (due to NaNs): ' + str(n1 - n2))
# Classification - predictions
X = data[features]
X = (X - Norm_mean) / Norm_std
pred_class = model.predict(X)
pred_prob = model.predict_proba(X)
pred_prob = pred_prob[range(0, len(pred_class)), pred_class]
# Relabel
for index, row in classes_encoder.iterrows():
pred_class = [
row['label'] if i == row['ID'] else i for i in pred_class
]
# update measurements
data['label_class'] = pred_class
data['confidence'] = pred_prob
# sort detections by ascending start date/time
data.sort_values('time_min_offset',
axis=0,
ascending=True,
inplace=True)
# save result as NetCDF file
print('Saving')
meas.data = data
meas.to_netcdf(os.path.join(outdir, file))