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
# Set virtual sources location (spherical grid)
S = loclib.defineSphereSurfaceGrid(nsources, radius, origin)
#S = loclib.defineCubeVolumeGrid(spacing, radius, origin)
nsources = S.shape[0]
# creates output folder
StartTimestamp_obj = datetime.datetime.now()
StartTimestamp_str = StartTimestamp_obj.strftime("%Y%m%d%H%M%S")
outdir = os.path.join(
outroot, StartTimestamp_str + '_' + 'Receivers' + str(nReceivers) + '_' +
'Bounds' + str(ReceiverBoundValue) + 'm_' + 'Sources' + str(nsources) +
'_' + 'Radius' + str(radius) + 'm')
os.mkdir(outdir)
# Define receiver pairs for TDOAs
Rpairs = loclib.defineReceiverPairs(nReceivers)
# Repeats optimization nIter times to ensure stability
for i in range(nIter):
# Closes all open figures
plt.close("all")
# Optimize array configuration
R, Rchanges, acceptRateChanges, Cost, processingTime = loclib.optimizeArray(
ReceiverBounds, nReceivers, AnnealingSchedule, S, Rpairs, V,
NoiseVariance)
# Get list of Jacobian matrice for each source
J2 = loclib.defineJacobian(R, S, V, Rpairs)
# Calculates localization uncertainty for each source
Uncertainties2 = loclib.getUncertainties(J2, NoiseVariance)
# Plots unceratinties of optimized array
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'])
if localization_config['GRIDSEARCH']['min_z']:
sources = sources.loc[sources['z']>=localization_config['GRIDSEARCH']['min_z']]
sources = sources.reset_index(drop=True)
# sources = defineCubeVolumeGrid(0.2, 2, origin=[0, 0, 0])
# sources = defineSphereSurfaceGrid(
# 10000,
# localization_config['GRIDSEARCH']['radius_m'],
# origin=localization_config['GRIDSEARCH']['origin'])
def getArrayUncertainties(R, radius, spacing, V, NoiseSTD, contoursValues):
# Virtual sources coordinates -> Cube of points (Cartesian coordinates)
vec = np.arange(-radius, radius + spacing, spacing)
X, Y, Z = np.meshgrid(vec, vec, vec, indexing='ij')
Sx = np.reshape(X, X.shape[0] * X.shape[1] * X.shape[2])
Sy = np.reshape(Y, Y.shape[0] * Y.shape[1] * Y.shape[2])
Sz = np.reshape(Z, Z.shape[0] * Z.shape[1] * Z.shape[2])
S = pd.DataFrame({'x': Sx, 'y': Sy, 'z': Sz})
# find location of slice
ind = np.argmin(abs(vec))
sliceValue = vec[ind]
# Nb of receivers
nReceivers = R.shape[0]
# Variance of TDOA measurement errors
NoiseVariance = NoiseSTD**2
# Define receiver pairs for TDOAs
Rpairs = loclib.defineReceiverPairs(nReceivers)
# Get list of Jacobian matrice for each source
J = loclib.defineJacobian(R, S, V, Rpairs)
# Calculates localization uncertainty for each source
Uncertainties = loclib.getUncertainties(J, NoiseVariance)
# Plots unceratinties of optimized array
loclib.plotArrayUncertainties(R, S, Uncertainties)
# PLot hydrophone locations
f0 = plt.figure()
ax0 = f0.add_subplot(111, projection='3d')
ax0.scatter(R['x'], R['y'], R['z'], s=30, c='black')
ax0.set_xlabel('X (m)', labelpad=10)
ax0.set_ylabel('Y (m)', labelpad=10)
ax0.set_zlabel('Z (m)', labelpad=10)
plt.show()
# Define and plot plane slices
f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=False, figsize=(16, 5))
## XY plane
XY = np.zeros([len(vec), len(vec)])
for i in range(len(vec)):
for jj in range(len(vec)):
idx = S.index[(S['x'] == vec[i]) & (S['y'] == vec[jj]) &
(S['z'] == sliceValue)][0]
XY[i, jj] = Uncertainties['rms'][idx]
CS_XY = ax1.contour(vec, vec, XY, levels=contoursValues, colors=['k'])
# Receivers
ax1.plot(R['x'], R['y'], 'go')
ax1.set_xlabel('X(m)')
ax1.set_ylabel('Y(m)')
ax1.grid(True)
im = ax1.imshow(XY,
interpolation='bilinear',
origin='lower',
cmap=cm.jet,
extent=(-radius, radius, -radius, radius),
norm=colors.Normalize(vmin=0, vmax=10))
ax1.set_aspect('auto')
cbar = f.colorbar(im, ax=ax1)
cbar.ax.set_ylabel('Uncertainty (m)')
## XZ plane
XZ = np.zeros([len(vec), len(vec)])
for i in range(len(vec)):
for jj in range(len(vec)):
idx = S.index[(S['x'] == vec[i]) & (S['z'] == vec[jj]) &
(S['y'] == sliceValue)][0]
XZ[i, jj] = Uncertainties['rms'][idx]
CS_XZ = ax2.contour(vec, vec, XZ, levels=contoursValues, colors=['k'])
# Receivers
ax2.plot(R['x'], R['z'], 'go')
ax2.set_xlabel('X(m)')
ax2.set_ylabel('Z(m)')
ax2.grid(True)
im = ax2.imshow(XZ,
interpolation='bilinear',
origin='lower',
cmap=cm.jet,
extent=(-radius, radius, -radius, radius),
norm=colors.Normalize(vmin=0, vmax=10))
ax2.set_aspect('auto')
cbar = f.colorbar(im, ax=ax2)
cbar.ax.set_ylabel('Uncertainty (m)')
## YZ plane
YZ = np.zeros([len(vec), len(vec)])
for i in range(len(vec)):
for jj in range(len(vec)):
idx = S.index[(S['y'] == vec[i]) & (S['z'] == vec[jj]) &
(S['x'] == sliceValue)][0]
YZ[i, jj] = Uncertainties['rms'][idx]
CS_YZ = ax3.contour(vec, vec, YZ, levels=contoursValues, colors=['k'])
# Receivers
ax3.plot(R['y'], R['z'], 'go')
ax3.set_xlabel('Y(m)')
ax3.set_ylabel('Z(m)')
ax3.grid(True)
im = ax3.imshow(YZ,
interpolation='bilinear',
origin='lower',
cmap='jet',
extent=(-radius, radius, -radius, radius),
norm=colors.Normalize(vmin=0, vmax=10))
cbar = f.colorbar(im, ax=ax3)
cbar.ax.set_ylabel('Uncertainty (m)')
# from mpl_toolkits.axes_grid1 import make_axes_locatable
# divider = make_axes_locatable(plt.gca())
# cax = divider.append_axes("right", "5%", pad="3%")
# plt.colorbar(im, cax=cax)
#plt.colorbar(im,ax=ax3)
ax3.set_aspect('auto')
#plt.tight_layout()
plt.show()
#plt.tight_layout()
# Extract contours lines
coord_CS_XY = []
coord_CS_XZ = []
coord_CS_YZ = []
for i in range(len(contoursValues)):
p1 = CS_XY.collections[i].get_paths()[0]
coord_CS_XY.append(p1.vertices)
p2 = CS_XZ.collections[i].get_paths()[0]
coord_CS_XZ.append(p2.vertices)
p3 = CS_YZ.collections[i].get_paths()[0]
coord_CS_YZ.append(p3.vertices)
return coord_CS_XY, coord_CS_XZ, coord_CS_YZ
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
