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
'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) &
# annot4.from_netcdf(netcdf_files, verbose=True) # print(len(annot4)) # # ## netcdf folder # netcdf_files = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\test' # annot4 = Annotation() # annot4.from_netcdf(netcdf_files, verbose=True) # print(len(annot4)) # # ## netcdf folder from Measurements # netcdf_files = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\test2' # annot4 = Annotation() # annot4.from_netcdf(netcdf_files, verbose=True) # print(len(annot4)) # ## netcdf folder from Measurements netcdf_files = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\test2' annot4 = Measurement() annot4.from_netcdf(netcdf_files, verbose=True) print(len(annot4)) # import xarray as xr # d=annot3.data # index = range(0,len(d),1) # d['index']=index # #d = d.set_index(['index','entry_date', 'frequency_min','label_class']) # d = d.set_index(['index']) # data = d.to_xarray() # data2=data.sel(index=0)
'uncertainty_style': ['-'],
'uncertainty_alpha': [1], #0.7
'uncertainty_width': [0.2], #0.2
'x_min':[-3],
'x_max':[3],
'y_min':[-3],
'y_max':[3],
'z_min':[-3],
'z_max':[3],
})
## ###########################################################################
## load localization results 0 degrees
file1 = '0_deg.nc'
loc1 = Measurement()
loc1.from_netcdf(os.path.join(indir,file1))
# loc1_data = loc1.data
# # Filter
# loc1_data = loc1_data.dropna(subset=['x', 'y','z']) # remove NaN
# loc1_data = loc1_data.loc[(loc1_data['x']>=min(filter_x)) &
# (loc1_data['x']<=max(filter_x)) &
# (loc1_data['y']>=min(filter_y)) &
# (loc1_data['y']<=max(filter_y)) &
# (loc1_data['z']>=min(filter_z)) &
# (loc1_data['z']<=max(filter_z)) &
# (loc1_data['x_std']<= filter_x_std) &
# (loc1_data['y_std']<= filter_y_std) &
# (loc1_data['z_std']<= filter_z_std)
# ]
detection_config = read_yaml(detection_config_file)
localization_config = read_yaml(localization_config_file)
# 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)
detections = Measurement()
detections.from_pamlab(annotation_file)
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'])
# 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']]
# if localization_config['METHOD']['grid_search']:
# localizations = Measurement()
# localizations.metadata['measurer_name'] = localization_method_name
# localizations.metadata['measurer_version'] = '0.1'
# localizations.metadata['measurements_name'] = [['theta', 'phi', 'theta_std', 'phi_std', 'tdoa_errors_std']]
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','tdoa_sec_1','tdoa_sec_2','tdoa_sec_3','tdoa_sec_4','tdoa_sec_5']]
# 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]
# # Plot
# graph = GrapherFactory('SoundPlotter', title='Recording', frequency_max=1000)
# graph.add_data(sound) # add waveform data
# graph.add_data(spectro) # add spectrogram
# graph.add_annotation(detec, panel=0, color='green', label='Detections') # overlay detections on waveform plot
# graph.add_annotation(detec, panel=1, color='green', label='Detections') # overlay detections on spectrogram plot
# graph.colormap = 'binary'
# #graph.colormap = 'jet'
# graph.show()
from ecosound.core.measurement import Measurement
import seaborn as sns
# load detections
detection_file = r"C:\Users\xavier.mouy\Desktop\Tutorial\results\67674121.181017060806.wav.nc"
detec = Measurement()
detec.from_netcdf(detection_file)
detec.data['freq_peak'].groupby(detec.data['label_class'])
fig = sns.violinplot(y=detec.data['label_class'], x=detec.data['freq_peak'])
fig.set(xlabel='Detections peak frequency (Hz)', ylabel='Detections class')
#sns.show()
# ## Input paraneters ##########################################################
# audio_file = r"C:\Users\xavier.mouy\Desktop\Tutorial\data\67674121.181017060806.wav"
# #audio_file = r"C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\UVIC_hornby-island_2019\audio_data\AMAR173.4.20190916T011248Z.wav"
# #annotation_file = r"C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\UVIC_hornby-island_2019\manual_annotations\AMAR173.4.20190916T011248Z.Table.1.selections.txt"
# #detection_file = r"C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\Full_dataset\AMAR173.4.20190916T011248Z.wav.nc"
# filter_x=[-1.5, 1.5]
# filter_y=[-1.5, 1.5]
# filter_z=[-2, 2]
# filter_x_std=0.3
# filter_y_std=0.3
# filter_z_std=0.3
# load data
print('')
print('Loading dataset')
idx = 0
for infile in os.listdir(indir):
if infile.endswith(".nc"):
print(infile)
locs = Measurement()
locs.from_netcdf(os.path.join(indir, infile))
loc_data = locs.data
# 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)]
def main():
# input arguments
input_args = dict()
input_args['positive_class_label'] = 'FS'
input_args['train_ratio'] = 0.75
input_args['cv_splits'] = 10 #5
input_args['cv_repeats'] = 1
input_args['rebalance_classes'] = True
#input_args['data_file']= r'C:\Users\xavier.mouy\Documents\PhD\Projects\Detector\results\dataset_FS-NN_modified_20201105145300.nc'
input_args[
'data_file'] = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Detector\results\dataset_FS-NN_modified_20200902194334.nc'
input_args[
'out_dir'] = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Detector\results\Classification'
input_args['run_CV'] = False
input_args['train_final_model'] = True
input_args['final_model_name'] = 'RF50'
## DEFINITION OF CLASSIFIERS -------------------------------------------------
models = []
models.append(('Dummy', DummyClassifier(strategy="constant", constant=1)))
models.append(
('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
#models.append(('KNN', KNeighborsClassifier()))
#models.append(('KNN', KNeighborsClassifier(n_neighbors=4, metric='euclidean')))
models.append(('CART', DecisionTreeClassifier()))
#models.append(('NB', GaussianNB()))
models.append(('XGBoost', XGBClassifier()))
#models.append(('MLP', MLPClassifier(solver='sgd', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=0)))
models.append(('RF5',
RandomForestClassifier(n_estimators=5,
min_samples_split=100,
min_samples_leaf=50,
random_state=0)))
models.append(('RF10',
RandomForestClassifier(n_estimators=10,
min_samples_split=100,
min_samples_leaf=50,
random_state=0)))
models.append(('RF30',
RandomForestClassifier(n_estimators=30,
min_samples_split=100,
min_samples_leaf=50,
random_state=0)))
models.append(('RF50',
RandomForestClassifier(n_estimators=50,
min_samples_split=100,
min_samples_leaf=50,
random_state=0)))
#models.append(('RF100', RandomForestClassifier(n_estimators=100,min_samples_split= 100, min_samples_leaf=50,random_state=0)))
## setup output folder
now = datetime.now()
now_str = now.strftime("%Y%m%dT%H%M%S")
out_dir = os.path.join(input_args['out_dir'], now_str)
os.mkdir(out_dir)
## Save input args to txt file
text_file = open(os.path.join(out_dir, 'input_args_' + now_str + '.txt'),
"w")
n = text_file.write(str(input_args))
text_file.close()
## Checks that model name exists before running all the processing
if input_args['train_final_model']:
model_idx = [model[0]
for model in models].index(input_args['final_model_name'])
## LOAD DATSET ---------------------------------------------------------------
dataset = Measurement()
dataset.from_netcdf(input_args['data_file'])
print(dataset.summary())
## DATA PREPARATION ----------------------------------------------------------
# features
features = dataset.metadata['measurements_name'][
0] # list of features used for the classification
# data
data = dataset.data
# drop FS observations at Mill Bay
indexNames = data[(data['label_class'] == 'FS')
& (data['location_name'] == 'Mill bay')].index
data.drop(indexNames, inplace=True)
# add subclass + IDs
data, class_encoder = add_class_ID(data,
input_args['positive_class_label'])
data, _ = add_subclass(data)
#subclass2class_table = subclass2class_conversion(data)
# add group ID
data, group_encoder = add_group(data)
## DATA CLEAN-UP -------------------------------------------------------------
# Basic stats on all features
data_stats = data[features].describe()
#print(data_stats)
# how many NaNs and Infs per column
data = data.replace([np.inf, -np.inf], np.nan)
Nnan = data[features].isna().sum()
ax = Nnan.plot(kind='bar', title='Number of NaN/Inf', grid=True)
ax.set_ylabel('Number of observations with NaNs/Infs')
# Drop some features with too many NaNs
features.remove('freq_flatness')
features.remove('snr')
features.remove('uuid')
# drop observations/rows with NaNs
data.dropna(subset=features, axis=0, how='any', thresh=None, inplace=True)
data_stats2 = data[features].describe()
# ## VISUALIZATION -------------------------------------------------------------
# # box and whisker plots
# data[features].plot(kind='box', subplots=True, layout=(7,7), sharex=False, sharey=False)
# # histograms
# data[features].hist()
# # scatter plot matrix
# pd.plotting.scatter_matrix(data[features])
# scatter plot PCA
# pca = PCA(n_components=2)
# X = pca.fit_transform(data[features])
# y = data['class_ID']
# plot_2d_space(X, y, 'Imbalanced dataset (2 PCA components)')
## SPLIT DATA INTO TRAIN & TEST SETS ------------------------------------------
n_splits = round(1 / (1 - input_args['train_ratio']))
skf = StratifiedGroupKFold(n_splits=n_splits,
shuffle=True,
random_state=None)
for train_index, test_index in skf.split(data,
data['subclass_ID'],
groups=data['group_ID']):
data_train, data_test = data.iloc[train_index], data.iloc[test_index]
break
# plot class repartition
plot_datasets_distrib(data_train, data_test)
plot_dataset_distrib(data,
attr_list=['subclass_label', 'label_class'],
title='Full dataset')
plot_dataset_distrib(data_train,
attr_list=['subclass_label', 'label_class'],
title='Training set')
plot_dataset_distrib(data_test,
attr_list=['subclass_label', 'label_class'],
title='Test set')
# verify groups are not used in both datasets
groups_intersection = plot_datasets_groups(data_train,
data_test,
show=True)
## CROSS VALIDATION ON TRAIN SET ----------------------------------------------
if input_args['run_CV']:
# run train/test experiments
cv_predictions, cv_performance = cross_validation(
data_train,
models,
features,
cv_splits=input_args['cv_splits'],
cv_repeats=input_args['cv_repeats'],
rebalance=input_args['rebalance_classes'])
# display summary results
performance_report = summarize_performance(cv_performance,
threshold=0.5)
print(performance_report)
# plot mean Precision and Recall curves
plot_PR_curves(cv_performance)
plot_F_curves(cv_performance)
# save results
CV_results = {
'cv_predictions': cv_predictions,
'cv_performance': cv_performance,
'models': models,
'input_args': input_args,
}
pickle.dump(
CV_results,
open(os.path.join(out_dir, 'CV_' + now_str + '.sav'), 'wb'))
## FINAL EVALUATION ON TEST SET -----------------------------------------------
if input_args['train_final_model']:
print(' ')
print('Final evaluation on test set:')
print(' ')
model_name = models[model_idx][0]
model = models[model_idx][1] # RF50
print(model)
X_train = data_train[features] # features
Y_train = data_train['class_ID'] #labels
X_test = data_test[features] # features
Y_test = data_test['class_ID'] #labels
# feature normalization
Norm_mean = X_train.mean()
Norm_std = X_train.std()
X_train = (X_train - Norm_mean) / Norm_std
X_test = (X_test - Norm_mean) / Norm_std
# Train on entire train set
final_model = classification_train(
X_train, Y_train, model, rebalance=input_args['rebalance_classes'])
# Evaluate on full test set
pred_class, pred_prob = classification_predict(X_test, final_model)
# Print evaluation report
CR = classification_report(Y_test, pred_class)
print(CR)
# save the model to disk
model = {
'name': model_name,
'model': final_model,
'features': features,
'normalization_mean': Norm_mean,
'normalization_std': Norm_std,
'classes': class_encoder,
'input_args': input_args,
}
pickle.dump(
model,
open(
os.path.join(out_dir,
model_name + '_model_' + now_str + '.sav'), 'wb'))
deployment_file = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\UVIC_mill-bay_2019\deployment_info.csv'
data_dir = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\UVIC_mill-bay_2019\audio_data'
# load meta data
operator_name = platform.uname().node
dep_info = DeploymentInfo()
dep_info.read(deployment_file)
#list files
files = ecosound.core.tools.list_files(indir,
ext,
recursive=False,
case_sensitive=True)
for idx, file in enumerate(files):
print(str(idx) + r'/' + str(len(files)) + ': ' + file)
meas = Measurement()
meas.from_netcdf(file)
meas.insert_metadata(deployment_file)
file_name = os.path.splitext(os.path.basename(file))[0]
meas.insert_values(
operator_name=platform.uname().node,
audio_file_name=os.path.splitext(os.path.basename(file_name))[0],
audio_file_dir=data_dir,
audio_file_extension='.wav',
audio_file_start_date=ecosound.core.tools.filename_to_datetime(
file_name)[0])
meas.to_netcdf(os.path.join(outdir, file_name + '.nc'))
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
# # load annotations
# print('-----------------')
# print(' Annotations ')
# annot = Annotation()
# annot.from_netcdf(annotation_file)
# print(annot.summary())
# annot_perfile = annot.summary(rows='audio_file_name',columns='label_class')
# annot_perfile.rename(columns={"FS": "FS-annot"}, inplace=True)
# annot_perfile = annot_perfile['FS-annot'].to_frame()
# #annot_perfile.to_csv('annot.csv')
print(' ')
print('-----------------')
print(' Detections ')
# load detections
detec = Measurement()
detec.from_netcdf(detec_file)
print(detec.summary())
detec_perfile = detec.summary(rows='audio_file_name', columns='label_class')
detec_perfile.rename(columns={"FS": "FS-detec"}, inplace=True)
detec_perfile = detec_perfile['FS-detec'].to_frame()
dd = pd.concat([annot_perfile, detec_perfile], axis=1)
dd['diff'] = dd['FS-annot'] - dd['FS-detec']
dd.plot()
# outdir=r'C:\Users\xavier.mouy\Documents\Workspace\GitHub\ecosound\tests\detec_export'
# detec.to_pamlab(outdir, single_file=False)
# outdir=r'C:\Users\xavier.mouy\Documents\Workspace\GitHub\ecosound\tests\annot_export'
# annot.to_pamlab(outdir, single_file=False)
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')
from ecosound.core.annotation import Annotation import pandas as pd """ Gathers measuremenst for all annotations and noise. Merges into a single dataset, and re-label classes to create a 2-class dataset 'FS' vs 'NN'. """ # Define input and output files annot_file = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\dataset_annotations_only.nc' noise_file = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\Noise_dataset' outfile=r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\dataset_FS-NN_modified_20201105145300.nc' # Load measurements meas_annot = Measurement() meas_annot.from_netcdf(annot_file) meas_noise = Measurement() meas_noise.from_netcdf(noise_file) ## Label noise measurement as 'NN' meas_noise.insert_values(label_class='NN') print(meas_noise.summary()) ## relabel annotations that are not 'FS' as 'NN' print(meas_annot.summary()) meas_annot.data['label_class'].replace(to_replace=['', 'ANT','HS','KW','UN'], value='NN', inplace=True) print(meas_annot.summary()) ## merge the 2 datasets meas_NN_FS = meas_noise + meas_annot
import time
import pandas as pd
## Input paraneters ##########################################################
annotation_file = r"C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\Master_annotations_dataset.nc"
detection_file = r"C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\Full_dataset_with_metadata2"
outfile=r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\dataset_annotations_only2.nc'
# load annotations
annot = Annotation()
annot.from_netcdf(annotation_file)
# load detections
detec = Measurement()
detec.from_netcdf(detection_file)
print(detec)
freq_ovp = True # default True
dur_factor_max = None # default None
dur_factor_min = 0.1 # default None
ovlp_ratio_min = 0.3 # defaulkt None
remove_duplicates = True # dfault - False
inherit_metadata = True # default False
filter_deploymentID = False # default True
detec.filter_overlap_with(annot,
freq_ovp=freq_ovp,
dur_factor_max=dur_factor_max,
dur_factor_min=dur_factor_min,
from ecosound.core.measurement import Measurement # # ## netcdf folder # netcdf_files = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\test' # annot4 = Annotation() # annot4.from_netcdf(netcdf_files, verbose=True) # print(len(annot4)) # # ## Load netcdf measurmeent folder from folder # netcdf_files = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\Noise_dataset' # outfile=r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\Noise_dataset\dataset_noise.nc' # meas = Measurement() # meas.from_netcdf(netcdf_files, verbose=True) # print(len(meas)) # #meas.to_netcdf(outfile) # # ## Load netcdf measurmeent folder from single file netcdf_files = r'C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\results\Full_dataset_with_metadata2\JASCOAMARHYDROPHONE742_20140913T115018.797Z.wav.nc' meas = Measurement() meas.from_netcdf(netcdf_files, verbose=True) print(len(meas)) # # ## Load netcdf measurmeent folder from list of files # netcdf_files = [] # netcdf_files.append(r"C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\test2\67391492.181017121114.wav.nc") # netcdf_files.append(r"C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\test2\67391492.181017151114.wav.nc") # netcdf_files.append(r"C:\Users\xavier.mouy\Documents\PhD\Projects\Dectector\datasets\test2\67391492.181017181114.wav.nc") # meas = Measurement() # meas.from_netcdf(netcdf_files, verbose=True) # print(len(meas))
# laod classif model
classif_model = pickle.load(open(classif_model_file, 'rb'))
features = classif_model['features']
model = classif_model['model']
Norm_mean = classif_model['normalization_mean']
Norm_std = classif_model['normalization_std']
classes_encoder = classif_model['classes']
# loops thrugh each file
files_list = os.listdir(indir) # list of files
for file in files_list:
if os.path.isfile(os.path.join(indir, file)) & file.endswith(file_ext):
if os.path.isfile(os.path.join(outdir, file)) is False:
# load file
print(file)
meas = Measurement()
meas.from_netcdf(os.path.join(indir, file))
# reclassify
data = meas.data
n1 = len(data)
# drop observations/rows with NaNs
data = data.replace([np.inf, -np.inf], np.nan)
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]