def main(audio_file, channel=0, output_folder='.' + os.path.sep + 'analysis_output', verbose=None, beat_plot= False):
# get config dictionary
cfg = ct.get_config_dict()
if verbose is not None:
cfg['verboseLevel'][0] = verbose
channel = channel
with audioread.audio_open(audio_file) as af:
tracen = af.channels
if channel >= tracen:
print('number of traces in file is', tracen)
quit()
ft = FT.FishTracker(audio_file.split(os.path.sep)[-1], af.samplerate)
index = 0
data = ft.get_data()
for buffer in af:
fulldata = np.fromstring(buffer, dtype='<i2').reshape(-1, af.channels)
n = fulldata.shape[0]
if index + n > len(data):
if index == 0:
print("panic!!!! I need a larger buffer!")
# ft.processdata(data[:index] / 2.0 ** 15)
index = 0
if n > 0:
data[index:index + n] = fulldata[:n, channel]
index += n
else:
break
# long file analysis
good_file = ft.exclude_short_files(data, index)
if good_file == False:
print("file too short !!!")
exit()
# best window algorithm
mod_file = aux.conv_to_single_ch_audio(audio_file)
Fish = FR.FishRecording(mod_file)
bwin, win_width = Fish.detect_best_window()
print ('\nbest window is between: %.2f' % bwin, '& %.2f' % (bwin + win_width), 'seconds.\n')
os.remove(mod_file)
# fish_type algorithm
fish_type, r_value = Fish.type_detector()
print('current fish is a ' + fish_type + '-fish')
# data process: creation of fish-lists containing frequencies, power of fundamentals and harmonics of all fish
if index > 0:
power_fres1, freqs_fres1, psd_type, fish_type,\
fishlist, mean_proportions = ft.processdata(data[:index] / 2.0 ** 15, fish_type, bwin, win_width, config_dict=cfg)
#####################################################
# collect data for mat meth figure:
# bw_data = data[(bwin * af.samplerate):(bwin * af.samplerate + win_width * af.samplerate)]
# np.save('pulse_trace_data.npy', bw_data)
if beat_plot:
# designed for parama_data/20140519_Rioanita/EN099.wav
beat_data = data[(1.2 * af.samplerate): (1.9 * af.samplerate)] / 2.0 ** 15
beat_time = np.arange(len(beat_data)) * 1.0 / af.samplerate
aux.beat_plot(beat_data, beat_time)
embed()
quit()
#####################################################
# Pulse analysis
pulse_data = []
pulse_freq = []
if psd_type == 'pulse' or fish_type == 'pulse':
print('')
print('try to create MEAN PULSE-EOD')
print('')
pulse_data, pulse_freq = ft.pulse_sorting(bwin, win_width, data[:index] / 2.0 ** 15, fish_type, psd_type)
wave_data = []
if fish_type == 'wave' and len(fishlist) == 1:
print('')
print('try to create MEAN WAVE-EOD')
print('')
wave_data = ft.wave_sorting(bwin, win_width, data[:index] / 2.0 ** 15)
# create EOD plots
out_folder = aux.create_outp_folder(audio_file, output_folder)
ft.bw_psd_and_eod_plot(power_fres1, freqs_fres1, bwin, win_width, data[:index] / 2.0 ** 15, psd_type, fish_type,
fishlist, pulse_data, pulse_freq, out_folder, mean_proportions, r_value, wave_data)
# saves fundamentals of all wave fish !!!
st.save_fundamentals(fishlist, out_folder)
print('\nAnalysis completed! .npy arrays located in %s\n' %out_folder)
def main(audio_file, channel=0, output_folder='.' + os.path.sep + 'analysis_output', verbose=None):
# get config dictionary
cfg = ct.get_config_dict()
if verbose is not None:
cfg['verboseLevel'][0] = verbose
channel = channel
with audioread.audio_open(audio_file) as af:
tracen = af.channels
if channel >= tracen:
print 'number of traces in file is', tracen
quit()
ft = FT.FishTracker(audio_file.split(os.path.sep)[-1], af.samplerate)
index = 0
data = ft.get_data()
for buffer in af:
fulldata = np.fromstring(buffer, dtype='<i2').reshape(-1, af.channels)
n = fulldata.shape[0]
if index + n > len(data):
if index == 0:
print "panic!!!! I need a larger buffer!"
# ft.processdata(data[:index] / 2.0 ** 15)
index = 0
if n > 0:
data[index:index + n] = fulldata[:n, channel]
index += n
else:
break
# long file analysis
good_file = ft.exclude_short_files(data, index)
if good_file == False:
print "file too short !!!"
exit()
# best window algorithm
mod_file = aux.conv_to_single_ch_audio(audio_file)
Fish = FR.FishRecording(mod_file)
bwin, win_width = Fish.detect_best_window()
print '\nbest window is between: %.2f' % bwin, '& %.2f' % (bwin + win_width), 'seconds.\n'
os.remove(mod_file)
# fish_type algorithm
fish_type = Fish.type_detector()
print('current fish is a ' + fish_type + '-fish')
# data process: creation of fish-lists containing frequencies, power of fundamentals and harmonics of all fish
if index > 0:
power_fres1, freqs_fres1, psd_type, fish_type,\
fishlist = ft.processdata(data[:index] / 2.0 ** 15, fish_type, bwin, win_width, config_dict=cfg)
# Pulse analysis
pulse_data = []
pulse_freq = []
if psd_type == 'pulse' or fish_type == 'pulse':
print ''
print 'try to create MEAN PULSE-EOD'
print ''
pulse_data, pulse_freq = ft.pulse_sorting(bwin, win_width, data[:index] / 2.0 ** 15)
# create EOD plots
out_folder = aux.create_outp_folder(audio_file, output_folder)
ft.bw_psd_and_eod_plot(power_fres1, freqs_fres1, bwin, win_width, data[:index] / 2.0 ** 15, psd_type, fish_type,
fishlist, pulse_data, pulse_freq, out_folder)
# saves fundamentals of all wave fish !!!
st.save_fundamentals(fishlist, out_folder)
print('\nAnalysis completed! .npy arrays located in %s\n' %out_folder)