def process_azfp(site, data_directory, xml_file, output_directory, dates,
tilt_correction):
"""
Use echopype to convert and process the ASL AZFP bio-acoustic sonar data
(in *.01A files) to generate echograms for use by the community
:param site:
:param data_directory:
:param xml_file:
:param output_directory:
:param dates:
:param tilt_correction:
:return data:
"""
# generate a list of data files given the input dates
file_list = azfp_file_list(data_directory, dates)
# reset the file_list to a single index
file_list = [file for sub in file_list for file in sub]
if not file_list:
# if there are no files to process, exit cleanly
return None
# make sure the data output directory exists
output_directory = os.path.join(output_directory,
dates[0] + '-' + dates[1])
if not os.path.isdir(output_directory):
os.mkdir(output_directory)
# convert the list of .01A files using echopype and save the output as NetCDF files
dc = Convert(file_list, xml_file)
dc.platform_name = site # OOI site name
dc.platform_type = 'Mooring' # ICES platform type
dc.platform_code_ICES = '48' # ICES code: tethered collection of instruments at a fixed location that may
# include seafloor, mid-water or surface components
dc.raw2nc(save_path=output_directory)
# process the data, calculating the volume acoustic backscatter strength and the vertical range
echo = []
nc_files = glob.glob(output_directory + '/[12]???????.nc')
for nc in nc_files:
tmp_echo = Process(nc)
tmp_echo.calibrate() # calculate Sv
data = tmp_echo.Sv # extract the Sv dataset
echo.append(data.sortby('ping_time')) # append to the echogram list
# concatenate the data into a single dataset
data = xr.concat(echo, dim='ping_time', join='outer')
data = data.sortby(['frequency', 'ping_time'])
data['frequency'] = data['frequency'].astype(np.float32)
data['range_bin'] = data['range_bin'].astype(np.int32)
data['range'] = data['range'].sel(ping_time=data.ping_time[0], drop=True)
data = data.set_coords('range')
if tilt_correction:
range_correction(
data, tilt_correction) # apply a tilt correction, if applicable
# pass the Sv data back for further processing
return data
def process_ek60(site, data_directory, output_directory, dates,
tilt_correction):
"""
:param site:
:param data_directory:
:param output_directory:
:param dates:
:param tilt_correction:
:return data:
"""
# generate a list of data files given the input dates
file_list = ek60_file_list(data_directory, dates)
# reset the file_list to a single index
file_list = [file for sub in file_list for file in sub]
if not file_list:
# if there are no files to process, exit cleanly
return None
# make sure the data output directory exists
output_directory = os.path.join(output_directory,
dates[0] + '-' + dates[1])
if not os.path.isdir(output_directory):
os.mkdir(output_directory)
# convert the list of .raw files using echopype and save the output as NetCDF files
dc = Convert(file_list)
dc.platform_name = site # OOI site name
if site == 'CE02SHBP':
dc.platform_type = 'Fixed Benthic Node' # ICES platform type
dc.platform_code_ICES = '11' # ICES code
else:
dc.platform_type = 'Mooring' # ICES platform type
dc.platform_code_ICES = '48' # ICES code: tethered collection of instruments at a fixed location that may
# include seafloor, mid-water or surface components
dc.raw2nc(save_path=output_directory)
# process the data, calculating the volume acoustic backscatter strength and the vertical range
echo = []
sample_thickness = []
tvg_correction_factor = []
nc_files = glob.glob(output_directory + '/*OOI-D*.nc')
for nc in nc_files:
tmp_echo = Process(nc)
tmp_echo.calibrate() # calculate Sv
data = tmp_echo.Sv # extract the Sv dataset
echo.append(data.sortby('ping_time')) # append to the echogram list
sample_thickness.append(tmp_echo.sample_thickness.values)
tvg_correction_factor.append(tmp_echo.tvg_correction_factor)
# concatenate the data into a single dataset
data = xr.concat(echo, dim='ping_time', join='outer')
data = data.sortby(['frequency', 'ping_time'])
data['range_bin'] = data['range_bin'].astype(np.int32)
data['range'] = data['range'].sel(ping_time=data.ping_time[0], drop=True)
data = data.set_coords('range')
# recalculate the range to deal with some discrepancies caused by the xarray concat
thickness = np.max(np.array(sample_thickness), 0)
correction_factor = np.max(tvg_correction_factor)
range_meter = calc_range(data, thickness, correction_factor)
data['range'] = data['range'].fillna(range_meter)
if tilt_correction:
range_correction(
data, tilt_correction) # apply a tilt correction, if applicable
# pass the Sv data back for further processing
return data