self.data.wake_time_count.append(float(match.group(55)))
self.data.wake_power_count.append(int(match.group(56)))
self.data.power_state.append(int(match.group(57)))
self.data.power_board_mode.append(int(match.group(58)))
self.data.power_voltage_select.append(int(match.group(59)))
self.data.power_voltage_main.append(float(match.group(60)))
self.data.power_current_main.append(float(match.group(61)))
self.data.power_voltage_12.append(float(match.group(62)))
self.data.power_current_12.append(float(match.group(63)))
self.data.power_voltage_24.append(float(match.group(64)))
self.data.power_current_24.append(float(match.group(65)))
if __name__ == '__main__':
# load the input arguments
args = inputs()
infile = os.path.abspath(args.infile)
outfile = os.path.abspath(args.outfile)
# initialize the Parser object for METBK
superv = Parser(infile)
# load the data into a buffered object and parse the data into a dictionary
superv.load_ascii()
superv.parse_data()
# write the resulting Bunch object via the toDict method to a matlab
# formatted structured array.
sio.savemat(outfile, superv.data.toDict())
"""
Extract the data from the relevant regex groups and assign to elements
of the data dictionary.
"""
# Use the date_time_string to calculate an epoch timestamp (seconds since
# 1970-01-01)
epts = dcl_to_epoch(match.group(1))
self.data.time.append(epts)
self.data.dcl_date_time_string.append(str(match.group(1)))
# Assign the remaining MET data to the named parameters
self.data.hydrogen_concentration.append(float(match.group(2)))
if __name__ == '__main__':
# load the input arguments
args = inputs()
infile = os.path.abspath(args.infile)
outfile = os.path.abspath(args.outfile)
# initialize the Parser object for hydrogen
hydrogen = Parser(infile)
# load the data into a buffered object and parse the data into a dictionary
hydrogen.load_ascii()
hydrogen.parse_data()
# write the resulting Bunch object via the toDict method to a matlab
# formatted structured array.
sio.savemat(outfile, hydrogen.data.toDict())
self.data.internal_current.append(float(match.group(47)))
self.data.internal_temperature.append(float(match.group(48)))
self.data.fuel_cell_volume.append(float(match.group(49)))
self.data.seawater_ground_state.append(int(match.group(50)))
self.data.seawater_ground_positve.append(float(match.group(51)))
self.data.seawater_ground_negative.append(float(match.group(52)))
self.data.cvt_state.append(int(match.group(53)))
self.data.cvt_voltage.append(float(match.group(54)))
self.data.cvt_current.append(float(match.group(55)))
self.data.cvt_interlock.append(int(match.group(56)))
self.data.cvt_temperature.append(float(match.group(57)))
self.data.error_flag3.append(str(match.group(58)))
if __name__ == '__main__':
# load the input arguments
args = inputs()
infile = os.path.abspath(args.infile)
outfile = os.path.abspath(args.outfile)
# initialize the Parser object for PWRSYS
pwrsys = Parser(infile)
# load the data into a buffered object and parse the data into a dictionary
pwrsys.load_ascii()
pwrsys.parse_data()
# write the resulting Bunch object via the toDict method to a matlab
# formatted structured array.
sio.savemat(outfile, pwrsys.data.toDict())
self.data.mean_spectral_period.append(float(match.group(7)))
self.data.maximum_wave_height.append(float(match.group(8)))
self.data.significant_wave_height.append(float(match.group(9)))
self.data.significant_wave_period.append(float(match.group(10)))
self.data.average_tenth_height.append(float(match.group(11)))
self.data.average_tenth_period.append(float(match.group(12)))
self.data.average_wave_period.append(float(match.group(13)))
self.data.peak_period.append(float(match.group(14)))
self.data.peak_period_read.append(float(match.group(15)))
self.data.spectral_wave_height.append(float(match.group(16)))
self.data.mean_wave_direction.append(float(match.group(17)))
self.data.mean_directional_spread.append(float(match.group(18)))
if __name__ == '__main__':
# load the input arguments
args = inputs()
infile = os.path.abspath(args.infile)
outfile = os.path.abspath(args.outfile)
# initialize the Parser object for wavss
wavss = Parser(infile)
# load the data into a buffered object and parse the data into a dictionary
wavss.load_ascii()
wavss.parse_data()
# write the resulting Bunch object via the toDict method to a matlab
# formatted structured array.
sio.savemat(outfile, wavss.data.toDict())
for row in range(1, N):
(a, b, c, d) = unpack('<4B', chunk[offset + 2: offset + 6])
percent1.append(a)
percent2.append(b)
percent3.append(c)
percent4.append(d)
offset += 4
self.data.percent.good_3beam.append(percent1)
self.data.percent.transforms_reject.append(percent2)
self.data.percent.bad_beams.append(percent3)
self.data.percent.good_4beam.append(percent4)
if __name__ == '__main__':
# load the input arguments
args = inputs()
infile = os.path.abspath(args.infile)
outfile = os.path.abspath(args.outfile)
# initialize the Parser object for PWRSYS
adcp = Parser(infile)
# load the data into a buffered object and parse the data into a dictionary
adcp.load_ascii()
adcp.parse_data()
# write the resulting Bunch object via the toDict method to a matlab
# formatted structured array.
sio.savemat(outfile, adcp.data.toDict())
# Assign the remaining MET data to the named parameters
self.data.barometric_pressure.append(float(match.group(2)))
self.data.relative_humidity.append(float(match.group(3)))
self.data.air_temperature.append(float(match.group(4)))
self.data.longwave_irradiance.append(float(match.group(5)))
self.data.precipitation_level.append(float(match.group(6)))
self.data.sea_surface_temperature.append(float(match.group(7)))
self.data.sea_surface_conductivity.append(float(match.group(8)))
self.data.shortwave_irradiance.append(float(match.group(9)))
self.data.eastward_wind_velocity.append(float(match.group(10)))
self.data.northward_wind_velocity.append(float(match.group(11)))
if __name__ == '__main__':
# load the input arguments
args = inputs()
infile = os.path.abspath(args.infile)
outfile = os.path.abspath(args.outfile)
# initialize the Parser object for METBK
metbk = Parser(infile)
# load the data into a buffered object and parse the data into a dictionary
metbk.load_ascii()
metbk.parse_data()
# write the resulting Bunch object via the toDict method to a matlab
# formatted structured array.
sio.savemat(outfile, metbk.data.toDict())