def compute_LPET_elevations(input_file, output_file,
FORMAT='csv', VARIABLES=['time','lat','lon','data'], HEADER=0, TYPE='drift',
TIME_UNITS='days since 1858-11-17T00:00:00', TIME=None, PROJECTION='4326',
VERBOSE=False, MODE=0o775):
#-- output netCDF4 and HDF5 file attributes
#-- will be added to YAML header in csv files
attrib = {}
#-- latitude
attrib['lat'] = {}
attrib['lat']['long_name'] = 'Latitude'
attrib['lat']['units'] = 'Degrees_North'
#-- longitude
attrib['lon'] = {}
attrib['lon']['long_name'] = 'Longitude'
attrib['lon']['units'] = 'Degrees_East'
#-- long-period equilibrium tides
attrib['tide_lpe'] = {}
attrib['tide_lpe']['long_name'] = 'Equilibrium_Tide'
attrib['tide_lpe']['description'] = ('Long-period_equilibrium_tidal_'
'elevation_from_the_summation_of_fifteen_tidal_spectral_lines')
attrib['tide_lpe']['reference'] = ('https://doi.org/10.1111/'
'j.1365-246X.1973.tb03420.x')
attrib['tide_lpe']['units'] = 'meters'
#-- time
attrib['time'] = {}
attrib['time']['long_name'] = 'Time'
attrib['time']['units'] = 'days since 1992-01-01T00:00:00'
attrib['time']['calendar'] = 'standard'
#-- read input file to extract time, spatial coordinates and data
if (FORMAT == 'csv'):
dinput = pyTMD.spatial.from_ascii(input_file, columns=VARIABLES,
header=HEADER, verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
dinput = pyTMD.spatial.from_netCDF4(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
dinput = pyTMD.spatial.from_HDF5(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
elif (FORMAT == 'geotiff'):
dinput = pyTMD.spatial.from_geotiff(input_file, verbose=VERBOSE)
#-- copy global geotiff attributes for projection and grid parameters
for att_name in ['projection','wkt','spacing','extent']:
attrib[att_name] = dinput['attributes'][att_name]
#-- update time variable if entered as argument
if TIME is not None:
dinput['time'] = np.copy(TIME)
#-- converting x,y from projection to latitude/longitude
#-- could try to extract projection attributes from netCDF4 and HDF5 files
try:
crs1 = pyproj.CRS.from_string("epsg:{0:d}".format(int(PROJECTION)))
except (ValueError,pyproj.exceptions.CRSError):
crs1 = pyproj.CRS.from_string(PROJECTION)
crs2 = pyproj.CRS.from_string("epsg:{0:d}".format(4326))
transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)
if (TYPE == 'grid'):
ny,nx = (len(dinput['y']),len(dinput['x']))
gridx,gridy = np.meshgrid(dinput['x'],dinput['y'])
lon,lat = transformer.transform(gridx.flatten(),gridy.flatten())
elif (TYPE == 'drift'):
lon,lat = transformer.transform(dinput['x'].flatten(),
dinput['y'].flatten())
#-- extract time units from netCDF4 and HDF5 attributes or from TIME_UNITS
try:
time_string = dinput['attributes']['time']['units']
except (TypeError, KeyError):
epoch1,to_secs = pyTMD.time.parse_date_string(TIME_UNITS)
else:
epoch1,to_secs = pyTMD.time.parse_date_string(time_string)
#-- convert time from units to days since 1992-01-01T00:00:00
tide_time = pyTMD.time.convert_delta_time(to_secs*dinput['time'].flatten(),
epoch1=epoch1, epoch2=(1992,1,1,0,0,0), scale=1.0/86400.0)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data','merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- number of time points
nt = len(tide_time)
#-- predict long-period equilibrium tides at time
if (TYPE == 'grid'):
tide_lpe = np.zeros((ny,nx,nt))
for i in range(nt):
lpet = compute_equilibrium_tide(tide_time[i] + deltat[i], lat)
tide_lpe[:,:,i] = np.reshape(lpet,(ny,nx))
elif (TYPE == 'drift'):
tide_lpe = compute_equilibrium_tide(tide_time + deltat, lat)
#-- output to file
output = dict(time=tide_time,lon=lon,lat=lat,tide_lpe=tide_lpe)
if (FORMAT == 'csv'):
pyTMD.spatial.to_ascii(output, attrib, output_file, delimiter=',',
columns=['time','lat','lon','tide_lpe'], verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
pyTMD.spatial.to_netCDF4(output, attrib, output_file, verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
pyTMD.spatial.to_HDF5(output, attrib, output_file, verbose=VERBOSE)
elif (FORMAT == 'geotiff'):
pyTMD.spatial.to_geotiff(output, attrib, output_file, verbose=VERBOSE,
varname='tide_lpe')
#-- change the permissions level to MODE
os.chmod(output_file, MODE)
def compute_OPT_icebridge_data(tide_dir,
arg,
METHOD=None,
VERBOSE=False,
MODE=0o775):
#-- extract file name and subsetter indices lists
match_object = re.match(r'(.*?)(\[(.*?)\])?$', arg)
input_file = os.path.expanduser(match_object.group(1))
#-- subset input file to indices
if match_object.group(2):
#-- decompress ranges and add to list
input_subsetter = []
for i in re.findall(r'((\d+)-(\d+)|(\d+))', match_object.group(3)):
input_subsetter.append(int(i[3])) if i[3] else \
input_subsetter.extend(range(int(i[1]),int(i[2])+1))
else:
input_subsetter = None
#-- output directory for input_file
DIRECTORY = os.path.dirname(input_file)
#-- calculate if input files are from ATM or LVIS (+GH)
regex = {}
regex[
'ATM'] = r'(BLATM2|ILATM2)_(\d+)_(\d+)_smooth_nadir(.*?)(csv|seg|pt)$'
regex['ATM1b'] = r'(BLATM1b|ILATM1b)_(\d+)_(\d+)(.*?).(qi|TXT|h5)$'
regex['LVIS'] = r'(BLVIS2|BVLIS2|ILVIS2)_(.*?)(\d+)_(\d+)_(R\d+)_(\d+).H5$'
regex['LVGH'] = r'(ILVGH2)_(.*?)(\d+)_(\d+)_(R\d+)_(\d+).H5$'
for key, val in regex.items():
if re.match(val, os.path.basename(input_file)):
OIB = key
#-- invalid value
fill_value = -9999.0
#-- output netCDF4 and HDF5 file attributes
#-- will be added to YAML header in csv files
attrib = {}
#-- latitude
attrib['lat'] = {}
attrib['lat']['long_name'] = 'Latitude_of_measurement'
attrib['lat']['description'] = ('Corresponding_to_the_measurement_'
'position_at_the_acquisition_time')
attrib['lat']['units'] = 'Degrees_North'
#-- longitude
attrib['lon'] = {}
attrib['lon']['long_name'] = 'Longitude_of_measurement'
attrib['lon']['description'] = ('Corresponding_to_the_measurement_'
'position_at_the_acquisition_time')
attrib['lon']['units'] = 'Degrees_East'
#-- ocean pole tides
attrib['tide_oc_pole'] = {}
attrib['tide_oc_pole']['long_name'] = 'Ocean_Pole_Tide'
attrib['tide_oc_pole']['description'] = (
'Ocean_pole_tide_radial_'
'displacements_at_the_measurement_position_at_the_acquisition_time_due_'
'to_polar_motion')
attrib['tide_oc_pole']['reference'] = (
'ftp://tai.bipm.org/iers/conv2010/'
'chapter7/opoleloadcoefcmcor.txt.gz')
attrib['tide_oc_pole']['units'] = 'meters'
#-- Modified Julian Days
attrib['time'] = {}
attrib['time']['long_name'] = 'Time'
attrib['time']['units'] = 'days since 1858-11-17T00:00:00'
attrib['time']['description'] = 'Modified Julian Days'
attrib['time']['calendar'] = 'standard'
#-- extract information from first input file
#-- acquisition year, month and day
#-- number of points
#-- instrument (PRE-OIB ATM or LVIS, OIB ATM or LVIS)
if OIB in ('ATM', 'ATM1b'):
M1, YYMMDD1, HHMMSS1, AX1, SF1 = re.findall(regex[OIB],
input_file).pop()
#-- early date strings omitted century and millenia (e.g. 93 for 1993)
if (len(YYMMDD1) == 6):
ypre, MM1, DD1 = YYMMDD1[:2], YYMMDD1[2:4], YYMMDD1[4:]
if (np.float(ypre) >= 90):
YY1 = '{0:4.0f}'.format(np.float(ypre) + 1900.0)
else:
YY1 = '{0:4.0f}'.format(np.float(ypre) + 2000.0)
elif (len(YYMMDD1) == 8):
YY1, MM1, DD1 = YYMMDD1[:4], YYMMDD1[4:6], YYMMDD1[6:]
elif OIB in ('LVIS', 'LVGH'):
M1, RG1, YY1, MMDD1, RLD1, SS1 = re.findall(regex[OIB],
input_file).pop()
MM1, DD1 = MMDD1[:2], MMDD1[2:]
#-- read data from input_file
print('{0} -->'.format(input_file)) if VERBOSE else None
if (OIB == 'ATM'):
#-- load IceBridge ATM data from input_file
dinput, file_lines, HEM = read_ATM_icessn_file(input_file,
input_subsetter)
elif (OIB == 'ATM1b'):
#-- load IceBridge Level-1b ATM data from input_file
dinput, file_lines, HEM = read_ATM_qfit_file(input_file,
input_subsetter)
elif OIB in ('LVIS', 'LVGH'):
#-- load IceBridge LVIS data from input_file
dinput, file_lines, HEM = read_LVIS_HDF5_file(input_file,
input_subsetter)
#-- extract lat/lon
lon = dinput['lon'][:]
lat = dinput['lat'][:]
#-- convert time from UTC time of day to modified julian days (MJD)
#-- J2000: seconds since 2000-01-01 12:00:00 UTC
t = dinput['time'][:] / 86400.0 + 51544.5
#-- convert from MJD to calendar dates
YY, MM, DD, HH, MN, SS = convert_julian(t + 2400000.5, FORMAT='tuple')
#-- convert calendar dates into year decimal
tdec = convert_calendar_decimal(YY,
MM,
DAY=DD,
HOUR=HH,
MINUTE=MN,
SECOND=SS)
#-- elevation
h1 = dinput['data'][:]
#-- degrees to radians and arcseconds to radians
dtr = np.pi / 180.0
atr = np.pi / 648000.0
#-- earth and physical parameters (IERS)
G = 6.67428e-11 #-- universal constant of gravitation [m^3/(kg*s^2)]
GM = 3.986004418e14 #-- geocentric gravitational constant [m^3/s^2]
ge = 9.7803278 #-- mean equatorial gravity [m/s^2]
a_axis = 6378136.6 #-- equatorial radius of the Earth [m]
flat = 1.0 / 298.257223563 #-- flattening of the ellipsoid
omega = 7.292115e-5 #-- mean rotation rate of the Earth [radians/s]
rho_w = 1025.0 #-- density of sea water [kg/m^3]
ge = 9.7803278 #-- mean equatorial gravitational acceleration [m/s^2]
#-- Linear eccentricity and first numerical eccentricity
lin_ecc = np.sqrt((2.0 * flat - flat**2) * a_axis**2)
ecc1 = lin_ecc / a_axis
#-- tidal love number differential (1 + kl - hl) for pole tide frequencies
gamma = 0.6870 + 0.0036j
#-- convert from geodetic latitude to geocentric latitude
#-- geodetic latitude in radians
latitude_geodetic_rad = lat * dtr
#-- prime vertical radius of curvature
N = a_axis / np.sqrt(1.0 - ecc1**2.0 * np.sin(latitude_geodetic_rad)**2.0)
#-- calculate X, Y and Z from geodetic latitude and longitude
X = (N + h1) * np.cos(latitude_geodetic_rad) * np.cos(lon * dtr)
Y = (N + h1) * np.cos(latitude_geodetic_rad) * np.sin(lon * dtr)
Z = (N * (1.0 - ecc1**2.0) + h1) * np.sin(latitude_geodetic_rad)
rr = np.sqrt(X**2.0 + Y**2.0 + Z**2.0)
#-- calculate geocentric latitude and convert to degrees
latitude_geocentric = np.arctan(Z / np.sqrt(X**2.0 + Y**2.0)) / dtr
#-- pole tide displacement scale factor
Hp = np.sqrt(8.0 * np.pi / 15.0) * (omega**2 * a_axis**4) / GM
K = 4.0 * np.pi * G * rho_w * Hp * a_axis / (3.0 * ge)
K1 = 4.0 * np.pi * G * rho_w * Hp * a_axis**3 / (3.0 * GM)
#-- read ocean pole tide map from Desai (2002)
ocean_pole_tide_file = get_data_path(['data', 'opoleloadcoefcmcor.txt.gz'])
iur, iun, iue, ilon, ilat = read_ocean_pole_tide(ocean_pole_tide_file)
#-- pole tide files (mean and daily)
# mean_pole_file = os.path.join(tide_dir,'mean-pole.tab')
mean_pole_file = os.path.join(tide_dir, 'mean_pole_2017-10-23.tab')
pole_tide_file = os.path.join(tide_dir, 'finals_all_2017-09-01.tab')
#-- read IERS daily polar motion values
EOP = read_iers_EOP(pole_tide_file)
#-- create cubic spline interpolations of daily polar motion values
xSPL = scipy.interpolate.UnivariateSpline(EOP['MJD'], EOP['x'], k=3, s=0)
ySPL = scipy.interpolate.UnivariateSpline(EOP['MJD'], EOP['y'], k=3, s=0)
#-- bad value
fill_value = -9999.0
#-- output ocean pole tide HDF5 file
#-- form: rg_NASA_OCEAN_POLE_TIDE_WGS84_fl1yyyymmddjjjjj.H5
#-- where rg is the hemisphere flag (GR or AN) for the region
#-- fl1 and fl2 are the data flags (ATM, LVIS, GLAS)
#-- yymmddjjjjj is the year, month, day and second of the input file
#-- output region flags: GR for Greenland and AN for Antarctica
hem_flag = {'N': 'GR', 'S': 'AN'}
#-- use starting second to distinguish between files for the day
JJ1 = np.min(dinput['time']) % 86400
#-- output file format
args = (hem_flag[HEM], 'OCEAN_POLE_TIDE', OIB, YY1, MM1, DD1, JJ1)
FILENAME = '{0}_NASA_{1}_WGS84_{2}{3}{4}{5}{6:05.0f}.H5'.format(*args)
#-- print file information
print('\t{0}'.format(FILENAME)) if VERBOSE else None
#-- open output HDF5 file
fid = h5py.File(os.path.join(DIRECTORY, FILENAME), 'w')
#-- interpolate ocean pole tide map from Desai (2002)
if (METHOD == 'spline'):
#-- use scipy bivariate splines to interpolate to output points
f1 = scipy.interpolate.RectBivariateSpline(ilon,
ilat[::-1],
iur[:, ::-1].real,
kx=1,
ky=1)
f2 = scipy.interpolate.RectBivariateSpline(ilon,
ilat[::-1],
iur[:, ::-1].imag,
kx=1,
ky=1)
UR = np.zeros((file_lines), dtype=np.complex128)
UR.real = f1.ev(lon, latitude_geocentric)
UR.imag = f2.ev(lon, latitude_geocentric)
else:
#-- use scipy regular grid to interpolate values for a given method
r1 = scipy.interpolate.RegularGridInterpolator((ilon, ilat[::-1]),
iur[:, ::-1],
method=METHOD)
UR = r1.__call__(np.c_[lon, latitude_geocentric])
#-- calculate angular coordinates of mean pole at time tdec
mpx, mpy, fl = iers_mean_pole(mean_pole_file, tdec, '2015')
#-- interpolate daily polar motion values to t using cubic splines
px = xSPL(t)
py = ySPL(t)
#-- calculate differentials from mean pole positions
mx = px - mpx
my = -(py - mpy)
#-- calculate radial displacement at time
Urad = np.ma.zeros((file_lines), fill_value=fill_value)
Urad.data[:] = K * atr * np.real(
(mx * gamma.real + my * gamma.imag) * UR.real +
(my * gamma.real - mx * gamma.imag) * UR.imag)
#-- replace fill values
Urad.mask = np.isnan(Urad.data)
Urad.data[Urad.mask] = Urad.fill_value
#-- add latitude and longitude to output file
for key in ['lat', 'lon']:
#-- Defining the HDF5 dataset variables for lat/lon
h5 = fid.create_dataset(key, (file_lines, ),
data=dinput[key][:],
dtype=dinput[key].dtype,
compression='gzip')
#-- add HDF5 variable attributes
for att_name, att_val in attrib[key].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- output tides to HDF5 dataset
h5 = fid.create_dataset('tide_oc_pole', (file_lines, ),
data=Urad,
dtype=Urad.dtype,
fillvalue=fill_value,
compression='gzip')
#-- add HDF5 variable attributes
h5.attrs['_FillValue'] = fill_value
for att_name, att_val in attrib['tide_oc_pole'].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- output days to HDF5 dataset
h5 = fid.create_dataset('time', (file_lines, ),
data=t,
dtype=t.dtype,
compression='gzip')
#-- add HDF5 variable attributes
for att_name, att_val in attrib['time'].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- HDF5 file attributes
fid.attrs['featureType'] = 'trajectory'
fid.attrs['title'] = 'Tidal_correction_for_elevation_measurements'
fid.attrs['summary'] = ('Ocean_pole_tide_radial_displacements_'
'computed_at_elevation_measurements.')
fid.attrs['project'] = 'NASA_Operation_IceBridge'
fid.attrs['processing_level'] = '4'
fid.attrs['date_created'] = time.strftime('%Y-%m-%d', time.localtime())
#-- add attributes for input files
fid.attrs['elevation_file'] = os.path.basename(input_file)
#-- add geospatial and temporal attributes
fid.attrs['geospatial_lat_min'] = dinput['lat'].min()
fid.attrs['geospatial_lat_max'] = dinput['lat'].max()
fid.attrs['geospatial_lon_min'] = dinput['lon'].min()
fid.attrs['geospatial_lon_max'] = dinput['lon'].max()
fid.attrs['geospatial_lat_units'] = "degrees_north"
fid.attrs['geospatial_lon_units'] = "degrees_east"
fid.attrs['geospatial_ellipsoid'] = "WGS84"
fid.attrs['time_type'] = 'UTC'
#-- convert start/end time from MJD into Julian days
JD_start = np.min(t) + 2400000.5
JD_end = np.max(t) + 2400000.5
#-- convert to calendar date with convert_julian.py
cal = convert_julian(np.array([JD_start, JD_end]), ASTYPE=np.int)
#-- add attributes with measurement date start, end and duration
args = (cal['hour'][0], cal['minute'][0], cal['second'][0])
fid.attrs['RangeBeginningTime'] = '{0:02d}:{1:02d}:{2:02d}'.format(*args)
args = (cal['hour'][-1], cal['minute'][-1], cal['second'][-1])
fid.attrs['RangeEndingTime'] = '{0:02d}:{1:02d}:{2:02d}'.format(*args)
args = (cal['year'][0], cal['month'][0], cal['day'][0])
fid.attrs['RangeBeginningDate'] = '{0:4d}-{1:02d}-{2:02d}'.format(*args)
args = (cal['year'][-1], cal['month'][-1], cal['day'][-1])
fid.attrs['RangeEndingDate'] = '{0:4d}-{1:02d}-{2:02d}'.format(*args)
duration = np.round(JD_end * 86400.0 - JD_start * 86400.0)
fid.attrs['DurationTimeSeconds'] = '{0:0.0f}'.format(duration)
#-- close the output HDF5 dataset
fid.close()
#-- change the permissions level to MODE
os.chmod(os.path.join(DIRECTORY, FILENAME), MODE)
def test_ocean_pole_tide(METHOD):
#-- degrees to radians and arcseconds to radians
dtr = np.pi / 180.0
atr = np.pi / 648000.0
#-- earth and physical parameters (IERS)
# G = 6.67428e-11#-- universal constant of gravitation [m^3/(kg*s^2)]
G = 6.673e-11 #-- test universal constant of gravitation [m^3/(kg*s^2)]
GM = 3.986004418e14 #-- geocentric gravitational constant [m^3/s^2]
a_axis = 6378136.6 #-- equatorial radius of the Earth [m]
flat = 1.0 / 298.257223563 #-- flattening of the ellipsoid
omega = 7.292115e-5 #-- mean rotation rate of the Earth [arcseconds/s]
rho_w = 1025.0 #-- density of sea water [kg/m^3]
ge = 9.7803278 #-- mean equatorial gravitational acceleration [m/s^2]
#-- Linear eccentricity and first numerical eccentricity
lin_ecc = np.sqrt((2.0 * flat - flat**2) * a_axis**2)
ecc1 = lin_ecc / a_axis
#-- tidal love number differential (1 + kl - hl) for pole tide frequencies
gamma = 0.6870 + 0.0036j
#-- pole tide displacement scale factor
Hp = np.sqrt(8.0 * np.pi / 15.0) * (omega**2 * a_axis**4) / GM
K = 4.0 * np.pi * G * rho_w * Hp * a_axis / (3.0 * ge)
K1 = 4.0 * np.pi * G * rho_w * Hp * a_axis**3 / (3.0 * GM)
#-- determine differences with values from test data
eps = np.finfo(np.float16).eps
assert (np.abs(Hp - 2.8577142980e+04) < eps)
assert (np.abs(K - 5.3394043696e+03) < eps)
#-- read test file for values
ocean_pole_test_file = os.path.join(filepath, 'opoleloadcmcor.test')
names = ('MJD', 'xbar_p', 'ybar_p', 'x_p', 'y_p', 'm1', 'm2', 'u_radial',
'u_north')
formats = ('i', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f')
validation = np.loadtxt(ocean_pole_test_file,
skiprows=26,
dtype=dict(names=names, formats=formats))
file_lines = len(validation)
#-- mean pole coordinates for test
xmean = np.array([5.4e-2, 8.30e-4])
ymean = np.array([3.57e-1, 3.95e-3])
t0 = 5.1544e4
#-- coordinates for test
lon, lat = (232.25, -43.75)
#-- read ocean pole tide map from Desai (2002)
ocean_pole_tide_file = get_data_path(['data', 'opoleloadcoefcmcor.txt.gz'])
iur, iun, iue, ilon, ilat = read_ocean_pole_tide(ocean_pole_tide_file)
#-- interpolate ocean pole tide map from Desai (2002)
if (METHOD == 'spline'):
#-- use scipy bivariate splines to interpolate to output points
f1 = scipy.interpolate.RectBivariateSpline(ilon,
ilat[::-1],
iur[:, ::-1].real,
kx=1,
ky=1)
f2 = scipy.interpolate.RectBivariateSpline(ilon,
ilat[::-1],
iur[:, ::-1].imag,
kx=1,
ky=1)
UR = np.zeros((file_lines), dtype=np.complex128)
UR.real = f1.ev(lon, lat)
UR.imag = f2.ev(lon, lat)
else:
#-- use scipy regular grid to interpolate values for a given method
r1 = scipy.interpolate.RegularGridInterpolator((ilon, ilat[::-1]),
iur[:, ::-1],
method=METHOD)
UR = r1.__call__(np.c_[lon, lat])
#-- calculate angular coordinates of mean pole at time
mpx = xmean[0] + xmean[1] * (validation['MJD'] - t0) / 365.25
mpy = ymean[0] + ymean[1] * (validation['MJD'] - t0) / 365.25
#-- calculate differentials from mean pole positions
mx = validation['x_p'] - mpx
my = -(validation['y_p'] - mpy)
#-- calculate radial displacement at time
u_radial = K * atr * np.real(
(mx * gamma.real + my * gamma.imag) * UR.real +
(my * gamma.real - mx * gamma.imag) * UR.imag)
assert np.all((u_radial - validation['u_radial']) < eps)
def compute_LPET_ICESat2(FILE, VERBOSE=False, MODE=0o775):
#-- read data from FILE
print('{0} -->'.format(os.path.basename(FILE))) if VERBOSE else None
IS2_atl11_mds, IS2_atl11_attrs, IS2_atl11_pairs = read_HDF5_ATL11(
FILE, ATTRIBUTES=True)
DIRECTORY = os.path.dirname(FILE)
#-- extract parameters from ICESat-2 ATLAS HDF5 file name
rx = re.compile(r'(processed_)?(ATL\d{2})_(\d{4})(\d{2})_(\d{2})(\d{2})_'
r'(\d{3})_(\d{2})(.*?).h5$')
SUB, PRD, TRK, GRAN, SCYC, ECYC, RL, VERS, AUX = rx.findall(FILE).pop()
#-- number of GPS seconds between the GPS epoch
#-- and ATLAS Standard Data Product (SDP) epoch
atlas_sdp_gps_epoch = IS2_atl11_mds['ancillary_data'][
'atlas_sdp_gps_epoch']
#-- copy variables for outputting to HDF5 file
IS2_atl11_tide = {}
IS2_atl11_fill = {}
IS2_atl11_dims = {}
IS2_atl11_tide_attrs = {}
#-- number of GPS seconds between the GPS epoch (1980-01-06T00:00:00Z UTC)
#-- and ATLAS Standard Data Product (SDP) epoch (2018-01-01T00:00:00Z UTC)
#-- Add this value to delta time parameters to compute full gps_seconds
IS2_atl11_tide['ancillary_data'] = {}
IS2_atl11_tide_attrs['ancillary_data'] = {}
for key in ['atlas_sdp_gps_epoch']:
#-- get each HDF5 variable
IS2_atl11_tide['ancillary_data'][key] = IS2_atl11_mds[
'ancillary_data'][key]
#-- Getting attributes of group and included variables
IS2_atl11_tide_attrs['ancillary_data'][key] = {}
for att_name, att_val in IS2_atl11_attrs['ancillary_data'][key].items(
):
IS2_atl11_tide_attrs['ancillary_data'][key][att_name] = att_val
#-- for each input beam pair within the file
for ptx in sorted(IS2_atl11_pairs):
#-- output data dictionaries for beam
IS2_atl11_tide[ptx] = dict(cycle_stats={})
IS2_atl11_fill[ptx] = dict(cycle_stats={})
IS2_atl11_dims[ptx] = dict(cycle_stats={})
IS2_atl11_tide_attrs[ptx] = dict(cycle_stats={})
#-- number of average segments and number of included cycles
invalid_time = IS2_atl11_attrs[ptx]['delta_time']['_FillValue']
n_points, n_cycles = IS2_atl11_mds[ptx]['delta_time'].shape
#-- latitudinal values
lat = IS2_atl11_mds[ptx]['latitude'].copy()
#-- find valid average segments for beam pair
fv = IS2_atl11_attrs[ptx]['h_corr']['_FillValue']
#-- convert time from ATLAS SDP to days relative to Jan 1, 1992
gps_seconds = atlas_sdp_gps_epoch + IS2_atl11_mds[ptx]['delta_time']
leap_seconds = pyTMD.time.count_leap_seconds(gps_seconds)
tide_time = pyTMD.time.convert_delta_time(gps_seconds - leap_seconds,
epoch1=(1980, 1, 6, 0, 0, 0),
epoch2=(1992, 1, 1, 0, 0, 0),
scale=1.0 / 86400.0)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data', 'merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- allocate for each cycle
tide_lpe = np.ma.empty((n_points, n_cycles), fill_value=fv)
tide_lpe.mask = (IS2_atl11_mds[ptx]['delta_time'] == invalid_time)
for cycle in range(n_cycles):
#-- find valid time and spatial points for cycle
valid, = np.nonzero(~tide_lpe.mask[:, cycle])
#-- predict long-period equilibrium tides at latitudes and time
t = tide_time[valid, cycle] + deltat[valid, cycle]
tide_lpe.data[valid,
cycle] = compute_equilibrium_tide(t, lat[valid])
#-- replace masked and nan values with fill value
invalid = np.nonzero(np.isnan(tide_lpe.data) | tide_lpe.mask)
tide_lpe.data[invalid] = tide_lpe.fill_value
tide_lpe.mask[invalid] = True
#-- group attributes for beam
IS2_atl11_tide_attrs[ptx]['description'] = (
'Contains the primary science parameters for this '
'data set')
IS2_atl11_tide_attrs[ptx]['beam_pair'] = IS2_atl11_attrs[ptx][
'beam_pair']
IS2_atl11_tide_attrs[ptx]['ReferenceGroundTrack'] = IS2_atl11_attrs[
ptx]['ReferenceGroundTrack']
IS2_atl11_tide_attrs[ptx]['first_cycle'] = IS2_atl11_attrs[ptx][
'first_cycle']
IS2_atl11_tide_attrs[ptx]['last_cycle'] = IS2_atl11_attrs[ptx][
'last_cycle']
IS2_atl11_tide_attrs[ptx]['equatorial_radius'] = IS2_atl11_attrs[ptx][
'equatorial_radius']
IS2_atl11_tide_attrs[ptx]['polar_radius'] = IS2_atl11_attrs[ptx][
'polar_radius']
#-- geolocation, time and reference point
#-- cycle_number
IS2_atl11_tide[ptx]['cycle_number'] = IS2_atl11_mds[ptx][
'cycle_number'].copy()
IS2_atl11_fill[ptx]['cycle_number'] = None
IS2_atl11_dims[ptx]['cycle_number'] = None
IS2_atl11_tide_attrs[ptx]['cycle_number'] = {}
IS2_atl11_tide_attrs[ptx]['cycle_number']['units'] = "1"
IS2_atl11_tide_attrs[ptx]['cycle_number'][
'long_name'] = "Orbital cycle number"
IS2_atl11_tide_attrs[ptx]['cycle_number']['source'] = "ATL06"
IS2_atl11_tide_attrs[ptx]['cycle_number']['description'] = (
"Number of 91-day periods "
"that have elapsed since ICESat-2 entered the science orbit. Each of the 1,387 "
"reference ground track (RGTs) is targeted in the polar regions once "
"every 91 days.")
#-- delta time
IS2_atl11_tide[ptx]['delta_time'] = IS2_atl11_mds[ptx][
'delta_time'].copy()
IS2_atl11_fill[ptx]['delta_time'] = IS2_atl11_attrs[ptx]['delta_time'][
'_FillValue']
IS2_atl11_dims[ptx]['delta_time'] = ['ref_pt', 'cycle_number']
IS2_atl11_tide_attrs[ptx]['delta_time'] = {}
IS2_atl11_tide_attrs[ptx]['delta_time'][
'units'] = "seconds since 2018-01-01"
IS2_atl11_tide_attrs[ptx]['delta_time'][
'long_name'] = "Elapsed GPS seconds"
IS2_atl11_tide_attrs[ptx]['delta_time']['standard_name'] = "time"
IS2_atl11_tide_attrs[ptx]['delta_time']['calendar'] = "standard"
IS2_atl11_tide_attrs[ptx]['delta_time']['source'] = "ATL06"
IS2_atl11_tide_attrs[ptx]['delta_time']['description'] = (
"Number of GPS "
"seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch offset "
"is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds "
"between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By "
"adding the offset contained within atlas_sdp_gps_epoch to delta time parameters, the "
"time in gps_seconds relative to the GPS epoch can be computed.")
IS2_atl11_tide_attrs[ptx]['delta_time']['coordinates'] = \
"ref_pt cycle_number latitude longitude"
#-- latitude
IS2_atl11_tide[ptx]['latitude'] = IS2_atl11_mds[ptx]['latitude'].copy()
IS2_atl11_fill[ptx]['latitude'] = IS2_atl11_attrs[ptx]['latitude'][
'_FillValue']
IS2_atl11_dims[ptx]['latitude'] = ['ref_pt']
IS2_atl11_tide_attrs[ptx]['latitude'] = {}
IS2_atl11_tide_attrs[ptx]['latitude']['units'] = "degrees_north"
IS2_atl11_tide_attrs[ptx]['latitude'][
'contentType'] = "physicalMeasurement"
IS2_atl11_tide_attrs[ptx]['latitude']['long_name'] = "Latitude"
IS2_atl11_tide_attrs[ptx]['latitude']['standard_name'] = "latitude"
IS2_atl11_tide_attrs[ptx]['latitude']['source'] = "ATL06"
IS2_atl11_tide_attrs[ptx]['latitude']['description'] = (
"Center latitude of "
"selected segments")
IS2_atl11_tide_attrs[ptx]['latitude']['valid_min'] = -90.0
IS2_atl11_tide_attrs[ptx]['latitude']['valid_max'] = 90.0
IS2_atl11_tide_attrs[ptx]['latitude']['coordinates'] = \
"ref_pt delta_time longitude"
#-- longitude
IS2_atl11_tide[ptx]['longitude'] = IS2_atl11_mds[ptx][
'longitude'].copy()
IS2_atl11_fill[ptx]['longitude'] = IS2_atl11_attrs[ptx]['longitude'][
'_FillValue']
IS2_atl11_dims[ptx]['longitude'] = ['ref_pt']
IS2_atl11_tide_attrs[ptx]['longitude'] = {}
IS2_atl11_tide_attrs[ptx]['longitude']['units'] = "degrees_east"
IS2_atl11_tide_attrs[ptx]['longitude'][
'contentType'] = "physicalMeasurement"
IS2_atl11_tide_attrs[ptx]['longitude']['long_name'] = "Longitude"
IS2_atl11_tide_attrs[ptx]['longitude']['standard_name'] = "longitude"
IS2_atl11_tide_attrs[ptx]['longitude']['source'] = "ATL06"
IS2_atl11_tide_attrs[ptx]['longitude']['description'] = (
"Center longitude of "
"selected segments")
IS2_atl11_tide_attrs[ptx]['longitude']['valid_min'] = -180.0
IS2_atl11_tide_attrs[ptx]['longitude']['valid_max'] = 180.0
IS2_atl11_tide_attrs[ptx]['longitude']['coordinates'] = \
"ref_pt delta_time latitude"
#-- reference point
IS2_atl11_tide[ptx]['ref_pt'] = IS2_atl11_mds[ptx]['ref_pt'].copy()
IS2_atl11_fill[ptx]['ref_pt'] = None
IS2_atl11_dims[ptx]['ref_pt'] = None
IS2_atl11_tide_attrs[ptx]['ref_pt'] = {}
IS2_atl11_tide_attrs[ptx]['ref_pt']['units'] = "1"
IS2_atl11_tide_attrs[ptx]['ref_pt'][
'contentType'] = "referenceInformation"
IS2_atl11_tide_attrs[ptx]['ref_pt'][
'long_name'] = "Reference point number"
IS2_atl11_tide_attrs[ptx]['ref_pt']['source'] = "ATL06"
IS2_atl11_tide_attrs[ptx]['ref_pt']['description'] = (
"The reference point is the 7 digit segment_id "
"number corresponding to the center of the ATL06 data used for each ATL11 point. These are "
"sequential, starting with 1 for the first segment after an ascending equatorial crossing node."
)
IS2_atl11_tide_attrs[ptx]['ref_pt']['coordinates'] = \
"delta_time latitude longitude"
#-- cycle statistics variables
IS2_atl11_tide_attrs[ptx]['cycle_stats']['Description'] = (
"The cycle_stats subgroup "
"contains summary information about segments for each reference point, including "
"the uncorrected mean heights for reference surfaces, blowing snow and cloud "
"indicators, and geolocation and height misfit statistics.")
IS2_atl11_tide_attrs[ptx]['cycle_stats']['data_rate'] = (
"Data within this group "
"are stored at the average segment rate.")
#-- computed long-period equilibrium tide
IS2_atl11_tide[ptx]['cycle_stats']['tide_equilibrium'] = tide_lpe
IS2_atl11_fill[ptx]['cycle_stats'][
'tide_equilibrium'] = tide_lpe.fill_value
IS2_atl11_dims[ptx]['cycle_stats']['tide_equilibrium'] = [
'ref_pt', 'cycle_number'
]
IS2_atl11_tide_attrs[ptx]['cycle_stats']['tide_equilibrium'] = {}
IS2_atl11_tide_attrs[ptx]['cycle_stats']['tide_equilibrium'][
'units'] = "meters"
IS2_atl11_tide_attrs[ptx]['cycle_stats']['tide_equilibrium'][
'contentType'] = "referenceInformation"
IS2_atl11_tide_attrs[ptx]['cycle_stats']['tide_equilibrium']['long_name'] = \
"Long Period Equilibrium Tide"
IS2_atl11_tide_attrs[ptx]['cycle_stats']['tide_equilibrium'][
'description'] = (
"Long-period "
"equilibrium tidal elevation from the summation of fifteen tidal spectral lines"
)
IS2_atl11_tide_attrs[ptx]['cycle_stats']['tide_equilibrium']['reference'] = \
"https://doi.org/10.1111/j.1365-246X.1973.tb03420.x"
IS2_atl11_tide_attrs[ptx]['cycle_stats']['tide_equilibrium']['coordinates'] = \
"../ref_pt ../cycle_number ../delta_time ../latitude ../longitude"
#-- output tidal HDF5 file
args = (PRD, TRK, GRAN, SCYC, ECYC, RL, VERS, AUX)
file_format = '{0}_LPET_{1}{2}_{3}{4}_{5}_{6}{7}.h5'
#-- print file information
print('\t{0}'.format(file_format.format(*args))) if VERBOSE else None
HDF5_ATL11_tide_write(IS2_atl11_tide,
IS2_atl11_tide_attrs,
CLOBBER=True,
INPUT=os.path.basename(FILE),
FILL_VALUE=IS2_atl11_fill,
DIMENSIONS=IS2_atl11_dims,
FILENAME=os.path.join(DIRECTORY,
file_format.format(*args)))
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY, file_format.format(*args)), MODE)
def compute_OPT_displacements(tide_dir, input_file, output_file,
FORMAT='csv', VARIABLES=['time','lat','lon','data'], HEADER=0, TYPE='drift',
TIME_UNITS='days since 1858-11-17T00:00:00', TIME=None, PROJECTION='4326',
METHOD='spline', VERBOSE=False, MODE=0o775):
#-- invalid value
fill_value = -9999.0
#-- output netCDF4 and HDF5 file attributes
#-- will be added to YAML header in csv files
attrib = {}
#-- latitude
attrib['lat'] = {}
attrib['lat']['long_name'] = 'Latitude'
attrib['lat']['units'] = 'Degrees_North'
#-- longitude
attrib['lon'] = {}
attrib['lon']['long_name'] = 'Longitude'
attrib['lon']['units'] = 'Degrees_East'
#-- ocean pole tides
attrib['tide_oc_pole'] = {}
attrib['tide_oc_pole']['long_name'] = 'Ocean_Pole_Tide'
attrib['tide_oc_pole']['description'] = ('Ocean_pole_tide_radial_'
'displacements_time_due_to_polar_motion')
attrib['tide_oc_pole']['reference'] = ('ftp://tai.bipm.org/iers/conv2010/'
'chapter7/opoleloadcoefcmcor.txt.gz')
attrib['tide_oc_pole']['units'] = 'meters'
attrib['tide_oc_pole']['_FillValue'] = fill_value
#-- Modified Julian Days
attrib['time'] = {}
attrib['time']['long_name'] = 'Time'
attrib['time']['units'] = 'days since 1858-11-17T00:00:00'
attrib['time']['description'] = 'Modified Julian Days'
attrib['time']['calendar'] = 'standard'
#-- read input file to extract time, spatial coordinates and data
if (FORMAT == 'csv'):
dinput = pyTMD.spatial.from_ascii(input_file, columns=VARIABLES,
header=HEADER, verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
dinput = pyTMD.spatial.from_netCDF4(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
dinput = pyTMD.spatial.from_HDF5(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
elif (FORMAT == 'geotiff'):
dinput = pyTMD.spatial.from_geotiff(input_file, verbose=VERBOSE)
#-- copy global geotiff attributes for projection and grid parameters
for att_name in ['projection','wkt','spacing','extent']:
attrib[att_name] = dinput['attributes'][att_name]
#-- update time variable if entered as argument
if TIME is not None:
dinput['time'] = np.copy(TIME)
#-- converting x,y from projection to latitude/longitude
#-- could try to extract projection attributes from netCDF4 and HDF5 files
try:
crs1 = pyproj.CRS.from_string("epsg:{0:d}".format(int(PROJECTION)))
except (ValueError,pyproj.exceptions.CRSError):
crs1 = pyproj.CRS.from_string(PROJECTION)
crs2 = pyproj.CRS.from_string("epsg:{0:d}".format(4326))
transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)
if (TYPE == 'grid'):
ny,nx = (len(dinput['y']),len(dinput['x']))
gridx,gridy = np.meshgrid(dinput['x'],dinput['y'])
lon,lat = transformer.transform(gridx.flatten(),gridy.flatten())
elif (TYPE == 'drift'):
lon,lat = transformer.transform(dinput['x'].flatten(),
dinput['y'].flatten())
#-- extract time units from netCDF4 and HDF5 attributes or from TIME_UNITS
try:
time_string = dinput['attributes']['time']['units']
except (TypeError, KeyError):
epoch1,to_secs = pyTMD.time.parse_date_string(TIME_UNITS)
else:
epoch1,to_secs = pyTMD.time.parse_date_string(time_string)
#-- convert dates to Modified Julian days (days since 1858-11-17T00:00:00)
MJD = pyTMD.time.convert_delta_time(to_secs*dinput['time'].flatten(),
epoch1=epoch1, epoch2=(1858,11,17,0,0,0), scale=1.0/86400.0)
#-- add offset to convert to Julian days and then convert to calendar dates
Y,M,D,h,m,s = pyTMD.time.convert_julian(2400000.5 + MJD, FORMAT='tuple')
#-- calculate time in year-decimal format
time_decimal = pyTMD.time.convert_calendar_decimal(Y,M,day=D,
hour=h,minute=m,second=s)
#-- number of time points
nt = len(time_decimal)
#-- degrees to radians and arcseconds to radians
dtr = np.pi/180.0
atr = np.pi/648000.0
#-- earth and physical parameters (IERS and WGS84)
G = 6.67428e-11#-- universal constant of gravitation [m^3/(kg*s^2)]
GM = 3.986004418e14#-- geocentric gravitational constant [m^3/s^2]
a_axis = 6378136.6#-- WGS84 equatorial radius of the Earth [m]
flat = 1.0/298.257223563#-- flattening of the WGS84 ellipsoid
omega = 7.292115e-5#-- mean rotation rate of the Earth [radians/s]
rho_w = 1025.0#-- density of sea water [kg/m^3]
ge = 9.7803278#-- mean equatorial gravitational acceleration [m/s^2]
#-- Linear eccentricity and first numerical eccentricity
lin_ecc = np.sqrt((2.0*flat - flat**2)*a_axis**2)
ecc1 = lin_ecc/a_axis
#-- tidal love number differential (1 + kl - hl) for pole tide frequencies
gamma = 0.6870 + 0.0036j
#-- flatten heights
h = dinput['data'].flatten() if ('data' in dinput.keys()) else 0.0
#-- convert from geodetic latitude to geocentric latitude
#-- calculate X, Y and Z from geodetic latitude and longitude
X,Y,Z = pyTMD.spatial.to_cartesian(lon,lat,h=h,a_axis=a_axis,flat=flat)
#-- calculate geocentric latitude and convert to degrees
latitude_geocentric = np.arctan(Z / np.sqrt(X**2.0 + Y**2.0))/dtr
#-- pole tide displacement scale factor
Hp = np.sqrt(8.0*np.pi/15.0)*(omega**2*a_axis**4)/GM
K = 4.0*np.pi*G*rho_w*Hp*a_axis/(3.0*ge)
K1 = 4.0*np.pi*G*rho_w*Hp*a_axis**3/(3.0*GM)
#-- pole tide files (mean and daily)
mean_pole_file = get_data_path(['data','mean-pole.tab'])
pole_tide_file = get_data_path(['data','finals.all'])
#-- calculate angular coordinates of mean pole at time
mpx,mpy,fl = iers_mean_pole(mean_pole_file,time_decimal,'2015')
#-- read IERS daily polar motion values
EOP = read_iers_EOP(pole_tide_file)
#-- interpolate daily polar motion values to t1 using cubic splines
xSPL = scipy.interpolate.UnivariateSpline(EOP['MJD'],EOP['x'],k=3,s=0)
ySPL = scipy.interpolate.UnivariateSpline(EOP['MJD'],EOP['y'],k=3,s=0)
px = xSPL(MJD)
py = ySPL(MJD)
#-- calculate differentials from mean pole positions
mx = px - mpx
my = -(py - mpy)
#-- read ocean pole tide map from Desai (2002)
ocean_pole_tide_file = get_data_path(['data','opoleloadcoefcmcor.txt.gz'])
iur,iun,iue,ilon,ilat = read_ocean_pole_tide(ocean_pole_tide_file)
#-- interpolate ocean pole tide map from Desai (2002)
if (METHOD == 'spline'):
#-- use scipy bivariate splines to interpolate to output points
f1 = scipy.interpolate.RectBivariateSpline(ilon, ilat[::-1],
iur[:,::-1].real, kx=1, ky=1)
f2 = scipy.interpolate.RectBivariateSpline(ilon, ilat[::-1],
iur[:,::-1].imag, kx=1, ky=1)
UR = np.zeros((len(latitude_geocentric)),dtype=np.complex128)
UR.real = f1.ev(lon,latitude_geocentric)
UR.imag = f2.ev(lon,latitude_geocentric)
else:
#-- use scipy regular grid to interpolate values for a given method
r1 = scipy.interpolate.RegularGridInterpolator((ilon,ilat[::-1]),
iur[:,::-1], method=METHOD)
UR = r1.__call__(np.c_[lon,latitude_geocentric])
#-- calculate radial displacement at time
if (TYPE == 'grid'):
Urad = np.ma.zeros((ny,nx,nt),fill_value=fill_value)
Urad.mask = np.zeros((ny,nx,nt),dtype=bool)
for i in range(nt):
URAD = K*atr*np.real((mx[i]*gamma.real + my[i]*gamma.imag)*UR.real +
(my[i]*gamma.real - mx[i]*gamma.imag)*UR.imag)
#-- reform grid
Urad.data[:,:,i] = np.reshape(URAD, (ny,nx))
Urad.mask[:,:,i] = np.isnan(URAD)
elif (TYPE == 'drift'):
Urad = np.ma.zeros((nt),fill_value=fill_value)
Urad.data[:] = K*atr*np.real((mx*gamma.real + my*gamma.imag)*UR.real +
(my*gamma.real - mx*gamma.imag)*UR.imag)
Urad.mask = np.isnan(Urad.data)
#-- replace invalid data with fill values
Urad.data[Urad.mask] = Urad.fill_value
#-- output to file
output = dict(time=MJD,lon=lon,lat=lat,tide_oc_pole=Urad)
if (FORMAT == 'csv'):
pyTMD.spatial.to_ascii(output, attrib, output_file, delimiter=',',
columns=['time','lat','lon','tide_oc_pole'], verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
pyTMD.spatial.to_netCDF4(output, attrib, output_file, verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
pyTMD.spatial.to_HDF5(output, attrib, output_file, verbose=VERBOSE)
elif (FORMAT == 'geotiff'):
pyTMD.spatial.to_geotiff(output, attrib, output_file, verbose=VERBOSE,
varname='tide_oc_pole')
#-- change the permissions level to MODE
os.chmod(output_file, MODE)
def compute_LPET_ICESat2(INPUT_FILE, VERBOSE=False, MODE=0o775):
#-- read data from input file
print('{0} -->'.format(os.path.basename(INPUT_FILE))) if VERBOSE else None
IS2_atl06_mds, IS2_atl06_attrs, IS2_atl06_beams = read_HDF5_ATL06(
INPUT_FILE, ATTRIBUTES=True)
DIRECTORY = os.path.dirname(INPUT_FILE)
#-- extract parameters from ICESat-2 ATLAS HDF5 file name
rx = re.compile(
r'(processed_)?(ATL\d{2})_(\d{4})(\d{2})(\d{2})(\d{2})'
r'(\d{2})(\d{2})_(\d{4})(\d{2})(\d{2})_(\d{3})_(\d{2})(.*?).h5$')
try:
SUB, PRD, YY, MM, DD, HH, MN, SS, TRK, CYCL, GRAN, RL, VERS, AUX = rx.findall(
INPUT_FILE).pop()
except:
#-- output long-period equilibrium tide HDF5 file (generic)
fileBasename, fileExtension = os.path.splitext(INPUT_FILE)
OUTPUT_FILE = '{0}_{1}{2}'.format(fileBasename, 'LPET', fileExtension)
else:
#-- output long-period equilibrium tide HDF5 file for ASAS/NSIDC granules
args = (PRD, YY, MM, DD, HH, MN, SS, TRK, CYCL, GRAN, RL, VERS, AUX)
file_format = '{0}_LPET_{1}{2}{3}{4}{5}{6}_{7}{8}{9}_{10}_{11}{12}.h5'
OUTPUT_FILE = file_format.format(*args)
#-- number of GPS seconds between the GPS epoch
#-- and ATLAS Standard Data Product (SDP) epoch
atlas_sdp_gps_epoch = IS2_atl06_mds['ancillary_data'][
'atlas_sdp_gps_epoch']
#-- copy variables for outputting to HDF5 file
IS2_atl06_tide = {}
IS2_atl06_fill = {}
IS2_atl06_dims = {}
IS2_atl06_tide_attrs = {}
#-- number of GPS seconds between the GPS epoch (1980-01-06T00:00:00Z UTC)
#-- and ATLAS Standard Data Product (SDP) epoch (2018-01-01T00:00:00Z UTC)
#-- Add this value to delta time parameters to compute full gps_seconds
IS2_atl06_tide['ancillary_data'] = {}
IS2_atl06_tide_attrs['ancillary_data'] = {}
for key in ['atlas_sdp_gps_epoch']:
#-- get each HDF5 variable
IS2_atl06_tide['ancillary_data'][key] = IS2_atl06_mds[
'ancillary_data'][key]
#-- Getting attributes of group and included variables
IS2_atl06_tide_attrs['ancillary_data'][key] = {}
for att_name, att_val in IS2_atl06_attrs['ancillary_data'][key].items(
):
IS2_atl06_tide_attrs['ancillary_data'][key][att_name] = att_val
#-- for each input beam within the file
for gtx in sorted(IS2_atl06_beams):
#-- output data dictionaries for beam
IS2_atl06_tide[gtx] = dict(land_ice_segments={})
IS2_atl06_fill[gtx] = dict(land_ice_segments={})
IS2_atl06_dims[gtx] = dict(land_ice_segments={})
IS2_atl06_tide_attrs[gtx] = dict(land_ice_segments={})
#-- number of segments
val = IS2_atl06_mds[gtx]['land_ice_segments']
n_seg = len(val['segment_id'])
#-- find valid segments for beam
fv = IS2_atl06_attrs[gtx]['land_ice_segments']['h_li']['_FillValue']
#-- convert time from ATLAS SDP to days relative to Jan 1, 1992
gps_seconds = atlas_sdp_gps_epoch + val['delta_time']
leap_seconds = pyTMD.time.count_leap_seconds(gps_seconds)
tide_time = pyTMD.time.convert_delta_time(gps_seconds - leap_seconds,
epoch1=(1980, 1, 6, 0, 0, 0),
epoch2=(1992, 1, 1, 0, 0, 0),
scale=1.0 / 86400.0)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data', 'merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- predict long-period equilibrium tides at latitudes and time
tide_lpe = np.ma.zeros((n_seg), fill_value=fv)
tide_lpe.data[:] = compute_equilibrium_tide(tide_time + deltat,
val['latitude'])
tide_lpe.mask = (val['latitude'] == fv) | (val['delta_time'] == fv)
#-- group attributes for beam
IS2_atl06_tide_attrs[gtx]['Description'] = IS2_atl06_attrs[gtx][
'Description']
IS2_atl06_tide_attrs[gtx]['atlas_pce'] = IS2_atl06_attrs[gtx][
'atlas_pce']
IS2_atl06_tide_attrs[gtx]['atlas_beam_type'] = IS2_atl06_attrs[gtx][
'atlas_beam_type']
IS2_atl06_tide_attrs[gtx]['groundtrack_id'] = IS2_atl06_attrs[gtx][
'groundtrack_id']
IS2_atl06_tide_attrs[gtx]['atmosphere_profile'] = IS2_atl06_attrs[gtx][
'atmosphere_profile']
IS2_atl06_tide_attrs[gtx]['atlas_spot_number'] = IS2_atl06_attrs[gtx][
'atlas_spot_number']
IS2_atl06_tide_attrs[gtx]['sc_orientation'] = IS2_atl06_attrs[gtx][
'sc_orientation']
#-- group attributes for land_ice_segments
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['Description'] = (
"The land_ice_segments group "
"contains the primary set of derived products. This includes geolocation, height, and "
"standard error and quality measures for each segment. This group is sparse, meaning "
"that parameters are provided only for pairs of segments for which at least one beam "
"has a valid surface-height measurement.")
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['data_rate'] = (
"Data within this group are "
"sparse. Data values are provided only for those ICESat-2 20m segments where at "
"least one beam has a valid land ice height measurement.")
#-- geolocation, time and segment ID
#-- delta time
delta_time = np.ma.array(val['delta_time'],
fill_value=fv,
mask=(val['delta_time'] == fv))
IS2_atl06_tide[gtx]['land_ice_segments']['delta_time'] = delta_time
IS2_atl06_fill[gtx]['land_ice_segments'][
'delta_time'] = delta_time.fill_value
IS2_atl06_dims[gtx]['land_ice_segments']['delta_time'] = None
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['delta_time'] = {}
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['delta_time'][
'units'] = "seconds since 2018-01-01"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['delta_time'][
'long_name'] = "Elapsed GPS seconds"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['delta_time'][
'standard_name'] = "time"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['delta_time'][
'calendar'] = "standard"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['delta_time'][
'description'] = (
"Number of GPS "
"seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch offset "
"is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds "
"between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By "
"adding the offset contained within atlas_sdp_gps_epoch to delta time parameters, the "
"time in gps_seconds relative to the GPS epoch can be computed."
)
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['delta_time']['coordinates'] = \
"segment_id latitude longitude"
#-- latitude
latitude = np.ma.array(val['latitude'],
fill_value=fv,
mask=(val['latitude'] == fv))
IS2_atl06_tide[gtx]['land_ice_segments']['latitude'] = latitude
IS2_atl06_fill[gtx]['land_ice_segments'][
'latitude'] = latitude.fill_value
IS2_atl06_dims[gtx]['land_ice_segments']['latitude'] = ['delta_time']
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude'] = {}
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude'][
'units'] = "degrees_north"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude'][
'contentType'] = "physicalMeasurement"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude'][
'long_name'] = "Latitude"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude'][
'standard_name'] = "latitude"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude'][
'description'] = ("Latitude of "
"segment center")
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude'][
'valid_min'] = -90.0
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude'][
'valid_max'] = 90.0
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['latitude']['coordinates'] = \
"segment_id delta_time longitude"
#-- longitude
longitude = np.ma.array(val['longitude'],
fill_value=fv,
mask=(val['longitude'] == fv))
IS2_atl06_tide[gtx]['land_ice_segments']['longitude'] = longitude
IS2_atl06_fill[gtx]['land_ice_segments'][
'longitude'] = longitude.fill_value
IS2_atl06_dims[gtx]['land_ice_segments']['longitude'] = ['delta_time']
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude'] = {}
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude'][
'units'] = "degrees_east"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude'][
'contentType'] = "physicalMeasurement"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude'][
'long_name'] = "Longitude"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude'][
'standard_name'] = "longitude"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude'][
'description'] = ("Longitude of "
"segment center")
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude'][
'valid_min'] = -180.0
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude'][
'valid_max'] = 180.0
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['longitude']['coordinates'] = \
"segment_id delta_time latitude"
#-- segment ID
IS2_atl06_tide[gtx]['land_ice_segments']['segment_id'] = val[
'segment_id']
IS2_atl06_fill[gtx]['land_ice_segments']['segment_id'] = None
IS2_atl06_dims[gtx]['land_ice_segments']['segment_id'] = ['delta_time']
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['segment_id'] = {}
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['segment_id'][
'units'] = "1"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['segment_id'][
'contentType'] = "referenceInformation"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['segment_id'][
'long_name'] = "Along-track segment ID number"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['segment_id'][
'description'] = (
"A 7 digit number "
"identifying the along-track geolocation segment number. These are sequential, starting with "
"1 for the first segment after an ascending equatorial crossing node. Equal to the segment_id for "
"the second of the two 20m ATL03 segments included in the 40m ATL06 segment"
)
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['segment_id']['coordinates'] = \
"delta_time latitude longitude"
#-- geophysical variables
IS2_atl06_tide[gtx]['land_ice_segments']['geophysical'] = {}
IS2_atl06_fill[gtx]['land_ice_segments']['geophysical'] = {}
IS2_atl06_dims[gtx]['land_ice_segments']['geophysical'] = {}
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical'] = {}
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical'][
'Description'] = (
"The geophysical group "
"contains parameters used to correct segment heights for geophysical effects, parameters "
"related to solar background and parameters indicative of the presence or absence of clouds."
)
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical'][
'data_rate'] = (
"Data within this group "
"are stored at the land_ice_segments segment rate.")
#-- computed long-period equilibrium tide
IS2_atl06_tide[gtx]['land_ice_segments']['geophysical'][
'tide_equilibrium'] = tide_lpe
IS2_atl06_fill[gtx]['land_ice_segments']['geophysical'][
'tide_equilibrium'] = tide_lpe.fill_value
IS2_atl06_dims[gtx]['land_ice_segments']['geophysical'][
'tide_equilibrium'] = ['delta_time']
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical'][
'tide_equilibrium'] = {}
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical'][
'tide_equilibrium']['units'] = "meters"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical'][
'tide_equilibrium']['contentType'] = "referenceInformation"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical']['tide_equilibrium']['long_name'] = \
"Long Period Equilibrium Tide"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical'][
'tide_equilibrium']['description'] = (
"Long-period "
"equilibrium tidal elevation from the summation of fifteen tidal spectral lines"
)
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical']['tide_equilibrium']['reference'] = \
"https://doi.org/10.1111/j.1365-246X.1973.tb03420.x"
IS2_atl06_tide_attrs[gtx]['land_ice_segments']['geophysical']['tide_equilibrium']['coordinates'] = \
"../segment_id ../delta_time ../latitude ../longitude"
#-- print file information
print('\t{0}'.format(OUTPUT_FILE)) if VERBOSE else None
HDF5_ATL06_tide_write(IS2_atl06_tide,
IS2_atl06_tide_attrs,
CLOBBER=True,
INPUT=os.path.basename(INPUT_FILE),
FILL_VALUE=IS2_atl06_fill,
DIMENSIONS=IS2_atl06_dims,
FILENAME=os.path.join(DIRECTORY, OUTPUT_FILE))
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY, OUTPUT_FILE), MODE)
def compute_tidal_currents(tide_dir, input_file, output_file,
TIDE_MODEL=None, FORMAT='csv', VARIABLES=['time','lat','lon','data'],
HEADER=0, TYPE='drift', TIME_UNITS='days since 1858-11-17T00:00:00',
TIME=None, PROJECTION='4326', METHOD='spline', EXTRAPOLATE=False,
VERBOSE=False, MODE=0o775):
#-- select between tide models
if (TIDE_MODEL == 'CATS0201'):
grid_file = os.path.join(tide_dir,'cats0201_tmd','grid_CATS')
model_file = os.path.join(tide_dir,'cats0201_tmd','UV0_CATS02_01')
reference = 'https://mail.esr.org/polar_tide_models/Model_CATS0201.html'
model_format = 'OTIS'
EPSG = '4326'
TYPES = ['u','v']
elif (TIDE_MODEL == 'CATS2008'):
grid_file = os.path.join(tide_dir,'CATS2008','grid_CATS2008')
model_file = os.path.join(tide_dir,'CATS2008','uv.CATS2008.out')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/cats2008/')
model_format = 'OTIS'
EPSG = 'CATS2008'
TYPES = ['u','v']
elif (TIDE_MODEL == 'TPXO9-atlas'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas')
grid_file = 'grid_tpxo9_atlas.nc.gz'
model_files = {}
model_files['u'] = ['u_q1_tpxo9_atlas_30.nc.gz','u_o1_tpxo9_atlas_30.nc.gz',
'u_p1_tpxo9_atlas_30.nc.gz','u_k1_tpxo9_atlas_30.nc.gz',
'u_n2_tpxo9_atlas_30.nc.gz','u_m2_tpxo9_atlas_30.nc.gz',
'u_s2_tpxo9_atlas_30.nc.gz','u_k2_tpxo9_atlas_30.nc.gz',
'u_m4_tpxo9_atlas_30.nc.gz','u_ms4_tpxo9_atlas_30.nc.gz',
'u_mn4_tpxo9_atlas_30.nc.gz','u_2n2_tpxo9_atlas_30.nc.gz']
model_files['v'] = ['v_q1_tpxo9_atlas_30.nc.gz','v_o1_tpxo9_atlas_30.nc.gz',
'v_p1_tpxo9_atlas_30.nc.gz','v_k1_tpxo9_atlas_30.nc.gz',
'v_n2_tpxo9_atlas_30.nc.gz','v_m2_tpxo9_atlas_30.nc.gz',
'v_s2_tpxo9_atlas_30.nc.gz','v_k2_tpxo9_atlas_30.nc.gz',
'v_m4_tpxo9_atlas_30.nc.gz','v_ms4_tpxo9_atlas_30.nc.gz',
'v_mn4_tpxo9_atlas_30.nc.gz','v_2n2_tpxo9_atlas_30.nc.gz']
reference = 'http://volkov.oce.orst.edu/tides/tpxo9_atlas.html'
model_format = 'netcdf'
TYPES = ['u','v']
model_scale = 1.0/100.0
GZIP = True
elif (TIDE_MODEL == 'TPXO9-atlas-v2'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas_v2')
grid_file = 'grid_tpxo9_atlas_30_v2.nc.gz'
model_files = {}
model_files['u'] = ['u_q1_tpxo9_atlas_30_v2.nc.gz','u_o1_tpxo9_atlas_30_v2.nc.gz',
'u_p1_tpxo9_atlas_30_v2.nc.gz','u_k1_tpxo9_atlas_30_v2.nc.gz',
'u_n2_tpxo9_atlas_30_v2.nc.gz','u_m2_tpxo9_atlas_30_v2.nc.gz',
'u_s2_tpxo9_atlas_30_v2.nc.gz','u_k2_tpxo9_atlas_30_v2.nc.gz',
'u_m4_tpxo9_atlas_30_v2.nc.gz','u_ms4_tpxo9_atlas_30_v2.nc.gz',
'u_mn4_tpxo9_atlas_30_v2.nc.gz','u_2n2_tpxo9_atlas_30_v2.nc.gz']
model_files['v'] = ['v_q1_tpxo9_atlas_30_v2.nc.gz','v_o1_tpxo9_atlas_30_v2.nc.gz',
'v_p1_tpxo9_atlas_30_v2.nc.gz','v_k1_tpxo9_atlas_30_v2.nc.gz',
'v_n2_tpxo9_atlas_30_v2.nc.gz','v_m2_tpxo9_atlas_30_v2.nc.gz',
'v_s2_tpxo9_atlas_30_v2.nc.gz','v_k2_tpxo9_atlas_30_v2.nc.gz',
'v_m4_tpxo9_atlas_30_v2.nc.gz','v_ms4_tpxo9_atlas_30_v2.nc.gz',
'v_mn4_tpxo9_atlas_30_v2.nc.gz','v_2n2_tpxo9_atlas_30_v2.nc.gz']
reference = 'https://www.tpxo.net/global/tpxo9-atlas'
model_format = 'netcdf'
TYPES = ['u','v']
model_scale = 1.0/100.0
GZIP = True
elif (TIDE_MODEL == 'TPXO9.1'):
grid_file = os.path.join(tide_dir,'TPXO9.1','DATA','grid_tpxo9')
model_file = os.path.join(tide_dir,'TPXO9.1','DATA','u_tpxo9.v1')
reference = 'http://volkov.oce.orst.edu/tides/global.html'
model_format = 'OTIS'
EPSG = '4326'
TYPES = ['u','v']
elif (TIDE_MODEL == 'TPXO8-atlas'):
grid_file = os.path.join(tide_dir,'tpxo8_atlas','grid_tpxo8atlas_30_v1')
model_file = os.path.join(tide_dir,'tpxo8_atlas','uv.tpxo8_atlas_30_v1')
reference = 'http://volkov.oce.orst.edu/tides/tpxo8_atlas.html'
model_format = 'ATLAS'
EPSG = '4326'
TYPES = ['u','v']
elif (TIDE_MODEL == 'TPXO7.2'):
grid_file = os.path.join(tide_dir,'TPXO7.2_tmd','grid_tpxo7.2')
model_file = os.path.join(tide_dir,'TPXO7.2_tmd','u_tpxo7.2')
reference = 'http://volkov.oce.orst.edu/tides/global.html'
model_format = 'OTIS'
EPSG = '4326'
TYPES = ['u','v']
elif (TIDE_MODEL == 'AODTM-5'):
grid_file = os.path.join(tide_dir,'aodtm5_tmd','grid_Arc5km')
model_file = os.path.join(tide_dir,'aodtm5_tmd','UV0_Arc5km')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aodtm-5/')
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPES = ['u','v']
elif (TIDE_MODEL == 'AOTIM-5'):
grid_file = os.path.join(tide_dir,'aotim5_tmd','grid_Arc5km')
model_file = os.path.join(tide_dir,'aotim5_tmd','UV_Arc5km')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aotim-5/')
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPES = ['u','v']
elif (TIDE_MODEL == 'AOTIM-5-2018'):
grid_file = os.path.join(tide_dir,'Arc5km2018','grid_Arc5km2018')
model_file = os.path.join(tide_dir,'Arc5km2018','UV_Arc5km2018')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aotim-5/')
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPES = ['u','v']
elif (TIDE_MODEL == 'FES2014'):
model_directory = {}
model_directory['u'] = os.path.join(tide_dir,'fes2014','eastward_velocity')
model_directory['v'] = os.path.join(tide_dir,'fes2014','northward_velocity')
model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',
'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',
'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',
'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',
'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',
'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',
'ssa.nc.gz','t2.nc.gz']
c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6',
'm8','mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2',
'n4','nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']
reference = ('https://www.aviso.altimetry.fr/en/data/products'
'auxiliary-products/global-tide-fes.html')
model_format = 'FES'
TYPES = ['u','v']
model_scale = 1.0
#-- invalid value
fill_value = -9999.0
#-- output netCDF4 and HDF5 file attributes
#-- will be added to YAML header in csv files
attrib = {}
#-- latitude
attrib['lat'] = {}
attrib['lat']['long_name'] = 'Latitude'
attrib['lat']['units'] = 'Degrees_North'
#-- longitude
attrib['lon'] = {}
attrib['lon']['long_name'] = 'Longitude'
attrib['lon']['units'] = 'Degrees_East'
#-- zonal tidal currents
attrib['u'] = {}
attrib['u']['description'] = ('depth_averaged_tidal_zonal_current_'
'from_harmonic_constants')
attrib['u']['model'] = TIDE_MODEL
attrib['u']['units'] = 'cm/s'
attrib['u']['long_name'] = 'zonal_tidal_current'
attrib['u']['_FillValue'] = fill_value
#-- meridional tidal currents
attrib['v'] = {}
attrib['v']['description'] = ('depth_averaged_tidal_meridional_current_'
'from_harmonic_constants')
attrib['v']['model'] = TIDE_MODEL
attrib['v']['units'] = 'cm/s'
attrib['v']['long_name'] = 'meridional_tidal_current'
attrib['v']['_FillValue'] = fill_value
#-- time
attrib['time'] = {}
attrib['time']['long_name'] = 'Time'
attrib['time']['units'] = 'days since 1992-01-01T00:00:00'
attrib['time']['calendar'] = 'standard'
#-- read input file to extract time, spatial coordinates and data
if (FORMAT == 'csv'):
dinput = pyTMD.spatial.from_ascii(input_file, columns=VARIABLES,
header=HEADER, verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
dinput = pyTMD.spatial.from_netCDF4(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
dinput = pyTMD.spatial.from_HDF5(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
elif (FORMAT == 'geotiff'):
dinput = pyTMD.spatial.from_geotiff(input_file, verbose=VERBOSE)
#-- copy global geotiff attributes for projection and grid parameters
for att_name in ['projection','wkt','spacing','extent']:
attrib[att_name] = dinput['attributes'][att_name]
#-- update time variable if entered as argument
if TIME is not None:
dinput['time'] = np.copy(TIME)
#-- converting x,y from projection to latitude/longitude
#-- could try to extract projection attributes from netCDF4 and HDF5 files
try:
crs1 = pyproj.CRS.from_string("epsg:{0:d}".format(int(PROJECTION)))
except (ValueError,pyproj.exceptions.CRSError):
crs1 = pyproj.CRS.from_string(PROJECTION)
crs2 = pyproj.CRS.from_string("epsg:{0:d}".format(4326))
transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)
if (TYPE == 'grid'):
ny,nx = (len(dinput['y']),len(dinput['x']))
gridx,gridy = np.meshgrid(dinput['x'],dinput['y'])
lon,lat = transformer.transform(gridx,gridy)
elif (TYPE == 'drift'):
lon,lat = transformer.transform(dinput['x'],dinput['y'])
#-- extract time units from netCDF4 and HDF5 attributes or from TIME_UNITS
try:
time_string = dinput['attributes']['time']['units']
except (TypeError, KeyError):
epoch1,to_secs = pyTMD.time.parse_date_string(TIME_UNITS)
else:
epoch1,to_secs = pyTMD.time.parse_date_string(time_string)
#-- convert time from units to days since 1992-01-01T00:00:00
tide_time = pyTMD.time.convert_delta_time(to_secs*dinput['time'].flatten(),
epoch1=epoch1, epoch2=(1992,1,1,0,0,0), scale=1.0/86400.0)
#-- number of time points
nt = len(tide_time)
#-- python dictionary with output data
output = {'time':tide_time,'lon':lon,'lat':lat}
#-- iterate over u and v currents
for t in TYPES:
#-- read tidal constants and interpolate to grid points
if model_format in ('OTIS','ATLAS'):
amp,ph,D,c = extract_tidal_constants(lon.flatten(), lat.flatten(),
grid_file, model_file, EPSG, TYPE=t, METHOD=METHOD,
EXTRAPOLATE=EXTRAPOLATE, GRID=model_format)
deltat = np.zeros((nt))
elif (model_format == 'netcdf'):
amp,ph,D,c = extract_netcdf_constants(lon.flatten(), lat.flatten(),
model_directory, grid_file, model_files[t], TYPE=t,
METHOD=METHOD, EXTRAPOLATE=EXTRAPOLATE, SCALE=model_scale,
GZIP=GZIP)
deltat = np.zeros((nt))
elif (model_format == 'FES'):
amp,ph = extract_FES_constants(lon.flatten(), lat.flatten(),
model_directory[t], model_files, TYPE=t, VERSION=TIDE_MODEL,
METHOD=METHOD, EXTRAPOLATE=EXTRAPOLATE, SCALE=model_scale)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data','merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- calculate complex phase in radians for Euler's
cph = -1j*ph*np.pi/180.0
#-- calculate constituent oscillation
hc = amp*np.exp(cph)
#-- predict tidal currents at time and infer minor corrections
if (TYPE == 'grid'):
output[t] = np.ma.zeros((ny,nx,nt),fill_value=fill_value)
output[t].mask = np.zeros((ny,nx,nt),dtype=np.bool)
for i in range(nt):
TIDE = predict_tide(tide_time[i], hc, c,
DELTAT=deltat[i], CORRECTIONS=model_format)
MINOR = infer_minor_corrections(tide_time[i], hc, c,
DELTAT=deltat[i], CORRECTIONS=model_format)
#-- add major and minor components and reform grid
output[t][:,:,i] = np.reshape((TIDE+MINOR), (ny,nx))
output[t].mask[:,:,i] = np.reshape((TIDE.mask | MINOR.mask),
(ny,nx))
elif (TYPE == 'drift'):
output[t] = np.ma.zeros((nt), fill_value=fill_value)
output[t].mask = np.any(hc.mask,axis=1)
output[t].data[:] = predict_tide_drift(tide_time, hc, c,
DELTAT=deltat, CORRECTIONS=model_format)
minor = infer_minor_corrections(tide_time, hc, c,
DELTAT=deltat, CORRECTIONS=model_format)
output[t].data[:] += minor.data[:]
#-- replace invalid values with fill value
output[t].data[output[t].mask] = output[t].fill_value
#-- output to file
if (FORMAT == 'csv'):
pyTMD.spatial.to_ascii(output, attrib, output_file, delimiter=',',
columns=['time','lat','lon','u','v'], verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
pyTMD.spatial.to_netCDF4(output, attrib, output_file, verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
pyTMD.spatial.to_HDF5(output, attrib, output_file, verbose=VERBOSE)
elif (FORMAT == 'geotiff'):
#-- merge current variables into a single variable
output['data'] = np.concatenate((output['u'],output['v']),axis=-1)
attrib['data'] = {'_FillValue':fill_value}
pyTMD.spatial.to_geotiff(output, attrib, output_file, verbose=VERBOSE,
varname='data')
#-- change the permissions level to MODE
os.chmod(output_file, MODE)
def compute_tidal_elevations(tide_dir, input_file, output_file,
TIDE_MODEL=None, FORMAT='csv', VARIABLES=['time','lat','lon','data'],
TIME_UNITS='days since 1858-11-17T00:00:00', PROJECTION='4326',
METHOD='spline', VERBOSE=False, MODE=0o775):
#-- select between tide models
if (TIDE_MODEL == 'CATS0201'):
grid_file = os.path.join(tide_dir,'cats0201_tmd','grid_CATS')
model_file = os.path.join(tide_dir,'cats0201_tmd','h0_CATS02_01')
reference = 'https://mail.esr.org/polar_tide_models/Model_CATS0201.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'CATS2008'):
grid_file = os.path.join(tide_dir,'CATS2008','grid_CATS2008')
model_file = os.path.join(tide_dir,'CATS2008','hf.CATS2008.out')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/cats2008/')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = 'CATS2008'
TYPE = 'z'
elif (TIDE_MODEL == 'CATS2008_load'):
grid_file = os.path.join(tide_dir,'CATS2008a_SPOTL_Load','grid_CATS2008a_opt')
model_file = os.path.join(tide_dir,'CATS2008a_SPOTL_Load','h_CATS2008a_SPOTL_load')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/cats2008/')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'OTIS'
EPSG = 'CATS2008'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO9-atlas'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas')
grid_file = 'grid_tpxo9_atlas.nc.gz'
model_files = ['h_q1_tpxo9_atlas_30.nc.gz','h_o1_tpxo9_atlas_30.nc.gz',
'h_p1_tpxo9_atlas_30.nc.gz','h_k1_tpxo9_atlas_30.nc.gz',
'h_n2_tpxo9_atlas_30.nc.gz','h_m2_tpxo9_atlas_30.nc.gz',
'h_s2_tpxo9_atlas_30.nc.gz','h_k2_tpxo9_atlas_30.nc.gz',
'h_m4_tpxo9_atlas_30.nc.gz','h_ms4_tpxo9_atlas_30.nc.gz',
'h_mn4_tpxo9_atlas_30.nc.gz','h_2n2_tpxo9_atlas_30.nc.gz']
reference = 'http://volkov.oce.orst.edu/tides/tpxo9_atlas.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'netcdf'
TYPE = 'z'
SCALE = 1.0/1000.0
elif (TIDE_MODEL == 'TPXO9-atlas-v2'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas_v2')
grid_file = 'grid_tpxo9_atlas_30_v2.nc.gz'
model_files = ['h_q1_tpxo9_atlas_30_v2.nc.gz','h_o1_tpxo9_atlas_30_v2.nc.gz',
'h_p1_tpxo9_atlas_30_v2.nc.gz','h_k1_tpxo9_atlas_30_v2.nc.gz',
'h_n2_tpxo9_atlas_30_v2.nc.gz','h_m2_tpxo9_atlas_30_v2.nc.gz',
'h_s2_tpxo9_atlas_30_v2.nc.gz','h_k2_tpxo9_atlas_30_v2.nc.gz',
'h_m4_tpxo9_atlas_30_v2.nc.gz','h_ms4_tpxo9_atlas_30_v2.nc.gz',
'h_mn4_tpxo9_atlas_30_v2.nc.gz','h_2n2_tpxo9_atlas_30_v2.nc.gz']
reference = 'https://www.tpxo.net/global/tpxo9-atlas'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'netcdf'
TYPE = 'z'
SCALE = 1.0/1000.0
elif (TIDE_MODEL == 'TPXO9-atlas-v3'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas_v3')
grid_file = 'grid_tpxo9_atlas_30_v3.nc.gz'
model_files = ['h_q1_tpxo9_atlas_30_v3.nc.gz','h_o1_tpxo9_atlas_30_v3.nc.gz',
'h_p1_tpxo9_atlas_30_v3.nc.gz','h_k1_tpxo9_atlas_30_v3.nc.gz',
'h_n2_tpxo9_atlas_30_v3.nc.gz','h_m2_tpxo9_atlas_30_v3.nc.gz',
'h_s2_tpxo9_atlas_30_v3.nc.gz','h_k2_tpxo9_atlas_30_v3.nc.gz',
'h_m4_tpxo9_atlas_30_v3.nc.gz','h_ms4_tpxo9_atlas_30_v3.nc.gz',
'h_mn4_tpxo9_atlas_30_v3.nc.gz','h_2n2_tpxo9_atlas_30_v3.nc.gz',
'h_mf_tpxo9_atlas_30_v3.nc.gz','h_mm_tpxo9_atlas_30_v3.nc.gz']
reference = 'https://www.tpxo.net/global/tpxo9-atlas'
variable = 'tide_ocean'
long_name = "Ocean_Tide"
model_format = 'netcdf'
TYPE = 'z'
SCALE = 1.0/1000.0
elif (TIDE_MODEL == 'TPXO9.1'):
grid_file = os.path.join(tide_dir,'TPXO9.1','DATA','grid_tpxo9')
model_file = os.path.join(tide_dir,'TPXO9.1','DATA','h_tpxo9.v1')
reference = 'http://volkov.oce.orst.edu/tides/global.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO8-atlas'):
grid_file = os.path.join(tide_dir,'tpxo8_atlas','grid_tpxo8atlas_30_v1')
model_file = os.path.join(tide_dir,'tpxo8_atlas','hf.tpxo8_atlas_30_v1')
reference = 'http://volkov.oce.orst.edu/tides/tpxo8_atlas.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'ATLAS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO7.2'):
grid_file = os.path.join(tide_dir,'TPXO7.2_tmd','grid_tpxo7.2')
model_file = os.path.join(tide_dir,'TPXO7.2_tmd','h_tpxo7.2')
reference = 'http://volkov.oce.orst.edu/tides/global.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO7.2_load'):
grid_file = os.path.join(tide_dir,'TPXO7.2_load','grid_tpxo6.2')
model_file = os.path.join(tide_dir,'TPXO7.2_load','h_tpxo7.2_load')
reference = 'http://volkov.oce.orst.edu/tides/global.html'
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'AODTM-5'):
grid_file = os.path.join(tide_dir,'aodtm5_tmd','grid_Arc5km')
model_file = os.path.join(tide_dir,'aodtm5_tmd','h0_Arc5km.oce')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aodtm-5/')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPE = 'z'
elif (TIDE_MODEL == 'AOTIM-5'):
grid_file = os.path.join(tide_dir,'aotim5_tmd','grid_Arc5km')
model_file = os.path.join(tide_dir,'aotim5_tmd','h_Arc5km.oce')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aotim-5/')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPE = 'z'
elif (TIDE_MODEL == 'AOTIM-5-2018'):
grid_file = os.path.join(tide_dir,'Arc5km2018','grid_Arc5km2018')
model_file = os.path.join(tide_dir,'Arc5km2018','h_Arc5km2018')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aotim-5/')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPE = 'z'
elif (TIDE_MODEL == 'GOT4.7'):
model_directory = os.path.join(tide_dir,'GOT4.7','grids_oceantide')
model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',
'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']
c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'GOT'
SCALE = 1.0/100.0
elif (TIDE_MODEL == 'GOT4.7_load'):
model_directory = os.path.join(tide_dir,'GOT4.7','grids_loadtide')
model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',
'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',
's1load.d.gz','m4load.d.gz']
c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'GOT'
SCALE = 1.0/1000.0
elif (TIDE_MODEL == 'GOT4.8'):
model_directory = os.path.join(tide_dir,'got4.8','grids_oceantide')
model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',
'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']
c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'GOT'
SCALE = 1.0/100.0
elif (TIDE_MODEL == 'GOT4.8_load'):
model_directory = os.path.join(tide_dir,'got4.8','grids_loadtide')
model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',
'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',
's1load.d.gz','m4load.d.gz']
c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'GOT'
SCALE = 1.0/1000.0
elif (TIDE_MODEL == 'GOT4.10'):
model_directory = os.path.join(tide_dir,'GOT4.10c','grids_oceantide')
model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',
'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']
c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'GOT'
SCALE = 1.0/100.0
elif (TIDE_MODEL == 'GOT4.10_load'):
model_directory = os.path.join(tide_dir,'GOT4.10c','grids_loadtide')
model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',
'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',
's1load.d.gz','m4load.d.gz']
c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'GOT'
SCALE = 1.0/1000.0
elif (TIDE_MODEL == 'FES2014'):
model_directory = os.path.join(tide_dir,'fes2014','ocean_tide')
model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',
'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',
'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',
'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',
'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',
'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',
'ssa.nc.gz','t2.nc.gz']
c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6',
'm8','mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2',
'n4','nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']
reference = ('https://www.aviso.altimetry.fr/data/products/'
'auxiliary-products/global-tide-fes.html')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'FES'
TYPE = 'z'
SCALE = 1.0/100.0
elif (TIDE_MODEL == 'FES2014_load'):
model_directory = os.path.join(tide_dir,'fes2014','load_tide')
model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',
'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',
'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',
'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',
'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',
'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',
'ssa.nc.gz','t2.nc.gz']
c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6',
'm8','mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2',
'n4','nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']
reference = ('https://www.aviso.altimetry.fr/data/products/'
'auxiliary-products/global-tide-fes.html')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'FES'
TYPE = 'z'
SCALE = 1.0/100.0
#-- invalid value
fill_value = -9999.0
#-- output netCDF4 and HDF5 file attributes
#-- will be added to YAML header in csv files
attrib = {}
#-- latitude
attrib['lat'] = {}
attrib['lat']['long_name'] = 'Latitude'
attrib['lat']['units'] = 'Degrees_North'
#-- longitude
attrib['lon'] = {}
attrib['lon']['long_name'] = 'Longitude'
attrib['lon']['units'] = 'Degrees_East'
#-- tides
attrib[output_variable] = {}
attrib[output_variable]['description'] = ('tidal_elevation_from_harmonic_'
'constants')
attrib[output_variable]['model'] = TIDE_MODEL
attrib[output_variable]['units'] = 'meters'
attrib[output_variable]['long_name'] = variable_long_name
attrib[output_variable]['_FillValue'] = fill_value
#-- time
attrib['time'] = {}
attrib['time']['long_name'] = 'Time'
attrib['time']['units'] = 'days since 1992-01-01T00:00:00'
attrib['time']['calendar'] = 'standard'
#-- read input file to extract time, spatial coordinates and data
if (FORMAT == 'csv'):
dinput = pyTMD.spatial.from_ascii(input_file, columns=VARIABLES,
header=0, verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
dinput = pyTMD.spatial.from_netCDF4(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
dinput = pyTMD.spatial.from_HDF5(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
#-- converting x,y from projection to latitude/longitude
#-- could try to extract projection attributes from netCDF4 and HDF5 files
try:
crs1 = pyproj.CRS.from_string("epsg:{0:d}".format(int(PROJECTION)))
except (ValueError,pyproj.exceptions.CRSError):
crs1 = pyproj.CRS.from_string(PROJECTION)
crs2 = pyproj.CRS.from_string("epsg:{0:d}".format(4326))
transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)
lon,lat = transformer.transform(dinput['x'].flatten(),dinput['y'].flatten())
#-- extract time units from netCDF4 and HDF5 attributes or from TIME_UNITS
try:
time_string = dinput['attributes']['time']['units']
except (TypeError, KeyError):
epoch1,to_secs = pyTMD.time.parse_date_string(TIME_UNITS)
else:
epoch1,to_secs = pyTMD.time.parse_date_string(time_string)
#-- convert time from units to days since 1992-01-01T00:00:00
tide_time = pyTMD.time.convert_delta_time(to_secs*dinput['time'].flatten(),
epoch1=epoch1, epoch2=(1992,1,1,0,0,0), scale=1.0/86400.0)
n_time = len(tide_time)
#-- read tidal constants and interpolate to grid points
if model_format in ('OTIS','ATLAS'):
amp,ph,D,c = extract_tidal_constants(lon, lat, grid_file, model_file,
EPSG, TYPE=TYPE, METHOD=METHOD)
deltat = np.zeros((n_time))
elif (model_format == 'netcdf'):
amp,ph,D,c = extract_netcdf_constants(lon, lat, model_directory,
grid_file, model_files, TYPE=TYPE, METHOD=METHOD, SCALE=SCALE)
deltat = np.zeros((n_time))
elif (model_format == 'GOT'):
amp,ph = extract_GOT_constants(lon, lat, model_directory, model_files,
METHOD=METHOD, SCALE=SCALE)
#-- convert times from modified julian days to days since 1992-01-01
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data','merged_deltat.data'])
deltat = calc_delta_time(delta_file,tide_time)
elif (model_format == 'FES'):
amp,ph = extract_FES_constants(lon, lat, model_directory, model_files,
TYPE=TYPE, VERSION=TIDE_MODEL, METHOD=METHOD, SCALE=SCALE)
#-- convert times from modified julian days to days since 1992-01-01
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data','merged_deltat.data'])
deltat = calc_delta_time(delta_file,tide_time)
#-- calculate complex phase in radians for Euler's
cph = -1j*ph*np.pi/180.0
#-- calculate constituent oscillation
hc = amp*np.exp(cph)
#-- predict tidal elevations at time and infer minor corrections
tide = np.ma.zeros((n_time), fill_value=fill_value)
tide.mask = np.any(hc.mask,axis=1)
tide.data[:] = predict_tide_drift(tide_time, hc, c,
DELTAT=deltat, CORRECTIONS=model_format)
minor = infer_minor_corrections(tide_time, hc, c,
DELTAT=deltat, CORRECTIONS=model_format)
tide.data[:] += minor.data[:]
#-- replace invalid values with fill value
tide.data[tide.mask] = tide.fill_value
#-- output to file
output = {'time':tide_time,'lon':lon,'lat':lat,output_variable:tide}
if (FORMAT == 'csv'):
pyTMD.spatial.to_ascii(output, attrib, output_file, delimiter=',',
columns=['time','lat','lon',output_variable], verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
pyTMD.spatial.to_netCDF4(output, attrib, output_file, verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
pyTMD.spatial.to_HDF5(output, attrib, output_file, verbose=VERBOSE)
#-- change the permissions level to MODE
os.chmod(output_file, MODE)
def compute_OPT_ICESat(FILE, METHOD=None, VERBOSE=False, MODE=0o775):
#-- get directory from FILE
print('{0} -->'.format(os.path.basename(FILE))) if VERBOSE else None
DIRECTORY = os.path.dirname(FILE)
#-- compile regular expression operator for extracting information from file
rx = re.compile((r'GLAH(\d{2})_(\d{3})_(\d{1})(\d{1})(\d{2})_(\d{3})_'
r'(\d{4})_(\d{1})_(\d{2})_(\d{4})\.H5'), re.VERBOSE)
#-- extract parameters from ICESat/GLAS HDF5 file name
#-- PRD: Product number (01, 05, 06, 12, 13, 14, or 15)
#-- RL: Release number for process that created the product = 634
#-- RGTP: Repeat ground-track phase (1=8-day, 2=91-day, 3=transfer orbit)
#-- ORB: Reference orbit number (starts at 1 and increments each time a
#-- new reference orbit ground track file is obtained.)
#-- INST: Instance number (increments every time the satellite enters a
#-- different reference orbit)
#-- CYCL: Cycle of reference orbit for this phase
#-- TRK: Track within reference orbit
#-- SEG: Segment of orbit
#-- GRAN: Granule version number
#-- TYPE: File type
PRD, RL, RGTP, ORB, INST, CYCL, TRK, SEG, GRAN, TYPE = rx.findall(
FILE).pop()
#-- read GLAH12 HDF5 file
fileID = h5py.File(FILE, 'r')
n_40HZ, = fileID['Data_40HZ']['Time']['i_rec_ndx'].shape
#-- get variables and attributes
rec_ndx_40HZ = fileID['Data_40HZ']['Time']['i_rec_ndx'][:].copy()
#-- seconds since 2000-01-01 12:00:00 UTC (J2000)
DS_UTCTime_40HZ = fileID['Data_40HZ']['DS_UTCTime_40'][:].copy()
#-- Latitude (degrees North)
lat_TPX = fileID['Data_40HZ']['Geolocation']['d_lat'][:].copy()
#-- Longitude (degrees East)
lon_40HZ = fileID['Data_40HZ']['Geolocation']['d_lon'][:].copy()
#-- Elevation (height above TOPEX/Poseidon ellipsoid in meters)
elev_TPX = fileID['Data_40HZ']['Elevation_Surfaces']['d_elev'][:].copy()
fv = fileID['Data_40HZ']['Elevation_Surfaces']['d_elev'].attrs[
'_FillValue']
#-- convert time from UTC time of day to Modified Julian Days (MJD)
#-- J2000: seconds since 2000-01-01 12:00:00 UTC
t = DS_UTCTime_40HZ[:] / 86400.0 + 51544.5
#-- convert from MJD to calendar dates
YY, MM, DD, HH, MN, SS = pyTMD.time.convert_julian(t + 2400000.5,
FORMAT='tuple')
#-- convert calendar dates into year decimal
tdec = pyTMD.time.convert_calendar_decimal(YY,
MM,
day=DD,
hour=HH,
minute=MN,
second=SS)
#-- semimajor axis (a) and flattening (f) for TP and WGS84 ellipsoids
atop, ftop = (6378136.3, 1.0 / 298.257)
awgs, fwgs = (6378137.0, 1.0 / 298.257223563)
#-- convert from Topex/Poseidon to WGS84 Ellipsoids
lat_40HZ, elev_40HZ = pyTMD.spatial.convert_ellipsoid(lat_TPX,
elev_TPX,
atop,
ftop,
awgs,
fwgs,
eps=1e-12,
itmax=10)
#-- degrees to radians and arcseconds to radians
dtr = np.pi / 180.0
atr = np.pi / 648000.0
#-- earth and physical parameters (IERS)
G = 6.67428e-11 #-- universal constant of gravitation [m^3/(kg*s^2)]
GM = 3.986004418e14 #-- geocentric gravitational constant [m^3/s^2]
ge = 9.7803278 #-- mean equatorial gravity [m/s^2]
a_axis = 6378136.6 #-- equatorial radius of the Earth [m]
flat = 1.0 / 298.257223563 #-- flattening of the ellipsoid
omega = 7.292115e-5 #-- mean rotation rate of the Earth [radians/s]
rho_w = 1025.0 #-- density of sea water [kg/m^3]
ge = 9.7803278 #-- mean equatorial gravitational acceleration [m/s^2]
#-- Linear eccentricity and first numerical eccentricity
lin_ecc = np.sqrt((2.0 * flat - flat**2) * a_axis**2)
ecc1 = lin_ecc / a_axis
#-- tidal love number differential (1 + kl - hl) for pole tide frequencies
gamma = 0.6870 + 0.0036j
#-- convert from geodetic latitude to geocentric latitude
#-- geodetic latitude in radians
latitude_geodetic_rad = lat_40HZ * dtr
#-- prime vertical radius of curvature
N = a_axis / np.sqrt(1.0 - ecc1**2.0 * np.sin(latitude_geodetic_rad)**2.0)
#-- calculate X, Y and Z from geodetic latitude and longitude
X = (N + elev_40HZ) * np.cos(latitude_geodetic_rad) * np.cos(
lon_40HZ * dtr)
Y = (N + elev_40HZ) * np.cos(latitude_geodetic_rad) * np.sin(
lon_40HZ * dtr)
Z = (N * (1.0 - ecc1**2.0) + elev_40HZ) * np.sin(latitude_geodetic_rad)
rr = np.sqrt(X**2.0 + Y**2.0 + Z**2.0)
#-- calculate geocentric latitude and convert to degrees
latitude_geocentric = np.arctan(Z / np.sqrt(X**2.0 + Y**2.0)) / dtr
#-- pole tide displacement scale factor
Hp = np.sqrt(8.0 * np.pi / 15.0) * (omega**2 * a_axis**4) / GM
K = 4.0 * np.pi * G * rho_w * Hp * a_axis / (3.0 * ge)
K1 = 4.0 * np.pi * G * rho_w * Hp * a_axis**3 / (3.0 * GM)
#-- read ocean pole tide map from Desai (2002)
ocean_pole_tide_file = get_data_path(['data', 'opoleloadcoefcmcor.txt.gz'])
iur, iun, iue, ilon, ilat = read_ocean_pole_tide(ocean_pole_tide_file)
#-- pole tide files (mean and daily)
mean_pole_file = get_data_path(['data', 'mean-pole.tab'])
pole_tide_file = get_data_path(['data', 'finals.all'])
#-- read IERS daily polar motion values
EOP = read_iers_EOP(pole_tide_file)
#-- create cubic spline interpolations of daily polar motion values
xSPL = scipy.interpolate.UnivariateSpline(EOP['MJD'], EOP['x'], k=3, s=0)
ySPL = scipy.interpolate.UnivariateSpline(EOP['MJD'], EOP['y'], k=3, s=0)
#-- interpolate ocean pole tide map from Desai (2002)
if (METHOD == 'spline'):
#-- use scipy bivariate splines to interpolate to output points
f1 = scipy.interpolate.RectBivariateSpline(ilon,
ilat[::-1],
iur[:, ::-1].real,
kx=1,
ky=1)
f2 = scipy.interpolate.RectBivariateSpline(ilon,
ilat[::-1],
iur[:, ::-1].imag,
kx=1,
ky=1)
UR = np.zeros((n_40HZ), dtype=np.complex128)
UR.real = f1.ev(lon_40HZ, latitude_geocentric)
UR.imag = f2.ev(lon_40HZ, latitude_geocentric)
else:
#-- use scipy regular grid to interpolate values for a given method
r1 = scipy.interpolate.RegularGridInterpolator((ilon, ilat[::-1]),
iur[:, ::-1],
method=METHOD)
UR = r1.__call__(np.c_[lon_40HZ, latitude_geocentric])
#-- calculate angular coordinates of mean pole at time tdec
mpx, mpy, fl = iers_mean_pole(mean_pole_file, tdec, '2015')
#-- interpolate daily polar motion values to t using cubic splines
px = xSPL(t)
py = ySPL(t)
#-- calculate differentials from mean pole positions
mx = px - mpx
my = -(py - mpy)
#-- calculate radial displacement at time
Urad = np.ma.zeros((n_40HZ), fill_value=fv)
Urad.data[:] = K * atr * np.real(
(mx * gamma.real + my * gamma.imag) * UR.real +
(my * gamma.real - mx * gamma.imag) * UR.imag)
#-- replace fill values
Urad.mask = np.isnan(Urad.data)
Urad.data[Urad.mask] = Urad.fill_value
#-- copy variables for outputting to HDF5 file
IS_gla12_tide = dict(Data_40HZ={})
IS_gla12_fill = dict(Data_40HZ={})
IS_gla12_tide_attrs = dict(Data_40HZ={})
#-- copy global file attributes
global_attribute_list = [
'featureType', 'title', 'comment', 'summary', 'license', 'references',
'AccessConstraints', 'CitationforExternalPublication',
'contributor_role', 'contributor_name', 'creator_name',
'creator_email', 'publisher_name', 'publisher_email', 'publisher_url',
'platform', 'instrument', 'processing_level', 'date_created',
'spatial_coverage_type', 'history', 'keywords', 'keywords_vocabulary',
'naming_authority', 'project', 'time_type', 'date_type',
'time_coverage_start', 'time_coverage_end', 'time_coverage_duration',
'source', 'HDFVersion', 'identifier_product_type',
'identifier_product_format_version', 'Conventions', 'institution',
'ReprocessingPlanned', 'ReprocessingActual', 'LocalGranuleID',
'ProductionDateTime', 'LocalVersionID', 'PGEVersion', 'OrbitNumber',
'StartOrbitNumber', 'StopOrbitNumber', 'EquatorCrossingLongitude',
'EquatorCrossingTime', 'EquatorCrossingDate', 'ShortName', 'VersionID',
'InputPointer', 'RangeBeginningTime', 'RangeEndingTime',
'RangeBeginningDate', 'RangeEndingDate', 'PercentGroundHit',
'OrbitQuality', 'Cycle', 'Track', 'Instrument_State', 'Timing_Bias',
'ReferenceOrbit', 'SP_ICE_PATH_NO', 'SP_ICE_GLAS_StartBlock',
'SP_ICE_GLAS_EndBlock', 'Instance', 'Range_Bias',
'Instrument_State_Date', 'Instrument_State_Time', 'Range_Bias_Date',
'Range_Bias_Time', 'Timing_Bias_Date', 'Timing_Bias_Time',
'identifier_product_doi', 'identifier_file_uuid',
'identifier_product_doi_authority'
]
for att in global_attribute_list:
IS_gla12_tide_attrs[att] = fileID.attrs[att]
#-- add attributes for input GLA12 file
IS_gla12_tide_attrs['input_files'] = os.path.basename(FILE)
#-- update geospatial ranges for ellipsoid
IS_gla12_tide_attrs['geospatial_lat_min'] = np.min(lat_40HZ)
IS_gla12_tide_attrs['geospatial_lat_max'] = np.max(lat_40HZ)
IS_gla12_tide_attrs['geospatial_lon_min'] = np.min(lon_40HZ)
IS_gla12_tide_attrs['geospatial_lon_max'] = np.max(lon_40HZ)
IS_gla12_tide_attrs['geospatial_lat_units'] = "degrees_north"
IS_gla12_tide_attrs['geospatial_lon_units'] = "degrees_east"
IS_gla12_tide_attrs['geospatial_ellipsoid'] = "WGS84"
#-- copy 40Hz group attributes
for att_name, att_val in fileID['Data_40HZ'].attrs.items():
IS_gla12_tide_attrs['Data_40HZ'][att_name] = att_val
#-- copy attributes for time, geolocation and geophysical groups
for var in ['Time', 'Geolocation', 'Geophysical']:
IS_gla12_tide['Data_40HZ'][var] = {}
IS_gla12_fill['Data_40HZ'][var] = {}
IS_gla12_tide_attrs['Data_40HZ'][var] = {}
for att_name, att_val in fileID['Data_40HZ'][var].attrs.items():
IS_gla12_tide_attrs['Data_40HZ'][var][att_name] = att_val
#-- J2000 time
IS_gla12_tide['Data_40HZ']['DS_UTCTime_40'] = DS_UTCTime_40HZ
IS_gla12_fill['Data_40HZ']['DS_UTCTime_40'] = None
IS_gla12_tide_attrs['Data_40HZ']['DS_UTCTime_40'] = {}
for att_name, att_val in fileID['Data_40HZ']['DS_UTCTime_40'].attrs.items(
):
if att_name not in ('DIMENSION_LIST', 'CLASS', 'NAME'):
IS_gla12_tide_attrs['Data_40HZ']['DS_UTCTime_40'][
att_name] = att_val
#-- record
IS_gla12_tide['Data_40HZ']['Time']['i_rec_ndx'] = rec_ndx_40HZ
IS_gla12_fill['Data_40HZ']['Time']['i_rec_ndx'] = None
IS_gla12_tide_attrs['Data_40HZ']['Time']['i_rec_ndx'] = {}
for att_name, att_val in fileID['Data_40HZ']['Time'][
'i_rec_ndx'].attrs.items():
if att_name not in ('DIMENSION_LIST', 'CLASS', 'NAME'):
IS_gla12_tide_attrs['Data_40HZ']['Time']['i_rec_ndx'][
att_name] = att_val
#-- latitude
IS_gla12_tide['Data_40HZ']['Geolocation']['d_lat'] = lat_40HZ
IS_gla12_fill['Data_40HZ']['Geolocation']['d_lat'] = None
IS_gla12_tide_attrs['Data_40HZ']['Geolocation']['d_lat'] = {}
for att_name, att_val in fileID['Data_40HZ']['Geolocation'][
'd_lat'].attrs.items():
if att_name not in ('DIMENSION_LIST', 'CLASS', 'NAME'):
IS_gla12_tide_attrs['Data_40HZ']['Geolocation']['d_lat'][
att_name] = att_val
#-- longitude
IS_gla12_tide['Data_40HZ']['Geolocation']['d_lon'] = lon_40HZ
IS_gla12_fill['Data_40HZ']['Geolocation']['d_lon'] = None
IS_gla12_tide_attrs['Data_40HZ']['Geolocation']['d_lon'] = {}
for att_name, att_val in fileID['Data_40HZ']['Geolocation'][
'd_lon'].attrs.items():
if att_name not in ('DIMENSION_LIST', 'CLASS', 'NAME'):
IS_gla12_tide_attrs['Data_40HZ']['Geolocation']['d_lon'][
att_name] = att_val
#-- geophysical variables
#-- computed ocean pole tide
IS_gla12_tide['Data_40HZ']['Geophysical']['d_opElv'] = Urad
IS_gla12_fill['Data_40HZ']['Geophysical']['d_opElv'] = Urad.fill_value
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_opElv'] = {}
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_opElv'][
'units'] = "meters"
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_opElv']['long_name'] = \
"Ocean Pole Tide"
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_opElv'][
'description'] = ("Ocean "
"pole tide radial displacements due to polar motion")
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_opElv']['reference'] = \
'ftp://tai.bipm.org/iers/conv2010/chapter7/opoleloadcoefcmcor.txt.gz'
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_opElv']['coordinates'] = \
"../DS_UTCTime_40"
#-- close the input HDF5 file
fileID.close()
#-- output tidal HDF5 file
args = (PRD, RL, RGTP, ORB, INST, CYCL, TRK, SEG, GRAN, TYPE)
file_format = 'GLAH{0}_{1}_OPT_{2}{3}{4}_{5}_{6}_{7}_{8}_{9}.h5'
#-- print file information
print('\t{0}'.format(file_format.format(*args))) if VERBOSE else None
HDF5_GLA12_tide_write(IS_gla12_tide,
IS_gla12_tide_attrs,
FILENAME=os.path.join(DIRECTORY,
file_format.format(*args)),
FILL_VALUE=IS_gla12_fill,
CLOBBER=True)
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY, file_format.format(*args)), MODE)
def compute_LPET_ICESat2(INPUT_FILE, VERBOSE=False, MODE=0o775):
#-- read data from input file
print('{0} -->'.format(os.path.basename(INPUT_FILE))) if VERBOSE else None
IS2_atl07_mds, IS2_atl07_attrs, IS2_atl07_beams = read_HDF5_ATL07(
INPUT_FILE, ATTRIBUTES=True)
DIRECTORY = os.path.dirname(INPUT_FILE)
#-- extract parameters from ICESat-2 ATLAS HDF5 sea ice file name
rx = re.compile(
r'(processed_)?(ATL\d{2})-(\d{2})_(\d{4})(\d{2})(\d{2})'
r'(\d{2})(\d{2})(\d{2})_(\d{4})(\d{2})(\d{2})_(\d{3})_(\d{2})(.*?).h5$'
)
try:
SUB, PRD, HEM, YY, MM, DD, HH, MN, SS, TRK, CYCL, SN, RL, VERS, AUX = rx.findall(
INPUT_FILE).pop()
except:
#-- output long-period equilibrium tide HDF5 file (generic)
fileBasename, fileExtension = os.path.splitext(INPUT_FILE)
OUTPUT_FILE = '{0}_{1}{2}'.format(fileBasename, 'LPET', fileExtension)
else:
#-- output long-period equilibrium tide HDF5 file for ASAS/NSIDC granules
args = (PRD, HEM, YY, MM, DD, HH, MN, SS, TRK, CYCL, SN, RL, VERS, AUX)
file_format = '{0}-{1}_LPET_{2}{3}{4}{5}{6}{7}_{8}{9}{10}_{11}_{12}{13}.h5'
OUTPUT_FILE = file_format.format(*args)
#-- number of GPS seconds between the GPS epoch
#-- and ATLAS Standard Data Product (SDP) epoch
atlas_sdp_gps_epoch = IS2_atl07_mds['ancillary_data'][
'atlas_sdp_gps_epoch']
#-- copy variables for outputting to HDF5 file
IS2_atl07_tide = {}
IS2_atl07_fill = {}
IS2_atl07_dims = {}
IS2_atl07_tide_attrs = {}
#-- number of GPS seconds between the GPS epoch (1980-01-06T00:00:00Z UTC)
#-- and ATLAS Standard Data Product (SDP) epoch (2018-01-01T00:00:00Z UTC)
#-- Add this value to delta time parameters to compute full gps_seconds
IS2_atl07_tide['ancillary_data'] = {}
IS2_atl07_tide_attrs['ancillary_data'] = {}
for key in ['atlas_sdp_gps_epoch']:
#-- get each HDF5 variable
IS2_atl07_tide['ancillary_data'][key] = IS2_atl07_mds[
'ancillary_data'][key]
#-- Getting attributes of group and included variables
IS2_atl07_tide_attrs['ancillary_data'][key] = {}
for att_name, att_val in IS2_atl07_attrs['ancillary_data'][key].items(
):
IS2_atl07_tide_attrs['ancillary_data'][key][att_name] = att_val
#-- for each input beam within the file
for gtx in sorted(IS2_atl07_beams):
#-- output data dictionaries for beam
IS2_atl07_tide[gtx] = dict(sea_ice_segments={})
IS2_atl07_fill[gtx] = dict(sea_ice_segments={})
IS2_atl07_dims[gtx] = dict(sea_ice_segments={})
IS2_atl07_tide_attrs[gtx] = dict(sea_ice_segments={})
#-- number of segments
val = IS2_atl07_mds[gtx]['sea_ice_segments']
#-- convert time from ATLAS SDP to days relative to Jan 1, 1992
gps_seconds = atlas_sdp_gps_epoch + val['delta_time']
leap_seconds = pyTMD.time.count_leap_seconds(gps_seconds)
tide_time = pyTMD.time.convert_delta_time(gps_seconds - leap_seconds,
epoch1=(1980, 1, 6, 0, 0, 0),
epoch2=(1992, 1, 1, 0, 0, 0),
scale=1.0 / 86400.0)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data', 'merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- predict long-period equilibrium tides at latitudes and time
tide_lpe = compute_equilibrium_tide(tide_time + deltat,
val['latitude'])
#-- group attributes for beam
IS2_atl07_tide_attrs[gtx]['Description'] = IS2_atl07_attrs[gtx][
'Description']
IS2_atl07_tide_attrs[gtx]['atlas_pce'] = IS2_atl07_attrs[gtx][
'atlas_pce']
IS2_atl07_tide_attrs[gtx]['atlas_beam_type'] = IS2_atl07_attrs[gtx][
'atlas_beam_type']
IS2_atl07_tide_attrs[gtx]['groundtrack_id'] = IS2_atl07_attrs[gtx][
'groundtrack_id']
IS2_atl07_tide_attrs[gtx]['atmosphere_profile'] = IS2_atl07_attrs[gtx][
'atmosphere_profile']
IS2_atl07_tide_attrs[gtx]['atlas_spot_number'] = IS2_atl07_attrs[gtx][
'atlas_spot_number']
IS2_atl07_tide_attrs[gtx]['sc_orientation'] = IS2_atl07_attrs[gtx][
'sc_orientation']
#-- group attributes for sea_ice_segments
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['Description'] = (
"Top group for sea "
"ice segments as computed by the ATBD algorithm.")
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['data_rate'] = (
"Data within this "
"group are stored at the variable segment rate.")
#-- geolocation, time and segment ID
#-- delta time
IS2_atl07_tide[gtx]['sea_ice_segments']['delta_time'] = val[
'delta_time'].copy()
IS2_atl07_fill[gtx]['sea_ice_segments']['delta_time'] = None
IS2_atl07_dims[gtx]['sea_ice_segments']['delta_time'] = None
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['delta_time'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['delta_time'][
'units'] = "seconds since 2018-01-01"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['delta_time'][
'long_name'] = "Elapsed GPS seconds"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['delta_time'][
'standard_name'] = "time"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['delta_time'][
'source'] = "telemetry"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['delta_time'][
'calendar'] = "standard"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['delta_time']['description'] = (
"Number of "
"GPS seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch "
"offset is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS "
"seconds between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP "
"epoch. By adding the offset contained within atlas_sdp_gps_epoch to delta time "
"parameters, the time in gps_seconds relative to the GPS epoch can be computed."
)
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['delta_time']['coordinates'] = \
"height_segment_id latitude longitude"
#-- latitude
IS2_atl07_tide[gtx]['sea_ice_segments']['latitude'] = val[
'latitude'].copy()
IS2_atl07_fill[gtx]['sea_ice_segments']['latitude'] = None
IS2_atl07_dims[gtx]['sea_ice_segments']['latitude'] = ['delta_time']
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude'][
'units'] = "degrees_north"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude'][
'contentType'] = "physicalMeasurement"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude'][
'long_name'] = "Latitude"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude'][
'standard_name'] = "latitude"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude'][
'description'] = ("Latitude of "
"segment center")
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude'][
'valid_min'] = -90.0
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude'][
'valid_max'] = 90.0
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['latitude']['coordinates'] = \
"height_segment_id delta_time longitude"
#-- longitude
IS2_atl07_tide[gtx]['sea_ice_segments']['longitude'] = val[
'longitude'].copy()
IS2_atl07_fill[gtx]['sea_ice_segments']['longitude'] = None
IS2_atl07_dims[gtx]['sea_ice_segments']['longitude'] = ['delta_time']
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude'][
'units'] = "degrees_east"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude'][
'contentType'] = "physicalMeasurement"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude'][
'long_name'] = "Longitude"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude'][
'standard_name'] = "longitude"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude'][
'description'] = ("Longitude of "
"segment center")
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude'][
'valid_min'] = -180.0
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude'][
'valid_max'] = 180.0
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['longitude']['coordinates'] = \
"height_segment_id delta_time latitude"
#-- segment ID
IS2_atl07_tide[gtx]['sea_ice_segments']['height_segment_id'] = val[
'height_segment_id']
IS2_atl07_fill[gtx]['sea_ice_segments']['height_segment_id'] = None
IS2_atl07_dims[gtx]['sea_ice_segments']['height_segment_id'] = [
'delta_time'
]
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['height_segment_id'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['height_segment_id'][
'units'] = "1"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['height_segment_id'][
'contentType'] = "referenceInformation"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['height_segment_id']['long_name'] = \
"Identifier of each height segment"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['height_segment_id']['description'] = \
"Identifier of each height segment"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['height_segment_id']['coordinates'] = \
"delta_time latitude longitude"
#-- geolocation segment beginning
IS2_atl07_tide[gtx]['sea_ice_segments']['geoseg_beg'] = val[
'geoseg_beg'].copy()
IS2_atl07_fill[gtx]['sea_ice_segments']['geoseg_beg'] = None
IS2_atl07_dims[gtx]['sea_ice_segments']['geoseg_beg'] = ['delta_time']
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_beg'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_beg'][
'units'] = "1"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_beg'][
'contentType'] = "referenceInformation"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_beg'][
'long_name'] = "Beginning GEOSEG"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_beg']['description'] = \
"Geolocation segment (geoseg) ID associated with the first photon used in this sea ice segment"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_beg']['coordinates'] = \
"height_segment_id delta_time latitude longitude"
#-- geolocation segment ending
IS2_atl07_tide[gtx]['sea_ice_segments']['geoseg_end'] = val[
'geoseg_end'].copy()
IS2_atl07_fill[gtx]['sea_ice_segments']['geoseg_end'] = None
IS2_atl07_dims[gtx]['sea_ice_segments']['geoseg_end'] = ['delta_time']
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_end'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_end'][
'units'] = "1"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_end'][
'contentType'] = "referenceInformation"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_end'][
'long_name'] = "Ending GEOSEG"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_end']['description'] = \
"Geolocation segment (geoseg) ID associated with the last photon used in this sea ice segment"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geoseg_end']['coordinates'] = \
"height_segment_id delta_time latitude longitude"
#-- along track distance
IS2_atl07_tide[gtx]['sea_ice_segments']['seg_dist_x'] = val[
'seg_dist_x'].copy()
IS2_atl07_fill[gtx]['sea_ice_segments']['seg_dist_x'] = None
IS2_atl07_dims[gtx]['sea_ice_segments']['seg_dist_x'] = ['delta_time']
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['seg_dist_x'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['seg_dist_x'][
'units'] = "meters"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['seg_dist_x'][
'contentType'] = "referenceInformation"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['seg_dist_x'][
'long_name'] = "Along track distance"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['seg_dist_x']['description'] = \
"Along-track distance from the equator crossing to the segment center."
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['seg_dist_x']['coordinates'] = \
"height_segment_id delta_time latitude longitude"
#-- geophysical variables
IS2_atl07_tide[gtx]['sea_ice_segments']['geophysical'] = {}
IS2_atl07_fill[gtx]['sea_ice_segments']['geophysical'] = {}
IS2_atl07_dims[gtx]['sea_ice_segments']['geophysical'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical'][
'Description'] = (
"Contains geophysical "
"parameters and corrections used to correct photon heights for geophysical effects, such as tides."
)
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical'][
'data_rate'] = ("Data within this group "
"are stored at the sea_ice_height segment rate.")
#-- computed long-period equilibrium tide
IS2_atl07_tide[gtx]['sea_ice_segments']['geophysical'][
'height_segment_lpe'] = tide_lpe
IS2_atl07_fill[gtx]['sea_ice_segments']['geophysical'][
'height_segment_lpe'] = None
IS2_atl07_dims[gtx]['sea_ice_segments']['geophysical'][
'height_segment_lpe'] = ['delta_time']
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical'][
'height_segment_lpe'] = {}
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical'][
'height_segment_lpe']['units'] = "meters"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical'][
'height_segment_lpe']['contentType'] = "referenceInformation"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical']['height_segment_lpe']['long_name'] = \
"Long Period Equilibrium Tide"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical'][
'height_segment_lpe']['description'] = (
"Long-period "
"equilibrium tidal elevation from the summation of fifteen tidal spectral lines"
)
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical']['height_segment_lpe']['reference'] = \
"https://doi.org/10.1111/j.1365-246X.1973.tb03420.x"
IS2_atl07_tide_attrs[gtx]['sea_ice_segments']['geophysical']['height_segment_lpe']['coordinates'] = \
"../height_segment_id ../delta_time ../latitude ../longitude"
#-- print file information
print('\t{0}'.format(OUTPUT_FILE)) if VERBOSE else None
HDF5_ATL07_tide_write(IS2_atl07_tide,
IS2_atl07_tide_attrs,
CLOBBER=True,
INPUT=os.path.basename(INPUT_FILE),
FILL_VALUE=IS2_atl07_fill,
DIMENSIONS=IS2_atl07_dims,
FILENAME=os.path.join(DIRECTORY, OUTPUT_FILE))
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY, OUTPUT_FILE), MODE)
def compute_LPET_ICESat2(INPUT_FILE, VERBOSE=False, MODE=0o775):
#-- read data from input file
print('{0} -->'.format(os.path.basename(INPUT_FILE))) if VERBOSE else None
IS2_atl03_mds, IS2_atl03_attrs, IS2_atl03_beams = read_HDF5_ATL03_main(
INPUT_FILE, ATTRIBUTES=True)
DIRECTORY = os.path.dirname(INPUT_FILE)
#-- extract parameters from ICESat-2 ATLAS HDF5 file name
rx = re.compile(
r'(processed_)?(ATL\d{2})_(\d{4})(\d{2})(\d{2})(\d{2})'
r'(\d{2})(\d{2})_(\d{4})(\d{2})(\d{2})_(\d{3})_(\d{2})(.*?).h5$')
try:
SUB, PRD, YY, MM, DD, HH, MN, SS, TRK, CYCL, GRAN, RL, VERS, AUX = rx.findall(
INPUT_FILE).pop()
except:
#-- output long-period equilibrium tide HDF5 file (generic)
fileBasename, fileExtension = os.path.splitext(INPUT_FILE)
OUTPUT_FILE = '{0}_{1}{2}'.format(fileBasename, 'LPET', fileExtension)
else:
#-- output long-period equilibrium tide HDF5 file for ASAS/NSIDC granules
args = (PRD, YY, MM, DD, HH, MN, SS, TRK, CYCL, GRAN, RL, VERS, AUX)
file_format = '{0}_LPET_{1}{2}{3}{4}{5}{6}_{7}{8}{9}_{10}_{11}{12}.h5'
OUTPUT_FILE = file_format.format(*args)
#-- number of GPS seconds between the GPS epoch
#-- and ATLAS Standard Data Product (SDP) epoch
atlas_sdp_gps_epoch = IS2_atl03_mds['ancillary_data'][
'atlas_sdp_gps_epoch']
#-- copy variables for outputting to HDF5 file
IS2_atl03_tide = {}
IS2_atl03_fill = {}
IS2_atl03_dims = {}
IS2_atl03_tide_attrs = {}
#-- number of GPS seconds between the GPS epoch (1980-01-06T00:00:00Z UTC)
#-- and ATLAS Standard Data Product (SDP) epoch (2018-01-01T00:00:00Z UTC)
#-- Add this value to delta time parameters to compute full gps_seconds
IS2_atl03_tide['ancillary_data'] = {}
IS2_atl03_tide_attrs['ancillary_data'] = {}
for key in ['atlas_sdp_gps_epoch']:
#-- get each HDF5 variable
IS2_atl03_tide['ancillary_data'][key] = IS2_atl03_mds[
'ancillary_data'][key]
#-- Getting attributes of group and included variables
IS2_atl03_tide_attrs['ancillary_data'][key] = {}
for att_name, att_val in IS2_atl03_attrs['ancillary_data'][key].items(
):
IS2_atl03_tide_attrs['ancillary_data'][key][att_name] = att_val
#-- for each input beam within the file
for gtx in sorted(IS2_atl03_beams):
#-- output data dictionaries for beam
IS2_atl03_tide[gtx] = dict(geolocation={}, geophys_corr={})
IS2_atl03_fill[gtx] = dict(geolocation={}, geophys_corr={})
IS2_atl03_dims[gtx] = dict(geolocation={}, geophys_corr={})
IS2_atl03_tide_attrs[gtx] = dict(geolocation={}, geophys_corr={})
#-- read data and attributes for beam
val, attrs = read_HDF5_ATL03_beam(INPUT_FILE, gtx, ATTRIBUTES=True)
#-- number of segments
n_seg = len(val['geolocation']['segment_id'])
#-- extract variables for computing equilibrium tides
segment_id = val['geolocation']['segment_id'].copy()
delta_time = val['geolocation']['delta_time'].copy()
lon = val['geolocation']['reference_photon_lon'].copy()
lat = val['geolocation']['reference_photon_lat'].copy()
#-- invalid value
fv = attrs['geolocation']['sigma_h']['_FillValue']
#-- convert time from ATLAS SDP to days relative to Jan 1, 1992
gps_seconds = atlas_sdp_gps_epoch + delta_time
leap_seconds = pyTMD.time.count_leap_seconds(gps_seconds)
tide_time = pyTMD.time.convert_delta_time(gps_seconds - leap_seconds,
epoch1=(1980, 1, 6, 0, 0, 0),
epoch2=(1992, 1, 1, 0, 0, 0),
scale=1.0 / 86400.0)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data', 'merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- predict long-period equilibrium tides at latitudes and time
tide_lpe = compute_equilibrium_tide(tide_time + deltat, lat)
#-- group attributes for beam
IS2_atl03_tide_attrs[gtx]['Description'] = attrs['Description']
IS2_atl03_tide_attrs[gtx]['atlas_pce'] = attrs['atlas_pce']
IS2_atl03_tide_attrs[gtx]['atlas_beam_type'] = attrs['atlas_beam_type']
IS2_atl03_tide_attrs[gtx]['groundtrack_id'] = attrs['groundtrack_id']
IS2_atl03_tide_attrs[gtx]['atmosphere_profile'] = attrs[
'atmosphere_profile']
IS2_atl03_tide_attrs[gtx]['atlas_spot_number'] = attrs[
'atlas_spot_number']
IS2_atl03_tide_attrs[gtx]['sc_orientation'] = attrs['sc_orientation']
#-- group attributes for geolocation
IS2_atl03_tide_attrs[gtx]['geolocation']['Description'] = (
"Contains parameters related to "
"geolocation. The rate of all of these parameters is at the rate corresponding to the "
"ICESat-2 Geolocation Along Track Segment interval (nominally 20 m along-track)."
)
IS2_atl03_tide_attrs[gtx]['geolocation']['data_rate'] = (
"Data within this group are "
"stored at the ICESat-2 20m segment rate.")
#-- group attributes for geophys_corr
IS2_atl03_tide_attrs[gtx]['geophys_corr']['Description'] = (
"Contains parameters used to "
"correct photon heights for geophysical effects, such as tides. These parameters are "
"posted at the same interval as the ICESat-2 Geolocation Along-Track Segment interval "
"(nominally 20m along-track).")
IS2_atl03_tide_attrs[gtx]['geophys_corr']['data_rate'] = (
"These parameters are stored at "
"the ICESat-2 Geolocation Along Track Segment rate (nominally every 20 m along-track)."
)
#-- geolocation, time and segment ID
#-- delta time in geolocation group
IS2_atl03_tide[gtx]['geolocation']['delta_time'] = delta_time
IS2_atl03_fill[gtx]['geolocation']['delta_time'] = None
IS2_atl03_dims[gtx]['geolocation']['delta_time'] = None
IS2_atl03_tide_attrs[gtx]['geolocation']['delta_time'] = {}
IS2_atl03_tide_attrs[gtx]['geolocation']['delta_time'][
'units'] = "seconds since 2018-01-01"
IS2_atl03_tide_attrs[gtx]['geolocation']['delta_time'][
'long_name'] = "Elapsed GPS seconds"
IS2_atl03_tide_attrs[gtx]['geolocation']['delta_time'][
'standard_name'] = "time"
IS2_atl03_tide_attrs[gtx]['geolocation']['delta_time'][
'calendar'] = "standard"
IS2_atl03_tide_attrs[gtx]['geolocation']['delta_time']['description'] = (
"Elapsed seconds "
"from the ATLAS SDP GPS Epoch, corresponding to the transmit time of the reference "
"photon. The ATLAS Standard Data Products (SDP) epoch offset is defined within "
"/ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds between the GPS epoch "
"(1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By adding the offset "
"contained within atlas_sdp_gps_epoch to delta time parameters, the time in gps_seconds "
"relative to the GPS epoch can be computed.")
IS2_atl03_tide_attrs[gtx]['geolocation']['delta_time']['coordinates'] = \
"segment_id reference_photon_lat reference_photon_lon"
#-- delta time in geophys_corr group
IS2_atl03_tide[gtx]['geophys_corr']['delta_time'] = delta_time
IS2_atl03_fill[gtx]['geophys_corr']['delta_time'] = None
IS2_atl03_dims[gtx]['geophys_corr']['delta_time'] = None
IS2_atl03_tide_attrs[gtx]['geophys_corr']['delta_time'] = {}
IS2_atl03_tide_attrs[gtx]['geophys_corr']['delta_time'][
'units'] = "seconds since 2018-01-01"
IS2_atl03_tide_attrs[gtx]['geophys_corr']['delta_time'][
'long_name'] = "Elapsed GPS seconds"
IS2_atl03_tide_attrs[gtx]['geophys_corr']['delta_time'][
'standard_name'] = "time"
IS2_atl03_tide_attrs[gtx]['geophys_corr']['delta_time'][
'calendar'] = "standard"
IS2_atl03_tide_attrs[gtx]['geophys_corr']['delta_time']['description'] = (
"Elapsed seconds "
"from the ATLAS SDP GPS Epoch, corresponding to the transmit time of the reference "
"photon. The ATLAS Standard Data Products (SDP) epoch offset is defined within "
"/ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds between the GPS epoch "
"(1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By adding the offset "
"contained within atlas_sdp_gps_epoch to delta time parameters, the time in gps_seconds "
"relative to the GPS epoch can be computed.")
IS2_atl03_tide_attrs[gtx]['geophys_corr']['delta_time']['coordinates'] = (
"../geolocation/segment_id "
"../geolocation/reference_photon_lat ../geolocation/reference_photon_lon"
)
#-- latitude
IS2_atl03_tide[gtx]['geolocation']['reference_photon_lat'] = lat
IS2_atl03_fill[gtx]['geolocation']['reference_photon_lat'] = None
IS2_atl03_dims[gtx]['geolocation']['reference_photon_lat'] = [
'delta_time'
]
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat'] = {}
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat'][
'units'] = "degrees_north"
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat'][
'contentType'] = "physicalMeasurement"
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat'][
'long_name'] = "Latitude"
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat'][
'standard_name'] = "latitude"
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat'][
'description'] = (
"Latitude of each "
"reference photon. Computed from the ECF Cartesian coordinates of the bounce point."
)
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat'][
'valid_min'] = -90.0
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat'][
'valid_max'] = 90.0
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lat']['coordinates'] = \
"segment_id delta_time reference_photon_lon"
#-- longitude
IS2_atl03_tide[gtx]['geolocation']['reference_photon_lon'] = lon
IS2_atl03_fill[gtx]['geolocation']['reference_photon_lon'] = None
IS2_atl03_dims[gtx]['geolocation']['reference_photon_lon'] = [
'delta_time'
]
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon'] = {}
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon'][
'units'] = "degrees_east"
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon'][
'contentType'] = "physicalMeasurement"
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon'][
'long_name'] = "Longitude"
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon'][
'standard_name'] = "longitude"
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon'][
'description'] = (
"Longitude of each "
"reference photon. Computed from the ECF Cartesian coordinates of the bounce point."
)
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon'][
'valid_min'] = -180.0
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon'][
'valid_max'] = 180.0
IS2_atl03_tide_attrs[gtx]['geolocation']['reference_photon_lon']['coordinates'] = \
"segment_id delta_time reference_photon_lat"
#-- segment ID
IS2_atl03_tide[gtx]['geolocation']['segment_id'] = segment_id
IS2_atl03_fill[gtx]['geolocation']['segment_id'] = None
IS2_atl03_dims[gtx]['geolocation']['segment_id'] = ['delta_time']
IS2_atl03_tide_attrs[gtx]['geolocation']['segment_id'] = {}
IS2_atl03_tide_attrs[gtx]['geolocation']['segment_id']['units'] = "1"
IS2_atl03_tide_attrs[gtx]['geolocation']['segment_id'][
'contentType'] = "referenceInformation"
IS2_atl03_tide_attrs[gtx]['geolocation']['segment_id'][
'long_name'] = "Along-track segment ID number"
IS2_atl03_tide_attrs[gtx]['geolocation']['segment_id']['description'] = (
"A 7 digit number "
"identifying the along-track geolocation segment number. These are sequential, starting with "
"1 for the first segment after an ascending equatorial crossing node. Equal to the segment_id for "
"the second of the two 20m ATL03 segments included in the 40m ATL03 segment"
)
IS2_atl03_tide_attrs[gtx]['geolocation']['segment_id']['coordinates'] = \
"delta_time reference_photon_lat reference_photon_lon"
#-- computed long-period equilibrium tide
IS2_atl03_tide[gtx]['geophys_corr']['tide_equilibrium'] = tide_lpe
IS2_atl03_fill[gtx]['geophys_corr']['tide_equilibrium'] = None
IS2_atl03_dims[gtx]['geophys_corr']['tide_equilibrium'] = [
'delta_time'
]
IS2_atl03_tide_attrs[gtx]['geophys_corr']['tide_equilibrium'] = {}
IS2_atl03_tide_attrs[gtx]['geophys_corr']['tide_equilibrium'][
'units'] = "meters"
IS2_atl03_tide_attrs[gtx]['geophys_corr']['tide_equilibrium'][
'contentType'] = "referenceInformation"
IS2_atl03_tide_attrs[gtx]['geophys_corr']['tide_equilibrium']['long_name'] = \
"Long Period Equilibrium Tide"
IS2_atl03_tide_attrs[gtx]['geophys_corr']['tide_equilibrium'][
'description'] = (
"Long-period "
"equilibrium tidal elevation from the summation of fifteen tidal spectral lines"
)
IS2_atl03_tide_attrs[gtx]['geophys_corr']['tide_equilibrium']['reference'] = \
"https://doi.org/10.1111/j.1365-246X.1973.tb03420.x"
IS2_atl03_tide_attrs[gtx]['geophys_corr']['tide_equilibrium']['coordinates'] = \
("../geolocation/segment_id ../geolocation/delta_time "
"../geolocation/reference_photon_lat ../geolocation/reference_photon_lon")
#-- print file information
print('\t{0}'.format(OUTPUT_FILE)) if VERBOSE else None
HDF5_ATL03_tide_write(IS2_atl03_tide,
IS2_atl03_tide_attrs,
CLOBBER=True,
INPUT=os.path.basename(INPUT_FILE),
FILL_VALUE=IS2_atl03_fill,
DIMENSIONS=IS2_atl03_dims,
FILENAME=os.path.join(DIRECTORY, OUTPUT_FILE))
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY, OUTPUT_FILE), MODE)
def compute_LPT_ICESat(FILE, VERBOSE=False, MODE=0o775):
#-- get directory from FILE
print('{0} -->'.format(os.path.basename(FILE))) if VERBOSE else None
DIRECTORY = os.path.dirname(FILE)
#-- compile regular expression operator for extracting information from file
rx = re.compile((r'GLAH(\d{2})_(\d{3})_(\d{1})(\d{1})(\d{2})_(\d{3})_'
r'(\d{4})_(\d{1})_(\d{2})_(\d{4})\.H5'), re.VERBOSE)
#-- extract parameters from ICESat/GLAS HDF5 file name
#-- PRD: Product number (01, 05, 06, 12, 13, 14, or 15)
#-- RL: Release number for process that created the product = 634
#-- RGTP: Repeat ground-track phase (1=8-day, 2=91-day, 3=transfer orbit)
#-- ORB: Reference orbit number (starts at 1 and increments each time a
#-- new reference orbit ground track file is obtained.)
#-- INST: Instance number (increments every time the satellite enters a
#-- different reference orbit)
#-- CYCL: Cycle of reference orbit for this phase
#-- TRK: Track within reference orbit
#-- SEG: Segment of orbit
#-- GRAN: Granule version number
#-- TYPE: File type
PRD, RL, RGTP, ORB, INST, CYCL, TRK, SEG, GRAN, TYPE = rx.findall(
FILE).pop()
#-- read GLAH12 HDF5 file
fileID = h5py.File(FILE, 'r')
n_40HZ, = fileID['Data_40HZ']['Time']['i_rec_ndx'].shape
#-- get variables and attributes
rec_ndx_40HZ = fileID['Data_40HZ']['Time']['i_rec_ndx'][:].copy()
#-- seconds since 2000-01-01 12:00:00 UTC (J2000)
DS_UTCTime_40HZ = fileID['Data_40HZ']['DS_UTCTime_40'][:].copy()
#-- Latitude (degrees North)
lat_TPX = fileID['Data_40HZ']['Geolocation']['d_lat'][:].copy()
#-- Longitude (degrees East)
lon_40HZ = fileID['Data_40HZ']['Geolocation']['d_lon'][:].copy()
#-- Elevation (height above TOPEX/Poseidon ellipsoid in meters)
elev_TPX = fileID['Data_40HZ']['Elevation_Surfaces']['d_elev'][:].copy()
fv = fileID['Data_40HZ']['Elevation_Surfaces']['d_elev'].attrs[
'_FillValue']
#-- convert time from UTC time of day to Modified Julian Days (MJD)
#-- J2000: seconds since 2000-01-01 12:00:00 UTC
t = DS_UTCTime_40HZ[:] / 86400.0 + 51544.5
#-- convert from MJD to calendar dates
YY, MM, DD, HH, MN, SS = pyTMD.time.convert_julian(t + 2400000.5,
FORMAT='tuple')
#-- convert calendar dates into year decimal
tdec = pyTMD.time.convert_calendar_decimal(YY,
MM,
day=DD,
hour=HH,
minute=MN,
second=SS)
#-- semimajor axis (a) and flattening (f) for TP and WGS84 ellipsoids
atop, ftop = (6378136.3, 1.0 / 298.257)
awgs, fwgs = (6378137.0, 1.0 / 298.257223563)
#-- convert from Topex/Poseidon to WGS84 Ellipsoids
lat_40HZ, elev_40HZ = pyTMD.spatial.convert_ellipsoid(lat_TPX,
elev_TPX,
atop,
ftop,
awgs,
fwgs,
eps=1e-12,
itmax=10)
#-- degrees to radians
dtr = np.pi / 180.0
atr = np.pi / 648000.0
#-- earth and physical parameters (IERS and WGS84)
G = 6.67428e-11 #-- universal constant of gravitation [m^3/(kg*s^2)]
GM = 3.986004418e14 #-- geocentric gravitational constant [m^3/s^2]
ge = 9.7803278 #-- mean equatorial gravity [m/s^2]
a_axis = 6378136.6 #-- semimajor axis of the WGS84 ellipsoid [m]
flat = 1.0 / 298.257223563 #-- flattening of the WGS84 ellipsoid
b_axis = (1.0 -
flat) * a_axis #-- semiminor axis of the WGS84 ellipsoid [m]
omega = 7.292115e-5 #-- mean rotation rate of the Earth [radians/s]
#-- tidal love number appropriate for the load tide
hb2 = 0.6207
#-- Linear eccentricity, first and second numerical eccentricity
lin_ecc = np.sqrt((2.0 * flat - flat**2) * a_axis**2)
ecc1 = lin_ecc / a_axis
ecc2 = lin_ecc / b_axis
#-- m parameter [omega^2*a^2*b/(GM)]. p. 70, Eqn.(2-137)
m = omega**2 * ((1 - flat) * a_axis**3) / GM
#-- flattening components
f_2 = -flat + (5.0/2.0)*m + (1.0/2.0)*flat**2.0 - (26.0/7.0)*flat*m + \
(15.0/4.0)*m**2.0
f_4 = -(1.0 / 2.0) * flat**2.0 + (5.0 / 2.0) * flat * m
#-- convert from geodetic latitude to geocentric latitude
#-- geodetic latitude in radians
latitude_geodetic_rad = lat_40HZ * dtr
#-- prime vertical radius of curvature
N = a_axis / np.sqrt(1.0 - ecc1**2.0 * np.sin(latitude_geodetic_rad)**2.0)
#-- calculate X, Y and Z from geodetic latitude and longitude
X = (N + elev_40HZ) * np.cos(latitude_geodetic_rad) * np.cos(
lon_40HZ * dtr)
Y = (N + elev_40HZ) * np.cos(latitude_geodetic_rad) * np.sin(
lon_40HZ * dtr)
Z = (N * (1.0 - ecc1**2.0) + elev_40HZ) * np.sin(latitude_geodetic_rad)
rr = np.sqrt(X**2.0 + Y**2.0 + Z**2.0)
#-- calculate geocentric latitude and convert to degrees
latitude_geocentric = np.arctan(Z / np.sqrt(X**2.0 + Y**2.0)) / dtr
#-- colatitude and longitude in radians
theta = dtr * (90.0 - latitude_geocentric)
phi = lon_40HZ * dtr
#-- compute normal gravity at spatial location and elevation of points.
#-- normal gravity at the equator. p. 79, Eqn.(2-186)
gamma_a = (GM / (a_axis * b_axis)) * (1.0 - (3.0 / 2.0) * m -
(3.0 / 14.0) * ecc2**2.0 * m)
#-- Normal gravity. p. 80, Eqn.(2-199)
gamma_0 = gamma_a * (1.0 + f_2 * np.cos(theta)**2.0 + f_4 *
np.sin(np.pi * latitude_geocentric / 180.0)**4.0)
#-- Normal gravity at height h. p. 82, Eqn.(2-215)
gamma_h = gamma_0*(1.0 -
(2.0/a_axis)*(1.0+flat+m-2.0*flat*np.cos(theta)**2.0)*elev_40HZ + \
(3.0/a_axis**2.0)*elev_40HZ**2.0)
#-- pole tide files (mean and daily)
mean_pole_file = get_data_path(['data', 'mean-pole.tab'])
pole_tide_file = get_data_path(['data', 'finals.all'])
#-- read IERS daily polar motion values
EOP = read_iers_EOP(pole_tide_file)
#-- create cubic spline interpolations of daily polar motion values
xSPL = scipy.interpolate.UnivariateSpline(EOP['MJD'], EOP['x'], k=3, s=0)
ySPL = scipy.interpolate.UnivariateSpline(EOP['MJD'], EOP['y'], k=3, s=0)
#-- calculate angular coordinates of mean pole at time tdec
mpx, mpy, fl = iers_mean_pole(mean_pole_file, tdec, '2015')
#-- interpolate daily polar motion values to time using cubic splines
px = xSPL(t)
py = ySPL(t)
#-- calculate differentials from mean pole positions
mx = px - mpx
my = -(py - mpy)
#-- calculate radial displacement at time
dfactor = -hb2 * atr * (omega**2 * rr**2) / (2.0 * gamma_h)
Srad = np.ma.zeros((n_40HZ), fill_value=fv)
Srad.data[:] = dfactor * np.sin(
2.0 * theta) * (mx * np.cos(phi) + my * np.sin(phi))
#-- replace fill values
Srad.mask = np.isnan(Srad.data)
Srad.data[Srad.mask] = Srad.fill_value
#-- copy variables for outputting to HDF5 file
IS_gla12_tide = dict(Data_40HZ={})
IS_gla12_fill = dict(Data_40HZ={})
IS_gla12_tide_attrs = dict(Data_40HZ={})
#-- copy global file attributes
global_attribute_list = [
'featureType', 'title', 'comment', 'summary', 'license', 'references',
'AccessConstraints', 'CitationforExternalPublication',
'contributor_role', 'contributor_name', 'creator_name',
'creator_email', 'publisher_name', 'publisher_email', 'publisher_url',
'platform', 'instrument', 'processing_level', 'date_created',
'spatial_coverage_type', 'history', 'keywords', 'keywords_vocabulary',
'naming_authority', 'project', 'time_type', 'date_type',
'time_coverage_start', 'time_coverage_end', 'time_coverage_duration',
'source', 'HDFVersion', 'identifier_product_type',
'identifier_product_format_version', 'Conventions', 'institution',
'ReprocessingPlanned', 'ReprocessingActual', 'LocalGranuleID',
'ProductionDateTime', 'LocalVersionID', 'PGEVersion', 'OrbitNumber',
'StartOrbitNumber', 'StopOrbitNumber', 'EquatorCrossingLongitude',
'EquatorCrossingTime', 'EquatorCrossingDate', 'ShortName', 'VersionID',
'InputPointer', 'RangeBeginningTime', 'RangeEndingTime',
'RangeBeginningDate', 'RangeEndingDate', 'PercentGroundHit',
'OrbitQuality', 'Cycle', 'Track', 'Instrument_State', 'Timing_Bias',
'ReferenceOrbit', 'SP_ICE_PATH_NO', 'SP_ICE_GLAS_StartBlock',
'SP_ICE_GLAS_EndBlock', 'Instance', 'Range_Bias',
'Instrument_State_Date', 'Instrument_State_Time', 'Range_Bias_Date',
'Range_Bias_Time', 'Timing_Bias_Date', 'Timing_Bias_Time',
'identifier_product_doi', 'identifier_file_uuid',
'identifier_product_doi_authority'
]
for att in global_attribute_list:
IS_gla12_tide_attrs[att] = fileID.attrs[att]
#-- add attributes for input GLA12 file
IS_gla12_tide_attrs['input_files'] = os.path.basename(FILE)
#-- update geospatial ranges for ellipsoid
IS_gla12_tide_attrs['geospatial_lat_min'] = np.min(lat_40HZ)
IS_gla12_tide_attrs['geospatial_lat_max'] = np.max(lat_40HZ)
IS_gla12_tide_attrs['geospatial_lon_min'] = np.min(lon_40HZ)
IS_gla12_tide_attrs['geospatial_lon_max'] = np.max(lon_40HZ)
IS_gla12_tide_attrs['geospatial_lat_units'] = "degrees_north"
IS_gla12_tide_attrs['geospatial_lon_units'] = "degrees_east"
IS_gla12_tide_attrs['geospatial_ellipsoid'] = "WGS84"
#-- copy 40Hz group attributes
for att_name, att_val in fileID['Data_40HZ'].attrs.items():
IS_gla12_tide_attrs['Data_40HZ'][att_name] = att_val
#-- copy attributes for time, geolocation and geophysical groups
for var in ['Time', 'Geolocation', 'Geophysical']:
IS_gla12_tide['Data_40HZ'][var] = {}
IS_gla12_fill['Data_40HZ'][var] = {}
IS_gla12_tide_attrs['Data_40HZ'][var] = {}
for att_name, att_val in fileID['Data_40HZ'][var].attrs.items():
IS_gla12_tide_attrs['Data_40HZ'][var][att_name] = att_val
#-- J2000 time
IS_gla12_tide['Data_40HZ']['DS_UTCTime_40'] = DS_UTCTime_40HZ
IS_gla12_fill['Data_40HZ']['DS_UTCTime_40'] = None
IS_gla12_tide_attrs['Data_40HZ']['DS_UTCTime_40'] = {}
for att_name, att_val in fileID['Data_40HZ']['DS_UTCTime_40'].attrs.items(
):
if att_name not in ('DIMENSION_LIST', 'CLASS', 'NAME'):
IS_gla12_tide_attrs['Data_40HZ']['DS_UTCTime_40'][
att_name] = att_val
#-- record
IS_gla12_tide['Data_40HZ']['Time']['i_rec_ndx'] = rec_ndx_40HZ
IS_gla12_fill['Data_40HZ']['Time']['i_rec_ndx'] = None
IS_gla12_tide_attrs['Data_40HZ']['Time']['i_rec_ndx'] = {}
for att_name, att_val in fileID['Data_40HZ']['Time'][
'i_rec_ndx'].attrs.items():
if att_name not in ('DIMENSION_LIST', 'CLASS', 'NAME'):
IS_gla12_tide_attrs['Data_40HZ']['Time']['i_rec_ndx'][
att_name] = att_val
#-- latitude
IS_gla12_tide['Data_40HZ']['Geolocation']['d_lat'] = lat_40HZ
IS_gla12_fill['Data_40HZ']['Geolocation']['d_lat'] = None
IS_gla12_tide_attrs['Data_40HZ']['Geolocation']['d_lat'] = {}
for att_name, att_val in fileID['Data_40HZ']['Geolocation'][
'd_lat'].attrs.items():
if att_name not in ('DIMENSION_LIST', 'CLASS', 'NAME'):
IS_gla12_tide_attrs['Data_40HZ']['Geolocation']['d_lat'][
att_name] = att_val
#-- longitude
IS_gla12_tide['Data_40HZ']['Geolocation']['d_lon'] = lon_40HZ
IS_gla12_fill['Data_40HZ']['Geolocation']['d_lon'] = None
IS_gla12_tide_attrs['Data_40HZ']['Geolocation']['d_lon'] = {}
for att_name, att_val in fileID['Data_40HZ']['Geolocation'][
'd_lon'].attrs.items():
if att_name not in ('DIMENSION_LIST', 'CLASS', 'NAME'):
IS_gla12_tide_attrs['Data_40HZ']['Geolocation']['d_lon'][
att_name] = att_val
#-- geophysical variables
#-- computed Solid Earth load pole tide
IS_gla12_tide['Data_40HZ']['Geophysical']['d_poElv'] = Srad
IS_gla12_fill['Data_40HZ']['Geophysical']['d_poElv'] = Srad.fill_value
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_poElv'] = {}
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_poElv'][
'units'] = "meters"
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_poElv']['long_name'] = \
"Solid Earth Pole Tide"
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_poElv'][
'description'] = (
"Solid "
"Earth pole tide radial displacements due to polar motion")
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_poElv']['reference'] = \
'ftp://tai.bipm.org/iers/conv2010/chapter7/tn36_c7.pdf'
IS_gla12_tide_attrs['Data_40HZ']['Geophysical']['d_poElv']['coordinates'] = \
"../DS_UTCTime_40"
#-- close the input HDF5 file
fileID.close()
#-- output tidal HDF5 file
args = (PRD, RL, RGTP, ORB, INST, CYCL, TRK, SEG, GRAN, TYPE)
file_format = 'GLAH{0}_{1}_LPT_{2}{3}{4}_{5}_{6}_{7}_{8}_{9}.h5'
#-- print file information
print('\t{0}'.format(file_format.format(*args))) if VERBOSE else None
HDF5_GLA12_tide_write(IS_gla12_tide,
IS_gla12_tide_attrs,
FILENAME=os.path.join(DIRECTORY,
file_format.format(*args)),
FILL_VALUE=IS_gla12_fill,
CLOBBER=True)
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY, file_format.format(*args)), MODE)
def compute_tides_icebridge_data(tide_dir, arg, TIDE_MODEL,
METHOD='spline', EXTRAPOLATE=False, VERBOSE=False, MODE=0o775):
#-- extract file name and subsetter indices lists
match_object = re.match(r'(.*?)(\[(.*?)\])?$',arg)
input_file = os.path.expanduser(match_object.group(1))
#-- subset input file to indices
if match_object.group(2):
#-- decompress ranges and add to list
input_subsetter = []
for i in re.findall(r'((\d+)-(\d+)|(\d+))',match_object.group(3)):
input_subsetter.append(int(i[3])) if i[3] else \
input_subsetter.extend(range(int(i[1]),int(i[2])+1))
else:
input_subsetter = None
#-- output directory for input_file
DIRECTORY = os.path.dirname(input_file)
#-- calculate if input files are from ATM or LVIS (+GH)
regex = {}
regex['ATM'] = r'(BLATM2|ILATM2)_(\d+)_(\d+)_smooth_nadir(.*?)(csv|seg|pt)$'
regex['ATM1b'] = r'(BLATM1b|ILATM1b)_(\d+)_(\d+)(.*?).(qi|TXT|h5)$'
regex['LVIS'] = r'(BLVIS2|BVLIS2|ILVIS2)_(.*?)(\d+)_(\d+)_(R\d+)_(\d+).H5$'
regex['LVGH'] = r'(ILVGH2)_(.*?)(\d+)_(\d+)_(R\d+)_(\d+).H5$'
for key,val in regex.items():
if re.match(val, os.path.basename(input_file)):
OIB = key
#-- select between tide models
if (TIDE_MODEL == 'CATS0201'):
grid_file = os.path.join(tide_dir,'cats0201_tmd','grid_CATS')
model_file = os.path.join(tide_dir,'cats0201_tmd','h0_CATS02_01')
reference = 'https://mail.esr.org/polar_tide_models/Model_CATS0201.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'CATS2008'):
grid_file = os.path.join(tide_dir,'CATS2008','grid_CATS2008')
model_file = os.path.join(tide_dir,'CATS2008','hf.CATS2008.out')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/cats2008/')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = 'CATS2008'
TYPE = 'z'
elif (TIDE_MODEL == 'CATS2008_load'):
grid_file = os.path.join(tide_dir,'CATS2008a_SPOTL_Load','grid_CATS2008a_opt')
model_file = os.path.join(tide_dir,'CATS2008a_SPOTL_Load','h_CATS2008a_SPOTL_load')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/cats2008/')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'OTIS'
EPSG = 'CATS2008'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO9-atlas'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas')
grid_file = os.path.join(model_directory,'grid_tpxo9_atlas.nc.gz')
model_files = ['h_q1_tpxo9_atlas_30.nc.gz','h_o1_tpxo9_atlas_30.nc.gz',
'h_p1_tpxo9_atlas_30.nc.gz','h_k1_tpxo9_atlas_30.nc.gz',
'h_n2_tpxo9_atlas_30.nc.gz','h_m2_tpxo9_atlas_30.nc.gz',
'h_s2_tpxo9_atlas_30.nc.gz','h_k2_tpxo9_atlas_30.nc.gz',
'h_m4_tpxo9_atlas_30.nc.gz','h_ms4_tpxo9_atlas_30.nc.gz',
'h_mn4_tpxo9_atlas_30.nc.gz','h_2n2_tpxo9_atlas_30.nc.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = 'http://volkov.oce.orst.edu/tides/tpxo9_atlas.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'netcdf'
TYPE = 'z'
SCALE = 1.0/1000.0
GZIP = True
elif (TIDE_MODEL == 'TPXO9-atlas-v2'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas_v2')
grid_file = os.path.join(model_directory,'grid_tpxo9_atlas_30_v2.nc.gz')
model_files = ['h_q1_tpxo9_atlas_30_v2.nc.gz','h_o1_tpxo9_atlas_30_v2.nc.gz',
'h_p1_tpxo9_atlas_30_v2.nc.gz','h_k1_tpxo9_atlas_30_v2.nc.gz',
'h_n2_tpxo9_atlas_30_v2.nc.gz','h_m2_tpxo9_atlas_30_v2.nc.gz',
'h_s2_tpxo9_atlas_30_v2.nc.gz','h_k2_tpxo9_atlas_30_v2.nc.gz',
'h_m4_tpxo9_atlas_30_v2.nc.gz','h_ms4_tpxo9_atlas_30_v2.nc.gz',
'h_mn4_tpxo9_atlas_30_v2.nc.gz','h_2n2_tpxo9_atlas_30_v2.nc.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = 'https://www.tpxo.net/global/tpxo9-atlas'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'netcdf'
TYPE = 'z'
SCALE = 1.0/1000.0
GZIP = True
elif (TIDE_MODEL == 'TPXO9-atlas-v3'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas_v3')
grid_file = os.path.join(model_directory,'grid_tpxo9_atlas_30_v3.nc.gz')
model_files = ['h_q1_tpxo9_atlas_30_v3.nc.gz','h_o1_tpxo9_atlas_30_v3.nc.gz',
'h_p1_tpxo9_atlas_30_v3.nc.gz','h_k1_tpxo9_atlas_30_v3.nc.gz',
'h_n2_tpxo9_atlas_30_v3.nc.gz','h_m2_tpxo9_atlas_30_v3.nc.gz',
'h_s2_tpxo9_atlas_30_v3.nc.gz','h_k2_tpxo9_atlas_30_v3.nc.gz',
'h_m4_tpxo9_atlas_30_v3.nc.gz','h_ms4_tpxo9_atlas_30_v3.nc.gz',
'h_mn4_tpxo9_atlas_30_v3.nc.gz','h_2n2_tpxo9_atlas_30_v3.nc.gz',
'h_mf_tpxo9_atlas_30_v3.nc.gz','h_mm_tpxo9_atlas_30_v3.nc.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = 'https://www.tpxo.net/global/tpxo9-atlas'
output_variable = 'tide_ocean'
variable_long_name = "Ocean Tide"
model_format = 'netcdf'
TYPE = 'z'
SCALE = 1.0/1000.0
GZIP = True
elif (TIDE_MODEL == 'TPXO9-atlas-v4'):
model_directory = os.path.join(tide_dir,'TPXO9_atlas_v4')
grid_file = os.path.join(model_directory,'grid_tpxo9_atlas_30_v4')
model_files = ['h_q1_tpxo9_atlas_30_v4','h_o1_tpxo9_atlas_30_v4',
'h_p1_tpxo9_atlas_30_v4','h_k1_tpxo9_atlas_30_v4',
'h_n2_tpxo9_atlas_30_v4','h_m2_tpxo9_atlas_30_v4',
'h_s2_tpxo9_atlas_30_v4','h_k2_tpxo9_atlas_30_v4',
'h_m4_tpxo9_atlas_30_v4','h_ms4_tpxo9_atlas_30_v4',
'h_mn4_tpxo9_atlas_30_v4','h_2n2_tpxo9_atlas_30_v4',
'h_mf_tpxo9_atlas_30_v4','h_mm_tpxo9_atlas_30_v4']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = 'https://www.tpxo.net/global/tpxo9-atlas'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO9.1'):
grid_file = os.path.join(tide_dir,'TPXO9.1','DATA','grid_tpxo9')
model_file = os.path.join(tide_dir,'TPXO9.1','DATA','h_tpxo9.v1')
reference = 'http://volkov.oce.orst.edu/tides/global.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO8-atlas'):
grid_file = os.path.join(tide_dir,'tpxo8_atlas','grid_tpxo8atlas_30_v1')
model_file = os.path.join(tide_dir,'tpxo8_atlas','hf.tpxo8_atlas_30_v1')
reference = 'http://volkov.oce.orst.edu/tides/tpxo8_atlas.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'ATLAS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO7.2'):
grid_file = os.path.join(tide_dir,'TPXO7.2_tmd','grid_tpxo7.2')
model_file = os.path.join(tide_dir,'TPXO7.2_tmd','h_tpxo7.2')
reference = 'http://volkov.oce.orst.edu/tides/global.html'
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'TPXO7.2_load'):
grid_file = os.path.join(tide_dir,'TPXO7.2_load','grid_tpxo6.2')
model_file = os.path.join(tide_dir,'TPXO7.2_load','h_tpxo7.2_load')
reference = 'http://volkov.oce.orst.edu/tides/global.html'
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'OTIS'
EPSG = '4326'
TYPE = 'z'
elif (TIDE_MODEL == 'AODTM-5'):
grid_file = os.path.join(tide_dir,'aodtm5_tmd','grid_Arc5km')
model_file = os.path.join(tide_dir,'aodtm5_tmd','h0_Arc5km.oce')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aodtm-5/')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPE = 'z'
elif (TIDE_MODEL == 'AOTIM-5'):
grid_file = os.path.join(tide_dir,'aotim5_tmd','grid_Arc5km')
model_file = os.path.join(tide_dir,'aotim5_tmd','h_Arc5km.oce')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aotim-5/')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPE = 'z'
elif (TIDE_MODEL == 'AOTIM-5-2018'):
grid_file = os.path.join(tide_dir,'Arc5km2018','grid_Arc5km2018')
model_file = os.path.join(tide_dir,'Arc5km2018','h_Arc5km2018')
reference = ('https://www.esr.org/research/polar-tide-models/'
'list-of-polar-tide-models/aotim-5/')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'OTIS'
EPSG = 'PSNorth'
TYPE = 'z'
elif (TIDE_MODEL == 'GOT4.7'):
model_directory = os.path.join(tide_dir,'GOT4.7','grids_oceantide')
model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',
'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'GOT'
SCALE = 1.0/100.0
GZIP = True
elif (TIDE_MODEL == 'GOT4.7_load'):
model_directory = os.path.join(tide_dir,'GOT4.7','grids_loadtide')
model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',
'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',
's1load.d.gz','m4load.d.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'GOT'
SCALE = 1.0/1000.0
GZIP = True
elif (TIDE_MODEL == 'GOT4.8'):
model_directory = os.path.join(tide_dir,'got4.8','grids_oceantide')
model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',
'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'GOT'
SCALE = 1.0/100.0
GZIP = True
elif (TIDE_MODEL == 'GOT4.8_load'):
model_directory = os.path.join(tide_dir,'got4.8','grids_loadtide')
model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',
'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',
's1load.d.gz','m4load.d.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'GOT'
SCALE = 1.0/1000.0
GZIP = True
elif (TIDE_MODEL == 'GOT4.10'):
model_directory = os.path.join(tide_dir,'GOT4.10c','grids_oceantide')
model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',
'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'GOT'
SCALE = 1.0/100.0
GZIP = True
elif (TIDE_MODEL == 'GOT4.10_load'):
model_directory = os.path.join(tide_dir,'GOT4.10c','grids_loadtide')
model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',
'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',
's1load.d.gz','m4load.d.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
reference = ('https://denali.gsfc.nasa.gov/personal_pages/ray/'
'MiscPubs/19990089548_1999150788.pdf')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'GOT'
SCALE = 1.0/1000.0
GZIP = True
elif (TIDE_MODEL == 'FES2014'):
model_directory = os.path.join(tide_dir,'fes2014','ocean_tide')
model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',
'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',
'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',
'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',
'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',
'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',
'ssa.nc.gz','t2.nc.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6',
'm8','mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2',
'n4','nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']
reference = ('https://www.aviso.altimetry.fr/en/data/products'
'auxiliary-products/global-tide-fes.html')
output_variable = 'tide_ocean'
variable_long_name = 'Ocean_Tide'
model_format = 'FES'
TYPE = 'z'
SCALE = 1.0/100.0
GZIP = True
elif (TIDE_MODEL == 'FES2014_load'):
model_directory = os.path.join(tide_dir,'fes2014','load_tide')
model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',
'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',
'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',
'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',
'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',
'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',
'ssa.nc.gz','t2.nc.gz']
model_file = [os.path.join(model_directory,m) for m in model_files]
c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6',
'm8','mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2',
'n4','nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']
reference = ('https://www.aviso.altimetry.fr/en/data/products'
'auxiliary-products/global-tide-fes.html')
output_variable = 'tide_load'
variable_long_name = 'Load_Tide'
model_format = 'FES'
TYPE = 'z'
SCALE = 1.0/100.0
GZIP = True
#-- HDF5 file attributes
attrib = {}
#-- latitude
attrib['lat'] = {}
attrib['lat']['long_name'] = 'Latitude_of_measurement'
attrib['lat']['description'] = ('Corresponding_to_the_measurement_'
'position_at_the_acquisition_time')
attrib['lat']['units'] = 'Degrees_North'
#-- longitude
attrib['lon'] = {}
attrib['lon']['long_name'] = 'Longitude_of_measurement'
attrib['lon']['description'] = ('Corresponding_to_the_measurement_'
'position_at_the_acquisition_time')
attrib['lon']['units'] = 'Degrees_East'
#-- tides
attrib[output_variable] = {}
attrib[output_variable]['description'] = ('Tidal_elevation_from_harmonic_'
'constants_at_the_measurement_position_at_the_acquisition_time')
attrib[output_variable]['reference'] = reference
attrib[output_variable]['model'] = TIDE_MODEL
attrib[output_variable]['units'] = 'meters'
attrib[output_variable]['long_name'] = variable_long_name
#-- time
attrib['time'] = {}
attrib['time']['long_name'] = 'Time'
attrib['time']['description'] = ('Time_corresponding_to_the_measurement_'
'position')
attrib['time']['units'] = 'Days since 1992-01-01T00:00:00'
attrib['time']['calendar'] = 'standard'
#-- extract information from first input file
#-- acquisition year, month and day
#-- number of points
#-- instrument (PRE-OIB ATM or LVIS, OIB ATM or LVIS)
if OIB in ('ATM','ATM1b'):
M1,YYMMDD1,HHMMSS1,AX1,SF1 = re.findall(regex[OIB], input_file).pop()
#-- early date strings omitted century and millenia (e.g. 93 for 1993)
if (len(YYMMDD1) == 6):
ypre,MM1,DD1 = YYMMDD1[:2],YYMMDD1[2:4],YYMMDD1[4:]
if (np.float64(ypre) >= 90):
YY1 = '{0:4.0f}'.format(np.float64(ypre) + 1900.0)
else:
YY1 = '{0:4.0f}'.format(np.float64(ypre) + 2000.0)
elif (len(YYMMDD1) == 8):
YY1,MM1,DD1 = YYMMDD1[:4],YYMMDD1[4:6],YYMMDD1[6:]
elif OIB in ('LVIS','LVGH'):
M1,RG1,YY1,MMDD1,RLD1,SS1 = re.findall(regex[OIB], input_file).pop()
MM1,DD1 = MMDD1[:2],MMDD1[2:]
#-- read data from input_file
print('{0} -->'.format(input_file)) if VERBOSE else None
if (OIB == 'ATM'):
#-- load IceBridge ATM data from input_file
dinput,file_lines,HEM = read_ATM_icessn_file(input_file,input_subsetter)
elif (OIB == 'ATM1b'):
#-- load IceBridge Level-1b ATM data from input_file
dinput,file_lines,HEM = read_ATM_qfit_file(input_file,input_subsetter)
elif OIB in ('LVIS','LVGH'):
#-- load IceBridge LVIS data from input_file
dinput,file_lines,HEM = read_LVIS_HDF5_file(input_file,input_subsetter)
#-- convert time from J2000 to days relative to Jan 1, 1992 (48622mjd)
#-- J2000: seconds since 2000-01-01 12:00:00 UTC
t = pyTMD.time.convert_delta_time(dinput['time'],
epoch1=(2000,1,1,12,0,0), epoch2=(1992,1,1,0,0,0),
scale=1.0/86400.0)
#-- delta time (TT - UT1) file
delta_file = get_data_path(['data','merged_deltat.data'])
#-- read tidal constants and interpolate to grid points
if model_format in ('OTIS','ATLAS'):
amp,ph,D,c = extract_tidal_constants(dinput['lon'], dinput['lat'],
grid_file, model_file, EPSG, TYPE=TYPE, METHOD=METHOD,
EXTRAPOLATE=EXTRAPOLATE, GRID=model_format)
deltat = np.zeros_like(t)
elif model_format in ('netcdf'):
amp,ph,D,c = extract_netcdf_constants(dinput['lon'], dinput['lat'],
grid_file, model_file, TYPE=TYPE, METHOD=METHOD,
EXTRAPOLATE=EXTRAPOLATE, SCALE=SCALE, GZIP=GZIP)
deltat = np.zeros_like(t)
elif (model_format == 'GOT'):
amp,ph,c = extract_GOT_constants(dinput['lon'], dinput['lat'],
model_file, METHOD=METHOD, EXTRAPOLATE=EXTRAPOLATE,
SCALE=SCALE, GZIP=GZIP)
#-- interpolate delta times from calendar dates to tide time
deltat = calc_delta_time(delta_file, t)
elif (model_format == 'FES'):
amp,ph = extract_FES_constants(dinput['lon'], dinput['lat'],
model_file, TYPE=TYPE, VERSION=TIDE_MODEL, METHOD=METHOD,
EXTRAPOLATE=EXTRAPOLATE, SCALE=SCALE, GZIP=GZIP)
#-- interpolate delta times from calendar dates to tide time
deltat = calc_delta_time(delta_file, t)
#-- calculate complex phase in radians for Euler's
cph = -1j*ph*np.pi/180.0
#-- calculate constituent oscillation
hc = amp*np.exp(cph)
#-- output tidal HDF5 file
#-- form: rg_NASA_model_TIDES_WGS84_fl1yyyymmddjjjjj.H5
#-- where rg is the hemisphere flag (GR or AN) for the region
#-- model is the tidal TIDE_MODEL flag (e.g. CATS0201)
#-- fl1 and fl2 are the data flags (ATM, LVIS, GLAS)
#-- yymmddjjjjj is the year, month, day and second of the input file
#-- output region flags: GR for Greenland and AN for Antarctica
hem_flag = {'N':'GR','S':'AN'}
#-- use starting second to distinguish between files for the day
JJ1 = np.min(dinput['time']) % 86400
#-- output file format
args = (hem_flag[HEM],TIDE_MODEL,OIB,YY1,MM1,DD1,JJ1)
FILENAME = '{0}_NASA_{1}_TIDES_WGS84_{2}{3}{4}{5}{6:05.0f}.H5'.format(*args)
#-- print file information
print('\t{0}'.format(FILENAME)) if VERBOSE else None
#-- open output HDF5 file
fid = h5py.File(os.path.join(DIRECTORY,FILENAME), 'w')
#-- predict tidal elevations at time and infer minor corrections
fill_value = -9999.0
tide = np.ma.empty((file_lines),fill_value=fill_value)
tide.mask = np.any(hc.mask,axis=1)
tide.data[:] = predict_tide_drift(t, hc, c,
DELTAT=deltat, CORRECTIONS=model_format)
minor = infer_minor_corrections(t, hc, c,
DELTAT=deltat, CORRECTIONS=model_format)
tide.data[:] += minor.data[:]
#-- replace invalid values with fill value
tide.data[tide.mask] = tide.fill_value
#-- add latitude and longitude to output file
for key in ['lat','lon']:
#-- Defining the HDF5 dataset variables for lat/lon
h5 = fid.create_dataset(key, (file_lines,), data=dinput[key][:],
dtype=dinput[key].dtype, compression='gzip')
#-- add HDF5 variable attributes
for att_name,att_val in attrib[key].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- output tides to HDF5 dataset
h5 = fid.create_dataset(output_variable, (file_lines,), data=tide,
dtype=tide.dtype, fillvalue=fill_value, compression='gzip')
#-- add HDF5 variable attributes
tide_count = np.count_nonzero(tide != fill_value)
h5.attrs['tide_count'] = tide_count
h5.attrs['_FillValue'] = fill_value
for att_name,att_val in attrib[output_variable].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- output days to HDF5 dataset
h5 = fid.create_dataset('time', (file_lines,), data=t,
dtype=t.dtype, compression='gzip')
#-- add HDF5 variable attributes
for att_name,att_val in attrib['time'].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- HDF5 file attributes
fid.attrs['featureType'] = 'trajectory'
fid.attrs['title'] = 'Tidal_correction_for_elevation_measurements'
fid.attrs['summary'] = ('Tidal_correction_computed_at_elevation_'
'measurements_using_a_tidal_model_driver.')
fid.attrs['project'] = 'NASA_Operation_IceBridge'
fid.attrs['processing_level'] = '4'
fid.attrs['date_created'] = time.strftime('%Y-%m-%d',time.localtime())
#-- add attributes for input file
fid.attrs['elevation_file'] = os.path.basename(input_file)
fid.attrs['tide_model'] = TIDE_MODEL
#-- add geospatial and temporal attributes
fid.attrs['geospatial_lat_min'] = dinput['lat'].min()
fid.attrs['geospatial_lat_max'] = dinput['lat'].max()
fid.attrs['geospatial_lon_min'] = dinput['lon'].min()
fid.attrs['geospatial_lon_max'] = dinput['lon'].max()
fid.attrs['geospatial_lat_units'] = "degrees_north"
fid.attrs['geospatial_lon_units'] = "degrees_east"
fid.attrs['geospatial_ellipsoid'] = "WGS84"
fid.attrs['time_type'] = 'UTC'
#-- convert start/end time from days since 1992-01-01 into Julian days
time_range = np.array([np.min(t),np.max(t)])
time_julian = 2400000.5 + pyTMD.time.convert_delta_time(time_range,
epoch1=(1992,1,1,0,0,0), epoch2=(1858,11,17,0,0,0), scale=1.0)
#-- convert to calendar date
cal = pyTMD.time.convert_julian(time_julian,ASTYPE=int)
#-- add attributes with measurement date start, end and duration
args = (cal['hour'][0],cal['minute'][0],cal['second'][0])
fid.attrs['RangeBeginningTime'] = '{0:02d}:{1:02d}:{2:02d}'.format(*args)
args = (cal['hour'][-1],cal['minute'][-1],cal['second'][-1])
fid.attrs['RangeEndingTime'] = '{0:02d}:{1:02d}:{2:02d}'.format(*args)
args = (cal['year'][0],cal['month'][0],cal['day'][0])
fid.attrs['RangeBeginningDate'] = '{0:4d}-{1:02d}-{2:02d}'.format(*args)
args = (cal['year'][-1],cal['month'][-1],cal['day'][-1])
fid.attrs['RangeEndingDate'] = '{0:4d}-{1:02d}-{2:02d}'.format(*args)
duration = np.round(time_julian[-1]*86400.0 - time_julian[0]*86400.0)
fid.attrs['DurationTimeSeconds'] = '{0:0.0f}'.format(duration)
#-- close the output HDF5 dataset
fid.close()
#-- change the permissions level to MODE
os.chmod(os.path.join(DIRECTORY,FILENAME), MODE)
def compute_LPET_icebridge_data(arg, VERBOSE=False, MODE=0o775):
#-- extract file name and subsetter indices lists
match_object = re.match(r'(.*?)(\[(.*?)\])?$', arg)
input_file = os.path.expanduser(match_object.group(1))
#-- subset input file to indices
if match_object.group(2):
#-- decompress ranges and add to list
input_subsetter = []
for i in re.findall(r'((\d+)-(\d+)|(\d+))', match_object.group(3)):
input_subsetter.append(int(i[3])) if i[3] else \
input_subsetter.extend(range(int(i[1]),int(i[2])+1))
else:
input_subsetter = None
#-- output directory for input_file
DIRECTORY = os.path.dirname(input_file)
#-- calculate if input files are from ATM or LVIS (+GH)
regex = {}
regex[
'ATM'] = r'(BLATM2|ILATM2)_(\d+)_(\d+)_smooth_nadir(.*?)(csv|seg|pt)$'
regex['ATM1b'] = r'(BLATM1b|ILATM1b)_(\d+)_(\d+)(.*?).(qi|TXT|h5)$'
regex['LVIS'] = r'(BLVIS2|BVLIS2|ILVIS2)_(.*?)(\d+)_(\d+)_(R\d+)_(\d+).H5$'
regex['LVGH'] = r'(ILVGH2)_(.*?)(\d+)_(\d+)_(R\d+)_(\d+).H5$'
for key, val in regex.items():
if re.match(val, os.path.basename(input_file)):
OIB = key
#-- HDF5 file attributes
attrib = dict(lon={}, lat={}, tide_lpe={}, day={})
#-- latitude
attrib['lat']['long_name'] = 'Latitude_of_measurement'
attrib['lat']['description'] = ('Corresponding_to_the_measurement_'
'position_at_the_acquisition_time')
attrib['lat']['units'] = 'Degrees_North'
#-- longitude
attrib['lon']['long_name'] = 'Longitude_of_measurement'
attrib['lon']['description'] = ('Corresponding_to_the_measurement_'
'position_at_the_acquisition_time')
attrib['lon']['units'] = 'Degrees_East'
#-- long-period equilibrium tides
attrib['tide_lpe']['long_name'] = 'Equilibrium_Tide'
attrib['tide_lpe']['description'] = (
'Long-period_equilibrium_tidal_elevation_'
'from_the_summation_of_fifteen_tidal_spectral_lines_at_the_measurement_'
'position_at_the_acquisition_time')
attrib['tide_lpe']['reference'] = ('https://doi.org/10.1111/'
'j.1365-246X.1973.tb03420.x')
attrib['tide_lpe']['units'] = 'meters'
#-- time
attrib['time'] = {}
attrib['time']['long_name'] = 'Time'
attrib['time']['units'] = 'days since 1992-01-01T00:00:00'
attrib['time']['calendar'] = 'standard'
#-- extract information from first input file
#-- acquisition year, month and day
#-- number of points
#-- instrument (PRE-OIB ATM or LVIS, OIB ATM or LVIS)
if OIB in ('ATM', 'ATM1b'):
M1, YYMMDD1, HHMMSS1, AX1, SF1 = re.findall(regex[OIB],
input_file).pop()
#-- early date strings omitted century and millenia (e.g. 93 for 1993)
if (len(YYMMDD1) == 6):
ypre, MM1, DD1 = YYMMDD1[:2], YYMMDD1[2:4], YYMMDD1[4:]
if (np.float(ypre) >= 90):
YY1 = '{0:4.0f}'.format(np.float(ypre) + 1900.0)
else:
YY1 = '{0:4.0f}'.format(np.float(ypre) + 2000.0)
elif (len(YYMMDD1) == 8):
YY1, MM1, DD1 = YYMMDD1[:4], YYMMDD1[4:6], YYMMDD1[6:]
elif OIB in ('LVIS', 'LVGH'):
M1, RG1, YY1, MMDD1, RLD1, SS1 = re.findall(regex[OIB],
input_file).pop()
MM1, DD1 = MMDD1[:2], MMDD1[2:]
#-- read data from input_file
print('{0} -->'.format(input_file)) if VERBOSE else None
if (OIB == 'ATM'):
#-- load IceBridge ATM data from input_file
dinput, file_lines, HEM = read_ATM_icessn_file(input_file,
input_subsetter)
elif (OIB == 'ATM1b'):
#-- load IceBridge Level-1b ATM data from input_file
dinput, file_lines, HEM = read_ATM_qfit_file(input_file,
input_subsetter)
elif OIB in ('LVIS', 'LVGH'):
#-- load IceBridge LVIS data from input_file
dinput, file_lines, HEM = read_LVIS_HDF5_file(input_file,
input_subsetter)
#-- convert time from J2000 to days relative to Jan 1, 1992 (48622mjd)
#-- J2000: seconds since 2000-01-01 12:00:00 UTC
tide_time = pyTMD.time.convert_delta_time(dinput['time'],
epoch1=(2000, 1, 1, 12, 0, 0),
epoch2=(1992, 1, 1, 0, 0, 0),
scale=1.0 / 86400.0)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data', 'merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- output tidal HDF5 file
#-- form: rg_NASA_model_EQUILIBRIUM_TIDES_WGS84_fl1yyyymmddjjjjj.H5
#-- where rg is the hemisphere flag (GR or AN) for the region
#-- fl1 and fl2 are the data flags (ATM, LVIS, GLAS)
#-- yymmddjjjjj is the year, month, day and second of the input file
#-- output region flags: GR for Greenland and AN for Antarctica
hem_flag = {'N': 'GR', 'S': 'AN'}
#-- use starting second to distinguish between files for the day
JJ1 = np.min(dinput['time']) % 86400
#-- output file format
file_format = '{0}_NASA_EQUILIBRIUM_TIDES_WGS84_{1}{2}{3}{4}{5:05.0f}.H5'
FILENAME = file_format.format(hem_flag[HEM], OIB, YY1, MM1, DD1, JJ1)
#-- print file information
print('\t{0}'.format(FILENAME)) if VERBOSE else None
#-- open output HDF5 file
fid = h5py.File(os.path.join(DIRECTORY, FILENAME), 'w')
#-- predict long-period equilibrium tides at time
tide_lpe = compute_equilibrium_tide(tide_time + deltat, dinput['lat'])
#-- add latitude and longitude to output file
for key in ['lat', 'lon']:
#-- Defining the HDF5 dataset variables for lat/lon
h5 = fid.create_dataset(key, (file_lines, ),
data=dinput[key][:],
dtype=dinput[key].dtype,
compression='gzip')
#-- add HDF5 variable attributes
for att_name, att_val in attrib[key].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- output tides to HDF5 dataset
h5 = fid.create_dataset('tide_lpe', (file_lines, ),
data=tide_lpe,
dtype=tide_lpe.dtype,
compression='gzip')
#-- add HDF5 variable attributes
for att_name, att_val in attrib['tide_lpe'].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- output days to HDF5 dataset
h5 = fid.create_dataset('time', (file_lines, ),
data=tide_time,
dtype=tide_time.dtype,
compression='gzip')
#-- add HDF5 variable attributes
for att_name, att_val in attrib['time'].items():
h5.attrs[att_name] = att_val
#-- attach dimensions
h5.dims[0].label = 'RECORD_SIZE'
#-- HDF5 file attributes
fid.attrs['featureType'] = 'trajectory'
fid.attrs['title'] = ('Long-Period_Equilibrium_tidal_correction_for_'
'elevation_measurements')
fid.attrs['summary'] = ('Tidal_correction_computed_at_elevation_'
'measurements_using_fifteen_spectral_lines.')
fid.attrs['project'] = 'NASA_Operation_IceBridge'
fid.attrs['processing_level'] = '4'
fid.attrs['date_created'] = time.strftime('%Y-%m-%d', time.localtime())
#-- add attributes for input files
fid.attrs['elevation_file'] = os.path.basename(input_file)
#-- add geospatial and temporal attributes
fid.attrs['geospatial_lat_min'] = dinput['lat'].min()
fid.attrs['geospatial_lat_max'] = dinput['lat'].max()
fid.attrs['geospatial_lon_min'] = dinput['lon'].min()
fid.attrs['geospatial_lon_max'] = dinput['lon'].max()
fid.attrs['geospatial_lat_units'] = "degrees_north"
fid.attrs['geospatial_lon_units'] = "degrees_east"
fid.attrs['geospatial_ellipsoid'] = "WGS84"
fid.attrs['time_type'] = 'UTC'
#-- convert start/end time from days since 1992-01-01 into Julian days
time_range = np.array([np.min(tide_time), np.max(tide_time)])
time_julian = 2400000.5 + pyTMD.time.convert_delta_time(
time_range,
epoch1=(1992, 1, 1, 0, 0, 0),
epoch2=(1858, 11, 17, 0, 0, 0),
scale=1.0)
#-- convert to calendar date
cal = pyTMD.time.convert_julian(time_julian, ASTYPE=np.int)
#-- add attributes with measurement date start, end and duration
args = (cal['hour'][0], cal['minute'][0], cal['second'][0])
fid.attrs['RangeBeginningTime'] = '{0:02d}:{1:02d}:{2:02d}'.format(*args)
args = (cal['hour'][-1], cal['minute'][-1], cal['second'][-1])
fid.attrs['RangeEndingTime'] = '{0:02d}:{1:02d}:{2:02d}'.format(*args)
args = (cal['year'][0], cal['month'][0], cal['day'][0])
fid.attrs['RangeBeginningDate'] = '{0:4d}-{1:02d}-{2:02d}'.format(*args)
args = (cal['year'][-1], cal['month'][-1], cal['day'][-1])
fid.attrs['RangeEndingDate'] = '{0:4d}-{1:02d}-{2:02d}'.format(*args)
duration = np.round(time_julian[-1] * 86400.0 - time_julian[0] * 86400.0)
fid.attrs['DurationTimeSeconds'] = '{0:0.0f}'.format(duration)
#-- close the output HDF5 dataset
fid.close()
#-- change the permissions level to MODE
os.chmod(os.path.join(DIRECTORY, FILENAME), MODE)
def compute_LPET_elevations(input_file, output_file,
FORMAT='csv', VARIABLES=['time','lat','lon','data'],
TIME_UNITS='days since 1858-11-17T00:00:00', PROJECTION='4326',
VERBOSE=False, MODE=0o775):
#-- output netCDF4 and HDF5 file attributes
#-- will be added to YAML header in csv files
attrib = {}
#-- latitude
attrib['lat'] = {}
attrib['lat']['long_name'] = 'Latitude'
attrib['lat']['units'] = 'Degrees_North'
#-- longitude
attrib['lon'] = {}
attrib['lon']['long_name'] = 'Longitude'
attrib['lon']['units'] = 'Degrees_East'
#-- long-period equilibrium tides
attrib['tide_lpe'] = {}
attrib['tide_lpe']['long_name'] = 'Equilibrium_Tide'
attrib['tide_lpe']['description'] = ('Long-period_equilibrium_tidal_'
'elevation_from_the_summation_of_fifteen_tidal_spectral_lines')
attrib['tide_lpe']['reference'] = ('https://doi.org/10.1111/'
'j.1365-246X.1973.tb03420.x')
attrib['tide_lpe']['units'] = 'meters'
#-- time
attrib['time'] = {}
attrib['time']['long_name'] = 'Time'
attrib['time']['units'] = 'days since 1992-01-01T00:00:00'
attrib['time']['calendar'] = 'standard'
#-- read input file to extract time, spatial coordinates and data
if (FORMAT == 'csv'):
dinput = pyTMD.spatial.from_ascii(input_file, columns=VARIABLES,
header=0, verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
dinput = pyTMD.spatial.from_netCDF4(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
dinput = pyTMD.spatial.from_HDF5(input_file, timename=VARIABLES[0],
xname=VARIABLES[2], yname=VARIABLES[1], varname=VARIABLES[3],
verbose=VERBOSE)
#-- converting x,y from projection to latitude/longitude
#-- could try to extract projection attributes from netCDF4 and HDF5 files
try:
crs1 = pyproj.CRS.from_string("epsg:{0:d}".format(int(PROJECTION)))
except (ValueError,pyproj.exceptions.CRSError):
crs1 = pyproj.CRS.from_string(PROJECTION)
crs2 = pyproj.CRS.from_string("epsg:{0:d}".format(4326))
transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)
lon,lat = transformer.transform(dinput['x'].flatten(),dinput['y'].flatten())
#-- extract time units from netCDF4 and HDF5 attributes or from TIME_UNITS
try:
time_string = dinput['attributes']['time']['units']
except (TypeError, KeyError):
epoch1,to_secs = pyTMD.time.parse_date_string(TIME_UNITS)
else:
epoch1,to_secs = pyTMD.time.parse_date_string(time_string)
#-- convert time from units to days since 1992-01-01T00:00:00
tide_time = pyTMD.time.convert_delta_time(to_secs*dinput['time'].flatten(),
epoch1=epoch1, epoch2=(1992,1,1,0,0,0), scale=1.0/86400.0)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data','merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- predict long-period equilibrium tides at time
tide_lpe = compute_equilibrium_tide(tide_time + deltat, lat)
#-- output to file
output = dict(time=tide_time,lon=lon,lat=lat,tide_lpe=tide_lpe)
if (FORMAT == 'csv'):
pyTMD.spatial.to_ascii(output, attrib, output_file, delimiter=',',
columns=['time','lat','lon','tide_lpe'], verbose=VERBOSE)
elif (FORMAT == 'netCDF4'):
pyTMD.spatial.to_netCDF4(output, attrib, output_file, verbose=VERBOSE)
elif (FORMAT == 'HDF5'):
pyTMD.spatial.to_HDF5(output, attrib, output_file, verbose=VERBOSE)
#-- change the permissions level to MODE
os.chmod(output_file, MODE)
def compute_LPET_ICESat2(FILE, VERBOSE=False, MODE=0o775):
#-- read data from FILE
print('{0} -->'.format(os.path.basename(FILE))) if VERBOSE else None
IS2_atl12_mds,IS2_atl12_attrs,IS2_atl12_beams = read_HDF5_ATL12(FILE,
ATTRIBUTES=True)
DIRECTORY = os.path.dirname(FILE)
#-- extract parameters from ICESat-2 ATLAS HDF5 ocean surface file name
rx = re.compile(r'(processed_)?(ATL\d{2})_(\d{4})(\d{2})(\d{2})(\d{2})'
r'(\d{2})(\d{2})_(\d{4})(\d{2})(\d{2})_(\d{3})_(\d{2})(.*?).h5$')
SUB,PRD,YY,MM,DD,HH,MN,SS,TRK,CYCL,GRAN,RL,VERS,AUX = rx.findall(FILE).pop()
#-- number of GPS seconds between the GPS epoch
#-- and ATLAS Standard Data Product (SDP) epoch
atlas_sdp_gps_epoch = IS2_atl12_mds['ancillary_data']['atlas_sdp_gps_epoch']
#-- copy variables for outputting to HDF5 file
IS2_atl12_tide = {}
IS2_atl12_fill = {}
IS2_atl12_dims = {}
IS2_atl12_tide_attrs = {}
#-- number of GPS seconds between the GPS epoch (1980-01-06T00:00:00Z UTC)
#-- and ATLAS Standard Data Product (SDP) epoch (2018-01-01T00:00:00Z UTC)
#-- Add this value to delta time parameters to compute full gps_seconds
IS2_atl12_tide['ancillary_data'] = {}
IS2_atl12_tide_attrs['ancillary_data'] = {}
for key in ['atlas_sdp_gps_epoch']:
#-- get each HDF5 variable
IS2_atl12_tide['ancillary_data'][key] = IS2_atl12_mds['ancillary_data'][key]
#-- Getting attributes of group and included variables
IS2_atl12_tide_attrs['ancillary_data'][key] = {}
for att_name,att_val in IS2_atl12_attrs['ancillary_data'][key].items():
IS2_atl12_tide_attrs['ancillary_data'][key][att_name] = att_val
#-- for each input beam within the file
for gtx in sorted(IS2_atl12_beams):
#-- output data dictionaries for beam
IS2_atl12_tide[gtx] = dict(ssh_segments={})
IS2_atl12_fill[gtx] = dict(ssh_segments={})
IS2_atl12_dims[gtx] = dict(ssh_segments={})
IS2_atl12_tide_attrs[gtx] = dict(ssh_segments={})
#-- number of segments
val = IS2_atl12_mds[gtx]['ssh_segments']
#-- convert time from ATLAS SDP to days relative to Jan 1, 1992
gps_seconds = atlas_sdp_gps_epoch + val['delta_time']
leap_seconds = pyTMD.time.count_leap_seconds(gps_seconds)
tide_time = pyTMD.time.convert_delta_time(gps_seconds-leap_seconds,
epoch1=(1980,1,6,0,0,0), epoch2=(1992,1,1,0,0,0), scale=1.0/86400.0)
#-- interpolate delta times from calendar dates to tide time
delta_file = get_data_path(['data','merged_deltat.data'])
deltat = calc_delta_time(delta_file, tide_time)
#-- predict long-period equilibrium tides at latitudes and time
tide_lpe = compute_equilibrium_tide(tide_time + deltat, val['latitude'])
#-- group attributes for beam
IS2_atl12_tide_attrs[gtx]['Description'] = IS2_atl12_attrs[gtx]['Description']
IS2_atl12_tide_attrs[gtx]['atlas_pce'] = IS2_atl12_attrs[gtx]['atlas_pce']
IS2_atl12_tide_attrs[gtx]['atlas_beam_type'] = IS2_atl12_attrs[gtx]['atlas_beam_type']
IS2_atl12_tide_attrs[gtx]['groundtrack_id'] = IS2_atl12_attrs[gtx]['groundtrack_id']
IS2_atl12_tide_attrs[gtx]['atmosphere_profile'] = IS2_atl12_attrs[gtx]['atmosphere_profile']
IS2_atl12_tide_attrs[gtx]['atlas_spot_number'] = IS2_atl12_attrs[gtx]['atlas_spot_number']
IS2_atl12_tide_attrs[gtx]['sc_orientation'] = IS2_atl12_attrs[gtx]['sc_orientation']
#-- group attributes for ssh_segments
IS2_atl12_tide_attrs[gtx]['ssh_segments']['Description'] = ("Contains "
"parameters relating to the calculated surface height.")
IS2_atl12_tide_attrs[gtx]['ssh_segments']['data_rate'] = ("Data within "
"this group are stored at the variable ocean processing segment rate.")
#-- geolocation, time and segment ID
#-- delta time
IS2_atl12_tide[gtx]['ssh_segments']['delta_time'] = val['delta_time'].copy()
IS2_atl12_fill[gtx]['ssh_segments']['delta_time'] = None
IS2_atl12_dims[gtx]['ssh_segments']['delta_time'] = None
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delta_time'] = {}
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delta_time']['units'] = "seconds since 2018-01-01"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delta_time']['long_name'] = "Elapsed GPS seconds"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delta_time']['standard_name'] = "time"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delta_time']['source'] = "telemetry"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delta_time']['calendar'] = "standard"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delta_time']['description'] = ("Number of "
"GPS seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch "
"offset is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS "
"seconds between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP "
"epoch. By adding the offset contained within atlas_sdp_gps_epoch to delta time "
"parameters, the time in gps_seconds relative to the GPS epoch can be computed.")
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delta_time']['coordinates'] = \
"latitude longitude"
#-- latitude
IS2_atl12_tide[gtx]['ssh_segments']['latitude'] = val['latitude'].copy()
IS2_atl12_fill[gtx]['ssh_segments']['latitude'] = None
IS2_atl12_dims[gtx]['ssh_segments']['latitude'] = ['delta_time']
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude'] = {}
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude']['units'] = "degrees_north"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude']['contentType'] = "physicalMeasurement"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude']['long_name'] = "Latitude"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude']['standard_name'] = "latitude"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude']['description'] = ("Latitude of "
"segment center")
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude']['valid_min'] = -90.0
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude']['valid_max'] = 90.0
IS2_atl12_tide_attrs[gtx]['ssh_segments']['latitude']['coordinates'] = \
"delta_time longitude"
#-- longitude
IS2_atl12_tide[gtx]['ssh_segments']['longitude'] = val['longitude'].copy()
IS2_atl12_fill[gtx]['ssh_segments']['longitude'] = None
IS2_atl12_dims[gtx]['ssh_segments']['longitude'] = ['delta_time']
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude'] = {}
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude']['units'] = "degrees_east"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude']['contentType'] = "physicalMeasurement"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude']['long_name'] = "Longitude"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude']['standard_name'] = "longitude"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude']['description'] = ("Longitude of "
"segment center")
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude']['valid_min'] = -180.0
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude']['valid_max'] = 180.0
IS2_atl12_tide_attrs[gtx]['ssh_segments']['longitude']['coordinates'] = \
"delta_time latitude"
#-- Ocean Segment Duration
IS2_atl12_tide[gtx]['ssh_segments']['delt_seg'] = val['delt_seg']
IS2_atl12_fill[gtx]['ssh_segments']['delt_seg'] = None
IS2_atl12_dims[gtx]['ssh_segments']['delt_seg'] = ['delta_time']
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delt_seg'] = {}
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delt_seg']['units'] = "seconds"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delt_seg']['contentType'] = \
"referenceInformation"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delt_seg']['long_name'] = \
"Ocean Segment Duration"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delt_seg']['description'] = \
"Time duration segment"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['delt_seg']['coordinates'] = \
"delta_time latitude longitude"
#-- stats variables
IS2_atl12_tide[gtx]['ssh_segments']['stats'] = {}
IS2_atl12_fill[gtx]['ssh_segments']['stats'] = {}
IS2_atl12_dims[gtx]['ssh_segments']['stats'] = {}
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats'] = {}
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['Description'] = ("Contains parameters "
"related to quality and corrections on the sea surface height parameters.")
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['data_rate'] = ("Data within this group "
"are stored at the variable ocean processing segment rate.")
#-- computed long-period equilibrium tide
IS2_atl12_tide[gtx]['ssh_segments']['stats']['tide_equilibrium_seg'] = tide_lpe
IS2_atl12_fill[gtx]['ssh_segments']['stats']['tide_equilibrium_seg'] = None
IS2_atl12_dims[gtx]['ssh_segments']['stats']['tide_equilibrium_seg'] = ['delta_time']
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['tide_equilibrium_seg'] = {}
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['tide_equilibrium_seg']['units'] = "meters"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['tide_equilibrium_seg']['contentType'] = "referenceInformation"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['tide_equilibrium']['long_name'] = \
"Long Period Equilibrium Tide"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['tide_equilibrium_seg']['description'] = ("Long-period "
"equilibrium tidal elevation from the summation of fifteen tidal spectral lines")
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['tide_equilibrium_seg']['reference'] = \
"https://doi.org/10.1111/j.1365-246X.1973.tb03420.x"
IS2_atl12_tide_attrs[gtx]['ssh_segments']['stats']['tide_equilibrium_seg']['coordinates'] = \
"../delta_time ../latitude ../longitude"
#-- output equilibrium tide HDF5 file
args = (PRD,YY,MM,DD,HH,MN,SS,TRK,CYCL,GRAN,RL,VERS,AUX)
file_format = '{0}_LPET_{1}{2}{3}{4}{5}{6}_{7}{8}{9}_{10}_{11}{12}.h5'
#-- print file information
print('\t{0}'.format(file_format.format(*args))) if VERBOSE else None
HDF5_ATL12_tide_write(IS2_atl12_tide, IS2_atl12_tide_attrs,
CLOBBER=True, INPUT=os.path.basename(FILE),
FILL_VALUE=IS2_atl12_fill, DIMENSIONS=IS2_atl12_dims,
FILENAME=os.path.join(DIRECTORY,file_format.format(*args)))
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY,file_format.format(*args)), MODE)
