def test_read_EOP(EPOCH):
#-- convert dates to Modified Julian days (days since 1858-11-17T00:00:00)
delta_time = 86400.0 * np.arange(0, 365)
MJD = pyTMD.time.convert_delta_time(delta_time,
epoch1=(2000, 1, 1, 0, 0, 0),
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)
#-- mean and daily EOP files
mean_pole_file = pyTMD.utilities.get_data_path(['data', 'mean-pole.tab'])
pole_tide_file = pyTMD.utilities.get_data_path(['data', 'finals.all'])
#-- calculate angular coordinates of mean pole at time
#-- iterate over different IERS conventional mean pole (CMP) formulations
mpx, mpy, fl = iers_mean_pole(mean_pole_file, time_decimal, EPOCH)
#-- check flags
assert np.all(fl)
#-- read IERS daily polar motion values
EOP = read_iers_EOP(pole_tide_file)
#-- check validity
assert np.all(np.isfinite(EOP['x'])) & np.all(np.isfinite(EOP['y']))
#-- interpolate daily polar motion values to time 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)
#-- check validity of differentials
assert np.all(np.isfinite(mx)) & np.all(np.isfinite(my))
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 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_LPT_displacements(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):
#-- 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'
#-- load pole tides
attrib['tide_pole'] = {}
attrib['tide_pole']['long_name'] = 'Solid_Earth_Pole_Tide'
attrib['tide_pole']['description'] = ('Solid_Earth_pole_tide_radial_'
'displacements_due_to_polar_motion')
attrib['tide_pole']['reference'] = ('ftp://tai.bipm.org/iers/conv2010/'
'chapter7/tn36_c7.pdf')
attrib['tide_pole']['units'] = 'meters'
attrib['tide_pole']['_FillValue'] = fill_value
#-- time
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
dtr = np.pi/180.0
atr = np.pi/648000.0
#-- earth and physical parameters (IERS and WGS84)
GM = 3.986004418e14#-- geocentric gravitational constant [m^3/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
#-- 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)
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
#-- geocentric colatitude and longitude in radians
theta = dtr*(90.0 - latitude_geocentric)
phi = lon*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)*h + \
(3.0/a_axis**2.0)*h**2.0)
#-- pole tide files (mean and daily)
mean_pole_file = pyTMD.utilities.get_data_path(['data','mean-pole.tab'])
pole_tide_file = pyTMD.utilities.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 MJD 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)
#-- calculate radial displacement at time
dfactor = -hb2*atr*(omega**2*rr**2)/(2.0*gamma_h)
Srad = np.ma.zeros((len(latitude_geocentric)),fill_value=fill_value)
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
#-- calculate radial displacement at time
if (TYPE == 'grid'):
Srad = np.ma.zeros((ny,nx,nt),fill_value=fill_value)
Srad.mask = np.zeros((ny,nx,nt),dtype=bool)
for i in range(nt):
SRAD=dfactor*np.sin(2.0*theta)*(mx[i]*np.cos(phi)+my[i]*np.sin(phi))
#-- reform grid
Srad.data[:,:,i]=np.reshape(SRAD, (ny,nx))
Srad.mask[:,:,i]=np.isnan(SRAD)
elif (TYPE == 'drift'):
Srad = np.ma.zeros((nt),fill_value=fill_value)
Srad.data[:] = dfactor*np.sin(2.0*theta)*(mx*np.cos(phi)+my*np.sin(phi))
Srad.mask = np.isnan(Srad.data)
#-- replace invalid data with fill values
Srad.data[Srad.mask] = Srad.fill_value
#-- output to file
output = dict(time=MJD,lon=lon,lat=lat,tide_pole=Srad)
if (FORMAT == 'csv'):
pyTMD.spatial.to_ascii(output, attrib, output_file, delimiter=',',
columns=['time','lat','lon','tide_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_pole')
#-- 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_OPT_displacements(tide_dir,
input_file,
output_file,
METHOD=None,
VERBOSE=False,
MODE=0775):
#-- read input *.csv file to extract MJD, latitude, longitude and elevation
dtype = dict(names=('MJD', 'lat', 'lon', 'h'),
formats=('f', 'f', 'f', 'f'))
dinput = np.loadtxt(input_file, delimiter=',', dtype=dtype)
file_lines, = np.shape(dinput['h'])
#-- convert from MJD to calendar dates, then to year-decimal
YY, MM, DD, HH, MN, SS = convert_julian(dinput['MJD'] + 2400000.5,
FORMAT='tuple')
tdec = convert_calendar_decimal(YY,
MM,
DAY=DD,
HOUR=HH,
MINUTE=MN,
SECOND=SS)
#-- 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 = 3.98004418e14 #-- 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]
#-- 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 = dinput['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 + dinput['h']) * np.cos(latitude_geodetic_rad) * np.cos(
dinput['lon'] * dtr)
Y = (N + dinput['h']) * np.cos(latitude_geodetic_rad) * np.sin(
dinput['lon'] * dtr)
Z = (N * (1.0 - ecc1**2.0) + dinput['h']) * np.sin(latitude_geodetic_rad)
#-- 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 / (3.0 * GM)
#-- pole tide files (mean and daily)
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')
#-- calculate angular coordinates of mean pole at time tdec
mpx, mpy, fl = iers_mean_pole(mean_pole_file, tdec, '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(dinput['MJD'])
py = ySPL(dinput['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 = os.path.join(tide_dir, 'opoleloadcoefcmcor.txt.gz')
iur, 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(dinput['lon'], latitude_geocentric)
UR.imag = f2.ev(dinput['lon'], latitude_geocentric)
else:
#-- create mesh grids of latitude and longitude
gridlon, gridlat = np.meshgrid(ilon, ilat, indexing='ij')
interp_points = zip(gridlon.flatten(), gridlat.flatten())
#-- use scipy griddata to interpolate to output points
UR = scipy.interpolate.griddata(interp_points,
iur.flatten(),
zip(dinput['lon'],
latitude_geocentric),
method=METHOD)
#-- calculate radial displacement at time
Urad = K * atr * np.real((mx * gamma.real + my * gamma.imag) * UR.real +
(my * gamma.real - mx * gamma.imag) * UR.imag)
#-- output to file
with open(output_file) as f:
for d, lt, ln, u in zip(dinput['MJD'], dinput['lat'], dinput['lon'],
Urad):
print('{0:g},{1:g},{2:g},{3:f}'.format(d, lt, ln, u), file=f)
#-- change the permissions level to MODE
os.chmod(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_LPT_icebridge_data(tide_dir, arg, VERBOSE=False, MODE=0o775):
#-- extract file name and subsetter indices lists
match_object = re.match('(.*?)(\[(.*?)\])?$', arg)
input_file = os.path.expanduser(match_object.group(1))
#-- subset input file to indices
if match_object.group(2):
#-- decompress ranges and add to list
input_subsetter = []
for i in re.findall('((\d+)-(\d+)|(\d+))', match_object.group(3)):
input_subsetter.append(int(i[3])) if i[3] else \
input_subsetter.extend(range(int(i[1]),int(i[2])+1))
else:
input_subsetter = None
#-- output directory for input_file
DIRECTORY = os.path.dirname(input_file)
#-- calculate if input files are from ATM or LVIS (+GH)
regex = {}
regex['ATM'] = '(BLATM2|ILATM2)_(\d+)_(\d+)_smooth_nadir(.*?)(csv|seg|pt)$'
regex['ATM1b'] = '(BLATM1b|ILATM1b)_(\d+)_(\d+)(.*?).(qi|TXT|h5)$'
regex['LVIS'] = '(BLVIS2|BVLIS2|ILVIS2)_(.*?)(\d+)_(\d+)_(R\d+)_(\d+).H5$'
regex['LVGH'] = '(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 = {}
#-- 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'
#-- load pole tides
attrib['tide_pole'] = {}
attrib['tide_pole']['long_name'] = 'Solid_Earth_Pole_Tide'
attrib['tide_pole']['description'] = (
'Solid_Earth_pole_tide_radial_'
'displacements_at_the_measurement_position_at_the_acquisition_'
'time_due_to_polar_motion')
attrib['tide_pole']['reference'] = ('ftp://tai.bipm.org/iers/conv2010/'
'chapter7/opoleloadcoefcmcor.txt.gz')
attrib['tide_pole']['units'] = 'meters'
#-- Modified Julian Days
attrib['MJD'] = {}
attrib['MJD']['long_name'] = 'Time'
attrib['MJD']['description'] = 'Modified Julian Days'
attrib['MJD']['units'] = 'Days'
#-- 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
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 = 3.98004418e14 #-- 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 * 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
#-- colatitude and longitude in radians
theta = dtr * (90.0 - latitude_geocentric)
phi = lon * 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) * h1 +
(3.0 / a_axis**2.0) * h1**2.0)
#-- 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 load pole tide HDF5 file
#-- form: rg_NASA_LOAD_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], 'LOAD_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')
#-- 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)
Sr = dfactor * np.sin(2.0 * theta) * (mx * np.cos(phi) + my * np.sin(phi))
#-- 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_pole', (file_lines, ),
data=Sr,
dtype=Sr.dtype,
compression='gzip')
#-- add HDF5 variable attributes
h5.attrs['_FillValue'] = fill_value
for att_name, att_val in attrib['tide_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('MJD', (file_lines, ),
data=t,
dtype=t.dtype,
compression='gzip')
#-- add HDF5 variable attributes
for att_name, att_val in attrib['MJD'].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'] = 'Load_Pole_Tide_correction_for_elevation_measurements'
fid.attrs['summary'] = ('Solid_Earth_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)
