def test_decimal_dates(YEAR, MONTH):
#-- days per month in a leap and a standard year
#-- only difference is February (29 vs. 28)
dpm_leap = np.array([31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
dpm_stnd = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
DPM = dpm_stnd if np.mod(YEAR, 4) else dpm_leap
#-- calculate Modified Julian Day (MJD) from calendar date
DAY = np.random.randint(1, DPM[MONTH - 1] + 1)
HOUR = np.random.randint(0, 23 + 1)
MINUTE = np.random.randint(0, 59 + 1)
SECOND = 60.0 * np.random.random_sample(1)
#-- calculate year-decimal time
tdec = convert_calendar_decimal(YEAR,
MONTH,
DAY=DAY,
HOUR=HOUR,
MINUTE=MINUTE,
SECOND=SECOND)
#-- day of the year 1 = Jan 1, 365 = Dec 31 (std)
day_temp = np.mod(tdec, 1) * np.sum(DPM)
DofY = np.floor(day_temp) + 1
#-- cumulative sum of the calendar dates
day_cumulative = np.cumsum(np.concatenate(([0], DPM))) + 1
#-- finding which month date is in
i = np.nonzero((DofY >= day_cumulative[0:-1])
& (DofY < day_cumulative[1:]))
month_range = np.arange(1, 13)
month = month_range[i]
#-- finding day of the month
day = (DofY - day_cumulative[i]) + 1
#-- convert residuals into time (hour, minute and second)
hour_temp = np.mod(day_temp, 1) * 24.0
minute_temp = np.mod(hour_temp, 1) * 60.0
second = np.mod(minute_temp, 1) * 60.0
#-- assert dates
eps = np.finfo(np.float16).eps
assert (np.floor(tdec) == YEAR)
assert (month == MONTH)
assert (day == DAY)
assert (np.floor(hour_temp) == HOUR)
assert (np.floor(minute_temp) == MINUTE)
assert (np.abs(second - SECOND) < eps)
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,
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_OPT_displacements(tide_dir,
input_file,
output_file,
FORMAT='csv',
VARIABLES=['time', 'lat', 'lon', 'data'],
TIME_UNITS='days since 1858-11-17T00:00:00',
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=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 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 = convert_julian(2400000.5 + MJD, FORMAT='tuple')
#-- calculate time in year-decimal format
time_decimal = convert_calendar_decimal(Y,
M,
DAY=D,
HOUR=h,
MINUTE=m,
SECOND=s)
#-- number of data points
n_time = 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
#-- 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 + dinput['data']) * np.cos(latitude_geodetic_rad) * np.cos(
lon * dtr)
Y = (N + dinput['data']) * np.cos(latitude_geodetic_rad) * np.sin(
lon * dtr)
Z = (N *
(1.0 - ecc1**2.0) + dinput['data']) * 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 / (3.0 * 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 = 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
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((n_time), 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
Urad = np.ma.zeros((n_time), 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
#-- 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)
#-- change the permissions level to MODE
os.chmod(output_file, 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)
def compute_LPT_displacements(tide_dir,
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):
#-- 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/opoleloadcoefcmcor.txt.gz')
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=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 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 = convert_julian(2400000.5 + MJD, FORMAT='tuple')
#-- calculate time in year-decimal format
time_decimal = convert_calendar_decimal(Y,
M,
DAY=D,
HOUR=h,
MINUTE=m,
SECOND=s)
#-- number of data points
n_time = 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
#-- 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 + dinput['data']) * np.cos(latitude_geodetic_rad) * np.cos(
lon * dtr)
Y = (N + dinput['data']) * np.cos(latitude_geodetic_rad) * np.sin(
lon * dtr)
Z = (N *
(1.0 - ecc1**2.0) + dinput['data']) * 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
#-- 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) * dinput['data'] +
(3.0 / a_axis**2.0) * dinput['data']**2.0)
#-- 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
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)
#-- calculate radial displacement at time
dfactor = -hb2 * atr * (omega**2 * rr**2) / (2.0 * gamma_h)
Srad = np.ma.zeros((n_time), 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
#-- 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)
#-- change the permissions level to MODE
os.chmod(output_file, MODE)
