def combine_harmonics(INPUT_FILE,
OUTPUT_FILE,
LMAX=None,
MMAX=None,
LOVE_NUMBERS=0,
REFERENCE=None,
RAD=None,
DESTRIPE=False,
UNITS=None,
DDEG=None,
INTERVAL=None,
BOUNDS=None,
REDISTRIBUTE=False,
LSMASK=None,
MEAN_FILE=None,
DATAFORM=None,
VERBOSE=False,
MODE=0o775):
#-- verify that output directory exists
DIRECTORY = os.path.abspath(os.path.dirname(OUTPUT_FILE))
if not os.access(DIRECTORY, os.F_OK):
os.makedirs(DIRECTORY, MODE, exist_ok=True)
#-- read input spherical harmonic coefficients from file in DATAFORM
if (DATAFORM == 'ascii'):
input_Ylms = harmonics().from_ascii(INPUT_FILE)
elif (DATAFORM == 'netCDF4'):
#-- read input netCDF4 file (.nc)
input_Ylms = harmonics().from_netCDF4(INPUT_FILE)
elif (DATAFORM == 'HDF5'):
#-- read input HDF5 file (.H5)
input_Ylms = harmonics().from_HDF5(INPUT_FILE)
#-- reform harmonic dimensions to be l,m,t
#-- truncate to degree and order LMAX, MMAX
input_Ylms = input_Ylms.truncate(lmax=LMAX, mmax=MMAX).expand_dims()
#-- remove mean file from input Ylms
if MEAN_FILE and (DATAFORM == 'ascii'):
mean_Ylms = harmonics().from_ascii(MEAN_FILE, date=False)
input_Ylms.subtract(mean_Ylms)
elif MEAN_FILE and (DATAFORM == 'netCDF4'):
#-- read input netCDF4 file (.nc)
mean_Ylms = harmonics().from_netCDF4(MEAN_FILE, date=False)
input_Ylms.subtract(mean_Ylms)
elif MEAN_FILE and (DATAFORM == 'HDF5'):
#-- read input HDF5 file (.H5)
mean_Ylms = harmonics().from_HDF5(MEAN_FILE, date=False)
input_Ylms.subtract(mean_Ylms)
#-- read arrays of kl, hl, and ll Love Numbers
hl, kl, ll = load_love_numbers(LMAX,
LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE=REFERENCE)
#-- distribute total mass uniformly over the ocean
if REDISTRIBUTE:
#-- read Land-Sea Mask and convert to spherical harmonics
ocean_Ylms = ocean_stokes(LSMASK, LMAX, MMAX=MMAX, LOVE=(hl, kl, ll))
#-- calculate ratio between total mass and a uniformly distributed
#-- layer of water over the ocean
ratio = input_Ylms.clm[0, 0, :] / ocean_Ylms['clm'][0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove the ratio*ocean Ylms from Ylms
#-- note: x -= y is equivalent to x = x - y
input_Ylms.clm[l, m, :] -= ratio * ocean_Ylms['clm'][l, m]
input_Ylms.slm[l, m, :] -= ratio * ocean_Ylms['slm'][l, m]
#-- if using a decorrelation filter (Isabella's destriping Routine)
if DESTRIPE:
input_Ylms = input_Ylms.destripe()
#-- Gaussian smoothing
if (RAD != 0):
wt = 2.0 * np.pi * gauss_weights(RAD, LMAX)
else:
wt = np.ones((LMAX + 1))
#-- Output spatial data
grid = spatial()
grid.time = np.copy(input_Ylms.time)
grid.month = np.copy(input_Ylms.month)
#-- Output Degree Spacing
if (len(DDEG) == 1):
#-- dlon == dlat
dlon = DDEG
dlat = DDEG
else:
#-- dlon != dlat
dlon, dlat = DDEG
#-- Output Degree Interval
if (INTERVAL == 1):
#-- (0:360,90:-90)
nlon = np.int((360.0 / dlon) + 1.0)
nlat = np.int((180.0 / dlat) + 1.0)
grid.lon = dlon * np.arange(0, nlon)
grid.lat = 90.0 - dlat * np.arange(0, nlat)
elif (INTERVAL == 2):
#-- (Degree spacing)/2
grid.lon = np.arange(dlon / 2.0, 360 + dlon / 2.0, dlon)
grid.lat = np.arange(90.0 - dlat / 2.0, -90.0 - dlat / 2.0, -dlat)
nlon = len(grid.lon)
nlat = len(grid.lat)
elif (INTERVAL == 3):
#-- non-global grid set with BOUNDS parameter
minlon, maxlon, minlat, maxlat = BOUNDS.copy()
grid.lon = np.arange(minlon + dlon / 2.0, maxlon + dlon / 2.0, dlon)
grid.lat = np.arange(maxlat - dlat / 2.0, minlat - dlat / 2.0, -dlat)
nlon = len(grid.lon)
nlat = len(grid.lat)
#-- Setting units factor for output
#-- dfactor computes the degree dependent coefficients
if (UNITS == 1):
#-- 1: cmwe, centimeters water equivalent
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).cmwe
elif (UNITS == 2):
#-- 2: mmGH, mm geoid height
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).mmGH
elif (UNITS == 3):
#-- 3: mmCU, mm elastic crustal deformation
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).mmCU
elif (UNITS == 4):
#-- 4: micGal, microGal gravity perturbations
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).microGal
elif (UNITS == 5):
#-- 5: Pa, equivalent surface pressure in Pascals
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).Pa
else:
raise ValueError(('UNITS is invalid:\n1: cmwe\n2: mmGH\n3: mmCU '
'(elastic)\n4:microGal\n5: Pa'))
#-- Computing plms for converting to spatial domain
theta = (90.0 - grid.lat) * np.pi / 180.0
PLM, dPLM = plm_holmes(LMAX, np.cos(theta))
#-- output spatial grid
nt = len(input_Ylms.time)
grid.data = np.zeros((nlat, nlon, nt))
#-- converting harmonics to truncated, smoothed coefficients in output units
for t in range(nt):
#-- spherical harmonics for time t
Ylms = input_Ylms.index(t)
Ylms.convolve(dfactor * wt)
#-- convert spherical harmonics to output spatial grid
grid.data[:, :, t] = harmonic_summation(Ylms.clm,
Ylms.slm,
grid.lon,
grid.lat,
LMAX=LMAX,
PLM=PLM).T
#-- if verbose output: print input and output file names
if VERBOSE:
print('{0}:'.format(os.path.basename(sys.argv[0])))
print('{0} -->\n\t{1}\n'.format(INPUT_FILE, OUTPUT_FILE))
#-- outputting data to file
output_data(grid.squeeze(),
FILENAME=OUTPUT_FILE,
DATAFORM=DATAFORM,
UNITS=UNITS)
#-- change output permissions level to MODE
os.chmod(OUTPUT_FILE, MODE)
def mascon_reconstruct(DSET,
LMAX,
RAD,
START=None,
END=None,
MMAX=None,
DESTRIPE=False,
LOVE_NUMBERS=0,
REFERENCE=None,
GIA=None,
GIA_FILE=None,
ATM=False,
DATAFORM=None,
MASCON_FILE=None,
REDISTRIBUTE_MASCONS=False,
RECONSTRUCT_FILE=None,
LANDMASK=None,
OUTPUT_DIRECTORY=None,
MODE=0o775):
#-- for datasets not GSM: will add a label for the dataset
dset_str = '' if (DSET == 'GSM') else '_{0}'.format(DSET)
#-- atmospheric ECMWF "jump" flag (if ATM)
atm_str = '_wATM' if ATM else ''
#-- Gaussian smoothing string for radius RAD
gw_str = '_r{0:0.0f}km'.format(RAD) if (RAD != 0) else ''
#-- input GIA spherical harmonic datafiles
GIA_Ylms_rate = read_GIA_model(GIA_FILE, GIA=GIA, LMAX=LMAX, MMAX=MMAX)
gia_str = '_{0}'.format(GIA_Ylms_rate['title']) if GIA else ''
#-- output string for both LMAX==MMAX and LMAX != MMAX cases
MMAX = np.copy(LMAX) if not MMAX else MMAX
order_str = 'M{0:d}'.format(MMAX) if (MMAX != LMAX) else ''
#-- filter grace coefficients flag
ds_str = '_FL' if DESTRIPE else ''
#-- output filename suffix
suffix = dict(ascii='txt', netCDF4='nc', HDF5='H5')
#-- file parser for reading index files
#-- removes commented lines (can comment out files in the index)
#-- removes empty lines (if there are extra empty lines)
parser = re.compile(r'^(?!\#|\%|$)', re.VERBOSE)
#-- create initial reconstruct index for calc_mascon.py
fid = open(RECONSTRUCT_FILE, 'w')
#-- output file format
file_format = '{0}{1}{2}{3}{4}_L{5:d}{6}{7}{8}_{9:03d}-{10:03d}.{11}'
#-- read load love numbers
hl, kl, ll = load_love_numbers(LMAX,
LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE=REFERENCE)
#-- Earth Parameters
factors = units(lmax=LMAX).harmonic(hl, kl, ll)
#-- Average Density of the Earth [g/cm^3]
rho_e = factors.rho_e
#-- Average Radius of the Earth [cm]
rad_e = factors.rad_e
#-- Read Ocean function and convert to Ylms for redistribution
if REDISTRIBUTE_MASCONS:
#-- read Land-Sea Mask and convert to spherical harmonics
ocean_Ylms = ocean_stokes(LANDMASK, LMAX, MMAX=MMAX, LOVE=(hl, kl, ll))
ocean_str = '_OCN'
else:
#-- not distributing uniformly over ocean
ocean_str = ''
#-- input mascon spherical harmonic datafiles
with open(MASCON_FILE, 'r') as f:
mascon_files = [l for l in f.read().splitlines() if parser.match(l)]
for k, fi in enumerate(mascon_files):
#-- read mascon spherical harmonics
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
Ylms = harmonics().from_ascii(os.path.expanduser(fi), date=False)
elif (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
Ylms = harmonics().from_netCDF4(os.path.expanduser(fi), date=False)
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
Ylms = harmonics().from_HDF5(os.path.expanduser(fi), date=False)
#-- Calculating the total mass of each mascon (1 cmwe uniform)
total_area = 4.0 * np.pi * (rad_e**3) * rho_e * Ylms.clm[0, 0] / 3.0
#-- distribute mascon mass uniformly over the ocean
if REDISTRIBUTE_MASCONS:
#-- calculate ratio between total mascon mass and
#-- a uniformly distributed cm of water over the ocean
ratio = Ylms.clm[0, 0] / ocean_Ylms.clm[0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove ratio*ocean Ylms from mascon Ylms
#-- note: x -= y is equivalent to x = x - y
Ylms.clm[l, m] -= ratio * ocean_Ylms.clm[l, m]
Ylms.slm[l, m] -= ratio * ocean_Ylms.slm[l, m]
#-- truncate mascon spherical harmonics to d/o LMAX/MMAX
Ylms = Ylms.truncate(lmax=LMAX, mmax=MMAX)
#-- mascon base is the file without directory or suffix
mascon_base = os.path.basename(fi)
mascon_base = os.path.splitext(mascon_base)[0]
#-- if lower case, will capitalize
mascon_base = mascon_base.upper()
#-- if mascon name contains degree and order info, remove
mascon_name = mascon_base.replace('_L{0:d}'.format(LMAX), '')
#-- input filename format (for both LMAX==MMAX and LMAX != MMAX cases):
#-- mascon name, GRACE dataset, GIA model, LMAX, (MMAX,)
#-- Gaussian smoothing, filter flag, remove reconstructed fields flag
#-- output GRACE error file
args = (mascon_name, dset_str, gia_str.upper(), atm_str, ocean_str,
LMAX, order_str, gw_str, ds_str)
file_input = '{0}{1}{2}{3}{4}_L{5:d}{6}{7}{8}.txt'.format(*args)
mascon_data_input = np.loadtxt(
os.path.join(OUTPUT_DIRECTORY, file_input))
#-- convert mascon time-series from Gt to cmwe
mascon_sigma = 1e15 * mascon_data_input[:, 2] / total_area
#-- mascon time-series Ylms
mascon_Ylms = Ylms.scale(mascon_sigma)
mascon_Ylms.time = mascon_data_input[:, 1].copy()
mascon_Ylms.month = mascon_data_input[:, 0].astype(np.int64)
#-- output to file: no ascii option
args = (mascon_name, dset_str, gia_str.upper(), atm_str, ocean_str,
LMAX, order_str, gw_str, ds_str, START, END, suffix[DATAFORM])
FILE = file_format.format(*args)
#-- output harmonics to file
if (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
mascon_Ylms.to_netCDF4(os.path.join(OUTPUT_DIRECTORY, FILE))
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
mascon_Ylms.to_HDF5(os.path.join(OUTPUT_DIRECTORY, FILE))
#-- print file name to index
print(tilde_compress(os.path.join(OUTPUT_DIRECTORY, FILE)), file=fid)
#-- change the permissions mode
os.chmod(os.path.join(OUTPUT_DIRECTORY, FILE), MODE)
#-- close the reconstruct index
fid.close()
#-- change the permissions mode of the index file
os.chmod(RECONSTRUCT_FILE, MODE)
def least_squares_mascons(parameters,
LOVE_NUMBERS=0,
REFERENCE=None,
MODE=0o775):
#-- convert parameters to variables
#-- index file of data files (containing path)
#-- path.expanduser = tilde expansion of path
INDEX_FILE = os.path.expanduser(parameters['INDEX_FILE'])
DATAFORM = parameters['DATAFORM']
#-- spherical harmonic degree range
LMIN = np.int(parameters['LMIN'])
LMAX = np.int(parameters['LMAX'])
#-- maximum spherical harmonic order
if (parameters['MMAX'].title() == 'None'):
MMAX = np.copy(LMAX)
else:
MMAX = np.int(parameters['MMAX'])
#-- gaussian smoothing radius
RAD = np.float(parameters['RAD'])
#-- filter coefficients for stripe effects
DESTRIPE = parameters['DESTRIPE'] in ('Y', 'y')
#-- index of mascons spherical harmonics
MASCON_INDEX = os.path.expanduser(parameters['MASCON_INDEX'])
#-- mascon distribution over the ocean
MASCON_OCEAN = parameters['MASCON_OCEAN'] in ('Y', 'y')
#-- destribute total mass of spherical harmonics over the ocean
REDISTRIBUTE = parameters['REDISTRIBUTE'] in ('Y', 'y')
#-- calculating with data errors
DATA_ERROR = parameters['DATA_ERROR'] in ('Y', 'y')
#-- input data have dates
DATE = parameters['DATE'] in ('Y', 'y')
#-- output directory
DIRECTORY = os.path.expanduser(parameters['DIRECTORY'])
#-- 1: fit mass, 2: fit geoid
FIT_METHOD = np.int(parameters['FIT_METHOD'])
#-- Recursively create output directory if not currently existing
if (not os.access(DIRECTORY, os.F_OK)):
os.makedirs(DIRECTORY, mode=MODE, exist_ok=True)
#-- list object of output files for file logs (full path)
output_files = []
#-- read arrays of kl, hl, and ll Love Numbers
hl, kl, ll = load_love_numbers(LMAX,
LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE='CF')
#-- Earth Parameters
factors = units(lmax=LMAX).harmonic(hl, kl, ll)
#-- Average Density of the Earth [g/cm^3]
rho_e = factors.rho_e
#-- Average Radius of the Earth [cm]
rad_e = factors.rad_e
#-- Calculating the Gaussian smoothing for radius RAD
if (RAD != 0):
wt = 2.0 * np.pi * gauss_weights(RAD, LMAX)
gw_str = '_r{0:0.0f}km'.format(RAD)
else:
#-- else = 1
wt = np.ones((LMAX + 1))
gw_str = ''
#-- output string for both LMAX==MMAX and LMAX != MMAX cases
order_str = 'M{0:d}'.format(MMAX) if (MMAX != LMAX) else ''
#-- output string for destriped harmonics
ds_str = '_FL' if DESTRIPE else ''
#-- Read Ocean function and convert to Ylms for redistribution
if (MASCON_OCEAN | REDISTRIBUTE):
#-- read Land-Sea Mask and convert to spherical harmonics
LANDMASK = os.path.expanduser(parameters['LANDMASK'])
ocean_Ylms = ocean_stokes(LANDMASK, LMAX, MMAX=MMAX, LOVE=(hl, kl, ll))
ocean_str = '_OCN'
else:
#-- not distributing uniformly over ocean
ocean_str = ''
#-- input spherical harmonic datafile index
#-- correspond file names with GRACE month
#-- this allows additional months to be in the index
data_Ylms = harmonics().from_index(INDEX_FILE, DATAFORM, date=DATE)
#-- number of files within the index
n_files = data_Ylms.shape[-1]
#-- truncate to degree and order
data_Ylms = data_Ylms.truncate(lmax=LMAX, mmax=MMAX)
#-- Total mass in the simulated input data in gigatonnes
rmass = 4.0 * np.pi * (rad_e**
3.0) * rho_e * data_Ylms.clm[0, 0, :] / 3.0 / 1e15
#-- distribute Ylms uniformly over the ocean
if REDISTRIBUTE:
#-- calculate ratio between total removed mass and
#-- a uniformly distributed cm of water over the ocean
ratio = data_Ylms.clm[0, 0, :] / ocean_Ylms['clm'][0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove the ratio*ocean Ylms from Ylms
#-- note: x -= y is equivalent to x = x - y
data_Ylms.clm[l, m, :] -= ratio * ocean_Ylms['clm'][l, m]
data_Ylms.slm[l, m, :] -= ratio * ocean_Ylms['slm'][l, m]
#-- filter data coefficients
if DESTRIPE:
data_Ylms = data_Ylms.destripe()
#-- input mascon spherical harmonic datafiles
with open(MASCON_INDEX, 'r') as f:
mascon_files = f.read().splitlines()
#-- number of mascons
n_mas = len(mascon_files)
#-- spatial area of the mascon
area_tot = np.zeros((n_mas))
#-- name of each mascon
mascon_name = []
#-- for each valid file in the index (iterate over mascons)
mascon_list = []
for k in range(n_mas):
#-- read mascon spherical harmonics
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
Ylms = harmonics().from_ascii(mascon_files[k], date=False)
elif (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
Ylms = harmonics().from_netCDF4(mascon_files[k], date=False)
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
Ylms = harmonics().from_HDF5(mascon_files[k], date=False)
#-- Calculating the total mass of each mascon (1 cmH2O uniform)
area_tot[k] = 4.0 * np.pi * (rad_e**3) * rho_e * Ylms.clm[0, 0] / 3.0
#-- distribute MASCON mass uniformly over the ocean
if MASCON_OCEAN:
#-- calculate ratio between total mascon mass and
#-- a uniformly distributed cm of water over the ocean
ratio = Ylms.clm[0, 0] / ocean_Ylms['clm'][0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove ratio*ocean Ylms from mascon Ylms
#-- note: x -= y is equivalent to x = x - y
Ylms.clm[l, m] -= ratio * ocean_Ylms['clm'][l, m]
Ylms.slm[l, m] -= ratio * ocean_Ylms['slm'][l, m]
#-- truncate mascon spherical harmonics to d/o LMAX/MMAX and add to list
mascon_list.append(Ylms.truncate(lmax=LMAX, mmax=MMAX))
#-- mascon base is the file without directory or suffix
mascon_base = os.path.basename(mascon_files[k])
mascon_base = os.path.splitext(mascon_base)[0]
#-- if lower case, will capitalize
mascon_base = mascon_base.upper()
#-- if mascon name contains degree and order info, remove
mascon_name.append(mascon_base.replace('_L{0:d}'.format(LMAX), ''))
#-- create single harmonics object from list
mascon_Ylms = harmonics().from_list(mascon_list, date=False)
#-- Calculating the number of cos and sin harmonics between LMIN and LMAX
#-- taking into account MMAX (if MMAX == LMAX then LMAX-MMAX=0)
n_harm = np.int(LMAX**2 - LMIN**2 + 2 * LMAX + 1 - (LMAX - MMAX)**2 -
(LMAX - MMAX))
#-- Initialing harmonics for least squares fitting
#-- mascon kernel
M_lm = np.zeros((n_harm, n_mas))
#-- mascon kernel converted to output unit
MA_lm = np.zeros((n_harm, n_mas))
#-- corrected clm and slm
Y_lm = np.zeros((n_harm, n_files))
#-- sensitivity kernel
A_lm = np.zeros((n_harm, n_mas))
#-- Initializing output Mascon time-series
mascon = np.zeros((n_mas, n_files))
#-- Initializing conversion factors
#-- factor for converting to coefficients of mass
fact = np.zeros((n_harm))
#-- smoothing factor
wt_lm = np.zeros((n_harm))
#-- ii is a counter variable for building the mascon column array
ii = 0
#-- Creating column array of clm/slm coefficients
#-- Order is [C00...C6060,S11...S6060]
coeff = rho_e * rad_e / 3.0
#-- Switching between Cosine and Sine Stokes
for cs, csharm in enumerate(['clm', 'slm']):
#-- copy cosine and sin harmonics
mascon_harm = getattr(mascon_Ylms, csharm)
data_harm = getattr(data_Ylms, csharm)
#-- for each spherical harmonic degree
#-- +1 to include LMAX
for l in range(LMIN, LMAX + 1):
#-- for each spherical harmonic order
#-- Sine Stokes for (m=0) = 0
mm = np.min([MMAX, l])
#-- +1 to include l or MMAX (whichever is smaller)
for m in range(cs, mm + 1):
#-- Mascon Spherical Harmonics
M_lm[ii, :] = np.copy(mascon_harm[l, m, :])
#-- Data Spherical Harmonics
Y_lm[ii, :] = np.copy(data_harm[l, m, :])
#-- degree dependent factor to convert to mass
fact[ii] = (2.0 * l + 1.0) / (1.0 + kl[l])
#-- degree dependent smoothing
wt_lm[ii] = np.copy(wt[l])
#-- add 1 to counter
ii += 1
#-- free up memory from data harmonics
data_Ylms.clm = None
data_Ylms.slm = None
#-- Converting mascon coefficients to fit method
if (FIT_METHOD == 1):
#-- Fitting Sensitivity Kernel as mass coefficients
#-- converting M_lm to mass coefficients of the kernel
for i in range(n_harm):
MA_lm[i, :] = wt_lm[i] * M_lm[i, :] * fact[i]
fit_factor = wt_lm * fact
else:
#-- Fitting Sensitivity Kernel as geoid coefficients
MA_lm[:, :] = np.copy(M_lm)
fit_factor = wt_lm * np.ones((n_harm))
#-- Fitting the sensitivity kernel from the input kernel
for i in range(n_harm):
#-- setting kern_i equal to 1 for d/o
kern_i = np.zeros((n_harm))
#-- converting to mass coefficients if specified
kern_i[i] = 1.0 * fit_factor[i]
#-- spherical harmonics solution for the
#-- mascon sensitivity kernels
#-- Least Squares Solutions: Inv(X'.X).(X'.Y)
kern_lm = np.linalg.lstsq(MA_lm, kern_i, rcond=-1)[0]
for k in range(n_mas):
A_lm[i, k] = kern_lm[k] * area_tot[k]
#-- output formatting string if containing date variables
if DATE:
formatting_string = '{0:03d} {1:12.4f} {2:16.10f} {3:16.5f} {4:16.10f}'
else:
formatting_string = '{0:16.10f} {1:16.5f} {2:16.10f}'
#-- for each mascon
for k in range(n_mas):
#-- output filename format:
#-- mascon name, LMAX, Gaussian smoothing radii, filter flag
file_out = '{0}{1}_L{2:d}{3}{4}{5}{6}.txt'.format(
parameters['FILENAME'], mascon_name[k], LMAX, order_str, gw_str,
ds_str, ocean_str)
#-- add output files to list object
output_files.append(os.path.join(DIRECTORY, file_out))
#-- Output mascon datafiles
#-- Will output each mascon mass series
#-- if with dates:
#-- month, date, mascon mass, mascon area
#-- else:
#-- mascon mass, mascon area
#-- open output mascon time-series file
fid = open(os.path.join(DIRECTORY, file_out), 'w')
#-- for each date
for f in range(n_files):
#-- Summing over all spherical harmonics for mascon k, and time t
#-- multiplies by the degree dependent factor to convert
#-- the harmonics into mass coefficients
#-- Converting mascon mass time-series from g to gigatonnes
if DATA_ERROR:
#-- if input data are errors (i.e. estimated dealiasing errors)
mascon[k, f] = np.sqrt(np.sum(
(A_lm[:, k] * Y_lm[:, f])**2)) / 1e15
else:
mascon[k, f] = np.sum(A_lm[:, k] * Y_lm[:, f]) / 1e15
#-- output to file
if DATE:
#-- if files contain date information
args = (data_Ylms.month[f], data_Ylms.time[f], mascon[k, f],
area_tot[k] / 1e10, rmass[f])
print(formatting_string.format(*args), file=fid)
else:
#-- just print the time-series and mascon areas
args = (mascon[k, f], area_tot[k] / 1e10, rmass[f])
print(formatting_string.format(*args), file=fid)
#-- close the output file
fid.close()
#-- change the permissions mode of the output file
os.chmod(os.path.join(DIRECTORY, file_out), MODE)
#-- add output files to list object
output_files.append(os.path.join(DIRECTORY, file_out))
#-- return the list of output files
return output_files
def grace_spatial_maps(base_dir,
PROC,
DREL,
DSET,
LMAX,
RAD,
START=None,
END=None,
MISSING=None,
LMIN=None,
MMAX=None,
LOVE_NUMBERS=0,
REFERENCE=None,
DESTRIPE=False,
UNITS=None,
DDEG=None,
INTERVAL=None,
BOUNDS=None,
GIA=None,
GIA_FILE=None,
ATM=False,
POLE_TIDE=False,
DEG1=None,
DEG1_FILE=None,
MODEL_DEG1=False,
SLR_C20=None,
SLR_21=None,
SLR_22=None,
SLR_C30=None,
SLR_C50=None,
DATAFORM=None,
MEAN_FILE=None,
MEANFORM=None,
REMOVE_FILES=None,
REMOVE_FORMAT=None,
REDISTRIBUTE_REMOVED=False,
LANDMASK=None,
OUTPUT_DIRECTORY=None,
FILE_PREFIX=None,
VERBOSE=False,
MODE=0o775):
#-- recursively create output directory if not currently existing
if not os.access(OUTPUT_DIRECTORY, os.F_OK):
os.makedirs(OUTPUT_DIRECTORY, mode=MODE, exist_ok=True)
#-- list object of output files for file logs (full path)
output_files = []
#-- file information
suffix = dict(ascii='txt', netCDF4='nc', HDF5='H5')
#-- read arrays of kl, hl, and ll Love Numbers
hl, kl, ll = load_love_numbers(LMAX,
LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE=REFERENCE)
#-- Calculating the Gaussian smoothing for radius RAD
if (RAD != 0):
wt = 2.0 * np.pi * gauss_weights(RAD, LMAX)
gw_str = '_r{0:0.0f}km'.format(RAD)
else:
#-- else = 1
wt = np.ones((LMAX + 1))
gw_str = ''
#-- flag for spherical harmonic order
MMAX = np.copy(LMAX) if not MMAX else MMAX
order_str = 'M{0:d}'.format(MMAX) if (MMAX != LMAX) else ''
#-- reading GRACE months for input date range
#-- replacing low-degree harmonics with SLR values if specified
#-- include degree 1 (geocenter) harmonics if specified
#-- correcting for Pole-Tide and Atmospheric Jumps if specified
Ylms = grace_input_months(base_dir,
PROC,
DREL,
DSET,
LMAX,
START,
END,
MISSING,
SLR_C20,
DEG1,
MMAX=MMAX,
SLR_21=SLR_21,
SLR_22=SLR_22,
SLR_C30=SLR_C30,
SLR_C50=SLR_C50,
DEG1_FILE=DEG1_FILE,
MODEL_DEG1=MODEL_DEG1,
ATM=ATM,
POLE_TIDE=POLE_TIDE)
#-- convert to harmonics object and remove mean if specified
GRACE_Ylms = harmonics().from_dict(Ylms)
GRACE_Ylms.directory = Ylms['directory']
#-- use a mean file for the static field to remove
if MEAN_FILE:
#-- read data form for input mean file (ascii, netCDF4, HDF5, gfc)
mean_Ylms = harmonics().from_file(MEAN_FILE,
format=MEANFORM,
date=False)
#-- remove the input mean
GRACE_Ylms.subtract(mean_Ylms)
else:
GRACE_Ylms.mean(apply=True)
#-- date information of GRACE/GRACE-FO coefficients
nfiles = len(GRACE_Ylms.time)
#-- filter GRACE/GRACE-FO coefficients
if DESTRIPE:
#-- destriping GRACE/GRACE-FO coefficients
ds_str = '_FL'
GRACE_Ylms = GRACE_Ylms.destripe()
else:
#-- using standard GRACE/GRACE-FO harmonics
ds_str = ''
#-- input GIA spherical harmonic datafiles
GIA_Ylms_rate = read_GIA_model(GIA_FILE, GIA=GIA, LMAX=LMAX, MMAX=MMAX)
gia_str = '_{0}'.format(GIA_Ylms_rate['title']) if GIA else ''
#-- calculate the monthly mass change from GIA
GIA_Ylms = GRACE_Ylms.zeros_like()
GIA_Ylms.time[:] = np.copy(GRACE_Ylms.time)
GIA_Ylms.month[:] = np.copy(GRACE_Ylms.month)
#-- monthly GIA calculated by gia_rate*time elapsed
#-- finding change in GIA each month
for t in range(nfiles):
GIA_Ylms.clm[:, :,
t] = GIA_Ylms_rate['clm'] * (GIA_Ylms.time[t] - 2003.3)
GIA_Ylms.slm[:, :,
t] = GIA_Ylms_rate['slm'] * (GIA_Ylms.time[t] - 2003.3)
#-- default file prefix
if not FILE_PREFIX:
fargs = (PROC, DREL, DSET, Ylms['title'], gia_str)
FILE_PREFIX = '{0}_{1}_{2}{3}{4}_'.format(*fargs)
#-- Read Ocean function and convert to Ylms for redistribution
if REDISTRIBUTE_REMOVED:
#-- read Land-Sea Mask and convert to spherical harmonics
ocean_Ylms = ocean_stokes(LANDMASK, LMAX, MMAX=MMAX, LOVE=(hl, kl, ll))
ocean_str = '_OCN'
else:
ocean_str = ''
#-- input spherical harmonic datafiles to be removed from the GRACE data
#-- Remove sets of Ylms from the GRACE data before returning
remove_Ylms = GRACE_Ylms.zeros_like()
remove_Ylms.time[:] = np.copy(GRACE_Ylms.time)
remove_Ylms.month[:] = np.copy(GRACE_Ylms.month)
if REMOVE_FILES:
#-- extend list if a single format was entered for all files
if len(REMOVE_FORMAT) < len(REMOVE_FILES):
REMOVE_FORMAT = REMOVE_FORMAT * len(REMOVE_FILES)
#-- for each file to be removed
for REMOVE_FILE, REMOVEFORM in zip(REMOVE_FILES, REMOVE_FORMAT):
if REMOVEFORM in ('ascii', 'netCDF4', 'HDF5'):
#-- ascii (.txt)
#-- netCDF4 (.nc)
#-- HDF5 (.H5)
Ylms = harmonics().from_file(REMOVE_FILE, format=REMOVEFORM)
elif REMOVEFORM in ('index-ascii', 'index-netCDF4', 'index-HDF5'):
#-- read from index file
_, removeform = REMOVEFORM.split('-')
#-- index containing files in data format
Ylms = harmonics().from_index(REMOVE_FILE, format=removeform)
#-- reduce to GRACE/GRACE-FO months and truncate to degree and order
Ylms = Ylms.subset(GRACE_Ylms.month).truncate(lmax=LMAX, mmax=MMAX)
#-- distribute removed Ylms uniformly over the ocean
if REDISTRIBUTE_REMOVED:
#-- calculate ratio between total removed mass and
#-- a uniformly distributed cm of water over the ocean
ratio = Ylms.clm[0, 0, :] / ocean_Ylms.clm[0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove the ratio*ocean Ylms from Ylms
#-- note: x -= y is equivalent to x = x - y
Ylms.clm[l, m, :] -= ratio * ocean_Ylms.clm[l, m]
Ylms.slm[l, m, :] -= ratio * ocean_Ylms.slm[l, m]
#-- filter removed coefficients
if DESTRIPE:
Ylms = Ylms.destripe()
#-- add data for month t and INDEX_FILE to the total
#-- remove_clm and remove_slm matrices
#-- redistributing the mass over the ocean if specified
remove_Ylms.add(Ylms)
#-- Output spatial data object
grid = spatial()
#-- Output Degree Spacing
dlon, dlat = (DDEG[0], DDEG[0]) if (len(DDEG) == 1) else (DDEG[0], DDEG[1])
#-- Output Degree Interval
if (INTERVAL == 1):
#-- (-180:180,90:-90)
nlon = np.int64((360.0 / dlon) + 1.0)
nlat = np.int64((180.0 / dlat) + 1.0)
grid.lon = -180 + dlon * np.arange(0, nlon)
grid.lat = 90.0 - dlat * np.arange(0, nlat)
elif (INTERVAL == 2):
#-- (Degree spacing)/2
grid.lon = np.arange(-180 + dlon / 2.0, 180 + dlon / 2.0, dlon)
grid.lat = np.arange(90.0 - dlat / 2.0, -90.0 - dlat / 2.0, -dlat)
nlon = len(grid.lon)
nlat = len(grid.lat)
elif (INTERVAL == 3):
#-- non-global grid set with BOUNDS parameter
minlon, maxlon, minlat, maxlat = BOUNDS.copy()
grid.lon = np.arange(minlon + dlon / 2.0, maxlon + dlon / 2.0, dlon)
grid.lat = np.arange(maxlat - dlat / 2.0, minlat - dlat / 2.0, -dlat)
nlon = len(grid.lon)
nlat = len(grid.lat)
#-- Computing plms for converting to spatial domain
theta = (90.0 - grid.lat) * np.pi / 180.0
PLM, dPLM = plm_holmes(LMAX, np.cos(theta))
#-- Earth Parameters
#-- output spatial units
unit_list = ['cmwe', 'mmGH', 'mmCU', u'\u03BCGal', 'mbar']
unit_name = [
'Equivalent Water Thickness', 'Geoid Height', 'Elastic Crustal Uplift',
'Gravitational Undulation', 'Equivalent Surface Pressure'
]
#-- Setting units factor for output
#-- dfactor computes the degree dependent coefficients
if (UNITS == 1):
#-- 1: cmwe, centimeters water equivalent
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).cmwe
elif (UNITS == 2):
#-- 2: mmGH, mm geoid height
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).mmGH
elif (UNITS == 3):
#-- 3: mmCU, mm elastic crustal deformation
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).mmCU
elif (UNITS == 4):
#-- 4: micGal, microGal gravity perturbations
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).microGal
elif (UNITS == 5):
#-- 5: mbar, millibars equivalent surface pressure
dfactor = units(lmax=LMAX).harmonic(hl, kl, ll).mbar
else:
raise ValueError('Invalid units code {0:d}'.format(UNITS))
#-- output file format
file_format = '{0}{1}_L{2:d}{3}{4}{5}_{6:03d}.{7}'
#-- converting harmonics to truncated, smoothed coefficients in units
#-- combining harmonics to calculate output spatial fields
for i, grace_month in enumerate(GRACE_Ylms.month):
#-- GRACE/GRACE-FO harmonics for time t
Ylms = GRACE_Ylms.index(i)
#-- Remove GIA rate for time
Ylms.subtract(GIA_Ylms.index(i))
#-- Remove monthly files to be removed
Ylms.subtract(remove_Ylms.index(i))
#-- smooth harmonics and convert to output units
Ylms.convolve(dfactor * wt)
#-- convert spherical harmonics to output spatial grid
grid.data = harmonic_summation(Ylms.clm,
Ylms.slm,
grid.lon,
grid.lat,
LMIN=LMIN,
LMAX=LMAX,
MMAX=MMAX,
PLM=PLM).T
#-- copy time variables for month
grid.time = np.copy(Ylms.time)
grid.month = np.copy(Ylms.month)
#-- output monthly files to ascii, netCDF4 or HDF5
args = (FILE_PREFIX, unit_list[UNITS - 1], LMAX, order_str, gw_str,
ds_str, grace_month, suffix[DATAFORM])
FILE = os.path.join(OUTPUT_DIRECTORY, file_format.format(*args))
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
grid.to_ascii(FILE, date=True, verbose=VERBOSE)
elif (DATAFORM == 'netCDF4'):
#-- netCDF4
grid.to_netCDF4(FILE,
date=True,
verbose=VERBOSE,
units=unit_list[UNITS - 1],
longname=unit_name[UNITS - 1],
title='GRACE/GRACE-FO Spatial Data')
elif (DATAFORM == 'HDF5'):
#-- HDF5
grid.to_HDF5(FILE,
date=True,
verbose=VERBOSE,
units=unit_list[UNITS - 1],
longname=unit_name[UNITS - 1],
title='GRACE/GRACE-FO Spatial Data')
#-- set the permissions mode of the output files
os.chmod(FILE, MODE)
#-- add file to list
output_files.append(FILE)
#-- return the list of output files
return output_files
def calc_sensitivity_kernel(LMAX,
RAD,
LMIN=None,
MMAX=None,
LOVE_NUMBERS=0,
REFERENCE=None,
DATAFORM=None,
MASCON_FILE=None,
REDISTRIBUTE_MASCONS=False,
FIT_METHOD=0,
LANDMASK=None,
DDEG=None,
INTERVAL=None,
OUTPUT_DIRECTORY=None,
MODE=0o775):
#-- file information
suffix = dict(ascii='txt', netCDF4='nc', HDF5='H5')
#-- file parser for reading index files
#-- removes commented lines (can comment out files in the index)
#-- removes empty lines (if there are extra empty lines)
parser = re.compile(r'^(?!\#|\%|$)', re.VERBOSE)
#-- Create output Directory if not currently existing
if (not os.access(OUTPUT_DIRECTORY, os.F_OK)):
os.mkdir(OUTPUT_DIRECTORY)
#-- list object of output files for file logs (full path)
output_files = []
#-- read arrays of kl, hl, and ll Love Numbers
hl, kl, ll = load_love_numbers(LMAX,
LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE=REFERENCE)
#-- Earth Parameters
factors = units(lmax=LMAX).harmonic(hl, kl, ll)
#-- Average Density of the Earth [g/cm^3]
rho_e = factors.rho_e
#-- Average Radius of the Earth [cm]
rad_e = factors.rad_e
#-- input/output string for both LMAX==MMAX and LMAX != MMAX cases
MMAX = np.copy(LMAX) if not MMAX else MMAX
order_str = 'M{0:d}'.format(MMAX) if (MMAX != LMAX) else ''
#-- Calculating the Gaussian smoothing for radius RAD
if (RAD != 0):
wt = 2.0 * np.pi * gauss_weights(RAD, LMAX)
gw_str = '_r{0:0.0f}km'.format(RAD)
else:
#-- else = 1
wt = np.ones((LMAX + 1))
gw_str = ''
#-- Read Ocean function and convert to Ylms for redistribution
if REDISTRIBUTE_MASCONS:
#-- read Land-Sea Mask and convert to spherical harmonics
ocean_Ylms = ocean_stokes(LANDMASK, LMAX, MMAX=MMAX, LOVE=(hl, kl, ll))
ocean_str = '_OCN'
else:
#-- not distributing uniformly over ocean
ocean_str = ''
#-- input mascon spherical harmonic datafiles
with open(MASCON_FILE, 'r') as f:
mascon_files = [l for l in f.read().splitlines() if parser.match(l)]
#-- number of mascons
n_mas = len(mascon_files)
#-- spatial area of the mascon
total_area = np.zeros((n_mas))
#-- name of each mascon
mascon_name = []
#-- for each valid file in the index (iterate over mascons)
mascon_list = []
for k, fi in enumerate(mascon_files):
#-- read mascon spherical harmonics
Ylms = harmonics().from_file(os.path.expanduser(fi),
format=DATAFORM,
date=False)
#-- Calculating the total mass of each mascon (1 cmwe uniform)
total_area[k] = 4.0 * np.pi * (rad_e**3) * rho_e * Ylms.clm[0, 0] / 3.0
#-- distribute mascon mass uniformly over the ocean
if REDISTRIBUTE_MASCONS:
#-- calculate ratio between total mascon mass and
#-- a uniformly distributed cm of water over the ocean
ratio = Ylms.clm[0, 0] / ocean_Ylms.clm[0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove ratio*ocean Ylms from mascon Ylms
#-- note: x -= y is equivalent to x = x - y
Ylms.clm[l, m] -= ratio * ocean_Ylms.clm[l, m]
Ylms.slm[l, m] -= ratio * ocean_Ylms.slm[l, m]
#-- truncate mascon spherical harmonics to d/o LMAX/MMAX and add to list
mascon_list.append(Ylms.truncate(lmax=LMAX, mmax=MMAX))
#-- mascon base is the file without directory or suffix
mascon_base = os.path.basename(mascon_files[k])
mascon_base = os.path.splitext(mascon_base)[0]
#-- if lower case, will capitalize
mascon_base = mascon_base.upper()
#-- if mascon name contains degree and order info, remove
mascon_name.append(mascon_base.replace('_L{0:d}'.format(LMAX), ''))
#-- create single harmonics object from list
mascon_Ylms = harmonics().from_list(mascon_list, date=False)
#-- Output spatial data object
grid = spatial()
#-- Output Degree Spacing
dlon, dlat = (DDEG[0], DDEG[0]) if (len(DDEG) == 1) else (DDEG[0], DDEG[1])
#-- Output Degree Interval
if (INTERVAL == 1):
#-- (-180:180,90:-90)
n_lon = np.int64((360.0 / dlon) + 1.0)
n_lat = np.int64((180.0 / dlat) + 1.0)
grid.lon = -180 + dlon * np.arange(0, n_lon)
grid.lat = 90.0 - dlat * np.arange(0, n_lat)
elif (INTERVAL == 2):
#-- (Degree spacing)/2
grid.lon = np.arange(-180 + dlon / 2.0, 180 + dlon / 2.0, dlon)
grid.lat = np.arange(90.0 - dlat / 2.0, -90.0 - dlat / 2.0, -dlat)
n_lon = len(grid.lon)
n_lat = len(grid.lat)
#-- Computing plms for converting to spatial domain
theta = (90.0 - grid.lat) * np.pi / 180.0
PLM, dPLM = plm_holmes(LMAX, np.cos(theta))
#-- Calculating the number of cos and sin harmonics between LMIN and LMAX
#-- taking into account MMAX (if MMAX == LMAX then LMAX-MMAX=0)
n_harm = np.int64(LMAX**2 - LMIN**2 + 2 * LMAX + 1 - (LMAX - MMAX)**2 -
(LMAX - MMAX))
#-- Initialing harmonics for least squares fitting
#-- mascon kernel
M_lm = np.zeros((n_harm, n_mas))
#-- mascon kernel converted to output unit
MA_lm = np.zeros((n_harm, n_mas))
#-- sensitivity kernel
A_lm = np.zeros((n_harm, n_mas))
#-- Initializing conversion factors
#-- factor for converting to smoothed coefficients of mass
fact = np.zeros((n_harm))
#-- factor for converting back into geoid coefficients
fact_inv = np.zeros((n_harm))
#-- smoothing factor
wt_lm = np.zeros((n_harm))
#-- ii is a counter variable for building the mascon column array
ii = 0
#-- Creating column array of clm/slm coefficients
#-- Order is [C00...C6060,S11...S6060]
#-- Calculating factor to convert geoid spherical harmonic coefficients
#-- to coefficients of mass (Wahr, 1998)
coeff = rho_e * rad_e / 3.0
coeff_inv = 0.75 / (np.pi * rho_e * rad_e**3)
#-- Switching between Cosine and Sine Stokes
for cs, csharm in enumerate(['clm', 'slm']):
#-- copy cosine and sin harmonics
mascon_harm = getattr(mascon_Ylms, csharm)
#-- for each spherical harmonic degree
#-- +1 to include LMAX
for l in range(LMIN, LMAX + 1):
#-- for each spherical harmonic order
#-- Sine Stokes for (m=0) = 0
mm = np.min([MMAX, l])
#-- +1 to include l or MMAX (whichever is smaller)
for m in range(cs, mm + 1):
#-- Mascon Spherical Harmonics
M_lm[ii, :] = np.copy(mascon_harm[l, m, :])
#-- degree dependent factor to convert to mass
fact[ii] = (2.0 * l + 1.0) / (1.0 + kl[l])
#-- degree dependent factor to convert from mass
fact_inv[ii] = coeff_inv * (1.0 + kl[l]) / (2.0 * l + 1.0)
#-- degree dependent smoothing
wt_lm[ii] = np.copy(wt[l])
#-- add 1 to counter
ii += 1
#-- Converting mascon coefficients to fit method
if (FIT_METHOD == 1):
#-- Fitting Sensitivity Kernel as mass coefficients
#-- converting M_lm to mass coefficients of the kernel
for i in range(n_harm):
MA_lm[i, :] = M_lm[i, :] * wt_lm[i] * fact[i]
fit_factor = wt_lm * fact
inv_fit_factor = np.copy(fact_inv)
else:
#-- Fitting Sensitivity Kernel as geoid coefficients
for i in range(n_harm):
MA_lm[:, :] = M_lm[i, :] * wt_lm[i]
fit_factor = wt_lm * np.ones((n_harm))
inv_fit_factor = np.ones((n_harm))
#-- Fitting the sensitivity kernel from the input kernel
for i in range(n_harm):
#-- setting kern_i equal to 1 for d/o
kern_i = np.zeros((n_harm))
#-- converting to mass coefficients if specified
kern_i[i] = 1.0 * fit_factor[i]
#-- spherical harmonics solution for the
#-- mascon sensitivity kernels
#-- Least Squares Solutions: Inv(X'.X).(X'.Y)
kern_lm = np.linalg.lstsq(MA_lm, kern_i, rcond=-1)[0]
for k in range(n_mas):
A_lm[i, k] = kern_lm[k] * total_area[k]
#-- for each mascon
for k in range(n_mas):
#-- reshaping harmonics of sensitivity kernel to LMAX+1,MMAX+1
#-- calculating the spatial sensitivity kernel of each mascon
#-- kernel calculated as outlined in Tiwari (2009) and Jacobs (2012)
#-- Initializing output sensitivity kernel (both spatial and Ylms)
kern_Ylms = harmonics(lmax=LMAX, mmax=MMAX)
kern_Ylms.clm = np.zeros((LMAX + 1, MMAX + 1))
kern_Ylms.slm = np.zeros((LMAX + 1, MMAX + 1))
kern_Ylms.time = total_area[k]
#-- counter variable for deconstructing the mascon column arrays
ii = 0
#-- Switching between Cosine and Sine Stokes
for cs, csharm in enumerate(['clm', 'slm']):
#-- for each spherical harmonic degree
#-- +1 to include LMAX
for l in range(LMIN, LMAX + 1):
#-- for each spherical harmonic order
#-- Sine Stokes for (m=0) = 0
mm = np.min([MMAX, l])
#-- +1 to include l or MMAX (whichever is smaller)
for m in range(cs, mm + 1):
#-- inv_fit_factor: normalize from mass harmonics
temp = getattr(kern_Ylms, csharm)
temp[l, m] = inv_fit_factor[ii] * A_lm[ii, k]
#-- add 1 to counter
ii += 1
#-- convert spherical harmonics to output spatial grid
grid.data = harmonic_summation(kern_Ylms.clm,
kern_Ylms.slm,
grid.lon,
grid.lat,
LMAX=LMAX,
MMAX=MMAX,
PLM=PLM).T
grid.time = total_area[k]
#-- output names for sensitivity kernel Ylm and spatial files
#-- for both LMAX==MMAX and LMAX != MMAX cases
args = (mascon_name[k], ocean_str, LMAX, order_str, gw_str,
suffix[DATAFORM])
FILE1 = '{0}_SKERNEL_CLM{1}_L{2:d}{3}{4}.{5}'.format(*args)
FILE2 = '{0}_SKERNEL{1}_L{2:d}{3}{4}.{5}'.format(*args)
#-- output sensitivity kernel to file
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
kern_Ylms.to_ascii(os.path.join(OUTPUT_DIRECTORY, FILE1),
date=False)
grid.to_ascii(os.path.join(OUTPUT_DIRECTORY, FILE2),
date=False,
units='unitless',
longname='Sensitivity_Kernel')
elif (DATAFORM == 'netCDF4'):
#-- netCDF4 (.nc)
kern_Ylms.to_netCDF4(os.path.join(OUTPUT_DIRECTORY, FILE1),
date=False)
grid.to_netCDF4(os.path.join(OUTPUT_DIRECTORY, FILE2),
date=False,
units='unitless',
longname='Sensitivity_Kernel')
elif (DATAFORM == 'HDF5'):
#-- netcdf (.H5)
kern_Ylms.to_HDF5(os.path.join(OUTPUT_DIRECTORY, FILE1),
date=False)
grid.to_HDF5(os.path.join(OUTPUT_DIRECTORY, FILE2),
date=False,
units='unitless',
longname='Sensitivity_Kernel')
#-- change the permissions mode
os.chmod(os.path.join(OUTPUT_DIRECTORY, FILE1), MODE)
os.chmod(os.path.join(OUTPUT_DIRECTORY, FILE2), MODE)
#-- add output files to list object
output_files.append(os.path.join(OUTPUT_DIRECTORY, FILE1))
output_files.append(os.path.join(OUTPUT_DIRECTORY, FILE2))
#-- return the list of output files
return output_files
def scale_grace_maps(base_dir, PROC, DREL, DSET, LMAX, RAD,
START=None,
END=None,
MISSING=None,
LMIN=None,
MMAX=None,
LOVE_NUMBERS=0,
REFERENCE=None,
DESTRIPE=False,
DDEG=None,
INTERVAL=None,
GIA=None,
GIA_FILE=None,
ATM=False,
POLE_TIDE=False,
DEG1=None,
DEG1_FILE=None,
MODEL_DEG1=False,
SLR_C20=None,
SLR_21=None,
SLR_22=None,
SLR_C30=None,
SLR_C50=None,
DATAFORM=None,
MEAN_FILE=None,
MEANFORM=None,
REMOVE_FILES=None,
REMOVE_FORMAT=None,
REDISTRIBUTE_REMOVED=False,
SCALE_FILE=None,
ERROR_FILE=None,
POWER_FILE=None,
LANDMASK=None,
OUTPUT_DIRECTORY=None,
FILE_PREFIX=None,
VERBOSE=False,
MODE=0o775):
#-- recursively create output Directory if not currently existing
if not os.access(OUTPUT_DIRECTORY, os.F_OK):
os.makedirs(OUTPUT_DIRECTORY, mode=MODE, exist_ok=True)
#-- list object of output files for file logs (full path)
output_files = []
#-- file information
suffix = dict(ascii='txt', netCDF4='nc', HDF5='H5')
#-- output file format
file_format = '{0}{1}{2}_L{3:d}{4}{5}{6}_{7:03d}-{8:03d}.{9}'
#-- read arrays of kl, hl, and ll Love Numbers
hl,kl,ll = load_love_numbers(LMAX, LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE=REFERENCE)
#-- atmospheric ECMWF "jump" flag (if ATM)
atm_str = '_wATM' if ATM else ''
#-- output string for both LMAX==MMAX and LMAX != MMAX cases
MMAX = np.copy(LMAX) if not MMAX else MMAX
order_str = 'M{0:d}'.format(MMAX) if (MMAX != LMAX) else ''
#-- output spatial units
unit_str = 'cmwe'
unit_name = 'Equivalent Water Thickness'
#-- invalid value
fill_value = -9999.0
#-- Calculating the Gaussian smoothing for radius RAD
if (RAD != 0):
wt = 2.0*np.pi*gauss_weights(RAD,LMAX)
gw_str = '_r{0:0.0f}km'.format(RAD)
else:
#-- else = 1
wt = np.ones((LMAX+1))
gw_str = ''
#-- Read Ocean function and convert to Ylms for redistribution
if REDISTRIBUTE_REMOVED:
#-- read Land-Sea Mask and convert to spherical harmonics
ocean_Ylms = ocean_stokes(LANDMASK, LMAX, MMAX=MMAX, LOVE=(hl,kl,ll))
#-- Grid spacing
dlon,dlat = (DDEG[0],DDEG[0]) if (len(DDEG) == 1) else (DDEG[0],DDEG[1])
#-- Grid dimensions
if (INTERVAL == 1):#-- (0:360, 90:-90)
nlon = np.int64((360.0/dlon)+1.0)
nlat = np.int64((180.0/dlat)+1.0)
elif (INTERVAL == 2):#-- degree spacing/2
nlon = np.int64((360.0/dlon))
nlat = np.int64((180.0/dlat))
#-- read data for input scale files (ascii, netCDF4, HDF5)
if (DATAFORM == 'ascii'):
kfactor = spatial(spacing=[dlon,dlat],nlat=nlat,nlon=nlon).from_ascii(
SCALE_FILE,date=False)
k_error = spatial(spacing=[dlon,dlat],nlat=nlat,nlon=nlon).from_ascii(
ERROR_FILE,date=False)
k_power = spatial(spacing=[dlon,dlat],nlat=nlat,nlon=nlon).from_ascii(
POWER_FILE,date=False)
elif (DATAFORM == 'netCDF4'):
kfactor = spatial().from_netCDF4(SCALE_FILE,date=False)
k_error = spatial().from_netCDF4(ERROR_FILE,date=False)
k_power = spatial().from_netCDF4(POWER_FILE,date=False)
elif (DATAFORM == 'HDF5'):
kfactor = spatial().from_HDF5(SCALE_FILE,date=False)
k_error = spatial().from_HDF5(ERROR_FILE,date=False)
k_power = spatial().from_HDF5(POWER_FILE,date=False)
#-- input data shape
nlat,nlon = kfactor.shape
#-- input GRACE/GRACE-FO spherical harmonic datafiles for date range
#-- replacing low-degree harmonics with SLR values if specified
#-- include degree 1 (geocenter) harmonics if specified
#-- correcting for Pole-Tide and Atmospheric Jumps if specified
Ylms = grace_input_months(base_dir, PROC, DREL, DSET, LMAX,
START, END, MISSING, SLR_C20, DEG1, MMAX=MMAX,
SLR_21=SLR_21, SLR_22=SLR_22, SLR_C30=SLR_C30, SLR_C50=SLR_C50,
DEG1_FILE=DEG1_FILE, MODEL_DEG1=MODEL_DEG1, ATM=ATM,
POLE_TIDE=POLE_TIDE)
#-- create harmonics object from GRACE/GRACE-FO data
GRACE_Ylms = harmonics().from_dict(Ylms)
GRACE_Ylms.directory = Ylms['directory']
#-- use a mean file for the static field to remove
if MEAN_FILE:
#-- read data form for input mean file (ascii, netCDF4, HDF5, gfc)
mean_Ylms = harmonics().from_file(MEAN_FILE,format=MEANFORM,date=False)
#-- remove the input mean
GRACE_Ylms.subtract(mean_Ylms)
else:
GRACE_Ylms.mean(apply=True)
#-- date information of GRACE/GRACE-FO coefficients
nfiles = len(GRACE_Ylms.time)
#-- filter GRACE/GRACE-FO coefficients
if DESTRIPE:
#-- destriping GRACE/GRACE-FO coefficients
ds_str = '_FL'
GRACE_Ylms = GRACE_Ylms.destripe()
else:
#-- using standard GRACE/GRACE-FO harmonics
ds_str = ''
#-- input GIA spherical harmonic datafiles
GIA_Ylms_rate = read_GIA_model(GIA_FILE,GIA=GIA,LMAX=LMAX,MMAX=MMAX)
gia_str = '_{0}'.format(GIA_Ylms_rate['title']) if GIA else ''
#-- calculate the monthly mass change from GIA
GIA_Ylms = GRACE_Ylms.zeros_like()
GIA_Ylms.time[:] = np.copy(GRACE_Ylms.time)
GIA_Ylms.month[:] = np.copy(GRACE_Ylms.month)
#-- monthly GIA calculated by gia_rate*time elapsed
#-- finding change in GIA each month
for t in range(nfiles):
GIA_Ylms.clm[:,:,t] = GIA_Ylms_rate['clm']*(GIA_Ylms.time[t]-2003.3)
GIA_Ylms.slm[:,:,t] = GIA_Ylms_rate['slm']*(GIA_Ylms.time[t]-2003.3)
#-- default file prefix
if not FILE_PREFIX:
fargs = (PROC,DREL,DSET,Ylms['title'],gia_str)
FILE_PREFIX = '{0}_{1}_{2}{3}{4}_'.format(*fargs)
#-- input spherical harmonic datafiles to be removed from the GRACE data
#-- Remove sets of Ylms from the GRACE data before returning
remove_Ylms = GRACE_Ylms.zeros_like()
remove_Ylms.time[:] = np.copy(GRACE_Ylms.time)
remove_Ylms.month[:] = np.copy(GRACE_Ylms.month)
if REMOVE_FILES:
#-- extend list if a single format was entered for all files
if len(REMOVE_FORMAT) < len(REMOVE_FILES):
REMOVE_FORMAT = REMOVE_FORMAT*len(REMOVE_FILES)
#-- for each file to be removed
for REMOVE_FILE,REMOVEFORM in zip(REMOVE_FILES,REMOVE_FORMAT):
if REMOVEFORM in ('ascii','netCDF4','HDF5'):
#-- ascii (.txt)
#-- netCDF4 (.nc)
#-- HDF5 (.H5)
Ylms = harmonics().from_file(REMOVE_FILE, format=REMOVEFORM)
elif REMOVEFORM in ('index-ascii','index-netCDF4','index-HDF5'):
#-- read from index file
_,removeform = REMOVEFORM.split('-')
#-- index containing files in data format
Ylms = harmonics().from_index(REMOVE_FILE, format=removeform)
#-- reduce to GRACE/GRACE-FO months and truncate to degree and order
Ylms = Ylms.subset(GRACE_Ylms.month).truncate(lmax=LMAX,mmax=MMAX)
#-- distribute removed Ylms uniformly over the ocean
if REDISTRIBUTE_REMOVED:
#-- calculate ratio between total removed mass and
#-- a uniformly distributed cm of water over the ocean
ratio = Ylms.clm[0,0,:]/ocean_Ylms.clm[0,0]
#-- for each spherical harmonic
for m in range(0,MMAX+1):#-- MMAX+1 to include MMAX
for l in range(m,LMAX+1):#-- LMAX+1 to include LMAX
#-- remove the ratio*ocean Ylms from Ylms
#-- note: x -= y is equivalent to x = x - y
Ylms.clm[l,m,:] -= ratio*ocean_Ylms.clm[l,m]
Ylms.slm[l,m,:] -= ratio*ocean_Ylms.slm[l,m]
#-- filter removed coefficients
if DESTRIPE:
Ylms = Ylms.destripe()
#-- add data for month t and INDEX_FILE to the total
#-- remove_clm and remove_slm matrices
#-- redistributing the mass over the ocean if specified
remove_Ylms.add(Ylms)
#-- calculating GRACE/GRACE-FO error (Wahr et al. 2006)
#-- output GRACE error file (for both LMAX==MMAX and LMAX != MMAX cases)
args = (PROC,DREL,DSET,LMAX,order_str,ds_str,atm_str,GRACE_Ylms.month[0],
GRACE_Ylms.month[-1], suffix[DATAFORM])
delta_format = '{0}_{1}_{2}_DELTA_CLM_L{3:d}{4}{5}{6}_{7:03d}-{8:03d}.{9}'
DELTA_FILE = os.path.join(GRACE_Ylms.directory,delta_format.format(*args))
#-- check full path of the GRACE directory for delta file
#-- if file was previously calculated: will read file
#-- else: will calculate the GRACE/GRACE-FO error
if not os.access(DELTA_FILE, os.F_OK):
#-- add output delta file to list object
output_files.append(DELTA_FILE)
#-- Delta coefficients of GRACE time series (Error components)
delta_Ylms = harmonics(lmax=LMAX,mmax=MMAX)
delta_Ylms.clm = np.zeros((LMAX+1,MMAX+1))
delta_Ylms.slm = np.zeros((LMAX+1,MMAX+1))
#-- Smoothing Half-Width (CNES is a 10-day solution)
#-- All other solutions are monthly solutions (HFWTH for annual = 6)
if ((PROC == 'CNES') and (DREL in ('RL01','RL02'))):
HFWTH = 19
else:
HFWTH = 6
#-- Equal to the noise of the smoothed time-series
#-- for each spherical harmonic order
for m in range(0,MMAX+1):#-- MMAX+1 to include MMAX
#-- for each spherical harmonic degree
for l in range(m,LMAX+1):#-- LMAX+1 to include LMAX
#-- Delta coefficients of GRACE time series
for cs,csharm in enumerate(['clm','slm']):
#-- calculate GRACE Error (Noise of smoothed time-series)
#-- With Annual and Semi-Annual Terms
val1 = getattr(GRACE_Ylms, csharm)
smth = tssmooth(GRACE_Ylms.time, val1[l,m,:], HFWTH=HFWTH)
#-- number of smoothed points
nsmth = len(smth['data'])
tsmth = np.mean(smth['time'])
#-- GRACE delta Ylms
#-- variance of data-(smoothed+annual+semi)
val2 = getattr(delta_Ylms, csharm)
val2[l,m] = np.sqrt(np.sum(smth['noise']**2)/nsmth)
#-- save GRACE/GRACE-FO delta harmonics to file
delta_Ylms.time = np.copy(tsmth)
delta_Ylms.month = np.int64(nsmth)
delta_Ylms.to_file(DELTA_FILE,format=DATAFORM)
else:
#-- read GRACE/GRACE-FO delta harmonics from file
delta_Ylms = harmonics().from_file(DELTA_FILE,format=DATAFORM)
#-- copy time and number of smoothed fields
tsmth = np.squeeze(delta_Ylms.time)
nsmth = np.int64(delta_Ylms.month)
#-- Output spatial data object
grid = spatial()
grid.lon = np.copy(kfactor.lon)
grid.lat = np.copy(kfactor.lat)
grid.time = np.zeros((nfiles))
grid.month = np.zeros((nfiles),dtype=np.int64)
grid.data = np.zeros((nlat,nlon,nfiles))
grid.mask = np.zeros((nlat,nlon,nfiles),dtype=bool)
#-- Computing plms for converting to spatial domain
phi = grid.lon[np.newaxis,:]*np.pi/180.0
theta = (90.0-grid.lat)*np.pi/180.0
PLM,dPLM = plm_holmes(LMAX,np.cos(theta))
#-- square of legendre polynomials truncated to order MMAX
mm = np.arange(0,MMAX+1)
PLM2 = PLM[:,mm,:]**2
#-- dfactor is the degree dependent coefficients
#-- for converting to centimeters water equivalent (cmwe)
dfactor = units(lmax=LMAX).harmonic(hl,kl,ll).cmwe
#-- converting harmonics to truncated, smoothed coefficients in units
#-- combining harmonics to calculate output spatial fields
for i,gm in enumerate(GRACE_Ylms.month):
#-- GRACE/GRACE-FO harmonics for time t
Ylms = GRACE_Ylms.index(i)
#-- Remove GIA rate for time
Ylms.subtract(GIA_Ylms.index(i))
#-- Remove monthly files to be removed
Ylms.subtract(remove_Ylms.index(i))
#-- smooth harmonics and convert to output units
Ylms.convolve(dfactor*wt)
#-- convert spherical harmonics to output spatial grid
grid.data[:,:,i] = harmonic_summation(Ylms.clm, Ylms.slm,
grid.lon, grid.lat, LMAX=LMAX, MMAX=MMAX, PLM=PLM).T
#-- copy time variables for month
grid.time[i] = np.copy(Ylms.time)
grid.month[i] = np.copy(Ylms.month)
#-- update spacing and dimensions
grid.update_spacing()
grid.update_extents()
grid.update_dimensions()
#-- scale output data with kfactor
grid = grid.scale(kfactor.data)
grid.replace_invalid(fill_value, mask=kfactor.mask)
#-- output monthly files to ascii, netCDF4 or HDF5
args = (FILE_PREFIX,'',unit_str,LMAX,order_str,gw_str,ds_str,
grid.month[0],grid.month[-1],suffix[DATAFORM])
FILE=os.path.join(OUTPUT_DIRECTORY,file_format.format(*args))
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
grid.to_ascii(FILE, date=True, verbose=VERBOSE)
elif (DATAFORM == 'netCDF4'):
#-- netCDF4
grid.to_netCDF4(FILE, date=True, verbose=VERBOSE,
units=unit_str, longname=unit_name,
title='GRACE/GRACE-FO Spatial Data')
elif (DATAFORM == 'HDF5'):
#-- HDF5
grid.to_HDF5(FILE, date=True, verbose=VERBOSE,
units=unit_str, longname=unit_name,
title='GRACE/GRACE-FO Spatial Data')
#-- set the permissions mode of the output files
os.chmod(FILE, MODE)
#-- add file to list
output_files.append(FILE)
#-- calculate power of scaled GRACE/GRACE-FO data
scaled_power = grid.sum(power=2.0).power(0.5)
#-- calculate residual leakage errors
#-- scaled by ratio of GRACE and synthetic power
ratio = scaled_power.scale(k_power.power(-1).data)
error = k_error.scale(ratio.data)
#-- output monthly error files to ascii, netCDF4 or HDF5
args = (FILE_PREFIX,'ERROR_',unit_str,LMAX,order_str,gw_str,ds_str,
grid.month[0],grid.month[-1],suffix[DATAFORM])
FILE = os.path.join(OUTPUT_DIRECTORY,file_format.format(*args))
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
error.to_ascii(FILE, date=False, verbose=VERBOSE)
elif (DATAFORM == 'netCDF4'):
#-- netCDF4
error.to_netCDF4(FILE, date=False, verbose=VERBOSE,
units=unit_str, longname=unit_name,
title='GRACE/GRACE-FO Scaling Error')
elif (DATAFORM == 'HDF5'):
#-- HDF5
error.to_HDF5(FILE, date=False, verbose=VERBOSE,
units=unit_str, longname=unit_name,
title='GRACE/GRACE-FO Scaling Error')
#-- set the permissions mode of the output files
os.chmod(FILE, MODE)
#-- add file to list
output_files.append(FILE)
#-- Output spatial data object
delta = spatial()
delta.lon = np.copy(kfactor.lon)
delta.lat = np.copy(kfactor.lat)
delta.time = np.copy(tsmth)
delta.month = np.copy(nsmth)
delta.data = np.zeros((nlat,nlon))
delta.mask = np.zeros((nlat,nlon),dtype=bool)
#-- calculate scaled spatial error
#-- Calculating cos(m*phi)^2 and sin(m*phi)^2
m = delta_Ylms.m[:,np.newaxis]
ccos = np.cos(np.dot(m,phi))**2
ssin = np.sin(np.dot(m,phi))**2
#-- truncate delta harmonics to spherical harmonic range
Ylms = delta_Ylms.truncate(LMAX,lmin=LMIN,mmax=MMAX)
#-- convolve delta harmonics with degree dependent factors
#-- smooth harmonics and convert to output units
Ylms = Ylms.convolve(dfactor*wt).power(2.0).scale(1.0/nsmth)
#-- Calculate fourier coefficients
d_cos = np.zeros((MMAX+1,nlat))#-- [m,th]
d_sin = np.zeros((MMAX+1,nlat))#-- [m,th]
#-- Calculating delta spatial values
for k in range(0,nlat):
#-- summation over all spherical harmonic degrees
d_cos[:,k] = np.sum(PLM2[:,:,k]*Ylms.clm, axis=0)
d_sin[:,k] = np.sum(PLM2[:,:,k]*Ylms.slm, axis=0)
#-- Multiplying by c/s(phi#m) to get spatial error map
delta.data[:] = np.sqrt(np.dot(ccos.T,d_cos) + np.dot(ssin.T,d_sin)).T
#-- update spacing and dimensions
delta.update_spacing()
delta.update_extents()
delta.update_dimensions()
#-- scale output harmonic errors with kfactor
delta = delta.scale(kfactor.data)
delta.replace_invalid(fill_value, mask=kfactor.mask)
#-- output monthly files to ascii, netCDF4 or HDF5
args = (FILE_PREFIX,'DELTA_',unit_str,LMAX,order_str,gw_str,ds_str,
grid.month[0],grid.month[-1],suffix[DATAFORM])
FILE=os.path.join(OUTPUT_DIRECTORY,file_format.format(*args))
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
delta.to_ascii(FILE, date=True, verbose=VERBOSE)
elif (DATAFORM == 'netCDF4'):
#-- netCDF4
delta.to_netCDF4(FILE, date=True, verbose=VERBOSE,
units=unit_str, longname=unit_name,
title='GRACE/GRACE-FO Spatial Error')
elif (DATAFORM == 'HDF5'):
#-- HDF5
delta.to_HDF5(FILE, date=True, verbose=VERBOSE,
units=unit_str, longname=unit_name,
title='GRACE/GRACE-FO Spatial Error')
#-- set the permissions mode of the output files
os.chmod(FILE, MODE)
#-- add file to list
output_files.append(FILE)
#-- return the list of output files
return output_files
def mascon_reconstruct(parameters, LOVE_NUMBERS=0, REFERENCE=None, MODE=0o775):
#-- convert parameters into variables
#-- Data processing center
PROC = parameters['PROC']
#-- Data Release
DREL = parameters['DREL']
#-- GRACE dataset
DSET = parameters['DSET']
#-- Date Range
START_MON = np.int(parameters['START'])
END_MON = np.int(parameters['END'])
#-- spherical harmonic parameters
LMAX = np.int(parameters['LMAX'])
#-- maximum spherical harmonic order
if (parameters['MMAX'].title() == 'None'):
MMAX = np.copy(LMAX)
else:
MMAX = np.int(parameters['MMAX'])
#-- gaussian smoothing radius
RAD = np.float(parameters['RAD'])
#-- filtered coefficients for stripe effects
DESTRIPE = parameters['DESTRIPE'] in ('Y', 'y')
#-- ECMWF jump corrections
ATM = parameters['ATM'] in ('Y', 'y')
#-- Glacial Isostatic Adjustment file to read
GIA = parameters['GIA'] if (parameters['GIA'].title() != 'None') else None
GIA_FILE = os.path.expanduser(parameters['GIA_FILE'])
#-- input/output data format (ascii, netCDF4, HDF5)
DATAFORM = parameters['DATAFORM']
#-- index of mascons spherical harmonics
#-- path.expanduser = tilde expansion of path
MASCON_INDEX = os.path.expanduser(parameters['MASCON_INDEX'])
#-- mascon distribution over the ocean
MASCON_OCEAN = parameters['MASCON_OCEAN'] in ('Y', 'y')
#-- for datasets not GSM: will add a label for the dataset
dset_str = '' if (DSET == 'GSM') else '_{0}'.format(DSET)
#-- atmospheric ECMWF "jump" flag (if ATM)
atm_str = '_wATM' if ATM else ''
#-- Gaussian smoothing string for radius RAD
gw_str = '_r{0:0.0f}km'.format(RAD) if (RAD != 0) else ''
#-- input GIA spherical harmonic datafiles
GIA_Ylms_rate = read_GIA_model(GIA_FILE, GIA=GIA, LMAX=LMAX, MMAX=MMAX)
gia_str = '_{0}'.format(GIA_Ylms_rate['title']) if GIA else ''
#-- output string for both LMAX==MMAX and LMAX != MMAX cases
order_str = 'M{0:d}'.format(MMAX) if (MMAX != LMAX) else ''
#-- filter grace coefficients flag
ds_str = '_FL' if DESTRIPE else ''
#-- output filename suffix
suffix = dict(ascii='txt', netCDF4='nc', HDF5='H5')
#-- GRACE output filename prefix
#-- mascon directory for GRACE product, processing and date range
DIRECTORY = os.path.expanduser(parameters['DIRECTORY'])
#-- create initial reconstruct index for calc_mascon.py
fid = open(os.path.expanduser(parameters['RECONSTRUCT_INDEX']), 'w')
HOME = os.path.expanduser('~')
#-- output file format
file_format = '{0}{1}{2}{3}{4}_L{5:d}{6}{7}{8}_{9:03d}-{10:03d}.{11}'
#-- read load love numbers
hl, kl, ll = load_love_numbers(LMAX,
LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE=REFERENCE)
#-- Earth Parameters
factors = units(lmax=LMAX).harmonic(hl, kl, ll)
#-- Average Density of the Earth [g/cm^3]
rho_e = factors.rho_e
#-- Average Radius of the Earth [cm]
rad_e = factors.rad_e
#-- Read Ocean function and convert to Ylms for redistribution
if MASCON_OCEAN:
#-- read Land-Sea Mask and convert to spherical harmonics
LANDMASK = os.path.expanduser(parameters['LANDMASK'])
ocean_Ylms = ocean_stokes(LANDMASK, LMAX, MMAX=MMAX, LOVE=(hl, kl, ll))
ocean_str = '_OCN'
else:
#-- not distributing uniformly over ocean
ocean_str = ''
#-- input mascon spherical harmonic datafiles
with open(MASCON_INDEX, 'r') as f:
mascon_files = f.read().splitlines()
for k, fi in enumerate(mascon_files):
#-- read mascon spherical harmonics
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
Ylms = harmonics().from_ascii(os.path.expanduser(fi), date=False)
elif (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
Ylms = harmonics().from_netCDF4(os.path.expanduser(fi), date=False)
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
Ylms = harmonics().from_HDF5(os.path.expanduser(fi), date=False)
#-- Calculating the total mass of each mascon (1 cmwe uniform)
total_area = 4.0 * np.pi * (rad_e**3) * rho_e * Ylms.clm[0, 0] / 3.0
#-- distribute MASCON mass uniformly over the ocean
if MASCON_OCEAN:
#-- calculate ratio between total mascon mass and
#-- a uniformly distributed cm of water over the ocean
ratio = Ylms.clm[0, 0] / ocean_Ylms['clm'][0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove ratio*ocean Ylms from mascon Ylms
#-- note: x -= y is equivalent to x = x - y
Ylms.clm[l, m] -= ratio * ocean_Ylms['clm'][l, m]
Ylms.slm[l, m] -= ratio * ocean_Ylms['slm'][l, m]
#-- truncate mascon spherical harmonics to d/o LMAX/MMAX
Ylms = Ylms.truncate(lmax=LMAX, mmax=MMAX)
#-- mascon base is the file without directory or suffix
mascon_base = os.path.basename(fi)
mascon_base = os.path.splitext(mascon_base)[0]
#-- if lower case, will capitalize
mascon_base = mascon_base.upper()
#-- if mascon name contains degree and order info, remove
mascon_name = mascon_base.replace('_L{0:d}'.format(LMAX), '')
#-- input filename format (for both LMAX==MMAX and LMAX != MMAX cases):
#-- mascon name, GRACE dataset, GIA model, LMAX, (MMAX,)
#-- Gaussian smoothing, filter flag, remove reconstructed fields flag
#-- output GRACE error file
args = (mascon_name, dset_str, gia_str.upper(), atm_str, ocean_str,
LMAX, order_str, gw_str, ds_str)
file_input = '{0}{1}{2}{3}{4}_L{5:d}{6}{7}{8}.txt'.format(*args)
mascon_data_input = np.loadtxt(os.path.join(DIRECTORY, file_input))
nmon = np.shape(mascon_data_input)[0]
#-- convert mascon time-series from Gt to cmwe
mascon_sigma = 1e15 * mascon_data_input[:, 2] / total_area
#-- mascon time-series Ylms
mascon_Ylms = Ylms.scale(mascon_sigma)
mascon_Ylms.time = mascon_data_input[:, 1].copy()
mascon_Ylms.month = mascon_data_input[:, 0].astype(np.int)
#-- output to file: no ascii option
args = (mascon_name, dset_str, gia_str.upper(), atm_str, ocean_str,
LMAX, order_str, gw_str, ds_str, START_MON, END_MON,
suffix[DATAFORM])
FILE = file_format.format(*args)
#-- output harmonics to file
if (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
mascon_Ylms.to_netCDF4(os.path.join(DIRECTORY, FILE))
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
mascon_Ylms.to_HDF5(os.path.join(DIRECTORY, FILE))
#-- print file name to index
print(os.path.join(DIRECTORY, FILE).replace(HOME, '~'), file=fid)
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY, FILE), MODE)
#-- close the reconstruct index
fid.close()
#-- change the permissions mode of the index file
os.chmod(os.path.expanduser(parameters['RECONSTRUCT_INDEX']), MODE)
def calc_mascon(base_dir,
parameters,
LOVE_NUMBERS=0,
REFERENCE=None,
MODE=0o775):
#-- convert parameters to variables
#-- Data processing center
PROC = parameters['PROC']
#-- Data Release
DREL = parameters['DREL']
#-- GRACE dataset
DSET = parameters['DSET']
#-- Date Range and missing months
start_mon = np.int(parameters['START'])
end_mon = np.int(parameters['END'])
missing = np.array(parameters['MISSING'].split(','), dtype=np.int)
#-- spherical harmonic degree range
LMIN = np.int(parameters['LMIN'])
LMAX = np.int(parameters['LMAX'])
#-- maximum spherical harmonic order
if (parameters['MMAX'].title() == 'None'):
MMAX = np.copy(LMAX)
else:
MMAX = np.int(parameters['MMAX'])
#-- degree 1 coefficients
#-- None: No degree 1
#-- Tellus: GRACE/GRACE-FO TN-13 from PO.DAAC
#-- https://grace.jpl.nasa.gov/data/get-data/geocenter/
#-- SLR: satellite laser ranging from CSR
#-- ftp://ftp.csr.utexas.edu/pub/slr/geocenter/
#-- SLF: Sutterley and Velicogna, Remote Sensing (2019)
#-- https://www.mdpi.com/2072-4292/11/18/2108
DEG1 = parameters['DEG1']
#-- replace C20, C30 with coefficients from SLR
SLR_C20 = parameters['SLR_C20']
SLR_C30 = parameters['SLR_C30']
#-- ECMWF jump corrections
ATM = parameters['ATM'] in ('Y', 'y')
#-- Pole-Tide from Wahr et al. (2015)
POLE_TIDE = parameters['POLE_TIDE'] in ('Y', 'y')
#-- Glacial Isostatic Adjustment file to read
GIA = parameters['GIA'] if (parameters['GIA'].title() != 'None') else None
GIA_FILE = os.path.expanduser(parameters['GIA_FILE'])
#-- remove a set of spherical harmonics from the GRACE data
REMOVE_INDEX = parameters['REMOVE_INDEX']
REDISTRIBUTE_REMOVED = parameters['REDISTRIBUTE_REMOVED'] in ('Y', 'y')
#-- remove reconstructed fields
RECONSTRUCT = parameters['RECONSTRUCT'] in ('Y', 'y')
#-- gaussian smoothing radius
RAD = np.float(parameters['RAD'])
#-- filter coefficients for stripe effects
DESTRIPE = parameters['DESTRIPE'] in ('Y', 'y')
#-- input/output data format (ascii, netCDF4, HDF5)
DATAFORM = parameters['DATAFORM']
#-- index of mascons spherical harmonics
#-- path.expanduser = tilde expansion of path
MASCON_INDEX = os.path.expanduser(parameters['MASCON_INDEX'])
#-- output directory for mascon time series files
DIRECTORY = os.path.expanduser(parameters['DIRECTORY'])
#-- 1: fit mass, 2: fit geoid
FIT_METHOD = np.int(parameters['FIT_METHOD'])
#-- mascon redistribution
MASCON_OCEAN = parameters['MASCON_OCEAN'] in ('Y', 'y')
#-- recursively create output Directory if not currently existing
if (not os.access(DIRECTORY, os.F_OK)):
os.makedirs(DIRECTORY, mode=MODE, exist_ok=True)
#-- list object of output files for file logs (full path)
output_files = []
#-- file information
suffix = dict(ascii='txt', netCDF4='nc', HDF5='H5')
#-- read arrays of kl, hl, and ll Love Numbers
hl, kl, ll = load_love_numbers(LMAX,
LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE=REFERENCE)
#-- Earth Parameters
factors = units(lmax=LMAX).harmonic(hl, kl, ll)
#-- Average Density of the Earth [g/cm^3]
rho_e = factors.rho_e
#-- Average Radius of the Earth [cm]
rad_e = factors.rad_e
#-- for datasets not GSM: will add a label for the dataset
dset_str = '' if (DSET == 'GSM') else '_{0}'.format(DSET)
#-- atmospheric ECMWF "jump" flag (if ATM)
atm_str = '_wATM' if ATM else ''
#-- output string for both LMAX==MMAX and LMAX != MMAX cases
order_str = 'M{0:d}'.format(MMAX) if (MMAX != LMAX) else ''
#-- Calculating the Gaussian smoothing for radius RAD
if (RAD != 0):
wt = 2.0 * np.pi * gauss_weights(RAD, LMAX)
gw_str = '_r{0:0.0f}km'.format(RAD)
else:
#-- else = 1
wt = np.ones((LMAX + 1))
gw_str = ''
#-- Read Ocean function and convert to Ylms for redistribution
if (MASCON_OCEAN | REDISTRIBUTE_REMOVED):
#-- read Land-Sea Mask and convert to spherical harmonics
LSMASK = os.path.expanduser(parameters['LANDMASK'])
ocean_Ylms = ocean_stokes(LSMASK, LMAX, MMAX=MMAX, LOVE=(hl, kl, ll))
ocean_str = '_OCN'
else:
#-- not distributing uniformly over ocean
ocean_str = ''
#-- input GRACE/GRACE-FO spherical harmonic datafiles
#-- reading GRACE months for input range with grace_input_months.py
#-- replacing SLR and Degree 1 if specified
#-- correcting for Pole-Tide and Atmospheric Jumps if specified
Ylms = grace_input_months(base_dir,
PROC,
DREL,
DSET,
LMAX,
start_mon,
end_mon,
missing,
SLR_C20,
DEG1,
MMAX=MMAX,
SLR_C30=SLR_C30,
MODEL_DEG1=True,
ATM=ATM,
POLE_TIDE=POLE_TIDE)
#-- full path to directory for specific GRACE/GRACE-FO product
grace_dir = Ylms['directory']
#-- create harmonics object from GRACE/GRACE-FO data
GRACE_Ylms = harmonics().from_dict(Ylms)
#-- use a mean file for the static field to remove
if (parameters['MEAN_FILE'].title() == 'None'):
GRACE_Ylms.mean(apply=True)
else:
#-- read data form for input mean file (ascii, netCDF4, HDF5, gfc)
if (parameters['MEANFORM'] == 'ascii'):
mean_Ylms = harmonics().from_ascii(parameters['MEAN_FILE'],
date=False)
elif (parameters['MEANFORM'] == 'netCDF4'):
mean_Ylms = harmonics().from_netCDF4(parameters['MEAN_FILE'],
date=False)
elif (parameters['MEANFORM'] == 'HDF5'):
mean_Ylms = harmonics().from_HDF5(parameters['MEAN_FILE'],
date=False)
elif (parameters['MEANFORM'] == 'gfc'):
mean_Ylms = harmonics().from_gfc(parameters['MEAN_FILE'])
#-- remove the input mean
GRACE_Ylms.subtract(mean_Ylms)
#-- date information of GRACE/GRACE-FO coefficients
n_files = len(GRACE_Ylms.time)
#-- filter GRACE/GRACE-FO coefficients
if DESTRIPE:
#-- destriping GRACE/GRACE-FO coefficients
ds_str = '_FL'
GRACE_Ylms = GRACE_Ylms.destripe()
else:
#-- using standard GRACE/GRACE-FO harmonics
ds_str = ''
#-- input GIA spherical harmonic datafiles
GIA_Ylms_rate = read_GIA_model(GIA_FILE, GIA=GIA, LMAX=LMAX, MMAX=MMAX)
gia_str = '_{0}'.format(GIA_Ylms_rate['title']) if GIA else ''
#-- calculate the monthly mass change from GIA
GIA_Ylms = GRACE_Ylms.zeros_like()
GIA_Ylms.time[:] = np.copy(GRACE_Ylms.time)
GIA_Ylms.month[:] = np.copy(GRACE_Ylms.month)
#-- monthly GIA calculated by gia_rate*time elapsed
#-- finding change in GIA each month
for t in range(n_files):
GIA_Ylms.clm[:, :,
t] = GIA_Ylms_rate['clm'] * (GIA_Ylms.time[t] - 2003.3)
GIA_Ylms.slm[:, :,
t] = GIA_Ylms_rate['slm'] * (GIA_Ylms.time[t] - 2003.3)
#-- input spherical harmonic datafiles to be removed from the GRACE data
#-- Remove sets of Ylms from the GRACE data before returning
remove_Ylms = GRACE_Ylms.zeros_like()
remove_Ylms.time[:] = np.copy(GRACE_Ylms.time)
remove_Ylms.month[:] = np.copy(GRACE_Ylms.month)
if (parameters['REMOVE_INDEX'].title() != 'None'):
#-- for each file index separated by commas
for REMOVE_INDEX in parameters['REMOVE_INDEX'].split(','):
Ylms = harmonics().from_index(REMOVE_INDEX, DATAFORM)
#-- reduce to GRACE/GRACE-FO months and truncate to degree and order
Ylms = Ylms.subset(GRACE_Ylms.month).truncate(lmax=LMAX, mmax=MMAX)
#-- distribute removed Ylms uniformly over the ocean
if REDISTRIBUTE_REMOVED:
#-- calculate ratio between total removed mass and
#-- a uniformly distributed cm of water over the ocean
ratio = Ylms.clm[0, 0, :] / ocean_Ylms['clm'][0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove the ratio*ocean Ylms from Ylms
#-- note: x -= y is equivalent to x = x - y
Ylms.clm[l, m, :] -= ratio * ocean_Ylms['clm'][l, m]
Ylms.slm[l, m, :] -= ratio * ocean_Ylms['slm'][l, m]
#-- filter removed coefficients
if DESTRIPE:
Ylms = Ylms.destripe()
#-- add data for month t and INDEX_FILE to the total
#-- remove_clm and remove_slm matrices
#-- redistributing the mass over the ocean if specified
remove_Ylms.add(Ylms)
#-- input reconstructed spherical harmonic datafiles
construct_Ylms = GRACE_Ylms.zeros_like()
construct_Ylms.time[:] = np.copy(GRACE_Ylms.time)
construct_Ylms.month[:] = np.copy(GRACE_Ylms.month)
if RECONSTRUCT:
#-- input index for reconstructed spherical harmonic datafiles
RECONSTRUCT_INDEX = os.path.expanduser(parameters['RECONSTRUCT_INDEX'])
with open(RECONSTRUCT_INDEX, 'r') as f:
reconstruct_files = f.read().splitlines()
#-- remove commented lines (can comment out files in the index)
reconstruct_files = [
f for f in reconstruct_files if not re.match('#', f)
]
#-- for each valid file in the index (iterate over mascons)
for construct_file in reconstruct_files:
#-- read reconstructed spherical harmonics
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
Ylms = harmonics().from_ascii(construct_file)
elif (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
Ylms = harmonics().from_netCDF4(construct_file)
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
Ylms = harmonics().from_HDF5(construct_file)
#-- truncate clm and slm matrices to LMAX/MMAX
#-- add harmonics object to total
construct_Ylms.add(Ylms.truncate(lmax=LMAX, mmax=MMAX))
#-- filter reconstructed coefficients
if DESTRIPE:
construct_Ylms = construct_Ylms.destripe()
#-- set flag for removing reconstructed coefficients
construct_str = '_LEAKAGE'
else:
#-- set flag for not removing the reconstructed coefficients
construct_str = ''
#-- input mascon spherical harmonic datafiles
with open(MASCON_INDEX, 'r') as f:
mascon_files = f.read().splitlines()
#-- number of mascons
n_mas = len(mascon_files)
#-- spatial area of the mascon
total_area = np.zeros((n_mas))
#-- name of each mascon
mascon_name = []
#-- for each valid file in the index (iterate over mascons)
mascon_list = []
for k, fi in enumerate(mascon_files):
#-- read mascon spherical harmonics
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
Ylms = harmonics().from_ascii(os.path.expanduser(fi), date=False)
elif (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
Ylms = harmonics().from_netCDF4(os.path.expanduser(fi), date=False)
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
Ylms = harmonics().from_HDF5(os.path.expanduser(fi), date=False)
#-- Calculating the total mass of each mascon (1 cmwe uniform)
total_area[k] = 4.0 * np.pi * (rad_e**3) * rho_e * Ylms.clm[0, 0] / 3.0
#-- distribute MASCON mass uniformly over the ocean
if MASCON_OCEAN:
#-- calculate ratio between total mascon mass and
#-- a uniformly distributed cm of water over the ocean
ratio = Ylms.clm[0, 0] / ocean_Ylms['clm'][0, 0]
#-- for each spherical harmonic
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- remove ratio*ocean Ylms from mascon Ylms
#-- note: x -= y is equivalent to x = x - y
Ylms.clm[l, m] -= ratio * ocean_Ylms['clm'][l, m]
Ylms.slm[l, m] -= ratio * ocean_Ylms['slm'][l, m]
#-- truncate mascon spherical harmonics to d/o LMAX/MMAX and add to list
mascon_list.append(Ylms.truncate(lmax=LMAX, mmax=MMAX))
#-- mascon base is the file without directory or suffix
mascon_base = os.path.basename(mascon_files[k])
mascon_base = os.path.splitext(mascon_base)[0]
#-- if lower case, will capitalize
mascon_base = mascon_base.upper()
#-- if mascon name contains degree and order info, remove
mascon_name.append(mascon_base.replace('_L{0:d}'.format(LMAX), ''))
#-- create single harmonics object from list
mascon_Ylms = harmonics().from_list(mascon_list, date=False)
#-- calculating GRACE/GRACE-FO error (Wahr et al. 2006)
#-- output GRACE error file (for both LMAX==MMAX and LMAX != MMAX cases)
args = (PROC, DREL, DSET, LMAX, order_str, ds_str, atm_str,
GRACE_Ylms.month[0], GRACE_Ylms.month[-1], suffix[DATAFORM])
delta_format = '{0}_{1}_{2}_DELTA_CLM_L{3:d}{4}{5}{6}_{7:03d}-{8:03d}.{9}'
DELTA_FILE = delta_format.format(*args)
#-- check full path of the GRACE directory for delta file
#-- if file was previously calculated: will read file
#-- else: will calculate the GRACE/GRACE-FO error
if (not os.access(os.path.join(grace_dir, DELTA_FILE), os.F_OK)):
#-- add output delta file to list object
output_files.append(os.path.join(grace_dir, DELTA_FILE))
#-- Delta coefficients of GRACE time series (Error components)
delta_Ylms = harmonics(lmax=LMAX, mmax=MMAX)
delta_Ylms.clm = np.zeros((LMAX + 1, MMAX + 1))
delta_Ylms.slm = np.zeros((LMAX + 1, MMAX + 1))
#-- Smoothing Half-Width (CNES is a 10-day solution)
#-- All other solutions are monthly solutions (HFWTH for annual = 6)
if ((PROC == 'CNES') and (DREL in ('RL01', 'RL02'))):
HFWTH = 19
else:
HFWTH = 6
#-- Equal to the noise of the smoothed time-series
#-- for each spherical harmonic order
for m in range(0, MMAX + 1): #-- MMAX+1 to include MMAX
#-- for each spherical harmonic degree
for l in range(m, LMAX + 1): #-- LMAX+1 to include LMAX
#-- Delta coefficients of GRACE time series
for cs, csharm in enumerate(['clm', 'slm']):
#-- calculate GRACE Error (Noise of smoothed time-series)
#-- With Annual and Semi-Annual Terms
val1 = getattr(GRACE_Ylms, csharm)
smth = tssmooth(GRACE_Ylms.time,
val1[l, m, :],
HFWTH=HFWTH)
#-- number of smoothed points
nsmth = len(smth['data'])
tsmth = np.mean(smth['time'])
#-- GRACE delta Ylms
#-- variance of data-(smoothed+annual+semi)
val2 = getattr(delta_Ylms, csharm)
val2[l, m] = np.sqrt(np.sum(smth['noise']**2) / nsmth)
#-- save GRACE/GRACE-FO delta harmonics to file
delta_Ylms.time = np.copy(tsmth)
delta_Ylms.month = np.int(nsmth)
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
delta_Ylms.to_ascii(os.path.join(grace_dir, DELTA_FILE))
elif (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
delta_Ylms.to_netCDF4(os.path.join(grace_dir, DELTA_FILE))
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
delta_Ylms.to_HDF5(os.path.join(grace_dir, DELTA_FILE))
else:
#-- read GRACE/GRACE-FO delta harmonics from file
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
delta_Ylms = harmonics().from_ascii(
os.path.join(grace_dir, DELTA_FILE))
elif (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
delta_Ylms = harmonics().from_netCDF4(
os.path.join(grace_dir, DELTA_FILE))
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
delta_Ylms = harmonics().from_HDF5(
os.path.join(grace_dir, DELTA_FILE))
#-- truncate GRACE/GRACE-FO delta clm and slm to d/o LMAX/MMAX
delta_Ylms = delta_Ylms.truncate(lmax=LMAX, mmax=MMAX)
tsmth = np.squeeze(delta_Ylms.time)
nsmth = np.int(delta_Ylms.month)
#-- Calculating the number of cos and sin harmonics between LMIN and LMAX
#-- taking into account MMAX (if MMAX == LMAX then LMAX-MMAX=0)
n_harm = np.int(LMAX**2 - LMIN**2 + 2 * LMAX + 1 - (LMAX - MMAX)**2 -
(LMAX - MMAX))
#-- Initialing harmonics for least squares fitting
#-- mascon kernel
M_lm = np.zeros((n_harm, n_mas))
#-- mascon kernel converted to output unit
MA_lm = np.zeros((n_harm, n_mas))
#-- corrected clm and slm
Y_lm = np.zeros((n_harm, n_files))
#-- sensitivity kernel
A_lm = np.zeros((n_harm, n_mas))
#-- Satellite error harmonics
delta_lm = np.zeros((n_harm))
#-- Initializing output Mascon time-series
mascon = np.zeros((n_mas, n_files))
#-- Mascon satellite error component
M_delta = np.zeros((n_mas))
#-- Initializing conversion factors
#-- factor for converting to coefficients of mass
fact = np.zeros((n_harm))
#-- smoothing factor
wt_lm = np.zeros((n_harm))
#-- ii is a counter variable for building the mascon column array
ii = 0
#-- Creating column array of clm/slm coefficients
#-- Order is [C00...C6060,S11...S6060]
#-- Calculating factor to convert geoid spherical harmonic coefficients
#-- to coefficients of mass (Wahr, 1998)
coeff = rho_e * rad_e / 3.0
#-- Switching between Cosine and Sine Stokes
for cs, csharm in enumerate(['clm', 'slm']):
#-- copy cosine and sin harmonics
mascon_harm = getattr(mascon_Ylms, csharm)
grace_harm = getattr(GRACE_Ylms, csharm)
GIA_harm = getattr(GIA_Ylms, csharm)
remove_harm = getattr(remove_Ylms, csharm)
construct_harm = getattr(construct_Ylms, csharm)
delta_harm = getattr(delta_Ylms, csharm)
#-- for each spherical harmonic degree
#-- +1 to include LMAX
for l in range(LMIN, LMAX + 1):
#-- for each spherical harmonic order
#-- Sine Stokes for (m=0) = 0
mm = np.min([MMAX, l])
#-- +1 to include l or MMAX (whichever is smaller)
for m in range(cs, mm + 1):
#-- Mascon Spherical Harmonics
M_lm[ii, :] = np.copy(mascon_harm[l, m, :])
#-- GRACE Spherical Harmonics
#-- Correcting GRACE Harmonics for GIA and Removed Terms
Y_lm[ii,:] = grace_harm[l,m,:] - GIA_harm[l,m,:] - \
remove_harm[l,m,:] - construct_harm[l,m,:]
#-- GRACE delta spherical harmonics
delta_lm[ii] = np.copy(delta_harm[l, m])
#-- degree dependent factor to convert to mass
fact[ii] = (2.0 * l + 1.0) / (1.0 + kl[l])
#-- degree dependent smoothing
wt_lm[ii] = np.copy(wt[l])
#-- add 1 to counter
ii += 1
#-- Converting mascon coefficients to fit method
if (FIT_METHOD == 1):
#-- Fitting Sensitivity Kernel as mass coefficients
#-- converting M_lm to mass coefficients of the kernel
for i in range(n_harm):
MA_lm[i, :] = M_lm[i, :] * wt_lm[i] * fact[i]
fit_factor = wt_lm * fact
else:
#-- Fitting Sensitivity Kernel as geoid coefficients
for i in range(n_harm):
MA_lm[:, :] = M_lm[i, :] * wt_lm[i]
fit_factor = wt_lm * np.ones((n_harm))
#-- Fitting the sensitivity kernel from the input kernel
for i in range(n_harm):
#-- setting kern_i equal to 1 for d/o
kern_i = np.zeros((n_harm))
#-- converting to mass coefficients if specified
kern_i[i] = 1.0 * fit_factor[i]
#-- spherical harmonics solution for the
#-- mascon sensitivity kernels
#-- Least Squares Solutions: Inv(X'.X).(X'.Y)
kern_lm = np.linalg.lstsq(MA_lm, kern_i, rcond=-1)[0]
for k in range(n_mas):
A_lm[i, k] = kern_lm[k] * total_area[k]
#-- for each mascon
for k in range(n_mas):
#-- Multiply the Satellite error (noise of a smoothed time-series
#-- with annual and semi-annual components) by the sensitivity kernel
#-- Converting to Gigatonnes
M_delta[k] = np.sqrt(np.sum((delta_lm * A_lm[:, k])**2)) / 1e15
#-- output filename format (for both LMAX==MMAX and LMAX != MMAX cases):
#-- mascon name, GRACE dataset, GIA model, LMAX, (MMAX,)
#-- Gaussian smoothing, filter flag, remove reconstructed fields flag
#-- output GRACE error file
file_out = '{0}{1}{2}{3}{4}_L{5:d}{6}{7}{8}{9}.txt'.format(
mascon_name[k], dset_str, gia_str.upper(), atm_str, ocean_str,
LMAX, order_str, gw_str, ds_str, construct_str)
#-- Output mascon datafiles
#-- Will output each mascon time series
#-- month, date, mascon mass [Gt], satellite error [Gt], mascon area [km^2]
#-- open output mascon time-series file
fid = open(os.path.join(DIRECTORY, file_out), 'w')
#-- for each date
formatting_string = '{0:03d} {1:12.4f} {2:16.10f} {3:16.10f} {4:16.5f}'
for t, mon in enumerate(GRACE_Ylms.month):
#-- Summing over all spherical harmonics for mascon k, and time t
#-- multiplies by the degree dependent factor to convert
#-- the harmonics into mass coefficients
#-- Converting mascon mass time-series from g to gigatonnes
mascon[k, t] = np.sum(A_lm[:, k] * Y_lm[:, t]) / 1e15
#-- output to file
args = (mon, GRACE_Ylms.time[t], mascon[k, t], M_delta[k],
total_area[k] / 1e10)
print(formatting_string.format(*args), file=fid)
#-- close the output file
fid.close()
#-- change the permissions mode
os.chmod(os.path.join(DIRECTORY, file_out), MODE)
#-- add output files to list object
output_files.append(os.path.join(DIRECTORY, file_out))
#-- return the list of output files
return output_files