def from_SHM(self, filename, **kwargs):
"""
Read a harmonics object from a spherical harmonic model file
Inputs: full path of input SHM file
Options:
keyword arguments for SHM input
"""
#-- set filename
self.case_insensitive_filename(filename)
#-- set default verbosity
kwargs.setdefault('verbose', False)
#-- read data from SHM file
Ylms = read_GRACE_harmonics(self.filename, self.lmax, **kwargs)
#-- Output file information
logging.info(self.filename)
logging.info(list(Ylms.keys()))
#-- copy variables for gravity model
self.clm = Ylms['clm'].copy()
self.slm = Ylms['slm'].copy()
self.time = Ylms['time'].copy()
self.month = np.int64(calendar_to_grace(self.time))
#-- copy header information for gravity model
self.header = Ylms['header']
#-- assign shape and ndim attributes
self.update_dimensions()
return self
def from_HDF5(self, filename, date=True, **kwargs):
"""
Read a spatial object from a HDF5 file
Inputs: full path of input HDF5 file
Options:
HDF5 file contains date information
keyword arguments for HDF5 reader
"""
#-- set filename
self.case_insensitive_filename(filename)
#-- set default parameters
kwargs.setdefault('verbose', False)
kwargs.setdefault('compression', None)
kwargs.setdefault('varname', 'z')
kwargs.setdefault('lonname', 'lon')
kwargs.setdefault('latname', 'lat')
kwargs.setdefault('timename', 'time')
#-- read data from HDF5 file
data = hdf5_read(self.filename,
DATE=date,
COMPRESSION=kwargs['compression'],
VARNAME=kwargs['varname'],
LONNAME=kwargs['lonname'],
LATNAME=kwargs['latname'],
TIMENAME=kwargs['timename'])
#-- copy variables to spatial object
self.data = data['data'].copy()
if '_FillValue' in data['attributes']['data'].keys():
self.fill_value = data['attributes']['_FillValue']
self.mask = np.zeros(self.data.shape, dtype=bool)
self.lon = data['lon'].copy()
self.lat = data['lat'].copy()
#-- if the HDF5 file contains date variables
if date:
self.time = data['time'].copy()
self.month = calendar_to_grace(self.time)
#-- adjust months to fix special cases if necessary
self.month = adjust_months(self.month)
#-- update attributes
self.attributes.update(data['attributes'])
#-- get spacing and dimensions
self.update_spacing()
self.update_extents()
self.update_dimensions()
self.update_mask()
return self
def from_ascii(self, filename, date=True, **kwargs):
"""
Read a spatial object from an ascii file
Inputs: full path of input ascii file
Options:
ascii file contains date information
keyword arguments for ascii input
"""
#-- set filename
self.case_insensitive_filename(filename)
#-- set default parameters
kwargs.setdefault('verbose', False)
kwargs.setdefault('compression', None)
kwargs.setdefault('columns', ['lon', 'lat', 'data', 'time'])
kwargs.setdefault('header', 0)
#-- open the ascii file and extract contents
logging.info(self.filename)
if (kwargs['compression'] == 'gzip'):
#-- read input ascii data from gzip compressed file and split lines
with gzip.open(self.filename, 'r') as f:
file_contents = f.read().decode('ISO-8859-1').splitlines()
elif (kwargs['compression'] == 'zip'):
#-- read input ascii data from zipped file and split lines
base, _ = os.path.splitext(self.filename)
with zipfile.ZipFile(self.filename) as z:
file_contents = z.read(base).decode('ISO-8859-1').splitlines()
elif (kwargs['compression'] == 'bytes'):
#-- read input file object and split lines
file_contents = self.filename.read().splitlines()
else:
#-- read input ascii file (.txt, .asc) and split lines
with open(self.filename, 'r') as f:
file_contents = f.read().splitlines()
#-- compile regular expression operator for extracting numerical values
#-- from input ascii files of spatial data
regex_pattern = r'[-+]?(?:(?:\d*\.\d+)|(?:\d+\.?))(?:[EeD][+-]?\d+)?'
rx = re.compile(regex_pattern, re.VERBOSE)
#-- output spatial dimensions
if (None not in self.extent):
self.lat = np.linspace(self.extent[3], self.extent[2],
self.shape[0])
self.lon = np.linspace(self.extent[0], self.extent[1],
self.shape[1])
else:
self.lat = np.zeros((self.shape[0]))
self.lon = np.zeros((self.shape[1]))
#-- output spatial data
self.data = np.zeros((self.shape[0], self.shape[1]))
self.mask = np.zeros((self.shape[0], self.shape[1]), dtype=bool)
#-- remove time from list of column names if not date
columns = [c for c in kwargs['columns'] if (c != 'time')]
#-- extract spatial data array and convert to matrix
#-- for each line in the file
header = kwargs['header']
for line in file_contents[header:]:
#-- extract columns of interest and assign to dict
#-- convert fortran exponentials if applicable
d = {
c: r.replace('D', 'E')
for c, r in zip(columns, rx.findall(line))
}
#-- convert line coordinates to integers
ilon = np.int64(np.float64(d['lon']) / self.spacing[0])
ilat = np.int64((90.0 - np.float64(d['lat'])) // self.spacing[1])
self.data[ilat, ilon] = np.float64(d['data'])
self.mask[ilat, ilon] = False
self.lon[ilon] = np.float64(d['lon'])
self.lat[ilat] = np.float64(d['lat'])
#-- if the ascii file contains date variables
if date:
self.time = np.array(d['time'], dtype='f')
self.month = calendar_to_grace(self.time)
#-- if the ascii file contains date variables
if date:
#-- adjust months to fix special cases if necessary
self.month = adjust_months(self.month)
#-- get spacing and dimensions
self.update_spacing()
self.update_extents()
self.update_dimensions()
self.update_mask()
return self
def convert_harmonics(INPUT_FILE,
OUTPUT_FILE,
LMAX=None,
MMAX=None,
UNITS=None,
LOVE_NUMBERS=0,
REFERENCE=None,
DDEG=None,
INTERVAL=None,
FILL_VALUE=None,
HEADER=None,
DATAFORM=None,
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)
#-- Grid spacing
dlon, dlat = (DDEG, DDEG) if (np.ndim(DDEG) == 0) 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 spatial file in data format
#-- expand dimensions
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
input_spatial = spatial(spacing=[dlon, dlat],
nlat=nlat,
nlon=nlon,
fill_value=FILL_VALUE).from_ascii(
INPUT_FILE, header=HEADER).expand_dims()
elif (DATAFORM == 'netCDF4'):
#-- netcdf (.nc)
input_spatial = spatial().from_netCDF4(INPUT_FILE).expand_dims()
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
input_spatial = spatial().from_HDF5(INPUT_FILE).expand_dims()
#-- convert missing values to zero
input_spatial.replace_invalid(0.0)
#-- input data shape
nlat, nlon, nt = input_spatial.shape
#-- read arrays of kl, hl, and ll Love Numbers
LOVE = load_love_numbers(LMAX,
LOVE_NUMBERS=LOVE_NUMBERS,
REFERENCE=REFERENCE)
#-- upper bound of spherical harmonic orders (default = LMAX)
if MMAX is None:
MMAX = np.copy(LMAX)
#-- calculate associated Legendre polynomials
th = (90.0 - input_spatial.lat) * np.pi / 180.0
PLM, dPLM = plm_holmes(LMAX, np.cos(th))
#-- create list of harmonics objects
Ylms_list = []
for i, t in enumerate(input_spatial.time):
#-- convert spatial field to spherical harmonics
output_Ylms = gen_stokes(input_spatial.data[:, :, i].T,
input_spatial.lon,
input_spatial.lat,
UNITS=UNITS,
LMIN=0,
LMAX=LMAX,
MMAX=MMAX,
PLM=PLM,
LOVE=LOVE)
output_Ylms.time = np.copy(t)
output_Ylms.month = calendar_to_grace(t)
#-- append to list
Ylms_list.append(output_Ylms)
#-- convert Ylms list for output spherical harmonics
Ylms = harmonics().from_list(Ylms_list)
Ylms_list = None
#-- outputting data to file
if (DATAFORM == 'ascii'):
#-- ascii (.txt)
Ylms.to_ascii(OUTPUT_FILE)
elif (DATAFORM == 'netCDF4'):
#-- netCDF4 (.nc)
Ylms.to_netCDF4(OUTPUT_FILE)
elif (DATAFORM == 'HDF5'):
#-- HDF5 (.H5)
Ylms.to_HDF5(OUTPUT_FILE)
#-- change output permissions level to MODE
os.chmod(OUTPUT_FILE, MODE)
def from_ascii(self, filename, **kwargs):
"""
Read a harmonics object from an ascii file
Inputs: full path of input ascii file
Options:
ascii file contains date information
keyword arguments for ascii input
"""
#-- set filename
self.case_insensitive_filename(filename)
#-- set default parameters
kwargs.setdefault('date', True)
kwargs.setdefault('verbose', False)
kwargs.setdefault('compression', None)
#-- open the ascii file and extract contents
logging.info(self.filename)
if (kwargs['compression'] == 'gzip'):
#-- read input ascii data from gzip compressed file and split lines
with gzip.open(self.filename, 'r') as f:
file_contents = f.read().decode('ISO-8859-1').splitlines()
elif (kwargs['compression'] == 'zip'):
#-- read input ascii data from zipped file and split lines
base, _ = os.path.splitext(self.filename)
with zipfile.ZipFile(self.filename) as z:
file_contents = z.read(base).decode('ISO-8859-1').splitlines()
elif (kwargs['compression'] == 'bytes'):
#-- read input file object and split lines
file_contents = self.filename.read().splitlines()
else:
#-- read input ascii file (.txt, .asc) and split lines
with open(self.filename, 'r') as f:
file_contents = f.read().splitlines()
#-- compile regular expression operator for extracting numerical values
#-- from input ascii files of spherical harmonics
regex_pattern = r'[-+]?(?:(?:\d*\.\d+)|(?:\d+\.?))(?:[EeD][+-]?\d+)?'
rx = re.compile(regex_pattern, re.VERBOSE)
#-- find maximum degree and order of harmonics
self.lmax = 0
self.mmax = 0
#-- for each line in the file
for line in file_contents:
if kwargs['date']:
l1, m1, clm1, slm1, time = rx.findall(line)
else:
l1, m1, clm1, slm1 = rx.findall(line)
#-- convert line degree and order to integers
l1, m1 = np.array([l1, m1], dtype=np.int64)
self.lmax = np.copy(l1) if (l1 > self.lmax) else self.lmax
self.mmax = np.copy(m1) if (m1 > self.mmax) else self.mmax
#-- output spherical harmonics dimensions array
self.l = np.arange(self.lmax + 1)
self.m = np.arange(self.mmax + 1)
#-- output spherical harmonics data
self.clm = np.zeros((self.lmax + 1, self.mmax + 1))
self.slm = np.zeros((self.lmax + 1, self.mmax + 1))
#-- if the ascii file contains date variables
if kwargs['date']:
self.time = np.float64(time)
self.month = np.int64(calendar_to_grace(self.time))
#-- adjust months to fix special cases if necessary
self.month = adjust_months(self.month)
#-- extract harmonics and convert to matrix
#-- for each line in the file
for line in file_contents:
if kwargs['date']:
l1, m1, clm1, slm1, time = rx.findall(line)
else:
l1, m1, clm1, slm1 = rx.findall(line)
#-- convert line degree and order to integers
ll, mm = np.array([l1, m1], dtype=np.int64)
#-- convert fortran exponentials if applicable
self.clm[ll, mm] = np.float64(clm1.replace('D', 'E'))
self.slm[ll, mm] = np.float64(slm1.replace('D', 'E'))
#-- assign shape and ndim attributes
self.update_dimensions()
return self