def from_HDF5(self, filename, **kwargs):
"""
Read a harmonics 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('date', True)
kwargs.setdefault('verbose', False)
kwargs.setdefault('compression', None)
#-- read data from HDF5 file
Ylms = hdf5_read_stokes(self.filename,
DATE=kwargs['date'],
COMPRESSION=kwargs['compression'])
#-- copy variables to harmonics object
self.clm = Ylms['clm'].copy()
self.slm = Ylms['slm'].copy()
self.l = Ylms['l'].copy()
self.m = Ylms['m'].copy()
self.lmax = np.max(Ylms['l'])
self.mmax = np.max(Ylms['m'])
if kwargs['date']:
self.time = Ylms['time'].copy()
#-- adjust months to fix special cases if necessary
self.month = adjust_months(Ylms['month'])
#-- assign shape and ndim attributes
self.update_dimensions()
return self
def from_list(self, object_list, **kwargs):
"""
Build a sorted harmonics object from a list of other harmonics objects
Inputs: list of harmonics object to be merged
Options:
harmonics objects contain date information
sort harmonics objects by date information
clear the harmonics list from memory
"""
#-- set default keyword arguments
kwargs.setdefault('date', True)
kwargs.setdefault('sort', True)
kwargs.setdefault('clear', False)
#-- number of harmonic objects in list
n = len(object_list)
#-- indices to sort data objects if harmonics list contain dates
if kwargs['date'] and kwargs['sort']:
list_sort = np.argsort([d.time for d in object_list], axis=None)
else:
list_sort = np.arange(n)
#-- truncate to maximum degree and order
self.lmax = np.min([d.lmax for d in object_list])
self.mmax = np.min([d.mmax for d in object_list])
#-- output degree and order
self.l = np.arange(self.lmax + 1)
self.m = np.arange(self.mmax + 1)
#-- create output harmonics
self.clm = np.zeros((self.lmax + 1, self.mmax + 1, n))
self.slm = np.zeros((self.lmax + 1, self.mmax + 1, n))
#-- create list of files
self.filename = []
#-- output dates
if kwargs['date']:
self.time = np.zeros((n))
self.month = np.zeros((n), dtype=np.int64)
#-- for each indice
for t, i in enumerate(list_sort):
self.clm[:, :,
t] = object_list[i].clm[:self.lmax + 1, :self.mmax + 1]
self.slm[:, :,
t] = object_list[i].slm[:self.lmax + 1, :self.mmax + 1]
if kwargs['date']:
self.time[t] = np.atleast_1d(object_list[i].time)
self.month[t] = np.atleast_1d(object_list[i].month)
#-- append filename to list
if getattr(object_list[i], 'filename'):
self.filename.append(object_list[i].filename)
#-- adjust months to fix special cases if necessary
if kwargs['date']:
self.month = adjust_months(self.month)
#-- assign shape and ndim attributes
self.update_dimensions()
#-- clear the input list to free memory
if kwargs['clear']:
object_list = None
#-- return the single harmonic object
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_gfc(self, filename, **kwargs):
"""
Read a harmonics object from a gfc gravity model file
Inputs: full path of input gfc file
Options:
keyword arguments for gfc input
"""
#-- set filename
self.case_insensitive_filename(filename)
#-- set default parameters
kwargs.setdefault('date', False)
kwargs.setdefault('tide', None)
kwargs.setdefault('verbose', False)
#-- read data from gfc file
if kwargs['date']:
Ylms = read_gfc_harmonics(self.filename, TIDE=kwargs['tide'])
else:
Ylms = read_ICGEM_harmonics(self.filename, TIDE=kwargs['tide'])
#-- 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.lmax = np.int64(Ylms['max_degree'])
self.mmax = np.int64(Ylms['max_degree'])
self.l = np.arange(self.lmax + 1)
self.m = np.arange(self.mmax + 1)
#-- copy date variables
if kwargs['date']:
self.time = Ylms['time'].copy()
#-- adjust months to fix special cases if necessary
self.month = adjust_months(Ylms['month'])
#-- geophysical parameters of gravity model
self.GM = np.float64(Ylms['earth_gravity_constant'])
self.R = np.float64(Ylms['radius'])
self.tide = Ylms['tide_system']
#-- assign shape and ndim attributes
self.update_dimensions()
return self
def from_list(self, object_list, **kwargs):
"""
Build a sorted spatial object from a list of other spatial objects
Inputs: list of spatial object to be merged
Options:
spatial objects contain date information
sort spatial objects by date information
clear the spatial list from memory
"""
#-- set default keyword arguments
kwargs.setdefault('date', True)
kwargs.setdefault('sort', True)
kwargs.setdefault('clear', False)
#-- number of spatial objects in list
n = len(object_list)
#-- indices to sort data objects if spatial list contain dates
if kwargs['date'] and kwargs['sort']:
list_sort = np.argsort([d.time for d in object_list], axis=None)
else:
list_sort = np.arange(n)
#-- extract dimensions and grid spacing
self.spacing = object_list[0].spacing
self.extent = object_list[0].extent
self.shape = object_list[0].shape
#-- create output spatial grid and mask
self.data = np.zeros((self.shape[0], self.shape[1], n))
self.mask = np.zeros((self.shape[0], self.shape[1], n), dtype=bool)
self.fill_value = object_list[0].fill_value
self.lon = object_list[0].lon.copy()
self.lat = object_list[0].lat.copy()
#-- create list of files and attributes
self.filename = []
self.attributes = []
#-- output dates
if kwargs['date']:
self.time = np.zeros((n))
self.month = np.zeros((n), dtype=np.int64)
#-- for each indice
for t, i in enumerate(list_sort):
self.data[:, :, t] = object_list[i].data[:, :].copy()
self.mask[:, :, t] |= object_list[i].mask[:, :]
if kwargs['date']:
self.time[t] = np.atleast_1d(object_list[i].time)
self.month[t] = np.atleast_1d(object_list[i].month)
#-- append filename to list
if getattr(object_list[i], 'filename'):
self.filename.append(object_list[i].filename)
#-- append attributes to list
if getattr(object_list[i], 'attributes'):
self.attributes.append(object_list[i].attributes)
#-- adjust months to fix special cases if necessary
if kwargs['date']:
self.month = adjust_months(self.month)
#-- update the dimensions
self.update_dimensions()
self.update_mask()
#-- clear the input list to free memory
if kwargs['clear']:
object_list = None
#-- return the single spatial object
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 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