def specsmooth(self, datagrid, smooth):
"""
isotropic spectral smoothing on a sphere.
@param datagrid: rank 2 or 3 numpy float32 array with shape (nlat,nlon) or
(nlat,nlon,nt), where nt is the number of grids to be smoothed. If
datagrid is rank 2, nt is assumed to be 1.
@param smooth: rank 1 array of length nlat containing smoothing factors
as a function of total wavenumber.
@return: C{B{datagrid}} - rank 2 or 3 numpy float32 array with shape
(nlat,nlon) or (nlat,nlon,nt) containing the smoothed grids.
"""
# check that datagrid is rank 2 or 3 with size (self.nlat, self.nlon) or
# (self.nlat, self.nlon, nt) where nt is number of grids to transform.
if len(datagrid.shape) > 3:
msg = 'specsmooth needs a rank two or three array, got %d' % (len(
datagrid.shape), )
raise ValueError(msg)
if datagrid.shape[0] != self.nlat or datagrid.shape[1] != self.nlon:
msg = 'specsmooth needs an array of size %d by %d, got %d by %d' % (
self.nlat,
self.nlon,
datagrid.shape[0],
datagrid.shape[1],
)
raise ValueError(msg)
# make sure smooth is rank 1, same size as datagrid.shape[0]
if len(smooth.shape) != 1 or smooth.shape[0] != datagrid.shape[0]:
msg = 'smooth must be rank 1 and same size as datagrid.shape[0] in specsmooth!'
raise ValueError(msg)
# grid to spectral transform.
nlat = self.nlat
dataspec = self.grdtospec(datagrid, nlat - 1)
# multiply spectral coeffs. by smoothing factor.
dataspec = _spherepack.multsmoothfact(dataspec, smooth)
# spectral to grid transform.
datagrid = self.spectogrd(dataspec)
return datagrid
def regrid(grdin, grdout, datagrid, ntrunc=None, smooth=None):
"""
regrid data using spectral interpolation, while performing
optional spectral smoothing and/or truncation.
@param grdin: Spharmt class instance describing input grid.
@param grdout: Spharmt class instance describing output grid.
@param datagrid: data on input grid (grdin.nlat x grdin.nlon). If
datagrid is rank 3, last dimension is the number of grids to interpolate.
@keyword ntrunc: optional spectral truncation limit for datagrid
(default min(grdin.nlat-1,grdout.nlat-1)).
@keyword smooth: rank 1 array of length grdout.nlat containing smoothing
factors as a function of total wavenumber (default is no smoothing).
@return: C{B{datagrid}} - interpolated (and optionally smoothed) array(s)
on grdout.nlon x grdout.nlat grid.
"""
# check that datagrid is rank 2 or 3 with size (grdin.nlat, grdin.nlon) or
# (grdin.nlat, grdin.nlon, nt) where nt is number of grids to transform.
if len(datagrid.shape) > 3:
msg = 'regrid needs a rank two or three array, got %d' % (len(
datagrid.shape), )
raise ValueError(msg)
if datagrid.shape[0] != grdin.nlat or datagrid.shape[1] != grdin.nlon:
msg = 'grdtospec needs an array of size %d by %d, got %d by %d' % (
grdin.nlat,
grdin.nlon,
datagrid.shape[0],
datagrid.shape[1],
)
raise ValueError(msg)
if smooth is not None and (len(smooth.shape) != 1
or smooth.shape[0] != grdout.nlat):
msg = 'smooth must be rank 1 size grdout.nlat in regrid!'
raise ValueError(msg)
if ntrunc is None:
ntrunc = min(grdout.nlat - 1, grdin.nlat - 1)
dataspec = grdin.grdtospec(datagrid, ntrunc)
if smooth is not None:
dataspec = _spherepack.multsmoothfact(dataspec, smooth)
return grdout.spectogrd(dataspec)
def specsmooth(self, datagrid, smooth):
"""
isotropic spectral smoothing on a sphere.
@param datagrid: rank 2 or 3 numpy float32 array with shape (nlat,nlon) or
(nlat,nlon,nt), where nt is the number of grids to be smoothed. If
datagrid is rank 2, nt is assumed to be 1.
@param smooth: rank 1 array of length nlat containing smoothing factors
as a function of total wavenumber.
@return: C{B{datagrid}} - rank 2 or 3 numpy float32 array with shape
(nlat,nlon) or (nlat,nlon,nt) containing the smoothed grids.
"""
# check that datagrid is rank 2 or 3 with size (self.nlat, self.nlon) or
# (self.nlat, self.nlon, nt) where nt is number of grids to transform.
if len(datagrid.shape) > 3:
msg = 'specsmooth needs a rank two or three array, got %d' % (len(datagrid.shape),)
raise ValueError(msg)
if datagrid.shape[0] != self.nlat or datagrid.shape[1] != self.nlon:
msg = 'specsmooth needs an array of size %d by %d, got %d by %d' % (self.nlat, self.nlon, datagrid.shape[0], datagrid.shape[1],)
raise ValueError(msg)
# make sure smooth is rank 1, same size as datagrid.shape[0]
if len(smooth.shape) !=1 or smooth.shape[0] != datagrid.shape[0]:
msg = 'smooth must be rank 1 and same size as datagrid.shape[0] in specsmooth!'
raise ValueError(msg)
# grid to spectral transform.
nlat = self.nlat
dataspec = self.grdtospec(datagrid, nlat-1)
# multiply spectral coeffs. by smoothing factor.
dataspec = _spherepack.multsmoothfact(dataspec, smooth)
# spectral to grid transform.
datagrid = self.spectogrd(dataspec)
return datagrid
def regrid(grdin, grdout, datagrid, ntrunc=None, smooth=None):
"""
regrid data using spectral interpolation, while performing
optional spectral smoothing and/or truncation.
@param grdin: Spharmt class instance describing input grid.
@param grdout: Spharmt class instance describing output grid.
@param datagrid: data on input grid (grdin.nlat x grdin.nlon). If
datagrid is rank 3, last dimension is the number of grids to interpolate.
@keyword ntrunc: optional spectral truncation limit for datagrid
(default min(grdin.nlat-1,grdout.nlat-1)).
@keyword smooth: rank 1 array of length grdout.nlat containing smoothing
factors as a function of total wavenumber (default is no smoothing).
@return: C{B{datagrid}} - interpolated (and optionally smoothed) array(s)
on grdout.nlon x grdout.nlat grid.
"""
# check that datagrid is rank 2 or 3 with size (grdin.nlat, grdin.nlon) or
# (grdin.nlat, grdin.nlon, nt) where nt is number of grids to transform.
if len(datagrid.shape) > 3:
msg = 'regrid needs a rank two or three array, got %d' % (len(datagrid.shape),)
raise ValueError(msg)
if datagrid.shape[0] != grdin.nlat or datagrid.shape[1] != grdin.nlon:
msg = 'grdtospec needs an array of size %d by %d, got %d by %d' % (grdin.nlat, grdin.nlon, datagrid.shape[0], datagrid.shape[1],)
raise ValueError(msg)
if smooth is not None and (len(smooth.shape) !=1 or smooth.shape[0] != grdout.nlat):
msg = 'smooth must be rank 1 size grdout.nlat in regrid!'
raise ValueError(msg)
if ntrunc is None:
ntrunc = min(grdout.nlat-1,grdin.nlat-1)
dataspec = grdin.grdtospec(datagrid,ntrunc)
if smooth is not None:
dataspec = _spherepack.multsmoothfact(dataspec, smooth)
return grdout.spectogrd(dataspec)
