def interp(datain,lonsin,latsin,lonsout,latsout,checkbounds=False,mode='nearest',cval=0.0,order=3):
"""
dataout = interp(datain,lonsin,latsin,lonsout,latsout,mode='constant',cval=0.0,order=3)
interpolate data (datain) on a regularly spaced lat/lon grid (with lons=lonsin
lats=latsin) to a grid with lons=lonsout, lats=latsout.
datain is a rank-2 array with 1st dimension corresponding to longitude,
2nd dimension latitude.
lonsin, latsin are rank-1 Numeric arrays containing longitudes and latitudes
of datain grid in increasing order (i.e. from Greenwich meridian eastward, and
South Pole northward)
lonsout, latsout are rank-2 Numeric arrays containing lons and lats out desired
output grid (typically a native map projection grid).
If checkbounds=True, values of lonsout and latsout are checked to see that
they lie within the range specified by lonsin and latsing. Default is
False, and values outside the borders are handled in the manner described
by the 'mode' parameter (default mode='nearest', which means the nearest
boundary value is used). See section 20.2 of the numarray docs for
information on the 'mode' keyword.
See numarray.nd_image.map_coordinates documentation for information on
the other optional keyword parameters. The order keyword can be 0
for nearest neighbor interpolation (nd_image only allows 1-6) - if
order=0 bounds checking is done even if checkbounds=False.
"""
# lons and lats must be monotonically increasing.
# lonsin, latsin assumed equally spaced.
# make sure lonsin, latsin increasing.
if lonsin[-1]-lonsin[0] < 0 or latsin[-1]-latsin[0] < 0:
raise ValueError, 'lonsin and latsin must be increasing!'
# make sure lonsin, latsin are regularly spaced.
delon = lonsin[1:]-lonsin[0:-1]
delat = latsin[1:]-latsin[0:-1]
if max(delat)-min(delat) > 1.e-4 or max(delon)-min(delon) > 1.e-4:
raise ValueError, 'lat/lon grid must be uniform!'
# optionally, check that lonsout,latsout are
# within region defined by lonsin,latsin.
if checkbounds or order == 0:
if min(N.ravel(lonsout)) < min(lonsin) or \
max(N.ravel(lonsout)) > max(lonsin) or \
min(N.ravel(latsout)) < min(latsin) or \
max(N.ravel(latsout)) > max(latsin):
raise ValueError, 'latsout or lonsout outside range of latsin or lonsin'
xcoords = (len(lonsin)-1)*(lonsout-lonsin[0])/(lonsin[-1]-lonsin[0])
ycoords = (len(latsin)-1)*(latsout-latsin[0])/(latsin[-1]-latsin[0])
coords = [ycoords,xcoords]
# interpolate to projection grid using numarray.nd_image spline filter.
if order:
return nd_image.map_coordinates(datain,coords,mode=mode,cval=cval,order=order)
else:
# nearest neighbor interpolation if order=0.
# uses index arrays, so first convert to numarray.
datatmp = N.array(datain,datain.typecode())
xi = N.around(xcoords).astype('i')
yi = N.around(ycoords).astype('i')
return datatmp[yi,xi]
def interp(datain,lonsin,latsin,lonsout,latsout,checkbounds=False,mode='nearest',cval=0.0,order=3):
"""
dataout = interp(datain,lonsin,latsin,lonsout,latsout,mode='constant',cval=0.0,order=3)
interpolate data (datain) on a rectilinear lat/lon grid (with lons=lonsin
lats=latsin) to a grid with lons=lonsout, lats=latsout.
datain is a rank-2 array with 1st dimension corresponding to longitude,
2nd dimension latitude.
lonsin, latsin are rank-1 Numeric arrays containing longitudes and latitudes
of datain grid in increasing order (i.e. from Greenwich meridian eastward, and
South Pole northward)
lonsout, latsout are rank-2 Numeric arrays containing lons and lats out desired
output grid (typically a native map projection grid).
If checkbounds=True, values of lonsout and latsout are checked to see that
they lie within the range specified by lonsin and latsing. Default is
False, and values outside the borders are handled in the manner described
by the 'mode' parameter (default mode='nearest', which means the nearest
boundary value is used). See section 20.2 of the numarray docs for
information on the 'mode' keyword.
See numarray.nd_image.map_coordinates documentation for information on
the other optional keyword parameters. The order keyword can be 0
for nearest neighbor interpolation (nd_image only allows 1-6) - if
order=0 bounds checking is done even if checkbounds=False.
"""
# lonsin and latsin must be monotonically increasing.
if lonsin[-1]-lonsin[0] < 0 or latsin[-1]-latsin[0] < 0:
raise ValueError, 'lonsin and latsin must be increasing!'
# optionally, check that lonsout,latsout are
# within region defined by lonsin,latsin.
# (this check is always done if nearest neighbor
# interpolation (order=0) requested).
if checkbounds or order == 0:
if min(N.ravel(lonsout)) < min(lonsin) or \
max(N.ravel(lonsout)) > max(lonsin) or \
min(N.ravel(latsout)) < min(latsin) or \
max(N.ravel(latsout)) > max(latsin):
raise ValueError, 'latsout or lonsout outside range of latsin or lonsin'
# compute grid coordinates of output grid.
delon = lonsin[1:]-lonsin[0:-1]
delat = latsin[1:]-latsin[0:-1]
if max(delat)-min(delat) < 1.e-4 and max(delon)-min(delon) < 1.e-4:
# regular input grid.
xcoords = (len(lonsin)-1)*(lonsout-lonsin[0])/(lonsin[-1]-lonsin[0])
ycoords = (len(latsin)-1)*(latsout-latsin[0])/(latsin[-1]-latsin[0])
else:
# irregular (but still rectilinear) input grid.
lonsoutflat = N.ravel(lonsout)
latsoutflat = N.ravel(latsout)
ix = N.searchsorted(lonsin,lonsoutflat)-1
iy = N.searchsorted(latsin,latsoutflat)-1
xcoords = N.zeros(ix.shape,'f')
ycoords = N.zeros(iy.shape,'f')
for n,i in enumerate(ix):
if i < 0:
xcoords[n] = -1 # outside of range on lonsin (lower end)
elif i >= len(lonsin)-1:
xcoords[n] = len(lonsin) # outside range on upper end.
else:
xcoords[n] = float(i)+(lonsoutflat[n]-lonsin[i])/(lonsin[i+1]-lonsin[i])
xcoords = N.reshape(xcoords,lonsout.shape)
for m,j in enumerate(iy):
if j < 0:
ycoords[m] = -1 # outside of range of latsin (on lower end)
elif j >= len(latsin)-1:
ycoords[m] = len(latsin) # outside range on upper end
else:
ycoords[m] = float(j)+(latsoutflat[m]-latsin[j])/(latsin[j+1]-latsin[j])
ycoords = N.reshape(ycoords,latsout.shape)
coords = [ycoords,xcoords]
# interpolate to output grid using numarray.nd_image spline filter.
if order:
return nd_image.map_coordinates(datain,coords,mode=mode,cval=cval,order=order)
else:
# nearest neighbor interpolation if order=0.
# uses index arrays, so first convert to numarray.
datatmp = N.array(datain,datain.typecode())
xi = N.around(xcoords).astype('i')
yi = N.around(ycoords).astype('i')
return datatmp[yi,xi]
def interp(datain,
lonsin,
latsin,
lonsout,
latsout,
checkbounds=False,
mode='nearest',
cval=0.0,
order=3):
"""
dataout = interp(datain,lonsin,latsin,lonsout,latsout,mode='constant',cval=0.0,order=3)
interpolate data (datain) on a rectilinear lat/lon grid (with lons=lonsin
lats=latsin) to a grid with lons=lonsout, lats=latsout.
datain is a rank-2 array with 1st dimension corresponding to longitude,
2nd dimension latitude.
lonsin, latsin are rank-1 Numeric arrays containing longitudes and latitudes
of datain grid in increasing order (i.e. from Greenwich meridian eastward, and
South Pole northward)
lonsout, latsout are rank-2 Numeric arrays containing lons and lats out desired
output grid (typically a native map projection grid).
If checkbounds=True, values of lonsout and latsout are checked to see that
they lie within the range specified by lonsin and latsing. Default is
False, and values outside the borders are handled in the manner described
by the 'mode' parameter (default mode='nearest', which means the nearest
boundary value is used). See section 20.2 of the numarray docs for
information on the 'mode' keyword.
See numarray.nd_image.map_coordinates documentation for information on
the other optional keyword parameters. The order keyword can be 0
for nearest neighbor interpolation (nd_image only allows 1-6) - if
order=0 bounds checking is done even if checkbounds=False.
"""
# lonsin and latsin must be monotonically increasing.
if lonsin[-1] - lonsin[0] < 0 or latsin[-1] - latsin[0] < 0:
raise ValueError, 'lonsin and latsin must be increasing!'
# optionally, check that lonsout,latsout are
# within region defined by lonsin,latsin.
# (this check is always done if nearest neighbor
# interpolation (order=0) requested).
if checkbounds or order == 0:
if min(N.ravel(lonsout)) < min(lonsin) or \
max(N.ravel(lonsout)) > max(lonsin) or \
min(N.ravel(latsout)) < min(latsin) or \
max(N.ravel(latsout)) > max(latsin):
raise ValueError, 'latsout or lonsout outside range of latsin or lonsin'
# compute grid coordinates of output grid.
delon = lonsin[1:] - lonsin[0:-1]
delat = latsin[1:] - latsin[0:-1]
if max(delat) - min(delat) < 1.e-4 and max(delon) - min(delon) < 1.e-4:
# regular input grid.
xcoords = (len(lonsin) - 1) * (lonsout - lonsin[0]) / (lonsin[-1] -
lonsin[0])
ycoords = (len(latsin) - 1) * (latsout - latsin[0]) / (latsin[-1] -
latsin[0])
else:
# irregular (but still rectilinear) input grid.
lonsoutflat = N.ravel(lonsout)
latsoutflat = N.ravel(latsout)
ix = N.searchsorted(lonsin, lonsoutflat) - 1
iy = N.searchsorted(latsin, latsoutflat) - 1
xcoords = N.zeros(ix.shape, 'f')
ycoords = N.zeros(iy.shape, 'f')
for n, i in enumerate(ix):
if i < 0:
xcoords[n] = -1 # outside of range on lonsin (lower end)
elif i >= len(lonsin) - 1:
xcoords[n] = len(lonsin) # outside range on upper end.
else:
xcoords[n] = float(i) + (lonsoutflat[n] - lonsin[i]) / (
lonsin[i + 1] - lonsin[i])
xcoords = N.reshape(xcoords, lonsout.shape)
for m, j in enumerate(iy):
if j < 0:
ycoords[m] = -1 # outside of range of latsin (on lower end)
elif j >= len(latsin) - 1:
ycoords[m] = len(latsin) # outside range on upper end
else:
ycoords[m] = float(j) + (latsoutflat[m] - latsin[j]) / (
latsin[j + 1] - latsin[j])
ycoords = N.reshape(ycoords, latsout.shape)
coords = [ycoords, xcoords]
# interpolate to output grid using numarray.nd_image spline filter.
if order:
return nd_image.map_coordinates(datain,
coords,
mode=mode,
cval=cval,
order=order)
else:
# nearest neighbor interpolation if order=0.
# uses index arrays, so first convert to numarray.
datatmp = N.array(datain, datain.typecode())
xi = N.around(xcoords).astype('i')
yi = N.around(ycoords).astype('i')
return datatmp[yi, xi]
