def interpolate_g(xi, yi, zi, xx, yy, knots=10, error=False, mask=None):
"""Create a grid of zi values interpolating the values from xi,yi,zi
**ARGUMENTS**
========= ==================================================================
xi,yi,zi 1D Lists or arrays containing the values to use as base for the
interpolation
xx,yy 1D vectors or lists containing the output coordinates
samples tuple containing the shape of the output array.
knots number of knots to be used in each direction
error if set to true, half of the points (x, y, z) are used to create
the interpolation, and half are used to evaluate the interpolation
error
========= ==================================================================
"""
xi = array(xi)
yi = array(yi)
zi = array(zi)
#print xi
#print yi
#print zi
assert xi.ndim == 1, "xi must ba a 1D array or list"
assert yi.ndim == 1, "yi must ba a 1D array or list"
assert zi.ndim == 1, "zi must ba a 1D array or list"
assert xx.ndim == 1, "xx must ba a 1D array or list"
assert yy.ndim == 1, "yy must ba a 1D array or list"
assert len(xi) == len(yi) and len(xi) == len(
zi), "xi, yi, zi must have the same number of items"
if error == True:
# Create a list of indexes to be able to select the points that are going
# to be used as spline generators, and as control points
idx = where(arange(len(xi)) % 2 == 0, False, True)
# Use only half of the samples to create the Spline,
if error == True:
isp = argwhere(idx == True)
ich = argwhere(idx == False)
xsp = xi[isp]
ysp = yi[isp]
zsp = zi[isp]
xch = xi[ich]
ych = yi[ich]
zch = zi[ich]
else:
xsp = xi
ysp = yi
zsp = zi
#Distribute homogeneously the knots
xk = linspace(xsp.min(), xsp.max(), knots)
yk = linspace(ysp.min(), ysp.max(), knots)
# LSQBivariateSpline using some knots gives smaller error than
# SmoothBivariateSpline
di = interpolate.LSQBivariateSpline(xsp, ysp, zsp, xk[1:-1], yk[1:-1])
#print xsp,ysp,zsp
#di=interpolate.SmoothBivariateSpline(xsp, ysp, zsp)
# Evaluate error
if error == True:
zch1 = di.ev(xch, ych)
er = (zch.flatten() - zch1).std()
if mask == None:
#d=griddata(xi, yi, zi, xx, yy) #
d = di(xx, yy).transpose()
else:
d = ma_array(di(xx, yy).transpose(), mask=mask)
if error == True: return d, er
else: return d
def interpolate_g(xi,yi,zi,xx,yy,knots=10, error=False,mask=None):
"""Create a grid of zi values interpolating the values from xi,yi,zi
xi,yi,zi 1D Lists or arrays containing the values to use as base for the interpolation
xx,yy 1D vectors or lists containing the output coordinates
samples tuple containing the shape of the output array.
knots number of knots to be used in each direction
error if set to true, half of the points (x, y, z) are used to create
the interpolation, and half are used to evaluate the interpolation error
"""
xi=array(xi)
yi=array(yi)
zi=array(zi)
#print xi
#print yi
#print zi
assert xi.ndim==1 ,"xi must ba a 1D array or list"
assert yi.ndim==1 ,"yi must ba a 1D array or list"
assert zi.ndim==1 ,"zi must ba a 1D array or list"
assert xx.ndim==1 ,"xx must ba a 1D array or list"
assert yy.ndim==1 ,"yy must ba a 1D array or list"
assert len(xi)==len(yi) and len(xi)==len(zi), "xi, yi, zi must have the same number of items"
if error==True:
# Create a list of indexes to be able to select the points that are going
# to be used as spline generators, and as control points
idx=where(arange(len(xi)) %2 ==0, False, True)
# Use only half of the samples to create the Spline,
if error == True:
isp=argwhere(idx==True)
ich=argwhere(idx==False)
xsp=xi[isp]
ysp=yi[isp]
zsp=zi[isp]
xch=xi[ich]
ych=yi[ich]
zch=zi[ich]
else:
xsp=xi
ysp=yi
zsp=zi
#Distribute homogeneously the knots
xk=linspace(xsp.min(), xsp.max(),knots)
yk=linspace(ysp.min(), ysp.max(),knots)
# LSQBivariateSpline using some knots gives smaller error than
# SmoothBivariateSpline
di=interpolate.LSQBivariateSpline(xsp, ysp, zsp, xk[1:-1], yk[1:-1])
#print xsp,ysp,zsp
#di=interpolate.SmoothBivariateSpline(xsp, ysp, zsp)
# Evaluate error
if error==True:
zch1=di.ev(xch, ych)
er=(zch.flatten()-zch1).std()
if mask==None:
#d=griddata(xi, yi, zi, xx, yy) #
d=di(xx,yy).transpose()
else:
d=ma_array(di(xx,yy).transpose(), mask=mask)
if error==True: return d, er
else: return d