def __init__(self, x, y, z, bbox=[None] * 4, kx=3, ky=3, s=0):
x, y = ravel(x), ravel(y)
if not all(diff(x) > 0.0):
raise TypeError('x must be strictly increasing')
if not all(diff(y) > 0.0):
raise TypeError('y must be strictly increasing')
if not ((x.min() == x[0]) and (x.max() == x[-1])):
raise TypeError('x must be strictly ascending')
if not ((y.min() == y[0]) and (y.max() == y[-1])):
raise TypeError('y must be strictly ascending')
if not x.size == z.shape[0]:
raise TypeError('x dimension of z must have same number of '
'elements as x')
if not y.size == z.shape[1]:
raise TypeError('y dimension of z must have same number of '
'elements as y')
z = ravel(z)
xb, xe, yb, ye = bbox
nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(
x, y, z, xb, xe, yb, ye, kx, ky, s)
if not ier in [0, -1, -2]:
msg = _surfit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(msg)
self.fp = fp
self.tck = tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)]
self.degrees = kx, ky
def __init__(self, x, y, z, bbox = [None]*4, kx=3, ky=3, s=0):
x,y = ravel(x),ravel(y)
if not all(diff(x) > 0.0):
raise TypeError,'x must be strictly increasing'
if not all(diff(y) > 0.0):
raise TypeError,'y must be strictly increasing'
if not ((x.min() == x[0]) and (x.max() == x[-1])):
raise TypeError, 'x must be strictly ascending'
if not ((y.min() == y[0]) and (y.max() == y[-1])):
raise TypeError, 'y must be strictly ascending'
if not x.size == z.shape[0]:
raise TypeError,\
'x dimension of z must have same number of elements as x'
if not y.size == z.shape[1]:
raise TypeError,\
'y dimension of z must have same number of elements as y'
z = ravel(z)
xb,xe,yb,ye = bbox
nx,tx,ny,ty,c,fp,ier = dfitpack.regrid_smth(x,y,z,
xb,xe,yb,ye,
kx,ky,s)
if ier in [0,-1,-2]: # normal return
pass
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx],ty[:ny],c[:(nx-kx-1)*(ny-ky-1)]
self.degrees = kx,ky
def __init__(self,
x,
y,
z,
kind='linear',
copy=True,
bounds_error=False,
fill_value=np.nan):
self.x, self.y, self.z = map(asarray, [x, y, z])
self.x, self.y = map(ravel, [self.x, self.y])
if self.z.size == len(self.x) * len(self.y):
rectangular_grid = True
if not all(self.x[1:] > self.x[:-1]):
j = np.argsort(self.x)
self.x = self.x[j]
self.z = self.z[:, j]
if not all(self.y[1:] > self.y[:-1]):
j = np.argsort(self.y)
self.y = self.y[j]
self.z = self.z[j, :]
self.z = ravel(self.z.T)
else:
rectangular_grid = False
self.z = ravel(self.z)
if len(self.x) != len(self.y):
raise ValueError(
"x and y must have equal lengths for non rectangular grid")
if len(self.z) != len(self.x):
raise ValueError(
"Invalid length for input z for non rectangular grid")
try:
kx = ky = {'linear': 1, 'cubic': 3, 'quintic': 5}[kind]
except KeyError:
raise ValueError("Unsupported interpolation type.")
if not rectangular_grid:
# TODO: surfit is really not meant for interpolation!
self.tck = fitpack.bisplrep(self.x,
self.y,
self.z,
kx=kx,
ky=ky,
s=0.0)
else:
nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(self.x,
self.y,
self.z,
None,
None,
None,
None,
kx=kx,
ky=ky,
s=0.0)
self.tck = (tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)],
kx, ky)
def __init__(self, x, y, z,
bbox = [None]*4, kx=3, ky=3, s=0):
"""
Input:
x,y - 1-d sequences of coordinates in strictly ascending order
z - 2-d array of data with shape (x.size,y.size)
Optional input:
bbox - 4-sequence specifying the boundary of
the rectangular approximation domain.
By default, bbox=[min(x,tx),max(x,tx),
min(y,ty),max(y,ty)]
kx,ky=3,3 - degrees of the bivariate spline.
s - positive smoothing factor defined for
estimation condition:
sum((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) <= s
Default s=0 which is for interpolation
"""
x,y = ravel(x),ravel(y)
if not all(diff(x) > 0.0):
raise TypeError,'x must be strictly increasing'
if not all(diff(y) > 0.0):
raise TypeError,'y must be strictly increasing'
if not ((x.min() == x[0]) and (x.max() == x[-1])):
raise TypeError, 'x must be strictly ascending'
if not ((y.min() == y[0]) and (y.max() == y[-1])):
raise TypeError, 'y must be strictly ascending'
if not x.size == z.shape[0]:
raise TypeError,\
'x dimension of z must have same number of elements as x'
if not y.size == z.shape[1]:
raise TypeError,\
'y dimension of z must have same number of elements as y'
z = ravel(z)
xb,xe,yb,ye = bbox
nx,tx,ny,ty,c,fp,ier = dfitpack.regrid_smth(x,y,z,
xb,xe,yb,ye,
kx,ky,s)
if ier in [0,-1,-2]: # normal return
pass
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx],ty[:ny],c[:(nx-kx-1)*(ny-ky-1)]
self.degrees = kx,ky
def __init__(self, x, y, z,
bbox = [None]*4, kx=3, ky=3, s=0):
"""
Input:
x,y - 1-d sequences of coordinates in strictly ascending order
z - 2-d array of data with shape (x.size,y.size)
Optional input:
bbox - 4-sequence specifying the boundary of
the rectangular approximation domain.
By default, bbox=[min(x,tx),max(x,tx),
min(y,ty),max(y,ty)]
kx,ky=3,3 - degrees of the bivariate spline.
s - positive smoothing factor defined for
estimation condition:
sum((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) <= s
Default s=0 which is for interpolation
"""
x,y = ravel(x),ravel(y)
if not all(diff(x) > 0.0):
raise TypeError,'x must be strictly increasing'
if not all(diff(y) > 0.0):
raise TypeError,'y must be strictly increasing'
if not ((x.min() == x[0]) and (x.max() == x[-1])):
raise TypeError, 'x must be strictly ascending'
if not ((y.min() == y[0]) and (y.max() == y[-1])):
raise TypeError, 'y must be strictly ascending'
if not x.size == z.shape[0]:
raise TypeError,\
'x dimension of z must have same number of elements as x'
if not y.size == z.shape[1]:
raise TypeError,\
'y dimension of z must have same number of elements as y'
z = ravel(z)
xb,xe,yb,ye = bbox
nx,tx,ny,ty,c,fp,ier = dfitpack.regrid_smth(x,y,z,
xb,xe,yb,ye,
kx,ky,s)
if ier in [0,-1,-2]: # normal return
pass
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx],ty[:ny],c[:(nx-kx-1)*(ny-ky-1)]
self.degrees = kx,ky
def __init__(self, x, y, z, kind='linear', copy=True, bounds_error=False,
fill_value=np.nan):
self.x, self.y, self.z = map(asarray, [x, y, z])
self.x, self.y = map(ravel, [self.x, self.y])
if self.z.size == len(self.x) * len(self.y):
rectangular_grid = True
if not all(self.x[1:] > self.x[:-1]):
j = np.argsort(self.x)
self.x = self.x[j]
self.z = self.z[:,j]
if not all(self.y[1:] > self.y[:-1]):
j = np.argsort(self.y)
self.y = self.y[j]
self.z = self.z[j,:]
self.z = ravel(self.z.T)
else:
rectangular_grid = False
self.z = ravel(self.z)
if len(self.x) != len(self.y):
raise ValueError(
"x and y must have equal lengths for non rectangular grid")
if len(self.z) != len(self.x):
raise ValueError(
"Invalid length for input z for non rectangular grid")
try:
kx = ky = {'linear' : 1,
'cubic' : 3,
'quintic' : 5}[kind]
except KeyError:
raise ValueError("Unsupported interpolation type.")
if not rectangular_grid:
# TODO: surfit is really not meant for interpolation!
self.tck = fitpack.bisplrep(self.x, self.y, self.z,
kx=kx, ky=ky, s=0.0)
else:
nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(
self.x, self.y, self.z, None, None, None, None,
kx=kx, ky=ky, s=0.0)
self.tck = (tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)],
kx, ky)