def __init__(self, x, y, z, w=None, bbox=[None] * 4, kx=3, ky=3, s=None, eps=None): xb, xe, yb, ye = bbox nx, tx, ny, ty, c, fp, wrk1, ier = dfitpack.surfit_smth(x, y, z, w, xb, xe, yb, ye, kx, ky, s=s, eps=eps, lwrk2=1) 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, w=None, bbox=[None] * 4, kx=3, ky=3, s=None, eps=None): """ Input: x,y,z - 1-d sequences of data points (order is not important) Optional input: w - positive 1-d sequence of weights 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=len(w) which should be a good value if 1/w[i] is an estimate of the standard deviation of z[i]. eps - a threshold for determining the effective rank of an over-determined linear system of equations. 0 < eps < 1, default is 1e-16. """ xb, xe, yb, ye = bbox nx, tx, ny, ty, c, fp, wrk1, ier = dfitpack.surfit_smth( x, y, z, w, xb, xe, yb, ye, kx, ky, s=s, eps=eps, lwrk2=1 ) 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, w=None, bbox=[None] * 4, kx=3, ky=3, s=None, eps=None): """ Input: x,y,z - 1-d sequences of data points (order is not important) Optional input: w - positive 1-d sequence of weights 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=len(w) which should be a good value if 1/w[i] is an estimate of the standard deviation of z[i]. eps - a threshold for determining the effective rank of an over-determined linear system of equations. 0 < eps < 1, default is 1e-16. """ xb, xe, yb, ye = bbox nx, tx, ny, ty, c, fp, wrk1, ier = dfitpack.surfit_smth(x, y, z, w, xb, xe, yb, ye, kx, ky, s=s, eps=eps, lwrk2=1) 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, w=None, bbox=[None] * 4, kx=3, ky=3, s=None, eps=None): xb, xe, yb, ye = bbox nx, tx, ny, ty, c, fp, wrk1, ier = dfitpack.surfit_smth( x, y, z, w, xb, xe, yb, ye, kx, ky, s=s, eps=eps, lwrk2=1 ) 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