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Methods defined here:
- __init__(self, x1, bcs1=None)
- Constructor.
x1: original grid array
bcs1: boundary conditions. Use bcs1=1 to apply
periodic boundary conditions (bcs1 defaults to None for
not-a-knot boundary conditions, this should be fine in most cases.
More general boundary conditions can be applied by setting
bcs1=(bmin, bmax)
where bmin/bmax can take values from 0 to 7, as described in
http://w3.pppl.gov/NTCC/PSPLINE/.
The boundary conditions (if inhomogeneous) must then be applied
by setting the class members
self.bcval1min and/or self.bcval1max
explicitly *prior* to calling setup(f).
1 -- match slope
2 -- match 2nd derivative
3 -- boundary condition is slope=0
4 -- boundary condition is d2f/dx2=0
5 -- match 1st derivative to 1st divided difference
6 -- match 2nd derivative to 2nd divided difference
7 -- match 3rd derivative to 3rd divided difference
For example, if one wishes to apply df/dx = a on the left and
d^2f/dx^2 = b
on the right of x1, use
bcs1=(1, 2)
and set both
self.bcval1min = a
and
self.bcval1max = b
The returned value is a spline object.
- derivative(self, i1, p1, meth=None)
- Compute the derivative d^i1 f/dx1^i1 at p1. Must have
i1>=0 and i1<=2. See interp method for a list of possible p1 shapes.
The meth argument has no effect, its purpose is to provide compatibility
with higher order spline methods.
With checks enabled.
- derivative_array(self, i1, p1)
- Compute the derivative d^i1 f/dx1^i1 for a grid-array p1. Must have
i1>=0 and i1<=2. Same as derivative_cloud in 1-D.
Return the interpolated function, an error flag (=0 if ok) and a warning flag (=0 if ok).
- derivative_cloud(self, i1, p1)
- Compute the derivative d^i1 f/dx1^i1 for a cloud p1. Must have
i1>=0 and i1<=2.
Return the interpolated function, an error flag (=0 if ok) and a warning flag (=0 if ok).
- derivative_point(self, i1, p1)
- Compute a single point derivative d^i1 f/dx1^i1 at p1. Must have
i1>=0 and i1<=2.
Return the interpolated function, an error flag (=0 if ok) and a warning flag (=0 if ok).
- interp(self, p1, meth=None)
- Interpolatate onto p1, the coordinate which can either be a single point
(point interpolation) or an array (cloud/array interpolation).
The returned value is a single float for point interpolation,
it is a rank-1 array of length len(p1) for cloud/array interpolation.
The meth argument has no effect, its purpose is to provide compatibility
with higher order spline methods.
With checks enabled.
- interp_array(self, p1)
- Array interpolation for all p1[i1], i1=0:len( p1 ).
In 1-D, this is the same as interp_cloud.
Return the interpolated function, an error flag (=0 if ok) and a warning flag (=0 if ok).
- interp_cloud(self, p1)
- Cloud interpolation for all p1[:].
Return the interpolated function, an error flag (=0 if ok) and a warning flag (=0 if ok).
- interp_point(self, p1)
- Point interpolation at p1.
Return the interpolated function, an error flag (=0 if ok) and a warning flag (=0 if ok).
- load(self, filename)
- Save state in NetCDF file
- save(self, filename)
- Save state in NetCDF file
- setup(self, f)
- Set up (compute) cubic spline coefficients.
See __init__ for comment about boundary conditions.
Input is f[ix], a rank-1 array for the function values.
Data and non-method functions defined here:
- __doc__ = None
- __module__ = 'pspline1_r8'
- str(object) -> string
Return a nice string representation of the object.
If the argument is a string, the return value is the same object.
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