Example 3 - `stripy` interpolation

SRFPACK is a Fortran 77 software package that constructs a smooth interpolatory or approximating surface to data values associated with arbitrarily distributed points. It employs automatically selected tension factors to preserve shape properties of the data and avoid overshoot and undershoot associated with steep gradients.

The next three examples demonstrate the interface to SRFPACK provided through stripy

The next example is Ex4-Gradients

Define two different meshes

Create a fine and a coarse mesh without common points



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import stripy as stripy

xmin = 0.0
xmax = 10.0
ymin = 0.0
ymax = 10.0
extent = [xmin, xmax, ymin, ymax]

spacingX = 1.0
spacingY = 1.0

cmesh = stripy.cartesian_meshes.elliptical_mesh(extent, spacingX, spacingY, refinement_levels=1)
fmesh = stripy.cartesian_meshes.elliptical_mesh(extent, spacingX, spacingY, refinement_levels=3)

print("coarse mesh points = {}".format(cmesh.npoints))
print("fine mesh points   = {}".format(fmesh.npoints))



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help(cmesh.interpolate)



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%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np



def mesh_fig(mesh, meshR, name):

    fig = plt.figure(figsize=(10, 10), facecolor="none")
    ax  = plt.subplot(111)
    ax.axis('off')

    generator = mesh
    refined   = meshR

    x0 = generator.x
    y0 = generator.y

    xR = refined.x
    yR = refined.y
    

    ax.scatter(x0, y0, color="Red", marker="o", s=150.0)
    ax.scatter(xR, yR, color="DarkBlue", marker="o", s=50.0)
    
    ax.triplot(xR, yR, refined.simplices, color="black", linewidth=0.5)

    fig.savefig(name, dpi=250, transparent=True)
    
    return

mesh_fig(cmesh,  fmesh, "Two grids" )

Analytic function

Define a relatively smooth function that we can interpolate from the coarse mesh to the fine mesh and analyse



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def analytic(xs, ys, k1, k2):
     return np.cos(k1*xs) * np.sin(k2*ys)

coarse_afn = analytic(cmesh.x, cmesh.y, 0.1, 1.0)
fine_afn   = analytic(fmesh.x, fmesh.y, 0.1, 1.0)

The analytic function on the different samplings

It is helpful to be able to view a mesh to verify that it is an appropriate choice. Here, for example, we visualise the analytic function on the elliptical mesh.



In [ ]:

    
def mesh_field_fig(mesh, field, name):

    fig = plt.figure(figsize=(10, 10), facecolor="none")
    ax  = plt.subplot(111)
    ax.axis('off')
    
    ax.tripcolor(mesh.x, mesh.y, mesh.simplices, field)

    fig.savefig(name, dpi=250, transparent=True)
    
    return

mesh_field_fig(cmesh, coarse_afn, "coarse analytic")
mesh_field_fig(fmesh, fine_afn, "fine analytic")

Interpolation from coarse to fine

The interpolate method of the Triangulation takes arrays of x, y points and an array of data on the mesh vertices. It returns an array of interpolated values and a status array that states whether each value represents an interpolation, extrapolation or neither (an error condition).

The interpolation can be:

nearest-neighbour (order=0)
linear (order=1)
cubic spline (order=3)



In [ ]:

    
interp_c2f1, err = cmesh.interpolate(fmesh.x, fmesh.y, order=1, zdata=coarse_afn)
interp_c2f3, err = cmesh.interpolate(fmesh.x, fmesh.y, order=3, zdata=coarse_afn)

err_c2f1 = interp_c2f1-fine_afn
err_c2f3 = interp_c2f3-fine_afn



In [ ]:

    
def axis_mesh_field(ax, mesh, field, label):

    ax.axis('off')

    x0 = mesh.x
    y0 = mesh.y
    
    im = ax.tripcolor(x0, y0, mesh.simplices, field)
    ax.set_title(str(label))
    fig.colorbar(im, ax=ax)
    return

    
fig, ax = plt.subplots(2,2, figsize=(10,8))

axis_mesh_field(ax[0,0], fmesh, interp_c2f1, "interp1")
axis_mesh_field(ax[0,1], fmesh, interp_c2f3, "interp3")
axis_mesh_field(ax[1,0], fmesh, err_c2f1, "interp_err1")
axis_mesh_field(ax[1,1], fmesh, err_c2f3, "interp_err3")