Example 5 - stripy smoothing operations

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.

Here we demonstrate how to access SRFPACK smoothing through the stripy interface.

Define a computational mesh

Use the (usual) icosahedron with face points included.

import stripy as stripy

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

spacingX = 0.2
spacingY = 0.2

mesh = stripy.cartesian_meshes.elliptical_mesh(extent, spacingX, spacingY, refinement_levels=1)
mesh = stripy.Triangulation(mesh.x, mesh.y, permute=True)

Analytic function with noise and short wavelengths

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

import numpy as np

def analytic(xs, ys, k1, k2):
     return  np.cos(k1*xs) * np.sin(k2*ys) 
def analytic_noisy(xs, ys, k1, k2, noise, short):
     return  np.cos(k1*xs) * np.sin(k2*ys) + \
             short * (np.cos(k1*5.0*xs) * np.sin(k2*5.0*ys)) + \
             noise * np.random.random(xs.shape)

# def analytic_ddlon(xs, ys, k1, k2):
#      return -k1 * np.sin(k1*xs) * np.sin(k2*ys) / np.cos(ys)

# def analytic_ddlat(xs, ys, k1, k2):
#      return k2 * np.cos(k1*xs) * np.cos(k2*ys) 

analytic_sol   = analytic(mesh.x, mesh.y, 0.1, 1.0)
analytic_sol_n = analytic_noisy(mesh.x, mesh.y, 0.1, 1.0, 0.2, 0.0)
%matplotlib inline
import matplotlib.pyplot as plt

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


    x0 = mesh.x
    y0 = mesh.y
    trip = ax.tripcolor(x0, y0, mesh.simplices, field, cmap=plt.cm.RdBu)
    fig.colorbar(trip, ax=ax)

fig = plt.figure(figsize=(10, 8), facecolor="none")
ax = fig.add_subplot(111)
axis_mesh_field(fig, ax, mesh, analytic_sol-analytic_sol_n, "noisy solution")

Smoothing operations

The Triangulation.smoothing method directly wraps the SRFPACK smoother that smooths a surface f described by values on the mesh vertices to find a new surface f’ (also described on the mesh vertices) by choosing nodal function values and gradients to minimize the linearized curvature of F subject to a bound on the deviation from the data values.

stripy_smoothed,  dds, err = mesh.smoothing(analytic_sol_n, np.ones_like(analytic_sol_n), 10.0, 0.01, 0.01)
stripy_smoothed2, dds2, err = mesh.smoothing(analytic_sol_n, np.ones_like(analytic_sol_n), 1.0, 0.1, 0.01)
stripy_smoothed3, dds3, err = mesh.smoothing(analytic_sol_n, np.ones_like(analytic_sol_n), 20.0, 0.1, 0.01)

delta_n  = analytic_sol_n - stripy_smoothed
delta_ns = analytic_sol   - stripy_smoothed

delta_n2  = analytic_sol_n - stripy_smoothed2
delta_ns2 = analytic_sol   - stripy_smoothed2

delta_n3  = analytic_sol_n - stripy_smoothed3
delta_ns3 = analytic_sol   - stripy_smoothed3
stripy_smoothed, dds

Results of smoothing with different value of sm

fig, ax = plt.subplots(2,2, figsize=(12, 10), facecolor="none")

axis_mesh_field(fig, ax[0,0], mesh, analytic_sol_n, label="original")
axis_mesh_field(fig, ax[0,1], mesh, stripy_smoothed, label="smoothed1")
axis_mesh_field(fig, ax[1,0], mesh, stripy_smoothed2, label="smoothed2")
axis_mesh_field(fig, ax[1,1], mesh, stripy_smoothed3, label="smoothed3")


The next example is Ex6-Scattered-Data

fig, ax = plt.subplots(2,2, figsize=(12,10), facecolor="none")

axis_mesh_field(fig, ax[0,0], mesh, analytic_sol_n, label="original")
axis_mesh_field(fig, ax[0,1], mesh, dds[1], label="smoothed1")
axis_mesh_field(fig, ax[1,0], mesh, dds2[1], label="smoothed2")
axis_mesh_field(fig, ax[1,1], mesh, dds3[1], label="smoothed3")


The next notebook is Ex6-Scattered-Data