def spline_points(cls, X, Y, Z, xi, yi):
"""Interpolates for given set of points"""
# SBS requires at least m=(kx+1)*(ky+1) points,
# where kx=ky=3 (default) is the degree of bivariate spline.
# Thus, if less than 16=(3+1)*(3+1) points, adjust kx & ky.
spline = SBS(X, Y, Z)
return spline(xi, yi)
#!/usr/bin/env python3 #-*- coding:utf-8 -*- __author__ = 'tongzi' import numpy as np from scipy.interpolate import SmoothBivariateSpline as SBS import matplotlib.pyplot as plt #定义一个波纹函数 ripple = lambda x, y: np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2) #采样数据，用于插值 xy = np.random.rand(1000, 2) x, y = xy[:, 0], xy[:, 1] sample = ripple(xy[:, 0] * 5, xy[:, 1] * 5) #插值 fit = SBS(x * 5, y * 5, sample, s=0.01, kx=4, ky=4) interp = fit(np.linspace(0, 5, 1000), np.linspace(0, 5, 1000)) plt.imshow(interp) plt.show()
import matplotlib.pyplot as plt
from scipy.interpolate import SmoothBivariateSpline as SBS
# Defining a function
ripple = lambda x, y: np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)
# Generarting gridded data
grid_x, grid_y = np.mgrid[0:5:1000j, 0:5:1000j]
# Generating saplte that interpolation function will see
xy = np.random.rand(1000, 2)
x, y = xy[:, 0], xy[:, 1]
saplte = ripple(xy[:, 0] * 5, xy[:, 1] * 5)
# Interpolating data
fit = SBS(x * 5, y * 5, saplte, s=0, kx=4, ky=4)
interp = fit(np.linspace(0, 5, 1000), np.linspace(0, 5, 1000))
# Making figure.
fig = plt.figure(figsize=(8, 4))
x0, x1 = 0, 1000
y0, y1 = 0, 1000
ax1 = fig.add_subplot(121)
ax1.imshow(ripple(grid_x, grid_y).T, cmap=plt.cm.PRGn, interpolation='nearest')
ax1.scatter(xy[:, 0] * 1e3,
xy[:, 1] * 1e3,
facecolor='black',
edgecolor='none',
s=1)
ax1.xaxis.set_visible(False)
# Interpolate onto our grid
print(
("Starting ST4 interpolation for init time {0}".format(date)))
if method1:
ST4data_SALgrid = griddata(
(ST4_SALgrid_xx.flat, ST4_SALgrid_yy.flat),
ST4data_ST4grid.flat,
(SAL_native_mx.flat, SAL_native_my.flat),
method='nearest').reshape(308, 537)
elif method2:
ST4rbs = RBS(ST4_SALgrid_xx, ST4_SALgrid_yy, ST4data_ST4grid)
ST4data_SALgrid = N.swapaxes(
ST4rbs(SAL_native_xx, SAL_native_yy), 1, 0)
elif method3:
ST4_F = SBS(ST4_SALgrid_xx.flatten(), ST4_SALgrid_yy.flatten(),
ST4data_ST4grid.flatten())
ST4data_SALgrid = ST4_F(SAL_native_xx, SAL_native_yy)
print("Done.")
# import pdb; pdb.set_trace()
obplotproj = False
# TEST
if obplotproj:
SALmap.drawcoastlines()
SALmap.drawstates()
data2 = data_SALgrid.reshape(308, 537)
SALmap.contourf(SAL_native_mx,
SAL_native_my,
data2,
levels=N.arange(5, 75, 5))
plt.savefig(