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#!/usr/bin/env python
# Created by Pearu Peterson, June 2003
# Modified by John Travers, October 2006
""" Test functions for spline.spline module
"""
__usage__ = """
Build spline:
python setup_spline.py build
Run tests if scipy is installed:
python -c 'import scipy;scipy.spline.test(<level>)'
Run tests if spline is not installed:
python tests/test_spline.py [<level>]
"""
import sys
from numpy.testing import *
from numpy import array, arange, around, pi, sin, cos
set_package_path()
from spline.spline import UnivariateSpline,LSQUnivariateSpline,\
InterpolatedUnivariateSpline
from spline.spline import LSQBivariateSpline, SmoothBivariateSpline,\
RectBivariateSpline
restore_path()
set_local_path()
from dierckx_test_data import *
restore_path()
class test_UnivariateSpline(NumpyTestCase):
def check_linear_constant(self):
x = [1,2,3]
y = [3,3,3]
lut = UnivariateSpline(x,y,k=1)
assert_array_almost_equal(lut.get_knots(),[1,3])
assert_array_almost_equal(lut.get_coeffs(),[3,3])
assert_almost_equal(lut.get_residual(),0.0)
assert_array_almost_equal(lut([1,1.5,2]),[3,3,3])
def check_linear_1d(self):
x = [1,2,3]
y = [0,2,4]
lut = UnivariateSpline(x,y,k=1)
assert_array_almost_equal(lut.get_knots(),[1,3])
assert_array_almost_equal(lut.get_coeffs(),[0,4])
assert_almost_equal(lut.get_residual(),0.0)
assert_array_almost_equal(lut([1,1.5,2]),[0,1,2])
def check_curfit_against_dierckx(self):
""" Test against results obtined from the pure fortran routines.
Here we check simple spline creation and evaluation.
"""
x,y = curfit_test['x'],curfit_test['y']
k,s = curfit_test_smth['k'],curfit_test_smth['s']
iopt = curfit_test_smth['iopt']
for i in range(len(k)):
if iopt[i] == 0:
uspl = UnivariateSpline(x,y,k=k[i],s=s[i])
elif iopt[i] == 1:
uspl.set_smoothing_factor(s[i])
assert_almost_equal(uspl.get_residual(),
curfit_test_smth['fp'][i], decimal=2)
n = uspl._data[7]
assert_equal(n,curfit_test_smth['n'][i])
assert_array_almost_equal(around(uspl.get_knots(),1),
curfit_test_smth['t'][i][k[i]:n-k[i]])
assert_array_almost_equal(around(uspl.get_coeffs(),4),
curfit_test_smth['c'][i], decimal=3)
assert_array_almost_equal(around(uspl(x),1),
curfit_test_smth['sp'][i])
def check_spint_spalde(self):
per = [0, 0, 0]
N = [20, 20, 50]
ia = [0, 0, 0.2*pi]
ib = [0, 0, pi]
a,b = 0,2*pi
dx = 0.2*pi
k = range(1,6)
for i in range(len(per)):
x=a+(b-a)*arange(N[i]+1,dtype=float)/float(N[i])
v=f1(x)
for j in range(len(k)):
uspl = UnivariateSpline(x,v,k=k[j],s=0)
ir = uspl.integral(ia[i],ib[i])
dr = uspl.derivatives(dx)
assert_almost_equal(ir, f1(ib[i],-1)-f1(ia[i],-1), decimal=2)
d=0
for ddr in dr:
if d<k[j]-1:
assert_almost_equal(1, ddr/f1(dx,d), decimal=2)
d=d+1
def check_sproot(self):
a=0
b=15
N=20
x=a+(b-a)*arange(N+1,dtype=float)/float(N)
v=f1(x)
k=3
uspl = UnivariateSpline(x,v,k=k,s=0)
ex = array([0.0, pi, 2.0*pi, 3.0*pi, 4.0*pi])
assert_array_almost_equal(uspl.roots(),ex, decimal=3)
class test_LSQUnivariateSpline(NumpyTestCase):
def check_curfit_against_dierckx(self):
""" Test against results obtined from the pure fortran routines.
Here we check simple spline creation and evaluation.
"""
x,y = curfit_test['x'],curfit_test['y']
k = curfit_test_lsq['k']
for i in range(len(k)):
t = curfit_test_lsq['t'][i]
lsquspl = LSQUnivariateSpline(x,y,t,k=k[i])
assert_almost_equal(lsquspl.get_residual(),
curfit_test_lsq['fp'][i], decimal=2)
assert_array_almost_equal(around(lsquspl.get_coeffs(),4),
curfit_test_lsq['c'][i], decimal=3)
assert_array_almost_equal(around(lsquspl(x),1),
curfit_test_lsq['sp'][i])
class test_LSQBivariateSpline(NumpyTestCase):
def check_linear_constant(self):
x = [1,1,1,2,2,2,3,3,3]
y = [1,2,3,1,2,3,1,2,3]
z = [3,3,3,3,3,3,3,3,3]
s = 0.1
tx = [1+s,3-s]
ty = [1+s,3-s]
lut = LSQBivariateSpline(x,y,z,tx,ty,kx=1,ky=1)
#print lut.get_knots()
#print lut.get_coeffs()
#print lut.get_residual()
class test_SmoothBivariateSpline(NumpyTestCase):
def check_linear_constant(self):
x = [1,1,1,2,2,2,3,3,3]
y = [1,2,3,1,2,3,1,2,3]
z = [3,3,3,3,3,3,3,3,3]
lut = SmoothBivariateSpline(x,y,z,kx=1,ky=1)
assert_array_almost_equal(lut.get_knots(),([1,1,3,3],[1,1,3,3]))
assert_array_almost_equal(lut.get_coeffs(),[3,3,3,3])
assert_almost_equal(lut.get_residual(),0.0)
assert_array_almost_equal(lut([1,1.5,2],[1,1.5]),[[3,3],[3,3],[3,3]])
def check_linear_1d(self):
x = [1,1,1,2,2,2,3,3,3]
y = [1,2,3,1,2,3,1,2,3]
z = [0,0,0,2,2,2,4,4,4]
lut = SmoothBivariateSpline(x,y,z,kx=1,ky=1)
assert_array_almost_equal(lut.get_knots(),([1,1,3,3],[1,1,3,3]))
assert_array_almost_equal(lut.get_coeffs(),[0,0,4,4])
assert_almost_equal(lut.get_residual(),0.0)
assert_array_almost_equal(lut([1,1.5,2],[1,1.5]),[[0,0],[1,1],[2,2]])
class test_RectBivariateSpline(NumpyTestCase):
def check_defaults(self):
x = array([1,2,3,4,5])
y = array([1,2,3,4,5])
z = array([[1,2,1,2,1],[1,2,1,2,1],[1,2,3,2,1],[1,2,2,2,1],[1,2,1,2,1]])
lut = RectBivariateSpline(x,y,z)
assert_array_almost_equal(lut(x,y),z)
if __name__ == "__main__":
NumpyTest().run()
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