def testChebyshev1Function1D(self):
errMsg = "{}: {} != {} for x={}, xMin={}, xMax={}, xNorm={}, params={}; {}"
maxOrder = 6
deltaParam = 0.3
ranges = ((-1, 1), (-1, 0), (0, 1), (-17, -2), (-65.3, 2.132))
rangeIter = itertools.cycle(ranges)
nPoints = 9
for order in range(maxOrder + 1):
xMin, xMax = next(rangeIter)
xMean = (xMin + xMax) / 2.0
xDelta = (xMax - xMin) / float(nPoints - 1)
f = afwMath.Chebyshev1Function1D(order, xMin, xMax)
numParams = f.getNParameters()
params = np.arange(deltaParam, deltaParam *
numParams + (deltaParam / 2.0), deltaParam)
f.setParameters(params)
g = afwMath.Chebyshev1Function1D(params, xMin, xMax)
h = f.clone()
self.assertEqual(f.getNParameters(), g.getNParameters())
self.assertEqual(f.getMinX(), xMin)
self.assertEqual(f.getMaxX(), xMax)
self.assertEqual(f.getOrder(), order)
self.assertEqual(g.getMinX(), xMin)
self.assertEqual(g.getMaxX(), xMax)
self.assertEqual(g.getOrder(), order)
minXNorm = None
maxXNorm = None
for x in np.arange(xMin, xMax + xDelta/2.0, xDelta):
xNorm = 2.0 * (x - xMean) / float(xMax - xMin)
if minXNorm is None or xNorm < minXNorm:
minXNorm = xNorm
if maxXNorm is None or xNorm > maxXNorm:
maxXNorm = xNorm
predVal = referenceChebyshev1Polynomial1(xNorm, params)
msg = errMsg.format(type(f).__name__, f(x), predVal, x, xMin, xMax, xNorm, params,
"order constructor")
self.assertFloatsAlmostEqual(
f(x), predVal, msg=msg, atol=self.atol)
msg = errMsg.format(type(g).__name__, g(x), predVal, x, xMin, xMax, xNorm, params,
"params constructor")
self.assertFloatsAlmostEqual(
g(x), predVal, msg=msg, atol=self.atol)
msg = errMsg.format(type(h).__name__, h(x),
predVal, x, xMin, xMax, xNorm, params,
"clone")
self.assertFloatsAlmostEqual(
h(x), predVal, msg=msg, atol=self.atol)
msg = self.normErr.format("x", xMin, xMax, minXNorm, maxXNorm)
self.assertFloatsAlmostEqual(
minXNorm, -1., msg=msg, atol=self.atol)
self.assertFloatsAlmostEqual(maxXNorm, 1., msg=msg, atol=self.atol)
def testCast(self):
for instance in (afwMath.Chebyshev1Function1F(2),
afwMath.GaussianFunction1F(1.0),
afwMath.LanczosFunction1F(3),
afwMath.NullFunction1F(),
afwMath.PolynomialFunction1F(2)):
Class = type(instance)
base = instance.clone()
self.assertEqual(type(base), afwMath.Function1F)
derived = Class.cast(base)
self.assertEqual(type(derived), Class)
for instance in (afwMath.Chebyshev1Function1D(2),
afwMath.GaussianFunction1D(1.0),
afwMath.LanczosFunction1D(3),
afwMath.NullFunction1D(),
afwMath.PolynomialFunction1D(2)):
Class = type(instance)
base = instance.clone()
self.assertEqual(type(base), afwMath.Function1D)
derived = Class.cast(base)
self.assertEqual(type(derived), Class)
for instance in (afwMath.Chebyshev1Function2F(2),
afwMath.GaussianFunction2F(1.0, 1.0),
afwMath.DoubleGaussianFunction2F(1.0),
afwMath.LanczosFunction2F(3),
afwMath.NullFunction2F(),
afwMath.PolynomialFunction2F(2)):
Class = type(instance)
base = instance.clone()
self.assertEqual(type(base), afwMath.Function2F)
derived = Class.cast(base)
self.assertEqual(type(derived), Class)
for instance in (afwMath.Chebyshev1Function2D(2),
afwMath.GaussianFunction2D(1.0, 1.0),
afwMath.DoubleGaussianFunction2D(1.0),
afwMath.LanczosFunction2D(3),
afwMath.NullFunction2D(),
afwMath.PolynomialFunction2D(2)):
Class = type(instance)
base = instance.clone()
self.assertEqual(type(base), afwMath.Function2D)
derived = Class.cast(base)
self.assertEqual(type(derived), Class)
def testChebyshev1Function1D(self):
"""A test for Chebyshev1Function1D"""
maxOrder = 6
deltaParam = 0.3
allParams = numpy.arange(
deltaParam,
deltaParam * (maxOrder + 1) + (deltaParam / 2.0), deltaParam)
ranges = ((-1, 1), (-1, 0), (0, 1), (-17, -2), (-65.3, 2.132))
rangeIter = itertools.cycle(ranges)
nPoints = 9
for order in range(maxOrder + 1):
xMin, xMax = rangeIter.next()
xMean = (xMin + xMax) / 2.0
xDelta = (xMax - xMin) / float(nPoints - 1)
f = afwMath.Chebyshev1Function1D(order, xMin, xMax)
numParams = f.getNParameters()
params = numpy.arange(deltaParam,
deltaParam * numParams + (deltaParam / 2.0),
deltaParam)
f.setParameters(params)
g = afwMath.Chebyshev1Function1D(params, xMin, xMax)
h = f.clone()
self.assertEqual(f.getNParameters(), g.getNParameters())
self.assertEqual(f.getMinX(), xMin)
self.assertEqual(f.getMaxX(), xMax)
self.assertEqual(f.getOrder(), order)
self.assertEqual(g.getMinX(), xMin)
self.assertEqual(g.getMaxX(), xMax)
self.assertEqual(g.getOrder(), order)
# self.assertEqual(h.getMinX(), xMin)
# self.assertEqual(h.getMaxX(), xMax)
minXNorm = None
maxXNorm = None
for x in numpy.arange(xMin, xMax + xDelta / 2.0, xDelta):
xNorm = 2.0 * (x - xMean) / float(xMax - xMin)
if minXNorm == None or xNorm < minXNorm:
minXNorm = xNorm
if maxXNorm == None or xNorm > maxXNorm:
maxXNorm = xNorm
predVal = referenceChebyshev1Polynomial1(xNorm, params)
if not numpy.allclose(predVal, f(x)):
self.fail(
"%s = %s != %s for x=%s, xMin=%s, xMax=%s, xNorm=%s, params=%s; order constructor" % \
(type(f).__name__, f(x), predVal, x, xMin, xMax, xNorm, params))
if not numpy.allclose(predVal, g(x)):
self.fail(
"%s = %s != %s for x=%s, xMin=%s, xMax=%s, xNorm=%s, params=%s; params constructor" %\
(type(f).__name__, g(x), predVal, x, xMin, xMax, xNorm, params))
if not numpy.allclose(predVal, h(x)):
self.fail(
"%s = %s != %s for x=%s, xMin=%s, xMax=%s, xNorm=%s, params=%s; clone" %\
(type(f).__name__, h(x), predVal, x, xMin, xMax, xNorm, params))
if not numpy.allclose((minXNorm, maxXNorm), (-1.0, 1.0)):
raise RuntimeError(
"Invalid x normalization: xMin=%s, xMax=%s, min/max xNorm=(%s, %s) != (-1, 1)"
% (xMin, xMax, minXNorm, maxXNorm))