def testImplicit(self):
print("==== BracketModel Test =======================")
m1 = GaussModel()
m1 += PolynomialModel(0) # Gauss on a constant background
m2 = BracketModel(m1)
m3 = SineModel()
m3 *= m2 # sine * ( gauss + const )
print("Explicit Use")
print(m3)
g1 = GaussModel()
g1 += PolynomialModel(0) # m1 is a chain of models
g3 = SineModel()
g3 *= g1 # sine * ( gauss + const )
print("Implicit Use")
print(g3) # exactly the same
p = [8.0, 1.0, 0.0, 1.0, 0.0, 0.2, 1.0]
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
assertAE(g3.result(x, p), m3.result(x, p))
assertAE(g3.partial(x, p), m3.partial(x, p))
assertAE(g3.derivative(x, p), m3.derivative(x, p))
stdModeltest(g3, p, plot=self.doplot)
def test2(self):
print("\n CombiModel Test 2\n")
gm = GaussModel()
pars = numpy.linspace(0.0, 1.0, 3, dtype=float)
gm.parameters = pars
cm = CombiModel(gm, 3)
Tools.printclass(cm)
self.assertTrue(cm.deep == 1)
print(str(cm._head))
self.assertTrue(str(cm._head) == "Combi of 3 times Gauss")
self.assertTrue(cm._next is None)
self.assertTrue(cm._npchain == 9 and cm.npchain == 9)
assertAE(cm.addindex, [])
assertAE(cm.addvalue, [])
assertAE(cm.mulindex, [])
assertAE(cm.mulvalue, [])
assertAE(cm.expandindex, numpy.arange(9, dtype=int))
assertAE(cm.select, numpy.arange(9, dtype=int))
assertAE(cm.parameters, [0.0, 0.5, 1.0] * 3)
p = numpy.ones(9, dtype=float)
p[1:9:3] /= 2
p[2:9:3] /= 10
stdModeltest(cm, p, plot=self.doplot)
def testConstantModel(self):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******NULL***********************")
m = ConstantModel()
self.assertTrue(m.npchain == 0)
p = []
stdModeltest(m, p)
print("******CONSTANT VALUE*******************")
sm = SineModel()
m = ConstantModel(fixedModel=sm)
m.values = [2.0, 0.0, 0.5]
stdModeltest(m, p, silent=False)
print("******CONSTANT MODEL*******************")
tbl = numpy.linspace(0, 1, 11)
m = ConstantModel(table=tbl)
stdModeltest(m, p)
print("******CONSTANT TABLE*******************")
m = ConstantModel(values=5.5)
stdModeltest(m, p)
print("******COMPOUND*******************")
m = ConstantModel(values=1.0)
m.addModel(ExpModel())
self.assertTrue(m.npbase == 0)
self.assertTrue(m.npchain == 2)
p = [1.0, -3.0]
stdModeltest(m, p, plot=self.doplot)
def testSplinesModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******SPLINES*************************")
knots = numpy.arange(11, dtype=float)
m0 = SplinesModel(knots=knots)
self.assertTrue(m0.order == 3)
self.assertTrue(m0.getNumberOfParameters() == 13)
self.assertTrue(m0.npbase == 13)
m1 = SplinesModel(nrknots=5, min=-1, max=1)
self.assertTrue(m1.order == 3)
self.assertTrue(m1.getNumberOfParameters() == 7)
self.assertTrue(m1.npbase == 7)
for k in range(len(m1.knots)):
self.assertTrue(m1.knots[k] == -1 + k / 2)
xin = numpy.linspace(0, 10, 101, dtype=float)
m2 = SplinesModel(xrange=xin, order=2, nrknots=11)
self.assertTrue(m2.order == 2)
self.assertTrue(m2.getNumberOfParameters() == 12)
self.assertTrue(m2.npbase == 12)
for k in range(len(m2.knots)):
self.assertTrue(m2.knots[k] == k)
self.assertRaises(ValueError, SplinesModel)
p = numpy.asarray([0, 1, 0, 1, 0, 1, 0], dtype=float)
stdModeltest(m1, p, plot=plot)
def testVoigtModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******VOIGT***********************")
m = VoigtModel()
p = numpy.asarray([1.2, 0.2, 0.3, 0.4], dtype=float)
print(p)
stdModeltest(m, p, plot=plot, warn=["nopart"])
def test1b(self):
model = SoftMaxModel(ndim=1, ndout=2, offset=True, normed=False)
print(model.npars)
p = numpy.asarray([1.0, 1.0, 2.0, 2.0])
x = numpy.linspace(0, 10, 101, dtype=float)
stdModeltest(model, p, x=x, plot=self.doplot)
def testArctanModel(self):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******ARCTAN***********************")
m = ArctanModel()
p = numpy.asarray([1.2, -0.1, 30], dtype=float)
stdModeltest(m, p, plot=self.doplot)
def testGaussModel(self):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******GAUSS*************************")
m = GaussModel()
p = numpy.asarray([1.2, -0.2, 0.3], dtype=float)
stdModeltest(m, p, plot=self.doplot)
def test2(self):
x = numpy.linspace(0, 10, 101, dtype=float)
x += 0.01 ## avoid awkward non-continuities
print("****** FREESHAPE test 2 ********************")
m = FreeShapeModel(10, nconvolve=3, center=0.0)
p = numpy.asarray([1.2, 0.2, 1.0, 0.5, 0.3, 1.2, 0.2, 1.0, 0.5, 0.3])
stdModeltest(m, p, x=x, plot=self.doplot)
def testEtalonModel(self):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******ETALON***********************")
m = EtalonModel()
p = numpy.asarray([1.2, 0.6, 2.0, 0.2], dtype=float)
stdModeltest(m, p, plot=self.doplot)
def testGaussModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******GAUSS*************************")
m = GaussModel(fixed={2: 0.3})
p = numpy.asarray([1.2, -0.2], dtype=float)
stdModeltest(m, p, plot=plot)
def testExpModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******EXP**************************")
m = ExpModel(fixed={1: 0.1})
p = numpy.asarray([1.2], dtype=float)
stdModeltest(m, p, plot=plot)
def testLorentzModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******LORENTZ***********************")
m = LorentzModel(fixed={0: 4})
p = numpy.asarray([-0.2, 0.3], dtype=float)
stdModeltest(m, p, plot=plot)
def testLorentzModel(self):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******LORENTZ***********************")
m = LorentzModel()
p = numpy.asarray([1.2, -0.2, 0.3], dtype=float)
stdModeltest(m, p, plot=self.doplot)
def testArctanModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******ARCTAN******************")
m = ArctanModel(fixed={1: -1.2})
p = numpy.asarray([1.2, 30], dtype=float)
stdModeltest(m, p, plot=plot)
def test2(self):
print("******ASTROPY MODEL 2***********************")
gm = modeling.models.Gaussian1D()
m = AstropyModel(gm, fixed={0: 2.6})
p = numpy.asarray([-0.1, 30], dtype=float)
stdModeltest(m, p, plot=self.doplot)
def testEtalonModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******ETALON***********************")
m = EtalonModel(fixed={0: 1.5})
p = numpy.asarray([30.0, 2.0, 0.2], dtype=float)
stdModeltest(m, p, plot=plot)
def testPowerLawModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******POWERLAW*******************")
m = PowerLawModel(fixed={1: -2})
self.assertTrue(m.npchain == 2)
self.assertTrue(m.npbase == 2)
p = [2.3, 0.5]
stdModeltest(m, p, plot=plot)
def testChebyshevPolynomialModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******CHEBYSHEV POLYNOMIAL*************")
m = ChebyshevPolynomialModel(4, fixed={4: 0.1})
self.assertTrue(m.getNumberOfParameters() == 4)
self.assertTrue(m.npbase == 4)
p = numpy.asarray([1, -2, 3, -2], dtype=float)
stdModeltest(m, p, plot=plot)
def testSineDriftModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******SINEDRIFT******************")
m = SineDriftModel()
self.assertTrue(m.npchain == 4)
self.assertTrue(m.npbase == 4)
p = [1.1, 0.5, 0.8, 0.4]
stdModeltest(m, p, plot=plot)
def testSineModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******SINE***********************")
m = SineModel(fixed={0: 1})
self.assertTrue(m.npchain == 2)
self.assertTrue(m.npbase == 2)
p = [-1.1, 0.5]
stdModeltest(m, p, plot=plot)
def testFreeShapeModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******FREESHAPE********************")
m = FreeShapeModel(5, pixperbin=1, xlo=-1, xhi=1.1)
self.assertTrue(m.npbase == 5)
p = numpy.asarray([1.2, 0.2, 1.0, 0.5, 0.3], dtype=float)
stdModeltest(m, p, plot=plot)
def test1(self):
x = numpy.linspace(0, 10, 101, dtype=float)
x += 0.01 ## avoid awkward non-continuities
print("****** FREESHAPE test 1 ********************")
m = FreeShapeModel(10)
self.assertTrue(m.npbase == 10)
p = numpy.asarray([1.2, 0.2, 1.0, 0.5, 0.3, 1.2, 0.2, 1.0, 0.5, 0.3])
stdModeltest(m, p, x=x, plot=self.doplot)
def testPolynomialModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******POLYNOMIAL**********************")
m = PolynomialModel(3)
self.assertTrue(m.getNumberOfParameters() == 4)
self.assertTrue(m.npbase == 4)
p = numpy.asarray([1, -2, 3, -2], dtype=float)
stdModeltest(m, p, plot=plot)
def testPadeModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******PADE**********************")
m = PadeModel(3, 2)
self.assertTrue(m.getNumberOfParameters() == 6)
self.assertTrue(m.npbase == 6)
p = numpy.asarray([1, -2, 3, -2, 0.3, 1], dtype=float)
stdModeltest(m, p, plot=plot)
def testPowerModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******POWER**********************")
m = PowerModel(3, fixed={0: 1.2})
self.assertTrue(m.getNumberOfParameters() == 0)
self.assertTrue(m.npbase == 0)
p = []
stdModeltest(m, p, plot=plot)
def testSincModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******SINC***********************")
m = SincModel(fixed={2: 0.1, 1: 2})
self.assertTrue(m.npchain == 1)
self.assertTrue(m.npbase == 1)
p = [2.3]
stdModeltest(m, p, plot=plot)
def testEtalonModel2(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******ETALON 2***********************")
fm = PolynomialModel(1)
m = EtalonModel(fixed={0: 1.5, 1: fm})
p = numpy.asarray([2.0, 0.2, 1.0, 0.9], dtype=float)
stdModeltest(m, p, plot=plot)
def testSineAmpModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******SINEAMP********************")
m = SineAmpModel(1.3, fixed={1: 1})
self.assertTrue(m.npchain == 1)
self.assertTrue(m.npbase == 1)
p = [-1.1]
stdModeltest(m, p, plot=plot)
def testSincModel(self, plot=False):
x = numpy.asarray(
[-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
print("******SINC***********************")
m = SincModel()
self.assertTrue(m.npchain == 3)
self.assertTrue(m.npbase == 3)
p = [2.3, -1.1, 0.5]
stdModeltest(m, p, plot=plot)