def test_not_allowed():
param1 = Parameter("param1", 1.0)
param2 = Parameter("param2", 2.0)
param3 = Parameter("param3", 3.0, floating=False)
def func1_pure(self, x):
return param1 * x
def func2_pure(self, x):
return param2 * x + param3
func1 = SimpleFuncV1(func=func1_pure, obs=obs1, p1=param1)
pdf1 = SimplePDF(func=lambda self, x: x * param1, obs=obs1)
pdf2 = SimplePDF(func=lambda self, x: x * param2, obs=obs1)
with pytest.raises(BreakingAPIChangeError):
pdf1 + pdf2
ext_pdf1 = pdf1.create_extended(param1)
with pytest.raises(BreakingAPIChangeError):
ext_pdf1 + pdf2
with pytest.raises(BreakingAPIChangeError):
param2 * pdf1
with pytest.raises(NotImplementedError):
param1 + func1
with pytest.raises(NotImplementedError):
func1 + param1
with pytest.raises(ModelIncompatibleError):
func1 * pdf2
with pytest.raises(ModelIncompatibleError):
pdf1 * func1
def test_projection_pdf():
return # HACK(Mayou36) check again on projection and dimensions. Why was there an expand_dims?
# x = zfit.Space("x", limits=(-5, 5))
# y = zfit.Space("y", limits=(-5, 5))
# gauss_x = Gauss(mu=mu, sigma=sigma, obs=x, name="gauss_x")
# gauss_y = Gauss(mu=mu, sigma=sigma, obs=y, name="gauss_x")
# gauss_xy = ProductPDF([gauss_x, gauss_y])
x = zfit.Space("x", limits=(-1, 1))
y = zfit.Space("y", limits=(-0.1, 1))
gauss_x = Gauss(mu=mu, sigma=sigma, obs=x, name="gauss_x")
gauss_y = Gauss(mu=mu, sigma=sigma, obs=y, name="gauss_x")
# gauss_xy = ProductPDF([gauss_x, gauss_y])
import tensorflow as tf
def correlated_func(self, x):
x, y = x.unstack_x()
value = 1 / (tf.abs(x - y) + 0.1) ** 2
return value
gauss_xy = SimplePDF(func=correlated_func, obs=x * y)
assert gauss_xy.create_projection_pdf(limits_to_integrate=y).norm_range == x
proj_pdf = gauss_xy.create_projection_pdf(limits_to_integrate=y)
test_values = np.array([-0.95603563, -0.84636306, -0.83895759, 2.62608006, 1.02336499,
-0.99631608, -1.22185623, 0.83838586, 2.77894762, -2.48259488,
1.5440374, 0.1109899, 0.20873491, -2.45271623, 2.04510553,
0.31566277, -1.55696965, 0.36304538, 0.77765786, 3.92630088])
true_probs = np.array([0.0198562, 0.02369336, 0.02399382, 0.00799939, 0.2583654, 0.01868723,
0.01375734, 0.53971816, 0.00697152, 0.00438797, 0.03473656, 0.55963737,
0.58296759, 0.00447878, 0.01517764, 0.59553535, 0.00943439, 0.59838703,
0.56315399, 0.00312496])
probs = proj_pdf.pdf(x=test_values)
probs = zfit.run(probs)
np.testing.assert_allclose(probs, true_probs, rtol=1e-5) # MC normalization
def test_not_allowed():
param1 = Parameter('param1', 1.)
param2 = Parameter('param2', 2.)
param3 = Parameter('param3', 3., floating=False)
param4 = Parameter('param4', 4.)
def func1_pure(self, x):
return param1 * x
def func2_pure(self, x):
return param2 * x + param3
func1 = SimpleFunc(func=func1_pure, obs=obs1, p1=param1)
func2 = SimpleFunc(func=func2_pure, obs=obs1, p2=param2, p3=param3)
pdf1 = SimplePDF(func=lambda self, x: x * param1, obs=obs1)
pdf2 = SimplePDF(func=lambda self, x: x * param2, obs=obs1)
with pytest.raises(ModelIncompatibleError):
pdf1 + pdf2
with pytest.raises(NotImplementedError):
param1 + func1
with pytest.raises(NotImplementedError):
func1 + param1
with pytest.raises(ModelIncompatibleError):
func1 * pdf2
with pytest.raises(ModelIncompatibleError):
pdf1 * func1
def test_implicit_extended():
# return # TODO(Mayou36): deps: impl_copy,
param1 = Parameter('param1', 12.)
yield1 = Parameter('yield1', 21.)
param2 = Parameter('param2', 13., floating=False)
yield2 = Parameter('yield2', 31., floating=False)
pdf1 = SimplePDF(func=lambda self, x: x * param1, obs=obs1)
pdf2 = SimplePDF(func=lambda self, x: x * param2, obs=obs1)
extended_pdf = yield1 * pdf1 + yield2 * pdf2
with pytest.raises(ModelIncompatibleError):
true_extended_pdf = SumPDF(pdfs=[pdf1, pdf2], obs=obs1)
assert isinstance(extended_pdf, SumPDF)
assert extended_pdf.is_extended
def test_projection_pdf(test_values):
import numpy as np
import zfit
x = zfit.Space("x", limits=(-1, 1))
y = zfit.Space("y", limits=(-1, 1))
def correlated_func(self, x):
x, y = x.unstack_x()
value = (((x - y**3)**2) + 0.1)
return value
def correlated_func_integrate_x(y, limits):
lower, upper = limits.rect_limits
def integ(x, y):
return 0.333333333333333 * x**3 - 1.0 * x**2 * y**3 + x * (
1.0 * y**6 + 0.1)
return integ(y, upper) - integ(y, lower)
def correlated_func_integrate_y(x, limits):
lower, upper = limits.rect_limits
def integ(x, y):
return -0.5 * x * y**4 + 0.142857142857143 * y**7 + y * (
1.0 * x**2 + 0.1)
return (integ(x, upper) - integ(x, lower))[0]
obs = x * y
from zfit.models.special import SimplePDF
gauss_xy = SimplePDF(func=correlated_func, obs=obs)
assert gauss_xy.create_projection_pdf(
limits_to_integrate=y).norm_range == x
proj_pdf = gauss_xy.create_projection_pdf(limits_to_integrate=y)
test_values = np.array([
-0.95603563, -0.84636306, -0.83895759, 2.62608006, 1.02336499,
-0.99631608, -1.22185623, 0.83838586, 2.77894762, -2.48259488,
1.5440374, 0.1109899, 0.20873491, -2.45271623, 2.04510553, 0.31566277,
-1.55696965, 0.36304538, 0.77765786, 3.92630088
])
true_probs = correlated_func_integrate_y(
test_values, y) / gauss_xy.integrate(limits=obs, norm_range=False)
probs = proj_pdf.pdf(x=test_values)
probs = probs.numpy()
np.testing.assert_allclose(probs, true_probs,
rtol=1e-3) # MC normalization
def test_param_pdf():
# return # TODO(Mayou36): deps: impl_copy,
param1 = Parameter('param1', 12.)
param2 = Parameter('param2', 22.)
yield1 = Parameter('yield1', 21.)
yield2 = Parameter('yield2', 22.)
pdf1 = SimplePDF(func=lambda self, x: x * param1, obs=obs1)
pdf2 = SimplePDF(func=lambda self, x: x * param2, obs=obs1)
assert not pdf1.is_extended
extended_pdf = yield1 * pdf1
assert extended_pdf.is_extended
with pytest.raises(ModelIncompatibleError):
_ = pdf2 * yield2
with pytest.raises(AlreadyExtendedPDFError):
_ = yield2 * extended_pdf
def test_implicit_sumpdf():
# return # TODO(Mayou36): deps: impl_copy, (mostly for Simple{PDF,Func})
# tf.reset_default_graph()
norm_range = (-5.7, 13.6)
param1 = Parameter('param13s', 1.1)
frac1 = 0.11
frac1_param = Parameter('frac13s', frac1)
frac2 = 0.56
frac2_param = Parameter('frac23s', frac2)
frac3 = 1 - frac1 - frac2 # -frac1_param -frac2_param
param2 = Parameter('param23s', 1.5, floating=False)
param3 = Parameter('param33s', 0.4, floating=False)
pdf1 = SimplePDF(func=lambda self, x: x * param1**2, obs=obs1)
pdf2 = SimplePDF(func=lambda self, x: x * param2, obs=obs1)
pdf3 = SimplePDF(func=lambda self, x: x * 2 + param3, obs=obs1)
# sugar 1
# sum_pdf = frac1_param * pdf1 + frac2_param * pdf2 + pdf3 # TODO(Mayou36): deps, correct copy
sum_pdf = zfit.pdf.SumPDF(pdfs=[pdf1, pdf2, pdf3],
fracs=[frac1_param, frac2_param])
true_values = pdf1.pdf(rnd_test_values, norm_range=norm_range)
true_values *= frac1_param
true_values += pdf2.pdf(rnd_test_values,
norm_range=norm_range) * frac2_param
true_values += pdf3.pdf(rnd_test_values,
norm_range=norm_range) * ztf.constant(frac3)
assert isinstance(sum_pdf, SumPDF)
assert not sum_pdf.is_extended
assert zfit.run(sum(sum_pdf.fracs)) == 1.
true_values = zfit.run(true_values)
test_values = zfit.run(sum_pdf.pdf(rnd_test_values, norm_range=norm_range))
np.testing.assert_allclose(true_values, test_values,
rtol=5e-2) # it's MC normalized
# sugar 2
sum_pdf2_part1 = frac1 * pdf1 + frac2 * pdf3
