def set_range(self, lower, upper):
self.fit_lower = lower
self.fit_upper = upper
self._calc_fit_bins()
self.obs = zfit.Space('x', limits=(self.fit_lower, self.fit_upper))
def integrate_one(limits):
l, u = tf.unstack(limits)
limits_space = zfit.Space(obs=self.obs, limits=[l, u])
return pdf.integrate(limits_space, norm=False, options=options)
cond = cond & (abs(df['Lb_TRUEID']) == 5122) & (abs(df['Lc_TRUEID']) == 4122) & (abs(df['p_TRUEID']) == 2212) & (abs(df['K_TRUEID']) == 321) & (abs(df['pi_TRUEID']) == 211) & (abs(df['mu_TRUEID']) == 13)
cond = cond & (abs(df['mu_MC_MOTHER_ID']) == 15) & (df['Lc_BKGCAT'] < 30)
#Below cuts only for FOM optimisation (ie. define signal region)
cond = cond & (df['Lc_M'] > limits[0][0][0]) & (df['Lc_M'] < limits[1][0][0])
num_bef = np.sum(df['Event_PIDCalibEffWeight'])
num_after = np.sum(df[cond]['Event_PIDCalibEffWeight'])
df = df[cond]
return df[['Lc_M', 'bdt', 'Event_PIDCalibEffWeight']], num_after, num_bef
###################
##################
#define space to fit in the upperside band region
upper = (( 2327.,),)
lower = (( 2310.,),);
limits = (lower, upper)
obs = zfit.Space(obs=['Lc_M'], limits=limits)
#define a space which includes full sig + bkg regs
lowerfull = (( 2260.,),)
upperfull = (( 2327.,),)
limitsfull = (lowerfull, upperfull)
obsfull = zfit.Space(obs=['Lc_M'], limits=limitsfull)
##################
##################- Get real data read_root
path_data = '/disk/lhcb_data2/amathad/Lb2Lclnu_analysis/Data/'
vars_data = ['Lc_M', 'bdt', 'Lb_L0Global_TIS', 'Lc_L0HadronDecision_TOS', 'Lc_Hlt1TrackMVADecision_TOS', 'Lc_Hlt1TwoTrackMVADecision_TOS', 'Lb_Hlt2XcMuXForTauB2XcMuDecision_TOS', '*_PT', '*_P', 'nTracks', 'mu_PID*', 'mu_isMuon']
dfu = GetData(path_data+"Data_MagUp_2016_MVA.root", vars_data, limits = limits)
dfd = GetData(path_data+"Data_MagDown_2016_MVA.root", vars_data, limits = limits)
data_df = pd.concat([dfu,dfd], ignore_index=True)
print('Total Data', data_df.shape)
#plt.hist((zfit.run(data)).ravel(), bins=200)
def test_morphing_templates(alphas):
bins1 = 15
irregular_str = "irregular templates" if alphas is not None else ""
counts1 = np.random.uniform(70, high=100, size=bins1) # generate counts
counts = [
counts1 - np.random.uniform(high=20, size=bins1),
counts1,
counts1 + np.random.uniform(high=20, size=bins1),
]
if alphas is not None:
counts.append(counts1 + np.random.uniform(high=5, size=bins1))
binning = zfit.binned.RegularBinning(bins1, 0, 10, name="obs1")
obs = zfit.Space(obs="obs1", binning=binning)
histos = [BinnedData.from_tensor(obs, count) for count in counts]
pdfs = [zfit.pdf.HistogramPDF(h) for h in histos]
if alphas is not None:
pdfs = {a: p for a, p in zip(alphas, pdfs)}
alpha = zfit.Parameter("alpha", 0, -5, 5)
morph = SplineMorphingPDF(alpha=alpha, hists=pdfs)
if alphas is None:
alphas = [-1, 0, 1]
for i, a in enumerate(alphas):
alpha.set_value(a)
np.testing.assert_allclose(morph.counts(), counts[i])
if len(alphas) > i + 1:
alpha.set_value((a + alphas[i + 1]) / 2)
max_dist = (counts[i] - counts[i + 1])**2 + 5 # tolerance
max_dist *= 1.1 # not strict, it can be a bit higher
numpy.testing.assert_array_less((morph.counts() - counts[i])**2,
max_dist)
numpy.testing.assert_array_less(
(morph.counts() - counts[i + 1])**2, max_dist)
import matplotlib.cm as cm
amin, amax = -2, 2
n = 5
template_alphas = np.array(list(alphas))
for do_3d in [True, False]:
plt.figure()
if do_3d:
ax = plt.gcf().add_subplot(111, projection="3d")
else:
ax = plt.gca()
plotstyle = "3d plot" if do_3d else "hist plot"
plt.title(f"Morphing with splines {irregular_str} {plotstyle}")
for a in list(znp.linspace(amin, amax, n * 2)) + list(template_alphas):
normed_a = (a - amin) / (amax -
amin) / 1.3 # 3 is a scaling factor
color = cm.get_cmap("winter")(normed_a)
alpha.set_value(a)
histo = morph.ext_pdf(None)
histo = BinnedData.from_tensor(obs, histo)
histo = histo.to_hist()
values = histo.values()
x = histo.axes.edges[0][:-1]
y = np.broadcast_to(a, values.shape)
z = values
label = None
if do_3d:
ax.step(x, y, z, color=color, where="pre", label=label)
else:
if np.min((a - template_alphas)**2) < 0.0001:
label = f"alpha={a}"
mplhep.histplot(histo, label=label, color=color)
ax.set_xlabel("observable")
ax.set_ylabel("alpha")
if do_3d:
ax.set_zlabel("ext_pdf")
plt.legend()
pytest.zfit_savefig()
def test_conditional_pdf_simple():
xobs = zfit.Space('x', (-2, 5))
muobs = zfit.Space('y', (-1, 15))
sigmaobs = zfit.Space('z', (0.2, 13))
mu = zfit.Parameter('mu', 2, -5, 25)
xmuobs = xobs * muobs
obs = xobs * muobs * sigmaobs
nsample = 5000
uniform_sample = np.random.uniform(size=(nsample, 1), low=-1, high=4)
normal_sample1 = np.random.normal(loc=2, scale=3.5, size=(nsample, 1))
normal_sample2 = np.abs(np.random.normal(loc=5, scale=2, size=(nsample, 1))) + 0.9
sigma = zfit.Parameter('sigma', 3, 0.1, 15)
gauss = zfit.pdf.Gauss(obs=xobs, mu=mu, sigma=sigma)
# gauss = zfit.pdf.Gauss(xmuobs=muobs, mu=mu, sigma=sigma)
data2d = zfit.Data.from_numpy(array=np.stack([uniform_sample, normal_sample1], axis=-1),
obs=xmuobs)
data1d = zfit.Data.from_numpy(array=uniform_sample, obs=xobs)
data3d = zfit.Data.from_numpy(array=np.stack([uniform_sample, normal_sample1, normal_sample2], axis=-1),
obs=obs)
data1dmu = zfit.Data.from_numpy(array=uniform_sample, obs=muobs)
cond_gauss2d = zfit.pdf.ConditionalPDFV1(pdf=gauss, cond={mu: muobs}, use_vectorized_map=False)
prob2 = cond_gauss2d.pdf(data2d)
assert prob2.shape[0] == data2d.nevents
assert prob2.shape.rank == 1
prob_check2 = np.ones_like(prob2)
for i, (xval, muval) in enumerate(data2d.value()):
mu.assign(muval)
prob_check2[i] = gauss.pdf(xval)
np.testing.assert_allclose(prob_check2, prob2, rtol=1e-5)
cond_gauss1d = zfit.pdf.ConditionalPDFV1(pdf=gauss, cond={mu: xobs})
prob1 = cond_gauss1d.pdf(data1d)
assert prob1.shape[0] == data1d.nevents
assert prob1.shape.rank == 1
prob_check1 = np.ones_like(prob1)
for i, xval in enumerate(data1d.value()[:, 0]):
mu.assign(xval)
prob_check1[i] = gauss.pdf(xval)
np.testing.assert_allclose(prob_check1, prob1, rtol=1e-5)
cond_gauss3d = zfit.pdf.ConditionalPDFV1(pdf=gauss, cond={mu: muobs, sigma: sigmaobs})
prob3 = cond_gauss3d.pdf(data3d)
assert prob3.shape[0] == data3d.nevents
assert prob3.shape.rank == 1
prob_check3 = np.ones_like(prob3)
for i, (xval, muval, sigmaval) in enumerate(data3d.value()):
zfit.param.assign_values([mu, sigma], [muval, sigmaval])
prob_check3[i] = gauss.pdf(xval)
np.testing.assert_allclose(prob_check3, prob3, rtol=1e-5)
nll = zfit.loss.UnbinnedNLL(model=cond_gauss2d, data=data2d)
nll.value()
minimizer = zfit.minimize.Minuit()
result = minimizer.minimize(nll)
assert result.valid
integrals2 = cond_gauss2d.integrate(limits=xobs, x=data2d)
assert integrals2.shape[0] == data2d.nevents
assert integrals2.shape.rank == 1
np.testing.assert_allclose(integrals2, np.ones_like(integrals2), atol=1e-5)
sample2 = cond_gauss2d.sample(n=data1dmu.nevents, limits=xobs, x=data1dmu)
assert sample2.value().shape == (data1dmu.nevents, cond_gauss2d.n_obs)
# Copyright (c) 2022 zfit
import copy
import numpy as np
import pytest
import tensorflow as tf
import zfit
obs1_random = zfit.Space(obs="obs1", limits=(-1.5, 1.2))
obs1 = zfit.Space(obs="obs1", limits=(-1, 1))
coeffs_parametrization = [
1.4,
[0.6],
[1.42, 1.2],
[0.2, 0.8, 0.5],
[8.1, 1.4, 3.6, 4.1],
[1.1, 1.42, 1.2, 0.4, 0.7],
[11.1, 1.4, 5.6, 3.1, 18.1, 3.1],
]
rel_integral = 7e-2
default_sampling = 100000
poly_pdfs = [
(zfit.pdf.Legendre, default_sampling),
(zfit.pdf.Chebyshev, default_sampling),
(zfit.pdf.Chebyshev2, default_sampling),
(zfit.pdf.Hermite, default_sampling * 20),
(zfit.pdf.Laguerre, default_sampling * 20),
# Copyright (c) 2021 zfit
import numpy as np
import pytest
import zfit
from zfit import Parameter
from zfit.models.dist_tfp import Poisson
lamb_true = 50
obs = zfit.Space(obs="Nobs", limits=(0, 200))
test_values = np.random.uniform(low=0, high=100, size=100)
def create_poisson():
N = Parameter("N", lamb_true)
poisson = Poisson(obs=obs, lamb=N)
return poisson
def create_poisson_composed_rate():
N1 = Parameter("N1", lamb_true / 2)
N2 = Parameter("N2", lamb_true / 2)
N = zfit.param.ComposedParameter("N",
lambda n1, n2: n1 + n2,
params=[N1, N2])
poisson = Poisson(obs=obs, lamb=N)
return poisson
param1 = params['super_param']
param2 = params['param2']
param3 = params['param3']
lower = z.convert_to_tensor(lower)
upper = z.convert_to_tensor(upper)
# calculate the integral here, dummy integral, wrong!
integral = param1 * param2 * param3 + z.reduce_sum([lower, upper])
return integral
# define the space over which it is defined. Here, we use the axes
lower_full = (-10, zfit.Space.ANY_LOWER)
upper_full = (10, zfit.Space.ANY_UPPER)
integral_full_limits = zfit.Space(axes=(0, 1),
limits=(lower_full, upper_full))
CustomPDF2D.register_analytic_integral(func=integral_full,
limits=integral_full_limits)
# define the partial integral function
def integral_axis1(x, limits, norm_range, params, model):
data_0 = x.unstack_x() # data from axis 0
param1 = params['super_param']
param2 = params['param2']
param3 = params['param3']
lower, upper = limits.limit1d
lower = z.convert_to_tensor(lower) # the limits are now 1-D, for axis 1
from zfit.core.space import Limit
from zfit.util.exception import AnalyticSamplingNotImplemented
@pytest.fixture(autouse=True, scope="module")
def setup_teardown_vectors():
Limit._experimental_allow_vectors = True
yield
Limit._experimental_allow_vectors = False
mu_true = 1.5
sigma_true = 1.2
low, high = -3.8, 2.9
obs1 = zfit.Space("obs1", (low, high))
class GaussNoAnalyticSampling(zfit.pdf.Gauss):
def _analytic_sample(self, n, limits: Space):
raise AnalyticSamplingNotImplemented # HACK do make importance sampling work
class UniformNoAnalyticSampling(zfit.pdf.Uniform):
def _analytic_sample(self, n, limits: Space):
raise AnalyticSamplingNotImplemented # HACK do make importance sampling work
def create_gauss1():
mu = zfit.Parameter("mu_sampling1", mu_true, mu_true - 2.0, mu_true + 7.0)
sigma = zfit.Parameter(
setup_func_general()
def teardown_function():
Limit._experimental_allow_vectors = False
teardown_func_general()
ztf = z
from zfit.core.sample import accept_reject_sample
mu_true = 1.5
sigma_true = 1.2
low, high = -3.8, 2.9
obs1 = zfit.Space('obs1', (low, high))
class GaussNoAnalyticSampling(zfit.pdf.Gauss):
def _analytic_sample(self, n, limits: Space):
raise AnalyticSamplingNotImplementedError # HACK do make importance sampling work
class UniformNoAnalyticSampling(zfit.pdf.Uniform):
def _analytic_sample(self, n, limits: Space):
raise AnalyticSamplingNotImplementedError # HACK do make importance sampling work
def create_gauss1():
mu = zfit.Parameter("mu_sampling1", mu_true, mu_true - 2., mu_true + 7.)
sigma = zfit.Parameter("sigma_sampling1", sigma_true, sigma_true - 10.,
def test_analytic_integral():
obs = zfit.Space("obs1", (-2, 5))
def test_conv_2D_simple():
zfit.run.set_graph_mode(False) # TODO: remove, just for debugging
raise WorkInProgressError("2D convolution not yet implemented, re-activate if so")
n_points = 1000
obs1 = zfit.Space("obs1", limits=(-2, 4))
obs2 = zfit.Space("obs2", limits=(-6, 4))
obskernel = obs1 * obs2
param2 = zfit.Parameter('param2', 0.4)
gauss1 = zfit.pdf.Gauss(1., 0.5, obs=obs1)
gauss22 = zfit.pdf.CrystalBall(0.0, param2, -0.2, 3, obs=obs2)
obs1func = zfit.Space("obs1", limits=(4, 10))
obs2func = zfit.Space("obs2", limits=(-6, 4))
obs_func = obs1func * obs2func
gauss21 = zfit.pdf.Gauss(-0.5, param2, obs=obs2func)
func1 = zfit.pdf.Uniform(5, 8, obs=obs1func)
func2 = zfit.pdf.Uniform(6, 7, obs=obs1func)
func = zfit.pdf.SumPDF([func1, func2], 0.5)
func = func * gauss21
gauss = gauss1 * gauss22
conv = zfit.pdf.FFTConvPDFV1(func=func, kernel=gauss)
start = obs_func.rect_lower
stop = obs_func.rect_upper
x_tensor = tf.random.uniform((n_points, 2), start, stop)
x_tensor = tf.reshape(x_tensor, (-1, 2))
linspace = tf.linspace(start, stop, num=n_points)
linspace = tf.transpose(tf.meshgrid(*tf.unstack(linspace, axis=-1)))
linspace_func = tf.reshape(linspace, (-1, 2))
# linspace_full = tf.linspace((-8, -8), (12, 12), num=n_points)
# linspace_full = tf.transpose(tf.meshgrid(*tf.unstack(linspace_full, axis=-1)))
# linspace_full = tf.reshape(linspace_full, (-1, 2))
linspace_kernel = tf.linspace(obskernel.rect_lower,
obskernel.rect_upper, num=n_points)
linspace_kernel = tf.transpose(tf.meshgrid(*tf.unstack(linspace_kernel, axis=-1)))
linspace_kernel = tf.reshape(linspace_kernel, (-1, 2))
# linspace_kernel = obskernel.filter(linspace_full)
# linspace_func = obs_func.filter(linspace_full)
x = zfit.Data.from_tensor(obs=obs_func, tensor=x_tensor)
linspace_data = zfit.Data.from_tensor(obs=obs_func, tensor=linspace)
probs_rnd = conv.pdf(x=x)
probs = conv.pdf(x=linspace_data)
# Numpy doesn't support ndim convolution?
true_probs = true_conv_2d_np(func, gauss, obsfunc=obs_func,
xfunc=linspace_func, xkernel=linspace_kernel)
import matplotlib.pyplot as plt
# np.testing.assert_allclose(probs, true_probs, rtol=0.2, atol=0.1)
integral = conv.integrate(limits=obs_func)
assert pytest.approx(1, rel=1e-3) == integral.numpy()
probs_np = probs_rnd.numpy()
assert len(probs_np) == n_points
# probs_plot = np.reshape(probs_np, (-1, n_points))
# x_plot = linspace[0:, ]
# probs_plot_projx = np.sum(probs_plot, axis=0)
# plt.plot(x_plot, probs_np)
# probs_plot = np.reshape(probs_np, (n_points, n_points))
# plt.imshow(probs_plot)
# plt.show()
true_probsr = tf.reshape(true_probs, (n_points, n_points))
probsr = tf.reshape(probs, (n_points, n_points))
plt.figure()
plt.imshow(true_probsr, label='true probs')
plt.title('true probs')
plt.figure()
plt.imshow(probsr, label='zfit conv')
plt.title('zfit conv')
plt.show(block=False)
# test the sampling
conv_nosample = FFTConvPDFV1NoSampling(func=func, kernel=gauss)
npoints_sample = 10000
sample = conv.sample(npoints_sample)
sample_nosample = conv_nosample.sample(npoints_sample)
x, y = z.unstack_x(sample)
xns, yns = z.unstack_x(sample_nosample)
plt.figure()
plt.hist2d(x, y, bins=30)
plt.title('custom sampling, addition')
plt.figure()
plt.hist2d(xns, yns, bins=30)
plt.title('fallback sampling, accept-reject')
plt.figure()
plt.hist(x.numpy(), bins=50, label='custom', alpha=0.5)
plt.hist(xns.numpy(), bins=50, label='fallback', alpha=0.5)
plt.legend()
plt.title('x')
plt.show()
plt.figure()
plt.hist(y.numpy(), bins=50, label='custom', alpha=0.5)
plt.hist(yns.numpy(), bins=50, label='fallback', alpha=0.5)
plt.title('y')
plt.legend()
plt.show()
from zfit.core.space import ANY, Limit
rect_limits = (1., 3)
rect_limits_tf = (tf.constant(1.), tf.constant(3))
rect_limit_enlarged = rect_limits[0] - 1, rect_limits[1] + 1.5
rect_limits_any = (ANY, ANY)
def inside_rect_limits(x):
return tf.logical_and(rect_limits[0] < x, x < rect_limits[1])[:, 0]
@pytest.mark.parametrize('graph', [True, False])
@pytest.mark.parametrize('rect_limits', [rect_limits, rect_limits_tf])
@pytest.mark.parametrize('testclass', [
Limit, lambda limit_fn=None, rect_limits=None: zfit.Space(
'obs1', limits=limit_fn, rect_limits=rect_limits)
])
def test_rect_limits_1d(graph, rect_limits, testclass):
def test(allow_graph=True):
limit = testclass(rect_limits)
limit2 = testclass(rect_limits=rect_limits)
lower, upper = limit.rect_limits
lower2, upper2 = limit2.rect_limits
assert limit.has_rect_limits
inside = limit.inside(2)
inside2 = limit.inside(2, guarantee_limits=True)
outside = limit.inside(4)
if graph:
equal = limit.equal(limit2, allow_graph=allow_graph)
less_equal = limit.less_equal(limit2, allow_graph=allow_graph)
def set_bins(self, bins):
self.bins = bins
self._calc_fit_bins()
self.obs = zfit.Space('x', limits=(self.fit_lower, self.fit_upper))
import zfit
from math import pi
costk = zfit.Space('costk', (-1, 1))
costl = zfit.Space('costl', (-1, 1))
phi = zfit.Space('phi', (-pi, pi))
def getobs():
obs = costk * costl * phi
return obs
def getobslist():
obslist = [costk, costl, phi]
return obslist
from zfit.core.space import Space
from zfit.models.dist_tfp import Gauss
from zfit.models.functor import ProductPDF, SumPDF
low, high = -0.64, 5.9
mu1_true = 1.
mu2_true = 2.
mu3_true = 0.6
sigma1_true = 1.4
sigma2_true = 2.3
sigma3_true = 1.8
fracs = [0.3, 0.15]
obs1 = zfit.Space('obs1', (-3, 6))
obs2 = zfit.Space('obs2', (-2, 5))
obs3 = zfit.Space('obs3', (-3.5, 4))
def true_gaussian_sum(x):
def norm(sigma):
return np.sqrt(2 * np.pi) * sigma
sum_gauss = fracs[0] * np.exp(-(x - mu1_true)**2 /
(2 * sigma1_true**2)) / norm(sigma1_true)
sum_gauss += fracs[1] * np.exp(-(x - mu2_true)**2 /
(2 * sigma2_true**2)) / norm(sigma2_true)
sum_gauss += (1. - sum(fracs)) * np.exp(
-(x - mu3_true)**2 / (2 * sigma3_true**2)) / norm(sigma3_true)
return sum_gauss
def get_folded_pdf(self, name):
pdf_class, param_names, _ = self.FOLDS[name]
def get_params(param_list):
out = {}
for param in param_list:
if param not in self.params:
config = [0.8, 0, 1] if param == "FL" else [0.0, -1, 1]
self.params.update({param: zfit.Parameter(param, *config)})
out[param] = self.params[param]
return out
# Make sure params exist
params = get_params(param_names)
pdf = pdf_class(obs=self.obs, **params)
return pdf
def fold_dataset(self, name, dataset):
*_, data_transform = self.FOLDS[name]
return data_transform(dataset, self.obs.obs)
if __name__ == "__main__":
costheta_l = zfit.Space("costhetal", limits=(-1.0, 1.0))
costheta_k = zfit.Space("costhetaK", limits=(-1.0, 1.0))
phi = zfit.Space("phi", limits=(-pi, pi))
decay = B2Kstll(costheta_l, costheta_k, phi)
pdf = decay.get_folded_pdf("P5p")
# EOFs
# Copyright (c) 2020 zfit
import numpy as np
import pytest
import zfit
from zfit.core.sample import extract_extended_pdfs, extended_sampling
# noinspection PyUnresolvedReferences
from zfit.core.testing import setup_function, teardown_function, tester
obs1 = zfit.Space('obs1', limits=(-3, 4))
@pytest.mark.flaky(reruns=3) # poissonian sampling
def test_extract_extended_pdfs():
gauss1 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)
gauss2 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)
gauss3 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)
gauss4 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)
gauss5 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)
gauss6 = zfit.pdf.Gauss(obs=obs1, mu=1.3, sigma=5.4)
yield1 = zfit.Parameter('yield123' + str(np.random.random()), 200.)
# sum1 = 0.3 * gauss1 + gauss2
gauss3_ext = gauss3.create_extended(45)
gauss4_ext = gauss4.create_extended(100)
sum2_ext_daughters = gauss3_ext + gauss4_ext
sum3 = zfit.pdf.SumPDF((gauss5, gauss6), 0.4)
sum3_ext = sum3.create_extended(yield1)
class CustomPDF(zfit.pdf.ZPDF):
"""3-dimensional PDF calculating doing something fancy. Takes x, y, z as data."""
_PARAMS = ['alpha', 'beta'] # specify which parameters to take
_N_OBS = 3
def _unnormalized_pdf(self, x): # implement function
x, y, z = x.unstack_x()
alpha = self.params['alpha']
beta = self.params['beta']
x_new = tf.math.cos(alpha) * x
y_new = tf.math.sinh(beta) * y
z_new = z + 4.2
return x_new**2 + y_new**2 + z_new**2
xobs = zfit.Space('xobs', (-4, 4))
yobs = zfit.Space('yobs', (-3, 3))
zobs = zfit.Space('z', (-2, 2))
obs = xobs * yobs * zobs
alpha = zfit.Parameter("alpha", 0.2) # floating
beta = zfit.Parameter("beta", 0.4, floating=False) # non-floating
custom_pdf = CustomPDF(obs=obs, alpha=alpha, beta=beta)
integral = custom_pdf.integrate(limits=obs) # = 1 since normalized
sample = custom_pdf.sample(n=1000) # DO NOT USE THIS FOR TOYS!
prob = custom_pdf.pdf(sample) # DO NOT USE THIS FOR TOYS!
integral_np, sample_np, prob_np = zfit.run([integral, sample, prob])
from zfit import Parameter
from zfit.core.testing import setup_function, teardown_function, tester
from zfit.models.dist_tfp import Gauss
mu1_true = 1.
mu2_true = 2.
mu3_true = 0.6
sigma1_true = 1.4
sigma2_true = 2.3
sigma3_true = 1.8
test_values = np.random.uniform(low=-3, high=5, size=100)
norm_range1 = (-4., 2.)
obs1 = 'obs1'
limits1 = zfit.Space(obs=obs1, limits=(-0.3, 1.5))
def create_gauss():
mu1 = Parameter("mu1a", mu1_true)
mu2 = Parameter("mu2a", mu2_true)
mu3 = Parameter("mu3a", mu3_true)
sigma1 = Parameter("sigma1a", sigma1_true)
sigma2 = Parameter("sigma2a", sigma2_true)
sigma3 = Parameter("sigma3a", sigma3_true)
gauss1 = Gauss(mu=mu1, sigma=sigma1, obs=obs1, name="gauss1a")
normal1 = Gauss(mu=mu1, sigma=sigma1, obs=obs1, name="normal1a")
gauss2 = Gauss(mu=mu2, sigma=sigma2, obs=obs1, name="gauss2a")
normal2 = Gauss(mu=mu2, sigma=sigma2, obs=obs1, name="normal2a")
gauss3 = Gauss(mu=mu3, sigma=sigma3, obs=obs1, name="gauss3a")
normal3 = Gauss(mu=mu3, sigma=sigma3, obs=obs1, name="normal3a")
def test_binned_template_pdf_bbfull(TemplateLikePDF):
bins1 = 15
bins2 = 10
counts1 = np.random.uniform(high=150,
size=(bins1, bins2)) # generate counts
counts2 = np.random.normal(loc=50, size=(bins1, bins2))
counts3 = (np.linspace(10, 100, num=bins1)[:, None] *
np.linspace(10, 500, num=bins2)[None, :])
binnings = [
zfit.binned.RegularBinning(bins1, 0, 10, name="obs1"),
zfit.binned.RegularBinning(bins2, -10, 7, name="obs2"),
]
binning = binnings
obs = zfit.Space(obs=["obs1", "obs2"], binning=binning)
mc1 = BinnedData.from_tensor(space=obs,
values=counts1,
variances=znp.ones_like(counts1) * 1.3)
mc2 = BinnedData.from_tensor(obs, counts2)
mc3 = BinnedData.from_tensor(obs, counts3)
counts_mc = counts1 + counts2 + counts3
counts1_data = np.random.uniform(high=150,
size=(bins1, bins2)) # generate counts
counts2_data = np.random.normal(loc=50, size=(bins1, bins2))
counts3_data = (np.linspace(10, 100, num=bins1)[:, None] *
np.linspace(20, 490, num=bins2)[None, :])
counts_data = counts1_data + counts2_data + counts3_data
counts_data *= 1.1
data = BinnedData.from_tensor(space=obs, values=counts_data)
pdf1 = TemplateLikePDF(data=mc1, sysshape=True)
pdf2 = TemplateLikePDF(data=mc2, sysshape=True)
pdf3 = TemplateLikePDF(data=mc3, sysshape=True)
assert len(pdf1.counts()) > 0
pdf_sum = BinnedSumPDF(pdfs=[pdf1, pdf2, pdf3], obs=obs)
counts1_flat = np.reshape(counts1, -1)
constraints1 = zfit.constraint.GaussianConstraint(
pdf1.params.values(),
observation=np.ones_like(counts1_flat),
uncertainty=np.sqrt(counts1_flat) / counts1_flat,
)
counts2_flat = np.reshape(counts2, -1)
constraints2 = zfit.constraint.GaussianConstraint(
pdf2.params.values(),
observation=np.ones_like(counts2_flat),
uncertainty=np.sqrt(counts2_flat) / counts2_flat,
)
counts3_flat = np.reshape(counts3, -1)
constraints3 = zfit.constraint.GaussianConstraint(
pdf3.params.values(),
observation=np.ones_like(counts3_flat),
uncertainty=np.sqrt(counts3_flat) / counts3_flat,
)
# constraints2 = zfit.constraint.PoissonConstraint(pdf2.params.values(), np.reshape(counts2, -1))
# constraints3 = zfit.constraint.PoissonConstraint(pdf3.params.values(), np.reshape(counts3, -1))
loss = ExtendedBinnedNLL(
pdf_sum, data, constraints=[constraints1, constraints2, constraints3])
# for i in progressbar.progressbar(range(1000000)):
loss.value()
loss.gradients()
print("start minimization")
minimizer = zfit.minimize.Minuit(verbosity=8, gradient=False)
minimizer.minimize(loss)
counts = pdf_sum.counts()
np.testing.assert_array_less(
counts_mc, counts_data
) # this is an assumption, if that is wrong, the test is flawed
np.testing.assert_array_less(counts, counts_data)
np.testing.assert_array_less(counts_mc, counts)
# DoubleCB.register_analytic_integral(func=double_crystalball_integral, limits=crystalball_integral_limits)
if __name__ == '__main__':
mu = ztf.constant(0)
sigma = ztf.constant(0.5)
alpha = ztf.constant(3)
n = ztf.constant(1)
# res = crystalball_func(np.random.random(size=100), mu, sigma, alpha, n)
# int1 = crystalball_integral(limits=zfit.Space(obs='obs1', limits=(-3, 5)),
# params={'mu': mu, "sigma": sigma, "alpha": alpha, "n": n})
from tensorflow.contrib import autograph
import matplotlib.pyplot as plt
new_code = autograph.to_code(crystalball_integral)
obs = zfit.Space(obs='obs1', limits=(-3, 1))
cb1 = CrystalBall(mu, sigma, alpha, n, obs=obs)
res = cb1.pdf(np.random.random(size=100))
int1 = cb1.integrate(limits=(-0.01, 2), norm_range=obs)
# tf.add_check_numerics_ops()
x = np.linspace(-5, 1, num=1000)
vals = cb1.pdf(x=x)
y = zfit.run(vals)[0]
plt.plot(x, y)
plt.show()
# print(new_code)
print(zfit.run(res))
print(zfit.run(int1))
# Copyright (c) 2021 zfit
import numpy as np
import zfit
# create space
obs = zfit.Space("x", limits=(-2, 3))
# parameters
mu = zfit.Parameter("mu", 1.2, -4, 6)
sigma = zfit.Parameter("sigma", 1.3, 0.5, 10)
# model building, pdf creation
gauss = zfit.pdf.Gauss(mu=mu, sigma=sigma, obs=obs)
# data
normal_np = np.random.normal(loc=2., scale=3., size=10000)
data = zfit.Data.from_numpy(obs=obs, array=normal_np)
# create NLL
nll = zfit.loss.UnbinnedNLL(model=gauss, data=data)
# # create a minimizer
minimizer = zfit.minimize.Minuit()
result = minimizer.minimize(nll)
# do the error calculations with a hessian approximation
param_errors = result.hesse()
# or here with minos
import zfit
simple_limit1 = zfit.Space(obs='obs1', limits=(-5, 1))
simple_limit2 = zfit.Space(obs='obs1', limits=(3, 7.5))
added_limits = simple_limit1 + simple_limit2
# OR equivalently
added_limits = simple_limit1.add(simple_limit2)
first_limit_lower = (-5, 6)
first_limit_upper = (5, 10)
second_limit_lower = (7, 12)
second_limit_upper = (9, 15)
third_limit_lower = (13, 20)
third_limit_upper = (14, 25)
lower = (first_limit_lower, second_limit_lower, third_limit_lower)
upper = (first_limit_upper, second_limit_upper, third_limit_upper)
limits = (lower, upper)
space1 = zfit.Space(obs=['obs1', 'obs2'], limits=limits)
assert space1.obs == ('obs1', 'obs2')
assert space1.n_obs == 2
assert space1.n_limits == 3
# noinspection PyUnresolvedReferences
from zfit.core.testing import setup_function, teardown_function, tester
from zfit.util.exception import (
LimitsIncompatibleError,
LimitsNotSpecifiedError,
SpaceIncompatibleError,
)
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import zfit
from zfit.core.dimension import add_spaces, limits_consistent, limits_overlap, combine_spaces
obs = ['obs' + str(i) for i in range(4)]
space1 = zfit.Space(obs=obs)
space2 = zfit.Space(obs=reversed(obs))
space3 = zfit.Space(obs=obs)
space4 = zfit.Space(obs=obs[0:2])
lower1d_1 = ((1, ), )
upper1d_1 = ((2, ), )
lower1d_12 = ((3, ), )
upper1d_12 = ((4, ), )
lower1d_22 = ((5, ), )
upper1d_22 = ((6, ), )
combined_lim_1d_12_and_22 = (((3, ), (5, )), ((4, ), (6, )))
def test_get_params():
obs = zfit.Space("obs", (-4, 5))
mu_nofloat = zfit.Parameter("mu_nofloat", 1, floating=False)
mu2 = zfit.Parameter("mu2", 1)
sigma2 = zfit.Parameter("sigma2", 2)
sigma_comp = zfit.ComposedParameter("sigma_comp",
lambda s: s * 0.7,
params=sigma2)
yield1 = zfit.Parameter("yield1", 10)
yield2 = zfit.Parameter("yield2", 200)
yield2_comp = zfit.ComposedParameter("yield2_comp",
lambda y: y * 0.9,
params=yield2)
gauss = zfit.pdf.Gauss(mu_nofloat, sigma_comp, obs)
gauss2 = zfit.pdf.Gauss(mu2, sigma2, obs)
gauss_ext = gauss.create_extended(yield1)
gauss2_ext = gauss2.create_extended(yield2_comp)
frac = zfit.Parameter("frac", 0.4)
sum1 = zfit.pdf.SumPDF([gauss, gauss2], fracs=frac)
sum1_ext = zfit.pdf.SumPDF([gauss_ext, gauss2_ext])
assert set(gauss.get_params()) == {sigma2}
with pytest.raises(ValueError):
set(
gauss.get_params(floating=True,
is_yield=False,
extract_independent=False))
with pytest.raises(ValueError):
set(
gauss_ext.get_params(floating=True,
is_yield=False,
extract_independent=False))
assert set(
gauss_ext.get_params(floating=None,
is_yield=False,
extract_independent=False)) == {
mu_nofloat, sigma_comp
}
with pytest.raises(ValueError):
set(
gauss_ext.get_params(floating=False,
is_yield=False,
extract_independent=False))
with pytest.raises(ValueError):
set(
gauss_ext.get_params(floating=True,
is_yield=None,
extract_independent=False))
assert set(
gauss_ext.get_params(floating=None,
is_yield=None,
extract_independent=False)) == {
mu_nofloat, sigma_comp, yield1
}
with pytest.raises(ValueError):
set(
gauss_ext.get_params(floating=False,
is_yield=None,
extract_independent=False))
assert (set(
gauss_ext.get_params(floating=False,
is_yield=True,
extract_independent=False)) == set())
assert set(
gauss_ext.get_params(floating=None,
is_yield=True,
extract_independent=False)) == {yield1}
assert set(
gauss_ext.get_params(floating=True,
is_yield=True,
extract_independent=False)) == {yield1}
# with extract deps
assert set(
gauss_ext.get_params(floating=True,
is_yield=False,
extract_independent=True)) == {sigma2}
assert set(
gauss_ext.get_params(floating=None,
is_yield=False,
extract_independent=True)) == {
mu_nofloat, sigma2
}
assert set(
gauss_ext.get_params(floating=False,
is_yield=False,
extract_independent=True)) == {mu_nofloat}
assert set(
gauss_ext.get_params(floating=True,
is_yield=None,
extract_independent=True)) == {sigma2, yield1}
assert set(
gauss_ext.get_params(floating=None,
is_yield=None,
extract_independent=True)) == {
mu_nofloat, sigma2, yield1
}
assert set(
gauss_ext.get_params(floating=False,
is_yield=None,
extract_independent=True)) == {mu_nofloat}
assert set(
gauss_ext.get_params(floating=True,
is_yield=True,
extract_independent=True)) == {yield1}
assert set(
gauss_ext.get_params(floating=None,
is_yield=True,
extract_independent=True)) == {yield1}
assert (set(
gauss_ext.get_params(floating=False,
is_yield=True,
extract_independent=True)) == set())
# Gauss ext2
assert set(
gauss2_ext.get_params(floating=True,
is_yield=False,
extract_independent=False)) == {mu2, sigma2}
assert set(
gauss2_ext.get_params(floating=None,
is_yield=False,
extract_independent=False)) == {mu2, sigma2}
assert (set(
gauss2_ext.get_params(floating=False,
is_yield=False,
extract_independent=False)) == set())
with pytest.raises(ValueError):
set(
gauss2_ext.get_params(floating=True,
is_yield=None,
extract_independent=False))
assert set(
gauss2_ext.get_params(floating=None,
is_yield=None,
extract_independent=False)) == {
mu2, sigma2, yield2_comp
}
with pytest.raises(ValueError):
set(
gauss2_ext.get_params(floating=False,
is_yield=None,
extract_independent=False))
with pytest.raises(ValueError):
set(
gauss2_ext.get_params(floating=False,
is_yield=True,
extract_independent=False))
assert set(
gauss2_ext.get_params(floating=None,
is_yield=True,
extract_independent=False)) == {yield2_comp}
with pytest.raises(ValueError):
set(
gauss2_ext.get_params(floating=True,
is_yield=True,
extract_independent=False))
# with extract deps
assert set(
gauss2_ext.get_params(floating=True,
is_yield=False,
extract_independent=True)) == {mu2, sigma2}
assert set(
gauss2_ext.get_params(floating=None,
is_yield=False,
extract_independent=True)) == {mu2, sigma2}
yield2.floating = False
assert (set(
gauss2_ext.get_params(floating=False,
is_yield=False,
extract_independent=True)) == set())
yield2.floating = True
assert (set(
gauss2_ext.get_params(floating=False,
is_yield=False,
extract_independent=True)) == set())
assert set(
gauss2_ext.get_params(floating=True,
is_yield=None,
extract_independent=True)) == {
mu2, sigma2, yield2
}
assert set(
gauss2_ext.get_params(floating=None,
is_yield=None,
extract_independent=True)) == {
mu2, sigma2, yield2
}
assert (set(
gauss2_ext.get_params(floating=False,
is_yield=None,
extract_independent=True)) == set())
assert set(
gauss2_ext.get_params(floating=True,
is_yield=True,
extract_independent=True)) == {yield2}
assert set(
gauss2_ext.get_params(floating=None,
is_yield=True,
extract_independent=True)) == {yield2}
assert (set(
gauss2_ext.get_params(floating=False,
is_yield=True,
extract_independent=True)) == set())
# sum extended
with pytest.raises(ValueError):
assert set(
sum1_ext.get_params(floating=True,
is_yield=False,
extract_independent=False))
frac0 = sum1_ext.fracs[0]
frac1 = sum1_ext.fracs[1]
assert set(
sum1_ext.get_params(floating=None,
is_yield=False,
extract_independent=False)) == {
mu_nofloat, sigma_comp, mu2, sigma2, frac0,
frac1
}
with pytest.raises(ValueError):
assert set(
sum1_ext.get_params(floating=False,
is_yield=False,
extract_independent=False))
with pytest.raises(ValueError):
set(
sum1_ext.get_params(floating=True,
is_yield=None,
extract_independent=False))
assert set(
sum1_ext.get_params(floating=None,
is_yield=None,
extract_independent=False)) == {
mu_nofloat, sigma_comp, mu2, sigma2, frac0,
frac1,
sum1_ext.get_yield()
}
with pytest.raises(ValueError):
set(
sum1_ext.get_params(floating=False,
is_yield=None,
extract_independent=False))
with pytest.raises(ValueError):
set(
sum1_ext.get_params(floating=False,
is_yield=True,
extract_independent=False))
assert set(
sum1_ext.get_params(floating=None,
is_yield=True,
extract_independent=False)) == {
sum1_ext.get_yield()
}
with pytest.raises(ValueError):
set(
sum1_ext.get_params(floating=True,
is_yield=True,
extract_independent=False))
# with extract deps
assert set(
sum1_ext.get_params(floating=True,
is_yield=False,
extract_independent=True)) == {
mu2,
sigma2,
yield1,
yield2, # fracs depend on them
}
assert set(
sum1_ext.get_params(floating=None,
is_yield=False,
extract_independent=True)) == {
mu_nofloat, mu2, sigma2, yield1, yield2
}
assert set(
sum1_ext.get_params(floating=False,
is_yield=False,
extract_independent=True)) == {mu_nofloat}
assert set(
sum1_ext.get_params(floating=True,
is_yield=None,
extract_independent=True)) == {
mu2, sigma2, yield1, yield2, yield1, yield2
}
assert set(
sum1_ext.get_params(floating=None,
is_yield=None,
extract_independent=True)) == {
mu_nofloat, mu2, sigma2, yield1, yield2
}
assert set(
sum1_ext.get_params(floating=False,
is_yield=None,
extract_independent=True)) == {mu_nofloat}
yield1.floating = False
assert set(
sum1_ext.get_params(floating=False,
is_yield=None,
extract_independent=True)) == {mu_nofloat, yield1}
yield1.floating = True
assert set(
sum1_ext.get_params(floating=True,
is_yield=True,
extract_independent=True)) == {yield1, yield2}
assert set(
sum1_ext.get_params(floating=None,
is_yield=True,
extract_independent=True)) == {yield1, yield2}
assert (set(
sum1_ext.get_params(floating=False,
is_yield=True,
extract_independent=True)) == set())
import numpy as np
import zfit
# create space
obs = zfit.Space("x", limits=(-10, 10))
# parameters
mu = zfit.Parameter("mu", 1., -4, 6)
sigma = zfit.Parameter("sigma", 1., 0.1, 10)
# model building, pdf creation
gauss = zfit.pdf.Gauss(mu=mu, sigma=sigma, obs=obs)
# data
normal_np = np.random.normal(loc=2., scale=3., size=10000)
data = zfit.data.Data.from_numpy(obs=obs, array=normal_np)
# create NLL
nll = zfit.loss.UnbinnedNLL(model=gauss, data=data)
# create a minimizer
minimizer = zfit.minimize.MinuitMinimizer()
result = minimizer.minimize(nll)
# do the error calculations, here with minos
param_errors = result.error()
# Copyright (c) 2022 zfit
import matplotlib.pyplot as plt
import mplhep
import numpy as np
import zfit
import zfit.models.tobinned
n_bins = 50
# create space
obs_binned = zfit.Space("x",
binning=zfit.binned.RegularBinning(50,
-10,
10,
name="x"))
obs = obs_binned.with_binning(None)
# parameters
mu = zfit.Parameter("mu", 1.0, -4, 6)
sigma = zfit.Parameter("sigma", 1.0, 0.1, 10)
lambd = zfit.Parameter("lambda", -0.06, -1, -0.01)
# model building, pdf creation
gauss = zfit.pdf.Gauss(mu=mu, sigma=sigma, obs=obs)
exponential = zfit.pdf.Exponential(lambd, obs=obs)
n_bkg = zfit.Parameter("n_bkg", 20000)
n_sig = zfit.Parameter("n_sig", 1000)
gauss_extended = gauss.create_extended(n_sig)
def test_cb_dcb():
obs = zfit.Space("x", limits=bounds)
mu_ = zfit.Parameter("mu_cb5", mu)
sigma_ = zfit.Parameter("sigma_cb5", sigma)
alphal_ = zfit.Parameter("alphal_cb5", alphal)
nl_ = zfit.Parameter("nl_cb5", nl)
alphar_ = zfit.Parameter("alphar_cb5", alphar)
nr_ = zfit.Parameter("nr_cb5", nr)
cbl = CrystalBall(obs=obs, mu=mu_, sigma=sigma_, alpha=alphal_, n=nl_)
cbr = CrystalBall(obs=obs, mu=mu_, sigma=sigma_, alpha=-alphar_, n=nr_)
dcb = DoubleCB(obs=obs,
mu=mu_,
sigma=sigma_,
alphal=alphal_,
nl=nl_,
alphar=alphar_,
nr=nr_)
sample_testing(cbl)
sample_testing(cbr)
sample_testing(dcb)
x = np.random.normal(mu, sigma, size=10000)
probsl = eval_testing(cbl, x)
probsr = eval_testing(cbr, x)
assert not any(np.isnan(probsl))
assert not any(np.isnan(probsr))
probsl_scipy = crystalball.pdf(x, beta=alphal, m=nl, loc=mu, scale=sigma)
probsr_scipy = crystalball.pdf(-x + 2 * mu,
beta=alphar,
m=nr,
loc=mu,
scale=sigma)
# We take the ration as the normalization is not fixed
ratio_l = probsl_scipy / probsl
ratio_r = probsr_scipy / probsr
assert np.allclose(ratio_l, ratio_l[0])
assert np.allclose(ratio_r, ratio_r[0])
kwargs = dict(limits=(-5.0, mu), norm_range=lbounds)
intl = cbl.integrate(**kwargs) - dcb.integrate(**kwargs)
assert pytest.approx(intl.numpy(), abs=1e-3) == 0.0
intl = cbr.integrate(**kwargs) - dcb.integrate(**kwargs)
assert pytest.approx(intl.numpy(), abs=1e-3) != 0
# TODO: update test to fixed DCB integral
kwargs = dict(limits=(mu, 2.0), norm_range=rbounds)
dcb_integr1 = dcb.integrate(**kwargs)
intr = cbr.integrate(**kwargs) - dcb_integr1
assert pytest.approx(intr.numpy(), abs=1e-3) == 0.0
intr = cbl.integrate(**kwargs) - dcb.integrate(**kwargs)
assert pytest.approx(intr.numpy(), abs=1e-3) != 0.0
xl = x[x <= mu]
xr = x[x > mu]
probs_dcb_l = eval_testing(dcb, xl)
probs_dcb_r = eval_testing(dcb, xr)
probsl_scipy = crystalball.pdf(xl, beta=alphal, m=nl, loc=mu, scale=sigma)
probsr_scipy = crystalball.pdf(-xr + 2 * mu,
beta=alphar,
m=nr,
loc=mu,
scale=sigma)
ratio_l = probsl_scipy / probs_dcb_l
ratio_r = probsr_scipy / probs_dcb_r
assert np.allclose(ratio_l, ratio_l[0])
assert np.allclose(ratio_r, ratio_r[0])
rnd_limits = sorted(np.random.uniform(*bounds, 130))
integrals = []
for low, up in zip(rnd_limits[:-1], rnd_limits[1:]):
integrals.append(dcb.integrate((low, up), norm=False))
integral = np.sum(integrals)
integral_full = zfit.run(dcb.integrate((bounds[0], up), norm=False))
assert pytest.approx(integral_full, integral)
def test_cb_dcb():
obs = zfit.Space('x', limits=bounds)
mu_ = zfit.Parameter('mu_cb', mu)
sigma_ = zfit.Parameter('sigma_cb', sigma)
alphal_ = zfit.Parameter('alphal_cb', alphal)
nl_ = zfit.Parameter('nl_cb', nl)
alphar_ = zfit.Parameter('alphar_cb', alphar)
nr_ = zfit.Parameter('nr_cb', nr)
cbl = CrystalBall(obs=obs, mu=mu_, sigma=sigma_, alpha=alphal_, n=nl_)
cbr = CrystalBall(obs=obs, mu=mu_, sigma=sigma_, alpha=-alphar_, n=nr_)
dcb = DoubleCB(obs=obs,
mu=mu_,
sigma=sigma_,
alphal=alphal_,
nl=nl_,
alphar=alphar_,
nr=nr_)
sample_testing(cbl)
sample_testing(cbr)
sample_testing(dcb)
x = np.random.normal(mu, sigma, size=10000)
probsl = eval_testing(cbl, x)
probsr = eval_testing(cbr, x)
assert not any(np.isnan(probsl))
assert not any(np.isnan(probsr))
probsl_scipy = crystalball.pdf(x, beta=alphal, m=nl, loc=mu, scale=sigma)
probsr_scipy = crystalball.pdf(-x, beta=alphar, m=nr, loc=mu, scale=sigma)
ratio_l = probsl_scipy / probsl
ratio_r = probsr_scipy / probsr
assert np.allclose(ratio_l, ratio_l[0])
assert np.allclose(ratio_r, ratio_r[0])
kwargs = dict(limits=(-5.0, mu), norm_range=lbounds)
intl = cbl.integrate(**kwargs) - dcb.integrate(**kwargs)
assert pytest.approx(zfit.run(intl)) == 0.
intl = cbr.integrate(**kwargs) - dcb.integrate(**kwargs)
assert pytest.approx(zfit.run(intl)) != 0
kwargs = dict(limits=(mu, 2.0), norm_range=rbounds)
intr = cbr.integrate(**kwargs) - dcb.integrate(**kwargs)
assert pytest.approx(zfit.run(intr)) == 0.
intr = cbl.integrate(**kwargs) - dcb.integrate(**kwargs)
assert pytest.approx(zfit.run(intr)) != 0.
xl = x[x <= mu]
xr = x[x > mu]
probs_dcb_l = eval_testing(dcb, xl)
probs_dcb_r = eval_testing(dcb, xr)
probsl_scipy = crystalball.pdf(xl, beta=alphal, m=nl, loc=mu, scale=sigma)
probsr_scipy = crystalball.pdf(-xr, beta=alphar, m=nr, loc=mu, scale=sigma)
ratio_l = probsl_scipy / probs_dcb_l
ratio_r = probsr_scipy / probs_dcb_r
assert np.allclose(ratio_l, ratio_l[0])
assert np.allclose(ratio_r, ratio_r[0])
