def test_range():
'''
Test the "Range" class.
'''
# Simple constructor
v = [(1, 2), (5, 6)]
r = minkit.Range(v)
assert np.allclose(r.bounds, v)
# Do calculations in a range
m = minkit.Parameter('m', bounds=(0, 10))
k = minkit.Parameter('k', -0.5, bounds=(-0.8, -0.3))
e = minkit.Exponential('exponential', m, k)
m.set_range('sides', [(0, 4), (6, 10)])
assert np.allclose(e.norm(range='sides'),
e.numerical_normalization(range='sides'))
data = e.generate(10000)
with helpers.fit_test(e) as test:
with minkit.minimizer('uml', e, data, minimizer='minuit', range='sides') as minuit:
test.result = minuit.migrad()
# Test generation of data only in the range
data = e.generate(10000, range='sides')
with helpers.fit_test(e) as test:
with minkit.minimizer('uml', e, data, minimizer='minuit', range='sides') as minuit:
test.result = minuit.migrad()
def test_range():
'''
Test for disjointed ranges.
'''
# Do calculations in a range
m = minkit.Parameter('m', bounds=(0, 10))
k = minkit.Parameter('k', -0.5, bounds=(-0.8, -0.3))
e = minkit.Exponential('exponential', m, k)
m.set_range('sides', [(0, 4), (6, 10)])
helpers.check_numerical_normalization(e, range='sides')
data = e.generate(10000)
with helpers.fit_test(e) as test:
with minkit.minimizer('uml',
e,
data,
minimizer='minuit',
range='sides') as minuit:
test.result = minuit.migrad()
# Test generation of data only in the range
data = e.generate(10000, range='sides')
with helpers.fit_test(e) as test:
with minkit.minimizer('uml',
e,
data,
minimizer='minuit',
range='sides') as minuit:
test.result = minuit.migrad()
def test_unbinned_extended_maximum_likelihood():
'''
Test the "unbinned_extended_maximum_likelihood" FCN.
'''
m = minkit.Parameter('m', bounds=(-5, +15))
# Create an Exponential PDF
k = minkit.Parameter('k', -0.1, bounds=(-0.2, 0))
e = minkit.Exponential('exponential', m, k)
# Create a Gaussian PDF
c = minkit.Parameter('c', 10., bounds=(8, 12))
s = minkit.Parameter('s', 1., bounds=(0.5, 2))
g = minkit.Gaussian('gaussian', m, c, s)
# Add them together
ng = minkit.Parameter('ng', 10000, bounds=(0, 100000))
ne = minkit.Parameter('ne', 1000, bounds=(0, 100000))
pdf = minkit.AddPDFs.two_components('model', g, e, ng, ne)
data = pdf.generate(int(ng.value + ne.value))
with helpers.fit_test(pdf) as test:
with minkit.minimizer('ueml', pdf, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Add constraints
cc = minkit.Parameter('cc', 10)
sc = minkit.Parameter('sc', 1)
gc = minkit.Gaussian('constraint', c, cc, sc)
with helpers.fit_test(pdf) as test:
with minkit.minimizer('ueml', pdf, data, minimizer='minuit', constraints=[gc]) as minuit:
test.result = minuit.migrad()
def test_unbinned_maximum_likelihood():
'''
Test the "unbinned_maximum_likelihood" FCN.
'''
# Simple fit to a Gaussian
m = minkit.Parameter('m', bounds=(5, 15))
c = minkit.Parameter('c', 10., bounds=(5, 15))
s = minkit.Parameter('s', 1., bounds=(0.5, 2))
g = minkit.Gaussian('gaussian', m, c, s)
data = g.generate(10000)
with helpers.fit_test(g) as test:
with minkit.minimizer('uml', g, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Add constraints
cc = minkit.Parameter('cc', 10)
sc = minkit.Parameter('sc', 0.1)
gc = minkit.Gaussian('constraint', c, cc, sc)
with helpers.fit_test(g) as test:
with minkit.minimizer('uml',
g,
data,
minimizer='minuit',
constraints=[gc]) as minuit:
test.result = minuit.migrad()
def test_formulapdf(tmpdir):
'''
Test the "FormulaPDF" class.
'''
x = minkit.Parameter('x', bounds=(-2. * np.pi, +2 * np.pi))
a = minkit.Parameter('a', 1., bounds=(0.9, 1.1))
b = minkit.Parameter('b', 0., bounds=(-0.1, 0.1))
pdf = minkit.FormulaPDF.unidimensional('pdf', 'pow(sin(a * x + b), 2)', x,
[a, b])
norm = pdf.norm()
data = pdf.generate(10000)
with helpers.fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data) as minimizer:
test.result = minimizer.migrad()
# Include the integral
pdf = minkit.FormulaPDF.unidimensional(
'pdf',
'pow(sin(a * x + b), 2)',
x, [a, b],
primitive='- sin(2 * (a * x + b)) -2 * (a * x + b) / (4 * a)')
assert np.allclose(norm, pdf.norm())
with helpers.fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data) as minimizer:
test.result = minimizer.migrad()
# In two dimensions
x = minkit.Parameter('x', bounds=(0, 10))
y = minkit.Parameter('y', bounds=(0, 10))
ax = minkit.Parameter('ax', -0.01, bounds=(-1, 0))
ay = minkit.Parameter('ay', -0.01, bounds=(-1, 0))
pdf = minkit.FormulaPDF('pdf', 'exp(ax * x) * exp(ay * y)', [x, y],
[ax, ay])
data = pdf.generate(10000)
with helpers.fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data) as minimizer:
test.result = minimizer.migrad()
# Test the JSON conversion
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(pdf), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
p = minkit.pdf_from_json(json.load(fi))
check_pdfs(p, pdf)
def test_interppdf(tmpdir):
'''
Test the InterpPDF class.
'''
m = minkit.Parameter('m', bounds=(-3, +3))
centers = np.linspace(*m.bounds, 100)
values = np.exp(-0.5 * centers**2)
ip = minkit.InterpPDF.from_ndarray('ip', m, centers, values)
ip.max() # check that we can calculate the maximum
# Test the JSON conversion
with open(os.path.join(tmpdir, 'ip.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(ip), fi)
with open(os.path.join(tmpdir, 'ip.json'), 'rt') as fi:
p = minkit.pdf_from_json(json.load(fi))
check_pdfs(p, ip)
# Check copying the PDF
ip.copy()
# Combine the PDF with another
k = minkit.Parameter('k', -0.1, bounds=(-1, +1))
e = minkit.Exponential('exp', m, k)
y = minkit.Parameter('y', 0.5, bounds=(0, 1))
pdf = minkit.AddPDFs.two_components('pdf', ip, e, y)
data = pdf.generate(10000)
with fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data,
minimizer='minuit') as minimizer:
test.result = minimizer.migrad()
bdata = data.make_binned(20)
with fit_test(pdf) as test:
with minkit.minimizer('bml', pdf, bdata,
minimizer='minuit') as minimizer:
test.result = minimizer.migrad()
# Test the construction from a binned data set
minkit.InterpPDF.from_binned_dataset('pdf', bdata)
def test_simultaneous_minimizer():
'''
Test the "simultaneous_minimizer" function.
'''
m = minkit.Parameter('m', bounds=(10, 20))
# Common mean
s = minkit.Parameter('s', 1, bounds=(0.1, +3))
# First Gaussian
c1 = minkit.Parameter('c1', 15, bounds=(10, 20))
g1 = minkit.Gaussian('g1', m, c1, s)
data1 = g1.generate(size=1000)
# Second Gaussian
c2 = minkit.Parameter('c2', 15, bounds=(10, 20))
g2 = minkit.Gaussian('g2', m, c2, s)
data2 = g2.generate(size=10000)
categories = [
minkit.Category('uml', g1, data1),
minkit.Category('uml', g2, data2)
]
with helpers.fit_test(categories, simultaneous=True) as test:
with minkit.simultaneous_minimizer(categories,
minimizer='minuit') as minuit:
test.result = minuit.migrad()
def test_constpdf(tmpdir):
'''
Test a fit with a constant PDF.
'''
pdf = helpers.default_add_pdfs(extended=False)
# Check for "get_values" and "set_values"
p = pdf.norm()
pdf.set_values(**pdf.get_values())
assert np.allclose(p, pdf.norm())
# Test a simple fit
data = pdf.generate(10000)
with fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Test the JSON conversion
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(pdf), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
s = minkit.pdf_from_json(json.load(fi))
check_multi_pdfs(s, pdf)
def test_minimizer():
'''
Test the "minimizer" function
'''
m = minkit.Parameter('m', bounds=(20, 80))
c = minkit.Parameter('c', 50, bounds=(30, 70))
s = minkit.Parameter('s', 5, bounds=(1, 10))
g = minkit.Gaussian('gaussian', m, c, s)
initials = g.get_values()
arr = np.random.normal(c.value, s.value, 10000)
data = minkit.DataSet.from_array(arr, m)
with helpers.fit_test(g) as test:
with minkit.minimizer('uml', g, data, minimizer='minuit') as minuit:
test.result = pytest.shared_result = minuit.migrad()
pytest.shared_names = [p.name for p in g.all_args]
# Unweighted fit to uniform distribution fails
arr = np.random.uniform(*m.bounds, 100000)
data = minkit.DataSet.from_array(arr, m)
with minkit.minimizer('uml', g, data, minimizer='minuit') as minuit:
r = minuit.migrad()
print(r)
reg = minkit.minuit_to_registry(r.params)
assert not np.allclose(reg.get(s.name).value, initials[s.name])
# With weights fits correctly
data.weights = minkit.as_ndarray(g(data))
with helpers.fit_test(g) as test:
with minkit.minimizer('uml', g, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Test the binned case
data = data.make_binned(bins=100)
with helpers.fit_test(g) as test:
with minkit.minimizer('bml', g, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
def test_convpdfs(tmpdir):
'''
Test the "ConvPDFs" class.
'''
m = minkit.Parameter('m', bounds=(-20, +20))
# Create two Gaussians
c1 = minkit.Parameter('c1', 0, bounds=(-2, +2))
s1 = minkit.Parameter('s1', 3, bounds=(0.5, +10))
g1 = minkit.Gaussian('g1', m, c1, s1)
c2 = minkit.Parameter('c2', 0, bounds=(-2, +2))
s2 = minkit.Parameter('s2', 4, bounds=(0.5, +10))
g2 = minkit.Gaussian('g2', m, c2, s2)
pdf = minkit.ConvPDFs('convolution', g1, g2)
data = pdf.generate(10000)
# Check that the output is another Gaussian with bigger standard deviation
mean = minkit.core.aop.sum(data[m.name]) / len(data)
var = minkit.core.aop.sum((data[m.name] - mean)**2) / len(data)
assert np.allclose(var, s1.value**2 + s2.value**2, rtol=0.1)
# Check that the normalization is correct
with pdf.bind() as proxy:
assert np.allclose(proxy.integral(), 1.)
assert np.allclose(proxy.norm(), 1.)
assert np.allclose(proxy.numerical_normalization(), 1.)
# Ordinary check for PDFs
values, edges = np.histogram(minkit.as_ndarray(data[m.name]),
bins=100,
range=m.bounds)
centers = minkit.DataSet.from_array(0.5 * (edges[1:] + edges[:-1]), m)
pdf_values = minkit.plotting.scaled_pdf_values(pdf, centers, values, edges)
assert np.allclose(np.sum(pdf_values), np.sum(values), rtol=0.01)
# Test a fit
with fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Test the JSON conversion
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(pdf), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
s = minkit.pdf_from_json(json.load(fi))
check_multi_pdfs(s, pdf)
def test_sweights():
'''
Test the "sweights" function.
'''
pdf = helpers.default_add_pdfs(extended=True, yields=['ng', 'ne'])
ng = pdf.args.get('ng')
ne = pdf.args.get('ne')
data = pdf.generate(int(ng.value + ne.value))
with helpers.fit_test(pdf) as test:
with minkit.minimizer('ueml', pdf, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Now we fix the parameters that are not yields, and we re-run the fit
for p in pdf.pdfs:
for a in p.args:
a.constant = True
with helpers.fit_test(pdf) as test:
with minkit.minimizer('ueml', pdf, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
result = pdf.args.copy()
# Calculate the s-weights (first comes from the Gaussian, second from the exponential)
sweights, V = minkit.sweights(pdf.pdfs,
result.reduce(['ng', 'ne']),
data,
return_covariance=True)
# The s-weights are normalized
assert np.allclose(aop.sum(sweights[0]), result.get(ng.name).value)
assert np.allclose(aop.sum(sweights[1]), result.get(ne.name).value)
# The uncertainty on the yields is reflected in the s-weights
assert np.allclose(aop.sum(sweights[0]**2), V[0][0])
assert np.allclose(aop.sum(sweights[1]**2), V[1][1])
# Check the calculation of the uncertainties of the s-weights
minkit.sweights_u(data.values.as_ndarray(), sweights[0].as_ndarray())
def test_binned_chisquare():
'''
Test the "binned_chisquare" FCN.
'''
# Single PDF
m = minkit.Parameter('m', bounds=(0, 20))
c = minkit.Parameter('c', 10., bounds=(8, 12))
# all bins must be highly populated
s = minkit.Parameter('s', 3., bounds=(2, 7))
g = minkit.Gaussian('gaussian', m, c, s)
data = g.generate(10000)
values, edges = np.histogram(
data[m.name].as_ndarray(), range=m.bounds, bins=100)
data = minkit.BinnedDataSet.from_ndarray(edges, m, values)
with helpers.fit_test(g) as test:
with minkit.minimizer('chi2', g, data) as minimizer:
test.result = minimizer.migrad()
# Many PDfs
k = minkit.Parameter('k', -0.1, bounds=(-1, 0))
e = minkit.Exponential('exponential', m, k)
ng = minkit.Parameter('ng', 10000, bounds=(0, 100000))
ne = minkit.Parameter('ne', 1000, bounds=(0, 100000))
pdf = minkit.AddPDFs.two_components('pdf', g, e, ng, ne)
data = pdf.generate(int(ng.value + ne.value))
values, edges = np.histogram(
data[m.name].as_ndarray(), range=m.bounds, bins=100)
data = minkit.BinnedDataSet.from_ndarray(edges, m, values)
with helpers.fit_test(pdf) as test:
with minkit.minimizer('chi2', pdf, data) as minimizer:
test.result = minimizer.migrad()
def test_binned_extended_maximum_likelihood():
'''
Test the "binned_extended_maximum_likelihood" FCN.
'''
pdf = helpers.default_add_pdfs(extended=True, yields=('ng', 'ne'))
ntot = int(pdf.args.get('ng').value + pdf.args.get('ne').value)
data = pdf.generate(ntot).make_binned(100)
with helpers.fit_test(pdf) as test:
with minkit.minimizer('beml', pdf, data) as minimizer:
test.result = minimizer.migrad()
def test_prodpdfs(tmpdir):
'''
Test the "ProdPDFs" class.
'''
# Create two Gaussians
mx = minkit.Parameter('mx', bounds=(-5, +5))
cx = minkit.Parameter('cx', 0., bounds=(-2, +2))
sx = minkit.Parameter('sx', 1., bounds=(0.1, +3))
gx = minkit.Gaussian('gx', mx, cx, sx)
my = minkit.Parameter('my', bounds=(-5, +5))
cy = minkit.Parameter('cy', 0., bounds=(-2, +2))
sy = minkit.Parameter('sy', 2., bounds=(0.5, +3))
gy = minkit.Gaussian('gy', my, cy, sy)
pdf = minkit.ProdPDFs('pdf', [gx, gy])
# Test integration
helpers.check_numerical_normalization(pdf)
# Test consteness of the PDFs
for p in gx.all_args:
p.constant = True
assert gx.constant and not pdf.constant
for p in gy.all_args:
p.constant = True
assert pdf.constant
# Test the JSON conversion
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(pdf), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
s = minkit.pdf_from_json(json.load(fi))
check_multi_pdfs(s, pdf)
# Check copying the PDF
pdf.copy()
# Do a simple fit
for p in pdf.all_real_args:
p.constant = False
data = pdf.generate(10000)
with fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data) as minimizer:
test.result = minimizer.migrad()
def test_formula(tmpdir):
'''
Test the "Formula" class.
'''
a = minkit.Parameter('a', 1)
b = minkit.Parameter('b', 2)
c = minkit.Formula('c', 'a * b', [a, b])
assert np.allclose(c.value, a.value * b.value)
# Test its use on a PDF
m = minkit.Parameter('m', bounds=(10, 20))
c = minkit.Parameter('c', 15, bounds=(10, 20))
s = minkit.Formula('s', '0.1 + c / 10', [c])
g = minkit.Gaussian('gaussian', m, c, s)
data = g.generate(10000)
nd = np.random.normal(c.value, s.value, 10000)
compare_with_numpy(g, nd, m)
with helpers.fit_test(g) as test:
with minkit.minimizer('uml', g, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Test the JSON (only for formula)
with open(os.path.join(tmpdir, 'r.json'), 'wt') as fi:
json.dump(s.to_json_object(), fi)
with open(os.path.join(tmpdir, 'r.json'), 'rt') as fi:
s = minkit.Formula.from_json_object(json.load(fi), g.all_real_args)
# Test the JSON (whole PDF)
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(g), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
s = minkit.pdf_from_json(json.load(fi))
def test_constpdf(tmpdir):
'''
Test a fit with a constant PDF.
'''
m = minkit.Parameter('m', bounds=(0, 10))
# Create an Exponential PDF
k = minkit.Parameter('k', -0.05)
e = minkit.Exponential('exponential', m, k)
# Create a Gaussian PDF
c = minkit.Parameter('c', 5., bounds=(0, 10))
s = minkit.Parameter('s', 1., bounds=(0.5, 3))
g = minkit.Gaussian('gaussian', m, c, s)
# Add them together
g2e = minkit.Parameter('g2e', 0.5, bounds=(0, 1))
pdf = minkit.AddPDFs.two_components('model', g, e, g2e)
# Check for "get_values" and "set_values"
p = pdf.norm()
pdf.set_values(**pdf.get_values())
assert np.allclose(p, pdf.norm())
# Test a simple fit
data = pdf.generate(10000)
with fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Test the JSON conversion
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(pdf), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
s = minkit.pdf_from_json(json.load(fi))
check_multi_pdfs(s, pdf)
def test_formula(tmpdir):
'''
Test the "Formula" class.
'''
a = minkit.Parameter('a', 1)
b = minkit.Parameter('b', 2)
c = minkit.Formula('c', '{a} * {b}', [a, b])
assert np.allclose(c.value, a.value * b.value)
# Test its use on a PDF
m = minkit.Parameter('m', bounds=(10, 20))
c = minkit.Parameter('c', 15, bounds=(10, 20))
s = minkit.Formula('s', '0.1 + {c} / 10', [c])
g = minkit.Gaussian('gaussian', m, c, s)
data = g.generate(10000)
nd = rndm_gen.normal(c.value, s.value, 10000)
compare_with_numpy(g, nd, m)
with helpers.fit_test(g) as test:
with minkit.minimizer('uml', g, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Test the JSON (only for formula)
with open(os.path.join(tmpdir, 'r.json'), 'wt') as fi:
json.dump(s.to_json_object(), fi)
with open(os.path.join(tmpdir, 'r.json'), 'rt') as fi:
s = minkit.Formula.from_json_object(json.load(fi), g.all_real_args)
# Test the JSON (whole PDF)
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(g), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
minkit.pdf_from_json(json.load(fi))
# Test the copy of a formula
new_args = s.args.copy()
assert all(not o is p for o, p in zip(s.args, s.copy(new_args).args))
# Test for a formula depending on another formula
m = minkit.Parameter('m', bounds=(10, 20))
c = minkit.Parameter('c', 15, bounds=(10, 20))
d = minkit.Formula('d', '0.1 + {c} / 10', [c])
s = minkit.Formula('s', '2 * {d}', [d])
g = minkit.Gaussian('gaussian', m, c, s)
assert s.value == 3.2
data = g.generate(10000)
with helpers.fit_test(g) as test:
with minkit.minimizer('uml', g, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Test the JSON (only for formula)
with open(os.path.join(tmpdir, 'r.json'), 'wt') as fi:
json.dump(s.to_json_object(), fi)
with open(os.path.join(tmpdir, 'r.json'), 'rt') as fi:
s = minkit.Formula.from_json_object(json.load(fi), g.all_args)
# Test the copy of a formula depending on another formula
new_args = s.args.copy()
assert all(not o is p for o, p in zip(s.args, s.copy(new_args).args))
# Test the JSON (whole PDF)
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(g), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
minkit.pdf_from_json(json.load(fi))
def test_convpdfs(tmpdir):
'''
Test the "ConvPDFs" class.
'''
m = minkit.Parameter('m', bounds=(-20, +20))
# Create two Gaussians
c1 = minkit.Parameter('c1', 0, bounds=(-2, +2))
s1 = minkit.Parameter('s1', 3, bounds=(0.5, +10))
g1 = minkit.Gaussian('g1', m, c1, s1)
c2 = minkit.Parameter('c2', 0, bounds=(-2, +2))
s2 = minkit.Parameter('s2', 4, bounds=(0.5, +10))
g2 = minkit.Gaussian('g2', m, c2, s2)
pdf = minkit.ConvPDFs('convolution', g1, g2)
data = pdf.generate(10000)
# Check that the output is another Gaussian with bigger standard deviation
mean = aop.sum(data[m.name]) / len(data)
var = aop.sum((data[m.name] - mean)**2) / len(data)
assert np.allclose(var, s1.value**2 + s2.value**2, rtol=0.1)
# Ordinary check for PDFs
values, edges = np.histogram(data[m.name].as_ndarray(),
bins=100,
range=m.bounds)
centers = minkit.DataSet.from_ndarray(0.5 * (edges[1:] + edges[:-1]), m)
pdf_values = minkit.utils.core.scaled_pdf_values(pdf, centers, values,
edges)
assert np.allclose(np.sum(pdf_values), np.sum(values), rtol=0.01)
# Test a fit
s2.constant = True # otherwise the minimization is undefined
with fit_test(pdf) as test:
with minkit.minimizer('uml', pdf, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Test the JSON conversion
with open(os.path.join(tmpdir, 'pdf.json'), 'wt') as fi:
json.dump(minkit.pdf_to_json(pdf), fi)
with open(os.path.join(tmpdir, 'pdf.json'), 'rt') as fi:
s = minkit.pdf_from_json(json.load(fi))
check_multi_pdfs(s, pdf)
# Check copying the PDF
pdf.copy()
# Test for binned data samples
bdata = data.make_binned(20)
with fit_test(pdf) as test:
with minkit.minimizer('bml', pdf, bdata, minimizer='minuit') as minuit:
test.result = minuit.migrad()
def test_binned_maximum_likelihood():
'''
Tets the "binned_maximum_likelihood" FCN.
'''
# Simple fit to a Gaussian
m = minkit.Parameter('m', bounds=(5, 15))
c = minkit.Parameter('c', 10., bounds=(8, 12))
s = minkit.Parameter('s', 1., bounds=(0.5, 2))
g = minkit.Gaussian('gaussian', m, c, s)
values, edges = np.histogram(np.random.normal(c.value, s.value, 10000),
bins=100)
data = minkit.BinnedDataSet.from_array(edges, m, values)
with helpers.fit_test(g) as test:
with minkit.minimizer('bml', g, data, minimizer='minuit') as minuit:
test.result = minuit.migrad()
# Add constraints
cc = minkit.Parameter('cc', 10)
sc = minkit.Parameter('sc', 0.1)
gc = minkit.Gaussian('constraint', c, cc, sc)
with helpers.fit_test(g) as test:
with minkit.minimizer('bml',
g,
data,
minimizer='minuit',
constraints=[gc]) as minuit:
test.result = minuit.migrad()
# Test for a composed PDF
k = minkit.Parameter('k', -0.1, bounds=(-1, 0))
e = minkit.Exponential('e', m, k)
y = minkit.Parameter('y', 0.5, bounds=(0, 1))
pdf = minkit.AddPDFs.two_components('pdf', g, e, y)
data = pdf.generate(10000)
values, edges = np.histogram(minkit.as_ndarray(data[m.name]), bins=100)
data = minkit.BinnedDataSet.from_array(edges, m, values)
with helpers.fit_test(pdf) as test:
with minkit.minimizer('bml', pdf, data) as minimizer:
test.result = minimizer.migrad()
# Test for a PDF with no "evaluate_binned" function defined
m = minkit.Parameter('m', bounds=(0, 10))
a = minkit.Parameter('a', 0)
theta = minkit.Parameter('theta', 2, bounds=(0, 3))
alpha = minkit.Parameter('alpha', 0.5)
beta = minkit.Parameter('beta', 2)
pdf = minkit.Amoroso('amoroso', m, a, theta, alpha, beta)
data = pdf.generate(1000)
values, edges = np.histogram(minkit.as_ndarray(data[m.name]),
range=m.bounds,
bins=100)
data = minkit.BinnedDataSet.from_array(edges, m, values)
with helpers.fit_test(pdf) as test:
with minkit.minimizer('bml', pdf, data) as minimizer:
test.result = minimizer.migrad()
