def model(fit_range, bin):
nrm_bkg_pdf = Normalized(rename(exp, ['x', 'l%d' % bin]), fit_range)
ext_bkg_pdf = Extended(nrm_bkg_pdf, extname='Ncomb_%d' % bin)
ext_sig_pdf = Extended(rename(gaussian, ['x', 'm%d' % bin, "sigma%d" % bin]), extname='Nsig_%d' % bin)
tot_pdf = AddPdf(ext_bkg_pdf, ext_sig_pdf)
print('pdf: {}'.format(describe(tot_pdf)))
return tot_pdf
def test_rename():
def f(x, y, z):
return None
assert describe(f) == ["x", "y", "z"]
g = rename(f, ["x", "a", "b"])
assert describe(g) == ["x", "a", "b"]
def test_rename():
def f(x, y, z):
return None
assert describe(f) == ['x', 'y', 'z']
g = rename(f, ['x', 'a', 'b'])
assert describe(g) == ['x', 'a', 'b']
def test_rename():
def f(x, y, z):
return None
assert_equal(describe(f), ["x", "y", "z"])
g = rename(f, ["x", "a", "b"])
assert_equal(describe(g), ["x", "a", "b"])
def test_rename():
def f(x, y, z):
return None
assert describe(f) == ['x', 'y', 'z']
g = rename(f, ['x', 'a', 'b'])
assert describe(g) == ['x', 'a', 'b']
bound = (1.83,1.91) bgpdf = Normalized(linear,bound) # <codecell> describe(bgpdf) # <codecell> #remember our breit wigner also has m argument which means different thing describe(rtv_breitwigner) # <codecell> #renaming is easy signalpdf = Normalized(rename(rtv_breitwigner,['x','mass','gamma']),(1.83,1.91)) # <codecell> describe(signalpdf) # <codecell> #now we can add them total_pdf = AddPdfNorm(signalpdf,bgpdf) #if you want to just directly add them up with out the factor(eg. adding extended pdf) #use AddPdf describe(total_pdf) # <codecell>
bound = (1.83,1.91) bgpdf = Normalized(linear,bound) # <codecell> describe(bgpdf) # <codecell> #remember our breit wigner also has m argument which means different thing describe(rtv_breitwigner) # <codecell> #renaming is easy signalpdf = Normalized(rename(rtv_breitwigner,['x','mass','gamma']),(1.83,1.91)) # <codecell> describe(signalpdf) # <codecell> #now we can add them total_pdf = AddPdfNorm(signalpdf,bgpdf) #if you want to just directly add them up with out the factor(eg. adding extended pdf) #use AddPdf describe(total_pdf) # <codecell>
from iminuit import Minuit
from probfit import AddPdfNorm, BinnedLH, gaussian, Extended, rename
from matplotlib import pyplot as plt
from numpy.random import randn
import numpy as np
peak1 = randn(1000)*0.5 + 1.
peak2 = randn(500)*0.5 + 0.
#two peaks data with shared width
data = np.concatenate([peak1, peak2])
#Share the width
#If you use Normalized here. Do not reuse the object.
#It will be really slow due to cache miss. Read Normalized doc for more info.
pdf1 = rename(gaussian, ('x', 'm_1', 'sigma'))
pdf2 = rename(gaussian, ('x', 'm_2', 'sigma'))
compdf = AddPdfNorm(pdf1, pdf2) # merge by name (merge sigma)
ulh = BinnedLH(compdf, data, extended=False)
m = Minuit(ulh, m_1=1.1, m_2=-0.1, sigma=0.48, f_0=0.6, limit_f_0=(0,1))
plt.figure(figsize=(8, 3))
plt.subplot(121)
ulh.draw(m, parts=True)
plt.title('Before')
m.migrad() # fit
plt.subplot(122)
ulh.draw(m, parts=True)
abund_guess = 0.5 * \
np.array([RSLT[0][DFS_func.nearest_point(i, REF_M)] for i in mass_guess])
# Arbitrary values for resolution and cup width, easy to tweak with
# a rough scan after the fit objects are created
res_guess = np.array([float(30000)])
sigma_guess = ((min(REF_M) + max(REF_M)) / 2) / \
res_guess / 2.0 / 1.645 / np.sqrt(2.0)
cup_guess = np.array([float(0.00015 * REF_M[0] / 18)])
# CONSTRUCTING MODEL
# Building one function with arguments for each peak, multiple concurrent fits
peak0 = peak1 = peak2 = peak3 = []
for i in range(PEAKS):
name = 'peak' + str(i)
vars()[name] = probfit.Extended(
probfit.rename(DFS_func.peakshape,
['mass', 'center' + str(i), 'sigma', 'cupwidth']),
extname='amp' + str(i))
# Sum those with the special iminut functions
if PEAKS == 1:
MODEL = peak0
if PEAKS == 2:
MODEL = probfit.AddPdf(peak0, peak1)
if PEAKS == 3:
MODEL = probfit.AddPdf(peak0, peak1, peak2)
if PEAKS == 4:
MODEL = probfit.AddPdf(peak0, peak1, peak2, peak3)
# STEP ONE : fit the first row (or only row)
print('Fitting the first peak shape')
# Setting up a chi2 function
label=['Signal 1', 'Signal 2', 'Background', 'Total'],
bins=200, histtype='step', range=data_range)
plt.legend(loc='upper left');
# <codecell>
# Using a polynomial to fit a distribution is problematic, because the
# polynomial can assume negative values, which results in NaN (not a number)
# values in the likelihood function.
# To avoid this problem we restrict the fit to the range (0, 5) where
# the polynomial is clearly positive.
fit_range = (0, 5)
normalized_poly = probfit.Normalized(probfit.Polynomial(2), fit_range)
normalized_poly = probfit.Extended(normalized_poly, extname='NBkg')
gauss1 = probfit.Extended(probfit.rename(probfit.gaussian, ['x', 'mu1', 'sigma1']), extname='N1')
gauss2 = probfit.Extended(probfit.rename(probfit.gaussian, ['x', 'mu2', 'sigma2']), extname='N2')
# Define an extended PDF consisting of three components
pdf = probfit.AddPdf(normalized_poly, gauss1, gauss2)
print('normalized_poly: {}'.format(probfit.describe(normalized_poly)))
print('gauss1: {}'.format(probfit.describe(gauss1)))
print('gauss2: {}'.format(probfit.describe(gauss2)))
print('pdf: {}'.format(probfit.describe(pdf)))
# <codecell>
# Define the cost function in the usual way ...
binned_likelihood = probfit.BinnedLH(pdf, data_all, bins=200, extended=True, bound=fit_range)
abund_guess = 0.5 * \
np.array([RSLT[0][DFS_func.nearest_point(i, REF_M)] for i in mass_guess])
# Arbitrary values for resolution and cup width, easy to tweak with
# a rough scan after the fit objects are created
res_guess = np.array([float(30000)])
sigma_guess = ((min(REF_M) + max(REF_M)) / 2) / \
res_guess / 2.0 / 1.645 / np.sqrt(2.0)
cup_guess = np.array([float(0.00015 * REF_M[0] / 18)])
# CONSTRUCTING MODEL
# Building one function with arguments for each peak, multiple concurrent fits
peak0 = peak1 = peak2 = peak3 = []
for i in range(PEAKS):
name = 'peak' + str(i)
vars()[name] = probfit.Extended(
probfit.rename(DFS_func.peakshape,
['mass', 'center' + str(i), 'sigma', 'cupwidth']),
extname='amp' + str(i))
# Sum those with the special iminut functions
if PEAKS == 1:
MODEL = peak0
if PEAKS == 2:
MODEL = probfit.AddPdf(peak0, peak1)
if PEAKS == 3:
MODEL = probfit.AddPdf(peak0, peak1, peak2)
if PEAKS == 4:
MODEL = probfit.AddPdf(peak0, peak1, peak2, peak3)
# STEP ONE : fit the first row (or only row)
print('Fitting the first peak shape')
# Setting up a chi2 function
import numpy as np
from iminuit import Minuit
from matplotlib import pyplot as plt
from numpy.random import randn
from probfit import AddPdf, BinnedLH, Extended, gaussian, rename
peak1 = randn(1000) * 0.5 + 1.0
peak2 = randn(500) * 0.5 + 0.0
# two peaks data with shared width
data = np.concatenate([peak1, peak2])
# Share the width
# If you use Normalized here. Do not reuse the object.
# It will be really slow due to cache miss. Read Normalized doc for more info.
pdf1 = rename(gaussian, ("x", "m_1", "sigma"))
pdf2 = rename(gaussian, ("x", "m_2", "sigma"))
ext_pdf1 = Extended(pdf1, extname="N_1")
ext_pdf2 = Extended(pdf2, extname="N_2")
compdf = AddPdf(ext_pdf1, ext_pdf2) # merge by name (merge sigma)
ulh = BinnedLH(compdf, data, extended=True)
m = Minuit(ulh, m_1=0.1, m_2=-0.1, sigma=0.1, N_1=900, N_2=480)
plt.figure(figsize=(8, 3))
plt.subplot(121)
ulh.draw(m, parts=True)
plt.title("Before")
def test_rename():
def f(x, y, z):
return None
assert_equal(describe(f), ['x', 'y', 'z'])
g = rename(f, ['x', 'a', 'b'])
assert_equal(describe(g), ['x', 'a', 'b'])
from iminuit import Minuit
from probfit import AddPdf, BinnedLH, gaussian, Extended, rename
from matplotlib import pyplot as plt
from numpy.random import randn
import numpy as np
peak1 = randn(1000)*0.5 + 1.
peak2 = randn(500)*0.5 + 0.
#two peaks data with shared width
data = np.concatenate([peak1, peak2])
#Share the width
#If you use Normalized here. Do not reuse the object.
#It will be really slow due to cache miss. Read Normalized doc for more info.
pdf1 = rename(gaussian, ('x', 'm_1', 'sigma'))
pdf2 = rename(gaussian, ('x', 'm_2', 'sigma'))
ext_pdf1 = Extended(pdf1, extname='N_1')
ext_pdf2 = Extended(pdf2, extname='N_2')
compdf = AddPdf(ext_pdf1, ext_pdf2) # merge by name (merge sigma)
ulh = BinnedLH(compdf, data, extended=True)
m = Minuit(ulh, m_1=0.1, m_2=-0.1, sigma=0.1, N_1=900, N_2=480)
plt.figure(figsize=(8, 3))
plt.subplot(121)
ulh.draw(m, parts=True)
plt.title('Before')
m.migrad() # fit
sigma_guess = ((min(REF_M) + max(REF_M)) / 2) / \
res_guess / 2.0 / 1.645 / np.sqrt(2.0)
cup_guess = np.array([float(0.00015 * REF_M[0] / 18)])
# CONSTRUCTING MODEL
# Building one function with arguments for each peak, multiple concurrent fits
peak0 = peak1 = peak2 = []
# for i in range(PEAKS):
# name = 'peak' + str(i)
# vars()[name] = probfit.Extended(
# probfit.rename(DFS_func.peakshape,
# ['mass', 'center13' , 'sigma', 'cupwidth']),
# extname='amp' + str(i))
name = 'peak0'
vars()[name] = probfit.Extended(
probfit.rename(DFS_func.peakshape_13,
['mass', 'center13' , 'sigma', 'cupwidth']),
extname='amp' + str(0))
name = 'peak1'
vars()[name] = probfit.Extended(
probfit.rename(DFS_func.peakshape_D,
['mass', 'center13' , 'sigma', 'cupwidth']),
extname='amp' + str(1))
name = 'peak2'
vars()[name] = probfit.Extended(
probfit.rename(DFS_func.peakshape_adduct,
['mass', 'center13' , 'sigma', 'cupwidth']),
extname='amp' + str(2))
# Sum those with the special iminut functions
if PEAKS == 1:
MODEL = peak0
