def test_morphing1():
logger = getLogger('test_morphing1')
if ROOT.gROOT.GetVersionInt() < 62301:
logger.warning('Test is disabled for ROOT version %s' %
ROOT.gROOT.GetVersion())
return
pdf1 = Models.Gauss_pdf('G1', xvar=mass, mean=10, sigma=1)
pdf2 = Models.Gauss_pdf('G2', xvar=mass, mean=10, sigma=2)
pdf3 = Models.Gauss_pdf('G3', xvar=mass, mean=10, sigma=3)
pdf4 = Models.Gauss_pdf('G4', xvar=mass, mean=10, sigma=4)
pdf = Morphing1D_pdf('M1', {
1.0: pdf1,
2.0: pdf2,
3.0: pdf3,
4.0: pdf4,
},
xvar=mass)
for mu in vrange(1, 3, 6):
pdf.mu = mu
logger.info('Mu= %s' % mu)
pdf.draw()
r, f = pdf.fitHisto(h1, draw=True, nbins=100, silent=True)
logger.info('Morphing: \n%s' % r.table(prefix="# "))
def test_morphing1():
pdf1 = Models.Gauss_pdf('G1', xvar=mass, mean=10, sigma=1)
pdf2 = Models.Gauss_pdf('G2', xvar=mass, mean=10, sigma=2)
pdf3 = Models.Gauss_pdf('G3', xvar=mass, mean=10, sigma=3)
pdf4 = Models.Gauss_pdf('G4', xvar=mass, mean=10, sigma=4)
pdf = Morphing1D_pdf('M', {
1.0: pdf1,
2.0: pdf2,
3.0: pdf3,
4.0: pdf4,
},
xvar=mass)
for mu in vrange(1, 3, 6):
pdf.mu = mu
logger.info('Mu= %s' % mu)
pdf.draw()
r, f = pdf.fitHisto(h1, draw=True, nbins=100, silent=True)
logger.info('Morphing: \n%s' % r.table(prefix="# "))
rw.reweight(original=dmc, target=ddata)
## new weights
wnew = rw.weight(original=dmc)
mc.chain.add_new_branch('w', wnew)
## reload data
data = Data('DATA_tree', testdata)
mc = Data(tag_mc, testdata)
wsum = mc.chain.sumVar('w')
wvar = '%d*w/%s' % (len(data.chain), wsum.value())
nn = '%s' % (len(data.chain) * 1.0 / len(mc.chain))
for phi in vrange(0, 2 * math.pi, 10):
dvar = '%.5f*x+%.5f*y' % (math.cos(phi), math.sin(phi))
mn, mx = data.chain.statVar(dvar).minmax()
h1 = ROOT.TH1D(hID(), '', 100, *axis_range(mn, mx, delta=0.05))
h2 = h1.clone()
h3 = h1.clone()
data.chain.project(h1, dvar) ## data
mc.chain.project(h2, dvar, nn) ## original (non-weighted) MC
mc.chain.project(h3, dvar, wvar) ## weighted MC
mn, mx = h1.minmax()
mn, mx = axis_range(0, mx, delta=0.7)
h1.SetMaximum(mx)
def _dp0_points_(dp, M, npoints=250):
"""Make a graph of Dalitz plot
>>> d = Dalitz ( 0.1 , 0.2 , 0.3 )
>>> points = d.points ( M = 5 )
"""
m1 = dp.m1()
m2 = dp.m2()
m3 = dp.m3()
assert m1 + m2 + m3 <= M, \
'Dalitz0.points: Invalid mass %s>%s+%s+%s' % ( M , m1 , m2 , m3 )
s = M * M
s1_min = dp.s1_min()
s1_max = dp.s1_max(M)
s2_min = dp.s2_min()
s2_max = dp.s2_max(M)
s3_min = dp.s3_min()
s3_max = dp.s3_max(M)
## four reference minmax points ..
P = [(s1_min, dp.s2_minmax_for_s_s1(s, s1_min).first),
(dp.s1_minmax_for_s_s2(s, s2_max).second, s2_max),
(s1_max, dp.s2_minmax_for_s_s1(s, s1_max).second),
(dp.s1_minmax_for_s_s2(s, s2_min).first, s2_min)]
P = [p for p in P if s1_min <= p[0] <= s1_max and s2_min <= p[1] <= s2_max]
pnts = []
from ostap.utils.utils import vrange
## fill branches 1 and 3
for v in vrange(s1_min, s1_max, npoints):
s1 = v
s2 = dp.s2_minmax_for_s_s1(s, s1)
s2min = s2.first
s2max = s2.second
if not s2min < s2max: continue
x = s1
y1, y2 = s2min, s2max
if s1_min <= x <= s1_max:
if s2_min <= y1 <= s2_max: pnts.append((x, y1))
if s2_min <= y2 <= s2_max: pnts.append((x, y2))
## fill branches 2 and 4 :
for v in vrange(s2_min, s2_max, npoints):
s2 = v
s1 = dp.s1_minmax_for_s_s2(s, s2)
s1min = s1.first
s1max = s1.second
if s1min < s1max:
y = s2
x1, x2 = s1min, s1max
if s2_min <= y <= s2_max:
if s1_min <= x1 <= s1_max: pnts.append((x1, y))
if s1_min <= x2 <= s1_max: pnts.append((x2, y))
pnts = P + pnts
pnts = [
p for p in pnts if dp.inside(s, p[0], p[1])
or abs(dp.distance(s, p[0], p[1])) < 1.e-8 * s
]
## find some point "inside" Dalitz plot
## first guess
x0 = 0.5 * (s1_min + s1_max)
y0 = 0.5 * (s2_min + s2_max)
## find another point if needed
while not dp.inside(s, x0, y0):
import random
x0 = random.uniform(s1_min, s1_max)
y0 = random.uniform(s2_min, s2_max)
## collect, eliminate the duplicates and ogranize all points according to phi
from ostap.math.base import isequal
points = set()
for p in pnts:
x, y = p
dx = x - x0
dy = y - y0
phi = math.atan2(dy, dx)
in_list = False
for entry in points:
if isequal(phi, entry[0]):
in_list = True
break
if in_list: continue
point = phi, x, y
points.add(point)
## convert set to the list
points = list(points)
## sort the list according to phi
points.sort()
## make a closed loop from the points
if points and points[0][1:] != points[-1][1:]:
points.append(points[0])
return tuple([p[1:] for p in points])
def test_gbreweight():
logger = getLogger("test_gbreweight")
try:
from ostap.tools.reweighter import Reweighter
rw = Reweighter()
except ImportError:
logger.error('GBReweighter is not available!')
return
if not os.path.exists(testdata):
with timing("Prepare input data", logger=logger):
prepare_data()
# =========================================================================
## Input data/mc samples
# =========================================================================
data = Data('DATA_tree', testdata)
mc = Data(tag_mc, testdata)
ddata, wdata = data.chain.slice('x y', transpose=True)
dmc, wmc = mc.chain.slice('x y', transpose=True)
## train BDT
rw.reweight(original=dmc, target=ddata)
## new weights
wnew = rw.weight(original=dmc)
mc.chain.add_new_branch('w', wnew)
## reload data
data = Data('DATA_tree', testdata)
mc = Data(tag_mc, testdata)
wsum = mc.chain.sumVar('w')
wvar = '%d*w/%s' % (len(data.chain), wsum.value())
nn = '%s' % (len(data.chain) * 1.0 / len(mc.chain))
for phi in vrange(0, 2 * math.pi, 10):
dvar = '%.5f*x+%.5f*y' % (math.cos(phi), math.sin(phi))
mn, mx = data.chain.statVar(dvar).minmax()
h1 = ROOT.TH1D(hID(), '', 100, *axis_range(mn, mx, delta=0.05))
h2 = h1.clone()
h3 = h1.clone()
data.chain.project(h1, dvar) ## data
mc.chain.project(h2, dvar, nn) ## original (non-weighted) MC
mc.chain.project(h3, dvar, wvar) ## weighted MC
mn, mx = h1.minmax()
mn, mx = axis_range(0, mx, delta=0.7)
h1.SetMaximum(mx)
h1.blue()
h2.green()
h3.red()
h1.draw('')
h2.draw('same hist')
h3.draw('same')
time.sleep(2)
Berrut2nd ( results ) ,
Barycentric ( results )
] + [
FloaterHormann ( results , i ) for i in range ( 10 ) ]
graphs = []
with wait ( 5 ) , use_canvas ( 'test_fitresults' ) :
f1_draw ( sigma , linecolor=1 , linewidth = 4 , minimum = 0 , xmin = 0 , xmax = 100 )
gr.red()
gr.draw ('pe1')
K = 255
for i,f in enumerate ( interpolants ) :
g = ROOT.TGraphErrors ( K + 1 )
for j,x in enumerate ( vrange ( 0 , 100 , K ) ) :
g[j] = x , f ( x )
graphs.append ( g )
for i, g in enumerate ( graphs ) :
g.draw ('lpe1', linecolor = i+3 , markercolor = i+3 )
gr.draw ('pe1')
# =============================================================================
## if '__main__' == __name__ :
## test_fitresults ()
def uniform_abscissas(low, high, N):
"""Uniform interpoaltion abscissas
for a in uniform_abscissas ( 0, 1 , 10 ) :
... print ( a )
"""
return vrange(low, high, max(N, 2) - 1)
