In [1]:
import pandas as pd
import seaborn as sns

from nnfwtbn import Variable, Process, Cut, hist, McStack, DataStack, Stack
from nnfwtbn import toydata
In [2]:
df = toydata.get()
In [3]:
df
Out[3]:
Dask DataFrame Structure:
jet_1_pt jet_1_eta jet_1_phi jet_2_pt jet_2_eta jet_2_phi met_phi met_pt tau_phi tau_eta tau_pt lep_phi lep_eta lep_pt higgs_pt higgs_eta higgs_phi higgs_m m_jj lep_centrality tau_centrality random fpid weight
npartitions=1
float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 int64
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Dask Name: read-hdf, 1 tasks
In [4]:
p_ztt = Process(r"$Z\rightarrow\tau\tau$", range=(0, 0))
p_sig = Process(r"Signal", range=(1, 1))

p_asimov = Process(r"Asimov", selection=lambda d: d.fpid >= 0)
In [5]:
colors = ["windows blue", "amber", "greyish", "faded green", "dusty purple"]
palette = sns.xkcd_palette(colors)

s_bkg = McStack(p_ztt, p_sig, palette=palette)
s_data = DataStack(p_asimov)
In [6]:
v_higgs_m = Variable(r"$m^H$", "higgs_m", "GeV")
In [7]:
c_vbf = Cut(lambda d: d.dijet_p4__M  > 400) & \
        Cut(lambda d: d.jet_1_p4__Pt >= 30) & \
        Cut(lambda d: d.is_dijet_centrality == 1) & \
        Cut(lambda d: d.jet_0_p4__Eta * df.jet_1_p4__Eta < 0) & \
        Cut(lambda d: (d.jet_0_p4__Eta - df.jet_1_p4__Eta).abs() > 3)
In [8]:
hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight",  ratio_label="Data / SM")
None
In [9]:
hist(df, v_higgs_m, 22, [s_bkg, s_data], range=(75, 130), selection=None,
     weight="weight",  ratio_label="Data / SM", include_outside=True)
None
In [10]:
hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight",  ratio_label="Data / SM", y_log=True, numerator=None)
None
In [11]:
hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight", ratio_label="MC / Data", y_log=True, y_min=1e-1,
     vlines=[80, {'x': 100, 'color': 'b'}])
None
In [12]:
s_sig = McStack(p_sig, color=palette[1], histtype='step')
s_ztt = McStack(p_ztt, color=palette[0], histtype='step')

hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(40, 120), selection=None,
     weight="weight", ratio_label="Signal / Bkg", y_log=True, y_min=1e-1,
     vlines=[80, {'x': 100, 'color': 'b'}], numerator=s_sig, denominator=s_ztt)
None
In [13]:
hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight", ratio_label="Data - Bkg", y_log=True, y_min=1e-1, diff=True,
     enlarge=1.5,
     vlines=[80, {'x': 100, 'color': 'b'}], numerator=s_data, denominator=s_ztt)
None
In [14]:
hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight",  ratio_label="Data / SM", info=False, atlas="Work in progress")
None
In [15]:
import nnfwtbn.plot as nnp
nnp.INFO = "$\sqrt{s} = 13\,\mathrm{TeV}$, $140\,\mathrm{fb}^{-1}$"
In [16]:
hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight",  ratio_label="Data / SM")
None
In [17]:
s_sig = McStack(p_sig, color=palette[1], histtype='step')
s_ztt = McStack(p_ztt, color=palette[0], histtype='step')

hist(df, v_higgs_m, 20, [s_sig, s_ztt], range=(0, 200), selection=None,
     weight="weight",  numerator=None, density=True)
None
In [18]:
hist(df, v_higgs_m, 30, [s_bkg, s_data], range=(25, 175),
     selection=None, numerator=[s_ztt, s_sig], denominator=s_data,
     weight="weight",  ratio_label="Process / Asimov")
None
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