Histograms

This notebook shows how to generate histograms with various settings.

[1]:

import pandas as pd
import seaborn as sns

from freeforestml import Variable, Process, Cut, hist, McStack, DataStack, Stack
from freeforestml import toydata, example_style
example_style()

2023-08-02 16:39:22.212654: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-02 16:39:22.347807: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2023-08-02 16:39:22.347836: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2023-08-02 16:39:23.179132: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2023-08-02 16:39:23.179231: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2023-08-02 16:39:23.179242: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

Setup

Load or generate toy dataset.

[2]:

df = toydata.get()

Define processes to plot as deparate colors.

[3]:

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)

Define colors and how to stack the processes. Data should not be stacked on top of the MC prediction.

[4]:

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)

Define the variable to use on the x-axis.

[5]:

v_higgs_m = Variable(r"$m^H$", "higgs_m", "GeV")

Examples

[6]:

hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight",  ratio_label="Data / SM")
None

[7]:

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

[8]:

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

[9]:

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

[10]:

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

[11]:

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

[12]:

import freeforestml.plot as nnp
nnp.INFO = "$\sqrt{s} = 13\,\mathrm{TeV}$, $140\,\mathrm{fb}^{-1}$"

[13]:

hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,
     weight="weight",  ratio_label="Data / SM")
None

[14]:

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

[15]:

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