Logo ROOT  
Reference Guide
 
Loading...
Searching...
No Matches
hist007_TH1_liveupdate_uhi.py File Reference

Detailed Description

View in nbviewer Open in SWAN
Simple example illustrating how to use the C++ interpreter.

:

import matplotlib.pyplot as plt
import mplhep as hep
import numpy as np
from ROOT import TH1F, gBenchmark, gRandom
# Create a new canvas, and enable interactive mode.
fig, ax = plt.subplots(figsize=(8, 6), num="The HSUM Example")
# Create some histograms.
BINS = 100
RANGE_MIN, RANGE_MAX = -4, 4
total = TH1F("total", "This is the total distribution", BINS, RANGE_MIN, RANGE_MAX)
main = TH1F("main", "Main contributor", BINS, RANGE_MIN, RANGE_MAX)
s1 = TH1F("s1", "This is the first signal", BINS, RANGE_MIN, RANGE_MAX)
s2 = TH1F("s2", "This is the second signal", BINS, RANGE_MIN, RANGE_MAX)
# total.Sumw2()
# initialize a dictionary that holds the histogram counts as numpy arrays
counts = {"total": np.zeros(BINS), "main": np.zeros(BINS), "s1": np.zeros(BINS), "s2": np.zeros(BINS)}
# Initialize random number generator.
gauss, landau = gRandom.Gaus, gRandom.Landau
# def gauss(loc, scale):
# return np.random.normal(loc, scale)
# def landau(loc, scale):
# return np.random.standard_cauchy() * scale + loc
# initialize the histogram filling method
def fill_hist(hist_name, x, weight=1.0):
if RANGE_MIN <= x < RANGE_MAX:
idx = int((x - RANGE_MIN) / (RANGE_MAX - RANGE_MIN) * BINS)
counts[hist_name][idx] += weight
# Fill histograms randomly
kUPDATE = 500
N_EVENTS = 10000
for i in range(1, N_EVENTS + 1):
# Generate random values.
xmain = gauss(-1, 1.5)
xs1 = gauss(-0.5, 0.5)
xs2 = landau(1, 0.15)
# Fill histograms
# Compute the counts
fill_hist("main", xmain)
fill_hist("s1", xs1, 0.3)
fill_hist("s2", xs2, 0.2)
fill_hist("total", xmain)
fill_hist("total", xs1, 0.3)
fill_hist("total", xs2, 0.2)
# Set the bin contents
total[...] = counts["total"]
main[...] = counts["main"]
s1[...] = counts["s1"]
s2[...] = counts["s2"]
# Update display every kUPDATE events.
if i % kUPDATE == 0:
entries = total.GetEntries()
mean = total.GetMean()
stddev = total.GetStdDev()
stats_text = f"Entries = {entries:.0f}\nMean = {mean:.2f}\nStd Dev = {stddev:.2f}"
hep.histplot(main, histtype="fill", color="gray", alpha=0.5, edgecolor="blue", linewidth=1.5, ax=ax)
hep.histplot(total, histtype="errorbar", color="black", ecolor="blue", linewidth=2, ax=ax)
hep.histplot(s1, histtype="errorbar", color="blue", alpha=0.7, ecolor="blue", linewidth=2, marker="+", ax=ax)
hep.histplot(s2, histtype="errorbar", color="blue", alpha=0.7, ecolor="blue", linewidth=2, marker="+", ax=ax)
ax.set_title("This is the total distribution", pad=20, fontsize=14, loc="center")
0.95,
0.90,
stats_text,
transform=ax.transAxes,
ha="right",
va="top",
fontsize=12,
bbox=dict(facecolor="white", edgecolor="black", boxstyle="round,pad=0.2", alpha=0.9),
)
# Plot formatting
ax.set_xlim(RANGE_MIN, RANGE_MAX)
ax.set_ylim(0, max(counts["total"]) * 1.2)
plt.pause(0.001)
# Done, show final plot.
plt.grid(True)
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
1-D histogram with a float per channel (see TH1 documentation)
Definition TH1.h:879
Date
July 2025
Author
Wim Lavrijsen, Nursena Bitirgen

Definition in file hist007_TH1_liveupdate_uhi.py.