Basic Plotting

This section covers the core plotting functionality of mplhep for plotting 1D and 2D histograms.

Prerequisites

Throughout this guide the following codeblock is assumed.

import matplotlib.pyplot as plt
import numpy as np
import hist
np.random.seed(42)
import mplhep as mh
# mh.style.use('<as appropriate>')

1D Histogram Plotting

mh.histplot() works with multiple histogram formats through the UHI protocol:

Supported Input Formats

DefaultplothistCMSATLASLHCbALICEDUNE

Array-Likenumpy.histogramhist.Histuproot.TH1

fig, ax = plt.subplots()
mh.histplot([1, 2, 3, 6, 3, 5, 2, 1], ax=ax)

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
mh.histplot(*np.histogram(data, bins=40), ax=ax)

data = np.random.normal(0, 1, 1000)
h_obj = hist.new.Reg(40, -4, 4).Weight().fill(data)
fig, ax = plt.subplots()
mh.histplot(h_obj, ax=ax)
# Note that errorbars are now automatically plotted because hist.Hist inputs objects has .variances() available

import uproot

file = uproot.open(uproot_file_name)
h_root = file['hpx']
fig, ax = plt.subplots()
mh.histplot(h_root, ax=ax)

Array-Likenumpy.histogramhist.Histuproot.TH1

fig, ax = plt.subplots()
mh.histplot([1, 2, 3, 6, 3, 5, 2, 1], ax=ax)

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
mh.histplot(*np.histogram(data, bins=40), ax=ax)

data = np.random.normal(0, 1, 1000)
h_obj = hist.new.Reg(40, -4, 4).Weight().fill(data)
fig, ax = plt.subplots()
mh.histplot(h_obj, ax=ax)
# Note that errorbars are now automatically plotted because hist.Hist inputs objects has .variances() available

import uproot

file = uproot.open(uproot_file_name)
h_root = file['hpx']
fig, ax = plt.subplots()
mh.histplot(h_root, ax=ax)

Array-Likenumpy.histogramhist.Histuproot.TH1

fig, ax = plt.subplots()
mh.histplot([1, 2, 3, 6, 3, 5, 2, 1], ax=ax)

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
mh.histplot(*np.histogram(data, bins=40), ax=ax)

data = np.random.normal(0, 1, 1000)
h_obj = hist.new.Reg(40, -4, 4).Weight().fill(data)
fig, ax = plt.subplots()
mh.histplot(h_obj, ax=ax)
# Note that errorbars are now automatically plotted because hist.Hist inputs objects has .variances() available

import uproot

file = uproot.open(uproot_file_name)
h_root = file['hpx']
fig, ax = plt.subplots()
mh.histplot(h_root, ax=ax)

Array-Likenumpy.histogramhist.Histuproot.TH1

fig, ax = plt.subplots()
mh.histplot([1, 2, 3, 6, 3, 5, 2, 1], ax=ax)

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
mh.histplot(*np.histogram(data, bins=40), ax=ax)

data = np.random.normal(0, 1, 1000)
h_obj = hist.new.Reg(40, -4, 4).Weight().fill(data)
fig, ax = plt.subplots()
mh.histplot(h_obj, ax=ax)
# Note that errorbars are now automatically plotted because hist.Hist inputs objects has .variances() available

import uproot

file = uproot.open(uproot_file_name)
h_root = file['hpx']
fig, ax = plt.subplots()
mh.histplot(h_root, ax=ax)

Array-Likenumpy.histogramhist.Histuproot.TH1

fig, ax = plt.subplots()
mh.histplot([1, 2, 3, 6, 3, 5, 2, 1], ax=ax)

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
mh.histplot(*np.histogram(data, bins=40), ax=ax)

data = np.random.normal(0, 1, 1000)
h_obj = hist.new.Reg(40, -4, 4).Weight().fill(data)
fig, ax = plt.subplots()
mh.histplot(h_obj, ax=ax)
# Note that errorbars are now automatically plotted because hist.Hist inputs objects has .variances() available

import uproot

file = uproot.open(uproot_file_name)
h_root = file['hpx']
fig, ax = plt.subplots()
mh.histplot(h_root, ax=ax)

Array-Likenumpy.histogramhist.Histuproot.TH1

fig, ax = plt.subplots()
mh.histplot([1, 2, 3, 6, 3, 5, 2, 1], ax=ax)

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
mh.histplot(*np.histogram(data, bins=40), ax=ax)

data = np.random.normal(0, 1, 1000)
h_obj = hist.new.Reg(40, -4, 4).Weight().fill(data)
fig, ax = plt.subplots()
mh.histplot(h_obj, ax=ax)
# Note that errorbars are now automatically plotted because hist.Hist inputs objects has .variances() available

import uproot

file = uproot.open(uproot_file_name)
h_root = file['hpx']
fig, ax = plt.subplots()
mh.histplot(h_root, ax=ax)

Array-Likenumpy.histogramhist.Histuproot.TH1

fig, ax = plt.subplots()
mh.histplot([1, 2, 3, 6, 3, 5, 2, 1], ax=ax)

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
mh.histplot(*np.histogram(data, bins=40), ax=ax)

data = np.random.normal(0, 1, 1000)
h_obj = hist.new.Reg(40, -4, 4).Weight().fill(data)
fig, ax = plt.subplots()
mh.histplot(h_obj, ax=ax)
# Note that errorbars are now automatically plotted because hist.Hist inputs objects has .variances() available

import uproot

file = uproot.open(uproot_file_name)
h_root = file['hpx']
fig, ax = plt.subplots()
mh.histplot(h_root, ax=ax)

Histogram Styles

Control the appearance with the histtype parameter. Select an experiment style and then choose a histogram type:

DefaultplothistCMSATLASLHCbALICEDUNE

StepFillErrorbarBandBarBarstep

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='step', label='Step histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='fill', alpha=0.5, label='Filled histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', label='Data', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='band', alpha=0.5, label='Band histogram', ax=ax)
# Can be used to visualize uncertainties

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='bar', label='Bar histogram', ax=ax)
# If only one histogram is provided, it will be treated as "fill" histtype, if multiple data are given the bars are arranged side by side (see next section)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='barstep', label='Barstep histogram', ax=ax)
# If one histogram is provided, it will be treated as "step" histtype. If multiple data are given the bars are arranged side by side (see next section)

StepFillErrorbarBandBarBarstep

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='step', label='Step histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='fill', alpha=0.5, label='Filled histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', label='Data', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='band', alpha=0.5, label='Band histogram', ax=ax)
# Can be used to visualize uncertainties

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='bar', label='Bar histogram', ax=ax)
# If only one histogram is provided, it will be treated as "fill" histtype, if multiple data are given the bars are arranged side by side (see next section)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='barstep', label='Barstep histogram', ax=ax)
# If one histogram is provided, it will be treated as "step" histtype. If multiple data are given the bars are arranged side by side (see next section)

StepFillErrorbarBandBarBarstep

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='step', label='Step histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='fill', alpha=0.5, label='Filled histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', label='Data', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='band', alpha=0.5, label='Band histogram', ax=ax)
# Can be used to visualize uncertainties

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='bar', label='Bar histogram', ax=ax)
# If only one histogram is provided, it will be treated as "fill" histtype, if multiple data are given the bars are arranged side by side (see next section)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='barstep', label='Barstep histogram', ax=ax)
# If one histogram is provided, it will be treated as "step" histtype. If multiple data are given the bars are arranged side by side (see next section)

StepFillErrorbarBandBarBarstep

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='step', label='Step histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='fill', alpha=0.5, label='Filled histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', label='Data', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='band', alpha=0.5, label='Band histogram', ax=ax)
# Can be used to visualize uncertainties

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='bar', label='Bar histogram', ax=ax)
# If only one histogram is provided, it will be treated as "fill" histtype, if multiple data are given the bars are arranged side by side (see next section)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='barstep', label='Barstep histogram', ax=ax)
# If one histogram is provided, it will be treated as "step" histtype. If multiple data are given the bars are arranged side by side (see next section)

StepFillErrorbarBandBarBarstep

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='step', label='Step histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='fill', alpha=0.5, label='Filled histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', label='Data', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='band', alpha=0.5, label='Band histogram', ax=ax)
# Can be used to visualize uncertainties

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='bar', label='Bar histogram', ax=ax)
# If only one histogram is provided, it will be treated as "fill" histtype, if multiple data are given the bars are arranged side by side (see next section)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='barstep', label='Barstep histogram', ax=ax)
# If one histogram is provided, it will be treated as "step" histtype. If multiple data are given the bars are arranged side by side (see next section)

StepFillErrorbarBandBarBarstep

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='step', label='Step histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='fill', alpha=0.5, label='Filled histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', label='Data', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='band', alpha=0.5, label='Band histogram', ax=ax)
# Can be used to visualize uncertainties

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='bar', label='Bar histogram', ax=ax)
# If only one histogram is provided, it will be treated as "fill" histtype, if multiple data are given the bars are arranged side by side (see next section)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='barstep', label='Barstep histogram', ax=ax)
# If one histogram is provided, it will be treated as "step" histtype. If multiple data are given the bars are arranged side by side (see next section)

StepFillErrorbarBandBarBarstep

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='step', label='Step histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='fill', alpha=0.5, label='Filled histogram', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', label='Data', ax=ax)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='band', alpha=0.5, label='Band histogram', ax=ax)
# Can be used to visualize uncertainties

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='bar', label='Bar histogram', ax=ax)
# If only one histogram is provided, it will be treated as "fill" histtype, if multiple data are given the bars are arranged side by side (see next section)

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, histtype='barstep', label='Barstep histogram', ax=ax)
# If one histogram is provided, it will be treated as "step" histtype. If multiple data are given the bars are arranged side by side (see next section)

Multiple Histograms

Stack

mh.histplot() supports plotting multiple histograms in one call. They can be stacked using the stack=True parameter:

DefaultplothistCMSATLASLHCbALICEDUNE

Unstacked fillStacked fillSorted (by yield)Sorted (by label)Sorted (by label - reversed)With errorbars

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 alpha=0.7,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='yield',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='label',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='l_r',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)

ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Unstacked fillStacked fillSorted (by yield)Sorted (by label)Sorted (by label - reversed)With errorbars

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 alpha=0.7,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='yield',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='label',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='l_r',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
txt_obj = mh.add_text('plothist', loc='over left')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Unstacked fillStacked fillSorted (by yield)Sorted (by label)Sorted (by label - reversed)With errorbars

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 alpha=0.7,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='yield',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='label',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='l_r',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
mh.cms.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Unstacked fillStacked fillSorted (by yield)Sorted (by label)Sorted (by label - reversed)With errorbars

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 alpha=0.7,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='yield',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='label',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='l_r',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
mh.atlas.label(data=False)
mh.mpl_magic(soft_fail=True)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Unstacked fillStacked fillSorted (by yield)Sorted (by label)Sorted (by label - reversed)With errorbars

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 alpha=0.7,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='yield',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='label',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='l_r',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
mh.lhcb.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Unstacked fillStacked fillSorted (by yield)Sorted (by label)Sorted (by label - reversed)With errorbars

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 alpha=0.7,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='yield',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='label',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='l_r',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
mh.alice.label(data=False)
mh.mpl_magic(soft_fail=True)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Unstacked fillStacked fillSorted (by yield)Sorted (by label)Sorted (by label - reversed)With errorbars

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 alpha=0.7,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='yield',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='label',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='fill',
 stack=True,
 sort='l_r',
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms and fill them
h1 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(-1, 0.8, 800))
h2 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(2, 0.6, 600))
h3 = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0.5, 1.2, 1200))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 stack=True,
 label=['Background 1', 'Background 2', 'Signal'],
 ax=ax
)
mh.dune.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Side by Side

When plotting multiple histograms, the histtype can be set to 'bar' or 'barstep' to create side-by-side bar plots, just like matplotlib:

DefaultplothistCMSATLASLHCbALICEDUNE

BarBarstep

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='bar',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)

ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='barstep',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)

ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

BarBarstep

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='bar',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
txt_obj = mh.add_text('plothist', loc='over left')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='barstep',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
txt_obj = mh.add_text('plothist', loc='over left')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

BarBarstep

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='bar',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.cms.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='barstep',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.cms.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

BarBarstep

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='bar',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.atlas.label(data=False)
mh.mpl_magic(soft_fail=True)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='barstep',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.atlas.label(data=False)
mh.mpl_magic(soft_fail=True)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

BarBarstep

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='bar',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.lhcb.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='barstep',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.lhcb.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

BarBarstep

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='bar',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.alice.label(data=False)
mh.mpl_magic(soft_fail=True)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='barstep',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.alice.label(data=False)
mh.mpl_magic(soft_fail=True)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

BarBarstep

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='bar',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.dune.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

# Create histograms and fill them
h1 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(0.5, 200))
h2 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.0, 300))
h3 = hist.new.Reg(6, 0, 6).Weight().fill(np.random.poisson(1.2, 500))

fig, ax = plt.subplots()
mh.histplot(
 [h1, h2, h3],
 histtype='barstep',
 label=['$h_1$', '$h_2$', '$h_3$'],
 ax=ax
)
mh.dune.label(data=False)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)
# Optional: move the x-ticks to the bin centers
ax.set_xticks(h1.axes[0].centers)
ax.set_xticklabels([x+0.5 for x in h1.axes[0].centers])
ax.minorticks_off()

Blinding

Blinding allows you to hide specific regions of a histogram, which is useful for blind analyses where signal regions should not be examined until the analysis is finalized. The blind parameter in mh.histplot() supports multiple specification formats to accommodate different use cases.

Blinding syntax overview

Blinding regions can be specified in several ways:

• Value-based: Specify physical values (coordinates) using tuples or j suffix
• Index-based: Specify bin indices directly using integers
• Multiple regions: Use a list to blind multiple regions at once

All formats can be combined in a list for maximum flexibility.

Value-Based Blinding

Blind a region by specifying physical values (coordinates) on the x-axis:

DefaultplothistCMSATLASLHCbALICEDUNE

Tuple notationString with j suffixloc slice notation

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=(-1.0, 1.0), label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind="-1j:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=loc[-1.0:1.0], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Tuple notationString with j suffixloc slice notation

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=(-1.0, 1.0), label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind="-1j:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=loc[-1.0:1.0], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Tuple notationString with j suffixloc slice notation

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=(-1.0, 1.0), label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind="-1j:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=loc[-1.0:1.0], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Tuple notationString with j suffixloc slice notation

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=(-1.0, 1.0), label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind="-1j:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=loc[-1.0:1.0], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Tuple notationString with j suffixloc slice notation

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=(-1.0, 1.0), label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind="-1j:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=loc[-1.0:1.0], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Tuple notationString with j suffixloc slice notation

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=(-1.0, 1.0), label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind="-1j:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=loc[-1.0:1.0], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Tuple notationString with j suffixloc slice notation

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=(-1.0, 1.0), label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind="-1j:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
mh.histplot(h, ax=ax, blind=loc[-1.0:1.0], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Index-Based Blinding

Blind specific bins by their index:

DefaultplothistCMSATLASLHCbALICEDUNE

Single binRange of binsList of bins

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind the 20th bin (index 19)
mh.histplot(h, ax=ax, blind=19, label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind bins 15 to 25 (indices 15-24)
mh.histplot(h, ax=ax, blind="15:25", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind specific bins
mh.histplot(h, ax=ax, blind=[18, 19, 20, 21, 22], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Single binRange of binsList of bins

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind the 20th bin (index 19)
mh.histplot(h, ax=ax, blind=19, label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind bins 15 to 25 (indices 15-24)
mh.histplot(h, ax=ax, blind="15:25", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind specific bins
mh.histplot(h, ax=ax, blind=[18, 19, 20, 21, 22], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Single binRange of binsList of bins

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind the 20th bin (index 19)
mh.histplot(h, ax=ax, blind=19, label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind bins 15 to 25 (indices 15-24)
mh.histplot(h, ax=ax, blind="15:25", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind specific bins
mh.histplot(h, ax=ax, blind=[18, 19, 20, 21, 22], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Single binRange of binsList of bins

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind the 20th bin (index 19)
mh.histplot(h, ax=ax, blind=19, label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind bins 15 to 25 (indices 15-24)
mh.histplot(h, ax=ax, blind="15:25", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind specific bins
mh.histplot(h, ax=ax, blind=[18, 19, 20, 21, 22], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Single binRange of binsList of bins

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind the 20th bin (index 19)
mh.histplot(h, ax=ax, blind=19, label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind bins 15 to 25 (indices 15-24)
mh.histplot(h, ax=ax, blind="15:25", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind specific bins
mh.histplot(h, ax=ax, blind=[18, 19, 20, 21, 22], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Single binRange of binsList of bins

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind the 20th bin (index 19)
mh.histplot(h, ax=ax, blind=19, label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind bins 15 to 25 (indices 15-24)
mh.histplot(h, ax=ax, blind="15:25", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind specific bins
mh.histplot(h, ax=ax, blind=[18, 19, 20, 21, 22], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Single binRange of binsList of bins

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind the 20th bin (index 19)
mh.histplot(h, ax=ax, blind=19, label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind bins 15 to 25 (indices 15-24)
mh.histplot(h, ax=ax, blind="15:25", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind specific bins
mh.histplot(h, ax=ax, blind=[18, 19, 20, 21, 22], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Multiple Regions

Blind multiple regions by providing a list of specifications:

DefaultplothistCMSATLASLHCbALICEDUNE

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind multiple regions: value-based and index-based
mh.histplot(h, ax=ax, blind=[(-2.5, -1.5), (1.5, 2.5), 5], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind multiple regions: value-based and index-based
mh.histplot(h, ax=ax, blind=[(-2.5, -1.5), (1.5, 2.5), 5], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind multiple regions: value-based and index-based
mh.histplot(h, ax=ax, blind=[(-2.5, -1.5), (1.5, 2.5), 5], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind multiple regions: value-based and index-based
mh.histplot(h, ax=ax, blind=[(-2.5, -1.5), (1.5, 2.5), 5], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind multiple regions: value-based and index-based
mh.histplot(h, ax=ax, blind=[(-2.5, -1.5), (1.5, 2.5), 5], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind multiple regions: value-based and index-based
mh.histplot(h, ax=ax, blind=[(-2.5, -1.5), (1.5, 2.5), 5], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind multiple regions: value-based and index-based
mh.histplot(h, ax=ax, blind=[(-2.5, -1.5), (1.5, 2.5), 5], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Mixed-Mode Blinding

Mix value-based and index-based specifications in the same slice:

DefaultplothistCMSATLASLHCbALICEDUNE

Index start, value endValue start, index end

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from bin 5 to value 1.0
mh.histplot(h, ax=ax, blind="5:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from value -1.0 to bin 30
mh.histplot(h, ax=ax, blind=loc[-1.0:30], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Index start, value endValue start, index end

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from bin 5 to value 1.0
mh.histplot(h, ax=ax, blind="5:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from value -1.0 to bin 30
mh.histplot(h, ax=ax, blind=loc[-1.0:30], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Index start, value endValue start, index end

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from bin 5 to value 1.0
mh.histplot(h, ax=ax, blind="5:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from value -1.0 to bin 30
mh.histplot(h, ax=ax, blind=loc[-1.0:30], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Index start, value endValue start, index end

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from bin 5 to value 1.0
mh.histplot(h, ax=ax, blind="5:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from value -1.0 to bin 30
mh.histplot(h, ax=ax, blind=loc[-1.0:30], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Index start, value endValue start, index end

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from bin 5 to value 1.0
mh.histplot(h, ax=ax, blind="5:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from value -1.0 to bin 30
mh.histplot(h, ax=ax, blind=loc[-1.0:30], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Index start, value endValue start, index end

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from bin 5 to value 1.0
mh.histplot(h, ax=ax, blind="5:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from value -1.0 to bin 30
mh.histplot(h, ax=ax, blind=loc[-1.0:30], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Index start, value endValue start, index end

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from bin 5 to value 1.0
mh.histplot(h, ax=ax, blind="5:1j", label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

h = hist.new.Reg(40, -4, 4).Weight().fill(np.random.normal(0, 1, 1000))
fig, ax = plt.subplots()
# Blind from value -1.0 to bin 30
mh.histplot(h, ax=ax, blind=loc[-1.0:30], label='Data')
ax.legend()
ax.set_xlabel('Observable [GeV]')
ax.set_ylabel('Events')

Error Bars

Control error bar display with yerr and w2method parameters:

DefaultplothistCMSATLASLHCbALICEDUNE

Automatic (Poisson)Set explicitlySet yerr calculation method ('sqrt'/'poisson')Set yerr calculation function - full control

# Simple histogram with Weight storage for automatic errors
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.poisson(5, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=True, label='Data with Poisson errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Simple histogram with custom error bars
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))
custom_errors = np.minimum(np.sqrt(h.values()), np.random.uniform(0, 20, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=custom_errors, label='Data with custom errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram - errors from sqrt of sum of weights squared
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method='sqrt', label='Weighted data (sqrt method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram with custom error calculation
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

# Custom error function: error = sqrt(sum(weights^2)) / 2
def custom_w2_method(weights, variances):
 up = weights - np.ones_like(weights) * 0.2 * np.mean(weights)
 down = weights + np.ones_like(weights) * 0.2 * np.mean(weights)
 return up, down

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method=custom_w2_method, label='Weighted data (custom error method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Automatic (Poisson)Set explicitlySet yerr calculation method ('sqrt'/'poisson')Set yerr calculation function - full control

# Simple histogram with Weight storage for automatic errors
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.poisson(5, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=True, label='Data with Poisson errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Simple histogram with custom error bars
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))
custom_errors = np.minimum(np.sqrt(h.values()), np.random.uniform(0, 20, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=custom_errors, label='Data with custom errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram - errors from sqrt of sum of weights squared
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method='sqrt', label='Weighted data (sqrt method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram with custom error calculation
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

# Custom error function: error = sqrt(sum(weights^2)) / 2
def custom_w2_method(weights, variances):
 up = weights - np.ones_like(weights) * 0.2 * np.mean(weights)
 down = weights + np.ones_like(weights) * 0.2 * np.mean(weights)
 return up, down

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method=custom_w2_method, label='Weighted data (custom error method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Automatic (Poisson)Set explicitlySet yerr calculation method ('sqrt'/'poisson')Set yerr calculation function - full control

# Simple histogram with Weight storage for automatic errors
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.poisson(5, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=True, label='Data with Poisson errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Simple histogram with custom error bars
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))
custom_errors = np.minimum(np.sqrt(h.values()), np.random.uniform(0, 20, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=custom_errors, label='Data with custom errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram - errors from sqrt of sum of weights squared
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method='sqrt', label='Weighted data (sqrt method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram with custom error calculation
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

# Custom error function: error = sqrt(sum(weights^2)) / 2
def custom_w2_method(weights, variances):
 up = weights - np.ones_like(weights) * 0.2 * np.mean(weights)
 down = weights + np.ones_like(weights) * 0.2 * np.mean(weights)
 return up, down

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method=custom_w2_method, label='Weighted data (custom error method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Automatic (Poisson)Set explicitlySet yerr calculation method ('sqrt'/'poisson')Set yerr calculation function - full control

# Simple histogram with Weight storage for automatic errors
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.poisson(5, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=True, label='Data with Poisson errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Simple histogram with custom error bars
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))
custom_errors = np.minimum(np.sqrt(h.values()), np.random.uniform(0, 20, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=custom_errors, label='Data with custom errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram - errors from sqrt of sum of weights squared
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method='sqrt', label='Weighted data (sqrt method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram with custom error calculation
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

# Custom error function: error = sqrt(sum(weights^2)) / 2
def custom_w2_method(weights, variances):
 up = weights - np.ones_like(weights) * 0.2 * np.mean(weights)
 down = weights + np.ones_like(weights) * 0.2 * np.mean(weights)
 return up, down

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method=custom_w2_method, label='Weighted data (custom error method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Automatic (Poisson)Set explicitlySet yerr calculation method ('sqrt'/'poisson')Set yerr calculation function - full control

# Simple histogram with Weight storage for automatic errors
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.poisson(5, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=True, label='Data with Poisson errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Simple histogram with custom error bars
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))
custom_errors = np.minimum(np.sqrt(h.values()), np.random.uniform(0, 20, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=custom_errors, label='Data with custom errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram - errors from sqrt of sum of weights squared
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method='sqrt', label='Weighted data (sqrt method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram with custom error calculation
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

# Custom error function: error = sqrt(sum(weights^2)) / 2
def custom_w2_method(weights, variances):
 up = weights - np.ones_like(weights) * 0.2 * np.mean(weights)
 down = weights + np.ones_like(weights) * 0.2 * np.mean(weights)
 return up, down

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method=custom_w2_method, label='Weighted data (custom error method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Automatic (Poisson)Set explicitlySet yerr calculation method ('sqrt'/'poisson')Set yerr calculation function - full control

# Simple histogram with Weight storage for automatic errors
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.poisson(5, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=True, label='Data with Poisson errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Simple histogram with custom error bars
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))
custom_errors = np.minimum(np.sqrt(h.values()), np.random.uniform(0, 20, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=custom_errors, label='Data with custom errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram - errors from sqrt of sum of weights squared
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method='sqrt', label='Weighted data (sqrt method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram with custom error calculation
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

# Custom error function: error = sqrt(sum(weights^2)) / 2
def custom_w2_method(weights, variances):
 up = weights - np.ones_like(weights) * 0.2 * np.mean(weights)
 down = weights + np.ones_like(weights) * 0.2 * np.mean(weights)
 return up, down

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method=custom_w2_method, label='Weighted data (custom error method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Automatic (Poisson)Set explicitlySet yerr calculation method ('sqrt'/'poisson')Set yerr calculation function - full control

# Simple histogram with Weight storage for automatic errors
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.poisson(5, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=True, label='Data with Poisson errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Simple histogram with custom error bars
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))
custom_errors = np.minimum(np.sqrt(h.values()), np.random.uniform(0, 20, 20))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', yerr=custom_errors, label='Data with custom errors', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram - errors from sqrt of sum of weights squared
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method='sqrt', label='Weighted data (sqrt method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Weighted histogram with custom error calculation
h = hist.new.Reg(20, 0, 20).Weight().fill(np.random.normal(10, 5, 2000))

# Custom error function: error = sqrt(sum(weights^2)) / 2
def custom_w2_method(weights, variances):
 up = weights - np.ones_like(weights) * 0.2 * np.mean(weights)
 down = weights + np.ones_like(weights) * 0.2 * np.mean(weights)
 return up, down

fig, ax = plt.subplots()
mh.histplot(h, histtype='errorbar', w2method=custom_w2_method, label='Weighted data (custom error method)', ax=ax)
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

Normalization Options

Control histogram normalization with density and binwnorm parameters:

DefaultplothistCMSATLASLHCbALICEDUNE

NominalDensityBin Width Normalized

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'density': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Density')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'binwnorm': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events / Bin Width')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

NominalDensityBin Width Normalized

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'density': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Density')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'binwnorm': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events / Bin Width')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

NominalDensityBin Width Normalized

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'density': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Density')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'binwnorm': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events / Bin Width')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

NominalDensityBin Width Normalized

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'density': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Density')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'binwnorm': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events / Bin Width')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

NominalDensityBin Width Normalized

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'density': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Density')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'binwnorm': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events / Bin Width')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

NominalDensityBin Width Normalized

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'density': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Density')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'binwnorm': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events / Bin Width')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

NominalDensityBin Width Normalized

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'density': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Density')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

# Create histograms with different binning schemes
bins1 = np.r_[np.linspace(-4, 0, 30)[:-1], np.linspace(0, 4, 10)] # Variable binning
bins2 = np.linspace(-4, 4, 40) #  Regular binning
h1 = hist.new.Var(bins1).Weight().fill(np.random.normal(0, 1, 2000))
h2 = hist.new.Var(bins2).Weight().fill(np.random.normal(0, 1, 2000))

# Plot
kwargs = {'binwnorm': True}
fig, ax = plt.subplots()
mh.histplot(h1, histtype='fill', alpha=0.7, label='Variable bins (same data)', ax=ax, **kwargs)
mh.histplot(h2, histtype='fill', alpha=0.7, label='Regular bins (same data)', ax=ax, **kwargs)

# Style
ax.set_xlabel('Observable')
ax.set_ylabel('Events / Bin Width')
ax.legend(loc='upper right')
mh.yscale_legend(soft_fail=True)

2D Histograms

Use mh.hist2dplot() for 2D histogram visualization:

DefaultplothistCMSATLASLHCbALICEDUNE

# Generate 2D data
x = np.random.normal(0, 1, 5000)
y = np.random.normal(0, 1, 5000)
H, xedges, yedges = np.histogram2d(x, y, bins=30)

fig, ax = plt.subplots()
mh.hist2dplot(H, xedges, yedges, ax=ax, cbar=True)
ax.set_xlabel('Variable 1')
ax.set_ylabel('Variable 2')

# Generate 2D data
x = np.random.normal(0, 1, 5000)
y = np.random.normal(0, 1, 5000)
H, xedges, yedges = np.histogram2d(x, y, bins=30)

fig, ax = plt.subplots()
mh.hist2dplot(H, xedges, yedges, ax=ax, cbar=True)
ax.set_xlabel('Variable 1')
ax.set_ylabel('Variable 2')

# Generate 2D data
x = np.random.normal(0, 1, 5000)
y = np.random.normal(0, 1, 5000)
H, xedges, yedges = np.histogram2d(x, y, bins=30)

fig, ax = plt.subplots()
mh.hist2dplot(H, xedges, yedges, ax=ax, cbar=True)
ax.set_xlabel('Variable 1')
ax.set_ylabel('Variable 2')

# Generate 2D data
x = np.random.normal(0, 1, 5000)
y = np.random.normal(0, 1, 5000)
H, xedges, yedges = np.histogram2d(x, y, bins=30)

fig, ax = plt.subplots()
mh.hist2dplot(H, xedges, yedges, ax=ax, cbar=True)
ax.set_xlabel('Variable 1')
ax.set_ylabel('Variable 2')

# Generate 2D data
x = np.random.normal(0, 1, 5000)
y = np.random.normal(0, 1, 5000)
H, xedges, yedges = np.histogram2d(x, y, bins=30)

fig, ax = plt.subplots()
mh.hist2dplot(H, xedges, yedges, ax=ax, cbar=True)
ax.set_xlabel('Variable 1')
ax.set_ylabel('Variable 2')

# Generate 2D data
x = np.random.normal(0, 1, 5000)
y = np.random.normal(0, 1, 5000)
H, xedges, yedges = np.histogram2d(x, y, bins=30)

fig, ax = plt.subplots()
mh.hist2dplot(H, xedges, yedges, ax=ax, cbar=True)
ax.set_xlabel('Variable 1')
ax.set_ylabel('Variable 2')

# Generate 2D data
x = np.random.normal(0, 1, 5000)
y = np.random.normal(0, 1, 5000)
H, xedges, yedges = np.histogram2d(x, y, bins=30)

fig, ax = plt.subplots()
mh.hist2dplot(H, xedges, yedges, ax=ax, cbar=True)
ax.set_xlabel('Variable 1')
ax.set_ylabel('Variable 2')