https://github.com/JelleAalbers/multihist
Thin wrapper around numpy’s histogram and histogramdd.
Numpy has great histogram functions, which return (histogram, bin_edges) tuples. This package wraps these in a class with methods for adding new data to existing histograms, take averages, projecting, etc.
For 1-dimensional histograms you can access cumulative and density information, as well as basic statistics (mean and std). For d-dimensional histograms you can name the axes, and refer to them by their names when projecting / summing / averaging.
NB: For a faster and richer histogram package, check out hist from scikit-hep. Alternatively, look at its parent library boost-histogram, which has numpy-compatible features. Multihist was created back in 2015, long before those libraries existed.
Synopsis:
# Create histograms just like from numpy...
m = Hist1d([0, 3, 1, 6, 2, 9], bins=3)
# ...or add data incrementally:
m = Hist1d(bins=100, range=(-3, 4))
m.add(np.random.normal(0, 0.5, 10**4))
m.add(np.random.normal(2, 0.2, 10**3))
# Get the data back out:
print(m.histogram, m.bin_edges)
# Access derived quantities like bin_centers, normalized_histogram, density, cumulative_density, mean, std
plt.plot(m.bin_centers, m.normalized_histogram, label="Normalized histogram", drawstyle='steps')
plt.plot(m.bin_centers, m.density, label="Empirical PDF", drawstyle='steps')
plt.plot(m.bin_centers, m.cumulative_density, label="Empirical CDF", drawstyle='steps')
plt.title("Estimated mean %0.2f, estimated std %0.2f" % (m.mean, m.std))
plt.legend(loc='best')
plt.show()
# Slicing and arithmetic behave just like ordinary ndarrays
print("The fourth bin has %d entries" % m[3])
m[1:4] += 4 + 2 * m[-27:-24]
print("Now it has %d entries" % m[3])
# Of course I couldn't resist adding a canned plotting function:
m.plot()
plt.show()
# Create and show a 2d histogram. Axis names are optional.
m2 = Histdd(bins=100, range=[[-5, 3], [-3, 5]], axis_names=['x', 'y'])
m2.add(np.random.normal(1, 1, 10**6), np.random.normal(1, 1, 10**6))
m2.add(np.random.normal(-2, 1, 10**6), np.random.normal(2, 1, 10**6))
m2.plot()
plt.show()
# x and y projections return Hist1d objects
m2.projection('x').plot(label='x projection')
m2.projection(1).plot(label='y projection')
plt.legend()
plt.show()
History
0.6.6 (2026-03-02)
Add Hist1d methods for compatibility with Histdd (#18)
0.6.5 (2022-01-26)
‘model’ option for error bars, showing Poisson quantiles (#14)
Fix vmin/vmax for matplotlib >3.3, resume CI tests (#15)
Hist1d.data_for_plot returns numbers used in error calculation
0.6.4 (2021-01-17)
Prevent object array creation (#12)
0.6.3 (2020-01-22)
Feldman-Cousins errors for Hist1d.plot (#10)
0.6.2 (2020-01-15)
Fix rebinning for empty histograms (#9)
0.6.1 (2019-12-05)
Fixes for #7 (#8)
0.6.0 (2019-06-30)
Correct step plotting at edges, other plotting fixes
Histogram numpy structured arrays
Fix deprecation warnings (#6)
lookup_hist
.max() and .min() methods
percentile support for higher-dimensional histograms
Improve Hist1d.get_random (also randomize in bin)
0.5.4 (2017-09-20)
Fix issue with input from dask
0.5.3 (2017-09-18)
Fix python 2 support
0.5.2 (2017-08-08)
Fix colorbar arguments to Histdd.plot (#4)
percentile for Hist1d
rebin method for Histdd (experimental)
0.5.1 (2017-03-22)
get_random for Histdd no longer just returns bin centers (Hist1d does stil…)
lookup for Hist1d. When will I finally merge the classes…
0.5.0 (2016-10-07)
pandas.DataFrame and dask.dataframe support
dimensions option to Histdd to init axis_names and bin_centers at once
0.4.3 (2016-10-03)
Remove matplotlib requirement (still required for plotting features)
0.4.2 (2016-08-10)
Fix small bug for >=3 d histograms
0.4.1 (2016-17-14)
get_random and lookup for Histdd. Not really tested yet.
0.4.0 (2016-02-05)
.std function for Histdd
Fix off-by-one errors
0.3.0 (2015-09-28)
Several new histdd functions: cumulate, normalize, percentile…
Python 2 compatibility
0.2.1 (2015-08-18)
Histdd functions sum, slice, average now also work
0.2 (2015-08-06)
Multidimensional histograms
Axes naming
0.1.1-4 (2015-08-04)
Correct various rookie mistakes in packaging… Hey, it’s my first pypi package!
0.1 (2015-08-04)
Initial release
Hist1d, Hist2d
Basic test suite
Basic readme
1 maintainer
Metadata
1 maintainer
Release files for multihist 0.6.6
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