.. image:: https://github.com/JelleAalbers/multihist/actions/workflows/tests.yml/badge.svg :target: https://github.com/JelleAalbers/multihist/actions/workflows/tests.yml
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 <https://github.com/scikit-hep/hist>_ from scikit-hep. Alternatively, look at its parent library boost-histogram <https://github.com/scikit-hep/boost-histogram>, which has numpy-compatible features <https://boost-histogram.readthedocs.io/en/latest/usage/numpy.html>. 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()
lookup_hist.max() and .min() methodsCorrect various rookie mistakes in packaging... Hey, it's my first pypi package!
Initial release
FAQs
Convenience wrappers around numpy histograms
We found that multihist demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
Malicious PyPI package ‘set-utils’ steals Ethereum private keys by exfiltrating them through blockchain transactions via the Polygon RPC.
Research
Security News
Malicious Go packages are impersonating popular libraries to install hidden loader malware on Linux and macOS, targeting developers with obfuscated payloads.
Security News
Bybit's $1.46B hack by North Korea's Lazarus Group pushes 2025 crypto losses to $1.6B in just two months, already surpassing all of 2024's $1.49B total.