multihist 0.1.3

Convenience wrappers around numpy histograms

multihist
===========
`https://github.com/JelleAalbers/multihist`

Thin wrapper around numpy's histogram and histogram2d.

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, slicing and projecting 2d histograms, etc.
You can also access cumulative and density information, as well as basic statistics (mean and std).

Synopsis::

import numpy as np
from matplotlib import pyplot as plt

from multihist import Hist1d, Hist2d

# 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", linestyle='steps')
plt.plot(m.bin_centers, m.density, label="Empirical PDF", linestyle='steps')
plt.plot(m.bin_centers, m.cumulative_density, label="Empirical CDF", linestyle='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
m2 = Hist2d(bins=100, range=[[-5, 3], [-3, 5]])
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('y').plot(label='y projection')
plt.legend()
plt.show()

Alternatives
------------
Of course, the easiest alternative is just to use np.histogram without any wrappers.

If you're looking for a more fancy histogram class, and don't mind installing a big framework,
you might like the particle physics workhorse ROOT (`https://root.cern.ch/root/html/TH1.html`) and one of its python bindings (pyROOT, rootpy).

If you do come from a ROOT background, you might appreciate pyhistogram (`https://github.com/cbourjau/pyhistogram`) instead,
which has a much more ROOT-like interface.

Another python histogram package oriented towards physics is `http://docs.danse.us/histogram/0.2.1/intro.html`. This has support for physical units in histograms and error propagation, but the interface is further removed from numpy.



History
-------

------------------
0.1.1-3 (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