Ensemble averages

enstat

Overview

enstat is a library to facilitate the computation of ensemble averages (and their variances) or histograms.

The key feature is that a class stores the sum of the first and second statistical moments and the number of samples. This gives access to the mean (and variance) at all times, while you can keep adding samples.

For the histogram something similar holds, but this time the count per bin is stored.

Ensemble average

Suppose that we have 100 realisations, each with 1000 'blocks', and we want to know the ensemble average of each block:

import enstat

ensemble = enstat.static()

for realisation in range(100):

sample = np.random.random(1000)
ensemble += sample

print(ensemble.mean())


Ensemble histogram

Same example, but now we want the histogram for pre-defined bins:

import enstat

bin_edges = np.linspace(0, 1, 11)
hist = enstat.histogram(bin_edges=bin_edges)

for realisation in range(100):

sample = np.random.random(1000)
hist += sample

print(hist.p)


which prints the probability density of each bin (so list of values around 0.1 for these bins).

Histogram: bins and plotting

The histogram class contains two nice features.

1. It has several bin algorithms that NumPy does not have.

2. It can be used for plotting with an ultra-sort interface, for example:

import enstat
import matplotlib.pyplot as plt

data = np.random.random(1000)
hist = enstat.histogram.from_data(data, bins=10, mode="log")

fig, ax = plt.subplots()
ax.plot(hist.x, hist.p)
plt.show()


You can even use ax.plot(*hist.plot).

Installation

• Using conda

conda install -c conda-forge enstat

• Using PyPi

python -m pip install enstat


Disclaimer

This library is free to use under the MIT license. Any additions are very much appreciated. As always, the code comes with no guarantee. None of the developers can be held responsible for possible mistakes.

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