Ensemble averages
Project description
enstat
Documentation: enstat.readthedocs.io
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.
-
It has several bin algorithms that NumPy does not have.
-
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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