P(i/y)thon h(i/y)stograms.
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README.md

physt Physt logo

P(i/y)thon h(i/y)stograms. Inspired (and based on) numpy.histogram, but designed for humans(TM) on steroids(TM).

The goal is to unify different concepts of histograms as occurring in numpy, pandas, matplotlib, ROOT, etc. and to create one representation that is easily manipulated with from the data point of view and at the same time provides nice integration into IPython notebook and various plotting options. In short, whatever you want to do with histograms, physt aims to be on your side.

Note: Version 0.3.x is feature-full while a re-design in the form of 0.4 is on the way.

Note: bokeh plotting backend has been discontinued (due to external library being redesigned.)

Join the chat at https://gitter.im/physt/Lobby PyPI version ReadTheDocs Anaconda-Server Badge Anaconda-Server Badge

Simple example

from physt import h1

# Create the sample
heights = [160, 155, 156, 198, 177, 168, 191, 183, 184, 179, 178, 172, 173, 175,
           172, 177, 176, 175, 174, 173, 174, 175, 177, 169, 168, 164, 175, 188,
           178, 174, 173, 181, 185, 166, 162, 163, 171, 165, 180, 189, 166, 163,
           172, 173, 174, 183, 184, 161, 162, 168, 169, 174, 176, 170, 169, 165]

hist = h1(heights, 10)           # <--- get the histogram data
hist << 190                      # <--- add a forgotten value
hist.plot()                      # <--- and plot it

Heights plot

2D example

from physt import h2
import seaborn as sns

iris = sns.load_dataset('iris')
iris_hist = h2(iris["sepal_length"], iris["sepal_width"], "human", (12, 7), name="Iris")
iris_hist.plot(show_zero=False, cmap="gray_r", show_values=True);

Iris 2D plot

3D directional example

import numpy as np
from physt import special

# Generate some sample data
data = np.empty((1000, 3))
data[:,0] = np.random.normal(0, 1, 1000)
data[:,1] = np.random.normal(0, 1.3, 1000)
data[:,2] = np.random.normal(1, .6, 1000)

# Get histogram data (in spherical coordinates)
h = special.spherical_histogram(data)                 

# And plot its projection on a globe
h.projection("theta", "phi").plot.globe_map(density=True, figsize=(7, 7), cmap="rainbow")   

Directional 3D plot

See more in docstring's and notebooks:

Installation

Using pip:

pip install physt

Features

Implemented

  • 1D histograms
  • 2D histograms
  • ND histograms
  • Some special histograms
    • 2D polar coordinates (with plotting)
    • 3D spherical / cylindrical coordinates (beta)
  • Adaptive rebinning for on-line filling of unknown data (beta)
  • Non-consecutive bins
  • Memory-effective histogramming of dask arrays (beta)
  • Understands any numpy-array-like object
  • Keep underflow / overflow / missed bins
  • Basic numeric operations (* / + -)
  • Items / slice selection (including mask arrays)
  • Add new values (fill, fill_n)
  • Cumulative values, densities
  • Simple statistics for original data (mean, std, sem)
  • Plotting with several backends
    • matplotlib (static plots with many options)
    • vega (interactive plots, beta)
    • folium (experimental for geo-data)
    • plotly (very basic)
  • Algorithms for optimized binning
    • human-friendly
    • mathematical
  • IO, conversions
    • I/O JSON
    • I/O xarray.DataSet (experimental)
    • I/O protobuf (experimental)
    • O ROOT file (experimental)
    • O pandas.DataFrame (basic)

Planned

  • Rebinning
    • using reference to original data?
    • merging bins
  • Statistics (based on original data)?
  • Stacked histograms (with names)
  • Potentially holoviews plotting backend (instead of the discontinued bokeh one)

Not planned

  • Kernel density estimates - use your favourite statistics package (like seaborn)
  • Rebinning using interpolation - it should be trivial to use rebin (https://github.com/jhykes/rebin) with physt

Rationale (for both): physt is dumb, but precise.

Dependencies

  • Python 3.5+ targeted, 2.7 mostly working (support will be dropped in 2019)
  • numpy
  • (optional) matplotlib - simple output
  • (optional) xarray - I/O
  • (optional) protobuf - I/O
  • (optional) uproot - I/O
  • (optional) astropy - additional binning algorithms
  • (optional) folium - map plotting
  • (optional) vega3 - for vega in-line in IPython notebook (note that to generate vega JSON, this is not necessary)
  • (testing) py.test, pandas
  • (docs) sphinx, sphinx_rtd_theme, ipython

Publicity

Talk at PyData Berlin 2018:

Contribution

I am looking for anyone interested in using / developing physt. You can contribute by reporting errors, implementing missing features and suggest new one.

Thanks to:

Alternatives and inspirations