phy

physt

Python histogram library - histograms as updateable, fully semantic objects with visualization tools. [P]ython [HYST]ograms.

Showing:

Popularity

Downloads/wk

0

GitHub Stars

115

Maintenance

Last Commit

3mos ago

Contributors

6

Package

Dependencies

0

License

MIT

Categories

Readme

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

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

Versioning

  • Versions 0.3.x support Python 2.7 (no new releases in 2019)
  • Versions 0.4.x support Python 3.5+ while continuing the 0.3 API
  • Versions 0.4.9+ support only Python 3.6+ while continuing the 0.3 API
  • Versions 0.5.x slightly change the interpretation of *args in h1, h2, ...

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", bin_count=[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_histograms

# 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_histograms.spherical(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, help wanted!)
    • folium (experimental for geo-data)
    • plotly (very basic, help wanted!)
    • ascii (experimental)
  • Algorithms for optimized binning
    • human-friendly
    • mathematical
  • IO, conversions
    • I/O JSON
    • I/O xarray.DataSet (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 3.6+
  • 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)
  • (optional) asciiplotlib - for ASCII bar plots
  • (optional) xtermcolot - for ASCII color maps
  • (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:

Patches:

Alternatives and inspirations

Rate & Review

Great Documentation0
Easy to Use0
Performant0
Highly Customizable0
Bleeding Edge0
Responsive Maintainers0
Poor Documentation0
Hard to Use0
Slow0
Buggy0
Abandoned0
Unwelcoming Community0
100
No reviews found
Be the first to rate

Alternatives

No alternatives found

Tutorials

No tutorials found
Add a tutorial