plotly-scientific-plots 0.1.0.3

Python package extending plotly for scientific computing and visualization

plotly-scientific-plots

This python library is meant to augment the plotly and dash visualization libraries. It is designed to facilitate rapid and beautiful data visualizations for research scientists and data scientists.

Its advantages over naive plotly are:

• One-line commands to make plots
• Integrated scatistical testing above plots
• Expanded plot types (such as confusion amtrices, ROC plots)
• more 'Matlab-like' interface for those making the Matlab --> python transition

Requirements and installation

Required packages:

• numpy
• scipy
• plotly
• colorlover
• dash
• dash_core_components
• dash_html_components

To install, simply use pip install plotly-scientific-plots

Examples & Usage

Plots 2 overlapping normalized histograms, including overhead boxplots and data points.

Furthermore performs statistical testing to differentiate the two population samples. Tests are:

• KS: Kolmogorov-Smirnov statistic on 2 samples.
• MW: Mann-Whitney rank test on two samples.
• T: T-test for the means of two independent samples of scores.

All tests are done via scipy

plot2Hists(data_source_1, data_source_2, names=['Data 1','Data 2'],
           normHist=True, title='Comparison of 2 Data Sources',
           KS=True, MW=True, T=True))