Metadata-Version: 2.1
Name: plotly-scientific-plots
Version: 0.1.0.6
Summary: Python package extending plotly for scientific computing and visualization
Home-page: https://github.com/rsandler00/plotly-scientific-tools
Author: Roman Sandler
Author-email: rsandler00@gmail.com
License: MIT
Description: # 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
        * Easily make full multi-figure dashboards in a single line using Dash
        
        ## 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`
        
        
        **NOTE**: to get latest version install directly from git, which is more commonly updated. Use the following: 
        `pip install git+https://github.com/rsandler00/plotly-scientific-plots.git`
        
        
        To import use `import plotly_scientific_plots as psp`
        
        ## Examples and Usage
        
        Plotly's key strength is its ability to do interactive visualizations. 
        For scientists, this allows novel ways of exploring data with mouse clicks and hovers. **To see a full list of plotly-scientific-tools examples and their descriptions, go through the `examples.ipynb` 
        in nbviewer by clicking [here][1]**
        
        Below, are a limited set of examples to give the feel of how `psp` works:
        
        #### Sample plots
        
        ##### Two dataset histograms:
        
        ```python
        psp.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)
        ```
        
        Notice that the statistics box only appears when mouse hovers on the databar of the given color. 
        
        ![plot2Hist_1](images/plot2Hist_1.png?raw=true "plot2Hist_1")
        
        
        ##### Scatter + Contour Plot:
        
        ```python
        psp.scatterHistoPlot(data_source_1, data_source_3, title='Contour of x_var & y_var', 
                    xlbl='x_var label', ylbl='y_var label')
        ```
        ![plot2Hist_1](images/contour_and_scatter.png?raw=true "contour_and_scatter")
        
        
        ##### Multiple Dataset Correlations + Stats:
        
        ```python
        psp.corrPlot([data_source_1, data_source_11, data_source_12], [data_source_3, data_source_31, 
                    data_source_32], names=['datasetA', 'datasetB', 'datasetC'],addCorr=True, 
                    addCorrLine=True, title='Correlation of x_var & y_var', xlbl='x_var label', 
                    ylbl='y_var label')
        ```
        ![plot2Hist_1](images/corrPlot_multi.png?raw=true "corrPlot_multi")
        
        
        ##### Polar Plot
        
        ```python
        psp.plotPolar([polar1], numbins=20, title='Polar Distribution')
        ```
        ![plot2Hist_1](images/polar1.png?raw=true "polar1")
        
        
        #### Dashboards
        
        To make multi-figure dashboards simply collect all desired figures in a nested list. Each outer list will correspond 
        to a column in the dashboard, and each figure within each outer list will be a row in the column. The pass that list 
        to `psp.startDashboard`. A flask-based web-server will start showing the figures in the browser at the provided port 
        (default port=8050). For example:
        
        ```python
        plot1 = psp.plotHist(..., plot=False)
        plot2 = psp.plot2Hists(..., plot=False)
        plot3 = psp.corrPlot(..., plot=False)
        plot4 = psp.plotPolar(..., plot=False)
        dash_plots = [
                    [plot1, plot2],
                    [plot3, plot4]
                ]
        psp.startDashboard(dash_plots, port=8052)
        ```
        
        An example dashboard appears below:
        
        ![plot2Hist_1](images/dashboard1.png?raw=true "dashboard1")
        
        
        
        
        [1]: https://nbviewer.jupyter.org/github/rsandler00/plotly-scientific-plots/blob/master/examples.ipynb
         
        
         
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
