roc_plot
===============================================

.. currentmodule:: sdt_metrics.plotting

.. autofunction:: roc_plot

Producing plot from probabilities
----------------------------------

ROC plots can be generated from dprime, aprime, or amzs

    >>> from sdt_metrics.plotting import roc_plot
    >>> roc_plot(.67, .43, metric='amzs', fname='roc_example01.png')
    
.. image:: static/roc_example01.png
    :width: 600px
    :align: center
    :height: 600px
    :alt: roc_example01.png
    
Producing plot from frequencies
--------------------------------------------------

    >>> roc_plot(116, 30, 50, 50, 
                 metric='aprime',
                 fname='roc_example02.png')
    
.. image:: static/roc_example02.png
    :width: 600px
    :align: center
    :height: 600px
    :alt: roc_example02.png
    
Producing plot from :class:`SDT` Object
----------------------------------------

    >>> from sdt_metrics import HI,MI,CR,FA, SDT
    >>> from random import choice
    >>> sdt_obj = SDT([choice([HI,MI,CR,FA]) for i in xrange(1000)])
    >>> print(sdt_obj)
    SDT(HI=251, MI=245, CR=264, FA=240)
    >>> roc_plot(sdt_obj, fname='roc_example03.png')
    
.. image:: static/roc_example03.png
    :width: 600px
    :align: center
    :height: 600px
    :alt: roc_example03.png
    
        
Specifiying Bias Isopleths (Contours)
------------------------------------------------

The `isopleths` keyword allows specifying isopleths for either 
`beta`, `c`, `bppd`, or `bmz`.

    >>> roc_plot(116, 30, 50, 50,
                 metric='dprime',
                 isopleths='beta',
                 fname='roc_example04.png')
    
.. image:: static/roc_example04.png
    :width: 600px
    :align: center
    :height: 600px
    :alt: roc_example04.png
    
The values inside the brackets specify [`start` : `stop` : `step`]. On
The figure the thinner lines denot larger values (going up hill). This
is a little counterintuitive but the uphill gradients tend to be steeper
and having thinner lines makes the plots look better.