.. _plotter:

========
Plotting
========

.. autofunction:: curie.colormap
.. autofunction:: curie.set_style


Class Plotting Method
=====================

The following classes have plotting methods callable with the `cls.plot()` function, where `cls` is an instance of the appropriate class:

* Calibration
* Spectrum
* DecayChain
* Reaction
* Stack

All `cls.plot()` methods have no required arguments, and while some have specific optional arguments that are documented in the respective classes,
all plotting methods share the same optional **keyword arguments**, denoted in the code by `**kwargs`.

**f**
: `matplotlib.pyplot figure`  
	If figure *and* axes are supplied, then plot will be drawn *on top* of those axes.

**ax**
: `matplotlib.pyplot axes`
	If figure *and* axes are supplied, then plot will be drawn *on top* of those axes.

**scale**
: `str`
	Can specify the scale for the x and y axes with the following options:

* `log` or `logy` : Set *only* y-scale to log
* `logx` : Set *only* x-scale to log
* `lin`, `liny` or `linear` : Set *only* y-scale to linear
* `linx` : Set *only* x-scale to linear
* `linlin` : x-scale *and* y-scale linear
* `loglog` : x-scale *and* y-scale log
* `linlog` : linear x-scale and log y-scale
* `loglin` : log x-scale and linear y-scale

**show**
: `bool`
	Whether or not to show the figure using the matplotlib GUI.

**saveas**
: `str`
	Full filename for saving the figure in one of the matplotlib supported filetypes.

**return_plot**
: `bool`
	If `True`, then a tuple of (fig, axes) will be returned when calling the `cls.plot()` method.  This can be used to draw multiple plots over each other, e.g. for plotting multiple cross-sections.

**style**, **palette** and **shade** are also available as keyword arguments, and have the same behavior as in `curie.set_style()`.