vice.mirror
===========

**[DEPRECATED]**

Obtain an instance of either vice.singlezone or vice.multizone class
given only an instance of the vice.output class or the path to the output.
The returned object will have the same parameters as that which produced
the output, allowing re-simulation with whatever modifications the user
desires.

**Signature**: vice.mirror(arg)

.. deprecated:: 1.1.0
	Users should instead call vice.singlezone.from_output or
	vice.multizone.from_output to achieve this functionality.

Parameters
----------
arg : ``str`` or ``output``
	Either the path to the output (type ``str``) or the output object
	itself.

Returns
-------
obj : ``singlezone`` or ``multizone``
	If arg is of type ``output``, then the ``singlezone`` object which
	produced the output is returned. If arg is of type ``str``, then obj
	is either of type vice.singlezone or vice.multizone, depending on
	which type of simulation produced the output. If arg is of type
	``multioutput``, then the corresponding ``multizone`` object is
	returned.

Raises
------
* ImportError
	- 	The output has encoded functional attributes and the user does not
		have dill_ installed.
* UserWarning
	- 	The output was produced with functional attributes, but was ran on
		a system without dill_, and they have thus been lost.

.. note:: Saving and reinstancing functional simulation parameters from
	VICE outputs requires dill_, an extenstion to ``pickle`` in the
	python standard library. It is recommended that VICE users install
	dill_ >= 0.2.0.

.. _dill: https://pypi.org/dill/

Example Code
------------
>>> out = vice.output("example")
>>> new = vice.mirror(out)
>>> new
vice.singlezone{
	name -----------> onezonemodel
	func -----------> <function _DEFAULT_FUNC_ at 0x1085a6ae8>
	mode -----------> ifr
	verbose --------> False
	elements -------> ('fe', 'sr', 'o')
	IMF ------------> kroupa
	eta ------------> 2.5
	enhancement ----> 1.0
	Zin ------------> 0.0
	recycling ------> continuous
	delay ----------> 0.15
	RIa ------------> plaw
	Mg0 ------------> 6000000000.0
	smoothing ------> 0.0
	tau_ia ---------> 1.5
	tau_star -------> 2.0
	schmidt --------> False
	schmidt_index --> 0.5
	MgSchmidt ------> 6000000000.0
	dt -------------> 0.01
	m_upper --------> 100.0
	m_lower --------> 0.08
	Z_solar --------> 0.014
	bins -----------> [-3, -2.95, -2.9, ... , 0.9, 0.95, 1]
}
>>> import numpy as np
>>> new.run(np.linspace(0, 10, 1001))