Handling of operator input types to models

[DanPuzzuoli]

What is the expected behavior?

There are a few type-handling issues that we need to sort out. Currently, the model classes support operation in both dense and sparse modes, however, regardless of the chosen mode of operation, at instantiation, everything is passed through the to_array function, which converts everything to a dense array. If a sparse mode is selected, it will then convert these arrays to sparse. We should set things up so that if a user specifies things in a sparse format then it never gets converted to a dense format.

To do:

• Add a to_csr analog of to_array, which does the same job but converts everything to the required format for the sparse types (see e.g. the constructor to SparseOperatorCollection for the required format).
• Add handling of qutip operator input types to both to_array and to_csr. This should be done without actually importing qutip, so need to decide how to detect this (op.__name__ ?) .
• Make sure the use-case of specifying things operators in a sparse format, and requesting models to operate in sparse format, never converts to dense.

[DanPuzzuoli]

Possible code suggestion from @chriseclectic for detecting/conversion from qutip without importing qutip.

def isinstance_qutip_qobj(obj):
    if (type(obj).__name__ == 'Qobj'
            and hasattr(obj, '_data')
            and type(obj._data).__name__ == 'fast_csr_matrix'):
        return True
    return False

import qutip
op = qutip.Qobj([[0, 1], [1, 0]])

if isinstance_qutip_qobj(op):
    op = op._data  # csr matrix