## loopmodel() — prepare to build models with loop refinement

`loopmodel(env, sequence, alnfile=None, knowns=[], inimodel=None, deviation=None, library_schedule=None, csrfile=None, inifile=None, assess_methods=None, loop_assess_methods=None)`

> This creates a new object for loop modeling. It can either build standard comparative models (in identical fashion to the `automodel` class) and then refine each of them, in which case you should set the alnfile and knowns arguments appropriately (see the automodel() documentation) or it can refine a given region of a PDB or mmCIF file, in which case you should set inimodel to the name of the PDB or mmCIF file instead. In both cases, sequence identifies the code of the target sequence.

> All other arguments are the same as those for automodel(), with the exception of those below:

> loop_assess_methods is the analog of automodel.assess_methods for loop modeling, and allows you to request assessment of the generated loop models. (This can also be set after the object is created, by assigning to `'loopmodel.loop.assess_methods'`.)

> See section 2.3 for examples.