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## automodel() -- prepare to build one or more comparative models

`automodel(env, alnfile, knowns, sequence, deviation=None, library_schedule=None, csrfile=None, inifile=None, assess_methods=None)`

> alnfile is required, and specifies the PIR file which contains an alignment between knowns (the templates) and sequence (the target sequence).

> deviation controls the amount of randomization done by `randomize.xyz` or `randomize.dihedrals`. (This can also be set after the object is created, by assigning to `'automodel.deviation'`.)

> library_schedule, if given, sets an initial value for automodel.library_schedule.

> If csrfile is set, restraints are not constructed, but are instead read from the user-supplied file of the same name. See section 2.2.8 for an example.

> If inifile is set, an initial model is read from the user-supplied file of the same name. See section 2.2.9 for an example.

> assess_methods allows you to request assessment of the generated models (by default, none is done). You can provide a function, or list of functions, for this purpose, or use one or more of the standard functions provided in the `assess` module -- `assess.GA341`, which uses the GA341 method (see model.assess_ga341()), `assess.DOPE`, which uses the DOPE method (see selection.assess_dope()), or `assess.DOPEHR`, which uses the DOPE-HR method (see selection.assess_dopehr()). (This can also be set after the object is created, by assigning to `'automodel.assess_methods'`.) See section 2.2.3 for an example. Note that only standard models are assessed in this way; if you are also building loop models, see loopmodel.loop.assess_methods.

> By default, models are built using heavy atom-only parameters and topology. If you want to use different parameters, read them in before creating the `automodel` object with topology.read() and parameters.read().

> See section 2.1 for a general example of using this class.

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