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

env = <environ>		MODELLER environment
alnfile = <str:1>		file containing template-sequence alignment
knowns = <str:0>		template codes in alignment
sequence = <str:1>		sequence code in alignment
deviation = <float:1>		control amount of randomization
library_schedule = <int:1>		optimization schedule
toplib = <str:1>		topology library to use
parlib = <str:1>		parameter library to use
topology_model = <int:1>		topology submodel
csrfile = <str:1>		user-provided restraints file
inifile = <str:1>		user-provided initial model file
assess_methods = function(s)		assessment functions

Description:

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 selects the optimization schedule for model building. See model.schedule.make() for more information. (This can also be set after the object is created, by assigning to 'automodel.library_schedule'.)

If toplib and/or parlib are specified, the topology and parameters used to build the model(s) are taken from these files. In this case, topology_model can also be given to set topology.submodel appropriately.

If csrfile is set, restraints are not constructed, but are instead read from the user-supplied file of the same name. See section 2.2.5 for an example.

If inifile is set, an initial model is read from the user-supplied file of the same name. See section 2.2.6 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()) or assess.DOPE, which uses the DOPE method (see model.assess_dope()). (This can also be set after the object is created, by assigning to 'automodel.assess_methods'.) See section 2.2.2 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.

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