## model.assess_normalized_dope() — assess a model with the normalized DOPE method

`assess_normalized_dope()`

Output:
  Z-score

> This command assesses the quality of the model using the normalized DOPE method. This is a Z-score; positive scores are likely to be poor models, while scores lower than -1 or so are likely to be native-like.

> The normalized DOPE score is derived from the statistics of raw DOPE scores6.3. See selection.assess_dope() for more information on these raw scores.

> When using `automodel` or `loopmodel`, automatic normalized DOPE assessment of each model can be requested by adding `assess.normalized_dope` to automodel.assess_methods or loopmodel.loop.assess_methods respectively.

Example: examples/assessment/assess_normalized_dope.py

      # Example for: model.assess_normalized_dope()

      from modeller import *
      from modeller.scripts import complete_pdb

      env = environ()
      env.libs.topology.read(file='$(LIB)/top_heav.lib')
      env.libs.parameters.read(file='$(LIB)/par.lib')

      # Read a model previously generated by Modeller's automodel class
      mdl = complete_pdb(env, '../atom_files/1fdx.B99990001.pdb')

      zscore = mdl.assess_normalized_dope()