Automatic decorating of class methods to run them in a context

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We previously examined approaches to running code in a context. With hy, we even managed to remove the need for a with statement through the use of a macro that expanded behind the scenes to manage the context. In our jasp code, we frequently need a context manager that temporarily changes the working directory to run some code, then changes back. The use of the context manager was a design decision to avoid decorating every single function. Why? There are a lot of functions that need decorating, and they are spread over a lot of files. Not all of the entries from the next block are methods, but most of them are.

from jasp import *

c = Vasp()
print(dir(c))
['__doc__', '__init__', '__module__', '__repr__', '__str__', 'add_to_db', 'archive', 'atoms', 'bader', 'bool_params', 'calculate', 'calculation_required', 'check_state', 'chgsum', 'clean', 'clone', 'create_metadata', 'dict', 'dict_params', 'exp_params', 'float_params', 'get_atoms', 'get_beefens', 'get_bz_k_points', 'get_charge_density', 'get_default_number_of_electrons', 'get_dipole_moment', 'get_eigenvalues', 'get_elapsed_time', 'get_electronic_temperature', 'get_elf', 'get_energy_components', 'get_fermi_level', 'get_forces', 'get_ibz_k_points', 'get_ibz_kpoints', 'get_infrared_intensities', 'get_k_point_weights', 'get_local_potential', 'get_magnetic_moment', 'get_magnetic_moments', 'get_name', 'get_nearest_neighbor_table', 'get_neb', 'get_nonselfconsistent_energies', 'get_number_of_bands', 'get_number_of_electrons', 'get_number_of_grid_points', 'get_number_of_ionic_steps', 'get_number_of_iterations', 'get_number_of_spins', 'get_occupation_numbers', 'get_orbital_occupations', 'get_potential_energy', 'get_property', 'get_pseudo_density', 'get_pseudo_wavefunction', 'get_pseudopotentials', 'get_required_memory', 'get_spin_polarized', 'get_stress', 'get_valence_electrons', 'get_version', 'get_vibrational_frequencies', 'get_vibrational_modes', 'get_xc_functional', 'initialize', 'input_params', 'int_params', 'is_neb', 'job_in_queue', 'json', 'list_params', 'name', 'nbands', 'org', 'output_template', 'plot_neb', 'positions', 'post_run_hooks', 'prepare_input_files', 'pretty_json', 'python', 'read', 'read_convergence', 'read_default_number_of_electrons', 'read_dipole', 'read_eigenvalues', 'read_electronic_temperature', 'read_energy', 'read_fermi', 'read_forces', 'read_ibz_kpoints', 'read_incar', 'read_k_point_weights', 'read_kpoints', 'read_ldau', 'read_magnetic_moment', 'read_magnetic_moments', 'read_metadata', 'read_nbands', 'read_number_of_electrons', 'read_number_of_iterations', 'read_occupation_numbers', 'read_outcar', 'read_potcar', 'read_relaxed', 'read_stress', 'read_version', 'read_vib_freq', 'register_post_run_hook', 'register_pre_run_hook', 'restart', 'restart_load', 'results', 'run', 'run_counts', 'set', 'set_atoms', 'set_nbands', 'set_results', 'special_params', 'string_params', 'strip', 'strip_warnings', 'todict', 'track_output', 'update', 'write_incar', 'write_kpoints', 'write_metadata', 'write_potcar', 'write_sort_file', 'xml']

The use of a context manager is really useful for a single calculation, and it saves us a lot of boilerplate code to manage changing directories. It limits us though for looping through calculations. We are stuck with traditional for loops that have the with statement embedded in them. We also cannot get too functional, e.g. with list comprehension.

In other words, this is ok:

E = []
for d in np.linspace(1, 1.5):
    atoms = Atoms(...,d)
    with jasp('calculated-name-{}'.format(d),
              ...,
              atoms=atoms) as calc:
        E.append(atoms.get_potential_energy())

But this code is not possible:

bond_lengths = np.linspace(1, 1.5)

A = [Atoms(...,d) for d in bond_lengths]

calcs = [JASP('calculated-name-{}'.format(d),...,atoms=atoms)
         for d, atoms in zip(bond-lengths, A)]

E = [atoms.get_potential_energy() for atoms in A]

It is not legal syntax to embed a with statement inside a list comprehension. The code will not work because to get the potential energy we have to switch into the calculation directory and read it from a file there, then switch back.

To make that possible, we need to decorate the class functions so that the right thing happens when needed. I still do not want to decorate each function manually. Although there is a case to make for it, as I mentioned earlier, the functions are all over the place, and numerous. Now is not the time to fix it.

Instead, we consider a solution that will automatically decorate class functions for us! Enter the Metaclass. This is a class that modifies how classes are created. The net effect of the code below is our Calculator class now has all functions automatically decorated with a function that changes to the working directory, runs the function, and then ensures we change back even in the event of an exception. This approach is adapted from http://stackoverflow.com/questions/3467526/attaching-a-decorator-to-all-functions-within-a-class .

I am pretty sure this is the right way to do this. We cannot simply decorate the functions of ase.calculators.vasp.Vasp because our decorator needs access to the directory defined in a class instance. That is what the init method of the metaclass enables.

We will put this code into a library called meta_calculator.py for reuse in later examples.

import os
import types

class WithCurrentDirectory(type):
   """Metaclass that decorates all of its methods except __init__."""
   def __new__(cls, name, bases, attrs):
      return super(WithCurrentDirectory, cls).__new__(cls, name, bases, attrs)

   def __init__(cls, name, bases, attrs):
      """Decorate all the methods of the class instance with the classmethod cd.

      We skip __init__ because that is where the attributes are actually set.
      It is an error to access them before they are set.
      """
      for attr_name, attr_value in attrs.iteritems():
         if attr_name != '__init__' and isinstance(attr_value, types.FunctionType):
            setattr(cls, attr_name, cls.cd(attr_value))


   @classmethod
   def cd(cls, func):
      """Decorator to temporarily run cls.func in the directory stored in cls.wd.

      The working directory is restored to the original directory afterwards.
      """
      def wrapper(self, *args, **kwargs):
         if self.verbose:
            print('\nRunning {}'.format(func.__name__))
            print("Started in {}".format(os.getcwd()))
         os.chdir(self.wd)
         if self.verbose:
            print("  Entered {}".format(os.getcwd()))
         try:
            result = func(self, *args, **kwargs)
            if self.verbose:
               print('  {}'.format(result))
            return result
         except Exception, e:
            # this is where you would use an exception handling function
            print('  Caught {}'.format(e))
            pass
         finally:
            os.chdir(self.owd)
            if self.verbose:
               print("  Exited to {}\n".format(os.getcwd()))

      wrapper.__name__ = func.__name__
      wrapper.__doc__ = func.__doc__
      return wrapper


class Calculator(object):
   """Class we use for a calculator.

   Every method is decorated by the metaclass so it runs in the working
   directory defined by the class instance.

   """

   __metaclass__ = WithCurrentDirectory

   def __init__(self, wd, verbose=False):
      self.owd = os.getcwd()
      self.wd = wd
      self.verbose = verbose
      if not os.path.isdir(wd):
         os.makedirs(wd)


   def create_input(self, **kwargs):
      with open('INCAR', 'w') as f:
         for key, val in kwargs.iteritems():
            f.write('{} = {}\n'.format(key, val))


   def exc(self):
      "This raises an exception to see what happens"
      1 / 0

   def read_input(self):
      with open('INCAR', 'r') as f:
         return f.read()

   def __str__(self):
      return 'In {}. Contains: {}'.format(os.getcwd(),
                                          os.listdir('.'))

Here is how we might use it.

from meta_calculator import *

c = Calculator('/tmp/calc-1', verbose=True)
print c.create_input(xc='PBE', encut=450)
print c.read_input()
print c.exc()
print c
Running create_input
Started in /Users/jkitchin/blogofile-jkitchin.github.com/_blog
  Entered /private/tmp/calc-1
  None
  Exited to /Users/jkitchin/blogofile-jkitchin.github.com/_blog

None

Running read_input
Started in /Users/jkitchin/blogofile-jkitchin.github.com/_blog
  Entered /private/tmp/calc-1
  xc = PBE
encut = 450

  Exited to /Users/jkitchin/blogofile-jkitchin.github.com/_blog

xc = PBE
encut = 450


Running exc
Started in /Users/jkitchin/blogofile-jkitchin.github.com/_blog
  Entered /private/tmp/calc-1
  Caught integer division or modulo by zero
  Exited to /Users/jkitchin/blogofile-jkitchin.github.com/_blog

None

Running __str__
Started in /Users/jkitchin/blogofile-jkitchin.github.com/_blog
  Entered /private/tmp/calc-1
  In /private/tmp/calc-1. Contains: ['INCAR']
  Exited to /Users/jkitchin/blogofile-jkitchin.github.com/_blog

In /private/tmp/calc-1. Contains: ['INCAR']

As we can see, in each function call, we evidently do change into the path that /tmp/calc-1 points to (it is apparently /private/tmp on Mac OSX), runs the method, and then changes back out, even when exceptions occur.

Here is a functional approach to using our new calculator.

from meta_calculator import *

encuts = [100, 200, 300, 400]
calcs = [Calculator('encut-{}'.format(encut)) for encut in encuts]

# list-comprehension for the side-effect
[calc.create_input(encut=encut) for calc,encut in zip(calcs, encuts)]

inputs = [calc.read_input() for calc in calcs]

print(inputs)
print([calc.wd for calc in calcs])
['encut = 100\n', 'encut = 200\n', 'encut = 300\n', 'encut = 400\n']
['encut-100', 'encut-200', 'encut-300', 'encut-400']

Sweet. And here is our evidence that the directories got created and have the files in them.

find . -type f -print | grep "encut-[1-4]00" | xargs -n 1 -I {} -i bash -c 'echo {}; cat {}; echo'
./encut-100/INCAR
encut = 100

./encut-200/INCAR
encut = 200

./encut-300/INCAR
encut = 300

./encut-400/INCAR
encut = 400

This looks like another winner that will be making its way into jasp soon. I guess it will require at least some minor surgery on a class in ase.calculators.vasp. It might be time to move a copy of it all the way into jasp.

Copyright (C) 2016 by John Kitchin. See the License for information about copying.

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