Merge pull request #989 from rbharath/rmdir

import deepchem as dc

import numpy as np

from tensorflow.python.framework import test_util

from deepchem.models.tf_new_models.vina_model import VinaModel

from deepchem.models.tf_new_models.vina_model import get_cells

from deepchem.models.tf_new_models.vina_model import put_atoms_in_cells

from deepchem.models.tf_new_models.vina_model import compute_neighbor_cells

from deepchem.models.tf_new_models.vina_model import compute_closest_neighbors

from deepchem.models.tf_new_models.vina_model import get_cells_for_atoms

from deepchem.models.tf_new_models.vina_model import compute_neighbor_list

from deepchem.models.tensorflow_models.vina_model import VinaModel

from deepchem.models.tensorflow_models.vina_model import get_cells

from deepchem.models.tensorflow_models.vina_model import put_atoms_in_cells

from deepchem.models.tensorflow_models.vina_model import compute_neighbor_cells

from deepchem.models.tensorflow_models.vina_model import compute_closest_neighbors

from deepchem.models.tensorflow_models.vina_model import get_cells_for_atoms

from deepchem.models.tensorflow_models.vina_model import compute_neighbor_list

import deepchem.utils.rdkit_util as rdkit_util

from deepchem.utils.save import load_sdf_files

from deepchem.utils import pad_array

  cells: tf.Tensor

    (box_size**ndim, ndim) shape.

  """

  return tf.reshape(

      tf.transpose(

          tf.stack(

              tf.meshgrid(

                  * [tf.range(start, stop, nbr_cutoff) for _ in range(ndim)]))),

      (-1, ndim))

  ranges = [tf.range(start, stop, nbr_cutoff) for _ in range(ndim)]

  return tf.reshape(tf.transpose(tf.stack(tf.meshgrid(*ranges))), (-1, ndim))

def put_atoms_in_cells(coords, cells, N, n_cells, ndim, k=5):

def hydrophobic(d):

  """Compute hydrophobic interaction term."""

  return tf.where(d < 0.5,

                  tf.ones_like(d), tf.where(d < 1.5, 1.5 - d, tf.zeros_like(d)))

  where = tf.where(d < 1.5, 1.5 - d, tf.zeros_like(d))

  return tf.where(d < 0.5, tf.ones_like(d), where)

def hbond(d):

  """Computes hydrogen bond term."""

  return tf.where(d < -0.7,

                  tf.ones_like(d),

                  tf.where(d < 0, (1.0 / 0.7) * (0 - d), tf.zeros_like(d)))

  where = tf.where(d < 0, (1.0 / 0.7) * (0 - d), tf.zeros_like(d))

  return tf.where(d < -0.7, tf.ones_like(d), where)

def g(c, Nrot):