added verbose log

feature_calculator.py

@@ -80,6 +80,7 @@ def f_proteins_interaction(df_protein_A, df_protein_B, atom_types, cutoff):
  #calculate the euclidean distance and heaviside step value:
  sum_interaction += f_h_step(x=f_euclid_dist(a_coord, b_coord), a=cutoff) 
  x_vector[idx] = sum_interaction
+ print(x_vector)
  idx+=1
  return x_vector
 
@@ -185,6 +186,7 @@ def f_proteins_interaction_mp(df_protein_A, df_protein_B, atom_types, cutoff, po
  sum_interaction = np.sum(heavisides)
  x_vector[idx] = sum_interaction
  idx+=1
+ print(x_vector)
  return x_vector
 
 def x_atom_dist_mp(params):
@@ -250,6 +252,7 @@ def x_hydrophobic_acid_mp(params):
 # heavisides = np.array(list(map(f_heaviside_mp, paramlist)))
  sum_interactions += np.sum(heavisides)
  x_vector[patch_idx] = sum_interactions
+ print("ha: ",x_vector)
  patch_idx+=1
  return {'id':id_name, 'h_a_vector':x_vector}
 
@@ -259,7 +262,7 @@ def x_processor_mp(params):
  atom_types = params[2]
  cutoff = params[3]
  pool = params[4]
- x_dict = x_atom_dist([chains, id_name, atom_types, cutoff, pool])
- ha_dict = x_hydrophobic_acid([chains, id_name, cutoff, pool])
+ x_dict = x_atom_dist_mp([chains, id_name, atom_types, cutoff, pool])
+ ha_dict = x_hydrophobic_acid_mp([chains, id_name, cutoff, pool])
  x_dict['x_vector'] = np.concatenate((x_dict['x_vector'], ha_dict["h_a_vector"]))
  return x_dict