added features-calculator

feature_calculator.py

@@ -0,0 +1,106 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jan 13 20:41:10 2020
+
+@author: Saint8312
+"""
+
+"""
+functions to calculate the features of protein-protein interactions
+"""
+
+import numpy as np
+import pandas as pd
+
+'''
+math functions
+'''
+f_euclid_dist = lambda a,b: np.linalg.norm(a-b)
+
+def f_h_step(x, a):
+    return 1 if (x<=a) else 0
+
+f_y = lambda k : -np.log10(k)
+
+'''
+feature and labels calculator functions
+'''
+def y_processor(path):
+    '''
+    Create dataframes of log_10^y
+    '''
+    mol_units = {'uM':1.e-6, 'pM':1.e-12, 'fM':1.e-15, 'nM':1.e-9, 'mM':1.e-3}
+
+    #load the index file
+    l = []
+    with open(path, 'r') as f:
+        for line in f:
+            if not line.startswith('#'):
+                l.append((line.rstrip()).split())
+    df_idx = (pd.DataFrame(l)).rename(columns={0:'id',3:'k'})
+
+    #generate the -log_10k values
+    op_tokens = ['=','~','>','<']
+    logys = np.zeros(df_idx.shape[0])
+    for i in range(df_idx.shape[0]):
+        string = df_idx.loc[i]['k']
+        for s in string:
+            if s in op_tokens:
+                split_str = string.split(s)
+                break
+        logys[i] = f_y( float(split_str[-1][:-2]) * mol_units[split_str[-1][-2:]] )
+    df_idx["log_y"] = logys
+    return df_idx
+
+def f_proteins_interaction(df_protein_A, df_protein_B, atom_types, cutoff):
+    '''
+    calculate the combination of euclidian distance and heaviside step between chains in a protein, 
+    e.g chains=[A,B,C,D], hence the interactions are: [[A-B],[A-C],[A-D],[B-C],[B-D],[C-D]]
+    
+    'atom_types' are the type of atoms used for calculation
+    'cutoff' is the distance cutoff between atoms for heaviside step function (in Angstrom)
+    '''
+    type_len = len(atom_types)
+    x_vector = np.zeros(type_len**2)
+    idx = 0
+    for a_type in atom_types:
+        for b_type in atom_types:
+            #calculate the interaction of each atoms:
+            sum_interaction = 0
+            a_atoms = df_protein_A.loc[df_protein_A['atom_type'] == a_type]
+            b_atoms = df_protein_B.loc[df_protein_B['atom_type'] == b_type]
+            for i in range(a_atoms.shape[0]):
+                for j in range(b_atoms.shape[0]):
+                    #get the (x,y,z):
+                    a_atom = a_atoms.iloc[i]
+                    b_atom = b_atoms.iloc[j]
+                    a_coord = np.array([float(a_atom['x_coor']), float(a_atom['y_coor']), float(a_atom['z_coor'])]) 
+                    b_coord = np.array([float(b_atom['x_coor']), float(b_atom['y_coor']), float(b_atom['z_coor'])])
+                    #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
+            idx+=1
+            print(x_vector)
+    return x_vector
+
+def x_processor(chains, id_name, atom_types, cutoff):
+    #vector calculation:
+    x_vector = np.zeros(len(atom_types)**2)
+    length = len(chains)
+    for i in range(length):
+        for j in range(length):
+            if j>i:
+                #sum each chain interaction values:
+                print('protein chain :', i, j)
+                x_vector += f_proteins_interaction(chains[i], chains[j], atom_types, cutoff)
+    return {'id':id_name, 'x_vector':x_vector}
+
+'''
+multiprocessor feature functions
+'''
+
+
+if __name__ == '__main__':
+    import pdb_processor as pdbp
+
+