Merge pull request #146 from DeepRank/tidy_examples

Tidy examples

example/alignH5.py

@@ -0,0 +1,42 @@
+# This script is used to align complexes in hdf5.
+# INPUT:
+#   1. a hdf5 file that contains pdb of all complexes (it is the output of generate_dataset_noalign.py).
+#   2. pssm files if the pssm feature is needed for the training later
+
+
+from deeprank.generate import *
+from mpi4py import MPI
+
+comm = MPI.COMM_WORLD
+
+# name of the hdf5 to align
+h5file = './hdf5/1ak4.hdf5'
+
+# where to find the pssm
+pssm_source = '../test/1AK4/pssm_new/'
+
+
+# align the principle component 1 of complexes to axis z
+newdb = DataGenerator(hdf5=h5file)
+newdb.realign_complexes(align={'axis':'z'})
+
+
+# define the 3D grid
+# grid_info = {
+#   'number_of_points' : [30,30,30],
+#   'resolution' : [1.,1.,1.],
+#   'atomic_densities': {'C': 1.7, 'N': 1.55, 'O': 1.52, 'S': 1.8},
+# }
+
+# generate the grid
+#print('{:25s}'.format('Generate the grid') + database.hdf5)
+#database.precompute_grid(grid_info,try_sparse=True, time=False, prog_bar=True)
+
+
+# print('{:25s}'.format('Map features in database') + database.hdf5)
+# database.map_features(grid_info,try_sparse=True, time=False, prog_bar=True)
+
+# # get the normalization of the features
+# print('{:25s}'.format('Normalization') + database.hdf5)
+# norm = NormalizeData(database.hdf5)
+# norm.get()

example/generate_dataset_alignPDB.py

@@ -1,3 +1,8 @@
+# generate a hdf5 file
+# 1. calculate features/targets
+# 2. align pdb
+# 3. map features to 3D grids
+# 4. write to a hdf5 file
 from deeprank.generate import *
 from mpi4py import MPI
 
@@ -22,7 +27,8 @@
     pdb_source=pdb_source,
     pdb_native=pdb_native,
     pssm_source=pssm_source,
-    align={"axis":'x','export':False},
+    #align={"axis":'x','export':False},
+    align={"selection":"interface","plane":"xy", 'export':True},
     data_augmentation=None,
     compute_targets=[
         'deeprank.targets.binary_class'],

example/generate_dataset_noalign.py

@@ -1,3 +1,7 @@
+# generate a hdf5 file
+# 1. calculate features/targets
+# 2. map features to 3D grids
+# 3. write to a hdf5 file
 from deeprank.generate import *
 from mpi4py import MPI
 
@@ -23,7 +27,7 @@
     pdb_source=pdb_source,
     pdb_native=pdb_native,
     pssm_source=pssm_source,
-    data_augmentation=2,
+    data_augmentation=None,
     compute_targets=[
         'deeprank.targets.dockQ',
         'deeprank.targets.binary_class'],