MDCATH

tests/test_mdcath.py

@@ -0,0 +1,184 @@
+import h5py
+import psutil
+import numpy as np
+from pytest import mark
+from os.path import join
+from torchmdnet.datasets.mdcath import MDCATH
+from torch_geometric.loader import DataLoader
+from tqdm import tqdm
+
+
+def test_mdcath(tmpdir):
+    num_atoms_list = np.linspace(50, 1000, 50)
+    source_file = h5py.File(join(tmpdir, "mdcath_source.h5"), mode="w")
+    for num_atoms in num_atoms_list:
+        z = np.zeros(int(num_atoms))
+        pos = np.zeros((100, int(num_atoms), 3))
+        forces = np.zeros((100, int(num_atoms), 3))
+
+        s_group = source_file.create_group(f"A{num_atoms}")
+
+        s_group.attrs["numChains"] = 1
+        s_group.attrs["numNoHAtoms"] = int(num_atoms) / 2
+        s_group.attrs["numProteinAtoms"] = int(num_atoms)
+        s_group.attrs["numResidues"] = int(num_atoms) / 10
+        s_temp_group = s_group.create_group("348")
+        s_replica_group = s_temp_group.create_group("0")
+        s_replica_group.attrs["numFrames"] = 100
+        s_replica_group.attrs["alpha"] = 0.30
+        s_replica_group.attrs["beta"] = 0.25
+        s_replica_group.attrs["coil"] = 0.45
+        s_replica_group.attrs["max_gyration_radius"] = 2
+        s_replica_group.attrs["max_num_neighbors_5A"] = 55
+        s_replica_group.attrs["max_num_neighbors_9A"] = 200
+        s_replica_group.attrs["min_gyration_radius"] = 1
+
+        # write the dataset
+        data = h5py.File(join(tmpdir, f"mdcath_dataset_A{num_atoms}.h5"), mode="w")
+        group = data.create_group(f"A{num_atoms}")
+        group.create_dataset("z", data=z)
+        tempgroup = group.create_group("348")
+        replicagroup = tempgroup.create_group("0")
+        replicagroup.create_dataset("coords", data=pos)
+        replicagroup.create_dataset("forces", data=forces)
+        # add some attributes
+        replicagroup.attrs["numFrames"] = 100
+        replicagroup["coords"].attrs["unit"] = "Angstrom"
+        replicagroup["forces"].attrs["unit"] = "kcal/mol/Angstrom"
+
+        data.flush()
+        data.close()
+
+    dataset = MDCATH(root=tmpdir)
+    dl = DataLoader(
+        dataset,
+        batch_size=1,
+        shuffle=False,
+        num_workers=0,
+        pin_memory=True,
+        persistent_workers=False,
+    )
+    for _, data in enumerate(tqdm(dl)):
+        pass
+
+
+def test_mdcath_multiprocessing(tmpdir, num_entries=100, numFrames=10):
+    # generate sample data
+    z = np.zeros(num_entries)
+    pos = np.zeros((numFrames, num_entries, 3))
+    forces = np.zeros((numFrames, num_entries, 3))
+
+    source_file = h5py.File(join(tmpdir, "mdcath_source.h5"), mode="w")
+    s_group = source_file.create_group("A00")
+
+    s_group.attrs["numChains"] = 1
+    s_group.attrs["numNoHAtoms"] = num_entries / 2
+    s_group.attrs["numProteinAtoms"] = num_entries
+    s_group.attrs["numResidues"] = num_entries / 10
+    s_temp_group = s_group.create_group("348")
+    s_replica_group = s_temp_group.create_group("0")
+    s_replica_group.attrs["numFrames"] = numFrames
+    s_replica_group.attrs["alpha"] = 0.30
+    s_replica_group.attrs["beta"] = 0.25
+    s_replica_group.attrs["coil"] = 0.45
+    s_replica_group.attrs["max_gyration_radius"] = 2
+    s_replica_group.attrs["max_num_neighbors_5A"] = 55
+    s_replica_group.attrs["max_num_neighbors_9A"] = 200
+    s_replica_group.attrs["min_gyration_radius"] = 1
+
+    # write the dataset
+    data = h5py.File(join(tmpdir, "mdcath_dataset_A00.h5"), mode="w")
+    group = data.create_group("A00")
+    group.create_dataset("z", data=z)
+    tempgroup = group.create_group("348")
+    replicagroup = tempgroup.create_group("0")
+    replicagroup.create_dataset("coords", data=pos)
+    replicagroup.create_dataset("forces", data=forces)
+    # add some attributes
+    replicagroup.attrs["numFrames"] = numFrames
+    replicagroup["coords"].attrs["unit"] = "Angstrom"
+    replicagroup["forces"].attrs["unit"] = "kcal/mol/Angstrom"
+
+    data.flush()
+    data.close()
+
+    # make sure creating the dataset doesn't open any files on the main process
+    proc = psutil.Process()
+    n_open = len(proc.open_files())
+
+    dset = MDCATH(
+        root=tmpdir,
+    )
+    assert len(proc.open_files()) == n_open, "creating the dataset object opened a file"
+
+
+def replacer(arr, skipframes):
+    tmp_arr = arr.copy()
+    # function that take a numpy array of zeros and based on a skipframes value, replaces the zeros with 1s in that position
+    for i in range(0, len(tmp_arr), skipframes):
+        tmp_arr[i, :, :] = 1
+    return tmp_arr
+
+
+@mark.parametrize("skipframes", [1, 2, 5])
+@mark.parametrize("batch_size", [1, 10])
+@mark.parametrize("pdb_list", [["A50", "A612", "A1000"], None])
+def test_mdcath_args(tmpdir, skipframes, batch_size, pdb_list):
+    with h5py.File(join(tmpdir, "mdcath_source.h5"), mode="w") as source_file:
+        num_frames_list = np.linspace(50, 1000, 50).astype(int)
+        for num_frame in tqdm(num_frames_list, desc="Creating tmp files"):
+            z = np.zeros(100)
+            pos = np.zeros((num_frame, 100, 3))
+            forces = np.zeros((num_frame, 100, 3))
+
+            pos = replacer(pos, skipframes)
+            forces = replacer(forces, skipframes)
+
+            s_group = source_file.create_group(f"A{num_frame}")
+
+            s_group.attrs["numChains"] = 1
+            s_group.attrs["numNoHAtoms"] = 100 / 2
+            s_group.attrs["numProteinAtoms"] = 100
+            s_group.attrs["numResidues"] = 100 / 10
+            s_temp_group = s_group.create_group("348")
+            s_replica_group = s_temp_group.create_group("0")
+            s_replica_group.attrs["numFrames"] = num_frame
+            s_replica_group.attrs["alpha"] = 0.30
+            s_replica_group.attrs["beta"] = 0.25
+            s_replica_group.attrs["coil"] = 0.45
+            s_replica_group.attrs["max_gyration_radius"] = 2
+            s_replica_group.attrs["max_num_neighbors_5A"] = 55
+            s_replica_group.attrs["max_num_neighbors_9A"] = 200
+            s_replica_group.attrs["min_gyration_radius"] = 1
+
+            # write the dataset
+            data = h5py.File(join(tmpdir, f"mdcath_dataset_A{num_frame}.h5"), mode="w")
+            group = data.create_group(f"A{num_frame}")
+            group.create_dataset("z", data=z)
+            tempgroup = group.create_group("348")
+            replicagroup = tempgroup.create_group("0")
+            replicagroup.create_dataset("coords", data=pos)
+            replicagroup.create_dataset("forces", data=forces)
+            # add some attributes
+            replicagroup.attrs["numFrames"] = num_frame
+            replicagroup["coords"].attrs["unit"] = "Angstrom"
+            replicagroup["forces"].attrs["unit"] = "kcal/mol/Angstrom"
+
+            data.flush()
+            data.close()
+
+    dataset = MDCATH(
+        root=tmpdir, skip_frames=skipframes, pdb_list=pdb_list
+    )
+    dl = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=0,
+        pin_memory=True,
+        persistent_workers=False,
+    )
+    for _, data in enumerate(tqdm(dl)):
+        # if the skipframes works correclty, data returned should be only 1s
+        assert data.pos.all() == 1, "skipframes not working correctly for positions"
+        assert data.neg_dy.all() == 1, "skipframes not working correctly for forces"

torchmdnet/datasets/__init__.py

@@ -14,6 +14,7 @@
     COMP6v1,
     COMP6v2,
 )
+from .mdcath import MDCATH
 from .custom import Custom
 from .water import WaterBox
 from .hdf import HDF5
@@ -40,6 +41,7 @@
     "GDB10to13",
     "GenentechTorsions",
     "HDF5",
+    "MDCATH",
     "MD17",
     "MD22",
     "QM9",