Refactor RMSD analyses into MDAnalysis AnaysisBase classes

src/openfe_analysis/rmsd.py

@@ -5,10 +5,9 @@
 import MDAnalysis as mda
 import netCDF4 as nc
 import numpy as np
-import tqdm
-from MDAnalysis.analysis import rms
+from MDAnalysis.analysis import diffusionmap, rms
+from MDAnalysis.analysis.base import AnalysisBase
 from MDAnalysis.transformations import unwrap
-from numpy import typing as npt
 
 from .reader import FEReader
 from .transformations import Aligner, ClosestImageShift, NoJump
@@ -100,6 +99,130 @@ def make_Universe(top: pathlib.Path, trj: nc.Dataset, state: int) -> mda.Univers
     return u
 
 
+class Protein2DRMSD(AnalysisBase):
+    """
+    Flattened 2D RMSD matrix
+
+    For all unique frame pairs ``(i, j)`` with ``i < j``, this function
+    computes the RMSD between atomic coordinates after optimal alignment.
+    """
+
+    def __init__(self, atomgroup, weights=None, **kwargs):
+        """
+        Parameters
+        ----------
+        atomgroup: AtomGroup
+          Protein atoms (e.g. CA selection)
+        weights: np.ndarray, optional
+          Per-atom weights to use in the RMSD calculation. If ``None``,
+          all atoms are weighted equally.
+        """
+        super(Protein2DRMSD, self).__init__(atomgroup.universe.trajectory, **kwargs)
+
+        self._weights = weights
+        self._ag = atomgroup
+
+    def _prepare(self):
+        self._coords = []
+        self.results.rmsd2d = []
+
+    def _single_frame(self):
+        self._coords.append(self._ag.positions)
+
+    def _conclude(self):
+        positions = np.asarray(self._coords)
+        nframes, _, _ = positions.shape
+
+        output = []
+        for i, j in itertools.combinations(range(nframes), 2):
+            posi, posj = positions[i], positions[j]
+            rmsd = rms.rmsd(
+                posi,
+                posj,
+                self._weights,
+                center=True,
+                superposition=True,
+            )
+            output.append(rmsd)
+
+        self.results.rmsd2d = np.asarray(output)
+
+
+class RMSDAnalysis(AnalysisBase):
+    """
+    1D RMSD time series for an AtomGroup.
+
+    Parameters
+    ----------
+    atomgroup : MDAnalysis.AtomGroup
+      Atoms to compute RMSD for.
+    reference: Optional[MDAnalysis.AtomGroup]
+      Reference AtomGroup. If None, the first frame of the trajectory will be used.
+    mass_weighted : bool, optional
+      If True, compute mass-weighted RMSD.
+    """
+
+    def __init__(
+        self, atomgroup, reference=None, mass_weighted=False, superposition=False, **kwargs
+    ):
+        super(RMSDAnalysis, self).__init__(atomgroup.universe.trajectory, **kwargs)
+
+        self._ag = atomgroup
+        self._reference = reference if reference is not None else self._ag
+        self._mass_weighted = mass_weighted
+        self._superposition = superposition
+
+    def _prepare(self):
+        self.results.rmsd = []
+
+        self._reference_pos = self._reference.positions
+
+        if self._mass_weighted:
+            self._weights = self._ag.masses / np.mean(self._ag.masses)
+        else:
+            self._weights = None
+
+    def _single_frame(self):
+        rmsd = rms.rmsd(
+            self._ag.positions,
+            self._reference_pos,
+            self._weights,
+            center=False,
+            superposition=self._superposition,
+        )
+        self.results.rmsd.append(rmsd)
+
+    def _conclude(self):
+        self.results.rmsd = np.asarray(self.results.rmsd)
+
+
+class LigandCOMDrift(AnalysisBase):
+    """
+    Ligand center-of-mass displacement from initial position.
+    """
+
+    def __init__(self, atomgroup, **kwargs):
+        super(LigandCOMDrift, self).__init__(atomgroup.universe.trajectory, **kwargs)
+
+        self._ag = atomgroup
+
+    def _prepare(self):
+        self.results.com_drift = []
+        self._initial_com = self._ag.center_of_mass()
+
+    def _single_frame(self):
+        # distance between start and current ligand position
+        # ignores PBC, but we've already centered the traj
+        drift = mda.lib.distances.calc_bonds(
+            self._ag.center_of_mass(),
+            self._initial_com,
+        )
+        self.results.com_drift.append(drift)
+
+    def _conclude(self):
+        self.results.com_drift = np.asarray(self.results.com_drift)
+
+
 def gather_rms_data(
     pdb_topology: pathlib.Path, dataset: pathlib.Path, skip: Optional[int] = None
 ) -> dict[str, list[float]]:
@@ -161,8 +284,6 @@ def gather_rms_data(
             # max against 1 to avoid skip=0 case
             skip = max(n_frames // 500, 1)
 
-        pb = tqdm.tqdm(total=int(n_frames / skip) * n_lambda)
-
         u_top = mda.Universe(pdb_topology)
 
         for i in range(n_lambda):
@@ -173,93 +294,47 @@ def gather_rms_data(
             prot = u.select_atoms("protein and name CA")
             ligand = u.select_atoms("resname UNK")
 
-            # save coordinates for 2D RMSD matrix
-            # TODO: Some smart guard to avoid allocating a silly amount of memory?
-            prot2d = np.empty((len(u.trajectory[::skip]), len(prot), 3), dtype=np.float32)
-
-            prot_start = prot.positions
-            ligand_start = ligand.positions
-            ligand_initial_com = ligand.center_of_mass()
-            ligand_weights = ligand.masses / np.mean(ligand.masses)
-
-            this_protein_rmsd = []
-            this_ligand_rmsd = []
-            this_ligand_wander = []
-
-            for ts_i, ts in enumerate(u.trajectory[::skip]):
-                pb.update()
-
-                if prot:
-                    prot2d[ts_i, :, :] = prot.positions
-                    this_protein_rmsd.append(
-                        rms.rmsd(
-                            prot.positions,
-                            prot_start,
-                            None,  # prot_weights,
-                            center=False,
-                            superposition=False,
-                        )
-                    )
-                if ligand:
-                    this_ligand_rmsd.append(
-                        rms.rmsd(
-                            ligand.positions,
-                            ligand_start,
-                            ligand_weights,
-                            center=False,
-                            superposition=False,
-                        )
-                    )
-                    this_ligand_wander.append(
-                        # distance between start and current ligand position
-                        # ignores PBC, but we've already centered the traj
-                        mda.lib.distances.calc_bonds(ligand.center_of_mass(), ligand_initial_com)
-                    )
-
             if prot:
-                # can ignore weights here as it's all Ca
-                rmsd2d = twoD_RMSD(prot2d, w=None)  # prot_weights)
-                output["protein_RMSD"].append(this_protein_rmsd)
-                output["protein_2D_RMSD"].append(rmsd2d)
-            if ligand:
-                output["ligand_RMSD"].append(this_ligand_rmsd)
-                output["ligand_wander"].append(this_ligand_wander)
+                prot_rmsd = RMSDAnalysis(prot).run(step=skip)
+                output["protein_RMSD"].append(prot_rmsd.results.rmsd)
+                # # Using the MDAnalysis RMSD class instead
+                # gs = ["protein and name CA"]
+                # prot_rmsd = rms.RMSD(
+                #    u, select="protein and name CA", groupselections=gs, weights="mass")
+                # prot_rmsd.run(step=skip)
+                # # The results contain:
+                # # - frame number
+                # # - time
+                # # - RMSD based on select (after superimposing)
+                # # - RMSD based on groupselections, one array per selection
+                # output["protein_RMSD"].append(prot_rmsd.results.rmsd.T[3])
+
+                prot_rmsd2d = Protein2DRMSD(prot).run(step=skip)
+                output["protein_2D_RMSD"].append(prot_rmsd2d.results.rmsd2d)
+                # # Using the MDAnalysis DistanceMatrix class
+                # prot_rmsd2d = diffusionmap.DistanceMatrix(u, select="protein and name CA")
+                # prot_rmsd2d.run(step=skip)
+                # dist_mat = prot_rmsd2d.results.dist_matrix
+                # i, j = np.triu_indices_from(dist_mat, k=1)
+                # flattened = dist_mat[i, j]
+                # output["protein_2D_RMSD"].append(flattened)
 
-            output["time(ps)"] = list(np.arange(len(u.trajectory))[::skip] * u.trajectory.dt)
-
-    return output
-
-
-def twoD_RMSD(positions, w: Optional[npt.NDArray]) -> list[float]:
-    """
-    Compute a flattened 2D RMSD matrix from a trajectory.
-
-    For all unique frame pairs ``(i, j)`` with ``i < j``, this function
-    computes the RMSD between atomic coordinates after optimal alignment.
-
-    Parameters
-    ----------
-    positions : np.ndarray
-      Atomic coordinates for all frames in the trajectory.
-    w : np.ndarray, optional
-      Per-atom weights to use in the RMSD calculation. If ``None``,
-      all atoms are weighted equally.
-
-    Returns
-    -------
-    list of float
-      Flattened list of RMSD values corresponding to all frame pairs
-      ``(i, j)`` with ``i < j``.
-    """
-    nframes, _, _ = positions.shape
-
-    output = []
-
-    for i, j in itertools.combinations(range(nframes), 2):
-        posi, posj = positions[i], positions[j]
-
-        rmsd = rms.rmsd(posi, posj, w, center=True, superposition=True)
-
-        output.append(rmsd)
+            if ligand:
+                lig_rmsd = RMSDAnalysis(ligand, mass_weighted=True).run(step=skip)
+                output["ligand_RMSD"].append(lig_rmsd.results.rmsd)
+                # # Using the MDAnalysis RMSD class instead
+                # groupselections = ["resname UNK"]
+                # lig_rmsd = rms.RMSD(
+                #     u,
+                #     select="protein and name CA",
+                #     groupselections=groupselections,
+                #     weights="mass",
+                # )
+                # lig_rmsd.run(step=skip)
+                # output["ligand_RMSD"].append(lig_rmsd.results.rmsd.T[3])
+                lig_com_drift = LigandCOMDrift(ligand).run(step=skip)
+                output["ligand_wander"].append(lig_com_drift.results.com_drift)
+
+            output["time(ps)"] = np.arange(len(u.trajectory))[::skip] * u.trajectory.dt
 
     return output