Refactor Entropy Calculations with Averaging Over Timesteps, Robust State Handling, and Enhanced Eigenvalue Filtering

CodeEntropy/config/arg_config_manager.py

@@ -67,7 +67,7 @@
     "grouping": {
         "type": str,
         "help": "How to group molecules for averaging",
-        "default": "each",
+        "default": "molecules",
     },
 }
 

CodeEntropy/entropy.py

@@ -247,7 +247,7 @@ def _get_trajectory_bounds(self):
             Tuple of (start, end, step) frame indices.
         """
         start = self._args.start or 0
-        end = self._args.end or -1
+        end = len(self._universe.trajectory) if self._args.end == -1 else self._args.end
         step = self._args.step or 1
 
         return start, end, step
@@ -343,11 +343,9 @@ def _process_united_atom_entropy(
 
             f_matrix = force_matrix[key]
             f_matrix = self._level_manager.filter_zero_rows_columns(f_matrix)
-            f_matrix = f_matrix / number_frames
 
             t_matrix = torque_matrix[key]
             t_matrix = self._level_manager.filter_zero_rows_columns(t_matrix)
-            t_matrix = t_matrix / number_frames
 
             S_trans_res = ve.vibrational_entropy_calculation(
                 f_matrix, "force", self._args.temperature, highest
@@ -356,8 +354,16 @@ def _process_united_atom_entropy(
                 t_matrix, "torque", self._args.temperature, highest
             )
 
-            S_conf_res = ce.conformational_entropy_calculation(
-                states[key], number_frames
+            values = states[key]
+
+            contains_non_empty_states = (
+                np.any(values) if isinstance(values, np.ndarray) else any(values)
+            )
+
+            S_conf_res = (
+                ce.conformational_entropy_calculation(values, number_frames)
+                if contains_non_empty_states
+                else 0
             )
 
             S_trans += S_trans_res
@@ -395,10 +401,8 @@ def _process_vibrational_entropy(
             level.
         """
         force_matrix = self._level_manager.filter_zero_rows_columns(force_matrix)
-        force_matrix = force_matrix / number_frames
 
         torque_matrix = self._level_manager.filter_zero_rows_columns(torque_matrix)
-        torque_matrix = torque_matrix / number_frames
 
         S_trans = ve.vibrational_entropy_calculation(
             force_matrix, "force", self._args.temperature, highest
@@ -425,7 +429,22 @@ def _process_conformational_entropy(
             start, end, step (int): Frame bounds.
             n_frames (int): Number of frames used.
         """
-        S_conf = ce.conformational_entropy_calculation(states[group_id], number_frames)
+        group_states = states[group_id] if group_id < len(states) else None
+
+        if group_states is not None:
+            contains_state_data = (
+                group_states.any()
+                if isinstance(group_states, np.ndarray)
+                else any(group_states)
+            )
+        else:
+            contains_state_data = False
+
+        S_conf = (
+            ce.conformational_entropy_calculation(group_states, number_frames)
+            if contains_state_data
+            else 0
+        )
 
         self._data_logger.add_results_data(group_id, level, "Conformational", S_conf)
 
@@ -619,13 +638,19 @@ def frequency_calculation(self, lambdas, temp):
         lambdas = np.array(lambdas)  # Ensure input is a NumPy array
         logger.debug(f"Eigenvalues (lambdas): {lambdas}")
 
-        # Check for negatives and raise an error if any are found
-        if np.any(lambdas < 0):
-            logger.error(f"Negative eigenvalues encountered: {lambdas[lambdas < 0]}")
-            raise ValueError(
-                f"Negative eigenvalues encountered: {lambdas[lambdas < 0]}"
+        lambdas = np.real_if_close(lambdas, tol=1000)
+        valid_mask = (
+            np.isreal(lambdas) & (lambdas > 0) & (~np.isclose(lambdas, 0, atol=1e-07))
+        )
+
+        if len(lambdas) > np.count_nonzero(valid_mask):
+            logger.warning(
+                f"{len(lambdas) - np.count_nonzero(valid_mask)} "
+                f"invalid eigenvalues excluded (complex, non-positive, or near-zero)."
             )
 
+        lambdas = lambdas[valid_mask].real
+
         # Compute frequencies safely
         frequencies = 1 / (2 * pi) * np.sqrt(lambdas / kT)
         logger.debug(f"Calculated frequencies: {frequencies}")
@@ -748,8 +773,11 @@ def assign_conformation(
 
         # get the values of the angle for the dihedral
         # dihedral angle values have a range from -180 to 180
-        for timestep in data_container.trajectory[start:end:step]:
-            timestep_index = timestep.frame - start
+        indices = list(range(start, end, step))
+        for timestep_index, _ in zip(
+            indices, data_container.trajectory[start:end:step]
+        ):
+            timestep_index = timestep_index - start
             value = dihedral.value()
             # we want postive values in range 0 to 360 to make the peak assignment
             # work using the fact that dihedrals have circular symetry

CodeEntropy/levels.py

@@ -171,7 +171,7 @@ def get_matrices(
                 f"{force_matrix.shape}, new {force_block.shape}"
             )
         else:
-            force_matrix += force_block
+            force_matrix = force_block
 
         if torque_matrix is None:
             torque_matrix = np.zeros_like(torque_block)
@@ -181,7 +181,7 @@ def get_matrices(
                 f"{torque_matrix.shape}, new {torque_block.shape}"
             )
         else:
-            torque_matrix += torque_block
+            torque_matrix = torque_block
 
         return force_matrix, torque_matrix
 
@@ -290,18 +290,27 @@ def compute_dihedral_conformations(
                 - dihedrals (list): List of dihedral angle definitions.
         """
         dihedrals = self.get_dihedrals(selector, level)
-        num_dihedrals = len(dihedrals)
 
-        conformation = np.zeros((num_dihedrals, number_frames))
-        for i, dihedral in enumerate(dihedrals):
-            conformation[i] = ce.assign_conformation(
-                selector, dihedral, number_frames, bin_width, start, end, step
-            )
+        if len(dihedrals) == 0:
+            logger.debug("No dihedrals found; skipping conformation assignment.")
+            states = []
+        else:
+            num_dihedrals = len(dihedrals)
+            conformation = np.zeros((num_dihedrals, number_frames))
 
-        states = [
-            "".join(str(int(conformation[d][f])) for d in range(num_dihedrals))
-            for f in range(number_frames)
-        ]
+            for i, dihedral in enumerate(dihedrals):
+                conformation[i] = ce.assign_conformation(
+                    selector, dihedral, number_frames, bin_width, start, end, step
+                )
+
+            states = [
+                state
+                for state in (
+                    "".join(str(int(conformation[d][f])) for d in range(num_dihedrals))
+                    for f in range(number_frames)
+                )
+                if state
+            ]
 
         return states
 
@@ -733,40 +742,58 @@ def build_covariance_matrices(
         number_frames,
     ):
         """
-        Construct force and torque covariance matrices for all molecules and levels.
+        Construct average force and torque covariance matrices for all molecules and
+        entropy levels.
 
-        Parameters:
-            entropy_manager (EntropyManager): Instance of the EntropyManager
-            reduced_atom (Universe): The reduced atom selection.
-            number_molecules (int): Number of molecules in the system.
-            levels (list): List of entropy levels per molecule.
-            start (int): Start frame index.
-            end (int): End frame index.
-            step (int): Step size for frame iteration.
-            number_frames (int): Total number of frames to process.
+        Parameters
+        ----------
+        entropy_manager : EntropyManager
+            Instance of the EntropyManager.
+        reduced_atom : Universe
+            The reduced atom selection.
+        levels : dict
+            Dictionary mapping molecule IDs to lists of entropy levels.
+        groups : dict
+            Dictionary mapping group IDs to lists of molecule IDs.
+        start : int
+            Start frame index.
+        end : int
+            End frame index.
+        step : int
+            Step size for frame iteration.
+        number_frames : int
+            Total number of frames to process.
 
-        Returns:
-            tuple: A tuple containing:
-                - force_matrices (dict): Force covariance matrices by level.
-                - torque_matrices (dict): Torque covariance matrices by level.
+        Returns
+        -------
+        tuple
+            force_avg : dict
+                Averaged force covariance matrices by entropy level.
+            torque_avg : dict
+                Averaged torque covariance matrices by entropy level.
         """
         number_groups = len(groups)
-        force_matrices = {
+
+        force_avg = {
             "ua": {},
             "res": [None] * number_groups,
             "poly": [None] * number_groups,
         }
-        torque_matrices = {
+        torque_avg = {
             "ua": {},
             "res": [None] * number_groups,
             "poly": [None] * number_groups,
         }
+        frame_counts = {
+            "ua": {},
+            "res": np.zeros(number_groups, dtype=int),
+            "poly": np.zeros(number_groups, dtype=int),
+        }
 
-        for timestep in reduced_atom.trajectory[start:end:step]:
-            time_index = timestep.frame - start
+        indices = list(range(start, end, step))
+        for time_index, _ in zip(indices, reduced_atom.trajectory[start:end:step]):
 
-            for group_id in groups.keys():
-                molecules = groups[group_id]
+            for group_id, molecules in groups.items():
                 for mol_id in molecules:
                     mol = entropy_manager._get_molecule_container(reduced_atom, mol_id)
                     for level in levels[mol_id]:
@@ -776,13 +803,14 @@ def build_covariance_matrices(
                             group_id,
                             level,
                             levels[mol_id],
-                            time_index,
+                            time_index - start,
                             number_frames,
-                            force_matrices,
-                            torque_matrices,
+                            force_avg,
+                            torque_avg,
+                            frame_counts,
                         )
 
-        return force_matrices, torque_matrices
+        return force_avg, torque_avg
 
     def update_force_torque_matrices(
         self,
@@ -793,22 +821,55 @@ def update_force_torque_matrices(
         level_list,
         time_index,
         num_frames,
-        force_matrices,
-        torque_matrices,
+        force_avg,
+        torque_avg,
+        frame_counts,
     ):
         """
-        Update force and torque matrices for a given molecule and entropy level.
+        Update the running averages of force and torque covariance matrices
+        for a given molecule and entropy level.
 
-        Parameters:
-            entropy_manager (EntropyManager): Instance of the EntropyManager
-            mol (AtomGroup): The molecule to process.
-            group_id (int): Index of the group.
-            level (str): Current entropy level ("united_atom", "residue", or "polymer").
-            level_list (list): List of levels for the molecule.
-            time_index (int): Index of the current frame.
-            num_frames (int): Total number of frames.
-            force_matrices (dict): Dictionary of force matrices to update.
-            torque_matrices (dict): Dictionary of torque matrices to update.
+        This function computes the force and torque covariance matrices for the
+        current frame and updates the existing averages in-place using the incremental
+        mean formula:
+
+            new_avg = old_avg + (value - old_avg) / n
+
+        where n is the number of frames processed so far for that molecule/level
+        combination. This ensures that the averages are maintained without storing
+        all previous frame data.
+
+        Parameters
+        ----------
+        entropy_manager : EntropyManager
+            Instance of the EntropyManager.
+        mol : AtomGroup
+            The molecule to process.
+        group_id : int
+            Index of the group to which the molecule belongs.
+        level : str
+            Current entropy level ("united_atom", "residue", or "polymer").
+        level_list : list
+            List of entropy levels for the molecule.
+        time_index : int
+            Index of the current frame relative to the start of the trajectory slice.
+        num_frames : int
+            Total number of frames to process.
+        force_avg : dict
+            Dictionary holding the running average force matrices, keyed by entropy
+            level.
+        torque_avg : dict
+            Dictionary holding the running average torque matrices, keyed by entropy
+            level.
+        frame_counts : dict
+            Dictionary holding the count of frames processed for each molecule/level
+            combination.
+
+        Returns
+        -------
+        None
+            Updates are performed in-place on `force_avg`, `torque_avg`, and
+            `frame_counts`.
         """
         highest = level == level_list[-1]
 
@@ -825,11 +886,19 @@ def update_force_torque_matrices(
                     level,
                     num_frames,
                     highest,
-                    force_matrices["ua"].get(key),
-                    torque_matrices["ua"].get(key),
+                    None if key not in force_avg["ua"] else force_avg["ua"][key],
+                    None if key not in torque_avg["ua"] else torque_avg["ua"][key],
                 )
-                force_matrices["ua"][key] = f_mat
-                torque_matrices["ua"][key] = t_mat
+
+                if key not in force_avg["ua"]:
+                    force_avg["ua"][key] = f_mat.copy()
+                    torque_avg["ua"][key] = t_mat.copy()
+                    frame_counts["ua"][key] = 1
+                else:
+                    frame_counts["ua"][key] += 1
+                    n = frame_counts["ua"][key]
+                    force_avg["ua"][key] += (f_mat - force_avg["ua"][key]) / n
+                    torque_avg["ua"][key] += (t_mat - torque_avg["ua"][key]) / n
 
         elif level in ["residue", "polymer"]:
             mol.trajectory[time_index]
@@ -839,11 +908,23 @@ def update_force_torque_matrices(
                 level,
                 num_frames,
                 highest,
-                force_matrices[key][group_id],
-                torque_matrices[key][group_id],
+                None if force_avg[key][group_id] is None else force_avg[key][group_id],
+                (
+                    None
+                    if torque_avg[key][group_id] is None
+                    else torque_avg[key][group_id]
+                ),
             )
-            force_matrices[key][group_id] = f_mat
-            torque_matrices[key][group_id] = t_mat
+
+            if force_avg[key][group_id] is None:
+                force_avg[key][group_id] = f_mat.copy()
+                torque_avg[key][group_id] = t_mat.copy()
+                frame_counts[key][group_id] = 1
+            else:
+                frame_counts[key][group_id] += 1
+                n = frame_counts[key][group_id]
+                force_avg[key][group_id] += (f_mat - force_avg[key][group_id]) / n
+                torque_avg[key][group_id] += (t_mat - torque_avg[key][group_id]) / n
 
     def filter_zero_rows_columns(self, arg_matrix):
         """