Merge pull request mala-project#485 from RandomDefaultUser/add_malada…

…_feature

Add uncorrelated snapshot parsing from MALADA to MALA

mala/common/parameters.py

@@ -1068,6 +1068,17 @@ class ParametersDataGeneration(ParametersBase):
         from the end). Usually, 10% is a fine assumption. This value usually
         does not need to be changed.
 
+    trajectory_analysis_correlation_metric_cutoff : float
+        Cutoff value to be used when sampling uncorrelated snapshots
+        during trajectory analysis. If negative, a value will be determined
+        numerically. This value is a cutoff for the minimum euclidean distance
+        between any two ions in two subsequent ionic configurations.
+
+    trajectory_analysis_temperature_tolerance_percent : float
+        Maximum deviation of temperature between snapshot and desired
+        temperature for snapshot to be considered for DFT calculation
+        (in percent)
+
     local_psp_path : string
         Path to where the local pseudopotential is stored (for OF-DFT-MD).
 
@@ -1095,6 +1106,8 @@ def __init__(self):
         self.trajectory_analysis_denoising_width = 100
         self.trajectory_analysis_below_average_counter = 50
         self.trajectory_analysis_estimated_equilibrium = 0.1
+        self.trajectory_analysis_correlation_metric_cutoff = -0.1
+        self.trajectory_analysis_temperature_tolerance_percent = 1
         self.local_psp_path = None
         self.local_psp_name = None
         self.ofdft_timestep = 0

mala/datageneration/trajectory_analyzer.py

@@ -1,13 +1,16 @@
 """Tools for analyzing a trajectory."""
-
+from functools import cached_property
+import os
 from warnings import warn
 
-from ase.io.trajectory import TrajectoryReader, Trajectory
+from ase.io.trajectory import TrajectoryReader, Trajectory, TrajectoryWriter
 import numpy as np
 from scipy.spatial import distance
 
-from mala.common.parameters import Parameters, ParametersDataGeneration
+from mala.common.parameters import ParametersDataGeneration
+from mala.common.parallelizer import printout
 from mala.targets import Target
+from mala.descriptors.descriptor import Descriptor
 
 
 class TrajectoryAnalyzer:
@@ -27,26 +30,42 @@ class TrajectoryAnalyzer:
         one will be generated ad-hoc (recommended).
     """
 
-    def __init__(self, parameters, trajectory, target_calculator=None):
+    def __init__(self, parameters, trajectory, temperatures=None,
+                 target_calculator=None, target_temperature=None,
+                 malada_compatability=False):
         warn("The class TrajectoryAnalyzer is experimental. The algorithms "
              "within have been tested, but the API may still be subject to "
              "large changes.")
 
         self.params: ParametersDataGeneration = parameters.datageneration
 
-        # Save or read the trajectory
+        # If needed, read the trajectory
+        self.trajectory = None
         if isinstance(trajectory, TrajectoryReader):
             self.trajectory = trajectory
         elif isinstance(trajectory, str):
             self.trajectory = Trajectory(trajectory)
         else:
             raise Exception("Incompatible trajectory format provided.")
 
+        # If needed, read the temperature files
+        self.temperatures = None
+        if temperatures is not None:
+            if isinstance(temperatures, np.ndarray):
+                self.temperatures = temperatures
+            elif isinstance(temperatures, str):
+                self.temperatures = np.load(temperatures)
+            else:
+                raise Exception("Incompatible temperature format provided.")
+
         # Create target calculator.
         self.target_calculator = target_calculator
         if target_calculator is None:
             self.target_calculator = Target(parameters)
 
+        if target_temperature is not None:
+            self.target_calculator.temperature = target_temperature
+
         # Initialize variables.
         self.distance_metrics = []
         self.distance_metrics_denoised = []
@@ -55,6 +74,43 @@ def __init__(self, parameters, trajectory, target_calculator=None):
         self.first_considered_snapshot = None
         self.last_considered_snapshot = None
 
+        # MALADA used two times the minimum imaging convention (MIC) to
+        # determine the cutoff radius for the RDF.
+        # In the future, only the MIC radius should be used, but to
+        # recreate old plots, one may need to use two times the MIC.
+        if malada_compatability:
+            self.target_calculator.parameters.rdf_parameters["rMax"] = "2mic"
+
+    @property
+    def trajectory(self):
+        """Trajectory to be analyzed."""
+        return self._trajectory
+
+    @trajectory.setter
+    def trajectory(self, new_trajectory):
+        self._trajectory = new_trajectory
+        self.uncache_properties()
+
+    def _is_property_cached(self, property_name):
+        return property_name in self.__dict__.keys()
+
+    def uncache_properties(self):
+        """Uncache all cached properties of this calculator."""
+        if self._is_property_cached("first_snapshot"):
+            del self.first_snapshot
+        if self._is_property_cached("snapshot_correlation_cutoff"):
+            del self.snapshot_correlation_cutoff
+
+    @cached_property
+    def first_snapshot(self):
+        """First equilibrated snapshot."""
+        return self.get_first_snapshot()
+
+    @cached_property
+    def snapshot_correlation_cutoff(self):
+        """Cutoff for the snapshot correlation analysis."""
+        return self.get_snapshot_correlation_cutoff()
+
     def get_first_snapshot(self, equilibrated_snapshot=None,
                            distance_threshold=None):
         """
@@ -133,9 +189,104 @@ def get_first_snapshot(self, equilibrated_snapshot=None,
                     first_snapshot = idx
                     break
 
-        print("First equilibrated timestep of trajectory is", first_snapshot)
+        printout("First equilibrated timestep of trajectory is", first_snapshot)
         return first_snapshot
 
+    def get_snapshot_correlation_cutoff(self):
+        """
+        Determine the cutoff for the distance metric.
+
+        If a cutoff is set by the user via the Parameters object, this
+        function simply returns this value. Elsewise, the value
+        is determined numerically. The distance metric used here is realspace
+        (i.e. the smallest displacement of an atom between two snapshots). The
+        cutoff gives a lower estimate for the oscillations of the trajectory.
+        Any distance above this cutoff can be attributed to the oscillations
+        in the trajectory. Any cutoff below is the consquence of temporal
+        neighborhood of these snapshots.
+
+        Returns
+        -------
+        cutoff : float
+            Cutoff below which two snapshots can be assumed to be similar
+            to each other to a degree that suggests temporal neighborhood.
+
+        """
+        if self.params.trajectory_analysis_correlation_metric_cutoff < 0:
+            return self._analyze_distance_metric(self.trajectory)
+        else:
+            return self.params.trajectory_analysis_correlation_metric_cutoff
+
+    def get_uncorrelated_snapshots(self, filename_uncorrelated_snapshots):
+        """
+        Calculate a set of uncorrelated configurations from a trajectory.
+
+        If not priorly determined with the object calling this function, the
+        first equilibrated configuration will be determined automatically.
+        This function will create two files, one archiving the MD step number
+        of all snapshots sampled, and one with the actual snapshots themselves.
+
+        Parameters
+        ----------
+        filename_uncorrelated_snapshots : string
+            Name of the file in which to save the uncorrelated snapshots.
+        """
+        filename_base = \
+            os.path.basename(filename_uncorrelated_snapshots).split(".")[0]
+        allowed_temp_diff_K = (self.params.
+                               trajectory_analysis_temperature_tolerance_percent
+                               / 100) * self.target_calculator.temperature
+        current_snapshot = self.first_snapshot
+        begin_snapshot = self.first_snapshot+1
+        end_snapshot = len(self.trajectory)
+        j = 0
+        md_iteration = []
+        for i in range(begin_snapshot, end_snapshot):
+            if self.__check_if_snapshot_is_valid(self.trajectory[i],
+                                                 self.temperatures[i],
+                                                 self.trajectory[current_snapshot],
+                                                 self.temperatures[current_snapshot],
+                                                 self.snapshot_correlation_cutoff,
+                                                 allowed_temp_diff_K):
+                current_snapshot = i
+                md_iteration.append(current_snapshot)
+                j += 1
+        np.random.shuffle(md_iteration)
+        for i in range(0, len(md_iteration)):
+            if i == 0:
+                traj_writer = TrajectoryWriter(filename_base+".traj", mode='w')
+            else:
+                traj_writer = TrajectoryWriter(filename_base+".traj", mode='a')
+            atoms_to_write = Descriptor.enforce_pbc(self.trajectory[md_iteration[i]])
+            traj_writer.write(atoms=atoms_to_write)
+        np.save(filename_base+"_numbers.npy", md_iteration)
+        printout(j, "possible snapshots found in MD trajectory.")
+
+    def _analyze_distance_metric(self, trajectory):
+        # distance metric usefdfor the snapshot parsing (realspace similarity
+        # of the snapshot), we first find the center of the equilibrated part
+        # of the trajectory and calculate the differences w.r.t to to it.
+        center = int((np.shape(self.distance_metrics_denoised)[
+                          0] - self.first_snapshot) / 2) + self.first_snapshot
+        width = int(
+            self.params.trajectory_analysis_estimated_equilibrium *
+            np.shape(self.distance_metrics_denoised)[0])
+        self.distances_realspace = []
+        self.__saved_rdf = None
+        for i in range(center - width, center + width):
+            self.distances_realspace.append(
+                self._calculate_distance_between_snapshots(
+                    trajectory[center], trajectory[i],
+                    "realspace", "minimal_distance", save_rdf1=True))
+
+        # From these metrics, we assume mean - 2.576 std as limit.
+        # This translates to a confidence interval of ~99%, which should
+        # make any coincidental similarites unlikely.
+        cutoff = np.mean(self.distances_realspace) - 2.576 * np.std(
+            self.distances_realspace)
+        printout("Distance metric cutoff is", cutoff)
+        return cutoff
+
     def _calculate_distance_between_snapshots(self, snapshot1, snapshot2,
                                               distance_metric, reduction,
                                               save_rdf1=False):
@@ -193,3 +344,19 @@ def __denoise(self, signal):
                                       mode='same')
         return denoised_signal
 
+    def __check_if_snapshot_is_valid(self, snapshot_to_test, temp_to_test,
+                                     reference_snapshot, reference_temp,
+                                     distance_metric,
+                                     allowed_temp_diff):
+        distance = self.\
+            _calculate_distance_between_snapshots(snapshot_to_test,
+                                                  reference_snapshot,
+                                                   "realspace",
+                                                   "minimal_distance")
+        temp_diff = np.abs(temp_to_test-reference_temp)
+        if distance > distance_metric and temp_diff < allowed_temp_diff:
+            return True
+        else:
+            return False
+
+