N merge max REVO resampler

src/wepy/resampling/distances/distance.py

@@ -118,6 +118,28 @@ def image(self, state):
     def image_distance(self, image_a, image_b):
         return np.sqrt((image_a[0] - image_b[0]) ** 2 + (image_a[1] - image_b[1]) ** 2)
 
+class ProjectorDistance(Distance):
+    """Take a projector as input 
+
+    """
+    def __init__(self, projector):
+        """Construct a distance metric.
+
+        Parameters
+        ----------
+
+        projector : A Projector object, which implementes the project function 
+        """
+
+        self.projector = projector
+
+    def image(self, state):
+        return self.projector.project(state)
+
+    def image_distance(self, image_a, image_b):
+        return np.sqrt(np.sum(np.square(image_a - image_b)))
+
+
 class AtomPairDistance(Distance):
     """Constructs a vector of atomic distances for each state.
     Distance is the root mean squared distance between the vectors.

src/wepy/resampling/projectors/centroid.py

@@ -0,0 +1,53 @@
+"""Projector for determining centroid distances.
+"""
+# Standard Library
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Third Party Library
+import numpy as np
+
+from wepy.resampling.projectors.projector import Projector
+from wepy.util.util import box_vectors_to_lengths_angles
+from geomm.grouping import group_pair
+
+class CentroidProjector(Projector):
+    """Projects a state onto the centroid distance between two groups.
+    """
+
+    def __init__(self, group1_idxs, group2_idxs, periodic=True):
+        """Construct a centroid distance projector.
+
+        Parameters
+        ----------
+
+        group1_idxs : list of int - indices of atoms in group1
+        group2_idxs : list of int - indices of atoms in group2
+        periodic :    bool (default = True) - whether to use periodic boundary conditions to
+                      minimize centroid distances
+        """
+        self.group1_idxs = group1_idxs
+        self.group2_idxs = group2_idxs
+        self.periodic = periodic
+
+    def project(self, state):
+
+        # cut out only the coordinates we need
+        coords = np.concatenate([state['positions'][self.group1_idxs],state['positions'][self.group2_idxs]])
+        idxs1 = list(range(len(self.group1_idxs)))
+        idxs2 = list(range(len(self.group1_idxs),len(self.group1_idxs) + len(self.group2_idxs)))
+
+        if self.periodic:
+            # get the box lengths from the vectors
+            box_lengths, box_angles = box_vectors_to_lengths_angles(state["box_vectors"])
+            coords = group_pair(coords,box_lengths,idxs1,idxs2)
+
+        # determine coordinate centroids
+        c1 = coords[idxs1].mean(axis=0)
+        c2 = coords[idxs2].mean(axis=0)
+
+        # return the distance between the centroids
+        return np.sqrt(np.sum(np.square(c1-c2)))
+
+

src/wepy/resampling/projectors/projector.py

@@ -0,0 +1,43 @@
+"""Modular component for defining "projectors" that project a 
+walker state into a one- or low-dimensional subspace. These are
+usable within different resamplers.
+
+This module contains an abstract base class for Projector classes.
+
+This is similar to the 'image' function in a Distance object
+"""
+# Standard Library
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Third Party Library
+import numpy as np
+
+class Projector(object):
+    """Abstract Base class for Projector classes."""
+
+    def __init__(self):
+        """Constructor for Projector class."""
+        pass
+
+    def project(self, state):
+        """Compute the 'projection' of a walker state onto one
+        or more variables.
+
+        The abstract implementation is naive and just returns the 
+        numpy array [1].
+
+        Parameters
+        ----------
+        state : object implementing WalkerState
+            The state which will be transformed to an image
+
+        Returns
+        -------
+        projection : numpy array
+            The same state that was given as an argument.
+
+        """
+
+        return np.ones((1))