add user warning and position wrapping to KDTreePairs+, attribute acc…

…ess to Memory pair-finders

hippynn/graphs/nodes/pairs.py

@@ -68,7 +68,23 @@ def __init__(self, name, parents, dist_hard_max, module="auto", module_kwargs=No
         parents = self.expand_parents(parents)
         super().__init__(name, parents, module=module, **kwargs)
 
-class PeriodicPairIndexerMemory(PeriodicPairIndexer):
+class Memory:
+    @property
+    def skin(self):
+        return self.torch_module.skin
+
+    @skin.setter
+    def skin(self, skin):
+        self.torch_module.skin = skin
+
+    @property
+    def reuse_percentage(self):
+        return self.torch_module.reuse_percentage
+
+    def reset_reuse_percentage(self):
+        self.torch_module.reset_reuse_percentage()
+
+class PeriodicPairIndexerMemory(PeriodicPairIndexer, Memory):
     '''
     Implementation of PeriodicPairIndexer with additional memory component.
 
@@ -86,9 +102,9 @@ class PeriodicPairIndexerMemory(PeriodicPairIndexer):
     def __init__(self, name, parents, dist_hard_max, skin, module="auto", module_kwargs=None, **kwargs):
         if module_kwargs is None:
             module_kwargs = {}
-        module_kwargs = {"skin": skin, **module_kwargs}
+        self.expand0module_kwargs = {"skin": skin, **module_kwargs}
 
-        super().__init__(name, parents, dist_hard_max, module=module, module_kwargs=module_kwargs, **kwargs)
+        super().__init__(name, parents, dist_hard_max, module=module, module_kwargs=self.module_kwargs, **kwargs)
 
 
 class ExternalNeighborIndexer(ExpandParents, PairIndexer, AutoKw, MultiNode):
@@ -379,7 +395,7 @@ class KDTreePairs(_DispatchNeighbors):
     '''
     _auto_module_class = pairs_modules.dispatch.KDTreeNeighbors
 
-class KDTreePairsMemory(_DispatchNeighbors):
+class KDTreePairsMemory(_DispatchNeighbors, Memory):
     '''
     Implementation of KDTreePairs with an added memory component.
 

hippynn/layers/indexers.py

@@ -2,6 +2,7 @@
 Layers for encoding, decoding, index states, besides pairs
 """
 
+import warnings
 import torch
 
 
@@ -249,7 +250,23 @@ def __init__(self, length, vmin, vmax):
         self.bins = torch.nn.Parameter(torch.linspace(vmin, vmax, length), requires_grad=False)
         self.sigma = (vmax - vmin) / length
 
+        self.vmin = vmin
+        self.vmax = vmax
+
     def forward(self, values):
+        # Warn user if provided values lie outside the range of the histogram bins
+        values_out_of_range = (values < self.vmin) + (values > self.vmax)
+
+        if values_out_of_range.sum() > 0:
+            perc_out_of_range = values_out_of_range.float().mean()
+            warnings.warn(
+                "Values out of range for FuzzyHistogrammer\n"
+                f"Number of values out of range: {values_out_of_range.sum()}\n"
+                f"Percentage of values out of range: {perc_out_of_range * 100:.2f}%\n"
+                f"Set range for FuzzyHistogrammer: {(self.vmin, self.vmax)}\n"
+                f"Range of values: ({values.min().item():.2f}, {values.max().item():.2f})"
+            )
+
         if values.shape[-1] != 1:
             values = values[...,None]
         x = values - self.bins

hippynn/layers/pairs/dispatch.py

@@ -159,6 +159,8 @@ def neighbor_list_kdtree(cutoff, coords, cell):
         new_cell = cell.clone()
         new_coords = coords.clone()
 
+    new_coords = new_coords % torch.diag(new_cell)  # KD Tree will not work if positions are outside of periodic box
+
     # Find pair indices
     tree = KDTree(
         data=new_coords.detach().cpu().numpy(),