Fixed parse jumps for triclinic box case

src/mdevaluate/pbc.py

@@ -156,7 +156,7 @@ def nojump(frame: CoordinateFrame, usecache: bool = True) -> CoordinateFrame:
                     [m[i0 : abstep + 1].sum(axis=0) for m in reader.nojump_matrices]
                 ).T
             )
-            * frame.box.diagonal()
+            @ frame.box
         )
 
         reader._nojump_cache[abstep] = delta
@@ -173,7 +173,7 @@ def nojump(frame: CoordinateFrame, usecache: bool = True) -> CoordinateFrame:
                     ]
                 ).T
             )
-            * frame.box.diagonal()
+            @ frame.box
         )
     return frame - delta
 

src/mdevaluate/reader.py

@@ -187,19 +187,33 @@ def nojump_save_filename(reader: BaseReader):
 
 def parse_jumps(trajectory: Coordinates):
     prev = trajectory[0].whole
-    box = prev.box.diagonal()
+    box = prev.box
     SparseData = namedtuple("SparseData", ["data", "row", "col"])
     jump_data = (
         SparseData(data=array("b"), row=array("l"), col=array("l")),
         SparseData(data=array("b"), row=array("l"), col=array("l")),
         SparseData(data=array("b"), row=array("l"), col=array("l")),
     )
-
     for i, curr in enumerate(trajectory):
         if i % 500 == 0:
             logger.debug("Parse jumps Step: %d", i)
-        delta = ((curr - prev) / box).round().astype(np.int8)
+        r3 = np.subtract(curr, prev)
+        delta_z = np.array(np.rint(np.divide(r3[:, 2], box[2][2])), dtype=np.int8)
+        r2 = np.subtract(
+            r3,
+            (np.rint(np.divide(r3[:, 2], box[2][2])))[:, np.newaxis]
+            * box[2][np.newaxis, :],
+            )
+        delta_y = np.array(np.rint(np.divide(r2[:, 1], box[1][1])), dtype=np.int8)
+        r1 = np.subtract(
+            r2,
+            (np.rint(np.divide(r2[:, 1], box[1][1])))[:, np.newaxis]
+            * box[1][np.newaxis, :],
+            )
+        delta_x = np.array(np.rint(np.divide(r1[:, 0], box[0][0])), dtype=np.int8)
+        delta = np.array([delta_x, delta_y, delta_z]).T
         prev = curr
+        box = prev.box
         for d in range(3):
             (col,) = np.where(delta[:, d] != 0)
             jump_data[d].col.extend(col)