Fix handling of COO matrices by ripser mirroring scikit-tda/ripser.py…

…#104 (#465)

gtda/externals/python/ripser_interface.py

@@ -207,16 +207,27 @@ def ripser(X, maxdim=1, thresh=np.inf, coeff=2, metric="euclidean",
         dm = sparse.coo_matrix(dm)
 
     if sparse.issparse(dm):
-        coo = dm.tocoo()
+        if sparse.isspmatrix_coo(dm):
+            # If the matrix is already COO, we need to order the row and column
+            # indices lexicographically to avoid errors. See
+            # https://github.com/scikit-tda/ripser.py/issues/103
+            row, col, data = dm.row, dm.col, dm.data
+            lex_sort_idx = np.lexsort((col, row))
+            row, col, data = \
+                row[lex_sort_idx], col[lex_sort_idx], data[lex_sort_idx]
+        else:
+            # Lexicographic sorting is performed by scipy upon conversion
+            coo = dm.tocoo()
+            row, col, data = coo.row, coo.col, coo.data
         res = DRFDMSparse(
-            coo.row.astype(dtype=np.int32, order="C"),
-            coo.col.astype(dtype=np.int32, order="C"),
-            np.array(coo.data, dtype=np.float32, order="C"),
+            row.astype(dtype=np.int32, order="C"),
+            col.astype(dtype=np.int32, order="C"),
+            np.array(data, dtype=np.float32, order="C"),
             n_points,
             maxdim,
             thresh,
             coeff,
-        )
+            )
     else:
         I, J = np.meshgrid(np.arange(n_points), np.arange(n_points))
         DParam = np.array(dm[I > J], dtype=np.float32)