put back original make_unique function

Author: erdogant

findpeaks/stats.py

@@ -711,51 +711,21 @@ def disable_tqdm():
     """Set the logger for verbosity messages."""
     return (True if (logger.getEffectiveLevel()>=30) else False)
 
-# def _make_unique(arr: np.ndarray):
-#     """Method iterates through elements of the input array to ensure all values are unique.
-#        Duplicate values are reduced by the smallest possible increment for the given data type.
-#        """
-#     logger.debug('Making values unique')
-#     res = np.empty_like(arr)
-#     it = np.nditer([arr, res], [], [['readonly'], ['writeonly', 'allocate']])
-#     seen = set()
-#     with it:
-#         while not it.finished:
-#             a = it[0].item()
-#             while a in seen and np.isfinite(a):
-#                 a = np.nextafter(a, -np.inf)
-#             it[1] = a
-#             if a not in seen:
-#                 seen.add(a)
-#             it.iternext()
-#     return res
-
-def _make_unique(arr: np.ndarray) -> np.ndarray:
-    """Return array where duplicate finite values are slightly perturbed to make all values unique."""
-    # Convert to float64 if needed
-    if not np.issubdtype(arr.dtype, np.floating):
-        arr = arr.astype(np.float64)
-    dtype = arr.dtype
-
-    arr_flat = arr.ravel()
-    res = arr_flat.copy()
-    is_finite = np.isfinite(res)
-    finite_vals = res[is_finite]
-    unique, counts = np.unique(finite_vals, return_counts=True)
-    dupes = unique[counts > 1]
-    if dupes.size == 0:
-        return arr.copy()
-    
-    seen = {}
-    mask = np.isin(finite_vals, dupes)
-    indices = np.where(mask)[0]
-    values = finite_vals[mask]
-
-    for i, val in tqdm(zip(indices, values), total=len(values), disable=disable_tqdm(), desc=logger.info("Making values unique")):
-        count = seen.get(val, 0)
-        perturbed = np.nextafter(val, -np.inf, dtype=dtype) - count * np.finfo(dtype).eps
-        finite_vals[i] = perturbed
-        seen[val] = count + 1
-
-    res[is_finite] = finite_vals
-    return res.reshape(arr.shape)
+def _make_unique(arr: np.ndarray):
+    """Method iterates through elements of the input array to ensure all values are unique.
+       Duplicate values are reduced by the smallest possible increment for the given data type.
+       """
+    logger.debug('Making values unique')
+    res = np.empty_like(arr)
+    it = np.nditer([arr, res], [], [['readonly'], ['writeonly', 'allocate']])
+    seen = set()
+    with it:
+        while not it.finished:
+            a = it[0].item()
+            while a in seen and np.isfinite(a):
+                a = np.nextafter(a, -np.inf)
+            it[1] = a
+            if a not in seen:
+                seen.add(a)
+            it.iternext()
+    return res

findpeaks/tests/test_findpeaks.py

@@ -26,6 +26,7 @@ def test_fit(self):
         assert len(results['Xranked'][results['Xranked'] != 0]) > 18
 
     def test_topology_limit(self):
+        from findpeaks import findpeaks
         # CHECK RESULTS METHOD with LIMIT functionality
         fp = findpeaks(method="topology", whitelist=['peak'], limit=0)
         X = fp.import_example('2dpeaks')