Do the wrong thing for the sake of test coverage

Author: lmcinnes

hdbscan/hdbscan_.py

@@ -40,52 +40,49 @@
 FAST_METRICS = KDTree.valid_metrics + BallTree.valid_metrics
 
 # Supporting numpy prior to version 1.7 is a little painful ...
-if hasattr(np, 'isclose'):
-    from numpy import isclose
-else:
-    def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
-
-        def within_tol(x, y, atol, rtol):
-            with np.errstate(invalid='ignore'):
-                result = np.less_equal(abs(x-y), atol + rtol * abs(y))
-            if np.isscalar(a) and np.isscalar(b):
-                result = bool(result)
-            return result
-
-        x = np.array(a, copy=False, subok=True, ndmin=1)
-        y = np.array(b, copy=False, subok=True, ndmin=1)
-
-        # Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT).
-        # This will cause casting of x later. Also, make sure to allow subclasses
-        # (e.g., for numpy.ma).
-        dt = np.core.multiarray.result_type(y, 1.)
-        y = np.array(y, dtype=dt, copy=False, subok=True)
-
-        xfin = np.isfinite(x)
-        yfin = np.isfinite(y)
-        if np.all(xfin) and np.all(yfin):
-            return within_tol(x, y, atol, rtol)
+def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
+
+    def within_tol(x, y, atol, rtol):
+        with np.errstate(invalid='ignore'):
+            result = np.less_equal(abs(x-y), atol + rtol * abs(y))
+        if np.isscalar(a) and np.isscalar(b):
+            result = bool(result)
+        return result
+
+    x = np.array(a, copy=False, subok=True, ndmin=1)
+    y = np.array(b, copy=False, subok=True, ndmin=1)
+
+    # Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT).
+    # This will cause casting of x later. Also, make sure to allow subclasses
+    # (e.g., for numpy.ma).
+    dt = np.core.multiarray.result_type(y, 1.)
+    y = np.array(y, dtype=dt, copy=False, subok=True)
+
+    xfin = np.isfinite(x)
+    yfin = np.isfinite(y)
+    if np.all(xfin) and np.all(yfin):
+        return within_tol(x, y, atol, rtol)
+    else:
+        finite = xfin & yfin
+        cond = np.zeros_like(finite, subok=True)
+        # Because we're using boolean indexing, x & y must be the same shape.
+        # Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in
+        # lib.stride_tricks, though, so we can't import it here.
+        x = x * np.ones_like(cond)
+        y = y * np.ones_like(cond)
+        # Avoid subtraction with infinite/nan values...
+        cond[finite] = within_tol(x[finite], y[finite], atol, rtol)
+        # Check for equality of infinite values...
+        cond[~finite] = (x[~finite] == y[~finite])
+        if equal_nan:
+            # Make NaN == NaN
+            both_nan = np.isnan(x) & np.isnan(y)
+            cond[both_nan] = both_nan[both_nan]
+
+        if np.isscalar(a) and np.isscalar(b):
+            return bool(cond)
         else:
-            finite = xfin & yfin
-            cond = np.zeros_like(finite, subok=True)
-            # Because we're using boolean indexing, x & y must be the same shape.
-            # Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in
-            # lib.stride_tricks, though, so we can't import it here.
-            x = x * np.ones_like(cond)
-            y = y * np.ones_like(cond)
-            # Avoid subtraction with infinite/nan values...
-            cond[finite] = within_tol(x[finite], y[finite], atol, rtol)
-            # Check for equality of infinite values...
-            cond[~finite] = (x[~finite] == y[~finite])
-            if equal_nan:
-                # Make NaN == NaN
-                both_nan = np.isnan(x) & np.isnan(y)
-                cond[both_nan] = both_nan[both_nan]
-
-            if np.isscalar(a) and np.isscalar(b):
-                return bool(cond)
-            else:
-                return cond
+            return cond
 
 
 def _tree_to_labels(X, single_linkage_tree, min_cluster_size=10, allow_single_cluster=False):