Update use of np.ptp()

src/SALib/analyze/delta.py

@@ -137,7 +137,7 @@ def calc_delta(Y, Ygrid, X, m):
 
         # if not np.all(np.equal(Y_ix, Y_ix[0])):
         Y_ix = Y[ix]
-        if Y_ix.ptp() != 0.0:
+        if np.ptp(Y_ix) != 0.0:
             fyc = gaussian_kde(Y_ix, bw_method="silverman")(Ygrid)
             fy_ = np.abs(fy - fyc)
         else:
@@ -168,7 +168,7 @@ def bias_reduced_delta(Y, Ygrid, X, m, num_resamples, conf_level, y_resamples):
 def sobol_first(Y, X, m):
     # pre-process to catch constant array
     # see: https://github.com/numpy/numpy/issues/9631
-    if Y.ptp() == 0.0:
+    if np.ptp(Y) == 0.0:
         # Catch constant results
         # If Y does not change then it is not sensitive to anything...
         return 0.0

src/SALib/analyze/sobol.py

@@ -154,7 +154,7 @@ def analyze(
             if keep_resamples:
                 S["S1_conf_all"][:, j] = S1_conf_j
 
-            var_diff = np.r_[A[r], B[r]].ptp()
+            var_diff = np.ptp(np.r_[A[r], B[r]])
             if var_diff != 0.0:
                 S["S1_conf"][j] = Z * S1_conf_j.std(ddof=1)
             else:
@@ -212,7 +212,7 @@ def first_order(A, AB, B):
     sample variance
     """
     y = np.r_[A, B]
-    if y.ptp() == 0:
+    if np.ptp(y) == 0:
         warn(CONST_RESULT_MSG)
         return np.array([0.0])
 
@@ -225,7 +225,7 @@ def total_order(A, AB, B):
     sample variance
     """
     y = np.r_[A, B]
-    if y.ptp() == 0:
+    if np.ptp(y) == 0:
         warn(CONST_RESULT_MSG)
         return np.array([0.0])
 
@@ -235,7 +235,7 @@ def total_order(A, AB, B):
 def second_order(A, ABj, ABk, BAj, B):
     """Second order estimator following Saltelli 2002"""
     y = np.r_[A, B]
-    if y.ptp() == 0:
+    if np.ptp(y) == 0:
         warn(CONST_RESULT_MSG)
         return np.array([0.0])
 

tests/sample/test_latin.py

@@ -137,8 +137,8 @@ def test_latin_group_constant(self):
         # Group samples should have the same values
         # Get (max - min) with the `ptp()` method, the result of which should be
         # an array of zeros
-        diff = samples[:, ::2].ptp(axis=1)
+        diff = np.ptp(samples[:, ::2], axis=1)
         assert np.all(diff == 0), "Grouped samples do not have the same values"
 
-        diff = samples[:, 1::2].ptp(axis=1)
+        diff = np.ptp(samples[:, 1::2], axis=1)
         assert np.all(diff == 0), "Grouped samples do not have the same values"