Revert to interpolation arg

Author: andreped

tests/test_tf.py

@@ -15,7 +15,7 @@ def test_cov():
 def test_percentile():
     x = np.random.randn(10, 10)
     p = 20
-    p_np = np.percentile(x, p, method='nearest')
+    p_np = np.percentile(x, p, interpolation='nearest')
     p_t = torchstain.tf.utils.percentile(x, p)
 
     np.testing.assert_almost_equal(p_np, p_t)

tests/test_torch.py

@@ -21,7 +21,7 @@ def test_cov():
 def test_percentile():
     x = np.random.randn(10, 10)
     p = 20
-    p_np = np.percentile(x, p, method='nearest')
+    p_np = np.percentile(x, p, interpolation='nearest')
     p_t = torchstain.torch.utils.percentile(torch.tensor(x), p)
 
     np.testing.assert_almost_equal(p_np, p_t)

torchstain/tf/utils/percentile.py

@@ -7,7 +7,7 @@ def percentile(t: tf.Tensor, q: float) -> Union[int, float]:
 
     CAUTION:
      * Values are not interpolated, which corresponds to
-       ``numpy.percentile(..., method="nearest")``.
+       ``numpy.percentile(..., interpolation="nearest")``.
 
     :param t: Input tensor.
     :param q: Percentile to compute, which must be between 0 and 100 inclusive.

torchstain/torch/utils/percentile.py

@@ -11,7 +11,7 @@ def percentile(t: torch.Tensor, q: float) -> Union[int, float]:
     CAUTION:
      * Needs PyTorch >= 1.1.0, as ``torch.kthvalue()`` is used.
      * Values are not interpolated, which corresponds to
-       ``numpy.percentile(..., method="nearest")``.
+       ``numpy.percentile(..., interpolation="nearest")``.
 
     :param t: Input tensor.
     :param q: Percentile to compute, which must be between 0 and 100 inclusive.