BUG: Make interp handle zero dimensional ndarrays as interpolation

points. Add some tests for interp. Fixes ticket #1177.
unc_api.txt.tmp

2 files changed, 37 insertions, 7 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 3f293ae4c..680103924 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -55,7 +55,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None):
     range : (float, float), optional
         The lower and upper range of the bins.  If not provided, range
         is simply ``(a.min(), a.max())``.  Values outside the range are
-        ignored. 
+        ignored.
     normed : bool, optional
         If False, the result will contain the number of samples
         in each bin.  If True, the result is the value of the
@@ -68,7 +68,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None):
         only contributes its associated weight towards the bin count
         (instead of 1).  If `normed` is True, the weights are normalized,
         so that the integral of the density over the range remains 1
- 
+
     Returns
     -------
     hist : array
@@ -76,7 +76,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None):
         description of the possible semantics.
     bin_edges : array of dtype float
         Return the bin edges ``(length(hist)+1)``.
-        
+
 
     See Also
     --------
@@ -112,7 +112,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None):
     1.0
 
     """
- 
+
     a = asarray(a)
     if weights is not None:
         weights = asarray(weights)
@@ -985,6 +985,8 @@ def interp(x, xp, fp, left=None, right=None):
     """
     if isinstance(x, (float, int, number)):
         return compiled_interp([x], xp, fp, left, right).item()
+    elif isinstance(x, np.ndarray) and x.ndim == 0:
+        return compiled_interp([x], xp, fp, left, right).item()
     else:
         return compiled_interp(x, xp, fp, left, right)
 
@@ -2043,7 +2045,7 @@ def blackman(M):
     >>> plt.xlabel("Sample")
     <matplotlib.text.Text object at 0x...>
     >>> plt.show()
-    
+
     >>> plt.figure()
     <matplotlib.figure.Figure object at 0x...>
     >>> A = fft(window, 2048) / 25.5
@@ -2152,7 +2154,7 @@ def bartlett(M):
     >>> plt.xlabel("Sample")
     <matplotlib.text.Text object at 0x...>
     >>> plt.show()
-    
+
     >>> plt.figure()
     <matplotlib.figure.Figure object at 0x...>
     >>> A = fft(window, 2048) / 25.5
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 6381c0a72..10e3790ea 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -69,7 +69,7 @@ class TestAverage(TestCase):
         actual = average(y, weights=w)
         desired = (arange(10) ** 2).sum()*1. / arange(10).sum()
         assert_almost_equal(actual, desired)
-        
+
         y1 = array([[1, 2, 3], [4, 5, 6]])
         w0 = [1, 2]
         actual = average(y1, weights=w0, axis=0)
@@ -934,6 +934,34 @@ class TestBincount(TestCase):
         assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))
 
 
+class TestInterp(TestCase):
+    def test_basic(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = np.linspace(0, 1, 50)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+
+    def test_scalar_interpolation_point(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = 0
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = .3
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.float32(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.float64(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+
+    def test_zero_dimensional_interpolation_point(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = np.array(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.array(.3, dtype=object)
+        assert_almost_equal(np.interp(x0, x, y), .3)
+
+
 def compare_results(res, desired):
     for i in range(len(desired)):
         assert_array_equal(res[i], desired[i])