Fix interpolateArray for order=0

pyqtgraph/functions.py

@@ -602,37 +602,44 @@ def interpolateArray(data, x, default=0.0, order=1):
     if md > nd:
         raise TypeError("x.shape[-1] must be less than or equal to data.ndim")
 
-    # First we generate arrays of indexes that are needed to 
-    # extract the data surrounding each point
-    fields = np.mgrid[(slice(0,order+1),) * md]
-    xmin = np.floor(x).astype(int)
-    xmax = xmin + 1
-    indexes = np.concatenate([xmin[np.newaxis, ...], xmax[np.newaxis, ...]])
-    fieldInds = []
     totalMask = np.ones(x.shape[:-1], dtype=bool) # keep track of out-of-bound indexes
-    for ax in range(md):
-        mask = (xmin[...,ax] >= 0) & (x[...,ax] <= data.shape[ax]-1) 
-        # keep track of points that need to be set to default
-        totalMask &= mask
-        
-        # ..and keep track of indexes that are out of bounds 
-        # (note that when x[...,ax] == data.shape[ax], then xmax[...,ax] will be out
-        #  of bounds, but the interpolation will work anyway)
-        mask &= (xmax[...,ax] < data.shape[ax])
-        axisIndex = indexes[...,ax][fields[ax]]
-        axisIndex[axisIndex < 0] = 0
-        axisIndex[axisIndex >= data.shape[ax]] = 0
-        fieldInds.append(axisIndex)
-    prof()
-
-    # Get data values surrounding each requested point
-    fieldData = data[tuple(fieldInds)]
-    prof()
-    
-    ## Interpolate
     if order == 0:
-        result = fieldData[0,0]
-    else:
+        xinds = np.round(x).astype(int)  # NOTE: for 0.5 this rounds to the nearest *even* number
+        for ax in range(md):
+            mask = (xinds[...,ax] >= 0) & (xinds[...,ax] <= data.shape[ax]-1) 
+            xinds[...,ax][~mask] = 0
+            # keep track of points that need to be set to default
+            totalMask &= mask
+        result = data[tuple([xinds[...,i] for i in range(xinds.shape[-1])])]
+        
+    elif order == 1:
+        # First we generate arrays of indexes that are needed to 
+        # extract the data surrounding each point
+        fields = np.mgrid[(slice(0,order+1),) * md]
+        xmin = np.floor(x).astype(int)
+        xmax = xmin + 1
+        indexes = np.concatenate([xmin[np.newaxis, ...], xmax[np.newaxis, ...]])
+        fieldInds = []
+        for ax in range(md):
+            mask = (xmin[...,ax] >= 0) & (x[...,ax] <= data.shape[ax]-1) 
+            # keep track of points that need to be set to default
+            totalMask &= mask
+            
+            # ..and keep track of indexes that are out of bounds 
+            # (note that when x[...,ax] == data.shape[ax], then xmax[...,ax] will be out
+            #  of bounds, but the interpolation will work anyway)
+            mask &= (xmax[...,ax] < data.shape[ax])
+            axisIndex = indexes[...,ax][fields[ax]]
+            axisIndex[axisIndex < 0] = 0
+            axisIndex[axisIndex >= data.shape[ax]] = 0
+            fieldInds.append(axisIndex)
+        prof()
+
+        # Get data values surrounding each requested point
+        fieldData = data[tuple(fieldInds)]
+        prof()
+    
+        ## Interpolate
         s = np.empty((md,) + fieldData.shape, dtype=float)
         dx = x - xmin
         # reshape fields for arithmetic against dx

pyqtgraph/tests/test_functions.py

@@ -58,8 +58,8 @@ def check_interpolateArray(order):
     
     x = np.array([[  0.3,   0.6],
                   [  1. ,   1. ],
-                  [  0.5,   1. ],
-                  [  0.5,   2.5],
+                  [  0.501,   1. ],   # NOTE: testing at exactly 0.5 can yield different results from map_coordinates
+                  [  0.501,   2.501],  # due to differences in rounding
                   [ 10. ,  10. ]])
     
     result = interpolateArray(data, x)
@@ -82,8 +82,8 @@ def check_interpolateArray(order):
     
     
     # test mapping 2D array of locations
-    x = np.array([[[0.5, 0.5], [0.5, 1.0], [0.5, 1.5]],
-                  [[1.5, 0.5], [1.5, 1.0], [1.5, 1.5]]])
+    x = np.array([[[0.501, 0.501], [0.501, 1.0], [0.501, 1.501]],
+                  [[1.501, 0.501], [1.501, 1.0], [1.501, 1.501]]])
     
     r1 = interpolateArray(data, x)
     r2 = scipy.ndimage.map_coordinates(data, x.transpose(2,0,1), order=order)