add tests and fixes for raster.misc

- adds tests for gradient, normed_potential_vectors (sort of), and
  divergence
- fixes divergence

reference issue #20

karta/raster/misc.py

@@ -86,8 +86,7 @@ def gradient(grid, band=0):
     Parameters
     ----------
     grid: RegularGrid
-    band: int, optional
-        band to compute hillshade for (default 0)
+    band: int, optional (default 0)
 
     Returns
     -------
@@ -108,23 +107,24 @@ def _div(U, V, res=(1.0, 1.0)):
                + np.pad(dVdy, ((1, 1), (0, 0)), "constant", constant_values=(np.nan,))
     return divergence
 
-def divergence(grid, band=0):
+def divergence(grid, bands=(0, 1)):
     """ Compute divergence from a grid.
 
     Parameters
     ----------
     grid: RegularGrid
-    band: int, optional
-        band to compute hillshade for (default 0)
+    band: tuple of ints, optional (default (0, 1))
 
     Returns
     -------
     RegularGrid
     """
     if grid.skew != (0, 0):
         raise NotImplementedError("divergence calculations not implemented on skewed grids")
-    D = np.where(grid.data_mask, grid[:,:,band].astype(np.float32), np.nan)
-    return RegularGrid(grid.transform, _div(D, grid.resolution),
+    return RegularGrid(grid.transform,
+                       _div(grid[:,:,bands[0]],
+                            grid[:,:,bands[1]],
+                            grid.resolution),
                        crs=grid.crs, nodata_value=np.nan)
 
 def _normed_potential_vectors(D, res=(1.0, 1.0)):
@@ -149,8 +149,7 @@ def normed_potential_vectors(grid, band=0):
     Parameters
     ----------
     grid: RegularGrid
-    band: int, optional
-        band to compute hillshade for (default 0)
+    band: int, optional (default 0)
 
     Returns
     -------

tests/raster_misc_tests.py

@@ -109,7 +109,37 @@ def test_aspect(self):
         grid = RegularGrid((0, 0, 10, 10, 0, 0), values=np.ones_like(x)*y)
         aspect = misc.aspect(grid)
         self.assertTrue(np.allclose(0.5*np.pi, aspect[1:-1,1:-1]))
-        pass
+        return
+
+    def test_gradient(self):
+        x = np.linspace(-np.pi, np.pi, 256)
+        y = np.linspace(-np.pi, np.pi, 256).reshape(-1, 1)
+        g = RegularGrid((0, 0, 2*np.pi/255, 2*np.pi/255, 0, 0), values=np.sin(x)*np.cos(y))
+        gx, gy = misc.gradient(g)
+        self.assertTrue(np.nansum(np.abs(gx[:,:] - np.cos(x)*np.cos(y))) < 7.0)
+        self.assertTrue(np.nansum(np.abs(gy[:,:] + np.sin(x)*np.sin(y))) < 7.0)
+        return
+
+    def test_normed_potential_vectors(self):
+        x = np.linspace(-np.pi, np.pi, 256)
+        y = np.linspace(-np.pi, np.pi, 256).reshape(-1, 1)
+        g = RegularGrid((0, 0, 2*np.pi/255, 2*np.pi/255, 0, 0), values=np.sin(x)+np.sin(y))
+        u, v = misc.normed_potential_vectors(g)
+        # TODO: add test for correctness
+        return
+
+    def test_divergence(self):
+        x = np.linspace(-np.pi, np.pi, 256)
+        y = np.linspace(-np.pi, np.pi, 256).reshape(-1, 1)
+        g = RegularGrid((0, 0, 2*np.pi/255, 2*np.pi/255, 0, 0), values=np.sin(x)+np.sin(y))
+        u, v = misc.normed_potential_vectors(g)
+        twoband = RegularGrid(g.transform, np.dstack([u[:,:], v[:,:]]))
+        div = misc.divergence(twoband)
+
+        self.assertTrue(
+            np.nansum(np.abs((g[:,:]/g.max() + div[:,:]/div.max()))) < 1.32
+        )
+        return
 
 if __name__ == "__main__":
     unittest.main()