Unverified Commit b9b36dd1 authored by Ross Beyer's avatar Ross Beyer Committed by GitHub
Browse files

image_to_ground fix (#580) 

* test(test_isis.py): image_to_ground() failure with map-projected image and single lon and lat.

* test(test_isis.py): image_to_ground() failure with non-map-projected image and arrays of lon and lat.

* fix(spatial/isis.py): image_to_ground() now properly returns correct values or numpy arrays, tests improved.
parent 193056a6

autocnet/spatial/isis.py

+19 −4
Original line number Diff line number Diff line
@@ -308,15 +308,30 @@ def image_to_ground( 
    res = point_info(cube_path, sample, line, "image")



    if isinstance(sample, (abc.Sequence, np.ndarray)):

        lons, lats = np.asarray([

            [r[lontype], r[lattype]] for r in res

        ]).T

        lon_list = list()

        lat_list = list()

        for r in res:

            lon_list.append(_get_value(r[lontype]))

            lat_list.append(_get_value(r[lattype]))



        lons = np.asarray(lon_list)

        lats = np.asarray(lat_list)

    else:

        lons, lats = res[lontype].value, res[lattype].value

        lons = _get_value(res[lontype])

        lats = _get_value(res[lattype])



    return lons, lats





def _get_value(obj):

    """Returns *obj*, unless *obj* is of type pvl.collections.Quantity, in

    which case, the .value component of the object is returned."""

    if isinstance(obj, pvl.collections.Quantity):

        return obj.value

    else:

        return obj





def ground_to_image(cube_path, lon, lat):

    """

    Returns a two-tuple of numpy arrays or a two-tuple of floats, where

autocnet/spatial/tests/test_isis.py

+47 −11
Original line number Diff line number Diff line
@@ -118,26 +118,62 @@ class TestISIS(unittest.TestCase): 


    def test_image_to_ground(self):

        lon, lat = si.image_to_ground(self.cube, 20, 20)

        self.assertEqual(274.14948072713, lon)

        self.assertEqual(28.537396673529, lat)

        x = 274.14948

        y = 28.537396

        self.assertAlmostEqual(x, lon, places=5)

        self.assertAlmostEqual(y, lat, places=5)



        lons, lats = si.image_to_ground(

            self.cube, np.array([10, 20]), np.array([10, 20])

        )

        npt.assert_allclose(np.array([274.14961, x]), lons, rtol=1e-05)

        npt.assert_allclose(np.array([28.53731, y]), lats, rtol=1e-05)



        lon, lat = si.image_to_ground(self.map, 20, 20)

        x = 274.13914

        y = 28.57541

        self.assertAlmostEqual(x, lon, places=5)

        self.assertAlmostEqual(y, lat, places=5)



        lons, lats = si.image_to_ground(

            self.map, np.array([10, 20]), np.array([10, 20])

        )

        npt.assert_allclose(np.array([274.13902925, 274.13913915]), lons)

        npt.assert_allclose(np.array([28.57551247, 28.57541596]), lats)

        npt.assert_allclose(np.array([274.13903, x]), lons, rtol=1e-05)

        npt.assert_allclose(np.array([28.57551, y]), lats, rtol=1e-05)



    def test_ground_to_image(self):

        lon = 274.14948072713

        lat = 28.537396673529

        goal_samp = 20.001087

        goal_line = 20.004109

        sample, line = si.ground_to_image(

            self.cube, lon, lat

        )

        self.assertAlmostEqual(goal_samp, sample, places=6)

        self.assertAlmostEqual(goal_line, line, places=6)



        samples, lines = si.ground_to_image(

            self.cube,

            np.array([lon, 274.1495]),

            np.array([lat, 28.5374])

        )

        npt.assert_allclose(np.array([goal_samp, 18.241668]), samples)

        npt.assert_allclose(np.array([goal_line, 20.145382]), lines)



        lon = 274.13903475

        lat = 28.57550764

        goal_samp = 10.5001324

        goal_line = 10.49999466

        sample, line = si.ground_to_image(

            self.cube, 274.14948072713, 28.537396673529

            self.map, lon, lat

        )

        self.assertEqual(20.001087366213, sample)

        self.assertEqual(20.004109124452, line)

        self.assertAlmostEqual(goal_samp, sample)

        self.assertAlmostEqual(goal_line, line)



        samples, lines = si.ground_to_image(

            self.map,

            np.array([274.13903475, 274.14948072713]),

            np.array([28.57550764, 28.57541113])

            np.array([lon, 274.14948072713]),

            np.array([lat, 28.57541113])

        )

        npt.assert_allclose(np.array([10.5001324, 961.03569217]), samples)

        npt.assert_allclose(np.array([10.49999466, 20.50009032]), lines)
 
        npt.assert_allclose(np.array([goal_samp, 961.03569217]), samples)

        npt.assert_allclose(np.array([goal_line, 20.50009032]), lines)