def test_lonRange0To360_outOfBounds(self):
"""
Tests that lon_range_0_to_360 aborts gracefully when lon = 361
"""
with self.assertRaisesRegex(
SystemExit, "lon_in needs to be in the range 0 to 360"):
_ = lon_range_0_to_360(361)
def test_lonRange0To360_lonIsNegGreaterThan1(self):
"""
Tests that negative inputs to lon_range_0_to_360 get 360 added to them
"""
inval = -0.001
result = lon_range_0_to_360(inval)
self.assertEqual(result, inval + 360)
def test_lonRange0To360_lonIs360(self):
"""
Tests that input to lon_range_0_to_360 of 360 remains unchanged
"""
inval = 360
result = lon_range_0_to_360(inval)
self.assertEqual(result, inval)
def _get_not_rectangle(lon_1, lon_2, lat_1, lat_2, longxy, latixy):
"""
Description
-----------
"""
# ensure that lon ranges 0-360 in case user entered -180 to 180
lon_1 = lon_range_0_to_360(lon_1)
lon_2 = lon_range_0_to_360(lon_2)
# determine the rectangle(s)
# TODO This is not really "nearest" for the edges but isel didn't work
rectangle_1 = (longxy >= lon_1)
rectangle_2 = (longxy <= lon_2)
eps = np.finfo(np.float32).eps # to avoid roundoff issue
rectangle_3 = (latixy >= (lat_1 - eps))
rectangle_4 = (latixy <= (lat_2 + eps))
if lon_1 <= lon_2:
# rectangles overlap
union_1 = np.logical_and(rectangle_1, rectangle_2)
else:
# rectangles don't overlap: stradling the 0-degree meridian
union_1 = np.logical_or(rectangle_1, rectangle_2)
if lat_1 < -90 or lat_1 > 90 or lat_2 < -90 or lat_2 > 90:
errmsg = 'lat_1 and lat_2 need to be in the range -90 to 90'
abort(errmsg)
elif lat_1 <= lat_2:
# rectangles overlap
union_2 = np.logical_and(rectangle_3, rectangle_4)
else:
# rectangles don't overlap: one in the north, one in the south
union_2 = np.logical_or(rectangle_3, rectangle_4)
# union rectangles overlap
rectangle = np.logical_and(union_1, union_2)
not_rectangle = np.logical_not(rectangle)
return not_rectangle
def _get_longxy_latixy(self, _min_lon, _max_lon, _min_lat, _max_lat):
"""
Return longxy, latixy, cols, rows
"""
cols = _max_lon - _min_lon + 1
rows = _max_lat - _min_lat + 1
long = np.arange(_min_lon, _max_lon + 1)
long = [lon_range_0_to_360(longitude) for longitude in long]
longxy = long * np.ones((rows,cols))
compare = np.repeat([long], rows, axis=0) # alternative way to form
# assert this to confirm intuitive understanding of these matrices
np.testing.assert_array_equal(longxy, compare)
lati = np.arange(_min_lat, _max_lat + 1)
self.assertEqual(min(lati), _min_lat)
self.assertEqual(max(lati), _max_lat)
latixy_transp = lati * np.ones((cols,rows))
compare = np.repeat([lati], cols, axis=0) # alternative way to form
# assert this to confirm intuitive understanding of these matrices
np.testing.assert_array_equal(latixy_transp, compare)
latixy = np.transpose(latixy_transp)
return longxy, latixy, cols, rows