def test_elev_gradient(self):
elevs0 = [0] * 9 + [-153.8]
points0 = titanlib.Points(lats, lons, elevs0)
flags = titanlib.buddy_check(points0, values, radius, num_min,
threshold, max_elev_diff, 0, min_std,
num_iterations)
np.testing.assert_array_equal(flags, [0] * 8 + [1, 1])
flags = titanlib.buddy_check(points0, values, radius, num_min,
threshold, max_elev_diff, elev_gradient,
min_std, num_iterations)
np.testing.assert_array_equal(flags, [0] * 8 + [1, 0])
def test_missing(self):
values0 = np.copy(values)
values0[0] = np.nan
# Check that NaNs are still flagged, eventhough they are not checked
flags = titanlib.buddy_check(points, values0, radius, num_min, 0.0001,
1, elev_gradient, min_std, num_iterations)
np.testing.assert_array_equal(flags, [1] * 10)
def test_max_elev_diff(self):
"""Check that test is not run on a point which has no other points within the elevation range"""
elevs0 = [0]*9 + [100]
points0 = titanlib.Points(lats, lons, elevs0)
flags = titanlib.buddy_check(points0, values, radius, num_min, threshold,
1, elev_gradient, min_std, num_iterations)
np.testing.assert_array_equal(flags, [0]*8 + [1, 0])
def test_1(self):
"""Check that the test doesn't fail"""
lats, lons, elevs, values = util.summer_temperature_example()
I = slice(0, len(lats))
lats = lats[I]
lons = lons[I]
elevs = elevs[I]
values = values[I]
s_time = time.time()
flags = titanlib.buddy_check(lats, lons, elevs, values, [10000], [5], [2], 5, 0.0065, 1, 1)
e_time = time.time()
print(e_time - s_time)
print("Fraction of stations removed: %.1f %%" % (np.mean(flags) * 100))
def test(self, dataset, mask, code=104):
if tit is None:
raise ModuleNotFoundError("titanlib was not loaded properly")
global_flags = dataset.flags
# Buddy does not work properly for dataset. Also without data set the values must be set without subscripts
# status = dataset.buddy_check(self.distance_lim, self.priorities, self.buddies_min, self.thresholds,
# self.diff_elev_max, self.adjust_for_elev_diff, self.obs_to_check, mask)
lons = []
lats = []
elevs = []
values = []
for i in range(0, len(mask)):
lons.append(dataset.lons[i])
lats.append(dataset.lats[i])
elevs.append(dataset.elevs[i])
values.append(dataset.values[i])
status, flags = tit.buddy_check(lats, lons, elevs, values,
self.distance_lim, self.priorities,
self.buddies_min, self.thresholds, self.diff_elev_max,
self.adjust_for_elev_diff, self.obs_to_check)
if not status:
raise Exception("Buddy check failed!")
for i in range(0, len(mask)):
if global_flags[mask[i]] == 0 and flags[i] == 1:
global_flags[mask[i]] = code
if self.debug:
for i in range(0, len(mask)):
print(self.name, i, dataset.values[i], dataset.flags[i], global_flags[i])
return global_flags
def test_num_iterations(self):
"""Check that num_iterations affects the flags"""
flags = titanlib.buddy_check(points, values, radius, num_min,
threshold, max_elev_diff, elev_gradient,
min_std, 1)
np.testing.assert_array_equal(flags, [0] * 9 + [1])
def test_min_num(self):
"""Check that when min_num is high enough, nothing is flagged"""
flags = titanlib.buddy_check(points, values, radius, [20], threshold,
max_elev_diff, elev_gradient, min_std,
num_iterations)
np.testing.assert_array_equal(flags, [0] * N)
def test_1(self):
flags = titanlib.buddy_check(points, values, radius, num_min,
threshold, max_elev_diff, elev_gradient,
min_std, num_iterations)
np.testing.assert_array_equal(flags, [0] * 8 + [1] * 2)