def test_4D(self):
np.random.seed(0)
a = np.random.rand(12, 12, 12, 12)
self.assertTrue(
np.allclose(rolling_sum(a, 3),
rolling_window(a, 3).sum(axis=(4, 5, 6, 7))))
self.assertTrue(
np.allclose(rolling_sum(a, 9),
rolling_window(a, 9).sum(axis=(4, 5, 6, 7))))
def test_2D(self):
np.random.seed(0)
a = np.random.rand(20, 20)
self.assertTrue(
np.allclose(rolling_sum(a, 3),
rolling_window(a, 3).sum(axis=(2, 3))))
self.assertTrue(
np.allclose(rolling_sum(a, 9),
rolling_window(a, 9).sum(axis=(2, 3))))
def test_3D(self):
np.random.seed(0)
a = np.random.rand(15, 15, 15)
self.assertTrue(
np.allclose(rolling_sum(a, 3),
rolling_window(a, 3).sum(axis=(3, 4, 5))))
self.assertTrue(
np.allclose(rolling_sum(a, 9),
rolling_window(a, 9).sum(axis=(3, 4, 5))))
def test_assumptions(self):
with self.assertRaises(ValueError):
# window_size bigger than dimensions of array should raise ValueError
rolling_sum(np.array([1, 2, 3]), 5)
def test_reduce(self):
np.random.seed(0)
a = np.random.rand(5, 5)
self.assertTrue(rolling_sum(a, window_size=5, reduce=True), a.sum())
def test_5D(self):
np.random.seed(0)
a = np.random.rand(10, 10, 10, 10, 10)
self.assertTrue(
np.allclose(rolling_sum(a, 3),
rolling_window(a, 3).sum(axis=(5, 6, 7, 8, 9))))
x = t.ppf(0.95, df=df)
r = x / np.sqrt(df + x**2)
sign_threshold[n] = x / np.sqrt(df + x**2)
mp.Raster.set_window_size(window_size)
M_corr.window_size = 1
mp.Raster.set_tiles((10, 10))
for i in M_th:
progress_bar((i + 1) / M_th.c_tiles)
th = M_th[i]
wtd = M_wtd[i]
fapar = M_fapar[i]
fapar[th < 3] = np.nan
corr = M_corr[i]
count_values = rolling_sum(np.logical_and(~np.isnan(fapar),
~np.isnan(wtd)),
window_size=15)
threshold = sign_threshold[count_values]
significance = np.full_like(corr, 0, dtype=np.float64)
significance[np.isnan(corr)] = np.nan
significance[corr < -threshold] = -1
significance[corr > threshold] = 1
M_sig[i] = significance
mp.Raster.close()
mp.Raster.set_window_size(15)
mp.Raster.set_tiles((20, 20))
d_p_pet = {}
d_wtd = {}
for i in M_th:
progress_bar((i+1) / M_th.c_tiles)
th = M_th[i]
fapar = M_fapar[i]
fapar[th < 3] = np.nan
p_pet = M_p_pet[i]
wtd = M_wtd[i]
# P_PET
nans = np.logical_or(np.isnan(fapar), np.isnan(p_pet))
mask = (rolling_sum(nans, window_size=15) == 0)
a = rolling_window(fapar, window_size=15)[mask]
b = rolling_window(p_pet, window_size=15)[mask]
if a.size == 0:
continue
sample_size = min(100, a.shape[0])
x = correlation_threshold_inference(a[np.random.choice(np.arange(a.shape[0]), sample_size, replace=False)],
b[np.random.choice(np.arange(b.shape[0]), sample_size, replace=False)])
d_p_pet[i] = x
# WTD
nans = np.logical_or(np.isnan(fapar), np.isnan(wtd))
