def test_pearson3_fitting_values(self):
"""
Test for the compute._pearson3_fitting_values() function
"""
# provide some bogus inputs to make sure these raise expected errors
np.testing.assert_raises(ValueError, compute._pearson3_fitting_values,
np.array([1.0, 0.0, 0.0]))
np.testing.assert_raises(ValueError, compute._pearson3_fitting_values,
np.array([1.0, 0.0, 0.0, 1.0, 0.0, 0.0]))
np.testing.assert_raises(
ValueError, compute._pearson3_fitting_values,
np.array([[1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 5.0],
[1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 4.7]]))
np.testing.assert_raises(
ValueError, compute._pearson3_fitting_values,
np.array([
np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN,
np.NaN
]))
np.testing.assert_raises(AttributeError,
compute._pearson3_fitting_values, None)
# try using a subset of the precipitation dataset (1897 - 1915, year indices 2 - 20)
computed_values = compute._pearson3_fitting_values(
self.fixture_precips_mm_monthly[2:21, :])
expected_values = np.array(
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
48.539987664499996, 53.9852487665, 44.284745065842102,
62.583727384894736, 125.72157689160528, 182.03053042784214,
159.00575657926319, 170.92269736865791, 189.8925781252895,
155.13420024692104, 72.953125000026319, 43.31532689144737
],
[
33.781507724523095, 43.572151699968387, 40.368173442404107,
44.05329691434887, 60.10621716019174, 59.343178125457186,
49.228795303727473, 66.775653341386999, 65.362977393206421,
94.467597091088265, 72.63706898364299, 34.250906049301463
],
[
0.76530966976335302, 1.2461447518219784, 2.275517179222323,
0.8069305098698194, -0.6783037020197018, 1.022194696224529,
0.40876120732817578, 1.2372551346168916, 0.73881116931924118,
0.91911763257003465, 2.3846715887263725, 1.4700559294571962
]])
np.testing.assert_allclose(
computed_values,
expected_values,
atol=0.001,
equal_nan=True,
err_msg=
'Failed to accurately compute Pearson Type III fitting values')
# add some zeros in order to exercise the parts where it gets a percentage of zeros
precips_mm = np.array(self.fixture_precips_mm_monthly, copy=True)
precips_mm[0, 1] = 0.0
precips_mm[3, 4] = 0.0
precips_mm[14, 9] = 0.0
precips_mm[2, 5] = 0.0
precips_mm[8, 3] = 0.0
precips_mm[7, 11] = 0.0
precips_mm[3, 9] = 0.0
precips_mm[11, 4] = 0.0
precips_mm[13, 5] = 0.0
computed_values = compute._pearson3_fitting_values(precips_mm)
expected_values = np.array(
[[
0.0, 0.008130081300813009, 0.0, 0.0081967213114754103,
0.016393442622950821, 0.016393442622950821, 0.0, 0.0, 0.0,
0.016393442622950821, 0.0, 0.0081967213114754103
],
[
45.85372999240245, 46.347934133658548, 48.324170722364769,
67.635750192172154, 121.1711705943935, 184.12836193667619,
154.96859791263938, 170.28928662930332, 196.42544505660646,
153.52549468512296, 58.400078445204926, 38.858758644995909
],
[
38.873650403487751, 35.333748953293423, 34.315010982762324,
50.257217545953182, 73.519095805475956, 100.17902892507252,
50.629474961599207, 63.070686393124326, 75.262836828223314,
93.674893334263402, 48.751881843917658, 33.011345617774751
],
[
1.7567209830258725, 1.2512959828378094, 1.1665495317869126,
1.1928621421474375, 0.76407610195548825, 0.836464024048587,
0.18285005633387616, 0.99639419415939923, 0.83383974102177649,
1.162401322000489, 1.8477937169727758, 1.8064543865073583
]])
np.testing.assert_allclose(
computed_values,
expected_values,
atol=0.001,
equal_nan=True,
err_msg=
'Failed to accurately compute Pearson Type III fitting values')
def test_pearson3_fitting_values(precips_mm_monthly):
"""
Test for the compute._pearson3_fitting_values() function
"""
# provide some bogus inputs to make sure these raise expected errors
np.testing.assert_raises(ValueError, compute._pearson3_fitting_values,
np.array([1.0, 0.0, 0.0]))
np.testing.assert_raises(
ValueError,
compute._pearson3_fitting_values,
np.array([1.0, 0.0, 0.0, 1.0, 0.0, 0.0]),
)
np.testing.assert_raises(
ValueError,
compute._pearson3_fitting_values,
np.array([[1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 5.0],
[1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 4.7]]),
)
np.testing.assert_raises(
ValueError,
compute._pearson3_fitting_values,
np.array([
np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN,
np.NaN
]),
)
np.testing.assert_raises(AttributeError, compute._pearson3_fitting_values,
None)
# try using a subset of the precipitation dataset (1897 - 1915, year indices 2 - 20)
computed_values = compute._pearson3_fitting_values(
precips_mm_monthly[2:21, :])
expected_values = np.array([
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[
48.539987664499996,
53.9852487665,
44.284745065842102,
62.583727384894736,
125.72157689160528,
182.03053042784214,
159.00575657926319,
170.92269736865791,
189.8925781252895,
155.13420024692104,
72.953125000026319,
43.31532689144737,
],
[
33.781507724523095,
43.572151699968387,
40.368173442404107,
44.05329691434887,
60.10621716019174,
59.343178125457186,
49.228795303727473,
66.775653341386999,
65.362977393206421,
94.467597091088265,
72.63706898364299,
34.250906049301463,
],
[
0.76530966976335302,
1.2461447518219784,
2.275517179222323,
0.8069305098698194,
-0.6783037020197018,
1.022194696224529,
0.40876120732817578,
1.2372551346168916,
0.73881116931924118,
0.91911763257003465,
2.3846715887263725,
1.4700559294571962,
],
])
np.testing.assert_allclose(
computed_values,
expected_values,
atol=0.001,
equal_nan=True,
err_msg="Failed to accurately compute Pearson Type III fitting values",
)
# add some zeros in order to exercise the parts where it gets a percentage of zeros
precips_mm = np.array(precips_mm_monthly, copy=True)
precips_mm[0, 1] = 0.0
precips_mm[3, 4] = 0.0
precips_mm[14, 9] = 0.0
precips_mm[2, 5] = 0.0
precips_mm[8, 3] = 0.0
precips_mm[7, 11] = 0.0
precips_mm[3, 9] = 0.0
precips_mm[11, 4] = 0.0
precips_mm[13, 5] = 0.0
computed_values = compute._pearson3_fitting_values(precips_mm)
expected_values = np.array([
[
0.0, 0.008, 0.0, 0.008, 0.0164, 0.0164, 0.0, 0.0, 0.0, 0.0164, 0.0,
0.008
],
[
45.85,
46.35,
48.32,
67.64,
121.17,
184.13,
154.97,
170.29,
196.43,
153.53,
58.40,
38.86,
],
[
38.87,
35.33,
34.32,
50.26,
73.52,
100.18,
50.63,
63.07,
75.26,
93.67,
48.75,
33.01,
],
[
1.76, 1.25, 1.17, 1.19, 0.76, 0.83, 0.18, 0.996, 0.83, 1.16, 1.85,
1.81
],
])
np.testing.assert_allclose(
computed_values,
expected_values,
atol=0.01,
equal_nan=True,
err_msg="Failed to accurately compute Pearson Type III fitting values",
)