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tests.py
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'''
Unit tests for the mod17 Python utilities library.
'''
import os
import unittest
import numpy as np
import mod17
from mod17 import MOD17
from mod17.utils import restore_bplut
from mod17.srs import modis_from_wgs84, modis_to_wgs84, modis_tile_from_wgs84, modis_row_col_from_wgs84, modis_row_col_to_wgs84
MOD17_BPLUT = os.path.join(
os.path.dirname(mod17.__file__), 'data/MOD17_BPLUT_CX.X_MERRA_NASA.csv')
class GPP(unittest.TestCase):
'''
Suite of GPP test cases based on the Collection 5.1 BPLUT.
'''
@classmethod
def setUp(cls):
cls.pft = np.arange(0, 13)
cls.params = dict([
(k, v[cls.pft]) for k, v in restore_bplut(MOD17_BPLUT).items()
])
cls.drivers_annual = [ # Sequence of daily driver data over 1 year
np.repeat(np.array((0.5,)), 365)[:,np.newaxis]\
.repeat(cls.pft.size, axis = -1), # fPAR
np.repeat(np.array((15,)), 365)[:,np.newaxis]\
.repeat(cls.pft.size, axis = -1), # Tmin
np.repeat(np.array((1000,)), 365)[:,np.newaxis]\
.repeat(cls.pft.size, axis = -1), # VPD
np.repeat(np.array((10,)), 365)[:,np.newaxis]\
.repeat(cls.pft.size, axis = -1), # PAR
np.repeat(np.array((1,)), 365)[:,np.newaxis]\
.repeat(cls.pft.size, axis = -1), # LAI
np.repeat(np.array((25,)), 365)[:,np.newaxis]\
.repeat(cls.pft.size, axis = -1), # Tmean
np.repeat(np.array((2010,)), 365)[:,np.newaxis]\
.repeat(cls.pft.size, axis = -1) # years
]
def test_gpp(self):
'Should model GPP as expected'
model = MOD17(self.params)
par = model.par(100)
answer = np.array([
np.nan, 2., 2.73, 2.04, 2.51, 2.01, 2.63,
1.81, 2.37, 2.31, 1.91, np.nan, 2.07
])
pred = model.daily_gpp(fpar = 0.5, tmin = 10, vpd = 1000, par = par)\
.round(2)
self.assertTrue(
np.logical_or(np.isnan(pred), np.equal(pred, answer)).all())
def test_gpp_static_method(self):
'Should model GPP as expected, from class method'
model = MOD17(self.params)
params = [
self.params[p][2] # i.e., PFT = 2
for p in ('LUE_max', 'tmin0', 'tmin1', 'vpd0', 'vpd1')
]
par = model.par(100)
predicted = MOD17._gpp(params, 0.5, 10, 1000, par).round(2)
self.assertTrue(np.equal(predicted, 2.73).all())
def test_gpp_temperature_ramp(self):
'Should accurately model GPP over a range of temperatures'
tmin_sweep = np.arange(-10, 35, 5)
answer = np.array([
np.full((9,), np.nan), # i.e., PFT == 0
[0., 0.37, 0.98, 1.6, 2., 2., 2., 2., 2. ],
[0., 0.48, 1.28, 2.08, 2.73, 2.73, 2.73, 2.73, 2.73],
[0., 0.34, 0.91, 1.48, 2.04, 2.09, 2.09, 2.09, 2.09],
[0., 0.16, 0.94, 1.73, 2.51, 2.51, 2.51, 2.51, 2.51],
[0., 0.24, 0.85, 1.46, 2.01, 2.01, 2.01, 2.01, 2.01],
[0., 0.47, 1.27, 2.06, 2.63, 2.63, 2.63, 2.63, 2.63],
[0., 0.32, 0.86, 1.4, 1.81, 1.81, 1.81, 1.81, 1.81],
[0., 0.4, 1.05, 1.71, 2.37, 2.55, 2.55, 2.55, 2.55],
[0., 0.39, 1.03, 1.67, 2.31, 2.49, 2.49, 2.49, 2.49],
[0., 0.32, 0.85, 1.38, 1.91, 2.13, 2.13, 2.13, 2.13],
np.full((9,), np.nan), # i.e, PFT == 11
[0., 0.34, 0.92, 1.49, 2.07, 2.3, 2.3, 2.3, 2.3 ]])
model = MOD17(self.params)
for i, tmin in enumerate(tmin_sweep.tolist()):
par = model.par(100)
pred = model.daily_gpp(fpar = 0.5, tmin = tmin, vpd = 1000, par = par).round(2)
self.assertTrue(np.logical_or(
np.isnan(pred),
np.equal(answer[:,i], pred)).all())
def test_gpp_vpd_ramp(self):
'Should accurately model GPP over a range of VPD'
vpd_sweep = np.arange(0, 6000, 1000)
answer = np.array([
np.full((6,), np.nan), # i.e., PFT == 0
[2.35, 2. , 1. , 0. , 0. , 0.],
[2.73, 2.73, 1.82, 0.91, 0. , 0.],
[2.39, 2.09, 1.26, 0.42, 0. , 0.],
[2.97, 2.51, 1.19, 0. , 0. , 0.],
[2.38, 2.01, 0.95, 0. , 0. , 0.],
[2.91, 2.63, 1.83, 1.04, 0.24, 0.],
[2. , 1.81, 1.28, 0.75, 0.21, 0.],
[2.91, 2.55, 1.53, 0.51, 0. , 0.],
[2.83, 2.49, 1.53, 0.57, 0. , 0.],
[2.36, 2.13, 1.46, 0.8 , 0.13, 0.],
np.full((6,), np.nan), # i.e., PFT == 11
[2.53, 2.3 , 1.64, 0.98, 0.33, 0.]])
model = MOD17(self.params)
for i, vpd in enumerate(vpd_sweep.tolist()):
par = model.par(100)
pred = model.daily_gpp(
fpar = 0.5, tmin = 20, vpd = vpd, par = par).round(2)
self.assertTrue(np.logical_or(
np.isnan(pred),
np.equal(answer[:,i], pred)).all())
def test_daily_respiration(self):
'Should accurately calculate daily respiration'
lai = 1
tmean = 25
model = MOD17(self.params)
pred_leaf, pred_froot = model.daily_respiration(lai, tmean)
self.assertTrue(np.logical_or(
np.isnan(pred_leaf),
np.equal(pred_leaf.round(3), np.array([
np.nan, 0.579, 0.323, 0.694, 0.453, 0.495, 1.33, 0.622, 0.434,
0.433, 0.371, np.nan, 0.371
]))
).all())
self.assertTrue(np.logical_or(
np.isnan(pred_froot),
np.equal(pred_froot.round(3), np.array([
np.nan, 0.587, 0.3, 0.738, 0.327, 0.357, 0.781, 0.795, 0.459,
0.457, 0.792, np.nan, 0.61
]))
).all())
def test_daily_respiration_lai_ramp(self):
'Should accurately calculate daily respiration over range of LAI'
lai_sweep = np.array((0.1, 1, 2, 3, 4.5))
tmean = 25
model = MOD17(self.params)
answer1 = np.array([
[np.nan, 0.06, 0.03, 0.07, 0.05, 0.05, 0.13, 0.06, 0.04, 0.04, 0.04, np.nan, 0.04],
[np.nan, 0.58, 0.32, 0.69, 0.45, 0.50, 1.33, 0.62, 0.43, 0.43, 0.37, np.nan, 0.37],
[np.nan, 1.16, 0.65, 1.39, 0.91, 0.99, 2.66, 1.24, 0.87, 0.87, 0.74, np.nan, 0.74],
[np.nan, 1.74, 0.97, 2.08, 1.36, 1.49, 3.99, 1.87, 1.3 , 1.30, 1.11, np.nan, 1.11],
[np.nan, 2.61, 1.45, 3.12, 2.04, 2.23, 5.99, 2.80, 1.95, 1.95, 1.67, np.nan, 1.67],
])
answer2 = np.array([
[np.nan, 0.06, 0.03, 0.07, 0.03, 0.04, 0.08, 0.08, 0.05, 0.05, 0.08, np.nan, 0.06],
[np.nan, 0.59, 0.30, 0.74, 0.33, 0.36, 0.78, 0.80, 0.46, 0.46, 0.79, np.nan, 0.61],
[np.nan, 1.17, 0.60, 1.48, 0.65, 0.71, 1.56, 1.59, 0.92, 0.91, 1.58, np.nan, 1.22],
[np.nan, 1.76, 0.90, 2.21, 0.98, 1.07, 2.34, 2.39, 1.38, 1.37, 2.38, np.nan, 1.83],
[np.nan, 2.64, 1.35, 3.32, 1.47, 1.61, 3.51, 3.58, 2.06, 2.06, 3.57, np.nan, 2.74],
])
for i, lai in enumerate(lai_sweep.tolist()):
pred1, pred2 = model.daily_respiration(lai, tmean)
pred1 = pred1.round(2)
pred2 = pred2.round(2)
self.assertTrue(np.logical_or(
np.isnan(answer1[i]), np.equal(answer1[i], pred1)).all())
self.assertTrue(np.logical_or(
np.isnan(answer2[i]), np.equal(answer2[i], pred2)).all())
def test_daily_respiration_temperature_ramp(self):
'Should accurately calculate daily respiration over temperature range'
tmean_sweep = np.arange(-10, 35, 5)
lai = 1
model = MOD17(self.params)
answer1 = np.array([
[np.nan, 0.01, 0. , 0.01, 0.01, 0.01, 0.02, 0.01, 0.01, 0.01, 0.01, np.nan, 0.01],
[np.nan, 0.02, 0.01, 0.02, 0.01, 0.02, 0.04, 0.02, 0.01, 0.01, 0.01, np.nan, 0.01],
[np.nan, 0.04, 0.02, 0.05, 0.03, 0.03, 0.09, 0.04, 0.03, 0.03, 0.02, np.nan, 0.02],
[np.nan, 0.08, 0.04, 0.09, 0.06, 0.07, 0.18, 0.08, 0.06, 0.06, 0.05, np.nan, 0.05],
[np.nan, 0.15, 0.08, 0.17, 0.11, 0.12, 0.33, 0.16, 0.11, 0.11, 0.09, np.nan, 0.09],
[np.nan, 0.25, 0.14, 0.3 , 0.2 , 0.22, 0.58, 0.27, 0.19, 0.19, 0.16, np.nan, 0.16],
[np.nan, 0.4 , 0.22, 0.48, 0.31, 0.34, 0.92, 0.43, 0.3 , 0.3 , 0.26, np.nan, 0.26],
[np.nan, 0.58, 0.32, 0.69, 0.45, 0.5 , 1.33, 0.62, 0.43, 0.43, 0.37, np.nan, 0.37],
[np.nan, 0.74, 0.41, 0.89, 0.58, 0.63, 1.7 , 0.8 , 0.56, 0.55, 0.47, np.nan, 0.47],
])
answer2 = np.array([
[np.nan, 0.05, 0.03, 0.07, 0.03, 0.03, 0.07, 0.07, 0.04, 0.04, 0.07, np.nan, 0.05],
[np.nan, 0.07, 0.04, 0.09, 0.04, 0.04, 0.1 , 0.1 , 0.06, 0.06, 0.1 , np.nan, 0.08],
[np.nan, 0.1 , 0.05, 0.13, 0.06, 0.06, 0.14, 0.14, 0.08, 0.08, 0.14, np.nan, 0.11],
[np.nan, 0.15, 0.08, 0.18, 0.08, 0.09, 0.2 , 0.2 , 0.11, 0.11, 0.2 , np.nan, 0.15],
[np.nan, 0.21, 0.11, 0.26, 0.12, 0.13, 0.28, 0.28, 0.16, 0.16, 0.28, np.nan, 0.22],
[np.nan, 0.29, 0.15, 0.37, 0.16, 0.18, 0.39, 0.4 , 0.23, 0.23, 0.4 , np.nan, 0.3 ],
[np.nan, 0.42, 0.21, 0.52, 0.23, 0.25, 0.55, 0.56, 0.32, 0.32, 0.56, np.nan, 0.43],
[np.nan, 0.59, 0.3 , 0.74, 0.33, 0.36, 0.78, 0.8 , 0.46, 0.46, 0.79, np.nan, 0.61],
[np.nan, 0.83, 0.42, 1.04, 0.46, 0.51, 1.1 , 1.12, 0.65, 0.65, 1.12, np.nan, 0.86],
])
for i, tmean in enumerate(tmean_sweep.tolist()):
pred1, pred2 = model.daily_respiration(lai, tmean)
pred1 = pred1.round(2)
pred2 = pred2.round(2)
self.assertTrue(np.logical_or(
np.isnan(answer1[i]), np.equal(answer1[i], pred1)).all())
self.assertTrue(np.logical_or(
np.isnan(answer2[i]), np.equal(answer2[i], pred2)).all())
def test_annual_respiration(self):
'Should correctly calculate annual respiration'
lai = np.repeat(np.array((1,)), 365)[:,np.newaxis]\
.repeat(self.pft.size, axis = -1)
tmean = np.repeat(np.array((25,)), 365)[:,np.newaxis]\
.repeat(self.pft.size, axis = -1)
years = np.repeat(np.array((2010,)), 365)[:,np.newaxis]\
.repeat(self.pft.size, axis = -1)
model = MOD17(self.params)
r_leaf, r_froot, r_livewood = model.annual_respiration(
lai, tmean, years)
self.assertTrue(np.equal(r_leaf.round(1), np.array([[
0., 211.5, 117.9, 253.2, 165.4, 180.8, 485.5, 227.1, 158.5,
157.9, 135.4, 0., 135.4
]])).all())
self.assertTrue(np.equal(r_froot.round(1), np.array([[
0., 214.3, 109.6, 269.5, 119.3, 130.4, 285., 290.2, 167.4,
166.9, 289.3, 0., 222.5
]])).all())
self.assertTrue(np.equal(r_livewood.round(1), np.array([[
0., 24.9, 12.3, 20., 15.7, 17.2, 18.9, 3.8, 5.1, 0.9, 0., 0., 0.
]])).all())
def test_annual_npp(self):
'Should correctly calculate annual NPP'
fpar, tmin, vpd, par, lai, tmean, years = self.drivers_annual
model = MOD17(self.params)
npp = model.annual_npp(fpar, tmin, vpd, par, lai, tmean, years)
npp[np.isnan(npp)] = 0
self.assertTrue(np.equal(npp.round(0), np.array([[
0., 1144., 1859., 1137., 1641., 1249., 1342., 943., 1654.,
1610., 1259., 0., 1439.
]])).all())
def test_annual_npp_static_method(self):
'Should calculate annual NPP same when using low-level API'
pft = 1
params = [
self.params[p][pft] for p in MOD17.required_parameters
]
# Get the driver data for the specific PFT
drivers = map(lambda x: x[:,pft], self.drivers_annual)
npp = MOD17._npp(params, *drivers)
self.assertEqual(npp.round(2), 1144.22)
class CoordinateTransformations(unittest.TestCase):
'''
Suite of tests related to coordinate transformations involving the MODIS
Sinusoidal projection.
'''
wgs84_coords = [ # (Longitude, Latitude)
( 30.5, -25.5),
(-50.5, -30.1),
(-10.1, 45.6),
(125.1, 65.5),
]
sinusoidal_coords = [
( 3061072.0, -2835473.8),
(-4858128.1, -3346971.1),
( -785770.9, 5070494.4),
( 5768590.8, 7283275.9),
]
tiles = [ # (h, v)
(20, 11), (13, 12), (17, 4), (23, 2),
]
row_col_500m = [
(1319, 1806), (23, 1513), (1055, 703), (1079, 450)
]
row_col_1000m = [
(659, 902), (11, 756), (527, 351), (539, 224)
]
def test_modis_from_wgs84(self):
'Should correctly determine Sinusoidal coordinates from WGS84 coords.'
for i, pair in enumerate(self.wgs84_coords):
x, y = modis_from_wgs84(pair)
answer = self.sinusoidal_coords[i]
self.assertTrue(
x.round(1) == answer[0] and y.round(1) == answer[1])
# Test vectorized version
self.assertTrue(np.equal(
np.stack(self.sinusoidal_coords, axis = -1),
modis_from_wgs84(np.stack(self.wgs84_coords, axis = -1)).round(1)
).all())
def test_modis_to_wgs84(self):
'Should correctly determine WGS84 coordinates from Sinusoidal coords.'
for i, pair in enumerate(self.sinusoidal_coords):
x, y = modis_to_wgs84(pair)
answer = self.wgs84_coords[i]
self.assertTrue(
x.round(1) == answer[0] and y.round(1) == answer[1])
# Test vectorized version
self.assertTrue(np.equal(
np.stack(self.wgs84_coords, axis = -1),
modis_to_wgs84(np.stack(self.sinusoidal_coords, axis = -1)).round(1)
).all())
def test_modis_row_col_from_wgs84_500m(self):
'Should correctly determine MODIS row, column from WGS84 coordinates'
for i, pair in enumerate(self.wgs84_coords):
r, c = modis_row_col_from_wgs84(pair, nominal = 500)
answer = self.row_col_500m[i]
self.assertTrue(r == answer[0] and c == answer[1])
# Test vectorized version
self.assertTrue(np.equal(
np.stack(self.row_col_500m, axis = -1),
modis_row_col_from_wgs84(np.stack(self.wgs84_coords, axis = -1))
).all())
def test_modis_row_col_from_wgs84_1000m(self):
'Should correctly determine MODIS row, column from WGS84 coordinates'
for i, pair in enumerate(self.wgs84_coords):
r, c = modis_row_col_from_wgs84(pair, nominal = 1000)
answer = self.row_col_1000m[i]
self.assertTrue(r == answer[0] and c == answer[1])
def test_modis_row_col_to_wgs84_500m(self):
'Should correctly determine WGS84 coordinates from MODIS row, column'
for i, pair in enumerate(self.row_col_500m):
x, y = modis_row_col_to_wgs84(
pair, h = self.tiles[i][0], v = self.tiles[i][1], nominal = 500)
x, y = list(map(lambda x: round(x, 1), (x, y)))
answer = self.wgs84_coords[i]
self.assertTrue(x == answer[0] and y == answer[1])
# Test vectorized version
self.assertTrue(np.equal(
np.stack(self.wgs84_coords, axis = -1),
modis_row_col_to_wgs84(
np.stack(self.row_col_500m, axis = -1),
*np.stack(self.tiles, axis = -1)).round(1)
).all())
def test_modis_row_col_to_wgs84_1000m(self):
'Should correctly determine WGS84 coordinates from MODIS row, column'
for i, pair in enumerate(self.row_col_1000m):
x, y = modis_row_col_to_wgs84(
pair, h = self.tiles[i][0], v = self.tiles[i][1], nominal = 1000)
x, y = list(map(lambda x: round(x, 1), (x, y)))
answer = self.wgs84_coords[i]
self.assertTrue(x == answer[0] and y == answer[1])
def test_modis_tile_from_wgs84(self):
'Should correctly determine the MODIS tile from WGS84 coordinates'
for i, pair in enumerate(self.wgs84_coords):
h, v = modis_tile_from_wgs84(pair)
self.assertTrue(h == self.tiles[i][0] and v == self.tiles[i][1])
# Test vectorized version
self.assertTrue(np.equal(
np.stack(self.tiles, axis = -1),
modis_tile_from_wgs84(np.stack(self.wgs84_coords, axis = 1))
).all())
if __name__ == '__main__':
unittest.main()