def test_glacier_melt_step_function(self):
dtf = 6.0
temperature = 10.0
area_m2 = 3600.0/0.001
sca = 0.5 * area_m2
gf = 1.0 * area_m2
m = glacier_melt_step(dtf, temperature, sca, gf)
self.assertAlmostEqual(1.25, m) # mm/h
self.assertAlmostEqual(0.0, glacier_melt_step(dtf, 0.0, sca, gf),5, 'no melt at 0.0 deg C')
self.assertAlmostEqual(0.0, glacier_melt_step(dtf, 10.0, 0.7, 0.6),5, 'no melt when glacier is covered')
def test_glacier_melt_step_function(self):
dtf = 6.0
temperature = 10.0
area_m2 = 3600.0 / 0.001
sca = 0.5 * area_m2
gf = 1.0 * area_m2
m = glacier_melt_step(dtf, temperature, sca, gf)
self.assertAlmostEqual(1.25, m) # mm/h
self.assertAlmostEqual(0.0, glacier_melt_step(dtf, 0.0, sca, gf), 5,
'no melt at 0.0 deg C')
self.assertAlmostEqual(0.0, glacier_melt_step(dtf, 10.0, 0.7, 0.6), 5,
'no melt when glacier is covered')
def test_glacier_melt_ts_m3s(self):
utc = Calendar()
t0 = utc.time(2016, 10, 1)
dt = deltahours(1)
n = 240
ta = TimeAxis(t0, dt, n)
area_m2 = 487 * 1000 * 1000 # Jostedalsbreen, largest i Europe
temperature = TimeSeries(ta=ta,
fill_value=10.0,
point_fx=fx_policy.POINT_AVERAGE_VALUE)
sca_values = dv.from_numpy(np.linspace(area_m2 * 1.0, 0.0, num=n))
sca = TimeSeries(ta=ta,
values=sca_values,
point_fx=fx_policy.POINT_AVERAGE_VALUE)
gf = 1.0 * area_m2
dtf = 6.0
melt_m3s = create_glacier_melt_ts_m3s(
temperature, sca, gf,
dtf) # Here we get back a melt_ts, that we can do ts-stuff with
self.assertIsNotNone(melt_m3s)
full_melt_m3s = glacier_melt_step(dtf, 10.0, 0.0, gf)
expected_melt_m3s = np.linspace(0.0, full_melt_m3s, num=n)
assert_array_almost_equal(expected_melt_m3s,
melt_m3s.values.to_numpy(), 4)
# Just to check we can work with the result as a ts in all aspects
mx2 = melt_m3s * 2.0
emx2 = expected_melt_m3s * 2.0
assert_array_almost_equal(emx2, mx2.values.to_numpy(), 4)
def test_glacier_melt_ts_m3s(self):
utc = Calendar()
t0 = utc.time(2016,10,1)
dt = deltahours(1)
n = 240
ta = Timeaxis(t0, dt, n)
area_m2 = 487*1000*1000 # Jostedalsbreen, largest i Europe
temperature = Timeseries(ta=ta, fill_value=10.0, point_fx=fx_policy.POINT_AVERAGE_VALUE)
sca_values = dv.from_numpy(np.linspace(area_m2*1.0,0.0,num=n))
sca = Timeseries(ta=ta, values=sca_values, point_fx=fx_policy.POINT_AVERAGE_VALUE)
gf = 1.0 *area_m2
dtf = 6.0
melt_m3s = create_glacier_melt_ts_m3s(temperature, sca, gf, dtf) # Here we get back a melt_ts, that we can do ts-stuff with
self.assertIsNotNone(melt_m3s)
full_melt_m3s = glacier_melt_step(dtf, 10.0, 0.0, gf)
expected_melt_m3s = np.linspace(0.0,full_melt_m3s,num=n)
assert_array_almost_equal(expected_melt_m3s,melt_m3s.values.to_numpy(),4)
# Just to check we can work with the result as a ts in all aspects
mx2 = melt_m3s*2.0
emx2 = expected_melt_m3s * 2.0;
assert_array_almost_equal(emx2, mx2.values.to_numpy(), 4)
