def get_climate(self, heights, year=None):
"""Average climate information at given heights.
Note that prcp is corrected with the precipitation factor and that
all other biases (precipitation, temp) are applied
Returns
-------
(temp, tempformelt, prcp, prcpsol)
"""
yrs = monthly_timeseries(self.years[0], self.years[-1],
include_last_year=True)
heights = np.atleast_1d(heights)
nh = len(heights)
shape = (len(yrs), nh)
temp = np.zeros(shape)
tempformelt = np.zeros(shape)
prcp = np.zeros(shape)
prcpsol = np.zeros(shape)
for i, yr in enumerate(yrs):
t, tm, p, ps = self.mbmod.get_monthly_climate(heights, year=yr)
temp[i, :] = t
tempformelt[i, :] = tm
prcp[i, :] = p
prcpsol[i, :] = ps
# Note that we do not weight for number of days per month - bad
return (np.mean(temp, axis=0),
np.mean(tempformelt, axis=0) * 12,
np.mean(prcp, axis=0) * 12,
np.mean(prcpsol, axis=0) * 12)
def get_climate(self, heights, year=None):
"""Average climate information at given heights.
Note that prcp is corrected with the precipitation factor and that
all other biases (precipitation, temp) are applied
Returns
-------
(temp, tempformelt, prcp, prcpsol)
"""
yrs = monthly_timeseries(self.years[0],
self.years[-1],
include_last_year=True)
heights = np.atleast_1d(heights)
nh = len(heights)
shape = (len(yrs), nh)
temp = np.zeros(shape)
tempformelt = np.zeros(shape)
prcp = np.zeros(shape)
prcpsol = np.zeros(shape)
for i, yr in enumerate(yrs):
t, tm, p, ps = self.mbmod.get_monthly_climate(heights, year=yr)
temp[i, :] = t
tempformelt[i, :] = tm
prcp[i, :] = p
prcpsol[i, :] = ps
# Note that we do not weight for number of days per month - bad
return (np.mean(temp, axis=0), np.mean(tempformelt, axis=0) * 12,
np.mean(prcp, axis=0) * 12, np.mean(prcpsol, axis=0) * 12)
def test_date_to_year(self):
r = utils.date_to_year(0, 1)
self.assertEqual(r, 0)
r = utils.date_to_year(1, 1)
self.assertEqual(r, 1)
r = utils.date_to_year([0, 1], [1, 1])
np.testing.assert_array_equal(r, [0, 1])
yr = utils.date_to_year([1998, 1998], [6, 7])
y, m = utils.year_to_date(yr)
np.testing.assert_array_equal(y, [1998, 1998])
np.testing.assert_array_equal(m, [6, 7])
yr = utils.date_to_year([1998, 1998], [2, 3])
y, m = utils.year_to_date(yr)
np.testing.assert_array_equal(y, [1998, 1998])
np.testing.assert_array_equal(m, [2, 3])
time = pd.date_range('1/1/1800', periods=300*12, freq='MS')
yr = utils.date_to_year(time.year, time.month)
y, m = utils.year_to_date(yr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
myr = utils.monthly_timeseries(1800, 2099)
y, m = utils.year_to_date(myr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
myr = utils.monthly_timeseries(1800, ny=300)
y, m = utils.year_to_date(myr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
time = pd.period_range('0001-01', '3000-12', freq='M')
myr = utils.monthly_timeseries(1, 3000)
y, m = utils.year_to_date(myr)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
with self.assertRaises(ValueError):
utils.monthly_timeseries(1)
