def test_tg10p_simple(self, tas_series):
i = 366
arr = np.asarray(np.arange(i), 'float')
tas = tas_series(arr, start='1/1/2000')
tasC = tas.copy()
tasC -= K2C
tasC.attrs['units'] = 'C'
t10 = percentile_doy(tas, per=.1)
# create cold spell in june
tas[175:180] = 1
tasC[175:180] = 1 - K2C
out = temp.tg10p(tas, t10, freq='MS')
outC = temp.tg10p(tasC, t10, freq='MS')
np.testing.assert_array_equal(out, outC)
assert out[0] == 1
assert out[5] == 5
# nan treatment
tas[33] = np.nan
tasC[33] = np.nan
out = temp.tg10p(tas, t10, freq='MS')
outC = temp.tg10p(tasC, t10, freq='MS')
np.testing.assert_array_equal(out, outC)
assert out[0] == 1
assert np.isnan(out[1])
assert out[5] == 5
def test_simple(self, tas_series):
a = np.zeros(365) + 17
a[:7] += [-3, -2, -1, 0, 1, 2, 3]
da = tas_series(a + K2C)
out = xci.heating_degree_days(da)
np.testing.assert_array_equal(out[:1], 6)
np.testing.assert_array_equal(out[1:], 0)
def test_missing_days(self, tas_series):
a = np.arange(360.0)
a[5:10] = np.nan
ts = tas_series(a)
out = checks.missing_any(ts, freq="MS")
assert out[0]
assert not out[1]
def test_tg90p_simple(self, tas_series):
i = 366
arr = np.asarray(np.arange(i), "float")
tas = tas_series(arr, start="1/1/2000")
tasC = tas.copy()
tasC -= K2C
tasC.attrs["units"] = "C"
t90 = percentile_doy(tas, per=0.1)
# create cold spell in june
tas[175:180] = 1
tasC[175:180] = 1 - K2C
out = atmos.tg90p(tas, t90, freq="MS")
outC = atmos.tg90p(tasC, t90, freq="MS")
np.testing.assert_array_equal(out, outC)
assert out[0] == 30
assert out[1] == 29
assert out[5] == 25
# nan treatment
tas[33] = np.nan
tasC[33] = np.nan
out = atmos.tg90p(tas, t90, freq="MS")
outC = atmos.tg90p(tasC, t90, freq="MS")
np.testing.assert_array_equal(out, outC)
assert out[0] == 30
assert np.isnan(out[1])
assert out[5] == 25
def test_no_start(self, tas_series):
tg = np.zeros(365) - 1
tg = tas_series(tg, start='1/1/2000')
out = xci.freshet_start(tg)
np.testing.assert_equal(out, [
np.nan,
])
def test_attrs(self, tas_series):
import datetime as dt
a = tas_series(np.arange(360.))
ind = UniIndTemp()
txm = ind(a, freq='YS')
assert txm.cell_methods == 'time: mean within days time: mean within years'
assert '{:%Y-%m-%d %H}'.format(dt.datetime.now()) in txm.attrs['history']
assert txm.name == "tmin0"
def test_simple(self, tas_series):
a = np.zeros(365) + K2C
a[10:20] -= 15 # 10 days
a[40:43] -= 50 # too short -> 0
a[80:100] -= 30 # at the end and beginning
ts = tas_series(a)
out = temp.cold_spell_days(ts, thresh='-10 C', freq='MS')
np.testing.assert_array_equal(out, [10, 0, 12, 8, 0, 0, 0, 0, 0, 0, 0, 0])
def test_temp_unit_conversion(self, tas_series):
a = tas_series(np.arange(360.))
ind = UniIndTemp()
txk = ind(a, freq='YS')
ind.units = 'degC'
txc = ind(a, freq='YS')
np.testing.assert_array_almost_equal(txk, txc + 273.15)
def test_simple(self, pr_series, tas_series):
pr = np.ones(450)
pr = pr_series(pr, start='12/1/2000')
tas = np.zeros(450) - 1
tas[10:20] += 10
tas = tas_series(tas + K2C, start='12/1/2000')
out = xci.winter_rain_ratio(pr, tas=tas)
np.testing.assert_almost_equal(out, [10. / (31 + 31 + 28), 0])
def test_single_year(self, tas_series):
a = np.zeros(366) + K2C
ts = tas_series(a, start='1/1/2000')
tt = (ts.time.dt.month >= 5) & (ts.time.dt.month <= 8)
offset = np.random.uniform(low=5.5, high=23, size=(tt.sum().values,))
ts[tt] = ts[tt] + offset
out = temp.growing_season_length(ts)
np.testing.assert_array_equal(out, tt.sum())
def test_convert_units(self, tas_series):
a = np.zeros(365)
a[10:20] -= 15 # 10 days
a[40:43] -= 50 # too short -> 0
a[80:100] -= 30 # at the end and beginning
ts = tas_series(a)
ts.attrs["units"] = "C"
out = atmos.cold_spell_days(ts, thresh="-10 C", freq="MS")
np.testing.assert_array_equal(out,
[10, 0, 12, 8, 0, 0, 0, 0, 0, 0, 0, 0])
def test_simple(self, tas_series):
a = np.zeros(365)
a[10:20] -= 15 # 10 days
a[40:43] -= 50 # too short -> 0
a[80:100] -= 30 # at the end and beginning
da = tas_series(a + K2C)
out = xci.cold_spell_days(da, thresh="-10. C", freq="M")
np.testing.assert_array_equal(out, [10, 0, 12, 8, 0, 0, 0, 0, 0, 0, 0, 0])
assert out.units == "days"
def test_nan_presence(self, tas_series):
a = np.zeros(365) + K2C
a[10:20] -= 15 # 10 days
a[40:43] -= 50 # too short -> 0
a[80:100] -= 30 # at the end and beginning
a[-1] = np.nan
ts = tas_series(a)
out = atmos.cold_spell_days(ts, thresh="-10 C", freq="MS")
np.testing.assert_array_equal(
out, [10, 0, 12, 8, 0, 0, 0, 0, 0, 0, 0, np.nan])
def test_simple(self, pr_series, tas_series):
pr = np.zeros(100)
pr[10:20] = 1
pr = pr_series(pr)
tas = np.zeros(100)
tas[:14] -= 20
tas[14:] += 10
tas = tas_series(tas + K2C)
out = xci.liquid_precip_ratio(pr, tas=tas, freq="M")
np.testing.assert_almost_equal(out[:1], [0.6])
def test_convert_units(self, tas_series):
a = np.zeros(366)
ts = tas_series(a, start="1/1/2000")
ts.attrs["units"] = "C"
tt = (ts.time.dt.month >= 5) & (ts.time.dt.month <= 8)
offset = np.random.uniform(low=5.5, high=23, size=(tt.sum().values, ))
ts[tt] = ts[tt] + offset
out = atmos.growing_season_length(ts)
np.testing.assert_array_equal(out, tt.sum())
def test_simple(self, tas_series, pr_series):
tas = np.zeros(30) - 1
pr = np.zeros(30)
tas[10] += 5
pr[10] += 2
tas = tas_series(tas + K2C)
pr = pr_series(pr / 3600 / 24)
out = xci.rain_on_frozen_ground_days(pr, tas, freq="MS")
assert out[0] == 1
def test_attrs(self, tas_series):
import datetime as dt
a = tas_series(np.arange(360.0))
ind = UniIndTemp()
txm = ind(a, thresh=5, freq="YS")
assert txm.cell_methods == "time: mean within days time: mean within years"
assert "{:%Y-%m-%d %H}".format(
dt.datetime.now()) in txm.attrs["history"]
assert "tmin(da, thresh=5, freq='YS')" in txm.attrs["history"]
assert "xclim version: {}.".format(__version__) in txm.attrs["history"]
assert txm.name == "tmin5"
def test_consecutive_rain(self, tas_series, pr_series):
tas = np.zeros(30) - 1
pr = np.zeros(30)
tas[10:16] += 5
pr[10:16] += 5
tas = tas_series(tas + K2C)
pr = pr_series(pr)
out = xci.rain_on_frozen_ground_days(pr, tas, freq='MS')
assert out[0] == 1
def test_tn10p_simple(self, tas_series):
i = 366
tas = np.array(range(i))
tas = tas_series(tas, start='1/1/2000')
t10 = percentile_doy(tas, per=.1)
# create cold spell in june
tas[175:180] = 1
out = xci.tn10p(tas, t10, freq='MS')
assert out[0] == 1
assert out[5] == 5
def test_small_rain(self, tas_series, pr_series):
tas = np.zeros(30) - 1
pr = np.zeros(30)
tas[10] += 5
pr[10] += .5
tas = tas_series(tas + K2C)
pr = pr_series(pr / 3600 / 24)
out = xci.rain_on_frozen_ground_days(pr, tas, freq='MS')
assert out[0] == 0
def test_tg90p_simple(self, tas_series):
i = 366
tas = np.array(range(i))
tas = tas_series(tas, start="1/1/2000")
t90 = percentile_doy(tas, per=0.1)
# create cold spell in june
tas[175:180] = 1
out = xci.tg90p(tas, t90, freq="MS")
assert out[0] == 30
assert out[1] == 29
assert out[5] == 25
def test_nan_presence(self, tas_series):
a = np.zeros(366)
a[50] = np.nan
ts = tas_series(a, start='1/1/2000')
ts.attrs['units'] = 'C'
tt = (ts.time.dt.month >= 5) & (ts.time.dt.month <= 8)
offset = np.random.uniform(low=5.5, high=23, size=(tt.sum().values,))
ts[tt] = ts[tt] + offset
out = temp.growing_season_length(ts)
np.testing.assert_array_equal(out, [np.nan])
def test_multiyear(self, tas_series):
a = np.zeros(366 * 10)
ts = tas_series(a, start='1/1/2000')
ts.attrs['units'] = 'C'
tt = (ts.time.dt.month >= 5) & (ts.time.dt.month <= 8)
offset = np.random.uniform(low=5.5, high=23, size=(tt.sum().values,))
ts[tt] = ts[tt] + offset
out = temp.growing_season_length(ts)
np.testing.assert_array_equal(out[3], tt[0:366].sum().values)
def test_deprecation(self, tas_series):
with pytest.warns(FutureWarning):
out = utils.convert_units_to(0, units.K)
assert out == 273.15
out = utils.convert_units_to(10, units.mm / units.day, context='hydro')
assert out == 10
with pytest.warns(FutureWarning):
tas = tas_series(np.arange(365), start='1/1/2001')
out = ind.tx_days_above(tas, 30)
out1 = ind.tx_days_above(tas, '30 degC')
out2 = ind.tx_days_above(tas, '303.15 K')
np.testing.assert_array_equal(out, out1)
np.testing.assert_array_equal(out, out2)
def test_simple(self, tas_series):
tg = np.zeros(365) - 1
w = 5
i = 10
tg[i:i + w - 1] += 6 # too short
i = 20
tg[i:i + w] += 6 # ok
i = 30
tg[i:i + w + 1] += 6 # Second valid condition, should be ignored.
tg = tas_series(tg + K2C, start='1/1/2000')
out = xci.freshet_start(tg, window=w)
assert out[0] == tg.indexes['time'][20].dayofyear
def test_simple(self, tas_series):
# test for different growing length
# generate 5 years of data
a = np.zeros(366 * 2 + 365 * 3)
tas = tas_series(a, start='2000/1/1')
# 2000 : no growing season
# 2001 : growing season all year
d1 = '27-12-2000'
d2 = '31-12-2001'
buffer = tas.sel(time=slice(d1, d2))
tas = tas.where(~tas.time.isin(buffer.time), 280)
# 2002 : growing season in June only
d1 = '6-1-2002'
d2 = '6-10-2002'
buffer = tas.sel(time=slice(d1, d2))
tas = tas.where(~tas.time.isin(buffer.time), 280)
#
# comment:
# correct answer should be 10 (i.e. there are 10 days
# with tas > 5 degC) but current definition imposes end
# of growing season to be equal or later than July 1st.
# growing season in Aug only
d1 = '8-1-2003'
d2 = '8-10-2003'
buffer = tas.sel(time=slice(d1, d2))
tas = tas.where(~tas.time.isin(buffer.time), 280)
# growing season from June to end of July
d1 = '6-1-2004'
d2 = '7-31-2004'
buffer = tas.sel(time=slice(d1, d2))
tas = tas.where(~tas.time.isin(buffer.time), 280)
gsl = xci.growing_season_length(tas)
target = [0, 365, 25, 10, 61]
np.testing.assert_array_equal(gsl, target)
def test_simple(self, tas_series):
tas = tas_series(np.arange(365), start='1/1/2001')
p1 = percentile_doy(tas, window=5, per=.5)
assert p1.sel(dayofyear=3).data == 2
assert p1.attrs['units'] == 'K'
def test_simple(self, tas_series):
a = np.zeros(365)
a[0] = 5 # default thresh at 4
da = tas_series(a + K2C)
assert xci.growing_degree_days(da)[0] == 1
def test_simple(self, tas_series):
ts = tas_series(np.arange(365))
out = utils.threshold_count(ts, '<', 50, 'Y')
np.testing.assert_array_equal(out, [50, 0])
def test_simple(self, tas_series):
tas = tas_series(np.arange(365), start="1/1/2001")
p1 = percentile_doy(tas, window=5, per=0.5)
assert p1.sel(dayofyear=3).data == 2
assert p1.attrs["units"] == "K"