def test_365_day(self):
# 365_day calendar
# generate test DataArray
units = "days since 2000-01-01 00:00"
time_365 = cftime.num2date(np.arange(0, 1 * 365), units, "365_day")
da_365 = xr.DataArray(np.arange(time_365.size),
coords=[time_365],
dims="time",
name="data")
units = "days since 2001-01-01 00:00"
time_std = cftime.num2date(np.arange(0, 1 * 365), units, "standard")
da_std = xr.DataArray(np.arange(time_std.size),
coords=[time_std],
dims="time",
name="data")
for freq in "YS MS QS-DEC".split():
resampler = da_std.resample(time=freq)
grouper = daily_downsampler(da_365, freq=freq)
x1 = resampler.mean()
x2 = grouper.mean()
# add time coords to x2 and change dimension tags to time
time1 = daily_downsampler(da_365.time, freq=freq).first()
x2.coords["time"] = ("tags", time1.values)
x2 = x2.swap_dims({"tags": "time"})
x2 = x2.sortby("time")
# assert the values of resampler of non leap year with standard calendar
# is identical to grouper
assert np.allclose(x1.values, x2.values)
def test_360_days(self):
#
# 360_day calendar
#
units = "days since 2000-01-01 00:00"
time_360 = cftime.num2date(np.arange(0, 360), units, "360_day")
da_360 = xr.DataArray(np.arange(1, time_360.size + 1),
coords=[time_360],
dims="time",
name="data")
for freq in "YS MS QS-DEC".split():
grouper = daily_downsampler(da_360, freq=freq)
x2 = grouper.mean()
# add time coords to x2 and change dimension tags to time
time1 = daily_downsampler(da_360.time, freq=freq).first()
x2.coords["time"] = ("tags", time1.values)
x2 = x2.swap_dims({"tags": "time"})
x2 = x2.sortby("time")
# assert grouper values == expected values
target_year = 180.5
target_month = [n * 30 + 15.5 for n in range(0, 12)]
target_season = [30.5] + [(n - 1) * 30 + 15.5
for n in [4, 7, 10, 12]]
target = {
"YS": target_year,
"MS": target_month,
"QS-DEC": target_season
}[freq]
assert np.allclose(x2.values, target)
def test_360_days(self):
#
# 360_day calendar
#
units = 'days since 2000-01-01 00:00'
time_360 = cftime.num2date(np.arange(0, 360), units, '360_day')
da_360 = xr.DataArray(np.arange(1, time_360.size + 1), coords=[time_360], dims='time', name='data')
for freq in 'YS MS QS-DEC'.split():
grouper = daily_downsampler(da_360, freq=freq)
x2 = grouper.mean()
# add time coords to x2 and change dimension tags to time
time1 = daily_downsampler(da_360.time, freq=freq).first()
x2.coords['time'] = ('tags', time1.values)
x2 = x2.swap_dims({'tags': 'time'})
x2 = x2.sortby('time')
# assert grouper values == expected values
target_year = 180.5
target_month = [n * 30 + 15.5 for n in range(0, 12)]
target_season = [30.5] + [(n - 1) * 30 + 15.5 for n in [4, 7, 10, 12]]
target = {'YS': target_year, 'MS': target_month, 'QS-DEC': target_season}[freq]
assert (np.allclose(x2.values, target))
def test_365_day(self):
# 365_day calendar
# generate test DataArray
units = 'days since 2000-01-01 00:00'
time_365 = cftime.num2date(np.arange(0, 1 * 365), units, '365_day')
da_365 = xr.DataArray(np.arange(time_365.size), coords=[time_365], dims='time', name='data')
units = 'days since 2001-01-01 00:00'
time_std = cftime.num2date(np.arange(0, 1 * 365), units, 'standard')
da_std = xr.DataArray(np.arange(time_std.size), coords=[time_std], dims='time', name='data')
for freq in 'YS MS QS-DEC'.split():
resampler = da_std.resample(time=freq)
grouper = daily_downsampler(da_365, freq=freq)
x1 = resampler.mean()
x2 = grouper.mean()
# add time coords to x2 and change dimension tags to time
time1 = daily_downsampler(da_365.time, freq=freq).first()
x2.coords['time'] = ('tags', time1.values)
x2 = x2.swap_dims({'tags': 'time'})
x2 = x2.sortby('time')
# assert the values of resampler of non leap year with standard calendar
# is identical to grouper
assert (np.allclose(x1.values, x2.values))
def test_std_calendar(self):
# standard calendar
# generate test DataArray
time_std = pd.date_range('2000-01-01', '2000-12-31', freq='D')
da_std = xr.DataArray(np.arange(time_std.size), coords=[time_std], dims='time')
for freq in 'YS MS QS-DEC'.split():
resampler = da_std.resample(time=freq)
grouper = daily_downsampler(da_std, freq=freq)
x1 = resampler.mean()
x2 = grouper.mean()
# add time coords to x2 and change dimension tags to time
time1 = daily_downsampler(da_std.time, freq=freq).first()
x2.coords['time'] = ('tags', time1.values)
x2 = x2.swap_dims({'tags': 'time'})
x2 = x2.sortby('time')
# assert the values of resampler and grouper are the same
assert (np.allclose(x1.values, x2.values))
def test_std_calendar(self):
# standard calendar
# generate test DataArray
time_std = pd.date_range("2000-01-01", "2000-12-31", freq="D")
da_std = xr.DataArray(np.arange(time_std.size),
coords=[time_std],
dims="time")
for freq in "YS MS QS-DEC".split():
resampler = da_std.resample(time=freq)
grouper = daily_downsampler(da_std, freq=freq)
x1 = resampler.mean()
x2 = grouper.mean()
# add time coords to x2 and change dimension tags to time
time1 = daily_downsampler(da_std.time, freq=freq).first()
x2.coords["time"] = ("tags", time1.values)
x2 = x2.swap_dims({"tags": "time"})
x2 = x2.sortby("time")
# assert the values of resampler and grouper are the same
assert np.allclose(x1.values, x2.values)
