def test_linear_trend():
# TODO implement a test for nans
data = dsa.from_array(np.random.random([10, 2, 2]), chunks=(10, 1, 1))
t = range(10)
x = range(2)
y = range(2)
data_da = xr.DataArray(
data,
dims=["time", "x", "y"],
coords={
"time": ("time", t),
"x": ("x", x),
"y": ("y", y)
},
)
fit_da = linear_trend(data_da, "time")
for xi in x:
for yi in y:
x_fit = t
y_fit = data[:, xi, yi]
fit = np.array(stats.linregress(x_fit, y_fit))
test = np.array([
fit_da.sel(x=xi, y=yi)[param].data for param in
["slope", "intercept", "r_value", "p_value", "std_err"]
])
assert np.allclose(fit, test)
# Test with other timedim (previously was not caught)
data = dsa.from_array(np.random.random([2, 10, 2]), chunks=(1, 10, 1))
data_da = xr.DataArray(
data,
dims=["x", "time", "y"],
coords={
"x": ("x", x),
"time": ("time", t),
"y": ("y", y)
},
)
fit_da = linear_trend(data_da, "time")
for xi in x:
for yi in y:
x_fit = t
y_fit = data[xi, :, yi]
fit = np.array(stats.linregress(x_fit, y_fit))
test = test = np.array([
fit_da.sel(x=xi, y=yi)[param].data for param in
["slope", "intercept", "r_value", "p_value", "std_err"]
])
assert np.allclose(fit, test)
def slope(da):
"""returns slope per century"""
assert len(da.time) < 300 # make sure the data is annual
assert len(
da.time
) > 95 # make sure this covers most of the century allows for small adjustments
reg = linear_trend(da.sel(time=trend_slice), 'time')
return reg.slope * 100, reg.p_value
def test_linear_trend():
#TODO implement a test for nans
data = dsa.from_array(np.random.random([10, 2, 2]), chunks=(10, 1, 1))
t = range(10)
x = range(2)
y = range(2)
data_da = xr.DataArray(data,
dims=['time', 'x', 'y'],
coords={
'time': ('time', t),
'x': ('x', x),
'y': ('y', y)
})
fit_da = linear_trend(data_da, 'time')
for xi in x:
for yi in y:
x_fit = t
y_fit = data[:, xi, yi]
fit = np.array(stats.linregress(x_fit, y_fit))
test = fit_da.sel(x=xi, y=yi).data
assert np.allclose(fit, test)
# Test with other timedim (previously was not caught)
data = dsa.from_array(np.random.random([2, 10, 2]), chunks=(1, 10, 1))
data_da = xr.DataArray(data,
dims=['x', 'time', 'y'],
coords={
'x': ('x', x),
'time': ('time', t),
'y': ('y', y)
})
fit_da = linear_trend(data_da, 'time')
for xi in x:
for yi in y:
x_fit = t
y_fit = data[xi, :, yi]
fit = np.array(stats.linregress(x_fit, y_fit))
assert np.allclose(fit, fit_da.sel(x=xi, y=yi).data)