def test_05(self):
x = np.arange(10).astype(dtype=np.float)
y = np.arange(10).astype(dtype=np.float)
p = ndpolyfit(x, y, deg=1, meta=False)
np.testing.assert_almost_equal(p, [1.0, 0.0])
def test_02(self):
x = xr.DataArray(np.arange(10).astype(dtype=np.float))
y = xr.DataArray(np.arange(10).astype(dtype=np.float))
p = ndpolyfit(x, y, deg=1)
np.testing.assert_almost_equal(p, [1.0, 0.0])
def test_00(self):
x = np.arange(10).astype(dtype=np.float).tolist()
y = np.arange(10).astype(dtype=np.float).tolist()
p = ndpolyfit(x, y, deg=1)
np.testing.assert_almost_equal(p, [1.0, 0.0])
def test_6(self):
x = np.arange(10).astype(dtype=np.float)
y = np.concatenate((np.arange(10).astype(dtype=np.float).reshape(
(-1, 1)), np.arange(10).astype(dtype=np.float).reshape((-1, 1))),
axis=1)
y[5, :] = 999
p = ndpolyfit(x, y, deg=1, missing_value=999)
np.testing.assert_almost_equal(p, [[1.0, 1.0], [0.0, 0.0]])
def test_04(self):
x = xr.DataArray(np.arange(10).astype(dtype=np.float))
y = xr.DataArray(np.arange(10).astype(dtype=np.float))
y.attrs["attr1"] = 1
y.attrs["attr2"] = 2
p = ndpolyfit(x, y, deg=1, meta=False)
np.testing.assert_almost_equal(p, [1.0, 0.0])
self.assertFalse("attr1" in p.attrs)
self.assertFalse("attr2" in p.attrs)
def test_04(self):
# Creating synthetic data
x = np.linspace(-8 * np.pi, 8 * np.pi, 33, dtype=np.float64)
y0 = 1.0 * x
y1 = np.sin(x)
y = np.tile((y0 + y1).reshape((1, -1, 1, 1)), (2, 1, 3, 4))
p = ndpolyfit(x, y, deg=1, axis=1)
y_trend = ndpolyval(p, x, axis=1)
y_detrended = detrend(y, x=x, axis=1)
np.testing.assert_almost_equal(y_detrended + y_trend, y)
def test_02(self):
# Creating synthetic data
x = np.linspace(-8 * np.pi, 8 * np.pi, 33, dtype=np.float64)
y0 = 1.0 * x
y1 = np.sin(x)
y = y0 + y1
p = ndpolyfit(np.arange(x.size), y, deg=1)
y_trend = ndpolyval(p, np.arange(x.size))
y_detrended = detrend(y)
np.testing.assert_almost_equal(y_detrended + y_trend, y)
def test_03(self):
x = xr.DataArray(np.arange(10).astype(dtype=np.float))
y = xr.DataArray(np.arange(10).astype(dtype=np.float))
y.attrs["attr1"] = 1
y.attrs["attr2"] = 2
p = ndpolyfit(x, y, deg=1)
np.testing.assert_almost_equal(p, [1.0, 0.0])
self.assertTrue("attr1" in p.attrs)
self.assertTrue("attr2" in p.attrs)
self.assertEqual(1, p.attrs["attr1"])
self.assertEqual(2, p.attrs["attr2"])
def test_03(self):
# Creating synthetic data
x = np.linspace(-8 * np.pi, 8 * np.pi, 33, dtype=np.float64)
y0 = 1.0 * x
y1 = np.sin(x)
y = y0 + y1
p = ndpolyfit(x, y, deg=1)
y_trend = ndpolyval(p, x)
y_detrended = detrend(y, x=x, return_info=False)
np.testing.assert_almost_equal(y_detrended + y_trend, y)
np.testing.assert_equal({}, y_detrended.attrs)
def test_8(self):
x = xr.DataArray(np.arange(-10, 10).astype(dtype=np.float))
max_dim = 3
max_dim_size = 5
for i in range(3):
expected_p = np.random.randint(-10, 10, size=2)
while expected_p[0] == 0:
expected_p = np.random.randint(-10, 10, size=2)
y = expected_p[0] * x + expected_p[1]
other_dims = np.random.randint(1, max_dim_size,
np.random.randint(1, max_dim))
axis = np.random.randint(0, other_dims.ndim + 1)
y = xr.DataArray(
np.moveaxis(np.tile(y, (*other_dims, 1)), -1, axis))
expected_p = np.moveaxis(np.tile(expected_p, (*other_dims, 1)), -1,
axis)
y = da.from_array(y, chunks=np.ones((y.ndim, )))
actual_p = ndpolyfit(x, y, deg=1, axis=axis)
np.testing.assert_almost_equal(expected_p, actual_p)
def test_7(self):
x = xr.DataArray(np.arange(-10, 10).astype(dtype=np.float))
max_dim = 6
max_dim_size = 11
for i in range(50):
expected_p = np.random.randint(-10, 10, size=2)
while expected_p[0] == 0:
expected_p = np.random.randint(-10, 10, size=2)
y = expected_p[0] * x + expected_p[1]
y[np.random.randint(0, 10, size=2)] = np.nan
other_dims = np.random.randint(1, max_dim_size,
np.random.randint(1, max_dim))
axis = np.random.randint(0, other_dims.ndim + 1)
y = xr.DataArray(
np.moveaxis(np.tile(y, (*other_dims, 1)), -1, axis))
expected_p = np.moveaxis(np.tile(expected_p, (*other_dims, 1)), -1,
axis)
actual_p = ndpolyfit(x, y, deg=1, axis=axis)
np.testing.assert_almost_equal(expected_p, actual_p)
