def test_03(self):
for i in range(5):
# these limits are just to limit the time it takes to test.
deg = np.random.randint(0, 4) # Maximum polynomial degree = 3
ndim = np.random.randint(1, 6) # Maximim 5-Dimensional array
axis = np.random.randint(0, ndim)
max_dim_size = 11 # The maximum number of elements along one dimension
if ndim > 1:
tmp_shape = np.random.randint(1, max_dim_size, size=ndim)
data_shape = tmp_shape.copy()
data_shape[axis] = np.random.randint(1, max_dim_size)
data_shape = tuple(data_shape)
p_shape = tmp_shape.copy()
p_shape[axis] = deg + 1
p_shape = tuple(p_shape)
else:
data_shape = (np.random.randint(1, max_dim_size), )
p_shape = (deg + 1, )
p = np.random.random(size=p_shape)
x_nparr = np.random.random(size=data_shape)
x = da.from_array(x_nparr, chunks=np.ones((ndim, )))
y_expected = np.zeros(data_shape)
for i in range(deg + 1):
y_expected += p.take([i], axis=axis) * \
np.power(x_nparr, deg - i)
y_actual = ndpolyval(p, x, axis=axis)
np.testing.assert_almost_equal(y_actual.data, y_expected.data)
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):
# 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)