def test_move_exp_nanmean_numeric():
array = np.array([10, 0, np.nan, 10])
result = move_exp_nanmean(array, alpha=0.5)
expected = np.array([10.0, 3.3333333, 3.3333333, 8.1818182])
assert_almost_equal(result, expected)
result = move_exp_nanmean(array, alpha=0.25)
expected = np.array([10.0, 4.2857143, 4.2857143, 7.1653543])
assert_almost_equal(result, expected)
def test_move_exp_nanmean(rand_array, alpha):
array = rand_array[0]
expected = pd.Series(array).ewm(alpha=alpha).mean()
result = move_exp_nanmean(array, alpha)
assert_almost_equal(expected, result)
def test_move_exp_nanmean(rand_array, alpha):
array = rand_array[0]
expected = pd.Series(array).ewm(alpha=alpha).mean()
result = move_exp_nanmean(array, alpha)
assert_almost_equal(expected, result)
def move_exp_nanmean(array, *, axis, alpha):
if isinstance(array, dask_array_type):
raise TypeError("rolling_exp is not currently support for dask arrays")
import numbagg
if axis == ():
return array.astype(np.float64)
else:
return numbagg.move_exp_nanmean(array, axis=axis, alpha=alpha)
def move_exp_nanmean(array, *, axis, alpha):
if is_duck_dask_array(array):
raise TypeError("rolling_exp is not currently support for dask-like arrays")
import numbagg
# No longer needed in numbag > 0.2.0; remove in time
if axis == ():
return array.astype(np.float64)
else:
return numbagg.move_exp_nanmean(array, axis=axis, alpha=alpha)
def test_move_exp_nanmean_2d(rand_array):
expected = pd.DataFrame(rand_array).T.ewm(alpha=0.1).mean().T
result = move_exp_nanmean(rand_array, 0.1)
assert_almost_equal(expected, result)
def test_tuple_axis_arg(rand_array):
result = move_exp_nanmean(rand_array, 0.1, axis=())
assert_equal(result, rand_array)
def test_move_exp_nanmean_2d(rand_array):
expected = pd.DataFrame(rand_array).T.ewm(alpha=0.1).mean().T
result = move_exp_nanmean(rand_array, 0.1)
assert_almost_equal(expected, result)
