def test_median_array_input(window_size=71, length=1000):
pipe = rq.Pipeline(rq.LowPass(window=window_size,
portion=window_size // 2))
x = example_input(length)
y = pipe.feed(x)
z = pd.Series(x).rolling(window_size).median()
assert np.equal(y[window_size:], z.values[window_size:]).all(
) # exact equality, since no arithmetic is done on the numbers
def test_median_scalar_inputs(window_size=3,
length=100): # no interpolation yet
pipe = rq.Pipeline(rq.LowPass(window=window_size,
portion=window_size // 2))
v = example_input(length)
for i, x in enumerate(v):
y = pipe.feed(x)
if i >= window_size:
assert y == np.median(v[(i - window_size + 1):(i + 1)])
def test_basic_nans(window_size=5, length=20):
# make sure the pipeline effectively flushes its contents with NaNs
pipe = rq.Pipeline(rq.LowPass(window=window_size,
portion=window_size // 2))
x = example_input(length)
y = pipe.feed(x)
for i in range(window_size):
pipe.feed(np.nan)
z = pipe.feed(x)
assert np.equal(y, z).all()
def test_fancy_interpolation(
window_size=10,
n_trials=200
): # small windows may be more prone to boundary/edge-condition bugs
for trial in range(n_trials):
x = example_input(window_size)
quantile = np.random.uniform()
alpha, beta = np.random.uniform(size=2)
pipe = rq.Pipeline(
rq.LowPass(window=window_size,
quantile=quantile,
alpha=alpha,
beta=beta))
y = pipe.feed(x)
z = mquantiles(x, quantile, alphap=alpha, betap=beta)
assert z == y[-1]
def test_innocuous_interpolation(window_size=1001, length=10000):
pipe = rq.Pipeline(rq.LowPass(window=window_size, quantile=0.5))
x = example_input(length)
y = pipe.feed(x)
z = pd.Series(x).rolling(window_size).median()
assert np.equal(y[window_size:], z.values[window_size:]).all()
def test_typical_interpolation(window_size=40, quantile=0.2):
x = example_input(window_size) # one window only, due to scipy
pipe = rq.Pipeline(rq.LowPass(window=window_size, quantile=quantile))
y = pipe.feed(x)
z = mquantiles(x, quantile, alphap=1, betap=1)
assert z == y[-1]