def test_fboxplot_rainbowplot(close_figures):
# Test fboxplot and rainbowplot together, is much faster.
def harmfunc(t):
"""Test function, combination of a few harmonic terms."""
# Constant, 0 with p=0.9, 1 with p=1 - for creating outliers
ci = int(np.random.random() > 0.9)
a1i = np.random.random() * 0.05
a2i = np.random.random() * 0.05
b1i = (0.15 - 0.1) * np.random.random() + 0.1
b2i = (0.15 - 0.1) * np.random.random() + 0.1
func = (1 - ci) * (a1i * np.sin(t) + a2i * np.cos(t)) + \
ci * (b1i * np.sin(t) + b2i * np.cos(t))
return func
np.random.seed(1234567)
# Some basic test data, Model 6 from Sun and Genton.
t = np.linspace(0, 2 * np.pi, 250)
data = [harmfunc(t) for _ in range(20)]
# fboxplot test
fig = plt.figure()
ax = fig.add_subplot(111)
_, depth, ix_depth, ix_outliers = fboxplot(data, wfactor=2, ax=ax)
ix_expected = np.array(
[13, 4, 15, 19, 8, 6, 3, 16, 9, 7, 1, 5, 2, 12, 17, 11, 14, 10, 0, 18])
assert_equal(ix_depth, ix_expected)
ix_expected2 = np.array([2, 11, 17, 18])
assert_equal(ix_outliers, ix_expected2)
# rainbowplot test (re-uses depth variable)
xdata = np.arange(data[0].size)
fig = rainbowplot(data, xdata=xdata, depth=depth, cmap=plt.cm.rainbow)
def test_fboxplot_rainbowplot(close_figures):
# Test fboxplot and rainbowplot together, is much faster.
def harmfunc(t):
"""Test function, combination of a few harmonic terms."""
# Constant, 0 with p=0.9, 1 with p=1 - for creating outliers
ci = int(np.random.random() > 0.9)
a1i = np.random.random() * 0.05
a2i = np.random.random() * 0.05
b1i = (0.15 - 0.1) * np.random.random() + 0.1
b2i = (0.15 - 0.1) * np.random.random() + 0.1
func = (1 - ci) * (a1i * np.sin(t) + a2i * np.cos(t)) + \
ci * (b1i * np.sin(t) + b2i * np.cos(t))
return func
np.random.seed(1234567)
# Some basic test data, Model 6 from Sun and Genton.
t = np.linspace(0, 2 * np.pi, 250)
data = [harmfunc(t) for _ in range(20)]
# fboxplot test
fig = plt.figure()
ax = fig.add_subplot(111)
_, depth, ix_depth, ix_outliers = fboxplot(data, wfactor=2, ax=ax)
ix_expected = np.array([13, 4, 15, 19, 8, 6, 3, 16, 9, 7, 1, 5, 2,
12, 17, 11, 14, 10, 0, 18])
assert_equal(ix_depth, ix_expected)
ix_expected2 = np.array([2, 11, 17, 18])
assert_equal(ix_outliers, ix_expected2)
# rainbowplot test (re-uses depth variable)
xdata = np.arange(data[0].size)
fig = rainbowplot(data, xdata=xdata, depth=depth, cmap=plt.cm.rainbow)
