def test_mvslice():
g = distl.gaussian(10, 2, label='gaussian')
u = distl.uniform(0, 5, label='uniform')
mvg = distl.mvgaussian([5, 10, 12],
np.array([[2, 1, -1], [1, 2, 1], [-1, 1, 2]]),
allow_singular=True,
labels=['mvg_a', 'mvg_b', 'mvg_c'])
# passing a multivariate (non-sliced) must raise a TypeError
assert_raises(TypeError, distl.DistributionCollection, g, u, mvg)
dc = distl.DistributionCollection(g, u, mvg.slice('mvg_a'))
dc.labels
dc = distl.from_dict(dc.to_dict())
dc.sample(size=2)
dc.sample()
dc.pdf()
dc.pdf([6, 4, 2])
dc.plot(size=100)
# passing the wrong shape to pdf should raise a ValueError
assert_raises(ValueError, dc.pdf, [1, 1])
assert_raises(TypeError, dc.pdf, 1)
assert_raises(TypeError, dc.pdf, 1.0)
def test_slice():
mvg = distl.mvgaussian([5, 10, 12],
np.array([[2, 1, -1], [1, 2, 1], [-1, 1, 2]]),
allow_singular=True,
labels=['a', 'b', 'c'])
mvg_ab = mvg.take_dimensions(['a', 'b'])
mvg_a = mvg.slice('a')
mvg_b = mvg.slice('b')
def test_mvsamples():
mvg = distl.mvgaussian([5, 10, 12],
np.array([[2, 1, -1], [1, 2, 1], [-1, 1, 2]]),
allow_singular=True,
labels=['a', 'b', 'c'])
d = distl.mvsamples(mvg.sample(size=100), labels=['a', 'b', 'c'])
d = d.copy()
_test_conversions(d)
_test_methods_properties(d)
_test_plotting(d)
_test_json(d)
def test_mvgaussianslice():
d = distl.mvgaussian([5, 10, 12],
np.array([[2, 1, -1], [1, 2, 1], [-1, 1, 2]]),
allow_singular=True,
labels=['a', 'b', 'c'])
d = d.slice('a')
d = d.copy()
_test_conversions(d)
_test_methods_properties(d)
_test_plotting(d)
_test_json(d)
def test_mvgaussian():
d = distl.mvgaussian([5, 10, 12],
np.array([[2, 1, -1], [1, 2, 1], [-1, 1, 2]]),
allow_singular=True,
labels=['a', 'b', 'c'])
d_with_units = d.copy()
d_with_units.units = ['solRad', 'deg', 'kg']
for d in [d, d_with_units]:
_test_conversions(d)
_test_methods_properties(d)
_test_plotting(d)
_test_json(d)
def test_math():
mvg = distl.mvgaussian([5, 10, 12],
np.array([[2, 1, -1], [1, 2, 1], [-1, 1, 2]]),
allow_singular=True,
labels=['a', 'b', 'c'])
mvg_a = mvg.slice('a')
mvg_b = mvg.slice('b')
g = distl.gaussian(10, 2)
assert_raises(TypeError, mvg_a.__and__, mvg_b)
assert_raises(TypeError, mvg_a.__and__, g)
assert_raises(TypeError, g.__and__, mvg_a)