def testLoads(self):
# test initiating from multiple chain arrays
samps = []
for i in range(3):
samps.append(Gaussian2D([1.5, -2], np.diagflat([1, 2])).MCSamples(1001 + i * 10, names=['x', 'y']))
fromChains = MCSamples(samples=[s.samples for s in samps], names=['x', 'y'])
mean = np.sum([s.norm * s.mean('x') for s in samps]) / np.sum([s.norm for s in samps])
meanChains = fromChains.mean('x')
self.assertAlmostEqual(mean, meanChains)
def testDensitySymmetries(self):
# check flipping samples gives flipped density
samps = Gaussian1D(0, 1, xmin=-1, xmax=4).MCSamples(12000)
d = samps.get1DDensity('x')
samps.samples[:, 0] *= -1
samps = MCSamples(samples=samps.samples, names=['x'], ranges={'x': [-4, 1]})
d2 = samps.get1DDensity('x')
self.assertTrue(np.allclose(d.P, d2.P[::-1]))
samps = Gaussian2D([0, 0], np.diagflat([1, 2]), xmin=-1, xmax=2, ymin=0, ymax=3).MCSamples(12000)
d = samps.get2DDensity('x', 'y')
samps.samples[:, 0] *= -1
samps = MCSamples(samples=samps.samples, names=['x', 'y'], ranges={'x': [-2, 1], 'y': [0, 3]})
d2 = samps.get2DDensity('x', 'y')
self.assertTrue(np.allclose(d.P, d2.P[:, ::-1]))
samps.samples[:, 0] *= -1
samps.samples[:, 1] *= -1
samps = MCSamples(samples=samps.samples, names=['x', 'y'], ranges={'x': [-1, 2], 'y': [-3, 0]})
d2 = samps.get2DDensity('x', 'y')
self.assertTrue(np.allclose(d.P, d2.P[::-1, ::], atol=1e-5))