def testLimits(self):
samples = self.testdists.cut_correlated.MCSamples(12000, logLikes=False)
stats = samples.getMargeStats()
lims = stats.parWithName('x').limits
self.assertAlmostEqual(lims[0].lower, 0.2205, 3)
self.assertAlmostEqual(lims[1].lower, 0.0491, 3)
self.assertTrue(lims[2].onetail_lower)
# check some analytics (note not very accurate actually)
samples = Gaussian1D(0, 1, xmax=1).MCSamples(1500000, logLikes=False)
stats = samples.getMargeStats()
lims = stats.parWithName('x').limits
self.assertAlmostEqual(lims[0].lower, -0.792815, 2)
self.assertAlmostEqual(lims[0].upper, 0.792815, 2)
self.assertAlmostEqual(lims[1].lower, -1.72718, 2)
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))