def test_basic_plot_features_nsimu_lt_maxpoints(self):
chains = np.random.random_sample(size = (100,3))
f = MP.plot_pairwise_correlation_panel(chains = chains)
x1, y1 = f.axes[0].lines[0].get_xydata().T
x2, y2 = f.axes[1].lines[0].get_xydata().T
x3, y3 = f.axes[1].lines[0].get_xydata().T
self.assertTrue(np.array_equal(x1, chains[:,0]), msg = 'Expect x1 to match column 0')
self.assertTrue(np.array_equal(y1, chains[:,1]), msg = 'Expect y1 to match column 1')
self.assertTrue(np.array_equal(x2, chains[:,0]), msg = 'Expect x2 to match column 0')
self.assertTrue(np.array_equal(y2, chains[:,2]), msg = 'Expect y2 to match column 2')
self.assertTrue(np.array_equal(x3, chains[:,0]), msg = 'Expect x3 to match column 1')
self.assertTrue(np.array_equal(y3, chains[:,2]), msg = 'Expect y3 to match column 2')
for ai in f.axes:
self.assertEqual(ai.get_xlabel(), '', msg = 'Should be blank')
self.assertEqual(f.axes[0].get_title(),'$p_{0}$', msg = 'Expect $p_{0}$')
self.assertEqual(f.axes[2].get_title(),'$p_{1}$', msg = 'Expect $p_{1}$')
self.assertEqual(f.axes[0].get_ylabel(),'$p_{1}$', msg = 'Expect $p_{1}$')
self.assertEqual(f.axes[1].get_ylabel(),'$p_{2}$', msg = 'Expect $p_{2}$')
plt.close()
def standard_check(self, figsize=None, expectfigsize=(7, 5)):
chains = np.random.random_sample(size=(100, 1))
f = MP.plot_chain_metrics(chain=chains,
name='a1',
figsizeinches=figsize)
x1, y1 = f.axes[0].lines[0].get_xydata().T
self.assertTrue(np.array_equal(y1, chains[:, 0]),
msg='Expect y1 to match column 1')
self.assertEqual(f.axes[0].get_xlabel(),
'Iterations',
msg='Should be Iterations')
self.assertEqual(f.axes[0].get_ylabel(),
'a1-chain',
msg='Should be a1-chain')
self.assertEqual(f.axes[1].get_xlabel(),
'a1',
msg='Strings should match')
self.assertEqual(f.axes[1].get_ylabel(),
'Histogram of a1-chain',
msg='Strings should match')
self.assertEqual(f.get_figwidth(),
expectfigsize[0],
msg='Width is 7in')
self.assertEqual(f.get_figheight(),
expectfigsize[1],
msg='Height is 5in')
plt.close()
def test_basic_plot_features_nsimu_lt_maxpoints(self):
npar = 3
chains = np.random.random_sample(size = (100,npar))
f = MP.plot_histogram_panel(chains = chains)
for ii in range(npar):
self.assertEqual(f.axes[ii].get_xlabel(), str('$p_{{{}}}$'.format(ii)), msg = str('Should be $p_{{{}}}$'.format(ii)))
self.assertEqual(f.axes[ii].get_ylabel(), '', msg = 'Should be blank')
plt.close()
def test_basic_plot_features_nsimu_lt_maxpoints(self):
chains = np.random.random_sample(size = (100,2))
f = MP.plot_chain_panel(chains = chains)
_, y1 = f.axes[0].lines[0].get_xydata().T
_, y2 = f.axes[1].lines[0].get_xydata().T
self.assertTrue(np.array_equal(y1, chains[:,0]), msg = 'Expect y1 to match column 1')
self.assertTrue(np.array_equal(y2, chains[:,1]), msg = 'Expect y2 to match column 2')
self.assertEqual(f.axes[0].get_xlabel(), '', msg = 'Should be blank')
self.assertEqual(f.axes[1].get_xlabel(), 'Iteration', msg = 'Should be Iteration')
plt.close()
def test_plot_attributes(self):
P = MP.Plot()
check_these = [
'plot_density_panel', 'plot_chain_panel',
'plot_pairwise_correlation_panel', 'plot_histogram_panel',
'plot_chain_metrics'
]
for ct in check_these:
self.assertTrue(hasattr(P, ct),
msg=str('P contains method {}'.format(ct)))
def standard_check(self, hist_on = True):
npar = 3
chains = np.random.random_sample(size = (100,npar))
f = MP.plot_density_panel(chains=chains)
for ii in range(npar):
name = str('$p_{{{}}}$'.format(ii))
self.assertEqual(f.axes[ii].get_xlabel(), name, msg = str('Should be {}'.format(name)))
self.assertEqual(f.axes[ii].get_ylabel(), str('$\pi$({}$|M^{}$)'.format(name, '{data}')), msg = 'Should be posterior')
self.assertEqual(f.get_figwidth(), 5.0, msg = 'Width is 5in')
self.assertEqual(f.get_figheight(), 4.0, msg = 'Height is 4in')
plt.close()
def test_basic_plot_features_nsimu_gt_maxpoints(self):
nsimu = 1000
chains = np.random.random_sample(size = (nsimu,2))
f = MP.plot_chain_panel(chains = chains)
x1, y1 = f.axes[0].lines[0].get_xydata().T
x2, y2 = f.axes[1].lines[0].get_xydata().T
skip = int(math.floor(nsimu/500))
self.assertTrue(np.array_equal(y1, chains[range(0,nsimu,skip),0]), msg = 'Expect y1 to match column 1')
self.assertTrue(np.array_equal(y2, chains[range(0,nsimu,skip),1]), msg = 'Expect y2 to match column 2')
self.assertEqual(f.axes[0].get_xlabel(), '', msg = 'Should be blank')
self.assertEqual(f.axes[1].get_xlabel(), 'Iteration', msg = 'Should be Iteration')
plt.close()
def test_return_kde_handles(self):
npar = 3
chains = np.random.random_sample(size=(100, npar))
f, kdehandle = MP.plot_density_panel(chains=chains, return_kde=True)
self.assertEqual(len(kdehandle), 3,
msg='Expect 3 elements in list')