def test_file(seed, maximum_size, runs, file_extension):
for _ in range(runs):
size = _rd_randint(2, maximum_size)
zeros = _rd_randint(0, size)
mc_to = _MarkovChain.random(size, zeros=zeros, seed=seed)
file_handler, file_path = _tf_mkstemp(suffix=file_extension)
_os_close(file_handler)
# noinspection PyBroadException
try:
mc_to.to_file(file_path)
mc_from = _MarkovChain.from_file(file_path)
exception = False
except Exception:
mc_from = None
exception = True
_os_remove(file_path)
assert exception is False
_npt.assert_allclose(mc_from.p, mc_to.p, rtol=1e-5, atol=1e-8)
def test_plot_redistributions(seed, maximum_size, maximum_distributions, runs):
rs = _rd_getstate()
_rd_seed(seed)
configs = []
for _ in range(runs):
size = _rd_randint(2, maximum_size)
zeros = _rd_randint(0, size)
configs.append((size, zeros))
_rd_setstate(rs)
mcs = []
plot_types = ['heatmap', 'projection']
for i in range(runs):
size, zeros = configs[i]
mc = _MarkovChain.random(size, zeros=zeros, seed=seed)
if i == 0:
distributions = mc.redistribute(1, output_last=False)
initial_status = None
plot_type = 'projection'
else:
r = _rd_randint(1, maximum_distributions)
distributions = r if _rd_random() < 0.5 else mc.redistribute(r, output_last=False)
initial_status = None if isinstance(distributions, int) or _rd_random() < 0.5 else distributions[0]
plot_type = _rd_choice(plot_types)
configs[i] = (distributions, initial_status, plot_type)
mcs.append(mc)
for i in range(runs):
mc = mcs[i]
distributions, initial_status, plot_type = configs[i]
# noinspection PyBroadException
try:
figure, _ = _plot_redistributions(mc, distributions, initial_status, plot_type)
_mplp.close(figure)
exception = False
except Exception:
exception = True
assert exception is False
def test_dictionary(seed, maximum_size, runs):
for _ in range(runs):
size = _rd_randint(2, maximum_size)
zeros = _rd_randint(0, size)
mc_to = _MarkovChain.random(size, zeros=zeros, seed=seed)
d = mc_to.to_dictionary()
mc_from = _MarkovChain.from_dictionary(d)
_npt.assert_allclose(mc_from.p, mc_to.p, rtol=1e-5, atol=1e-8)
def test_plot_walk(seed, maximum_size, maximum_simulations, runs):
rs = _rd_getstate()
_rd_seed(seed)
configs = []
for _ in range(runs):
size = _rd_randint(2, maximum_size)
zeros = _rd_randint(0, size)
configs.append((size, zeros))
_rd_setstate(rs)
mcs = []
plot_types = ['histogram', 'sequence', 'transitions']
for i in range(runs):
size, zeros = configs[i]
mc = _MarkovChain.random(size, zeros=zeros, seed=seed)
r = _rd_randint(2, maximum_simulations)
walk = r if _rd_random() < 0.5 else mc.walk(r, output_indices=True)
initial_state = None if isinstance(walk, int) or _rd_random() < 0.5 else walk[0]
plot_type = _rd_choice(plot_types)
configs[i] = (walk, initial_state, plot_type)
mcs.append(mc)
for i in range(runs):
mc = mcs[i]
walk, initial_state, plot_type = configs[i]
# noinspection PyBroadException
try:
figure, _ = _plot_walk(mc, walk, initial_state, plot_type)
_mplp.close(figure)
exception = False
except Exception:
exception = True
assert exception is False
def test_graph(seed, maximum_size, runs):
for _ in range(runs):
size = _rd_randint(2, maximum_size)
zeros = _rd_randint(0, size)
mc_to = _MarkovChain.random(size, zeros=zeros, seed=seed)
graph = mc_to.to_graph(False)
mc_from = _MarkovChain.from_graph(graph)
_npt.assert_allclose(mc_from.p, mc_to.p, rtol=1e-5, atol=1e-8)
graph = mc_to.to_graph(True)
mc_from = _MarkovChain.from_graph(graph)
_npt.assert_allclose(mc_from.p, mc_to.p, rtol=1e-5, atol=1e-8)
def test_plot_eigenvalues(seed, maximum_size, runs):
for _ in range(runs):
size = _rd_randint(2, maximum_size)
zeros = _rd_randint(0, size)
mc = _MarkovChain.random(size, zeros=zeros, seed=seed)
# noinspection PyBroadException
try:
figure, _ = _plot_eigenvalues(mc)
_mplp.close(figure)
exception = False
except Exception:
exception = True
assert exception is False
def test_plot_graph(seed, maximum_size, runs):
rs = _rd_getstate()
_rd_seed(seed)
configs = []
for _ in range(runs):
size = _rd_randint(2, maximum_size)
zeros = _rd_randint(0, size)
configs.append((size, zeros) + tuple(_rd_random() < 0.5 for _ in range(4)))
_rd_setstate(rs)
for i in range(runs):
size, zeros, nodes_color, nodes_type, edges_color, edges_value = configs[i]
mc = _MarkovChain.random(size, zeros=zeros, seed=seed)
# noinspection PyBroadException
try:
figure, _ = _plot_graph(mc, nodes_color=nodes_color, nodes_type=nodes_type, edges_color=edges_color, edges_value=edges_value, force_standard=True)
_mplp.close(figure)
figure, _ = _plot_graph(mc, nodes_color=nodes_color, nodes_type=nodes_type, edges_color=edges_color, edges_value=edges_value, force_standard=False)
_mplp.close(figure)
exception = False
except Exception:
exception = True
assert exception is False
def test_random(seed, size, zeros, mask, value):
mc = _MarkovChain.random(size, None, zeros, mask, seed)
actual = mc.p
expected = _np.asarray(value)
_npt.assert_allclose(actual, expected, rtol=1e-5, atol=1e-8)
if zeros > 0 and mask is None:
actual = size**2 - _np.count_nonzero(mc.p)
expected = zeros
assert actual == expected
if mask is not None:
indices = ~_np.isnan(_np.asarray(mask))
actual = mc.p[indices]
expected = _np.asarray(value)[indices]
_npt.assert_allclose(actual, expected, rtol=1e-5, atol=1e-8)