def test_multivariate_te_one_realisation_per_replication():
"""Test boundary case of one realisation per replication."""
# Create a data set where one pattern fits into the time series exactly
# once, this way, we get one realisation per replication for each variable.
# This is easyer to assert/verify later. We also test data.get_realisations
# this way.
analysis_opts = {'cmi_calc_name': 'jidt_kraskov'}
max_lag_target = 5
max_lag_sources = max_lag_target
min_lag_sources = 4
target = 0
dat = Data(normalise=False)
n_repl = 10
n_procs = 2
n_points = n_procs * (max_lag_sources + 1) * n_repl
dat.set_data(
np.arange(n_points).reshape(n_procs, max_lag_sources + 1, n_repl),
'psr')
nw_0 = Multivariate_te(max_lag_sources, min_lag_sources, analysis_opts,
max_lag_target)
nw_0._initialise(dat, 'all', target)
assert (not nw_0.selected_vars_full)
assert (not nw_0.selected_vars_sources)
assert (not nw_0.selected_vars_target)
assert ((nw_0._replication_index == np.arange(n_repl)).all())
assert (nw_0._current_value == (target, max(max_lag_sources,
max_lag_target)))
assert (nw_0._current_value_realisations[:, 0] == dat.data[target,
-1, :]).all()
def test_add_conditional_manually():
"""Adda variable that is not in the data set."""
analysis_opts = {
'cmi_calc_name': 'jidt_kraskov',
'add_conditionals': (8, 0)
}
nw_1 = Multivariate_te(max_lag_sources=5,
min_lag_sources=3,
options=analysis_opts,
max_lag_target=7)
dat = Data()
dat.generate_mute_data()
sources = [1, 2, 3]
target = 0
with pytest.raises(IndexError):
nw_1._initialise(dat, sources, target)
def test_faes_method():
"""Check if the Faes method is working."""
analysis_opts = {
'cmi_calc_name': 'jidt_kraskov',
'add_conditionals': 'faes'
}
nw_1 = Multivariate_te(max_lag_sources=5,
min_lag_sources=3,
max_lag_target=7,
options=analysis_opts)
dat = Data()
dat.generate_mute_data()
sources = [1, 2, 3]
target = 0
nw_1._initialise(dat, sources, target)
assert (nw_1._selected_vars_sources == [
i for i in it.product(sources, [nw_1.current_value[1]])
]), ('Did not add correct additional conditioning vars.')
def test_multivariate_te_initialise():
"""Test if all values are set correctly in _initialise()."""
# Create a data set where one pattern fits into the time series exactly
# once, this way, we get one realisation per replication for each variable.
# This is easyer to assert/verify later. We also test data.get_realisations
# this way.
analysis_opts = {'cmi_calc_name': 'jidt_kraskov'}
max_lag_target = 5
max_lag_sources = max_lag_target
min_lag_sources = 4
target = 1
dat = Data(normalise=False)
n_repl = 30
n_procs = 2
n_points = n_procs * (max_lag_sources + 1) * n_repl
dat.set_data(np.arange(n_points).reshape(n_procs, max_lag_sources + 1,
n_repl), 'psr')
nw_0 = Multivariate_te(max_lag_sources, min_lag_sources, max_lag_target,
analysis_opts)
nw_0._initialise(dat, 'all', target)
assert (not nw_0.selected_vars_full)
assert (not nw_0.selected_vars_sources)
assert (not nw_0.selected_vars_target)
assert ((nw_0._replication_index == np.arange(n_repl)).all())
assert (nw_0._current_value == (target, max(max_lag_sources,
max_lag_target)))
assert ((nw_0._current_value_realisations ==
np.arange(n_points - n_repl, n_points).reshape(n_repl, 1)).all())
# Check if the Faes method is working
analysis_opts['add_conditionals'] = 'faes'
nw_1 = Multivariate_te(max_lag_sources, min_lag_sources, max_lag_target,
analysis_opts)
dat.generate_mute_data()
sources = [1, 2, 3]
target = [0]
nw_1._initialise(dat, sources, target)
assert (nw_1._selected_vars_sources ==
[i for i in it.product(sources, [nw_1.current_value[1]])]), (
'Did not add correct additional conditioning vars.')
# Adding a variable that is not in the data set.
analysis_opts['add_conditionals'] = (8, 0)
nw_1 = Multivariate_te(max_lag_sources, min_lag_sources, max_lag_target,
analysis_opts)
dat.generate_mute_data()
sources = [1, 2, 3]
target = [0]
with pytest.raises(IndexError):
nw_1._initialise(dat, sources, target)
