def set_hypotheses(head, config):
# Formulate a VEP hypothesis manually
hyp_builder = HypothesisBuilder(head.connectivity.number_of_regions,
config) # .set_normalize(0.99)
# Regions of Pathological Excitability hypothesis:
x0_indices = [2, 24]
x0_values = [0.01, 0.01]
hyp_builder.set_x0_hypothesis(x0_indices, x0_values)
# Regions of Model Epileptogenicity hypothesis:
e_indices = [1, 26]
# e_indices = list(range(head.connectivity.number_of_regions))
# e_indices.remove(2)
# e_indices.remove(25)
# e_values = np.zeros((head.connectivity.number_of_regions,)) + 0.01
# e_values[[1, 26]] = 0.99
# e_values = np.delete(e_values, [2, 25]).tolist()
e_values = np.array([1.5, 1.25]) # np.array([0.99] * 2)
hyp_builder.set_e_hypothesis(e_indices, e_values)
# Regions of Connectivity hypothesis:
# w_indices = [] # [(0, 1), (0, 2)]
# w_values = [] # [0.5, 2.0]
# hypo_builder.set_w_indices(w_indices).set_w_values(w_values)
hypothesis1 = hyp_builder.build_hypothesis()
e_indices = [1, 26] # [1, 2, 25, 26]
hypothesis2 = hyp_builder.build_hypothesis_from_file(
"clinical_hypothesis_postseeg", e_indices)
# Change something manually if necessary
# hypothesis2.x0_values = [0.01, 0.01]
return (hypothesis1, hypothesis2)
def test_read_hypothesis(self):
test_file = os.path.join(self.config.out.FOLDER_TEMP,
"TestHypothesis.h5")
hypothesis_builder = HypothesisBuilder(3, self.config)
dummy_hypothesis = hypothesis_builder.set_e_hypothesis(
[0], [0.6]).build_hypothesis()
self.writer.write_hypothesis(dummy_hypothesis, test_file)
hypothesis = self.reader.read_hypothesis(test_file)
assert dummy_hypothesis.number_of_regions == hypothesis.number_of_regions
assert numpy.array_equal(dummy_hypothesis.x0_values,
hypothesis.x0_values)
assert dummy_hypothesis.x0_indices == hypothesis.x0_indices
assert numpy.array_equal(dummy_hypothesis.e_values,
hypothesis.e_values)
assert dummy_hypothesis.e_indices == hypothesis.e_indices
assert numpy.array_equal(dummy_hypothesis.w_values,
hypothesis.w_values)
assert dummy_hypothesis.w_indices == hypothesis.w_indices
assert numpy.array_equal(dummy_hypothesis.lsa_propagation_indices,
hypothesis.lsa_propagation_indices)
if len(dummy_hypothesis.lsa_propagation_indices) == 0:
assert numpy.array_equal([0, 0, 0],
hypothesis.lsa_propagation_strengths)
else:
assert numpy.array_equal(
dummy_hypothesis.lsa_propagation_strengths,
hypothesis.lsa_propagation_strengths)