def test_perturb_tmat():
# The transition matrix is perturbed under the CLT approximation, which is only valid for well-sampled data w.r.t. transition probability (tprob >> 1 / row-summed counts)
countsmat = 100 * np.ones((10,10)) # 10-state MSM, 1000 counts per state, 100 transition events between states, no zero entries
params = mfpt.create_perturb_params(countsmat)
new_transmat = (mfpt.perturb_tmat(params[0], params[1]))
# All transition probabilities are by design nonzero, so there should be no nonzero entries after the perturbation
assert len(np.where(new_transmat == 0)[0]) == 0
# Now let's assume you have a poorly sampled dataset where all elements in the counts matrix is 1
countsmat = np.ones((10,10))
params = mfpt.create_perturb_params(countsmat)
new_transmat = (mfpt.perturb_tmat(params[0], params[1]))
# Your perturbed transition matrix will have several negative values (set automatically to 0), indicating this method probably isn't appropriate for your dataset
# (This will also cause your distribution of MFPTs to have very obvious outliers to an otherwise approximately Gaussian distribution due to the artificial zeros in the transition matrix)
assert len(np.where(new_transmat == 0)[0] > 0)
def test_create_perturb_params():
# Test with a 10x10 counts matrix, with all entries in the counts set to 100
countsmat = 100 * np.ones((10,10))
params = mfpt.create_perturb_params(countsmat)
# Check dimensions of outputs are equal to those of inputs
for param in params:
assert np.shape(param) == np.shape(countsmat)
def test_create_perturb_params():
# Test with a 10x10 counts matrix, with all entries in the counts set to 100
countsmat = 100 * np.ones((10, 10))
params = mfpt.create_perturb_params(countsmat)
# Check dimensions of outputs are equal to those of inputs
for param in params:
assert np.shape(param) == np.shape(countsmat)
def test_perturb_tmat():
# The transition matrix is perturbed under the CLT approximation, which is only valid for well-sampled data w.r.t. transition probability (tprob >> 1 / row-summed counts)
countsmat = 100 * np.ones(
(10, 10)
) # 10-state MSM, 1000 counts per state, 100 transition events between states, no zero entries
params = mfpt.create_perturb_params(countsmat)
new_transmat = (mfpt.perturb_tmat(params[0], params[1]))
# All transition probabilities are by design nonzero, so there should be no nonzero entries after the perturbation
assert len(np.where(new_transmat == 0)[0]) == 0
# Now let's assume you have a poorly sampled dataset where all elements in the counts matrix is 1
countsmat = np.ones((10, 10))
params = mfpt.create_perturb_params(countsmat)
new_transmat = (mfpt.perturb_tmat(params[0], params[1]))
# Your perturbed transition matrix will have several negative values (set automatically to 0), indicating this method probably isn't appropriate for your dataset
# (This will also cause your distribution of MFPTs to have very obvious outliers to an otherwise approximately Gaussian distribution due to the artificial zeros in the transition matrix)
assert len(np.where(new_transmat == 0)[0] > 0)
