def test_pastis_poisson_diploid_ambig():
lengths = np.array([20])
ploidy = 2
seed = 42
bcc_lambda = 0
hsc_lambda = 0
hsc_r = None
alpha, beta = -3., 1.
random_state = np.random.RandomState(seed=seed)
n = lengths.sum()
X_true = random_state.rand(n * ploidy, 3)
dis = euclidean_distances(X_true)
dis[dis == 0] = np.inf
counts = beta * dis ** alpha
counts[np.isnan(counts) | np.isinf(counts)] = 0
counts = counts[:n, :n] + counts[n:, n:] + counts[:n, n:] + counts[n:, :n]
counts = np.triu(counts, 1)
counts = sparse.coo_matrix(counts)
struct_, infer_var = pastis_algorithms.pastis_poisson(
counts, lengths=lengths, ploidy=ploidy, outdir=None, alpha=alpha,
seed=seed, normalize=False, filter_threshold=0, bcc_lambda=bcc_lambda,
hsc_lambda=hsc_lambda, hsc_r=hsc_r, print_freq=None, history_freq=None,
save_freq=None)
assert infer_var['converged']
def test_pastis_poisson_diploid_combo():
lengths = np.array([20])
ploidy = 2
seed = 42
bcc_lambda = 0
hsc_lambda = 0
hsc_r = None
alpha, beta = -3., 1.
ratio_ambig, ratio_pa, ratio_ua = 0.2, 0.7, 0.1
random_state = np.random.RandomState(seed=seed)
n = lengths.sum()
X_true = random_state.rand(n * ploidy, 3)
dis = euclidean_distances(X_true)
dis[dis == 0] = np.inf
poisson_intensity = dis ** alpha
ambig_counts = ratio_ambig * beta * poisson_intensity
ambig_counts[np.isnan(ambig_counts) | np.isinf(ambig_counts)] = 0
ambig_counts = ambig_counts[:n, :n] + ambig_counts[n:, n:] + ambig_counts[:n, n:] + ambig_counts[n:, :n]
ambig_counts = np.triu(ambig_counts, 1)
ambig_counts = sparse.coo_matrix(ambig_counts)
pa_counts = ratio_pa * beta * poisson_intensity
pa_counts[np.isnan(pa_counts) | np.isinf(pa_counts)] = 0
pa_counts = pa_counts[:, :n] + pa_counts[:, n:]
np.fill_diagonal(pa_counts[:n, :], 0)
np.fill_diagonal(pa_counts[n:, :], 0)
pa_counts = sparse.coo_matrix(pa_counts)
ua_counts = ratio_ua * beta * poisson_intensity
ua_counts[np.isnan(ua_counts) | np.isinf(ua_counts)] = 0
ua_counts = np.triu(ua_counts, 1)
ua_counts = sparse.coo_matrix(ua_counts)
counts = [ambig_counts, pa_counts, ua_counts]
struct_, infer_var = pastis_algorithms.pastis_poisson(
counts, lengths=lengths, ploidy=ploidy, outdir=None, alpha=alpha,
seed=seed, normalize=False, filter_threshold=0, bcc_lambda=bcc_lambda,
hsc_lambda=hsc_lambda, hsc_r=hsc_r, print_freq=None, history_freq=None,
save_freq=None)
assert infer_var['converged']
# Make sure estimated betas are appropriate given nreads per counts matrix
infer_beta = np.array(infer_var['beta'])
sim_ratio = np.array([ratio_ambig, ratio_pa, ratio_ua])
assert_array_almost_equal(
infer_beta / infer_beta.sum(), sim_ratio / sim_ratio.sum(), decimal=2)
def test_pastis_poisson_diploid_unambig_hsc_constraint():
lengths = np.array([30])
ploidy = 2
seed = 42
bcc_lambda = 0
hsc_lambda = 1e10
true_interhomo_dis = np.array([10.])
hsc_r = None
alpha, beta = -3., 1.
random_state = np.random.RandomState(seed=seed)
n = lengths.sum()
X_true = np.zeros((n * ploidy, 3), dtype=float)
for i in range(X_true.shape[0]):
X_true[i:, random_state.choice([0, 1, 2])] += 1
X_true[n:] -= X_true[n:].mean(axis=0)
X_true[:n] -= X_true[:n].mean(axis=0)
begin = end = 0
for i in range(len(lengths)):
end += lengths[i]
X_true[begin:end, 0] += true_interhomo_dis[i]
begin = end
dis = euclidean_distances(X_true)
dis[dis == 0] = np.inf
counts = beta * dis ** alpha
counts[np.isnan(counts) | np.isinf(counts)] = 0
counts = np.triu(counts, 1)
counts = sparse.coo_matrix(counts)
struct_, infer_var = pastis_algorithms.pastis_poisson(
counts, lengths=lengths, ploidy=ploidy, outdir=None, alpha=alpha,
seed=seed, normalize=False, filter_threshold=0, beta=beta,
bcc_lambda=bcc_lambda, hsc_lambda=hsc_lambda, hsc_r=hsc_r,
print_freq=None, history_freq=None, save_freq=None, struct_true=X_true)
infer_hsc_r = infer_var['hsc_r']
interhomo_dis = _inter_homolog_dis(struct_, lengths=lengths)
assert infer_var['converged']
# Make sure inference of hsc_r yields an acceptable result
assert_array_almost_equal(true_interhomo_dis, infer_hsc_r, decimal=1)
# Make sure inferred homologs are separated >= inferred hsc_r
assert interhomo_dis >= infer_hsc_r - 1e-6