Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Halve time taken for test_tree_stats #3029

Merged
merged 1 commit into from
Oct 16, 2024
Merged

Halve time taken for test_tree_stats #3029

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
40 changes: 32 additions & 8 deletions python/tests/test_tree_stats.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1282,7 +1282,10 @@ def site_segregating_sites(ts, sample_sets, windows=None, span_normalise=True):
haps = ts.genotype_matrix(isolated_as_missing=False)
site_positions = [x.position for x in ts.sites()]
for i, X in enumerate(sample_sets):
X_index = np.where(np.isin(samples, X))[0]
set_X = set(X)
X_index = np.where(np.fromiter((s in set_X for s in samples), dtype=bool))[
0
]
for k in range(ts.num_sites):
if (site_positions[k] >= begin) and (site_positions[k] < end):
num_alleles = len(set(haps[k, X_index]))
Expand Down Expand Up @@ -1430,7 +1433,10 @@ def site_tajimas_d(ts, sample_sets, windows=None):
nn = n[i]
S = 0
T = 0
X_index = np.where(np.isin(samples, X))[0]
set_X = set(X)
X_index = np.where(np.fromiter((s in set_X for s in samples), dtype=bool))[
0
]
for k in range(ts.num_sites):
if (site_positions[k] >= begin) and (site_positions[k] < end):
hX = haps[k, X_index]
Expand Down Expand Up @@ -4891,7 +4897,10 @@ def branch_trait_covariance(ts, W, windows=None, span_normalise=True):
has_trees = True
SS = 0
for u in range(ts.num_nodes):
below = np.isin(samples, list(tr.samples(u)))
tree_samples = set(tr.samples(u))
below = np.fromiter(
(s in tree_samples for s in samples), dtype=bool
)
branch_length = tr.branch_length(u)
SS += covsq(w, below) * branch_length
S += SS * (min(end, tr.interval.right) - max(begin, tr.interval.left))
Expand Down Expand Up @@ -4926,7 +4935,10 @@ def node_trait_covariance(ts, W, windows=None, span_normalise=True):
break
SS = np.zeros(ts.num_nodes)
for u in range(ts.num_nodes):
below = np.isin(samples, list(tr.samples(u)))
tree_samples = set(tr.samples(u))
below = np.fromiter(
(s in tree_samples for s in samples), dtype=bool
)
SS[u] += covsq(w, below)
S += SS * (min(end, tr.interval.right) - max(begin, tr.interval.left))
out[j, :, i] = S
Expand Down Expand Up @@ -5102,7 +5114,10 @@ def branch_trait_correlation(ts, W, windows=None, span_normalise=True):
has_trees = True
SS = 0
for u in range(ts.num_nodes):
below = np.isin(samples, list(tr.samples(u)))
tree_samples = set(tr.samples(u))
below = np.fromiter(
(s in tree_samples for s in samples), dtype=bool
)
p = np.mean(below)
if p > 0 and p < 1:
branch_length = tr.branch_length(u)
Expand Down Expand Up @@ -5143,7 +5158,10 @@ def node_trait_correlation(ts, W, windows=None, span_normalise=True):
break
SS = np.zeros(ts.num_nodes)
for u in range(ts.num_nodes):
below = np.isin(samples, list(tr.samples(u)))
tree_samples = set(tr.samples(u))
below = np.fromiter(
(s in tree_samples for s in samples), dtype=bool
)
p = np.mean(below)
if p > 0 and p < 1:
# SS[u] += sum(w[below])**2 / 2
Expand Down Expand Up @@ -5366,7 +5384,10 @@ def branch_trait_linear_model(ts, W, Z, windows=None, span_normalise=True):
has_trees = True
SS = 0
for u in range(ts.num_nodes):
below = np.isin(samples, list(tr.samples(u)))
tree_samples = set(tr.samples(u))
below = np.fromiter(
(s in tree_samples for s in samples), dtype=bool
)
branch_length = tr.branch_length(u)
SS += linear_model(w, below, Z) * branch_length
S += SS * (min(end, tr.interval.right) - max(begin, tr.interval.left))
Expand Down Expand Up @@ -5401,7 +5422,10 @@ def node_trait_linear_model(ts, W, Z, windows=None, span_normalise=True):
break
SS = np.zeros(ts.num_nodes)
for u in range(ts.num_nodes):
below = np.isin(samples, list(tr.samples(u)))
tree_samples = set(tr.samples(u))
below = np.fromiter(
(s in tree_samples for s in samples), dtype=bool
)
SS[u] += linear_model(w, below, Z)
S += SS * (min(end, tr.interval.right) - max(begin, tr.interval.left))
out[j, :, i] = S
Expand Down
Loading