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Apply yapf to python files

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This commit is contained in:
Matt Weiden
2019-12-19 15:20:41 -08:00
parent 42a8d45bd7
commit cdca128a01
43 changed files with 1143 additions and 678 deletions
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server/app/scanpy_engine/diffexp.py
+11 -6
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@@ -32,8 +32,8 @@ def _mean_var_n(X):
v = sumsq / (n - 1)
if fp_err_occurred:
mean[np.isfinite(mean) == False] = 0 # noqa: E712
v[np.isfinite(v) == False] = 0 # noqa: E712
mean[np.isfinite(mean) == False] = 0 # noqa: E712
v[np.isfinite(v) == False] = 0 # noqa: E712
return mean, v, n
@@ -76,7 +76,7 @@ def diffexp_ttest(adata, maskA, maskB, top_n=8, diffexp_lfc_cutoff=0.01):
# degrees of freedom for Welch's t-test
with np.errstate(divide="ignore", invalid="ignore"):
dof = sum_vn ** 2 / (vnA ** 2 / (nA - 1) + vnB ** 2 / (nB - 1))
dof = sum_vn**2 / (vnA**2 / (nA - 1) + vnB**2 / (nB - 1))
dof[np.isnan(dof)] = 1
# Welch's t-test score calculation
@@ -93,13 +93,15 @@ def diffexp_ttest(adata, maskA, maskB, top_n=8, diffexp_lfc_cutoff=0.01):
logfoldchanges = np.log2(np.abs((meanA + 1e-9) / (meanB + 1e-9)))
# find all with lfc > cutoff
lfc_above_cutoff_idx = np.nonzero(np.abs(logfoldchanges) > diffexp_lfc_cutoff)[0]
lfc_above_cutoff_idx = np.nonzero(
np.abs(logfoldchanges) > diffexp_lfc_cutoff)[0]
stats_to_sort = np.abs(tscores)
# derive sort order
if lfc_above_cutoff_idx.shape[0] > top_n:
# partition top N
rel_t_partition = np.argpartition(stats_to_sort[lfc_above_cutoff_idx], -top_n)[-top_n:]
rel_t_partition = np.argpartition(stats_to_sort[lfc_above_cutoff_idx],
-top_n)[-top_n:]
t_partition = lfc_above_cutoff_idx[rel_t_partition]
# sort the top N partition
rel_sort_order = np.argsort(stats_to_sort[t_partition])[::-1]
@@ -117,5 +119,8 @@ def diffexp_ttest(adata, maskA, maskB, top_n=8, diffexp_lfc_cutoff=0.01):
pvals_adj_top_n = pvals_adj[sort_order]
# varIndex, logfoldchange, pval, pval_adj
result = [[sort_order[i], logfoldchanges_top_n[i], pvals_top_n[i], pvals_adj_top_n[i]] for i in range(top_n)]
result = [[
sort_order[i], logfoldchanges_top_n[i], pvals_top_n[i],
pvals_adj_top_n[i]
] for i in range(top_n)]
return result