Uni/boundary-aware-centrality
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WIP different small python scripts to generate corresponding images

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The final API will be derived from these scripts into a different
repository, which then only holds the corresponding functions that
provide the corresponding functionalities described in the associated
master thesis.
This commit is contained in:
Yves Biener
2026-03-28 15:04:38 +01:00
parent ead3d70c35
commit 7581966c88
8 changed files with 469 additions and 198 deletions
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8
diff_comparison.py
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@@ -108,8 +108,8 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
vp.a = np.nan_to_num(vp.a) # correct floating point values
# normalization
min_val, max_val = vp.a.min(), vp.a.max()
vp.a = (vp.a - min_val) / (max_val - min_val)
# min_val, max_val = vp.a.min(), vp.a.max()
# vp.a = (vp.a - min_val) / (max_val - min_val)
# generate model based on convex hull and associated centrality values
quantification = plot.quantification_data(g, vp, convex_hull)
@@ -147,8 +147,8 @@ def apply_corrected(g, seed, weight, convex_hull, ax, method, method_name):
vp.a = np.nan_to_num(vp.a) # correct floating point values
# normalization
min_val, max_val = vp.a.min(), vp.a.max()
vp.a = (vp.a - min_val) / (max_val - min_val)
# min_val, max_val = vp.a.min(), vp.a.max()
# vp.a = (vp.a - min_val) / (max_val - min_val)
# generate model based on convex hull and associated centrality values
quantification = plot.quantification_data(g, vp, convex_hull)