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Update Fig. 4 and 5#51

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huangchieh merged 8 commits into
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reviewer2
Jun 24, 2026
Merged

Update Fig. 4 and 5#51
huangchieh merged 8 commits into
mainfrom
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Copilot AI review requested due to automatic review settings June 24, 2026 20:23
@huangchieh huangchieh merged commit 5f1aa38 into main Jun 24, 2026
@huangchieh huangchieh deleted the reviewer2 branch June 24, 2026 20:23

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Pull request overview

Updates the plotting scripts and generated SVG used for “Fig. 4 and 5” style/performance figures, aligning annotations and adding additional views for prediction visualization.

Changes:

  • visualiseTrainingData.py: disables 3D axis indicators by default and adds a cross-section line with P0/P1 markers in the XY atomic view.
  • visualisePredictionsPureOnlyBoth.py: switches AFM inputs to NPZ-backed slices with a shared normalization + colorbar, and adds an XZ row under each predicted XY structure panel.
  • Regenerates results/train_data/style_difference.svg (metadata + internal IDs updated).

Reviewed changes

Copilot reviewed 2 out of 26 changed files in this pull request and generated 2 comments.

File Description
src/performanceEvaluation/visualiseTrainingData.py Adds cross-section and P0/P1 markers; gates axis indicator drawing behind showIndicator.
src/performanceEvaluation/visualisePredictionsPureOnlyBoth.py Refactors figure layout to include XZ panels; loads AFM slices from NPZ with shared color scaling and adds a colorbar.
results/train_data/style_difference.svg Updated generated figure output (timestamp/tooling metadata and SVG element IDs).

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Comment on lines +611 to +612
if showIndicator:
draw_3d_axis_indicator(ax1, anchor=(0.85, 0.65), length=36, style='x-out')
Comment on lines +89 to +99
for i, sample in enumerate(samples):
npz_file = '{}/{}.npz'.format(expNpz, sample)
npz_data = np.load(npz_file)
exp_data = npz_data['data']
close = exp_data[:, :, indexes[i][0]]
far = exp_data[:, :, indexes[i][1]]
close = np.rot90(close, k=(angle+90)//90)
far = np.rot90(far, k=(angle+90)//90)
x_size_nm = float(npz_data['lengthX']) / 10.0
y_size_nm = float(npz_data['lengthY']) / 10.0
exp_slices[sample] = (close, far, x_size_nm, y_size_nm)
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2 participants