MATLAB's minor grid ignored MinorTickValues and auto-subdivided finer than
0.02. Make 0.02 the actual (major) tick spacing for rate, label only every 0.1,
and let grid on draw the fine 0.02 gridlines deterministically. Regenerated all
figures.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
variation_plot: count y-axis keeps 5-unit ticks/grid but labels only multiples
of 10; rate y-axis adds tick-only minor marks every 0.02 (major labels stay at
0.2). Regenerated all figures.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
variation_plot: xlim([min(xd)-0.5, max(xd)+0.5]) on all learning-curve plots,
so the first point lifts off the y-axis (phase windows d6_*) and the last point
clears the right edge (d0_5 etc.). Regenerated all figures.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
variation_plot: set XTickLabelRotation 0 after xticklabels('auto') so many-day
windows (e.g. unmerge_d0_10, 11 ticks) no longer auto-rotate to vertical; nudge
the y-axis label left for breathing room from the tick numbers. Regenerated all
learning-curve figures.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
variation_plot: for the count metric set YTick to every 5 units (spanning the
data); grid on for both metrics (rate keeps auto 0.2 ticks). Regenerated all
learning-curve figures.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Port the unmerge_d0_5 fix (b6dc69c) into variation_plot.m: tight centred plot
box (Position [0.2 0.30 0.60 0.60]), TickDir out, and xticklabels('auto') --
which keeps x labels horizontal when they fit (<=~10 days) and auto-rotates
them when crowded (e.g. 14-day windows). Replaces the manual rotated-text hack.
Regenerated all learning-curve figures; removed the stray plotcurve.asv and
gitignored *.asv.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
exportgraphics on headless invisible figures ignores XTickLabelRotation/
xtickangle, so the labels stayed horizontal. Draw the tick labels as rotated
text objects (Rotation 90) instead, add bottom margin, and drop the 'training
day' axis label below them. Regenerated all learning-curve figures.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
xtickangle(90) did not apply in headless export; set XTickLabelRotation=90
directly and drawnow before exportgraphics. Regenerated all learning_curve
.png / learning_curve_rate.png.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Refactor variation_{analyze,power,logpower,plot}.m to run both count and rate
metrics; plot x-axis labels vertical (xtickangle 90). SUMMARY.csv gains
interaction_p_rate / interaction_p_rs_rate.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add variation_plot.m + make_variation_plot.m, dropping plotcurve.m +
learning_curve.png into all 28 variation folders. Each figure plots mean +/-
SEM successful reaches per training day for the anodal/treatment group (red)
vs control (blue), in the style of the paper ("Lines indicate mean (and SEM)
across animals in the anodal (red) and control (blue) groups"). Per-group N is
read from the data and shown in the legend; training day is 1-indexed (our day
0 = paper Day 1). Headless via exportgraphics. prev_f_full reproduces the
paper's own figure (anodal N=12 vs control N=12).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Experiments DB Dev