Add golden-batch and mid-course-correction figures for the batch chapter - #83
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Add golden-batch and mid-course-correction figures for the batch chapter#83kgdunn wants to merge 1 commit into
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Nine PNGs and their two generator scripts for the new batch process monitoring and control chapter in the book. Every quantitative claim in the mid-course-correction set is executed on the calibrated bioreactor simulator (corrected schedules re-simulated under the same disturbance seed), replicating the documented headline of evaluate_control_policies(y_target=8.0, random_state=0) in process-improve 1.68. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NZAyiPT8xURSdi24yFKCra
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Nine PNGs and their two generator scripts, in the house matplotlib dialect (Okabe-Ito palette, DPI 300,
save()helper, output directory from argv), for the new batch process monitoring and control chapter.Companion PRs: the chapter that embeds these figures is kgdunn/pid-book#272; the modelling and control code they exercise is kgdunn/process-improve#516 (v1.69.0, the
controlextra). Merge order: process-improve first (the scripts installprocess-improve[control]>=1.68), then this PR, then the book PR (its PDF step stays red until these files are onmain).batch/golden-batch-figures.py(4 PNGs)golden-batch-nylon-spread.png: the bundled nylon batches; setpoint replication at sub-percent spread while the response spread is roughly 14 times wider.golden-batch-replay-spread.png: 40 simulator batches replaying the identical golden schedule; final titers 6.29 to 9.19 g/L (9.6% CV) around a 8.01 g/L reference.golden-batch-z-scores.png: the initial-conditions score plot with the A/B/C feed classes as overlapping ranges along the first component.golden-batch-variance-decomposition.png: the three variance buckets (initial conditions, within-batch disturbances, replication noise).batch/midcourse-correction-figures.py(5 PNGs)Every quantitative claim is executed on the simulator (corrected schedules re-simulated under the same disturbance seed), replicating
evaluate_control_policies(y_target=8.0, random_state=0):mcc-monitoring-funnel.png: one poor batch's predicted final titer at every decision point with the 95% prediction band.mcc-correction-at-k.png: that batch's temperature and pH schedules before and after the day-4 correction (3.66 to 5.79 g/L executed).mcc-policy-comparison.png: 40 batches under four executed policies: replay 7.51 +/- 1.20, mid-course 7.71 +/- 0.78 (4 corrected, none harmed), oracle-from-k 7.83 +/- 0.63, adapted feedforward 7.82 +/- 1.01 g/L.mcc-decision-point-window.png: mean executed gain versus decision-point placement; the useful window is days 4 to 5.mcc-exploration-dial.png: predicted versus executed titer of the corrected batches as the T2 penalty is relaxed, caps off.All of these numbers were re-measured after process-improve#516 absorbed the repo-wide audit in #500, and are unchanged.
Regeneration commands are in each module docstring; the MCC set takes roughly 20 minutes because the policy comparison is re-executed, not read from a cache.
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https://claude.ai/code/session_01NZAyiPT8xURSdi24yFKCra
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