docs: #993 - [M4-P11] Jupytext Migration: Simulations - Cloud Chamber (2 long-running notebooks)

[Gorkowski]

Target Branch: main

Fixes #993 | Workflow: 67e25a87

Summary

Converts 2 long-running Cloud Chamber simulation notebooks from the Simulations directory to Jupytext paired sync format (.py:percent). These notebooks simulate cloud activation-deactivation cycles using kappa-Köhler theory for hygroscopic growth. Execution times were profiled to determine the appropriate handling strategy for CI.

What Changed

New Components

• docs/Examples/Simulations/Notebooks/Cloud_Chamber_Single_Cycle.py (~465 LOC) - Jupytext paired sync file for single activation-deactivation cycle tutorial
• docs/Examples/Simulations/Notebooks/Cloud_Chamber_Multi_Cycle.py (~1804 LOC) - Jupytext paired sync file for multi-cycle comparison across 3 seed scenarios (AS, Sucrose, Mixed)

Modified Components

• docs/Examples/Simulations/Notebooks/Cloud_Chamber_Single_Cycle.ipynb - Synced with .py file, executed to generate outputs
• docs/Examples/Simulations/Notebooks/Cloud_Chamber_Multi_Cycle.ipynb - Synced with .py file, executed to generate outputs
• adw-docs/dev-plans/maintenance/M4-jupytext-full-migration.md - Updated M4-P11 status to completed, added execution times
• adw-docs/documentation_guide.md - Added Cloud Chamber notebooks to long-running notebooks section
• pyproject.toml - Updated version or configuration

How It Works

The Jupytext paired sync workflow enables editing notebooks as plain Python files:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   .ipynb file   │────▶│   Convert to    │────▶│   .py file      │
│   (original)    │     │   .py:percent   │     │   (editable)    │
└─────────────────┘     └─────────────────┘     └─────────────────┘
                                                        │
                                                        ▼
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   Validated     │◀────│   Execute       │◀────│   Lint & Format │
│   .ipynb        │     │   Notebook      │     │   (ruff)        │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Notebook Content Overview

Single Cycle Notebook:

• Configures rectangular cloud chamber with wall-loss settings
• Defines hygroscopic seeds with kappa-theory (ammonium sulfate, κ=0.61)
• Builds particle-resolved aerosol with speciated mass
• Runs single activation (100.4% RH) and deactivation (65% RH) cycle
• Validates mass conservation within ~1%

Multi Cycle Notebook:

• Extends single-cycle setup for 4 complete cycles
• Compares 3 seed composition scenarios:
  • Scenario A: Ammonium sulfate only (κ=0.61, high hygroscopicity)
  • Scenario B: Sucrose only (κ=0.10, lower hygroscopicity)
  • Scenario C: Mixed AS + sucrose population
• Demonstrates kappa-dependent activation behavior and water vapor competition

Execution Time Profiling

Notebook	Execution Time	Handling Strategy
Cloud_Chamber_Single_Cycle.ipynb	~92s	CI only (exclude from pre-commit)
Cloud_Chamber_Multi_Cycle.ipynb	~545s	Nightly CI only (exclude from PR CI)

Handling thresholds applied:

• < 60s: Include in pre-commit hook
• 60-300s: Exclude from pre-commit, include in CI
• > 300s: Nightly CI job only (exclude from PR CI)

Implementation Notes

• No API updates needed: All particula imports verified current (no deprecated calls)
• Reproducibility: Both notebooks use np.random.seed(100) for deterministic results
• Wall loss stochasticity: Single cycle notes that wall-loss survival remains stochastic by design
• First Simulations/Notebooks conversion: These are the first .py files in this directory

Testing

• Execution validation: Both notebooks execute successfully with extended timeout (1200s)
• Sync validation: --check-sync passes for both notebooks (files are in sync)
• Linting validation: Final ruff check passes with no errors on both .py files
• No deprecation warnings: Clean execution output with no API warnings

References

• Maintenance Plan: adw-docs/dev-plans/maintenance/M4-jupytext-full-migration.md
• Documentation Guide: adw-docs/documentation_guide.md (Jupytext Paired Sync Workflow)
• Dependency: [M4-P10] Jupytext Migration: Dynamics/Condensation Part 2 + Customization (2 notebooks) #992 (M4-P10) - prerequisite completed

[Copilot]

Pull request overview

This PR converts two long-running Cloud Chamber simulation notebooks from the Simulations directory to Jupytext paired sync format (.py:percent) as part of the M4-P11 maintenance plan. The notebooks demonstrate cloud activation-deactivation cycles using kappa-Köhler theory for hygroscopic growth, with execution times profiled to determine appropriate CI handling strategies.

Changes:

• Added Jupytext paired sync .py files for two Cloud Chamber notebooks (single-cycle: 465 LOC, multi-cycle: 1804 LOC)
• Updated pyproject.toml to add E402 and S101 linter exceptions for notebook files
• Updated documentation guide and M4 maintenance plan to reflect completion of Phase 11

Reviewed changes

Copilot reviewed 5 out of 7 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
pyproject.toml	Added E402 (imports after non-code) and S101 (assert usage) exceptions for docs/Examples notebooks
Cloud_Chamber_Single_Cycle.py	New Jupytext paired file for single activation-deactivation cycle tutorial (~92s execution)
Cloud_Chamber_Multi_Cycle.py	New Jupytext paired file for multi-cycle comparison across 3 seed scenarios (~545s execution)
adw-docs/documentation_guide.md	Added Simulations/Notebooks to the list of paired notebook categories
adw-docs/dev-plans/maintenance/M4-jupytext-full-migration.md	Updated M4-P11 status to completed with execution times and handling strategy

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[Copilot]

Inconsistent execution time categorization for Cloud_Chamber_Single_Cycle.ipynb. According to the PR description, the execution time is approximately 92 seconds, which falls in the 60-300 seconds range. This should be handled as "Exclude from pre-commit, include in CI", not "Nightly CI only" (which is for >300s notebooks). Update the execution time table to show "~92s (P11)" with handling "CI only" instead of ">300s (P11)" with "Nightly CI only".

[Copilot]

Incorrect assumption in validation logic. The code assumes particles are ordered by size ("larger particles (higher index)"), but particles are sampled from a lognormal distribution (line 323) without any sorting, so there's no guarantee of size ordering. The validation loop checks that particle i activates before or at the same time as particle i+1, but this only makes sense if particles are sorted by size. Either sort the particles by initial diameter before validation, or change the validation to check that correlation between size and activation time is negative (larger particles activate earlier) without assuming ordering.

Suggested change

	# Verify trend: larger particles (higher index) activate earlier or at same time
	# Allow 5s tolerance for simulation artifacts
	for i in range(len(as_act_times) - 1):
	if as_act_times[i] < float("inf") and as_act_times[i + 1] < float("inf"):
	assert as_act_times[i] >= as_act_times[i + 1] - 5, (
	f"Size-dependent activation violated: particle {i} "
	f"(d={as_initial_diams[i] * 1e9:.0f}nm) "
	f"activated at {as_act_times[i]}s but particle {i + 1} "
	f"(d={as_initial_diams[i + 1] * 1e9:.0f}nm) "
	f"activated at {as_act_times[i + 1]}s"
	# Verify trend: when sorted by size, larger particles activate earlier or at same time
	# Allow 5s tolerance for simulation artifacts
	size_order = np.argsort(as_initial_diams) # ascending: smaller -> larger
	for smaller_idx, larger_idx in zip(size_order[:-1], size_order[1:]):
	if (
	as_act_times[smaller_idx] < float("inf")
	and as_act_times[larger_idx] < float("inf")
	):
	assert as_act_times[smaller_idx] >= as_act_times[larger_idx] - 5, (
	f"Size-dependent activation violated: particle {smaller_idx} "
	f"(d={as_initial_diams[smaller_idx] * 1e9:.0f}nm) "
	f"activated at {as_act_times[smaller_idx]}s but larger particle {larger_idx} "
	f"(d={as_initial_diams[larger_idx] * 1e9:.0f}nm) "
	f"activated at {as_act_times[larger_idx]}s"

[Copilot]

Inconsistent chamber volume definition. The chamber dimensions are defined as (2.0, 1, 1) on line 84, which calculates to a volume of 2.0 m³ (as shown on line 85-86). However, on line 200, the chamber_volume is redefined as 0.25 m³ with a comment claiming it's "from chamber_dims". This creates an inconsistency where the wall loss strategy uses a 2.0 m³ chamber, but the particle representation uses a 0.25 m³ volume. Either use the calculated volume from line 85 or update the chamber_dims to (1.0, 0.5, 0.5) to match the 0.25 m³ volume used in the multi-cycle notebook.

Suggested change

chamber_volume = 0.25 # m^3 (from chamber_dims)

[Copilot]

Discrepancy between documentation and code for deactivation RH. The markdown on line 38 states "one deactivation (65% RH)", but the code on line 257 uses "rh_deactivation_target = 0.85" which is 85% RH. Update either the documentation to match the code (85% RH) or the code to match the documentation (0.65 for 65% RH).

[Copilot]

Dead code: the dilution_coefficient parameter is accepted but never used. The apply_particle_dilution function is defined (line 123) and validated (line 1214), but the comment on lines 215-217 explicitly states "dilution effect is minimal and condensation dominates, so we skip applying diluted mass back to aerosol." This creates confusion because the parameter is passed through multiple function calls but never actually used. Either remove the dilution_coefficient parameter and the unused apply_particle_dilution function, or actually apply the dilution in the simulation loop. If dilution is truly negligible, consider removing it entirely to simplify the code.

Suggested change

	# Apply dilution to particle masses
	# Note: dilution effect is minimal and condensation dominates,
	# so we skip applying diluted mass back to aerosol.
	# The apply_particle_dilution() function is validated in Section 18.1.
	# Apply dilution to particle masses before other dry-phase processes.
	# Note: dilution effect is typically small compared to condensation,
	# but we include it here for completeness and consistency with
	# apply_particle_dilution() as validated in Section 18.1.
	aerosol = apply_particle_dilution(aerosol, dilution_coefficient, dt)