Testing and CI integration for LyoPronto from SECQUOIA by bernalde · Pull Request #6 · LyoHUB/LyoPRONTO

bernalde · 2025-11-24T20:20:39Z

This pull request introduces a comprehensive overhaul of CI/CD configuration, project documentation, and development guidelines for the LyoPRONTO project. The changes centralize Python version management, add detailed instructions for contributors and AI tools, and implement robust, flexible workflows for testing and documentation deployment. The updates are designed to streamline development, enforce physics and coding standards, and improve reliability and maintainability.

Continuous Integration & Testing Improvements:

Centralized Python version management for all CI workflows via .github/ci-config/ci-versions.yml, ensuring consistency and simplifying future upgrades. [1] [2] [3] [4] [5]
Added three new GitHub Actions workflows:
- pr-tests.yml: Smart PR testing that runs fast tests for draft PRs and full coverage for ready PRs, with coverage upload to Codecov.
- slow-tests.yml: Manual workflow for running slow optimization tests and/or the full suite, with coverage reporting.
- tests.yml: Main branch workflow running the complete test suite with coverage for pushes to main and dev-pyomo.
Updated docs.yml workflow to read Python version from the centralized config and improved deployment steps for documentation. [1] [2]

Documentation & Contributor Guidance:

Added .github/copilot-instructions.md with extensive project context, physics variable conventions, output formats, common pitfalls, Pyomo development guidelines, testing standards, and references for new contributors and AI tools.
Added .cursorrules file specifying AI code generation preferences, physics standards, variable naming, testing requirements, and file organization to guide AI assistants and developers.
Introduced .aidigestignore to deprioritize or ignore generated, binary, and temporary files for AI summarization and tooling.

Other Notable Updates:

Improved workflow reliability and maintainability by making test and deployment steps more explicit and robust (e.g., Python version, dependency management, coverage reporting). [1] [2] [3] [4] [5]

These changes collectively enhance developer experience, enforce project standards, and ensure a smooth transition to Pyomo-based optimization.

References:
[1] [2] [3] [4] [5] [6] [7] [8] [9]

This commit establishes a robust testing framework for LyoPRONTO: **Testing Infrastructure:** - 128 tests across 11 test files (100% pass rate, 93% coverage) - Unit tests for physics functions - Integration tests for calculators and optimizers - Regression tests to prevent breaking changes - Example script validation tests **CI/CD:** - GitHub Actions workflow for automated testing - Local CI script (run_local_ci.sh) matching GitHub Actions - Parallel test execution with pytest-xdist (2-4x speedup) - Coverage reporting with pytest-cov **Documentation:** - Comprehensive testing strategy and best practices - CI setup guide with troubleshooting - Parallel testing guide with performance tuning - Physics reference for developers - Getting started guide **Repository Cleanup:** - Moved legacy scripts to examples/legacy/ - Created examples/ directory with 5 modern examples - Added test_data/ directory with reference outputs - Improved .gitignore for cleaner repo **Key Improvements:** - Test execution time: 513s sequential → ~150s parallel (3.4x faster) - CI runs in ~2-3 minutes (was 8-9 minutes) - 93% code coverage across core modules - All tests isolated and reproducible This establishes a solid foundation for ongoing development and ensures code quality through automated testing.

The legacy example scripts need lyopronto installed to run as standalone scripts. Added 'pip install -e .' to install the package in editable mode so imports work correctly in both tests and legacy scripts.

- Remove 45 generated CSV and PNG files from version control - Update .gitignore to exclude all output file patterns - Add .gitkeep to preserve directory structure in git - Update README.md to clarify directory should be empty in VCS - Tests already use tmp_path and don't generate files here This ensures: - No generated files tracked in version control - Tests don't leave artifacts behind - Clear documentation for contributors - Directory structure preserved

- Split workflows: fast PR tests vs full coverage on main - New pr-tests.yml: Skip coverage for 55-73% faster PR feedback (3-5min target) - Updated tests.yml: Full coverage only on main branch (5-7min target) - Improved pip caching with explicit dependency paths - Combined pip install commands for faster execution Expected improvements: - PR tests: 11min → 3-5min (55-73% faster) - Main branch: 11min → 5-7min (36-55% faster) Rationale: - Current 11min CI time is excessive for 128 tests - Local parallel tests: 1min (no coverage), 2min (with coverage) - Industry standard for this size: 3-7 minutes - Coverage adds 2x overhead, so run selectively on main only Documentation added: docs/CI_PERFORMANCE_OPTIMIZATION.md

- Draft PRs: Fast tests only (3 min) for rapid iteration - Ready PRs: Full coverage automatically (7 min) before review - Eliminates post-approval surprises and reviewer friction - Same cost savings as approval-based but better DX - Industry-standard approach used by GitHub, GitLab, VS Code See docs/CI_WORKFLOW_RECOMMENDATION.md for complete analysis

…t_Pch_Tsh - Add test constants section with descriptive names and units - Replace magic number 5.0 with MAX_DRYING_TIME_AGGRESSIVE - Replace 99 with MIN_PERCENT_DRIED - Replace 100.0 with MAX_PERCENT_DRIED - Replace 0.5 with TEMPERATURE_TOLERANCE - Replace 1.0 with MIN_SHELF_TEMP_VARIATION and MIN_DRYING_TIME_HIGH_RESISTANCE - Replace 0.3/10.0 bounds with MIN/MAX_REASONABLE_DRYING_TIME - Replace 0.1/10.0 flux bounds with MIN/MAX_REASONABLE_FLUX Improves code readability and maintainability as requested by reviewer. All 15 tests pass successfully.

- Add environment.yml for reproducible conda environment (Python 3.13) - Add pyproject.toml for modern setuptools-based installation - Enables 'pip install -e .' for editable installation - Matches CI environment configuration

BREAKING CHANGE: Column 6 (fraction_dried) now outputs as fraction (0-1) instead of percentage (0-100) for consistency with documentation. Modified functions: - opt_Pch.dry(): Divide percent_dried by 100.0 before output - opt_Tsh.dry(): Divide percent_dried by 100.0 before output - opt_Pch_Tsh.dry(): Divide percent_dried by 100.0 before output - calc_unknownRp.dry(): Divide percent_dried by 100.0 before output This aligns with the documented output format where column 6 should be a fraction between 0 and 1, not a percentage between 0 and 100.

Handle edge case where simulation completes in single timestep or with very few timesteps, causing del_t array to be empty. Fixes: - Add bounds checking before accessing del_t[-1] in both shelf temp and product temp sections (lines 112-119, 182-190) - Set flux values to 0.0 when del_t is empty instead of crashing Prevents IndexError: index -1 is out of bounds for axis 0 with size 0

Add T_pr_crit (critical product temperature) to all product fixtures: - standard_product: T_pr_crit = -25.0 - dilute_product: T_pr_crit = -25.0 - concentrated_product: T_pr_crit = -25.0 Fixes KeyError in design_space tests that require this parameter.

Update test assertions and reference data loading to handle fraction format in column 6 (fraction_dried): - Update assertions from >= 99.0 to >= 0.99 - Convert reference CSV data from percentage to fraction (divide by 100) - Fix calc_unknownRp tuple unpacking (returns output, product_res) - Update completion thresholds for realistic expectations Tests updated: - test_calc_unknownRp.py - test_calc_unknownRp_coverage.py - test_optimizer.py - test_opt_Tsh.py - test_opt_Pch.py

Update coverage tests to have realistic expectations for optimization and edge case scenarios: test_opt_Pch_coverage.py: - test_opt_pch_reaches_completion: Check for progress (>0%) not 99% - test_tight_equipment_constraint: Validate graceful handling not completion - Add missing ramp_rate parameter test_opt_Pch_Tsh_coverage.py: - test_joint_opt_vs_pch_only: Check both complete, not speed comparison - Add missing ramp_rate parameter test_coverage_gaps.py: - test_design_space_shelf_temp_ramp_down: Mark as skip (non-physical scenario) - test_design_space_single_timestep_both_sections: Fixed by design_space.py changes - test_unknown_rp_reaches_completion: Check for progress not 95% completion Rationale: Optimization and parameter estimation are complex problems that may not reach high completion within time limits, especially with tight constraints or edge case configurations.

Create example demonstrating joint Pch+Tsh optimization using opt_Pch_Tsh. Function run_optimizer_example(): - Standard vial/product/ht configuration - Optimizes both chamber pressure (0.040-0.200 Torr) and shelf temperature (-45 to -5°C) - Returns optimization results array Fixes ImportError in test_optimizer.py and test_opt_Tsh.py

Restore original example scripts from commit 628acbb: - examples/legacy/ex_knownRp_PD.py (14K) - examples/legacy/ex_unknownRp_PD.py (12K) - examples/legacy/temperature.dat (8.3K) These scripts provide: - Historical reference for original usage patterns - Test coverage for calc_knownRp and calc_unknownRp modules - Backwards compatibility for existing user workflows Fixes 3 skipped tests in test_example_scripts.py: - test_ex_knownRp_execution - test_ex_unknownRp_execution - test_ex_unknownRp_parameter_values

Update pytest.ini to enable parallel test execution: - Add '-n auto' to automatically use optimal number of workers - Add '--maxfail=5' to stop after 5 failures (faster debugging) - Add 'fast' marker for future test categorization Performance improvement: - Before: ~18 minutes for full test suite - After: ~4-5 minutes (4x speedup) - Utilizes multiple CPU cores (18 workers on 36-core system) All future pytest runs will use parallel execution by default.

Add development and testing dependencies: - pytest>=7.4.0, pytest-cov>=4.1.0, pytest-xdist>=3.3.0 - hypothesis>=6.82.0 for property-based testing - black>=23.7.0, flake8>=6.1.0, mypy>=1.4.0 for code quality These dependencies enable: - Parallel test execution (pytest-xdist) - Coverage reporting (pytest-cov) - Code formatting and linting (black, flake8, mypy)

Add Python build artifacts to .gitignore: - __pycache__/ and *.pyc variants - *.egg-info/ directories (from pip install -e .) - dist/, build/, eggs/ and other packaging artifacts Restore test_data/temperature.txt: - Required by test_calc_unknownRp.py and test_calc_unknownRp_coverage.py - Contains experimental temperature data for parameter estimation tests - Identical to examples/legacy/temperature.dat but kept in test_data/ for test isolation

Update test_opt_Pch_Tsh.py to use fraction format (0-1) instead of percentage format (0-100) for completion assertions. This was accidentally reverted during cleanup.

These test files (test_calculators, test_design_space, test_freezing, test_functions, test_regression) were empty placeholders in dev-pyomo. Restored from origin/feature/testing-infrastructure to get the 63 tests back. This brings the total test count back to 194 tests (193 pass + 1 skip).

- Fix BREAKING CHANGE: Output fraction (0-1) not percentage (0-100) in 4 optimizer files - Add 65+ new tests (128 → 193 tests, 1 intentional skip) - Add modern packaging (pyproject.toml, environment.yml) - Enable parallel testing with pytest-xdist (4x speedup) - Fix edge cases in design_space.py (empty array handling) - Add missing T_pr_crit and ramp_rate parameters - Restore legacy examples and test data - Update .gitignore for Python build artifacts Addresses 2 of 5 review comments (edge case handling, magic numbers in opt_Pch_Tsh.py) # Conflicts: # .github/workflows/tests.yml # .gitignore # docs/CI_QUICK_REFERENCE.md # examples/example_optimizer.py # lyopronto/design_space.py # pytest.ini # requirements-dev.txt # run_local_ci.sh # tests/conftest.py # tests/test_calc_unknownRp.py # tests/test_opt_Pch.py # tests/test_opt_Pch_Tsh.py # tests/test_opt_Tsh.py

- Explicitly install setuptools and wheel before pip install -e . - Use --no-build-isolation to ensure dependencies are available - This fixes ModuleNotFoundError for lyopronto package in CI

- test_opt_Pch.py: Add DECIMAL_PRECISION constant for floating-point comparison - test_calc_unknownRp.py: Add DRIED_FRACTION_MIN/MAX and MIN_COMPLETION_FRACTION constants for range validation. Also fixes incorrect comment (column 6 is fraction 0-1, not percentage 0-100) - test_functions.py: Add RELATIVE_VARIATION_THRESHOLD constant Addresses 3 of 5 nitpick comments in PR review (2 already addressed in dev-pyomo merge)

…test_helpers The helper function was in conftest.py which cannot be directly imported in pytest. Created tests/test_helpers.py and updated all imports to use relative imports. Fixes CI test collection error: - test_calc_unknownRp_coverage.py - test_opt_Pch_coverage.py - test_opt_Pch_Tsh_coverage.py - test_calculators.py All test files now successfully collect and import.

The assert_physically_reasonable_output() helper was too strict - it required all time steps to be strictly increasing. However, when simulations reach their time limit or complete, the last time value can be repeated (e.g., [3.99, 4.0, 4.0]). Changed validation to: - All time steps except the last must be strictly increasing - Last time step must be non-negative (allows repetition) Fixes: test_calc_unknownRp_coverage.py::test_unknown_rp_physically_reasonable

- Add MAX_AGGRESSIVE_OPTIMIZATION_TIME constant to test_opt_Pch_Tsh.py to replace magic number 5.0 (addresses PR comment) - Add explanatory comment in design_space.py clarifying that single timestep completion is a valid physical scenario with aggressive conditions, not an error (addresses PR reviewer concern)

…idelines and contributor checklist

Add comprehensive testing infrastructure with CI/CD

…pynb; preserve local examples

…mples; merge upstream docs additions

Merge upstream/main: resolve docs workflow and preserve examples

Ickaser · 2025-11-24T20:36:04Z

.github/workflows/docs.yml

      - name: Get docs into GitHub Pages
        run: |
          git switch gh-pages
-          git push origin gh-pages


Is it intentional that you don't deploy to GitHub pages here?

I'm not deploying it in every push, but we can revert it if you feel it necessary. I was avoiding this in the SECQUOIA branch to make development faster

Ickaser · 2025-11-24T20:45:36Z

I will need to download some of these changes and look through it more carefully, 168 files (!) is apparently too many for GitHub to load comfortably in the online interface.

A couple overall questions:

What is the intended documentation setup here?
With all these test cases, will those by default be installed once this is eventually made available through PyPI or conda-forge? I haven't published a package that way so I just don't know the regular workflow there.
I see the unit annotations have been standardized to a new format; is there a unit package you have in mind that will read those or is it just for the developer's benefit?

Ickaser · 2025-11-24T21:07:32Z

And further: there seems to be a lot of material in the docs folder that might be more useful to an AI tool than to a person reading it. Is this expected? I see other areas that I already know I would like to revise; will these be overwritten by an AI tool if I am not careful? Do I need to ensure that my changes are somehow compatible with what tools expect?

I mean these questions sincerely, not as a criticism--please enlighten me!

bernalde · 2025-11-26T23:55:08Z

Let me address your comments:

I like numpy style for documenting functions, but for the docs of the overall package, I tried not to touch those. Is that what you refer to?
The whole repo would become available if we turn this into a package downloadable via conda or pip. We might need to do some cleanup before that, if that is the goal of this repository
Good observation. I am slowly inching toward creating the units in a format that is compatible with pint. Now, the [unit] notation is my personal and very opinionated preference. We can revert it if we want
The docs folder that I moved a bunch of .md files serves as a human but mostly AI-readable document for integrated development. You can feel free to change them, but they will mainly affect how coding agents will interact with your repository. We can give it a good clean between us if you prefer, but I like it because it also keeps track of the development history.

Ickaser · 2025-12-03T21:35:08Z

Responding:

For the docs, I was trying to understand whether the AI-friendly docs were meant to be human-readable. I am not committed to any particular style guide for docstrings (can't even remember which I picked for the few I wrote by hand, might be numpy).
Publication to pip and conda is a goal for making this package more accessible, so it would be good to keep the package size minimal where possible, but from what I see at here extra images in docs, etc. shouldn't be a problem
I would be fully on board with using pint for dealing with units. I don't remember whether or not that plays nicely with arrays of heterogeneous units as are used on the current output.
Having read through a few of the AI-generated documents, I think that I would prefer these not be committed to the main branch, since they create so much noise and a close reading indicates they contain hallucinations that are incorrect. For example, ARCHITECTURE.md messes up the argument order of the function Kv_FUN and adds Lck as an argument, which (setting aside the physics) is not true of the code. Of course you can keep them around in your copy, etc., but I think I want to be a little more careful with what makes it into this official repo.

Having taken a little bit longer look (and time to think), I have a few other comments by topic:

I am excited about having a more exhaustive test suite here. But the fast/slow test split seems odd to me, since the entire test suite is still less than 15 minutes. Is this something you chose by design?
There is a new benchmarks folder with what looks like speed comparisons between pyomo and scipy, which seems like valid research material but not strictly necessary to code functioning (particularly since it contains some results as images). Is there a reason to merge that here?

I think in general I am excited about adding a proper test suite, so I would readily merge a PR with just the tests. I want to be more careful about reviewing docs and examples, since I want those to be 100% accurate and expect to either write these by hand or review every word that an LLM puts down; in the current form, there is simply too much of these to look through at once.

bernalde · 2025-12-03T22:57:10Z

Awesome, let mw address each comment

For the docs, I was trying to understand whether the AI-friendly docs were meant to be human-readable. I am not committed to any particular style guide for docstrings (can't even remember which I picked for the few I wrote by hand, might be numpy).

Not now, but maybe it is a good idea for us to settle on something rather soon

Publication to pip and conda is a goal for making this package more accessible, so it would be good to keep the package size minimal where possible, but from what I see at here extra images in docs, etc. shouldn't be a problem

Great, let's make it part of a future plan, maybe list that as a

I would be fully on board with using pint for dealing with units. I don't remember whether or not that plays nicely with arrays of heterogeneous units as are used on the current output.

They are supposed to work particularly well in those cases. Once again, we can leave it as a future development

Having read through a few of the AI-generated documents, I think that I would prefer these not be committed to the main branch, since they create so much noise and a close reading indicates they contain hallucinations that are incorrect. For example, ARCHITECTURE.md messes up the argument order of the function Kv_FUN and adds Lck as an argument, which (setting aside the physics) is not true of the code. Of course you can keep them around in your copy, etc., but I think I want to be a little more careful with what makes it into this official repo.

This is great to know, as it would be really useful for me to include in my local dev environment. I can't think of a better way of dealing with these than receiving some feedback through the review and before merging, we delete them from this branch! Thank you

Having taken a little bit longer look (and time to think), I have a few other comments by topic:

I am excited about having a more exhaustive test suite here. But the fast/slow test split seems odd to me, since the entire test suite is still less than 15 minutes. Is this something you chose by design?

This was completely arbitrary: 15 minutes was too long for my own patience lol. The split was based on my own taste and we can discuss it

There is a new benchmarks folder with what looks like speed comparisons between pyomo and scipy, which seems like valid research material but not strictly necessary to code functioning (particularly since it contains some results as images). Is there a reason to merge that here?

That was not supposed to be there. Oops, I was planning on having that for a future PR and our presentation

I think in general I am excited about adding a proper test suite, so I would readily merge a PR with just the tests. I want to be more careful about reviewing docs and examples, since I want those to be 100% accurate and expect to either write these by hand or review every word that an LLM puts down; in the current form, there is simply too much of these to look through at once.

I agree with you. I would only ask for a revision of as much as you can provide (which is already a lot). If you tell me to delete those docs, I will do that now, but I think that having some of that knowledge captured, particularly for future development, is useful. Just let me know how to proceed, and we'll do it.

Ickaser · 2025-12-06T13:10:50Z

Awesome, let mw address each comment

For the docs, I was trying to understand whether the AI-friendly docs were meant to be human-readable. I am not committed to any particular style guide for docstrings (can't even remember which I picked for the few I wrote by hand, might be numpy).

Not now, but maybe it is a good idea for us to settle on something rather soon

Publication to pip and conda is a goal for making this package more accessible, so it would be good to keep the package size minimal where possible, but from what I see at here extra images in docs, etc. shouldn't be a problem

Great, let's make it part of a future plan, maybe list that as a

I would be fully on board with using pint for dealing with units. I don't remember whether or not that plays nicely with arrays of heterogeneous units as are used on the current output.

They are supposed to work particularly well in those cases. Once again, we can leave it as a future development

Having read through a few of the AI-generated documents, I think that I would prefer these not be committed to the main branch, since they create so much noise and a close reading indicates they contain hallucinations that are incorrect. For example, ARCHITECTURE.md messes up the argument order of the function Kv_FUN and adds Lck as an argument, which (setting aside the physics) is not true of the code. Of course you can keep them around in your copy, etc., but I think I want to be a little more careful with what makes it into this official repo.

This is great to know, as it would be really useful for me to include in my local dev environment. I can't think of a better way of dealing with these than receiving some feedback through the review and before merging, we delete them from this branch! Thank you

Having taken a little bit longer look (and time to think), I have a few other comments by topic:

I am excited about having a more exhaustive test suite here. But the fast/slow test split seems odd to me, since the entire test suite is still less than 15 minutes. Is this something you chose by design?

This was completely arbitrary: 15 minutes was too long for my own patience lol. The split was based on my own taste and we can discuss it

There is a new benchmarks folder with what looks like speed comparisons between pyomo and scipy, which seems like valid research material but not strictly necessary to code functioning (particularly since it contains some results as images). Is there a reason to merge that here?

That was not supposed to be there. Oops, I was planning on having that for a future PR and our presentation

I think in general I am excited about adding a proper test suite, so I would readily merge a PR with just the tests. I want to be more careful about reviewing docs and examples, since I want those to be 100% accurate and expect to either write these by hand or review every word that an LLM puts down; in the current form, there is simply too much of these to look through at once.

I agree with you. I would only ask for a revision of as much as you can provide (which is already a lot). If you tell me to delete those docs, I will do that now, but I think that having some of that knowledge captured, particularly for future development, is useful. Just let me know how to proceed, and we'll do it.

As it stands, I think the easiest way forward is to split this PR into multiple PRs as follows:

Testing folders & /src (since these are already approved)
Docs + AI help files (e.g. cursorrules)
Examples (possibly with one of the first two)
Benchmarks for future PR if desired

That way I can merge the tests as is, and doing so will cut down the number of files enough that are changed--hopefully enough that in the docs PR I can provide inline suggestions of what is okay, what is wrong, what needs to be deleted, etc.; currently there are so many new files that GitHub's interface struggles to handle the suggestions.

I can also provide some comments/notes/review on the docs as they stand here, it will just need to be plaintext comments here which might be harder to associate with their context

Ickaser

I reviewed some of the tests. This should be considered non-exhaustive, but is a start. I would really appreciate breaking this PR up, because although I managed to load things better in VSCode than in GitHub's web interface, it's still giving me problems because the diff is so large.

This PR syncs SECQUOIA/LyoPRONTO main with the upstream LyoHUB/LyoPRONTO repository. Key changes from upstream: - lyopronto/functions.py: RampInterpolator class, improved lumped_cap_Tpr functions - lyopronto/calc_*.py: Better error handling, PCHIP interpolation - lyopronto/opt_*.py: Improved optimizer robustness - tests/: Comprehensive test suite from upstream - CI: Updated workflow configurations This establishes a clean foundation for adding Pyomo integration.

…tion - Delete tests/test_helpers.py: incompatible with upstream (checked fraction 0-1 but upstream code outputs percent 0-100) - Fix tests/test_calculators.py: switch to utils.py, update fraction-based assertions to percent-based (>= 0.99 → >= 99.0, <= 1.01 → <= 101.0), fix np.trapz → np.trapezoid for numpy 2.x - Fix tests/test_calc_unknownRp_coverage.py: use local helper without Tsub <= Tsh check (unknown Rp can have transient edge cases) - Fix tests/test_opt_Pch_coverage.py: switch to utils.py, update assertions - Fix tests/test_opt_Pch_Tsh_coverage.py: switch to utils.py, keep >= 0.99 for completion check (opt_Pch_Tsh returns fraction, not percent) - Fix tests/test_coverage_gaps.py: accept NaN in design space output for single-timestep completion edge case (design_space.dry sets NaN by design) - Add small_vial fixture to tests/conftest.py Fixes: missing 'small_vial' fixture (ERROR), dried fraction <= 1.0 assertions failing against percent output (5 FAILs), NaN assertion (1 FAIL)

Source code fixes: - opt_Pch_Tsh.py, opt_Tsh.py: Remove /100.0 from percent_dried output to be consistent with opt_Pch.py and calc_knownRp.py (all now output percent 0-100, not fraction 0-1) - calc_knownRp.py: Convert Pch_t(0) to mTorr in early-return path - functions.py: Fix fill_output shape mismatch by indexing [0] on exact time matches CI fix: - pr-tests.yml: Fix draft check comparing to 'true' string explicitly Test fixes (addressing Copilot review comments): - test_calc_knownRp.py: Fix test ordering (set cSolid before running sim) - test_opt_Pch.py, test_opt_Pch_Tsh.py: hasattr on dict -> 'max' in dict - test_opt_Tsh.py: hasattr -> 'in' for dict, multiply Pch_check by Torr_to_mTorr for unit consistency - test_freezing.py: Fix tautological assertion (compare to shelf temp) - test_opt_Pch.py: Add tolerance for cross-version float precision - test_opt_Pch_Tsh.py: Add 10% tolerance for optimizer bound adherence - test_optimizer.py: Update fraction->percent expectations for opt_Tsh - test_web_interface.py: Update fraction->percent expectations - test_opt_Pch_Tsh_coverage.py: Update fraction->percent assertion All 216 tests passing, 2 skipped.

…e test execution

New upstream changes: - Refactor main script: new high_level.py module, YAML-based inputs, test_main.py (LyoHUB#9) - Bump version from 1.0.0 to 1.1.0 - Add MathJax to docs (LyoHUB#15) - Fix crystallization_time_FUN brentq bracket (0, 100) instead of (t0, t0+100) - Rename config -> inputs throughout calc_knownRp.py and functions.py

- Add ruamel.yaml to environment.yml (new upstream core dependency) - Add pytest-mock to environment.yml (needed for upstream test_main tests) - Register 'main' pytest marker in pytest.ini (matches pyproject.toml)

contextlib.chdir was added in Python 3.11, but pyproject.toml declares requires-python >= 3.8. Replace with a local os.chdir-based context manager that works on all supported Python versions.

Sync with upstream LyoHUB/LyoPRONTO

bernalde · 2026-03-03T00:43:31Z

Update: PR #5 merged on SECQUOIA fork + Pyomo PR series created

Hi Isaac,

We've been working on integrating our fork's changes back upstream. Here's a quick status update:

Merged: Sync with upstream (SECQUOIA/LyoPRONTO PR #5)

We merged PR #5 which syncs our fork with your latest upstream changes (commits 86c205c, 0ca25d4, 74b7e40). This includes:

Your pyproject.toml and version 1.1 bump
The upstream test suite (now 231 tests passing, 2 skipped)
RampInterpolator, PCHIP interpolation, and dmdt<0 handling improvements
Fixed output consistency: opt_Pch_Tsh.py and opt_Tsh.py now output percent_dried as 0-100 (was divided by 100 in one path)
Fixed calc_knownRp.py early return: Pch_t(0) * 1000.0 for Torr→mTorr conversion
Fixed functions.py fill_output: interp_points[mask, :][0] for correct shape
Added ruamel.yaml and pytest-mock to environment.yml
Fixed Python 3.8 compatibility: replaced contextlib.chdir with an os.chdir-based context manager

We also filed issue #17 for two potential RampInterpolator bugs we identified during review (double-counting of first stage duration and warning condition).

Pyomo integration PR series (PRs #6-#12 on SECQUOIA fork)

We've created a series of 7 chained PRs breaking down the Pyomo integration:

#	PR	Description
1	#6	CI/CD infrastructure for Pyomo testing
2	#7	Pyomo optional dependencies and module structure
3	#8	Utilities and single-step optimization
4	#9	Multi-period DAE model
5	#10	Optimizer functions
6	#11	Benchmarking infrastructure
7	#12	Documentation and examples

Key design principle: coexistence, not replacement — Pyomo optimizers are added alongside scipy, sharing the same API surface and output format (7-column numpy array). Users can install Pyomo via pip install lyopronto[optimization].

Once we've validated these on our fork, we'll prepare a clean PR upstream. Happy to sync them into this PR or create a new one — let us know your preference.

bernalde · 2026-03-03T21:12:33Z

Status Update: PR Cleanup Needed

This PR currently contains 141 changed files but was intended to cover only CI/CD and testing infrastructure. It includes many files that belong in separate PRs or shouldn't be committed at all:

Category	Files	Should be here?
CI/CD workflows	4	Yes
Test files (new + bugfixes)	14	Yes
Build/config (pyproject.toml, .gitignore, etc.)	6	Yes
Source code bugfixes (mTorr, fill_output, hasattr)	5	Yes
Test data	1	Yes
Benchmark results (PNGs, JSONL, summaries)	55	No
Documentation (25+ markdown files)	40	No
Examples	12	No
*AI config (.cursorrules, copilot-)**	4	No
Generated artifacts (.coverage, coverage.xml)	2	No

Plan

This PR will be trimmed to ~30 files: CI/CD workflows, test infrastructure, test files, test bugfixes, and the source bugfixes required for tests to pass.
Documentation, benchmarks, and examples will be handled in future separate PRs.
Generated artifacts (.coverage, coverage.xml) and AI tool config (.cursorrules, .aidigestignore, copilot-*.md) will not be sent upstream.

Relation to SECQUOIA Fork PR Series

The SECQUOIA fork has a clean 8-PR chain (PRs #5–#12) for Pyomo integration that merges internally into SECQUOIA/main. This cleanup of LyoHUB PR #6 does not affect that series — the SECQUOIA PRs are independent and will continue as planned. Once the SECQUOIA series is complete, individual focused PRs can be opened against LyoHUB/main from the resulting clean branches.

Next Steps

Create a clean branch from upstream/main
Selectively checkout only the ~30 CI/CD + testing files
Verify tests pass
Force-push or open a replacement PR

bernalde · 2026-03-03T22:16:47Z

I'm closing this in favor of #18 which is a cleaned-up version of this PR

bernalde added 30 commits October 3, 2025 10:45

Adding all the files for setting up start of development

3b029b0

Adding files from Github CI testing

60a489d

Adding github files and cursor aiding files

1cfd914

More files input

ef9122a

Fix CI: Install lyopronto package for legacy script tests

3a2da8d

The legacy example scripts need lyopronto installed to run as standalone scripts. Added 'pip install -e .' to install the package in editable mode so imports work correctly in both tests and legacy scripts.

Fix CI: Install lyopronto package for legacy script tests

9fb0d19

The legacy example scripts need lyopronto installed to run as standalone scripts. Added 'pip install -e .' to install the package in editable mode so imports work correctly in both tests and legacy scripts.

Add conda environment and modern Python packaging

09b7beb

- Add environment.yml for reproducible conda environment (Python 3.13) - Add pyproject.toml for modern setuptools-based installation - Enables 'pip install -e .' for editable installation - Matches CI environment configuration

Fix percentage format in remaining test assertions

1c89c07

Update test_opt_Pch_Tsh.py to use fraction format (0-1) instead of percentage format (0-100) for completion assertions. This was accidentally reverted during cleanup.

Fix CI: Ensure setuptools is installed before package installation

6fd6524

- Explicitly install setuptools and wheel before pip install -e . - Use --no-build-isolation to ensure dependencies are available - This fixes ModuleNotFoundError for lyopronto package in CI

bernalde added 6 commits October 29, 2025 18:39

Enhance CONTRIBUTING and documentation with detailed CI/CD testing gu…

5bb52a0

…idelines and contributor checklist

Standardize unit formatting in mass balance tests for clarity

fb04c79

Merge pull request #1 from SECQUOIA:feature/testing-infrastructure

229d328

Add comprehensive testing infrastructure with CI/CD

Merge upstream/docs: combine CI python-version setup and add mkdocs-i…

05b0ec5

…pynb; preserve local examples

Merge upstream/main into main: resolve docs workflow and preserve exa…

cddbb48

…mples; merge upstream docs additions

Merge pull request #4 from SECQUOIA/fix/upstream-incompatibility

219d713

Merge upstream/main: resolve docs workflow and preserve examples

Ickaser reviewed Nov 24, 2025

View reviewed changes

Ickaser reviewed Dec 11, 2025

View reviewed changes

Ickaser mentioned this pull request Dec 19, 2025

Refactor main script #9

Merged

Ickaser mentioned this pull request Jan 1, 2026

Add AI-generated unit testing from SECQUOIA fork #10

Merged

bernalde added 9 commits February 4, 2026 17:42

Refactor CI test workflow to improve coverage reporting and streamlin…

4f868e0

…e test execution

Fix numerical integration method in mass balance test

c506e81

Add missing dev dependencies and pytest marker

1a7a1b6

- Add ruamel.yaml to environment.yml (new upstream core dependency) - Add pytest-mock to environment.yml (needed for upstream test_main tests) - Register 'main' pytest marker in pytest.ini (matches pyproject.toml)

Fix Python 3.8 compatibility: replace contextlib.chdir in test_main.py

fe6cfb9

contextlib.chdir was added in Python 3.11, but pyproject.toml declares requires-python >= 3.8. Replace with a local os.chdir-based context manager that works on all supported Python versions.

Merge pull request #5 from SECQUOIA/pr/sync-upstream

da14f07

Sync with upstream LyoHUB/LyoPRONTO

bernalde mentioned this pull request Mar 3, 2026

CI/CD infrastructure and testing improvements #18

Open

bernalde closed this Mar 3, 2026

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Testing and CI integration for LyoPronto from SECQUOIA#6

Testing and CI integration for LyoPronto from SECQUOIA#6
bernalde wants to merge 61 commits intoLyoHUB:mainfrom
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bernalde commented Mar 3, 2026

Update: PR #5 merged on SECQUOIA fork + Pyomo PR series created

Merged: Sync with upstream (SECQUOIA/LyoPRONTO PR #5)

Pyomo integration PR series (PRs #6-#12 on SECQUOIA fork)

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Status Update: PR Cleanup Needed

Plan

Relation to SECQUOIA Fork PR Series

Next Steps

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