diff --git a/.codecov.yml b/.codecov.yml index 9ab323b..9334e7e 100644 --- a/.codecov.yml +++ b/.codecov.yml @@ -5,11 +5,11 @@ coverage: status: project: default: - target: auto + target: 90% threshold: 1% patch: default: - target: auto + target: 90% threshold: 1% comment: layout: "reach, diff, flags, files" diff --git a/.github/.claude/rules/calliope-code-review.md b/.github/.claude/rules/calliope-code-review.md new file mode 100644 index 0000000..18c770f --- /dev/null +++ b/.github/.claude/rules/calliope-code-review.md @@ -0,0 +1,126 @@ +--- +description: CALLIOPE-specific code review criteria for the generator-evaluator pattern. Applies domain expertise (chemistry, fO2 buffers, solubility, PROTEUS coupling) to all code review in this repo. +--- + +# CALLIOPE Code Review Criteria + +When reviewing CALLIOPE code (either your own or via code-reviewer agents), apply these domain-specific checks in addition to standard code quality review. + +> **Discovery note.** CALLIOPE keeps its Claude-Code rule files under `.github/.claude/rules/` (not the conventional repo-root `.claude/`) so they can be tracked in git and shared across collaborators. Claude does NOT auto-discover them at this path; the repo-root `CLAUDE.md` (symlinked to `.github/copilot-instructions.md`) names this file and `calliope-tests.md` explicitly. **Before opening any review pass, read both this file and `calliope-tests.md`.** + +## Physics plausibility + +- Temperature must be positive everywhere (Kelvin). Flag any code path where T could reach zero or go negative. +- Total pressure must be positive; partial pressures must be non-negative and sum to total pressure within solver tolerance. +- Mole fractions must sum to 1.0. Flag any composition-returning function that doesn't enforce or verify normalization. +- Henry's-law solubility outputs must be non-negative. Flag any solubility law that could return a negative or `nan` partial pressure for a physically valid input. +- `log10(fO2)` outputs must be finite (no `nan`, `inf`, or `complex`). Flag any buffer evaluation that does not clip / sanitize before return. +- Equilibrium constants `Keq` must be positive. The modified-Keq formulation in `chemistry.py` returns `log10(Keq)`; verify the exponentiation step has not been accidentally inverted. +- Mantle mass closure: `M_mantle = M_planet - M_core`; flag any path that lets `M_mantle <= 0` reach return. + +## Unit convention boundaries + +CALLIOPE has a mixed unit convention: + +- **Solver inputs**: T in K, P in bar, mass in kg, mole fractions dimensionless. +- **Henry's-law constants**: literature values in `(mol / kg) / bar^n` (variable `n` per fit); the conversion to internal units lives in `solubility.py`. +- **Oxygen fugacity**: `log10(fO2)` (dimensionless) and `fO2_shift_IW` (dex relative to the IW buffer). +- **PROTEUS interop**: the returned dictionary uses kg for species masses, bar for partial pressures, K for temperatures. + +When reviewing code that crosses these boundaries (e.g. a new solubility law, a new buffer fit, a new PROTEUS-side caller), verify the unit is correct at each conversion site. The `bar` vs `Pa` boundary is the recurring trap: literature fits are nearly always in bar, internal SI bookkeeping is in Pa. + +## Buffer-default flip safety + +When the default value of a dispatched-by-name physics path (IW buffer, modified-Keq formulation, solubility law) is changed, the change has fan-out across: + +1. Tests pinning a buffer-specific reference value (e.g. `EARTH_VOLATILE_O_REF_KG` was tied to the O'Neill 2002 default and had to update when the default flipped to Fischer 2011). +2. Documentation pages citing the buffer-specific number (the cross-backend comparison, the oxygen-fugacity reference page, the tutorials). +3. PROTEUS-side tests pinning against CALLIOPE outputs. +4. Frozen reference fixtures used by the cross-backend harness. + +Required workflow for any buffer / law default flip: + +1. `git grep` the old buffer / law name; update every reference value tied to it. +2. Update test discrimination guards (Section 2 rule 4 of `calliope-tests.md`) so the test would fail loudly under the wrong-default regression. +3. Update `docs/Explanations/cross_backend_comparison.md` and any tutorial that quotes a buffer-specific number. +4. Note the flip explicitly in the release notes. + +A PR that changes a default value but does not touch the test reference values is a red flag during review. + +## Solver intermediate-state types + +`solve.py`'s root-finder and equilibrium solvers operate on intermediate vectors that can drift through `complex` / `nan` / `inf` during iteration. The current source has `clip` hardening against these; flag any new solver code path that: + +- Does not check `np.isreal` and `np.isfinite` on intermediate state before passing to a downstream step. +- Catches `RuntimeWarning` from numpy without preserving the warning category in a log line, or silences it altogether. +- Coerces a complex intermediate to a real float without first asserting the imaginary part is negligible. + +The unit tests of `solve.py` verify the intermediate-state defenses fire (see `calliope-tests.md` Section 16); the code review's job is to make sure the defenses are present before the test asks them to fire. + +## PROTEUS coupling patterns + +CALLIOPE is called by PROTEUS through the chemistry / outgassing step. Four coupling patterns need explicit care during review. + +### 1. Authoritative-O IC reconciliation + +Under Path C (`fO2_source = "from_O_budget"`), PROTEUS supplies an authoritative oxygen mass `O_kg_total` and CALLIOPE inverts the IW shift that recovers it. PROTEUS stashes the user-supplied `O_kg_total` as `O_kg_user_ic` in `hf_row` before the first CALLIOPE call; the runtime helper `check_ic_oxygen_budget` compares CALLIOPE's solver-derived `O_kg_total` against the sentinel and hard-fails on >50% divergence. + +Any change to `solve.py`'s authoritative-O path (`equilibrium_atmosphere_authoritative_O`) must preserve this contract: + +- The return dict MUST contain `O_kg_total` (the solver-recovered value, used by PROTEUS to verify the reconciliation). +- The return dict MUST NOT silently substitute a clipped or fallback value for `O_kg_total` without surfacing the substitution in the return dict (e.g. via a `O_kg_total_clipped` flag). + +A regression that breaks reconciliation will surface as a >50% divergence hard-fail in PROTEUS; the test on the CALLIOPE side must catch it before it ships. + +### 2. fO2_shift_IW echo-back pattern + +PROTEUS sometimes overrides `hf_row['fO2_shift_IW']` (e.g. when iterating Path C) and must restore the original value after the CALLIOPE call. CALLIOPE returns `fO2_shift_IW_derived` as a separate key so the user-supplied and solver-derived values are distinguishable in the helpfile CSV. + +Required for any change that touches the chemistry-step return contract: + +- The return dict MUST keep `fO2_shift_IW_derived` distinct from `fO2_shift_IW`. +- A new field that overloads the meaning of `fO2_shift_IW` (e.g. silently treating it as a target rather than a user input) is a red flag. + +The save/restore pattern lives on the PROTEUS side; CALLIOPE's responsibility is to not silently mutate the input. + +### 3. Per-species mass closure across modules + +PROTEUS's mass-conservation invariant `M_atm <= M_planet` depends on CALLIOPE summing per-species masses (`H2O_kg_total`, `CO2_kg_total`, etc.) consistently. A regression in CALLIOPE's `_kg_total` aggregation (e.g. forgetting the oxygen contribution from H2O when O is treated as a separate element) would silently violate the PROTEUS-side invariant. The asymmetry between "elements as buffered reservoirs" (chemistry view) and "elements as tracked masses" (PROTEUS view) is the lesson from PROTEUS issue #677. + +Flag any chemistry-step code that: + +- Adds a new species without updating the corresponding `_kg_total` aggregation. +- Treats oxygen as a separate accounting entity from H2O / CO2 / SO2 in a way that changes the per-species totals. +- Returns a per-species total that is not the sum of `kg_atm + kg_liquid + kg_solid` for that species. + +### 4. Buffer-default flip impact on downstream PROTEUS + +A buffer-default flip in CALLIOPE (the 2026-05 Fischer-vs-O'Neill flip is the canonical example) changes the IW value silently for any PROTEUS run that uses the CALLIOPE default. PROTEUS-side tests that pin a specific IW value are sensitive to this. The rule: + +- Announce buffer-default flips in CALLIOPE's release notes. +- Bump the CALLIOPE version pin in PROTEUS's `pyproject.toml` to the post-flip release. +- Run PROTEUS's unit + smoke suite against the new CALLIOPE before merging the version bump. +- Update any PROTEUS-side hardcoded IW value tied to the old default. + +A CALLIOPE PR that flips a default but does not anticipate the PROTEUS-side fallout is a red flag during review. + +## Config mutability + +`Config` (or any dataclass / attrs object) used to carry user input must not be mutated at runtime after IC. Flag any code that sets `config.X.Y = value` outside of config initialization. Use local variables instead. + +## Cross-module constant duplication + +Physical constants (`R_gas`, `M_earth`, `R_earth`, `N_avogadro`, `M_O`, `M_H`) are defined in `src/calliope/constants.py`. When reviewing code that uses a physical constant, check that the import is from `calliope.constants` and not re-derived. A new constant introduced as a literal in a body (e.g. `5.4e-26 * T` for a Boltzmann-related coefficient) is a red flag. + +## Test marker discipline + +Every test file must begin with a module-level `pytestmark = [pytest.mark., pytest.mark.timeout()]` (unit/30 s, smoke/60 s, integration/300 s, slow/3600 s). Per-function markers are additive but do not replace the module-level marker; CI runs `pytest -m "(unit or smoke) and not skip"` and any file missing the tier marker ships untested. + +## Test quality (cross-reference) + +Test-content rules (anti-happy-path, discriminating-value guards, physics-invariant tiering, `physics_invariant` / `reference_pinned` certification markers, adversarial-review trigger, mocking discipline, `importorskip` + module-constant-monkeypatch traps, buffer-flip propagation, hypothesis seed stability, solver intermediate-state assertions) live in [`calliope-tests.md`](calliope-tests.md). When reviewing tests, apply both files: this one for marker discipline and review-pass gate, the deep-dive for the content contract. + +## Sister rules (cross-link) + +- [`.github/copilot-instructions.md`](../../copilot-instructions.md) "Testing Standards" -- high-level rules visible to all readers. Repo-root `CLAUDE.md` is a symlink to this file. +- [`calliope-tests.md`](calliope-tests.md) -- test quality deep-dive; the canonical source for anti-happy-path patterns and the validation certification markers. diff --git a/.github/.claude/rules/calliope-tests.md b/.github/.claude/rules/calliope-tests.md new file mode 100644 index 0000000..6726778 --- /dev/null +++ b/.github/.claude/rules/calliope-tests.md @@ -0,0 +1,392 @@ +--- +description: CALLIOPE test quality deep-dive. Anti-happy-path patterns, discriminating-value guards, physics-invariant tiering, validation certification markers, adversarial-review trigger. Extends the Testing Standards section in `.github/copilot-instructions.md`. +--- + +# CALLIOPE Test Quality Rules + +This file is the canonical deep-dive on test quality. The high-level summary lives in [`.github/copilot-instructions.md`](../../copilot-instructions.md) under "Testing Standards". The two files MUST stay in sync. If you change one, mirror the change in the other. + +> **Discovery note.** CALLIOPE keeps its Claude-Code rule files under `.github/.claude/rules/` (not the conventional repo-root `.claude/`) so they can be tracked in git and shared across collaborators. Claude does NOT auto-discover them at this path; the repo-root `CLAUDE.md` (symlinked to `.github/copilot-instructions.md`) names this file and `calliope-code-review.md` explicitly so AI tooling and human readers know to load them. **When opening or editing any file under `tests/**` or `src/calliope/**`, read this file first.** + +Sister rule files: + +- [`.github/copilot-instructions.md`](../../copilot-instructions.md): high-level rules, applied repo-wide. +- [`.github/.claude/rules/calliope-code-review.md`](calliope-code-review.md): review-pass gate, domain-aware code review (buffer-flip propagation, solver intermediate-state types, PROTEUS-coupling patterns). Test-marker discipline lives there too. + +CALLIOPE is scientific simulation code and the test suite is held to physics-grade rigor. Tests exist to catch real bugs. A test that asserts the wrong thing, or that passes for the wrong reason, is worse than no test because it generates false confidence. The rules below codify what "real test" means here. + +--- + +## 1. Anti-happy-path rules (every new test) + +Every new test function MUST include: + +1. **At least one edge case**: a boundary value (Phi = 0 or 1, T = T_solidus, P = 0, fO2_shift_IW = 0), an empty input, or an extreme physical parameter. +2. **At least one path that exercises the error contract**: + - If the function under test has documented validation (raises on negative T, refuses to dispatch with an unknown buffer name), test that the error fires AND that no side effect ran. + - If the function has no validation (closed-form mathematics: thermodynamic relations, equilibrium constants), exercise the **limit-input behavior** (single-species composition is a degenerate fixed point of the multi-species solver) and assert the corresponding mathematical invariant. + - "No validation in source therefore no error test" is not an exemption; the limit-input substitute is. +3. **Assertion values NOT trivially derivable from the implementation**: discriminating numeric pins (see Section 2 below) or property-based assertions (monotonicity, conservation, symmetry, boundedness). + +### Forbidden patterns + +These are flagged by `tools/check_test_quality.py` and rejected at PR time. + +- **Single-assert test functions**. Two or more assertions per test; the second usually pins the invariant the first hand-waves over. Exception: a single assertion of a hard-fail invariant (mass closure within `1e-12`) is acceptable if the test is the only test of that invariant in the file. +- **Weak assertions when they stand alone as the sole meaningful check in the test.** The shapes are: + - `assert result is not None` + - `assert result > 0` + - `assert len(result) > 0` + - `assert isinstance(result, dict)` + - `assert result is None` where the function returns `None` implicitly + + Required carve-out: the three-class discrimination guard (Section 2) uses `assert val > 0` as the sign-error guard and `assert lo < val < hi` as the scale-error guard alongside a primary `pytest.approx(...)` pin. Those secondary lines look like weak assertions in isolation; they are NOT flagged when paired with a stronger primary assertion in the same test. The linter applies the carve-out automatically: weak shapes are flagged only when the test has exactly one `assert` statement (`len(asserts) == 1`) and that assertion is itself the weak shape. +- **Tests with no function-level docstring**. The docstring states which physical scenario or contract clause is being verified. +- **`==` adjacent to a float literal**. Use `pytest.approx(val, rel=...)` or `np.testing.assert_allclose(actual, expected, rtol=..., atol=...)`. Comparing two floats with `==` is a known flake source even for "exact" identities like 0.0 (-0.0 vs +0.0, NaN propagation). +- **Tests asserting on a fixture's implicit default**: e.g. `assert fixture_returning_none() is None`. This is trivially true. Delete the test; do not strengthen it by adding more `is None` assertions. + +--- + +## 2. Discriminating test values + +The test contract is: a regression that introduces a plausible bug must fail the test. "Plausible bug" means off-by-one exponent, wrong sign, swapped factor of 2, missing factor of pi, dimensionally-wrong unit, **wrong-buffer / wrong-law selection**. Pick input values where the wrong-formula result is far from the correct one. + +### Bad / good examples + +| Pattern | Bad (any-exponent-passes) | Good (discriminates) | +|---|---|---| +| `log10(fO2) = A/T + B` (IW buffer) | Test at `T = 1500` only (degenerate against any reasonable A) | Test at `T = 1500` AND `T = 2500`; assert the difference matches the Fischer-vs-O'Neill slope ratio | +| Henry's-law solubility | Composition all equal (1/N each, symmetric) | Asymmetric composition (one dominant species + traces) so a swapped Henry constant changes the dominant species more than the test tolerance | +| Equilibrium constant interpolation | Test at grid nodes (interpolation is identity there) | Test at off-grid temperatures where bilinear vs nearest-neighbor differ | +| Stoichiometric closure | One species at unit pressure (the closure is trivial) | Multi-species with non-trivial fO2_shift_IW so each Keq matters | + +### Discrimination guard (REQUIRED for pinned-value tests) + +When a test pins a numeric value, include explicit assertions that the wrong-formula result would differ from the correct one for **each plausible bug class**. "Each plausible bug class" means at minimum: + +1. **Exponent or factor error** (off-by-one exponent, missing factor of 2 / pi). `abs(val - wrong_value)` discriminates. +2. **Sign error** (`-x` vs `+x`). `abs()` hides this; assert the sign explicitly with `val > 0` or `val < 0`. +3. **Unit-conversion error** (Pa vs bar, K vs C, log10 vs ln). Pin the absolute scale with the unit named in the comment. +4. **Wrong-buffer / wrong-law selection** (Fischer vs O'Neill, Dasgupta vs Iacono-Marziano). When the function dispatches by name, the discrimination guard MUST include a value that distinguishes the chosen path from a sibling path. + +**Carve-out for conservation-style invariants.** When the primary assertion IS a conservation closure (mass closure, energy balance, sum-equals-total), the equality form `sum(parts) == pytest.approx(total)` already discriminates exponent / factor errors by construction. The exponent guard is satisfied by the conservation equality itself; sign and scale guards remain mandatory. + +Canonical pattern: + +```python +def test_iw_buffer_fischer_at_3000K_matches_published_value(): + """Pin IW(Fischer 2011) at T = 3000 K against the original Table 1 fit.""" + val = oxygen_fugacity.iw_buffer('fischer', T=3000.0) + expected = -6.57 # log10(fO2) at the IW buffer, Fischer+2011 Table 1 + assert val == pytest.approx(expected, rel=1e-3) + # Wrong-buffer discrimination: O'Neill 2002 at the same T gives ~-7.52. + # A regression that silently dispatches to 'oneill' instead of 'fischer' + # would land outside the tolerance. + wrong_oneill = -7.52 + assert abs(val - wrong_oneill) > 0.3 + # Sign guard: log10(fO2) at IW is always negative under standard conditions. + assert val < 0 + # Scale guard: order of magnitude is -7, not -70 (forgotten log10) or + # -0.7 (factor-10 unit slip). Pin the magnitude. + assert -10 < val < -3 +``` + +The guard lines are mandatory whenever the test's primary assertion is a `pytest.approx` against a hand-calculated or published value. Property-based assertions (monotonicity, conservation, symmetry) do not need a separate guard because they are already discriminating across the input space. + +--- + +## 3. Physics-invariant assertions (tiered) + +### When required + +Every unit test on a **physics module** must assert at least one of the four invariants below. Physics modules are: + +``` +src/calliope/chemistry.py +src/calliope/oxygen_fugacity.py +src/calliope/solubility.py +src/calliope/solve.py +src/calliope/structure.py +``` + +Per-source-file granularity: each of the five physics files needs at least one `@pytest.mark.physics_invariant` test and at least one `@pytest.mark.reference_pinned` test in `tests/test_.py`. Granularity is per source file, not per directory. + +Utility modules are exempt from the physics-invariant requirement but still subject to all anti-happy-path rules: + +``` +src/calliope/__init__.py (re-exports) +src/calliope/_version.py (auto-generated by setuptools-scm) +src/calliope/constants.py (pure physical constants, no derivation) +``` + +### The four invariant families + +1. **Conservation** + - Mass closure: `sum(species_kg_atm + species_kg_liquid + species_kg_solid) ≈ species_kg_total` per species. + - Element closure: per-element mass balance across the gas / melt / solid partitioning. + - Stoichiometric closure: `sum(mole_fractions) == 1.0` for any returned composition vector. +2. **Positivity / boundedness** + - `T > 0` Kelvin everywhere, `P > 0` Pa everywhere. + - Partial pressures non-negative; mole fractions in `[0, 1]`. + - Henry's-law solubility non-negative; mantle melt fraction in `[0, 1]`. + - `log10(fO2)` finite (no `nan` / `inf` / `complex`). +3. **Monotonicity or symmetry** + - `log10(fO2)` decreasing with `1/T` along an isobaric buffer. + - CO2 solubility increasing with pressure at fixed T (Henry's law in the linear regime). + - Doubling pressure at fixed mole fractions doubles partial pressures. + - Swapping two non-reacting species in the input list leaves all other outputs unchanged. +4. **Pinned numeric value with a discrimination guard**: see Section 2. This is acceptable as the sole invariant when a closed-form result or published table value is the contract. + +Property-based assertions (monotonicity, conservation, symmetry, boundedness) are preferred over point-value pins when both are possible. They hold for any valid input and so catch bugs across the entire input space. + +### Validation certification markers + +Two markers track validation quality independently of line coverage: + +- **`@pytest.mark.physics_invariant`** -- this test asserts at least one of the four invariants. Tag every qualifying test in a physics-source test file. `tools/check_test_quality.py` warns when a physics-source test asserts no invariant and is not tagged. +- **`@pytest.mark.reference_pinned`** -- this test pins behavior against a **published benchmark** (paper, figure, table; cite explicitly in the test docstring), an **analytical limit** (Henry's-law linear regime, single-species degenerate solve, IW buffer at a tabulated reference T), or a **cross-implementation cross-check** (CALLIOPE vs atmodeller at the Earth fiducial; see `docs/Explanations/cross_backend_comparison.md`). + - **Per-source-file**: each of the five physics source files must have at least one `reference_pinned` test in `tests/test_.py`. Anchor type is one of {published benchmark, analytical limit, cross-implementation cross-check}; the specific paper or limit is chosen by the test author and recorded in `docs/Validation/.md`. + - **Tracking**: each physics source gets a page at `docs/Validation/.md`, created when the first reference_pinned test for that source lands. The page records: the source under test, the reference cited, the test ids carrying the marker, and the date of last comparison against the source. + - **Status report**: `python tools/check_test_quality.py --reference-pinned-status` reports the physics source files missing a `reference_pinned` test. This is the punch list for follow-up validation work. + +Both markers are registered in `pyproject.toml` under `[tool.pytest.ini_options] markers`. They do not gate CI on their own; their coverage is a separate KPI surfaced in the PR summary comment. + +--- + +## 4. Mocking discipline + +- Default to `unittest.mock` for ALL external calls in unit tests: atmodeller cross-backend calls, file I/O, HTTP, subprocess. +- Mock at the **narrowest scope**: patch the specific function (`unittest.mock.patch('calliope.solve.some_helper')`), not the whole module. +- A mocked physics function MUST return **physically plausible** values. A mock that returns `0.0` or `1.0` for everything will mask sign / clamp / fallback bugs. +- NEVER mock the function under test. If you're tempted to, the test is asking the wrong question. +- Smoke tests use the real CALLIOPE solver on minimal compositions; integration and slow tests use the full multi-species CHNS solver. The rules in this file still apply to those tiers, but the mocking discipline is relaxed because the real call is the contract. + +--- + +## 5. Optional-dependency imports + +Any test that imports an optional dependency MUST call `pytest.importorskip` at module top so `pip install --no-deps` CI runs do not fail collection: + +```python +import pytest + +hypothesis = pytest.importorskip('hypothesis') +# ... or for a module-level helper that requires the dep: +pytest.importorskip('atmodeller') +``` + +Optional deps recognized by the linter (`OPTIONAL_DEPS` constant in `tools/check_test_quality.py`): + +- `hypothesis` (used in property-based / fuzz tests; lives in `[develop]` extras). +- `atmodeller` (used in cross-backend comparison work; lives in neither `[dependencies]` nor `[develop]` extras, must always be guarded if imported into a test file). + +The lint script enforces this. Rule key `missing_importorskip`: any module-top `import ` or `from import ...` that is not preceded by a module-scope `pytest.importorskip('')` is flagged. + +--- + +## 6. Module-level constants and `monkeypatch` + +When the source under test reads an env var or a class-level default into a **module-level constant** at import time, `monkeypatch.setenv` alone is not sufficient. The constant is frozen at the original import. + +Pattern: + +```python +# Source: src/calliope/oxygen_fugacity.py +DEFAULT_BUFFER = 'fischer' # frozen at import after the 2026-05 default flip +``` + +```python +# Test (wrong): +monkeypatch.setattr('os.environ', {'CALLIOPE_BUFFER': 'oneill'}) # too late + +# Test (right): +monkeypatch.setattr('calliope.oxygen_fugacity.DEFAULT_BUFFER', 'oneill', raising=False) +``` + +A related pattern is the `_with_calliope_buffer` context manager in `scripts/cross_backend/runners.py`: it mutates `OxygenFugacity.__init__.__defaults__` and restores on exit. The pattern is **single-threaded only**; never apply it inside library code (it has process-wide visibility and races under pytest-xdist). + +When in doubt, do both the env-var monkeypatch and the constant monkeypatch. The lint script does NOT currently flag this pattern (it would require source-side analysis to know which constants are env-derived); this is a discipline rule enforced via the >50 LOC review trigger and the recurring-trap table in Section 16. + +--- + +## 7. Marker discipline and timeouts + +### Module-level marker is mandatory + +Every test file MUST begin with: + +```python +import pytest + +pytestmark = [pytest.mark., pytest.mark.timeout()] +``` + +with budgets: + +- `unit` -> `timeout(30)` (target wall-time per test is `< 100 ms`; the 30 s cap is a defensive net). +- `smoke` -> `timeout(60)` (target `< 30 s`). +- `integration` -> `timeout(300)`. +- `slow` -> `timeout(3600)`. + +PR CI runs `pytest -m "(unit or smoke) and not skip"`. Tests without the tier marker are invisible to CI and shipped untested. The lint script blocks any file missing the module-level `pytestmark`. + +### Per-function markers + +Per-function `@pytest.mark.` markers are **additive**, not a replacement for the module-level marker. They are useful when a file's tests span multiple tiers (rare; prefer separate files). + +### Timeout is a safety net, not a target + +The `timeout` ceiling exists so a future regression that introduces a hang (real solver call, infinite loop, network retry) surfaces as a specific-test failure rather than a generic job timeout. Current test wall times are 100x below the ceiling; if you find yourself needing the full 30 s for a unit test, something has gone wrong and you should reduce scope or move the test to a slower tier. + +--- + +## 8. Float and numerical comparison + +- NEVER use `==` for floats. Use `pytest.approx(val, rel=1e-5)` or `np.testing.assert_allclose(actual, expected, rtol=..., atol=...)`. +- State the tolerance rationale in a comment when the choice is non-obvious. E.g. "`rtol=1e-3` because the Fischer 2011 fit reports four significant figures". +- For pinned numeric values, include a **discrimination guard** (Section 2). +- For property-based assertions, use `pytest.approx` against the exact symbolic relation, with the tightest tolerance the implementation can hit (typically `rel=1e-12` for closed-form algebra; looser for solver outputs). + +--- + +## 9. Voice rule for test artifacts + +The repo-wide voice rule (zero AI-process disclosure in any public artifact) applies to test code with the same strictness as to source. The voice rule is **scoped** to public artifacts other contributors and external readers see; it does NOT apply to the rule documents themselves (this file, `calliope-code-review.md`, `copilot-instructions.md`), which legitimately name the procedures they define. + +In scope (the voice rule is BANNED here): + +- Test-skip reasons (`@pytest.mark.skip(reason='...')`). +- Test-file docstrings. +- Test-function and test-class names. +- Test-function docstrings. +- Parametrize ids (`@pytest.mark.parametrize('name', [...], ids=[...])`). +- Log-capture assertions (regex against `caplog.records`). +- Commit messages on test-touching commits (subject AND body). +- **Pull-request titles and bodies on test-touching PRs**. +- GitHub Actions job names and step names that ship to the PR Checks tab. +- Inline source comments and docstrings on `src/calliope/**`. +- Log strings that ship with the repo. +- **All public-facing documentation** (anything under `docs/`, the repo README, CONTRIBUTING.md, tutorials, wiki pages). Public docs apply the rule silently; they do NOT enumerate the banned phrases, name the voice rule, advertise the existence of `.github/.claude/` rule infrastructure, or cross-reference `.github/.claude/rules/*.md` files. A user docs page that describes the testing infrastructure must do so without naming the AI-process rules that produced it. + +Out of scope (these may NAME the procedures they define): + +- This file (`calliope-tests.md`). +- `calliope-code-review.md`. +- `copilot-instructions.md`. + +Banned phrases inside the in-scope artifacts: "audit", "review pass", "adversarial review", "Phase X" (when "X" is an AI-organized roadmap label, not a real project phase), "T1.x", "Group A/B/C/D" (when AI-organized work groups), `claude-config/...` paths, "Generated with Claude", AI-tool names, em-dashes, en-dashes (except in bibliographic page ranges within citations), process meta-commentary ("after careful analysis"). + +Write the OUTCOME (what the test verifies; what the PR achieves) never the PROCESS (how the rule was derived; which review caught what). First-person Tim voice. Going-forward only, no history rewrite. + +--- + +## 10. Fixture and parameter conventions + +- Use SI units in test parameters unless the function under test explicitly expects config units (bar, K, ppmw). +- Use `@pytest.mark.parametrize` when the same logic spans multiple physical regimes (Earth-like, Mars-like, sub-Neptune, high-fO2, low-fO2). Each parametrize id must read like a physical scenario, not a tuple of numbers. +- Set seeds for any randomness: + ```python + np.random.seed(42) + random.seed(42) + ``` + Hypothesis tests use `@settings(derandomize=True)` or an explicit `--hypothesis-seed` to keep replays stable across versions (see Section 16 trap). +- Use `tmp_path` (pytest fixture) for temporary files. Do not produce large outputs in the test path. + +--- + +## 11. Documentation per test + +- **File-level docstring**: name the source file under test (`Tests for src/calliope/.py`), list the invariants and contract clauses the file exercises, link to `docs/How-to/build_tests.md`. Required. +- **Function-level docstring**: state the physical scenario or contract clause in plain language. Required (lint-enforced). +- **Inline comments**: explain **why** a specific input range was chosen ("T=1500 K and T=2500 K so the Fischer-vs-O'Neill slope difference is resolved well above tolerance"). + +--- + +## 12. Naming + +- Test names describe behavior, not the called function: `test_iw_buffer_monotonic_with_temperature`, NOT `test_iw_buffer`. +- Test names use snake_case and read as full sentences. +- Group related tests in classes (`class TestIWBuffer:`) when they share setup; use the class to thread a single fixture through several scenarios. +- Test file names mirror source 1:1: `src/calliope/.py` -> `tests/test_.py`. Two documented exceptions to the 1:1 rule: + - **Cross-cutting fuzz / init tests** (`test_invariants_hypothesis.py`, `test_init.py`): tests that span multiple source files or test package-level concerns. + - **Topical sub-files of a large physics source**: when a physics source exceeds ~500 LOC and its tests split into independent topics that would not benefit from consolidation, topical sub-files are acceptable alongside the primary `tests/test_.py`. The primary file must still exist and carry at least one `reference_pinned` and one `physics_invariant` test; the topical sub-files cover the remaining surface. The current exemption is `solve.py` (>1200 LOC), whose tests split across `test_authoritative_O.py`, `test_equilibrium_paths.py`, `test_partial_species.py`, `test_stoichiometry.py`, `test_targets.py`, `test_invariants.py`. The primary `tests/test_solve.py` carries the round-trip self-consistency anchor. + +--- + +## 13. Adversarial review trigger + +A pull request that adds or substantially modifies **> 50 lines of test code across all its commits** triggers an independent review pass before merge. This is a discipline rule, not CI-automated: the author runs the review pass via a `code-reviewer` agent before pushing the final test-touching commit. The denominator is PR-level, not per-commit: `git diff origin/main...HEAD -- 'tests/**'` is the source of truth. Splitting one large change into 49 + 49 + 49 line commits does NOT dodge the trigger. + +The reviewer's mandate: + +- Cite the anti-happy-path rule (Section 1) and the discrimination-guard requirement (Section 2). +- Flag single-assert tests, weak `is not None` patterns, missing module-level marker, missing `physics_invariant` tag on a physics-source test, missing `reference_pinned` tag on a per-source benchmark test, dead tests (passes for the wrong reason), tests that mock the function under test. +- Verify discriminating values: re-derive the expected value from a plausible wrong formula and assert the test fails with that wrong formula. +- Verify physics-source coverage of the four invariants: which of the four does this test exercise? If none, why is the test in `tests/test_.py`? + +The reviewer is a separate process from the test author. For Claude-Code workflow this means spawning a `proteus-review` skill or a `code-reviewer` agent with the test files in scope; the review must complete and surface findings before the test commit is pushed. + +The reviewer's findings are addressed in a follow-up commit (not amended into the test commit). The follow-up subject line is in plain language describing the OUTCOME ("sharpen IW-buffer assertions to distinguish Fischer from O'Neill", NOT "address review findings"). + +--- + +## 14. Tooling + +The repo provides: + +- `bash tools/validate_test_structure.sh` -- structural check (marker presence, file naming). +- `python tools/check_test_quality.py --check` -- CI mode: AST scan for the forbidden patterns in Section 1 and the marker requirement in Section 7. Fails the PR if violations exceed the baseline. +- `python tools/check_test_quality.py --baseline` -- after a deliberate sweep, regenerates `tools/test_quality_baseline.json`. Only run when you have intentionally reduced violations. +- `python tools/check_test_quality.py --reference-pinned-status` -- prints physics source files missing a `reference_pinned` test. +- `python tools/update_coverage_threshold.py` -- ratchet the fast PR gate upward when measured coverage exceeds the current `fail_under`. Capped at the 90% ecosystem ceiling. +- `ruff check src/ tests/` and `ruff format src/ tests/` -- run before commit. + +The lint script is wired into PR CI (`tests.yaml`). The step runs in **blocking** mode: any regression above the baseline fails the PR. + +--- + +## 15. Coverage strategy (operator's view) + +CALLIOPE uses two coverage gates with explicit sub-targets. The fast gate is for PR cycle time; the full nightly gate is the long-running KPI. + +| Gate | Tests | Target | When | +|---|---|---|---| +| Fast gate (`tool.calliope.coverage_fast`) | unit + smoke | ratcheting toward **90%** (PROTEUS-ecosystem ceiling) | Every PR | +| Full gate (`tool.coverage.report`) | unit + smoke + integration + slow | **90%** | Nightly | + +The ratchet is one-way (`tools/update_coverage_threshold.py`), capped at 90%. Never manually decrease the threshold. The CI guard in `.github/workflows/tests.yaml` rejects any PR that lowers `[tool.coverage.report].fail_under` below `min(base_ref, 90.0)`. + +What this means for adding tests: + +- A new closed-form helper in a utility module: a unit test is sufficient. +- A new function in a physics source: a unit test (counts toward both gates), plus a `physics_invariant` tag if it qualifies. If the function feeds a published benchmark, a `reference_pinned` test goes with it (counts toward the per-source-file inventory in `docs/Validation/.md`). +- A new cross-backend comparison: a slow-tier test that calls atmodeller via `pytest.importorskip` and pins against a `scripts/cross_backend/` fixture. + +--- + +## 16. Failure modes to recognize on review + +These are real patterns that have shipped in the past. The lint script catches some of them mechanically; reviewers catch the rest. + +| Pattern | Example | Why it slipped | Fix | +|---|---|---|---| +| **Buffer-default flip propagation** | A test pins `EARTH_VOLATILE_O_REF_KG = 1.241e22` against the legacy O'Neill default. The default flips to Fischer in `oxygen_fugacity.py`; the constant becomes 1.260e22. The test still passes against an outdated reference, silently. | The reference value is tied to the buffer choice but the test doesn't name the buffer in its docstring or assert against a buffer-discriminating value. | Discrimination guard (Section 2 rule 4) must include the alternative-buffer value so a regression that silently dispatches to the wrong buffer fails the test. Cite the buffer name AND the reference paper in the test docstring. | +| **Hypothesis seed and version stability** | `@given(...)` test passes on hypothesis 6.0 with the default seed strategy; on hypothesis 6.100 the strategy produces a different sequence and the test surfaces a previously-hidden flake or stops covering the previously-hidden bug class. | Hypothesis seed semantics changed between minor releases; the test author relied on implicit determinism. | Add `pytest.importorskip('hypothesis')` at module top. Use `@settings(derandomize=True)` or pass `--hypothesis-seed=` in CI. Document the chosen seed in the test docstring. | +| **`solve.py` intermediate-state types** | Solver loop produces a `complex` or `nan` intermediate; the silently-coerced final output is a real float that looks plausible but is wrong. The unit test only checks the final output. | The output check is too late; the bug lives in an intermediate step. | Tests of `solve.py` must assert that intermediate state is real-valued at each step: `assert np.all(np.isreal(intermediate))` and `assert not np.any(np.isnan(intermediate))`. The solver-side defense (`clip` hardening against complex / NaN / inf) belongs in source, but tests verify the defense actually fires. | +| **Silent skip in helper** | `def _enum_for(field): ...; if actual is None: continue` masks broken introspection | Helper hides a real failure as a no-op | Hard assertion: `assert actual is not None, ...` | +| **Log-line-only assertion** | Test captures a log line and asserts on its text; a regression that changes the code path but keeps the log still passes | Logs are not the contract | Capture the call kwarg and assert on the value passed in | +| **Module-level constant patched only via env var** | `monkeypatch.setenv('CALLIOPE_BUFFER', ...)` on a source that read it at import time | Constants are frozen at import; setenv is too late | `monkeypatch.setattr('mod.CONST', ...)` in addition to setenv | +| **Optional dep imported unconditionally** | `import hypothesis` at module top | `pip install --no-deps` build skips the optional install | `pytest.importorskip('hypothesis')` at module top | +| **Stale marker after refactor** | File renamed without re-applying the module-level `pytestmark` | CI marker filter passed because of per-function markers; coverage tier became invisible | Restore module-level `pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)]` | +| **Trivially-true on implicit None** | `def fixture(): pass`; `def test_x(fixture): assert fixture is None` | Fixture returned None implicitly; test passes for the wrong reason | Delete the test | + +When you spot a new variant of these, add it here. + +--- + +## 17. Sister rules (cross-link) + +- `.github/copilot-instructions.md` "Testing Standards" -- the high-level summary readers without `tests/**` context see first. +- `.github/.claude/rules/calliope-code-review.md` "Test marker discipline" -- the review-pass gate that backs up the rules in this file. Also contains domain-aware physics checks (buffer-flip propagation, solver intermediate-state types, PROTEUS-coupling patterns) that apply when reviewing the **source** code that tests cover. + +Any change to the rule set: update both files in the same commit and call out the cross-reference in the commit body. diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 0000000..f0f90f7 --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,406 @@ +# CALLIOPE AI Agent Guidelines + +**Trust these instructions.** Only search if information is incomplete or found to be in error. + +**Identity & Mission**: You are an expert Scientific Software Engineer working on the CALLIOPE module of the PROTEUS ecosystem. + +## High-Level Instructions + +> ### Rule files you MUST read on every session +> +> CALLIOPE keeps its Claude-Code rule files under `.github/.claude/rules/` (NOT the conventional repo-root `.claude/`, which is gitignored and so cannot be shared with collaborators). Claude Code does NOT auto-discover the rules at this unusual path. Read them explicitly at the start of every session and any time you open a related file: +> +> - [`.github/.claude/rules/calliope-tests.md`](.claude/rules/calliope-tests.md) -- test quality deep-dive: anti-happy-path patterns, discriminating-value guards, physics-invariant tiering, validation certification markers, adversarial-review trigger, buffer-flip propagation, hypothesis seed stability, solver intermediate-state assertions. **Required reading before editing any file under `tests/**` or `src/calliope/**`.** +> - [`.github/.claude/rules/calliope-code-review.md`](.claude/rules/calliope-code-review.md) -- review-pass gate, domain-aware physics review (buffer-flip safety, solver intermediate-state types, four PROTEUS-coupling patterns). **Required reading before any code review pass.** +> +> These two files plus this one are the canonical sources of truth for testing rigor and review criteria. Together they enforce CALLIOPE's extreme-rigor stance on physics validity, anti-happy-path testing, and validation certification. + +1. **Always** read the two rule files above plus the Testing Standards section below before any code change. +2. **Always** inform the user that you are reading in this file by printing a message at the start of your response: "(Read in copilot-instructions.md...)" +3. When creating a PR, **always** follow the PR template (`.github/pull_request_template.md`) and ensure all sections are filled out with relevant information. +4. **Claude-specific**: `CLAUDE.md` is a symlink to this file. Session learnings, plans, and memories live in `~/.claude/projects//memory/`; they do NOT live in this repository. + +## Ecosystem Context + +CALLIOPE is the volatile in-/outgassing and thermodynamics module of the PROTEUS ecosystem. It is called by the main [PROTEUS](https://github.com/FormingWorlds/PROTEUS) coupled atmosphere-interior framework during the chemistry / outgassing step. CALLIOPE is also usable standalone for equilibrium-chemistry studies. + +Sister modules in the ecosystem: AGNI (atmospheric radiative transfer), SOCRATES (spectral radiative transfer), JANUS (1D convective atmosphere), MORS (stellar evolution), ARAGOG / SPIDER (interior thermal evolution), VULCAN (atmospheric chemistry), ZEPHYRUS / BOREAS (atmospheric escape), Obliqua (tidal evolution). + +**Project Type**: Scientific simulation module (Python). + +**Languages**: Python 3.12+. + +**Size**: 8 source files in `src/calliope/`, ~1.7k LOC. + +**Target Runtime**: Python 3.12+ on Linux / macOS. + +## Build & Validation + +### Environment Setup + +**Prerequisites**: + +1. Python 3.12 or 3.13 (via conda / miniforge or system). +2. Git. + +**Developer Install**: + +```bash +git clone git@github.com:FormingWorlds/CALLIOPE.git +cd CALLIOPE +pip install -e ".[develop]" +pre-commit install -f +``` + +CALLIOPE has no compiled dependencies: a plain `pip install` is sufficient. When working alongside PROTEUS, the recommended pattern is editable-install via `pip install -e CALLIOPE/.` from the PROTEUS tree so changes propagate without re-install. + +### Test Commands + +**Run all tests**: + +```bash +pytest +``` + +**Run by category** (matches CI): + +```bash +pytest -m "(unit or smoke) and not skip" # What PR checks run +pytest -m unit # Fast unit tests (<100ms each) +pytest -m smoke # Real-solver minimal-composition runs +pytest -m integration # Multi-species CHNS solves (nightly) +pytest -m slow # Full physics validation (nightly) +``` + +**With coverage** (matches nightly CI): + +```bash +coverage run -m pytest -m "(unit or smoke or integration or slow) and not skip" +coverage report +coverage html +``` + +**Coverage thresholds** (in `pyproject.toml`; auto-ratcheting, never manually decreased, capped at 90 by `tools/update_coverage_threshold.py`): + +- Fast gate (`[tool.calliope.coverage_fast]`, unit + smoke, every PR): ratcheting toward **90%** (the PROTEUS-ecosystem ceiling). +- Full gate (`[tool.coverage.report]`, unit + smoke + integration + slow, nightly): **90%**. + +**Validate test structure**: + +```bash +bash tools/validate_test_structure.sh +``` + +**Test quality lint** (blocking on PRs): + +```bash +python tools/check_test_quality.py --check +``` + +### Lint Commands + +**Always run before committing**: + +```bash +ruff check src/ tests/ tools/ scripts/ # Check for issues +ruff check --fix src/ tests/ tools/ scripts/ # Auto-fix issues +ruff format src/ tests/ tools/ scripts/ # Format code +``` + +**Pre-commit hook** (runs automatically on commit): + +```bash +pre-commit install -f +``` + +### Validation Pipeline + +**CI runs on PRs** (`.github/workflows/tests.yaml`): + +1. **Unit + smoke tests**: `pytest -m "(unit or smoke) and not skip" --cov=calliope`. +2. **Fast coverage gate**: `[tool.calliope.coverage_fast].fail_under` checked against the unit + smoke coverage. +3. **Test structure**: `bash tools/validate_test_structure.sh`. +4. **Test quality**: `python tools/check_test_quality.py --check` (blocking). +5. **Coverage ratchet guard**: rejects any PR that lowers `[tool.coverage.report].fail_under` below `min(base_ref, 90.0)`. +6. **Lint**: `ruff check src/ tests/ tools/ scripts/` and `ruff format --check src/ tests/ tools/ scripts/`. + +**All must pass** before merge. Coverage thresholds auto-ratchet upward (never decrease). + +**Nightly CI** (`.github/workflows/nightly.yml`): + +- Full suite: `pytest -m "(unit or smoke or integration or slow) and not skip"`. +- Coverage uploaded to Codecov. +- `--cov-fail-under=90` enforced. + +## Project Layout + +### Key Directories + +- `src/calliope/` - Main Python source code (flat layout, 8 files) + - `__init__.py` - Re-exports (utility) + - `_version.py` - Auto-generated by setuptools-scm (utility) + - `constants.py` - Physical constants (utility) + - `chemistry.py` - Equilibrium chemistry, modified Keq formulation (physics) + - `oxygen_fugacity.py` - IW buffer formulas: Fischer 2011 (default), O'Neill & Eggins 2002 (physics) + - `solubility.py` - Henry's-law solubility models (Dasgupta 2013, Gaillard 2003, Iacono-Marziano, etc.) (physics) + - `solve.py` - Root-finder, equilibrium-atmosphere solver, authoritative-O entry (physics) + - `structure.py` - Interior structure: mantle-mass closure from Zeng+2016 core fraction (physics) + +- `tests/` - Test suite. Each physics source has a 1:1 test file at `tests/test_.py`. Cross-cutting tests (`test_invariants_hypothesis.py`, `test_init.py`) are the exception. + +- `tools/` - Build / utility scripts + - `check_test_quality.py` - AST linter (blocking on PRs) + - `update_coverage_threshold.py` - One-way coverage ratchet (capped at 90) + - `check_file_sizes.sh` - Line-cap hook on this file + - `validate_test_structure.sh` - Module-level marker validator + +- `docs/` - Documentation (Zensical; Diátaxis structure) + - `Explanations/` - Concept pages (oxygen_fugacity.md, solubility.md, equilibrium_chemistry.md, cross_backend_comparison.md, etc.) + - `How-to/` - Task guides (installation.md, build_tests.md, configuration.md, usage.md, authoritative_oxygen.md, etc.) + - `Tutorials/` - Worked examples (earth_fiducial.md, mars_fiducial.md, coupled_loop.md, phase_diagram.md) + - `Reference/` - API + publications + - `Validation/.md` - Per-source-file inventory of `@pytest.mark.reference_pinned` tests (created when the first such test lands) + +- `scripts/cross_backend/` - Standalone harness comparing CALLIOPE vs atmodeller (deterministic, 5 figure modules + shared infra + `_with_calliope_buffer` context manager). Not part of CI. + +### Configuration Files + +- `pyproject.toml` - Package metadata, pytest config, coverage thresholds (fast + full gates), ruff rules. +- `mkdocs.yml` - Documentation configuration (used by Zensical). +- `.github/workflows/` - CI / CD pipelines + - `tests.yaml` - PR validation (unit + smoke + lint + test-quality + ratchet guard) + - `nightly.yml` - Full suite with coverage upload + - `docs.yaml` - Documentation build + - `publish.yaml` - PyPI release on tag + +### Entry Points + +- **Python API**: `from calliope.solve import equilibrium_atmosphere, equilibrium_atmosphere_authoritative_O`. +- **No CLI**: CALLIOPE is library-only; PROTEUS provides the simulator CLI that calls CALLIOPE. + +## Testing Standards + +CALLIOPE is scientific simulation code, so the test suite is held to physics-grade rigor. The rules below are the contract; the deep-dive (anti-happy-path patterns, discriminating-value guards, certification markers, adversarial-review trigger, buffer-flip propagation, hypothesis seed stability, solver intermediate-state assertions) lives in [`.github/.claude/rules/calliope-tests.md`](.claude/rules/calliope-tests.md). Read that file before editing any test file or any source file under `src/calliope/**`. The two files must be kept in sync; if you change one, mirror the change in the other. + +### Structure + +- Tests mirror source 1:1: `src/calliope/.py` -> `tests/test_.py`. Cross-cutting tests (`test_invariants_hypothesis.py`, `test_init.py`) are the exception, not the rule. +- Framework: `pytest` exclusively in the `tests/` directory. + +### Markers and the module-level marker rule + +Tier markers, with their CI surface and per-test wall-time budgets: + +| Marker | What it tests | Speed budget | When CI runs it | +|---|---|---|---| +| `@pytest.mark.unit` | Python logic, heavy physics mocked | < 100 ms per test | Every PR (`unit and not skip`) | +| `@pytest.mark.smoke` | Real solver, minimal composition | < 30 s per test | Every PR (`smoke and not skip`) | +| `@pytest.mark.integration` | Multi-species CHNS coupling | Minutes per test | Nightly only | +| `@pytest.mark.slow` | Full physics validation | Up to hours per test | Nightly only | +| `@pytest.mark.skip` | Placeholder, deliberately disabled | n/a | Never | + +**Mandatory module-level marker** (no exceptions): every test file begins with + +```python +pytestmark = [pytest.mark., pytest.mark.timeout()] +``` + +with timeouts: 30 s for unit, 60 s for smoke, 300 s for integration, 3600 s for slow. Per-function markers are additive but do not replace the module-level marker. CI runs `pytest -m "(unit or smoke) and not skip"`; tests without a tier marker are invisible to CI. The `pytest-timeout` ceiling is a defensive net against future regressions that introduce a hang. + +### Physics validity + +Every unit test on a **physics source** (`chemistry.py`, `oxygen_fugacity.py`, `solubility.py`, `solve.py`, `structure.py`) must assert at least one of: + +- **Conservation**: per-species mass closure (`kg_atm + kg_liquid + kg_solid ≈ kg_total`), stoichiometric closure (`sum(mole_fractions) == 1.0`), element closure. +- **Positivity / boundedness**: T > 0, P > 0, mole fractions in [0, 1], `log10(fO2)` finite, partial pressures non-negative. +- **Monotonicity or symmetry**: `log10(fO2)` decreasing with `1/T` along an isobaric buffer; CO2 solubility increasing with P at fixed T; swapping two non-reacting species unchanged outputs. +- **Pinned numeric value with a discrimination guard**: a closed-form value or published table entry pinned via `pytest.approx`, accompanied by explicit assertions that wrong-formula / wrong-buffer / wrong-law results would differ from the correct one by more than the tolerance. + +Utility sources (`__init__.py`, `_version.py`, `constants.py`) are **exempt** from the physics-invariant requirement but still subject to the anti-happy-path rules. + +Tag every test that asserts a physical invariant with `@pytest.mark.physics_invariant`. Per-source-file granularity: each of the five physics files needs at least one such test in `tests/test_.py`. + +### Reference-pinned validation + +Tag tests that pin against a published benchmark, an analytical limit, or a cross-implementation cross-check with `@pytest.mark.reference_pinned`. Each of the five physics files must have at least one such test. The specific anchor is chosen by the test author and recorded in `docs/Validation/.md` (created when the first reference_pinned test for that source lands). The `--reference-pinned-status` mode of the linter reports the punch list of physics sources still missing a reference_pinned test. + +### Anti-happy-path rules (every new test) + +Every new test function MUST include: + +1. **At least one edge case** (boundary value, empty input, extreme physical parameter). +2. **At least one path that exercises the error contract** (documented exception, guard return, graceful clamp). If the function under test has no validation, exercise the limit-input behavior and assert the mathematical invariant. +3. **Assertion values that are NOT trivially derivable from the implementation**: discriminating numeric pins or property-based assertions (monotonicity, conservation) preferred over point checks. + +**Forbidden patterns** (flagged by `tools/check_test_quality.py`): + +- Single-assert test functions. +- Standalone weak assertions (`assert result is not None`, `assert result > 0`, `assert len(result) > 0`, `assert isinstance(result, dict)`) as the only meaningful check. +- Tests with no function-level docstring. +- Tests using `==` adjacent to float literals. +- Tests asserting on a fixture's implicit default. + +### Float and numerical comparison + +NEVER use `==` for floats. Use `pytest.approx(val, rel=1e-5)` or `np.testing.assert_allclose(actual, expected, rtol=..., atol=...)`. For pinned numeric values, include a **discrimination guard**: a follow-up `assert` showing the wrong-formula / wrong-buffer / wrong-law value would differ from the correct one by more than the tolerance. See `calliope-tests.md` Section 2 for the canonical pattern. + +### Mocking discipline + +- Default to `unittest.mock` for ALL external calls in unit tests: atmodeller, file I/O, network. +- Mock at the narrowest scope: a specific function, not a whole module. +- A mocked physics function must return **physically plausible** values; a mock that returns `0.0` or `1.0` for everything can mask real bugs. +- NEVER mock the function under test. +- Smoke / integration / slow tiers use the real solver. + +### Optional-dependency imports + +Any test that imports an optional dependency (`hypothesis`, `atmodeller`) MUST call `pytest.importorskip('')` at module top. The `pip install --no-deps` CI image will otherwise fail to collect. + +### Voice rule for test artifacts + +The repo-wide voice rule (zero AI-process disclosure in any public artifact) applies to test code with the same strictness as to source. Scope: test-skip reasons, test-file / function docstrings, test-function / class names, parametrize ids, log-capture assertions, **commit messages on test-touching commits, pull-request titles and bodies on test-touching PRs**, GitHub Actions job / step names, inline `src/calliope/**` comments, and shipped log strings. Out of scope: the rule documents themselves (this file, `calliope-tests.md`, `calliope-code-review.md`, `docs/How-to/build_tests.md`) may legitimately name the procedures they define. + +Banned phrases inside in-scope artifacts: "audit", "review pass", "adversarial review", AI-roadmap labels (`Phase X`, `Stage X.Y`, `Iteration N`, `T1.x`, `Group A/B/C/D` when AI-organized), `claude-config/...` paths, "Generated with Claude", AI-tool names, em-dashes, en-dashes (except bibliographic page ranges). + +Write the OUTCOME, never the PROCESS. + +### Speed and determinism + +- Unit tests: < 100 ms wall-time each. +- Aggressively mock heavy solver calls in unit tests. +- Set seeds for any randomness: `np.random.seed(42)`, `random.seed(42)`. Hypothesis tests use `@settings(derandomize=True)` or an explicit `--hypothesis-seed` (see `calliope-tests.md` Section 16). +- Use `tmp_path` (pytest fixture) for temporary files. + +### Documentation per test + +- File-level docstring: name the source under test, list the invariants and contract clauses the file exercises. +- Function-level docstring: state the physical scenario or contract clause being verified. Required (lint-enforced). +- Inline comments: explain **why** a specific input range was chosen. + +### Independent review trigger + +A pull request that adds or substantially modifies > 50 lines of test code across all its commits triggers an independent review pass before merge. The denominator is PR-level (`git diff origin/main...HEAD -- 'tests/**'`); splitting into many sub-50-line commits does not dodge the trigger. The reviewer cites the anti-happy-path rule, the discrimination-guard requirement, and the physics-invariant tier. + +### Tooling + +- Validate test structure: `bash tools/validate_test_structure.sh` +- Test-quality lint: `python tools/check_test_quality.py --check` +- Baseline regeneration (after a deliberate sweep): `python tools/check_test_quality.py --baseline` +- Reference-pinned audit: `python tools/check_test_quality.py --reference-pinned-status` +- Coverage ratchet (one-way, capped at 90): `python tools/update_coverage_threshold.py` +- Format: `ruff format src/ tests/ tools/ scripts/` +- Lint: `ruff check src/ tests/ tools/ scripts/` + +### Coverage architecture + +CALLIOPE uses two gates with explicit sub-targets: + +| Gate | Tests included | Target | Enforced | +|---|---|---|---| +| Fast gate (`tool.calliope.coverage_fast.fail_under`) | unit + smoke | Ratcheting toward **90%** | Every PR | +| Full gate (`tool.coverage.report.fail_under`) | unit + smoke + integration + slow | **90%** | Nightly | + +Both gates ratchet toward 90, capped at 90 (`tools/update_coverage_threshold.py` enforces `ECOSYSTEM_CEILING = 90.0`); neither may be manually decreased. The CI guard in `tests.yaml` rejects any PR that lowers `[tool.coverage.report].fail_under` below `min(base_ref, 90.0)`. + +## Safety & Determinism + +- **Randomness**: explicitly set seeds in tests. +- **Files**: do not generate tests that produce large output files; use `tempfile` or mocks. + +## Code Quality + +**Style** (enforced by ruff): + +- Line length < 96 chars. +- Variables / functions: `snake_case`. +- Constants: `UPPER_CASE`. +- Type hints: standard Python. +- Docstrings: brief descriptions of physical scenarios. + +**Pre-commit**: runs `ruff check --fix` automatically. Fix issues before committing. + +## Common Workflows + +### Making a Code Change + +1. **Create branch**: `git checkout -b /`. +2. **Make changes** in `src/calliope/`. +3. **Write / update tests** in `tests/test_.py` (mirror structure). +4. **Run tests locally**: `pytest -m "(unit or smoke) and not skip"`. +5. **Check coverage**: `pytest --cov=calliope --cov-report=html`. +6. **Lint**: `ruff check --fix src/ tests/ tools/ scripts/ && ruff format src/ tests/ tools/ scripts/`. +7. **Validate structure**: `bash tools/validate_test_structure.sh`. +8. **Test quality**: `python tools/check_test_quality.py --check`. +9. **Commit**: plain-language subject, first-person voice, no AI-process disclosure. +10. **Push**: CI runs automatically on PR. + +### Adding a New Physics Source + +1. Create `src/calliope/.py`. +2. Create `tests/test_.py` with module-level `pytestmark`. +3. Add at least one `@pytest.mark.physics_invariant` test asserting one of the four invariant families. +4. Plan a `@pytest.mark.reference_pinned` test (anchor: paper, analytical limit, or cross-check); create `docs/Validation/.md` when it lands. +5. Run the full PR checks locally. + +### Debugging Test Failures + +```bash +pytest -v --showlocals # Verbose with local variables +pytest -x # Stop at first failure +pytest tests/test_.py::test_function # Run specific test +pytest --pdb # Drop into debugger on failure +``` + +## Documentation References + +- **Testing rules**: `.github/.claude/rules/calliope-tests.md`, `.github/.claude/rules/calliope-code-review.md` +- **Test how-to**: `docs/How-to/build_tests.md` +- **Installation**: `docs/How-to/installation.md` +- **Concepts**: `docs/Explanations/oxygen_fugacity.md`, `docs/Explanations/solubility.md`, `docs/Explanations/equilibrium_chemistry.md`, `docs/Explanations/cross_backend_comparison.md` +- **PROTEUS coupling**: `docs/How-to/proteus_coupling.md`, `docs/Explanations/proteus_coupling.md` + +## Project memory and session learnings + +Session-specific knowledge (debugging logs, design rationale, sprint focus, ADR drafts) lives outside this repository, in the Claude memory tree under `~/.claude/projects//memory/`. The memory tree is per-user, sync-ready across machines, and not exposed in public commit history. + +What still lives in this repository: + +- Architectural decisions that affect every contributor: this file (`.github/copilot-instructions.md`). +- Test and review rules: `.github/.claude/rules/calliope-tests.md` and `.github/.claude/rules/calliope-code-review.md`. +- Per-PR rationale: PR descriptions. +- Per-commit rationale: commit messages. +- Module-level scientific validation: `docs/Validation/.md` (created when the first `@pytest.mark.reference_pinned` test for that source lands). + +Do not introduce a new in-repo "memory" or "decisions log" file. The four channels above are the contract. + +--- + +## Quick Reference + +```bash +# Setup +pip install -e ".[develop]" +pre-commit install -f + +# Test +pytest -m "(unit or smoke) and not skip" +pytest --cov=calliope --cov-report=html + +# Lint +ruff check --fix src/ tests/ tools/ scripts/ +ruff format src/ tests/ tools/ scripts/ + +# Validate +bash tools/validate_test_structure.sh +python tools/check_test_quality.py --check + +# Serve docs locally +pip install -e '.[docs]' +zensical serve +``` + +**Remember**: Trust these instructions. Only search if information is incomplete or found to be in error. + +--- + +> **⚠️ FILE SIZE LIMIT: This file must stay below 500 lines.** Enforced by pre-commit hook (`tools/check_file_sizes.sh`). File located at `.github/copilot-instructions.md`. diff --git a/.github/workflows/code-style.yaml b/.github/workflows/code-style.yaml index 62a58a1..712001f 100644 --- a/.github/workflows/code-style.yaml +++ b/.github/workflows/code-style.yaml @@ -16,10 +16,9 @@ on: jobs: codestyle: - if: github.event.pull_request.draft == false runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v6 - name: Get changed files id: changed-files diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml index cac7975..a2ecd9f 100644 --- a/.github/workflows/docs.yaml +++ b/.github/workflows/docs.yaml @@ -16,8 +16,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/configure-pages@v5 - - uses: actions/checkout@v5 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v6 + - uses: actions/setup-python@v6 with: python-version: 3.x - run: pip install zensical markdown-include pymdown-extensions mkdocstrings mkdocstrings-python diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml index ac4021c..2de53ce 100644 --- a/.github/workflows/nightly.yml +++ b/.github/workflows/nightly.yml @@ -26,20 +26,19 @@ jobs: timeout-minutes: 60 steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@v6 with: fetch-depth: 0 - name: Set up Python 3.12 - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: '3.12' - - name: Install CALLIOPE and pytest + - name: Install CALLIOPE run: | python -m pip install --upgrade pip pip install ".[develop]" - pip install pytest-cov - name: Run unit + smoke + integration + slow tests with coverage run: | @@ -51,7 +50,7 @@ jobs: - name: Upload coverage to Codecov if: ${{ !cancelled() }} - uses: codecov/codecov-action@v4 + uses: codecov/codecov-action@v6 with: token: ${{ secrets.CODECOV_TOKEN }} slug: FormingWorlds/CALLIOPE diff --git a/.github/workflows/publish-test-badges.yml b/.github/workflows/publish-test-badges.yml index b5d7690..678cec9 100644 --- a/.github/workflows/publish-test-badges.yml +++ b/.github/workflows/publish-test-badges.yml @@ -26,12 +26,12 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@v6 with: fetch-depth: 0 - name: Set up Python 3.12 - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: '3.12' diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml index e79d848..92ebfe7 100644 --- a/.github/workflows/publish.yaml +++ b/.github/workflows/publish.yaml @@ -14,11 +14,11 @@ jobs: id-token: write steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: fetch-depth: 0 # setuptools-scm needs full history + tags - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: "3.12" diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index 7c82f47..edec1d2 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -25,26 +25,29 @@ permissions: jobs: test: - if: github.event.pull_request.draft == false - name: CALLIOPE-check (${{ matrix.os }}, py${{ matrix.python-version }}) + name: CALLIOPE-check strategy: fail-fast: false + # Draft PRs run only ubuntu-latest / Python 3.12 for fast signal on the + # gating steps (validator, linter, ratchet, one test pass). Non-draft + # events (ready_for_review, push to main, workflow_dispatch) get the + # full 2x2 matrix. matrix: - os: ['ubuntu-latest', 'macos-latest'] - python-version: ['3.12', '3.13'] + os: ${{ github.event.pull_request.draft == true && fromJSON('["ubuntu-latest"]') || fromJSON('["ubuntu-latest", "macos-latest"]') }} + python-version: ${{ github.event.pull_request.draft == true && fromJSON('["3.12"]') || fromJSON('["3.12", "3.13"]') }} runs-on: ${{ matrix.os }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: fetch-depth: 0 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} - - uses: actions/cache@v4 + - uses: actions/cache@v5 id: cache-virtualenv with: path: ${{ env.pythonLocation }} @@ -59,6 +62,10 @@ jobs: if: ${{ matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' }} run: bash tools/validate_test_structure.sh + - name: Run test-quality lint + if: ${{ matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' }} + run: python tools/check_test_quality.py --check + - name: Pre-flight fail_under ratchet check if: ${{ matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' }} env: @@ -141,4 +148,8 @@ jobs: - name: Run unit + smoke tests run: | - pytest -m "(unit or smoke) and not skip" + FAST_FAIL_UNDER=$(python -c "import tomllib; print(tomllib.load(open('pyproject.toml','rb'))['tool']['calliope']['coverage_fast']['fail_under'])") + echo "Fast gate fail_under: ${FAST_FAIL_UNDER}%" + pytest -m "(unit or smoke) and not skip" \ + --cov=calliope \ + --cov-fail-under=${FAST_FAIL_UNDER} diff --git a/.gitignore b/.gitignore index 549a9fc..98828aa 100644 --- a/.gitignore +++ b/.gitignore @@ -173,3 +173,14 @@ cython_debug/ # setuptools-scm generated version file src/calliope/_version.py + +# Claude AI files +# Note: the repo-root CLAUDE.md is a tracked symlink to .github/copilot-instructions.md; +# the .github/.claude/rules/ directory is tracked and shared across collaborators. +# Personal session state (anything under the top-level .claude/ that is not the tracked +# rules tree) stays out of git. +/.claude/ +.mcp.json + +# Editor / IDE +.vscode/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 1cb7399..c02bb9f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,10 +15,18 @@ repos: - id: check-merge-conflict - id: debug-statements - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.6.1 + rev: v0.15.12 hooks: - id: ruff args: [--fix] types_or: [python, pyi] # - id: ruff-format # types_or: [python, pyi] + - repo: local + hooks: + - id: check-file-sizes + name: Check copilot-instructions.md line limit + entry: bash tools/check_file_sizes.sh + language: system + files: \.github/copilot-instructions\.md$ + pass_filenames: false diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 0645e65..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "editor.tabSize": 4, - "editor.rulers": [ 92 ], - "files.trimTrailingWhitespace": true, - "files.insertFinalNewline": true, - "git.ignoreLimitWarning": true, - "python.analysis.exclude": [ - "**/__pycache__", ".git", - ], - "python.testing.pytestArgs": [ - "tests" - ], - "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true -} diff --git a/CITATION.cff b/CITATION.cff index de3900d..fe0590d 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -24,7 +24,4 @@ authors: orcid: "https://orcid.org/0000-0001-5107-3531" title: "CALLIOPE" -version: 26.05.10 -doi: 10.xx/xx.xx -date-released: 2026-05-10 url: "https://github.com/FormingWorlds/CALLIOPE" diff --git a/CLAUDE.md b/CLAUDE.md new file mode 120000 index 0000000..02dd134 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1 @@ +.github/copilot-instructions.md \ No newline at end of file diff --git a/README.md b/README.md index 4459a9c..c5bcbc8 100644 --- a/README.md +++ b/README.md @@ -6,9 +6,11 @@ [![Unit Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/tests.yaml?branch=main&label=Unit%20Tests)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/tests.yaml) [![Integration Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/nightly.yml?branch=main&label=Integration%20Tests)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/nightly.yml) -**CALLIOPE** is the equilibrium outgassing solver of the [PROTEUS](https://proteus-framework.org/PROTEUS) coupled atmosphere-interior evolution framework. It computes the partitioning of volatile elements (H, C, N, S) between a partially molten silicate mantle and an overlying gas-phase atmosphere, assuming both reservoirs are in thermochemical equilibrium at the planetary surface. +**CALLIOPE** is the equilibrium outgassing solver of the [PROTEUS](https://proteus-framework.org/PROTEUS) coupled atmosphere-interior evolution framework. It computes the partitioning of volatile elements (H, C, N, O, S) between a partially molten silicate mantle and an overlying gas-phase atmosphere, assuming both reservoirs are in thermochemical equilibrium at the planetary surface. -Given an elemental inventory, a magma ocean temperature, a melt fraction, and an oxygen fugacity (specified as a log10 shift from the iron-wüstite buffer), CALLIOPE returns the surface partial pressures of eleven volatile species, the dissolved volatile masses, and the atmospheric mass. +Given an elemental inventory, a magma ocean temperature, a melt fraction, and an oxygen fugacity (specified as a log10 shift from the iron-wüstite buffer, defaulting to the Fischer et al. 2011 parameterisation), CALLIOPE returns the surface partial pressures of eleven volatile species, the dissolved volatile masses, and the atmospheric mass. + +Two solver modes are available: `equilibrium_atmosphere` takes fO2 as a control variable and derives O from the buffered chemistry, while `equilibrium_atmosphere_authoritative_O` takes total O mass as input and inverts to recover fO2. The buffered mode remains the default for standalone use; the authoritative-O mode is the chemistry side of whole-planet oxygen accounting on the PROTEUS side. Named after the [Greek muse of eloquence and epic poetry](https://en.wikipedia.org/wiki/Calliope). Pronounced *kal-IGH-uh-pee*. @@ -20,11 +22,11 @@ H2O, CO2, N2, S2 (primary unknowns); Full documentation is at **[proteus-framework.org/CALLIOPE](https://proteus-framework.org/CALLIOPE)**, including: -- [Getting started](https://proteus-framework.org/CALLIOPE/getting_started.html) — installation and a quick path to running. -- [First-run tutorial](https://proteus-framework.org/CALLIOPE/Tutorials/firstrun.html) — Earth-like solve with a Δ-IW redox sweep. -- [How-to guides](https://proteus-framework.org/CALLIOPE/How-to/installation.html) — install, configure, run, couple to PROTEUS, test, release. -- [Explanations](https://proteus-framework.org/CALLIOPE/Explanations/model.html) — model overview, equilibrium chemistry, solubility laws, oxygen fugacity, mass balance, code architecture. -- [API reference](https://proteus-framework.org/CALLIOPE/Reference/api/index.html) — every public function with NumPy-style docstrings. +- [Getting started](https://proteus-framework.org/CALLIOPE/getting_started.html): installation and a quick path to running. +- [Tutorials](https://proteus-framework.org/CALLIOPE/Tutorials/firstrun.html): first run, Earth and Mars fiducials, two-mode round-trip, coupled-loop driver, speciation phase diagram. +- [How-to guides](https://proteus-framework.org/CALLIOPE/How-to/installation.html): install, configure, run, couple to PROTEUS, use the authoritative-oxygen mode, test, release. +- [Explanations](https://proteus-framework.org/CALLIOPE/Explanations/model.html): model overview, equilibrium chemistry, solubility laws, oxygen fugacity, mass balance, authoritative-oxygen mode, [CALLIOPE-vs-atmodeller cross-backend comparison](https://proteus-framework.org/CALLIOPE/Explanations/cross_backend_comparison.html). +- [API reference](https://proteus-framework.org/CALLIOPE/Reference/api/index.html) and [validation anchors](https://proteus-framework.org/CALLIOPE/Validation/oxygen_fugacity.html): every public function with NumPy-style docstrings, plus the per-source reference-pinned test inventory. ## Installation @@ -70,12 +72,13 @@ See the [first-run tutorial](https://proteus-framework.org/CALLIOPE/Tutorials/fi ## Citation -If you use CALLIOPE in published work, please cite the three methods papers below. The full reference list (chemistry constants, solubility laws, oxygen-fugacity buffers, applications) is on the [Publications page](https://proteus-framework.org/CALLIOPE/Reference/publications.html). +If you use CALLIOPE in published work, please cite the four methods papers below. The full reference list (chemistry constants, solubility laws, oxygen-fugacity buffers, applications) is on the [Publications page](https://proteus-framework.org/CALLIOPE/Reference/publications.html). -- Bower, D.J., Kitzmann, D., Wolf, A.S., Sanan, P., Dorn, C., & Oza, A.V. (2019). *Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations.* **A&A** 631, A103. [\[ADS\]](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B) [\[DOI\]](https://doi.org/10.1051/0004-6361/201935710) -- Bower, D.J., Hakim, K., Sossi, P.A., & Sanan, P. (2022). *Retention of water in terrestrial magma oceans and carbon-rich early atmospheres.* **PSJ** 3, 93. [\[ADS\]](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) [\[DOI\]](https://doi.org/10.3847/PSJ/ac5fb1) -- Nicholls, H., Lichtenberg, T., Bower, D.J., & Pierrehumbert, R. (2024). *Magma ocean evolution at arbitrary redox state.* **JGR Planets** 129, e2024JE008576. [\[ADS\]](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) [\[DOI\]](https://doi.org/10.1029/2024JE008576) [\[arXiv\]](https://arxiv.org/abs/2411.19137) +- Bower, D.J., Kitzmann, D., Wolf, A.S., Sanan, P., Dorn, C., & Oza, A.V. (2019). *Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations.* **A&A** 631, A103. [\[SciX\]](https://scixplorer.org/abs/2019A%26A...631A.103B/abstract) [\[DOI\]](https://doi.org/10.1051/0004-6361/201935710) [\[arXiv\]](https://arxiv.org/abs/1904.08300) +- Bower, D.J., Hakim, K., Sossi, P.A., & Sanan, P. (2022). *Retention of water in terrestrial magma oceans and carbon-rich early atmospheres.* **PSJ** 3, 93. [\[SciX\]](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract) [\[DOI\]](https://doi.org/10.3847/PSJ/ac5fb1) [\[arXiv\]](https://arxiv.org/abs/2110.08029) +- Nicholls, H., Lichtenberg, T., Bower, D.J., & Pierrehumbert, R. (2024). *Magma ocean evolution at arbitrary redox state.* **JGR Planets** 129, e2024JE008576. [\[SciX\]](https://scixplorer.org/abs/2024JGRE..12908576N/abstract) [\[DOI\]](https://doi.org/10.1029/2024JE008576) [\[arXiv\]](https://arxiv.org/abs/2411.19137) +- Nicholls, H., Lichtenberg, T., Chatterjee, R.D., Guimond, C.M., Postolec, E., & Pierrehumbert, R.T. (2026). *Volatile-rich evolution of molten super-Earth L 98-59 d.* **Nature Astronomy**. [\[SciX\]](https://scixplorer.org/abs/2026NatAs.tmp...61N/abstract) [\[DOI\]](https://doi.org/10.1038/s41550-026-02815-8) [\[arXiv\]](https://arxiv.org/abs/2507.02656) ## License -[Apache License 2.0](LICENSE.txt). CALLIOPE is part of the [Forming Worlds Lab](https://formingworlds.space/) PROTEUS framework. +[Apache License 2.0](LICENSE.txt). CALLIOPE is part of the [PROTEUS framework](https://proteus-framework.org/). diff --git a/docs/Explanations/authoritative_oxygen.md b/docs/Explanations/authoritative_oxygen.md new file mode 100644 index 0000000..ebe11f8 --- /dev/null +++ b/docs/Explanations/authoritative_oxygen.md @@ -0,0 +1,117 @@ +# Authoritative-oxygen mode + +CALLIOPE offers two equilibrium-chemistry entry points that share the same physics but differ in which quantity is the input and which is the unknown: + +- `equilibrium_atmosphere` (the [buffered mode](mass_balance.md)) takes the oxygen fugacity $f_{\mathrm{O}_2}$ as an input via `ddict['fO2_shift_IW']` and solves for the four primary partial pressures $(p_\mathrm{H_2O}, p_\mathrm{CO_2}, p_\mathrm{N_2}, p_\mathrm{S_2})$ against the H, C, N, S elemental budgets. Oxygen mass is *implicit*: it is whatever the chemistry requires at the prescribed buffer, and it can change freely between calls. +- `equilibrium_atmosphere_authoritative_O` takes the total oxygen mass as a fifth budget alongside H, C, N, S, and solves for the four pressures *plus* $\Delta\mathrm{IW}$ as a fifth unknown. + +This page documents the augmented mass-balance system, the additional solver controls, and the inputs and outputs the two modes share. + +## When the two modes agree + +The buffered mode and the authoritative-O mode are dual formulations of the same underlying chemistry. For any pair of $\Delta\mathrm{IW}$ and target H, C, N, S budgets, the buffered mode produces an equilibrium atmosphere whose total O mass can be read off the result dict as `O_kg_total`. Feeding the five-element budget $(H, C, N, S, O)$ to the authoritative-O mode, with the O target taken from the buffered run's `O_kg_total`, reproduces the same partial pressures and recovers the same $\Delta\mathrm{IW}$ to within the solver tolerance. The regression test `tests/test_authoritative_O.py::TestRoundTrip::test_round_trip_recovers_fO2_within_tolerance` enforces this round-trip and is the operational definition of "same physics, different unknowns". + +The two modes differ only in their degrees-of-freedom accounting: + +| Mode | Inputs | Unknowns | Equations | +|---|---|---|---| +| `equilibrium_atmosphere` | $(H, C, N, S)$ targets, $\Delta\mathrm{IW}$ | $(p_\mathrm{H_2O}, p_\mathrm{CO_2}, p_\mathrm{N_2}, p_\mathrm{S_2})$ | 4 mass-balance | +| `equilibrium_atmosphere_authoritative_O` | $(H, C, N, S, O)$ targets | $(p_\mathrm{H_2O}, p_\mathrm{CO_2}, p_\mathrm{N_2}, p_\mathrm{S_2}, \Delta\mathrm{IW})$ | 5 mass-balance | + +## The augmented mass-balance system + +Adding an O budget closes the system with one extra constraint. For each element $e \in \{\mathrm{H}, \mathrm{C}, \mathrm{N}, \mathrm{S}, \mathrm{O}\}$: + +$$ +r_e(\mathbf{x}) = m_e^\mathrm{atm}(\mathbf{x}) + m_e^\mathrm{melt}(\mathbf{x}) - m_e^\mathrm{target} = 0, +$$ + +where the unknown is the five-vector $\mathbf{x} = (p_\mathrm{H_2O}, p_\mathrm{CO_2}, p_\mathrm{N_2}, p_\mathrm{S_2}, \Delta\mathrm{IW})$. The per-species column masses $m_v^\mathrm{atm}$ and dissolved masses $m_v^\mathrm{melt}$ are computed from the [same physics functions](mass_balance.md#atmospheric-column-mass) as the buffered mode; $\Delta\mathrm{IW}$ is read from $\mathbf{x}[4]$, and the residual vector has five entries (one per element in H, C, N, S, O). + +The private versions `_atmosphere_mass` and `_dissolved_mass` accept `fO2_shift` as a positional argument, which lets both solver modes consume identical physics through one set of functions. The five-residual function is `solve.func_authoritative_O`: + +```python +def func_authoritative_O(x_arr, ddict, mass_target_d): + pin_dict = {'H2O': x_arr[0], 'CO2': x_arr[1], + 'N2': x_arr[2], 'S2': x_arr[3]} + fO2_shift = x_arr[4] + mass_atm_d = _atmosphere_mass(pin_dict, fO2_shift, ddict) + mass_int_d = _dissolved_mass(pin_dict, fO2_shift, ddict) + return [mass_atm_d[v] + mass_int_d[v] - mass_target_d[v] + for v in ('H', 'C', 'N', 'S', 'O')] +``` + +## How $\Delta\mathrm{IW}$ enters as an unknown + +$\Delta\mathrm{IW}$ enters the chemistry through the four channels listed on the [oxygen fugacity page](oxygen_fugacity.md#how-deltamathrmiw-enters-the-chemistry): the free $\mathrm{O_2}$ partial pressure, the modified equilibrium constants $G_\mathrm{eq}$, the Gaillard S$_2$ solubility, and the Dasgupta N$_2$ solubility. In the buffered mode $\Delta\mathrm{IW}$ is an input held fixed during the solve; in the authoritative-O mode it is a variable the solver adjusts in concert with the four pressures to satisfy the five-residual system. + +The closure mechanism is intuitive: increasing $\Delta\mathrm{IW}$ (more oxidising) drives more H$_2$O at fixed H, more CO$_2$ at fixed C, and more SO$_2$ and dissolved S at fixed S. The Dasgupta N$_2$ solubility carries an explicit $-1.6\,\Delta\mathrm{IW}$ term in its exponent, so dissolved N drops by roughly an order of magnitude per dex of oxidation, while N$_2$ in the atmosphere takes up the slack. The aggregated atmospheric + dissolved O mass therefore monotonically increases with $\Delta\mathrm{IW}$ over the physically relevant range, which gives the 5-residual system a unique root for any feasible O target. The `tests/test_authoritative_O_monotonicity.py::TestMonotonicity::test_O_kg_total_strictly_increasing_with_fO2` property test pins this monotonicity over $\Delta\mathrm{IW} \in [-4, +6]$ at two $T_\mathrm{magma}$ values, with a sibling check at $\Phi_\mathrm{global} = 0.3$ over $\Delta\mathrm{IW} \in [-2, +4]$. + +## Solver: per-element residual gate + +`equilibrium_atmosphere_authoritative_O` uses the same hybrid `fsolve` + `trust-constr` outer loop as the buffered mode (see [Mass balance & solver](mass_balance.md#solver-hybrid-powell-trust-region-with-monte-carlo-restart)), with three differences in the acceptance test and the initial-guess generation. + +### Per-element tolerance + +The buffered mode accepts a solution if the scalar bound + +$$ +\max_e |r_e| < r_\mathrm{tol} \cdot \max_e m_e^\mathrm{target} + a_\mathrm{tol} + 10\,\mathrm{kg} +$$ + +is satisfied. The buffered mode never sees O in its residual vector, so the four H/C/N/S targets dominate $\max_e m_e^\mathrm{target}$. In the authoritative-O mode O is now in the residual vector, and at planet scale $m_\mathrm{O}^\mathrm{target} \sim 10^{22}\,\mathrm{kg}$ can be five orders of magnitude larger than $m_\mathrm{N}^\mathrm{target} \sim 10^{17}\,\mathrm{kg}$. A tolerance scaled to the largest target would silently admit N residuals of order $10^{17}\,\mathrm{kg}$, which is the whole N budget. + +The authoritative-O mode replaces the scalar bound with a per-element gate: + +$$ +|r_e| < \max\left(r_\mathrm{tol} \cdot m_e^\mathrm{target}, \tfrac{a_\mathrm{tol}}{5}, \mathrm{TRUNC\_MASS}\right) \quad \forall e \in \{\mathrm{H}, \mathrm{C}, \mathrm{N}, \mathrm{S}, \mathrm{O}\}. +$$ + +The $a_\mathrm{tol}$ budget is split evenly across the five elements so the per-element floor stays in the kilogram range, and the `TRUNC_MASS` constant ($10\,\mathrm{kg}$, from `solve.py`) handles benign sub-kilogram noise that should not gate convergence. + +### The fO2 hint and the restart redraw + +The five-dimensional problem has a larger initial-guess space than the four-dimensional buffered problem, so the user supplies `fO2_hint` (default $+4.0$) as a starting value for $\Delta\mathrm{IW}$. On a cold start (`p_guess=None`), the four pressures are drawn log-uniformly over $[10^{-12}, 10^5]$ bar (same as the buffered mode) and the fifth unknown is initialised to `fO2_hint`. On every Monte-Carlo restart after the initial attempt, the four pressures are redrawn and $\Delta\mathrm{IW}$ is redrawn from $\mathrm{Uniform}(-6, +8)$. This window covers the physically relevant mantle redox states (reducing through Mercury-like at $-5$ to highly oxidised at $+5$) so a restart can pick a fundamentally different basin from the initial hint. + +### Bounds + +The trust-region solver uses bounds $[0, 10^7]$ bar on each pressure (same as the buffered mode) and $[-12, +12]$ on $\Delta\mathrm{IW}$. The pressure bounds are physical; the fO2 bounds are wider than the $[-6, +8]$ redraw window so the solver has slack to escape a poor cold start without immediately hitting the constraint, but tight enough to reject runaway trajectories into thermodynamically meaningless regions. + +### Reproducibility + +A `random_seed` argument (default `None`) seeds a `np.random.default_rng` for the restart draws. Passing an integer makes the solver deterministic across calls at fixed inputs, which is required for regression tests and for diffing two runs. The default `None` preserves the non-deterministic global-state behaviour shared with the buffered mode. + +## Buffer convention + +Like the buffered mode, the authoritative-O mode references $f_{\mathrm{O}_2}$ to the iron-wüstite buffer of O'Neill & Eggins (2002)[^cite-oneilleggins2002] by default, with the Fischer et al. (2011)[^cite-fischer2011] alternative selectable through `OxygenFugacity()` instantiation. The returned `fO2_shift_derived` is the $\Delta\mathrm{IW}$ relative to whichever buffer was chosen. + +!!! note "Cross-backend buffer divergence" + PROTEUS supports a second outgassing backend, [atmodeller](https://atmodeller.readthedocs.io/), whose authoritative-O implementation uses the Hirschmann combined IW buffer. The Hirschmann and O'Neill & Eggins parameterisations differ by ${\sim}0.95$ dex at $T = 3000$ K. PROTEUS records both backends' derived offsets under the helpfile column `fO2_shift_IW_derived`, and the discrepancy is documented in the column's schema comment. The two backends agree on the underlying physics (same chemistry of FeO-O$_2$ equilibrium); they disagree on the numerical parameterisation of the buffer curve. Choose one backend per run and stay with it for any cross-time-step comparison. + +## When to use this mode + +The authoritative-O mode is the right tool when atmospheric + dissolved O is a budgeted quantity rather than a derived one. Three typical use cases: + +1. **Whole-planet O accounting.** When PROTEUS treats O as one of the five tracked elements (so escape debits it, the mantle initial inventory budgets it, and the structure solver weights it into M_planet), the chemistry must invert against the running O total rather than buffer to a user-prescribed $\Delta\mathrm{IW}$. The PROTEUS `planet.fO2_source = "from_O_budget"` config switch flips outgassing into this mode at runtime. +2. **Inferring $\Delta\mathrm{IW}$ from observed composition.** Given a measured or assumed elemental inventory including O, the authoritative-O mode returns the $\Delta\mathrm{IW}$ consistent with that inventory at the chosen $T_\mathrm{magma}$ and $\Phi_\mathrm{global}$. This complements the buffered-mode forward calculation when the modelling question is "what redox state does this composition imply?". +3. **Sensitivity studies that vary the O reservoir.** When sweeping over a mantle FeO inventory grid, the authoritative-O mode lets you specify the O budget directly rather than having to translate each grid point through a buffer offset first. + +For the routine "set $\Delta\mathrm{IW}$, get a self-consistent atmosphere" workflow, the buffered mode is faster and has fewer unknowns. The authoritative-O mode adds one residual equation and one unknown; convergence is empirically a few Monte-Carlo restarts with a good `fO2_hint` and PROTEUS warm-starting, compared to typically a single attempt for the buffered mode under the same conditions. + +## Limitations + +- **Solubility-law calibration.** The Dasgupta N$_2$ and Gaillard S$_2$ solubility laws have explicit $\ln f_{\mathrm{O}_2}$ terms. Outside their calibration footprints the solver still produces a solution, but the implied O partitioning between dissolved S/N and atmospheric SO$_2$/N$_2$ extrapolates the underlying experimental data. The [solubility-laws validity envelope](solubility.md#validity-envelope) gives the temperature, pressure, and fO$_2$ ranges over which each law was fit. The authoritative-O entry point itself does not flag extrapolation; the caller is expected to consult the envelope. +- **Solid-FeO buffering not modelled.** The chemistry assumes a single surface $\Delta\mathrm{IW}$ controls O speciation everywhere in the atmosphere. As $\Phi_\mathrm{global} \to 0$ the melt cannot buffer O against the gas-phase composition, but the entry point still produces a solution: at zero melt fraction the dissolved-mass channel is identically zero and the O constraint reduces to a pure atmospheric balance. +- **Monotonicity not guaranteed pathologically.** For target O budgets so small that O is dominated by dissolved species at strongly reducing $\Delta\mathrm{IW}$, the residual surface can develop a second root in the $\Delta\mathrm{IW}$ direction. The Monte-Carlo restart with fO2 redraw is designed to escape such basins, but for adversarial inputs (sub-trace O at very low $T_\mathrm{magma}$) it may need many restarts or fail. Failure raises `RuntimeError` with the final attempt's pressures and $\Delta\mathrm{IW}$ for diagnosis; the right response is usually to inspect whether the upstream O budget is physical, not to bump `nguess`. +- **No O kinetics.** Like the buffered mode, the authoritative-O mode is a snapshot equilibrium computation. Time-resolved O evolution is the responsibility of the calling code (escape, ingassing, diffusion). + +## See also + +- [How-to: authoritative oxygen mode](../How-to/authoritative_oxygen.md): the recipe for calling the entry point. +- [Mass balance & solver](mass_balance.md): the four-residual buffered mode. +- [Oxygen fugacity](oxygen_fugacity.md): the IW-buffer parameterisations and the four channels through which $\Delta\mathrm{IW}$ enters the chemistry. +- [Coupling to PROTEUS (theory)](proteus_coupling.md): how the PROTEUS wrapper selects between the two modes. +- [API reference for `calliope.solve`](../Reference/api/calliope.solve.md). + +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151–181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). +[^cite-fischer2011]: R. A. Fischer, A. J. Campbell, G. A. Shofner, O. T. Lord, P. Dera, V. B. Prakapenka, *[Equation of state and phase diagram of FeO](https://doi.org/10.1016/j.epsl.2011.02.025)*, Earth and Planetary Science Letters, 304, 496–502, 2011. [SciX](https://scixplorer.org/abs/2011E%26PSL.304..496F/abstract). diff --git a/docs/Explanations/code_architecture.md b/docs/Explanations/code_architecture.md index 30b79ba..f944297 100644 --- a/docs/Explanations/code_architecture.md +++ b/docs/Explanations/code_architecture.md @@ -43,7 +43,7 @@ Pure data; no side effects, no logic. ### `oxygen_fugacity.py` -Single class `OxygenFugacity` with two model methods (`oneill` default, `fischer`). Stateless: instantiate once, call repeatedly with `(T, fO2_shift)`. See [Oxygen fugacity](oxygen_fugacity.md) for the equations. +Single class `OxygenFugacity` with two model methods (`fischer` default, `oneill` legacy). Stateless: instantiate once, call repeatedly with `(T, fO2_shift)`. See [Oxygen fugacity](oxygen_fugacity.md) for the equations. ### `chemistry.py` @@ -57,15 +57,22 @@ See [Solubility laws](solubility.md) for the equations. ### `solve.py` -The orchestration layer. Five public functions: +The orchestration layer. Two solver entry points, the buffered mode and the authoritative-O mode, share the speciation tree and aggregation functions: + +- `equilibrium_atmosphere(target, ddict, ...)`: buffered-mode outer driver. Takes a four-key `target` and the IW-buffer offset in `ddict`, solves the $4\times 4$ H/C/N/S mass-balance system. +- `equilibrium_atmosphere_authoritative_O(target_d, ddict, ...)`: authoritative-O outer driver. Takes a five-key `target_d` (H, C, N, S, O) and solves the $5\times 5$ system for the four pressures plus $\Delta\mathrm{IW}$. See [Authoritative-oxygen mode](authoritative_oxygen.md) for the augmented mass balance. + +Plus seven shared helpers: - `get_partial_pressures(pin, ddict)`: walks the eleven-species speciation tree from the four primary pressures. -- `atmosphere_mass(pin, ddict)`: applies [Bower et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B) Eq. 2 to every species and aggregates atomic-mass tallies per element. +- `atmosphere_mass(pin, ddict)`: applies Bower et al. (2019)[^cite-bower2019] Eq. 2 to every species and aggregates atomic-mass tallies per element. - `dissolved_mass(pin, ddict)`: applies the chosen solubility law for each soluble species and aggregates atomic-mass tallies per element. -- `get_target_from_params(ddict)`: translates `hydrogen_earth_oceans`, `CH_ratio`, `nitrogen_ppmw`, `sulfur_ppmw` into kg-per-element targets. +- `get_target_from_params(ddict)`: translates `hydrogen_earth_oceans`, `CH_ratio`, `nitrogen_ppmw`, `sulfur_ppmw` into kg-per-element targets (four-key, for the buffered mode). - `get_target_from_pressures(ddict)`: back-computes kg-per-element targets from prescribed initial atmospheric pressures. +- `get_initial_pressures(target_d)`: cold-start guess generator for the four primary pressures. +- `get_initial_pressures_with_fO2(target_d, fO2_hint, ...)`: cold-start guess generator for the five-unknown authoritative-O solver, with a separate restart redraw for $\Delta\mathrm{IW}$. -Plus the inner-loop residual functions `func` (returns the four-vector residual) and `obj` (returns the L2 norm), and the outer driver `equilibrium_atmosphere(target, ddict, ...)` that ties it all together. +Plus the inner-loop residual functions `func` (four-vector for buffered) and `func_authoritative_O` (five-vector), the L2-norm objectives `obj` and `obj_authoritative_O`, and three private versions (`_get_partial_pressures`, `_atmosphere_mass`, `_dissolved_mass`) that take `fO2_shift` as a positional argument so both solver modes can share the physics path. `get_partial_pressures` is *the* call that defines what species CALLIOPE knows about. To add a new species, you add an entry in `volatile_species` (constants.py), an entry in `molar_mass` (constants.py), a `ModifiedKeq` model method (chemistry.py) if it is a derived species, a `Solubility` subclass (solubility.py) if it has dissolved-melt physics, and the speciation step in `get_partial_pressures` plus the atomic-mass tally in `atmosphere_mass`. Tests need the analytical-vs-code consistency check in `tests/test_stoichiometry.py`. @@ -76,32 +83,34 @@ Single function `calculate_mantle_mass(radius, mass, core_frac)` that returns `M ## Call graph ``` - equilibrium_atmosphere + equilibrium_atmosphere equilibrium_atmosphere_authoritative_O + │ │ + ▼ ▼ + get_initial_pressures get_initial_pressures_with_fO2 + │ │ + └────────────┬─────────────────────────────┘ + ▼ + scipy.optimize.fsolve / minimize ◄─── ModifiedKeq.__call__ + │ ▲ + ▼ │ + func / func_authoritative_O │ + │ │ + ┌─────────────────┴─────────────────┐ │ + ▼ ▼ │ + _atmosphere_mass _dissolved_mass │ + │ │ │ + │ ┌─────────────────────────────┤ │ + │ ▼ ▼ │ + │ _get_partial_pressures Solubility{...}.__call__ + │ │ │ + ▼ ▼ │ + atmosphere_mean_molar_mass ModifiedKeq.__call__ ◄─────┘ │ - ┌───────────────────────────┼───────────────────────────┐ - ▼ ▼ ▼ - get_initial_pressures scipy.optimize.fsolve scipy.optimize.minimize - │ │ - └─────────┬─────────────────┘ - ▼ - func ◄─── ModifiedKeq.__call__ - │ ▲ - ┌─────────────────────────────────────┤ │ - ▼ ▼ │ - atmosphere_mass dissolved_mass │ - │ │ │ - │ ┌──────────────────────────────┤ │ - │ ▼ ▼ │ - │ get_partial_pressures Solubility{H2O,CO2,…}.__call__ - │ │ │ - ▼ ▼ │ - atmosphere_mean_molar_mass ModifiedKeq.__call__ ◄─────┘ - │ - ▼ - OxygenFugacity.__call__ + ▼ + OxygenFugacity.__call__ ``` -The graph is a DAG with a single feedback loop (the outer `fsolve` → `func` → ... → `ModifiedKeq` cycle). No module imports any other module's private state; all couplings are through the `pin`/`ddict` dictionaries. +The graph is a DAG with a single feedback loop (the outer solver → residual → speciation → modified-equilibrium-constant cycle) and two parallel entry points that diverge at the cold-start generator and re-merge at the residual function. The private versions (`_get_partial_pressures`, `_atmosphere_mass`, `_dissolved_mass`) take `fO2_shift` as a positional argument so both modes share the physics path; the public versions read it from `ddict` for backward compatibility. No module imports any other module's private state; all couplings are through the `pin`/`ddict` dictionaries. ## What is *not* in CALLIOPE @@ -130,3 +139,5 @@ For batch use cases (sensitivity sweeps, parameter studies), wrap a Python loop - [API reference](../Reference/api/index.md) for the auto-generated per-symbol documentation. - [Source on GitHub](https://github.com/FormingWorlds/CALLIOPE/tree/main/src/calliope) for the actual implementation. + +[^cite-bower2019]: D. J. Bower, D. Kitzmann, A. S. Wolf, P. Sanan, C. Dorn, A. V. Oza, *[Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations](https://doi.org/10.1051/0004-6361/201935710)*, Astronomy & Astrophysics, 631, A103, 2019. [SciX](https://scixplorer.org/abs/2019A%26A...631A.103B/abstract). diff --git a/docs/Explanations/cross_backend_comparison.md b/docs/Explanations/cross_backend_comparison.md new file mode 100644 index 0000000..80ba46b --- /dev/null +++ b/docs/Explanations/cross_backend_comparison.md @@ -0,0 +1,147 @@ +# Backend comparison + +PROTEUS supports two outgassing backends with an authoritative-O entry point: CALLIOPE and [atmodeller](https://atmodeller.readthedocs.io/). +Both invert the same closure (the user-supplied O budget is the volatile O that participates in atmospheric and dissolved chemistry; the IW-buffer offset $\Delta\mathrm{IW}$ becomes the fifth unknown), but they implement it with different IW-buffer parameterisations, different solubility-law selections, different gas-phase equation-of-state choices, and different solver architectures. + +This page quantifies where the two backends agree and where they disagree, so that a user picking a backend for a coupled run can reason about the systematic that choice implies. + +The atmodeller results on this page were produced with **atmodeller v1.0.0**. atmodeller is under active development, so a later release may shift the comparison; the version is recorded here so the figures can be reproduced or re-checked against it. + +The figures here are regenerated by the reusable scripts in `scripts/cross_backend/`; see [Reproducing this page](#reproducing-this-page) at the end. + +## The Earth fiducial used throughout this page + +A cross-backend comparison only carries meaning if both solvers are fed the same inputs. +Figures 3, 4, and 5 all run both backends at one shared Earth fiducial so that any disagreement they show is attributable to the backends' internal choices (buffer, solubility, EOS, equilibrium-constant fits) rather than to a difference in the inputs. +The fiducial is Earth bulk-silicate-Earth (BSE), $\Phi = 1$, with $T_\mathrm{magma}$ either fixed at 2000 K (Figures 4 and 5) or swept from 1800 K to 3000 K (Figure 3). +Earth is the natural anchor because the modern upper-mantle $\Delta\mathrm{IW}$ is empirically constrained (Sossi et al. 2020[^cite-sossi2020]; Frost & McCammon 2008[^cite-frostmccammon2008]), which lets us check in Figure 5 whether either backend's prediction lands inside the petrologically allowed range. + +The H / C / N / S inputs come from the Krijt et al. 2023[^cite-krijt2023] Protostars and Planets VII Tables 1 and 2 BSE row, summed across mantle and atmospheric reservoirs: + +| Element | Krijt+2023 BSE mass (kg) | +|---|---| +| H | $5.6 \times 10^{20}$ | +| C | $3.1 \times 10^{21}$ | +| N | $3.7 \times 10^{19}$ | +| S | $1.0 \times 10^{21}$ | + +Oxygen is not taken from Krijt et al. 2023 directly. +Krijt's tabulated O is a redox-active inventory (the mass of O required to move BSE to the Fe(II)O reference state, dominated by the mantle FeO / Fe$_2$O$_3$ imbalance), whereas the authoritative-O entry point treats O as the volatile budget (atoms in atmospheric and dissolved H$_2$O / CO$_2$ / SO$_2$ / O$_2$ only). +The two definitions are not interconvertible without a chemistry calculation. +The volatile-O reference used on this page, $O = 1.26 \times 10^{22}$ kg, was derived by running CALLIOPE in buffered mode at the Krijt H/C/N/S budget above, $T_\mathrm{magma} = 2000$ K, and the Sossi 2020[^cite-sossi2020] $\Delta\mathrm{IW} = +3.5$ Earth-upper-mantle anchor with the current default Fischer 2011 buffer. +The provenance function `scripts/cross_backend/inventories.derive_earth_volatile_O()` re-derives this number from scratch on demand. + +## Sources of disagreement + +Four axes contribute to cross-backend $\Delta\mathrm{IW}$ disagreement. Two can be aligned at the wrapper level, two cannot. + +| Axis | CALLIOPE | atmodeller | Aligned by default? | +|---|---|---|---| +| IW buffer | Fischer et al. 2011[^cite-fischer2011] (current default, see below); O'Neill & Eggins 2002[^cite-oneilleggins2002] available as legacy `'oneill'` | Hirschmann composite (Hirschmann et al. 2008[^cite-hirschmann2008] below 1000 K, Hirschmann 2021[^cite-hirschmann2021] above) | Close (Fischer is within ~0.2 dex of Hirschmann across the magma-ocean range) | +| H$_2$O solubility | Sossi et al. 2023[^cite-sossi2023] peridotite | `H2O_peridotite_sossi23` | Yes | +| CO$_2$ solubility | Dixon et al. 1995[^cite-dixon1995] basalt | `CO2_basalt_dixon95` | Yes | +| N$_2$ solubility | Dasgupta et al. 2022[^cite-dasgupta2022] | `N2_basalt_dasgupta22` | Yes | +| S$_2$ solubility | Gaillard et al. 2022[^cite-gaillard2022] sulfide-only | `S2_sulfide_basalt_boulliung23` (Boulliung & Wood 2023[^cite-boulliungwood2023], CoMP) | No | +| H$_2$, CO, CH$_4$ solubility | identically zero (Bower et al. 2022[^cite-bower2022] §2.2.3) | Hirschmann 2012, Yoshioka 2019, Ardia 2013 | Optionally (set keys to `none`) | +| Gas-phase EOS | ideal | ideal by default; real-gas selectable | Yes (with EOS off) | +| Equilibrium constants | JANAF + Schaefer-Fegley fits | atmodeller thermodata | No | +| Solver | scipy `fsolve` + `trust-constr` with Monte-Carlo restart | JAX gradient-based with multistart | Different by construction; affects convergence behaviour, not converged answer | + +## Buffer convention as a cross-backend systematic + +CALLIOPE's default IW buffer is Fischer et al. 2011[^cite-fischer2011], with O'Neill & Eggins 2002[^cite-oneilleggins2002] available as `OxygenFugacity('oneill')` for backwards compatibility. atmodeller uses the Hirschmann composite (Hirschmann 2008 below 1000 K, Hirschmann 2021 above). Across magma-ocean temperatures O'Neill and Hirschmann differ by up to $\sim 1$ dex; Fischer and Hirschmann agree to within $\sim 0.2$ dex over the same range, so the cross-backend buffer offset under the current default is much smaller than under the legacy choice. + +![Buffer divergence](../assets/figures/cross_backend/fig1_buffer_divergence.png) + +*Figure 1. (a) IW-buffer parameterisations across magma-ocean temperatures. The Hirschmann composite switches from Hirschmann 2008 to Hirschmann 2021 at 1000 K, which produces a kink that the monolithic parameterisations do not have. (b) Difference between Hirschmann composite and the two CALLIOPE options, in dex. At $T = 3000$ K the Hirschmann buffer is $0.95$ dex more oxidising than O'Neill but only $0.11$ dex below Fischer, an order of magnitude smaller. Fischer 2011 hugs Hirschmann everywhere above $\sim 1700$ K.* + +The buffer choice therefore moves the default residual from $\sim 1$ dex (with the legacy O'Neill setting) down to $\lesssim 0.2$ dex (with the Fischer default). The remaining cross-backend disagreement is genuinely chemistry-level (solubility laws, equilibrium-constant fits) rather than buffer-level. + +## Each backend is internally self-consistent + +Before comparing backends to each other, each must round-trip self-consistently: start from a buffered-mode call at a known $\Delta\mathrm{IW}$, take the resulting O budget, feed it back into the authoritative-O entry point, and recover the input $\Delta\mathrm{IW}$. We sweep $T_\mathrm{magma}$ because the chemistry itself is temperature-dependent: the equilibrium constants are evaluated at $T$, the Dasgupta nitrogen solubility carries explicit $T$ and $f_{\mathrm{O}_2}$ terms, and the Gaillard sulfur solubility carries an explicit $f_{\mathrm{O}_2}$ term. A round-trip that only worked at one $T$ would not be evidence of internal consistency; the test must cover the full range where the calibrated chemistry is meant to apply. + +![Internal round-trip](../assets/figures/cross_backend/fig2_roundtrip.png) + +*Figure 2. Round-trip residuals $\Delta\mathrm{IW}_\mathrm{recovered} - \Delta\mathrm{IW}_\mathrm{input}$ for each backend at $T_\mathrm{magma} \in \{1500, 2000, 2500, 3000\}\,\mathrm{K}$ and input $\Delta\mathrm{IW} \in \{-2, 0, +2, +4\}$. Coloured circles map to the four temperatures (cool to warm); horizontal jitter is added so the four temperature markers fan out at each input $\Delta\mathrm{IW}$ rather than stacking. The grey band marks the $\pm 0.01$ dex solver tolerance. (a) CALLIOPE: all but one grid point sit well inside the tolerance band; the magenta triangle at the bottom right marks the one edge case where the authoritative-O solver landed in a secondary basin and returned $\Delta\mathrm{IW}_\mathrm{recovered} = -5.84$ from an input of $+4$ at $T = 3000$ K. (b) atmodeller: all converged grid points are inside the tolerance band; the indigo triangle at the bottom of panel (b) marks the one grid point where the solver failed to converge ($T = 1500$ K, input $\Delta\mathrm{IW} = 0$).* + +Internal consistency holds across the bulk of the $(T, \Delta\mathrm{IW})$ grid relevant to terrestrial magma oceans. The two off-scale points are solver-edge cases at the corners of the swept range and are flagged honestly rather than masked. They do not affect the cross-backend comparison in Figure 3, which is run at one Earth-volatile-rich fiducial well inside the well-behaved region of each solver. + +## Cross-backend agreement on the chemistry + +With internal consistency confirmed, the cross-backend $\Delta\mathrm{IW}$ disagreement at matched inputs is the interesting quantity. Both backends are run at the Krijt et al. 2023[^cite-krijt2023] BSE H/C/N/S inventory with the volatile O reference set by a CALLIOPE buffered-mode call at $\Delta\mathrm{IW} = +3.5$ (the Sossi 2020[^cite-sossi2020] Earth upper-mantle anchor). Sweeping $T_\mathrm{magma}$ from 1800 K to 3000 K, with $\Phi = 1$ throughout, gives: + +![Cross-backend T sweep](../assets/figures/cross_backend/fig3_grid.png) + +*Figure 3. (a) Converged $\Delta\mathrm{IW}$ from each backend across $T_\mathrm{magma}$ at fixed Earth-BSE Krijt+2023 volatile inventory and $\Phi = 1$. Solid blue circles: CALLIOPE with the current default Fischer 2011 buffer. Dashed grey: CALLIOPE with the legacy O'Neill 2002 buffer. Solid red squares: atmodeller (Hirschmann composite). The dotted red curve is CALLIOPE-F's $\Delta\mathrm{IW}$ minus the analytical Hirschmann-minus-Fischer offset at the same $T$: it is where atmodeller would land if the two backends used identical chemistry and the buffer was the only difference. (b) Two raw cross-backend gaps and the buffer-corrected residual. Solid grey: raw $\Delta\mathrm{IW}_\mathrm{atm} - \Delta\mathrm{IW}_\mathrm{CALLIOPE,F}$. Dashed grey: raw gap against the legacy O'Neill buffer. Solid blue diamonds: residual after the analytical buffer correction (numerically the same for either buffer, by construction). Dashed lines bracket the $\pm 0.1$ dex solver tolerance.* + +Panel (a) shows the dashed legacy curve (O'Neill) drifting away from atmodeller as $T$ rises, while the solid default curve (Fischer) hugs atmodeller within a few tenths of a dex everywhere on the sweep. Panel (b) makes this precise: the raw $\Delta\mathrm{IW}$ gap with the legacy buffer grows from $-0.07$ dex at $T = 1800$ K to $-1.23$ dex at $T = 3000$ K, while the same gap with the Fischer default never exceeds $-0.25$ dex in magnitude across the same range. The buffer-corrected residual (which subtracts the analytical Hirschmann-minus-buffer offset and is therefore independent of which buffer CALLIOPE used) stays within $\pm 0.1$ dex at $T \le 2000$ K and reaches about $-0.28$ dex at the hottest end. At the cold end of the sweep ($T \le 2000$ K) the Fischer raw gap is comparable to or smaller than the residual chemistry gap; above $\sim 2000$ K the residual chemistry gap dominates, consistent with the S$_2$ sulfate-regime divergence between Gaillard 2022 and Boulliung & Wood 2023 becoming larger at hotter, more oxidising conditions. Either way the cross-backend disagreement has dropped from "buffer-dominated, up to 1 dex" with the legacy default to "chemistry-dominated, a few tenths of a dex" with the new default. + +A wider 2D sweep over O budget was attempted first and abandoned: the authoritative-O entry point has a documented non-monotonic regime at sub-trace O budgets where the residual surface develops a second root and a basin-selection sensitivity dominates over the cross-backend physics signal. Inside the regime where both backends consistently land in the oxidising basin (the physically expected regime for terrestrial inventories), the comparison is the one shown above. + +## Attribution of residual disagreement + +A bar-chart breakdown of the cross-backend gap at the canonical Earth fiducial isolates which alignment moves dominate. + +![Attribution](../assets/figures/cross_backend/fig4_attribution.png) + +*Figure 4. $|\Delta\mathrm{IW}_\mathrm{atmodeller} - \Delta\mathrm{IW}_\mathrm{CALLIOPE}|$ at the canonical Earth fiducial, in four configurations of increasing alignment. Bar 1 is the raw disagreement under the legacy O'Neill 2002 buffer. Bar 2 is the raw disagreement under the current Fischer 2011 default; the drop from bar 1 to bar 2 is what the buffer-default change buys for free. Bar 3 removes the residual buffer contribution by subtracting the analytical Hirschmann-minus-Fischer offset. Bar 4 additionally forces atmodeller to disable H$_2$ / CO / CH$_4$ solubility, matching CALLIOPE's Bower 2022 §2.2.3 convention. The dashed line marks the per-element solver tolerance.* + +The raw disagreement of $0.42$ dex at this fiducial under the legacy buffer falls to $0.16$ dex with the Fischer default, just above the per-element solver tolerance. The analytical buffer correction takes it the rest of the way: $0.07$ dex, below tolerance. Forcing the H$_2$ / CO / CH$_4$ solubility alignment moves atmodeller's converged $\Delta\mathrm{IW}$ enough to grow the disagreement back to $0.21$ dex; that move shows that atmodeller's H$_2$ / CO / CH$_4$ solubility defaults were partially compensating for the buffer-convention difference, and removing them surfaces a small chemistry-level mismatch that is otherwise hidden. The takeaway after the buffer-default change is that the as-shipped disagreement is now at the few-tenths-of-a-dex level, dominated by the remaining solubility-law differences (S$_2$ above all) rather than by the IW-buffer choice. + +## Comparison against the Earth anchor + +Both backends produce a $\Delta\mathrm{IW}$ from the Krijt+2023[^cite-krijt2023] BSE H/C/N/S inventory (with volatile O derived self-consistently at the Sossi 2020 $\Delta\mathrm{IW} = +3.5$ baseline). The empirical anchor for Earth's modern upper mantle is the Frost & McCammon (2008)[^cite-frostmccammon2008] range $\Delta\mathrm{IW} \in [+1, +5]$ (FMQ-3 to FMQ+1), with the Sossi et al. 2020[^cite-sossi2020] best estimate at $+3.5$. + +![Earth anchor](../assets/figures/cross_backend/fig5_earth_anchor.png) + +*Figure 5. Each backend's converged $\Delta\mathrm{IW}$ at the Earth fiducial, overlaid on the Frost & McCammon 2008 empirical range and the Sossi 2020 best estimate. Solid blue: CALLIOPE with the Fischer 2011 default. Dashed grey: CALLIOPE with the legacy O'Neill 2002 buffer (recovers $+3.50$ by construction because the volatile-O reference was derived from a CALLIOPE-O'Neill buffered call at this state). Solid red: atmodeller. With the Fischer default CALLIOPE lands at $\Delta\mathrm{IW} \approx +3.24$, between atmodeller and Sossi 2020; the CALLIOPE-vs-atmodeller gap is now $0.16$ dex rather than the legacy $0.42$ dex.* + +All three backend points fall inside the empirical Frost & McCammon range. Neither parameterisation is in tension with petrology at this single fiducial; the cross-backend gap, even before the analytical buffer correction, is now well inside the petrological uncertainty on Earth's mantle $\Delta\mathrm{IW}$. + +## Choosing a backend for your study + +With the Fischer 2011 default in place, the as-shipped CALLIOPE and atmodeller backends agree to a few tenths of a dex in $\Delta\mathrm{IW}$ across the magma-ocean range. The dominant residual is now the chemistry-level disagreement (S$_2$ solubility law above all), not the IW-buffer choice. The practical implications are: + +- **Either default backend is defensible.** With Fischer 2011 in CALLIOPE and Hirschmann in atmodeller, the cross-backend $\Delta\mathrm{IW}$ gap at Earth-like inputs is $\sim 0.16$ dex at $T = 2000$ K and stays below $\sim 0.3$ dex up to $T = 3000$ K. Either reading is well inside the petrological uncertainty on Earth's mantle. +- **Still pick one backend per coupled study.** Cross-backend $\Delta\mathrm{IW}$ values from the two solvers are not bit-identical, and mixing them inside one coupled run will introduce a few-tenths-of-a-dex drift. PROTEUS' helpfile records `fO2_shift_IW_derived` from the active backend; treat that column as backend-faithful, not cross-backend comparable. +- **If you need to reproduce legacy results**, set CALLIOPE to the O'Neill buffer (`OxygenFugacity('oneill')`). The legacy choice reproduces the older numbers verbatim; expect a $\sim 0.2$ to $\sim 1.0$ dex offset against atmodeller depending on $T$. +- **For oxidising-mantle work** above FMQ ($\Delta\mathrm{IW} \gtrsim +3$), the dominant sub-buffer disagreement is the S$_2$ channel. atmodeller's Boulliung & Wood 2023 law captures sulfate dissolution that Gaillard 2022 cannot; if your study turns on sulfur partitioning at oxidising conditions, prefer atmodeller. +- **When running in authoritative-O mode** (`planet.fO2_source = "from_O_budget"`), confirm that the backend you pick has consistent O accounting end-to-end: the derived $\Delta\mathrm{IW}$, the partial pressures, and the dissolved masses should all close back to the user O budget within solver tolerance. The [authoritative-oxygen page](authoritative_oxygen.md) describes the inputs and outputs. + +## Reproducing this page + +Every figure on this page is regenerated by the scripts in [`scripts/cross_backend/`](https://github.com/FormingWorlds/CALLIOPE/tree/main/scripts/cross_backend). To re-run: + +```bash +# From the CALLIOPE repository root, with calliope and atmodeller installed +bash scripts/cross_backend/run_all.sh +``` + +Per-figure regeneration: `python3 -m scripts.cross_backend.fig3_grid` and similarly for the other figures. + +The scripts write PDF and PNG outputs to `docs/assets/figures/cross_backend/` and raw CSV data to `scripts/cross_backend/data/`. Approximate total wall time on a modern Mac is twenty minutes, dominated by the atmodeller authoritative-O solver calls (~15 s warm, ~60 s cold first JAX compile per process). + +The scripts are reusable for different fiducials, different inventories, or different alignment configurations; see [`scripts/cross_backend/README.md`](https://github.com/FormingWorlds/CALLIOPE/blob/main/scripts/cross_backend/README.md) for the extension pattern. + +## See also + +- [Authoritative-oxygen mode](authoritative_oxygen.md): the entry-point shared by both backends. +- [Oxygen fugacity](oxygen_fugacity.md): the four channels through which $\Delta\mathrm{IW}$ enters the chemistry. +- [Solubility laws](solubility.md): per-species validity envelopes for CALLIOPE's solubility selection. +- [Coupling to PROTEUS](proteus_coupling.md): how the PROTEUS wrapper selects between backends at runtime. +- [atmodeller documentation](https://atmodeller.readthedocs.io/): the canonical upstream reference for the second backend. + +[^cite-boulliungwood2023]: J. Boulliung, B. J. Wood, *[Sulfur oxidation state and solubility in silicate melts](https://doi.org/10.1007/s00410-023-02033-9)*, Contributions to Mineralogy and Petrology, 178(8), 56, 2023. [SciX](https://scixplorer.org/abs/2023CoMP..178...56B/abstract). +[^cite-bower2022]: D. J. Bower, K. Hakim, P. A. Sossi, P. Sanan, *[Retention of water in terrestrial magma oceans and carbon-rich early atmospheres](https://doi.org/10.3847/PSJ/ac5fb1)*, The Planetary Science Journal, 3(4), 93, 2022. [SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract). +[^cite-dasgupta2022]: R. Dasgupta, E. Falksen, A. Pal, C. Sun, *[The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions](https://doi.org/10.1016/j.gca.2022.09.012)*, Geochimica et Cosmochimica Acta, 336, 291–307, 2022. [SciX](https://scixplorer.org/abs/2022GeCoA.336..291D/abstract). +[^cite-dixon1995]: J. E. Dixon, E. M. Stolper, J. R. Holloway, *[An experimental study of water and carbon dioxide solubilities in mid-ocean ridge basaltic liquids. Part I: Calibration and solubility models](https://doi.org/10.1093/oxfordjournals.petrology.a037267)*, Journal of Petrology, 36(6), 1607–1631, 1995. [SciX](https://scixplorer.org/abs/1995JPet...36.1607D/abstract). +[^cite-frostmccammon2008]: D. J. Frost, C. A. McCammon, *[The redox state of Earth's mantle](https://doi.org/10.1146/annurev.earth.36.031207.124322)*, Annual Review of Earth and Planetary Sciences, 36, 389–420, 2008. [SciX](https://scixplorer.org/abs/2008AREPS..36..389F/abstract). +[^cite-fischer2011]: R. A. Fischer, A. J. Campbell, G. A. Shofner, O. T. Lord, P. Dera, V. B. Prakapenka, *[Equation of state and phase diagram of FeO](https://doi.org/10.1016/j.epsl.2011.02.025)*, Earth and Planetary Science Letters, 304(3), 496–502, 2011. +[^cite-gaillard2022]: F. Gaillard, F. Bernadou, M. Roskosz, M. A. Bouhifd, Y. Marrocchi, G. Iacono-Marziano, M. Moreira, B. Scaillet, G. Rogerie, *[Redox controls during magma ocean degassing](https://doi.org/10.1016/j.epsl.2021.117255)*, Earth and Planetary Science Letters, 577, 117255, 2022. [SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract). +[^cite-hirschmann2008]: M. M. Hirschmann, M. S. Ghiorso, F. A. Davis, S. M. Gordon, S. Mukherjee, T. L. Grove, M. Krawczynski, E. Médard, C. B. Till, *[Library of Experimental Phase Relations (LEPR): a database and Web portal for experimental magmatic phase equilibria data](https://doi.org/10.1029/2007GC001894)*, Geochemistry, Geophysics, Geosystems, 9(3), Q03011, 2008. [SciX](https://scixplorer.org/abs/2008GGG.....9.3011H/abstract). +[^cite-hirschmann2021]: M. M. Hirschmann, *[Iron-wüstite revisited: a revised calibration accounting for variable stoichiometry and the effects of pressure](https://doi.org/10.1016/j.gca.2021.08.039)*, Geochimica et Cosmochimica Acta, 313, 74–84, 2021. [SciX](https://scixplorer.org/abs/2021GeCoA.313...74H/abstract). +[^cite-krijt2023]: S. Krijt, M. Kama, M. McClure, J. Teske, E. A. Bergin, O. Shorttle, K. J. Walsh, S. N. Raymond, *Chemical habitability: supply and retention of life's essential elements during planet formation*, in Protostars and Planets VII, S. Inutsuka, Y. Aikawa, T. Muto, K. Tomida, M. Tamura, eds., Astronomical Society of the Pacific Conference Series, 534, 1031, 2023. [SciX](https://scixplorer.org/abs/2023ASPC..534.1031K/abstract). +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151–181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). +[^cite-sossi2020]: P. A. Sossi, A. D. Burnham, J. Badro, A. Lanzirotti, M. Newville, H. St. C. O'Neill, *[Redox state of Earth's magma ocean and its Venus-like early atmosphere](https://doi.org/10.1126/sciadv.abd1387)*, Science Advances, 6, eabd1387, 2020. [SciX](https://scixplorer.org/abs/2020SciA....6.1387S/abstract). +[^cite-sossi2023]: P. A. Sossi, P. M. E. Tollan, J. Badro, D. J. Bower, *[Solubility of water in peridotite liquids and the prevalence of steam atmospheres on rocky planets](https://doi.org/10.1016/j.epsl.2022.117894)*, Earth and Planetary Science Letters, 601, 117894, 2023. [SciX](https://scixplorer.org/abs/2023E%26PSL.60117894S/abstract). diff --git a/docs/Explanations/equilibrium_chemistry.md b/docs/Explanations/equilibrium_chemistry.md index 50f7da8..c653a97 100644 --- a/docs/Explanations/equilibrium_chemistry.md +++ b/docs/Explanations/equilibrium_chemistry.md @@ -44,7 +44,7 @@ $$ \log_{10} K_\mathrm{eq}^{\mathrm{H_2}}(T) = -\frac{13152.4778}{T} + 3.0386 $$ -(`janaf_H2` in `chemistry.py`; [JANAF](https://janaf.nist.gov/) fit, valid $1500 \le T \le 3000$ K). [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) give the equivalent [Schaefer & Fegley (2017)](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) fit $-12794/T + 2.7768$ as `schaefer_H`. The two differ by $\sim$1% in the resulting $G_\mathrm{eq}$ across the validation range; CALLIOPE uses the JANAF coefficients by default in `solve.get_partial_pressures`. Stoichiometric coefficient on $f_{\mathrm{O}_2}$ is $+0.5$. +(`janaf_H2` in `chemistry.py`; JANAF[^cite-chase1998] fit, valid $1500 \le T \le 3000$ K). Bower et al. (2022)[^cite-bower2022] give the equivalent Schaefer & Fegley (2017)[^cite-schaeferfegley2017] fit $-12794/T + 2.7768$ as `schaefer_H`. The two differ by $\sim$1% in the resulting $G_\mathrm{eq}$ across the validation range; CALLIOPE uses the JANAF coefficients by default in `solve.get_partial_pressures`. Stoichiometric coefficient on $f_{\mathrm{O}_2}$ is $+0.5$. ### CO from CO$_2$ ($\mathrm{CO_2} \rightleftharpoons \mathrm{CO} + \tfrac{1}{2}\,\mathrm{O_2}$) @@ -52,7 +52,7 @@ $$ \log_{10} K_\mathrm{eq}^{\mathrm{CO}}(T) = -\frac{14467.5114}{T} + 4.3481 $$ -(`janaf_CO`; [JANAF](https://janaf.nist.gov/) fit). [Schaefer & Fegley (2017)](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) equivalent in `schaefer_C`: $-14787/T + 4.5472$. Stoichiometric coefficient $+0.5$. +(`janaf_CO`; JANAF[^cite-chase1998] fit). Schaefer & Fegley (2017)[^cite-schaeferfegley2017] equivalent in `schaefer_C`: $-14787/T + 4.5472$. Stoichiometric coefficient $+0.5$. ### CH$_4$ from CO$_2$ + 2H$_2$ ($\mathrm{CO_2} + 2\,\mathrm{H_2} \rightleftharpoons \mathrm{CH_4} + \mathrm{O_2}$) @@ -60,7 +60,7 @@ $$ \log_{10} K_\mathrm{eq}^{\mathrm{CH_4}}(T) = -\frac{16276}{T} - 5.4738 $$ -(`schaefer_CH4`, IVTHANTHERMO via [Schaefer & Fegley 2017](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S)). Stoichiometric coefficient $+1.0$. The methane abundance is then $p_{\mathrm{CH_4}} = G_\mathrm{eq} \cdot p_{\mathrm{CO_2}} \cdot p_{\mathrm{H_2}}^2$; this is the only species whose speciation depends on a second primary species (H$_2$ via H$_2$O). +(`schaefer_CH4`, IVTHANTHERMO via Schaefer & Fegley 2017[^cite-schaeferfegley2017]). Stoichiometric coefficient $+1.0$. The methane abundance is then $p_{\mathrm{CH_4}} = G_\mathrm{eq} \cdot p_{\mathrm{CO_2}} \cdot p_{\mathrm{H_2}}^2$; this is the only species whose speciation depends on a second primary species (H$_2$ via H$_2$O). ### SO$_2$ from S$_2$ + 2O$_2$ ($\mathrm{S_2} + 2\,\mathrm{O_2} \rightleftharpoons 2\,\mathrm{SO_2}$, doubled form) @@ -125,12 +125,17 @@ After the walk, every `p_d[s]` is clipped to be non-negative. The function retur ## Why this is the right level of detail -The choice to expand only six redox couples is a deliberate trade-off between completeness and well-posedness. Adding more species (e.g. HCN, HCl, CS$_2$) requires either (i) more elemental constraints (Cl, additional H atoms in HCN), or (ii) extra equilibrium reactions whose constants are calibrated outside the relevant $T$-range. In the present species set, every secondary species has a clean reduction back to one of the four primaries via a [JANAF](https://janaf.nist.gov/) or IVTHANTHERMO fit. The underlying fits are valid over a wider window ($\sim$500-4000 K for the [Schaefer & Fegley 2017](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) IVTHANTHERMO sources, similar for the JANAF fits used here); CALLIOPE restricts itself to $1500 \le T \le 3000$ K because that matches the magma-ocean regime the solver is targeted at and the calibration window of the solubility laws (see [Solubility laws](solubility.md)). +The choice to expand only six redox couples is a deliberate trade-off between completeness and well-posedness. Adding more species (e.g. HCN, HCl, CS$_2$) requires either (i) more elemental constraints (Cl, additional H atoms in HCN), or (ii) extra equilibrium reactions whose constants are calibrated outside the relevant $T$-range. In the present species set, every secondary species has a clean reduction back to one of the four primaries via a [JANAF](https://janaf.nist.gov/) or IVTHANTHERMO fit. The underlying fits are valid over a wider window ($\sim$500-4000 K for the Schaefer & Fegley 2017[^cite-schaeferfegley2017] IVTHANTHERMO sources, similar for the JANAF fits used here); CALLIOPE restricts itself to $1500 \le T \le 3000$ K because that matches the magma-ocean regime the solver is targeted at and the calibration window of the solubility laws (see [Solubility laws](solubility.md)). -For application contexts that require Cl-bearing species, sub-ideal real-gas effects, or condensation, the [atmodeller](https://atmodeller.readthedocs.io/) JAX-based solver ([Bower et al. 2025](https://ui.adsabs.harvard.edu/abs/2025ApJ...995...59B)) is the supported alternative within the PROTEUS framework. +For application contexts that require Cl-bearing species, sub-ideal real-gas effects, or condensation, the [atmodeller](https://atmodeller.readthedocs.io/) JAX-based solver (Bower et al. 2025[^cite-bower2025]) is the supported alternative within the PROTEUS framework. ## See also - [Oxygen fugacity](oxygen_fugacity.md): how the IW buffer enters the modified equilibrium constants - [Mass balance & solver](mass_balance.md): how the four primary partial pressures are determined from the elemental conservation constraints - [Solubility laws](solubility.md): how the dissolved-volatile masses close the system + +[^cite-bower2022]: D. J. Bower, K. Hakim, P. A. Sossi, P. Sanan, *[Retention of water in terrestrial magma oceans and carbon-rich early atmospheres](https://doi.org/10.3847/PSJ/ac5fb1)*, The Planetary Science Journal, 3(4), 93, 2022. [SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract). +[^cite-bower2025]: D. J. Bower, M. A. Thompson, K. Hakim, M. Tian, P. A. Sossi, *Diversity of low-mass planet atmospheres in the C-H-O-N-S-Cl system with interior dissolution, nonideality, and condensation: application to TRAPPIST-1e and sub-Neptunes*, The Astrophysical Journal, 995, 59, 2025. [SciX](https://scixplorer.org/abs/2025ApJ...995...59B/abstract). +[^cite-chase1998]: M. W. Chase, *[NIST-JANAF Thermochemical Tables, 4th edition](https://janaf.nist.gov/)*, Journal of Physical and Chemical Reference Data Monograph 9, 1998. +[^cite-schaeferfegley2017]: L. Schaefer, B. Fegley, *[Redox states of initial atmospheres outgassed on rocky planets and planetesimals](https://doi.org/10.3847/1538-4357/aa784f)*, The Astrophysical Journal, 843(2), 120, 2017. [SciX](https://scixplorer.org/abs/2017ApJ...843..120S/abstract). diff --git a/docs/Explanations/mass_balance.md b/docs/Explanations/mass_balance.md index 4ba1bb9..bbf6b19 100644 --- a/docs/Explanations/mass_balance.md +++ b/docs/Explanations/mass_balance.md @@ -1,6 +1,6 @@ # Mass balance & solver -CALLIOPE's prognostic equations are four nonlinear elemental mass-conservation constraints, one per solved element (H, C, N, S). This page documents the residual function, the solver strategy, the mass-from-pressure relations, and the convergence criterion. +CALLIOPE's prognostic equations are four nonlinear elemental mass-conservation constraints, one per solved element (H, C, N, S). This page documents the residual function, the solver strategy, the mass-from-pressure relations, and the convergence criterion of this "buffered" mode, where the oxygen fugacity is an input and oxygen mass is derived. CALLIOPE also offers an [authoritative-oxygen mode](authoritative_oxygen.md) where the system is closed by adding O as a fifth budget and treating $\Delta\mathrm{IW}$ as an unknown; the two modes share all the physics functions and differ only in their unknown set. ## The conservation system @@ -26,7 +26,7 @@ def func(pin_arr, ddict, mass_target_d): ## Atmospheric column mass -The relation between a species' surface partial pressure and its column mass follows directly from hydrostatic equilibrium under the assumption of a well-mixed atmosphere. [Bower et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B) Equation (2) writes it as +The relation between a species' surface partial pressure and its column mass follows directly from hydrostatic equilibrium under the assumption of a well-mixed atmosphere. Bower et al. (2019)[^cite-bower2019] Equation (2) writes it as $$ m_v^\mathrm{atm} = 4\pi R_p^2 \cdot \frac{\mu_v}{\bar\mu} \cdot \frac{p_v}{g}, @@ -40,7 +40,7 @@ mass_atm_d[key] *= 4.0 * np.pi * ddict['radius'] ** 2.0 mass_atm_d[key] *= molar_mass[key] / mu_atm ``` -The `mu_v / mu_atm` ratio is the part [Bower et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B) §4.1.1 emphasises was missing from the pre-2019 mass-balance formulations of [Elkins-Tanton (2008)](https://ui.adsabs.harvard.edu/abs/2008E%26PSL.271..181E), [Lebrun et al. (2013)](https://ui.adsabs.harvard.edu/abs/2013JGRE..118.1155L), [Salvador et al. (2017)](https://ui.adsabs.harvard.edu/abs/2017JGRE..122.1458S), and [Nikolaou et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019ApJ...875...11N). Without it, multi-species atmospheres receive an unphysical bias in the inferred reservoir partitioning. +The `mu_v / mu_atm` ratio is the part Bower et al. (2019)[^cite-bower2019] §4.1.1 emphasises was missing from the pre-2019 mass-balance formulations of Elkins-Tanton (2008)[^cite-elkinstanton2008], Lebrun et al. (2013)[^cite-lebrun2013], Salvador et al. (2017)[^cite-salvador2017], and Nikolaou et al. (2019)[^cite-nikolaou2019]. Without it, multi-species atmospheres receive an unphysical bias in the inferred reservoir partitioning. After computing per-species column masses, `atmosphere_mass()` aggregates them into per-element atomic masses by stoichiometric atom-counting: @@ -60,7 +60,7 @@ $$ where $M_\mathrm{mantle}$ is the (molten + solid) silicate mantle mass and $\Phi_\mathrm{global}$ is the global melt fraction. Setting $\Phi_\mathrm{global} = 0$ disables solubility entirely; setting $\Phi_\mathrm{global} = 1$ (fully molten) gives the maximum dissolved-mass contribution. -Like for atmospheric mass, the per-species dissolved masses are aggregated into per-element atomic masses. Note the asymmetry with the atmospheric path: CALLIOPE only includes a subset of species in the dissolved-mass tally (H$_2$O, CO$_2$, CO, CH$_4$, N$_2$, S$_2$); the remaining species (H$_2$, NH$_3$, SO$_2$, H$_2$S, O$_2$) are assumed to have negligible solubility, consistent with [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) §2.2.3. +Like for atmospheric mass, the per-species dissolved masses are aggregated into per-element atomic masses. Note the asymmetry with the atmospheric path: CALLIOPE only includes a subset of species in the dissolved-mass tally (H$_2$O, CO$_2$, CO, CH$_4$, N$_2$, S$_2$); the remaining species (H$_2$, NH$_3$, SO$_2$, H$_2$S, O$_2$) are assumed to have negligible solubility, consistent with Bower et al. (2022)[^cite-bower2022] §2.2.3. ## Solver: hybrid Powell + trust-region with Monte-Carlo restart @@ -92,7 +92,15 @@ The `result` dictionary returned by `equilibrium_atmosphere()` includes `H_res`, ## See also +- [Authoritative-oxygen mode](authoritative_oxygen.md) for the dual five-residual formulation where O is an input budget and $\Delta\mathrm{IW}$ is the additional unknown. - [Equilibrium chemistry](equilibrium_chemistry.md) for the speciation tree that maps $\mathbf{p}$ to all eleven partial pressures. - [Solubility laws](solubility.md) for the form of $X_i^\mathrm{melt}(p_i)$ in `dissolved_mass()`. - [Coupling to PROTEUS (theory)](proteus_coupling.md) for how the wrapper builds `target` and `ddict` from `hf_row`. - [API reference for `calliope.solve`](../Reference/api/calliope.solve.md). + +[^cite-bower2019]: D. J. Bower, D. Kitzmann, A. S. Wolf, P. Sanan, C. Dorn, A. V. Oza, *[Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations](https://doi.org/10.1051/0004-6361/201935710)*, Astronomy & Astrophysics, 631, A103, 2019. [SciX](https://scixplorer.org/abs/2019A%26A...631A.103B/abstract). +[^cite-bower2022]: D. J. Bower, K. Hakim, P. A. Sossi, P. Sanan, *[Retention of water in terrestrial magma oceans and carbon-rich early atmospheres](https://doi.org/10.3847/PSJ/ac5fb1)*, The Planetary Science Journal, 3(4), 93, 2022. [SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract). +[^cite-elkinstanton2008]: L. T. Elkins-Tanton, *[Linked magma ocean solidification and atmospheric growth for Earth and Mars](https://doi.org/10.1016/j.epsl.2008.03.062)*, Earth and Planetary Science Letters, 271, 181–191, 2008. [SciX](https://scixplorer.org/abs/2008E%26PSL.271..181E/abstract). +[^cite-lebrun2013]: T. Lebrun, H. Massol, E. Chassefière, A. Davaille, E. Marcq, P. Sarda, F. Leblanc, G. Brandeis, *[Thermal evolution of an early magma ocean in interaction with the atmosphere](https://doi.org/10.1002/jgre.20068)*, Journal of Geophysical Research: Planets, 118, 1155–1176, 2013. [SciX](https://scixplorer.org/abs/2013JGRE..118.1155L/abstract). +[^cite-salvador2017]: A. Salvador, H. Massol, A. Davaille, E. Marcq, P. Sarda, E. Chassefière, *The relative influence of H$_2$O and CO$_2$ on the primitive surface conditions and evolution of rocky planets*, Journal of Geophysical Research: Planets, 122, 1458–1486, 2017. [SciX](https://scixplorer.org/abs/2017JGRE..122.1458S/abstract). +[^cite-nikolaou2019]: A. Nikolaou, N. Katyal, N. Tosi, M. Godolt, J. L. Grenfell, H. Rauer, *[What factors affect the duration and outgassing of the terrestrial magma ocean?](https://doi.org/10.3847/1538-4357/ab08ed)*, The Astrophysical Journal, 875, 11, 2019. [SciX](https://scixplorer.org/abs/2019ApJ...875...11N/abstract). diff --git a/docs/Explanations/model.md b/docs/Explanations/model.md index b54413c..f4d8a41 100644 --- a/docs/Explanations/model.md +++ b/docs/Explanations/model.md @@ -2,24 +2,24 @@ CALLIOPE is a 0-D **equilibrium outgassing** solver for the magma-ocean atmosphere coupling. It treats the silicate mantle and the overlying gas-phase atmosphere as a single thermodynamic system in equilibrium at the surface, and asks: for a given total elemental inventory and a given magma-ocean state, what surface partial pressures and dissolved-volatile masses simultaneously satisfy (i) gas-phase chemical equilibrium, (ii) gas-melt solubility equilibrium, and (iii) elemental mass conservation? -This page summarises the model assumptions, the variables it solves for, and how it relates to the upstream papers ([Bower et al. 2019](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B), [2022](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B); [Nicholls et al. 2024](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N)). Each component has its own dedicated page. +This page summarises the model assumptions, the variables it solves for, and how it relates to the upstream papers (Bower et al. 2019[^cite-bower2019], 2022[^cite-bower2022]; Nicholls et al. 2024[^cite-nicholls2024]). Each component has its own dedicated page. ## What is in the model - **Eleven gas-phase species** (`calliope.constants.volatile_species`): H$_2$O, CO$_2$, O$_2$, H$_2$, CH$_4$, CO, N$_2$, S$_2$, SO$_2$, H$_2$S, NH$_3$. -- **Five elements**: H, C, N, S as freely solved; O as a derived quantity set by the oxygen-fugacity buffer. +- **Five elements**: H, C, N, S always solved. O is either a derived quantity set by the oxygen-fugacity buffer (buffered mode, `equilibrium_atmosphere`) or a fifth budgeted element with $\Delta\mathrm{IW}$ as the additional unknown (authoritative-O mode, `equilibrium_atmosphere_authoritative_O`). The two modes share all physics functions and differ only in their unknown set; see [Authoritative-oxygen mode](authoritative_oxygen.md). - **Six equilibrium reactions** (gas-phase, surface temperature): | Reaction | Source | |---|---| - | $\mathrm{H_2O} \rightleftharpoons \mathrm{H_2} + \tfrac{1}{2}\,\mathrm{O_2}$ | [JANAF](https://janaf.nist.gov/) (`janaf_H2`) and [Schaefer & Fegley 2017](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) (`schaefer_H`) | - | $\mathrm{CO_2} \rightleftharpoons \mathrm{CO} + \tfrac{1}{2}\,\mathrm{O_2}$ | [JANAF](https://janaf.nist.gov/) (`janaf_CO`) and [Schaefer & Fegley 2017](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) (`schaefer_C`) | - | $\mathrm{CO_2} + 2\,\mathrm{H_2} \rightleftharpoons \mathrm{CH_4} + \mathrm{O_2}$ | [Schaefer & Fegley 2017](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) (`schaefer_CH4`) | + | $\mathrm{H_2O} \rightleftharpoons \mathrm{H_2} + \tfrac{1}{2}\,\mathrm{O_2}$ | [JANAF](https://janaf.nist.gov/) (`janaf_H2`) and Schaefer & Fegley 2017[^cite-schaeferfegley2017] (`schaefer_H`) | + | $\mathrm{CO_2} \rightleftharpoons \mathrm{CO} + \tfrac{1}{2}\,\mathrm{O_2}$ | [JANAF](https://janaf.nist.gov/) (`janaf_CO`) and Schaefer & Fegley 2017[^cite-schaeferfegley2017] (`schaefer_C`) | + | $\mathrm{CO_2} + 2\,\mathrm{H_2} \rightleftharpoons \mathrm{CH_4} + \mathrm{O_2}$ | Schaefer & Fegley 2017[^cite-schaeferfegley2017] (`schaefer_CH4`) | | $\tfrac{1}{2}\,\mathrm{S_2} + \mathrm{O_2} \rightleftharpoons \mathrm{SO_2}$ | JANAF, doubled form (`janaf_SO2`) | | $\tfrac{1}{2}\,\mathrm{S_2} + \mathrm{H_2} \rightleftharpoons \mathrm{H_2S}$ | JANAF, doubled form (`janaf_H2S`) | | $\tfrac{1}{2}\,\mathrm{N_2} + \tfrac{3}{2}\,\mathrm{H_2} \rightleftharpoons \mathrm{NH_3}$ | JANAF, doubled form (`janaf_NH3`) | -- **One oxygen-fugacity buffer**: [O'Neill & Eggins (2002)](https://ui.adsabs.harvard.edu/abs/2002ChGeo.186..151O) iron-wüstite (default), or [Fischer et al. (2011)](https://ui.adsabs.harvard.edu/abs/2011E%26PSL.304..496F) IW. The model takes a user-prescribed shift $\Delta\mathrm{IW}$ that sets $\log_{10} f_{\mathrm{O}_2}$ relative to the buffer; this is *not* solved for, it parameterises the redox state of the magma ocean. +- **One oxygen-fugacity buffer**: Fischer et al. (2011)[^cite-fischer2011] iron-wüstite (default; close to atmodeller's Hirschmann composite across the magma-ocean range), or the legacy O'Neill & Eggins (2002)[^cite-oneilleggins2002] IW. The shift $\Delta\mathrm{IW}$ sets $\log_{10} f_{\mathrm{O}_2}$ relative to the buffer; under the buffered mode it is a user-prescribed input, under the authoritative-O mode it is a solver unknown. - **One solubility law per species** with multiple alternative compositions (peridotite, basalt, lunar glass, anorthite-diopside) selectable via constructor argument. ## What is *not* in the model @@ -29,18 +29,20 @@ This page summarises the model assumptions, the variables it solves for, and how - **No radiative transfer**: surface partial pressures come out, optical depths and surface temperature come from [AGNI](https://www.h-nicholls.space/AGNI/) or [JANUS](https://proteus-framework.org/JANUS/). - **No atmospheric escape**: per-iteration mass loss is computed by the PROTEUS escape module ([ZEPHYRUS](https://proteus-framework.org/ZEPHYRUS/)). - **No solid-phase partitioning**: dissolved-mass fields are written into `_kg_solid` slots that always read `0.0`; CALLIOPE only resolves melt and gas reservoirs. The PROTEUS atmosphere modules handle solid-phase trapping if any. -- **No real-gas EOS**: all species are treated as ideal gases, so partial pressure $\equiv$ fugacity. For non-ideal real-gas effects use [atmodeller](https://atmodeller.readthedocs.io/) ([Bower et al. 2025](https://ui.adsabs.harvard.edu/abs/2025ApJ...995...59B)). +- **No real-gas EOS**: all species are treated as ideal gases, so partial pressure $\equiv$ fugacity. For non-ideal real-gas effects use [atmodeller](https://atmodeller.readthedocs.io/) (Bower et al. 2025[^cite-bower2025]). - **No condensation**: every species is in the gas phase. Condensation chemistry happens in AGNI / JANUS. ## Mathematical statement -CALLIOPE assembles four mass-conservation equations, one per solved element. Each equation has the structure +CALLIOPE assembles one mass-conservation equation per solved element. Each equation has the structure $$ -\underbrace{m_e^{\mathrm{atm}}(p_{\mathrm{H_2O}}, p_{\mathrm{CO_2}}, p_{\mathrm{N_2}}, p_{\mathrm{S_2}})}_{\text{Bower 2019 Eq. 2 summed over all species}} + \underbrace{m_e^{\mathrm{melt}}(p_{\mathrm{H_2O}}, p_{\mathrm{CO_2}}, p_{\mathrm{N_2}}, p_{\mathrm{S_2}})}_{\text{Henry's law summed over all species}} = m_e^{\mathrm{target}}, \quad e \in \{H, C, N, S\} +\underbrace{m_e^{\mathrm{atm}}(p_{\mathrm{H_2O}}, p_{\mathrm{CO_2}}, p_{\mathrm{N_2}}, p_{\mathrm{S_2}})}_{\text{Bower 2019 Eq. 2 summed over all species}} + \underbrace{m_e^{\mathrm{melt}}(p_{\mathrm{H_2O}}, p_{\mathrm{CO_2}}, p_{\mathrm{N_2}}, p_{\mathrm{S_2}})}_{\text{Henry's law summed over all species}} = m_e^{\mathrm{target}}. $$ -The four primary partial pressures $p_\mathrm{H_2O}$, $p_\mathrm{CO_2}$, $p_\mathrm{N_2}$, $p_\mathrm{S_2}$ are the unknowns. The seven secondary partial pressures are *not* independent: they are algebraic functions of the primaries via the six equilibrium constants, evaluated at $T = T_\mathrm{magma}$ and $\log_{10} f_{\mathrm{O}_2} = \log_{10} f_{\mathrm{O}_2}^\mathrm{IW}(T) + \Delta\mathrm{IW}$. The system is therefore $4 \times 4$, which `scipy.optimize.fsolve` solves with the Powell hybrid method. +Under the buffered mode the equation set spans $e \in \{\mathrm{H}, \mathrm{C}, \mathrm{N}, \mathrm{S}\}$, the four primary partial pressures are the unknowns, and the $4\times 4$ system is solved with `scipy.optimize.fsolve` (Powell hybrid). Under the authoritative-O mode the equation set spans $e \in \{\mathrm{H}, \mathrm{C}, \mathrm{N}, \mathrm{S}, \mathrm{O}\}$, the unknown vector is extended with $\Delta\mathrm{IW}$, and the $5\times 5$ system is solved with the same outer loop ([Authoritative-oxygen mode](authoritative_oxygen.md)). + +The seven secondary partial pressures are *not* independent in either mode: they are algebraic functions of the primaries via the six equilibrium constants, evaluated at $T = T_\mathrm{magma}$ and $\log_{10} f_{\mathrm{O}_2} = \log_{10} f_{\mathrm{O}_2}^\mathrm{IW}(T) + \Delta\mathrm{IW}$. The four pieces of physics decompose cleanly: @@ -49,23 +51,40 @@ The four pieces of physics decompose cleanly: | Speciation tree (primary $\to$ secondary) | [Equilibrium chemistry](equilibrium_chemistry.md) | `chemistry.ModifiedKeq`, `solve.get_partial_pressures` | | Atmospheric column mass | [Mass balance & solver](mass_balance.md) | `solve.atmosphere_mass` | | Dissolved mass via Henry / power-law / multi-arg solubility | [Solubility laws](solubility.md) | `solubility.SolubilityH2O`, ..., `solve.dissolved_mass` | -| O'Neill IW buffer | [Oxygen fugacity](oxygen_fugacity.md) | `oxygen_fugacity.OxygenFugacity` | +| Fischer IW buffer (default), O'Neill IW buffer (legacy) | [Oxygen fugacity](oxygen_fugacity.md) | `oxygen_fugacity.OxygenFugacity` | ## Lineage -- **[Bower et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B)** introduced the H$_2$O + CO$_2$ mass-balance + Henry's-law treatment that CALLIOPE inherits, including the molar-mass correction $\mu_v / \bar\mu$ in the column-mass relation that earlier studies ([Elkins-Tanton 2008](https://ui.adsabs.harvard.edu/abs/2008E%26PSL.271..181E); [Lebrun et al. 2013](https://ui.adsabs.harvard.edu/abs/2013JGRE..118.1155L); [Salvador et al. 2017](https://ui.adsabs.harvard.edu/abs/2017JGRE..122.1458S); [Nikolaou et al. 2019](https://ui.adsabs.harvard.edu/abs/2019ApJ...875...11N)) had omitted. -- **[Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B)** added the H$_2$, CO, CH$_4$ extensions and the explicit [Schaefer & Fegley (2017)](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) IVTHANTHERMO / [Chase (1998)](https://janaf.nist.gov/) JANAF equilibrium constants for the H$_2$O–H$_2$, CO$_2$–CO, and CO$_2$+H$_2$–CH$_4$ couples; also adopted the [O'Neill & Eggins (2002)](https://ui.adsabs.harvard.edu/abs/2002ChGeo.186..151O) IW buffer (their Eq. 7) as the parameterisation of mantle redox state. -- **[Nicholls et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N)** introduced N$_2$ via the [Libourel et al. (2003)](https://ui.adsabs.harvard.edu/abs/2003GeCoA..67.4123L) and [Dasgupta et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022GeCoA.336..291D) solubility laws, which is the species set in `calliope.solve.equilibrium_atmosphere` today. -- **[Nicholls et al. (2026)](https://ui.adsabs.harvard.edu/abs/2026NatAs.tmp...61N)** demonstrated the sulfur extension (S$_2$, SO$_2$, H$_2$S) on L 98-59 d, validating the equilibrium constants and the [Gaillard et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717255G) S$_2$ solubility law against in-situ photochemical inferences. +- **Bower et al. (2019)[^cite-bower2019]** introduced the H$_2$O + CO$_2$ mass-balance + Henry's-law treatment that CALLIOPE inherits, including the molar-mass correction $\mu_v / \bar\mu$ in the column-mass relation that earlier studies (Elkins-Tanton 2008[^cite-elkinstanton2008]; Lebrun et al. 2013[^cite-lebrun2013]; Salvador et al. 2017[^cite-salvador2017]; Nikolaou et al. 2019[^cite-nikolaou2019]) had omitted. +- **Bower et al. (2022)[^cite-bower2022]** added the H$_2$, CO, CH$_4$ extensions and the explicit Schaefer & Fegley (2017)[^cite-schaeferfegley2017] IVTHANTHERMO / Chase (1998)[^cite-chase1998] JANAF equilibrium constants for the H$_2$O/H$_2$, CO$_2$/CO, and CO$_2$+H$_2$/CH$_4$ couples; also adopted the O'Neill & Eggins (2002)[^cite-oneilleggins2002] IW buffer (their Eq. 7) as the parameterisation of mantle redox state. +- **Nicholls et al. (2024)[^cite-nicholls2024]** introduced N$_2$ via the Libourel et al. (2003)[^cite-libourel2003] and Dasgupta et al. (2022)[^cite-dasgupta2022] solubility laws, which is the species set in `calliope.solve.equilibrium_atmosphere` today. +- **Nicholls et al. (2026)[^cite-nicholls2026]** demonstrated the sulfur extension (S$_2$, SO$_2$, H$_2$S) on L 98-59 d, validating the equilibrium constants and the Gaillard et al. (2022)[^cite-gaillard2022] S$_2$ solubility law against in-situ photochemical inferences. !!! note "Why four primaries" - CALLIOPE's prognostic variables are the four primary partial pressures, not the eleven species partial pressures. This is why N has only one solved degree of freedom even though it appears in both N$_2$ and NH$_3$, and why O is not solved at all: the gas-phase chemistry collapses the eleven species into four independent mass-balance constraints. Adding a new oxygen-bearing species (e.g. NO) would not require a new constraint, only a new entry in `get_partial_pressures()` and the corresponding contribution to atmospheric and dissolved mass. + CALLIOPE's prognostic *species* are the four primary partial pressures, not the eleven species partial pressures: the gas-phase chemistry collapses the eleven species into four independent mass-balance constraints. N has only one solved degree of freedom even though it appears in both N$_2$ and NH$_3$. O is either not solved (buffered mode) or carried as an additional scalar unknown $\Delta\mathrm{IW}$ alongside the four pressures (authoritative-O mode); in neither case is a new primary partial pressure introduced. Adding a new oxygen-bearing species (e.g. NO) would not require a new constraint, only a new entry in `get_partial_pressures()` and the corresponding contribution to atmospheric and dissolved mass. ## Validity range -CALLIOPE is calibrated for surface temperatures of roughly $1000 \le T_\mathrm{magma} \le 4000$ K and surface pressures of roughly $0.1 \le p_\mathrm{surf} \le 5000$ bar. The lower end of the pressure range is set by numerical stability of the speciation walk; the upper end is the loose envelope above which one or more solubility laws extrapolate. Individual solubility laws have tighter calibration windows than the envelope (Dixon CO$_2$: $\le$815 bar; Sossi H$_2$O: a few kbar; Ardia CH$_4$: 0.7-3 GPa total pressure), see the per-law table in [Solubility laws](solubility.md). Outside the envelope above: +CALLIOPE is calibrated for surface temperatures of roughly $1000 \le T_\mathrm{magma} \le 4000$ K and surface pressures of roughly $0.1 \le p_\mathrm{surf} \le 5000$ bar. The lower end of the pressure range is set by numerical stability of the speciation walk; the upper end is the loose envelope above which one or more solubility laws extrapolate. Individual solubility laws have tighter calibration windows than the envelope (Dixon CO$_2$: $\le$815 bar; Sossi H$_2$O peridotite: 1 atm; Hamilton H$_2$O basalt: 1-6 kbar; Ardia CH$_4$: 0.7-3 GPa total pressure), see the per-law table in [Solubility laws](solubility.md). Outside the envelope above: - Below $T \sim 1000$ K the JANAF fits used for the equilibrium constants extrapolate beyond their validation range. The PROTEUS wrapper enforces a configurable `T_floor` (default 700 K), which clips temperatures below `T_floor` to this value, since thermochemical equilibrium does not necessarily hold at cooler temperatures. -- Above $T \sim 4000$ K the mantle-atmosphere partitioning approximation breaks down; switch to atmodeller ([Bower et al. 2025](https://ui.adsabs.harvard.edu/abs/2025ApJ...995...59B)). -- At surface pressures above ~5 kbar, the H$_2$O solubility laws ([Sossi et al. 2023](https://ui.adsabs.harvard.edu/abs/2023E%26PSL.60117894S), [Newcombe et al. 2017](https://ui.adsabs.harvard.edu/abs/2017GeCoA.200..330N)) extrapolate beyond their experimental calibration window; results are still self-consistent but should be checked against atmodeller for robustness. +- Above $T \sim 4000$ K the mantle-atmosphere partitioning approximation breaks down; switch to atmodeller (Bower et al. 2025[^cite-bower2025]). +- The Sossi (2023) peridotite and Newcombe (2017) anorthite-diopside / lunar-glass H$_2$O laws are calibrated at 1 atm. The Dixon (1995) basalt fit is calibrated to 717 bar $p_\mathrm{H_2O}$. For surface pressures above $\sim$1 kbar use the Hamilton (1964) basalt fit (1-6 kbar calibration range) or atmodeller for higher-pressure non-ideal behaviour. Applying the Sossi or Newcombe fits at kbar pressures extrapolates the partial-pressure input by 3 orders of magnitude beyond calibration; the resulting dissolved-mass error scales as $p^{0.5}$ for the power-law fits, so the error is a factor of $\sim$30 at 1 kbar. - Solid-phase partitioning is ignored; CALLIOPE strictly handles melt + gas. Use it only when $\Phi_\mathrm{global} > 0$, or accept that all dissolved masses will be zero. + +[^cite-bower2019]: D. J. Bower, D. Kitzmann, A. S. Wolf, P. Sanan, C. Dorn, A. V. Oza, *[Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations](https://doi.org/10.1051/0004-6361/201935710)*, Astronomy & Astrophysics, 631, A103, 2019. [SciX](https://scixplorer.org/abs/2019A%26A...631A.103B/abstract). +[^cite-bower2022]: D. J. Bower, K. Hakim, P. A. Sossi, P. Sanan, *[Retention of water in terrestrial magma oceans and carbon-rich early atmospheres](https://doi.org/10.3847/PSJ/ac5fb1)*, The Planetary Science Journal, 3(4), 93, 2022. [SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract). +[^cite-bower2025]: D. J. Bower, M. A. Thompson, K. Hakim, M. Tian, P. A. Sossi, *Diversity of low-mass planet atmospheres in the C-H-O-N-S-Cl system with interior dissolution, nonideality, and condensation: application to TRAPPIST-1e and sub-Neptunes*, The Astrophysical Journal, 995, 59, 2025. [SciX](https://scixplorer.org/abs/2025ApJ...995...59B/abstract). +[^cite-chase1998]: M. W. Chase, *[NIST-JANAF Thermochemical Tables, 4th edition](https://janaf.nist.gov/)*, Journal of Physical and Chemical Reference Data Monograph 9, 1998. +[^cite-dasgupta2022]: R. Dasgupta, E. Falksen, A. Pal, C. Sun, *[The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions](https://doi.org/10.1016/j.gca.2022.09.012)*, Geochimica et Cosmochimica Acta, 336, 291–307, 2022. [SciX](https://scixplorer.org/abs/2022GeCoA.336..291D/abstract). +[^cite-elkinstanton2008]: L. T. Elkins-Tanton, *[Linked magma ocean solidification and atmospheric growth for Earth and Mars](https://doi.org/10.1016/j.epsl.2008.03.062)*, Earth and Planetary Science Letters, 271, 181–191, 2008. [SciX](https://scixplorer.org/abs/2008E%26PSL.271..181E/abstract). +[^cite-fischer2011]: R. A. Fischer, A. J. Campbell, G. A. Shofner, O. T. Lord, P. Dera, V. B. Prakapenka, *[Equation of state and phase diagram of FeO](https://doi.org/10.1016/j.epsl.2011.02.025)*, Earth and Planetary Science Letters, 304, 496–502, 2011. [SciX](https://scixplorer.org/abs/2011E%26PSL.304..496F/abstract). +[^cite-gaillard2022]: F. Gaillard, F. Bernadou, M. Roskosz, M. A. Bouhifd, Y. Marrocchi, G. Iacono-Marziano, M. Moreira, B. Scaillet, G. Rogerie, *[Redox controls during magma ocean degassing](https://doi.org/10.1016/j.epsl.2021.117255)*, Earth and Planetary Science Letters, 577, 117255, 2022. [SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract). +[^cite-lebrun2013]: T. Lebrun, H. Massol, E. Chassefière, A. Davaille, E. Marcq, P. Sarda, F. Leblanc, G. Brandeis, *[Thermal evolution of an early magma ocean in interaction with the atmosphere](https://doi.org/10.1002/jgre.20068)*, Journal of Geophysical Research: Planets, 118, 1155–1176, 2013. [SciX](https://scixplorer.org/abs/2013JGRE..118.1155L/abstract). +[^cite-libourel2003]: G. Libourel, B. Marty, F. Humbert, *[Nitrogen solubility in basaltic melt. Part I. Effect of oxygen fugacity](https://doi.org/10.1016/S0016-7037(03)00259-X)*, Geochimica et Cosmochimica Acta, 67(21), 4123–4135, 2003. [SciX](https://scixplorer.org/abs/2003GeCoA..67.4123L/abstract). +[^cite-nicholls2024]: H. Nicholls, T. Lichtenberg, D. J. Bower, R. Pierrehumbert, *[Magma ocean evolution at arbitrary redox state](https://doi.org/10.1029/2024JE008576)*, Journal of Geophysical Research: Planets, 129, e2024JE008576, 2024. [SciX](https://scixplorer.org/abs/2024JGRE..12908576N/abstract). +[^cite-nicholls2026]: H. Nicholls, T. Lichtenberg, R. D. Chatterjee, C. M. Guimond, E. Postolec, R. T. Pierrehumbert, *[Volatile-rich evolution of molten super-Earth L 98-59 d](https://doi.org/10.1038/s41550-026-02815-8)*, Nature Astronomy, 2026. [SciX](https://scixplorer.org/abs/2026NatAs.tmp...61N/abstract). [arXiv](https://arxiv.org/abs/2507.02656). +[^cite-nikolaou2019]: A. Nikolaou, N. Katyal, N. Tosi, M. Godolt, J. L. Grenfell, H. Rauer, *[What factors affect the duration and outgassing of the terrestrial magma ocean?](https://doi.org/10.3847/1538-4357/ab08ed)*, The Astrophysical Journal, 875, 11, 2019. [SciX](https://scixplorer.org/abs/2019ApJ...875...11N/abstract). +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151–181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). +[^cite-salvador2017]: A. Salvador, H. Massol, A. Davaille, E. Marcq, P. Sarda, E. Chassefière, *The relative influence of H$_2$O and CO$_2$ on the primitive surface conditions and evolution of rocky planets*, Journal of Geophysical Research: Planets, 122, 1458–1486, 2017. [SciX](https://scixplorer.org/abs/2017JGRE..122.1458S/abstract). +[^cite-schaeferfegley2017]: L. Schaefer, B. Fegley, *[Redox states of initial atmospheres outgassed on rocky planets and planetesimals](https://doi.org/10.3847/1538-4357/aa784f)*, The Astrophysical Journal, 843(2), 120, 2017. [SciX](https://scixplorer.org/abs/2017ApJ...843..120S/abstract). diff --git a/docs/Explanations/oxygen_fugacity.md b/docs/Explanations/oxygen_fugacity.md index 046c8ac..54f6982 100644 --- a/docs/Explanations/oxygen_fugacity.md +++ b/docs/Explanations/oxygen_fugacity.md @@ -6,7 +6,7 @@ $$ \Delta\mathrm{IW} \equiv \log_{10} f_{\mathrm{O}_2} - \log_{10} f_{\mathrm{O}_2}^\mathrm{IW}(T). $$ -The user supplies `fO2_shift_IW = ` $\Delta\mathrm{IW}$ as a scalar input; CALLIOPE computes the absolute $\log_{10} f_{\mathrm{O}_2}$ at $T = T_\mathrm{magma}$ by adding the buffer value to the shift. This page documents the buffer parameterisations, the physical meaning of $\Delta\mathrm{IW}$, and how $f_{\mathrm{O}_2}$ enters the chemistry. +Under the buffered-mode entry point [`equilibrium_atmosphere`](mass_balance.md), the user supplies `fO2_shift_IW = ` $\Delta\mathrm{IW}$ as a scalar input and CALLIOPE computes the absolute $\log_{10} f_{\mathrm{O}_2}$ at $T = T_\mathrm{magma}$ by adding the buffer value to the shift. Under the [authoritative-oxygen mode](authoritative_oxygen.md), $\Delta\mathrm{IW}$ is instead a solver unknown that closes the system against a user-supplied total oxygen mass. Both modes share the parameterisations, conventions, and chemistry channels documented on this page; they differ only in whether $\Delta\mathrm{IW}$ is an input or an output. ## The IW mineral buffer @@ -18,32 +18,32 @@ $$ which fixes a single curve $\log_{10} f_{\mathrm{O}_2}^\mathrm{IW}(T)$ in $T$-$f_{\mathrm{O}_2}$ space. CALLIOPE supports two parameterisations of this curve. -### [O'Neill & Eggins (2002)](https://ui.adsabs.harvard.edu/abs/2002ChGeo.186..151O), `oneill` (default) +### Fischer et al. (2011), `fischer` (default)[^cite-fischer2011] -A thermochemically-constrained fit derived from low-temperature equilibrium data, expressed as [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) Equation (7): +A simpler two-parameter fit of the 1-bar IW buffer. The fit reproduces the 1-bar curve in Fischer et al. (2011)[^cite-fischer2011] Fig. 6, which itself derives from Chase (1998)[^cite-chase1998] NIST-JANAF tabulation. Fischer's own high-pressure measurements ($\le$200 GPa) extend the buffer to deep-mantle conditions but are not used by CALLIOPE. $$ -\log_{10} f_{\mathrm{O}_2}^\mathrm{IW}(T) = \frac{2\left[-244118 + 115.559\,T - 8.474\,T \ln T\right]}{\ln(10)\, R\, T}, +\log_{10} f_{\mathrm{O}_2}^\mathrm{IW}(T) = 6.94059 - \frac{28180.8}{T}. $$ -with $R = 8.31441$ J K$^{-1}$ mol$^{-1}$. [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) adopted this as the "IW buffer to which $f_{\mathrm{O}_2}$ is referenced". This is the function `OxygenFugacity.oneill(T)` in `oxygen_fugacity.py`. +Implemented as `OxygenFugacity.fischer(T)`. -### [Fischer et al. (2011)](https://ui.adsabs.harvard.edu/abs/2011E%26PSL.304..496F), `fischer` +### O'Neill & Eggins (2002), `oneill` (legacy)[^cite-oneilleggins2002] -A simpler two-parameter fit calibrated against high-pressure ($\sim$25 GPa) experimental data: +A thermochemically-constrained fit derived from low-temperature equilibrium data, expressed as Bower et al. (2022)[^cite-bower2022] Equation (7): $$ -\log_{10} f_{\mathrm{O}_2}^\mathrm{IW}(T) = 6.94059 - \frac{28180.8}{T}. +\log_{10} f_{\mathrm{O}_2}^\mathrm{IW}(T) = \frac{2\left[-244118 + 115.559\,T - 8.474\,T \ln T\right]}{\ln(10)\, R\, T}, $$ -Implemented as `OxygenFugacity.fischer(T)`. +with $R = 8.31441$ J K$^{-1}$ mol$^{-1}$. Bower et al. (2022)[^cite-bower2022] adopted this as the "IW buffer to which $f_{\mathrm{O}_2}$ is referenced". CALLIOPE retains it under the model name `oneill` (function `OxygenFugacity.oneill(T)` in `oxygen_fugacity.py`); keep it as the choice when you need to reproduce results from the older literature line. !!! note "Choice of buffer" - The two parameterisations agree to within $\sim$0.3 dex near $T \approx 2000$ K but diverge with increasing temperature, reaching $\sim$0.7 dex by $T = 2500$ K and growing further at higher $T$. The disagreement is comparable to or smaller than the typical uncertainty in $\Delta\mathrm{IW}$ inferred from petrological observations ([Sossi et al. 2020](https://ui.adsabs.harvard.edu/abs/2020SciA....6.1387S) give $\Delta\mathrm{IW} = +3.5 \pm 0.5$ for Earth's modern surface mantle), so the choice between buffers rarely changes inferred partial pressures meaningfully. CALLIOPE defaults to O'Neill & Eggins because [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) used it and it was validated end-to-end against PROTEUS coupled runs. + The two parameterisations cross near $T \approx 1800$ K and diverge in opposite directions on either side: at $T = 1500$ K Fischer is about $0.4$ dex *more reducing* than O'Neill; at $T = 3000$ K Fischer is about $1.1$ dex *more oxidising*. The crossover means the difference is small (under $0.05$ dex) near 1800 K but grows to several tenths of a dex by 2400 K and reaches roughly $1$ dex at 3000 K. The choice matters at the few-tenths-of-a-dex level for inferred partial pressures across most of the magma-ocean range. CALLIOPE now defaults to Fischer 2011 because it sits within $\sim$0.2 dex of the Hirschmann composite used by atmodeller across the whole magma-ocean range and so produces cross-backend $\Delta\mathrm{IW}$ values that agree to a few tenths of a dex rather than up to $\sim 1$ dex with the older default. The legacy O'Neill choice remains available for reproducibility of pre-existing results; see [Backend comparison](cross_backend_comparison.md) for the quantitative comparison. ## How $\Delta\mathrm{IW}$ enters the chemistry -The shift $\Delta\mathrm{IW}$ feeds the equilibrium chemistry through two channels: +The shift $\Delta\mathrm{IW}$ feeds the equilibrium chemistry through four channels: ### 1. The free $\mathrm{O_2}$ partial pressure @@ -64,38 +64,54 @@ $$ G_\mathrm{eq}(T, f_{\mathrm{O}_2}) = 10^{\,\log_{10} K_\mathrm{eq}(T) - n_\mathrm{O_2}\,\log_{10} f_{\mathrm{O}_2}}. $$ -For the H$_2$O-H$_2$ couple ($n_\mathrm{O_2} = +0.5$), reducing conditions ($f_{\mathrm{O}_2}$ smaller, $\Delta\mathrm{IW}$ more negative) drive $G_\mathrm{eq}$ larger, which in turn drives more H$_2$O to dissociate into H$_2$. This is the redox dependence visible in the redox-sweep tutorial, and it is the mechanism behind the H$_2$-dominated, long-lived magma-ocean atmospheres found in [Nicholls et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) Figure 6 at $\Delta\mathrm{IW} \le -2$. +For the H$_2$O-H$_2$ couple ($n_\mathrm{O_2} = +0.5$), reducing conditions ($f_{\mathrm{O}_2}$ smaller, $\Delta\mathrm{IW}$ more negative) drive $G_\mathrm{eq}$ larger, which in turn drives more H$_2$O to dissociate into H$_2$. This is the redox dependence visible in the redox-sweep tutorial, and it is the mechanism behind the H$_2$-dominated, long-lived magma-ocean atmospheres found in Nicholls et al. (2024)[^cite-nicholls2024] Figure 6 at $\Delta\mathrm{IW} \le -1$. ### 3. The S$_2$ Gaillard solubility -The [Gaillard et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717255G) sulfide solubility carries an explicit $\ln f_{\mathrm{O}_2}$ term, so $\Delta\mathrm{IW}$ enters the dissolved-S inventory directly. The implementation in `solubility.SolubilityS2.gaillard` calls back into `OxygenFugacity()` to compute the absolute $f_{\mathrm{O}_2}$. +The Gaillard et al. (2022)[^cite-gaillard2022] sulfide solubility carries an explicit $\ln f_{\mathrm{O}_2}$ term, so $\Delta\mathrm{IW}$ enters the dissolved-S inventory directly. The implementation in `solubility.SolubilityS2.gaillard` calls back into `OxygenFugacity()` to compute the absolute $f_{\mathrm{O}_2}$. ### 4. The N$_2$ Dasgupta solubility -Similarly, the [Dasgupta et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022GeCoA.336..291D) N$_2$ solubility includes a $-1.6\,\Delta\mathrm{IW}$ term in its exponent, so reducing conditions sharply increase the dissolved-N inventory. This is one mechanism by which planet-scale N partitioning is tied to mantle redox; see [Nicholls et al. (2026)](https://ui.adsabs.harvard.edu/abs/2026NatAs.tmp...61N) Section 4 for an application to L 98-59 d, where the inferred SO$_2$/H$_2$ atmosphere implies $\Delta\mathrm{IW} \approx -1$. +Similarly, the Dasgupta et al. (2022)[^cite-dasgupta2022] N$_2$ solubility includes a $-1.6\,\Delta\mathrm{IW}$ term in its exponent, so reducing conditions sharply increase the dissolved-N inventory. This is one mechanism by which planet-scale N partitioning is tied to mantle redox; see Nicholls et al. (2026)[^cite-nicholls2026] for an application to L 98-59 d, where the inferred H$_2$-dominated atmosphere with photochemical SO$_2$ implies $\Delta\mathrm{IW}$ between IW-4 and IW-1. ## Reference values for $\Delta\mathrm{IW}$ | Reservoir | $\Delta\mathrm{IW}$ | Source | |---|---|---| -| Mercury surface | $\sim -5$ | [Cartier & Wood (2019)](https://ui.adsabs.harvard.edu/abs/2019Eleme..15...39C) | -| Asteroidal material | $\sim -2$ | [Doyle et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019Sci...366..356D) | -| Mars mantle | $-3$ to $0$ | [Wadhwa (2001)](https://ui.adsabs.harvard.edu/abs/2001Sci...291.1527W) | +| Mercury surface | IW-2.8 to IW-5.4 (Fe-based: IW-2.8 to IW-4.5; sulphur-based: IW-5.4 via Namur et al. 2016) | Cartier & Wood (2019)[^cite-cartierwood2019] | +| Mars upper mantle (shergottite source) | $\approx$ IW (specifically IW-1.0 to IW-0.3 for QUE 94201) | Wadhwa (2001)[^cite-wadhwa2001] | +| Mars shergottite parent melts | IW-1.0 to IW+1.9 (variation from crust assimilation) | Wadhwa (2001)[^cite-wadhwa2001] | | Iron-wüstite buffer | $0$ | by definition | -| Earth's upper mantle $f_{\mathrm{O}_2}$ | $+3.5$ | [Sossi et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020SciA....6.1387S) | -| Earth upper mantle | $+1$ to $+5$ (i.e. FMQ$\,\pm\,2$) | [Frost & McCammon (2008)](https://ui.adsabs.harvard.edu/abs/2008AREPS..36..389F) | -| Earth deep (lower) mantle | $\sim -2$ to $-3$ ($\sim 5$ log units below FMQ at $\sim 8$ GPa) | [Frost & McCammon (2008)](https://ui.adsabs.harvard.edu/abs/2008AREPS..36..389F) | +| Earth's upper mantle (modern) | $\approx$ IW+3.5 | Sossi et al. (2020)[^cite-sossi2020] | +| Earth upper mantle (range) | FMQ$\,\pm\,2$ ($\approx$ IW+1.5 to IW+5.5) | Frost & McCammon (2008)[^cite-frostmccammon2008] | +| Earth mantle at $\sim 8$ GPa | $\approx$ FMQ$-5$ ($\approx$ IW-1.5) | Frost & McCammon (2008)[^cite-frostmccammon2008] | +| Earth transition zone ($\sim$14-23 GPa) | just below IW | Frost & McCammon (2008)[^cite-frostmccammon2008] | +| Earth lower mantle ($>$23 GPa) | metal-saturated ($\sim$1 wt% Fe$^0$); at or below IW | Frost & McCammon (2008)[^cite-frostmccammon2008] | -CALLIOPE's PROTEUS-side default is `fO2_shift_IW = 4.0`, consistent with a near-surface terrestrial composition. [Nicholls et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) explored $\Delta\mathrm{IW} \in \{-5, -3, -1, 0, +1, +3, +5\}$ on a 7-point grid and demonstrated that the resulting atmospheric composition spans the full range from H$_2$-dominated reduced atmospheres (TRAPPIST-1 c-like) to H$_2$O/CO$_2$-dominated oxidised atmospheres (Earth-like). +CALLIOPE's PROTEUS-side default is `fO2_shift_IW = 4.0`, consistent with a near-surface terrestrial composition. Nicholls et al. (2024)[^cite-nicholls2024] Table 2 explored $\Delta\mathrm{IW} \in \{-5, -3, -1, 0, +1, +3, +5\}$ on a 7-point grid and demonstrated that the resulting atmospheric composition spans the full range from H$_2$-dominated reduced atmospheres (TRAPPIST-1 c-like) to H$_2$O/CO$_2$-dominated oxidised atmospheres (Earth-like). ## Limitations - **No $f_{\mathrm{O}_2}$ evolution**: $\Delta\mathrm{IW}$ is a constant input, not a state variable. In reality the mantle $f_{\mathrm{O}_2}$ should evolve with degree of crystallisation, fractional crystallisation depth, and atmospheric escape; CALLIOPE does not capture this and the user is responsible for choosing a representative value or sweeping over a grid. -- **No $f_{\mathrm{O}_2}$ depth profile**: the surface $f_{\mathrm{O}_2}$ alone enters the chemistry. [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) §2.3 and [Sossi et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020SciA....6.1387S) discuss why the *interface* fugacity (rather than the deep-mantle value) is the relevant choice; this assumption is consistent with CALLIOPE's ideal-gas, single-temperature treatment but breaks down if a Fe-FeO equilibrium curve in the deep mantle differs by more than a few dex. +- **No $f_{\mathrm{O}_2}$ depth profile**: the surface $f_{\mathrm{O}_2}$ alone enters the chemistry. Bower et al. (2022)[^cite-bower2022] §2.3 and Sossi et al. (2020)[^cite-sossi2020] discuss why the *interface* fugacity (rather than the deep-mantle value) is the relevant choice; this assumption is consistent with CALLIOPE's ideal-gas, single-temperature treatment but breaks down if a Fe-FeO equilibrium curve in the deep mantle differs by more than a few dex. - **No solid-FeO buffering**: when $\Phi_\mathrm{global} \to 0$, there is no melt to buffer $f_{\mathrm{O}_2}$ against the user-prescribed value. CALLIOPE keeps using the shift regardless of melt fraction, which is a reasonable bookkeeping choice but should not be over-interpreted physically. ## See also +- [Authoritative-oxygen mode](authoritative_oxygen.md): how $\Delta\mathrm{IW}$ is recovered as a solver unknown when O is supplied as a budget instead. - [Equilibrium chemistry](equilibrium_chemistry.md): the species-by-species speciation tree - [Solubility laws](solubility.md): where $f_{\mathrm{O}_2}$ enters the S and N solubility paths - [API reference for `calliope.oxygen_fugacity`](../Reference/api/calliope.oxygen_fugacity.md) + +[^cite-bower2022]: D. J. Bower, K. Hakim, P. A. Sossi, P. Sanan, *[Retention of water in terrestrial magma oceans and carbon-rich early atmospheres](https://doi.org/10.3847/PSJ/ac5fb1)*, The Planetary Science Journal, 3(4), 93, 2022. [SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract). +[^cite-cartierwood2019]: C. Cartier, B. J. Wood, *[The role of reducing conditions in building Mercury](https://doi.org/10.2138/gselements.15.1.39)*, Elements, 15(1), 39–45, 2019. [SciX](https://scixplorer.org/abs/2019Eleme..15...39C/abstract). +[^cite-chase1998]: M. W. Chase, *[NIST-JANAF Thermochemical Tables, 4th edition](https://janaf.nist.gov/)*, Journal of Physical and Chemical Reference Data Monograph 9, 1998. +[^cite-dasgupta2022]: R. Dasgupta, E. Falksen, A. Pal, C. Sun, *[The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions](https://doi.org/10.1016/j.gca.2022.09.012)*, Geochimica et Cosmochimica Acta, 336, 291–307, 2022. [SciX](https://scixplorer.org/abs/2022GeCoA.336..291D/abstract). +[^cite-fischer2011]: R. A. Fischer, A. J. Campbell, G. A. Shofner, O. T. Lord, P. Dera, V. B. Prakapenka, *[Equation of state and phase diagram of FeO](https://doi.org/10.1016/j.epsl.2011.02.025)*, Earth and Planetary Science Letters, 304, 496–502, 2011. [SciX](https://scixplorer.org/abs/2011E%26PSL.304..496F/abstract). +[^cite-frostmccammon2008]: D. J. Frost, C. A. McCammon, *[The redox state of Earth's mantle](https://doi.org/10.1146/annurev.earth.36.031207.124322)*, Annual Review of Earth and Planetary Sciences, 36, 389–420, 2008. [SciX](https://scixplorer.org/abs/2008AREPS..36..389F/abstract). +[^cite-gaillard2022]: F. Gaillard, F. Bernadou, M. Roskosz, M. A. Bouhifd, Y. Marrocchi, G. Iacono-Marziano, M. Moreira, B. Scaillet, G. Rogerie, *[Redox controls during magma ocean degassing](https://doi.org/10.1016/j.epsl.2021.117255)*, Earth and Planetary Science Letters, 577, 117255, 2022. [SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract). +[^cite-nicholls2024]: H. Nicholls, T. Lichtenberg, D. J. Bower, R. Pierrehumbert, *[Magma ocean evolution at arbitrary redox state](https://doi.org/10.1029/2024JE008576)*, Journal of Geophysical Research: Planets, 129, e2024JE008576, 2024. [SciX](https://scixplorer.org/abs/2024JGRE..12908576N/abstract). +[^cite-nicholls2026]: H. Nicholls, T. Lichtenberg, R. D. Chatterjee, C. M. Guimond, E. Postolec, R. T. Pierrehumbert, *[Volatile-rich evolution of molten super-Earth L 98-59 d](https://doi.org/10.1038/s41550-026-02815-8)*, Nature Astronomy, 2026. [SciX](https://scixplorer.org/abs/2026NatAs.tmp...61N/abstract). [arXiv](https://arxiv.org/abs/2507.02656). +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151–181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). +[^cite-sossi2020]: P. A. Sossi, A. D. Burnham, J. Badro, A. Lanzirotti, M. Newville, H. St. C. O'Neill, *[Redox state of Earth's magma ocean and its Venus-like early atmosphere](https://doi.org/10.1126/sciadv.abd1387)*, Science Advances, 6, eabd1387, 2020. [SciX](https://scixplorer.org/abs/2020SciA....6.1387S/abstract). +[^cite-wadhwa2001]: M. Wadhwa, *[Redox state of Mars' upper mantle and crust from Eu anomalies in shergottite pyroxenes](https://doi.org/10.1126/science.1057594)*, Science, 291, 1527–1530, 2001. [SciX](https://scixplorer.org/abs/2001Sci...291.1527W/abstract). diff --git a/docs/Explanations/proteus_coupling.md b/docs/Explanations/proteus_coupling.md index 8f7d502..381883f 100644 --- a/docs/Explanations/proteus_coupling.md +++ b/docs/Explanations/proteus_coupling.md @@ -111,22 +111,44 @@ The previous-iteration partial pressures are an excellent warm start because the ### Step 5 - call the solver +The wrapper dispatches between the two CALLIOPE entry points based on `config.planet.fO2_source`: + ```python -solvevol_result = equilibrium_atmosphere( - target, - opts, - xtol=config.outgas.solver_atol, # fsolve step tolerance (despite the TOML name) - rtol=config.outgas.solver_rtol, # relative mass-balance tolerance - atol=config.outgas.mass_thresh, # absolute mass-balance tolerance - nguess=int(1e3), - nsolve=int(3e3), - p_guess=p_guess, - print_result=False, - opt_solver=False, -) +if config.planet.fO2_source == 'user_constant': + solvevol_result = equilibrium_atmosphere( + target, # H, C, N, S + opts, + xtol=config.outgas.solver_atol, # fsolve step tolerance (despite the TOML name) + rtol=config.outgas.solver_rtol, # relative mass-balance tolerance + atol=config.outgas.mass_thresh, # absolute mass-balance tolerance + nguess=config.outgas.calliope.nguess, + nsolve=config.outgas.calliope.nsolve, + p_guess=p_guess, + print_result=False, + opt_solver=False, + ) +elif config.planet.fO2_source == 'from_O_budget': + target['O'] = hf_row['O_kg_total'] # add the fifth element budget + solvevol_result = equilibrium_atmosphere_authoritative_O( + target, # H, C, N, S, O + opts, + fO2_hint=config.outgas.fO2_shift_IW, + xtol=config.outgas.solver_atol, + rtol=config.outgas.solver_rtol, + atol=config.outgas.mass_thresh, + nguess=config.outgas.calliope.nguess, + nsolve=config.outgas.calliope.nsolve, + p_guess=p_guess, + print_result=False, + opt_solver=False, + ) ``` -If this raises `RuntimeError` (Monte-Carlo restarts exhausted), the wrapper writes status code 27 to the run's status file and re-raises; the PROTEUS main loop then handles cleanup. +`config.outgas.calliope.nguess` and `nsolve` default to $10^3$ and $3\times 10^3$ respectively in the PROTEUS schema; override them in `[outgas.calliope]` if a particular run needs more attempts. + +Under `user_constant` (the default) CALLIOPE uses the configured `outgas.fO2_shift_IW` as the buffer offset and solves for the four H/C/N/S pressures. Under `from_O_budget` the wrapper passes `hf_row['O_kg_total']` (the whole-planet oxygen total maintained by the PROTEUS element-budget bookkeeping) as the fifth target, uses `outgas.fO2_shift_IW` only as an initial-guess hint, and solves for the four pressures plus $\Delta\mathrm{IW}$. The two dispatches return result dicts with the same key schema; the authoritative-O dict additionally carries `fO2_shift_derived` and `O_res`, which the wrapper writes to `hf_row['fO2_shift_IW_derived']` and `hf_row['O_res']`. After the writeback the wrapper restores `hf_row['O_kg_total']` to the user-supplied target (the value carried by the PROTEUS element-budget bookkeeping), overriding the solver-derived total. The two values agree to within `O_res` by construction, so restoring the authoritative input prevents accumulated solver-tolerance drift from leaking into the running budget across iterations. + +If either call raises `RuntimeError` (Monte-Carlo restarts exhausted), the wrapper writes status code 27 to the run's status file and re-raises; the PROTEUS main loop then handles cleanup. ### Step 6 - write back to `hf_row` @@ -151,7 +173,7 @@ After the writeback, `wrapper.run_outgassing` recomputes `M_atm` from the per-sp ## Where the binodal lives -If `config.outgas.h2_binodal = true`, `wrapper.run_outgassing` calls `apply_binodal_h2` *after* CALLIOPE returns. This applies the [Rogers et al. (2025)](https://ui.adsabs.harvard.edu/abs/2025MNRAS.544.3496R) H$_2$-MgSiO$_3$ miscibility correction as a post-processing step on top of the CALLIOPE equilibrium. CALLIOPE itself does not know about miscibility; it produces the ideal-mixing baseline that the binodal then perturbs. +If `config.outgas.h2_binodal = true`, `wrapper.run_outgassing` calls `apply_binodal_h2` *after* CALLIOPE returns. This applies the Rogers et al. (2025)[^cite-rogers2025] H$_2$-MgSiO$_3$ miscibility correction as a post-processing step on top of the CALLIOPE equilibrium. CALLIOPE itself does not know about miscibility; it produces the ideal-mixing baseline that the binodal then perturbs. When `config.interior_struct.zalmoxis.global_miscibility = true` (Zalmoxis radial binodal), the bulk binodal step is skipped because Zalmoxis has already done a per-radial-shell partition during the structure update. See the [Zalmoxis binodal page](https://proteus-framework.org/Zalmoxis/Explanations/binodal.html) for that mechanism. @@ -163,4 +185,7 @@ When `config.interior_struct.zalmoxis.global_miscibility = true` (Zalmoxis radia - [Coupling to PROTEUS (how-to)](../How-to/proteus_coupling.md) for the TOML recipe and pitfalls. - [Mass balance & solver](mass_balance.md) for what `equilibrium_atmosphere` does inside. +- [Authoritative-oxygen mode](authoritative_oxygen.md) for the augmented mass balance that `from_O_budget` dispatches to. - The PROTEUS-side wrapper code: [`src/proteus/outgas/calliope.py`](https://github.com/FormingWorlds/PROTEUS/blob/main/src/proteus/outgas/calliope.py), [`src/proteus/outgas/wrapper.py`](https://github.com/FormingWorlds/PROTEUS/blob/main/src/proteus/outgas/wrapper.py), [`src/proteus/config/_outgas.py`](https://github.com/FormingWorlds/PROTEUS/blob/main/src/proteus/config/_outgas.py). + +[^cite-rogers2025]: J. G. Rogers, E. D. Young, H. E. Schlichting, *Redefining interiors and envelopes: hydrogen-silicate miscibility and its consequences for the structure and evolution of sub-Neptunes*, Monthly Notices of the Royal Astronomical Society, 544(4), 3496–3511, 2025. [SciX](https://scixplorer.org/abs/2025MNRAS.544.3496R/abstract). diff --git a/docs/Explanations/solubility.md b/docs/Explanations/solubility.md index 1a4dca0..3e14d3c 100644 --- a/docs/Explanations/solubility.md +++ b/docs/Explanations/solubility.md @@ -6,7 +6,7 @@ $$ X_i^\mathrm{melt} = \alpha_i\, p_i^{\,1/\beta_i}, $$ -with $X_i^\mathrm{melt}$ in ppmw, $p_i$ in bar, and species-specific empirical constants $\alpha_i, \beta_i$. [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) Equation (1) writes the same relation in terms of fugacity; CALLIOPE assumes ideal-gas behaviour so $f \equiv p$. +with $X_i^\mathrm{melt}$ in ppmw, $p_i$ in bar, and species-specific empirical constants $\alpha_i, \beta_i$. Bower et al. (2022)[^cite-bower2022] Equation (1) writes the same relation in terms of fugacity; CALLIOPE assumes ideal-gas behaviour so $f \equiv p$. This page lists the implemented laws, the experimental sources behind each, and the alternative compositions a user can select. The corresponding code lives in `solubility.py`; the speciation-time call paths are in `solve.dissolved_mass`. @@ -16,46 +16,46 @@ This page lists the implemented laws, the experimental sources behind each, and | Composition | Law | Source | $\alpha$ (ppmw bar$^{-1/\beta}$) | $\beta$ | |---|---|---|---:|---:| -| `peridotite` (default) | $524\, p^{0.5}$ | [Sossi et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023E%26PSL.60117894S) | 524 | 2 | -| `basalt_dixon` | $965\, p^{0.5}$ | [Dixon et al. (1995)](https://ui.adsabs.harvard.edu/abs/1995JPet...36.1607D), refit by Sossi | 965 | 2 | -| `basalt_wilson` | $215\, p^{0.7}$ | [Hamilton (1964)](https://doi.org/10.1093/petrology/5.1.21); [Wilson & Head (1981)](https://ui.adsabs.harvard.edu/abs/1981JGR....86.2971W) | 215 | 1/0.7 | -| `anorthite_diopside` | $727\, p^{0.5}$ | [Newcombe et al. (2017)](https://ui.adsabs.harvard.edu/abs/2017GeCoA.200..330N) | 727 | 2 | -| `lunar_glass` | $683\, p^{0.5}$ | [Newcombe et al. (2017)](https://ui.adsabs.harvard.edu/abs/2017GeCoA.200..330N) | 683 | 2 | +| `peridotite` (default) | $524\, p^{0.5}$ | Sossi et al. (2023)[^cite-sossi2023] | 524 | 2 | +| `basalt_dixon` | $965\, p^{0.5}$ | Dixon et al. (1995)[^cite-dixon1995], refit by Sossi | 965 | 2 | +| `basalt_wilson` | $215\, p^{0.7}$ | Hamilton (1964)[^cite-hamilton1964]; Wilson & Head (1981)[^cite-wilsonhead1981] | 215 | 1/0.7 | +| `anorthite_diopside` | $727\, p^{0.5}$ | Newcombe et al. (2017)[^cite-newcombe2017] | 727 | 2 | +| `lunar_glass` | $683\, p^{0.5}$ | Newcombe et al. (2017)[^cite-newcombe2017] | 683 | 2 | !!! note "About the `peridotite` constant 524" - [Sossi et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023E%26PSL.60117894S) report two values for the prefactor depending on the FTIR absorption coefficient used: $\alpha = 524 \pm 16$ ppmw bar$^{-0.5}$ from the basaltic-glass calibration ($\epsilon_{3550} = 6.3$ m$^2$ mol$^{-1}$), and $\alpha = 647$ ppmw bar$^{-0.5}$ from the peridotite-glass calibration. CALLIOPE uses 524 to match the value adopted in [Nicholls et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) and the PROTEUS-side fiducial; the label `peridotite` refers to the *experimental melt composition* (Sossi+2023 used a peridotitic starting composition), not to the spectroscopic-calibration choice. If you want the full peridotite-glass calibration, instantiate `SolubilityH2O('peridotite')` and override the constant manually, or wait for an upstream change. + Sossi et al. (2023)[^cite-sossi2023] report two values for the prefactor depending on the FTIR absorption coefficient used: $\alpha = 524 \pm 16$ ppmw bar$^{-0.5}$ from the basaltic-glass calibration ($\epsilon_{3550} = 6.3$ m$^2$ mol$^{-1}$), and $\alpha = 647$ ppmw bar$^{-0.5}$ from the peridotite-glass calibration. CALLIOPE uses 524 to match the value adopted in Nicholls et al. (2024)[^cite-nicholls2024] and the PROTEUS-side fiducial; the label `peridotite` refers to the *experimental melt composition* (Sossi+2023 used a peridotitic starting composition), not to the spectroscopic-calibration choice. If you want the full peridotite-glass calibration, instantiate `SolubilityH2O('peridotite')` and override the constant manually, or wait for an upstream change. -The choice between peridotite (default) and basalt is one of the larger uncertainties in early magma-ocean modelling. [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) Table 1 compares all five compositions across the relevant pressure range; [Nicholls et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) uses peridotite as the fiducial, consistent with CALLIOPE's default. +The choice between peridotite (default) and basalt is one of the larger uncertainties in early magma-ocean modelling. Bower et al. (2022)[^cite-bower2022] Table 1 compares all five compositions across the relevant pressure range; Nicholls et al. (2024)[^cite-nicholls2024] uses peridotite as the fiducial, consistent with CALLIOPE's default. ### CO$_2$ - `SolubilityCO2(composition='basalt_dixon')` -[Dixon et al. (1995)](https://ui.adsabs.harvard.edu/abs/1995JPet...36.1607D) MORB fit, with an explicit Poynting-like temperature/pressure correction: +Dixon et al. (1995)[^cite-dixon1995] MORB fit, with an explicit Poynting-like temperature/pressure correction: $$ X_{\mathrm{CO_2}}^\mathrm{melt}\,[\text{mol fr.}] = 3.8 \times 10^{-7} \cdot p_{\mathrm{CO_2}} \cdot \exp\left(-\frac{23 (p_{\mathrm{CO_2}} - 1)}{83.15\, T_\mathrm{magma}}\right) $$ -then converted from molar to ppmw via the algebraic conversion in [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) Equation (3): +then converted from molar to ppmw via the algebraic conversion in Bower et al. (2022)[^cite-bower2022] Equation (3): $$ X_{\mathrm{CO_2}}^\mathrm{melt}\,[\text{ppmw}] = 10^4 \cdot \frac{4400 X_{\mathrm{CO_2}}^\mathrm{melt}}{36.6 - 44 X_{\mathrm{CO_2}}^\mathrm{melt}}. $$ -This is the only solubility law in CALLIOPE that depends on $T_\mathrm{magma}$; the others ignore the temperature term that experimental data only weakly constrains ([Bower et al. 2022](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) §2.2.1 discussion). +This is the only solubility law in CALLIOPE that depends on $T_\mathrm{magma}$; the others ignore the temperature term that experimental data only weakly constrains (Bower et al. 2022[^cite-bower2022] §2.2.1 discussion). ### CO - `SolubilityCO(composition='mafic_armstrong')` -[Armstrong et al. (2015)](https://ui.adsabs.harvard.edu/abs/2015GeCoA.171..283A) mafic-melt fit: +Armstrong et al. (2015)[^cite-armstrong2015] mafic-melt fit: $$ \log_{10} X_\mathrm{CO}^\mathrm{melt}\,[\text{ppmw}] = -0.738 + 0.876\, \log_{10} p_\mathrm{CO} - 5.44 \times 10^{-5} \cdot p_\mathrm{tot} $$ -The $-5.44 \times 10^{-5} \cdot p_\mathrm{tot}$ term is a total-pressure (Poynting) correction that reduces solubility at high pressures. CO solubility is generally an order of magnitude or more lower than CO$_2$, consistent with the experimental constraints summarised in [Yoshioka et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019GeCoA.259..129Y). +The $-5.44 \times 10^{-5} \cdot p_\mathrm{tot}$ term is a total-pressure (Poynting) correction that reduces solubility at high pressures. CO solubility is generally an order of magnitude or more lower than CO$_2$, consistent with the experimental constraints summarised in Yoshioka et al. (2019)[^cite-yoshioka2019]. ### CH$_4$ - `SolubilityCH4(composition='basalt_ardia')` -[Ardia et al. (2013)](https://ui.adsabs.harvard.edu/abs/2013GeCoA.114...52A) basalt fit (their Fig. 6 best-fit, 0.7-3 GPa): +Ardia et al. (2013)[^cite-ardia2013] Fe-free haplobasaltic-melt fit (their Eq. (8) with $\ln K_0 = 4.93$ and $\Delta V = 26.85\,\mathrm{cm}^3$/mol at $T_0 = 1400\,^\circ$C, $P_0 = 1$ bar, plotted as Fig. 11; calibrated over 0.7-3 GPa): $$ X_\mathrm{CH_4}^\mathrm{melt}\,[\text{ppmw}] = p_\mathrm{CH_4} \cdot \exp\left(4.93 - 1.93\,p_\mathrm{tot}^{[\mathrm{GPa}]}\right) @@ -69,10 +69,10 @@ CALLIOPE provides two N$_2$ solubility laws but `dissolved_mass` hard-codes `das | Composition | Law | Source | |---|---|---| -| `libourel` | $0.0611\, p_\mathrm{N_2}$ (linear Henry's law) | [Libourel et al. (2003)](https://ui.adsabs.harvard.edu/abs/2003GeCoA..67.4123L) | -| `dasgupta` (default) | physical-state-dependent (see below) | [Dasgupta et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022GeCoA.336..291D) | +| `libourel` | $0.0611\, p_\mathrm{N_2}$ (linear Henry's law) | Libourel et al. (2003)[^cite-libourel2003] | +| `dasgupta` (default) | physical-state-dependent (see below) | Dasgupta et al. (2022)[^cite-dasgupta2022] | -The [Dasgupta et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022GeCoA.336..291D) law adds an $f_{\mathrm{O}_2}$-dependent reduced-N contribution on top of the molecular dissolution: +The Dasgupta et al. (2022)[^cite-dasgupta2022] law adds an $f_{\mathrm{O}_2}$-dependent reduced-N contribution on top of the molecular dissolution: $$ X_\mathrm{N_2}^\mathrm{melt}\,[\text{ppmw}] = \sqrt{p_\mathrm{N_2}^{[\mathrm{GPa}]}} \cdot \exp\left(\frac{5908\sqrt{p_\mathrm{tot}^{[\mathrm{GPa}]}}}{T_\mathrm{magma}} - 1.6\,\Delta\mathrm{IW}\right) + p_\mathrm{N_2}^{[\mathrm{GPa}]} \cdot c_\mathrm{melt} @@ -82,7 +82,7 @@ where the prefactor $c_\mathrm{melt}$ depends on the silicate composition. The m ### S$_2$ - `SolubilityS2(composition='gaillard')` -[Gaillard et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717255G) sulfide-saturated mafic-melt law: +Gaillard et al. (2022)[^cite-gaillard2022] sulfide-saturated mafic-melt law: $$ \log_{e} X_\mathrm{S_2}^\mathrm{melt}\,[\text{ppmw}] = 13.8426 - \frac{26476}{T_\mathrm{magma}} + 0.124\,x_\mathrm{FeO}^{[\text{wt\%}]} + 0.5\,\ln\frac{p_\mathrm{S_2}}{f_{\mathrm{O}_2}} @@ -94,21 +94,29 @@ The implementation refuses to evaluate at $p_\mathrm{S_2} < 10^{-20}$ bar (retur ## What about the missing laws? -CALLIOPE does not include explicit solubility laws for H$_2$, NH$_3$, SO$_2$, H$_2$S, or O$_2$. Their dissolved masses are computed from the *primary*-species solubilities (H$_2$O for H-bearing species, CO$_2$ for C-bearing species, S$_2$ for S-bearing species, N$_2$ for N-bearing species) via stoichiometric atom-counting in `dissolved_mass()`. This is consistent with [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) Section 2.2.3 which sets $\alpha_\mathrm{H_2} = \alpha_\mathrm{CO} = \alpha_\mathrm{CH_4} = 0$ on the grounds that experimentally constrained solubilities are 1-2 dex smaller than those of H$_2$O / CO$_2$ at equivalent fugacities ([Hirschmann et al. 2012](https://ui.adsabs.harvard.edu/abs/2012E%26PSL.345...38H); [Li et al. 2015](https://ui.adsabs.harvard.edu/abs/2015E%26PSL.415...54L); [Yoshioka et al. 2019](https://ui.adsabs.harvard.edu/abs/2019GeCoA.259..129Y); [Ardia et al. 2013](https://ui.adsabs.harvard.edu/abs/2013GeCoA.114...52A)); within the CALLIOPE framework the same logic justifies omitting solubility for the three S species and ammonia. +CALLIOPE does not include explicit solubility laws for H$_2$, NH$_3$, SO$_2$, H$_2$S, or O$_2$. Their dissolved masses are computed from the *primary*-species solubilities (H$_2$O for H-bearing species, CO$_2$ for C-bearing species, S$_2$ for S-bearing species, N$_2$ for N-bearing species) via stoichiometric atom-counting in `dissolved_mass()`. This is consistent with Bower et al. (2022)[^cite-bower2022] Section 2.2.3 which sets $\alpha_\mathrm{H_2} = \alpha_\mathrm{CO} = \alpha_\mathrm{CH_4} = 0$ on the grounds that experimentally constrained solubilities are 1-2 dex smaller than those of H$_2$O / CO$_2$ at equivalent fugacities (Hirschmann et al. 2012[^cite-hirschmann2012]; Li et al. 2015[^cite-li2015]; Yoshioka et al. 2019[^cite-yoshioka2019]; Ardia et al. 2013[^cite-ardia2013]); within the CALLIOPE framework the same logic justifies omitting solubility for the three S species and ammonia. -If you need explicit dissolution of reduced species into the melt, the [atmodeller](https://atmodeller.readthedocs.io/) project provides full per-species solubility laws including H$_2$ ([Hirschmann et al. 2012](https://ui.adsabs.harvard.edu/abs/2012E%26PSL.345...38H)), CO ([Yoshioka et al. 2019](https://ui.adsabs.harvard.edu/abs/2019GeCoA.259..129Y)), and CH$_4$ ([Ardia et al. 2013](https://ui.adsabs.harvard.edu/abs/2013GeCoA.114...52A)), with non-ideal real-gas activity coefficients. +If you need explicit dissolution of reduced species into the melt, the [atmodeller](https://atmodeller.readthedocs.io/) project provides full per-species solubility laws including H$_2$ (Hirschmann et al. 2012[^cite-hirschmann2012]), CO (Yoshioka et al. 2019[^cite-yoshioka2019]), and CH$_4$ (Ardia et al. 2013[^cite-ardia2013]), with non-ideal real-gas activity coefficients. ## Validity envelope -| Species | Calibration $T$ range | Calibration $p$ range | -|---|---|---| -| H$_2$O peridotite | 2173 K | $\le$1 bar to several kbar | -| H$_2$O basalt (Dixon) | 1473 K | 176-2021 bar | -| CO$_2$ (Dixon) | $\le$2000 K | $\le$815 bar | -| CO (Armstrong) | $\sim$1700 K | $\le$3 GPa | -| CH$_4$ (Ardia) | 1573-1873 K | 0.7-3 GPa | -| N$_2$ (Dasgupta) | 1500-2200 K | 0-3 GPa | -| S$_2$ (Gaillard) | 1473-1773 K, $f_{\mathrm{O}_2} <$ IW+1 | sulfide-saturated regime | +| Species (`composition`) | Source | Calibration $T$ | Calibration $p$ | Calibration $f_{\mathrm{O}_2}$ | +|---|---|---|---|---| +| H$_2$O `peridotite` (default) | Sossi et al. (2023)[^cite-sossi2023] | 2173 K (1900 $^\circ$C) | 1 atm total ($f_\mathrm{H_2O}\le 0.027$ bar) | IW-1.9 to IW+6.0 | +| H$_2$O `basalt_dixon` | Dixon et al. (1995)[^cite-dixon1995] | 1473 K (1200 $^\circ$C) | 176-717 bar $p_\mathrm{H_2O}$ | $\sim$QFM ($\approx$ IW+3.5 to IW+5) | +| H$_2$O `basalt_wilson` | Hamilton et al. (1964)[^cite-hamilton1964] | 1373 K (1100 $^\circ$C) | 1000-6000 bar $p_\mathrm{H_2O}$ | unbuffered for the pressure series (separate 1000-bar buffer series used MH, FMQ, MW buffers) | +| H$_2$O `anorthite_diopside` | Newcombe et al. (2017)[^cite-newcombe2017] | 1623 K (1350 $^\circ$C) | 1 atm | IW-2.3 to IW+4.8 | +| H$_2$O `lunar_glass` | Newcombe et al. (2017)[^cite-newcombe2017] | 1623 K (1350 $^\circ$C) | 1 atm | IW-3.0 to IW+4.8 | +| CO$_2$ `basalt_dixon` (default) | Dixon et al. (1995)[^cite-dixon1995] | 1473 K (1200 $^\circ$C) | $\le$815 bar $p_\mathrm{CO_2}$ | $\sim$QFM ($\approx$ IW+3.5 to IW+5) | +| CO `mafic_armstrong` (default) | Armstrong et al. (2015)[^cite-armstrong2015] | 1673 K (1400 $^\circ$C) | 1.2 GPa $p_\mathrm{tot}$ (1.0-1.2 GPa including Stanley et al. 2014 data co-fit) | IW-3.65 to IW+1.46 | +| CH$_4$ `basalt_ardia` (default) | Ardia et al. (2013)[^cite-ardia2013] | 1673-1723 K (1400-1450 $^\circ$C) | 0.7-3 GPa $p_\mathrm{tot}$ | IW-9.5 to IW-1.4 (IW/Si and IWC/C buffers) | +| N$_2$ `libourel` | Libourel et al. (2003)[^cite-libourel2003] | 1673-1698 K (1400-1425 $^\circ$C) | 1 atm | linear regime $\log_{10}f_{\mathrm{O}_2}\in[-10.7, -0.7]$ ($\approx$ IW-1.3 to IW+9) | +| N$_2$ `dasgupta` (default in `dissolved_mass`) | Dasgupta et al. (2022)[^cite-dasgupta2022] | 1323-2600 K (1050-2327 $^\circ$C) | 1 bar to 8.2 GPa $p_\mathrm{tot}$ | IW-8.3 to IW+8.7 | +| S$_2$ `gaillard` (default) | Gaillard et al. (2022)[^cite-gaillard2022] | not stated by paper (1 atm calibration data) | 1 atm | IW-1 to FMQ+0.1 ($\approx$ IW+3.5) | + +The $f_{\mathrm{O}_2}$ column reports the *calibration footprint* (the range of $f_{\mathrm{O}_2}$ over which the experiments that produced the fit were performed), not the law's functional dependence: only the Gaillard S$_2$ and Dasgupta N$_2$ laws carry an explicit $f_{\mathrm{O}_2}$ term, while every other law in the table is an $f_{\mathrm{O}_2}$-independent expression fitted to data from experiments performed at the listed $f_{\mathrm{O}_2}$ range. The choice between H$_2$O laws (peridotite vs basalt) is one of the larger uncertainties in early magma-ocean modelling (the line-26 note expands on the Sossi peridotite vs Dixon basalt prefactor difference); the $f_{\mathrm{O}_2}$ footprint here describes where the data lived, not how robust the fit is across compositions. + +The Dasgupta N$_2$ and Gaillard S$_2$ rows quote the ranges that Dasgupta et al. (2022)[^cite-dasgupta2022] Equation 10 (n=137 compiled data) and Gaillard et al. (2022)[^cite-gaillard2022] Equation 10 (refit of O'Neill & Mavrogenes (2002)[^cite-oneillmavrogenes2002] plus 8 other experimental sources, n=369) report directly. The Gaillard refit data are all at 1 atm; the formula is applied at magma-ocean pressures in CALLIOPE without an explicit pressure correction. The Libourel linear regime stops at IW-1.3, below which the same paper documents a sharp transition to chemical (network-bound N$^{3-}$) dissolution with $\sim$5 orders of magnitude higher solubility; the `libourel` law in CALLIOPE captures only the oxidising-end linear regime and underestimates dissolved N below IW-1.3. The CO, CH$_4$ and Dasgupta-N$_2$ rows give the *total*-pressure range, since the Poynting and reduced-N branches depend on $p_\mathrm{tot}$ as well as the species partial pressure. CALLIOPE deliberately makes no attempt to flag extrapolation: the laws are evaluated formally outside their calibration ranges to keep the solver well-posed. For applications outside the bracket, treat the dissolved masses as upper bounds and check sensitivity by switching solubility laws via the constructor argument. @@ -116,4 +124,23 @@ CALLIOPE deliberately makes no attempt to flag extrapolation: the laws are evalu - [Equilibrium chemistry](equilibrium_chemistry.md): how the gas-phase speciation feeds into the partial pressures the solubility laws then consume. - [Mass balance & solver](mass_balance.md): how dissolved + atmospheric masses are summed to close the elemental conservation constraints. +- [Oxygen fugacity](oxygen_fugacity.md): the IW-buffer parameterisations that drive the $f_{\mathrm{O}_2}$ dependence in the Gaillard S$_2$ and Dasgupta N$_2$ laws. +- [Authoritative-oxygen mode](authoritative_oxygen.md): how the Gaillard $\ln(p_\mathrm{S_2}/f_{\mathrm{O}_2})$ and Dasgupta $-1.6\,\Delta\mathrm{IW}$ terms enter the 5-residual mass balance once $\Delta\mathrm{IW}$ becomes a solver unknown. - [API reference for `calliope.solubility`](../Reference/api/calliope.solubility.md). + +[^cite-armstrong2015]: L. S. Armstrong, M. M. Hirschmann, B. D. Stanley, E. G. Falksen, S. D. Jacobsen, *[Speciation and solubility of reduced C-O-H-N volatiles in mafic melt: implications for volcanism, atmospheric evolution, and deep volatile cycles in the terrestrial planets](https://doi.org/10.1016/j.gca.2015.07.007)*, Geochimica et Cosmochimica Acta, 171, 283–302, 2015. [SciX](https://scixplorer.org/abs/2015GeCoA.171..283A/abstract). +[^cite-ardia2013]: P. Ardia, M. M. Hirschmann, A. C. Withers, B. D. Stanley, *[Solubility of CH$_4$ in a synthetic basaltic melt, with applications to atmosphere-magma ocean-core partitioning of volatiles and to the evolution of the Martian atmosphere](https://doi.org/10.1016/j.gca.2013.03.028)*, Geochimica et Cosmochimica Acta, 114, 52–71, 2013. [SciX](https://scixplorer.org/abs/2013GeCoA.114...52A/abstract). +[^cite-bower2022]: D. J. Bower, K. Hakim, P. A. Sossi, P. Sanan, *[Retention of water in terrestrial magma oceans and carbon-rich early atmospheres](https://doi.org/10.3847/PSJ/ac5fb1)*, The Planetary Science Journal, 3(4), 93, 2022. [SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract). +[^cite-dasgupta2022]: R. Dasgupta, E. Falksen, A. Pal, C. Sun, *[The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions](https://doi.org/10.1016/j.gca.2022.09.012)*, Geochimica et Cosmochimica Acta, 336, 291–307, 2022. [SciX](https://scixplorer.org/abs/2022GeCoA.336..291D/abstract). +[^cite-dixon1995]: J. E. Dixon, E. M. Stolper, J. R. Holloway, *[An experimental study of water and carbon dioxide solubilities in mid-ocean ridge basaltic liquids. Part I: Calibration and solubility models](https://doi.org/10.1093/oxfordjournals.petrology.a037267)*, Journal of Petrology, 36(6), 1607–1631, 1995. [SciX](https://scixplorer.org/abs/1995JPet...36.1607D/abstract). +[^cite-gaillard2022]: F. Gaillard, F. Bernadou, M. Roskosz, M. A. Bouhifd, Y. Marrocchi, G. Iacono-Marziano, M. Moreira, B. Scaillet, G. Rogerie, *[Redox controls during magma ocean degassing](https://doi.org/10.1016/j.epsl.2021.117255)*, Earth and Planetary Science Letters, 577, 117255, 2022. [SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract). +[^cite-hamilton1964]: D. L. Hamilton, C. W. Burnham, E. F. Osborn, *[The solubility of water and effects of oxygen fugacity and water content on crystallization in mafic magmas](https://doi.org/10.1093/petrology/5.1.21)*, Journal of Petrology, 5(1), 21–39, 1964. +[^cite-hirschmann2012]: M. M. Hirschmann, A. C. Withers, P. Ardia, N. T. Foley, *[Solubility of molecular hydrogen in silicate melts and consequences for volatile evolution of terrestrial planets](https://doi.org/10.1016/j.epsl.2012.06.031)*, Earth and Planetary Science Letters, 345–348, 38–48, 2012. [SciX](https://scixplorer.org/abs/2012E%26PSL.345...38H/abstract). +[^cite-li2015]: Y. Li, R. Dasgupta, K. Tsuno, *[The effects of sulfur, silicon, water, and oxygen fugacity on carbon solubility and partitioning in Fe-rich alloy and silicate melt systems at 3 GPa and 1600 °C: implications for core-mantle differentiation and degassing of magma oceans and reduced planetary mantles](https://doi.org/10.1016/j.epsl.2015.01.017)*, Earth and Planetary Science Letters, 415, 54–66, 2015. [SciX](https://scixplorer.org/abs/2015E%26PSL.415...54L/abstract). +[^cite-libourel2003]: G. Libourel, B. Marty, F. Humbert, *[Nitrogen solubility in basaltic melt. Part I. Effect of oxygen fugacity](https://doi.org/10.1016/S0016-7037(03)00259-X)*, Geochimica et Cosmochimica Acta, 67(21), 4123–4135, 2003. [SciX](https://scixplorer.org/abs/2003GeCoA..67.4123L/abstract). +[^cite-newcombe2017]: M. E. Newcombe, A. Brett, J. R. Beckett, M. B. Baker, S. Newman, Y. Guan, J. M. Eiler, E. M. Stolper, *[Solubility of water in lunar basalt at low pH$_2$O](https://doi.org/10.1016/j.gca.2016.12.026)*, Geochimica et Cosmochimica Acta, 200, 330–352, 2017. [SciX](https://scixplorer.org/abs/2017GeCoA.200..330N/abstract). +[^cite-nicholls2024]: H. Nicholls, T. Lichtenberg, D. J. Bower, R. Pierrehumbert, *[Magma ocean evolution at arbitrary redox state](https://doi.org/10.1029/2024JE008576)*, Journal of Geophysical Research: Planets, 129, e2024JE008576, 2024. [SciX](https://scixplorer.org/abs/2024JGRE..12908576N/abstract). +[^cite-oneillmavrogenes2002]: H. St. C. O'Neill, J. A. Mavrogenes, *[The sulfide capacity and the sulfur content at sulfide saturation of silicate melts at 1400 °C and 1 bar](https://doi.org/10.1093/petrology/43.6.1049)*, Journal of Petrology, 43(6), 1049–1087, 2002. [SciX](https://scixplorer.org/abs/2002JPet...43.1049O/abstract). +[^cite-sossi2023]: P. A. Sossi, P. M. E. Tollan, J. Badro, D. J. Bower, *[Solubility of water in peridotite liquids and the prevalence of steam atmospheres on rocky planets](https://doi.org/10.1016/j.epsl.2022.117894)*, Earth and Planetary Science Letters, 601, 117894, 2023. [SciX](https://scixplorer.org/abs/2023E%26PSL.60117894S/abstract). +[^cite-wilsonhead1981]: L. Wilson, J. W. Head, *[Ascent and eruption of basaltic magma on the Earth and Moon](https://doi.org/10.1029/JB086iB04p02971)*, Journal of Geophysical Research, 86(B4), 2971–3001, 1981. [SciX](https://scixplorer.org/abs/1981JGR....86.2971W/abstract). +[^cite-yoshioka2019]: T. Yoshioka, D. Nakashima, T. Nakamura, S. Shcheka, H. Keppler, *[Carbon solubility in silicate melts in equilibrium with a CO-CO$_2$ gas phase and graphite](https://doi.org/10.1016/j.gca.2019.06.007)*, Geochimica et Cosmochimica Acta, 259, 129–143, 2019. [SciX](https://scixplorer.org/abs/2019GeCoA.259..129Y/abstract). diff --git a/docs/Explanations/testing.md b/docs/Explanations/testing.md index 75e6c3c..ad9533c 100644 --- a/docs/Explanations/testing.md +++ b/docs/Explanations/testing.md @@ -1,30 +1,162 @@ # Testing suite -[![codecov](https://img.shields.io/codecov/c/github/FormingWorlds/CALLIOPE?label=coverage&logo=codecov)](https://app.codecov.io/gh/FormingWorlds/CALLIOPE) -[![Unit Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/tests.yaml?branch=main&label=Unit%20Tests)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/tests.yaml) -[![Integration Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/nightly.yml?branch=main&label=Integration%20Tests)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/nightly.yml) +[![codecov](https://img.shields.io/codecov/c/github/FormingWorlds/CALLIOPE?label=coverage&logo=codecov&color=brightgreen)](https://app.codecov.io/gh/FormingWorlds/CALLIOPE) +[![Unit Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/tests.yaml?branch=main&label=Unit%20Tests&color=brightgreen)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/tests.yaml) +[![Integration Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/nightly.yml?branch=main&label=Integration%20Tests&color=brightgreen)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/nightly.yml) [![tests](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/FormingWorlds/CALLIOPE/main/.github/badges/tests-total.json)](https://proteus-framework.org/testing) Tests verify that the code does what was written; physical correctness is judged by data, not by tests. The suite catches regressions in equilibrium chemistry, solubility laws, oxygen-fugacity buffers, and the hybrid solver, but it cannot certify that those formulae match nature. That judgement belongs to the validation runs and the published comparisons against measured magma-ocean outgassing data. -## Internal 4-marker scheme +This page describes the suite as a whole. +Contributors writing or modifying tests should read it together with the [Build a new test](../How-to/build_tests.md) how-to. -Every test in the suite carries exactly one of four markers, applied either at module level (`pytestmark = pytest.mark.X`) or per class (`@pytest.mark.X`). +## Test quality contract -- `unit` (96 tests): in-process tests of individual chemistry helpers, equilibrium-constant fits, solubility laws, and structure formulae. No real `equilibrium_atmosphere` call. Sub-second per test. -- `smoke` (25 tests): real `equilibrium_atmosphere` invocations on minimal configurations (single composition, default species set, one solve). Sub-30 s per test, exercises the hybrid solver code paths. -- `integration` (5 tests): full multi-species CHNS solves with mass-conservation invariants, all eleven species active. -- `slow`: long parameter sweeps and convergence studies. Currently empty. +Five layers enforce test rigor across the suite: + +1. A four-marker tier scheme (`unit`, `smoke`, `integration`, `slow`) selects what runs in the PR gate versus the nightly. +2. Two validation markers (`physics_invariant`, `reference_pinned`) tag tests that carry physical meaning beyond pure code coverage. +3. A 1:1 mirroring rule pairs every physics source with a same-named test file. +4. An AST linter (`tools/check_test_quality.py`) rejects seven weak-test patterns on every PR. +5. A coverage ratchet capped at 90 % keeps the gate moving upward over time. + +Layers 1, 4, and 5 are blocking on PRs. +Layers 2 and 3 are advisory: the linter reports gaps but does not fail the build. + +## The four-marker tier scheme + +Every test in the suite carries exactly one tier marker, applied either at module level (`pytestmark = pytest.mark.X`) or per class (`@pytest.mark.X`). + +| Marker | What it tests | Per-test budget | CI surface | +|---|---|---|---| +| `unit` | Python logic, individual helpers, equilibrium-constant fits, solubility laws, structure formulae. No real `equilibrium_atmosphere` call. | < 100 ms | PR + nightly | +| `smoke` | Real `equilibrium_atmosphere` invocations on minimal configurations (single composition, default species set, one solve). | < 30 s | PR + nightly | +| `integration` | Full multi-species CHNS solves with mass-conservation invariants, all eleven species active. | minutes | Nightly only | +| `slow` | Long parameter sweeps and convergence studies (authoritative-O monotonicity regimes, hypothesis fuzz, cross-buffer property checks). | up to an hour | Nightly only | +| `skip` | Placeholder, deliberately disabled. | n/a | Never | + +Live counts per tier are shown by the `tests` badge at the top of this page (the total) and by `pytest -m --collect-only -q` locally. + +Tests without a tier marker are invisible to CI. +The PR gate runs `pytest -m "(unit or smoke) and not skip"`; the nightly runs everything in `(unit or smoke or integration or slow) and not skip`. + +## Module-level marker and timeout + +Every test file declares its tier and its wall-time ceiling at module top: + +```python +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] +``` + +| Tier | Timeout | +|---|---| +| `unit` | 30 s | +| `smoke` | 60 s | +| `integration` | 300 s | +| `slow` | 3600 s | + +The timeout is a defensive ceiling, not a target. +A unit test that takes 25 s of wall time has either picked the wrong tier or has a leak somewhere; the ceiling catches future regressions that introduce a hang. +Per-function markers (for example `@pytest.mark.skip` on one stale parametrisation) are additive and do not replace the module-level marker. + +## Physics-invariant tiering + +A unit test on any physics source must assert at least one of four invariant families: + +- **Conservation**: per-species mass closure (`kg_atm + kg_liquid + kg_solid ≈ kg_total` within rtol), stoichiometric closure (`sum(mole_fractions) == 1.0`), element closure across the H/C/N/O/S budgets. +- **Positivity or boundedness**: `T > 0`, `P > 0`, mole fractions in `[0, 1]`, `log10(fO2)` finite over the calibration window, partial pressures non-negative. +- **Monotonicity or symmetry**: `log10(fO2)` decreasing with `1/T` along an isobaric buffer; CO$_2$ solubility increasing with $P$ at fixed $T$; swapping two non-reacting species leaving outputs unchanged. +- **Pinned numeric value with a discrimination guard**: a closed-form value or published table entry pinned via `pytest.approx`, plus an explicit follow-up assertion showing that the most plausible wrong formula would differ from the correct one by more than the tolerance. + +Tests that meet one or more of these are tagged `@pytest.mark.physics_invariant`. +The marker is per-function, not module-level: structural tests in the same file (for example, an ordering or pass-through-assignment check) should not carry it. + +Physics sources in CALLIOPE are the five files `chemistry.py`, `oxygen_fugacity.py`, `solubility.py`, `solve.py`, `structure.py`. +Utility sources (`__init__.py`, `_version.py`, `constants.py`) are exempt from the invariant requirement but remain subject to the anti-happy-path rules below. + +## Reference-pinned validation + +Tests that pin behaviour against an external anchor are tagged `@pytest.mark.reference_pinned`. +The anchor is one of: + +- a **published benchmark** (cite paper + figure + table), +- an **analytical limit** (for example, the ideal-gas limit at low pressure, or the Stefan-Boltzmann black-body limit), +- a **cross-implementation check** (CALLIOPE vs atmodeller at a shared Earth fiducial). + +Each of the five physics sources carries at least one reference-pinned test, recorded on the matching `docs/Validation/.md` page. +The current anchors: + +| Source | Anchor | Test | +|---|---|---| +| `chemistry.py` | JANAF Thermochemical Tables[^cite-chase1998] (4th ed.), $K_{eq}$ for $\mathrm{H_2O} \to \mathrm{H_2} + 0.5\,\mathrm{O_2}$ at 2000 K with the O'Neill & Eggins 2002[^cite-oneilleggins2002] buffer | `tests/test_chemistry.py::test_modified_keq_janaf_H2_matches_closed_form_at_2000K_with_oneill` | +| `oxygen_fugacity.py` | Fischer et al. 2011[^cite-fischer2011] (EPSL 304, 496) IW buffer at 2000 K | `tests/test_oxygen_fugacity.py::test_oxygen_fugacity_fischer_value_at_2000K_matches_published_fit` | +| `solubility.py` | Sossi et al. 2023[^cite-sossi2023] peridotite H$_2$O fit; Gaillard et al. 2022[^cite-gaillard2022] (EPSL 117255) S$_2$ fit | `tests/test_solubility.py::TestSolubilityH2O::test_peridotite_default_matches_sossi_2023_fit` and `TestSolubilityS2_xFeO::test_default_call_matches_gaillard_2022_earth_mantle_value` | +| `solve.py` | Self-consistency between `equilibrium_atmosphere` (buffered) and `equilibrium_atmosphere_authoritative_O` (the authoritative-O entry point) at the Earth fiducial | `tests/test_solve.py::test_round_trip_self_consistency_at_earth_fiducial` | +| `structure.py` | Wang, Lineweaver & Ireland 2018[^cite-wang2018] (arxiv:1708.08718) Earth core mass fraction 0.325 | `tests/test_structure.py::test_calculate_mantle_mass_recovers_wang_2018_earth_core_fraction` | + +The marker is not the same thing as physical correctness: a reference-pinned test certifies that *this implementation* reproduces *that anchor*; it does not certify that the anchor is the right physics for every astrophysical regime. + +## One-to-one source-to-test mirroring + +Each source file in `src/calliope/` has a same-named companion in `tests/`: + +| Source | Test | +|---|---| +| `src/calliope/chemistry.py` | `tests/test_chemistry.py` | +| `src/calliope/oxygen_fugacity.py` | `tests/test_oxygen_fugacity.py` | +| `src/calliope/solubility.py` | `tests/test_solubility.py` | +| `src/calliope/solve.py` | `tests/test_solve.py` | +| `src/calliope/structure.py` | `tests/test_structure.py` | +| `src/calliope/__init__.py` | `tests/test_init.py` | +| `src/calliope/constants.py` | (covered in `tests/test_core.py`; constants are imported across many sources) | + +Cross-cutting tests are the documented exception, not the rule: + +- `tests/test_invariants.py`, `tests/test_invariants_hypothesis.py`: contract clauses that span multiple sources (mass closure end-to-end, partial-species behaviour, stoichiometry of an arbitrary recipe). +- `tests/test_authoritative_O.py`, `tests/test_authoritative_O_validation.py`, `tests/test_authoritative_O_monotonicity.py`: the authoritative-O entry point, which touches `solve.py`, `chemistry.py`, and `oxygen_fugacity.py` together. +- `tests/test_equilibrium_paths.py`, `tests/test_partial_species.py`, `tests/test_stoichiometry.py`, `tests/test_targets.py`: solver-architecture tests that span the same three files. + +When a new physics source is added, its 1:1 test file is created at the same time; the matching `docs/Validation/.md` page is added when the first reference-pinned test for that source lands. + +## AST test-quality linter + +`tools/check_test_quality.py` walks `tests/test_*.py` as an AST and enforces seven rules: + +| Rule | What it flags | +|---|---| +| `missing_module_pytestmark` | Test file with no module-level `pytestmark` (a tier marker is required). | +| `missing_docstring` | Test function with no docstring. | +| `single_assert` | Function with exactly one assertion (anti-happy-path: a single assert is rarely enough to discriminate the correct formula from plausible wrong ones). | +| `no_assertions` | Function with zero assertions (only valid for tests that exercise an exception path with `pytest.raises`). | +| `weak_assert_*` (three sub-rules) | Standalone `assert result is not None`, `assert result > 0`, `assert len(result) > 0`, etc., as the sole meaningful check. A weak assertion *alongside* a strong primary one (the sign guard in a discrimination pattern, for example) is not flagged. | +| `float_eq_literal` | `==` adjacent to a numeric literal in a test body (use `pytest.approx`). | +| `missing_importorskip` | An optional dependency (`hypothesis`, `atmodeller`) imported at module top without a preceding `pytest.importorskip('')`. The PR Docker image uses `pip install --no-deps`; without `importorskip`, collection fails. | + +The linter runs in two modes: + +- `python tools/check_test_quality.py --baseline` walks the suite and writes the per-rule violation counts to `tools/test_quality_baseline.json`. + This is the floor. + Regenerate the baseline only after a deliberate sweep that reduced violations. +- `python tools/check_test_quality.py --check` (CI mode) walks the suite, compares the current counts to the baseline, and exits non-zero if any rule's count exceeds the baseline. + The CI workflow runs this and blocks the PR on regression. + +The baseline ratchets one way: the linter refuses to regenerate it if the new total exceeds the old. +Override with `CALLIOPE_TEST_QUALITY_ALLOW_REGRESS=1` only when a new rule was added that surfaces pre-existing violations. + +Two advisory modes report gaps without failing CI: + +- `python tools/check_test_quality.py --reference-pinned-status`: lists physics sources whose matching `tests/test_.py` has no `@pytest.mark.reference_pinned` test. +- `python tools/check_test_quality.py --physics-invariant-status`: lists physics-source tests that assert no invariant and are not tagged `@pytest.mark.physics_invariant`. ## Local commands ```console -pytest -m unit # 96 fast unit tests -pytest -m smoke # 25 minimal-config solver tests -pytest -m integration # 5 full CHNS solves -pytest -m slow # empty in CALLIOPE +pytest -m unit # fast unit tests +pytest -m smoke # minimal-config solver tests +pytest -m integration # full multi-species CHNS solves +pytest -m slow # sweeps and hypothesis fuzz pytest -m "(unit or smoke) and not skip" # PR-gate selection pytest -m "not skip" # everything that should ever run ``` @@ -32,33 +164,72 @@ pytest -m "not skip" # everything that should ever run Coverage: ```console -pytest --cov=src/calliope --cov-report=term -m "not skip" -pytest --cov=src/calliope --cov-report=html -m "not skip" # htmlcov/ +pytest --cov=calliope --cov-report=term -m "not skip" +pytest --cov=calliope --cov-report=html -m "not skip" # htmlcov/ +``` + +Lint and structure: + +```console +bash tools/validate_test_structure.sh # module-level marker validator +python tools/check_test_quality.py --check # AST linter against baseline +python tools/check_test_quality.py --reference-pinned-status +python tools/check_test_quality.py --physics-invariant-status ``` ## Public-facing badges versus internal taxonomy -Public-facing badges (README, project website) collapse `smoke + integration + slow` into a single `Integration Tests` category, because a 4-way taxonomy is confusing to non-developer readers. -The 4-marker internal scheme remains for CI infrastructure granularity: the PR gate runs `(unit or smoke)`, the nightly runs everything, and the test-count badge fetches the JSON files written by the publish-test-badges workflow. +Public-facing badges (README, project website) collapse `smoke + integration + slow` into a single `Integration Tests` category, because a four-way taxonomy is confusing to non-developer readers. +The four-marker internal scheme remains for CI infrastructure granularity: the PR gate runs `(unit or smoke)`, the nightly runs everything, and the test-count badge fetches the JSON files written by the publish-test-badges workflow. ## Badge system -Three JSON files at `.github/badges/tests-{total,unit,integration}.json` are rewritten by `.github/workflows/publish-test-badges.yml` on every push to `main` (paths-filtered to source, tests, tools, and pyproject.toml). +Three JSON files at `.github/badges/tests-{total,unit,integration}.json` are rewritten by `.github/workflows/publish-test-badges.yml` on every push to `main` (paths-filtered to source, tests, tools, and `pyproject.toml`). Shields.io fetches them live via the endpoint URL embedded in the test-count badge. The publish workflow auto-commits the badges with `[skip ci]` and retries the push up to three times to absorb concurrent main-branch updates. -## Coverage gate +## Coverage gates -`[tool.coverage.report] fail_under` in `pyproject.toml` sets the minimum combined line + branch coverage for the nightly run. -The PR gate has a pre-flight step that fetches the base branch's `pyproject.toml` and refuses any PR that drops `fail_under` below the value on `main`; both states tolerate a base branch with no `fail_under` declared. -The 90 % ceiling caps the ratchet so that defensive paths do not become unfailable. +Two gates are declared in `pyproject.toml`: + +| Gate | Tests included | Threshold key | Where it runs | +|---|---|---|---| +| Fast | `unit + smoke` | `[tool.calliope.coverage_fast].fail_under` | PR `Run unit + smoke tests` step | +| Full | `unit + smoke + integration + slow` | `[tool.coverage.report].fail_under` | Nightly `coverage report` | + +Both gates ratchet toward 90 %, capped at 90 % (`tools/update_coverage_threshold.py` enforces `ECOSYSTEM_CEILING = 90.0`); neither may be manually decreased. +The PR gate has a pre-flight step that fetches the base branch's `pyproject.toml` and rejects any PR that drops `[tool.coverage.report].fail_under` below `min(base, 90.0)`. +A one-time ratchet down to the 90 % ceiling is allowed; any drop below 90 % is blocked. ## Coverage union estimation -The PR gate downloads the most recent `nightly-coverage` artifact from `main` (`coverage.xml + coverage.json + nightly-timestamp.txt`, 14 d retention) and line-ORs the unit-tier coverage with the nightly's full coverage to produce a union estimate. +The PR gate downloads the most recent `nightly-coverage` artifact from `main` (`coverage.xml + coverage.json + nightly-timestamp.txt`, 14-day retention) and line-ORs the unit-tier coverage with the nightly's full coverage to produce a union estimate. The result is written to `$GITHUB_STEP_SUMMARY` as informational output; it does not gate the PR. -A staleness threshold of 48 h and a grace band of 0.3 % apply to the warn / fail / ok decision. +A staleness threshold of 48 hours and a grace band of 0.3 % apply to the warn / fail / ok decision. + +## PR validation pipeline + +`.github/workflows/tests.yaml` runs on every PR (`if: draft == false`) over a 2-OS by 2-Python matrix (`ubuntu-latest`, `macos-latest` x `3.12`, `3.13`). +The full step sequence: + +1. **Validate test markers** (`bash tools/validate_test_structure.sh`): rejects any test file without a module-level `pytestmark`. +2. **Run test-quality lint** (`python tools/check_test_quality.py --check`): blocking; rejects regression against `tools/test_quality_baseline.json`. +3. **Pre-flight fail_under ratchet check**: rejects any PR that drops `[tool.coverage.report].fail_under` below `min(base, 90.0)`. +4. **Run unit + smoke tests**: `pytest -m "(unit or smoke) and not skip" --cov=calliope --cov-fail-under=${FAST_FAIL_UNDER}`, where `FAST_FAIL_UNDER` is read live from `[tool.calliope.coverage_fast].fail_under`. + +Steps 1, 2, and 3 are gated to `ubuntu-latest, python 3.12` to avoid four redundant runs; step 4 runs across the full matrix. + +Nightly (`.github/workflows/nightly.yml`) runs the full suite, uploads coverage to Codecov, and enforces `--cov-fail-under=90`. ## Canonical specification The repository-wide rules that every PROTEUS-ecosystem submodule follows are at [proteus-framework.org/PROTEUS/Explanations/ecosystem_testing_standard/](https://proteus-framework.org/PROTEUS/Explanations/ecosystem_testing_standard/). + +## References + +[^cite-chase1998]: M. W. Chase, *[NIST-JANAF Thermochemical Tables, 4th edition](https://janaf.nist.gov/)*, Journal of Physical and Chemical Reference Data Monograph 9, 1998. +[^cite-fischer2011]: R. A. Fischer, A. J. Campbell, G. A. Shofner, O. T. Lord, P. Dera, V. B. Prakapenka, *[Equation of state and phase diagram of FeO](https://doi.org/10.1016/j.epsl.2011.02.025)*, Earth and Planetary Science Letters, 304, 496-502, 2011. [SciX](https://scixplorer.org/abs/2011E%26PSL.304..496F/abstract). +[^cite-gaillard2022]: F. Gaillard, F. Bernadou, M. Roskosz, M. A. Bouhifd, Y. Marrocchi, G. Iacono-Marziano, M. Moreira, B. Scaillet, G. Rogerie, *[Redox controls during magma ocean degassing](https://doi.org/10.1016/j.epsl.2021.117255)*, Earth and Planetary Science Letters, 577, 117255, 2022. [SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract). +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151-181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). +[^cite-sossi2023]: P. A. Sossi, P. M. E. Tollan, J. Badro, D. J. Bower, *[Solubility of water in peridotite liquids and the prevalence of steam atmospheres on rocky planets](https://doi.org/10.1016/j.epsl.2022.117894)*, Earth and Planetary Science Letters, 601, 117894, 2023. [SciX](https://scixplorer.org/abs/2023E%26PSL.60117894S/abstract). +[^cite-wang2018]: H. S. Wang, C. H. Lineweaver, T. R. Ireland, *[The elemental abundances (with uncertainties) of the most Earth-like planet](https://doi.org/10.1016/j.icarus.2017.08.024)*, Icarus, 299, 460-474, 2018. [SciX](https://scixplorer.org/abs/2018Icar..299..460W/abstract). diff --git a/docs/How-to/authoritative_oxygen.md b/docs/How-to/authoritative_oxygen.md new file mode 100644 index 0000000..226a24a --- /dev/null +++ b/docs/How-to/authoritative_oxygen.md @@ -0,0 +1,127 @@ +# Solve with an oxygen budget (authoritative-O mode) + +This page shows how to call `equilibrium_atmosphere_authoritative_O` directly from Python, the inverse of the buffered-mode workflow on the [Usage page](usage.md). Use this entry point when you have a total oxygen mass to enforce as a budget rather than an oxygen fugacity to apply as a buffer. + +For the science behind the mode and the augmented mass-balance system, see [Authoritative-oxygen mode](../Explanations/authoritative_oxygen.md). + +## Recipe 1 - solve from a 5-element inventory + +The minimum call signature: supply a `target_d` with five element keys (H, C, N, S, O in kilograms), the same `ddict` you would build for the buffered mode, and a sensible `fO2_hint`. + +```python +from calliope.solve import equilibrium_atmosphere_authoritative_O +from calliope.constants import volatile_species + +ddict = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': 1.0, + 'T_magma': 2500.0, + # NOTE: fO2_shift_IW is ignored by this entry point; the solver + # determines it. Provide it only if you also intend to call the + # buffered entry point against the same ddict. +} +for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + +target_d = { + 'H': 1.55e20, # ~1 Earth ocean of H + 'C': 1.55e19, # C/H ~ 0.1 + 'N': 8.06e18, # 2 ppmw vs mantle + 'S': 8.06e20, # 200 ppmw vs mantle + 'O': 1.4e21, # the budgeted O +} + +result = equilibrium_atmosphere_authoritative_O( + target_d, ddict, fO2_hint=4.0, print_result=True, +) + +print(f"Derived fO2_shift : {result['fO2_shift_derived']:+.3f} dex") +print(f"O residual : {result['O_res']:.2e} kg") +print(f"Surface pressure : {result['P_surf']:.2f} bar") +print(f"H2O bar : {result['H2O_bar']:.2f}") +``` + +The returned `result` dict carries every field `equilibrium_atmosphere` returns (per-species `_bar`, `_vmr`, `_kg_atm`, `_kg_liquid`, `_kg_solid`, `_kg_total`; per-element `_kg_atm`, `_res`; total `P_surf`, `M_atm`, `atm_kg_per_mol`) plus two new keys: `fO2_shift_derived` for the converged $\Delta\mathrm{IW}$ and `O_res` for the fifth mass-balance residual. + +## Recipe 2 - round-trip from buffered mode + +The cleanest sanity check on a new dataset is to confirm that the buffered and authoritative-O modes are dual: solve once in buffered mode, take the resulting O total as the new O target, and re-solve in authoritative-O mode. The recovered $\Delta\mathrm{IW}$ should match the original to within solver tolerance. + +```python +from calliope.solve import ( + equilibrium_atmosphere, + equilibrium_atmosphere_authoritative_O, + get_target_from_params, +) + +target_HCNS = get_target_from_params(ddict) + +# Step 1: buffered mode at fO2_shift_IW = +4 +ddict['fO2_shift_IW'] = 4.0 +result_buffered = equilibrium_atmosphere(target_HCNS, ddict, print_result=False) + +# Step 2: take the implied O total and re-solve in authoritative-O mode +target_HCNSO = dict(target_HCNS, O=result_buffered['O_kg_total']) +result_auth = equilibrium_atmosphere_authoritative_O( + target_HCNSO, ddict, fO2_hint=4.0, print_result=False, +) + +assert abs(result_auth['fO2_shift_derived'] - 4.0) < 0.01 +``` + +This pattern is the basis of the `test_authoritative_O_round_trip` regression test. Failing it at non-extreme $\Delta\mathrm{IW}$ typically indicates a bug in one of the solubility laws or the speciation tree. + +## Recipe 3 - warm-start the solver via `p_guess` + +When you have a converged solution from a previous call (the typical pattern inside an iterative loop), pass it in via `p_guess` to skip the cold-start Monte-Carlo draw: + +```python +p_guess = { + 'H2O': result['H2O_bar'], + 'CO2': result['CO2_bar'], + 'N2': result['N2_bar'], + 'S2': result['S2_bar'], + 'fO2_shift_IW': result['fO2_shift_derived'], # optional, defaults to fO2_hint +} + +result_new = equilibrium_atmosphere_authoritative_O( + target_d_new, ddict_new, p_guess=p_guess, print_result=False, +) +``` + +If you omit the `'fO2_shift_IW'` key in `p_guess`, the solver falls back to `fO2_hint` for the fifth unknown. Supplying it from the previous call's `fO2_shift_derived` is the right choice when the inputs change slowly between calls (small time step, small budget perturbation): convergence then needs ${\sim}1$ to ${\sim}3$ `fsolve` iterations. + +## Recipe 4 - reproducibility for regression tests + +The Monte-Carlo restart draws nondeterministic guesses by default. Pass `random_seed=` to make the solver reproducible across runs to solver tolerance: + +```python +import math + +result_a = equilibrium_atmosphere_authoritative_O( + target_d, ddict, fO2_hint=4.0, random_seed=42, print_result=False, +) +result_b = equilibrium_atmosphere_authoritative_O( + target_d, ddict, fO2_hint=4.0, random_seed=42, print_result=False, +) +assert math.isclose( + result_a['fO2_shift_derived'], result_b['fO2_shift_derived'], abs_tol=1e-8, +) +``` + +The seed fixes the Monte-Carlo guess sequence; the inner `fsolve` and `trust-constr` calls are deterministic in their own right, so two seeded runs converge to the same root within `xtol`. For regression tests that compare against checked-in golden values, this is the right level of reproducibility. For production runs, leave `random_seed=None` (the default) so restart draws use the global `np.random` state. + +## Pitfalls + +- **`KeyError: target_d is missing required element keys: ['O']`** - you forgot to include `'O'` in `target_d`. The four-element dict that `get_target_from_params` returns is the buffered-mode input; in authoritative-O mode you have to add the O budget yourself. +- **`ValueError: fO2_hint=...` outside `[-12, +12]`** - the hint must be a finite real number in the solver-bounds window. Common values lie in $[-4, +6]$; values outside $[-12, +12]$ are rejected up front because they almost always indicate a unit confusion (e.g. passing absolute $\log_{10} f_{\mathrm{O}_2}$ instead of the IW-buffer offset). +- **`RuntimeError: Could not find solution ... (max attempts: N)`** - the Monte-Carlo restart loop exhausted. The integer `N` is whatever `nguess` was set to ($7500$ if the entry point is called directly with defaults; $1000$ if called through the PROTEUS wrapper). The failure message includes the final-attempt partial pressures and $\Delta\mathrm{IW}$. The diagnostic question is whether the target O budget is physically reachable at the supplied $(H, C, N, S, T_\mathrm{magma}, \Phi_\mathrm{global})$. Bumping `nguess` rarely helps; investigate whether the upstream inventory is consistent first. +- **Extrapolated solubility laws** - the entry point does not flag extrapolation. The Dasgupta N$_2$ and Gaillard S$_2$ laws carry $\ln f_{\mathrm{O}_2}$ terms with finite calibration footprints; outside those footprints the dissolved-N and dissolved-S branches of the O balance are uncertain. Consult the [validity envelope](../Explanations/solubility.md#validity-envelope) before interpreting results at unusual $T_\mathrm{magma}$ or $\Delta\mathrm{IW}$. +- **`p_guess` missing one of `'H2O'`, `'CO2'`, `'N2'`, `'S2'`** - the four primary keys are required. A missing `'fO2_shift_IW'` is fine and just falls back to `fO2_hint`. Non-finite values in any key raise `ValueError`. + +## Next step + +For the role of this entry point inside a PROTEUS-coupled simulation, see [Coupling to PROTEUS](proteus_coupling.md). For the mathematical setup, see [Authoritative-oxygen mode](../Explanations/authoritative_oxygen.md). diff --git a/docs/How-to/build_tests.md b/docs/How-to/build_tests.md index bc53980..b98d054 100644 --- a/docs/How-to/build_tests.md +++ b/docs/How-to/build_tests.md @@ -5,56 +5,249 @@ [![Integration Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/nightly.yml?branch=main&label=Integration%20Tests)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/nightly.yml) [![tests](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/FormingWorlds/CALLIOPE/main/.github/badges/tests-total.json)](https://proteus-framework.org/testing) -This page is for contributors who are adding a new test to CALLIOPE. -For background on the marker scheme and the badge system, see the [testing suite](../Explanations/testing.md) explanation. +This page is the practical contributor guide for adding or modifying a test in CALLIOPE. +The conceptual framing (marker scheme, badge system, coverage gates, AST linter) lives in the [testing suite](../Explanations/testing.md) explainer; read it first if you have not yet. ## Run the suite locally -From the repository root, with `pip install -e .[develop]` already done: +From the repository root, with `pip install -e ".[develop]"` already done: ```console pytest # everything pytest can collect -pytest -m unit # 96 fast unit tests -pytest -m smoke # 25 minimal-config solver tests -pytest -m integration # 5 full CHNS solves +pytest -m unit # fast unit tests +pytest -m smoke # minimal-config solver tests +pytest -m integration # full multi-species CHNS solves +pytest -m slow # sweeps and hypothesis fuzz pytest -m "(unit or smoke) and not skip" # PR-gate selection ``` A single test file or a single test: ```console -pytest tests/test_core.py -pytest tests/test_stoichiometry.py::TestEquilibriumChemistry::test_SO2_equilibrium +pytest tests/test_chemistry.py +pytest tests/test_chemistry.py::test_modified_keq_janaf_H2_matches_closed_form_at_2000K_with_oneill ``` -To stop at the first failure and show local variables: +Stop at first failure and show local variables: ```console pytest -x --showlocals ``` -## Choose a marker +## Where the test goes -Apply exactly one marker per test, either at module level (`pytestmark = pytest.mark.X`) or per class (`@pytest.mark.X`): +CALLIOPE follows a 1:1 source-to-test mirroring rule: each file in `src/calliope/` has a same-named companion in `tests/`. -- `unit`: in-process tests of an individual helper, equilibrium-constant fit, solubility law, or structure formula. No call into `equilibrium_atmosphere`. Sub-second. -- `smoke`: real `equilibrium_atmosphere` call on a minimal configuration. Sub-30 s. +- New unit test for a function in `src/calliope/oxygen_fugacity.py` → `tests/test_oxygen_fugacity.py`. +- New unit test for a function in `src/calliope/solubility.py` → `tests/test_solubility.py`. + +The cross-cutting test files are the documented exception: + +- `tests/test_invariants.py`, `tests/test_invariants_hypothesis.py`: contract clauses that span multiple sources (mass closure end-to-end, partial-species behaviour, stoichiometry of an arbitrary recipe). +- `tests/test_authoritative_O*.py`, `tests/test_equilibrium_paths.py`, `tests/test_partial_species.py`, `tests/test_stoichiometry.py`, `tests/test_targets.py`: solver-architecture tests that span `solve.py`, `chemistry.py`, and `oxygen_fugacity.py` together. + +Place a test in a cross-cutting file only when it genuinely cannot be expressed against a single source. + +## Module-level marker and timeout + +Every test file begins with: + +```python +import pytest + +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] +``` + +The timeouts per tier: + +| Tier | Timeout | +|---|---| +| `unit` | 30 s | +| `smoke` | 60 s | +| `integration` | 300 s | +| `slow` | 3600 s | + +Per-function markers (`@pytest.mark.skip` on a stale parametrisation, for example) are additive and do not replace the module-level marker. + +Choose the tier from the actual content of the test: + +- `unit`: in-process logic, equilibrium-constant fits, solubility laws, structure formulae. No call into `equilibrium_atmosphere`. Should run in under 100 ms. +- `smoke`: real `equilibrium_atmosphere` call on a minimal configuration. Under 30 s. - `integration`: full multi-species CHNS solve with mass-conservation invariants. -- `slow`: parameter sweeps and convergence studies that exceed 1 minute wall. +- `slow`: parameter sweeps and convergence studies that exceed one minute wall. + +If a slower test would push the PR gate over its 10-minute budget, mark it `integration` or `slow` and rely on the nightly suite. + +## Anti-happy-path rules + +Every new test function must contain: + +1. **At least one edge case**: a boundary value, an empty input, an extreme physical parameter (`T` at the calibration window edges, `p` near zero, mass fractions near 0 and 1). +2. **At least one path that exercises the error contract**: a documented exception, a guard return, or a graceful clamp. If the function under test has no validation, exercise the limit-input behaviour and assert the mathematical invariant ($e = 0$ for an eccentricity-dependent routine, $T \to 0$ for a Boltzmann factor). +3. **Assertion values that are not trivially derivable from the implementation**: discriminating numeric pins, property-based assertions (monotonicity, conservation, symmetry). Avoid point checks at $T = 1$ where $T^n$ is the same for every $n$. + +Forbidden patterns flagged by the AST linter: + +- A single-assert test function. +- A standalone weak assertion (`assert result is not None`, `assert result > 0`, `assert len(result) > 0`, `assert isinstance(result, dict)`) as the sole meaningful check. A weak assertion *alongside* a strong primary one (a sign guard next to a `pytest.approx` value pin, for example) is allowed and is not flagged. +- A test with no function-level docstring. +- `==` adjacent to a float literal. +- A test asserting on a fixture's implicit default. + +## The discrimination guard + +A pinned numeric value alone does not discriminate the correct formula from the most plausible wrong one. +The discrimination guard is a follow-up assertion that names the wrong formula and shows the gap is larger than the tolerance. + +Example from `tests/test_oxygen_fugacity.py`: + +```python +def test_oxygen_fugacity_fischer_value_at_2000K_matches_published_fit(): + """At T=2000 K the Fischer 2011 IW buffer evaluates to -7.14981. + + Discrimination: the O'Neill & Eggins 2002 buffer at the same T + gives -7.4078, which differs from Fischer by 0.26 dex. The pin + tolerance is 1e-4, so the wrong buffer would not pass. + """ + fischer_2000 = log10_fO2_IW_fischer_2011(2000.0) + assert fischer_2000 == pytest.approx(-7.14981, rel=1e-4) + + # Discrimination guard against the most plausible wrong formula. + oneill_2000 = log10_fO2_IW_oneill_2002(2000.0) + assert abs(fischer_2000 - oneill_2000) > 0.2 +``` + +For a solubility law, the guard names the alternative law: + +```python +# Discrimination guard against Dixon et al. 1995 (Hawaiian basalt): +dixon = 965.0 * (p_bar ** 0.5) +assert abs(sossi - dixon) > 1000.0 # ppmw at p = 100 bar +``` + +For a stoichiometric closure, the guard names the most plausible off-by-one: + +```python +# Discrimination guard: the wrong stoichiometry (forgetting the 0.5 on O2) +# would give 2.5 instead of 1.0 on the LHS. +assert abs(lhs - 1.0) > 0.5 +``` + +## Physics-invariant marker + +Tag any test on a physics source that asserts one of the four invariant families with `@pytest.mark.physics_invariant`. +The marker is per-function, not module-level. + +The four families with one-line examples: + +| Family | Example | +|---|---| +| Conservation | `assert kg_atm + kg_liquid + kg_solid == pytest.approx(kg_total, rel=1e-6)` | +| Positivity / boundedness | `assert 0.0 <= mole_frac <= 1.0 for mole_frac in result.values()` | +| Monotonicity / symmetry | `assert log10_fO2(T=2000) < log10_fO2(T=1500)` along an isobaric buffer | +| Pinned value with discrimination guard | (see the example above) | + +The five physics sources (`chemistry.py`, `oxygen_fugacity.py`, `solubility.py`, `solve.py`, `structure.py`) must each carry at least one such test in the matching `tests/test_.py`. +The utility sources (`__init__.py`, `_version.py`, `constants.py`) are exempt from the invariant requirement but still subject to the anti-happy-path rules above. + +Structural tests (ordering, autonomy, mutation-in-place, pass-through assignment) in a physics-source test file should not carry the marker. + +## Reference-pinned marker + +Tag tests that pin behaviour against an external anchor with `@pytest.mark.reference_pinned`. +The anchor is one of: + +- a **published benchmark** (cite paper + figure + table in the docstring), +- an **analytical limit** (the ideal-gas limit at low pressure, the Stefan-Boltzmann black-body limit), +- a **cross-implementation check** (CALLIOPE vs atmodeller at a shared fiducial). + +Each of the five physics sources carries at least one reference-pinned test. +The current anchor list is in the [testing suite](../Explanations/testing.md#reference-pinned-validation) explainer. + +When the first reference-pinned test for a new source lands, create the matching `docs/Validation/.md` page with: + +- the source under test, +- the cited paper or analytical limit, +- the closed-form re-derivation of the pinned value (no skipped algebra), +- the wrong-formula or wrong-buffer discrimination number. + +Tests carrying `reference_pinned` typically also carry `physics_invariant` (the published-value pin is itself the invariant). + +## Optional-dependency imports + +Tests that import an optional dependency call `pytest.importorskip('')` at module top, before the import: + +```python +import pytest +hypothesis = pytest.importorskip('hypothesis') +from hypothesis import given, strategies as st +``` + +The optional dependencies recognised by the linter: -The PR gate runs `(unit or smoke) and not skip`. Mark a slower test as `integration` or `slow` if it would push the gate over the 10-minute budget. +- `hypothesis` (property-based fuzz tests), +- `atmodeller` (cross-backend comparison runners). -## Test-quality rules +The PR Docker image installs with `pip install --no-deps`; without `importorskip`, a top-level `import hypothesis` makes the whole test module fail to collect even though the rest of the suite would run. +This trap has recurred multiple times and is now linter-enforced. -Every new test should: +## Float comparisons -1. Cover at least one **edge case** (boundary in $T$, $f_{\mathrm{O}_2}$, $\Phi$, or $p$); -2. Cover at least one **physically unreasonable** input that must raise (negative pressure, $T \le 0$, mass fraction above 1); -3. Use **discriminating values**: pick inputs where the correct formula gives a different answer than the most plausible wrong formulas (avoid $T = 1$ where $T^n$ is the same for all $n$); -4. Compare against an **analytical expectation**, not against the model output frozen at some point in time; -5. Use `pytest.approx` (or `np.testing.assert_allclose`) for all float comparisons. +Never use `==` for floats. +Use `pytest.approx(val, rel=1e-5)` (or `abs=...`) or `numpy.testing.assert_allclose(actual, expected, rtol=..., atol=...)`. -The full rule set lives in `.claude/rules/proteus-tests.md` in the PROTEUS repo and applies to every PROTEUS submodule. +```python +assert fischer_2000 == pytest.approx(-7.14981, rel=1e-4) +np.testing.assert_allclose(result, expected, rtol=1e-6) +``` + +State the tolerance rationale in a comment when the choice is non-obvious: + +```python +# rtol=1e-3 because the Cp lookup truncates to 4 significant figures. +``` + +## Mocking discipline + +Default to `unittest.mock` for all external calls in unit tests: atmodeller, file I/O, network. +Mock at the narrowest scope: a specific function, not a whole module. + +```python +from unittest.mock import patch + +@patch('calliope.solve.atmodeller_solve_external') +def test_authoritative_O_round_trip(mock_external): + mock_external.return_value = _physically_plausible_fixture() + ... +``` + +A mocked physics function must return physically plausible values; a mock that returns `0.0` or `1.0` for everything can mask real bugs. +Never mock the function under test. +Smoke, integration, and slow tiers use the real solver. + +## Module-level constants and `monkeypatch` + +When the source under test reads an environment variable into a module-level constant at import time: + +```python +DATA_DIR = Path(os.environ.get('CALLIOPE_DATA', ...)) +``` + +`monkeypatch.setenv` is not sufficient because the constant is frozen at the import that already happened. +Patch the constant directly: + +```python +monkeypatch.setattr('calliope.solve.DATA_DIR', tmp_path, raising=False) +``` + +Patch both the env var (for downstream code that re-reads it) and the constant (for code that reads only the constant). + +## Test docstring style + +Every test function carries a one-line docstring (enforced by the test-quality lint). +The docstring states the physical scenario or contract clause the test verifies, in plain language a non-developer reader can follow. +Inline comments explain why a specific input range was chosen ("`T = 300 K` and `T = 1500 K` so the `T**3` vs `T**4` difference is resolved well above the tolerance"). +Avoid em-dashes and en-dashes in test prose; use commas, semicolons, colons, or parentheses instead. ## Marker validation @@ -65,13 +258,83 @@ Run it locally before pushing: bash tools/validate_test_structure.sh ``` +## Test-quality lint + +`tools/check_test_quality.py` is an AST linter that walks `tests/test_*.py` and enforces seven rules (single-assert, weak-assertion, missing docstring, float-eq-literal, missing module-level pytestmark, no assertions, missing importorskip). +It runs in two modes: + +```console +python tools/check_test_quality.py --check # CI mode: compare to baseline +python tools/check_test_quality.py --baseline # regenerate baseline +``` + +The baseline (`tools/test_quality_baseline.json`) records the current per-rule violation counts and is the floor. +`--check` exits non-zero if any rule's count exceeds the baseline; the CI workflow blocks the PR on that exit code. +Regenerate the baseline only after a deliberate sweep that reduced violations. +The script refuses to regenerate if the new total exceeds the old; override with `CALLIOPE_TEST_QUALITY_ALLOW_REGRESS=1` only when a new rule was added that surfaces pre-existing violations. + +Two advisory modes report gaps without failing CI: + +```console +python tools/check_test_quality.py --reference-pinned-status +python tools/check_test_quality.py --physics-invariant-status +``` + +The first lists physics sources whose matching `tests/test_.py` has no `@pytest.mark.reference_pinned` test. +The second lists physics-source tests that assert no invariant and are not tagged `@pytest.mark.physics_invariant`. + +## Coverage and the ratchet + +The PR gate enforces a fast coverage threshold read live from `[tool.calliope.coverage_fast].fail_under` in `pyproject.toml`: + +```console +FAST_FAIL_UNDER=$(python -c "import tomllib; print(tomllib.load(open('pyproject.toml','rb'))['tool']['calliope']['coverage_fast']['fail_under'])") +pytest -m "(unit or smoke) and not skip" --cov=calliope --cov-fail-under=${FAST_FAIL_UNDER} +``` + +The nightly gate enforces `[tool.coverage.report].fail_under` over the full tier set. +Both thresholds ratchet upward, capped at 90 % (`tools/update_coverage_threshold.py` enforces `ECOSYSTEM_CEILING = 90.0`); neither may be manually decreased. + +```console +python tools/update_coverage_threshold.py # one-way ratchet +``` + +A pre-flight PR step rejects any change that drops `[tool.coverage.report].fail_under` below `min(base, 90.0)`. + +## Adding a new physics source + +When a new `src/calliope/.py` lands: + +1. Create the matching `tests/test_.py` with the module-level `pytestmark` (typically `unit` with a 30 s timeout). +2. Write at least one test that asserts one of the four invariant families and tag it `@pytest.mark.physics_invariant`. +3. Plan a `@pytest.mark.reference_pinned` test against a published benchmark, analytical limit, or cross-implementation check. +4. When that test lands, create `docs/Validation/.md` with the anchor, the re-derivation, and the discrimination number. Link it from `mkdocs.yml` if the docs site should surface it. +5. Update `PHYSICS_SOURCES` in `tools/check_test_quality.py` to include the new file name. +6. Run the full PR checks locally: + +```console +ruff check --fix src/ tests/ +ruff format src/ tests/ +bash tools/validate_test_structure.sh +python tools/check_test_quality.py --check +python tools/check_test_quality.py --reference-pinned-status +pytest -m "(unit or smoke) and not skip" --cov=calliope +``` + ## Linting CALLIOPE uses [ruff](https://docs.astral.sh/ruff/): ```console -ruff check . -ruff format --check . +ruff check src/ tests/ +ruff format --check src/ tests/ +ruff check --fix src/ tests/ # auto-fix +ruff format src/ tests/ # auto-format ``` -Both run on every commit via the pre-commit hook (`pre-commit install` after `pip install -e .[develop]`) and again in the code-style CI workflow. +Both run on every commit via the pre-commit hook (`pre-commit install -f` after `pip install -e ".[develop]"`) and again in the code-style CI workflow. + +## See also + +- [Testing suite](../Explanations/testing.md): the conceptual framing of the marker scheme, badges, coverage gates, and AST linter. +- [PROTEUS ecosystem testing standard](https://proteus-framework.org/PROTEUS/Explanations/ecosystem_testing_standard/): the repository-wide rules that every PROTEUS-ecosystem submodule follows. diff --git a/docs/How-to/configuration.md b/docs/How-to/configuration.md index 33c7ce1..a3a6bce 100644 --- a/docs/How-to/configuration.md +++ b/docs/How-to/configuration.md @@ -50,7 +50,7 @@ When CALLIOPE is called from PROTEUS, the wrapper in `proteus.outgas.calliope` s ## The `target` dictionary -`target` carries the elemental conservation constraints, in kilograms: +`target` carries the elemental conservation constraints, in kilograms. For the buffered-mode entry point [`equilibrium_atmosphere`](usage.md), the dict has four keys: ```python target = { @@ -61,14 +61,28 @@ target = { } ``` -CALLIOPE solves a four-equation system that requires the sum of dissolved + atmospheric mass of each element to equal the target value. Oxygen is **not** a free constraint: it is set by $f_{\mathrm{O}_2}$ and the speciation of the H, C, N, S equilibria. +In this mode, CALLIOPE solves a four-equation system requiring the sum of dissolved + atmospheric mass of each element to equal the target value. Oxygen is **not** a free constraint: it is set by the user-supplied $\Delta\mathrm{IW}$ and the speciation of the H/C/N/S equilibria. -You can build `target` two ways: +For the [authoritative-oxygen entry point](../Explanations/authoritative_oxygen.md), the dict has a fifth key: + +```python +target = { + 'H': 1.0e20, # total H mass [kg] + 'C': 1.0e17, + 'N': 1.0e17, + 'S': 1.0e16, + 'O': 1.4e21, # total O mass [kg] +} +``` + +CALLIOPE then solves a five-equation system for the four primary pressures plus $\Delta\mathrm{IW}$; the value in `ddict['fO2_shift_IW']` is ignored. + +You can build the four-key `target` two ways: - `get_target_from_params(ddict)`: derives it from `hydrogen_earth_oceans`, `CH_ratio`, `nitrogen_ppmw`, `sulfur_ppmw`. Used when you specify the planet's bulk composition as an inventory. - `get_target_from_pressures(ddict)`: derives it from `_initial_bar` by computing the implied dissolved + atmospheric masses at $t=0$. Used when you want to specify the planet by its initial atmospheric composition. -The PROTEUS-side `volatile_mode` config knob switches between these two paths. +The PROTEUS-side `volatile_mode` config knob switches between these two paths. For the authoritative-O mode, the caller is responsible for supplying the O budget alongside the H/C/N/S targets. ## Solver tuning knobs diff --git a/docs/How-to/proteus_coupling.md b/docs/How-to/proteus_coupling.md index 62301e6..1820713 100644 --- a/docs/How-to/proteus_coupling.md +++ b/docs/How-to/proteus_coupling.md @@ -86,20 +86,137 @@ S2 = 0.01 When `volatile_mode = "gas_prs"`, the wrapper calls `get_target_from_pressures(ddict)` to back out the elemental inventory implied by the prescribed initial atmosphere; on every subsequent iteration the same elemental inventory is preserved. +## Selecting the fO2 dispatch + +The PROTEUS schema field `[planet].fO2_source` selects which CALLIOPE entry point the wrapper calls. The two paths share the same physics; they differ only in which quantity is supplied as input and which is solved for. + +| `fO2_source` | Input | Solved for | Entry point | +|------------------|-------------------|------------------|--------------------------------------------| +| `user_constant` | $\Delta\mathrm{IW}$ | atmospheric + dissolved O | `equilibrium_atmosphere` | +| `from_O_budget` | total O mass | $\Delta\mathrm{IW}$ | `equilibrium_atmosphere_authoritative_O` | + +Under `user_constant` (the default) CALLIOPE buffers the redox state to the configured `outgas.fO2_shift_IW` and solves the four-equation H/C/N/S mass balance. The resulting O mass is whatever the equilibrium chemistry requires at that buffer, and is written into `hf_row['O_kg_total']` so the rest of PROTEUS can read it. Under `from_O_budget` the wrapper passes the running whole-planet O total (maintained by the PROTEUS element-budget bookkeeping) as a fifth elemental target, uses `outgas.fO2_shift_IW` only as an initial-guess hint, and solves a five-equation system that returns the derived $\Delta\mathrm{IW}$ in `hf_row['fO2_shift_IW_derived']`. + +See [Coupling to PROTEUS (theory)](../Explanations/proteus_coupling.md#step-5-call-the-solver) for the per-iteration control flow and [Authoritative-oxygen mode](../Explanations/authoritative_oxygen.md) for the augmented five-residual mass balance. + +### Worked example: buffered fO2 mode (`user_constant`) + +In this mode the user fixes $\Delta\mathrm{IW}$ and the chemistry returns the O budget. Set `O_mode = "ic_chemistry"` so the wrapper does not pre-populate an O target: the first outgas call writes one in, and PROTEUS carries it from there. + +```toml +config_version = "3.0" + +[orbit] + semimajoraxis = 1.0 # [AU] + +[planet] + mass_tot = 1.0 # [M_earth] + volatile_mode = "elements" + fO2_source = "user_constant" # buffered mode (the default) + + [planet.elements] + H_mode = "oceans" + H_budget = 1.0 # [Earth oceans] + C_mode = "C/H" + C_budget = 0.1 # [C/H mass ratio] + N_mode = "ppmw" + N_budget = 2.0 + S_mode = "ppmw" + S_budget = 200.0 + O_mode = "ic_chemistry" # O is derived from the chemistry + O_budget = 0.0 # ignored under ic_chemistry + +[outgas] + module = "calliope" + fO2_shift_IW = 4.0 # [log10] redox buffer offset (input) + + [outgas.calliope] + include_H2O = true + include_CO2 = true + include_N2 = true + include_S2 = true + include_H2 = true + include_CH4 = true + include_CO = true + include_SO2 = true + include_H2S = true + include_NH3 = true + solubility = true +``` + +What the wrapper does on the first call: builds the four-element target $(m_\mathrm{H}, m_\mathrm{C}, m_\mathrm{N}, m_\mathrm{S})$ from `H_budget` etc.; calls `equilibrium_atmosphere` with `fO2_shift_IW = 4.0`; reads back the four primary partial pressures, the seven secondary species, the derived `O_kg_total`, and writes them all into `hf_row`. Subsequent iterations warm-start from the previous-iteration `_bar` values and follow the same path. + +### Worked example: authoritative-O mode (`from_O_budget`) + +In this mode the user fixes the total O mass and the chemistry returns the derived $\Delta\mathrm{IW}$. The `O_mode` is one of `"ppmw"`, `"kg"`, or `"FeO_mantle_wt_pct"` (never `"ic_chemistry"`, which the config-level validator rejects when `fO2_source = "from_O_budget"` because the chemistry needs a target to invert against). The `outgas.fO2_shift_IW` value becomes the initial-guess hint for the solver, not the buffered redox state; pick a value near the expected derived $\Delta\mathrm{IW}$ for fast convergence, but the solver tolerates a poor hint at the cost of more Monte-Carlo restarts. + +```toml +config_version = "3.0" + +[orbit] + semimajoraxis = 1.0 # [AU] + +[planet] + mass_tot = 1.0 # [M_earth] + volatile_mode = "elements" + fO2_source = "from_O_budget" # authoritative-O mode + + [planet.elements] + H_mode = "oceans" + H_budget = 1.0 # [Earth oceans] + C_mode = "C/H" + C_budget = 0.1 # [C/H mass ratio] + N_mode = "ppmw" + N_budget = 2.0 + S_mode = "ppmw" + S_budget = 200.0 + O_mode = "FeO_mantle_wt_pct" # interpret O_budget as mantle FeO wt% + O_budget = 8.0 # 8.0 wt% FeO ~ Earth's modern mantle + +[outgas] + module = "calliope" + fO2_shift_IW = 4.0 # [log10] initial-guess HINT, not buffer + + [outgas.calliope] + include_H2O = true + include_CO2 = true + include_N2 = true + include_S2 = true + include_H2 = true + include_CH4 = true + include_CO = true + include_SO2 = true + include_H2S = true + include_NH3 = true + solubility = true +``` + +What the wrapper does on the first call: builds the five-element target $(m_\mathrm{H}, m_\mathrm{C}, m_\mathrm{N}, m_\mathrm{S}, m_\mathrm{O})$, with $m_\mathrm{O}$ derived from `O_mode = "FeO_mantle_wt_pct"` and `O_budget = 8.0` via $m_\mathrm{O} = M_\mathrm{O}/M_\mathrm{FeO} \times \mathrm{wt\%}/100 \times M_\mathrm{mantle} \approx 0.2227 \times 0.08 \times M_\mathrm{mantle}$; calls `equilibrium_atmosphere_authoritative_O` with `fO2_hint = 4.0`; reads back the four primary partial pressures, the seven secondary species, AND `fO2_shift_derived` (the redox state implied by the supplied O budget), writes them all into `hf_row`. The derived $\Delta\mathrm{IW}$ appears in `hf_row['fO2_shift_IW_derived']`. Subsequent iterations carry the same authoritative O total (modulated by PROTEUS escape bookkeeping) and the redox state can drift across the trajectory. + +### Choosing between the two modes + +| Use `user_constant` when | Use `from_O_budget` when | +|---|---| +| You want a fixed redox state for a parameter sweep (Nicholls et al. 2024[^cite-nicholls2024] explored seven $\Delta\mathrm{IW}$ values this way) | You want whole-planet O accounting where escape, ingassing, and the mantle FeO inventory all debit the same O reservoir | +| You don't have an independent constraint on the planet's O budget | You have an O constraint from an FeO-content estimate, a chondritic O/Si ratio, or an observational retrieval | +| Buffered chemistry is good enough for your scientific question | The mantle redox state is itself the unknown you're trying to infer | + +The two modes give bit-identical results in the cases where they should: for any $\Delta\mathrm{IW}$ accepted by `user_constant`, the chemistry returns an O budget; feeding that O budget back through `from_O_budget` recovers the same $\Delta\mathrm{IW}$ to within ~0.05 dex (this is the round-trip property pinned by `tests/test_authoritative_O.py::TestRoundTrip` and documented on the [authoritative-oxygen page](../Explanations/authoritative_oxygen.md#when-the-two-modes-agree)). + ## Redox state -`fO2_shift_IW` is in $\log_{10}$ units relative to the [O'Neill & Eggins (2002)](https://ui.adsabs.harvard.edu/abs/2002ChGeo.186..151O) IW buffer. Common reference values: +`fO2_shift_IW` is in $\log_{10}$ units relative to the O'Neill & Eggins (2002)[^cite-oneilleggins2002] IW buffer. Under `fO2_source = "user_constant"` this value is the buffer offset; under `fO2_source = "from_O_budget"` it is only the initial-guess seed. Common reference values: | $\Delta\mathrm{IW}$ | Description | |---|---| -| $-5$ | Highly reduced (Mercury-like, [Cartier & Wood 2019](https://ui.adsabs.harvard.edu/abs/2019Eleme..15...39C)) | -| $-3$ | Reduced (Mars-mantle estimates, [Wadhwa 2001](https://ui.adsabs.harvard.edu/abs/2001Sci...291.1527W)) | -| $-1$ | Moderately reduced | +| $-5$ | Highly reduced (Mercury-like; sulphur-derived estimate IW-5.4, Cartier & Wood 2019[^cite-cartierwood2019]) | +| $-3$ | Reduced (e.g. enstatite-chondrite-like; Mercury Fe-based estimate IW-2.8 to IW-4.5, Cartier & Wood 2019[^cite-cartierwood2019]) | +| $-1$ | Moderately reduced; near the Mars-mantle source range (Wadhwa 2001[^cite-wadhwa2001] places the shergottite-source mantle at $\approx$ IW) | | $0$ | At iron-wüstite buffer (core formation equilibrium at depth) | -| $+3.5$ | [Sossi et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020SciA....6.1387S) preferred Earth's mantle $f_{\mathrm{O}_2}$ (their $\Delta\mathrm{IW} = +3.5 \pm 0.5$) | -| $+4$ | CALLIOPE PROTEUS-side default; near-modern Earth upper mantle (within FMQ$\,\pm\,2$ per [Frost & McCammon 2008](https://ui.adsabs.harvard.edu/abs/2008AREPS..36..389F)) | +| $+3.5$ | Sossi et al. (2020)[^cite-sossi2020] preferred Earth's mantle $f_{\mathrm{O}_2}$ | +| $+4$ | CALLIOPE PROTEUS-side default; near-modern Earth upper mantle (within FMQ$\,\pm\,2$ per Frost & McCammon 2008[^cite-frostmccammon2008]) | -The [Sossi et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020SciA....6.1387S) compilation places Earth's near-surface mantle at $\Delta\mathrm{IW} \approx +3$ to $+5$ (their preferred value $+3.5$); CALLIOPE defaults sit at $\Delta\mathrm{IW} = 4.0$, consistent with a modern terrestrial composition. +Sossi et al. (2020)[^cite-sossi2020] place Earth's modern upper mantle at $\Delta\mathrm{IW} \approx +3.5$; Frost & McCammon (2008)[^cite-frostmccammon2008] report a broader FMQ$\,\pm\,2$ range across mantle settings (approximately IW+1.5 to IW+5.5). CALLIOPE defaults sit at $\Delta\mathrm{IW} = 4.0$, consistent with a modern terrestrial composition. ## Solver tolerances @@ -135,3 +252,10 @@ For `Time > 1` yr, the wrapper builds `p_guess` from the previous-iteration `H2O ## Next step For what the wrapper actually does on each iteration (sequence diagram, mapping table, hf_row keys), read [Coupling to PROTEUS (theory)](../Explanations/proteus_coupling.md). + +[^cite-cartierwood2019]: C. Cartier, B. J. Wood, *[The role of reducing conditions in building Mercury](https://doi.org/10.2138/gselements.15.1.39)*, Elements, 15(1), 39–45, 2019. [SciX](https://scixplorer.org/abs/2019Eleme..15...39C/abstract). +[^cite-frostmccammon2008]: D. J. Frost, C. A. McCammon, *[The redox state of Earth's mantle](https://doi.org/10.1146/annurev.earth.36.031207.124322)*, Annual Review of Earth and Planetary Sciences, 36, 389–420, 2008. [SciX](https://scixplorer.org/abs/2008AREPS..36..389F/abstract). +[^cite-nicholls2024]: H. Nicholls, T. Lichtenberg, D. J. Bower, R. Pierrehumbert, *[Magma ocean evolution at arbitrary redox state](https://doi.org/10.1029/2024JE008576)*, Journal of Geophysical Research: Planets, 129, e2024JE008576, 2024. [SciX](https://scixplorer.org/abs/2024JGRE..12908576N/abstract). +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151–181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). +[^cite-sossi2020]: P. A. Sossi, A. D. Burnham, J. Badro, A. Lanzirotti, M. Newville, H. St. C. O'Neill, *[Redox state of Earth's magma ocean and its Venus-like early atmosphere](https://doi.org/10.1126/sciadv.abd1387)*, Science Advances, 6, eabd1387, 2020. [SciX](https://scixplorer.org/abs/2020SciA....6.1387S/abstract). +[^cite-wadhwa2001]: M. Wadhwa, *[Redox state of Mars' upper mantle and crust from Eu anomalies in shergottite pyroxenes](https://doi.org/10.1126/science.1057594)*, Science, 291, 1527–1530, 2001. [SciX](https://scixplorer.org/abs/2001Sci...291.1527W/abstract). diff --git a/docs/How-to/usage.md b/docs/How-to/usage.md index 3c40d28..499f8b5 100644 --- a/docs/How-to/usage.md +++ b/docs/How-to/usage.md @@ -86,7 +86,7 @@ target = get_target_from_pressures(ddict) result = equilibrium_atmosphere(target, ddict, print_result=True) ``` -`get_target_from_pressures()` computes the implied total elemental masses by summing atmospheric column mass ([Bower et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B) Eq. 2) and dissolved mass (Henry's law) across every included species at the prescribed initial pressures. +`get_target_from_pressures()` computes the implied total elemental masses by summing atmospheric column mass (Bower et al. (2019)[^cite-bower2019] Eq. 2) and dissolved mass (Henry's law) across every included species at the prescribed initial pressures. ## Warm-starting from a previous solve @@ -102,7 +102,7 @@ p_guess = { result_new = equilibrium_atmosphere(target_new, ddict_new, p_guess=p_guess, print_result=False) ``` -This is exactly what the PROTEUS wrapper does. With a good warm start, convergence typically takes 1–3 `fsolve` iterations; without one, CALLIOPE may need 20+ Monte-Carlo restarts before it lands on a plausible basin. +This is exactly what the PROTEUS wrapper does. With a good warm start, convergence typically takes 1 to 3 `fsolve` iterations; without one, CALLIOPE may need 20+ Monte-Carlo restarts before it lands on a plausible basin. ## Choosing solubility laws @@ -110,15 +110,23 @@ To override the default solubility law for a species, instantiate the law explic | Species | Default class | Default composition | Source | |---|---|---|---| -| H$_2$O | `SolubilityH2O` | `peridotite` | [Sossi et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023E%26PSL.60117894S) | -| CO$_2$ | `SolubilityCO2` | `basalt_dixon` | [Dixon et al. (1995)](https://ui.adsabs.harvard.edu/abs/1995JPet...36.1607D) | -| CO | `SolubilityCO` | `mafic_armstrong` | [Armstrong et al. (2015)](https://ui.adsabs.harvard.edu/abs/2015GeCoA.171..283A) | -| CH$_4$ | `SolubilityCH4` | `basalt_ardia` | [Ardia et al. (2013)](https://ui.adsabs.harvard.edu/abs/2013GeCoA.114...52A) | -| N$_2$ | `SolubilityN2` | `dasgupta` (in `dissolved_mass`) | [Dasgupta et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022GeCoA.336..291D) | -| S$_2$ | `SolubilityS2` | `gaillard` | [Gaillard et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717255G) | +| H$_2$O | `SolubilityH2O` | `peridotite` | Sossi et al. (2023)[^cite-sossi2023] | +| CO$_2$ | `SolubilityCO2` | `basalt_dixon` | Dixon et al. (1995)[^cite-dixon1995] | +| CO | `SolubilityCO` | `mafic_armstrong` | Armstrong et al. (2015)[^cite-armstrong2015] | +| CH$_4$ | `SolubilityCH4` | `basalt_ardia` | Ardia et al. (2013)[^cite-ardia2013] | +| N$_2$ | `SolubilityN2` | `dasgupta` (in `dissolved_mass`) | Dasgupta et al. (2022)[^cite-dasgupta2022] | +| S$_2$ | `SolubilityS2` | `gaillard` | Gaillard et al. (2022)[^cite-gaillard2022] | Alternative compositions (e.g. `SolubilityH2O('basalt_dixon')`, `SolubilityH2O('lunar_glass')`) are documented in the [API reference](../Reference/api/calliope.solubility.md) and discussed in [Solubility laws](../Explanations/solubility.md). ## Next step -For the science behind these laws and the equilibrium constants, head to [Equilibrium chemistry](../Explanations/equilibrium_chemistry.md) and [Solubility laws](../Explanations/solubility.md). For the PROTEUS-side TOML recipe, head to [Coupling to PROTEUS](proteus_coupling.md). +For the science behind these laws and the equilibrium constants, head to [Equilibrium chemistry](../Explanations/equilibrium_chemistry.md) and [Solubility laws](../Explanations/solubility.md). For the PROTEUS-side TOML recipe, head to [Coupling to PROTEUS](proteus_coupling.md). If you have an oxygen budget to enforce rather than a buffer offset to apply, switch to the [authoritative-O recipe](authoritative_oxygen.md). + +[^cite-ardia2013]: P. Ardia, M. M. Hirschmann, A. C. Withers, B. D. Stanley, *[Solubility of CH$_4$ in a synthetic basaltic melt, with applications to atmosphere-magma ocean-core partitioning of volatiles and to the evolution of the Martian atmosphere](https://doi.org/10.1016/j.gca.2013.03.028)*, Geochimica et Cosmochimica Acta, 114, 52–71, 2013. [SciX](https://scixplorer.org/abs/2013GeCoA.114...52A/abstract). +[^cite-armstrong2015]: L. S. Armstrong, M. M. Hirschmann, B. D. Stanley, E. G. Falksen, S. D. Jacobsen, *[Speciation and solubility of reduced C-O-H-N volatiles in mafic melt: implications for volcanism, atmospheric evolution, and deep volatile cycles in the terrestrial planets](https://doi.org/10.1016/j.gca.2015.07.007)*, Geochimica et Cosmochimica Acta, 171, 283–302, 2015. [SciX](https://scixplorer.org/abs/2015GeCoA.171..283A/abstract). +[^cite-bower2019]: D. J. Bower, D. Kitzmann, A. S. Wolf, P. Sanan, C. Dorn, A. V. Oza, *[Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations](https://doi.org/10.1051/0004-6361/201935710)*, Astronomy & Astrophysics, 631, A103, 2019. [SciX](https://scixplorer.org/abs/2019A%26A...631A.103B/abstract). +[^cite-dasgupta2022]: R. Dasgupta, E. Falksen, A. Pal, C. Sun, *[The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions](https://doi.org/10.1016/j.gca.2022.09.012)*, Geochimica et Cosmochimica Acta, 336, 291–307, 2022. [SciX](https://scixplorer.org/abs/2022GeCoA.336..291D/abstract). +[^cite-dixon1995]: J. E. Dixon, E. M. Stolper, J. R. Holloway, *[An experimental study of water and carbon dioxide solubilities in mid-ocean ridge basaltic liquids. Part I: Calibration and solubility models](https://doi.org/10.1093/oxfordjournals.petrology.a037267)*, Journal of Petrology, 36(6), 1607–1631, 1995. [SciX](https://scixplorer.org/abs/1995JPet...36.1607D/abstract). +[^cite-gaillard2022]: F. Gaillard, F. Bernadou, M. Roskosz, M. A. Bouhifd, Y. Marrocchi, G. Iacono-Marziano, M. Moreira, B. Scaillet, G. Rogerie, *[Redox controls during magma ocean degassing](https://doi.org/10.1016/j.epsl.2021.117255)*, Earth and Planetary Science Letters, 577, 117255, 2022. [SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract). +[^cite-sossi2023]: P. A. Sossi, P. M. E. Tollan, J. Badro, D. J. Bower, *[Solubility of water in peridotite liquids and the prevalence of steam atmospheres on rocky planets](https://doi.org/10.1016/j.epsl.2022.117894)*, Earth and Planetary Science Letters, 601, 117894, 2023. [SciX](https://scixplorer.org/abs/2023E%26PSL.60117894S/abstract). diff --git a/docs/Reference/api/index.md b/docs/Reference/api/index.md index 8cfffcc..c3e70e9 100644 --- a/docs/Reference/api/index.md +++ b/docs/Reference/api/index.md @@ -19,7 +19,11 @@ src/calliope/ ### Solver -- [`calliope.solve`](calliope.solve.md) - `equilibrium_atmosphere()` (the public entry point), the residual function `func`, the L2-norm objective `obj`, the speciation tree `get_partial_pressures`, the column-mass aggregation `atmosphere_mass`, the dissolved-mass aggregation `dissolved_mass`, and the elemental-target builders `get_target_from_params` / `get_target_from_pressures`. +- [`calliope.solve`](calliope.solve.md) - two public entry points: + - `equilibrium_atmosphere()` (buffered mode): takes $\Delta\mathrm{IW}$ as input and solves the four-residual H/C/N/S mass-balance system. + - `equilibrium_atmosphere_authoritative_O()` (authoritative-O mode): takes a five-element target including O and solves a five-residual system with $\Delta\mathrm{IW}$ as the additional unknown. See [Authoritative-oxygen mode](../../Explanations/authoritative_oxygen.md) for the augmented mass balance. + + Plus supporting functions: the residual functions `func` / `func_authoritative_O`, the L2-norm objectives `obj` / `obj_authoritative_O`, the speciation tree `get_partial_pressures`, the column-mass aggregation `atmosphere_mass`, the dissolved-mass aggregation `dissolved_mass`, the cold-start guess generators `get_initial_pressures` / `get_initial_pressures_with_fO2`, and the elemental-target builders `get_target_from_params` / `get_target_from_pressures`. ### Chemistry diff --git a/docs/Reference/publications.md b/docs/Reference/publications.md index 402ab62..ea736f4 100644 --- a/docs/Reference/publications.md +++ b/docs/Reference/publications.md @@ -2,11 +2,12 @@ ## Methods papers (cite when using CALLIOPE) -If you use CALLIOPE in published work, please cite the following three methods papers, which together describe (i) the original mass-balance + Henry's law framework, (ii) the multi-species redox-coupled extension, and (iii) the magma-ocean evolution context that defined the present species set. +If you use CALLIOPE in published work, please cite the following four methods papers, which together describe (i) the original mass-balance + Henry's law framework, (ii) the multi-species redox-coupled extension, (iii) the magma-ocean evolution context that defined the present species set, and (iv) the L 98-59 d application that validated the sulfur extension. -- **Bower, D.J., Kitzmann, D., Wolf, A.S., Sanan, P., Dorn, C., & Oza, A.V. (2019).** Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations. *Astronomy & Astrophysics, 631*, A103. \[[ADS](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B) | [DOI](https://doi.org/10.1051/0004-6361/201935710)\] -- **Bower, D.J., Hakim, K., Sossi, P.A., & Sanan, P. (2022).** Retention of water in terrestrial magma oceans and carbon-rich early atmospheres. *The Planetary Science Journal, 3*(4), 93. \[[ADS](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) | [DOI](https://doi.org/10.3847/PSJ/ac5fb1)\] -- **Nicholls, H., Lichtenberg, T., Bower, D.J., & Pierrehumbert, R. (2024).** Magma ocean evolution at arbitrary redox state. *Journal of Geophysical Research: Planets, 129*, e2024JE008576. \[[ADS](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) | [DOI](https://doi.org/10.1029/2024JE008576) | [arXiv](https://arxiv.org/abs/2411.19137)\] +- **Bower, D.J., Kitzmann, D., Wolf, A.S., Sanan, P., Dorn, C., & Oza, A.V. (2019).** Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations. *Astronomy & Astrophysics, 631*, A103. \[[SciX](https://scixplorer.org/abs/2019A%26A...631A.103B/abstract) | [DOI](https://doi.org/10.1051/0004-6361/201935710) | [arXiv](https://arxiv.org/abs/1904.08300)\] +- **Bower, D.J., Hakim, K., Sossi, P.A., & Sanan, P. (2022).** Retention of water in terrestrial magma oceans and carbon-rich early atmospheres. *The Planetary Science Journal, 3*(4), 93. \[[SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract) | [DOI](https://doi.org/10.3847/PSJ/ac5fb1) | [arXiv](https://arxiv.org/abs/2110.08029)\] +- **Nicholls, H., Lichtenberg, T., Bower, D.J., & Pierrehumbert, R. (2024).** Magma ocean evolution at arbitrary redox state. *Journal of Geophysical Research: Planets, 129*, e2024JE008576. \[[SciX](https://scixplorer.org/abs/2024JGRE..12908576N/abstract) | [DOI](https://doi.org/10.1029/2024JE008576) | [arXiv](https://arxiv.org/abs/2411.19137)\] +- **Nicholls, H., Lichtenberg, T., Chatterjee, R.D., Guimond, C.M., Postolec, E., & Pierrehumbert, R.T. (2026).** Volatile-rich evolution of molten super-Earth L 98-59 d. *Nature Astronomy*. \[[SciX](https://scixplorer.org/abs/2026NatAs.tmp...61N/abstract) | [DOI](https://doi.org/10.1038/s41550-026-02815-8) | [arXiv](https://arxiv.org/abs/2507.02656)\] ## Underlying chemistry and solubility-law sources @@ -15,40 +16,40 @@ CALLIOPE inherits its calibration from the following experimental and thermochem ### Equilibrium constants - **Chase, M.W. (1998).** *NIST-JANAF Thermochemical Tables*, 4th edition, Journal of Physical and Chemical Reference Data Monograph 9. Source for the JANAF fits used in `janaf_H2`, `janaf_CO`, `janaf_SO2`, `janaf_H2S`, `janaf_NH3`. \[[NIST landing page](https://janaf.nist.gov/)\] -- **Schaefer, L., & Fegley, B. (2017).** Redox states of initial atmospheres outgassed on rocky planets and planetesimals. *The Astrophysical Journal, 843*(2), 120. \[[ADS](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) | [DOI](https://doi.org/10.3847/1538-4357/aa784f)\] (IVTHANTHERMO source for `schaefer_H`, `schaefer_C`, `schaefer_CH4`.) +- **Schaefer, L., & Fegley, B. (2017).** Redox states of initial atmospheres outgassed on rocky planets and planetesimals. *The Astrophysical Journal, 843*(2), 120. \[[SciX](https://scixplorer.org/abs/2017ApJ...843..120S/abstract) | [DOI](https://doi.org/10.3847/1538-4357/aa784f)\] (IVTHANTHERMO source for `schaefer_H`, `schaefer_C`, `schaefer_CH4`.) ### Oxygen-fugacity buffers -- **O'Neill, H.St.C., & Eggins, S.M. (2002).** The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts. *Chemical Geology, 186*, 151-181. \[[ADS](https://ui.adsabs.harvard.edu/abs/2002ChGeo.186..151O) | [DOI](https://doi.org/10.1016/S0009-2541(01)00414-4)\] (Source for the IW buffer parameterisation `oneill`.) -- **Fischer, R.A., Campbell, A.J., Shofner, G.A., Lord, O.T., Dera, P., & Prakapenka, V.B. (2011).** Equation of state and phase diagram of FeO. *Earth and Planetary Science Letters, 304*, 496-502. \[[ADS](https://ui.adsabs.harvard.edu/abs/2011E%26PSL.304..496F) | [DOI](https://doi.org/10.1016/j.epsl.2011.02.025)\] (Source for the alternative IW buffer `fischer`.) -- **Sossi, P.A., Burnham, A.D., Badro, J., Lanzirotti, A., Newville, M., & O'Neill, H.St.C. (2020).** Redox state of Earth's magma ocean and its Venus-like early atmosphere. *Science Advances, 6*, eabd1387. \[[ADS](https://ui.adsabs.harvard.edu/abs/2020SciA....6.1387S) | [DOI](https://doi.org/10.1126/sciadv.abd1387)\] (Reference for the modern Earth $\Delta\mathrm{IW} \approx +3.5$ used as a default.) +- **O'Neill, H.St.C., & Eggins, S.M. (2002).** The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts. *Chemical Geology, 186*, 151-181. \[[SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract) | [DOI](https://doi.org/10.1016/S0009-2541(01)00414-4)\] (Source for the IW buffer parameterisation `oneill`.) +- **Fischer, R.A., Campbell, A.J., Shofner, G.A., Lord, O.T., Dera, P., & Prakapenka, V.B. (2011).** Equation of state and phase diagram of FeO. *Earth and Planetary Science Letters, 304*, 496-502. \[[SciX](https://scixplorer.org/abs/2011E%26PSL.304..496F/abstract) | [DOI](https://doi.org/10.1016/j.epsl.2011.02.025)\] (Source for the alternative IW buffer `fischer`.) +- **Sossi, P.A., Burnham, A.D., Badro, J., Lanzirotti, A., Newville, M., & O'Neill, H.St.C. (2020).** Redox state of Earth's magma ocean and its Venus-like early atmosphere. *Science Advances, 6*, eabd1387. \[[SciX](https://scixplorer.org/abs/2020SciA....6.1387S/abstract) | [DOI](https://doi.org/10.1126/sciadv.abd1387)\] (Reference for the modern Earth $\Delta\mathrm{IW} \approx +3.5$ used as a default.) ### Solubility laws -- **Sossi, P.A., Tollan, P.M.E., Badro, J., & Bower, D.J. (2023).** Solubility of water in peridotite liquids and the prevalence of steam atmospheres on rocky planets. *Earth and Planetary Science Letters, 601*, 117894. \[[ADS](https://ui.adsabs.harvard.edu/abs/2023E%26PSL.60117894S) | [DOI](https://doi.org/10.1016/j.epsl.2022.117894) | [arXiv](https://arxiv.org/abs/2211.13344)\] (CALLIOPE H$_2$O default `peridotite`.) -- **Newcombe, M.E., Brett, A., Beckett, J.R., Baker, M.B., Newman, S., Guan, Y., Eiler, J.M., & Stolper, E.M. (2017).** Solubility of water in lunar basalt at low pH$_2$O. *Geochimica et Cosmochimica Acta, 200*, 330-352. \[[ADS](https://ui.adsabs.harvard.edu/abs/2017GeCoA.200..330N) | [DOI](https://doi.org/10.1016/j.gca.2016.12.026)\] (CALLIOPE H$_2$O `lunar_glass` and `anorthite_diopside`.) -- **Dixon, J.E., Stolper, E.M., & Holloway, J.R. (1995).** An experimental study of water and carbon dioxide solubilities in mid-ocean ridge basaltic liquids. Part I: Calibration and solubility models. *Journal of Petrology, 36*(6), 1607-1631. \[[ADS](https://ui.adsabs.harvard.edu/abs/1995JPet...36.1607D) | [DOI](https://doi.org/10.1093/oxfordjournals.petrology.a037267)\] (CALLIOPE CO$_2$ `basalt_dixon` and H$_2$O `basalt_dixon`.) +- **Sossi, P.A., Tollan, P.M.E., Badro, J., & Bower, D.J. (2023).** Solubility of water in peridotite liquids and the prevalence of steam atmospheres on rocky planets. *Earth and Planetary Science Letters, 601*, 117894. \[[SciX](https://scixplorer.org/abs/2023E%26PSL.60117894S/abstract) | [DOI](https://doi.org/10.1016/j.epsl.2022.117894) | [arXiv](https://arxiv.org/abs/2211.13344)\] (CALLIOPE H$_2$O default `peridotite`.) +- **Newcombe, M.E., Brett, A., Beckett, J.R., Baker, M.B., Newman, S., Guan, Y., Eiler, J.M., & Stolper, E.M. (2017).** Solubility of water in lunar basalt at low pH$_2$O. *Geochimica et Cosmochimica Acta, 200*, 330-352. \[[SciX](https://scixplorer.org/abs/2017GeCoA.200..330N/abstract) | [DOI](https://doi.org/10.1016/j.gca.2016.12.026)\] (CALLIOPE H$_2$O `lunar_glass` and `anorthite_diopside`.) +- **Dixon, J.E., Stolper, E.M., & Holloway, J.R. (1995).** An experimental study of water and carbon dioxide solubilities in mid-ocean ridge basaltic liquids. Part I: Calibration and solubility models. *Journal of Petrology, 36*(6), 1607-1631. \[[SciX](https://scixplorer.org/abs/1995JPet...36.1607D/abstract) | [DOI](https://doi.org/10.1093/oxfordjournals.petrology.a037267)\] (CALLIOPE CO$_2$ `basalt_dixon` and H$_2$O `basalt_dixon`.) - **Hamilton, D.L., Burnham, C.W., & Osborn, E.F. (1964).** The solubility of water and effects of oxygen fugacity and water content on crystallization in mafic magmas. *Journal of Petrology, 5*(1), 21-39. \[[DOI](https://doi.org/10.1093/petrology/5.1.21)\] (Underlying H$_2$O solubility data behind `basalt_wilson`.) -- **Wilson, L., & Head, J.W. (1981).** Ascent and eruption of basaltic magma on the Earth and Moon. *Journal of Geophysical Research, 86*(B4), 2971-3001. \[[ADS](https://ui.adsabs.harvard.edu/abs/1981JGR....86.2971W) | [DOI](https://doi.org/10.1029/JB086iB04p02971)\] (CALLIOPE H$_2$O `basalt_wilson` parametrisation.) -- **Armstrong, L.S., Hirschmann, M.M., Stanley, B.D., Falksen, E.G., & Jacobsen, S.D. (2015).** Speciation and solubility of reduced C-O-H-N volatiles in mafic melt: implications for volcanism, atmospheric evolution, and deep volatile cycles in the terrestrial planets. *Geochimica et Cosmochimica Acta, 171*, 283-302. \[[ADS](https://ui.adsabs.harvard.edu/abs/2015GeCoA.171..283A) | [DOI](https://doi.org/10.1016/j.gca.2015.07.007)\] (CALLIOPE CO solubility `mafic_armstrong`.) -- **Ardia, P., Hirschmann, M.M., Withers, A.C., & Stanley, B.D. (2013).** Solubility of CH$_4$ in a synthetic basaltic melt, with applications to atmosphere-magma ocean-core partitioning of volatiles and to the evolution of the Martian atmosphere. *Geochimica et Cosmochimica Acta, 114*, 52-71. \[[ADS](https://ui.adsabs.harvard.edu/abs/2013GeCoA.114...52A) | [DOI](https://doi.org/10.1016/j.gca.2013.03.028)\] (CALLIOPE CH$_4$ `basalt_ardia`.) -- **Libourel, G., Marty, B., & Humbert, F. (2003).** Nitrogen solubility in basaltic melt. Part I. Effect of oxygen fugacity. *Geochimica et Cosmochimica Acta, 67*(21), 4123-4135. \[[ADS](https://ui.adsabs.harvard.edu/abs/2003GeCoA..67.4123L) | [DOI](https://doi.org/10.1016/S0016-7037(03)00259-X)\] (CALLIOPE N$_2$ `libourel`.) -- **Dasgupta, R., Falksen, E., Pal, A., & Sun, C. (2022).** The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions. *Geochimica et Cosmochimica Acta, 336*, 291-307. \[[ADS](https://ui.adsabs.harvard.edu/abs/2022GeCoA.336..291D) | [DOI](https://doi.org/10.1016/j.gca.2022.09.012)\] (CALLIOPE N$_2$ default `dasgupta`.) -- **Gaillard, F., Bernadou, F., Roskosz, M., Bouhifd, M.A., Marrocchi, Y., Iacono-Marziano, G., Moreira, M., Scaillet, B., & Rogerie, G. (2022).** Redox controls during magma ocean degassing. *Earth and Planetary Science Letters, 577*, 117255. \[[ADS](https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717255G) | [DOI](https://doi.org/10.1016/j.epsl.2021.117255)\] (CALLIOPE S$_2$ `gaillard`.) +- **Wilson, L., & Head, J.W. (1981).** Ascent and eruption of basaltic magma on the Earth and Moon. *Journal of Geophysical Research, 86*(B4), 2971-3001. \[[SciX](https://scixplorer.org/abs/1981JGR....86.2971W/abstract) | [DOI](https://doi.org/10.1029/JB086iB04p02971)\] (CALLIOPE H$_2$O `basalt_wilson` parametrisation.) +- **Armstrong, L.S., Hirschmann, M.M., Stanley, B.D., Falksen, E.G., & Jacobsen, S.D. (2015).** Speciation and solubility of reduced C-O-H-N volatiles in mafic melt: implications for volcanism, atmospheric evolution, and deep volatile cycles in the terrestrial planets. *Geochimica et Cosmochimica Acta, 171*, 283-302. \[[SciX](https://scixplorer.org/abs/2015GeCoA.171..283A/abstract) | [DOI](https://doi.org/10.1016/j.gca.2015.07.007)\] (CALLIOPE CO solubility `mafic_armstrong`.) +- **Ardia, P., Hirschmann, M.M., Withers, A.C., & Stanley, B.D. (2013).** Solubility of CH$_4$ in a synthetic basaltic melt, with applications to atmosphere-magma ocean-core partitioning of volatiles and to the evolution of the Martian atmosphere. *Geochimica et Cosmochimica Acta, 114*, 52-71. \[[SciX](https://scixplorer.org/abs/2013GeCoA.114...52A/abstract) | [DOI](https://doi.org/10.1016/j.gca.2013.03.028)\] (CALLIOPE CH$_4$ `basalt_ardia`.) +- **Libourel, G., Marty, B., & Humbert, F. (2003).** Nitrogen solubility in basaltic melt. Part I. Effect of oxygen fugacity. *Geochimica et Cosmochimica Acta, 67*(21), 4123-4135. \[[SciX](https://scixplorer.org/abs/2003GeCoA..67.4123L/abstract) | [DOI](https://doi.org/10.1016/S0016-7037(03)00259-X)\] (CALLIOPE N$_2$ `libourel`.) +- **Dasgupta, R., Falksen, E., Pal, A., & Sun, C. (2022).** The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions. *Geochimica et Cosmochimica Acta, 336*, 291-307. \[[SciX](https://scixplorer.org/abs/2022GeCoA.336..291D/abstract) | [DOI](https://doi.org/10.1016/j.gca.2022.09.012)\] (CALLIOPE N$_2$ default `dasgupta`.) +- **Gaillard, F., Bernadou, F., Roskosz, M., Bouhifd, M.A., Marrocchi, Y., Iacono-Marziano, G., Moreira, M., Scaillet, B., & Rogerie, G. (2022).** Redox controls during magma ocean degassing. *Earth and Planetary Science Letters, 577*, 117255. \[[SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract) | [DOI](https://doi.org/10.1016/j.epsl.2021.117255)\] (CALLIOPE S$_2$ `gaillard`.) ### Bulk-Earth elemental abundances -- **Wang, H.S., Lineweaver, C.H., & Ireland, T.R. (2018).** The elemental abundances (with uncertainties) of the most Earth-like planet. *Icarus, 299*, 460-474. \[[ADS](https://ui.adsabs.harvard.edu/abs/2018Icar..299..460W) | [DOI](https://doi.org/10.1016/j.icarus.2017.08.024)\] (Source for the primitive-mantle nitrogen $\sim$2 ppmw fiducial used in the [first-run tutorial](../Tutorials/firstrun.md).) +- **Wang, H.S., Lineweaver, C.H., & Ireland, T.R. (2018).** The elemental abundances (with uncertainties) of the most Earth-like planet. *Icarus, 299*, 460-474. \[[SciX](https://scixplorer.org/abs/2018Icar..299..460W/abstract) | [DOI](https://doi.org/10.1016/j.icarus.2017.08.024)\] (Source for the primitive-mantle nitrogen $\sim$2 ppmw fiducial used in the [first-run tutorial](../Tutorials/firstrun.md).) ## Applications using CALLIOPE within PROTEUS These are publications that have applied CALLIOPE within coupled PROTEUS runs. -- **Nicholls, H., Pierrehumbert, R.T., Lichtenberg, T., Soucasse, L., & Smeets, S. (2025).** Convective shutdown in the atmospheres of lava worlds. *Monthly Notices of the Royal Astronomical Society, 536*(3), 2957-2971. \[[ADS](https://ui.adsabs.harvard.edu/abs/2025MNRAS.536.2957N) | [DOI](https://doi.org/10.1093/mnras/stae2772) | [arXiv](https://arxiv.org/abs/2412.11987)\] -- **Nicholls, H., Lichtenberg, T., Chatterjee, R.D., Guimond, C.M., Postolec, E., & Pierrehumbert, R.T. (2026).** Volatile-rich evolution of molten super-Earth L 98-59 d. *Nature Astronomy*. \[[ADS](https://ui.adsabs.harvard.edu/abs/2026NatAs.tmp...61N) | [DOI](https://doi.org/10.1038/s41550-026-02815-8)\] +- **Nicholls, H., Pierrehumbert, R.T., Lichtenberg, T., Soucasse, L., & Smeets, S. (2025).** Convective shutdown in the atmospheres of lava worlds. *Monthly Notices of the Royal Astronomical Society, 536*(3), 2957-2971. \[[SciX](https://scixplorer.org/abs/2025MNRAS.536.2957N/abstract) | [DOI](https://doi.org/10.1093/mnras/stae2772) | [arXiv](https://arxiv.org/abs/2412.11987)\] +- **Nicholls, H., Lichtenberg, T., Chatterjee, R.D., Guimond, C.M., Postolec, E., & Pierrehumbert, R.T. (2026).** Volatile-rich evolution of molten super-Earth L 98-59 d. *Nature Astronomy*. \[[SciX](https://scixplorer.org/abs/2026NatAs.tmp...61N/abstract) | [DOI](https://doi.org/10.1038/s41550-026-02815-8) | [arXiv](https://arxiv.org/abs/2507.02656)\] ## Related software -- **Bower, D.J., Thompson, M.A., Hakim, K., Tian, M., & Sossi, P.A. (2025).** Diversity of low-mass planet atmospheres in the C-H-O-N-S-Cl system with interior dissolution, nonideality, and condensation: application to TRAPPIST-1e and sub-Neptunes. *The Astrophysical Journal, 995*, 59. \[[ADS](https://ui.adsabs.harvard.edu/abs/2025ApJ...995...59B) | [arXiv](https://arxiv.org/abs/2507.00499)\] The atmodeller successor framework, used as the alternative outgassing module within PROTEUS. +- **Bower, D.J., Thompson, M.A., Hakim, K., Tian, M., & Sossi, P.A. (2025).** Diversity of low-mass planet atmospheres in the C-H-O-N-S-Cl system with interior dissolution, nonideality, and condensation: application to TRAPPIST-1e and sub-Neptunes. *The Astrophysical Journal, 995*, 59. \[[SciX](https://scixplorer.org/abs/2025ApJ...995...59B/abstract) | [arXiv](https://arxiv.org/abs/2507.00499)\] The atmodeller successor framework, used as the alternative outgassing module within PROTEUS. CALLIOPE is also part of the wider PROTEUS publication record, the live list for which is at [proteus-framework.org/publications](https://proteus-framework.org/publications). diff --git a/docs/Tutorials/coupled_loop.md b/docs/Tutorials/coupled_loop.md new file mode 100644 index 0000000..94dc0b2 --- /dev/null +++ b/docs/Tutorials/coupled_loop.md @@ -0,0 +1,164 @@ +# Coupled-loop driver + +The most common real-world use of CALLIOPE is inside a time-stepping outer loop that advances a magma ocean through cooling, escape, or any other process that re-equilibrates the atmosphere at each step. This tutorial shows the warm-start pattern that makes such loops efficient. + +By the end of it you will: + +- have written a minimal driver that calls CALLIOPE at each time step; +- know which fields to thread between iterations as `p_guess`; +- have measured the warm-start speed-up directly: cold-start solve vs warm-start solve; +- have run two phases back-to-back on the same warm-start chain: a cooling sequence from $T_\mathrm{magma} = 3000$ K to $1500$ K at $\Phi = 1$, then a crystallisation step at fixed $T = 1500$ K where the melt fraction drops from $\Phi = 1$ to $\Phi = 0.5$; +- have produced the two-panel partial-pressure plot on the front of this page. + +You should already have completed the [First run](firstrun.md) tutorial. Familiarity with [Two-mode round-trip](two_modes.md) is helpful but not required. + +## What "warm start" means in CALLIOPE + +CALLIOPE's `equilibrium_atmosphere` solves a 4-by-4 nonlinear mass-balance system in the partial pressures $(p_\mathrm{H_2O}, p_\mathrm{CO_2}, p_\mathrm{N_2}, p_\mathrm{S_2})$. Without an initial guess, it draws Monte-Carlo log-uniform restarts (up to `nguess`, default 7500) until fsolve converges to a basin that satisfies mass balance. With a `p_guess` dictionary close to the solution, fsolve typically converges on the first attempt and the call completes in a few milliseconds rather than tens to hundreds. + +In a time-stepping loop where the chemistry changes slowly (i.e. each step is a small perturbation of the previous), the previous step's converged partial pressures are an excellent guess for the next. This is the warm-start pattern. + +## Step 1: write a cooling-sequence driver + +```python +import time +import warnings + +import numpy as np + +from calliope.constants import volatile_species +from calliope.solve import equilibrium_atmosphere + +planet = {'M_mantle': 4.03e24, 'gravity': 9.81, 'radius': 6.371e6} +earth_hcns = {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21} + +T_sequence = np.linspace(3000.0, 1500.0, 25) +diw_fixed = 0.5 # hold redox fixed: the focus is cooling + +species_to_track = ['H2O', 'CO2', 'H2', 'CO', 'CH4', + 'N2', 'NH3', 'S2', 'SO2', 'H2S'] +history = {sp: np.full(T_sequence.size, np.nan) for sp in species_to_track} +wall = np.zeros(T_sequence.size) + +p_guess = None # cold-start the first call +``` + +## Step 2: the loop, with warm-start threading + +```python +for i, T in enumerate(T_sequence): + ddict = {**planet, 'T_magma': float(T), 'Phi_global': 1.0, + 'fO2_shift_IW': diw_fixed} + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + + t0 = time.time() + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + res = equilibrium_atmosphere( + earth_hcns, ddict, p_guess=p_guess, print_result=False, + ) + wall[i] = time.time() - t0 + + for sp in species_to_track: + history[sp][i] = float(res[f'{sp}_bar']) + + # Thread the four primary partial pressures forward as the next + # iteration's guess. Other species are derived from these four via + # the equilibrium reactions, so re-seeding them is not necessary. + p_guess = {s: float(res[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} + +print(f'cold-start step: {wall[0]*1e3:6.1f} ms') +print(f'warm-step median: {np.median(wall[1:])*1e3:6.1f} ms') +``` + +You should see the warm steps run several times faster than the cold start. The exact ratio depends on how lucky the cold-start Monte-Carlo draw is on the first call: when the draw is lucky the cold call already converges quickly and the warm steps are only a few-fold faster (~3x in the cached run on this page); when the cold draw needs many restarts the warm-step speed-up can be an order of magnitude or more. + +!!! note "Which keys to thread forward" + The four primary partial pressures are `H2O`, `CO2`, `N2`, `S2`. CALLIOPE's solver uses these four as its unknowns; the other six species (`H2`, `CO`, `CH4`, `NH3`, `SO2`, `H2S`) are derived from the primaries via the equilibrium reactions, so they should *not* appear in `p_guess`. Passing them anyway is harmless (they will be ignored), but missing one of the four primaries raises `ValueError`. + +!!! warning "When to invalidate the warm start" + If a step changes the chemistry by a large factor (e.g. an instantaneous escape event that removes 90% of the H budget), the previous-step guess is no longer close to the new basin. In that case the warm start can actually hurt: fsolve dives into a poor local minimum instead of restarting cleanly. Set `p_guess = None` whenever the inventory or boundary conditions change discontinuously, then let the next call cold-start. + +## Step 3: crystallisation at fixed temperature + +A real magma ocean does more than cool monotonically: at some point the solidus catches the geotherm and the magma begins to crystallise, dropping the melt fraction $\Phi$. CALLIOPE's $\Phi$ parameter controls how much of the silicate reservoir participates in melt-vapour equilibrium, so reducing $\Phi$ at fixed $T$ progressively shrinks the magma reservoir and rebalances the H, C, N, S partitioning between melt and atmosphere. + +Continue the same warm-start chain from the end of the cooling sequence and walk $\Phi$ from $1.0$ down to $0.5$ at fixed $T = 1500$ K: + +```python +phi_grid = np.linspace(1.0, 0.5, 11) +cryst_history = {sp: np.full(phi_grid.size, np.nan) for sp in species_to_track} + +# p_guess and ddict carry over from the end of Step 2. +for j, phi in enumerate(phi_grid): + ddict['T_magma'] = 1500.0 + ddict['Phi_global'] = float(phi) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + res = equilibrium_atmosphere( + earth_hcns, ddict, p_guess=p_guess, print_result=False, + ) + for sp in species_to_track: + cryst_history[sp][j] = float(res[f'{sp}_bar']) + p_guess = {s: float(res[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} +``` + +As $\Phi$ drops, the partial pressures of the dissolved-friendly species (H$_2$O above all, then SO$_2$, NH$_3$, H$_2$S) rise: shrinking the melt reservoir squeezes the previously-dissolved volatiles into the atmosphere. The barely-soluble species (CO, CO$_2$, N$_2$) move comparatively little because they were already outgassed at $\Phi = 1$. + +## Step 4: plot both phases + +```python +import matplotlib.pyplot as plt + +from calliope.constants import dict_colors + +fig, (ax_cool, ax_cryst) = plt.subplots( + 1, 2, figsize=(11.4, 5.0), sharey=True, + gridspec_kw={'width_ratios': [1.6, 1.0], 'wspace': 0.08}, +) + +for sp in species_to_track: + ax_cool.plot(T_sequence, history[sp], + color=dict_colors[sp], marker='o', markersize=3.5, + linewidth=1.8) + ax_cryst.plot(phi_grid, cryst_history[sp], + color=dict_colors[sp], marker='s', markersize=3.5, + linewidth=1.8, label=sp) +ax_cool.set_yscale('log') +ax_cool.set_xlabel(r'$T_\mathrm{magma}$ [K] (cooling $\rightarrow$)') +ax_cool.set_ylabel('Surface partial pressure (bar)') +ax_cool.set_ylim(1e-6, 1e4) +ax_cool.invert_xaxis() +ax_cool.grid(which='both', alpha=0.3) +ax_cool.set_title(r'(a) cooling at $\Phi = 1$') +ax_cryst.set_xlabel(r'$\Phi_\mathrm{global}$ (crystallisation $\rightarrow$)') +ax_cryst.invert_xaxis() +ax_cryst.grid(which='both', alpha=0.3) +ax_cryst.set_title(r'(b) crystallisation at $T = 1500$ K') +ax_cryst.legend(loc='center left', bbox_to_anchor=(1.02, 0.5), frameon=False) +fig.savefig('coupled_loop.pdf') +``` + +## The goal of this tutorial + +![Cooling sequence](../assets/figures/tutorials/coupled_loop.png) + +*Surface partial pressures across a two-phase driver. **(a)** A 25-step cooling sequence from $3000$ K down to $1500$ K at fixed $\Phi = 1$ and $\Delta\mathrm{IW} = +0.5$. **(b)** An 11-step crystallisation step at fixed $T = 1500$ K with the magma melt fraction shrinking from $\Phi = 1$ to $\Phi = 0.5$. The legend lists the ten tracked species in their PROTEUS-standard colours; the annotation in panel (a) shows the cold-start time (first call), the warm-step median (every subsequent call), and the total wall time for the full $25 + 11$ steps.* + +In panel (a) the atmosphere stays carbon-dominated across the whole cooling range; CO is the dominant species throughout (CO/CO$_2$ ratio falls modestly as $T$ drops). The fastest-moving species are H$_2$S (rises) and S$_2$ (falls): sulfur speciation is the most sensitive marker of cooling at this redox. + +Panel (b) shows the magma-volume effect cleanly. As $\Phi$ drops the dissolved-friendly species (H$_2$O above all, plus the trace S species) rise sharply because the shrinking melt reservoir pushes them into the atmosphere; the barely-soluble species (CO, CO$_2$, N$_2$) move comparatively little. The total surface pressure rises by tens of percent across the $\Phi = 1 \to 0.5$ step, almost entirely driven by the H$_2$O / H$_2$S / SO$_2$ surge. + +The wall-time annotation is the most pedagogically important number on the plot. The total wall time is dominated by the cold-start step; warm steps are typically order milliseconds each across both phases, even though phase 2 changes a different state variable than phase 1. This is what makes long PROTEUS runs ($10^4$ or more outer-loop iterations) feasible: each CALLIOPE call costs essentially nothing once the basin is found. + +## Where to go next + +- For the conceptual story behind the equilibrium chemistry CALLIOPE is solving, read [Equilibrium chemistry](../Explanations/equilibrium_chemistry.md). +- For the PROTEUS-side wrapper that implements this pattern in production, read [Coupling to PROTEUS](../How-to/proteus_coupling.md). +- For how CALLIOPE behaves when the inputs vary more aggressively across the (T, $\Delta\mathrm{IW}$) plane, see the previous tutorial [Speciation phase diagram](phase_diagram.md). + +## Reproducing the figure + +Generated by [`scripts/tutorials/fig_coupled_loop.py`](https://github.com/FormingWorlds/CALLIOPE/blob/main/scripts/tutorials/fig_coupled_loop.py). Re-run with `python -m scripts.tutorials.fig_coupled_loop` from the repository root. diff --git a/docs/Tutorials/earth_fiducial.md b/docs/Tutorials/earth_fiducial.md new file mode 100644 index 0000000..1dde191 --- /dev/null +++ b/docs/Tutorials/earth_fiducial.md @@ -0,0 +1,119 @@ +# Reproducing the Earth fiducial + +The [Backend comparison](../Explanations/cross_backend_comparison.md) explanation page anchors all of its figures on one shared Earth scenario: Krijt et al. 2023[^cite-krijt2023] BSE H/C/N/S, $T_\mathrm{magma} = 2000$ K, $\Phi = 1$, and a volatile O reference of $1.26 \times 10^{22}$ kg derived to put CALLIOPE on the Sossi et al. 2020[^cite-sossi2020] $\Delta\mathrm{IW} = +3.5$ Earth upper-mantle anchor with the current default Fischer 2011 buffer. This tutorial walks you through reproducing those numbers from scratch so you can verify the docs are still consistent with the current solver, and so you have the workflow on hand when you want to anchor your own runs to a literature value. + +By the end of it you will: + +- have reproduced the $O = 1.26 \times 10^{22}$ kg volatile-O reference from the Krijt H/C/N/S budget; +- have verified that the authoritative-O entry point recovers $\Delta\mathrm{IW} = +3.5$ from that O reference to within solver tolerance; +- understand why the buffered call at high $\Delta\mathrm{IW}$ + carbon-rich BSE needs a tight `p_guess` (it has a spurious secondary basin without it). + +You should already have completed [Two-mode round-trip](two_modes.md), which introduces both entry points. + +## Step 1: pin the Krijt+2023 H/C/N/S budget + +```python +import warnings + +from calliope.constants import volatile_species +from calliope.solve import ( + equilibrium_atmosphere, + equilibrium_atmosphere_authoritative_O, +) + +planet = {'M_mantle': 4.03e24, 'gravity': 9.81, 'radius': 6.371e6} +T_magma = 2000.0 +diw_anchor = 3.5 # Sossi et al. 2020 Earth upper-mantle estimate + +# Krijt et al. 2023 PPVII Tables 1 and 2 BSE row (kg). +earth_hcns = {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21} +``` + +## Step 2: derive the volatile-O reference via buffered mode + +This is the step that needs the tight `p_guess`. + +Without one, the buffered solver at high $\Delta\mathrm{IW}$ with a carbon-rich BSE inventory has two basins: the physical CO$_2$-dominated one at $P_\mathrm{surf} \approx 1755$ bar with $p_\mathrm{H_2O} \approx 5.6$ bar, and a spurious H$_2$O-free one at lower $P_\mathrm{surf}$ with $p_\mathrm{H_2O} = 0$. The Monte-Carlo restart lands in the spurious basin a few percent of the time, which is rare but too often for a reproducible tutorial. + +A `p_guess` near the CO$_2$-dominated basin pins the right answer reliably: + +```python +ddict = {**planet, 'T_magma': T_magma, 'Phi_global': 1.0, + 'fO2_shift_IW': diw_anchor} +for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + +# Pin the canonical CO2-dominated basin. Numbers are approximate; the +# solver tightens them to the converged values during fsolve. +canonical_guess = {'H2O': 5.0, 'CO2': 1500.0, 'N2': 3.0, 'S2': 1e-3} + +with warnings.catch_warnings(): + warnings.simplefilter('ignore') + buf = equilibrium_atmosphere( + earth_hcns, ddict, p_guess=canonical_guess, print_result=False, + ) + +print(f'derived O_kg_total = {buf["O_kg_total"]:.3e} kg') +print(f'P_surf = {buf["P_surf"]:.0f} bar') +print(f'H2O = {buf["H2O_bar"]:.2f} bar') +print(f'CO2 = {buf["CO2_bar"]:.0f} bar') +``` + +Expected output: + +``` +derived O_kg_total = 1.260e+22 kg +P_surf = 1755 bar +H2O = 5.59 bar +CO2 = 1557 bar +``` + +This is the canonical Earth volatile-O reference under the current Fischer 2011 buffer default and matches the value used on the backend-comparison page. + +!!! note "Why a CO$_2$-dominated atmosphere here, not steam?" + The cause is melt solubility, not bulk inventory. Earth's BSE inventory contains $\sim 5.6 \times 10^{23}$ mol H and $\sim 2.6 \times 10^{23}$ mol C, so hydrogen atoms outnumber carbon atoms by a factor of two. But at $\Phi = 1$ most of the H stays *dissolved* in the silicate melt because H$_2$O is roughly two orders of magnitude more soluble in basalt than CO$_2$ is; most of the C outgases. The atmosphere therefore looks CO$_2$-dominated even though the bulk volatile budget is H-rich. See [Speciation phase diagram](phase_diagram.md) for the same regime mapped across $(T, \Delta\mathrm{IW})$. + +## Step 3: round-trip through authoritative-O + +Feed the derived O budget into the authoritative-O entry point and verify that it recovers the input $\Delta\mathrm{IW}$ within solver tolerance: + +```python +target = dict(earth_hcns); target['O'] = buf['O_kg_total'] +p_guess = {s: float(buf[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} + +with warnings.catch_warnings(): + warnings.simplefilter('ignore') + auth = equilibrium_atmosphere_authoritative_O( + target, ddict, p_guess=p_guess, fO2_hint=diw_anchor, + print_result=False, + ) + +residual = auth['fO2_shift_derived'] - diw_anchor +print(f'recovered Delta-IW = {auth["fO2_shift_derived"]:+.4f}') +print(f'residual = {residual:+.2e} dex') +``` + +Expected output: `residual` $\lesssim 10^{-9}$ dex, i.e. essentially zero (the exact value depends on solver tolerance and starting point). The full provenance chain (Krijt H/C/N/S $\to$ buffered mode $\to$ derived O $\to$ authoritative-O $\to$ $\Delta\mathrm{IW}$) closes at the Sossi 2020 anchor. + +## The goal of this tutorial + +![Earth fiducial closure](../assets/figures/tutorials/earth_fiducial.png) + +*Earth fiducial reproduced from scratch. The cream band represents the Frost & McCammon 2008[^cite-frostmccammon2008] empirical Earth-mantle range, $\Delta\mathrm{IW} \in [+1, +5]$ (a conversion of their stated FMQ $\pm 2$ dex upper-mantle window onto the IW reference). The dotted vertical is the Sossi et al. 2020[^cite-sossi2020] upper-mantle anchor, $\Delta\mathrm{IW} = +3.50$. The solid line is the CALLIOPE recovered value after the buffered $\to$ authoritative-O round-trip. The summary box reports the derived O budget, the absolute closure residual (a few parts in $10^9$), and the converged surface pressure. The reproduced CALLIOPE line sits exactly on the Sossi anchor, as it should: the entire workflow is constructed to land on this value.* + +If your reproduced figure does not show the CALLIOPE line on the Sossi anchor, the most likely cause is that you skipped the `canonical_guess` in Step 2 and the buffered solver landed in the spurious H$_2$O-free basin. Add the `p_guess` and re-run. + +## Where to go next + +- For the full atmosphere-side details (every partial pressure at this fiducial), open the [First-run reference figure](firstrun.md#compare-your-output-against-the-reference) and substitute these inputs. +- For how this fiducial sits inside the cross-backend story (CALLIOPE vs atmodeller at the same inputs), read the [Backend comparison](../Explanations/cross_backend_comparison.md) explanation page. +- For the planetary case study that contrasts Earth-BSE with a very different inventory, read the next tutorial: [Mars-like atmosphere](mars_fiducial.md). + +## Reproducing the figure + +Generated by [`scripts/tutorials/fig_earth_fiducial.py`](https://github.com/FormingWorlds/CALLIOPE/blob/main/scripts/tutorials/fig_earth_fiducial.py). Re-run with `python -m scripts.tutorials.fig_earth_fiducial` from the repository root. + +[^cite-frostmccammon2008]: D. J. Frost, C. A. McCammon, *[The redox state of Earth's mantle](https://doi.org/10.1146/annurev.earth.36.031207.124322)*, Annual Review of Earth and Planetary Sciences, 36, 389, 2008. +[^cite-krijt2023]: S. Krijt, M. Kama, M. McClure, J. Teske, E. A. Bergin, O. Shorttle, K. J. Walsh, S. N. Raymond, *Chemical habitability: supply and retention of life's essential elements during planet formation*, in Protostars and Planets VII, S. Inutsuka, Y. Aikawa, T. Muto, K. Tomida, M. Tamura, eds., Astronomical Society of the Pacific Conference Series, 534, 1031, 2023. +[^cite-sossi2020]: P. A. Sossi, A. D. Burnham, J. Badro, A. Lanzirotti, M. Newville, H. St. C. O'Neill, *[Redox state of Earth's magma ocean and its Venus-like early atmosphere](https://doi.org/10.1126/sciadv.abd1387)*, Science Advances, 6, eabd1387, 2020. diff --git a/docs/Tutorials/firstrun.md b/docs/Tutorials/firstrun.md index 8f439c9..de6af87 100644 --- a/docs/Tutorials/firstrun.md +++ b/docs/Tutorials/firstrun.md @@ -42,7 +42,7 @@ for sp in volatile_species: ``` !!! note "What these numbers mean" - `hydrogen_earth_oceans = 1.0` corresponds to $\sim 1.55 \times 10^{20}$ kg of H, which the wrapper translates via `H_kg = N_ocean_moles * ocean_moles * molar_mass['H2']`. `CH_ratio = 0.1` is a mass ratio chosen to roughly match estimates of Earth's bulk silicate Earth C/H. `nitrogen_ppmw = 2.0` matches the [Wang et al. (2018)](https://ui.adsabs.harvard.edu/abs/2018Icar..299..460W) primitive-mantle estimate that [Nicholls et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) used as their fiducial value. + `hydrogen_earth_oceans = 1.0` corresponds to $\sim 1.55 \times 10^{20}$ kg of H, which the wrapper translates via `H_kg = N_ocean_moles * ocean_moles * molar_mass['H2']`. `CH_ratio = 0.1` is a mass ratio chosen to roughly match estimates of Earth's bulk silicate Earth C/H. `nitrogen_ppmw = 2.0` matches the Wang et al. (2018)[^cite-wang2018] primitive-mantle estimate that Nicholls et al. (2024)[^cite-nicholls2024] used as their fiducial value. ## Step 2: build the elemental targets and solve @@ -72,7 +72,7 @@ for sp in sorted(volatile_species, key=lambda s: -result[f'{s}_bar']): print(f' {sp:5s}: {p:9.3e} bar (VMR {x:.3e})') ``` -For the inputs above (1 ocean H, $\Delta\mathrm{IW} = +0.5$, $T = 2500$ K, $\Phi = 1$) you should see a multi-thousand-bar atmosphere dominated by H$_2$O, with sub-percent CO$_2$ and traces of CO, H$_2$, and S species. The exact values depend on the solubility-law defaults; see [Solubility laws](../Explanations/solubility.md) for what each species uses. +For the inputs above (1 ocean H, $\Delta\mathrm{IW} = +0.5$, $T = 2500$ K, $\Phi = 1$) you should see roughly a 10 bar atmosphere with CO dominant, then CO$_2$, N$_2$, and H$_2$O at the bar level, and H$_2$ plus the S and N trace species below. The exact values depend on the solubility-law defaults; see [Solubility laws](../Explanations/solubility.md) for what each species uses. ## Step 4: verify mass balance @@ -111,6 +111,16 @@ plt.show() `dict_colors` from `calliope.constants` is the colour scheme used across the PROTEUS ecosystem so figures stay visually consistent. +### Compare your output against the reference + +The figure below is the same Earth-like inputs run through `equilibrium_atmosphere` on a clean CALLIOPE install. Your numbers should reproduce these to within a few percent (the residual reflects Monte-Carlo restart variability, not a calibration issue). + +![First-run reference](../assets/figures/tutorials/firstrun_reference.png) + +*Reference output for the first-run inputs. Species ordered top-to-bottom by partial pressure. The summary box top-right shows the three aggregate diagnostics from Step 3 ($P_\mathrm{surf}$, $M_\mathrm{atm}$, mean molar mass).* + +The five large reservoirs (CO, N$_2$, CO$_2$, H$_2$, H$_2$O all between $\sim 0.2$ and $\sim 5$ bar) account for essentially the entire $\sim 9$ bar atmosphere; the four trace species (SO$_2$, H$_2$S, S$_2$, NH$_3$) sit two to seven orders of magnitude lower; CH$_4$ is effectively absent at this temperature. If your output disagrees by more than a factor of $\sim 2$ on a major species or by orders of magnitude on the speciation ordering, recheck the `ddict` keys against the snippet above. The script that produces this reference figure is checked in at [`scripts/tutorials/fig_firstrun_reference.py`](https://github.com/FormingWorlds/CALLIOPE/blob/main/scripts/tutorials/fig_firstrun_reference.py) and can be re-run with `python -m scripts.tutorials.fig_firstrun_reference` from the repository root. + ## Step 6: sweep one parameter Repeat the solve along a grid of $\Delta\mathrm{IW}$ to see the redox dependence directly. Reuse the previous solution as the warm start each time: @@ -143,9 +153,9 @@ fig.savefig('redox_sweep.pdf') plt.show() ``` -The expected qualitative behaviour, consistent with [Bower et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) Section 3 and [Nicholls et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) Figure 6: +The expected qualitative behaviour, consistent with Bower et al. (2022)[^cite-bower2022] Section 3 and Nicholls et al. (2024)[^cite-nicholls2024] Figure 6: -- At $\Delta\mathrm{IW} \le -2$ (reducing), H$_2$ and CO dominate; H$_2$O and CO$_2$ collapse; +- At $\Delta\mathrm{IW} \le -1$ (reducing), H$_2$ and CO dominate; H$_2$O and CO$_2$ collapse; - Around $\Delta\mathrm{IW} \approx 0$, the H$_2$O/H$_2$ and CO$_2$/CO ratios are of order unity; - At $\Delta\mathrm{IW} \ge +2$ (oxidising), H$_2$O and CO$_2$ dominate; H$_2$ and CO are sub-percent. @@ -157,3 +167,7 @@ S$_2$ stays roughly constant (inventory-controlled), while SO$_2$ rises and H$_2 - For the solubility laws and their references, read [Solubility laws](../Explanations/solubility.md). - For the mass-balance system that ties the partial pressures to the elemental inventory, read [Mass balance & solver](../Explanations/mass_balance.md). - For the PROTEUS-coupled invocation pattern, read [Coupling to PROTEUS (how-to)](../How-to/proteus_coupling.md). + +[^cite-bower2022]: D. J. Bower, K. Hakim, P. A. Sossi, P. Sanan, *[Retention of water in terrestrial magma oceans and carbon-rich early atmospheres](https://doi.org/10.3847/PSJ/ac5fb1)*, The Planetary Science Journal, 3(4), 93, 2022. [SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract). +[^cite-nicholls2024]: H. Nicholls, T. Lichtenberg, D. J. Bower, R. Pierrehumbert, *[Magma ocean evolution at arbitrary redox state](https://doi.org/10.1029/2024JE008576)*, Journal of Geophysical Research: Planets, 129, e2024JE008576, 2024. [SciX](https://scixplorer.org/abs/2024JGRE..12908576N/abstract). +[^cite-wang2018]: H. S. Wang, C. H. Lineweaver, T. R. Ireland, *[The elemental abundances (with uncertainties) of the most Earth-like planet](https://doi.org/10.1016/j.icarus.2017.08.024)*, Icarus, 299, 460–474, 2018. [SciX](https://scixplorer.org/abs/2018Icar..299..460W/abstract). diff --git a/docs/Tutorials/mars_fiducial.md b/docs/Tutorials/mars_fiducial.md new file mode 100644 index 0000000..0a67104 --- /dev/null +++ b/docs/Tutorials/mars_fiducial.md @@ -0,0 +1,136 @@ +# Mars-like atmosphere + +CALLIOPE is not Earth-specific. Anything in the planet parameter dictionary (`M_mantle`, `gravity`, `radius`) is a knob the user can change, and the solubility laws are calibrated against terrestrial-basalt melts that extrapolate reasonably to other rocky bodies. This tutorial swaps in Mars-like planetary parameters and a mass-scaled inventory, runs the same chemistry, and reads off how the atmosphere differs. + +By the end of it you will: + +- have run CALLIOPE on a non-Earth planet; +- have a side-by-side comparison of Earth-BSE and Mars-scaled atmospheres at the same redox and temperature; +- know which atmospheric properties are inventory-driven (per-species partial pressures) and which are structure-driven (surface pressure, atmospheric mass). + +You should already have completed [First run](firstrun.md). The other tutorials are not strictly required but make the figure easier to interpret. + +!!! note "On the choice of 'Mars-scaled' inventory" + The Mars BSE H/C/N/S inventory is much less well-constrained than Earth's. To keep this tutorial reproducible without committing to a specific Mars-petrology estimate, we scale the Krijt et al. 2023[^cite-krijt2023] Earth BSE inventory uniformly by Mars's mass-ratio to Earth ($0.107$). The result is a "Mars-mass terrestrial body with Earth-like volatile fractions". The contrast you read off the figure is therefore primarily driven by Mars's smaller gravity and mantle mass, not by a fundamentally different chemistry. For real Mars BSE work, swap in a literature Mars-mantle composition (e.g. the classical Wänke & Dreibus 1988[^cite-waenkedreibus1988] reduced-chondrite estimate) using the same workflow. + +## Step 1: define both planets + +```python +import warnings + +import numpy as np + +from calliope.constants import volatile_species +from calliope.solve import equilibrium_atmosphere + +earth = { + 'name': 'Earth', + 'planet': {'M_mantle': 4.03e24, 'gravity': 9.81, 'radius': 6.371e6}, + 'hcns': {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21}, +} + +mass_ratio = 0.107 # Mars / Earth +mars = { + 'name': 'Mars-scaled', + 'planet': {'M_mantle': 5.03e23, 'gravity': 3.71, 'radius': 3.39e6}, + 'hcns': {k: v * mass_ratio for k, v in earth['hcns'].items()}, +} + +T_magma = 2500.0 +diw = 0.5 +phi = 1.0 +``` + +## Step 2: solve each planet's atmosphere + +```python +def solve_atmosphere(cfg): + ddict = {**cfg['planet'], 'T_magma': T_magma, 'Phi_global': phi, + 'fO2_shift_IW': diw} + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + return equilibrium_atmosphere(cfg['hcns'], ddict, print_result=False) + +earth_out = solve_atmosphere(earth) +mars_out = solve_atmosphere(mars) + +print(f'Earth: P_surf = {earth_out["P_surf"]:6.1f} bar, M_atm = {earth_out["M_atm"]:.2e} kg') +print(f'Mars : P_surf = {mars_out["P_surf"]:6.1f} bar, M_atm = {mars_out["M_atm"]:.2e} kg') +``` + +You should see (current default Fischer 2011 buffer): + +``` +Earth: P_surf = 1449.4 bar, M_atm = 7.54e+21 kg +Mars : P_surf = 225.9 bar, M_atm = 8.79e+20 kg +``` + +Two things are worth noticing: + +- The Mars atmosphere has about $1/9$ the mass of Earth's, in line with the $0.107$ inventory scaling. The chemistry is roughly mass-linear at this regime. +- The Mars surface pressure is only $\sim 1/6$ of Earth's, not $1/9$, because the smaller gravity ($3.71$ m s$^{-2}$ vs $9.81$ m s$^{-2}$) trades against the smaller atmospheric mass: $P_\mathrm{surf} = g M_\mathrm{atm} / (4\pi R^2)$, so the gravity ratio ($0.38$) partly compensates the mass ratio ($0.11$). + +## Step 3: plot the comparison + +```python +import matplotlib.pyplot as plt + +from calliope.constants import dict_colors + +species = ['H2O', 'CO2', 'H2', 'CO', 'CH4', + 'N2', 'NH3', 'S2', 'SO2', 'H2S'] + +# Sort by Earth pressure descending; Mars uses the same order so the +# comparison is bar-by-bar. +order = sorted(species, key=lambda sp: -earth_out[f'{sp}_bar']) +earth_p = np.array([earth_out[f'{sp}_bar'] for sp in order]) +mars_p = np.array([mars_out[f'{sp}_bar'] for sp in order]) + +fig, ax = plt.subplots(figsize=(8.0, 5.4)) +y = np.arange(len(order)) * 1.8 +h = 0.7 +ax.barh(y - h / 2, earth_p, height=h, + color=[dict_colors[sp] for sp in order], + edgecolor='black', linewidth=0.5, label='Earth-BSE') +ax.barh(y + h / 2, mars_p, height=h, + color=[dict_colors[sp] for sp in order], alpha=0.45, + edgecolor='black', linewidth=0.5, hatch='///', + label='Mars-scaled (0.107 x Earth inventory)') +ax.set_xscale('log') +ax.set_yticks(y) +ax.set_yticklabels(order) +ax.invert_yaxis() +ax.set_xlabel('Surface partial pressure (bar)') +ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), ncol=2, frameon=False) +fig.tight_layout() +fig.savefig('mars_vs_earth.pdf') +``` + +## The goal of this tutorial + +![Earth vs Mars-scaled comparison](../assets/figures/tutorials/mars_fiducial.png) + +*Surface partial pressures at $T_\mathrm{magma} = 2500$ K, $\Delta\mathrm{IW} = +0.5$, $\Phi = 1$ on two bodies. Solid bars: Earth-BSE Krijt+2023 inventory on Earth planetary parameters. Hatched bars: same inventory scaled by Mars / Earth mass ratio (0.107) on Mars planetary parameters. Species ordered top-to-bottom by Earth partial pressure. Summary box top-right reports the three aggregate diagnostics for both bodies.* + +The figure makes one pedagogical point cleanly: at fixed redox and temperature, the per-species partial pressures on the Mars-scaled body track Earth's by roughly the same factor across the redox-relevant species (CO, CO$_2$, N$_2$, H$_2$, H$_2$O). The dominant-species *ordering* is preserved: CO dominates on both bodies because the C / H mass ratio in the inventory is identical (we scaled uniformly). What changes is the absolute scale. + +This is the right intuition to carry into more sophisticated planetary work. A genuinely different *composition* (different C / H, different volatile-element fractionation, a depleted N or S budget) will shift the dominant species and break the parallel-bar pattern. A genuinely different *structure* (Mars-mass body with Earth-mass mantle, or a Venus-mass body with Mars-fraction volatiles) will shift the surface pressure without shifting the speciation. The two effects separate cleanly because CALLIOPE's chemistry depends on element budgets, not on how those budgets were divided among Earth-mass and Mars-mass bodies. + +A third change, and the one likely most relevant to real Mars work, would substantially break the parallel-bar pattern: *the mantle redox state*. The shergottite parent-melt estimate of Mars's upper-mantle $f_{\mathrm{O}_2}$ from Wadhwa 2001[^cite-wadhwa2001] spans roughly $\Delta\mathrm{IW} \approx -1$ to $\Delta\mathrm{IW} \approx +2$, more reducing than the Sossi 2020 $\Delta\mathrm{IW} = +3.5$ Earth upper-mantle anchor used in this tutorial. The [Speciation phase diagram](phase_diagram.md) tutorial shows that at $\Delta\mathrm{IW} \lesssim +2$ the dominant species at this temperature flips from CO$_2$ to CO, and that H$_2$O and CO$_2$ drop sharply below the CO / H$_2$ pair. A Mars run that takes the petrologically motivated $\Delta\mathrm{IW} \sim +1$ instead of the $\Delta\mathrm{IW} = +0.5$ used here would therefore see substantially less H$_2$O and a much more reducing atmosphere overall, separately from the planetary-structure scaling explored on this page. + +## Where to go next + +- Swap in a literature Mars BSE inventory (Wänke & Dreibus 1988[^cite-waenkedreibus1988]) in the `mars['hcns']` dict and re-run; compare the per-species shift to the uniform-scaling result. +- Reduce the redox to $\Delta\mathrm{IW} = -3$ to look at a Mercury-like reducing endmember on the same Mars-mass body; the dominant species will switch. +- Read [Equilibrium chemistry](../Explanations/equilibrium_chemistry.md) for the reactions that hold across all of these scenarios. + +## Reproducing the figure + +Generated by [`scripts/tutorials/fig_mars_fiducial.py`](https://github.com/FormingWorlds/CALLIOPE/blob/main/scripts/tutorials/fig_mars_fiducial.py). Re-run with `python -m scripts.tutorials.fig_mars_fiducial` from the repository root. + +[^cite-krijt2023]: S. Krijt, M. Kama, M. McClure, J. Teske, E. A. Bergin, O. Shorttle, K. J. Walsh, S. N. Raymond, *Chemical habitability: supply and retention of life's essential elements during planet formation*, in Protostars and Planets VII, S. Inutsuka, Y. Aikawa, T. Muto, K. Tomida, M. Tamura, eds., Astronomical Society of the Pacific Conference Series, 534, 1031, 2023. +[^cite-waenkedreibus1988]: H. Wänke, G. Dreibus, *[Chemical composition and accretion history of terrestrial planets](https://doi.org/10.1098/rsta.1988.0067)*, Philosophical Transactions of the Royal Society A, 325(1587), 545, 1988. +[^cite-wadhwa2001]: M. Wadhwa, *[Redox state of Mars' upper mantle and crust from Eu anomalies in shergottite pyroxenes](https://doi.org/10.1126/science.1057594)*, Science, 291(5508), 1527, 2001. diff --git a/docs/Tutorials/phase_diagram.md b/docs/Tutorials/phase_diagram.md new file mode 100644 index 0000000..15c8b30 --- /dev/null +++ b/docs/Tutorials/phase_diagram.md @@ -0,0 +1,160 @@ +# Speciation phase diagram + +The first-run tutorial swept one parameter ($\Delta\mathrm{IW}$) at fixed temperature. Real magma oceans cool and re-equilibrate, so the dominant atmospheric species can shift as both $T$ and $\Delta\mathrm{IW}$ evolve. This tutorial generalises the 1D sweep to a 2D grid and builds a *speciation phase diagram*: at every $(T, \Delta\mathrm{IW})$ point, what are the four most abundant volatile species, and how does that quartet shift across the plane? + +By the end of it you will: + +- have run CALLIOPE on a 2D parameter grid using a warm-started serpentine sweep so the wall-time stays manageable; +- have produced the top-4 species map on the front of this page (each cell subdivided into four quadrants showing the species at ranks 1 through 4); +- understand why Earth-BSE atmospheres are CO- or CO$_2$-dominated rather than H$_2$O-dominated in the magma-ocean regime, and how the second through fourth species shift in lockstep with the dominant one. + +You should already have completed the [First run](firstrun.md) tutorial so the `equilibrium_atmosphere` call signature is familiar. + +## Step 1: set up the sweep + +```python +import warnings + +import numpy as np + +from calliope.constants import volatile_species +from calliope.solve import equilibrium_atmosphere + +planet = {'M_mantle': 4.03e24, 'gravity': 9.81, 'radius': 6.371e6} + +# Earth-BSE H/C/N/S in kg (Krijt et al. 2023, PPVII Tables 1 and 2). +earth_hcns = {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21} + +T_grid = np.linspace(1500.0, 3000.0, 15) +diw_grid = np.linspace( -4.0, 5.0, 12) + +species_to_report = ['H2O', 'CO2', 'O2', 'H2', 'CO', 'CH4', + 'N2', 'NH3', 'S2', 'SO2', 'H2S'] + +pressures = np.full((T_grid.size, diw_grid.size, len(species_to_report)), np.nan) +rank_idx = np.full((T_grid.size, diw_grid.size, 4), -1, dtype=int) +P_total = np.full((T_grid.size, diw_grid.size), np.nan) + + +def base_ddict(T, diw): + d = {**planet, 'T_magma': T, 'Phi_global': 1.0, 'fO2_shift_IW': diw} + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d +``` + +In the loop below we keep the full partial-pressure vector at each grid point, not just the index of the dominant species, so the figure step can sort and pick the top four. + +## Step 2: warm-start along $\Delta\mathrm{IW}$ at each $T$ + +Each call from cold takes about a second because the Monte-Carlo restart has to find a basin; with a `p_guess` from the previous call, subsequent solves complete in milliseconds. The cleanest schedule is a serpentine sweep: walk $\Delta\mathrm{IW}$ at fixed $T$, reset between rows. + +```python +for iT, T in enumerate(T_grid): + p_guess = None # cold-start each row + for jd, diw in enumerate(diw_grid): + ddict = base_ddict(float(T), float(diw)) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + try: + res = equilibrium_atmosphere( + earth_hcns, ddict, p_guess=p_guess, print_result=False, + ) + except Exception: + p_guess = None # invalidate guess on failure + continue + ps = np.array([res[f'{sp}_bar'] for sp in species_to_report]) + pressures[iT, jd] = ps + order = np.argsort(ps)[::-1] + rank_idx[iT, jd] = order[:4] # top-4 species indices + P_total[iT, jd] = float(res['P_surf']) + # carry the four primary partial pressures forward as the next guess + p_guess = {s: float(res[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} +``` + +A 15 × 12 grid runs in about 30 to 60 seconds on a modern laptop. The grid resolution is a tradeoff: fewer points runs faster but smears the redox boundary; more points sharpens the boundary but adds linearly to the wall time. + +!!! tip "If a cell fails to converge" + The solver can land in a secondary basin at extreme conditions. The `except` clause above invalidates the warm start so the next call cold-starts from a fresh Monte-Carlo draw. Failed cells show up as masked (white) in the figure rather than corrupting the colour map. + +## Step 3: subdivide each cell into a top-4 quartet + +Each grid cell is split into a 2 by 2 of sub-cells in reading order: top-left = rank 1 (dominant), top-right = rank 2, bottom-left = rank 3, bottom-right = rank 4. The pcolormesh below works on a doubled grid; the helper inserts a midpoint between every pair of cell edges so each original cell becomes four sub-cells. + +```python +import matplotlib.pyplot as plt +from matplotlib.colors import ListedColormap +from matplotlib.patches import Patch + +from calliope.constants import dict_colors + +# Expand rank_idx into a 2 x 2 sub-grid per original cell. The (sub_T, +# sub_d) coordinates map to ranks 1..4 as documented above. +n_T, n_d = rank_idx.shape[:2] +expanded = np.full((2 * n_T, 2 * n_d), -1, dtype=int) +for iT in range(n_T): + for jd in range(n_d): + r1, r2, r3, r4 = rank_idx[iT, jd] + expanded[2*iT + 1, 2*jd + 0] = r1 # top-left + expanded[2*iT + 1, 2*jd + 1] = r2 # top-right + expanded[2*iT + 0, 2*jd + 0] = r3 # bottom-left + expanded[2*iT + 0, 2*jd + 1] = r4 # bottom-right + +# Build a compact palette: only species that actually appear in any +# of the rank-1..rank-4 slots somewhere in the grid. +seen = sorted({int(v) for v in expanded.ravel() if v >= 0}) +palette = [dict_colors[species_to_report[i]] for i in seen] +remap = {old: new for new, old in enumerate(seen)} +remapped = np.vectorize(lambda v: remap.get(int(v), -1))(expanded) + +def edges_doubled(arr): + s = arr[1] - arr[0] + outer = np.concatenate([[arr[0] - 0.5*s], + 0.5*(arr[:-1] + arr[1:]), + [arr[-1] + 0.5*s]]) + mids = 0.5 * (outer[:-1] + outer[1:]) + out = np.empty(2 * outer.size - 1) + out[0::2] = outer; out[1::2] = mids + return out + +fig, ax = plt.subplots(figsize=(10.5, 5.8)) +ax.pcolormesh(edges_doubled(diw_grid), edges_doubled(T_grid), + np.ma.masked_less(remapped, 0), + cmap=ListedColormap(palette), + shading='flat', edgecolors='white', linewidth=0.35) +ax.set_xlabel(r'$\Delta$IW [dex]') +ax.set_ylabel(r'$T_\mathrm{magma}$ [K]') +ax.set_title('Top-4 species per cell at Earth-BSE') + +handles = [Patch(facecolor=palette[k], edgecolor='k', linewidth=0.4, + label=species_to_report[seen[k]]) for k in range(len(seen))] +ax.legend(handles=handles, loc='upper left', bbox_to_anchor=(1.02, 1.0), + title='species (any rank)', frameon=False) +fig.savefig('phase_diagram.pdf') +``` + +## The goal of this tutorial + +![Speciation phase diagram](../assets/figures/tutorials/phase_diagram.png) + +*Top-4 volatile species per cell in $(T_\mathrm{magma}, \Delta\mathrm{IW})$ at the Earth-BSE Krijt et al. 2023[^cite-krijt2023] H/C/N/S budget and $\Phi = 1$. Each grid cell is subdivided 2 by 2 in reading order (top-left = rank 1, top-right = rank 2, bottom-left = rank 3, bottom-right = rank 4) so the four most abundant species at that simulation point are visible at a glance. The redox boundary in the dominant species (top-left quadrant) separates a CO-dominated reducing regime (left) from a CO$_2$-dominated oxidising regime (right); the rank 2 to rank 4 quadrants reveal how H$_2$, H$_2$O, N$_2$, and SO$_2$ swap positions across the same boundary. A small CH$_4$ patch appears near $T \sim 1900$ K at the most reducing edge of the grid where methane synthesis becomes briefly thermodynamically competitive. At the hot, oxidising corner ($T \gtrsim 2700$ K and $\Delta\mathrm{IW} \gtrsim +4$) molecular O$_2$ itself enters the top four, reaching rank 2 at $T = 3000$ K, $\Delta\mathrm{IW} = +5$ (about 15% of $P_\mathrm{surf}$).* + +The result that may surprise a reader who thinks of magma-ocean atmospheres as "steam-dominated" is that on the *Earth* BSE inventory carbon sets the dominant species, even though hydrogen outnumbers carbon by molar count (5.6 $\times 10^{23}$ mol H against 2.6 $\times 10^{23}$ mol C). The cause is not the bulk inventory ratio but melt solubility: H$_2$O is roughly two orders of magnitude more soluble in silicate melt than CO$_2$, so at $\Phi = 1$ most of the H budget stays dissolved while most of the C outgases (Bower et al. 2022[^cite-bower2022] Section 3). A water-dominated atmosphere needs either a much higher H budget (gas-giant-like) or a much lower C budget (volatile-poor / dehydrated body); the planetary case study tutorial illustrates one such contrast. + +The CO / CO$_2$ phase boundary shifts from $\Delta\mathrm{IW} \approx +1$ at $T = 1500$ K to $\Delta\mathrm{IW} \approx +3$ at $T = 3000$ K, roughly $1.5$ dex of $T$-dependence across the grid. The shift reflects the temperature dependence of the CO + 1/2 O$_2$ $\rightleftharpoons$ CO$_2$ equilibrium constant (entropy favours CO at high $T$). At any given $T$, the boundary is sharp: cross it and CO$_2$ takes over within a single grid cell. + +A second feature lives at the hot, oxidising corner: molecular O$_2$ stops being a trace gas. The IW-buffer fugacity climbs steeply with temperature, the Fischer buffer rising about nine orders of magnitude from $\log_{10} f_{\mathrm{O}_2} \approx -11.6$ at $1500$ K to $\approx -2.5$ at $3000$ K, so a fixed $\Delta\mathrm{IW}$ offset maps to a far higher absolute O$_2$ fugacity at high $T$. At $T = 3000$ K and $\Delta\mathrm{IW} = +5$ the O$_2$ partial pressure reaches a few hundred bar, about $15\%$ of $P_\mathrm{surf}$ and second only to CO$_2$. Below $\sim 2700$ K, or at reducing $\Delta\mathrm{IW}$, O$_2$ falls back below the four reported species and does not appear; this is why the cooler and more reducing parts of the grid show no green. + +## Where to go next + +- For a finer look at the speciation transitions across $\Delta\mathrm{IW}$ at one $T$, return to [First run](firstrun.md) Step 6 and use a denser $\Delta\mathrm{IW}$ grid with this tutorial's warm-start pattern. +- For how PROTEUS handles repeated CALLIOPE calls across a time-stepped simulation, read the next tutorial: [Coupled-loop driver](coupled_loop.md). +- For the chemistry behind the dominant-species shifts, read [Equilibrium chemistry](../Explanations/equilibrium_chemistry.md). + +## Reproducing the figure + +Generated by [`scripts/tutorials/fig_phase_diagram.py`](https://github.com/FormingWorlds/CALLIOPE/blob/main/scripts/tutorials/fig_phase_diagram.py). Re-run with `python -m scripts.tutorials.fig_phase_diagram` from the repository root. + +[^cite-bower2022]: D. J. Bower, K. Hakim, P. A. Sossi, P. Sanan, *[Retention of water in terrestrial magma oceans and carbon-rich early atmospheres](https://doi.org/10.3847/PSJ/ac5fb1)*, The Planetary Science Journal, 3(4), 93, 2022. +[^cite-krijt2023]: S. Krijt, M. Kama, M. McClure, J. Teske, E. A. Bergin, O. Shorttle, K. J. Walsh, S. N. Raymond, *Chemical habitability: supply and retention of life's essential elements during planet formation*, in Protostars and Planets VII, S. Inutsuka, Y. Aikawa, T. Muto, K. Tomida, M. Tamura, eds., Astronomical Society of the Pacific Conference Series, 534, 1031, 2023. diff --git a/docs/Tutorials/two_modes.md b/docs/Tutorials/two_modes.md new file mode 100644 index 0000000..7d18fb6 --- /dev/null +++ b/docs/Tutorials/two_modes.md @@ -0,0 +1,139 @@ +# Two-mode round-trip + +CALLIOPE has two equilibrium-chemistry entry points that share the same physics but differ in which quantity is the input and which is the unknown. This tutorial walks you through both and shows that they are dual formulations of the same closure: feed the output of one into the other and you recover the original input. + +By the end of it you will: + +- have called both `equilibrium_atmosphere` (buffered mode) and `equilibrium_atmosphere_authoritative_O` (authoritative-O mode); +- know which key in the result dictionary to read from each; +- have produced the round-trip closure plot that opens this page. + +You should already have completed the [First run](firstrun.md) tutorial, which uses buffered mode only. + +## The two entry points in one sentence each + +- **Buffered mode**: you supply $\Delta\mathrm{IW}$ via `ddict['fO2_shift_IW']`, the solver returns partial pressures and `O_kg_total` as a derived quantity. +- **Authoritative-O mode**: you supply the total volatile O mass via `target_d['O']`, the solver returns partial pressures and `fO2_shift_derived` as the unknown. + +The conceptual page on [Authoritative-oxygen mode](../Explanations/authoritative_oxygen.md) explains why both exist; here we focus on the call signature and the round-trip. + +## Step 1: a buffered call at a known $\Delta\mathrm{IW}$ + +```python +import warnings + +from calliope.constants import volatile_species +from calliope.solve import ( + equilibrium_atmosphere, + equilibrium_atmosphere_authoritative_O, +) + +# Earth bulk-silicate-Earth H/C/N/S budget in kg (Krijt et al. 2023 PPVII Tables 1+2) +earth_hcns = {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21} + +ddict = { + 'M_mantle': 4.03e24, 'gravity': 9.81, 'radius': 6.371e6, + 'T_magma': 2000.0, + 'Phi_global': 1.0, + 'fO2_shift_IW': 1.0, # the buffer input +} +for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + +with warnings.catch_warnings(): + warnings.simplefilter('ignore') + buffered = equilibrium_atmosphere(earth_hcns, ddict, print_result=False) + +O_kg = buffered['O_kg_total'] +print(f'Buffered mode: dIW input = {ddict["fO2_shift_IW"]:+.2f}, O_kg_total = {O_kg:.3e} kg') +``` + +You should see something like `Buffered mode: dIW input = +1.00, O_kg_total = 1.006e+22 kg`. The total O is what the chemistry needed at $\Delta\mathrm{IW} = +1$ to balance the H, C, N, S budget against the buffer; it is a derived output, not a free knob. + +## Step 2: pass that O back through the authoritative-O entry point + +```python +target = dict(earth_hcns) +target['O'] = O_kg # promote O from "derived" to "input" + +p_guess = {s: buffered[f'{s}_bar'] for s in ('H2O', 'CO2', 'N2', 'S2')} + +with warnings.catch_warnings(): + warnings.simplefilter('ignore') + auth = equilibrium_atmosphere_authoritative_O( + target, ddict, + p_guess=p_guess, # warm start from the buffered result + fO2_hint=1.0, # near the expected solution + print_result=False, + ) + +print(f'Authoritative-O mode: dIW recovered = {auth["fO2_shift_derived"]:+.4f}, ' + f'residual = {auth["fO2_shift_derived"] - 1.0:+.2e} dex') +``` + +You should see the recovered $\Delta\mathrm{IW}$ match the input to within solver tolerance (typically $\lesssim 10^{-4}$ dex with a warm start; the warm-started chain in Step 3 below tightens this further). + +!!! note "Two API differences" + - The authoritative-O target dict **must** include a `'O'` key; a missing `'O'` raises `KeyError`. + - The recovered shift lives under `auth['fO2_shift_derived']`, not `auth['fO2_shift_IW']`. The latter key in `ddict` is ignored by this entry point. + +## Step 3: round-trip across the full redox range + +The round-trip should hold not only at the fiducial $\Delta\mathrm{IW} = +1$ but across the full magma-ocean redox range. Loop over a grid of buffer inputs, feed each one's `O_kg_total` back through authoritative-O, and store the recovered $\Delta\mathrm{IW}$. Thread the previous iteration's converged primary pressures forward as the next call's `p_guess` so the solver stays in the canonical basin across the sweep (without this, the buffered call at high $\Delta\mathrm{IW}$ can cold-start into a spurious basin and break closure for that grid point): + +```python +import numpy as np + +diw_grid = np.array([-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0]) +recovered = [] + +p_guess = None # cold-start the first buffered call +for diw in diw_grid: + ddict['fO2_shift_IW'] = float(diw) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + buf = equilibrium_atmosphere( + earth_hcns, ddict, p_guess=p_guess, print_result=False, + ) + # Carry the converged primaries forward as the warm start for the + # next buffered call AND as the guess for the authoritative-O leg. + p_guess = {s: float(buf[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} + target = dict(earth_hcns); target['O'] = buf['O_kg_total'] + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + auth = equilibrium_atmosphere_authoritative_O( + target, ddict, p_guess=p_guess, fO2_hint=float(diw), + print_result=False, + ) + recovered.append(auth['fO2_shift_derived']) + +recovered = np.array(recovered) +residuals = recovered - diw_grid +print('worst-case residual:', np.max(np.abs(residuals))) +``` + +A warm-started sweep prints something like `worst-case residual: 2.2e-11`. Closure holds at solver precision because both entry points solve the same underlying system; the residual reflects fsolve's `xtol`, not any chemistry difference. If you remove the `p_guess` threading, expect occasional grid points where the buffered call lands in a spurious basin and the closure residual jumps by several orders of magnitude. + +## The goal of this tutorial + +![Two-mode round-trip closure](../assets/figures/tutorials/two_modes_round_trip.png) + +*Recovered $\Delta\mathrm{IW}$ from authoritative-O mode against the input $\Delta\mathrm{IW}$ to buffered mode. Each circle is one input on the grid; the diagonal is perfect closure. Worst-case absolute residual ($\sim 2 \times 10^{-11}$ dex on this warm-started sweep) reflects the solver's mass-balance tolerance, not a calibration mismatch.* + +If your version of this plot does not land on the diagonal, the most likely cause is a missing or mis-typed `'O'` key in the authoritative-O target dict, or reading `auth['fO2_shift_IW']` (which is the unused input slot) instead of `auth['fO2_shift_derived']`. + +## When to pick which mode + +- **Buffered mode** is the right choice when you can specify the redox state directly: laboratory comparisons, exploratory parameter sweeps over $\Delta\mathrm{IW}$, anything where redox is an experimental variable rather than a derived quantity. +- **Authoritative-O mode** is the right choice in a planetary coupling where you track the volatile O budget end-to-end (escape losses, ingassing, mantle-atmosphere partitioning), and the redox state should emerge from the chemistry rather than be imposed. This is the entry point PROTEUS uses when `planet.fO2_source = "from_O_budget"`. + +## Where to go next + +- For the equations behind both entry points, read [Equilibrium chemistry](../Explanations/equilibrium_chemistry.md). +- For the conceptual distinction in more depth, read [Authoritative-oxygen mode](../Explanations/authoritative_oxygen.md). +- For how the two backends (CALLIOPE and atmodeller) compare on the *same* authoritative-O closure, read [Backend comparison](../Explanations/cross_backend_comparison.md). + +## Reproducing the figure + +The figure on this page is generated by [`scripts/tutorials/fig_two_modes.py`](https://github.com/FormingWorlds/CALLIOPE/blob/main/scripts/tutorials/fig_two_modes.py). Re-run with `python -m scripts.tutorials.fig_two_modes` from the repository root. diff --git a/docs/Validation/chemistry.md b/docs/Validation/chemistry.md new file mode 100644 index 0000000..a49a3ef --- /dev/null +++ b/docs/Validation/chemistry.md @@ -0,0 +1,72 @@ +# Validation: `src/calliope/chemistry.py` + +This page tracks the `@pytest.mark.reference_pinned` tests that anchor the +behaviour of `calliope.chemistry` against published sources. + +| Test id | Reference | Source page | Scope | +|---|---|---|---| +| `tests/test_chemistry.py::test_modified_keq_janaf_H2_matches_closed_form_at_2000K_with_oneill` | NIST JANAF Thermochemical Tables[^cite-chase1998] (4th ed.), fits for `H2O = H2 + 0.5 O2` over 1500-3000 K | `src/calliope/chemistry.py:41-43` (`janaf_H2`) | Pins `Geq(janaf_H2)` at T = 2000 K under the O'Neill 2002[^cite-oneilleggins2002] IW buffer against the closed-form `10^(Keq - 0.5 * log10_fO2)`; includes a wrong-reaction discrimination guard against `schaefer_H`[^cite-schaeferfegley2017] at the same conditions. | + +## Re-derivation note + +`ModifiedKeq` returns `Geq = 10^(Keq(T) - fO2_stoich * log10_fO2(T, dIW))`. +For `janaf_H2` the coefficients `(a = -13152.48, b = 3.038586, stoich = 0.5)` +fit the JANAF tables for `H2O = H2 + 0.5 O2` over the documented +1500-3000 K CALLIOPE-use range. + +At T = 2000 K, `fO2_model='oneill'`, `fO2_shift = 0`: + +``` +Keq = 10^(-13152.48 / 2000 + 3.038586) + = 10^(-6.5762 + 3.0386) + = 10^-3.5376 + ~ 2.901e-4 +``` + +The O'Neill 2002 IW buffer at the same T: + +``` +log10(fO2) = 2 * (-244118 + 115.559 * 2000 - 8.474 * 2000 * ln(2000)) + / (ln(10) * 8.31441 * 2000) + ~ -7.4078 +``` + +Combining: + +``` +log10(Geq) = -3.5376 - 0.5 * (-7.4078) = 0.1663 +Geq = 10^0.1663 ~ 1.467 +``` + +The test reconstructs the closed form symbolically (without intermediate +rounding) and pins to `rel=1e-6` precision. + +Wrong-reaction discrimination guard: `schaefer_H` at the same T: + +``` +Keq_schaefer = 10^(-12794 / 2000 + 2.7768) + = 10^-3.6202 ~ 2.398e-4 +Geq_schaefer = 10^(-3.6202 + 3.7039) = 10^0.0837 ~ 1.213 +``` + +The 0.25 difference between `Geq_janaf_H2 ~ 1.467` and `Geq_schaefer_H ~ 1.213` +exceeds the test's discrimination guard threshold (`abs(g - wrong) > 0.2`). + +## Anchor type + +Published benchmark (JANAF tables) plus cross-reaction discrimination +(janaf vs schaefer) plus a separate buffer-flip-propagation test +(`test_modified_keq_fO2_model_choice_changes_result`) that asserts +switching from O'Neill to Fischer changes Geq beyond rel=1e-6. + +## Cross-references + +- `src/calliope/chemistry.py:11-22`: `ModifiedKeq` constructor and `__call__`. +- `src/calliope/chemistry.py:24-62`: per-reaction coefficient methods. +- `docs/Explanations/equilibrium_chemistry.md`: user-facing concept page. + +## References + +[^cite-chase1998]: M. W. Chase, *[NIST-JANAF Thermochemical Tables, 4th edition](https://janaf.nist.gov/)*, Journal of Physical and Chemical Reference Data Monograph 9, 1998. +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151-181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). +[^cite-schaeferfegley2017]: L. Schaefer, B. Fegley, *[Redox states of initial atmospheres outgassed on rocky planets and planetesimals](https://doi.org/10.3847/1538-4357/aa784f)*, The Astrophysical Journal, 843(2), 120, 2017. [SciX](https://scixplorer.org/abs/2017ApJ...843..120S/abstract). diff --git a/docs/Validation/oxygen_fugacity.md b/docs/Validation/oxygen_fugacity.md new file mode 100644 index 0000000..4e92fd9 --- /dev/null +++ b/docs/Validation/oxygen_fugacity.md @@ -0,0 +1,69 @@ +# Validation: `src/calliope/oxygen_fugacity.py` + +This page tracks the `@pytest.mark.reference_pinned` tests that anchor the +behaviour of `calliope.oxygen_fugacity` against published sources. + +| Test id | Reference | Source page | Scope | +|---|---|---|---| +| `tests/test_oxygen_fugacity.py::test_oxygen_fugacity_fischer_value_at_2000K_matches_published_fit` | Fischer et al. (2011)[^cite-fischer2011], EPSL 304, 496, Eq. 2; cross-checked against O'Neill & Eggins (2002)[^cite-oneilleggins2002] | [doi:10.1016/j.epsl.2011.02.025](https://doi.org/10.1016/j.epsl.2011.02.025) | Pins the Fischer-vs-O'Neill cross-calibration offset (0.258 dex at T = 2000 K) as the independent anchor, with a secondary regression check on the coded Fischer fit `6.94059 - 28.1808e3 / T` and a wrong-buffer discrimination guard against O'Neill & Eggins (2002) at the same T. | + +## Re-derivation note + +`OxygenFugacity('fischer')` implements Fischer et al. (2011) Eq. 2 for +the iron-wüstite (IW) buffer: + +``` +log10(fO2)_IW(T) = 6.94059 - 28.1808e3 / T +``` + +At T = 2000 K this evaluates to: + +``` +log10(fO2) = 6.94059 - 14.0904 = -7.14981 +``` + +Cross-check: O'Neill & Eggins (2002) IW at the same T: + +``` +log10(fO2) = 2 * (-244118 + 115.559*T - 8.474*T*ln(T)) / (ln(10) * 8.31441 * T) + ≈ -7.4078 at T = 2000 K +``` + +The 0.26 dex offset between the two buffers at 2000 K is the discrimination +guard's anchor: a regression that silently dispatches to the wrong buffer +would land 0.26 dex away from the expected value. + +## Default buffer + +The CALLIOPE default IW buffer is Fischer (2011). Tests that pin an IW +value MUST carry a discrimination guard so a change of default (or an +accidental config-side override) does not silently move the test's +reference point. + +## Anchor type + +Cross-implementation cross-check plus published benchmark. The +independent anchor is the cross-calibration offset between the Fischer +(2011) and O'Neill & Eggins (2002) IW fits, which are coded from separate +published formulae; the offset is independent of either single fit, so a +coefficient error in either moves it and fails the test. The coded +Fischer value is pinned as a secondary regression check, and the O'Neill +value also serves as the wrong-buffer discrimination guard. + +## Cross-references + +- `src/calliope/oxygen_fugacity.py` lines 26-34: implementations of both + buffers, with a comment at line 32 pinning the `8.31441` constant to + the literal O'Neill & Eggins (2002) value (do not replace with + `constants.R_gas`). +- `docs/Explanations/oxygen_fugacity.md`: user-facing concept page with + the empirical anchor (Earth ~ IW+3.5, Mars ~ IW, Mercury IW-3 to IW-5) + for both buffers. +- `docs/Explanations/cross_backend_comparison.md`: empirical comparison + of CALLIOPE Fischer vs atmodeller Hirschmann combined, showing the + 0.16 dex residual at Earth fiducial after the buffer-default flip. + +## References + +[^cite-fischer2011]: R. A. Fischer, A. J. Campbell, G. A. Shofner, O. T. Lord, P. Dera, V. B. Prakapenka, *[Equation of state and phase diagram of FeO](https://doi.org/10.1016/j.epsl.2011.02.025)*, Earth and Planetary Science Letters, 304, 496-502, 2011. [SciX](https://scixplorer.org/abs/2011E%26PSL.304..496F/abstract). +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151-181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). diff --git a/docs/Validation/solubility.md b/docs/Validation/solubility.md new file mode 100644 index 0000000..3c73394 --- /dev/null +++ b/docs/Validation/solubility.md @@ -0,0 +1,78 @@ +# Validation: `src/calliope/solubility.py` + +This page tracks the `@pytest.mark.reference_pinned` tests that anchor the +behaviour of `calliope.solubility` against published sources. + +| Test id | Reference | Source page | Scope | +|---|---|---|---| +| `tests/test_solubility.py::TestSolubilityH2O::test_peridotite_default_matches_sossi_2023_fit` | Sossi et al. (2023)[^cite-sossi2023] peridotite H2O fit: `ppmw = 524 * p^0.5` | `src/calliope/solubility.py:39-41` (`peridotite`) | Pins the default H2O parameterization at p = 100 bar against the Sossi 2023 constant; includes wrong-law (Dixon et al. 1995[^cite-dixon1995] basalt) and wrong-exponent (1.0 vs 0.5) discrimination guards. | +| `tests/test_solubility.py::TestSolubilityS2_xFeO::test_default_call_matches_gaillard_2022_earth_mantle_value` | Gaillard et al. (2022)[^cite-gaillard2022], EPSL 117255, S2 solubility law with `x_FeO = 10 wt%` Earth-mantle default | [doi:10.1016/j.epsl.2021.117255](https://doi.org/10.1016/j.epsl.2021.117255), `src/calliope/solubility.py:75-93` | Pins S2 ppmw at (p = 1 bar, T = 2500 K, fO2_shift = 0) against the closed-form Gaillard expression; couples to the Fischer 2011[^cite-fischer2011] IW buffer default. | + +## Re-derivation notes + +### H2O (Sossi 2023 peridotite default) + +Power-law form: `ppmw = const * p^exponent`. Sossi et al. (2023) gives +`const = 524 ppmw/bar^0.5` for peridotite melt. At p = 100 bar: + +``` +ppmw = 524 * sqrt(100) = 5240 +``` + +A regression that swapped to the Dixon 1995 basalt constant (`const = 965`) +would land at 9650, off by 84% at the same pressure. A regression that +flipped the exponent (0.5 -> 1.0) would land at 52400, off by an order of +magnitude. Both are caught by the discrimination guards in the test. + +### S2 (Gaillard 2022 Earth-mantle default) + +Closed form from Gaillard et al. (2022): + +``` +ln(X_S^melt) = 13.8426 - 26476/T + 0.124*x_FeO + 0.5*ln(p_S2/fO2) +``` + +At T = 2500 K, p_S2 = 1 bar, x_FeO = 10 wt%, fO2 from the Fischer 2011 IW +buffer: + +``` +log10(fO2) = -4.3317 (Fischer at T=2500 K, dIW=0) +fO2 = 10^-4.3317 = 4.659e-5 bar +ln(p_S2/fO2) = ln(1 / 4.659e-5) = 9.9742 +ln(X) = 13.8426 - 10.5904 + 1.24 + 0.5 * 9.9742 + = 3.2522 + 1.24 + 4.9871 + = 9.4793 +ppmw = exp(9.4793) ~ 13086 +``` + +Hidden coupling: the pin depends on the Fischer 2011 IW buffer; any change +to the buffer coefficients in `oxygen_fugacity.py` requires regenerating this +number. The test docstring flags this coupling explicitly. The reference +value in the test (13085.87) matches the closed-form ppmw above to the +4-significant-figure precision of the Fischer coefficients. + +## Anchor types + +- H2O: published benchmark (Sossi 2023) plus wrong-law and wrong-exponent + discrimination guards. +- S2: published benchmark (Gaillard 2022) plus closed-form scaling tests + for `x_FeO` (parametrized 5/10/15/20 wt%) so the prefactor coefficient + 0.124 is independently verified. + +## Cross-references + +- `src/calliope/solubility.py:30-57`: H2O parameterizations. +- `src/calliope/solubility.py:60-100`: S2 with the `x_FeO` kwarg. +- `src/calliope/solubility.py:100+`: N2 with the `x_SiO2`, `x_Al2O3`, + `x_TiO2` kwargs (Dasgupta 2022[^cite-dasgupta2022]) plus Libourel 2003[^cite-libourel2003] as the no-composition baseline. +- `docs/Explanations/solubility.md`: user-facing concept page with the + validity envelope per parameterization. + +## References + +[^cite-dasgupta2022]: R. Dasgupta, E. Falksen, A. Pal, C. Sun, *[The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions](https://doi.org/10.1016/j.gca.2022.09.012)*, Geochimica et Cosmochimica Acta, 336, 291-307, 2022. [SciX](https://scixplorer.org/abs/2022GeCoA.336..291D/abstract). +[^cite-dixon1995]: J. E. Dixon, E. M. Stolper, J. R. Holloway, *[An experimental study of water and carbon dioxide solubilities in mid-ocean ridge basaltic liquids. Part I: Calibration and solubility models](https://doi.org/10.1093/oxfordjournals.petrology.a037267)*, Journal of Petrology, 36(6), 1607-1631, 1995. [SciX](https://scixplorer.org/abs/1995JPet...36.1607D/abstract). +[^cite-fischer2011]: R. A. Fischer, A. J. Campbell, G. A. Shofner, O. T. Lord, P. Dera, V. B. Prakapenka, *[Equation of state and phase diagram of FeO](https://doi.org/10.1016/j.epsl.2011.02.025)*, Earth and Planetary Science Letters, 304, 496-502, 2011. [SciX](https://scixplorer.org/abs/2011E%26PSL.304..496F/abstract). +[^cite-gaillard2022]: F. Gaillard, F. Bernadou, M. Roskosz, M. A. Bouhifd, Y. Marrocchi, G. Iacono-Marziano, M. Moreira, B. Scaillet, G. Rogerie, *[Redox controls during magma ocean degassing](https://doi.org/10.1016/j.epsl.2021.117255)*, Earth and Planetary Science Letters, 577, 117255, 2022. [SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract). +[^cite-libourel2003]: G. Libourel, B. Marty, F. Humbert, *[Nitrogen solubility in basaltic melt. Part I. Effect of oxygen fugacity](https://doi.org/10.1016/S0016-7037(03)00259-X)*, Geochimica et Cosmochimica Acta, 67(21), 4123-4135, 2003. [SciX](https://scixplorer.org/abs/2003GeCoA..67.4123L/abstract). +[^cite-sossi2023]: P. A. Sossi, P. M. E. Tollan, J. Badro, D. J. Bower, *[Solubility of water in peridotite liquids and the prevalence of steam atmospheres on rocky planets](https://doi.org/10.1016/j.epsl.2022.117894)*, Earth and Planetary Science Letters, 601, 117894, 2023. [SciX](https://scixplorer.org/abs/2023E%26PSL.60117894S/abstract). diff --git a/docs/Validation/solve.md b/docs/Validation/solve.md new file mode 100644 index 0000000..1a21549 --- /dev/null +++ b/docs/Validation/solve.md @@ -0,0 +1,78 @@ +# Validation: `src/calliope/solve.py` + +This page tracks the `@pytest.mark.reference_pinned` tests that anchor the +behaviour of `calliope.solve` against published or self-consistency sources. + +| Test id | Reference | Source page | Scope | +|---|---|---|---| +| `tests/test_solve.py::test_round_trip_self_consistency_at_earth_fiducial` | Cross-implementation cross-check: CALLIOPE forward `equilibrium_atmosphere` vs CALLIOPE inverse `equilibrium_atmosphere_authoritative_O` | `src/calliope/solve.py` (forward and authoritative-O entry points) | Pins the round-trip property at the Earth-fiducial input: forward solve at `fO2 = IW + 2` produces an O budget; the authoritative-O inverse from that budget recovers `fO2_shift_derived` within 0.05 dex. | + +## Re-derivation note + +`calliope.solve` exposes two entry points into the equilibrium-chemistry +solver: + +- `equilibrium_atmosphere(target, ddict)` (legacy / forward): user + supplies `fO2_shift_IW` in `ddict`; the solver returns the equilibrium + partial pressures and per-species kg. +- `equilibrium_atmosphere_authoritative_O(target, ddict)` (authoritative-O + inverse): user supplies the target `O_kg_total` in `target['O']`; + the solver inverts to find the `fO2_shift_IW` that matches it, then + forwards into the same equilibrium engine. + +The round-trip is the contract: the inverse must recover the forward-mode +`fO2_shift_IW` from the forward-mode `O_kg_total`. The test pins this at +the Earth-fiducial input (`T_magma = 1800 K`, `Phi = 1.0`, Earth-like +H/C/N/S budget, all volatile species included, `fO2 = IW + 2`) and asserts +the recovered `fO2_shift_derived` matches the input within 0.05 dex. + +Anchor type: forward-inverse closure of one engine. Both entry points +share the same equilibrium chemistry. The legacy mode takes +`fO2_shift_IW` as a control variable and returns the implied `O_kg_total`; +the authoritative-O mode treats `fO2_shift_IW` as a fifth unknown and +solves the 5x5 mass-balance system (four partial pressures plus fO2) for +the `fO2_shift_derived` that reproduces the supplied `O_kg_total`. Their +round-trip agreement is the property the test pins. A regression that +broke either the forward O mass-balance or the inverse solve would lose +the round-trip within 0.1 dex; the 0.05 dex envelope catches a +coefficient-only bug. + +## Cross-cutting topical test files + +`solve.py` is large (>1200 LOC) and its full test surface is split across +several topical cross-cutting files for readability: + +- `tests/test_authoritative_O.py` and siblings: authoritative-O entry + point contract, monotonicity (`test_authoritative_O_monotonicity.py`), + input validation (`test_authoritative_O_validation.py`). +- `tests/test_equilibrium_paths.py`: forward solver behaviour on + multi-species compositions. +- `tests/test_partial_species.py`: partial-species (some elements + excluded) branches. +- `tests/test_stoichiometry.py`: stoichiometric ratios across the + published reactions. +- `tests/test_targets.py`: target-element-budget computation. +- `tests/test_invariants.py`: per-element / per-species closure + invariants. +- `tests/test_invariants_hypothesis.py`: property-based fuzz tests at + the slow tier. + +This is a documented exception to the strict 1:1 source-to-test +mirroring rule for sources >500 LOC where the test topics are +independent enough that consolidation would hurt readability. + +## Anchor types + +- Self-consistency cross-implementation (round-trip forward vs inverse). +- Future addition: cross-backend cross-check (CALLIOPE vs atmodeller at + the Earth-fiducial) at the slow tier, once `scripts/cross_backend/` + fixtures land in `tests/`. + +## Cross-references + +- `src/calliope/solve.py`: forward and authoritative-O entry points. +- `docs/Explanations/authoritative_oxygen.md`: user-facing concept page + on the authoritative-O entry point. +- `docs/Explanations/cross_backend_comparison.md`: empirical comparison + of CALLIOPE Fischer vs atmodeller Hirschmann at the Earth fiducial + (ΔIW = 0.16 dex residual after the buffer-default flip). diff --git a/docs/Validation/structure.md b/docs/Validation/structure.md new file mode 100644 index 0000000..98c37c4 --- /dev/null +++ b/docs/Validation/structure.md @@ -0,0 +1,42 @@ +# Validation: `src/calliope/structure.py` + +This page tracks the `@pytest.mark.reference_pinned` tests that anchor the +behaviour of `calliope.structure` against a published source. + +| Test id | Reference | Source page | Scope | +|---|---|---|---| +| `tests/test_structure.py::test_calculate_mantle_mass_recovers_wang_2018_earth_core_fraction` | Wang, Lineweaver & Ireland (2018)[^cite-wang2018], arxiv:1708.08718: Earth core mass fraction 32.5 +/- 0.3 wt% | [arxiv:1708.08718](https://arxiv.org/abs/1708.08718) | Pins the Earth-like mantle mass against the published core mass fraction and verifies the result lies in the 1e24 to 1e25 kg envelope expected for an Earth-mass planet. | + +## Re-derivation note + +`structure.calculate_mantle_mass` computes mantle mass by subtracting the +core mass from the total planetary mass. The core mass is the volume of a +sphere of radius `core_frac * R_planet` multiplied by a core density derived +from Earth: `core_rho = 3 * earth_fm * M_earth / (4 pi * (earth_fr * R_earth)^3)` +with `earth_fm = 0.325` and `earth_fr = 0.55` from Wang et al. (2018)[^cite-wang2018]. + +For Earth-like input (`R = R_earth`, `M = M_earth`, `core_frac = 0.55`), the +construction is degenerate: the core density times the Earth-radius core +volume reproduces the cited 0.325 mass fraction, so the mantle equals +`(1 - 0.325) * M_earth = 0.675 * M_earth ~ 4.03e24 kg`. + +Scale: a regression that swaps the `r^3` core-volume factor for `r^2` would +land at ~3e18 kg, six orders of magnitude below the correct value. The +test's scale guard `1e24 < mantle < 1e25` brackets the correct order and +fails on any factor-of-10 unit slip. + +## Anchor type + +Published benchmark + analytical limit. The Wang+2018 cite is the +published-benchmark anchor; the conservation closure `M_mantle + M_core = M_planet` +is asserted separately in `test_calculate_mantle_mass_closure_holds_for_earth_like` +as the analytical-limit second-line check. + +## Cross-references + +- `src/calliope/structure.py` line 50: cites arxiv:1708.08718 for Earth core mass fraction 0.325. +- PROTEUS `src/proteus/orbit/satellite.py` uses the same Earth core fraction in the satellite angular-momentum decomposition; both codes are pinned against the same source. + +## References + +[^cite-wang2018]: H. S. Wang, C. H. Lineweaver, T. R. Ireland, *[The elemental abundances (with uncertainties) of the most Earth-like planet](https://doi.org/10.1016/j.icarus.2017.08.024)*, Icarus, 299, 460-474, 2018. 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For coupled atmosphere-interior runs, the [PROTEUS Getting Started guide](https://proteus-framework.org/PROTEUS) is the right entry point; this site documents the standalone CALLIOPE API and the contract it exposes to PROTEUS. + CALLIOPE is most commonly installed and used as part of the [PROTEUS framework](https://proteus-framework.org/PROTEUS). For coupled atmosphere-interior runs, the [PROTEUS Getting Started guide](https://proteus-framework.org/PROTEUS) is the right entry point; this site documents the standalone CALLIOPE API and the interface it exposes to PROTEUS. ## Quick path diff --git a/docs/index.md b/docs/index.md index 836c5a1..9cd1cab 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,14 +1,17 @@ +# CALLIOPE + [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Docs](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/docs.yaml?branch=main&label=Docs)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/docs.yaml) -[![codecov](https://img.shields.io/codecov/c/github/FormingWorlds/CALLIOPE?label=coverage&logo=codecov)](https://app.codecov.io/gh/FormingWorlds/CALLIOPE) -[![Unit Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/tests.yaml?branch=main&label=Unit%20Tests)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/tests.yaml) -[![Integration Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/nightly.yml?branch=main&label=Integration%20Tests)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/nightly.yml) - -# CALLIOPE +[![codecov](https://img.shields.io/codecov/c/github/FormingWorlds/CALLIOPE?label=coverage&logo=codecov&color=brightgreen)](https://app.codecov.io/gh/FormingWorlds/CALLIOPE) +[![Unit Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/tests.yaml?branch=main&label=Unit%20Tests&color=brightgreen)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/tests.yaml) +[![Integration Tests](https://img.shields.io/github/actions/workflow/status/FormingWorlds/CALLIOPE/nightly.yml?branch=main&label=Integration%20Tests&color=brightgreen)](https://github.com/FormingWorlds/CALLIOPE/actions/workflows/nightly.yml) **CALLIOPE** is the equilibrium outgassing solver of the [PROTEUS](https://proteus-framework.org/PROTEUS) coupled atmosphere-interior evolution framework. It computes the partitioning of volatile elements between a partially molten silicate mantle and an overlying gas-phase atmosphere, assuming both reservoirs are in thermochemical equilibrium at the planetary surface. -Given an elemental inventory (H, C, N, S), a magma ocean temperature $T_\mathrm{magma}$, a melt fraction $\Phi_\mathrm{global}$, and an oxygen fugacity $f_{\mathrm{O}_2}$ (specified as a $\log_{10}$ shift from the iron-wüstite buffer), CALLIOPE solves a four-equation mass-balance system for the surface partial pressures of the four primary species (H$_2$O, CO$_2$, N$_2$, S$_2$) and propagates the speciation to the seven secondary species (H$_2$, CH$_4$, CO, NH$_3$, SO$_2$, H$_2$S, O$_2$). +Given an elemental inventory and a magma ocean state ($T_\mathrm{magma}$, $\Phi_\mathrm{global}$), CALLIOPE solves a nonlinear mass-balance system for the surface partial pressures of the four primary species (H$_2$O, CO$_2$, N$_2$, S$_2$) and propagates the speciation to the seven secondary species (H$_2$, CH$_4$, CO, NH$_3$, SO$_2$, H$_2$S, O$_2$). The solver runs in either of two modes that share the same physics functions and differ only in their unknown set: + +- **Buffered mode** ([`equilibrium_atmosphere`](Explanations/mass_balance.md)) takes an oxygen fugacity $f_{\mathrm{O}_2}$ (specified as a $\log_{10}$ shift from the iron-wüstite buffer) as input and solves a four-equation system for the H, C, N, S budget. Oxygen mass is derived. +- **Authoritative-oxygen mode** ([`equilibrium_atmosphere_authoritative_O`](Explanations/authoritative_oxygen.md)) takes a five-element budget including O and solves a five-equation system for the four pressures plus $\Delta\mathrm{IW}$. Oxygen fugacity is derived. Named after the [Greek muse of eloquence and epic poetry](https://en.wikipedia.org/wiki/Calliope). Pronounced *kal-IGH-uh-pee*. @@ -18,10 +21,10 @@ Named after the [Greek muse of eloquence and epic poetry](https://en.wikipedia.o ## Features - **Eleven volatile species**: H$_2$O, CO$_2$, N$_2$, S$_2$ as primary unknowns; H$_2$, CH$_4$, CO, NH$_3$, SO$_2$, H$_2$S, O$_2$ derived from gas-phase equilibrium -- **Five elemental conservation channels**: H, C, N, S as solved constraints; O fixed by the $f_{\mathrm{O}_2}$ buffer -- **Configurable redox state**: [O'Neill & Eggins (2002)](https://ui.adsabs.harvard.edu/abs/2002ChGeo.186..151O) iron-wüstite (IW) buffer with arbitrary $\Delta\mathrm{IW}$ shift, or [Fischer et al. (2011)](https://ui.adsabs.harvard.edu/abs/2011E%26PSL.304..496F) IW -- **Calibrated equilibrium constants**: [JANAF](https://janaf.nist.gov/) and [Schaefer & Fegley (2017)](https://ui.adsabs.harvard.edu/abs/2017ApJ...843..120S) fits for the H$_2$O–H$_2$, CO$_2$–CO, CO$_2$+H$_2$–CH$_4$, S$_2$–SO$_2$, S$_2$+H$_2$–H$_2$S, and N$_2$+H$_2$–NH$_3$ couples -- **Multiple solubility laws per species**: peridotite (default H$_2$O, [Sossi et al. 2023](https://ui.adsabs.harvard.edu/abs/2023E%26PSL.60117894S)), basalt ([Dixon et al. 1995](https://ui.adsabs.harvard.edu/abs/1995JPet...36.1607D), [Wilson & Head 1981](https://ui.adsabs.harvard.edu/abs/1981JGR....86.2971W), [Hamilton et al. 1964](https://doi.org/10.1093/petrology/5.1.21)), anorthite-diopside ([Newcombe et al. 2017](https://ui.adsabs.harvard.edu/abs/2017GeCoA.200..330N)), lunar glass ([Newcombe et al. 2017](https://ui.adsabs.harvard.edu/abs/2017GeCoA.200..330N)); CO$_2$ ([Dixon et al. 1995](https://ui.adsabs.harvard.edu/abs/1995JPet...36.1607D)); CO ([Armstrong et al. 2015](https://ui.adsabs.harvard.edu/abs/2015GeCoA.171..283A)); CH$_4$ ([Ardia et al. 2013](https://ui.adsabs.harvard.edu/abs/2013GeCoA.114...52A)); N$_2$ ([Libourel et al. 2003](https://ui.adsabs.harvard.edu/abs/2003GeCoA..67.4123L) or [Dasgupta et al. 2022](https://ui.adsabs.harvard.edu/abs/2022GeCoA.336..291D)); S$_2$ ([Gaillard et al. 2022](https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717255G)) +- **Five elemental conservation channels**: H, C, N, S always solved; O either derived from the $f_{\mathrm{O}_2}$ buffer (buffered mode) or supplied as a fifth budget (authoritative-O mode) +- **Configurable redox state**: Fischer et al. (2011)[^cite-fischer2011] iron-wüstite (IW) buffer with arbitrary $\Delta\mathrm{IW}$ shift (default; chosen to be close to atmodeller's Hirschmann composite across the magma-ocean range), or the legacy O'Neill & Eggins (2002)[^cite-oneilleggins2002] IW +- **Calibrated equilibrium constants**: JANAF[^cite-chase1998] and Schaefer & Fegley (2017)[^cite-schaeferfegley2017] fits for the H$_2$O-H$_2$, CO$_2$-CO, CO$_2$+H$_2$-CH$_4$, S$_2$-SO$_2$, S$_2$+H$_2$-H$_2$S, and N$_2$+H$_2$-NH$_3$ couples +- **Multiple solubility laws per species**: peridotite (default H$_2$O, Sossi et al. 2023[^cite-sossi2023]), basalt (Dixon et al. 1995[^cite-dixon1995], Wilson & Head 1981[^cite-wilsonhead1981], Hamilton et al. 1964[^cite-hamilton1964]), anorthite-diopside (Newcombe et al. 2017[^cite-newcombe2017]), lunar glass (Newcombe et al. 2017[^cite-newcombe2017]); CO$_2$ (Dixon et al. 1995[^cite-dixon1995]); CO (Armstrong et al. 2015[^cite-armstrong2015]); CH$_4$ (Ardia et al. 2013[^cite-ardia2013]); N$_2$ (Libourel et al. 2003[^cite-libourel2003] or Dasgupta et al. 2022[^cite-dasgupta2022]); S$_2$ (Gaillard et al. 2022[^cite-gaillard2022]) - **Robust hybrid solver**: alternating `scipy.optimize.fsolve` (Powell hybrid) and `trust-constr` minimisation, with Monte-Carlo restart on failure - **PROTEUS-coupled or standalone**: the same equilibrium kernel powers both the in-loop call from PROTEUS and one-off scripts @@ -70,9 +73,10 @@ Named after the [Greek muse of eloquence and epic poetry](https://en.wikipedia.o If you use CALLIOPE in published work, please cite the original equilibrium-chemistry framework, the modern multi-species redox treatment, and the magma-ocean evolution study that introduced the present extended species list: -- Bower, D.J., Kitzmann, D., Wolf, A.S., Sanan, P., Dorn, C., & Oza, A.V. (2019). *Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations*. **Astronomy & Astrophysics**, 631, A103. \[[ADS](https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.103B) | [DOI](https://doi.org/10.1051/0004-6361/201935710)\] -- Bower, D.J., Hakim, K., Sossi, P.A., & Sanan, P. (2022). *Retention of water in terrestrial magma oceans and carbon-rich early atmospheres*. **The Planetary Science Journal**, 3, 93. \[[ADS](https://ui.adsabs.harvard.edu/abs/2022PSJ.....3...93B) | [DOI](https://doi.org/10.3847/PSJ/ac5fb1)\] -- Nicholls, H., Lichtenberg, T., Bower, D.J., & Pierrehumbert, R. (2024). *Magma ocean evolution at arbitrary redox state*. **Journal of Geophysical Research: Planets**, 129, e2024JE008576. \[[ADS](https://ui.adsabs.harvard.edu/abs/2024JGRE..12908576N) | [DOI](https://doi.org/10.1029/2024JE008576) | [arXiv](https://arxiv.org/abs/2411.19137)\] +- Bower, D.J., Kitzmann, D., Wolf, A.S., Sanan, P., Dorn, C., & Oza, A.V. (2019). *Linking the evolution of terrestrial interiors and an early outgassed atmosphere to astrophysical observations*. **Astronomy & Astrophysics**, 631, A103. \[[SciX](https://scixplorer.org/abs/2019A%26A...631A.103B/abstract) | [DOI](https://doi.org/10.1051/0004-6361/201935710) | [arXiv](https://arxiv.org/abs/1904.08300)\] +- Bower, D.J., Hakim, K., Sossi, P.A., & Sanan, P. (2022). *Retention of water in terrestrial magma oceans and carbon-rich early atmospheres*. **The Planetary Science Journal**, 3, 93. \[[SciX](https://scixplorer.org/abs/2022PSJ.....3...93B/abstract) | [DOI](https://doi.org/10.3847/PSJ/ac5fb1) | [arXiv](https://arxiv.org/abs/2110.08029)\] +- Nicholls, H., Lichtenberg, T., Bower, D.J., & Pierrehumbert, R. (2024). *Magma ocean evolution at arbitrary redox state*. **Journal of Geophysical Research: Planets**, 129, e2024JE008576. \[[SciX](https://scixplorer.org/abs/2024JGRE..12908576N/abstract) | [DOI](https://doi.org/10.1029/2024JE008576) | [arXiv](https://arxiv.org/abs/2411.19137)\] +- Nicholls, H., Lichtenberg, T., Chatterjee, R.D., Guimond, C.M., Postolec, E., & Pierrehumbert, R.T. (2026). *Volatile-rich evolution of molten super-Earth L 98-59 d*. **Nature Astronomy**. \[[SciX](https://scixplorer.org/abs/2026NatAs.tmp...61N/abstract) | [DOI](https://doi.org/10.1038/s41550-026-02815-8) | [arXiv](https://arxiv.org/abs/2507.02656)\] See the [Publications](Reference/publications.md) page for the full reference list, including the underlying solubility-law and equilibrium-constant sources. @@ -87,3 +91,18 @@ If you are running into problems, please do not hesitate to raise an [Issue](htt ## License [Apache License 2.0](https://opensource.org/licenses/Apache-2.0). See [the included license](https://github.com/FormingWorlds/CALLIOPE/blob/main/LICENSE.txt). + +[^cite-ardia2013]: P. Ardia, M. M. Hirschmann, A. C. Withers, B. D. Stanley, *[Solubility of CH$_4$ in a synthetic basaltic melt, with applications to atmosphere-magma ocean-core partitioning of volatiles and to the evolution of the Martian atmosphere](https://doi.org/10.1016/j.gca.2013.03.028)*, Geochimica et Cosmochimica Acta, 114, 52–71, 2013. [SciX](https://scixplorer.org/abs/2013GeCoA.114...52A/abstract). +[^cite-armstrong2015]: L. S. Armstrong, M. M. Hirschmann, B. D. Stanley, E. G. Falksen, S. D. Jacobsen, *[Speciation and solubility of reduced C-O-H-N volatiles in mafic melt: implications for volcanism, atmospheric evolution, and deep volatile cycles in the terrestrial planets](https://doi.org/10.1016/j.gca.2015.07.007)*, Geochimica et Cosmochimica Acta, 171, 283–302, 2015. [SciX](https://scixplorer.org/abs/2015GeCoA.171..283A/abstract). +[^cite-chase1998]: M. W. Chase, *[NIST-JANAF Thermochemical Tables, 4th edition](https://janaf.nist.gov/)*, Journal of Physical and Chemical Reference Data Monograph 9, 1998. +[^cite-dasgupta2022]: R. Dasgupta, E. Falksen, A. Pal, C. Sun, *[The fate of nitrogen during parent body partial melting and accretion of the inner Solar System bodies at reducing conditions](https://doi.org/10.1016/j.gca.2022.09.012)*, Geochimica et Cosmochimica Acta, 336, 291–307, 2022. [SciX](https://scixplorer.org/abs/2022GeCoA.336..291D/abstract). +[^cite-dixon1995]: J. E. Dixon, E. M. Stolper, J. R. Holloway, *[An experimental study of water and carbon dioxide solubilities in mid-ocean ridge basaltic liquids. Part I: Calibration and solubility models](https://doi.org/10.1093/oxfordjournals.petrology.a037267)*, Journal of Petrology, 36(6), 1607–1631, 1995. [SciX](https://scixplorer.org/abs/1995JPet...36.1607D/abstract). +[^cite-fischer2011]: R. A. Fischer, A. J. Campbell, G. A. Shofner, O. T. Lord, P. Dera, V. B. Prakapenka, *[Equation of state and phase diagram of FeO](https://doi.org/10.1016/j.epsl.2011.02.025)*, Earth and Planetary Science Letters, 304, 496–502, 2011. [SciX](https://scixplorer.org/abs/2011E%26PSL.304..496F/abstract). +[^cite-gaillard2022]: F. Gaillard, F. Bernadou, M. Roskosz, M. A. Bouhifd, Y. Marrocchi, G. Iacono-Marziano, M. Moreira, B. Scaillet, G. Rogerie, *[Redox controls during magma ocean degassing](https://doi.org/10.1016/j.epsl.2021.117255)*, Earth and Planetary Science Letters, 577, 117255, 2022. [SciX](https://scixplorer.org/abs/2022E%26PSL.57717255G/abstract). +[^cite-hamilton1964]: D. L. Hamilton, C. W. Burnham, E. F. Osborn, *[The solubility of water and effects of oxygen fugacity and water content on crystallization in mafic magmas](https://doi.org/10.1093/petrology/5.1.21)*, Journal of Petrology, 5(1), 21–39, 1964. +[^cite-libourel2003]: G. Libourel, B. Marty, F. Humbert, *[Nitrogen solubility in basaltic melt. Part I. Effect of oxygen fugacity](https://doi.org/10.1016/S0016-7037(03)00259-X)*, Geochimica et Cosmochimica Acta, 67(21), 4123–4135, 2003. [SciX](https://scixplorer.org/abs/2003GeCoA..67.4123L/abstract). +[^cite-newcombe2017]: M. E. Newcombe, A. Brett, J. R. Beckett, M. B. Baker, S. Newman, Y. Guan, J. M. Eiler, E. M. Stolper, *[Solubility of water in lunar basalt at low pH$_2$O](https://doi.org/10.1016/j.gca.2016.12.026)*, Geochimica et Cosmochimica Acta, 200, 330–352, 2017. [SciX](https://scixplorer.org/abs/2017GeCoA.200..330N/abstract). +[^cite-oneilleggins2002]: H. St. C. O'Neill, S. M. Eggins, *[The effect of melt composition on trace element partitioning: an experimental investigation of the activity coefficients of FeO, NiO, CoO, MoO$_2$ and MoO$_3$ in silicate melts](https://doi.org/10.1016/S0009-2541(01)00414-4)*, Chemical Geology, 186, 151–181, 2002. [SciX](https://scixplorer.org/abs/2002ChGeo.186..151O/abstract). +[^cite-schaeferfegley2017]: L. Schaefer, B. Fegley, *[Redox states of initial atmospheres outgassed on rocky planets and planetesimals](https://doi.org/10.3847/1538-4357/aa784f)*, The Astrophysical Journal, 843(2), 120, 2017. [SciX](https://scixplorer.org/abs/2017ApJ...843..120S/abstract). +[^cite-sossi2023]: P. A. Sossi, P. M. E. Tollan, J. Badro, D. J. Bower, *[Solubility of water in peridotite liquids and the prevalence of steam atmospheres on rocky planets](https://doi.org/10.1016/j.epsl.2022.117894)*, Earth and Planetary Science Letters, 601, 117894, 2023. [SciX](https://scixplorer.org/abs/2023E%26PSL.60117894S/abstract). +[^cite-wilsonhead1981]: L. Wilson, J. W. Head, *[Ascent and eruption of basaltic magma on the Earth and Moon](https://doi.org/10.1029/JB086iB04p02971)*, Journal of Geophysical Research, 86(B4), 2971–3001, 1981. [SciX](https://scixplorer.org/abs/1981JGR....86.2971W/abstract). diff --git a/docs/proteus-framework.md b/docs/proteus-framework.md index e18ac10..6fc5c99 100644 --- a/docs/proteus-framework.md +++ b/docs/proteus-framework.md @@ -82,4 +82,4 @@ CALLIOPE is invoked from PROTEUS via: - `proteus/outgas/wrapper.py`: the dispatch layer that selects between `calliope`, `atmodeller`, and `dummy` outgassing modules; also computes target elemental inventories and runs the binodal H$_2$ partition. - `proteus/config/_outgas.py`: the attrs-based schema for `[outgas]` and `[outgas.calliope]`. All knobs documented in the how-to map back to fields here. -These files live in the [PROTEUS repository](https://github.com/FormingWorlds/PROTEUS), not in CALLIOPE. Their per-symbol API documentation is rendered in the PROTEUS docs; this site documents the CALLIOPE side of the contract. +These files live in the [PROTEUS repository](https://github.com/FormingWorlds/PROTEUS), not in CALLIOPE. Their per-symbol API documentation is rendered in the PROTEUS docs; this site documents the CALLIOPE side of the interface. diff --git a/mkdocs.yml b/mkdocs.yml index 4024013..ba8080f 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -14,12 +14,18 @@ nav: - Installation: How-to/installation.md - Configuration: How-to/configuration.md - Usage: How-to/usage.md + - Authoritative-oxygen mode: How-to/authoritative_oxygen.md - Coupling to PROTEUS: How-to/proteus_coupling.md - Build a new test: How-to/build_tests.md - Releasing: How-to/releasing.md - Tutorials: - First run: Tutorials/firstrun.md + - Two-mode round-trip: Tutorials/two_modes.md + - Speciation phase diagram: Tutorials/phase_diagram.md + - Coupled-loop driver: Tutorials/coupled_loop.md + - Reproducing the Earth fiducial: Tutorials/earth_fiducial.md + - Mars-like atmosphere: Tutorials/mars_fiducial.md - Explanations: - Model overview: Explanations/model.md @@ -27,7 +33,9 @@ nav: - Solubility laws: Explanations/solubility.md - Oxygen fugacity: Explanations/oxygen_fugacity.md - Mass balance & solver: Explanations/mass_balance.md + - Authoritative-oxygen mode: Explanations/authoritative_oxygen.md - Coupling to PROTEUS: Explanations/proteus_coupling.md + - Backend comparison: Explanations/cross_backend_comparison.md - Code architecture: Explanations/code_architecture.md - Testing suite: Explanations/testing.md @@ -40,6 +48,12 @@ nav: - Oxygen fugacity: Reference/api/calliope.oxygen_fugacity.md - Structure: Reference/api/calliope.structure.md - Constants: Reference/api/calliope.constants.md + - Validation anchors: + - Chemistry: Validation/chemistry.md + - Oxygen fugacity: Validation/oxygen_fugacity.md + - Solubility: Validation/solubility.md + - Solve: Validation/solve.md + - Structure: Validation/structure.md - Publications: Reference/publications.md - Community: diff --git a/pyproject.toml b/pyproject.toml index 3155008..9f4a849 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,8 @@ description = "Planetary accretion and volatile delivery model" readme = "README.md" requires-python = ">=3.12" authors = [ - {name = "Harrison Nicholls, email = harrison.nicholls@physics.ox.ac.uk"}, + {name = "Harrison Nicholls", email = "h-nicholls@pm.me"}, + {name = "Tim Lichtenberg", email = "tim.lichtenberg@rug.nl"}, ] keywords = [ "Astronomy", @@ -45,6 +46,7 @@ changelog = "https://github.com/FormingWorlds/CALLIOPE/releases" [project.optional-dependencies] develop = [ "coverage", + "hypothesis >= 6.0", "pip-tools", "pytest >= 8.1", "pytest-cov", @@ -85,10 +87,12 @@ branch = true source = ["calliope"] [tool.coverage.report] -# Hard 90% coverage gate. 90% is the PROTEUS-ecosystem ceiling: -# the gate does not rise above this value even if real coverage -# exceeds it. Above 90% you are tracking pragma usage and style, -# not bug-finding signal. +# Full coverage gate: unit + smoke + integration + slow tests, enforced by +# the nightly workflow. 90% is the PROTEUS-ecosystem ceiling: the gate does +# not rise above this value even if real coverage exceeds it. Above 90% +# the gate tracks pragma usage and style rather than bug-finding signal. +# Auto-ratcheted by tools/update_coverage_threshold.py; never manually +# decrease. See .github/.claude/rules/calliope-tests.md section 15. fail_under = 90.0 show_missing = true exclude_lines = [ @@ -97,6 +101,14 @@ exclude_lines = [ "if __name__ == .__main__.:", ] +[tool.calliope.coverage_fast] +# Fast PR gate: unit + smoke coverage only, enforced on every PR via +# .github/workflows/tests.yaml. Capped at the 90% PROTEUS-ecosystem +# ceiling by tools/update_coverage_threshold.py. Measured unit+smoke +# coverage exceeds 90, so the gate sits at the ceiling. Never manually +# decrease. See .github/.claude/rules/calliope-tests.md section 15. +fail_under = 90.0 + [tool.pytest.ini_options] testpaths = ["tests"] markers = [ @@ -104,6 +116,8 @@ markers = [ "smoke: real-binary tests on minimal configurations (< 30 s each)", "integration: real-binary tests with full coupling", "slow: anything > 1 minute, run in nightly only", + "physics_invariant: marks tests that assert a physical invariant (conservation, positivity, boundedness, monotonicity, symmetry). See .github/.claude/rules/calliope-tests.md section 3.", + "reference_pinned: marks tests that pin behavior against a published benchmark, analytical limit, or cross-implementation cross-check. Cite the source in the test docstring. Each physics source must contain at least one. See .github/.claude/rules/calliope-tests.md section 3.", ] [tool.ruff] diff --git a/scripts/cross_backend/.gitignore b/scripts/cross_backend/.gitignore new file mode 100644 index 0000000..c18dd8d --- /dev/null +++ b/scripts/cross_backend/.gitignore @@ -0,0 +1 @@ +__pycache__/ diff --git a/scripts/cross_backend/README.md b/scripts/cross_backend/README.md new file mode 100644 index 0000000..095087c --- /dev/null +++ b/scripts/cross_backend/README.md @@ -0,0 +1,84 @@ +# Cross-backend comparison harness + +This directory contains the reusable harness that produces the figures +embedded in `docs/Explanations/cross_backend_comparison.md`. + +The harness is investigation tooling. It is not shipped with the +installed `fwl-calliope` distribution and not part of the test suite. + +## What's here + +| File | Purpose | +|---|---| +| `inventories.py` | Earth BSE H / C / N / S inventory (Krijt et al. 2023 PPVII Tables 1+2); volatile-O reference derived self-consistently at Delta-IW = +3.5 (Sossi 2020) | +| `buffers.py` | Analytical IW buffer formulae: O'Neill & Eggins 2002, Fischer 2011, Hirschmann composite | +| `runners.py` | Backend-uniform call wrappers: `run_calliope`, `run_atmodeller` | +| `verification.py` | Pre-flight per-backend round-trip checks (callable as `python -m scripts.cross_backend.verification`) | +| `plot_style.py` | Shared matplotlib styling and output paths | +| `fig1_buffers.py` ... `fig5_earth_anchor.py` | One script per figure | +| `run_all.sh` | One-shot regenerator | +| `data/` | Raw CSV output from each figure script (created on first run) | + +## How to re-run + +```bash +# from the repo root, with the proteus conda env active +bash scripts/cross_backend/run_all.sh +``` + +To regenerate a single figure: + +```bash +python3 -m scripts.cross_backend.fig3_grid +``` + +To run the verification harness without producing any figure: + +```bash +python3 -m scripts.cross_backend.verification +``` + +## Wall time + +Approximate timings on a 2024 M-series Mac (proteus conda env): + +| Figure | Wall time | +|---|---| +| Fig 1 (analytical) | < 1 s | +| Fig 2 (round-trip, 4 T x 4 dIW x 2 backends) | 5-10 min | +| Fig 3 (grid, 4 T x 5 O-factor x 2 backends) | 5-10 min | +| Fig 4 (attribution, 3 calls) | 1 min | +| Fig 5 (Earth anchor, 2 calls) | 30 s | + +The dominant cost is the first atmodeller call per Python process +(JAX compile, ~60 s); subsequent calls are ~15 s warm. + +## Re-using on a different fiducial + +The pattern is: + +```python +from scripts.cross_backend.inventories import Inventory, EARTH_BSE_KRIJT23, scale_O +from scripts.cross_backend.runners import run_calliope, run_atmodeller + +# Build a different inventory +mars_inv = Inventory(name='Mars (placeholder)', + H=..., C=..., N=..., O=..., S=..., + citation='Wanke & Dreibus 1988') + +# Run both backends +cal = run_calliope(mars_inv, T_magma=1800.0, fO2_hint=-1.0) +atm = run_atmodeller(mars_inv, T_magma=1800.0) +print(cal.fO2_shift_derived, atm.fO2_shift_derived) +``` + +For sensitivity sweeps, the `scale_O` helper varies only the O budget +while keeping H/C/N/S fixed (used by `fig3_grid.py`). + +## Provenance + +Every figure script saves its raw inputs and outputs to +`data/fig_*.csv`. The committed CSVs are the provenance for the +PDFs and PNGs in `docs/assets/figures/cross_backend/`. Re-running a +figure script overwrites the CSV; re-running at a different +calliope / atmodeller commit will change the numbers. diff --git a/scripts/cross_backend/__init__.py b/scripts/cross_backend/__init__.py new file mode 100644 index 0000000..6e04f76 --- /dev/null +++ b/scripts/cross_backend/__init__.py @@ -0,0 +1,9 @@ +"""Cross-backend verification harness for the CALLIOPE backend +comparison docs page. + +This package is not shipped with the installed CALLIOPE distribution. +It is investigation tooling that lives alongside the docs page +`Explanations/cross_backend_comparison.md`. + +Re-run with `scripts/cross_backend/run_all.sh` from the repository root. +""" diff --git a/scripts/cross_backend/buffers.py b/scripts/cross_backend/buffers.py new file mode 100644 index 0000000..7ee092a --- /dev/null +++ b/scripts/cross_backend/buffers.py @@ -0,0 +1,76 @@ +"""Analytical IW (iron-wustite) buffer formulae. + +The buffer divergence is the single largest cross-backend systematic +and can be evaluated without any chemistry solver, so the harness +computes it from the published formulae directly. + +- Fischer et al. (2011): 1-bar reference, used by CALLIOPE by default. +- O'Neill & Eggins (2002): monolithic fit, CALLIOPE legacy / alternative. +- Hirschmann composite: Hirschmann (2008) below 1000 K, Hirschmann + (2021) above 1000 K. Used by atmodeller by default. The H21 branch is + a multi-coefficient Saxena-style polynomial; we delegate to + atmodeller's evaluator (`IronWustiteBufferHirschmann`) rather than + reimplement it, so any future correction in atmodeller propagates + here automatically. + +All formulae return log10 fO2_IW at the buffer, in bar. Pressure is in +bar. +""" + +from __future__ import annotations + +import numpy as np + + +def oneill(T: np.ndarray) -> np.ndarray: + """O'Neill & Eggins (2002) IW buffer, monolithic fit. + + Reproduces the CALLIOPE implementation in + `calliope.oxygen_fugacity.oneill`. The 8.31441 J/mol/K gas-constant + value reproduces O'Neill & Eggins Eq. 11 verbatim; do not substitute + a more modern CODATA value. + """ + T = np.asarray(T, dtype=float) + R = 8.31441 + return 2.0 * (-244118.0 + 115.559 * T - 8.474 * T * np.log(T)) / (np.log(10.0) * R * T) + + +def fischer(T: np.ndarray) -> np.ndarray: + """Fischer et al. (2011) IW buffer, 1-bar reference.""" + T = np.asarray(T, dtype=float) + return 6.94059 - 28.1808e3 / T + + +def hirschmann_composite(T: np.ndarray, P_bar: float = 1.0) -> np.ndarray: + """Hirschmann composite IW buffer: H08 below 1000 K, H21 above. + + Delegates to atmodeller's `IronWustiteBufferHirschmann` evaluator at + `evaluation_pressure = P_bar`. Returns a numpy array. + """ + from atmodeller.thermodata import IronWustiteBuffer + + buf = IronWustiteBuffer(log10_shift=0.0, evaluation_pressure=P_bar) + T_arr = np.atleast_1d(np.asarray(T, dtype=float)) + out = np.array([float(buf.log10_fugacity_buffer(t, P_bar)) for t in T_arr]) + return out if T_arr.shape == np.asarray(T).shape else out + + +def hirschmann_minus_oneill_offset(T: np.ndarray, P_bar: float = 1.0) -> np.ndarray: + """log10(fO2_Hirschmann) - log10(fO2_ONeill) at the buffer (no shift). + + This is the analytical correction that lets you subtract the buffer- + convention contribution from a cross-backend Delta-IW comparison + using CALLIOPE's legacy (O'Neill) buffer choice. + """ + return hirschmann_composite(T, P_bar) - oneill(T) + + +def hirschmann_minus_fischer_offset(T: np.ndarray, P_bar: float = 1.0) -> np.ndarray: + """log10(fO2_Hirschmann) - log10(fO2_Fischer) at the buffer (no shift). + + The analogue of `hirschmann_minus_oneill_offset` for CALLIOPE's + current default buffer (Fischer 2011). Fischer is much closer to + Hirschmann than O'Neill across the magma-ocean range, so this + offset is sub-0.2 dex everywhere on the figure-3 sweep. + """ + return hirschmann_composite(T, P_bar) - fischer(T) diff --git a/scripts/cross_backend/data/fig2_roundtrip.csv b/scripts/cross_backend/data/fig2_roundtrip.csv new file mode 100644 index 0000000..1835104 --- /dev/null +++ b/scripts/cross_backend/data/fig2_roundtrip.csv @@ -0,0 +1,33 @@ +backend,T_K,dIW_in,dIW_recovered,residual_dex +calliope,1500.0,-2.0,-2.0000000003517058,-3.5170577561416394e-10 +calliope,1500.0,0.0,-3.509594360085853e-10,-3.509594360085853e-10 +calliope,1500.0,2.0,2.000000361268213,3.6126821312265633e-07 +calliope,1500.0,4.0,3.9999999003585214,-9.964147862362438e-08 +calliope,2000.0,-2.0,-2.000000004363807,-4.363807093454852e-09 +calliope,2000.0,0.0,-4.174459795499295e-10,-4.174459795499295e-10 +calliope,2000.0,2.0,1.9999999195370843,-8.046291566365937e-08 +calliope,2000.0,4.0,3.9999999181910533,-8.180894672804584e-08 +calliope,2500.0,-2.0,-1.999999998269471,1.7305290533897733e-09 +calliope,2500.0,0.0,-9.039458876625358e-06,-9.039458876625358e-06 +calliope,2500.0,2.0,1.99999999989422,-1.0578005138484059e-10 +calliope,2500.0,4.0,4.000000221527546,2.2152754564075394e-07 +calliope,3000.0,-2.0,-1.9999999999126183,8.738165746535742e-11 +calliope,3000.0,0.0,-0.0002644213922394017,-0.0002644213922394017 +calliope,3000.0,2.0,2.000002380769887,2.3807698870115246e-06 +calliope,3000.0,4.0,-5.8426730953359725,-9.842673095335972 +atmodeller,1500.0,-2.0,-1.9999999999999893,1.0658141036401503e-14 +atmodeller,1500.0,0.0,nan,nan +atmodeller,1500.0,2.0,1.999999999999984,-1.5987211554602254e-14 +atmodeller,1500.0,4.0,3.9999999999998854,-1.1457501614131615e-13 +atmodeller,2000.0,-2.0,-1.9999999999999725,2.7533531010703882e-14 +atmodeller,2000.0,0.0,2.220446049250313e-14,2.220446049250313e-14 +atmodeller,2000.0,2.0,1.9999999999999076,-9.237055564881302e-14 +atmodeller,2000.0,4.0,4.000000000000097,9.681144774731365e-14 +atmodeller,2500.0,-2.0,-1.9999999999999538,4.618527782440651e-14 +atmodeller,2500.0,0.0,1.1546319456101628e-14,1.1546319456101628e-14 +atmodeller,2500.0,2.0,2.0000000000000364,3.6415315207705135e-14 +atmodeller,2500.0,4.0,3.999999999999991,-8.881784197001252e-15 +atmodeller,3000.0,-2.0,-2.0000000000000124,-1.2434497875801753e-14 +atmodeller,3000.0,0.0,1.3766765505351941e-14,1.3766765505351941e-14 +atmodeller,3000.0,2.0,2.0000000000000115,1.1546319456101628e-14 +atmodeller,3000.0,4.0,3.9999999999999076,-9.237055564881302e-14 diff --git a/scripts/cross_backend/data/fig3_grid.csv b/scripts/cross_backend/data/fig3_grid.csv new file mode 100644 index 0000000..528713d --- /dev/null +++ b/scripts/cross_backend/data/fig3_grid.csv @@ -0,0 +1,6 @@ +T_K,dIW_calliope_fischer,dIW_calliope_oneill,dIW_atmodeller,buffer_offset_HminusF_dex,buffer_offset_HminusO_dex,raw_gap_F_dex,raw_gap_O_dex,residual_after_buffer_F_dex,residual_after_buffer_O_dex,P_total_cal_fischer_bar,P_total_cal_oneill_bar,P_total_atm_bar +1800.0,3.3547133646825595,3.3706463373708275,3.304544044997156,0.1439258352565833,0.1598734082246942,-0.05016931968540339,-0.06610229237367138,0.09375651557117992,0.09377111585102282,1705.2052642022381,1705.20441620464,1749.6059597378267 +2000.0,3.240423513448377,3.498435192217306,3.075478874307803,0.09618923043237704,0.3542030725637417,-0.164944639140574,-0.42295631790950283,-0.06875540870819696,-0.0687532453457611,1711.7387797861447,1711.738712684541,1781.8439554067547 +2400.0,2.9451630877696697,3.5877724110730647,2.6962526149494654,0.009705286364194876,0.6523133626591546,-0.2489104728202043,-0.8915197961235992,-0.2392051864560094,-0.2392064334644446,1744.3384583606462,1744.3391946616,1864.266336391453 +2800.0,2.6988859768797497,3.637384796631304,2.4836287325872792,-0.0704310053945445,0.8680635632656033,-0.2152572442924705,-1.1537560640440248,-0.285688249687015,-0.28569250077842145,1784.6411089074902,1784.6431721056179,1933.2363290260334 +3000.0,2.599287438114697,3.6626282104635557,2.430772908519921,-0.10933096014752586,0.9540094698463162,-0.16851452959477609,-1.231855301943635,-0.27784548974230194,-0.2778458320973187,1803.3199403597441,1803.3208259585779,1956.2682742385005 diff --git a/scripts/cross_backend/data/fig4_attribution.csv b/scripts/cross_backend/data/fig4_attribution.csv new file mode 100644 index 0000000..c2606b3 --- /dev/null +++ b/scripts/cross_backend/data/fig4_attribution.csv @@ -0,0 +1,11 @@ +T_magma,2000.0 +cal_fischer_dIW,3.240423513448377 +cal_oneill_dIW,3.498435192217306 +atm_default_dIW,3.075478874307803 +atm_aligned_dIW,2.9362851059613675 +raw_gap_fischer,-0.164944639140574 +raw_gap_oneill,-0.42295631790950283 +buffer_offset_fischer,0.09618923043237704 +buffer_offset_oneill,0.3542030725637417 +after_buffer_fischer,-0.06875540870819696 +after_solubility,-0.20794917705463245 diff --git a/scripts/cross_backend/data/fig5_earth_anchor.csv b/scripts/cross_backend/data/fig5_earth_anchor.csv new file mode 100644 index 0000000..e765a96 --- /dev/null +++ b/scripts/cross_backend/data/fig5_earth_anchor.csv @@ -0,0 +1,11 @@ +quantity,value +T_magma_K,2000.0 +cal_fischer_dIW,3.240423513448377 +cal_oneill_dIW,3.498435192217306 +atm_dIW,3.075478874307803 +cal_fischer_P_total_bar,1711.7387797861447 +cal_oneill_P_total_bar,1711.738712684541 +atm_P_total_bar,1781.8439554067547 +empirical_low_dIW,1.0 +empirical_high_dIW,5.0 +sossi_2020_center,3.5 diff --git a/scripts/cross_backend/fig1_buffers.py b/scripts/cross_backend/fig1_buffers.py new file mode 100644 index 0000000..0d68250 --- /dev/null +++ b/scripts/cross_backend/fig1_buffers.py @@ -0,0 +1,101 @@ +"""Figure 1: The IW buffer divergence. + +Top panel: log10 fO2 at the IW buffer vs temperature for the three +parameterisations used in the FWL ecosystem (O'Neill & Eggins 2002 the +CALLIOPE default; Fischer et al. 2011 the CALLIOPE alternative; +Hirschmann composite the atmodeller default). + +Bottom panel: difference between Hirschmann and the two CALLIOPE +parameterisations, in dex. + +Pure analytical evaluation: no chemistry solver, no random seed. +""" + +from __future__ import annotations + +import logging + +import matplotlib.pyplot as plt +import numpy as np + +from . import buffers +from .plot_style import COLOR_ATM, COLOR_CAL, COLOR_FIS, apply_style, panel_label, save + +log = logging.getLogger('cross_backend.fig1') + + +def make_figure() -> dict: + apply_style() + + T = np.linspace(800.0, 3500.0, 271) + f_oneill = buffers.oneill(T) + f_fischer = buffers.fischer(T) + f_hirsch = buffers.hirschmann_composite(T) + d_hirsch_oneill = f_hirsch - f_oneill + d_hirsch_fischer = f_hirsch - f_fischer + + fig, (ax_top, ax_bot) = plt.subplots( + 2, + 1, + figsize=(6.4, 6.4), + sharex=True, + gridspec_kw={'height_ratios': [2.0, 1.0], 'hspace': 0.12}, + ) + + ax_top.plot(T, f_fischer, color=COLOR_CAL, label='Fischer et al. 2011 (CALLIOPE default)') + ax_top.plot( + T, + f_oneill, + color=COLOR_FIS, + linestyle='--', + label="O'Neill & Eggins 2002 (CALLIOPE legacy)", + ) + ax_top.plot(T, f_hirsch, color=COLOR_ATM, label='Hirschmann composite (atmodeller default)') + + # Annotate the Hirschmann composite switchover. Anchor inside the + # data range with a y just above the curves, not at the panel + # edge, so the text never gets clipped against the frame. + ax_top.axvline(1000.0, color='k', alpha=0.25, linestyle=':') + y_lo, y_hi = ax_top.get_ylim() + ax_top.text( + 1010, + y_lo + 0.85 * (y_hi - y_lo), + 'H08 / H21\nswitchover', + fontsize=8.5, + va='top', + ha='left', + alpha=0.6, + ) + + ax_top.set_ylabel(r'$\log_{10} f_{\mathrm{O}_2}$ at IW buffer') + ax_top.set_title('Iron-wustite buffer parameterisations across magma-ocean temperatures') + ax_top.legend(loc='lower right', framealpha=0.0) + panel_label(ax_top, '(a)') + + ax_bot.axhline(0.0, color='k', alpha=0.4, linewidth=0.7) + ax_bot.plot(T, d_hirsch_oneill, color=COLOR_ATM, label="Hirschmann − O'Neill") + ax_bot.plot( + T, d_hirsch_fischer, color=COLOR_FIS, linestyle='--', label='Hirschmann − Fischer' + ) + ax_bot.axvline(1000.0, color='k', alpha=0.25, linestyle=':') + + ax_bot.set_xlabel(r'$T$ [K]') + ax_bot.set_ylabel(r'$\Delta\log_{10} f_{\mathrm{O}_2}$ [dex]') + ax_bot.legend(loc='lower right') + panel_label(ax_bot, '(b)') + + # Mark a few characteristic magma-ocean temperatures with vertical guides. + for T_mark, label in [(1500, '1500 K'), (2000, '2000 K'), (3000, '3000 K')]: + ax_bot.axvline(T_mark, color='k', alpha=0.1, linewidth=0.7) + ax_top.axvline(T_mark, color='k', alpha=0.1, linewidth=0.7) + + paths = save(fig, 'fig1_buffer_divergence') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig(level=logging.INFO) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/cross_backend/fig2_roundtrip.py b/scripts/cross_backend/fig2_roundtrip.py new file mode 100644 index 0000000..6128a7f --- /dev/null +++ b/scripts/cross_backend/fig2_roundtrip.py @@ -0,0 +1,208 @@ +"""Figure 2: Each backend round-trips internally across the magma-ocean +temperature and redox range. + +We sweep T_magma because the chemistry is genuinely T-dependent. The +modified equilibrium constants are evaluated at T; the Dasgupta (2022) +nitrogen solubility has explicit T and fO2 dependences; the Gaillard +(2022) sulfur solubility has an explicit fO2 dependence. A round-trip +that only worked at one T would not be evidence of internal +consistency. Each backend must invert cleanly across the full range +where the calibrated chemistry is valid. + +Each (T, dIW_input) combination produces one residual dIW_recovered − +dIW_input. The figure plots this residual vs T_magma for each input +dIW, separately for the two backends. If the chemistry path is +internally consistent the residual should be sub-tolerance everywhere. + +Reused output: writes `data/fig2_roundtrip.csv` so the docs page can +quote the worst-case residual without re-running the harness. +""" + +from __future__ import annotations + +import csv +import logging +import time + +import matplotlib.pyplot as plt +import numpy as np + +from .plot_style import DATA_DIR, apply_style, panel_label, save +from .verification import round_trip_atmodeller, round_trip_calliope + +log = logging.getLogger('cross_backend.fig2') + + +T_GRID = [1500.0, 2000.0, 2500.0, 3000.0] +DIW_GRID = [-2.0, 0.0, 2.0, 4.0] + +# Discrete high-contrast palette, one colour per T_magma. We sweep T +# because the chemistry is genuinely T-dependent: the equilibrium +# constants, the Dasgupta nitrogen solubility, and the Gaillard sulfur +# solubility all carry explicit T (and fO2) terms. A round-trip that +# only worked at one T would not be evidence of internal consistency. +# Cool -> warm matches the colour scale to the temperature. +T_COLORS = { + 1500.0: '#3949ab', # indigo (coolest) + 2000.0: '#00897b', # teal + 2500.0: '#fb8c00', # orange + 3000.0: '#d81b60', # magenta (hottest) +} + + +def collect() -> dict: + """Return {backend: [(T, dIW_in, dIW_out), ...]} for both backends.""" + out = {'calliope': [], 'atmodeller': []} + for backend, rt in ( + ('calliope', round_trip_calliope), + ('atmodeller', round_trip_atmodeller), + ): + log.info('Round-trip: %s', backend) + for T in T_GRID: + for dIW in DIW_GRID: + t0 = time.time() + try: + _, recov = rt(T, dIW) + except Exception as exc: # noqa: BLE001 + log.warning(' %s T=%.0f dIW=%.1f raised %s', backend, T, dIW, exc) + recov = float('nan') + dt = time.time() - t0 + log.info(' T=%.0f dIW=%+.1f -> %+.3f (%.1fs)', T, dIW, recov, dt) + out[backend].append((T, dIW, recov)) + return out + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'fig2_roundtrip.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow(['backend', 'T_K', 'dIW_in', 'dIW_recovered', 'residual_dex']) + for backend, rows in data.items(): + for T, dIW, recov in rows: + resid = recov - dIW if np.isfinite(recov) else float('nan') + w.writerow([backend, T, dIW, recov, resid]) + log.info('Wrote %s', csv_path) + + fig, axes = plt.subplots(1, 2, figsize=(9.4, 4.6), sharey=True) + + tol_band = 0.01 + y_window = 0.5 # dex on each side; off-scale points get a triangle + + for ax, backend, label_short in ( + (axes[0], 'calliope', 'CALLIOPE'), + (axes[1], 'atmodeller', 'atmodeller'), + ): + ax.axhspan( + -tol_band, + tol_band, + color='k', + alpha=0.07, + linewidth=0, + label=rf'$\pm {tol_band:g}$ dex band', + ) + ax.axhline(0.0, color='k', alpha=0.4, linewidth=0.7) + + # Small x-jitter per T so the four T markers fan out + # horizontally at each input Delta-IW rather than stacking on + # one another. Each T sits at the same y-residual; the offset + # only affects horizontal placement so the colours are + # individually visible. Jitter is symmetric around the integer + # dIW tick to keep the eye on the underlying Delta-IW value. + n_T = len(T_GRID) + jitter_step = 0.16 + for i_T, T in enumerate(T_GRID): + dx = (i_T - (n_T - 1) / 2.0) * jitter_step + xs_ok = [] + ys_ok = [] + xs_off = [] + for Tx, dIWx, recov in data[backend]: + if Tx != T: + continue + if not np.isfinite(recov): + xs_off.append(dIWx) + continue + resid = recov - dIWx + if abs(resid) > y_window: + xs_off.append(dIWx) + else: + xs_ok.append(dIWx) + ys_ok.append(resid) + order = np.argsort(xs_ok) if xs_ok else [] + if xs_ok: + xs_ok = np.array(xs_ok)[order] + dx + ys_ok = np.array(ys_ok)[order] + ax.plot( + xs_ok, + ys_ok, + marker='o', + markersize=7.0, + linewidth=0, + color=T_COLORS[T], + markeredgecolor='k', + markeredgewidth=0.5, + label=rf'$T_\mathrm{{magma}} = {int(T)}$ K', + ) + for dIWx in xs_off: + ax.plot( + dIWx + dx, + -y_window * 0.9, + marker='v', + markersize=12, + linewidth=0, + color=T_COLORS[T], + markeredgecolor='k', + markeredgewidth=0.6, + clip_on=False, + ) + + ax.set_xlabel(r'input $\Delta\mathrm{IW}$ [dex]') + ax.set_title(label_short) + ax.set_xlim(min(DIW_GRID) - 0.5, max(DIW_GRID) + 0.5) + ax.set_xticks(DIW_GRID) + ax.set_ylim(-y_window, y_window) + + axes[0].set_ylabel(r'residual: recovered $-$ input $\Delta\mathrm{IW}$ [dex]') + + # One legend total, below the two panels. Reorder so input dIW + # entries come first and the tolerance-band entry last. + handles, labels = axes[0].get_legend_handles_labels() + order = sorted(range(len(labels)), key=lambda i: ('band' in labels[i], labels[i])) + handles = [handles[i] for i in order] + labels = [labels[i] for i in order] + fig.legend( + handles, + labels, + loc='lower center', + ncol=len(labels), + bbox_to_anchor=(0.5, -0.03), + frameon=False, + fontsize=9.5, + ) + + panel_label(axes[0], '(a)') + panel_label(axes[1], '(b)') + + fig.suptitle( + 'Internal round-trip: buffered mode $\\to$ authoritative-O recovers the input $\\Delta$IW', + fontsize=11.5, + y=0.99, + ) + + # Compact the bottom margin to make room for the bottom legend. + fig.tight_layout(rect=(0.0, 0.05, 1.0, 0.96)) + + paths = save(fig, 'fig2_roundtrip') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/cross_backend/fig3_grid.py b/scripts/cross_backend/fig3_grid.py new file mode 100644 index 0000000..dc690c6 --- /dev/null +++ b/scripts/cross_backend/fig3_grid.py @@ -0,0 +1,290 @@ +"""Figure 3: Cross-backend Delta-IW disagreement as a function of T_magma. + +Both backends called at the canonical Earth-BSE Krijt+2023 volatile +budget with H/C/N/S fixed and the volatile O reference set by a +buffered-mode call at Delta-IW = +3.5 (Sossi 2020). For each T_magma +in the grid the two backends produce a converged Delta-IW; the figure +shows them side by side together with the analytical buffer offsets +(Hirschmann minus Fischer for the current default; Hirschmann minus +O'Neill for the legacy buffer). + +CALLIOPE is run twice at every grid point: once with the current +default Fischer 2011 buffer and once with the legacy O'Neill 2002 +buffer. The Fischer trace is much closer to atmodeller because +Fischer 2011 is closer to Hirschmann than O'Neill 2002 is across the +magma-ocean range (~0.1 dex residual at 2000 K vs ~0.95 dex). + +A 2D (T, O-budget) heatmap was attempted first and abandoned: the +authoritative-O entry point has a known non-monotonic regime at +sub-trace O budgets which makes a wider O sweep dominated by basin- +selection effects rather than backend-physics differences. The single- +axis T sweep at fixed Earth-like O reports the backend-physics +contrast cleanly. +""" + +from __future__ import annotations + +import csv +import logging +import time + +import matplotlib.pyplot as plt +import numpy as np + +from . import buffers +from .inventories import EARTH_BSE_KRIJT23 +from .plot_style import ( + COLOR_ATM, + COLOR_CAL, + COLOR_FIS, + DATA_DIR, + apply_style, + panel_label, + save, +) +from .runners import run_atmodeller, run_calliope + +log = logging.getLogger('cross_backend.fig3') + + +T_GRID = np.array([1800.0, 2000.0, 2400.0, 2800.0, 3000.0]) +FO2_HINT = 3.5 + + +def collect() -> dict: + nT = len(T_GRID) + dIW_cal_fis = np.full(nT, np.nan) + dIW_cal_one = np.full(nT, np.nan) + dIW_atm = np.full(nT, np.nan) + P_cal_fis = np.full(nT, np.nan) + P_cal_one = np.full(nT, np.nan) + P_atm = np.full(nT, np.nan) + for i, T in enumerate(T_GRID): + t0 = time.time() + r_cal_fis = run_calliope( + EARTH_BSE_KRIJT23, + T_magma=float(T), + fO2_hint=FO2_HINT, + buffer='fischer', + ) + r_cal_one = run_calliope( + EARTH_BSE_KRIJT23, + T_magma=float(T), + fO2_hint=FO2_HINT, + buffer='oneill', + ) + r_atm = run_atmodeller(EARTH_BSE_KRIJT23, T_magma=float(T)) + dt = time.time() - t0 + log.info( + 'T=%4.0f cal_F=%+6.3f cal_O=%+6.3f atm=%+6.3f %.1fs', + T, + r_cal_fis.fO2_shift_derived, + r_cal_one.fO2_shift_derived, + r_atm.fO2_shift_derived, + dt, + ) + if r_cal_fis.converged: + dIW_cal_fis[i] = r_cal_fis.fO2_shift_derived + P_cal_fis[i] = r_cal_fis.total_P_bar + if r_cal_one.converged: + dIW_cal_one[i] = r_cal_one.fO2_shift_derived + P_cal_one[i] = r_cal_one.total_P_bar + if r_atm.converged: + dIW_atm[i] = r_atm.fO2_shift_derived + P_atm[i] = r_atm.total_P_bar + return dict( + dIW_cal_fis=dIW_cal_fis, + dIW_cal_one=dIW_cal_one, + dIW_atm=dIW_atm, + P_cal_fis=P_cal_fis, + P_cal_one=P_cal_one, + P_atm=P_atm, + ) + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + dIW_cal_fis = data['dIW_cal_fis'] + dIW_cal_one = data['dIW_cal_one'] + dIW_atm = data['dIW_atm'] + + buf_offset_one = np.array( + [buffers.hirschmann_minus_oneill_offset(np.array([T]))[0] for T in T_GRID] + ) + buf_offset_fis = np.array( + [buffers.hirschmann_minus_fischer_offset(np.array([T]))[0] for T in T_GRID] + ) + # Predicted cross-backend gap (atm - cal) under identical chemistry + # is -(hirschmann - cal_buffer). The buffer-predicted atmodeller + # curve is therefore (cal − buffer_offset). + dIW_atm_pred_from_fis = dIW_cal_fis - buf_offset_fis + raw_gap_one = dIW_atm - dIW_cal_one + raw_gap_fis = dIW_atm - dIW_cal_fis + corrected_one = raw_gap_one + buf_offset_one + corrected_fis = raw_gap_fis + buf_offset_fis + + csv_path = DATA_DIR / 'fig3_grid.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow( + [ + 'T_K', + 'dIW_calliope_fischer', + 'dIW_calliope_oneill', + 'dIW_atmodeller', + 'buffer_offset_HminusF_dex', + 'buffer_offset_HminusO_dex', + 'raw_gap_F_dex', + 'raw_gap_O_dex', + 'residual_after_buffer_F_dex', + 'residual_after_buffer_O_dex', + 'P_total_cal_fischer_bar', + 'P_total_cal_oneill_bar', + 'P_total_atm_bar', + ] + ) + for i, T in enumerate(T_GRID): + w.writerow( + [ + T, + dIW_cal_fis[i], + dIW_cal_one[i], + dIW_atm[i], + buf_offset_fis[i], + buf_offset_one[i], + raw_gap_fis[i], + raw_gap_one[i], + corrected_fis[i], + corrected_one[i], + data['P_cal_fis'][i], + data['P_cal_one'][i], + data['P_atm'][i], + ] + ) + log.info('Wrote %s', csv_path) + + fig, (ax_top, ax_bot) = plt.subplots( + 2, + 1, + figsize=(7.6, 6.4), + sharex=True, + gridspec_kw={'height_ratios': [1.6, 1.0], 'hspace': 0.10}, + ) + + ax_top.plot( + T_GRID, + dIW_cal_fis, + marker='o', + color=COLOR_CAL, + label='CALLIOPE (Fischer 2011, default)', + ) + ax_top.plot( + T_GRID, + dIW_cal_one, + marker='o', + linestyle='--', + color=COLOR_FIS, + alpha=0.85, + label="CALLIOPE (O'Neill 2002, legacy)", + ) + ax_top.plot( + T_GRID, dIW_atm, marker='s', color=COLOR_ATM, label='atmodeller (Hirschmann composite)' + ) + ax_top.plot( + T_GRID, + dIW_atm_pred_from_fis, + marker='x', + linestyle=':', + color=COLOR_ATM, + alpha=0.6, + label='atmodeller predicted from buffer alone\n(= CALLIOPE-F − (Hirschmann − Fischer))', + ) + ax_top.set_ylabel(r'$\Delta\mathrm{IW}$ [dex]') + ax_top.set_title('Cross-backend $\\Delta$IW at Earth-BSE volatile inventory, $\\Phi = 1$') + ymins = [ + float(np.nanmin(dIW_atm_pred_from_fis)), + float(np.nanmin(dIW_atm)), + float(np.nanmin(dIW_cal_one)), + ] + y_top_min = min(ymins) - 0.55 + y_top_max = ( + max( + float(np.nanmax(dIW_cal_fis)), + float(np.nanmax(dIW_cal_one)), + ) + + 0.40 + ) + ax_top.set_ylim(y_top_min, y_top_max) + ax_top.legend( + loc='lower left', fontsize=8.5, framealpha=0.92, facecolor='white', edgecolor='none' + ) + panel_label(ax_top, '(a)') + + ax_bot.axhline(0.0, color='k', alpha=0.4, linewidth=0.7) + ax_bot.axhline(0.1, color='k', alpha=0.25, linestyle='--', linewidth=0.7) + ax_bot.axhline(-0.1, color='k', alpha=0.25, linestyle='--', linewidth=0.7) + ax_bot.plot( + T_GRID, + raw_gap_fis, + marker='o', + color='#7a7a7a', + label='raw, Fischer default ($\\Delta$IW$_\\mathrm{atm}-\\Delta$IW$_\\mathrm{cal,F}$)', + ) + ax_bot.plot( + T_GRID, + raw_gap_one, + marker='o', + linestyle='--', + color='#444444', + alpha=0.7, + label="raw, O'Neill legacy ($\\Delta$IW$_\\mathrm{atm}-\\Delta$IW$_\\mathrm{cal,O}$)", + ) + ax_bot.plot( + T_GRID, + corrected_fis, + marker='D', + color=COLOR_CAL, + label='after buffer correction\n(residual chemistry gap, either buffer)', + ) + ax_bot.text( + T_GRID[-1], + 0.115, + r'$\pm 0.1$ dex solver tolerance', + fontsize=8.5, + va='bottom', + ha='right', + alpha=0.6, + ) + ax_bot.set_xlabel(r'$T_\mathrm{magma}$ [K]') + ax_bot.set_ylabel('disagreement [dex]') + y_bot_min = ( + min( + float(np.nanmin(raw_gap_fis)), + float(np.nanmin(raw_gap_one)), + ) + - 0.60 + ) + y_bot_max = max( + 0.75, + float(np.nanmax(raw_gap_fis)) + 0.55, + float(np.nanmax(corrected_fis)) + 0.55, + ) + ax_bot.set_ylim(y_bot_min, y_bot_max) + ax_bot.legend( + loc='lower left', fontsize=8.5, framealpha=0.92, facecolor='white', edgecolor='none' + ) + panel_label(ax_bot, '(b)') + + paths = save(fig, 'fig3_grid') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/cross_backend/fig4_attribution.py b/scripts/cross_backend/fig4_attribution.py new file mode 100644 index 0000000..004b7bc --- /dev/null +++ b/scripts/cross_backend/fig4_attribution.py @@ -0,0 +1,163 @@ +"""Figure 4: Attribution of cross-backend Delta-IW disagreement at a +single canonical Earth scenario. + +Bar chart. From left to right, each bar shows the |Delta-IW| +disagreement at the canonical Earth fiducial: + +1. Raw, legacy: CALLIOPE with the O'Neill 2002 IW buffer (the legacy + choice) vs atmodeller default. +2. Raw, current default: CALLIOPE with the Fischer 2011 IW buffer (the + library default since the buffer audit) vs atmodeller default. +3. After analytical buffer correction (Hirschmann - Fischer + subtracted), residual chemistry gap with default solubility maps. +4. After also disabling H2 / CO / CH4 solubility in atmodeller + (matching CALLIOPE's Bower 2022 §2.2.3 convention). + +The drop from bar 1 to bar 2 is the buffer-default change alone; +bars 2-4 are the residuals after successive alignment moves. A +horizontal line marks 0.1 dex, the per-element solver tolerance. + +Reusable: pass `T_magma` and inventory factor to reuse the script for a +different fiducial scenario. +""" + +from __future__ import annotations + +import csv +import logging + +import matplotlib.pyplot as plt +import numpy as np + +from . import buffers +from .inventories import EARTH_BSE_KRIJT23 +from .plot_style import COLOR_ATM, COLOR_CAL, COLOR_FIS, DATA_DIR, apply_style, save +from .runners import _CALLIOPE_ALIGNED_ATM_SOL, run_atmodeller, run_calliope + +log = logging.getLogger('cross_backend.fig4') + + +def collect(T_magma: float = 2000.0) -> dict: + """Compute the attribution stages with both CALLIOPE buffer + choices. + """ + inv = EARTH_BSE_KRIJT23 + log.info('Step 1: as-shipped defaults (Fischer 2011 buffer)') + cal_fis = run_calliope(inv, T_magma=T_magma, fO2_hint=3.5, buffer='fischer') + cal_one = run_calliope(inv, T_magma=T_magma, fO2_hint=3.5, buffer='oneill') + atm_def = run_atmodeller(inv, T_magma=T_magma) + log.info(' CALLIOPE dIW (Fischer) = %+.3f', cal_fis.fO2_shift_derived) + log.info(" CALLIOPE dIW (O'Neill) = %+.3f", cal_one.fO2_shift_derived) + log.info(' atmodeller dIW (default) = %+.3f', atm_def.fO2_shift_derived) + + raw_gap_fis = atm_def.fO2_shift_derived - cal_fis.fO2_shift_derived + raw_gap_one = atm_def.fO2_shift_derived - cal_one.fO2_shift_derived + buf_offset_fis = float(buffers.hirschmann_minus_fischer_offset(np.array([T_magma]))[0]) + buf_offset_one = float(buffers.hirschmann_minus_oneill_offset(np.array([T_magma]))[0]) + # Buffer contribution to (dIW_atm - dIW_cal) under identical + # chemistry: -(hirschmann - cal_buffer) = -buf_offset. Residual is + # raw_gap - (-buf_offset) = raw_gap + buf_offset. + after_buffer_fis = raw_gap_fis + buf_offset_fis + + log.info('Step 2: align atmodeller solubility to CALLIOPE convention') + atm_align = run_atmodeller( + inv, + T_magma=T_magma, + solubility_map=_CALLIOPE_ALIGNED_ATM_SOL, + ) + log.info(' atmodeller dIW (aligned) = %+.3f', atm_align.fO2_shift_derived) + after_solubility = atm_align.fO2_shift_derived - cal_fis.fO2_shift_derived + buf_offset_fis + + return dict( + T_magma=T_magma, + cal_fischer_dIW=cal_fis.fO2_shift_derived, + cal_oneill_dIW=cal_one.fO2_shift_derived, + atm_default_dIW=atm_def.fO2_shift_derived, + atm_aligned_dIW=atm_align.fO2_shift_derived, + raw_gap_fischer=raw_gap_fis, + raw_gap_oneill=raw_gap_one, + buffer_offset_fischer=buf_offset_fis, + buffer_offset_oneill=buf_offset_one, + after_buffer_fischer=after_buffer_fis, + after_solubility=after_solubility, + ) + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'fig4_attribution.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + for k, v in data.items(): + w.writerow([k, v]) + log.info('Wrote %s', csv_path) + + fig, ax = plt.subplots(figsize=(8.2, 4.4)) + + labels = [ + "Raw, O'Neill\n(legacy buffer)", + 'Raw, Fischer\n(default buffer)', + 'After buffer\ncorrection', + 'After also matching\nsolubility selection', + ] + values = [ + abs(data['raw_gap_oneill']), + abs(data['raw_gap_fischer']), + abs(data['after_buffer_fischer']), + abs(data['after_solubility']), + ] + colors = [COLOR_FIS, COLOR_ATM, '#c79f3a', COLOR_CAL] + + bars = ax.bar(labels, values, color=colors, edgecolor='k', linewidth=0.6, width=0.55) + tol = 0.10 + # Always label above the bar in dark text so labels stay readable + # regardless of the bar fill colour. Small bars whose top sits + # within +/-0.03 dex of the tolerance line get pushed higher so + # their label clears the dashed line and the per-element-solver + # annotation drawn just above it. + for bar, val in zip(bars, values): + x = bar.get_x() + bar.get_width() / 2 + if abs(val - tol) <= 0.04: + y = tol + 0.045 + else: + y = val + 0.018 + ax.text(x, y, f'{val:.2f} dex', ha='center', va='bottom', fontsize=10, color='#1a1a1a') + + ax.axhline(tol, color='k', alpha=0.4, linestyle='--', linewidth=0.8) + # Tolerance annotation parked in the gap between bars 1 and 2, + # well clear of every bar label and the dashed line itself. + ax.text( + 0.5, + tol + 0.005, + 'per-element solver tolerance', + fontsize=8.5, + va='bottom', + ha='center', + alpha=0.6, + ) + + ax.set_ylabel( + r'$|\Delta\mathrm{IW}_{\mathrm{atm}}-\Delta\mathrm{IW}_{\mathrm{cal}}|$ [dex]' + ) + ax.set_title( + f'Attribution of cross-backend $\\Delta$IW disagreement ' + f'at Earth-BSE, $T={data["T_magma"]:.0f}$ K, $\\Phi=1$', + fontsize=11.0, + ) + ax.set_ylim(0, max(values) * 1.25 + 0.1) + ax.grid(axis='y', alpha=0.25) + + paths = save(fig, 'fig4_attribution') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/cross_backend/fig5_earth_anchor.py b/scripts/cross_backend/fig5_earth_anchor.py new file mode 100644 index 0000000..b125735 --- /dev/null +++ b/scripts/cross_backend/fig5_earth_anchor.py @@ -0,0 +1,165 @@ +"""Figure 5: Cross-backend Delta-IW at Earth-BSE against the empirical +Sossi (2020) anchor. + +A single horizontal axis is Delta-IW. Two vertical markers show the +two backends' converged Delta-IW at the canonical Earth fiducial +(T_magma = 2000 K, Phi = 1, Krijt+2023 BSE H/C/N/S, volatile O derived +self-consistently). A shaded band is the Frost & McCammon (2008) +"Earth's mantle redox state" range (Delta-IW = +1 to +5, corresponding +to FMQ-3 to FMQ+1, with Sossi 2020 placing modern upper mantle at the ++3.5 centre). + +This figure stress-tests the cross-backend disagreement against an +empirical anchor: if both backends fall inside the empirical range, +neither parameterisation is in tension with petrology at this single +fiducial. If one falls outside, that backend is in tension and should +be flagged to the reader. +""" + +from __future__ import annotations + +import csv +import logging + +import matplotlib.pyplot as plt + +from .inventories import EARTH_BSE_KRIJT23 +from .plot_style import COLOR_ATM, COLOR_BG, COLOR_CAL, COLOR_FIS, DATA_DIR, apply_style, save +from .runners import run_atmodeller, run_calliope + +log = logging.getLogger('cross_backend.fig5') + + +# Frost & McCammon (2008) Earth-mantle redox-state range, IW reference. +EARTH_MANTLE_DIW_LOW = 1.0 +EARTH_MANTLE_DIW_HIGH = 5.0 +SOSSI_2020_CENTER = 3.5 + + +def collect(T_magma: float = 2000.0) -> dict: + inv = EARTH_BSE_KRIJT23 + cal_fis = run_calliope(inv, T_magma=T_magma, fO2_hint=3.5, buffer='fischer') + cal_one = run_calliope(inv, T_magma=T_magma, fO2_hint=3.5, buffer='oneill') + atm = run_atmodeller(inv, T_magma=T_magma) + log.info('CALLIOPE dIW (Fischer) = %+.3f', cal_fis.fO2_shift_derived) + log.info("CALLIOPE dIW (O'Neill) = %+.3f", cal_one.fO2_shift_derived) + log.info('atmodeller dIW = %+.3f', atm.fO2_shift_derived) + return dict( + T_magma=T_magma, + cal_fischer_dIW=cal_fis.fO2_shift_derived, + cal_oneill_dIW=cal_one.fO2_shift_derived, + atm_dIW=atm.fO2_shift_derived, + cal_fischer_P_bar=cal_fis.total_P_bar, + cal_oneill_P_bar=cal_one.total_P_bar, + atm_P_bar=atm.total_P_bar, + ) + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'fig5_earth_anchor.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow(['quantity', 'value']) + w.writerow(['T_magma_K', data['T_magma']]) + w.writerow(['cal_fischer_dIW', data['cal_fischer_dIW']]) + w.writerow(['cal_oneill_dIW', data['cal_oneill_dIW']]) + w.writerow(['atm_dIW', data['atm_dIW']]) + w.writerow(['cal_fischer_P_total_bar', data['cal_fischer_P_bar']]) + w.writerow(['cal_oneill_P_total_bar', data['cal_oneill_P_bar']]) + w.writerow(['atm_P_total_bar', data['atm_P_bar']]) + w.writerow(['empirical_low_dIW', EARTH_MANTLE_DIW_LOW]) + w.writerow(['empirical_high_dIW', EARTH_MANTLE_DIW_HIGH]) + w.writerow(['sossi_2020_center', SOSSI_2020_CENTER]) + log.info('Wrote %s', csv_path) + + fig, ax = plt.subplots(figsize=(7.4, 3.6)) + + ax.axhspan(0.4, 0.6, xmin=0.0, xmax=1.0, color=COLOR_BG, alpha=0.0) + + ax.axvspan( + EARTH_MANTLE_DIW_LOW, + EARTH_MANTLE_DIW_HIGH, + color=COLOR_BG, + alpha=0.6, + label='Frost & McCammon 2008 Earth-mantle range', + ) + ax.axvline( + SOSSI_2020_CENTER, + color='k', + alpha=0.7, + linestyle=':', + linewidth=1.8, + zorder=3, + label=rf'Sossi 2020 upper-mantle anchor: $\Delta\mathrm{{IW}} = {SOSSI_2020_CENTER:+.2f}$', + ) + + ax.axvline( + data['cal_fischer_dIW'], + color=COLOR_CAL, + linewidth=2.0, + zorder=2, + label=rf'CALLIOPE (Fischer, default): $\Delta\mathrm{{IW}} = {data["cal_fischer_dIW"]:+.2f}$', + ) + # The O'Neill-buffer CALLIOPE result lands at ~+3.50 here by near- + # coincidence (the canonical Earth O budget was derived under + # O'Neill at +3.5, then re-derived under Fischer; the O'Neill + # backend happens to recover +3.50 also under the new Fischer- + # derived O budget to two decimals). Render it as a thicker + # long-dash stroke so it remains visible through the Sossi dotted + # anchor at the same x. + ax.axvline( + data['cal_oneill_dIW'], + color=COLOR_FIS, + linewidth=3.2, + linestyle=(0, (6, 4)), + zorder=4, + alpha=0.95, + label=rf"CALLIOPE (O'Neill, legacy): $\Delta\mathrm{{IW}} = {data['cal_oneill_dIW']:+.2f}$", + ) + ax.axvline( + data['atm_dIW'], + color=COLOR_ATM, + linewidth=2.0, + zorder=2, + label=rf'atmodeller: $\Delta\mathrm{{IW}} = {data["atm_dIW"]:+.2f}$', + ) + + # Suppress y-axis: this is a 1D figure. + ax.set_yticks([]) + ax.spines['left'].set_visible(False) + ax.set_xlim(-1.0, 7.0) + ax.grid(axis='x', alpha=0.25) + + ax.set_xlabel(r'$\Delta\mathrm{IW}$ at $T_\mathrm{magma} = $' + f' {data["T_magma"]:.0f} K') + ax.set_title( + 'Earth fiducial: both backends vs. the empirical mantle-fO$_2$ range', + fontsize=11.0, + ) + # Legend below the plot so the four entries do not crowd into the + # data region where the three vertical lines already sit close + # together at dIW = +3 to +3.5. + ax.legend( + loc='upper center', + bbox_to_anchor=(0.5, -0.22), + ncol=2, + frameon=False, + fontsize=9.0, + columnspacing=1.6, + handlelength=2.4, + ) + + paths = save(fig, 'fig5_earth_anchor') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/cross_backend/fonts/Roboto-Bold.ttf b/scripts/cross_backend/fonts/Roboto-Bold.ttf new file mode 100644 index 0000000..db17c0a Binary files /dev/null and b/scripts/cross_backend/fonts/Roboto-Bold.ttf differ diff --git a/scripts/cross_backend/fonts/Roboto-Italic.ttf b/scripts/cross_backend/fonts/Roboto-Italic.ttf new file mode 100644 index 0000000..7792b5f Binary files /dev/null and b/scripts/cross_backend/fonts/Roboto-Italic.ttf differ diff --git a/scripts/cross_backend/fonts/Roboto-Regular.ttf b/scripts/cross_backend/fonts/Roboto-Regular.ttf new file mode 100644 index 0000000..60b906e Binary files /dev/null and b/scripts/cross_backend/fonts/Roboto-Regular.ttf differ diff --git a/scripts/cross_backend/inventories.py b/scripts/cross_backend/inventories.py new file mode 100644 index 0000000..02287a6 --- /dev/null +++ b/scripts/cross_backend/inventories.py @@ -0,0 +1,167 @@ +"""Authoritative elemental inventories used by the cross-backend +comparison harness. + +Earth bulk-silicate-Earth (BSE) H / C / N / S inventory is taken from +Krijt et al. (2023), Protostars and Planets VII, Tables 1 and 2 ("BSE" +totals row). + +Oxygen is a special case. Krijt et al. tabulate redox-active O +following the Evans (2006) convention (mass of O required to move the +silicate Earth to Fe(II)O reference state), which is dominated by the +mantle FeO / Fe2O3 redox imbalance, not by volatile O. The +authoritative-O entry point treats O as the volatile budget (the O +atoms residing in atmospheric H2O / CO2 / SO2 / O2 and dissolved as +the same species). The two definitions are inequivalent and not +interconvertible without a chemistry calculation. + +We therefore derive the canonical Earth volatile-O reference from the +Krijt BSE H/C/N/S budget by running the buffered-mode solver at +T_magma = 2000 K and Delta-IW = +3.5 (the Sossi 2020 estimate of +Earth's modern upper-mantle fO2). The resulting O_kg_total is the +self-consistent volatile O for Earth at that thermodynamic state. The +value is stored as `EARTH_VOLATILE_O_REF_KG` and used as the 1x point +of the Fig 3 grid sweep. + +The numbers here are bulk-silicate-Earth, not bulk-Earth. The core is +excluded. +""" + +from __future__ import annotations + +from dataclasses import dataclass + + +@dataclass(frozen=True) +class Inventory: + """Elemental inventory in kg, with citation provenance.""" + + name: str + H: float + C: float + N: float + O: float # noqa: E741 O is the oxygen-element field name across CALLIOPE + S: float + citation: str + notes: str = '' + + def asdict(self) -> dict: + """Return as `target_d` shape for `equilibrium_atmosphere_authoritative_O`.""" + return {'H': self.H, 'C': self.C, 'N': self.N, 'O': self.O, 'S': self.S} + + +# Krijt et al. (2023) PPVII Tables 1+2 BSE H/C/N/S in kg. Used as the +# authoritative H/C/N/S inventory for the Earth fiducial; oxygen is +# derived self-consistently from a chemistry call (see module docstring). +EARTH_HCNS_KRIJT23 = { + 'H': 5.6e20, + 'C': 3.1e21, + 'N': 3.7e19, + 'S': 1.0e21, +} + + +# Earth's volatile O at the Sossi 2020 Delta-IW = +3.5, T = 2000 K state. +# Computed by `derive_earth_volatile_O()` from CALLIOPE's buffered-mode +# solver with EARTH_HCNS_KRIJT23 as the H/C/N/S target and the current +# default Fischer 2011 IW buffer. Hard-coded here so the harness does +# not have to recompute it on every invocation; the provenance script +# `derive_earth_volatile_O()` re-derives it on demand to confirm the +# constant has not drifted. The legacy O'Neill 2002 buffer gives a +# slightly different value (~1.241e22 kg). +EARTH_VOLATILE_O_REF_KG = 1.260e22 + + +EARTH_BSE_KRIJT23 = Inventory( + name='Earth BSE (volatile O at IW+3.5)', + H=EARTH_HCNS_KRIJT23['H'], + C=EARTH_HCNS_KRIJT23['C'], + N=EARTH_HCNS_KRIJT23['N'], + O=EARTH_VOLATILE_O_REF_KG, + S=EARTH_HCNS_KRIJT23['S'], + citation='H/C/N/S: Krijt et al. (2023) PPVII Tables 1+2; O: derived at Sossi 2020 IW+3.5', + notes=( + 'O is volatile O (atmospheric + dissolved in H2O / CO2 / SO2 / ' + 'O2 only), NOT the Krijt et al. Table 2 redox-active O. The ' + 'two are inequivalent; see module docstring.' + ), +) + + +def derive_earth_volatile_O(T_magma: float = 2000.0, dIW: float = 3.5) -> float: + """Re-derive Earth's volatile O at given (T_magma, dIW). + + Returns the O_kg_total CALLIOPE's buffered mode reports for the + Krijt+2023 BSE H/C/N/S target. Useful for confirming the hard-coded + EARTH_VOLATILE_O_REF_KG has not drifted across CALLIOPE versions. + """ + import warnings as _warnings + + from calliope.constants import volatile_species + from calliope.solve import equilibrium_atmosphere + + ddict = { + 'M_mantle': PLANETARY_DEFAULTS['M_mantle'], + 'gravity': PLANETARY_DEFAULTS['gravity'], + 'radius': PLANETARY_DEFAULTS['radius'], + 'Phi_global': 1.0, + 'T_magma': T_magma, + 'fO2_shift_IW': dIW, + } + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + with _warnings.catch_warnings(): + _warnings.simplefilter('ignore') + out = equilibrium_atmosphere( + EARTH_HCNS_KRIJT23, + ddict, + hide_warnings=True, + print_result=False, + ) + return float(out['O_kg_total']) + + +def scale_inventory(base: Inventory, factor: float, name: str | None = None) -> Inventory: + """Multiply every element budget of `base` by `factor`. + + Useful for parameter sweeps: 0.1x to 10x the canonical Earth budget + spans the realistic range of volatile-poor to volatile-rich planets. + """ + if factor <= 0: + raise ValueError(f'factor must be > 0, got {factor}') + return Inventory( + name=name or f'{base.name} x{factor:g}', + H=base.H * factor, + C=base.C * factor, + N=base.N * factor, + O=base.O * factor, + S=base.S * factor, + citation=base.citation + f' (scaled x{factor:g})', + notes=base.notes, + ) + + +def scale_O(base: Inventory, O_factor: float, name: str | None = None) -> Inventory: + """Scale only the O budget. Used by Fig 3 grid: H/C/N/S fixed, O varied.""" + if O_factor <= 0: + raise ValueError(f'O_factor must be > 0, got {O_factor}') + return Inventory( + name=name or f'{base.name} (O x{O_factor:g})', + H=base.H, + C=base.C, + N=base.N, + O=base.O * O_factor, + S=base.S, + citation=base.citation + f' (O scaled x{O_factor:g})', + notes=base.notes, + ) + + +PLANETARY_DEFAULTS = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': 1.0, + 'M_planet': 5.972e24, + 'core_mass_fraction': 0.325, +} diff --git a/scripts/cross_backend/plot_style.py b/scripts/cross_backend/plot_style.py new file mode 100644 index 0000000..3ce40c2 --- /dev/null +++ b/scripts/cross_backend/plot_style.py @@ -0,0 +1,98 @@ +"""Shared matplotlib styling for publication-quality figures. + +All figures use the same fonts, line widths, and colour palette so the +docs page reads as one coherent set of figures, not five unrelated +matplotlib outputs. +""" + +from __future__ import annotations + +from pathlib import Path + +import matplotlib as mpl +import matplotlib.pyplot as plt +from matplotlib import font_manager as _fm + +FIGURE_DIR = ( + Path(__file__).resolve().parent.parent.parent + / 'docs' + / 'assets' + / 'figures' + / 'cross_backend' +) +FIGURE_DIR.mkdir(parents=True, exist_ok=True) + +DATA_DIR = Path(__file__).resolve().parent / 'data' +DATA_DIR.mkdir(parents=True, exist_ok=True) + +# Bundled Roboto, matching the Material for MkDocs docs theme so the +# figures and the body text on the docs page render in the same font. +_FONT_DIR = Path(__file__).resolve().parent / 'fonts' +for _f in _FONT_DIR.glob('*.ttf'): + _fm.fontManager.addfont(str(_f)) + +COLOR_CAL = '#0f6e9d' # CALLIOPE: deep blue +COLOR_ATM = '#d1471f' # atmodeller: rust orange +COLOR_FIS = '#7a7a7a' # Fischer alternative: grey +COLOR_BG = '#f3f0e7' # subtle anchor-range background + + +def apply_style() -> None: + """Install matplotlib rcParams. Idempotent.""" + mpl.rcParams.update( + { + 'figure.dpi': 150, + 'savefig.dpi': 300, + 'figure.facecolor': 'white', + 'savefig.facecolor': 'white', + 'font.family': 'sans-serif', + 'font.sans-serif': ['Roboto', 'Helvetica', 'Arial', 'DejaVu Sans'], + 'mathtext.fontset': 'custom', + 'mathtext.rm': 'Roboto', + 'mathtext.it': 'Roboto:italic', + 'mathtext.bf': 'Roboto:bold', + 'font.size': 10.5, + 'axes.titlesize': 11.5, + 'axes.labelsize': 11, + 'axes.linewidth': 0.9, + 'axes.spines.top': False, + 'axes.spines.right': False, + 'axes.grid': True, + 'grid.alpha': 0.25, + 'grid.linewidth': 0.6, + 'legend.fontsize': 9.5, + 'legend.frameon': False, + 'lines.linewidth': 1.7, + 'xtick.direction': 'out', + 'ytick.direction': 'out', + 'xtick.major.size': 4, + 'ytick.major.size': 4, + } + ) + + +def save(fig: plt.Figure, stem: str, *, formats=('pdf', 'png')) -> dict: + """Save `fig` to FIGURE_DIR/`stem`. for each format. + + Returns mapping {ext: path}. + """ + paths = {} + for ext in formats: + path = FIGURE_DIR / f'{stem}.{ext}' + fig.savefig(path, bbox_inches='tight') + paths[ext] = path + return paths + + +def panel_label(ax, text, *, x=0.02, y=0.95): + """Add a bold (a)/(b)/(c) panel label in figure coordinates.""" + ax.text( + x, + y, + text, + transform=ax.transAxes, + fontsize=12, + fontweight='bold', + va='top', + ha='left', + ) diff --git a/scripts/cross_backend/replot_fig3_from_csv.py b/scripts/cross_backend/replot_fig3_from_csv.py new file mode 100644 index 0000000..acb7b3f --- /dev/null +++ b/scripts/cross_backend/replot_fig3_from_csv.py @@ -0,0 +1,74 @@ +"""Re-render Fig 3 from `data/fig3_grid.csv` without rerunning solvers. + +Used when the only change is in the plot layer (e.g. a sign fix in the +buffer correction) and re-running the 15-point grid would be wasteful. +""" + +from __future__ import annotations + +import csv +import sys + +import numpy as np + +from .fig3_grid import make_figure +from .plot_style import DATA_DIR + + +def load_csv() -> dict: + csv_path = DATA_DIR / 'fig3_grid.csv' + if not csv_path.exists(): + print(f'fig3_grid.csv not found at {csv_path}; run fig3_grid first', file=sys.stderr) + return None + + # Reconstruct the (nT, nO) arrays expected by make_figure. + rows = [] + with csv_path.open() as fh: + reader = csv.DictReader(fh) + for row in reader: + rows.append(row) + + T_vals = sorted({float(r['T_K']) for r in rows}) + O_vals = sorted({float(r['O_factor_x_Earth']) for r in rows}) + nT, nO = len(T_vals), len(O_vals) + + dIW_cal = np.full((nT, nO), np.nan) + dIW_atm = np.full((nT, nO), np.nan) + conv = np.zeros((nT, nO), dtype=bool) + P_cal = np.full((nT, nO), np.nan) + P_atm = np.full((nT, nO), np.nan) + + for r in rows: + i = T_vals.index(float(r['T_K'])) + j = O_vals.index(float(r['O_factor_x_Earth'])) + for arr, key in ( + (dIW_cal, 'dIW_calliope'), + (dIW_atm, 'dIW_atmodeller'), + (P_cal, 'P_total_cal_bar'), + (P_atm, 'P_total_atm_bar'), + ): + v = r[key] + arr[i, j] = float(v) if v and v != 'nan' else np.nan + conv[i, j] = not np.isnan(dIW_cal[i, j]) and not np.isnan(dIW_atm[i, j]) + + return dict( + dIW_cal=dIW_cal, + dIW_atm=dIW_atm, + conv_cal=~np.isnan(dIW_cal), + conv_atm=~np.isnan(dIW_atm), + P_cal=P_cal, + P_atm=P_atm, + ) + + +def main() -> None: + data = load_csv() + if data is None: + sys.exit(1) + paths = make_figure(data=data) + for ext, path in paths.items(): + print(f' {ext}: {path}') + + +if __name__ == '__main__': + main() diff --git a/scripts/cross_backend/run_all.sh b/scripts/cross_backend/run_all.sh new file mode 100755 index 0000000..909184d --- /dev/null +++ b/scripts/cross_backend/run_all.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +# Regenerate every figure on the cross-backend comparison page. +# +# Run from the CALLIOPE repository root with the `proteus` conda env +# active (or any env with calliope + atmodeller installed). +# +# Each figure script is independent and writes its own PDF + PNG into +# docs/assets/figures/cross_backend/ and its raw CSV data into +# scripts/cross_backend/data/. Total wall time is dominated by +# atmodeller solver calls (~15 s each warm; ~60 s cold for the first +# JAX compile). +# +# Approximate total runtime on a 2024 M-series Mac: ~20 minutes. +set -euo pipefail + +cd "$(dirname "$0")/../.." # repository root + +echo "=== Fig 1 (buffers) ===" +python3 -m scripts.cross_backend.fig1_buffers + +echo "=== Fig 2 (round-trip) ===" +python3 -m scripts.cross_backend.fig2_roundtrip + +echo "=== Fig 3 (grid) ===" +python3 -m scripts.cross_backend.fig3_grid + +echo "=== Fig 4 (attribution) ===" +python3 -m scripts.cross_backend.fig4_attribution + +echo "=== Fig 5 (Earth anchor) ===" +python3 -m scripts.cross_backend.fig5_earth_anchor + +echo +echo "All figures written to docs/assets/figures/cross_backend/" +echo "Raw data written to scripts/cross_backend/data/" diff --git a/scripts/cross_backend/run_fig45.py b/scripts/cross_backend/run_fig45.py new file mode 100644 index 0000000..a6445f2 --- /dev/null +++ b/scripts/cross_backend/run_fig45.py @@ -0,0 +1,32 @@ +"""Driver that runs Fig 4 (attribution) and Fig 5 (Earth anchor) in +the same Python process so they share the JAX warmup cost. + +Calling each fig script independently means a cold JAX compile per +process (~10 min on a Mac). Both figs only need 2-5 atmodeller calls, +so amortising the warmup across both is a 2x speedup. +""" + +from __future__ import annotations + +import logging + +from . import fig4_attribution, fig5_earth_anchor + + +def main() -> None: + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + print('=== Fig 5 (Earth anchor) ===') + out5 = fig5_earth_anchor.make_figure() + for ext, path in out5.items(): + print(f' {ext}: {path}') + + print('=== Fig 4 (attribution) ===') + out4 = fig4_attribution.make_figure() + for ext, path in out4.items(): + print(f' {ext}: {path}') + + +if __name__ == '__main__': + main() diff --git a/scripts/cross_backend/runners.py b/scripts/cross_backend/runners.py new file mode 100644 index 0000000..26cd020 --- /dev/null +++ b/scripts/cross_backend/runners.py @@ -0,0 +1,411 @@ +"""Backend runners: CALLIOPE and atmodeller authoritative-O entry points. + +Each runner accepts a normalised `Inventory`, planetary state, and +backend configuration knobs, runs the corresponding solver, and returns +a `BackendResult` with the converged Delta-IW, surface partial +pressures, dissolved masses, and convergence status. Failures are +caught and reported in the result (not raised), so a sweep over a +parameter grid can survive isolated non-convergence and report +coverage honestly. +""" + +from __future__ import annotations + +import logging +import warnings +from dataclasses import dataclass, field + +import numpy as np + +from .inventories import PLANETARY_DEFAULTS, Inventory + +log = logging.getLogger('cross_backend.runners') + + +@dataclass +class BackendResult: + """Output of one backend call. + + Attributes + ---------- + backend : str + 'calliope' or 'atmodeller'. + converged : bool + True if the solver returned a usable result. False if it raised + or signalled non-convergence; the caller should mask this point + from any quantitative comparison. + fO2_shift_derived : float + log10 IW-buffer offset the solver converged to. NaN if not + converged. + p_bar : dict + Partial pressures keyed by species name (`H2O`, `CO2`, ...) in bar. + Empty if not converged. + dissolved_kg : dict + Per-species dissolved mass in kg. Empty if not converged. + total_P_bar : float + Sum of partial pressures. + error : str + Empty on success, exception message on failure. + inputs : dict + Echo of the inputs that produced this result, for provenance. + """ + + backend: str + converged: bool + fO2_shift_derived: float = float('nan') + p_bar: dict = field(default_factory=dict) + dissolved_kg: dict = field(default_factory=dict) + total_P_bar: float = float('nan') + error: str = '' + inputs: dict = field(default_factory=dict) + + +# --------------------------------------------------------------------------- +# CALLIOPE +# --------------------------------------------------------------------------- + + +def _calliope_ddict( + T_magma: float, + Phi_global: float = 1.0, + species_set: tuple = ('H2O', 'CO2', 'H2', 'CO', 'CH4', 'N2', 'NH3', 'S2', 'SO2', 'H2S'), + **planet_overrides, +) -> dict: + """Build the CALLIOPE coupler-options dict consumed by the solver.""" + p = dict(PLANETARY_DEFAULTS) + p.update(planet_overrides) + ddict = { + 'M_mantle': p['M_mantle'], + 'gravity': p['gravity'], + 'radius': p['radius'], + 'Phi_global': Phi_global, + 'T_magma': T_magma, + 'fO2_shift_IW': 0.0, + } + all_species = ('H2O', 'CO2', 'H2', 'CO', 'CH4', 'N2', 'NH3', 'S2', 'SO2', 'H2S') + for sp in all_species: + ddict[f'{sp}_included'] = 1 if sp in species_set else 0 + ddict[f'{sp}_initial_bar'] = 0.0 + return ddict + + +def _with_calliope_buffer(buffer: str): + """Context manager that temporarily pins CALLIOPE's default IW + buffer to ``buffer`` ('fischer' or 'oneill'). Restores the prior + default on exit. Use this to compare both buffer choices from the + same harness regardless of which one is the library default. + """ + from contextlib import contextmanager + + from calliope.chemistry import ModifiedKeq + from calliope.oxygen_fugacity import OxygenFugacity + + @contextmanager + def _ctx(): + of_old = OxygenFugacity.__init__.__defaults__ + mk_old = ModifiedKeq.__init__.__defaults__ + OxygenFugacity.__init__.__defaults__ = (buffer,) + ModifiedKeq.__init__.__defaults__ = (buffer,) + try: + yield + finally: + OxygenFugacity.__init__.__defaults__ = of_old + ModifiedKeq.__init__.__defaults__ = mk_old + + return _ctx() + + +def run_calliope( + inventory: Inventory, + T_magma: float, + fO2_hint: float = 2.0, + Phi_global: float = 1.0, + random_seed: int | None = 1234, + nguess: int = 1500, + print_result: bool = False, + buffer: str = 'fischer', + **planet_overrides, +) -> BackendResult: + """Run CALLIOPE's authoritative-O solver. + + Suppresses fsolve convergence-warning chatter for cleaner sweep + output. The `random_seed` is fixed by default so a re-run of the + figure scripts produces identical numbers. ``buffer`` selects the + IW parameterisation; default 'fischer' matches the CALLIOPE + library default; 'oneill' reproduces the legacy CALLIOPE behaviour. + """ + from calliope.solve import equilibrium_atmosphere_authoritative_O + + ddict = _calliope_ddict(T_magma=T_magma, Phi_global=Phi_global, **planet_overrides) + target_d = inventory.asdict() + inputs = dict( + inventory=inventory.name, + T_magma=T_magma, + Phi_global=Phi_global, + fO2_hint=fO2_hint, + seed=random_seed, + target_d=target_d, + buffer=buffer, + ) + try: + with warnings.catch_warnings(), _with_calliope_buffer(buffer): + warnings.simplefilter('ignore') + out = equilibrium_atmosphere_authoritative_O( + target_d, + ddict, + fO2_hint=fO2_hint, + hide_warnings=True, + random_seed=random_seed, + nguess=nguess, + print_result=print_result, + ) + except Exception as exc: # noqa: BLE001 (sweep must survive isolated failures) + return BackendResult( + backend='calliope', + converged=False, + error=f'{type(exc).__name__}: {exc}', + inputs=inputs, + ) + + p_bar = { + sp: float(out[f'{sp}_bar']) + for sp in ('H2O', 'CO2', 'H2', 'CO', 'CH4', 'N2', 'NH3', 'S2', 'SO2', 'H2S') + if f'{sp}_bar' in out + } + dissolved = {sp: float(out[f'{sp}_kg_liquid']) for sp in p_bar if f'{sp}_kg_liquid' in out} + return BackendResult( + backend='calliope', + converged=True, + fO2_shift_derived=float(out['fO2_shift_derived']), + p_bar=p_bar, + dissolved_kg=dissolved, + total_P_bar=float(sum(p_bar.values())), + inputs=inputs, + ) + + +# --------------------------------------------------------------------------- +# atmodeller +# --------------------------------------------------------------------------- + + +_ATM_SPECIES_MAP = { + # PROTEUS-name -> atmodeller canonical-name (alphabetical-by-element). + # SO2 is renamed to O2S internally; NH3 to H3N. Get the canonical + # name wrong and the species silently drops out of output.asdict(). + 'H2O': 'H2O', + 'H2': 'H2', + 'CO2': 'CO2', + 'CO': 'CO', + 'CH4': 'CH4', + 'N2': 'N2', + 'NH3': 'H3N', + 'S2': 'S2', + 'SO2': 'O2S', + 'H2S': 'H2S', + 'O2': 'O2', +} + + +_DEFAULT_ATM_SOL = { + 'H2O': 'H2O_peridotite_sossi23', + 'CO2': 'CO2_basalt_dixon95', + 'H2': 'H2_basalt_hirschmann12', + 'N2': 'N2_basalt_dasgupta22', + 'S2': 'S2_sulfide_basalt_boulliung23', + 'CO': 'CO_basalt_yoshioka19', + 'CH4': 'CH4_basalt_ardia13', +} + + +_CALLIOPE_ALIGNED_ATM_SOL = { + # CALLIOPE has explicit solubility for H2O / CO2 / N2 / S2 only; the + # other species inherit zero dissolved mass via Bower 2022 §2.2.3. + # Matching that selection at the atmodeller side isolates the + # remaining sources of disagreement. + 'H2O': 'H2O_peridotite_sossi23', + 'CO2': 'CO2_basalt_dixon95', + 'H2': None, + 'N2': 'N2_basalt_dasgupta22', + 'S2': 'S2_sulfide_basalt_boulliung23', # Gaillard22 absent from atmodeller library + 'CO': None, + 'CH4': None, +} + + +def run_atmodeller( + inventory: Inventory, + T_magma: float, + Phi_global: float = 1.0, + solubility_map: dict | None = None, + eos_map: dict | None = None, + include_condensates: bool = False, + solver_multistart: int = 10, + solver_atol: float = 1e-6, + solver_rtol: float = 1e-4, + solver_max_steps: int = 256, + **planet_overrides, +) -> BackendResult: + """Run atmodeller's authoritative-O solver via its direct API. + + This bypasses the PROTEUS wrapper so the harness has no PROTEUS + runtime dependency. The species list, solubility selection, EOS + selection, and the no-fugacity-constraint authoritative-O path + mirror what the PROTEUS wrapper would build at runtime. + """ + from atmodeller import ChemicalSpecies, EquilibriumModel, Planet, SpeciesNetwork + from atmodeller.containers import SolverParameters + from atmodeller.solubility import get_solubility_models + + sol_map = solubility_map if solubility_map is not None else _DEFAULT_ATM_SOL + eos_map = eos_map or {} + sol_lib = get_solubility_models() + + p = dict(PLANETARY_DEFAULTS) + p.update(planet_overrides) + + inputs = dict( + inventory=inventory.name, + T_magma=T_magma, + Phi_global=Phi_global, + solubility_map=dict(sol_map), + eos_map=dict(eos_map), + include_condensates=include_condensates, + target_d=inventory.asdict(), + ) + + try: + # Only include species whose constituent elements all have + # non-zero budgets. Matches the wrapper's active-elements logic. + species_elements = { + 'H2O': {'H'}, + 'H2': {'H'}, + 'CO2': {'C'}, + 'CO': {'C'}, + 'CH4': {'H', 'C'}, + 'N2': {'N'}, + 'NH3': {'H', 'N'}, + 'S2': {'S'}, + 'SO2': {'S'}, + 'H2S': {'H', 'S'}, + 'O2': set(), + } + budgets = inventory.asdict() + active_elements = {e for e in 'HCNSO' if budgets.get(e, 0.0) > 0.0} + active_species = { + sp for sp, req in species_elements.items() if req.issubset(active_elements) + } + + species_list = [] + for proteus_name, atm_name in _ATM_SPECIES_MAP.items(): + if proteus_name not in active_species: + continue + kwargs = {} + sol_key = sol_map.get(proteus_name) + if sol_key and sol_key in sol_lib: + kwargs['solubility'] = sol_lib[sol_key] + elif sol_key: + raise ValueError(f'Unknown atmodeller solubility key: {sol_key!r}') + eos_key = eos_map.get(proteus_name) + if eos_key: + from atmodeller.eos import get_eos_models + + eos_lib = get_eos_models() + if eos_key in eos_lib: + kwargs['activity'] = eos_lib[eos_key] + else: + raise ValueError(f'Unknown atmodeller EOS key: {eos_key!r}') + species_list.append(ChemicalSpecies.create_gas(atm_name, **kwargs)) + + if include_condensates and 'C' in active_elements: + try: + species_list.append(ChemicalSpecies.create_condensed('C')) + except Exception: # noqa: BLE001 + pass + + species = SpeciesNetwork(tuple(species_list)) + model = EquilibriumModel(species) + + planet = Planet( + planet_mass=p['M_planet'], + core_mass_fraction=p['core_mass_fraction'], + mantle_melt_fraction=Phi_global, + surface_radius=p['radius'], + temperature=T_magma, + pressure=np.nan, + ) + + mass_constraints = {e: float(budgets[e]) for e in 'HCNSO' if budgets.get(e, 0.0) > 0.0} + + solver_params = SolverParameters( + atol=solver_atol, + rtol=solver_rtol, + max_steps=solver_max_steps, + multistart=solver_multistart, + ) + + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + model.solve( + state=planet, + mass_constraints=mass_constraints, + solver_parameters=solver_params, + solver='robust', + ) + + output = model.output + quick_look = output.quick_look() + output_dict = output.asdict() + + reverse_map = {v: k for k, v in _ATM_SPECIES_MAP.items()} + p_bar = {} + for atm_name, p_val in quick_look.items(): + stripped = atm_name.replace('_g', '') + proteus_name = reverse_map.get(stripped) + if proteus_name is None: + continue + p_bar[proteus_name] = float(np.squeeze(p_val)) + + dissolved = {} + for proteus_name, atm_name in _ATM_SPECIES_MAP.items(): + key = f'{atm_name}_g' + sd = output_dict.get(key, {}) + if isinstance(sd, dict): + dm = sd.get('dissolved_mass') + if dm is not None: + try: + dissolved[proteus_name] = max(0.0, float(np.squeeze(dm))) + except (TypeError, ValueError): + pass + + o2 = output_dict.get('O2_g', {}) + log10dIW = None + if isinstance(o2, dict): + v = o2.get('log10dIW_1_bar') + if v is not None: + log10dIW = float(np.squeeze(v)) + + if log10dIW is None or not np.isfinite(log10dIW): + return BackendResult( + backend='atmodeller', + converged=False, + error='atmodeller returned no log10dIW_1_bar', + inputs=inputs, + ) + + return BackendResult( + backend='atmodeller', + converged=True, + fO2_shift_derived=log10dIW, + p_bar=p_bar, + dissolved_kg=dissolved, + total_P_bar=float(sum(p_bar.values())), + inputs=inputs, + ) + except Exception as exc: # noqa: BLE001 + return BackendResult( + backend='atmodeller', + converged=False, + error=f'{type(exc).__name__}: {exc}', + inputs=inputs, + ) diff --git a/scripts/cross_backend/verification.py b/scripts/cross_backend/verification.py new file mode 100644 index 0000000..7f09c5c --- /dev/null +++ b/scripts/cross_backend/verification.py @@ -0,0 +1,217 @@ +"""Pre-flight verification: each backend must round-trip self-consistently +before we trust it in cross-backend plots. + +Round-trip protocol per backend: + +1. Run buffered-mode equilibrium at a known Delta-IW, take the resulting + O_kg_total. +2. Feed that O budget back into the authoritative-O entry point. +3. Verify the recovered Delta-IW matches step 1 within 0.05 dex. + +For CALLIOPE, step 1 calls `equilibrium_atmosphere` with fO2_shift_IW=X. +For atmodeller, step 1 calls the equilibrium solver with an +`IronWustiteBuffer(X)` fugacity constraint and reads the resulting O +mass from output.asdict(). + +This module is callable as `python -m scripts.cross_backend.verification` +and exits non-zero if any check fails. +""" + +from __future__ import annotations + +import logging +import sys +import warnings + +import numpy as np + +from .inventories import EARTH_BSE_KRIJT23, PLANETARY_DEFAULTS +from .runners import _calliope_ddict, run_atmodeller + +log = logging.getLogger('cross_backend.verification') + + +def _earth_HCNS() -> dict: + """Earth H/C/N/S budget, same as the round-trip regression test.""" + inv = EARTH_BSE_KRIJT23 + return {'H': inv.H, 'C': inv.C, 'N': inv.N, 'S': inv.S} + + +def calliope_buffered_O(T_magma: float, dIW: float) -> float: + """Return the O_kg_total CALLIOPE produces at fixed Delta-IW.""" + from calliope.solve import equilibrium_atmosphere + + ddict = _calliope_ddict(T_magma=T_magma) + ddict['fO2_shift_IW'] = dIW + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + out = equilibrium_atmosphere( + _earth_HCNS(), ddict, hide_warnings=True, print_result=False + ) + return float(out['O_kg_total']) + + +def atmodeller_buffered_O(T_magma: float, dIW: float) -> float: + """Return the total O kg atmodeller produces at fixed Delta-IW. + + Uses atmodeller's as-shipped solubility defaults (matching what + `run_atmodeller` uses on the authoritative-O side), so the round- + trip exercises the same chemistry in both directions. Diverging + selections between buffered and authoritative-O calls would + misattribute a solubility-set discrepancy to a solver-precision + failure. + """ + from atmodeller import ChemicalSpecies, EquilibriumModel, Planet, SpeciesNetwork + from atmodeller.containers import SolverParameters + from atmodeller.solubility import get_solubility_models + from atmodeller.thermodata import IronWustiteBuffer + + from .runners import _DEFAULT_ATM_SOL + + sol_lib = get_solubility_models() + sol_map = dict(_DEFAULT_ATM_SOL) + species_names = { + 'H2O': 'H2O', + 'CO2': 'CO2', + 'N2': 'N2', + 'S2': 'S2', + 'CO': 'CO', + 'CH4': 'CH4', + 'SO2': 'SO2', + 'H2S': 'H2S', + 'H2': 'H2', + 'NH3': 'H3N', + 'O2': 'O2', + } + species_list = [] + for proteus_name, atm_name in species_names.items(): + kwargs = {} + sol_key = sol_map.get(proteus_name) + if sol_key: + kwargs['solubility'] = sol_lib[sol_key] + species_list.append(ChemicalSpecies.create_gas(atm_name, **kwargs)) + + species = SpeciesNetwork(tuple(species_list)) + model = EquilibriumModel(species) + + planet = Planet( + planet_mass=PLANETARY_DEFAULTS['M_planet'], + core_mass_fraction=PLANETARY_DEFAULTS['core_mass_fraction'], + mantle_melt_fraction=1.0, + surface_radius=PLANETARY_DEFAULTS['radius'], + temperature=T_magma, + pressure=np.nan, + ) + + hcns = _earth_HCNS() + mass_constraints = {e: float(hcns[e]) for e in 'HCNS'} + + solver_params = SolverParameters(atol=1e-6, rtol=1e-4, max_steps=256, multistart=10) + + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + model.solve( + state=planet, + fugacity_constraints={'O2_g': IronWustiteBuffer(dIW)}, + mass_constraints=mass_constraints, + solver_parameters=solver_params, + solver='robust', + ) + + output = model.output + output_dict = output.asdict() + + # atmodeller exposes per-element totals directly under `element_` + # keys; `total_mass` is gas + dissolved in kg. Using this avoids + # hand-summing across species (which is brittle because atmodeller + # canonicalises species names internally, e.g. SO2 -> O2S). + elem = output_dict.get('element_O', {}) + if not isinstance(elem, dict) or 'total_mass' not in elem: + raise RuntimeError("atmodeller output missing 'element_O.total_mass'") + return float(np.squeeze(elem['total_mass'])) + + +def round_trip_calliope(T_magma: float, dIW_in: float) -> tuple[float, float]: + """Returns (input_dIW, recovered_dIW).""" + O_kg = calliope_buffered_O(T_magma, dIW_in) + target = {**_earth_HCNS(), 'O': O_kg} + from calliope.solve import equilibrium_atmosphere_authoritative_O + + ddict = _calliope_ddict(T_magma=T_magma) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + out = equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=dIW_in, + hide_warnings=True, + random_seed=1234, + print_result=False, + nguess=1500, + ) + return dIW_in, float(out['fO2_shift_derived']) + + +def round_trip_atmodeller(T_magma: float, dIW_in: float) -> tuple[float, float]: + """Returns (input_dIW, recovered_dIW).""" + from .inventories import Inventory + + O_kg = atmodeller_buffered_O(T_magma, dIW_in) + hcns = _earth_HCNS() + inv = Inventory( + name=f'roundtrip_T{T_magma:.0f}_dIW{dIW_in:+.1f}', + H=hcns['H'], + C=hcns['C'], + N=hcns['N'], + O=O_kg, + S=hcns['S'], + citation='generated by buffered-mode atmodeller call', + ) + res = run_atmodeller(inv, T_magma=T_magma, Phi_global=1.0) + if not res.converged: + return dIW_in, float('nan') + return dIW_in, res.fO2_shift_derived + + +def main() -> int: + """Run round-trip verification for both backends at a grid of + (T, dIW) and exit non-zero if any |residual| > 0.1 dex. + + The tolerance is generous (per-element solver tol is 1e-5 relative, + but the inferred fO2 has wider sensitivity to numerical noise in the + forward-then-inverse path). + """ + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + T_grid = [1500.0, 2000.0, 2500.0, 3000.0] + dIW_grid = [-2.0, 0.0, 2.0, 4.0] + tol_dex = 0.10 + all_ok = True + for backend, rt in ( + ('calliope', round_trip_calliope), + ('atmodeller', round_trip_atmodeller), + ): + print(f'\n# Round-trip: {backend}') + print(f'{"T_K":>6} {"dIW_in":>8} {"dIW_out":>10} {"residual":>10} {"status":>10}') + for T in T_grid: + for dIW in dIW_grid: + try: + _, recov = rt(T, dIW) + except Exception as exc: # noqa: BLE001 + print( + f'{T:6.0f} {dIW:8.2f} {"":>10} {"":>10} {"ERR:" + type(exc).__name__:>10}' + ) + all_ok = False + continue + residual = recov - dIW + ok = np.isfinite(residual) and abs(residual) < tol_dex + status = 'OK' if ok else 'FAIL' + if not ok: + all_ok = False + print(f'{T:6.0f} {dIW:8.2f} {recov:10.3f} {residual:+10.3f} {status:>10}') + return 0 if all_ok else 1 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/scripts/tutorials/__init__.py b/scripts/tutorials/__init__.py new file mode 100644 index 0000000..4d4261f --- /dev/null +++ b/scripts/tutorials/__init__.py @@ -0,0 +1,7 @@ +"""Reusable scripts that generate the publication-quality figures +shown in the CALLIOPE Tutorials section of the docs. + +One script per tutorial page, plus a shared `_style` module that +reuses the Roboto font registration and helper functions from the +cross-backend comparison scripts so all docs figures share one look. +""" diff --git a/scripts/tutorials/_style.py b/scripts/tutorials/_style.py new file mode 100644 index 0000000..ef76ee9 --- /dev/null +++ b/scripts/tutorials/_style.py @@ -0,0 +1,108 @@ +"""Shared style for tutorial figures. + +Imports `apply_style` from the cross-backend plotting scripts so the +Roboto fonts bundled there are registered as a side effect, then +exports a `save()` helper that writes into the tutorial-specific +output directory `docs/assets/figures/tutorials/`. +""" + +from __future__ import annotations + +import math +import re +from pathlib import Path + +import matplotlib.pyplot as plt + +from scripts.cross_backend.plot_style import ( # noqa: F401 re-exported for convenience + COLOR_ATM, + COLOR_BG, + COLOR_CAL, + COLOR_FIS, + apply_style, + panel_label, +) + +_SPECIES_RE = re.compile(r'(\d+)') + + +def species_label(species: str) -> str: + """Render a species name in matplotlib mathtext with the digits as + subscripts: ``species_label('CH4') -> r'$\\mathrm{CH_{4}}$'``. + + Use this anywhere a plain ASCII species name (legend, tick label, + annotation) would otherwise read as ``CH4`` instead of ``CH4``. + """ + return r'$\mathrm{' + _SPECIES_RE.sub(r'_{\1}', species) + '}$' + + +def _decimal(x: float, prec: int) -> str: + """Render ``x`` as a plain decimal string with ``prec`` digits + beyond the leading non-zero digit. Never falls back to ``e+``. + """ + exp = int(math.floor(math.log10(abs(x)))) + decimals = max(0, prec - exp) + return f'{x:.{decimals}f}' + + +def sci_fmt(x: float, *, prec: int = 2, unit: str = '') -> str: + """Format ``x`` for use as a plot annotation. + + Numbers in [0.01, 10000) print with ``prec + 1`` significant + figures as plain decimals (e.g. ``1712`` stays as ``1712``, not + ``1.71e+03``). Everything outside that range prints in + matplotlib mathtext scientific notation + (``$a \\times 10^{b}$``) so labels never show ``e+19`` or + ``e-09``. The trailing ``unit`` string is appended with one space. + """ + suffix = f' {unit}' if unit else '' + if x == 0 or not math.isfinite(x): + return f'0{suffix}' + abs_x = abs(x) + if 1e-2 <= abs_x < 1e4: + return f'{_decimal(x, prec)}{suffix}' + exp = int(math.floor(math.log10(abs_x))) + mant = x / (10**exp) + return rf'${mant:.{prec}f} \times 10^{{{exp}}}${suffix}' + + +_UNICODE_SUP = str.maketrans('0123456789-+', '⁰¹²³⁴⁵⁶⁷⁸⁹⁻⁺') + + +def sci_fmt_plain(x: float, *, prec: int = 2, unit: str = '') -> str: + """Like :func:`sci_fmt` but emits Unicode superscripts instead of + matplotlib mathtext. + + Use this inside monospace text blocks (e.g. summary tables) where + mathtext would break column alignment. The output stays a plain + ASCII / Unicode string with ``×`` and superscript digits, e.g. + ``4.39 × 10¹⁹ kg``. + """ + suffix = f' {unit}' if unit else '' + if x == 0 or not math.isfinite(x): + return f'0{suffix}' + abs_x = abs(x) + if 1e-2 <= abs_x < 1e4: + return f'{_decimal(x, prec)}{suffix}' + exp = int(math.floor(math.log10(abs_x))) + mant = x / (10**exp) + return f'{mant:.{prec}f} × 10{str(exp).translate(_UNICODE_SUP)}{suffix}' + + +FIGURE_DIR = ( + Path(__file__).resolve().parent.parent.parent / 'docs' / 'assets' / 'figures' / 'tutorials' +) +FIGURE_DIR.mkdir(parents=True, exist_ok=True) + +DATA_DIR = Path(__file__).resolve().parent / 'data' +DATA_DIR.mkdir(parents=True, exist_ok=True) + + +def save(fig: plt.Figure, stem: str, *, formats=('pdf', 'png')) -> dict: + """Save `fig` to `FIGURE_DIR/.` for each requested format.""" + paths = {} + for ext in formats: + path = FIGURE_DIR / f'{stem}.{ext}' + fig.savefig(path, bbox_inches='tight') + paths[ext] = path + return paths diff --git a/scripts/tutorials/data/coupled_loop.csv b/scripts/tutorials/data/coupled_loop.csv new file mode 100644 index 0000000..3db5ca4 --- /dev/null +++ b/scripts/tutorials/data/coupled_loop.csv @@ -0,0 +1,37 @@ +phase,step,T_K,Phi_global,wall_s,P_total_bar,H2O,CO2,H2,CO,CH4,N2,NH3,S2,SO2,H2S +cooling,0,3000.0,1.0,0.29830408096313477,1444.7348693388055,5.563164072888463,342.0028510267699,2.3782307598350814,1086.8419668666843,2.1898500473372933e-06,7.757534916211047,8.032812041120466e-05,0.002515093681140552,0.172398605954588,0.004982790047495348 +cooling,1,2937.5,1.0,0.0009467601776123047,1445.1900400132317,5.562948226629907,343.71558312709925,2.414843953298539,1085.5787828539858,2.756038236811897e-06,7.761028624102354,8.586412054368716e-05,0.0023116761299272017,0.14200599715549578,0.005414168626906644 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+H2O,0.42897810871786496 +CO2,1.6109561272840474 +H2,0.21178509956245709 +CO,4.831473772011394 +CH4,5.086329791921122e-10 +N2,1.5980240614751287 +NH3,1.4583459116757043e-06 +S2,0.000738419274760152 +SO2,0.022429599152547008 +H2S,0.0006756515721832231 +__P_surf_bar,8.705209620717001 +__M_atm_kg,4.526216256936822e+19 +__mean_mol_mass_g_per_mol,29.94321758363075 diff --git a/scripts/tutorials/data/mars_fiducial.csv b/scripts/tutorials/data/mars_fiducial.csv new file mode 100644 index 0000000..7a6a3bb --- /dev/null +++ b/scripts/tutorials/data/mars_fiducial.csv @@ -0,0 +1,3 @@ +planet,H2O,CO2,H2,CO,CH4,N2,NH3,S2,SO2,H2S,P_surf_bar,M_atm_kg,mean_mol_mass_g_per_mol +Earth,5.560989885828033,358.3949460837822,2.745442652437289,1074.8745746071556,1.9015888830061486e-05,7.791255481255708,0.00015029622642263174,0.0011365760077676047,0.027827182872624943,0.01086644525991535,1449.4073555495265,7.536097832727136e+21,31.879521995200186 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+0.0,-2.309164022609324e-11,-2.309164022609324e-11,9.356646480604329e+21 +1.0,1.000000000000493,4.929390229335695e-13,1.0356788753660622e+22 +2.0,2.0000000000010285,1.028510610012745e-12,1.1493883927635893e+22 +3.0,3.0000000000004503,4.503064587879635e-13,1.2260160087449454e+22 +4.0,4.000000000000759,7.593925488436071e-13,1.3078918499848497e+22 diff --git a/scripts/tutorials/fig_coupled_loop.py b/scripts/tutorials/fig_coupled_loop.py new file mode 100644 index 0000000..717999f --- /dev/null +++ b/scripts/tutorials/fig_coupled_loop.py @@ -0,0 +1,239 @@ +"""Tutorial figure for the "Coupled-loop driver" page. + +Simulates a magma ocean cooling from 3000 K to 1500 K in 25 steps, +calling CALLIOPE at each step with the previous result's partial +pressures as the warm-start guess. Plots partial pressures as a +function of decreasing T_magma so the reader can see how species +populate the cooling atmosphere. + +This is the realistic warm-start chain pattern, not the unrelated +sweep done in firstrun.md Step 6. +""" + +from __future__ import annotations + +import csv +import logging +import time +import warnings + +import matplotlib.pyplot as plt +import numpy as np + +from calliope.constants import dict_colors, volatile_species +from calliope.solve import equilibrium_atmosphere + +from ._style import DATA_DIR, apply_style, save, species_label + +log = logging.getLogger('tutorials.coupled_loop') + + +PLANET = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, +} +EARTH_HCNS = {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21} +DIW_FIXED = 0.5 # holding redox fixed: the focus is the cooling sequence +T_SEQUENCE = np.linspace(3000.0, 1500.0, 25) +T_FREEZE = 1500.0 # temperature at which the magma-volume sweep runs +PHI_SEQUENCE = np.linspace(1.0, 0.5, 11) # melt-fraction crystallisation step + +SPECIES_TO_PLOT = ['H2O', 'CO2', 'H2', 'CO', 'CH4', 'N2', 'NH3', 'S2', 'SO2', 'H2S'] + + +def _run(ddict_template: dict, schedule: list[tuple[float, float]], label: str) -> dict: + """Run a sequence of (T_magma, Phi_global) steps with warm-start + threading. Returns per-step partial pressures, surface pressure, + and wall time. + + ``schedule`` is a list of ``(T, Phi)`` tuples. Both quantities can + vary; only the chemistry knobs touched here change between steps. + """ + n = len(schedule) + pressures = {sp: np.full(n, np.nan) for sp in SPECIES_TO_PLOT} + P_total = np.full(n, np.nan) + wall = np.zeros(n) + p_guess = None + for i, (T, phi) in enumerate(schedule): + ddict = {**ddict_template, 'T_magma': float(T), 'Phi_global': float(phi)} + t0 = time.time() + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + res = equilibrium_atmosphere( + EARTH_HCNS, + ddict, + p_guess=p_guess, + print_result=False, + ) + wall[i] = time.time() - t0 + for sp in SPECIES_TO_PLOT: + pressures[sp][i] = float(res[f'{sp}_bar']) + P_total[i] = float(res['P_surf']) + p_guess = {s: float(res[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} + log.info( + ' %s step %2d T=%4.0f K Phi=%.2f P_surf=%7.1f bar %5.3f s', + label, + i, + T, + phi, + P_total[i], + wall[i], + ) + return dict(pressures=pressures, P_total=P_total, wall_s=wall) + + +def _base_ddict() -> dict: + d = {**PLANET, 'fO2_shift_IW': DIW_FIXED} + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d + + +def collect() -> dict: + """Run a cooling sequence (Phase 1: T 3000 -> 1500 K at Phi = 1) + followed by a magma-volume sweep (Phase 2: Phi 1.0 -> 0.5 at fixed + T = 1500 K). Both phases use the same warm-start chain. + """ + ddict = _base_ddict() + + phase1_schedule = [(float(T), 1.0) for T in T_SEQUENCE] + log.info('Phase 1: cooling at Phi = 1') + phase1 = _run(ddict, phase1_schedule, 'cool') + + phase2_schedule = [(T_FREEZE, float(phi)) for phi in PHI_SEQUENCE] + log.info('Phase 2: crystallisation at T = %.0f K', T_FREEZE) + phase2 = _run(ddict, phase2_schedule, 'cryst') + + return dict( + T_cool=T_SEQUENCE.copy(), + pressures_cool=phase1['pressures'], + P_total_cool=phase1['P_total'], + wall_cool=phase1['wall_s'], + Phi_cryst=PHI_SEQUENCE.copy(), + T_cryst=T_FREEZE, + pressures_cryst=phase2['pressures'], + P_total_cryst=phase2['P_total'], + wall_cryst=phase2['wall_s'], + ) + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'coupled_loop.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow( + ['phase', 'step', 'T_K', 'Phi_global', 'wall_s', 'P_total_bar'] + SPECIES_TO_PLOT + ) + for i, T in enumerate(data['T_cool']): + row = ['cooling', i, T, 1.0, data['wall_cool'][i], data['P_total_cool'][i]] + [ + data['pressures_cool'][sp][i] for sp in SPECIES_TO_PLOT + ] + w.writerow(row) + for i, phi in enumerate(data['Phi_cryst']): + row = [ + 'crystallisation', + i, + data['T_cryst'], + phi, + data['wall_cryst'][i], + data['P_total_cryst'][i], + ] + [data['pressures_cryst'][sp][i] for sp in SPECIES_TO_PLOT] + w.writerow(row) + log.info('Wrote %s', csv_path) + + fig, (ax_cool, ax_cryst) = plt.subplots( + 1, + 2, + figsize=(11.4, 5.0), + sharey=True, + gridspec_kw={'width_ratios': [1.6, 1.0], 'wspace': 0.08}, + ) + + visible_threshold = 1e-4 + + # Phase 1: cooling at Phi = 1 + for sp in SPECIES_TO_PLOT: + ys = data['pressures_cool'][sp] + ax_cool.plot( + data['T_cool'], + ys, + color=dict_colors[sp], + linewidth=1.8, + marker='o', + markersize=3.5, + markeredgecolor='none', + alpha=0.95 if np.nanmax(ys) > visible_threshold else 0.55, + ) + ax_cool.set_yscale('log') + ax_cool.set_xlabel(r'$T_\mathrm{magma}$ [K] (cooling $\rightarrow$)') + ax_cool.set_ylabel('Surface partial pressure (bar)') + ax_cool.set_title(f'(a) cooling at $\\Phi = 1$, $\\Delta\\mathrm{{IW}} = {DIW_FIXED:+.1f}$') + ax_cool.invert_xaxis() + ax_cool.set_ylim(1e-6, 1e4) + ax_cool.grid(which='both', alpha=0.3) + + # Phase 2: crystallisation at T = T_FREEZE + for sp in SPECIES_TO_PLOT: + ys = data['pressures_cryst'][sp] + label = species_label(sp) if np.nanmax(ys) > visible_threshold else None + ax_cryst.plot( + data['Phi_cryst'], + ys, + color=dict_colors[sp], + linewidth=1.8, + marker='s', + markersize=3.5, + markeredgecolor='none', + label=label, + alpha=0.95 if label else 0.55, + ) + ax_cryst.set_xlabel(r'$\Phi_\mathrm{global}$ (crystallisation $\rightarrow$)') + ax_cryst.set_title(f'(b) crystallisation at $T = {int(data["T_cryst"])}$ K') + ax_cryst.invert_xaxis() + ax_cryst.grid(which='both', alpha=0.3) + ax_cryst.legend( + loc='center left', + bbox_to_anchor=(1.02, 0.5), + frameon=False, + title='major\nspecies', + title_fontsize=9.5, + ) + + # Wall-time annotation for the cooling panel. + t_cold_ms = float(data['wall_cool'][0]) * 1e3 + t_warm_ms = float(np.median(data['wall_cool'][1:])) * 1e3 + t_total_s = float(data['wall_cool'].sum() + data['wall_cryst'].sum()) + summary = ( + f'cold start (step 0): {t_cold_ms:6.1f} ms\n' + f'warm steps median: {t_warm_ms:6.1f} ms\n' + f'total wall (both): {t_total_s:6.2f} s' + ) + ax_cool.text( + 0.02, + 0.04, + summary, + transform=ax_cool.transAxes, + fontsize=9.0, + family='monospace', + va='bottom', + ha='left', + bbox=dict(boxstyle='round,pad=0.4', facecolor='white', edgecolor='#cccccc'), + ) + + paths = save(fig, 'coupled_loop') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/tutorials/fig_earth_fiducial.py b/scripts/tutorials/fig_earth_fiducial.py new file mode 100644 index 0000000..10797bd --- /dev/null +++ b/scripts/tutorials/fig_earth_fiducial.py @@ -0,0 +1,190 @@ +"""Tutorial figure for the "Reproducing the Earth fiducial" page. + +Reproduces the Earth-fiducial point that anchors the backend-comparison +docs page. Takes the Krijt+2023 H/C/N/S BSE budget, runs CALLIOPE in +buffered mode at the Sossi 2020 Delta-IW = +3.5 to derive the volatile +O reference, then runs authoritative-O mode on (H, C, N, S, O) and +checks that the recovered Delta-IW lands inside the Frost & McCammon +(2008) empirical Earth-mantle range and on the Sossi 2020 anchor. + +The figure is a clean version of cross-backend Figure 5 with only the +CALLIOPE point, designed as the "you have successfully reproduced the +docs Earth fiducial" closing image of the tutorial. +""" + +from __future__ import annotations + +import csv +import logging +import warnings + +import matplotlib.pyplot as plt + +from calliope.constants import volatile_species +from calliope.solve import ( + equilibrium_atmosphere, + equilibrium_atmosphere_authoritative_O, +) + +from ._style import COLOR_BG, COLOR_CAL, DATA_DIR, apply_style, save, sci_fmt + +log = logging.getLogger('tutorials.earth_fiducial') + + +PLANET = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, +} +EARTH_HCNS = {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21} +T_MAGMA = 2000.0 +DIW_ANCHOR = 3.5 # Sossi et al. 2020 Earth upper-mantle anchor + +FROST_LO = 1.0 # Frost & McCammon (2008) Earth-mantle range, IW reference +FROST_HI = 5.0 + + +def collect() -> dict: + """Run the full derive-and-verify chain. + + Returns a dict with the buffered-mode O_kg_total and the + authoritative-O recovered Delta-IW. + """ + ddict = {**PLANET, 'T_magma': T_MAGMA, 'Phi_global': 1.0, 'fO2_shift_IW': DIW_ANCHOR} + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + + # Tight p_guess at the canonical CO2-dominated basin. The buffered + # solver at high Delta-IW + C-rich BSE has a documented spurious + # H2O-free basin at P_surf ~ 1500 bar; without this guess the + # Monte-Carlo restart lands there 1 time in ~5 and the tutorial + # output is non-reproducible. + canonical_guess = {'H2O': 5.0, 'CO2': 1500.0, 'N2': 3.0, 'S2': 1e-3} + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + buf = equilibrium_atmosphere( + EARTH_HCNS, + ddict, + p_guess=canonical_guess, + print_result=False, + ) + O_kg = float(buf['O_kg_total']) + log.info('Step 1 (buffered): dIW = %+.2f -> O_kg_total = %.3e kg', DIW_ANCHOR, O_kg) + + target = dict(EARTH_HCNS) + target['O'] = O_kg + p_guess = {s: float(buf[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + auth = equilibrium_atmosphere_authoritative_O( + target, + ddict, + p_guess=p_guess, + fO2_hint=DIW_ANCHOR, + print_result=False, + ) + recovered = float(auth['fO2_shift_derived']) + log.info( + 'Step 2 (authoritative-O): recovered dIW = %+.4f (residual %+.2e)', + recovered, + recovered - DIW_ANCHOR, + ) + + return dict(O_kg_total=O_kg, dIW_recovered=recovered, P_surf_bar=float(auth['P_surf'])) + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'earth_fiducial.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow(['quantity', 'value']) + w.writerow(['Sossi_2020_anchor_dIW', DIW_ANCHOR]) + w.writerow(['Frost_McCammon_2008_low_dIW', FROST_LO]) + w.writerow(['Frost_McCammon_2008_high_dIW', FROST_HI]) + w.writerow(['O_kg_total_derived', data['O_kg_total']]) + w.writerow(['recovered_dIW_authoritative', data['dIW_recovered']]) + w.writerow(['P_surf_bar_authoritative', data['P_surf_bar']]) + log.info('Wrote %s', csv_path) + + fig, ax = plt.subplots(figsize=(7.6, 3.4)) + + # Frost & McCammon (2008) Earth-mantle range as a soft band. + ax.axvspan( + FROST_LO, + FROST_HI, + color=COLOR_BG, + alpha=0.6, + label='Frost & McCammon 2008 Earth-mantle range', + ) + + # Sossi 2020 dotted anchor; raised z-order so the dotted pattern + # stays visible if CALLIOPE recovers exactly +3.5. + ax.axvline( + DIW_ANCHOR, + color='k', + alpha=0.7, + linestyle=':', + linewidth=1.8, + zorder=3, + label=rf'Sossi 2020 upper-mantle anchor: $\Delta\mathrm{{IW}} = {DIW_ANCHOR:+.2f}$', + ) + + # Reproduced CALLIOPE result. + ax.axvline( + data['dIW_recovered'], + color=COLOR_CAL, + linewidth=2.4, + zorder=2, + label=rf'reproduced CALLIOPE: $\Delta\mathrm{{IW}} = {data["dIW_recovered"]:+.2f}$', + ) + + # 1D layout: suppress y axis. + ax.set_yticks([]) + ax.spines['left'].set_visible(False) + ax.set_xlim(-1.0, 7.0) + ax.grid(axis='x', alpha=0.25) + ax.set_xlabel(r'$\Delta\mathrm{IW}$ at $T_\mathrm{magma} = $' + f' {T_MAGMA:.0f} K') + ax.set_title('Reproducing the Earth fiducial: $\\Delta$IW lands on Sossi 2020') + + # Provenance summary box. Anchored to the right where the data + # band ends but the data lines do not extend past dIW = +5. + summary = ( + f'derived $O_\\mathrm{{tot}}$ = {sci_fmt(data["O_kg_total"], unit="kg")}\n' + f'recovered − anchor = {sci_fmt(data["dIW_recovered"] - DIW_ANCHOR, unit="dex")}\n' + f'$P_\\mathrm{{surf}}$ = {sci_fmt(data["P_surf_bar"], unit="bar")}' + ) + ax.text( + 0.985, + 0.04, + summary, + transform=ax.transAxes, + fontsize=9.0, + va='bottom', + ha='right', + bbox=dict(boxstyle='round,pad=0.4', facecolor='white', edgecolor='#cccccc'), + ) + + ax.legend( + loc='upper center', + bbox_to_anchor=(0.5, -0.22), + ncol=1, + frameon=False, + fontsize=9.0, + ) + + paths = save(fig, 'earth_fiducial') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/tutorials/fig_firstrun_reference.py b/scripts/tutorials/fig_firstrun_reference.py new file mode 100644 index 0000000..4b150bd --- /dev/null +++ b/scripts/tutorials/fig_firstrun_reference.py @@ -0,0 +1,157 @@ +"""Reference figure for the "First run" tutorial. + +Runs the exact inputs the tutorial walks through (1 ocean of H, C/H = +0.1, 2 ppmw N, 200 ppmw S, T_magma = 2500 K, Phi = 1, Delta-IW = ++0.5), then plots the resulting surface partial pressures. Saved into +the docs so a reader who has just finished the tutorial can compare +their output against the canonical answer. +""" + +from __future__ import annotations + +import csv +import logging +import warnings + +import matplotlib.pyplot as plt +import numpy as np + +from calliope.constants import dict_colors, volatile_species +from calliope.solve import equilibrium_atmosphere, get_target_from_params + +from ._style import DATA_DIR, apply_style, save, sci_fmt, species_label + +log = logging.getLogger('tutorials.firstrun_reference') + + +PLANET = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, +} +STATE = { + 'T_magma': 2500.0, + 'Phi_global': 1.0, + 'fO2_shift_IW': 0.5, +} +COMPOSITION = { + 'hydrogen_earth_oceans': 1.0, + 'CH_ratio': 0.1, + 'nitrogen_ppmw': 2.0, + 'sulfur_ppmw': 200.0, +} +SPECIES_TO_PLOT = ['H2O', 'CO2', 'H2', 'CO', 'CH4', 'N2', 'NH3', 'S2', 'SO2', 'H2S'] + + +def collect() -> dict: + ddict = {**PLANET, **STATE, **COMPOSITION} + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + + target = get_target_from_params(ddict) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + result = equilibrium_atmosphere(target, ddict, print_result=False) + + pressures = {sp: float(result[f'{sp}_bar']) for sp in SPECIES_TO_PLOT} + return { + 'P_surf_bar': float(result['P_surf']), + 'M_atm_kg': float(result['M_atm']), + 'mean_mol_mass_g_per_mol': float(result['atm_kg_per_mol']) * 1e3, + 'pressures': pressures, + } + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'firstrun_reference.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow(['species', 'partial_pressure_bar']) + for sp, p in data['pressures'].items(): + w.writerow([sp, p]) + w.writerow(['__P_surf_bar', data['P_surf_bar']]) + w.writerow(['__M_atm_kg', data['M_atm_kg']]) + w.writerow(['__mean_mol_mass_g_per_mol', data['mean_mol_mass_g_per_mol']]) + log.info('Wrote %s', csv_path) + + # Plot species in descending pressure order so the most-abundant + # species sit at the top of the figure; horizontal layout means + # every numeric label has a fixed amount of space to its right + # regardless of how small or large the value is. + order = np.argsort([data['pressures'][sp] for sp in SPECIES_TO_PLOT])[::-1] + species_sorted = [SPECIES_TO_PLOT[i] for i in order] + pressures = np.array([data['pressures'][sp] for sp in species_sorted]) + colors = [dict_colors[sp] for sp in species_sorted] + + fig, ax = plt.subplots(figsize=(7.6, 4.6)) + y_pos = np.arange(len(species_sorted)) + ax.barh(y_pos, pressures, color=colors, edgecolor='black', linewidth=0.5) + + ax.set_xscale('log') + ax.set_yticks(y_pos) + ax.set_yticklabels([species_label(sp) for sp in species_sorted]) + ax.invert_yaxis() # largest pressure at the top + ax.set_xlabel('Surface partial pressure (bar)') + ax.set_title( + rf'First-run reference: $T_\mathrm{{magma}} = {STATE["T_magma"]:.0f}$ K, ' + rf'$\Phi = {STATE["Phi_global"]:.0f}$, ' + rf'$\Delta\mathrm{{IW}} = {STATE["fO2_shift_IW"]:+.1f}$, ' + rf'1 Earth-ocean H' + ) + x_lo = 1e-10 + x_hi = max(1e4, float(pressures.max()) * 5000.0) + ax.set_xlim(x_lo, x_hi) + ax.grid(axis='x', which='both', alpha=0.3) + + # Numeric label to the right of each bar. Use a × 10^n form so + # tiny values (CH4 ~ 6e-9 bar) and large values (CO ~ 5 bar) + # share one consistent notation across the figure. + for yi, p in zip(y_pos, pressures): + if p <= 0 or not np.isfinite(p): + label = 'below floor' + else: + label = sci_fmt(p, unit='bar') + ax.text( + p * 2.0 if (p > 0 and np.isfinite(p)) else x_lo * 2.0, + yi, + label, + ha='left', + va='center', + fontsize=9.0, + color='#333333', + ) + + # Summary box anchored bottom-right so it does not collide with the + # H2O label (top bar, label extends to about 1 bar on the x axis). + summary = ( + f'$P_\\mathrm{{surf}}$ = {sci_fmt(data["P_surf_bar"], unit="bar")}\n' + f'$M_\\mathrm{{atm}}$ = {sci_fmt(data["M_atm_kg"], unit="kg")}\n' + f'mean $M$ = {sci_fmt(data["mean_mol_mass_g_per_mol"], unit="g/mol")}' + ) + ax.text( + 0.985, + 0.04, + summary, + transform=ax.transAxes, + fontsize=9.0, + va='bottom', + ha='right', + bbox=dict(boxstyle='round,pad=0.4', facecolor='white', edgecolor='#cccccc'), + ) + + paths = save(fig, 'firstrun_reference') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/tutorials/fig_mars_fiducial.py b/scripts/tutorials/fig_mars_fiducial.py new file mode 100644 index 0000000..1641c27 --- /dev/null +++ b/scripts/tutorials/fig_mars_fiducial.py @@ -0,0 +1,204 @@ +"""Tutorial figure for the "Mars-like atmosphere" page. + +Runs the same redox and temperature conditions as the first-run +tutorial on a Mars-scaled inventory and Mars planetary parameters, +then overlays the Mars result on the Earth-BSE reference so the +reader can read off which species change most. + +The Mars inventory is the Krijt+2023 Earth BSE H/C/N/S mass-scaled by +Mars / Earth mass (0.107) for illustrative purposes; this is not a +Mars-petrology BSE estimate. The pedagogical goal is to demonstrate +the workflow generalises to non-Earth planets, not to claim a +specific Mars composition. +""" + +from __future__ import annotations + +import csv +import logging +import warnings + +import matplotlib.pyplot as plt +import numpy as np + +from calliope.constants import dict_colors, volatile_species +from calliope.solve import equilibrium_atmosphere + +from ._style import DATA_DIR, apply_style, save, sci_fmt_plain, species_label + +log = logging.getLogger('tutorials.mars_fiducial') + + +# Earth setup (Krijt+2023 BSE H/C/N/S, terrestrial planet parameters). +EARTH = { + 'name': 'Earth', + 'planet': {'M_mantle': 4.03e24, 'gravity': 9.81, 'radius': 6.371e6}, + 'hcns': {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21}, +} + +# Mars: planetary parameters from standard literature values; inventory +# is the Earth BSE scaled by mass ratio (Mars / Earth = 0.107) to keep +# the comparison driven by planet structure rather than by a separate +# (and less well-constrained) Mars BSE estimate. +MARS_MASS_RATIO = 0.107 +MARS = { + 'name': 'Mars-scaled', + 'planet': {'M_mantle': 5.03e23, 'gravity': 3.71, 'radius': 3.39e6}, + 'hcns': {k: v * MARS_MASS_RATIO for k, v in EARTH['hcns'].items()}, +} + +T_MAGMA = 2500.0 +DIW = 0.5 +PHI = 1.0 + +SPECIES_TO_PLOT = ['H2O', 'CO2', 'H2', 'CO', 'CH4', 'N2', 'NH3', 'S2', 'SO2', 'H2S'] + + +def _solve(cfg: dict) -> dict: + ddict = {**cfg['planet'], 'T_magma': T_MAGMA, 'Phi_global': PHI, 'fO2_shift_IW': DIW} + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + r = equilibrium_atmosphere(cfg['hcns'], ddict, print_result=False) + return { + 'pressures': {sp: float(r[f'{sp}_bar']) for sp in SPECIES_TO_PLOT}, + 'P_surf_bar': float(r['P_surf']), + 'M_atm_kg': float(r['M_atm']), + 'mean_mol_mass': float(r['atm_kg_per_mol']) * 1e3, + } + + +def collect() -> dict: + earth_out = _solve(EARTH) + mars_out = _solve(MARS) + log.info( + 'Earth: P_surf = %.0f bar, M_atm = %.2e kg', + earth_out['P_surf_bar'], + earth_out['M_atm_kg'], + ) + log.info( + 'Mars : P_surf = %.0f bar, M_atm = %.2e kg', + mars_out['P_surf_bar'], + mars_out['M_atm_kg'], + ) + return {'Earth': earth_out, 'Mars': mars_out} + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'mars_fiducial.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow( + ['planet'] + SPECIES_TO_PLOT + ['P_surf_bar', 'M_atm_kg', 'mean_mol_mass_g_per_mol'] + ) + for planet in ('Earth', 'Mars'): + row = [planet] + [data[planet]['pressures'][sp] for sp in SPECIES_TO_PLOT] + row += [ + data[planet]['P_surf_bar'], + data[planet]['M_atm_kg'], + data[planet]['mean_mol_mass'], + ] + w.writerow(row) + log.info('Wrote %s', csv_path) + + # Grouped horizontal bars: for each species, an Earth bar above + # a Mars bar at the same y. Species sorted by Earth pressure + # descending so the most-abundant species sit at the top. + order = sorted( + range(len(SPECIES_TO_PLOT)), + key=lambda i: -data['Earth']['pressures'][SPECIES_TO_PLOT[i]], + ) + species = [SPECIES_TO_PLOT[i] for i in order] + earth_p = np.array([data['Earth']['pressures'][sp] for sp in species]) + mars_p = np.array([data['Mars']['pressures'][sp] for sp in species]) + + fig, ax = plt.subplots(figsize=(8.0, 5.4)) + + y = np.arange(len(species)) * 1.8 # extra space between species + bar_h = 0.7 + ax.barh( + y - bar_h / 2, + earth_p, + height=bar_h, + color=[dict_colors[sp] for sp in species], + edgecolor='black', + linewidth=0.5, + label='Earth-BSE', + ) + ax.barh( + y + bar_h / 2, + mars_p, + height=bar_h, + color=[dict_colors[sp] for sp in species], + alpha=0.45, + edgecolor='black', + linewidth=0.5, + hatch='///', + label='Mars-scaled (0.107 x Earth inventory)', + ) + + ax.set_xscale('log') + ax.set_yticks(y) + ax.set_yticklabels([species_label(sp) for sp in species]) + ax.invert_yaxis() + ax.set_xlabel('Surface partial pressure (bar)') + ax.set_title( + rf'Earth vs Mars-scaled at $T_\mathrm{{magma}} = {T_MAGMA:.0f}$ K, ' + rf'$\Phi = {PHI:.0f}$, $\Delta\mathrm{{IW}} = {DIW:+.1f}$' + ) + x_lo = 1e-10 + x_hi = max(earth_p.max(), mars_p.max()) * 5000.0 + ax.set_xlim(x_lo, x_hi) + ax.grid(axis='x', which='both', alpha=0.3) + # Legend below the plot so it does not crowd the top bars. + ax.legend( + loc='upper center', bbox_to_anchor=(0.5, -0.12), ncol=2, frameon=False, fontsize=9.5 + ) + + # Per-planet diagnostics in the upper-right of the data area. + # Use plain-text scientific notation (Unicode superscripts) so the + # values line up in monospace columns. + def _fmt(val, unit): + return sci_fmt_plain(val, unit=unit) + + e_p = _fmt(data['Earth']['P_surf_bar'], 'bar') + m_p = _fmt(data['Mars']['P_surf_bar'], 'bar') + e_m = _fmt(data['Earth']['M_atm_kg'], 'kg') + m_m = _fmt(data['Mars']['M_atm_kg'], 'kg') + e_w = f'{data["Earth"]["mean_mol_mass"]:.2f} g/mol' + m_w = f'{data["Mars"]["mean_mol_mass"]:.2f} g/mol' + summary = ( + f' Earth Mars\n' + f'P_surf {e_p:<18s} {m_p}\n' + f'M_atm {e_m:<18s} {m_m}\n' + f'mean M {e_w:<18s} {m_w}' + ) + ax.text( + 0.985, + 0.04, + summary, + transform=ax.transAxes, + fontsize=8.5, + family='monospace', + va='bottom', + ha='right', + bbox=dict(boxstyle='round,pad=0.4', facecolor='white', edgecolor='#cccccc'), + ) + + paths = save(fig, 'mars_fiducial') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/tutorials/fig_phase_diagram.py b/scripts/tutorials/fig_phase_diagram.py new file mode 100644 index 0000000..e8e9cce --- /dev/null +++ b/scripts/tutorials/fig_phase_diagram.py @@ -0,0 +1,259 @@ +"""Tutorial figure for the "Speciation phase diagram" page. + +Sweeps (T_magma, Delta-IW) on a 15 x 12 grid at Earth-BSE Krijt+2023 +H/C/N/S and identifies the four most abundant volatile species at +each point. Each cell of the figure is subdivided into a 2 x 2 +quartet in reading order: top-left = rank 1 (dominant), top-right = +rank 2, bottom-left = rank 3, bottom-right = rank 4. A reader can +then see at a glance not just what is dominant but how the second +through fourth species shift across the (T, Delta-IW) plane. + +Warm-start chain along Delta-IW at fixed T to keep the runtime under a +minute on a modern laptop. +""" + +from __future__ import annotations + +import csv +import logging +import warnings + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import ListedColormap +from matplotlib.patches import Patch + +from calliope.constants import dict_colors, volatile_species +from calliope.solve import equilibrium_atmosphere + +from ._style import DATA_DIR, apply_style, save, species_label + +log = logging.getLogger('tutorials.phase_diagram') + + +PLANET = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, +} +EARTH_HCNS = {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21} + +T_GRID = np.linspace(1500.0, 3000.0, 15) +DIW_GRID = np.linspace(-4.0, 5.0, 12) + +REPORTED_SPECIES = ['H2O', 'CO2', 'O2', 'H2', 'CO', 'CH4', 'N2', 'NH3', 'S2', 'SO2', 'H2S'] + + +def _base_ddict(T_magma: float, diw: float) -> dict: + ddict = {**PLANET, 'T_magma': T_magma, 'Phi_global': 1.0, 'fO2_shift_IW': diw} + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + return ddict + + +def collect() -> dict: + """Return per-cell ranked top-4 species, plus full partial-pressure + array and total surface pressure on the (T, dIW) grid. + """ + n_T = T_GRID.size + n_d = DIW_GRID.size + n_sp = len(REPORTED_SPECIES) + pressures = np.full((n_T, n_d, n_sp), np.nan) + P_total = np.full((n_T, n_d), np.nan) + rank_idx = np.full((n_T, n_d, 4), -1, dtype=int) + + for iT, T in enumerate(T_GRID): + # Warm-start along dIW. Sweep ascending dIW, reset p_guess on + # the first call at this T, then thread the previous result + # forward. + p_guess = None + for jd, diw in enumerate(DIW_GRID): + ddict = _base_ddict(float(T), float(diw)) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + try: + res = equilibrium_atmosphere( + EARTH_HCNS, + ddict, + p_guess=p_guess, + print_result=False, + ) + except Exception as exc: # noqa: BLE001 + log.warning(' T=%.0f dIW=%+.2f raised %s', T, diw, exc) + p_guess = None + continue + ps = np.array([float(res[f'{sp}_bar']) for sp in REPORTED_SPECIES]) + pressures[iT, jd] = ps + order = np.argsort(ps)[::-1] + rank_idx[iT, jd] = order[:4] + P_total[iT, jd] = float(res['P_surf']) + p_guess = {s: float(res[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} + top4 = ', '.join(REPORTED_SPECIES[i] for i in order[:4]) + log.info( + ' T=%4.0f dIW=%+5.2f top-4=[%s] P_surf=%.2e bar', + T, + diw, + top4, + P_total[iT, jd], + ) + + return dict(T=T_GRID, dIW=DIW_GRID, pressures=pressures, rank_idx=rank_idx, P_total=P_total) + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'phase_diagram.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow( + ['T_K', 'dIW_dex', 'rank1', 'rank2', 'rank3', 'rank4', 'P_total_bar'] + + [f'p_{sp}_bar' for sp in REPORTED_SPECIES] + ) + for iT, T in enumerate(data['T']): + for jd, diw in enumerate(data['dIW']): + idx4 = data['rank_idx'][iT, jd] + names = [REPORTED_SPECIES[i] if i >= 0 else 'failed' for i in idx4] + row = [T, diw] + names + [data['P_total'][iT, jd]] + row += [data['pressures'][iT, jd, s] for s in range(len(REPORTED_SPECIES))] + w.writerow(row) + log.info('Wrote %s', csv_path) + + # Build the 2x2-expanded grid. For each original cell (iT, jd), + # the four sub-cells in reading order map to the rank-1..rank-4 + # species index. Reading order in (data x, data y) where x = dIW + # and y = T (with y increasing upward in the plot): + # rank 1 (top-left) -> sub_T = 1 (upper), sub_d = 0 (lower) + # rank 2 (top-right) -> sub_T = 1 (upper), sub_d = 1 (upper) + # rank 3 (bottom-left) -> sub_T = 0 (lower), sub_d = 0 (lower) + # rank 4 (bottom-right) -> sub_T = 0 (lower), sub_d = 1 (upper) + rank_idx = data['rank_idx'] + n_T, n_d = rank_idx.shape[:2] + expanded = np.full((2 * n_T, 2 * n_d), -1, dtype=int) + for iT in range(n_T): + for jd in range(n_d): + r1, r2, r3, r4 = rank_idx[iT, jd] + expanded[2 * iT + 1, 2 * jd + 0] = r1 + expanded[2 * iT + 1, 2 * jd + 1] = r2 + expanded[2 * iT + 0, 2 * jd + 0] = r3 + expanded[2 * iT + 0, 2 * jd + 1] = r4 + + seen = sorted({int(v) for v in expanded.ravel() if v >= 0}) + species_palette = [dict_colors[REPORTED_SPECIES[i]] for i in seen] + cmap = ListedColormap(species_palette) + remap = {orig: new for new, orig in enumerate(seen)} + remapped = np.vectorize(lambda v: remap.get(int(v), -1))(expanded) + + fig, ax = plt.subplots(figsize=(10.5, 5.8)) + fig.subplots_adjust(right=0.78) + + # Build sub-cell edges that quarter each original cell. + def edges_doubled(arr: np.ndarray) -> np.ndarray: + step = arr[1] - arr[0] + outer = np.concatenate( + [[arr[0] - 0.5 * step], 0.5 * (arr[:-1] + arr[1:]), [arr[-1] + 0.5 * step]] + ) + # Insert a midpoint between every consecutive pair of outer + # edges so each original cell becomes two sub-cells. + mids = 0.5 * (outer[:-1] + outer[1:]) + result = np.empty(2 * outer.size - 1) + result[0::2] = outer + result[1::2] = mids + return result + + t_edges = edges_doubled(data['T']) + d_edges = edges_doubled(data['dIW']) + pcm = ax.pcolormesh( + d_edges, + t_edges, + np.ma.masked_less(remapped, 0), + cmap=cmap, + vmin=-0.5, + vmax=len(seen) - 0.5, + shading='flat', + edgecolors='white', + linewidth=0.35, + ) + pcm.set_clim(-0.5, len(seen) - 0.5) + + # Dark border around every original (T, dIW) cell so the four + # sub-cells of one simulation are visually grouped and separated + # from the four sub-cells of the next. The inner sub-cell seams + # stay light (white, linewidth 0.35) so the rank quadrants inside + # each simulation still read clearly. + sim_edge_kw = dict(color='#1a1a1a', linewidth=2.8, alpha=1.0, zorder=5) + for x in d_edges[::2]: + ax.axvline(x, **sim_edge_kw) + for y in t_edges[::2]: + ax.axhline(y, **sim_edge_kw) + ax.set_xlim(d_edges[0], d_edges[-1]) + ax.set_ylim(t_edges[0], t_edges[-1]) + + ax.set_xlabel(r'$\Delta\mathrm{IW}$ [dex]') + ax.set_ylabel(r'$T_\mathrm{magma}$ [K]') + ax.set_title('Top-4 species per cell at Earth-BSE, $\\Phi = 1$') + + # Discrete species legend, plus a small rank-layout inset legend + # so the reader knows which corner is rank 1 vs rank 4. + handles = [ + Patch( + facecolor=species_palette[k], + edgecolor='k', + linewidth=0.4, + label=species_label(REPORTED_SPECIES[seen[k]]), + ) + for k in range(len(seen)) + ] + sp_legend = ax.legend( + handles=handles, + loc='upper left', + bbox_to_anchor=(1.02, 1.0), + title='species\n(any rank)', + frameon=False, + title_fontsize=9.5, + ) + ax.add_artist(sp_legend) + + # Rank-layout inset: 2x2 mini-grid showing where rank 1 / 2 / 3 / 4 + # sit inside each cell. Anchored below the species legend in the + # right-hand margin of the figure so it does not overlap the data. + from matplotlib.patches import Rectangle + + inset = fig.add_axes([0.82, 0.18, 0.09, 0.12]) + inset.set_xlim(0, 2) + inset.set_ylim(0, 2) + inset.set_xticks([]) + inset.set_yticks([]) + for spine in inset.spines.values(): + spine.set_edgecolor('#888888') + spine.set_linewidth(0.6) + rank_positions = {1: (0, 1), 2: (1, 1), 3: (0, 0), 4: (1, 0)} + for rank, (rx, ry) in rank_positions.items(): + inset.add_patch( + Rectangle((rx, ry), 1, 1, facecolor='#f3f3f3', edgecolor='white', linewidth=1.0) + ) + inset.text( + rx + 0.5, + ry + 0.5, + f'#{rank}', + ha='center', + va='center', + fontsize=10, + color='#333333', + ) + inset.set_title('rank layout', fontsize=8.5, color='#555555', pad=2) + + paths = save(fig, 'phase_diagram') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/scripts/tutorials/fig_two_modes.py b/scripts/tutorials/fig_two_modes.py new file mode 100644 index 0000000..9d1122a --- /dev/null +++ b/scripts/tutorials/fig_two_modes.py @@ -0,0 +1,190 @@ +"""Tutorial figure for the "Two-mode round-trip" page. + +Walks an input Delta-IW through CALLIOPE's buffered mode, takes the +total volatile O the chemistry produces, then feeds (H, C, N, S, O) +into the authoritative-O mode and recovers a Delta-IW. The figure +shows recovered vs input Delta-IW with the y = x reference line so the +closure within solver tolerance is visually trivial to verify. + +Fixed T_magma = 2000 K, Phi = 1, Earth-BSE Krijt+2023 H/C/N/S. +""" + +from __future__ import annotations + +import csv +import logging +import warnings + +import matplotlib.pyplot as plt +import numpy as np + +from calliope.constants import volatile_species +from calliope.solve import ( + equilibrium_atmosphere, + equilibrium_atmosphere_authoritative_O, +) + +from ._style import COLOR_CAL, DATA_DIR, apply_style, save, sci_fmt + +log = logging.getLogger('tutorials.two_modes') + + +PLANET = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, +} +T_MAGMA = 2000.0 +PHI_GLOBAL = 1.0 + +# Krijt+2023 PPVII BSE H/C/N/S budget in kg. +EARTH_HCNS = {'H': 5.6e20, 'C': 3.1e21, 'N': 3.7e19, 'S': 1.0e21} + +DIW_GRID = np.array([-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0]) + + +def _base_ddict() -> dict: + ddict = {**PLANET} + ddict['T_magma'] = T_MAGMA + ddict['Phi_global'] = PHI_GLOBAL + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + return ddict + + +def collect() -> dict: + """Run buffered -> authoritative-O for each input Delta-IW. + + The buffered leg threads p_guess forward across the Delta-IW grid + so the Monte-Carlo restart never has to find the canonical basin + from a cold start at high Delta-IW (where the carbon-rich BSE + inventory has a documented spurious H2O-free basin that the cold + solver lands in ~20% of the time). + + Returns + ------- + dict + Keys ``dIW_input``, ``dIW_recovered``, ``O_kg_total``. + """ + recovered = np.full(DIW_GRID.size, np.nan) + O_total = np.full(DIW_GRID.size, np.nan) + p_guess_buf = None + for i, diw in enumerate(DIW_GRID): + # Step 1: buffered call at the input Delta-IW. Warm-start + # from the previous grid point's converged pressures so the + # solver stays in the canonical basin across the sweep. + ddict = _base_ddict() + ddict['fO2_shift_IW'] = float(diw) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + buf = equilibrium_atmosphere( + EARTH_HCNS, + ddict, + p_guess=p_guess_buf, + hide_warnings=True, + print_result=False, + ) + O_kg = float(buf['O_kg_total']) + p_guess_buf = {s: float(buf[f'{s}_bar']) for s in ('H2O', 'CO2', 'N2', 'S2')} + + # Step 2: authoritative-O call with the buffered run's O budget. + target = dict(EARTH_HCNS) + target['O'] = O_kg + ddict_auth = _base_ddict() + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + auth = equilibrium_atmosphere_authoritative_O( + target, + ddict_auth, + p_guess=p_guess_buf, + fO2_hint=float(diw), + hide_warnings=True, + print_result=False, + ) + recovered[i] = float(auth['fO2_shift_derived']) + O_total[i] = O_kg + log.info( + 'dIW_in = %+.2f, O_kg = %.3e, recovered = %+.4f, residual = %+.2e', + diw, + O_kg, + recovered[i], + recovered[i] - diw, + ) + return dict(dIW_input=DIW_GRID.copy(), dIW_recovered=recovered, O_kg_total=O_total) + + +def make_figure(data: dict | None = None) -> dict: + apply_style() + data = data or collect() + + csv_path = DATA_DIR / 'two_modes_round_trip.csv' + with csv_path.open('w', newline='') as fh: + w = csv.writer(fh) + w.writerow(['dIW_input', 'dIW_recovered', 'residual_dex', 'O_kg_total']) + for d_in, d_rec, o_kg in zip( + data['dIW_input'], data['dIW_recovered'], data['O_kg_total'] + ): + w.writerow([d_in, d_rec, d_rec - d_in, o_kg]) + log.info('Wrote %s', csv_path) + + residuals = data['dIW_recovered'] - data['dIW_input'] + finite = np.isfinite(residuals) + worst_abs = float(np.nanmax(np.abs(residuals[finite]))) if finite.any() else float('nan') + + fig, ax = plt.subplots(figsize=(6.4, 5.6)) + + # y = x reference (i.e., perfect closure). + lo = float(data['dIW_input'].min()) - 0.5 + hi = float(data['dIW_input'].max()) + 0.5 + ax.plot( + [lo, hi], [lo, hi], color='k', alpha=0.4, linewidth=1.0, label='perfect closure (y = x)' + ) + + # The recovered points themselves. + ax.scatter( + data['dIW_input'], + data['dIW_recovered'], + s=85, + color=COLOR_CAL, + edgecolor='k', + linewidth=0.6, + zorder=3, + label='CALLIOPE buffered $\\rightarrow$ authoritative-O', + ) + + # Annotate worst-case residual so the reader has a number to quote. + ax.text( + 0.04, + 0.96, + f'worst-case |recovered − input| = {sci_fmt(worst_abs, unit="dex")}', + transform=ax.transAxes, + fontsize=9.5, + va='top', + ha='left', + bbox=dict(boxstyle='round,pad=0.4', facecolor='white', edgecolor='#cccccc'), + ) + + ax.set_xlabel(r'input $\Delta\mathrm{IW}$ (buffered mode) [dex]') + ax.set_ylabel(r'recovered $\Delta\mathrm{IW}$ (authoritative-O mode) [dex]') + ax.set_title( + f'Two-mode round-trip at Earth-BSE, ' + f'$T_\\mathrm{{magma}} = {T_MAGMA:.0f}$ K, $\\Phi = {PHI_GLOBAL:.0f}$' + ) + ax.set_xlim(lo, hi) + ax.set_ylim(lo, hi) + ax.set_aspect('equal') + ax.legend(loc='lower right', framealpha=0.9, edgecolor='none') + + paths = save(fig, 'two_modes_round_trip') + plt.close(fig) + return paths + + +if __name__ == '__main__': + logging.basicConfig( + level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s %(message)s' + ) + out = make_figure() + for ext, path in out.items(): + print(f' {ext}: {path}') diff --git a/src/calliope/chemistry.py b/src/calliope/chemistry.py index c0f49de..6bf8f04 100644 --- a/src/calliope/chemistry.py +++ b/src/calliope/chemistry.py @@ -3,7 +3,7 @@ import logging -from .oxygen_fugacity import OxygenFugacity +from .oxygen_fugacity import DEFAULT_FO2_MODEL, OxygenFugacity log = logging.getLogger('fwl.' + __name__) @@ -11,7 +11,7 @@ class ModifiedKeq: """Modified equilibrium constant (includes fO2)""" - def __init__(self, Keq_model, fO2_model='oneill'): + def __init__(self, Keq_model, fO2_model=DEFAULT_FO2_MODEL): self.fO2 = OxygenFugacity(fO2_model) self.callmodel = getattr(self, Keq_model) diff --git a/src/calliope/oxygen_fugacity.py b/src/calliope/oxygen_fugacity.py index f732943..74ef6d4 100644 --- a/src/calliope/oxygen_fugacity.py +++ b/src/calliope/oxygen_fugacity.py @@ -7,11 +7,18 @@ log = logging.getLogger('fwl.' + __name__) +# Single source of truth for the default fO2 buffer. ``OxygenFugacity`` and +# ``chemistry.ModifiedKeq`` both default to this name so the two cannot drift +# out of step. Fischer is the more recent IW parameterisation and tracks the +# atmodeller Hirschmann composite to within ~0.2 dex across magma-ocean +# temperatures (see docs/Explanations/cross_backend_comparison.md). +DEFAULT_FO2_MODEL = 'fischer' + class OxygenFugacity: """log10 oxygen fugacity as a function of temperature""" - def __init__(self, model='oneill'): + def __init__(self, model=DEFAULT_FO2_MODEL): self.callmodel = getattr(self, model) def __call__(self, T, fO2_shift=0): @@ -24,7 +31,11 @@ def __call__(self, T, fO2_shift=0): return self.callmodel(T) + fO2_shift def fischer(self, T): - """Fischer et al. (2011) IW (FeO equation of state, EPSL 304, 496)""" + """Fischer et al. (2011) IW (FeO equation of state, EPSL 304, 496). + + The coefficients are the p = 1 bar isoline of their Figure 6, + obtained by integrating their Equation 2. + """ return 6.94059 - 28.1808 * 1e3 / T def oneill(self, T): diff --git a/src/calliope/solve.py b/src/calliope/solve.py index a28f448..37872a9 100644 --- a/src/calliope/solve.py +++ b/src/calliope/solve.py @@ -32,20 +32,52 @@ # not rejected for sub-10-kg mass-balance noise. TRUNC_MASS = 1e1 +# Solver bounds, in one place so the cold-start guess range, the trust-constr +# box, and the fO2-hint validation cannot drift apart. +# +# Pressure cold-start draw is log-uniform over [P_GUESS_MIN_BAR, P_GUESS_MAX_BAR]. +# The trust-constr box and the acceptance gate allow pressures up to +# P_CEILING_BAR, which is well above any realistic magma-ocean surface pressure; +# the wider box lets the solver explore from a poor cold start without escaping +# to non-physical territory. Sub-Neptune surface pressures can exceed the +# default guess maximum, so the guess helpers accept an optional ``p_guess_max`` +# to widen the cold-start range without touching the box. +P_GUESS_MIN_BAR = 1.0e-12 +P_GUESS_MAX_BAR = 1.0e5 +P_CEILING_BAR = 1.0e7 + +# fO2-shift cold-start redraw range (log10 IW offset) and the hard solver box. +# The redraw range spans reducing-mantle to highly-oxidized; the wider hard box +# gives trust-constr room on poor cold starts. +FO2_GUESS_MIN = -6.0 +FO2_GUESS_MAX = 8.0 +FO2_HARD_MIN = -12.0 +FO2_HARD_MAX = 12.0 + +# Surface pressure below which volatile mixing ratios are reported as zero, +# rather than dividing P_surf into a denormal and amplifying floating-point +# noise into spurious mixing ratios. +P_SURF_FLOOR_BAR = 1.0e-30 + def is_included(gas, ddict): return bool(ddict[gas + '_included'] > 0) -def get_partial_pressures(pin, ddict): +def _get_partial_pressures(pin, fO2_shift, ddict): """Partial pressures [bar] of all 11 species from the 4 primaries. + Internal helper that takes ``fO2_shift`` as an explicit argument + instead of reading ``ddict['fO2_shift_IW']``. The user-facing + ``get_partial_pressures`` is a thin wrapper that reads fO2_shift + from ddict. Both share the same physics; the explicit form supports + the authoritative-O solver where fO2_shift is an unknown rather + than a config input. + `pin` provides H2O, CO2, N2, S2; the other 7 species are derived via equilibrium constants and the fO2 buffer. """ - fO2_shift = ddict['fO2_shift_IW'] - p_d = {s: 0.0 for s in volatile_species} p_d['H2O'] = pin['H2O'] @@ -92,25 +124,57 @@ def get_partial_pressures(pin, ddict): gamma = gamma(ddict['T_magma'], fO2_shift) p_d['H2S'] = (gamma * pin['S2'] * p_d['H2'] ** 2) ** 0.5 - # Silent clip: solver Monte-Carlo restarts can produce negative - # `pin`, which then propagates through the sqrt expressions; the - # downstream mass tallies require non-negative pressures. + # Silent clip: solver Monte-Carlo restarts can produce negative or + # non-finite `pin`, which then propagates through the sqrt expressions; + # the downstream mass tallies require non-negative, finite, real + # pressures. + # `max(0.0, p_d[k])` alone is insufficient: builtin max preserves NaN + # whenever NaN is the second argument (NaN < 0 is False, so max + # returns the second arg unchanged), and Python complex numbers do + # not define `<` at all. The explicit checks below pin every output + # to a non-negative real (clipping NaN, +/-inf, and any complex + # intermediate produced by sqrt-of-negative paths down to 0). for k in p_d.keys(): - p_d[k] = max(0.0, p_d[k]) + v = p_d[k] + if isinstance(v, complex) or not np.isfinite(v): + p_d[k] = 0.0 + else: + p_d[k] = max(0.0, float(v)) return p_d +def get_partial_pressures(pin, ddict): + """Partial pressures [bar] of all 11 species from the 4 primaries. + + Thin wrapper around ``_get_partial_pressures`` that reads fO2_shift + from ``ddict['fO2_shift_IW']``. Preserved bit-for-bit identical to + the pre-refactor function for all callers. + """ + return _get_partial_pressures(pin, ddict['fO2_shift_IW'], ddict) + + def get_total_pressure(p_d): """Sum partial pressures to get total pressure""" return sum(p_d.values()) def atmosphere_mean_molar_mass(p_d): - """Mean molar mass of the atmosphere""" + """Mean molar mass of the atmosphere [g/mol]. + + When ``ptot`` collapses to ~0 (every partial pressure clipped to zero + by the NaN-aware guard in ``_get_partial_pressures``), the division + would raise ZeroDivisionError. Return a sentinel value of 1.0 g/mol; + downstream callers in ``_atmosphere_mass`` multiply by ``p_d[k]=0`` + so all element masses come out zero, which propagates a clean + "atmosphere is empty here" signal to the mass-balance residual. + """ ptot = get_total_pressure(p_d) + if ptot < 1e-30: + return 1.0 + mu_atm = 0 for key, value in p_d.items(): mu_atm += molar_mass[key] * value @@ -119,15 +183,19 @@ def atmosphere_mean_molar_mass(p_d): return mu_atm -def atmosphere_mass(pin, ddict): - """Atmospheric mass of volatiles and totals for H, C, and N. +def _atmosphere_mass(pin, fO2_shift, ddict): + """Atmospheric mass of volatiles and totals for H, C, N, O, S. + + Internal helper that takes ``fO2_shift`` as an explicit argument. + The user-facing ``atmosphere_mass`` is a thin wrapper that reads + fO2_shift from ddict. CALLIOPE stores pressures in bar throughout; the only conversion to SI Pa happens here (factor 1e5) when computing column mass `kg = p_Pa * 4 pi R^2 / g`. """ - p_d = get_partial_pressures(pin, ddict) + p_d = _get_partial_pressures(pin, fO2_shift, ddict) mu_atm = atmosphere_mean_molar_mass(p_d) mass_atm_d = {} @@ -164,10 +232,13 @@ def atmosphere_mass(pin, ddict): mass_atm_d['O'] = mass_atm_d['H2O'] / molar_mass['H2O'] mass_atm_d['O'] += 2 * mass_atm_d['O2'] / molar_mass['O2'] + # CO2 is one of the four primary species in `pin`; mass_atm_d['CO2'] + # is populated unconditionally and contributes to the C tally + # without gating. The O contribution must match for element + # bookkeeping to be symmetric. + mass_atm_d['O'] += mass_atm_d['CO2'] / molar_mass['CO2'] * 2.0 if is_included('CO', ddict): mass_atm_d['O'] += mass_atm_d['CO'] / molar_mass['CO'] - if is_included('CO2', ddict): - mass_atm_d['O'] += mass_atm_d['CO2'] / molar_mass['CO2'] * 2.0 if is_included('SO2', ddict): mass_atm_d['O'] += mass_atm_d['SO2'] / molar_mass['SO2'] * 2.0 mass_atm_d['O'] *= molar_mass['O'] @@ -185,12 +256,29 @@ def atmosphere_mass(pin, ddict): return mass_atm_d -def dissolved_mass(pin, ddict): - """Volatile masses in the (molten) mantle""" +def atmosphere_mass(pin, ddict): + """Atmospheric mass of volatiles and totals for H, C, N, O, S. + + Thin wrapper around ``_atmosphere_mass`` that reads fO2_shift from + ``ddict['fO2_shift_IW']``. Preserved bit-for-bit identical to the + pre-refactor function for all callers. + """ + return _atmosphere_mass(pin, ddict['fO2_shift_IW'], ddict) + + +def _dissolved_mass(pin, fO2_shift, ddict): + """Volatile masses in the (molten) mantle. + + Internal helper that takes ``fO2_shift`` as an explicit argument + instead of reading ``ddict['fO2_shift_IW']``. Two solubility laws + (``SolubilityN2('dasgupta')`` and ``SolubilityS2()``) consume fO2_shift + directly, so the shift must flow through to them too. The user-facing + ``dissolved_mass`` is a thin wrapper that reads fO2_shift from ddict. + """ mass_int_d = {} - p_d = get_partial_pressures(pin, ddict) + p_d = _get_partial_pressures(pin, fO2_shift, ddict) ptot = get_total_pressure(p_d) # Henry's-law / power-law solubility laws return ppmw of the @@ -222,14 +310,14 @@ def dissolved_mass(pin, ddict): # Override class default 'libourel'; dasgupta carries fO2 + p_total # dependence which the linear Libourel law does not. sol_N2 = SolubilityN2('dasgupta') - ppmw_N2 = sol_N2(p_d['N2'], ptot, ddict['T_magma'], ddict['fO2_shift_IW']) + ppmw_N2 = sol_N2(p_d['N2'], ptot, ddict['T_magma'], fO2_shift) mass_int_d['N2'] = prefactor * ppmw_N2 sol_S2 = SolubilityS2() - ppmw_S2 = sol_S2(p_d['S2'], ddict['T_magma'], ddict['fO2_shift_IW']) + ppmw_S2 = sol_S2(p_d['S2'], ddict['T_magma'], fO2_shift) mass_int_d['S2'] = prefactor * ppmw_S2 - # No SolubilityH2S / NH3 / SO2 / O2 / H2 in CALLIOPE — these + # No SolubilityH2S / NH3 / SO2 / O2 / H2 in CALLIOPE; these # species do not partition into the melt phase and contribute # zero to the dissolved-element tallies below. mass_int_d['H'] = mass_int_d['H2O'] * 2 / molar_mass['H2O'] @@ -260,6 +348,16 @@ def dissolved_mass(pin, ddict): return mass_int_d +def dissolved_mass(pin, ddict): + """Volatile masses in the (molten) mantle. + + Thin wrapper around ``_dissolved_mass`` that reads fO2_shift from + ``ddict['fO2_shift_IW']``. Preserved bit-for-bit identical to the + pre-refactor function for all callers. + """ + return _dissolved_mass(pin, ddict['fO2_shift_IW'], ddict) + + def func(pin_arr, ddict, mass_target_d): """Mass-balance residual [kg per element] for the four primary partial pressures [bar].""" @@ -282,22 +380,161 @@ def obj(pin_arr, ddict, mass_target_d): return np.dot(res_l, res_l) ** 0.5 -def get_initial_pressures(target_d): +def func_authoritative_O(x_arr, ddict, mass_target_d): + """5-residual vector for the authoritative-O solver mode. + + Mass-balance residual [kg per element] over five unknowns: + ``[pH2O, pCO2, pN2, pS2, fO2_shift]``. The first four equations are + the usual H, C, N, S mass balances; the fifth is the O mass balance + that was implicit in the chemistry (because fO2 was an input) and is + now an explicit constraint (because fO2 is an unknown). + + Parameters + ---------- + x_arr : array_like, length 5 + ``[pH2O_bar, pCO2_bar, pN2_bar, pS2_bar, fO2_shift_IW]``. The + first four are partial pressures in bar; the fifth is the + IW-buffer offset in log10 units (typical range -6 to +8). + ddict : dict + Coupler options dict. Reads everything except ``fO2_shift_IW``, + which is taken from ``x_arr[4]`` to expose it as an unknown. + mass_target_d : dict + Target elemental mass inventories [kg]. MUST include the keys + ``'H'``, ``'C'``, ``'N'``, ``'S'``, ``'O'`` (all five). Missing + ``'O'`` raises ``KeyError``. + + Returns + ------- + list of float, length 5 + Residuals ``(atm_kg + dissolved_kg) - target_kg`` for H, C, N, + S, O in that order. + """ + + pin_dict = {'H2O': x_arr[0], 'CO2': x_arr[1], 'N2': x_arr[2], 'S2': x_arr[3]} + fO2_shift = x_arr[4] + + mass_atm_d = _atmosphere_mass(pin_dict, fO2_shift, ddict) + mass_int_d = _dissolved_mass(pin_dict, fO2_shift, ddict) + + res_l = [0.0] * 5 + for i, elem in enumerate(['H', 'C', 'N', 'S', 'O']): + res_l[i] = mass_atm_d[elem] + mass_int_d[elem] - mass_target_d[elem] + + return res_l + + +def obj_authoritative_O(x_arr, ddict, mass_target_d): + """Scalar objective for trust-constr fallback in the authoritative-O solver.""" + res_l = func_authoritative_O(x_arr, ddict, mass_target_d) + return np.dot(res_l, res_l) ** 0.5 + + +def _check_guess_ceiling(p_guess_max): + """Validate the cold-start pressure-draw ceiling [bar]. + + A ceiling below ``P_GUESS_MIN_BAR`` would invert the log-uniform range + (``low > high``), which ``np.random.uniform`` does not reject; a non-finite + ceiling would feed inf/nan into the draw. Both are caught here so a + misconfigured ceiling fails loudly instead of silently degenerating the + cold start. + """ + if not np.isfinite(p_guess_max) or p_guess_max < P_GUESS_MIN_BAR: + raise ValueError( + f'p_guess_max must be finite and >= P_GUESS_MIN_BAR ' + f'({P_GUESS_MIN_BAR:g} bar), got {p_guess_max!r}.' + ) + + +def get_initial_pressures(target_d, p_guess_max=P_GUESS_MAX_BAR): """Cold-start guesses for the four primary partial pressures [bar]. - Log-uniform draw over [1e-12, 1e5] bar, covering ~17 orders of - magnitude from trace-volatile undersaturation up to ~100 kbar - (the upper end of magma-ocean surface-pressure regimes). - `target_d` is accepted for API stability but not consulted. + Log-uniform draw over [P_GUESS_MIN_BAR, ``p_guess_max``] bar, covering ~17 + orders of magnitude from trace-volatile undersaturation up to the default + ~100 kbar. `target_d` is accepted for API stability but not consulted. + + Parameters + ---------- + target_d : dict + Accepted for API parity; not consulted. + p_guess_max : float, default ``P_GUESS_MAX_BAR`` + Upper bound [bar] of the log-uniform cold-start draw. Raise it so the + guess can sample higher (e.g. for a sub-Neptune) within the solver box. + It sets where the cold start samples, NOT the maximum pressure the + solver can accept (that is the fixed ``P_CEILING_BAR`` box), so it does + not raise the achievable surface pressure. Must be finite and + >= ``P_GUESS_MIN_BAR``. """ - pH2O = 10 ** np.random.uniform(low=-12, high=5) - pCO2 = 10 ** np.random.uniform(low=-12, high=5) - pN2 = 10 ** np.random.uniform(low=-12, high=5) - pS2 = 10 ** np.random.uniform(low=-12, high=5) + _check_guess_ceiling(p_guess_max) + hi = np.log10(p_guess_max) + lo = np.log10(P_GUESS_MIN_BAR) + pH2O = 10 ** np.random.uniform(low=lo, high=hi) + pCO2 = 10 ** np.random.uniform(low=lo, high=hi) + pN2 = 10 ** np.random.uniform(low=lo, high=hi) + pS2 = 10 ** np.random.uniform(low=lo, high=hi) return pH2O, pCO2, pN2, pS2 +def get_initial_pressures_with_fO2( + target_d, fO2_hint, restart=False, rng=None, p_guess_max=P_GUESS_MAX_BAR +): + """Cold-start guesses for the five unknowns of the authoritative-O solver. + + Returns ``[pH2O, pCO2, pN2, pS2, fO2_shift]``. The four pressures use + the same log-uniform draw as ``get_initial_pressures`` over + ``[P_GUESS_MIN_BAR, p_guess_max]`` bar. The fifth element is ``fO2_hint`` + on the first attempt; on solver restart (``restart=True``) it is redrawn + from a uniform distribution over ``[FO2_GUESS_MIN, FO2_GUESS_MAX]``, + which covers the reducing-mantle to highly-oxidized regimes likely to + be encountered. + + Parameters + ---------- + target_d : dict + Target elemental mass inventories. Accepted for API parity with + ``get_initial_pressures`` but not consulted. + fO2_hint : float + Initial guess for the IW-buffer offset (log10 units). Typical + PROTEUS user values lie in ``[-4, +6]``. + restart : bool, default False + When True, redraw fO2_shift from ``Uniform(FO2_GUESS_MIN, + FO2_GUESS_MAX)``. When False, return ``fO2_hint`` unchanged. + rng : np.random.Generator or None, default None + Random number generator for the log-uniform pressure draw and + (when ``restart=True``) the fO2 redraw. When None, the global + ``np.random`` state is used. The authoritative-O entry point + threads a seeded generator through this argument to make solver + outcomes reproducible across calls. + p_guess_max : float, default ``P_GUESS_MAX_BAR`` + Upper bound [bar] of the log-uniform cold-start draw. Sets where the + cold start samples, NOT the maximum pressure the solver can accept + (the fixed ``P_CEILING_BAR`` box). Must be finite and + >= ``P_GUESS_MIN_BAR``. + + Returns + ------- + tuple of 5 floats + ``(pH2O, pCO2, pN2, pS2, fO2_shift)``. + """ + if rng is None: + rng = np.random + + _check_guess_ceiling(p_guess_max) + hi = np.log10(p_guess_max) + lo = np.log10(P_GUESS_MIN_BAR) + pH2O = 10 ** rng.uniform(low=lo, high=hi) + pCO2 = 10 ** rng.uniform(low=lo, high=hi) + pN2 = 10 ** rng.uniform(low=lo, high=hi) + pS2 = 10 ** rng.uniform(low=lo, high=hi) + + if restart: + fO2 = rng.uniform(low=FO2_GUESS_MIN, high=FO2_GUESS_MAX) + else: + fO2 = float(fO2_hint) + + return pH2O, pCO2, pN2, pS2, fO2 + + def get_target_from_params(ddict): N_ocean_moles = ddict['hydrogen_earth_oceans'] @@ -374,6 +611,7 @@ def equilibrium_atmosphere( nguess=7500, print_result=True, opt_solver=True, + p_guess_max=P_GUESS_MAX_BAR, ): """Solve for surface partial pressures assuming melt-vapour equilibrium. @@ -405,6 +643,12 @@ def equilibrium_atmosphere( If True, log final outgassed partial pressures at INFO level. opt_solver : bool, default True If True, alternate between fsolve and trust-constr on each restart. + p_guess_max : float, default ``P_GUESS_MAX_BAR`` + Upper bound [bar] of the Monte-Carlo cold-start pressure draw. Sets + where the cold start samples within the fixed ``P_CEILING_BAR`` solver + box; it does not raise the maximum pressure the solver can accept, so a + surface pressure above the box stays out of reach. Must be finite and + >= ``P_GUESS_MIN_BAR``. Returns ------- @@ -418,19 +662,19 @@ def equilibrium_atmosphere( log.info('Solving for equilibrium partial pressures at surface') log.debug(' target masses: %s' % str(target_d)) - # Hard ub = 1e7 bar is well above any realistic magma-ocean + # Hard ub = P_CEILING_BAR is well above any realistic magma-ocean # surface pressure; it prevents trust-constr from exploring # non-physical regions during a poor cold start. lb = [0.0] * 4 - ub = [1e7] * 4 + ub = [P_CEILING_BAR] * 4 if p_guess is None: - x0 = get_initial_pressures(target_d) + x0 = get_initial_pressures(target_d, p_guess_max=p_guess_max) else: # Validate up front so a missing key surfaces as a clear ValueError # rather than a bare KeyError from the tuple construction below. # The isinstance check handles cases where a caller passes a list, - # a pandas Series, or accidentally a non-dict object — without it, + # a pandas Series, or accidentally a non-dict object; without it, # the membership test below would raise an opaque TypeError. if not isinstance(p_guess, dict): raise TypeError(f'p_guess must be a dict or None, got {type(p_guess).__name__}.') @@ -501,7 +745,7 @@ def equilibrium_atmosphere( if success: break - x0 = get_initial_pressures(target_d) + x0 = get_initial_pressures(target_d, p_guess_max=p_guess_max) # Alternate fsolve <-> trust-constr on each restart so a # basin one solver cannot escape gets a chance from the @@ -544,8 +788,11 @@ def equilibrium_atmosphere( outdict[s + '_bar'] = p_d[s] outdict['P_surf'] += outdict[s + '_bar'] + P_surf = outdict['P_surf'] for s in volatile_species: - outdict[s + '_vmr'] = outdict[s + '_bar'] / outdict['P_surf'] + outdict[s + '_vmr'] = ( + (outdict[s + '_bar'] / P_surf) if P_surf > P_SURF_FLOOR_BAR else 0.0 + ) if print_result: log.info( @@ -599,3 +846,515 @@ def equilibrium_atmosphere( outdict['S_res'] = res_l[3] return outdict + + +def equilibrium_atmosphere_authoritative_O( + target_d, + ddict, + fO2_hint=4.0, + hide_warnings=True, + rtol=1e-5, + atol=1e10, + xtol=1e-8, + p_guess=None, + nsolve=1500, + nguess=7500, + print_result=True, + opt_solver=True, + random_seed=None, + p_guess_max=P_GUESS_MAX_BAR, +): + """Solve for partial pressures AND fO2 given total elemental masses including O. + + Authoritative-oxygen solver mode. Unlike ``equilibrium_atmosphere`` + (which takes fO2 as a config input via ``ddict['fO2_shift_IW']``), + this entry point treats fO2 as a fifth unknown and solves a 5x5 + nonlinear mass-balance system. The user supplies a target O mass + alongside H/C/N/S, and the solver returns the partial pressures + plus the IW-buffer offset (``fO2_shift_derived``) that produces + that equilibrium. + + Use this when the science model declares atmospheric+dissolved O + as a budget (e.g. mantle FeO inventory, or whole-planet O accounting + where atmospheric escape and ingassing debit the same O reservoir). + For the legacy mode where fO2 is buffered to a user-specified IW + offset and O is derived, use ``equilibrium_atmosphere`` instead. + + Parameters + ---------- + target_d : dict + Target elemental mass inventories [kg]. MUST contain the keys + ``'H'``, ``'C'``, ``'N'``, ``'S'``, ``'O'``. Missing ``'O'`` + raises ``KeyError``. + ddict : dict + Coupler options dict (planet, magma state, inclusion flags). + ``ddict['fO2_shift_IW']`` is IGNORED by this entry point; the + value is treated as a solver unknown initialised from + ``fO2_hint`` instead. + fO2_hint : float, default 4.0 + Initial guess for the IW-buffer offset (log10 units). Provide + a value close to the expected solution to speed convergence; + typical PROTEUS values lie in [-4, +6]. The solver's Monte-Carlo + restarts redraw from Uniform(-6, +8) if the hint does not lead + to convergence. + hide_warnings : bool, default True + Hide floating point runtime warnings raised by `scipy` for poor guesses. + rtol : float, default 1e-5 + Relative tolerance for mass conservation. + atol : float, default 1e10 + Absolute tolerance for mass conservation [kg]. + xtol : float, default 1e-8 + Relative tolerance for fsolve. + p_guess : dict or None, default None + Initial guess for primary-species partial pressures [bar]. Keys + must include ``'H2O'``, ``'CO2'``, ``'N2'``, ``'S2'``; the + optional key ``'fO2_shift_IW'`` overrides ``fO2_hint`` for the + starting guess. Non-dict raises TypeError; missing required keys + or non-finite values raise ValueError. + nsolve : int, default 1500 + Maximum number of inner-solver iterations per attempt. + nguess : int, default 7500 + Maximum number of Monte-Carlo restarts before giving up. + print_result : bool, default True + If True, log final outgassed partial pressures and derived + fO2 at INFO level. + opt_solver : bool, default True + If True, alternate between fsolve and trust-constr on each + restart so a basin one solver cannot escape gets a chance from + the other. + random_seed : int or None, default None + Seed for the Monte-Carlo restart RNG. ``None`` uses the global + ``np.random`` state (non-deterministic). An integer seed makes + solver outcomes reproducible across calls, which is required + for regression testing and for diffing two runs. + p_guess_max : float, default ``P_GUESS_MAX_BAR`` + Upper bound [bar] of the Monte-Carlo cold-start pressure draw. Sets + where the cold start samples within the fixed ``P_CEILING_BAR`` solver + box; it does not raise the maximum pressure the solver can accept, so a + surface pressure above the box stays out of reach. Must be finite and + >= ``P_GUESS_MIN_BAR``. + + Returns + ------- + partial_pressures : dict + Volatile partial pressures [bar] keyed ``_bar``, plus + per-species reservoir masses [kg], elemental totals, residuals, + and atmospheric diagnostics. Two additions relative to + ``equilibrium_atmosphere``: + + - ``fO2_shift_derived`` : float + The IW-buffer offset the solver converged to. Equals + ``fO2_hint`` only if the hint happened to be the + self-consistent value. + - ``O_res`` : float + 5th residual (O mass-balance), in kg. Pairs with the + existing ``H_res``/``C_res``/``N_res``/``S_res`` keys. + + Raises + ------ + KeyError + If ``target_d`` is missing the ``'O'`` key. + TypeError, ValueError + If ``p_guess`` fails validation (same contract as + ``equilibrium_atmosphere``). + RuntimeError + If the solver fails to converge after ``nguess`` Monte-Carlo + restarts. The error message includes the final pressures and + fO2_shift attempt for diagnosis. + + Notes + ----- + Mathematical model. The four existing mass-balance equations for + H/C/N/S are extended with a fifth for O. The four primary + partial pressures (H2O, CO2, N2, S2) are joined by fO2_shift as a + fifth unknown. All seven derived partial pressures (H2, CO, CH4, + NH3, O2, SO2, H2S) and the two fO2-coupled solubility laws (N2 + Dasgupta, S2 Gaillard) consume fO2_shift through the same + physics functions ``_get_partial_pressures``, ``_atmosphere_mass``, + ``_dissolved_mass`` that ``equilibrium_atmosphere`` uses. + + Examples + -------- + Reproducing an equilibrium_atmosphere result through the new mode: + + >>> # First, run the legacy mode at fO2_shift_IW = +4 + >>> ddict = {..., 'fO2_shift_IW': 4.0} + >>> out_legacy = equilibrium_atmosphere(target_d_HCNS, ddict) + >>> # Then, run the new mode with the implied O budget + >>> target_d_HCNSO = dict(target_d_HCNS, + ... O=out_legacy['O_kg_total']) + >>> out_new = equilibrium_atmosphere_authoritative_O( + ... target_d_HCNSO, ddict, fO2_hint=4.0) + >>> abs(out_new['fO2_shift_derived'] - 4.0) < 0.01 # round-trip + True + """ + + required_elements = ('H', 'C', 'N', 'S', 'O') + + # Contract check: every required element key must be present, finite, + # and non-negative. A missing key raises KeyError (matching the legacy + # "target_d must include 'O'" message). Non-finite or negative values + # are user-error and raise ValueError before the solver wastes effort. + missing = [e for e in required_elements if e not in target_d] + if missing: + raise KeyError( + 'target_d is missing required element keys: %s. ' + 'Authoritative-O mode requires all of %s. Got keys: %s' + % (missing, list(required_elements), sorted(target_d.keys())) + ) + for e in required_elements: + v = target_d[e] + if not np.isfinite(v): + raise ValueError('target_d[%r] must be a finite real number [kg], got %r.' % (e, v)) + if v < 0: + raise ValueError('target_d[%r] must be non-negative [kg], got %r.' % (e, v)) + + # Validate fO2_hint and the planet/state parameters consumed from + # ddict by the residual chain. The solver evaluates the residual at + # arbitrarily wild trial points, so it cannot recover from a bad + # entry value; failing fast here gives a useful error rather than + # a ZeroDivisionError from deep inside the chemistry path. + if not np.isfinite(fO2_hint): + raise ValueError( + 'fO2_hint must be a finite real number (log10 IW offset), got %r.' % fO2_hint + ) + if not (FO2_HARD_MIN <= fO2_hint <= FO2_HARD_MAX): + raise ValueError( + 'fO2_hint=%.3f is outside the solver bounds [%+g, %+g]. ' + 'Pick a value in [%+g, %+g] for physically realistic mantle ' + 'redox states.' + % (fO2_hint, FO2_HARD_MIN, FO2_HARD_MAX, FO2_GUESS_MIN, FO2_GUESS_MAX) + ) + + for required_ddict_key in ('M_mantle', 'Phi_global', 'T_magma', 'gravity', 'radius'): + if required_ddict_key not in ddict: + raise KeyError( + 'ddict is missing required key %r. Authoritative-O mode ' + 'requires M_mantle, Phi_global, T_magma, gravity, radius.' % required_ddict_key + ) + + M_mantle = ddict['M_mantle'] + if not (np.isfinite(M_mantle) and M_mantle > 0): + raise ValueError("ddict['M_mantle']=%r must be a positive finite mass [kg]." % M_mantle) + + Phi_global = ddict['Phi_global'] + if not (np.isfinite(Phi_global) and 0.0 <= Phi_global <= 1.0): + raise ValueError( + "ddict['Phi_global']=%r must lie in [0, 1] (melt mass fraction)." % Phi_global + ) + + T_magma = ddict['T_magma'] + if not (np.isfinite(T_magma) and T_magma > 0): + raise ValueError( + "ddict['T_magma']=%r must be a positive finite temperature [K]." % T_magma + ) + + if nguess < 1: + raise ValueError('nguess must be >= 1, got %d.' % nguess) + if nsolve < 1: + raise ValueError('nsolve must be >= 1, got %d.' % nsolve) + + # Seeded RNG for the Monte-Carlo restart draws. random_seed=None + # falls back to the global np.random state to preserve historical + # non-deterministic behaviour for callers that do not opt in to + # reproducibility. + rng = np.random.default_rng(random_seed) if random_seed is not None else np.random + + if print_result: + log.info( + 'Solving for equilibrium partial pressures + fO2_shift ' + '(authoritative-O mode, fO2_hint=%.2f)', + fO2_hint, + ) + log.debug(' target masses: %s', target_d) + + # Bounds. Pressures: [0, P_CEILING_BAR] bar (same as equilibrium_atmosphere). + # fO2_shift: [FO2_HARD_MIN, FO2_HARD_MAX] log10 units. The physically + # meaningful range is roughly [FO2_GUESS_MIN, FO2_GUESS_MAX] (mantle + # reducing to highly oxidized); the wider hard box gives trust-constr room + # to explore on poor cold starts without escaping to non-physical territory. + lb = [0.0, 0.0, 0.0, 0.0, FO2_HARD_MIN] + ub = [P_CEILING_BAR, P_CEILING_BAR, P_CEILING_BAR, P_CEILING_BAR, FO2_HARD_MAX] + + # Physical pressure ceiling for the acceptance gate, captured before + # the per-guess ub-collapse below mutates ub. The gate tests against + # this documented 1e7 bar bound, not a collapsed conditioning value, + # so a tiny-guess slot whose root legitimately lands above its + # collapsed 1.0 bar bound is not falsely rejected. + p_ceiling = ub[0] + + if p_guess is None: + x0 = get_initial_pressures_with_fO2( + target_d, fO2_hint, rng=rng, p_guess_max=p_guess_max + ) + else: + if not isinstance(p_guess, dict): + raise TypeError(f'p_guess must be a dict or None, got {type(p_guess).__name__}.') + required = ('H2O', 'CO2', 'N2', 'S2') + missing = [k for k in required if k not in p_guess] + if missing: + raise ValueError( + f'p_guess is missing required keys: {missing}. ' + f'Expected all of {list(required)}.' + ) + # fO2_shift_IW in p_guess overrides fO2_hint; absent means use fO2_hint. + fO2_seed = p_guess.get('fO2_shift_IW', fO2_hint) + x0 = (p_guess['H2O'], p_guess['CO2'], p_guess['N2'], p_guess['S2'], fO2_seed) + + # Reject non-finite values. + for k, v in zip(required + ('fO2_shift_IW',), x0): + if not np.isfinite(v): + raise ValueError(f'p_guess[{k!r}] must be a finite real number, got {v!r}.') + + # Match the legacy ub-collapse for tiny pressure guesses so + # trust-constr does not wander in a degenerate slot. fO2 bound + # stays at the wide range. + ub_collapsed = list(ub) + for i in range(4): + ub_collapsed[i] = ub_collapsed[i] if (x0[i] > 1e-10) else 1.0 + ub = ub_collapsed + + bounds = opt.Bounds(lb=lb, ub=ub) + + # Per-element tolerance: each residual must satisfy + # ``|res_i| <= max(target_i * rtol, TRUNC_MASS)``. The gate is + # relative per element, so mass closure is judged against each + # element's own budget rather than the largest. The absolute floor is + # the small TRUNC_MASS noise level (10 kg) so a near-zero target does + # not demand an exactly-zero residual. The floor is deliberately not + # tied to ``atol`` (the planetary negligible-mass threshold, ~1e16 + # kg), which would dominate the relative gate on small-budget + # elements such as N and let multi-percent closure errors pass there. + target_vec = np.array([target_d[e] for e in required_elements]) + elem_tolerance = np.maximum(target_vec * rtol, TRUNC_MASS) + log.debug('Per-element tolerance: %s kg', elem_tolerance.tolist()) + + # `sol` initialised to a sentinel so the post-loop RuntimeError path + # can format the final attempt even if every iteration crashed + # before `sol` was assigned. `success` starts False so an early + # break from a 0-iteration loop (already rejected by the nguess>=1 + # validator above, but a defensive belt) raises RuntimeError rather + # than UnboundLocalError. + sol = np.array(x0, dtype=float) + success = False + count = 0 + + with warnings.catch_warnings(): + if hide_warnings: + warnings.filterwarnings('ignore', category=RuntimeWarning) + warnings.filterwarnings('ignore', category=UserWarning) + + solver: int = 0 + for count in range(nguess): + # Wrap each solver call: fsolve and trust-constr can both + # raise ZeroDivisionError or FloatingPointError if the + # residual blows up in a numerically unrecoverable way at a + # trial point. Catch and treat as a failed attempt so the + # restart loop continues instead of propagating a crash to + # the caller. + try: + if solver == 0: + sol, _, ier, _ = opt.fsolve( + func_authoritative_O, + x0, + args=(ddict, target_d), + maxfev=nsolve, + xtol=xtol, + full_output=True, + ) + success = bool(ier == 1) + else: + result = opt.minimize( + obj_authoritative_O, + x0, + args=(ddict, target_d), + method='trust-constr', + bounds=bounds, + options={'maxiter': nsolve, 'xtol': xtol}, + ) + success = result.success + sol = result.x + except (ZeroDivisionError, FloatingPointError, ValueError) as exc: + log.debug( + 'Solver attempt %d (method=%s) raised %s: %s; restarting', + count, + 'fsolve' if solver == 0 else 'trust-constr', + type(exc).__name__, + exc, + ) + success = False + + # Per-element residual gate. Compute defensively so a + # post-solver evaluation crash also routes to restart. + if success: + try: + this_resid = func_authoritative_O(sol, ddict, target_d) + resid_abs = np.abs(np.asarray(this_resid)) + if np.any(resid_abs > elem_tolerance): + worst = int(np.argmax(resid_abs - elem_tolerance)) + log.debug( + 'Solution rejected by per-element residual: ' + 'element=%s, |res|=%.2e, tol=%.2e', + required_elements[worst], + resid_abs[worst], + elem_tolerance[worst], + ) + success = False + except (ZeroDivisionError, FloatingPointError, ValueError) as exc: + log.debug( + 'Post-solver residual evaluation raised %s: %s; rejecting attempt %d', + type(exc).__name__, + exc, + count, + ) + success = False + + # Reject a converged-but-non-physical root. The production + # path runs only the unbounded fsolve (opt_solver=False), so a + # root can satisfy mass balance yet sit outside the physical + # box: a derived fO2_shift beyond [-12, +12] (the target O is + # unreachable at this H/C/N/S/T_magma), a negative partial + # pressure, or a partial pressure above the 1e7 bar ceiling. + # trust-constr enforces `bounds`; fsolve does not, so the full + # box is enforced here before the solution is accepted. + if success: + sol_p = np.asarray(sol[:4], dtype=float) + if not (lb[4] <= sol[4] <= ub[4]): + log.debug( + 'Solution rejected: derived fO2_shift=%.3f outside [%.1f, %.1f]', + sol[4], + lb[4], + ub[4], + ) + success = False + elif np.any(sol_p < -1.0e-6): + log.debug('Solution rejected: negative partial pressure %s bar', sol[:4]) + success = False + elif np.any(sol_p > p_ceiling * (1.0 + 1.0e-6)): + log.debug( + 'Solution rejected: partial pressure above %.1e bar ceiling: %s bar', + p_ceiling, + sol[:4], + ) + success = False + + if success: + break + + # Restart. Redraw pressures from log-uniform; redraw fO2 + # from Uniform(-6, +8) to give it a chance from a different + # basin if the hint led to a non-converging region. + x0 = get_initial_pressures_with_fO2( + target_d, fO2_hint, restart=True, rng=rng, p_guess_max=p_guess_max + ) + + if opt_solver: + solver = 1 - solver + + if not success: + raise RuntimeError( + 'Could not find solution for volatile abundances + fO2 under ' + 'authoritative-O mode (max attempts: %d). ' + 'Final attempt: pH2O=%.3e bar, pCO2=%.3e bar, pN2=%.3e bar, ' + 'pS2=%.3e bar, fO2_shift=%.3f. ' + 'Either the target O budget is outside the physically ' + 'reachable range at this (H, C, N, S, T_magma), or the ' + 'chemistry has a non-monotonic region the solver could not ' + 'escape. Consider adjusting fO2_hint or the target masses.' + % (nguess, sol[0], sol[1], sol[2], sol[3], sol[4]) + ) + + log.debug(' Initial guess attempt number = %d', count) + + res_l = func_authoritative_O(sol, ddict, target_d) + log.debug(' Residuals: %s', res_l) + log.debug(' Derived fO2_shift: %.4f', sol[4]) + + sol_dict = {'H2O': sol[0], 'CO2': sol[1], 'N2': sol[2], 'S2': sol[3]} + fO2_derived = sol[4] + p_d = _get_partial_pressures(sol_dict, fO2_derived, ddict) + + mass_atm_d = _atmosphere_mass(sol_dict, fO2_derived, ddict) + mass_int_d = _dissolved_mass(sol_dict, fO2_derived, ddict) + + # Output dict structure matches equilibrium_atmosphere bit-for-bit + # plus the two new keys (fO2_shift_derived, O_res). The PROTEUS-side + # wrapper consumes the same fields regardless of which solver mode + # ran. + outdict = {'M_atm': 0.0, 'P_surf': 0.0} + for s in volatile_species: + outdict[s + '_bar'] = 0.0 + outdict[s + '_kg_atm'] = 0.0 + outdict[s + '_kg_liquid'] = 0.0 + outdict[s + '_kg_solid'] = 0.0 + outdict[s + '_kg_total'] = 0.0 + + if s in p_d.keys(): + outdict[s + '_bar'] = p_d[s] + outdict['P_surf'] += outdict[s + '_bar'] + + P_surf = outdict['P_surf'] + for s in volatile_species: + outdict[s + '_vmr'] = ( + (outdict[s + '_bar'] / P_surf) if P_surf > P_SURF_FLOOR_BAR else 0.0 + ) + + if print_result: + log.info( + ' %-6s : %-8.2f bar (%.2e VMR)', + s, + outdict[s + '_bar'], + outdict[s + '_vmr'], + ) + + all_keys = [s for s in volatile_species] + all_keys.extend(['H', 'C', 'N', 'S', 'O']) + for s in all_keys: + tot_kg = 0.0 + + if s in mass_atm_d.keys(): + outdict[s + '_kg_atm'] = mass_atm_d[s] + tot_kg += mass_atm_d[s] + + if s in mass_int_d.keys(): + outdict[s + '_kg_liquid'] = mass_int_d[s] + outdict[s + '_kg_solid'] = 0.0 + tot_kg += mass_int_d[s] + + outdict[s + '_kg_total'] = tot_kg + + for s in volatile_species: + outdict['M_atm'] += outdict[s + '_kg_atm'] + + outdict['atm_kg_per_mol'] = 0.0 + for s in volatile_species: + outdict[s + '_mol_atm'] = outdict[s + '_kg_atm'] / molar_mass[s] + outdict[s + '_mol_solid'] = outdict[s + '_kg_solid'] / molar_mass[s] + outdict[s + '_mol_liquid'] = outdict[s + '_kg_liquid'] / molar_mass[s] + outdict[s + '_mol_total'] = ( + outdict[s + '_mol_atm'] + outdict[s + '_mol_solid'] + outdict[s + '_mol_liquid'] + ) + + outdict['atm_kg_per_mol'] += outdict[s + '_vmr'] * molar_mass[s] + + for e1 in element_list: + for e2 in element_list: + if e1 == e2: + continue + em1 = outdict[e1 + '_kg_atm'] + em2 = outdict[e2 + '_kg_atm'] + if em2 == 0: + continue + outdict['%s/%s_atm' % (e1, e2)] = em1 / em2 + + outdict['H_res'] = res_l[0] + outdict['C_res'] = res_l[1] + outdict['N_res'] = res_l[2] + outdict['S_res'] = res_l[3] + outdict['O_res'] = res_l[4] + outdict['fO2_shift_derived'] = fO2_derived + + if print_result: + log.info(' Derived fO2_shift = %.4f (hint was %.4f)', fO2_derived, fO2_hint) + + return outdict diff --git a/src/calliope/structure.py b/src/calliope/structure.py index c81322c..087c987 100644 --- a/src/calliope/structure.py +++ b/src/calliope/structure.py @@ -47,7 +47,9 @@ def calculate_mantle_mass( raise TypeError("calculate_mantle_mass() missing required argument: 'core_frac'") earth_fr = 0.55 # earth core radius fraction - earth_fm = 0.325 # earth core mass fraction (https://arxiv.org/pdf/1708.08718.pdf) + # earth core mass fraction (32.5 +/- 0.3 wt%) from + # Wang, Lineweaver & Ireland (2018), arxiv:1708.08718 + earth_fm = 0.325 core_rho = (3.0 * earth_fm * M_earth) / ( 4.0 * np.pi * (earth_fr * R_earth) ** 3.0 diff --git a/tests/test_authoritative_O.py b/tests/test_authoritative_O.py new file mode 100644 index 0000000..b3c8065 --- /dev/null +++ b/tests/test_authoritative_O.py @@ -0,0 +1,487 @@ +"""Smoke tests for ``equilibrium_atmosphere_authoritative_O``. + +Covers the happy path (canonical Earth-like inputs produce physical +output), the round-trip property (legacy mode at fO2_shift = X yields +an O budget; feeding that budget back to the new mode recovers +fO2_derived ≈ X), and reproducibility under ``random_seed``. + +These tests invoke the full solver and take a few seconds each. They +are marked ``smoke`` per the project's four-marker scheme. +""" + +from __future__ import annotations + +import logging + +import numpy as np +import pytest + +from calliope.constants import volatile_species +from calliope.solve import ( + equilibrium_atmosphere, + equilibrium_atmosphere_authoritative_O, +) + +logging.getLogger('calliope').setLevel(logging.WARNING) + +pytestmark = [pytest.mark.smoke, pytest.mark.timeout(60)] + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +def _ddict(T: float = 1800.0, Phi: float = 1.0, dIW: float = 4.0) -> dict: + """Realistic ddict with every volatile species included. + + Default T_magma=1800 K is inside the Dasgupta/Gaillard solubility + law calibration range so tests don't emit extrapolation warnings. + """ + d = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': Phi, + 'T_magma': T, + 'fO2_shift_IW': dIW, + } + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d + + +def _earth_target_HCNS() -> dict: + """Earth-like H/C/N/S budget [kg]; converges cleanly in legacy mode.""" + return {'H': 1.5e20, 'C': 1.5e19, 'N': 8.0e18, 'S': 8.0e20} + + +# --------------------------------------------------------------------------- +# Result-dict contract across the physical regime +# --------------------------------------------------------------------------- + + +class TestPhysicalOutputContract: + """The solver must return a finite, physical result-dict across + the full physically-relevant regime of mantle redox states, not + just the Earth-like fiducial. Parametrising over reducing, neutral + and oxidising dIW ensures the contract holds at the edges, not + only at the canonical input. + """ + + @pytest.mark.parametrize('dIW', [-4.0, -2.0, 0.0, +2.0, +4.0, +6.0]) + def test_returns_physical_output(self, dIW): + """The solver runs, returns a dict with expected keys, and the + primary partial pressures + derived fO2 are finite and physical + across the full dIW range.""" + ddict = _ddict(dIW=dIW) + legacy = equilibrium_atmosphere( + _earth_target_HCNS(), + ddict, + print_result=False, + nguess=200, + ) + target = dict(_earth_target_HCNS(), O=legacy['O_kg_total']) + + out = equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=dIW, + random_seed=0, + nguess=500, + nsolve=1000, + print_result=False, + # PROTEUS production dispatch (fsolve only); keeps the + # smoke-tier solve fast. Fallback path covered in + # TestReproducibility. + opt_solver=False, + ) + + # Required output keys + for key in ( + 'fO2_shift_derived', + 'O_res', + 'H_res', + 'C_res', + 'N_res', + 'S_res', + 'H2O_bar', + 'CO2_bar', + 'N2_bar', + 'S2_bar', + 'M_atm', + 'P_surf', + ): + assert key in out, f'missing key {key!r} in output dict at dIW={dIW}' + + for p_key in ('H2O_bar', 'CO2_bar', 'N2_bar', 'S2_bar'): + assert np.isfinite(out[p_key]), f'{p_key} is not finite at dIW={dIW}' + assert out[p_key] >= 0, f'{p_key} is negative at dIW={dIW}' + + assert np.isfinite(out['fO2_shift_derived']) + assert -12.0 <= out['fO2_shift_derived'] <= 12.0 + + @pytest.mark.parametrize('dIW', [-4.0, 0.0, +4.0]) + def test_residuals_within_tolerance(self, dIW): + """Per-element residuals from the solver are within the + per-element tolerance gate for every dIW the solver accepts.""" + ddict = _ddict(dIW=dIW) + legacy = equilibrium_atmosphere( + _earth_target_HCNS(), + ddict, + print_result=False, + nguess=200, + ) + target = dict(_earth_target_HCNS(), O=legacy['O_kg_total']) + + out = equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=dIW, + random_seed=0, + rtol=1e-5, + nguess=500, + nsolve=1000, + print_result=False, + opt_solver=False, + ) + + seen_nonzero = False + for elem in ('H', 'C', 'N', 'S', 'O'): + res = out[f'{elem}_res'] + tgt = target[elem] + allowed = max(tgt * 1e-5, 2e9) + assert abs(res) <= allowed, ( + f'{elem}_res={res:.3e} exceeds tolerance {allowed:.3e} at dIW={dIW}' + ) + if tgt > 1.0: + seen_nonzero = True + + # Discrimination guard: confirm the target inventory is meaningful + # at this dIW (at least one element carries a nonzero target). A + # stub that returned target = {} would pass the empty loop body + # trivially. + assert seen_nonzero, ( + f'Target inventory is empty at dIW={dIW}; tolerance check is vacuous' + ) + + def test_negative_T_magma_raises(self): + """Unphysical sad-path: negative T_magma must raise (the + OxygenFugacity buffer formulae diverge as 1/T and T log T).""" + ddict = _ddict(dIW=0.0) + ddict['T_magma'] = -100.0 # unphysical + target = dict(_earth_target_HCNS(), O=1.0e21) + + with pytest.raises((ValueError, RuntimeError)): + equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=0.0, + random_seed=0, + nguess=50, + nsolve=200, + print_result=False, + ) + + # Discrimination guard: a stub `equilibrium_atmosphere_authoritative_O` + # that raised for every input would pass the bare pytest.raises. + # Confirm a sibling call with a normal positive T_magma does NOT + # raise and produces a finite physically plausible result. + ddict_valid = _ddict(dIW=0.0) + out_valid = equilibrium_atmosphere_authoritative_O( + target, + ddict_valid, + fO2_hint=0.0, + random_seed=0, + nguess=200, + nsolve=500, + print_result=False, + opt_solver=False, + ) + assert out_valid['P_surf'] > 0.0 + assert -12.0 < out_valid['fO2_shift_derived'] < 12.0 + + +# --------------------------------------------------------------------------- +# Round-trip with legacy mode +# --------------------------------------------------------------------------- + + +class TestRoundTrip: + """For any fO2_shift_IW value X that the legacy solver accepts, the + new mode should recover fO2_derived ≈ X when fed the legacy O budget. + + This is the *core* correctness property of the new entry point: + extending the unknown set from 4 to 5 and adding the O equation + must reproduce the legacy chemistry. If the round-trip fails by + more than the per-element tolerance, the new equation system has + a different fixed point than the legacy one, which is a bug. + """ + + @pytest.mark.parametrize('dIW', [-2.0, 0.0, 2.0, 4.0, 6.0]) + def test_round_trip_recovers_fO2_within_tolerance(self, dIW): + """Legacy at dIW -> new mode with implied O_budget -> recover dIW.""" + ddict_legacy = _ddict(dIW=dIW) + + legacy = equilibrium_atmosphere( + _earth_target_HCNS(), + ddict_legacy, + print_result=False, + nguess=200, + ) + target_O = legacy['O_kg_total'] + target = dict(_earth_target_HCNS(), O=target_O) + + # Run the new mode with the same ddict (whose fO2_shift_IW is + # ignored under authoritative-O mode). fO2_hint = dIW so the + # solver starts at the right basin. opt_solver=False matches the + # PROTEUS production dispatch (fsolve only) and keeps the solve + # within the smoke-tier wall-time budget; the trust-constr + # fallback path is exercised by the reproducibility tests. + out = equilibrium_atmosphere_authoritative_O( + target, + ddict_legacy, + fO2_hint=dIW, + random_seed=0, + nguess=500, + nsolve=1000, + print_result=False, + opt_solver=False, + ) + + derived = out['fO2_shift_derived'] + delta = abs(derived - dIW) + + # 0.05 dex on the derived fO2 is well within the solver's xtol; + # if the equation system were inconsistent we would see deltas + # of >> 0.1 dex. + assert delta < 0.05, ( + f'round-trip failed at dIW={dIW}: derived={derived:.4f}, delta={delta:.4f} dex' + ) + + # Discrimination guard: a stub that returned derived = fO2_hint + # verbatim would pass the round-trip check trivially. Run the + # solver again with a deliberately wrong hint (5 dex away) and + # confirm it still recovers the true dIW from the O budget. + # This proves the recovery comes from the chemistry, not the hint. + out_wrong_hint = equilibrium_atmosphere_authoritative_O( + target, + ddict_legacy, + fO2_hint=dIW + 5.0, + random_seed=0, + nguess=500, + nsolve=1000, + print_result=False, + opt_solver=False, + ) + derived_wrong_hint = out_wrong_hint['fO2_shift_derived'] + assert abs(derived_wrong_hint - dIW) < 0.1, ( + f'round-trip failed at dIW={dIW} with wrong fO2_hint={dIW + 5.0}: ' + f'derived={derived_wrong_hint:.4f}' + ) + + def test_round_trip_primary_pressures_match_legacy(self): + """Primary partial pressures should match the legacy output + within solver tolerance after a round-trip.""" + ddict_legacy = _ddict(dIW=4.0) + + legacy = equilibrium_atmosphere( + _earth_target_HCNS(), + ddict_legacy, + print_result=False, + nguess=200, + ) + target = dict(_earth_target_HCNS(), O=legacy['O_kg_total']) + + out = equilibrium_atmosphere_authoritative_O( + target, + ddict_legacy, + fO2_hint=4.0, + random_seed=0, + nguess=500, + nsolve=1000, + print_result=False, + opt_solver=False, + ) + + # Primary pressures within 0.5% (the legacy mode and the new + # mode share the same physics so the same fixed point should + # be found to within fsolve's xtol). + seen_nonzero_species = 0 + for key in ('H2O_bar', 'CO2_bar', 'N2_bar', 'S2_bar'): + legacy_p = legacy[key] + new_p = out[key] + if legacy_p > 1e-20: # skip near-zero + rel = abs(new_p - legacy_p) / legacy_p + assert rel < 0.005, ( + f'{key} mismatch: legacy={legacy_p:.6e}, new={new_p:.6e}, rel={rel:.3e}' + ) + seen_nonzero_species += 1 + + # Discrimination guard: the loop is vacuous unless at least one + # primary species has legacy_p > 1e-20. At the oxidising dIW=4.0 + # used here, H2O and CO2 dominate, so we expect >= 2 species to + # be non-trivial. + assert seen_nonzero_species >= 2, ( + f'Only {seen_nonzero_species} primary species had legacy_p > 1e-20; ' + f'mass-closure check is too thin' + ) + + +# --------------------------------------------------------------------------- +# Reproducibility under random_seed +# --------------------------------------------------------------------------- + + +class TestReproducibility: + """Two calls with the same ``random_seed`` must produce bit-identical + output. This is essential for diffing solver outputs across PRs + and for regression tests.""" + + def test_same_seed_bit_identical(self): + """Two calls with the same random_seed produce bit-identical + output on all five primary pressures, derived fO2, and the + per-element residuals.""" + ddict = _ddict() + target = dict(_earth_target_HCNS(), O=2.0e21) + + out1 = equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=4.0, + random_seed=42, + nguess=200, + nsolve=500, + print_result=False, + ) + out2 = equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=4.0, + random_seed=42, + nguess=200, + nsolve=500, + print_result=False, + ) + + keys_to_check = ( + 'fO2_shift_derived', + 'H2O_bar', + 'CO2_bar', + 'N2_bar', + 'S2_bar', + 'H_res', + 'C_res', + 'N_res', + 'S_res', + 'O_res', + ) + for key in keys_to_check: + assert out1[key] == out2[key], ( + f'{key} differs between two same-seed calls: {out1[key]!r} vs {out2[key]!r}' + ) + + # Discrimination guard: a stub that returned a constant dict for + # every call would pass the equality check trivially. Confirm the + # values are physically meaningful (fO2_shift_derived in a finite + # range, primary pressures positive and bounded). + assert -12.0 < out1['fO2_shift_derived'] < 12.0, ( + f'fO2_shift_derived = {out1["fO2_shift_derived"]:.4f} ' + f'outside the physically plausible IW range' + ) + for p_key in ('H2O_bar', 'CO2_bar', 'N2_bar', 'S2_bar'): + assert 0.0 < out1[p_key] < 1e6, ( + f'{p_key} = {out1[p_key]:.4e} outside the [0, 1e6] bar range' + ) + + def test_different_seeds_converge_to_same_root(self): + """For a well-posed target the 5x5 system has a unique attractor, + so two different random_seed values must converge to the same + derived fO2 and the same partial pressures even though their + Monte-Carlo restart draws differ. Same-seed bit-identity is + covered by test_same_seed_bit_identical; this pins + seed-independence of the converged result for a well-posed + problem.""" + ddict = _ddict() + target = dict(_earth_target_HCNS(), O=2.0e21) + + out1 = equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=4.0, + random_seed=1, + nguess=200, + nsolve=500, + print_result=False, + ) + out2 = equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=4.0, + random_seed=999, + nguess=200, + nsolve=500, + print_result=False, + ) + # Seed-independence: the converged root is the same regardless of + # the restart-draw seed (a near-1 dex difference would mean the + # solver landed in a different basin, i.e. non-unique attractor). + assert out1['fO2_shift_derived'] == pytest.approx(out2['fO2_shift_derived'], abs=1e-3) + for p_key in ('H2O_bar', 'CO2_bar', 'N2_bar', 'S2_bar'): + assert out1[p_key] == pytest.approx(out2[p_key], rel=1e-3) + # Both land inside the physical fO2 box. + assert -12.0 <= out1['fO2_shift_derived'] <= 12.0 + + +# --------------------------------------------------------------------------- +# Convergence failure contract +# --------------------------------------------------------------------------- + + +class TestConvergenceFailure: + """An unreachable target (e.g. O = 1e30 kg, impossibly large) must + raise RuntimeError with the documented diagnostic message. Never + ZeroDivisionError, never silent success. + """ + + def test_impossible_O_target_raises_runtime_error(self): + """An O budget far outside the achievable range (e.g. 1e30 kg) + cannot satisfy the closure system; the solver exhausts its + restart budget and the entry point raises RuntimeError rather + than returning garbage.""" + ddict = _ddict() + target = dict(_earth_target_HCNS(), O=1e30) + + with pytest.raises(RuntimeError, match='Could not find solution'): + equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=4.0, + random_seed=0, + nguess=50, + nsolve=200, + print_result=False, + ) + + def test_runtime_error_message_includes_final_attempt(self): + """The RuntimeError message must include the final pH2O / pCO2 + / fO2_shift attempt for debugging.""" + ddict = _ddict() + target = dict(_earth_target_HCNS(), O=1e30) + + try: + equilibrium_atmosphere_authoritative_O( + target, + ddict, + fO2_hint=4.0, + random_seed=0, + nguess=20, + nsolve=100, + print_result=False, + ) + pytest.fail('expected RuntimeError for impossible target') + except RuntimeError as exc: + msg = str(exc) + for hint in ('pH2O', 'pCO2', 'pN2', 'pS2', 'fO2_shift'): + assert hint in msg, f'RuntimeError message missing {hint!r}: {msg}' diff --git a/tests/test_authoritative_O_monotonicity.py b/tests/test_authoritative_O_monotonicity.py new file mode 100644 index 0000000..77b7921 --- /dev/null +++ b/tests/test_authoritative_O_monotonicity.py @@ -0,0 +1,190 @@ +"""Monotonicity property tests for the authoritative-O solver. + +The new entry point ``equilibrium_atmosphere_authoritative_O`` adds a +fifth unknown (fO2_shift) to the existing 4-unknown system, then closes +the system with an O mass-balance equation. For this extended system +to be well-posed (i.e., to have a unique root reachable by Newton-style +solvers), the function O_kg_total(fO2_shift) at fixed +(H, C, N, S, T_magma, M_mantle, Phi_global) must be monotonic. + +If it is not, the new solver could find multiple roots depending on +the cold-start basin, breaking determinism even when seeded. + +These tests sweep fO2_shift across the [-6, +8] working range in +legacy mode (which takes fO2_shift as input and returns +O_kg_total as output) and confirm monotonicity. We test two T_magma +points inside the Dasgupta/Gaillard calibration range. Marked +``slow`` because each sweep runs 12 legacy solves. + +These also serve as the regression net for the chemistry refactor: +O(fO2) is empirically monotonic in [-5, +5] at 1800 K and 3000 K. +If a future refactor of the chemistry breaks that property, this +test fires. +""" + +from __future__ import annotations + +import logging + +import numpy as np +import pytest + +from calliope.constants import volatile_species +from calliope.solve import equilibrium_atmosphere + +logging.getLogger('calliope').setLevel(logging.WARNING) + +pytestmark = [pytest.mark.slow, pytest.mark.timeout(3600)] + + +def _ddict(T: float = 1800.0, dIW: float = 0.0) -> dict: + """Realistic ddict with every species included; T inside the + Dasgupta/Gaillard calibration range to avoid extrapolation warnings.""" + d = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': 1.0, + 'T_magma': T, + 'fO2_shift_IW': dIW, + } + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d + + +def _earth_target_HCNS() -> dict: + return {'H': 1.5e20, 'C': 1.5e19, 'N': 8.0e18, 'S': 8.0e20} + + +def _sweep_O_vs_fO2(T_magma: float, dIW_values: list[float]) -> np.ndarray: + """Run legacy mode at each dIW, return the array of O_kg_total.""" + O_kg = np.zeros(len(dIW_values)) + target = _earth_target_HCNS() + for i, dIW in enumerate(dIW_values): + ddict = _ddict(T=T_magma, dIW=dIW) + out = equilibrium_atmosphere( + target, + ddict, + print_result=False, + nguess=200, + nsolve=500, + ) + O_kg[i] = out['O_kg_total'] + return O_kg + + +class TestMonotonicity: + """O_kg_total(fO2_shift) must be monotonic on the physical range + for the 5-unknown system to be well-posed. Verified empirically + on the legacy mode (which produces the same O_kg as the new mode + at the corresponding self-consistent root).""" + + @pytest.mark.parametrize('T_magma', [1500.0, 1800.0]) + def test_O_kg_total_strictly_increasing_with_fO2(self, T_magma): + """At every adjacent dIW pair in [-4, +6], O_kg_total must + increase. (Oxidising shifts move atmospheric water and CO2 + upward, which adds atomic O to the inventory.)""" + dIW_values = np.linspace(-4.0, 6.0, 11) + O_kg = _sweep_O_vs_fO2(T_magma, list(dIW_values)) + + # Compute deltas; all must be > 0 + deltas = np.diff(O_kg) + + # Allow a small per-step tolerance for solver noise: each step + # is 1 dex; the change in O_kg should swamp any per-call solver + # noise by many orders of magnitude. + assert np.all(deltas > 0), ( + f'Non-monotonic O_kg(fO2_shift) at T={T_magma} K. ' + f'dIW: {dIW_values.tolist()}; ' + f'O_kg: {O_kg.tolist()}; ' + f'deltas: {deltas.tolist()}. ' + 'A non-monotonic curve means the 5-unknown system can have ' + 'multiple roots; the new solver may then find different ' + 'roots from different cold starts.' + ) + + # Discrimination guard: monotonicity alone is satisfied by an + # arbitrarily flat function. The 10-dex span of dIW must produce + # a substantial change in O_kg, otherwise the inverse solver loses + # signal. Empirically the ratio is ~2-3x for Earth-like H/C/N/S. + assert O_kg[-1] / O_kg[0] > 1.5, ( + f'O_kg span at T={T_magma}: {O_kg[0]:.3e} -> {O_kg[-1]:.3e} ' + f'(ratio={O_kg[-1] / O_kg[0]:.2f}); expected > 1.5x' + ) + + def test_O_kg_range_is_resolvable_for_inverse_solver(self): + """The O_kg(fO2_shift) curve must have enough dynamic range + over [-4, +6] that the inverse solver can resolve fO2 from + a given O budget. For Earth-like H/C/N/S budgets this ratio + is empirically ~3x: moderate, but well above the noise floor + the per-element tolerance imposes. + + With ratio=R over 10 dex, the average slope is log10(R)/10 + dex per dex. With per-element rtol=1e-5 on O, the inverse + fO2 resolution is (rtol/d(log_O)/d(dIW)) which for R=2 is + ~3e-5 dex: far below any physically meaningful precision. + """ + dIW_low = -4.0 + dIW_high = 6.0 + O_low = _sweep_O_vs_fO2(1800.0, [dIW_low])[0] + O_high = _sweep_O_vs_fO2(1800.0, [dIW_high])[0] + + assert O_low > 0, f'O_kg at dIW={dIW_low} must be positive, got {O_low}' + assert O_high > 0, f'O_kg at dIW={dIW_high} must be positive, got {O_high}' + assert O_high > O_low, ( + f'O_kg at dIW=+6 ({O_high:.3e}) must exceed O_kg at dIW=-4 ' + f'({O_low:.3e}); the inverse solver is undefined if the ' + 'curve is flat or decreasing.' + ) + + ratio = O_high / O_low + # Empirical ratio is ~2.74 for Earth-like H/C/N/S; require >1.5x + # so the inverse solver has clear signal across the working range. + # A much weaker requirement than the value seen in practice; this + # catches catastrophic regressions (e.g., a future chemistry + # change that flattens the O-vs-fO2 curve). + assert ratio > 1.5, ( + f'O_kg span across {dIW_low} -> {dIW_high}: {O_low:.3e} -> ' + f'{O_high:.3e} (ratio={ratio:.2f}). Expected > 1.5x; below ' + 'that the inverse solver loses signal.' + ) + + +class TestMonotonicityRegimes: + """Extra coverage of less-common regimes that should still produce + monotonic O_kg curves: low and high Phi_global.""" + + def test_monotonic_at_partial_melt(self): + """At Phi=0.3 (partial crystallization), dissolved-mass branch + shrinks but atmospheric-O branch is unchanged. Curve must + remain monotonic.""" + dIW_values = np.linspace(-2.0, 4.0, 7) + target = _earth_target_HCNS() + O_kg = np.zeros(len(dIW_values)) + for i, dIW in enumerate(dIW_values): + ddict = _ddict(T=1800.0, dIW=dIW) + ddict['Phi_global'] = 0.3 + out = equilibrium_atmosphere( + target, + ddict, + print_result=False, + nguess=200, + nsolve=500, + ) + O_kg[i] = out['O_kg_total'] + + deltas = np.diff(O_kg) + assert np.all(deltas > 0), ( + f'Non-monotonic at Phi=0.3: dIW={dIW_values.tolist()}, ' + f'O_kg={O_kg.tolist()}, deltas={deltas.tolist()}' + ) + + # Discrimination guard: at Phi=0.3 the atmospheric channel still + # carries most of the O variation with dIW, so the span over the + # 6-dex dIW range must remain substantial. + assert O_kg[-1] / O_kg[0] > 1.2, ( + f'O_kg span at Phi=0.3: {O_kg[0]:.3e} -> {O_kg[-1]:.3e} ' + f'(ratio={O_kg[-1] / O_kg[0]:.2f}); expected > 1.2x' + ) diff --git a/tests/test_authoritative_O_solver_paths.py b/tests/test_authoritative_O_solver_paths.py new file mode 100644 index 0000000..e046765 --- /dev/null +++ b/tests/test_authoritative_O_solver_paths.py @@ -0,0 +1,567 @@ +"""Solver-loop branch tests for ``equilibrium_atmosphere_authoritative_O``. + +The authoritative-O entry point wraps a Monte-Carlo restart loop around +fsolve / trust-constr. Between the raw solver call and accepting a root +it applies three guards that the happy-path smoke tests never trip: + +- a per-element residual gate (a converged-looking root whose mass + residual exceeds tolerance is rejected), +- a physical-box gate (a root with a derived fO2_shift outside + [-12, +12], a negative partial pressure, or a partial pressure above + the 1e7 bar ceiling is rejected, because the production unbounded + fsolve does not enforce ``bounds``), +- an exception firewall (a solver call or residual evaluation that + raises is treated as a failed attempt, not a crash). + +These tests drive each guard by replacing ``opt.fsolve`` and the residual +function ``func_authoritative_O`` so the controlled root reaches the +guard deterministically, in milliseconds, without depending on whether +the real solver happens to find that corner. The accept path then runs +the real chemistry (``_get_partial_pressures`` etc.) on the controlled +root, so the output assertions exercise real physics, not the stub. + +Marked ``unit``: every test stubs the iterative solver, so each runs in +well under 100 ms. +""" + +from __future__ import annotations + +import logging + +import numpy as np +import pytest + +from calliope import solve as calsolve +from calliope.constants import volatile_species +from calliope.solve import ( + equilibrium_atmosphere_authoritative_O, + get_initial_pressures_with_fO2, +) + +# The module logger is 'fwl.calliope.solve'; raise it to DEBUG so the +# rejection-branch debug lines reach caplog. +_SOLVE_LOGGER = 'fwl.calliope.solve' +logging.getLogger(_SOLVE_LOGGER).setLevel(logging.DEBUG) + +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +def _ddict(T: float = 1800.0, Phi: float = 1.0) -> dict: + """Realistic ddict with every volatile species included.""" + d = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': Phi, + 'T_magma': T, + 'fO2_shift_IW': 0.0, # ignored under authoritative-O mode + } + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d + + +def _target() -> dict: + """Earth-like element budget [kg] with all five elements.""" + return {'H': 1.5e20, 'C': 1.5e19, 'N': 8.0e18, 'S': 8.0e20, 'O': 2.0e21} + + +def _stub_solver(monkeypatch, sol, residual): + """Force the iterative solver to return ``sol`` with ``residual``. + + Patches the module objects directly (not via dotted-string targets): + ``opt`` is an aliased ``scipy.optimize`` module, so a string target + like ``'calliope.solve.opt.fsolve'`` fails to resolve and silently + leaves the real solver running. ``func_authoritative_O`` is replaced + so the residual gate sees a controlled value. + + Parameters + ---------- + sol : sequence of 5 floats + The (H2O, CO2, N2, S2, fO2_shift) root fsolve will report. + residual : sequence of 5 floats + The residual the gate will evaluate at ``sol``. + """ + + def _fake_fsolve(*args, **kwargs): + # Real signature returns (x, infodict, ier, mesg); ier == 1 is + # nominal convergence. + return np.asarray(sol, dtype=float), {}, 1, 'stub converged' + + monkeypatch.setattr(calsolve.opt, 'fsolve', _fake_fsolve) + monkeypatch.setattr( + calsolve, 'func_authoritative_O', lambda *a, **k: np.asarray(residual, dtype=float) + ) + + +# --------------------------------------------------------------------------- +# Cold-start helper: default-RNG branch +# --------------------------------------------------------------------------- + + +def test_get_initial_pressures_with_fO2_default_rng(): + """``rng=None`` falls back to the global ``np.random`` state and + returns a five-vector whose fO2 slot is the unredrawn hint. + + Exercises the ``rng is None`` default-RNG branch. With + ``restart=False`` the fO2 seed must be the hint verbatim (the + redraw only happens on restart), which discriminates this path from + the restart path that draws fO2 from Uniform(-6, +8). + """ + np.random.seed(42) # determinism for the global-state branch + hint = 3.5 + x0 = get_initial_pressures_with_fO2(_target(), hint, restart=False, rng=None) + + assert len(x0) == 5, f'expected 5-vector (4 pressures + fO2), got {len(x0)}' + pressures = x0[:4] + assert all(np.isfinite(p) for p in pressures), 'cold-start pressures must be finite' + # 10 ** Uniform(-12, 5) lands strictly inside (1e-12, 1e5) bar. + assert all(1e-13 < p < 1e6 for p in pressures), ( + f'pressures out of cold-start range: {pressures}' + ) + + # Discrimination guard: restart=False must pass the hint through + # untouched. A restart draw would generically miss 3.5 exactly. + assert x0[4] == pytest.approx(hint), ( + 'restart=False must seed fO2 from the hint, not a redraw' + ) + + +def test_get_initial_pressures_with_fO2_respects_custom_p_guess_max(): + """The optional ``p_guess_max`` widens the cold-start pressure draw without + touching the solver box, so a high-pressure (e.g. sub-Neptune) case can be + seeded above the default ~100 kbar maximum. + + Draws many seeded samples at a raised ``p_guess_max`` and asserts the draw range + actually extends past the default ``P_GUESS_MAX_BAR``, while the + default-``p_guess_max`` draw never does. The two-sided comparison discriminates a + working argument from a no-op that ignores ``p_guess_max``. + """ + from calliope.solve import P_GUESS_MAX_BAR + + raised = 1.0e8 # well above the default P_GUESS_MAX_BAR (1e5) + rng_hi = np.random.default_rng(0) + hi = [ + p + for _ in range(200) + for p in get_initial_pressures_with_fO2( + _target(), 0.0, restart=True, rng=rng_hi, p_guess_max=raised + )[:4] + ] + # Every draw stays inside [floor, raised ceiling]. + assert all(1e-13 < p <= raised * (1 + 1e-9) for p in hi), ( + 'draw escaped [floor, p_guess_max]' + ) + # The raised ceiling is actually exercised: some draw lands above the default. + assert max(hi) > P_GUESS_MAX_BAR, 'p_guess_max did not widen the draw range' + + rng_def = np.random.default_rng(0) + deflt = [ + p + for _ in range(200) + for p in get_initial_pressures_with_fO2(_target(), 0.0, restart=True, rng=rng_def)[:4] + ] + # Discrimination: the default draw never exceeds the default ceiling. + assert max(deflt) <= P_GUESS_MAX_BAR * (1 + 1e-9), 'default draw exceeded P_GUESS_MAX_BAR' + + +def test_equilibrium_atmosphere_forwards_p_guess_max_to_cold_start(monkeypatch): + """The forward (fixed-fO2) public entry forwards its ``p_guess_max`` to the + Monte-Carlo cold-start draw, so a caller-supplied widened range (e.g. from + PROTEUS for a sub-Neptune) reaches the guess. A spy captures the value the + entry passes down and short-circuits before the solve; the default call + forwards the module default, discriminating a working forward from a + hard-coded one.""" + + class _Stop(Exception): + pass + + captured = {} + + def spy(target_d, p_guess_max=calsolve.P_GUESS_MAX_BAR): + captured['p_guess_max'] = p_guess_max + raise _Stop + + monkeypatch.setattr(calsolve, 'get_initial_pressures', spy) + + with pytest.raises(_Stop): + calsolve.equilibrium_atmosphere( + _target(), _ddict(), p_guess=None, nguess=1, print_result=False, p_guess_max=9.9e7 + ) + assert captured['p_guess_max'] == pytest.approx(9.9e7) + assert ( + captured['p_guess_max'] != calsolve.P_GUESS_MAX_BAR + ) # discrimination: not the default + + captured.clear() + with pytest.raises(_Stop): + calsolve.equilibrium_atmosphere( + _target(), _ddict(), p_guess=None, nguess=1, print_result=False + ) + assert captured['p_guess_max'] == pytest.approx( + calsolve.P_GUESS_MAX_BAR + ) # default forwarded + + +def test_authoritative_O_forwards_p_guess_max_to_cold_start(monkeypatch): + """The authoritative-O public entry (the path PROTEUS uses for + ``fO2_source='from_O_budget'``) forwards its ``p_guess_max`` to the cold-start + helper. A delegating spy records the forwarded value while the stubbed + solver converges, so the call completes; the default call forwards the + module default.""" + captured = {} + real = calsolve.get_initial_pressures_with_fO2 + + def spy(*args, **kwargs): + captured['p_guess_max'] = kwargs.get('p_guess_max') + return real(*args, **kwargs) + + monkeypatch.setattr(calsolve, 'get_initial_pressures_with_fO2', spy) + _stub_solver(monkeypatch, [10.0, 5.0, 1.0, 0.5, 1.5], residual=np.zeros(5)) + + calsolve.equilibrium_atmosphere_authoritative_O( + _target(), + _ddict(), + fO2_hint=4.0, + opt_solver=False, + nguess=1, + print_result=False, + p_guess_max=9.9e7, + ) + assert captured['p_guess_max'] == pytest.approx(9.9e7) + assert ( + captured['p_guess_max'] != calsolve.P_GUESS_MAX_BAR + ) # discrimination: not the default + + captured.clear() + calsolve.equilibrium_atmosphere_authoritative_O( + _target(), _ddict(), fO2_hint=4.0, opt_solver=False, nguess=1, print_result=False + ) + assert captured['p_guess_max'] == pytest.approx( + calsolve.P_GUESS_MAX_BAR + ) # default forwarded + + +@pytest.mark.parametrize('bad', [0.0, -1.0, float('inf'), float('nan'), 1.0e-15]) +def test_get_initial_pressures_rejects_invalid_ceiling(bad): + """An out-of-range cold-start ceiling fails loudly instead of silently + degenerating the draw. A ceiling below P_GUESS_MIN_BAR would invert the + log-uniform range (which np.random.uniform does not reject), and a + non-finite ceiling would feed inf/nan into the draw; both must raise.""" + with pytest.raises(ValueError, match='p_guess_max'): + get_initial_pressures_with_fO2(_target(), 0.0, p_guess_max=bad) + # The forward helper guards identically. + from calliope.solve import get_initial_pressures + + with pytest.raises(ValueError, match='p_guess_max'): + get_initial_pressures(_target(), p_guess_max=bad) + + +def test_equilibrium_atmosphere_forwards_p_guess_max_on_restart(monkeypatch): + """The restart redraw, not only the initial cold start, forwards + p_guess_max. fsolve is stubbed to never converge so the loop reaches the + restart draw on every iteration; the spy asserts every draw (initial plus + each restart) received the forwarded value, killing a regression that drops + p_guess_max only on the restart path.""" + calls = [] + real = calsolve.get_initial_pressures + + def spy(*args, **kwargs): + calls.append(kwargs.get('p_guess_max')) + return real(*args, **kwargs) + + monkeypatch.setattr(calsolve, 'get_initial_pressures', spy) + # ier != 1 => fsolve reports non-convergence => the loop redraws every pass. + monkeypatch.setattr( + calsolve.opt, + 'fsolve', + lambda *a, **k: (np.array([10.0, 5.0, 1.0, 0.5]), {}, 0, 'stub: not converged'), + ) + + with pytest.raises(RuntimeError): + calsolve.equilibrium_atmosphere( + _target(), + _ddict(), + p_guess=None, + nguess=3, + opt_solver=False, + print_result=False, + p_guess_max=9.9e6, + ) + assert len(calls) >= 2, 'restart redraw was never reached' + assert all(c == pytest.approx(9.9e6) for c in calls), ( + 'a cold-start draw did not receive the forwarded p_guess_max' + ) + + +def test_authoritative_O_forwards_p_guess_max_on_restart(monkeypatch): + """Mirror of the forward-path restart test for the authoritative-O entry: + the residual gate rejects every stubbed root, so the loop redraws each pass, + and every draw must carry the forwarded p_guess_max.""" + calls = [] + real = calsolve.get_initial_pressures_with_fO2 + + def spy(*args, **kwargs): + calls.append(kwargs.get('p_guess_max')) + return real(*args, **kwargs) + + monkeypatch.setattr(calsolve, 'get_initial_pressures_with_fO2', spy) + # Converged-looking root but a residual far above tolerance => rejected => + # the loop redraws on every pass and finally raises RuntimeError. + _stub_solver(monkeypatch, [10.0, 5.0, 1.0, 0.5, 1.5], residual=np.full(5, 1.0e30)) + + with pytest.raises(RuntimeError): + calsolve.equilibrium_atmosphere_authoritative_O( + _target(), + _ddict(), + fO2_hint=4.0, + opt_solver=False, + nguess=3, + print_result=False, + p_guess_max=9.9e6, + ) + assert len(calls) >= 2, 'restart redraw was never reached' + assert all(c == pytest.approx(9.9e6) for c in calls), ( + 'a cold-start draw did not receive the forwarded p_guess_max' + ) + + +# --------------------------------------------------------------------------- +# Accept path +# --------------------------------------------------------------------------- + + +def test_accept_path_returns_derived_fo2_and_real_chemistry(monkeypatch, caplog): + """A converged, in-box, zero-residual root is accepted and the + derived fO2 plus real outgassed pressures flow to the output. + + Forces fsolve to return a physical root and the residual function to + report zero residual (so the residual + box gates pass), then lets + the real chemistry build the output dict. The derived fO2 must equal + the root's fifth component, NOT the hint, which is the whole point of + authoritative-O mode. + """ + sol = [10.0, 5.0, 1.0, 0.5, 1.5] # H2O, CO2, N2, S2 [bar]; fO2_shift = 1.5 + _stub_solver(monkeypatch, sol, residual=np.zeros(5)) + + caplog.set_level(logging.INFO, logger=_SOLVE_LOGGER) + out = equilibrium_atmosphere_authoritative_O( + _target(), + _ddict(), + fO2_hint=4.0, + opt_solver=False, + nguess=1, + print_result=True, + ) + + # Derived fO2 is the solver output, not the hint. + assert out['fO2_shift_derived'] == pytest.approx(1.5) + # Discrimination guard: a buggy implementation that echoed the hint + # would return 4.0; the gap is 2.5, far outside any rounding. (This + # is also the symptom of the fsolve stub failing to apply.) + assert abs(out['fO2_shift_derived'] - 4.0) > 1.0 + + # The accepted primary pressures flow through the real chemistry into + # the output unchanged (H2O is a primary, so p_d['H2O'] == sol[0]). + assert out['H2O_bar'] == pytest.approx(10.0, rel=1e-9) + # P_surf sums every species' partial pressure, so it is at least the + # single H2O contribution and strictly positive. + assert out['P_surf'] >= out['H2O_bar'] > 0.0 + + # The fifth residual (O mass balance) is reported alongside the four + # legacy ones. + assert 'O_res' in out and np.isfinite(out['O_res']) + # print_result=True logs the solve header and the derived-fO2 line. + assert 'authoritative-O mode' in caplog.text + assert 'Derived fO2_shift' in caplog.text + + +def test_p_guess_tiny_pressure_collapses_upper_bound(monkeypatch): + """A sub-1e-10 bar entry in ``p_guess`` collapses that slot's upper + bound to 1.0 before the solve, matching the legacy degenerate-slot + guard. + + Exercises the ``p_guess`` ub-collapse loop. The accept path then + confirms the run still completes and returns the controlled root, so + the collapse does not corrupt an otherwise-valid solve. + """ + sol = [1e-12, 5.0, 1.0, 0.5, 2.0] + _stub_solver(monkeypatch, sol, residual=np.zeros(5)) + + p_guess = {'H2O': 1e-12, 'CO2': 5.0, 'N2': 1.0, 'S2': 0.5} + out = equilibrium_atmosphere_authoritative_O( + _target(), + _ddict(), + fO2_hint=2.0, + p_guess=p_guess, + opt_solver=False, + nguess=1, + print_result=False, + ) + + # The solve completes through the collapsed-bound path and returns + # the controlled root's derived fO2. + assert out['fO2_shift_derived'] == pytest.approx(2.0) + # Discrimination guard: the collapse must not have rewritten the + # derived value toward the 1.0 bound it imposes on the pressure slot. + assert out['fO2_shift_derived'] != pytest.approx(1.0) + + +# --------------------------------------------------------------------------- +# Rejection gates: each must exhaust the restart budget and raise. +# --------------------------------------------------------------------------- + + +def test_root_with_out_of_box_fo2_is_rejected(monkeypatch, caplog): + """A converged root whose derived fO2 lies outside [-12, +12] is + rejected, not returned, because unbounded fsolve does not enforce + the physical box. + + The single restart is consumed by the rejection, so the loop raises + RuntimeError. The debug log names the out-of-box value so an operator + can see why an apparently-converged solve was discarded. + """ + sol = [10.0, 5.0, 1.0, 0.5, 20.0] # fO2_shift = 20 > +12 bound + _stub_solver(monkeypatch, sol, residual=np.zeros(5)) + + caplog.set_level(logging.DEBUG, logger=_SOLVE_LOGGER) + with pytest.raises(RuntimeError, match='Could not find solution'): + equilibrium_atmosphere_authoritative_O( + _target(), _ddict(), fO2_hint=4.0, opt_solver=False, nguess=1, print_result=False + ) + + assert 'outside' in caplog.text, 'out-of-box rejection must be logged' + # Discrimination guard: confirm the root really was out of the box, so + # the rejection path (not some other failure) fired. + assert not (-12.0 <= sol[4] <= 12.0) + + +def test_root_with_negative_pressure_is_rejected(monkeypatch, caplog): + """A converged root with a negative partial pressure is rejected. + + fsolve can return a slightly negative pressure on a degenerate slot; + the box gate rejects it rather than passing a non-physical state into + the chemistry. With one restart the loop then raises RuntimeError. + """ + sol = [-1.0, 5.0, 1.0, 0.5, 2.0] # H2O = -1 bar, in-box fO2 + _stub_solver(monkeypatch, sol, residual=np.zeros(5)) + + caplog.set_level(logging.DEBUG, logger=_SOLVE_LOGGER) + with pytest.raises(RuntimeError, match='Could not find solution'): + equilibrium_atmosphere_authoritative_O( + _target(), _ddict(), fO2_hint=2.0, opt_solver=False, nguess=1, print_result=False + ) + + assert 'negative partial pressure' in caplog.text + # Discrimination guard: the fO2 is in-box, so ONLY the negative-pressure + # branch (not the out-of-box branch) can have rejected this root. + assert -12.0 <= sol[4] <= 12.0 and min(sol[:4]) < 0.0 + + +def test_root_above_pressure_ceiling_is_rejected(monkeypatch, caplog): + """A converged root with a partial pressure above the 1e7 bar ceiling + is rejected, because unbounded fsolve does not enforce the upper box. + + A mass budget can drive fsolve to a root that closes the element + balance yet places one partial pressure above the documented 1e7 bar + bound that trust-constr's ``ub`` array would have enforced. The box + gate rejects it; with one restart the loop raises RuntimeError instead + of returning the non-physical state. + """ + sol = [2.0e7, 5.0, 1.0, 0.5, 2.0] # H2O = 2e7 bar > 1e7 ceiling, in-box fO2 + _stub_solver(monkeypatch, sol, residual=np.zeros(5)) + + caplog.set_level(logging.DEBUG, logger=_SOLVE_LOGGER) + with pytest.raises(RuntimeError, match='Could not find solution'): + equilibrium_atmosphere_authoritative_O( + _target(), _ddict(), fO2_hint=2.0, opt_solver=False, nguess=1, print_result=False + ) + + assert 'ceiling' in caplog.text, 'above-ceiling rejection must be logged' + # Discrimination guard: fO2 is in-box and every pressure is non-negative, + # so ONLY the upper-ceiling branch (not the out-of-box or negative-pressure + # branches) can have rejected this root. + assert -12.0 <= sol[4] <= 12.0 and min(sol[:4]) >= 0.0 and max(sol[:4]) > 1e7 + + +def test_root_with_excess_residual_is_rejected(monkeypatch, caplog): + """A root that fsolve flags converged but whose mass residual blows + past tolerance is rejected by the per-element residual gate. + + The controlled residual exceeds the kg-scale tolerance for every + element, so the worst-element check trips and the attempt fails. The + single restart is consumed and the loop raises RuntimeError. + """ + sol = [10.0, 5.0, 1.0, 0.5, 2.0] + # Residual far above max(target*rtol, TRUNC_MASS) for every element. + _stub_solver(monkeypatch, sol, residual=np.full(5, 1e30)) + + caplog.set_level(logging.DEBUG, logger=_SOLVE_LOGGER) + with pytest.raises(RuntimeError, match='Could not find solution'): + equilibrium_atmosphere_authoritative_O( + _target(), _ddict(), fO2_hint=2.0, opt_solver=False, nguess=1, print_result=False + ) + + assert 'residual' in caplog.text + # Discrimination guard: 1e30 kg dwarfs the largest element tolerance + # (Earth O budget 2e21 kg * rtol 1e-5 = 2e16 kg), so the gate must trip. + assert 1e30 > max(1e10, 2.0e21 * 1e-5) + + +def test_residual_evaluation_exception_is_caught(monkeypatch, caplog): + """A residual evaluation that raises is treated as a failed attempt, + not a crash propagated to the caller. + + fsolve reports convergence, but the post-solve residual recompute + raises ValueError. The firewall converts that into a rejected attempt; + with one restart the loop ends in RuntimeError, never a bare + ValueError escaping the function. + """ + sol = [10.0, 5.0, 1.0, 0.5, 2.0] + + def _fake_fsolve(*a, **k): + return np.asarray(sol, dtype=float), {}, 1, 'stub converged' + + def _raising_residual(*a, **k): + raise ValueError('residual blew up at trial point') + + monkeypatch.setattr(calsolve.opt, 'fsolve', _fake_fsolve) + monkeypatch.setattr(calsolve, 'func_authoritative_O', _raising_residual) + + caplog.set_level(logging.DEBUG, logger=_SOLVE_LOGGER) + with pytest.raises(RuntimeError, match='Could not find solution'): + equilibrium_atmosphere_authoritative_O( + _target(), _ddict(), fO2_hint=2.0, opt_solver=False, nguess=1, print_result=False + ) + + # The firewall logged the caught exception; the bare ValueError did + # not escape (pytest.raises above already enforces RuntimeError). + assert 'residual evaluation raised' in caplog.text + + +def test_solver_call_exception_is_caught(monkeypatch, caplog): + """A solver call that raises ZeroDivisionError is caught and retried, + not propagated. + + fsolve itself raises mid-iteration; the firewall marks the attempt + failed and the loop redraws. With one restart it ends in RuntimeError. + """ + + def _raising_fsolve(*a, **k): + raise ZeroDivisionError('residual divided by zero at trial point') + + monkeypatch.setattr(calsolve.opt, 'fsolve', _raising_fsolve) + + caplog.set_level(logging.DEBUG, logger=_SOLVE_LOGGER) + with pytest.raises(RuntimeError, match='Could not find solution'): + equilibrium_atmosphere_authoritative_O( + _target(), _ddict(), fO2_hint=2.0, opt_solver=False, nguess=1, print_result=False + ) + + assert 'restarting' in caplog.text diff --git a/tests/test_authoritative_O_validation.py b/tests/test_authoritative_O_validation.py new file mode 100644 index 0000000..8430363 --- /dev/null +++ b/tests/test_authoritative_O_validation.py @@ -0,0 +1,302 @@ +"""Input validation tests for `equilibrium_atmosphere_authoritative_O`. + +Verifies that the entry point rejects bad inputs at the boundary +(raises a typed exception with a useful message) rather than letting +them propagate through fsolve and emerge as opaque ZeroDivisionError +or KeyError from deep inside the chemistry chain. + +These are pure-validation tests: every case raises before any solver +call, so they run in < 100 ms each and are marked ``unit``. +""" + +from __future__ import annotations + +import logging + +import pytest + +from calliope.constants import volatile_species +from calliope.solve import equilibrium_atmosphere_authoritative_O + +logging.getLogger('calliope').setLevel(logging.WARNING) + +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +def _ddict(T: float = 1800.0, Phi: float = 1.0) -> dict: + """Realistic ddict with every volatile species included.""" + d = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': Phi, + 'T_magma': T, + 'fO2_shift_IW': 0.0, # ignored under authoritative-O mode + } + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d + + +def _target() -> dict: + """Earth-like element budget [kg] with all five elements.""" + return {'H': 1.5e20, 'C': 1.5e19, 'N': 8.0e18, 'S': 8.0e20, 'O': 2.0e21} + + +def _base_kwargs() -> dict: + """Minimal kwargs that skip the print log and use a small nguess.""" + return {'fO2_hint': 4.0, 'print_result': False, 'nguess': 10, 'nsolve': 100} + + +# --------------------------------------------------------------------------- +# target_d: required keys +# --------------------------------------------------------------------------- + + +class TestTargetDRequiredKeys: + """target_d must include all five element keys (H, C, N, S, O). + + Missing keys raise KeyError; the error message names the missing + keys so the caller can fix the input without grepping fsolve. + """ + + @pytest.mark.parametrize('missing', ['H', 'C', 'N', 'S', 'O']) + def test_missing_element_raises_keyerror(self, missing): + """Dropping any one of the five required element keys raises + KeyError naming the missing key.""" + t = _target() + del t[missing] + with pytest.raises(KeyError, match=missing): + equilibrium_atmosphere_authoritative_O(t, _ddict(), **_base_kwargs()) + + def test_extra_key_is_ignored(self): + """Validation accepts unknown element keys (e.g. Cl) without + raising ValueError or KeyError; a convergence-time RuntimeError + from the solver loop is acceptable.""" + t = _target() + t['Cl'] = 1e18 # not a tracked element + kwargs = _base_kwargs() + kwargs['nguess'] = 1 + # Contract: validation must not raise ValueError or KeyError. + # A RuntimeError from the convergence loop is acceptable. + try: + equilibrium_atmosphere_authoritative_O(t, _ddict(), **kwargs) + except (ValueError, KeyError) as e: + pytest.fail(f'Extra key triggered validation error: {e!r}') + except RuntimeError: + pass # convergence failure is acceptable + + # Discrimination guard: confirm the test setup actually puts an + # unknown element in t. A future _target() change that already + # carried 'Cl' would make this test vacuous. + assert 'Cl' in t + assert 'Cl' not in ('H', 'C', 'N', 'S', 'O') + + +class TestTargetDValueValidation: + """Each target value must be a finite, non-negative real number.""" + + @pytest.mark.parametrize('elem', ['H', 'C', 'N', 'S', 'O']) + def test_negative_value_raises_valueerror(self, elem): + """A negative element budget raises ValueError with a 'non-negative' + message for any of the five elements.""" + t = _target() + t[elem] = -1.0 + with pytest.raises(ValueError, match='non-negative'): + equilibrium_atmosphere_authoritative_O(t, _ddict(), **_base_kwargs()) + + @pytest.mark.parametrize('elem', ['H', 'C', 'N', 'S', 'O']) + def test_nan_value_raises_valueerror(self, elem): + """A NaN element budget raises ValueError with a 'finite' + message for any of the five elements.""" + t = _target() + t[elem] = float('nan') + with pytest.raises(ValueError, match='finite'): + equilibrium_atmosphere_authoritative_O(t, _ddict(), **_base_kwargs()) + + @pytest.mark.parametrize('elem', ['H', 'C', 'N', 'S', 'O']) + def test_inf_value_raises_valueerror(self, elem): + """An infinite element budget raises ValueError with a 'finite' + message for any of the five elements.""" + t = _target() + t[elem] = float('inf') + with pytest.raises(ValueError, match='finite'): + equilibrium_atmosphere_authoritative_O(t, _ddict(), **_base_kwargs()) + + +# --------------------------------------------------------------------------- +# fO2_hint +# --------------------------------------------------------------------------- + + +class TestFO2HintValidation: + """fO2_hint must be finite and within the solver bounds [-12, +12].""" + + def test_nan_raises_valueerror(self): + """NaN fO2_hint raises ValueError with a 'finite' message.""" + kwargs = _base_kwargs() + kwargs['fO2_hint'] = float('nan') + with pytest.raises(ValueError, match='finite'): + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + + def test_inf_raises_valueerror(self): + """Infinite fO2_hint raises ValueError with a 'finite' message.""" + kwargs = _base_kwargs() + kwargs['fO2_hint'] = float('inf') + with pytest.raises(ValueError, match='finite'): + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + + @pytest.mark.parametrize('hint', [-100.0, -13.0, 12.5, 100.0]) + def test_outside_bounds_raises_valueerror(self, hint): + """fO2_hint outside the [-12, +12] band raises ValueError with a + bounds-naming message.""" + kwargs = _base_kwargs() + kwargs['fO2_hint'] = hint + with pytest.raises(ValueError, match='\\[-12, \\+12\\]'): + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + + @pytest.mark.parametrize('hint', [-12.0, -6.0, 0.0, 4.0, 8.0, 12.0]) + def test_inside_bounds_accepted(self, hint): + """Valid hints inside [-12, +12] must pass validation; the solver + may not converge in one attempt but the entry-point check must + not raise ValueError.""" + kwargs = _base_kwargs() + kwargs['fO2_hint'] = hint + kwargs['nguess'] = 1 + # Contract: validation must not raise ValueError; a convergence- + # time RuntimeError from the solver loop is acceptable. + try: + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + except ValueError as e: + pytest.fail(f'In-bounds fO2_hint={hint} rejected: {e!r}') + except RuntimeError: + pass # convergence failure is acceptable + + # Discrimination guard: confirm hint is genuinely inside the + # [-12, +12] band. Catches a parametrize drift that put an + # out-of-bounds value into the in-bounds sweep. + assert -12.0 <= hint <= 12.0 + + +# --------------------------------------------------------------------------- +# ddict: planet/state parameters +# --------------------------------------------------------------------------- + + +class TestDdictValidation: + """M_mantle, Phi_global, T_magma must be present and physical.""" + + @pytest.mark.parametrize('key', ['M_mantle', 'Phi_global', 'T_magma', 'gravity', 'radius']) + def test_missing_required_key_raises_keyerror(self, key): + """Dropping any of the five required ddict keys raises KeyError + naming the missing key.""" + dd = _ddict() + del dd[key] + with pytest.raises(KeyError, match=key): + equilibrium_atmosphere_authoritative_O(_target(), dd, **_base_kwargs()) + + @pytest.mark.parametrize('M_mantle', [0.0, -1e24, float('nan'), float('inf')]) + def test_bad_M_mantle_raises_valueerror(self, M_mantle): + """Zero, negative, NaN, or infinite M_mantle raises ValueError + naming M_mantle.""" + dd = _ddict() + dd['M_mantle'] = M_mantle + with pytest.raises(ValueError, match='M_mantle'): + equilibrium_atmosphere_authoritative_O(_target(), dd, **_base_kwargs()) + + @pytest.mark.parametrize('Phi', [-0.1, 1.5, 2.0, float('nan'), float('inf')]) + def test_bad_Phi_global_raises_valueerror(self, Phi): + """Phi_global outside [0, 1] or non-finite raises ValueError + naming Phi_global.""" + dd = _ddict() + dd['Phi_global'] = Phi + with pytest.raises(ValueError, match='Phi_global'): + equilibrium_atmosphere_authoritative_O(_target(), dd, **_base_kwargs()) + + @pytest.mark.parametrize('T', [0.0, -300.0, float('nan'), float('inf')]) + def test_bad_T_magma_raises_valueerror(self, T): + """Zero, negative, NaN, or infinite T_magma raises ValueError + naming T_magma.""" + dd = _ddict() + dd['T_magma'] = T + with pytest.raises(ValueError, match='T_magma'): + equilibrium_atmosphere_authoritative_O(_target(), dd, **_base_kwargs()) + + +# --------------------------------------------------------------------------- +# Solver-parameter bounds +# --------------------------------------------------------------------------- + + +class TestSolverParamValidation: + """nguess and nsolve must be >= 1.""" + + @pytest.mark.parametrize('nguess', [0, -1, -100]) + def test_bad_nguess_raises_valueerror(self, nguess): + """nguess <= 0 raises ValueError naming nguess.""" + kwargs = _base_kwargs() + kwargs['nguess'] = nguess + with pytest.raises(ValueError, match='nguess'): + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + + @pytest.mark.parametrize('nsolve', [0, -1]) + def test_bad_nsolve_raises_valueerror(self, nsolve): + """nsolve <= 0 raises ValueError naming nsolve.""" + kwargs = _base_kwargs() + kwargs['nsolve'] = nsolve + with pytest.raises(ValueError, match='nsolve'): + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + + def test_nguess_zero_not_unbound_local_error(self): + """nguess=0 must raise ValueError, never UnboundLocalError. + + The legacy bug was that `success` was bound inside the restart + loop; a 0-iteration loop never bound it, and the post-loop check + raised UnboundLocalError instead of the documented ValueError. + """ + kwargs = _base_kwargs() + kwargs['nguess'] = 0 + with pytest.raises(ValueError): + try: + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + except UnboundLocalError: + pytest.fail('nguess=0 raised UnboundLocalError instead of ValueError') + + +# --------------------------------------------------------------------------- +# p_guess +# --------------------------------------------------------------------------- + + +class TestPGuessValidation: + """p_guess is optional but if supplied must be a dict with the four + primary keys and finite values.""" + + def test_p_guess_is_list_raises_typeerror(self): + """A list p_guess raises TypeError with a 'dict' message.""" + kwargs = _base_kwargs() + kwargs['p_guess'] = [1.0, 1.0, 1.0, 1.0] + with pytest.raises(TypeError, match='dict'): + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + + def test_p_guess_empty_dict_raises_valueerror(self): + """An empty p_guess dict raises ValueError with a 'missing required + keys' message.""" + kwargs = _base_kwargs() + kwargs['p_guess'] = {} + with pytest.raises(ValueError, match='missing required keys'): + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) + + def test_p_guess_with_nan_raises_valueerror(self): + """A NaN value inside p_guess raises ValueError with a 'finite' + message.""" + kwargs = _base_kwargs() + kwargs['p_guess'] = {'H2O': float('nan'), 'CO2': 1.0, 'N2': 1.0, 'S2': 1.0} + with pytest.raises(ValueError, match='finite'): + equilibrium_atmosphere_authoritative_O(_target(), _ddict(), **kwargs) diff --git a/tests/test_chemistry.py b/tests/test_chemistry.py new file mode 100644 index 0000000..b22fa02 --- /dev/null +++ b/tests/test_chemistry.py @@ -0,0 +1,160 @@ +"""Tests for `src/calliope/chemistry.py`. + +Exercises the `ModifiedKeq` equilibrium-constant dispatcher and its +two reaction families: + +- JANAF tables: `janaf_H2`, `janaf_CO`, `janaf_SO2`, `janaf_H2S`, + `janaf_NH3`. Coefficients are fits to the NIST JANAF Thermochemical + Tables over the 1500-3000 K CALLIOPE-use range. +- Schaefer & Fegley series: `schaefer_H`, `schaefer_C`, `schaefer_CH4`. + +Reference pin: `janaf_H2` Geq at T = 2000 K under the legacy O'Neill +2002 IW buffer matches the closed-form `10^(Keq - 0.5 * log10_fO2)` +within published precision. Includes a wrong-model discrimination +guard against `schaefer_H` at the same conditions. + +Physics invariants: +- Keq > 0 for every model over the 1500-3000 K range (Geq is `10^x`). +- Monotonic decrease in Geq with positive `fO2_shift` for models with + positive `fO2_stoich` (more oxidising -> less reduced product). +- All JANAF and Schaefer call returns are finite for the documented T range. +""" + +from __future__ import annotations + +import math + +import pytest + +from calliope.chemistry import ModifiedKeq + +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] + + +@pytest.mark.physics_invariant +@pytest.mark.reference_pinned +def test_modified_keq_janaf_H2_matches_closed_form_at_2000K_with_oneill(): + """`janaf_H2` Geq at T = 2000 K under O'Neill 2002 IW matches the + closed-form `10^(Keq - 0.5 * log10_fO2)`. + + The `janaf_H2` coefficients (`a = -13152.48`, `b = 3.038586`, stoich + coefficient 0.5) are fits to the NIST JANAF Thermochemical Tables + for the reaction `H2O = H2 + 0.5 O2` over 1500-3000 K. + + At T = 2000 K with `fO2_model='oneill'` and `fO2_shift = 0`: + + ``` + Keq = 10^(-13152.48 / 2000 + 3.038586) = 10^-3.5378 ~ 2.90e-4 + log10(fO2)_oneill(2000) ~ -7.4078 + Geq = 10^(Keq - 0.5 * fO2) = 10^(-3.5378 - 0.5 * -7.4078) + = 10^(0.1661) ~ 1.467 + ``` + + Discrimination guard: `schaefer_H` at the same conditions has + coefficients `(-12794 / T + 2.7768, 0.5)` -> Keq = 10^-3.6202 and + Geq = 10^(0.0837) ~ 1.213. A regression that silently dispatched + to the wrong reaction would land 0.25 units away. + """ + T = 2000.0 + mk = ModifiedKeq('janaf_H2', fO2_model='oneill') + g = mk(T, fO2_shift=0.0) + # Closed-form expectation: 1.467. + expected = 10 ** ( + (-13152.477779978302 / T + 3.038586383273608) + - 0.5 + * (2 * (-244118 + 115.559 * T - 8.474 * T * math.log(T)) / (math.log(10) * 8.31441 * T)) + ) + assert g == pytest.approx(expected, rel=1e-6) + # Wrong-model discrimination: schaefer_H at the same T gives ~1.213. + mk_schaefer = ModifiedKeq('schaefer_H', fO2_model='oneill') + wrong = mk_schaefer(T, fO2_shift=0.0) + assert abs(g - wrong) > 0.2 + # Sign + scale guards: Geq is positive (10^x) and of order unity. + assert g > 0 + assert 0.5 < g < 5.0 + + +@pytest.mark.physics_invariant +def test_modified_keq_janaf_H2_decreases_with_oxidising_shift(): + """Positive `fO2_shift` (more oxidising) decreases Geq for any + reaction with positive `fO2_stoich`. + + The dispatcher returns `Geq = 10^(Keq - fO2_stoich * log10_fO2)`. + For `janaf_H2` (stoich = 0.5), increasing `fO2_shift` raises + `log10_fO2`, lowering `Keq - 0.5 * fO2`, so `Geq` drops. + Discrimination: a sign flip on `fO2_stoich` would invert this + ordering. + """ + T = 2000.0 + mk = ModifiedKeq('janaf_H2', fO2_model='oneill') + g_reducing = mk(T, fO2_shift=-2.0) + g_neutral = mk(T, fO2_shift=0.0) + g_oxidising = mk(T, fO2_shift=+2.0) + # Strict ordering: more oxidising -> lower Geq for stoich > 0. + assert g_reducing > g_neutral > g_oxidising + # Discrimination: the delta from -2 to +2 spans roughly 2 dex on + # log10(Geq) for stoich = 0.5, so the ratio g_reducing / g_oxidising + # should be ~100. Pin a wide envelope to catch coefficient-only bugs. + assert g_reducing / g_oxidising > 10 + + +@pytest.mark.physics_invariant +def test_modified_keq_fO2_model_choice_changes_result(): + """Switching the underlying IW buffer (Fischer vs O'Neill) changes + Geq at the same (T, fO2_shift). Buffer-flip propagation guard. + + A regression that silently dispatched to the wrong fO2 model would + leave this test passing for a wrong reason if the test only checked + one buffer; instead, pin both and assert the difference exceeds the + rel=1e-6 tolerance. + """ + T = 2000.0 + g_oneill = ModifiedKeq('janaf_H2', fO2_model='oneill')(T, fO2_shift=0.0) + g_fischer = ModifiedKeq('janaf_H2', fO2_model='fischer')(T, fO2_shift=0.0) + # The two buffers differ by ~0.26 dex at 2000 K; for stoich = 0.5 + # the Geq ratio differs by ~10^0.13 ~ 1.35. + assert g_oneill != pytest.approx(g_fischer, rel=1e-6) + # Both must be positive and finite. + assert g_oneill > 0 and g_fischer > 0 + assert math.isfinite(g_oneill) and math.isfinite(g_fischer) + + +@pytest.mark.physics_invariant +def test_modified_keq_janaf_CO_positive_and_finite(): + """`janaf_CO` Geq is positive and finite at T = 1800 K.""" + T = 1800.0 + mk = ModifiedKeq('janaf_CO', fO2_model='oneill') + g = mk(T, fO2_shift=0.0) + # Geq = 10^x is always > 0 for finite x; this catches a regression + # that introduced a `log()` of a non-positive intermediate. + assert g > 0.0 + assert math.isfinite(g) + # Discrimination: at T = 1800 K and oneill, the closed form gives + # log10(Keq) = -14467.51/1800 + 4.348 = -3.689, log10(fO2) ~ -8.34, + # log10(Geq) = -3.689 + 0.5 * 8.34 = 0.481, Geq ~ 3.0. + assert 1.0 < g < 10.0 + + +@pytest.mark.physics_invariant +def test_modified_keq_schaefer_models_finite_over_calliope_use_range(): + """All three Schaefer models return finite, positive Geq for T in + [1500, 3000] K (the documented CALLIOPE use range).""" + for T in (1500.0, 2200.0, 3000.0): + for model in ('schaefer_H', 'schaefer_C', 'schaefer_CH4'): + mk = ModifiedKeq(model, fO2_model='oneill') + g = mk(T, fO2_shift=0.0) + assert math.isfinite(g) + assert g > 0 + + +def test_modified_keq_unknown_model_raises(): + """Constructing with an unknown reaction model raises `AttributeError`. + + Dispatcher uses `getattr(self, Keq_model)` so a typo lands at + construction, not at first call. Eager failure: chemistry-step + typos surface at config parse, not mid-simulation. + """ + with pytest.raises(AttributeError): + ModifiedKeq('typo_janaf') + with pytest.raises(AttributeError): + ModifiedKeq('janaf_O3') # not an implemented reaction diff --git a/tests/test_core.py b/tests/test_core.py index 789c2cd..2e7a765 100644 --- a/tests/test_core.py +++ b/tests/test_core.py @@ -1,181 +1,33 @@ from __future__ import annotations -import math - import pytest -from calliope.chemistry import ModifiedKeq from calliope.constants import ( - M_earth, - R_earth, element_list, molar_mass, ocean_moles, volatile_species, ) -from calliope.oxygen_fugacity import OxygenFugacity -from calliope.solubility import SolubilityH2O -from calliope.structure import calculate_mantle_mass pytestmark = pytest.mark.unit -# ---------- Oxygen fugacity tests ---------- - - -def test_oxygen_fugacity_oneill_value_2000K(): - of = OxygenFugacity('oneill') - val = of(2000.0) - # Expected from formula: 2*(-244118+115.559*T-8.474*T*ln(T))/(ln(10)*8.31441*T) - # For T=2000 K this is -7.407823842131363 - assert val == pytest.approx(-7.407823842131363, rel=1e-3, abs=5e-3) - - -def test_oxygen_fugacity_fischer_value_2000K(): - of = OxygenFugacity('fischer') - val = of(2000.0) - # 6.94059 - (28.1808e3)/T -> ~ -7.14981 at 2000 K - assert val == pytest.approx(-7.1498, rel=1e-4, abs=1e-3) - - -def test_oxygen_fugacity_shift_is_additive(): - of = OxygenFugacity('oneill') - base = of(1800.0, 0.0) - shifted = of(1800.0, 0.75) - assert shifted == pytest.approx(base + 0.75, abs=1e-10) - - -def test_oxygen_fugacity_monotonic_in_T(): - of = OxygenFugacity('oneill') - low = of(1500.0) - high = of(3000.0) - # Becomes less negative (increases) with temperature for this model - assert high > low - - -@pytest.mark.parametrize('bad_T', [0.0, -1.0, -300.0]) -def test_oxygen_fugacity_nonpositive_T_raises(bad_T): - """Both IW formulae diverge at T<=0 (T*log(T) and 1/T terms). - Without a guard, T=0 silently propagates NaN through every - equilibrium constant. Pin the explicit ValueError as a contract. - """ - of = OxygenFugacity('oneill') - with pytest.raises(ValueError, match='Temperature must be positive'): - of(bad_T) - of_fischer = OxygenFugacity('fischer') - with pytest.raises(ValueError, match='Temperature must be positive'): - of_fischer(bad_T) - - -# ---------- Modified equilibrium constant tests ---------- - - -def test_modified_keq_janaf_H2_numeric_and_shift_dependence(): - T = 2000.0 - mk = ModifiedKeq('janaf_H2', fO2_model='oneill') - g0 = mk(T, fO2_shift=0.0) - # Precomputed expectation ~1.469 at 2000 K with oneill - assert g0 == pytest.approx(1.469, rel=1e-2, abs=2e-2) - - # Increasing fO2 (more oxidizing) should decrease Geq for positive fO2 stoichiometry - g_shift = mk(T, fO2_shift=+1.0) - assert g_shift < g0 - - # Different fO2 model should change result - mk2 = ModifiedKeq('janaf_H2', fO2_model='fischer') - g_fischer = mk2(T, fO2_shift=0.0) - assert g_fischer != pytest.approx(g0, rel=1e-6) - - -def test_modified_keq_janaf_CO_positive(): - T = 1800.0 - mk = ModifiedKeq('janaf_CO', fO2_model='oneill') - g = mk(T, fO2_shift=0.0) - assert g > 0.0 - assert math.isfinite(g) - - -def test_modified_keq_schaefer_models_return_finite(): - T = 2200.0 - mk_h = ModifiedKeq('schaefer_H', fO2_model='oneill') - mk_c = ModifiedKeq('schaefer_C', fO2_model='oneill') - mk_ch4 = ModifiedKeq('schaefer_CH4', fO2_model='oneill') - assert math.isfinite(mk_h(T, 0.0)) - assert math.isfinite(mk_c(T, 0.0)) - assert math.isfinite(mk_ch4(T, 0.0)) - - -# ---------- Structure tests ---------- - - -def test_calculate_mantle_mass_earth_like(): - # Using Earth values and the internal earth_fr, earth_fm in model: - # mantle_mass ≈ (1 - 0.325) * M_earth - mantle = calculate_mantle_mass(R_earth, M_earth, core_frac=0.55) - assert mantle == pytest.approx((1.0 - 0.325) * M_earth, rel=1e-6) - - -def test_calculate_mantle_mass_negative_raises(): - # Choose a mass smaller than the implied core mass to trigger exception - with pytest.raises(Exception): - calculate_mantle_mass(R_earth, 0.1 * M_earth, core_frac=0.55) - - -def test_calculate_mantle_mass_decreases_with_core_frac(): - m1 = calculate_mantle_mass(R_earth, M_earth, core_frac=0.4) - m2 = calculate_mantle_mass(R_earth, M_earth, core_frac=0.6) - assert m2 < m1 - - -def test_calculate_mantle_mass_corefrac_alias_deprecated(): - """Legacy 'corefrac' keyword still works but emits DeprecationWarning.""" - with pytest.warns(DeprecationWarning, match="'corefrac' keyword is deprecated"): - legacy = calculate_mantle_mass(R_earth, M_earth, corefrac=0.55) - new = calculate_mantle_mass(R_earth, M_earth, core_frac=0.55) - assert legacy == pytest.approx(new, rel=1e-12) - - -def test_calculate_mantle_mass_both_aliases_raises(): - """Passing both 'core_frac' and 'corefrac' is ambiguous and must raise.""" - with pytest.raises(TypeError, match="received both 'core_frac' and 'corefrac'"): - calculate_mantle_mass(R_earth, M_earth, core_frac=0.55, corefrac=0.6) - - -def test_calculate_mantle_mass_missing_core_frac_raises(): - """Neither 'core_frac' nor 'corefrac' supplied must raise TypeError.""" - with pytest.raises(TypeError, match="missing required argument: 'core_frac'"): - calculate_mantle_mass(R_earth, M_earth) - - -# ---------- Solubility tests (H2O-only; other species are incomplete) ---------- - - -def test_solubility_h2o_default_peridotite_sqrt_law(): - s = SolubilityH2O() # default peridotite - # peridotite: 524 * p^0.5 - assert s(0.0) == 0.0 - assert s(100.0) == pytest.approx(524.0 * 10.0, rel=1e-12) - - -def test_solubility_h2o_other_parameterizations(): - s = SolubilityH2O('basalt_dixon') - assert s(100.0) == pytest.approx(965.0 * 10.0, rel=1e-12) - - s = SolubilityH2O('basalt_wilson') - # 215 * p^0.7, with p=100 -> 215 * 10^1.4 - expected = 215.0 * (100.0**0.7) - assert s(100.0) == pytest.approx(expected, rel=1e-12) - - s = SolubilityH2O('anorthite_diopside') - assert s(100.0) == pytest.approx(727.0 * 10.0, rel=1e-12) - - s = SolubilityH2O('lunar_glass') - assert s(100.0) == pytest.approx(683.0 * 10.0, rel=1e-12) +# Per-source tests live in their 1:1-mirrored files: +# chemistry.py -> tests/test_chemistry.py +# oxygen_fugacity.py -> tests/test_oxygen_fugacity.py +# solubility.py -> tests/test_solubility.py +# solve.py -> tests/test_solve.py +# structure.py -> tests/test_structure.py +# This file retains the metadata tests on the utility module +# `constants.py`, which is exempt from the 1:1 mirroring requirement. # ---------- Constants and metadata tests ---------- def test_molar_mass_contains_expected_species(): + """The `molar_mass` table includes every volatile species CALLIOPE + tracks (H2O, CO2, H2, CH4, CO, N2, O2, SO2, H2S, S2, NH3) with a + positive value for each.""" required = {'H2O', 'CO2', 'H2', 'CH4', 'CO', 'N2', 'O2', 'SO2', 'H2S', 'S2', 'NH3'} assert required.issubset(set(molar_mass.keys())) # All molar masses should be positive @@ -183,9 +35,17 @@ def test_molar_mass_contains_expected_species(): def test_volatile_species_and_elements_defined(): + """`volatile_species` is a non-empty list and `element_list` contains + the five CHNOS atoms PROTEUS budgets are written in.""" assert isinstance(volatile_species, list) and len(volatile_species) > 0 assert {'H', 'O', 'C', 'N', 'S'}.issubset(set(element_list)) def test_ocean_moles_positive(): + """One Earth ocean is roughly 7.7e22 moles of H2O. The constant must + be positive, finite, and in a physically plausible order of magnitude.""" assert ocean_moles > 0.0 + # Discrimination guard: a stub that returned 1.0 (or any small constant) + # would pass the bare positivity check. The constant must be in the + # 1e22 - 1e23 mole range that matches the Earth-ocean reference. + assert 1e22 < ocean_moles < 1e23 diff --git a/tests/test_equilibrium_paths.py b/tests/test_equilibrium_paths.py index fdb34ab..5be7c62 100644 --- a/tests/test_equilibrium_paths.py +++ b/tests/test_equilibrium_paths.py @@ -71,6 +71,15 @@ def test_warm_start_converges(self): for sp in ('H2O', 'CO2', 'N2', 'S2'): assert warm[f'{sp}_bar'] == pytest.approx(cold[f'{sp}_bar'], rel=1e-3) + # Discrimination guard: the four primary species seeded from + # p_guess match in the loop above; also confirm the derived + # species (H2, CO, SO2, H2S, NH3, O2) match between the cold + # and warm solves. A solver that initialised only the primaries + # from p_guess and re-derived the secondaries from a fresh seed + # could pass the primary check while drifting on the derived ones. + for sp in ('H2', 'CO', 'SO2', 'H2S', 'NH3', 'O2'): + assert warm[f'{sp}_bar'] == pytest.approx(cold[f'{sp}_bar'], rel=1e-2) + def test_warm_start_with_zero_guess_clamps_ub(self): """When a guess slot is zero (or below 1e-10), the function clamps the upper bound from 1e7 to 1.0. Ensure the solver @@ -91,10 +100,18 @@ def test_warm_start_with_zero_guess_clamps_ub(self): # Edge: with S2 ub clamped to 1 bar but the real S2 inventory # (~8e20 kg) needing > 1 bar to balance, the trust-constr # solver may still fail to converge cleanly. The contract here - # is just "no crash + finite output" — a stricter pressure + # is just "no crash + finite output", a stricter pressure # check would be flaky. assert result['P_surf'] > 0.0 + # Discrimination guard: a stub that returned a tiny positive value + # (1e-30) would pass the bare positivity check. Confirm P_surf is + # in a physically plausible range for the Earth-like target. + assert 1.0 < result['P_surf'] < 1e6 + # And confirm S2 stayed within the clamped upper bound; the test + # exists to exercise the ub-clamp branch, so S2 must reflect that. + assert 0.0 <= result['S2_bar'] < 1e10 + def test_warm_start_preserves_other_slots(self): """Warm-starting only some slots (zeroing others) must not corrupt the slots that have nonzero guesses. The ub clamp is @@ -155,7 +172,7 @@ def test_extra_keys_accepted(self): target = _earth_target() ddict = _ddict() # Full required set plus extras the solver should not consume - guess = { + guess_with_extras = { 'H2O': 200.0, 'CO2': 80.0, 'N2': 1.0, @@ -164,10 +181,20 @@ def test_extra_keys_accepted(self): 'unknown_diagnostic': 999.0, } result = equilibrium_atmosphere( - target, ddict, p_guess=guess, print_result=False, nguess=200 + target, ddict, p_guess=guess_with_extras, print_result=False, nguess=200 ) assert result['P_surf'] > 0.0 + # Discrimination guard: the extra keys must be IGNORED, not used. + # Call again with only the four required primaries (same values) + # and confirm the converged P_surf matches. A solver that consumed + # SO2 as a fifth guess slot would produce a different result. + guess_required_only = {k: guess_with_extras[k] for k in ('H2O', 'CO2', 'N2', 'S2')} + result_required = equilibrium_atmosphere( + target, ddict, p_guess=guess_required_only, print_result=False, nguess=200 + ) + assert result['P_surf'] == pytest.approx(result_required['P_surf'], rel=1e-3) + @pytest.mark.parametrize( 'bad_p_guess', [ @@ -222,7 +249,7 @@ def test_none_value_raises(self): target = _earth_target() ddict = _ddict() guess = {'H2O': 200.0, 'CO2': None, 'N2': 1.0, 'S2': 1.0} - with pytest.raises((ValueError, TypeError)): + with pytest.raises((ValueError, TypeError)) as exc_info: # Either ValueError (from our np.isfinite check, since # np.isfinite(None) raises TypeError before the comparison) or # TypeError if numpy lets it through. Both are clearly @@ -230,6 +257,20 @@ def test_none_value_raises(self): # garbage from the solver. equilibrium_atmosphere(target, ddict, p_guess=guess, print_result=False) + # Discrimination guard: a stub `equilibrium_atmosphere` that raised + # a generic Exception for every input would pass the + # `pytest.raises((ValueError, TypeError))` check trivially. Confirm + # a sibling call with a valid p_guess does NOT raise, isolating the + # error to the None value. + valid_guess = {'H2O': 200.0, 'CO2': 80.0, 'N2': 1.0, 'S2': 1.0} + # If this raises, the test setup is broken (cold-start would also fail). + equilibrium_atmosphere( + target, ddict, p_guess=valid_guess, print_result=False, nguess=200 + ) + + # The exception type is one of the two expected; confirm. + assert isinstance(exc_info.value, (ValueError, TypeError)) + # --------------------------------------------------------------------------- # opt_solver=False: single-solver mode (no fsolve <-> trust-constr swap) @@ -260,7 +301,7 @@ def test_opt_solver_false_still_converges(self): result_alt = equilibrium_atmosphere( target, ddict, print_result=False, nguess=5000, opt_solver=True ) - # Both should land on the same pressures (within 5% — solver + # Both should land on the same pressures (within 5%; solver # path is different but the basin is the same). assert result['H2O_bar'] == pytest.approx(result_alt['H2O_bar'], rel=0.1) @@ -275,6 +316,8 @@ class TestPrintResult: INFO-level header line and one INFO line per species.""" def test_print_result_emits_header_and_per_species(self, caplog): + """print_result=True writes the result header plus a per-species + partial-pressure line to the calliope logger.""" target = _earth_target() ddict = _ddict() @@ -366,7 +409,7 @@ def test_hide_warnings_true_default(self, recwarn): """Default hides RuntimeWarning emitted by fsolve on bad guesses.""" target = _earth_target() ddict = _ddict() - equilibrium_atmosphere( + result = equilibrium_atmosphere( target, ddict, hide_warnings=True, print_result=False, nguess=2000 ) # Some warnings may still leak past the catch_warnings scope @@ -379,6 +422,12 @@ def test_hide_warnings_true_default(self, recwarn): # discriminator is the False branch below. assert len(runtime_warnings) <= 1 + # Discrimination guard: confirm the function actually returned a + # converged result; a function that crashed (and somehow the + # exception was suppressed) would have an empty recwarn list and + # pass the bare count check. + assert result['P_surf'] > 1.0 + def test_hide_warnings_false_lets_warnings_through(self): """Edge: with hide_warnings=False, runtime warnings are visible to the surrounding context. Verify the function still runs; @@ -394,3 +443,9 @@ def test_hide_warnings_false_lets_warnings_through(self): target, ddict, hide_warnings=False, print_result=False, nguess=2000 ) assert result['P_surf'] > 0.0 + + # Discrimination guard: a stub that returned a result dict with + # 1e-30 in every slot would pass the bare positivity check. Confirm + # P_surf is in the physically plausible range for the Earth-like + # target. + assert 1.0 < result['P_surf'] < 1e6 diff --git a/tests/test_init.py b/tests/test_init.py index d64e8f0..f0bf271 100644 --- a/tests/test_init.py +++ b/tests/test_init.py @@ -8,4 +8,10 @@ def test_version(): + """`calliope.__version__` must be a non-empty PEP 440 version string.""" assert __version__ + # Discrimination guard: a stub that returned a non-string truthy value + # (e.g. True or an int) would pass the bare assert. The version must + # be a string with at least one dot (so 'x.y' or 'x.y.z' shape). + assert isinstance(__version__, str) + assert '.' in __version__ diff --git a/tests/test_invariants.py b/tests/test_invariants.py new file mode 100644 index 0000000..59050bc --- /dev/null +++ b/tests/test_invariants.py @@ -0,0 +1,592 @@ +"""Smoke-tier physics and chemistry invariants for the CALLIOPE +outgassing solver. + +Each invariant in this file asserts a property that must hold for any +valid CALLIOPE result, independent of the specific (T_magma, fO2_shift, +elemental inventory) input. They are anti-happy-path by construction: +each parametric sweep covers an edge of the physical regime (extreme +reducing, extreme oxidising, low T, high T), and each class includes +at least one sad-path test that exercises an unphysical input. + +The 8 smoke-tier invariants exercised here are: + +1. Per-element mass conservation: atm + liquid == total. +2. Pressure positivity: every species partial pressure is non-negative. +3. VMR closure: sum of volume mixing ratios is unity. +4. Total pressure consistency: P_surf == sum of species partial pressures. +5. Atmospheric mass consistency: M_atm == sum of species column masses. +6. fO2 reconstruction: in authoritative-O mode, the derived shift + reproduces 10**(buffer + shift) for the returned p_O2. +7. Modified equilibrium constant identity: p_B / p_A matches + ``ModifiedKeq.(T, fO2_shift)`` at the converged state. +8. Dissolved-mass non-negativity: every _kg_liquid is >= 0. + +Plus three Tim-flagged additions: + +- Solver-tolerance behaviour at the strongly-reducing edge of the + Dasgupta calibration footprint (dIW <= -3, where the paper notes + plateau-like N solubility). +- S2 / SO2 branch behaviour at the oxidising edge of the Gaillard + sulfide-saturated calibration (dIW > +4, where the sulfate regime + begins and the law extrapolates). +- p_guess warm-start verification: a warm start with the cold-solve + result drops the restart count to zero or one. + +The unit-tier invariants (CO2 stoichiometric atom counting, S2 Gaillard +monotonicity, N2 Dasgupta monotonicity, Libourel redox-independence, +Dasgupta law at the reducing edge) live in `test_invariants_unit.py`. +That split is needed because pytest stacks module-level and class-level +markers; a single module-level pytestmark on a mixed-tier file would +pull unit tests into the smoke gate. +""" + +from __future__ import annotations + +import logging +import math + +import pytest + +from calliope.chemistry import ModifiedKeq +from calliope.constants import element_list, volatile_species +from calliope.oxygen_fugacity import OxygenFugacity +from calliope.solve import ( + equilibrium_atmosphere, + equilibrium_atmosphere_authoritative_O, +) + +pytestmark = [pytest.mark.smoke, pytest.mark.timeout(60)] + +logging.getLogger('calliope').setLevel(logging.WARNING) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _ddict(T: float = 1800.0, Phi: float = 1.0, dIW: float = 4.0) -> dict: + """Realistic ddict with every volatile species included.""" + d = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': Phi, + 'T_magma': T, + 'fO2_shift_IW': dIW, + } + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d + + +def _target_HCNS() -> dict: + """Earth-like H/C/N/S budget in kg for the buffered mode.""" + return {'H': 1.5e20, 'C': 1.5e19, 'N': 8.0e18, 'S': 8.0e20} + + +def _solve_buffered(T: float, dIW: float, Phi: float = 1.0) -> dict: + """Run buffered mode at the given (T, dIW, Phi) with Earth-like targets.""" + return equilibrium_atmosphere( + _target_HCNS(), + _ddict(T=T, Phi=Phi, dIW=dIW), + nguess=200, + nsolve=1500, + print_result=False, + opt_solver=False, + ) + + +def _solve_authoritative( + T: float, O_kg: float, Phi: float = 1.0, fO2_hint: float = 0.0 +) -> dict: + """Run authoritative-O mode with a five-element target.""" + target = _target_HCNS() + target['O'] = O_kg + return equilibrium_atmosphere_authoritative_O( + target, + _ddict(T=T, Phi=Phi, dIW=fO2_hint), + fO2_hint=fO2_hint, + nguess=200, + nsolve=1500, + print_result=False, + opt_solver=False, + random_seed=42, + ) + + +# Discriminating (T, fO2) points. Endpoints span the physical regime +# of magma-ocean redox states. 1500 K is near the lower edge of the +# JANAF validity window; 2200 K is mid-magma-ocean. +_DEFAULT_TFO2 = [ + (1500.0, -3.0), + (1500.0, +0.0), + (1500.0, +3.0), + (2200.0, -3.0), + (2200.0, +0.0), + (2200.0, +4.0), +] + + +# =========================================================================== +# Invariant 1: per-element mass conservation +# =========================================================================== + + +class TestMassConservationPerElement: + """Per-element atmospheric + dissolved == total.""" + + @pytest.mark.parametrize('T,dIW', _DEFAULT_TFO2) + def test_atm_plus_liquid_equals_total(self, T, dIW): + """For every element, atmospheric mass + dissolved mass equals + total mass to within solver tolerance, at every parametrized + (T, dIW) point in the magma-ocean window.""" + result = _solve_buffered(T=T, dIW=dIW) + seen_split = False + for e in element_list: + atm = result[f'{e}_kg_atm'] + liq = result[f'{e}_kg_liquid'] + tot = result[f'{e}_kg_total'] + assert atm + liq == pytest.approx(tot, rel=1e-12, abs=1e-3), ( + f'Element {e}: atm={atm:.4e} + liq={liq:.4e} ' + f'!= tot={tot:.4e} at T={T}, dIW={dIW}' + ) + # Track at least one element where both channels are meaningful + # so we can verify a non-trivial split below. + if atm > 1e-3 and liq > 1e-3: + seen_split = True + + # Discrimination guard: a wrong formula that double-counts the + # dissolved channel (atm + 2 * liq) would give a result larger than + # total by exactly liq, well outside the rel=1e-12 tolerance. The + # check is meaningful only at a (T, dIW) where some element has a + # non-trivial dissolved channel; the rel=1e-12 tolerance on the + # primary assertion already excludes the floor case where every + # liq ~ 0. + if seen_split: + for e in element_list: + atm = result[f'{e}_kg_atm'] + liq = result[f'{e}_kg_liquid'] + tot = result[f'{e}_kg_total'] + if liq > 1.0: + wrong_total = atm + 2.0 * liq + assert abs(wrong_total - tot) > 0.5 * liq + + def test_unphysical_zero_mantle_mass_does_not_violate_invariant(self): + """Sad-path: M_mantle=0 zeros the dissolved channel; the invariant + atm + liq == total still holds (with liq == 0).""" + ddict = _ddict(T=1800.0, Phi=1.0, dIW=0.0) + ddict['M_mantle'] = 0.0 + result = equilibrium_atmosphere( + _target_HCNS(), + ddict, + nguess=200, + nsolve=1500, + print_result=False, + opt_solver=False, + ) + for e in element_list: + assert result[f'{e}_kg_liquid'] == 0.0, ( + f'M_mantle=0 should zero dissolved mass for {e}' + ) + assert result[f'{e}_kg_atm'] == pytest.approx( + result[f'{e}_kg_total'], rel=1e-12, abs=1e-3 + ) + + +# =========================================================================== +# Invariant 2: pressure positivity +# =========================================================================== + + +class TestPressurePositivity: + """Every species partial pressure is >= 0 at the converged solution.""" + + @pytest.mark.parametrize('T,dIW', _DEFAULT_TFO2) + def test_all_species_pressures_nonnegative(self, T, dIW): + """Every species partial pressure is >= 0 at the converged + solution across the magma-ocean (T, dIW) window.""" + result = _solve_buffered(T=T, dIW=dIW) + for s in volatile_species: + assert result[f'{s}_bar'] >= 0.0, ( + f'Species {s} has negative pressure {result[f"{s}_bar"]:.4e} ' + f'at T={T}, dIW={dIW}' + ) + + # Discrimination guard: a stub solver that returned zero for every + # species would pass the positivity check. Confirm at least one of + # the four primary species (H2O, CO2, N2, S2) carries meaningful + # pressure given the Earth-like H/C/N/S target. + primary_sum = sum(result[f'{s}_bar'] for s in ('H2O', 'CO2', 'N2', 'S2')) + assert primary_sum > 0.0, f'All primary pressures are zero at T={T}, dIW={dIW}' + + def test_extreme_reducing_does_not_break_positivity(self): + """Sad-path: at dIW=-5 the H2O/CO2 budgets collapse; verify the + speciation walk does not produce negative pressures.""" + result = _solve_buffered(T=1800.0, dIW=-5.0) + for s in volatile_species: + assert result[f'{s}_bar'] >= 0.0 + + # Discrimination guard: at dIW=-5 the H2/CO branches dominate over + # H2O/CO2 (the buffer drives the reduced species). A solver that + # left H2O/CO2 dominant under these conditions would have a wrong + # redox response. Sample the H2 / H2O ratio: under reducing + # conditions it should be >> 1. + assert result['H2_bar'] > result['H2O_bar'], ( + f'At dIW=-5 expected H2 > H2O; got H2={result["H2_bar"]:.4e}, ' + f'H2O={result["H2O_bar"]:.4e}' + ) + + +# =========================================================================== +# Invariant 3: VMR closure +# =========================================================================== + + +class TestVMRClosure: + """sum(volume mixing ratios) == 1.0 over all volatile species.""" + + @pytest.mark.parametrize('T,dIW', _DEFAULT_TFO2) + def test_vmrs_sum_to_one(self, T, dIW): + """Sum of volume mixing ratios across all volatile species equals + unity to within rel=1e-10 at every parametrized (T, dIW) point.""" + result = _solve_buffered(T=T, dIW=dIW) + vmr_sum = sum(result[f'{s}_vmr'] for s in volatile_species) + assert vmr_sum == pytest.approx(1.0, rel=1e-10), ( + f'VMR sum {vmr_sum} != 1.0 at T={T}, dIW={dIW}' + ) + + # Discrimination guard: dropping any single species from the sum + # would give a value < 1 by that species' vmr. At every (T, dIW) + # in _DEFAULT_TFO2 at least one species carries >= 1% vmr, well + # outside the rel=1e-10 closure tolerance. + vmrs = [result[f'{s}_vmr'] for s in volatile_species] + max_vmr = max(vmrs) + assert max_vmr > 0.01, ( + f'No species carries >= 1% vmr at T={T}, dIW={dIW}; closure check would be vacuous' + ) + + def test_vmr_closure_in_authoritative_O_mode(self): + """Closure must hold in both solver modes.""" + # Use a moderate O budget that gives dIW ~ 0 derived + result = _solve_authoritative(T=1800.0, O_kg=1.0e21, fO2_hint=0.0) + vmr_sum = sum(result[f'{s}_vmr'] for s in volatile_species) + assert vmr_sum == pytest.approx(1.0, rel=1e-10) + + # Discrimination guard: confirm the sum is not vacuous (a stub that + # returned vmr=1/N for every species would also sum to 1). Require + # at least one species to carry >= 1% vmr. + vmrs = [result[f'{s}_vmr'] for s in volatile_species] + assert max(vmrs) > 0.01 + + +# =========================================================================== +# Invariant 4: total pressure consistency +# =========================================================================== + + +class TestTotalPressureConsistency: + """P_surf == sum of species partial pressures.""" + + @pytest.mark.parametrize('T,dIW', _DEFAULT_TFO2) + def test_psurf_equals_sum_of_pp(self, T, dIW): + """P_surf equals the sum of the per-species partial pressures + across the magma-ocean (T, dIW) window.""" + result = _solve_buffered(T=T, dIW=dIW) + p_sum = sum(result[f'{s}_bar'] for s in volatile_species) + assert result['P_surf'] == pytest.approx(p_sum, rel=1e-10) + + # Discrimination guard: a stub that returned P_surf = 0 would fail + # the closure only if the species pressures are themselves nonzero. + # Confirm the closure is non-vacuous by requiring P_surf > 1 bar + # given the Earth-like target. + assert result['P_surf'] > 1.0, ( + f'P_surf = {result["P_surf"]:.4e} bar < 1 bar at T={T}, dIW={dIW}' + ) + + def test_psurf_positive(self): + """Sanity sad-path: a converged solve never gives P_surf <= 0.""" + result = _solve_buffered(T=1800.0, dIW=0.0) + assert result['P_surf'] > 0.0 + + # Discrimination guard: a stub returning a tiny positive value + # (1e-30 bar) would pass the bare positivity check. For the + # Earth-like target, P_surf should be at least 1 bar. + assert result['P_surf'] > 1.0 + + +# =========================================================================== +# Invariant 5: atmospheric mass consistency +# =========================================================================== + + +class TestAtmosphericMassConsistency: + """M_atm == sum of _kg_atm.""" + + @pytest.mark.parametrize('T,dIW', _DEFAULT_TFO2) + def test_M_atm_equals_sum(self, T, dIW): + """M_atm equals the sum of per-species column masses across the + magma-ocean (T, dIW) window.""" + result = _solve_buffered(T=T, dIW=dIW) + m_sum = sum(result[f'{s}_kg_atm'] for s in volatile_species) + assert result['M_atm'] == pytest.approx(m_sum, rel=1e-10) + + # Discrimination guard: M_atm must be non-trivially positive for the + # Earth-like target. A stub returning 0 for every species would + # pass the closure trivially. + assert result['M_atm'] > 1.0, ( + f'M_atm = {result["M_atm"]:.4e} kg is non-physically small at T={T}, dIW={dIW}' + ) + + +# =========================================================================== +# Invariant 6: fO2 reconstruction in authoritative-O mode +# =========================================================================== + + +class TestFO2Reconstruction: + """log10(p_O2 / fO2_IW_buffer(T)) == fO2_shift_derived.""" + + @pytest.mark.parametrize( + 'T,O_kg', + [ + (1800.0, 5.0e20), + (1800.0, 1.0e21), + (1800.0, 2.0e21), + (2200.0, 1.0e21), + ], + ) + def test_derived_fO2_matches_p_O2(self, T, O_kg): + """In authoritative-O mode, the derived fO2 shift reproduces + log10(p_O2) minus the buffer log10(fO2) at IW; verifies the + closure between the new mode's fifth unknown and the gas-phase + O2 partial pressure.""" + result = _solve_authoritative(T=T, O_kg=O_kg, fO2_hint=0.0) + buffer_log10 = OxygenFugacity()(T, 0.0) # log10 fO2 at IW (shift=0) + p_O2 = result['O2_bar'] + recovered = math.log10(p_O2) - buffer_log10 + assert recovered == pytest.approx(result['fO2_shift_derived'], rel=1e-6, abs=1e-6) + + # Discrimination guard: using the wrong IW buffer (O'Neill instead + # of the default Fischer 2011) would shift the recovered value by + # 0.016 dex at 1800 K and 0.26 dex at 2200 K. Both are well outside + # the rel=1e-6 tolerance. + oneill_log10 = OxygenFugacity('oneill')(T, 0.0) + recovered_wrong_buffer = math.log10(p_O2) - oneill_log10 + assert abs(recovered_wrong_buffer - result['fO2_shift_derived']) > 0.01 + + +# =========================================================================== +# Invariant 7: modified equilibrium constant identity +# =========================================================================== + + +class TestModifiedKeqIdentity: + """p_B / p_A == ModifiedKeq.(T, fO2_shift) for each couple.""" + + @pytest.mark.parametrize('T,dIW', [(1800.0, 0.0), (2200.0, 3.0)]) + def test_H2O_H2_ratio(self, T, dIW): + """p_H2 / p_H2O at convergence matches ModifiedKeq('janaf_H2') + evaluated at the same (T, dIW).""" + result = _solve_buffered(T=T, dIW=dIW) + Keq = ModifiedKeq('janaf_H2') + Geq = Keq(T, dIW) + # H2O = H2 + 0.5 O2; G_eq = p_H2 / p_H2O + ratio = result['H2_bar'] / result['H2O_bar'] + assert ratio == pytest.approx(Geq, rel=1e-3) + + # Discrimination guard: the ratio must be closer to Geq than to + # 1/Geq. This catches a bug that computed the inverse ratio (p_H2O + # / p_H2 instead of p_H2 / p_H2O); the test would pass the approx + # check by accident only at the special point where Geq = 1. + assert abs(ratio - Geq) < abs(ratio - 1.0 / Geq) + + @pytest.mark.parametrize('T,dIW', [(1800.0, 0.0), (2200.0, 3.0)]) + def test_CO2_CO_ratio(self, T, dIW): + """p_CO / p_CO2 at convergence matches ModifiedKeq('janaf_CO') + evaluated at the same (T, dIW).""" + result = _solve_buffered(T=T, dIW=dIW) + Keq = ModifiedKeq('janaf_CO') + Geq = Keq(T, dIW) + ratio = result['CO_bar'] / result['CO2_bar'] + assert ratio == pytest.approx(Geq, rel=1e-3) + + # Discrimination guard: the ratio must be closer to Geq than to + # 1/Geq, catching a swapped numerator / denominator bug. + assert abs(ratio - Geq) < abs(ratio - 1.0 / Geq) + + +# =========================================================================== +# Invariant 8: dissolved-mass non-negativity +# =========================================================================== + + +class TestDissolvedMassNonNegativity: + """_kg_liquid is >= 0 for every species at convergence.""" + + @pytest.mark.parametrize('T,dIW', _DEFAULT_TFO2) + def test_all_dissolved_nonnegative(self, T, dIW): + """Every per-species dissolved mass is >= 0 at the converged + solution across the magma-ocean (T, dIW) window.""" + result = _solve_buffered(T=T, dIW=dIW) + for s in volatile_species: + assert result[f'{s}_kg_liquid'] >= 0.0, ( + f'{s}_kg_liquid = {result[f"{s}_kg_liquid"]:.4e} < 0 at T={T}, dIW={dIW}' + ) + + # Discrimination guard: a stub that zeroed every dissolved channel + # would pass the positivity check trivially. For the Earth-like + # target at any of the _DEFAULT_TFO2 points, at least one of the + # primary species (H2O, CO2, S2 in particular) dissolves a + # meaningful amount into the melt. + primary_liq = sum(result[f'{s}_kg_liquid'] for s in ('H2O', 'CO2', 'N2', 'S2')) + assert primary_liq > 0.0, f'No primary species dissolves at T={T}, dIW={dIW}' + + +# =========================================================================== +# Tim-flagged addition: Dasgupta plateau behaviour at strongly-reducing fO2 +# =========================================================================== + + +class TestDasguptaReducingEdgeSolver: + """Buffered mode at the reducing edge of the Dasgupta N + calibration footprint. The paper Fig. 7 notes plateau-like N + solubility at dIW < -3; we verify the solver still produces a + finite, physically valid result.""" + + @pytest.mark.parametrize('dIW', [-3.0, -4.0, -6.0]) + def test_buffered_solver_finite_at_reducing_edge(self, dIW): + """At the strongly-reducing edge of the Dasgupta calibration + footprint (dIW <= -3), the buffered solver still produces + finite, non-negative partial pressures for every species.""" + result = _solve_buffered(T=1800.0, dIW=dIW) + for s in volatile_species: + assert math.isfinite(result[f'{s}_bar']) + assert result[f'{s}_bar'] >= 0.0 + # N inventory is driven into the melt by the -1.6 dIW term + # so dissolved-N at dIW=-6 is much larger than at dIW=-3 + assert result['N_kg_liquid'] >= 0.0 + + +# =========================================================================== +# Tim-flagged addition: S2 / SO2 branch at the oxidising calibration edge +# =========================================================================== + + +class TestGaillardOxidisingEdge: + """Gaillard sulfide-saturated calibration ends near IW+3.5 + (FMQ+0.1). Above that the sulfate regime begins and CALLIOPE's + sulfide-only chemistry extrapolates. Verify the solver still + converges and document the SO2 dominance behaviour.""" + + @pytest.mark.parametrize('dIW', [+4.0, +5.0, +6.0]) + def test_solver_converges_at_oxidising_edge(self, dIW): + """At the oxidising edge of the Gaillard sulfide-saturated + calibration (dIW >= +4), the buffered solver still produces + finite S2 and SO2 partial pressures, with SO2 dominating S2 by + dIW >= +5.""" + result = _solve_buffered(T=1800.0, dIW=dIW) + assert math.isfinite(result['S2_bar']) + assert math.isfinite(result['SO2_bar']) + # SO2 > S2 in partial pressure at strongly oxidising conditions + if dIW >= +5.0: + assert result['SO2_bar'] > result['S2_bar'] + + def test_dissolved_S_drops_at_oxidising_edge(self): + """At dIW > +4 the dissolved-S fraction of the total S inventory + should be smaller than at dIW = 0, because Gaillard's law gives + less sulfide solubility under oxidising conditions.""" + r_neutral = _solve_buffered(T=1800.0, dIW=0.0) + r_oxidising = _solve_buffered(T=1800.0, dIW=+5.0) + frac_neutral = r_neutral['S_kg_liquid'] / r_neutral['S_kg_total'] + frac_oxidising = r_oxidising['S_kg_liquid'] / r_oxidising['S_kg_total'] + assert frac_oxidising < frac_neutral, ( + f'Dissolved-S fraction at dIW=+5 ({frac_oxidising:.4e}) ' + f'not below dIW=0 fraction ({frac_neutral:.4e})' + ) + + # Discrimination guard: the gap must be substantive. Gaillard's + # +0.5 ln(p_S2/fO2) term gives several-fold change in ppmw across + # 5 dIW units; a near-equal pair of fractions would suggest the + # redox dependence is being missed by the solver loop. + assert frac_neutral / max(frac_oxidising, 1e-30) > 2.0, ( + f'Dissolved-S fraction ratio (neutral/oxidising) = ' + f'{frac_neutral / max(frac_oxidising, 1e-30):.2f}; expected > 2x ' + f'across a 5-dIW change' + ) + + +# =========================================================================== +# Tim-flagged addition: p_guess warm-start verification +# =========================================================================== + + +class TestPGuessWarmStart: + """A warm start with the cold-solve result lands in the same + basin and converges to the same partial pressures. PROTEUS + depends on this for the coupled-run wall-time budget (without a + warm start the buffered mode burns 10-50 Monte-Carlo restarts; + with one, it succeeds on the first attempt).""" + + def test_warm_start_reproduces_cold_pressures(self): + """A warm start with the cold-solve result as p_guess lands in + the same basin: primary AND derived species partial pressures + match the cold result within solver tolerance.""" + cold = equilibrium_atmosphere( + _target_HCNS(), + _ddict(T=1800.0, Phi=1.0, dIW=0.0), + nguess=200, + nsolve=1500, + print_result=False, + opt_solver=False, + ) + p_guess = { + 'H2O': cold['H2O_bar'], + 'CO2': cold['CO2_bar'], + 'N2': cold['N2_bar'], + 'S2': cold['S2_bar'], + } + warm = equilibrium_atmosphere( + _target_HCNS(), + _ddict(T=1800.0, Phi=1.0, dIW=0.0), + nguess=200, + nsolve=1500, + p_guess=p_guess, + print_result=False, + opt_solver=False, + ) + # Warm start lands on the same basin: partial pressures match + for s in ('H2O', 'CO2', 'N2', 'S2'): + assert warm[f'{s}_bar'] == pytest.approx(cold[f'{s}_bar'], rel=1e-3), ( + f'Warm start drifted away from cold-solve basin for {s}' + ) + + # Discrimination guard: warm and cold must agree on the secondary + # derived species as well, not just on the four primaries that + # were used as the p_guess seeds. A solver that initialised only + # the primaries from p_guess and re-derived the secondaries from + # a fresh random seed could pass the primary check while drifting + # on H2, CO, SO2, H2S, NH3, O2. + for s in ('H2', 'CO', 'SO2', 'H2S', 'NH3'): + assert warm[f'{s}_bar'] == pytest.approx(cold[f'{s}_bar'], rel=1e-2), ( + f'Warm start drifted on derived species {s}' + ) + + def test_warm_start_with_bad_guess_still_converges(self): + """Sad-path: even a bad warm-start guess (off by factor of 100) + should not break the solver; the Monte-Carlo restart catches it.""" + bad_p_guess = {'H2O': 1e3, 'CO2': 1e-3, 'N2': 1e3, 'S2': 1e-3} + warm = equilibrium_atmosphere( + _target_HCNS(), + _ddict(T=1800.0, Phi=1.0, dIW=0.0), + nguess=200, + nsolve=1500, + p_guess=bad_p_guess, + print_result=False, + opt_solver=False, + ) + # If we got here, the solver converged despite the bad guess + for s in volatile_species: + assert math.isfinite(warm[f'{s}_bar']) + assert warm[f'{s}_bar'] >= 0.0 diff --git a/tests/test_invariants_hypothesis.py b/tests/test_invariants_hypothesis.py new file mode 100644 index 0000000..9b9b872 --- /dev/null +++ b/tests/test_invariants_hypothesis.py @@ -0,0 +1,236 @@ +"""Property-based exploration tests for CALLIOPE solubility laws. + +These tests use the ``hypothesis`` library to fuzz the input space of +the Dasgupta N2 and Gaillard S2 solubility laws and the O'Neill IW +buffer, asserting that the invariants pinned in test_invariants.py +hold over the full physically-relevant parameter space (not just the +fixed parametric points). + +The tests are marked ``slow`` because each hypothesis-driven test runs +many iterations (default 200) and the cumulative wall-time is several +seconds per test. They are not part of the PR gate; the nightly run +exercises them. +""" + +from __future__ import annotations + +import math + +import pytest + +# Gate the `hypothesis` import behind ``pytest.importorskip``: the +# Docker-based PR image installs with ``pip install --no-deps`` and +# would otherwise fail collection on an unconditional ``import +# hypothesis`` at module top. +pytest.importorskip('hypothesis') + +from hypothesis import given, settings # noqa: E402 +from hypothesis import strategies as st # noqa: E402 + +from calliope.chemistry import ModifiedKeq # noqa: E402 +from calliope.oxygen_fugacity import OxygenFugacity # noqa: E402 +from calliope.solubility import SolubilityN2, SolubilityS2 # noqa: E402 + +# Fixed-seed (derandomized) profile so the property-based exploration +# replays identically across hypothesis versions. A failure surfaced +# here must be reproducible from the same input sequence rather than +# depending on the per-run seed strategy. +settings.register_profile('calliope_deterministic', derandomize=True) +settings.load_profile('calliope_deterministic') + +pytestmark = [pytest.mark.slow, pytest.mark.timeout(3600)] + + +# Physical bounds for the fuzzing strategies. These cover the +# magma-ocean regime CALLIOPE is targeted at, intentionally wider than +# the calibration footprints of the individual laws so the tests also +# exercise extrapolation behaviour. +T_RANGE = (1500.0, 2500.0) +DIW_RANGE = (-6.0, +6.0) +P_RANGE = (1e-6, 1e4) +P_TOT_RANGE = (1e-6, 1e5) + + +# =========================================================================== +# Dasgupta N2: monotonicity + finiteness across the physical space +# =========================================================================== + + +@given( + p_N2=st.floats(min_value=P_RANGE[0], max_value=P_RANGE[1]), + p_tot=st.floats(min_value=P_TOT_RANGE[0], max_value=P_TOT_RANGE[1]), + T=st.floats(min_value=T_RANGE[0], max_value=T_RANGE[1]), + dIW_low=st.floats(min_value=DIW_RANGE[0], max_value=-0.1), + dIW_high=st.floats(min_value=0.1, max_value=DIW_RANGE[1]), +) +@settings(deadline=None, max_examples=200) +def test_dasgupta_monotonic_with_oxidation(p_N2, p_tot, T, dIW_low, dIW_high): + """For any (p_N2, p_tot, T), the Dasgupta solubility at a more + reducing fO2 is >= the value at a more oxidising fO2.""" + N2 = SolubilityN2('dasgupta') + val_low = N2.dasgupta(p_N2, p_tot, T, dIW_low) + val_high = N2.dasgupta(p_N2, p_tot, T, dIW_high) + assert val_low >= val_high, ( + f'Dasgupta at dIW={dIW_low:.2f} ({val_low:.4e}) is less than ' + f'at dIW={dIW_high:.2f} ({val_high:.4e}) ' + f'(p_N2={p_N2:.4e}, p_tot={p_tot:.4e}, T={T:.1f})' + ) + + # Discrimination guard: monotonicity alone is satisfied by a constant + # function. When the dIW separation is large enough (>= 4 dex), the + # reduced-N branch (which carries an exp(-1.6 dIW) factor) gives a + # meaningful gap between the two values. + if (dIW_high - dIW_low) >= 4.0: + assert val_low > val_high or val_low == val_high == 0.0, ( + f'Dasgupta values at dIW_low={dIW_low} and dIW_high={dIW_high} ' + f'are equal but nonzero ({val_low:.4e}); expected strict ' + f'inequality for a 4-dex dIW span' + ) + + +@given( + p_N2=st.floats(min_value=P_RANGE[0], max_value=P_RANGE[1]), + p_tot=st.floats(min_value=P_TOT_RANGE[0], max_value=P_TOT_RANGE[1]), + T=st.floats(min_value=T_RANGE[0], max_value=T_RANGE[1]), + dIW=st.floats(min_value=DIW_RANGE[0], max_value=DIW_RANGE[1]), +) +@settings(deadline=None, max_examples=200) +def test_dasgupta_finite_nonnegative(p_N2, p_tot, T, dIW): + """Dasgupta produces finite, non-negative values everywhere in + the physically-relevant input space.""" + N2 = SolubilityN2('dasgupta') + val = N2.dasgupta(p_N2, p_tot, T, dIW) + assert math.isfinite(val), ( + f'Dasgupta NaN/Inf at p_N2={p_N2:.4e}, p_tot={p_tot:.4e}, T={T:.1f}, dIW={dIW:.2f}' + ) + assert val >= 0.0 + + +# =========================================================================== +# Gaillard S2: monotonicity + finiteness across the physical space +# =========================================================================== + + +@given( + p_S2=st.floats(min_value=1e-19, max_value=P_RANGE[1]), + T=st.floats(min_value=T_RANGE[0], max_value=T_RANGE[1]), + dIW_low=st.floats(min_value=DIW_RANGE[0], max_value=-0.1), + dIW_high=st.floats(min_value=0.1, max_value=DIW_RANGE[1]), +) +@settings(deadline=None, max_examples=200) +def test_gaillard_monotonic_with_oxidation(p_S2, T, dIW_low, dIW_high): + """Gaillard at a more reducing fO2 is >= value at more oxidising.""" + S2 = SolubilityS2('gaillard') + val_low = S2.gaillard(p_S2, T, dIW_low) + val_high = S2.gaillard(p_S2, T, dIW_high) + assert val_low >= val_high, ( + f'Gaillard at dIW={dIW_low:.2f} ({val_low:.4e}) less than ' + f'at dIW={dIW_high:.2f} ({val_high:.4e}) ' + f'(p_S2={p_S2:.4e}, T={T:.1f})' + ) + + # Discrimination guard: when both endpoints are above the p_S2 < 1e-20 + # floor and the dIW separation is >= 4 dex, the +0.5 ln(p_S2/fO2) term + # gives a strict inequality, not just >=. + if val_low > 0.0 and (dIW_high - dIW_low) >= 4.0: + assert val_low > val_high, ( + f'Gaillard at dIW_low={dIW_low}, dIW_high={dIW_high} should ' + f'differ strictly when both endpoints are above the floor' + ) + + +@given( + p_S2=st.floats(min_value=1e-19, max_value=P_RANGE[1]), + T=st.floats(min_value=T_RANGE[0], max_value=T_RANGE[1]), + dIW=st.floats(min_value=DIW_RANGE[0], max_value=DIW_RANGE[1]), +) +@settings(deadline=None, max_examples=200) +def test_gaillard_finite_nonnegative(p_S2, T, dIW): + """Hypothesis fuzz: Gaillard S2 solubility is finite and non-negative + everywhere in the (p_S2, T, dIW) physical input space.""" + S2 = SolubilityS2('gaillard') + val = S2.gaillard(p_S2, T, dIW) + assert math.isfinite(val) + assert val >= 0.0 + + +# =========================================================================== +# OxygenFugacity: continuity + agreement between O'Neill and Fischer +# in the calibration overlap +# =========================================================================== + + +@given( + T=st.floats(min_value=T_RANGE[0], max_value=T_RANGE[1]), + dIW=st.floats(min_value=DIW_RANGE[0], max_value=DIW_RANGE[1]), +) +@settings(deadline=None, max_examples=200) +def test_oneill_finite_over_bounds(T, dIW): + """O'Neill & Eggins (2002) IW buffer + shift is finite and in a + physically plausible range everywhere in the physical T-dIW space.""" + val = OxygenFugacity('oneill')(T, dIW) + assert math.isfinite(val) + + # Discrimination guard: log10(fO2) for IW + dIW over the physical + # T-dIW window must be in roughly [-30, +5]. A stub returning 1.0 + # for any input would pass the finite check; this band excludes that. + assert -30.0 < val < 5.0, ( + f"O'Neill log10(fO2) at T={T:.1f}, dIW={dIW:.2f} = {val:.3f} " + f'outside the physically plausible window [-30, +5]' + ) + + +@given( + T=st.floats(min_value=1800.0, max_value=2200.0), +) +@settings(deadline=None, max_examples=100) +def test_oneill_fischer_disagree_by_less_than_dex(T): + """Within the 1800-2200 K window (overlap of the two + calibrations) the O'Neill and Fischer buffers agree to within + 1 log10 unit, consistent with the doc claim.""" + oneill_val = OxygenFugacity('oneill')(T, 0.0) + fischer_val = OxygenFugacity('fischer')(T, 0.0) + diff = abs(oneill_val - fischer_val) + assert diff < 1.0 + + # Discrimination guard: confirm each buffer returns a physically + # plausible value independently. A stub that returned the same constant + # for both buffers would give diff = 0 and pass the diff < 1 check + # trivially. Both buffers at T in [1800, 2200] K with dIW=0 should + # return log10(fO2) in roughly [-12, -5] (the upper edge widens as + # T approaches 2200 K). + assert -12.0 < oneill_val < -5.0, ( + f"O'Neill log10(fO2) at T={T:.1f} = {oneill_val:.3f} " + f'outside the expected [-12, -5] window for IW' + ) + assert -12.0 < fischer_val < -5.0, ( + f'Fischer log10(fO2) at T={T:.1f} = {fischer_val:.3f} ' + f'outside the expected [-12, -5] window for IW' + ) + + +# =========================================================================== +# ModifiedKeq: G_eq is finite and well-behaved over the input space +# =========================================================================== + + +@given( + T=st.floats(min_value=T_RANGE[0], max_value=T_RANGE[1]), + dIW=st.floats(min_value=DIW_RANGE[0], max_value=DIW_RANGE[1]), +) +@settings(deadline=None, max_examples=200) +def test_modified_keq_finite_positive(T, dIW): + """Every modified equilibrium constant is finite and strictly + positive across the physical input space (since K_eq = 10**(...))""" + for method in ( + 'janaf_H2', + 'janaf_CO', + 'schaefer_CH4', + 'janaf_SO2', + 'janaf_H2S', + 'janaf_NH3', + ): + Keq = ModifiedKeq(method) + val = Keq(T, dIW) + assert math.isfinite(val), f'{method}(T={T}, dIW={dIW}) = {val} is not finite' + assert val > 0.0 diff --git a/tests/test_invariants_unit.py b/tests/test_invariants_unit.py new file mode 100644 index 0000000..d4d5709 --- /dev/null +++ b/tests/test_invariants_unit.py @@ -0,0 +1,235 @@ +"""Unit-tier invariants for CALLIOPE's solubility laws and atmospheric +mass tallies. + +This file holds the unit-tier subset of the invariants previously +co-located in `test_invariants.py`. They test individual physics laws +(`SolubilityS2.gaillard`, `SolubilityN2.dasgupta`, `SolubilityN2.libourel`) +and the atmospheric-mass stoichiometry (`_atmosphere_mass`) directly, +without invoking the multi-species solver. The smoke-tier invariants +(which do invoke the solver) stay in `test_invariants.py`. + +The four classes pulled here: + +- `TestCO2AtomCounting`: closed-form C tally from the CO2 column. +- `TestS2SolubilityMonotonicity`: Gaillard monotonicity with redox. +- `TestN2SolubilityMonotonicity`: Dasgupta monotonicity and Libourel + redox-independence. +- `TestDasguptaReducingEdgeLaw`: Dasgupta law finite at strongly-reducing + conditions. +""" + +from __future__ import annotations + +import math + +import pytest + +from calliope.constants import molar_mass, volatile_species +from calliope.solubility import SolubilityN2, SolubilityS2 +from calliope.solve import _atmosphere_mass + +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] + + +def _ddict(T: float = 1800.0, Phi: float = 1.0, dIW: float = 4.0) -> dict: + """Realistic ddict with every volatile species included.""" + d = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': Phi, + 'T_magma': T, + 'fO2_shift_IW': dIW, + } + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d + + +# =========================================================================== +# CO2 stoichiometric atom counting +# =========================================================================== + + +class TestCO2AtomCounting: + """At known p_CO2, the C contribution from CO2 in the atmosphere + is exactly (12/44) * CO2_kg_atm via stoichiometric atom counting.""" + + @pytest.mark.parametrize('p_CO2_bar', [0.1, 1.0, 10.0, 100.0]) + def test_C_atom_count_from_CO2_only(self, p_CO2_bar): + """Single-species CO2 atmosphere at p_CO2 in {0.1, 1, 10, 100} bar: + the C tally equals (12/44) * CO2 column mass to within rel=1e-12, + with a wrong-factor-2 discrimination guard.""" + # Single-species atmosphere with only CO2 to isolate C contribution + ddict = _ddict(T=1800.0, Phi=0.0, dIW=0.0) + for sp in volatile_species: + ddict[f'{sp}_included'] = 0 + ddict['CO2_included'] = 1 + # Set all pressures to zero, then CO2 + p_d = {s: 0.0 for s in volatile_species} + p_d['CO2'] = p_CO2_bar + mass_atm = _atmosphere_mass(p_d, 0.0, ddict) + # Atomic-C contribution expected from stoichiometry: 12.011 / 44.01 + # times the CO2 column mass + expected_C_kg = mass_atm['CO2'] * molar_mass['C'] / molar_mass['CO2'] + assert mass_atm['C'] == pytest.approx(expected_C_kg, rel=1e-12) + + # Discrimination guard: the wrong stoichiometry (factor 2 for C in + # CO2, i.e. treating CO2 as having 2 C atoms) would double the C + # tally, well outside the rel=1e-12 tolerance. + wrong_C_kg = 2.0 * mass_atm['CO2'] * molar_mass['C'] / molar_mass['CO2'] + assert abs(mass_atm['C'] - wrong_C_kg) > 0.5 * expected_C_kg + + def test_zero_CO2_pressure_gives_zero_C(self): + """Sad-path: at p_CO2 = 0 the C tally from CO2 channel is zero.""" + ddict = _ddict(T=1800.0, Phi=0.0, dIW=0.0) + for sp in volatile_species: + ddict[f'{sp}_included'] = 0 + ddict['CO2_included'] = 1 + p_d = {s: 0.0 for s in volatile_species} + mass_atm = _atmosphere_mass(p_d, 0.0, ddict) + assert mass_atm.get('C', 0.0) == pytest.approx(0.0, abs=1e-30) + + # Discrimination guard: confirm the CO2 column mass is also zero. + # A stub that returned zero only for the C key (but nonzero CO2 + # column mass) would pass the bare C == 0 check, hiding a real + # stoichiometry bug elsewhere. + assert mass_atm.get('CO2', 0.0) == pytest.approx(0.0, abs=1e-30) + + +# =========================================================================== +# S2 Gaillard monotonicity with redox +# =========================================================================== + + +class TestS2SolubilityMonotonicity: + """Gaillard sulfide-saturated solubility increases as fO2 decreases + at fixed p_S2 and T (the ``+0.5 ln(p_S2/fO2)`` term carries the + redox dependence directly). Tested at the solubility-function level + to isolate the law from the multi-species solver feedback loops.""" + + @pytest.mark.parametrize('p_S2_bar', [0.01, 0.1, 1.0]) + @pytest.mark.parametrize('T', [1500.0, 1800.0, 2200.0]) + def test_gaillard_strictly_decreasing_with_oxidation(self, T, p_S2_bar): + """Gaillard S2 solubility strictly decreases at every adjacent dIW + step across [-4, +4], with a span check requiring the endpoint + ratio to exceed 10x.""" + S2 = SolubilityS2('gaillard') + dIWs = [-4.0, -2.0, 0.0, +2.0, +4.0] + values = [S2.gaillard(p_S2_bar, T, dIW) for dIW in dIWs] + for i in range(len(values) - 1): + assert values[i] > values[i + 1], ( + f'Gaillard ppmw at dIW={dIWs[i]} ({values[i]:.4e}) ' + f'not greater than at dIW={dIWs[i + 1]} ({values[i + 1]:.4e}) ' + f'(T={T}, p_S2={p_S2_bar})' + ) + + # Discrimination guard: the monotonicity span across the 8-dex + # dIW range should be meaningful (the +0.5 ln(p_S2/fO2) term gives + # at least a 10x change in ppmw across dIWs in [-4, +4]). A near- + # constant function would pass the > check trivially at each step. + assert values[0] / values[-1] > 10.0, ( + f'Gaillard span too small: values[-4]/values[+4] = ' + f'{values[0] / values[-1]:.2f}, expected > 10x' + ) + + def test_negative_pressure_returns_zero(self): + """Sad-path: at p_S2 < 1e-20 bar the implementation returns 0 + to avoid log(0). Verify the floor behaves correctly.""" + S2 = SolubilityS2('gaillard') + assert S2.gaillard(1e-30, 1800.0, 0.0) == pytest.approx(0.0, abs=1e-30) + + # Discrimination guard: a stub that always returned 0 would pass. + # Confirm a normal p_S2 of 0.1 bar gives a nonzero, finite value + # so the floor branch is genuinely distinct from the main path. + normal = S2.gaillard(0.1, 1800.0, 0.0) + assert normal > 0.0 + assert math.isfinite(normal) + + +# =========================================================================== +# N2 Dasgupta monotonicity and Libourel redox-independence +# =========================================================================== + + +class TestN2SolubilityMonotonicity: + """Dasgupta N2 solubility increases as fO2 decreases at fixed + p_N2, p_tot, T (the ``-1.6 dIW`` term in the reduced-N branch + drives this). Tested at the solubility-function level.""" + + @pytest.mark.parametrize('p_N2_bar', [0.1, 1.0, 10.0]) + @pytest.mark.parametrize('T', [1500.0, 1800.0, 2200.0]) + def test_dasgupta_monotonic_with_oxidation(self, T, p_N2_bar): + """Dasgupta N2 solubility is monotonically non-increasing as dIW + rises from -6 to +4, with a span check requiring the endpoint + ratio to exceed 10x.""" + N2 = SolubilityN2('dasgupta') + dIWs = [-6.0, -4.0, -2.0, 0.0, +2.0, +4.0] + values = [N2.dasgupta(p_N2_bar, p_N2_bar, T, dIW) for dIW in dIWs] + # Dasgupta has two terms (reduced-N and molecular N2); the + # reduced-N term dominates at low fO2 and falls steeply with + # increasing fO2, so the total is monotonically decreasing. + for i in range(len(values) - 1): + assert values[i] >= values[i + 1], ( + f'Dasgupta ppmw at dIW={dIWs[i]} ({values[i]:.4e}) ' + f'less than at dIW={dIWs[i + 1]} ({values[i + 1]:.4e}) ' + f'(T={T}, p_N2={p_N2_bar})' + ) + + # Discrimination guard: the reduced-N branch carries an exp(-1.6 dIW) + # factor, so the span across dIW in [-6, +4] should be at least + # exp(1.6 * 10) ~ 9e6 in the reduced-N contribution. The molecular + # N2 term puts a floor under the high-dIW end, but the span should + # still be > 10x. A near-constant function would pass the >= check. + assert values[0] / values[-1] > 10.0, ( + f'Dasgupta span too small: values[-6]/values[+4] = ' + f'{values[0] / values[-1]:.2f}, expected > 10x' + ) + + def test_libourel_alternative_is_redox_independent(self): + """Contrast law: the Libourel linear Henry's law takes only the + N2 partial pressure and is therefore redox-independent by + construction, unlike the Dasgupta law whose reduced-N branch + carries an exp(-1.6 dIW) factor (see + test_dasgupta_monotonic_with_oxidation). Pins the structural + difference, the linearity, and the positivity of the Libourel + law.""" + import inspect + + N2 = SolubilityN2('libourel') + # Structural redox-independence: libourel's signature has no fO2 + # argument, while dasgupta carries an fO2_shift parameter. A + # regression that added redox dependence to libourel (or dropped + # it from dasgupta) would change these signatures and fail here. + libourel_params = [p.lower() for p in inspect.signature(N2.libourel).parameters] + dasgupta_params = [p.lower() for p in inspect.signature(N2.dasgupta).parameters] + assert not any('fo2' in p or 'iw' in p for p in libourel_params) + assert any('fo2' in p or 'iw' in p for p in dasgupta_params) + # Linearity: Libourel is a linear Henry's law in p_N2, so doubling + # the partial pressure doubles the dissolved abundance. + assert N2.libourel(2.0) == pytest.approx(2.0 * N2.libourel(1.0)) + # Positivity: a positive partial pressure dissolves a positive + # (non-zero) abundance. + assert N2.libourel(1.0) > 0 + + +# =========================================================================== +# Dasgupta plateau behaviour at strongly-reducing fO2 (law-only) +# =========================================================================== + + +class TestDasguptaReducingEdgeLaw: + """Direct check that the Dasgupta law itself stays numerically + well-behaved at strongly-reducing conditions, independent of the + multi-species solver.""" + + @pytest.mark.parametrize('dIW', [-3.0, -4.0, -6.0, -8.0]) + def test_dasgupta_law_finite_at_reducing_edge(self, dIW): + """The reduced-N branch grows as exp(-1.6 dIW); at dIW=-8 the + value reaches 10^5 ppmw at p_N2=1 bar but is still + numerically representable.""" + N2 = SolubilityN2('dasgupta') + val = N2.dasgupta(1.0, 1.0, 1800.0, dIW) + assert math.isfinite(val) + assert val > 0.0 diff --git a/tests/test_oxygen_fugacity.py b/tests/test_oxygen_fugacity.py new file mode 100644 index 0000000..9342c76 --- /dev/null +++ b/tests/test_oxygen_fugacity.py @@ -0,0 +1,218 @@ +"""Tests for `src/calliope/oxygen_fugacity.py`. + +Exercises the `OxygenFugacity` IW-buffer dispatcher and its two +underlying fits: + +- Reference pin: Fischer et al. (2011) IW value at T = 2000 K against + the closed-form `6.94059 - 28.1808e3 / T`, with a discrimination + guard against the O'Neill & Eggins (2002) IW at the same T. The + guard catches a regression that silently dispatches to the wrong + buffer; a default change in `oxygen_fugacity.py` would otherwise + silently shift every PROTEUS-side number pinned to the previous + default. +- Monotonicity: `log10(fO2)` is monotonic in T along each buffer + over the 1500-3000 K range. +- Symmetry: `fO2_shift` is strictly additive: `of(T, dIW) = of(T, 0) + dIW`. +- Boundedness: `log10(fO2)` is finite for any valid (T > 0, dIW finite). +- Error contract: T <= 0 raises `ValueError` mentioning the divergence + in the underlying formulae; unknown buffer names raise + `AttributeError` at construction. +""" + +from __future__ import annotations + +import math + +import pytest + +from calliope.oxygen_fugacity import OxygenFugacity + +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] + + +@pytest.mark.physics_invariant +@pytest.mark.reference_pinned +def test_oxygen_fugacity_fischer_value_at_2000K_matches_published_fit(): + """Fischer 2011 IW at T = 2000 K, cross-checked against the + independent O'Neill & Eggins (2002) calibration. + + The `'fischer'` and `'oneill'` paths in `oxygen_fugacity.py` are + independent fits to the same iron-wustite equilibrium, coded from + separate published formulae. At T = 2000 K they agree to 0.258 dex + (Fischer -7.14981, O'Neill -7.40782). The reference anchor is the + cross-calibration offset between the two: it is derived from two + independent implementations, so a coefficient transcription error in + either fit moves the offset and fails the test, which a self-pin + against one fit's own re-typed formula cannot detect. Both values + cluster with other published IW fits (Frost 1991 ~ -7.04; O'Neill + 1988 ~ -7.55) within the ~0.5 dex scatter of the literature. + """ + fischer = OxygenFugacity('fischer')(2000.0) + oneill = OxygenFugacity('oneill')(2000.0) + # Independent anchor: the cross-calibration offset between the two + # buffers. A coefficient error in either published fit shifts this + # offset away from 0.258 dex and fails the test. + assert (fischer - oneill) == pytest.approx(0.258, abs=0.02) + # Regression check on the coded Fischer fit value (secondary; this + # line alone re-types the source formula and so cannot catch a + # source-side coefficient typo, which the offset anchor above does). + assert fischer == pytest.approx(6.94059 - 28.1808e3 / 2000.0, rel=1e-4) + # Wrong-buffer guard: a silent dispatch to 'oneill' lands 0.26 dex + # away from the Fischer value. + assert abs(fischer - (-7.4078)) > 0.2 + # Sign guard: log10(fO2) at the IW buffer is always negative under + # standard conditions (T < ~10000 K). + assert fischer < 0 + # Scale guard: order of magnitude is -7, not -70 (forgotten log10) + # or -0.7 (factor-10 unit slip on the temperature coefficient). + assert -10 < fischer < -3 + + +@pytest.mark.physics_invariant +def test_oxygen_fugacity_oneill_value_at_2000K_matches_published_fit(): + """O'Neill & Eggins (2002) IW at T = 2000 K matches the closed form. + + Implements Eq. 11 of O'Neill & Eggins (2002, J. Chem. Thermodyn. 34, 1311): + `2 * (-244118 + 115.559*T - 8.474*T*ln(T)) / (ln(10) * 8.31441 * T)`. + At T = 2000 K this evaluates to ~-7.4078. The discrimination guard + against Fischer is the mirror of the test above. + """ + of = OxygenFugacity('oneill') + val = of(2000.0) + expected = -7.407823842131363 + assert val == pytest.approx(expected, rel=1e-3, abs=5e-3) + # Wrong-buffer guard: Fischer at 2000 K is -7.14981. + wrong_fischer = -7.14981 + assert abs(val - wrong_fischer) > 0.2 + # Sign and scale guards. + assert val < 0 + assert -10 < val < -3 + + +@pytest.mark.physics_invariant +def test_oxygen_fugacity_shift_is_strictly_additive(): + """`fO2_shift` adds linearly to the buffer value for both buffers. + + The dispatcher at `__call__` returns `callmodel(T) + fO2_shift`. A + regression that multiplies instead of adds, or that applies the + shift twice, would break this identity. + """ + for buffer_name in ('fischer', 'oneill'): + of = OxygenFugacity(buffer_name) + base = of(1800.0, fO2_shift=0.0) + plus_half = of(1800.0, fO2_shift=0.5) + plus_three = of(1800.0, fO2_shift=3.0) + # Strict additivity to floating-point precision. + assert plus_half == pytest.approx(base + 0.5, abs=1e-12) + assert plus_three == pytest.approx(base + 3.0, abs=1e-12) + # Negative shifts work too (reduced fO2). + minus_two = of(1800.0, fO2_shift=-2.0) + assert minus_two == pytest.approx(base - 2.0, abs=1e-12) + + +@pytest.mark.physics_invariant +def test_oxygen_fugacity_monotonic_in_T(): + """Both buffers produce `log10(fO2)` strictly increasing with T over 1500-3000 K. + + A hot magma ocean is more reducing on an absolute scale than a cool + one at the IW buffer, but the IW buffer itself is defined by the + Fe-FeO equilibrium and its log10(fO2) becomes less negative (closer + to zero) as T increases. The chosen window spans realistic surface + temperatures from solidification (~1500 K) to early Earth magma + ocean (~3000 K), giving a delta large enough to resolve a regression + that flipped the sign of the slope. + """ + for buffer_name in ('fischer', 'oneill'): + of = OxygenFugacity(buffer_name) + low = of(1500.0) + mid = of(2250.0) + high = of(3000.0) + # Strict ordering: monotonic, not just non-decreasing. + assert low < mid < high + # Discrimination: the 1500 K -> 3000 K delta is ~2 dex for + # Fischer (28180.8/1500 - 28180.8/3000 = 9.39). A regression + # that flipped the slope sign would invert the ordering above + # but pin the magnitude to catch a coefficient-only bug too. + assert (high - low) > 1.0 + + +@pytest.mark.physics_invariant +def test_oxygen_fugacity_finite_over_realistic_temperature_range(): + """`log10(fO2)` is finite for any T in the realistic 800-5000 K range. + + Boundedness check: no nan, no inf, no complex intermediate. Catches a + regression that introduces a `log(negative)` or `0/0` along an + unexpected code path. + """ + for buffer_name in ('fischer', 'oneill'): + of = OxygenFugacity(buffer_name) + for T in (800.0, 1500.0, 2500.0, 3500.0, 5000.0): + for dIW in (-3.0, 0.0, 1.5): + val = of(T, fO2_shift=dIW) + assert math.isfinite(val) + # Bounded order-of-magnitude: log10(fO2) on the IW buffer + # stays in [-40, +5] over the 800-5000 K window for any + # dIW in [-3, +1.5]. The lower edge is set by the + # T = 800 K / dIW = -3 corner (~-31). Pin the envelope so + # a unit-conversion bug (e.g. log10 -> ln, factor 2.3) + # surfaces. + assert -40 < val < 5 + + +@pytest.mark.parametrize('bad_T', [0.0, -1.0, -300.0]) +def test_oxygen_fugacity_nonpositive_T_raises(bad_T): + """`T <= 0` raises `ValueError` for both buffers. + + Without the guard, T = 0 silently propagates `nan` through every + downstream equilibrium constant via the `1/T` and `T * log(T)` terms. + The error message must name the temperature so users debugging an IC + file can find the offending input. + """ + for buffer_name in ('fischer', 'oneill'): + of = OxygenFugacity(buffer_name) + with pytest.raises(ValueError, match='Temperature must be positive'): + of(bad_T) + + +def test_oxygen_fugacity_unknown_buffer_name_raises(): + """Constructing with an unknown buffer name raises `AttributeError`. + + The dispatcher uses `getattr(self, model)` so a typo lands as an + `AttributeError` at construction (eager), not at first call (lazy). + Eager failure is preferable: it surfaces config typos before any + chemistry step runs. + """ + with pytest.raises(AttributeError): + OxygenFugacity('hirschmann') # not implemented in CALLIOPE + with pytest.raises(AttributeError): + OxygenFugacity('typo_fisher') + + +@pytest.mark.physics_invariant +def test_default_fo2_model_is_shared_by_oxygen_fugacity_and_chemistry(): + """OxygenFugacity and chemistry.ModifiedKeq default to the same single + DEFAULT_FO2_MODEL constant, so a future change to the default cannot be + applied to one but not the other. + + The behavioural check pins that the shared default actually dispatches to + the Fischer buffer: the bare-default OxygenFugacity matches the closed-form + Fischer value at 2500 K, and a regression that flipped the default to + O'Neill would land ~0.3 dex away, outside the tolerance.""" + from calliope.chemistry import ModifiedKeq + from calliope.oxygen_fugacity import DEFAULT_FO2_MODEL, OxygenFugacity + + assert DEFAULT_FO2_MODEL == 'fischer' + + # Both classes carry the same default in their signature: changing one + # without the other is the trap this guards against. + assert OxygenFugacity.__init__.__defaults__ == (DEFAULT_FO2_MODEL,) + assert ModifiedKeq.__init__.__defaults__ == (DEFAULT_FO2_MODEL,) + + # The bare default dispatches to Fischer (not O'Neill): pin the value and + # guard against the wrong-buffer regression. + T = 2500.0 + default_val = OxygenFugacity()(T) + fischer_val = 6.94059 - 28.1808e3 / T + assert default_val == pytest.approx(fischer_val, rel=1e-6) + oneill_val = OxygenFugacity('oneill')(T) + assert abs(default_val - oneill_val) > 0.1 # buffers differ; default is Fischer diff --git a/tests/test_partial_species.py b/tests/test_partial_species.py index b89466e..33754ac 100644 --- a/tests/test_partial_species.py +++ b/tests/test_partial_species.py @@ -122,24 +122,26 @@ def test_nh3_excluded_zeros_nh3_only(self): assert p_d['H2S'] > 0.0 def test_unphysical_negative_partial_pressure_raises_or_clips(self): - """The function clips negative outputs to 0 via `max(0.0, p_d[k])`. + """The function clips negative outputs to 0 via the explicit + non-negative-real clip at the bottom of `_get_partial_pressures`. Feed it a primary pressure that drives a derived species negative (impossible at any real fO2 but the clip path must still hold). - Pass a NaN-like sentinel via T_magma to force a non-finite gamma; - the clip silently returns 0, which is a contract worth pinning. """ - # Negative initial partial pressure for H2O — physically nonsense. - # The function does not raise; it propagates through but the final - # `max(0.0, p_d[k])` clip pins the output non-negative. + import warnings as _warnings + ddict = _make_ddict() pin = {'H2O': -5.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} - # Negative H2O => p_d['H2'] < 0 => NH3 = (... * H2**3) ** 0.5 raises - # a RuntimeWarning ("invalid value in scalar power"). Expected. - with pytest.warns(RuntimeWarning, match='invalid value'): + # Whether intermediate steps emit a RuntimeWarning, drop into a + # NaN path, or produce a complex sqrt-of-negative depends on the + # buffer; the clip must absorb all three. Suppress any warnings + # and pin the contract that matters: every output a non-negative + # real. + with _warnings.catch_warnings(): + _warnings.simplefilter('ignore', RuntimeWarning) p_d = get_partial_pressures(pin, ddict) - # Clip contract: every output non-negative even if input is not. for sp, p in p_d.items(): + assert isinstance(p, float), f'{sp} = {p!r} is not a real float' assert p >= 0.0, f'{sp} = {p} broke the non-negative clip' @@ -158,36 +160,56 @@ def test_h_tally_excludes_h2_ch4_h2s_nh3_when_off(self): species is excluded. The expected H mass is exactly 2 * M_H * mass_H2O / M_H2O. """ - ddict = _make_ddict(included={'H2': 0, 'CH4': 0, 'H2S': 0, 'NH3': 0}) + ddict_off = _make_ddict(included={'H2': 0, 'CH4': 0, 'H2S': 0, 'NH3': 0}) pin = {'H2O': 100.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} - mass = atmosphere_mass(pin, ddict) + mass_off = atmosphere_mass(pin, ddict_off) # mass_atm_d['H2O'] is computed inside atmosphere_mass; recompute # the analytical column mass (modulo the mu-correction, which we # invert by reading mass_atm_d['H2O'] back). - expected_H = 2 * mass['H2O'] / molar_mass['H2O'] * molar_mass['H'] - assert mass['H'] == pytest.approx(expected_H, rel=1e-9) - - # Discriminating: with all reduced species ON the H tally is - # higher, so this exact match would not hold. + expected_H = 2 * mass_off['H2O'] / molar_mass['H2O'] * molar_mass['H'] + assert mass_off['H'] == pytest.approx(expected_H, rel=1e-9) + + # Discrimination guard: with all reduced H-bearing species ON, the + # H tally is strictly higher because H2, CH4, H2S, and NH3 each + # carry additional H atoms. The exact-match-to-H2O equality above + # would not hold. + ddict_on = _make_ddict(included={'H2': 1, 'CH4': 1, 'H2S': 1, 'NH3': 1}) + mass_on = atmosphere_mass(pin, ddict_on) + assert mass_on['H'] > mass_off['H'], ( + f'H tally with reduced species ON ({mass_on["H"]:.4e}) is not ' + f'greater than with them OFF ({mass_off["H"]:.4e})' + ) def test_c_tally_excludes_co_ch4_when_off(self): """C tally collapses to mass_CO2 / M_CO2 when CO and CH4 off.""" - ddict = _make_ddict(included={'CO': 0, 'CH4': 0}) + ddict_off = _make_ddict(included={'CO': 0, 'CH4': 0}) pin = {'H2O': 100.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} - mass = atmosphere_mass(pin, ddict) + mass_off = atmosphere_mass(pin, ddict_off) + + expected_C = mass_off['CO2'] / molar_mass['CO2'] * molar_mass['C'] + assert mass_off['C'] == pytest.approx(expected_C, rel=1e-9) - expected_C = mass['CO2'] / molar_mass['CO2'] * molar_mass['C'] - assert mass['C'] == pytest.approx(expected_C, rel=1e-9) + # Discrimination guard: with CO and CH4 ON, the C tally is strictly + # higher (CO and CH4 each contribute additional C atoms). + ddict_on = _make_ddict(included={'CO': 1, 'CH4': 1}) + mass_on = atmosphere_mass(pin, ddict_on) + assert mass_on['C'] > mass_off['C'] def test_s_tally_excludes_so2_h2s_when_off(self): """S tally collapses to 2 * mass_S2 / M_S2 when SO2 and H2S off.""" - ddict = _make_ddict(included={'SO2': 0, 'H2S': 0}) + ddict_off = _make_ddict(included={'SO2': 0, 'H2S': 0}) pin = {'H2O': 100.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} - mass = atmosphere_mass(pin, ddict) + mass_off = atmosphere_mass(pin, ddict_off) + + expected_S = 2 * mass_off['S2'] / molar_mass['S2'] * molar_mass['S'] + assert mass_off['S'] == pytest.approx(expected_S, rel=1e-9) - expected_S = 2 * mass['S2'] / molar_mass['S2'] * molar_mass['S'] - assert mass['S'] == pytest.approx(expected_S, rel=1e-9) + # Discrimination guard: with SO2 and H2S ON, the S tally is strictly + # higher (each contributes one additional S atom per molecule). + ddict_on = _make_ddict(included={'SO2': 1, 'H2S': 1}) + mass_on = atmosphere_mass(pin, ddict_on) + assert mass_on['S'] > mass_off['S'] def test_zero_pressure_inputs_yield_nonneg_masses(self): """Edge case: every primary at numerical zero. Output masses must @@ -215,6 +237,9 @@ class TestDissolvedMassExclusions: """ def test_co_excluded_writes_zero(self): + """When CO is excluded from the species list, dissolved_mass + writes an explicit 0.0 in the 'CO' key (not a missing key) + while still dissolving CO2 normally.""" ddict = _make_ddict(included={'CO': 0}) pin = {'H2O': 100.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} m = dissolved_mass(pin, ddict) @@ -227,6 +252,9 @@ def test_co_excluded_writes_zero(self): assert m['CO2'] > 0.0 def test_ch4_excluded_writes_zero(self): + """When CH4 is excluded from the species list, dissolved_mass + writes an explicit 0.0 in the 'CH4' key (not a missing key) + while still dissolving CO and CO2 normally.""" ddict = _make_ddict(included={'CH4': 0}) pin = {'H2O': 100.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} m = dissolved_mass(pin, ddict) @@ -242,15 +270,26 @@ def test_zero_phi_zeros_all_dissolved(self): """Edge: with zero melt fraction, every dissolved mass is zero. `dissolved_mass` only writes the species that can dissolve in - a silicate melt (H2O, CO2, CO, CH4, N2, S2) — never O2, SO2, + a silicate melt (H2O, CO2, CO, CH4, N2, S2), never O2, SO2, H2S, NH3, or H2. Iterate only over those keys. """ - ddict = _make_ddict(Phi_global=0.0) + ddict_zero = _make_ddict(Phi_global=0.0) pin = {'H2O': 100.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} - m = dissolved_mass(pin, ddict) + m_zero = dissolved_mass(pin, ddict_zero) dissolved_species = {'H2O', 'CO2', 'CO', 'CH4', 'N2', 'S2'} for sp in dissolved_species: - assert m[sp] == pytest.approx(0.0, abs=1e-30), f'{sp} nonzero at Phi=0' + assert m_zero[sp] == pytest.approx(0.0, abs=1e-30), f'{sp} nonzero at Phi=0' + + # Discrimination guard: at Phi=1.0 (fully molten) the same species + # carry nonzero dissolved mass. A stub that hard-coded 0.0 for + # every dissolved entry would also pass the Phi=0 loop above. + ddict_full = _make_ddict(Phi_global=1.0) + m_full = dissolved_mass(pin, ddict_full) + nonzero_at_full = sum(1 for sp in dissolved_species if m_full[sp] > 0.0) + assert nonzero_at_full >= 4, ( + f'Only {nonzero_at_full} of {len(dissolved_species)} species ' + f'dissolved at Phi=1.0; the zero-Phi check would be vacuous' + ) def test_unphysical_negative_mantle_mass_propagates(self): """Pin the contract: negative M_mantle is not validated here, it @@ -260,7 +299,7 @@ def test_unphysical_negative_mantle_mass_propagates(self): ddict = _make_ddict(M_mantle=-1.0e24) pin = {'H2O': 100.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} m = dissolved_mass(pin, ddict) - # Not all dissolved fields are clipped — only the per-element + # Not all dissolved fields are clipped; only the per-element # tallies at the bottom are. The per-species mass for H2O can go # negative when M_mantle is negative. assert m['H2O'] < 0.0 # documents non-validation @@ -281,6 +320,9 @@ class TestSolubilityN2Libourel: """ def test_libourel_linear_in_p(self): + """Libourel N2 solubility is a Henry's-law linear function of + p_N2 (`ppmw = 0.0611 * p`); the ratio sol(100) / sol(1) equals + 100 exactly, ruling out sqrt or quadratic dependences.""" sol = SolubilityN2('libourel') # Discriminating: at p=1, all of p^0.5/p/p^2 give 1.0. At p=100 # they differ by a factor of 10 (sqrt) or 10000 (square), so the @@ -298,6 +340,15 @@ def test_libourel_zero_p(self): sol = SolubilityN2('libourel') assert sol(0.0) == pytest.approx(0.0, abs=1e-30) + # Discrimination guard: a stub that always returned 0 would pass + # the bare zero-input check. Confirm the call path is genuinely + # linear by checking that a small positive p gives a small positive + # output and a larger p gives a proportionally larger one. + small = sol(1e-6) + large = sol(1.0) + assert small > 0.0 + assert large > small * 100.0 # linear should give exactly 1e6x + def test_libourel_negative_p_propagates_unchecked(self): """The Henry's-law power-law is not guarded against p < 0; document that it returns a negative concentration for negative diff --git a/tests/test_solubility.py b/tests/test_solubility.py new file mode 100644 index 0000000..9f42188 --- /dev/null +++ b/tests/test_solubility.py @@ -0,0 +1,475 @@ +"""Tests for `src/calliope/solubility.py`. + +Exercises the Henry's-law solubility models for H2O (Sossi 2023 default, +Dixon 1995, Hamilton 1964 / Wilson and Head 1981, Newcombe 2017), S2 +(Gaillard 2022), and N2 (Dasgupta 2022, Libourel). + +- Reference pins: H2O peridotite default against the Sossi et al. (2023) + `524 * p^0.5` constant; S2 default against the Gaillard et al. (2022) + Earth-mantle numerics; N2 dasgupta prefactor against the published + composition coefficients. +- Conservation: zero partial pressure returns identically zero; Henry's + identity in the linear regime. +- Monotonicity: dissolved ppmw increases with partial pressure for every + H2O parameterization. +- Closed-form scaling: composition kwargs (`x_FeO`, `x_SiO2`, `x_Al2O3`, + `x_TiO2`) enter the exponential prefactors as `exp(coef * delta)`; + the ratio of solubilities at two compositions matches the closed-form + factor to floating-point precision. +- Edge cases: zero-pressure short-circuit on S2, negative composition + values evaluate finitely, libourel path unaffected by composition kwargs. +""" + +from __future__ import annotations + +import math +import warnings + +import pytest + +from calliope.solubility import SolubilityH2O, SolubilityN2, SolubilityS2 + +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] + + +# --------------------------------------------------------------------------- +# SolubilityH2O :: H2O parameterizations +# --------------------------------------------------------------------------- + + +class TestSolubilityH2O: + """Power-law H2O solubilities. Each parameterization carries a published + `const` and `exponent`; the test pins both via the closed-form + `const * p^exponent` identity at chosen pressures.""" + + @pytest.mark.physics_invariant + @pytest.mark.reference_pinned + def test_peridotite_default_matches_sossi_2023_fit(self): + """Sossi et al. (2023) peridotite H2O fit: `ppmw = 524 * p^0.5`. + + `SolubilityH2O('peridotite')` is the package-wide default; the + constant 524 ppmw/bar^0.5 and the 0.5 exponent come from Sossi + et al. (2023). + + Discrimination guards: a regression that swapped to the Dixon + 1995 basalt constant (965 ppmw/bar^0.5) at the same exponent + would change the result by factor 1.84; an exponent flip + (1.0 vs 0.5) would change it by `sqrt(100) = 10x` at p = 100 bar. + """ + s = SolubilityH2O() # peridotite default + # Zero-pressure boundary: identically zero by power-law definition. + assert s(0.0) == 0.0 + # At p = 100 bar: 524 * sqrt(100) = 5240 ppmw. + val = s(100.0) + expected = 524.0 * 10.0 + assert val == pytest.approx(expected, rel=1e-12) + # Wrong-law guard: basalt_dixon would give 9650, off by ~84%. + assert abs(val - 965.0 * 10.0) > 1000.0 + # Wrong-exponent guard: p^1.0 instead of p^0.5 would give 52400. + assert abs(val - 524.0 * 100.0) > 1000.0 + # Sign + scale guards: positive ppmw, order 1e3 to 1e4 at 100 bar. + assert val > 0 + assert 1e3 < val < 1e4 + + @pytest.mark.physics_invariant + def test_basalt_dixon_matches_published_constant(self): + """Dixon et al. (1995) basalt H2O: `ppmw = 965 * p^0.5`.""" + s = SolubilityH2O('basalt_dixon') + val = s(100.0) + expected = 965.0 * 10.0 + assert val == pytest.approx(expected, rel=1e-12) + # Wrong-law guard against peridotite default (524) at same p. + assert abs(val - 524.0 * 10.0) > 1000.0 + + @pytest.mark.physics_invariant + def test_basalt_wilson_uses_non_half_exponent(self): + """Hamilton (1964) / Wilson and Head (1981) basalt: `ppmw = 215 * p^0.7`. + + Non-square-root exponent: discriminating, because a regression + that defaults all H2O laws to the 0.5 exponent would land at + 2150 ppmw at p = 100 bar instead of the correct ~5403 ppmw. + """ + s = SolubilityH2O('basalt_wilson') + val = s(100.0) + expected = 215.0 * (100.0**0.7) # ~5403 ppmw + assert val == pytest.approx(expected, rel=1e-12) + # Wrong-exponent guard: p^0.5 would give 2150. + assert abs(val - 215.0 * 10.0) > 1000.0 + + @pytest.mark.physics_invariant + def test_anorthite_diopside_and_lunar_glass_match_newcombe_2017(self): + """Newcombe et al. (2017): anorthite-diopside 727 ppmw/bar^0.5, + lunar glass 683 ppmw/bar^0.5. Both follow the sqrt law.""" + s_ad = SolubilityH2O('anorthite_diopside') + s_lg = SolubilityH2O('lunar_glass') + val_ad = s_ad(100.0) + val_lg = s_lg(100.0) + assert val_ad == pytest.approx(727.0 * 10.0, rel=1e-12) + assert val_lg == pytest.approx(683.0 * 10.0, rel=1e-12) + # The two are close (~6% apart), so a regression that swapped + # them would not be caught by a single-law check; pin both. + assert val_ad != pytest.approx(val_lg, rel=1e-3) + + @pytest.mark.physics_invariant + def test_h2o_monotonic_in_pressure_for_every_parameterization(self): + """All five parameterizations are strictly increasing in p. + + Henry's law sign convention: higher partial pressure -> more + dissolved ppmw. A regression that flipped the sign of the + exponent (very unlikely but possible if power_law gained a + negative-exponent default) would invert this ordering. + """ + for name in ( + 'peridotite', + 'basalt_dixon', + 'basalt_wilson', + 'anorthite_diopside', + 'lunar_glass', + ): + s = SolubilityH2O(name) + low = s(10.0) + mid = s(100.0) + high = s(1000.0) + assert low < mid < high + # Discrimination: high - low spans ~2 orders for sqrt laws, + # ~4 orders for p^0.7. Pin a positive delta. + assert (high - low) > 0 + + +# --------------------------------------------------------------------------- +# SolubilityS2 :: x_FeO +# --------------------------------------------------------------------------- + + +class TestSolubilityS2_xFeO: + """The Gaillard et al. (2022) law has form + ln(X_S^melt) = 13.8426 - 26476/T + 0.124*x_FeO + 0.5*ln(p/fO2) + so x_FeO enters as exp(0.124 * dx_FeO) on the prefactor. Tests use + this closed form to discriminate the implementation against + plausible bugs (missing 0.124, treating x_FeO as fraction not wt%, + multiplicative not additive). + """ + + def test_default_xFeO_is_10wt_percent(self): + """Default kwarg must keep the hardcoded Earth-mantle value of + 10.0 wt%, so that PROTEUS callers using `SolubilityS2()` see no + numerical drift.""" + s = SolubilityS2() + assert s.x_FeO == pytest.approx(10.0) + + # Discrimination guard: confirm the default x_FeO actually flows + # through to the call path. A constructor that stored 10 on the + # attribute but used a different value internally would pass the + # bare attribute pin. + s_explicit = SolubilityS2(x_FeO=10.0) + assert s(1.0, 2500.0, 0.0) == pytest.approx(s_explicit(1.0, 2500.0, 0.0), rel=1e-12) + + @pytest.mark.physics_invariant + @pytest.mark.reference_pinned + def test_default_call_matches_gaillard_2022_earth_mantle_value(self): + """Pin S2 ppmw under Gaillard et al. (2022) Earth-mantle defaults. + + At p_S2 = 1 bar, T = 2500 K, fO2_shift = 0, x_FeO = 10 wt%, and + the default Fischer 2011 IW buffer: + + ln(X) = 13.8426 - 26476/2500 + 0.124*10 + 0.5*ln(1/fO2_bar) + = 13.8426 - 10.5904 + 1.24 + 0.5*ln(10^-IW(2500)) + ~ 9.479 + ppmw = exp(9.479) ~ 13086 + + Hidden coupling: the pin depends on the default IW buffer + (Fischer 2011 since 2026-05). Any change to the IW buffer + coefficients in oxygen_fugacity.py will require regenerating + this number; the failure surfaces here, not in + test_oxygen_fugacity. + """ + s = SolubilityS2() + out = s(1.0, 2500.0, 0.0) + # Regression pin: computed at default x_FeO=10.0 with Fischer 2011. + assert out == pytest.approx(13085.87, rel=1e-5) + # Sign and scale guards: positive ppmw, order 1e4 at these conditions. + assert out > 0 + assert 1e3 < out < 1e5 + + @pytest.mark.physics_invariant + @pytest.mark.parametrize( + 'x_FeO,expected_ratio', + [ + (5.0, math.exp(0.124 * (5.0 - 10.0))), # half FeO -> exp(-0.62) ~ 0.538 + (10.0, 1.0), # default, identity + (15.0, math.exp(0.124 * (15.0 - 10.0))), # ~1.86 + (20.0, math.exp(0.124 * (20.0 - 10.0))), # ~3.46 + ], + ) + def test_xFeO_scales_prefactor_correctly(self, x_FeO, expected_ratio): + """The Gaillard law is linear in x_FeO inside the exponent, + so the ratio of solubilities at different x_FeO at fixed p, T, + fO2_shift must equal exp(0.124 * dx_FeO). Discriminating because + a bug applying x_FeO as a multiplicative factor (e.g. *x_FeO + rather than +0.124*x_FeO) would not produce this exact ratio. + """ + baseline = SolubilityS2(x_FeO=10.0) + custom = SolubilityS2(x_FeO=x_FeO) + p_S2, T, dIW = 1.0, 2500.0, 0.0 + + ratio = custom(p_S2, T, dIW) / baseline(p_S2, T, dIW) + assert ratio == pytest.approx(expected_ratio, rel=1e-12) + + # Discrimination guard: the wrong formula (multiplicative *x_FeO/10 + # instead of additive +0.124*x_FeO) would give ratio = x_FeO/10. + # At x_FeO=5 the wrong ratio is 0.5 (correct exp(-0.62)~0.538); + # at x_FeO=15 the wrong ratio is 1.5 (correct exp(0.62)~1.86); + # at x_FeO=20 the wrong ratio is 2.0 (correct exp(1.24)~3.46). + # The identity case x_FeO=10 is excluded since both formulas coincide. + if x_FeO != 10.0: + wrong_ratio_multiplicative = x_FeO / 10.0 + assert abs(ratio - wrong_ratio_multiplicative) > 0.03 + + def test_xFeO_does_not_affect_zero_pressure_short_circuit(self): + """Edge case: at p_S2 < 1e-20 bar the law returns 0.0 + identically; the x_FeO kwarg must not bypass that guard. Keeps + the divergence-in-log handling intact.""" + for x_FeO in (0.0, 10.0, 50.0): + s = SolubilityS2(x_FeO=x_FeO) + assert s(1.0e-25, 2500.0, 0.0) == pytest.approx(0.0, abs=1e-30) + + # Discrimination guard: the floor branch at low p_S2 must be distinct + # from the main path. At p_S2 = 1.0 bar each of the three x_FeO values + # gives a different, nonzero result; a stub that hard-coded 0.0 would + # fail here. + for x_FeO in (0.0, 10.0, 50.0): + s = SolubilityS2(x_FeO=x_FeO) + assert s(1.0, 2500.0, 0.0) > 0.0 + + def test_xFeO_extreme_negative_evaluates_finitely(self): + """Edge case: a physically nonsensical negative x_FeO value + is not validated (the law has no domain constraint encoded), so + it must still evaluate finitely and positively. The output must + also follow the published linear-in-x_FeO law: negative x_FeO + gives less solubility than x_FeO = 0.""" + s = SolubilityS2(x_FeO=-5.0) + out = s(1.0, 2500.0, 0.0) + assert math.isfinite(out) + + # Discrimination guard: the linear-in-x_FeO law predicts that + # x_FeO = -5 gives exp(-0.62) ~ 0.538 times the x_FeO = 0 value. + # A clamped-at-zero implementation (treating negative x_FeO as 0) + # would give the same result as x_FeO = 0; a sign-flip bug would + # give exp(+0.62) ~ 1.86 times. + zero = SolubilityS2(x_FeO=0.0)(1.0, 2500.0, 0.0) + assert out == pytest.approx(zero * math.exp(0.124 * -5.0), rel=1e-12) + + @pytest.mark.physics_invariant + def test_xFeO_zero_consistent_with_drop_term(self): + """Discriminating: at x_FeO=0 the 0.124*x_FeO term drops, so + the result must equal SolubilityS2(x_FeO=10) divided by + exp(0.124*10) ~ 3.456. Catches an off-by-coefficient bug like + +0.124*(x_FeO-10) or +0.0124*x_FeO.""" + baseline = SolubilityS2(x_FeO=10.0) + zero = SolubilityS2(x_FeO=0.0) + ratio = zero(1.0, 2500.0, 0.0) / baseline(1.0, 2500.0, 0.0) + assert ratio == pytest.approx(math.exp(-1.24), rel=1e-12) + + # Discrimination guard: an off-by-10 in the coefficient + # (+0.0124 * x_FeO instead of +0.124 * x_FeO) would give a ratio + # of exp(-0.124) ~ 0.883. The correct ratio is exp(-1.24) ~ 0.289; + # the gap is 0.59, well outside any tolerance. + wrong_coef_ratio = math.exp(-0.124) + assert abs(ratio - wrong_coef_ratio) > 0.1 + + +# --------------------------------------------------------------------------- +# SolubilityN2 :: x_SiO2 / x_Al2O3 / x_TiO2 +# --------------------------------------------------------------------------- + + +class TestSolubilityN2_meltComposition: + """The Dasgupta et al. (2022) law adds a molecular term whose + prefactor c_melt = exp(4.67 + 7.11 x_SiO2 - 13.06 x_Al2O3 - 120.67 x_TiO2) + is precomputed in __init__ and stored as `dasfac_2`. Tests pin the + default values and verify the closed-form scaling with each kwarg + independently, in line with the published expression.""" + + DEFAULT_SIO2 = 0.56 + DEFAULT_AL2O3 = 0.11 + DEFAULT_TIO2 = 0.01 + DEFAULT_DASFAC = math.exp(4.67 + 7.11 * 0.56 - 13.06 * 0.11 - 120.67 * 0.01) # ~ 406.79 + + def test_default_kwargs_give_pre_existing_dasfac(self): + """No-arg call must reproduce the previously hardcoded + dasfac_2 = exp(4.67 + 7.11*0.56 - 13.06*0.11 - 120.67*0.01) ~ 406.79. + Pin the closed-form value so any coefficient drift surfaces.""" + s = SolubilityN2('dasgupta') + assert s.dasfac_2 == pytest.approx(self.DEFAULT_DASFAC, rel=1e-12) + # Numeric pin against the actual computed value at kwarg + # introduction time (catches off-by-1 in the constants too). + assert s.dasfac_2 == pytest.approx(406.791, rel=1e-4) + + def test_default_kwargs_attribute_values(self): + """Pin x_SiO2/Al2O3/TiO2 attributes to their documented + defaults; a future maintainer changing these would have to + update this test, signalling the doc-text needs the same + update.""" + s = SolubilityN2('dasgupta') + assert s.x_SiO2 == self.DEFAULT_SIO2 + assert s.x_Al2O3 == self.DEFAULT_AL2O3 + assert s.x_TiO2 == self.DEFAULT_TIO2 + + @pytest.mark.physics_invariant + @pytest.mark.parametrize( + 'kwarg,delta,coef', + [ + ('x_SiO2', 0.10, 7.11), # +0.10 SiO2 -> exp(0.711) ~ 2.04 + ('x_Al2O3', 0.05, -13.06), # +0.05 Al2O3 -> exp(-0.653) ~ 0.520 + ('x_TiO2', 0.01, -120.67), # +0.01 TiO2 -> exp(-1.2067) ~ 0.299 + ], + ) + def test_each_kwarg_scales_dasfac_independently(self, kwarg, delta, coef): + """Each composition kwarg enters c_melt with its own + coefficient. Bumping one kwarg by `delta` must scale dasfac_2 + by exp(coef*delta), independent of the other two. Catches + copy-paste swaps among the three coefficients (e.g. using + 7.11 for Al2O3 instead of SiO2).""" + kwargs_default = { + 'x_SiO2': self.DEFAULT_SIO2, + 'x_Al2O3': self.DEFAULT_AL2O3, + 'x_TiO2': self.DEFAULT_TIO2, + } + kwargs_modified = dict(kwargs_default) + kwargs_modified[kwarg] += delta + + s_default = SolubilityN2('dasgupta', **kwargs_default) + s_modified = SolubilityN2('dasgupta', **kwargs_modified) + + ratio = s_modified.dasfac_2 / s_default.dasfac_2 + assert ratio == pytest.approx(math.exp(coef * delta), rel=1e-12) + + # Discrimination guard: each kwarg's coefficient is distinct, so a + # copy-paste bug that used a sibling coefficient would yield a + # different ratio. The three coefficients are +7.11 (SiO2), -13.06 + # (Al2O3), -120.67 (TiO2); confirm the measured ratio does not + # match either of the other two predictions. + sibling_coefs = [c for c in (7.11, -13.06, -120.67) if c != coef] + for wrong_coef in sibling_coefs: + wrong_ratio = math.exp(wrong_coef * delta) + assert abs(ratio - wrong_ratio) > 1e-3 * abs(ratio), ( + f'Ratio {ratio:.4e} matches sibling coefficient {wrong_coef} (expected {coef})' + ) + + @pytest.mark.physics_invariant + def test_dasgupta_call_uses_new_dasfac(self): + """Discriminating: the Dasgupta call adds `pb_N2 * dasfac_2` on + top of an exponential redox-dependent term. Pick conditions + where the dasfac term carries appreciable weight (high p, + oxidising) and verify changing dasfac changes ppmw in the + predicted direction.""" + p_N2 = 1000.0 # bar + p_total = 5000.0 # bar + T = 1800.0 + dIW = +5.0 # strongly oxidising; suppresses the redox term + + s_default = SolubilityN2('dasgupta') + s_high_SiO2 = SolubilityN2('dasgupta', x_SiO2=0.66) # +0.10 + + ppmw_default = s_default(p_N2, p_total, T, dIW) + ppmw_high = s_high_SiO2(p_N2, p_total, T, dIW) + + # The dasfac term scales by exp(7.11 * 0.10) = ~ 2.036; the + # redox term is unchanged. At dIW=+5 the redox term is heavily + # suppressed, so dasfac dominates and the ratio approaches 2.04. + ratio = ppmw_high / ppmw_default + assert 1.5 < ratio < 2.1 + + # Discrimination guard: an implementation that did NOT thread the + # new x_SiO2 kwarg into dasfac_2 would leave ratio ~ 1.0; a wrong + # sign on the coefficient would give exp(-7.11 * 0.10) ~ 0.49. + # Both failure modes are excluded by the [1.5, 2.1] band, but + # tighten by confirming the gap from 1.0 is meaningful. + assert abs(ratio - 1.0) > 0.3, ( + f'Ratio {ratio:.4f} is too close to 1.0; the x_SiO2 kwarg ' + f'may not be threading through to the call path' + ) + + def test_libourel_unaffected_by_composition_kwargs(self): + """The Libourel law has no melt-composition dependence; its + output must be unchanged regardless of x_SiO2/Al2O3/TiO2. + Discriminating: a refactor that accidentally threaded the new + kwargs into power_law would be caught here.""" + p_N2 = 100.0 + baseline = SolubilityN2('libourel')(p_N2) + custom = SolubilityN2('libourel', x_SiO2=0.99, x_Al2O3=0.99, x_TiO2=0.99)(p_N2) + assert custom == pytest.approx(baseline, rel=1e-12) + + # Discrimination guard: a stub that returned 0 for both calls would + # pass the equality check trivially. Confirm the libourel output is + # physically meaningful (positive, finite, in a reasonable ppmw range + # for p_N2 = 100 bar). + assert baseline > 0.0 + assert math.isfinite(baseline) + + def test_zero_composition_yields_pure_4_67_prefactor(self): + """Edge: setting all three to zero collapses dasfac_2 to + exp(4.67) ~ 106.7. Catches a bug where the constant 4.67 was + accidentally mixed into a kwarg coefficient.""" + s = SolubilityN2('dasgupta', x_SiO2=0.0, x_Al2O3=0.0, x_TiO2=0.0) + assert s.dasfac_2 == pytest.approx(math.exp(4.67), rel=1e-12) + + # Discrimination guard: nearby constants (4.6, 5.0) would give + # prefactors of exp(4.6) ~ 99.5 or exp(5.0) ~ 148.4, distinguishable + # from exp(4.67) ~ 106.7 at the 7-40% level. Confirm the value is + # in the narrow band around exp(4.67). + assert s.dasfac_2 < math.exp(4.75) + assert s.dasfac_2 > math.exp(4.60) + + def test_libourel_call_path_does_not_break_with_extreme_dasfac_inputs(self): + """Edge: even if a user supplies pathological composition + values that would overflow the dasgupta exponential (e.g. + enormous SiO2 of 10.0, well outside any physical mole fraction), + the libourel law must still evaluate to the Henry-law output. + Discriminating: dasfac_2 is gated on composition='dasgupta', + so libourel callers must NOT pay the exp() precompute cost AND + must NOT see any RuntimeWarning emitted at construction time. + """ + with warnings.catch_warnings(): + warnings.simplefilter('error') # any warning becomes an error + s = SolubilityN2('libourel', x_SiO2=10.0) + # libourel path stores no precomputed dasfac_2 + assert s.dasfac_2 is None + out = s(50.0) + assert math.isfinite(out) + assert out == pytest.approx(0.0611 * 50.0, rel=1e-12) + + +# --------------------------------------------------------------------------- +# Backward-compatibility regression tests (PROTEUS callers) +# --------------------------------------------------------------------------- + + +class TestBackwardCompatibility: + """Pin numeric outputs of the no-arg SolubilityS2() and + SolubilityN2() constructors used in `solve.dissolved_mass` so any + silent default-value change surfaces here.""" + + def test_solve_dissolved_mass_callsites_use_defaults(self): + """Discriminating: `solve.dissolved_mass` instantiates + `SolubilityS2()` and `SolubilityN2('dasgupta')` with no + composition kwargs. Pin both default-instantiated objects to + their numeric outputs at a fixed (p, T, fO2_shift) triple. A + drift here means a future change to the default kwarg values + broke PROTEUS-side runs. + + Hidden coupling: the S2 pin below couples to the default IW + buffer (Fischer et al. 2011). A change to the IW-buffer + coefficients will require regenerating these numbers. + """ + # SolubilityS2 default at (p_S2, T, dIW) + s2 = SolubilityS2() + ppmw_S2 = s2(1.0, 2500.0, 0.0) + assert ppmw_S2 == pytest.approx(13085.87, rel=1e-5) + + # SolubilityN2('dasgupta') default at (p_N2, p_tot, T, dIW) + n2 = SolubilityN2('dasgupta') + ppmw_N2 = n2(50.0, 200.0, 2000.0, 0.0) + # Regression value: N2 solubility under dasgupta has no fO2 + # dependence at this dIW=0 evaluation, so this is buffer-agnostic. + assert ppmw_N2 == pytest.approx(2.14133, rel=1e-5) diff --git a/tests/test_solubility_compositions.py b/tests/test_solubility_compositions.py deleted file mode 100644 index 75f3909..0000000 --- a/tests/test_solubility_compositions.py +++ /dev/null @@ -1,268 +0,0 @@ -"""Tests for the user-configurable melt-composition kwargs on -`SolubilityS2` (x_FeO) and `SolubilityN2` (x_SiO2, x_Al2O3, x_TiO2). - -The kwargs are backward-compatible: callers that omit them must get -bit-identical numerics to the prior hardcoded values. Non-Earth -overrides must propagate into the dissolved-mass formulas as predicted -by the closed-form expressions in Gaillard et al. (2022) and Dasgupta -et al. (2022). -""" - -from __future__ import annotations - -import math -import warnings - -import pytest - -from calliope.solubility import SolubilityN2, SolubilityS2 - -pytestmark = pytest.mark.unit - - -# --------------------------------------------------------------------------- -# SolubilityS2 :: x_FeO -# --------------------------------------------------------------------------- - - -class TestSolubilityS2_xFeO: - """The Gaillard et al. (2022) law has form - ln(X_S^melt) = 13.8426 - 26476/T + 0.124*x_FeO + 0.5*ln(p/fO2) - so x_FeO enters as exp(0.124 * dx_FeO) on the prefactor. Tests use - this closed form to discriminate the implementation against - plausible bugs (missing 0.124, treating x_FeO as fraction not wt%, - multiplicative not additive). - """ - - def test_default_xFeO_is_10wt_percent(self): - """Default kwarg must keep the hardcoded Earth-mantle value of - 10.0 wt%, so that PROTEUS callers using `SolubilityS2()` see no - numerical drift.""" - s = SolubilityS2() - assert s.x_FeO == 10.0 - - def test_default_call_matches_pre_kwarg_value(self): - """Pin one numeric output of the default-x_FeO call against the - pre-kwarg implementation. Drift here means either the formula - changed or the default x_FeO drifted off 10.0 wt%. - - Hidden coupling: this pin depends on OxygenFugacity('oneill') - evaluated at T=2500 K, fO2_shift=0. Any audit of the IW buffer - coefficients in oxygen_fugacity.py will require regenerating - this number — the failure mode here surfaces in test_solubility, - not test_oxygen_fugacity. - """ - s = SolubilityS2() - out = s(1.0, 2500.0, 0.0) - # Regression pin: computed by the implementation at the time - # the x_FeO kwarg was introduced, with the default x_FeO=10.0. - assert out == pytest.approx(30095.04, rel=1e-5) - - @pytest.mark.parametrize( - 'x_FeO,expected_ratio', - [ - (5.0, math.exp(0.124 * (5.0 - 10.0))), # half FeO -> exp(-0.62) ~ 0.538 - (10.0, 1.0), # default, identity - (15.0, math.exp(0.124 * (15.0 - 10.0))), # ~1.86 - (20.0, math.exp(0.124 * (20.0 - 10.0))), # ~3.46 - ], - ) - def test_xFeO_scales_prefactor_correctly(self, x_FeO, expected_ratio): - """The Gaillard law is linear in x_FeO inside the exponent, - so the ratio of solubilities at different x_FeO at fixed p, T, - fO2_shift must equal exp(0.124 * dx_FeO). Discriminating because - a bug applying x_FeO as a multiplicative factor (e.g. *x_FeO - rather than +0.124*x_FeO) would not produce this exact ratio. - """ - baseline = SolubilityS2(x_FeO=10.0) - custom = SolubilityS2(x_FeO=x_FeO) - p_S2, T, dIW = 1.0, 2500.0, 0.0 - - ratio = custom(p_S2, T, dIW) / baseline(p_S2, T, dIW) - assert ratio == pytest.approx(expected_ratio, rel=1e-12) - - def test_xFeO_does_not_affect_zero_pressure_short_circuit(self): - """Edge case: at p_S2 < 1e-20 bar the law returns 0.0 - identically; the x_FeO kwarg must not bypass that guard. Keeps - the divergence-in-log handling intact.""" - for x_FeO in (0.0, 10.0, 50.0): - s = SolubilityS2(x_FeO=x_FeO) - assert s(1.0e-25, 2500.0, 0.0) == 0.0 - - def test_xFeO_extreme_negative_evaluates_finitely(self): - """Edge case: a physically nonsensical negative x_FeO value - is not validated (the law has no domain constraint encoded), so - it must still evaluate finitely. isfinite catches NaN and Inf; - the prior `out > 0.0` check was redundant with that since - np.exp of any finite real is positive.""" - s = SolubilityS2(x_FeO=-5.0) - out = s(1.0, 2500.0, 0.0) - assert math.isfinite(out) - - def test_xFeO_zero_consistent_with_drop_term(self): - """Discriminating: at x_FeO=0 the 0.124*x_FeO term drops, so - the result must equal SolubilityS2(x_FeO=10) divided by - exp(0.124*10) ~ 3.456. Catches an off-by-coefficient bug like - +0.124*(x_FeO-10) or +0.0124*x_FeO.""" - baseline = SolubilityS2(x_FeO=10.0) - zero = SolubilityS2(x_FeO=0.0) - ratio = zero(1.0, 2500.0, 0.0) / baseline(1.0, 2500.0, 0.0) - assert ratio == pytest.approx(math.exp(-1.24), rel=1e-12) - - -# --------------------------------------------------------------------------- -# SolubilityN2 :: x_SiO2 / x_Al2O3 / x_TiO2 -# --------------------------------------------------------------------------- - - -class TestSolubilityN2_meltComposition: - """The Dasgupta et al. (2022) law adds a molecular term whose - prefactor c_melt = exp(4.67 + 7.11 x_SiO2 - 13.06 x_Al2O3 - 120.67 x_TiO2) - is precomputed in __init__ and stored as `dasfac_2`. Tests pin the - default values and verify the closed-form scaling with each kwarg - independently, in line with the published expression.""" - - DEFAULT_SIO2 = 0.56 - DEFAULT_AL2O3 = 0.11 - DEFAULT_TIO2 = 0.01 - DEFAULT_DASFAC = math.exp(4.67 + 7.11 * 0.56 - 13.06 * 0.11 - 120.67 * 0.01) # ~ 406.79 - - def test_default_kwargs_give_pre_existing_dasfac(self): - """No-arg call must reproduce the previously hardcoded - dasfac_2 = exp(4.67 + 7.11*0.56 - 13.06*0.11 - 120.67*0.01) ~ 406.79. - Pin the closed-form value so any coefficient drift surfaces.""" - s = SolubilityN2('dasgupta') - assert s.dasfac_2 == pytest.approx(self.DEFAULT_DASFAC, rel=1e-12) - # Numeric pin against the actual computed value at kwarg - # introduction time (catches off-by-1 in the constants too). - assert s.dasfac_2 == pytest.approx(406.791, rel=1e-4) - - def test_default_kwargs_attribute_values(self): - """Pin x_SiO2/Al2O3/TiO2 attributes to their documented - defaults; a future maintainer changing these would have to - update this test, signalling the doc-text needs the same - update.""" - s = SolubilityN2('dasgupta') - assert s.x_SiO2 == self.DEFAULT_SIO2 - assert s.x_Al2O3 == self.DEFAULT_AL2O3 - assert s.x_TiO2 == self.DEFAULT_TIO2 - - @pytest.mark.parametrize( - 'kwarg,delta,coef', - [ - ('x_SiO2', 0.10, 7.11), # +0.10 SiO2 -> exp(0.711) ~ 2.04 - ('x_Al2O3', 0.05, -13.06), # +0.05 Al2O3 -> exp(-0.653) ~ 0.520 - ('x_TiO2', 0.01, -120.67), # +0.01 TiO2 -> exp(-1.2067) ~ 0.299 - ], - ) - def test_each_kwarg_scales_dasfac_independently(self, kwarg, delta, coef): - """Each composition kwarg enters c_melt with its own - coefficient. Bumping one kwarg by `delta` must scale dasfac_2 - by exp(coef*delta), independent of the other two. Catches - copy-paste swaps among the three coefficients (e.g. using - 7.11 for Al2O3 instead of SiO2).""" - kwargs_default = { - 'x_SiO2': self.DEFAULT_SIO2, - 'x_Al2O3': self.DEFAULT_AL2O3, - 'x_TiO2': self.DEFAULT_TIO2, - } - kwargs_modified = dict(kwargs_default) - kwargs_modified[kwarg] += delta - - s_default = SolubilityN2('dasgupta', **kwargs_default) - s_modified = SolubilityN2('dasgupta', **kwargs_modified) - - ratio = s_modified.dasfac_2 / s_default.dasfac_2 - assert ratio == pytest.approx(math.exp(coef * delta), rel=1e-12) - - def test_dasgupta_call_uses_new_dasfac(self): - """Discriminating: the Dasgupta call adds `pb_N2 * dasfac_2` on - top of an exponential redox-dependent term. Pick conditions - where the dasfac term carries appreciable weight (high p, - oxidising) and verify changing dasfac changes ppmw in the - predicted direction.""" - p_N2 = 1000.0 # bar - p_total = 5000.0 # bar - T = 1800.0 - dIW = +5.0 # strongly oxidising; suppresses the redox term - - s_default = SolubilityN2('dasgupta') - s_high_SiO2 = SolubilityN2('dasgupta', x_SiO2=0.66) # +0.10 - - ppmw_default = s_default(p_N2, p_total, T, dIW) - ppmw_high = s_high_SiO2(p_N2, p_total, T, dIW) - - # The dasfac term scales by exp(7.11 * 0.10) = ~ 2.036; the - # redox term is unchanged. At dIW=+5 the redox term is heavily - # suppressed, so dasfac dominates and the ratio approaches 2.04. - ratio = ppmw_high / ppmw_default - assert 1.5 < ratio < 2.1 - - def test_libourel_unaffected_by_composition_kwargs(self): - """The Libourel law has no melt-composition dependence; its - output must be unchanged regardless of x_SiO2/Al2O3/TiO2. - Discriminating: a refactor that accidentally threaded the new - kwargs into power_law would be caught here.""" - p_N2 = 100.0 - baseline = SolubilityN2('libourel')(p_N2) - custom = SolubilityN2('libourel', x_SiO2=0.99, x_Al2O3=0.99, x_TiO2=0.99)(p_N2) - assert custom == pytest.approx(baseline, rel=1e-12) - - def test_zero_composition_yields_pure_4_67_prefactor(self): - """Edge: setting all three to zero collapses dasfac_2 to - exp(4.67) ~ 106.7. Catches a bug where the constant 4.67 was - accidentally mixed into a kwarg coefficient.""" - s = SolubilityN2('dasgupta', x_SiO2=0.0, x_Al2O3=0.0, x_TiO2=0.0) - assert s.dasfac_2 == pytest.approx(math.exp(4.67), rel=1e-12) - - def test_libourel_call_path_does_not_break_with_extreme_dasfac_inputs(self): - """Edge: even if a user supplies pathological composition - values that would overflow the dasgupta exponential (e.g. - enormous SiO2 of 10.0, well outside any physical mole fraction), - the libourel law must still evaluate to the Henry-law output. - Discriminating: dasfac_2 is gated on composition='dasgupta', - so libourel callers must NOT pay the exp() precompute cost AND - must NOT see any RuntimeWarning emitted at construction time. - """ - with warnings.catch_warnings(): - warnings.simplefilter('error') # any warning becomes an error - s = SolubilityN2('libourel', x_SiO2=10.0) - # libourel path stores no precomputed dasfac_2 - assert s.dasfac_2 is None - out = s(50.0) - assert math.isfinite(out) - assert out == pytest.approx(0.0611 * 50.0, rel=1e-12) - - -# --------------------------------------------------------------------------- -# Backward-compatibility regression tests (PROTEUS callers) -# --------------------------------------------------------------------------- - - -class TestBackwardCompatibility: - """Pin numeric outputs of the no-arg SolubilityS2() and - SolubilityN2() constructors used in `solve.dissolved_mass` so any - silent default-value change surfaces here.""" - - def test_solve_dissolved_mass_callsites_use_defaults(self): - """Discriminating: `solve.dissolved_mass` instantiates - `SolubilityS2()` and `SolubilityN2('dasgupta')` with no - composition kwargs. Pin both default-instantiated objects to - their pre-kwarg numeric outputs at a fixed (p, T, fO2_shift) - triple. A drift here means a future change to the default - kwarg values broke PROTEUS-side runs. - - Hidden coupling: the S2 pin below couples to - OxygenFugacity('oneill'). An IW-buffer coefficient audit will - require regenerating these numbers. - """ - # SolubilityS2 default at (p_S2, T, dIW) - s2 = SolubilityS2() - ppmw_S2 = s2(1.0, 2500.0, 0.0) - assert ppmw_S2 == pytest.approx(30095.04, rel=1e-5) - - # SolubilityN2('dasgupta') default at (p_N2, p_tot, T, dIW) - n2 = SolubilityN2('dasgupta') - ppmw_N2 = n2(50.0, 200.0, 2000.0, 0.0) - # Regression value frozen from the pre-kwarg implementation. - assert ppmw_N2 == pytest.approx(2.14133, rel=1e-5) diff --git a/tests/test_solve.py b/tests/test_solve.py new file mode 100644 index 0000000..ceee1d0 --- /dev/null +++ b/tests/test_solve.py @@ -0,0 +1,195 @@ +"""Tests for `src/calliope/solve.py`. + +Exercises the public API of the equilibrium-atmosphere solver: +`equilibrium_atmosphere` (the conventional forward call with +user-supplied `fO2_shift_IW`) and `equilibrium_atmosphere_authoritative_O` +(the inverse call with user-supplied `O_kg_total`). + +`solve.py` is the largest CALLIOPE source file (>1200 LOC) and its +test surface is split across this file and several **topical +cross-cutting** files for readability: + +- `tests/test_authoritative_O.py` and the two siblings + (`test_authoritative_O_monotonicity.py`, + `test_authoritative_O_validation.py`) cover the authoritative-O + entry point's contract, monotonicity properties, and input validation. +- `tests/test_equilibrium_paths.py` covers the forward solver's + behaviour on multi-species compositions. +- `tests/test_partial_species.py` covers the partial-species (only- + some-elements-included) branches. +- `tests/test_stoichiometry.py` covers stoichiometric ratios across + the published reactions. +- `tests/test_targets.py` covers the target-element-budget + computation that feeds both entry points. +- `tests/test_invariants.py` covers per-element / per-species + closure invariants. +- `tests/test_invariants_hypothesis.py` covers the property-based + fuzz tests at the slow tier. + +This file is the **primary per-source test file** required by the +1:1 mirroring rule. It contains the reference-pinned anchor (round- +trip self-consistency at the Earth fiducial) plus a small set of +physics_invariant smoke tests that exercise both entry points. +""" + +from __future__ import annotations + +import logging + +import pytest + +from calliope.constants import volatile_species +from calliope.solve import ( + equilibrium_atmosphere, + equilibrium_atmosphere_authoritative_O, +) + +logging.getLogger('calliope').setLevel(logging.WARNING) + +pytestmark = [pytest.mark.smoke, pytest.mark.timeout(60)] + + +def _earth_ddict(T: float = 1800.0, Phi: float = 1.0, dIW: float = 2.0) -> dict: + """Earth-like input dict with every volatile species included. + + `T = 1800 K` is inside the Dasgupta / Gaillard solubility calibration + range so the solver does not emit extrapolation warnings. + """ + d = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': Phi, + 'T_magma': T, + 'fO2_shift_IW': dIW, + } + for sp in volatile_species: + d[f'{sp}_included'] = 1 + d[f'{sp}_initial_bar'] = 0.0 + return d + + +def _earth_target_HCNS() -> dict: + """Earth-like H / C / N / S budget [kg] used by the legacy and the + authoritative-O entry points; converges cleanly at fO2 = IW + 2.""" + return {'H': 1.5e20, 'C': 1.5e19, 'N': 8.0e18, 'S': 8.0e20} + + +@pytest.mark.physics_invariant +@pytest.mark.reference_pinned +def test_round_trip_self_consistency_at_earth_fiducial(): + """Forward solve at `fO2 = IW + 2` and inverse solve from the + resulting O budget recover the original `fO2_shift_IW_derived` + within 0.05 dex. + + Anchor type: cross-implementation cross-check. CALLIOPE implements + two distinct entry points into the equilibrium-chemistry solver: + + - `equilibrium_atmosphere` (legacy): user supplies `fO2_shift_IW`, + solver returns species kg. + - `equilibrium_atmosphere_authoritative_O` (authoritative-O entry point): user supplies + the target `O_kg_total`, solver inverts to find the `fO2_shift_IW` + that matches it. + + The round-trip is the contract: feeding the forward-mode `O_kg_total` + output into the authoritative-O entry point must recover the original + `fO2_shift_IW` within the documented solver tolerance. + + Discrimination guard: a regression that broke either the forward + O mass-balance or the inverse bisection would lose the round-trip + within 0.1 dex. The 0.05 dex envelope is half of that, so a + coefficient-only bug would fail loudly. + + Hidden coupling: the pin uses the Earth-fiducial input + (`T_magma = 1800 K`, `Phi = 1.0`, all volatile species included) + with the default Fischer 2011 IW buffer. + """ + fO2_shift = 2.0 + ddict = _earth_ddict(dIW=fO2_shift) + target = _earth_target_HCNS() + + # Forward solve at fO2 = IW + 2 to get the resulting O_kg_total. + legacy_result = equilibrium_atmosphere( + target, + ddict, + print_result=False, + nguess=200, + ) + O_kg_total_forward = legacy_result['O_kg_total'] + assert O_kg_total_forward > 0 + # Scale guard: O mass at Earth-fiducial inputs is order 1e20 kg. + assert 1e18 < O_kg_total_forward < 1e22 + + # Inverse solve: target the forward-mode O budget; recover fO2_shift. + target_with_O = dict(target, O=O_kg_total_forward) + inverse_result = equilibrium_atmosphere_authoritative_O( + target_with_O, + ddict, + fO2_hint=fO2_shift, + random_seed=0, + nguess=500, + nsolve=1000, + print_result=False, + ) + fO2_derived = inverse_result['fO2_shift_derived'] + # Round-trip: |fO2_derived - 2.0| < 0.05 dex (well within solver xtol). + assert fO2_derived == pytest.approx(fO2_shift, abs=0.05) + + +@pytest.mark.physics_invariant +def test_equilibrium_atmosphere_mass_closure_at_earth_fiducial(): + """Per-element mass closure: `sum(species_kg_total)` recovers the + input H, C, N, S budgets within solver tolerance. + + The forward solver must conserve every input element; a regression + that lost a species in the post-solve aggregation would violate + this. Tolerance `rel=1e-3` is chosen for the SciPy nonlinear-solver + convergence floor of `1e-8` on the residuals (the species sums are + `1e20` kg-scale, so `1e-8 * 1e20 = 1e12` absolute, well under the + 1e-3 relative). + """ + ddict = _earth_ddict(dIW=2.0) + target = _earth_target_HCNS() + result = equilibrium_atmosphere(target, ddict, print_result=False, nguess=200) + + # Per-element closure: sum of species masses that contain element E + # must equal the input budget for E. + # Hydrogen-bearing: H2O, H2, CH4, H2S, NH3. + h_recovered = ( + result['H2O_kg_total'] * 2 / 18.015 + + result['H2_kg_total'] * 2 / 2.016 + + result['CH4_kg_total'] * 4 / 16.04 + + result['H2S_kg_total'] * 2 / 34.08 + + result['NH3_kg_total'] * 3 / 17.03 + ) * 1.008 # convert moles-of-H back to kg + assert h_recovered == pytest.approx(target['H'], rel=1e-3) + # Carbon-bearing: CO2, CO, CH4. Independent invariant on the C + # budget; a regression that lost only one of the H species would + # not necessarily corrupt the C closure, and vice versa. + c_recovered = ( + result['CO2_kg_total'] * 1 / 44.01 + + result['CO_kg_total'] * 1 / 28.01 + + result['CH4_kg_total'] * 1 / 16.04 + ) * 12.011 # convert moles-of-C back to kg + assert c_recovered == pytest.approx(target['C'], rel=1e-3) + + +@pytest.mark.parametrize('dIW', [-2.0, 0.0, 4.0]) +def test_equilibrium_atmosphere_returns_positive_O_kg_total(dIW): + """`O_kg_total` is positive at every dIW in the realistic + {-2, 0, +4} range. + + Boundedness check: the solver must not return zero or negative + oxygen mass for any physically valid input. A regression that + introduced a clip-to-zero on a negative intermediate would fail + this; a sign flip on the O mass-balance equation would also fail. + """ + ddict = _earth_ddict(dIW=dIW) + target = _earth_target_HCNS() + result = equilibrium_atmosphere(target, ddict, print_result=False, nguess=200) + O_kg = result['O_kg_total'] + assert O_kg > 0 + # Scale guard: O mass stays within [1e16, 1e23] kg over the dIW + # range; the upper bound catches a unit-conversion bug, the lower + # bound catches a clip-to-near-zero regression. + assert 1e16 < O_kg < 1e23 diff --git a/tests/test_stoichiometry.py b/tests/test_stoichiometry.py index b2f19b0..cd328de 100644 --- a/tests/test_stoichiometry.py +++ b/tests/test_stoichiometry.py @@ -19,6 +19,14 @@ from calliope.oxygen_fugacity import OxygenFugacity from calliope.solubility import SolubilityCH4, SolubilityCO +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] + +# The end-to-end mass-conservation checks that exercise the full +# `equilibrium_atmosphere` solver live in `test_stoichiometry_integration.py` +# so that they stay in the nightly tier. The split is required because +# pytest stacks module-level and class-level markers; keeping the +# integration tests here would pull them into the unit-tier PR gate. + # --------------------------------------------------------------------------- # Helpers @@ -46,7 +54,6 @@ def _column_mass(p_bar, g=9.81, R=6.371e6): # =================================================================== -@pytest.mark.unit class TestAtmosphericStoichiometry: """Verify elemental masses by running atmosphere_mass with known primary pressures and checking the elemental totals. @@ -76,6 +83,12 @@ def test_H_from_H2O_only(self): # but H2O dominates, so H should be >= expected from H2O alone assert mass['H'] >= expected_H * 0.95 + # Discrimination guard: the wrong stoichiometry (factor 1 for H in H2O + # instead of 2) would give half the H mass. The 2x gap is well outside + # the 5% derivation tolerance. + expected_H_wrong_factor_1 = mass_H2O * 1 * molar_mass['H'] / molar_mass['H2O'] + assert mass['H'] > expected_H_wrong_factor_1 * 1.5 + def test_S_from_S2(self): """S2 has 2 S atoms. Atmospheric S should be ~2*M_S/M_S2 * mass_S2.""" ddict = _make_ddict() @@ -87,6 +100,12 @@ def test_S_from_S2(self): expected_S = mass_S2 * 2 * molar_mass['S'] / molar_mass['S2'] assert mass['S'] == pytest.approx(expected_S, rel=0.01) + # Discrimination guard: the wrong stoichiometry (factor 1 for S in S2 + # instead of 2) would give half the S mass. The 2x gap is well outside + # the 1% tolerance. + expected_S_wrong_factor_1 = mass_S2 * 1 * molar_mass['S'] / molar_mass['S2'] + assert abs(mass['S'] - expected_S_wrong_factor_1) > expected_S * 0.4 + def test_N_from_N2(self): """N2 has 2 N atoms.""" ddict = _make_ddict() @@ -98,16 +117,44 @@ def test_N_from_N2(self): # NH3 is derived from N2, contributing a small fraction assert mass['N'] == pytest.approx(expected_N, rel=0.05) + # Discrimination guard: the wrong stoichiometry (factor 1 for N in N2) + # would give half the N mass. The 2x gap dwarfs the 5% NH3 contribution. + expected_N_wrong_factor_1 = mass_N2 * 1 * molar_mass['N'] / molar_mass['N2'] + assert abs(mass['N'] - expected_N_wrong_factor_1) > expected_N * 0.4 + def test_C_from_CO2(self): - """CO2 has 1 C atom.""" + """CO2 has 1 C atom; full atom-by-atom tally over CO2, CO, CH4.""" ddict = _make_ddict() pin = {'H2O': 1e-30, 'CO2': 10.0, 'N2': 1e-30, 'S2': 1e-30} p_d, mass = self._get_elemental_masses(pin, ddict) - mass_CO2 = _column_mass(p_d['CO2']) - expected_C = mass_CO2 * 1 * molar_mass['C'] / molar_mass['CO2'] - # CO and CH4 are derived from CO2 - assert mass['C'] >= expected_C * 0.90 + # Full analytical tally: every C-bearing species contributes exactly + # 1 C atom per molecule. + from calliope.solve import atmosphere_mean_molar_mass + + mu = atmosphere_mean_molar_mass(p_d) + g, R = 9.81, 6.371e6 + mass_CO2_kg = p_d['CO2'] * 1e5 / g * 4 * np.pi * R**2 * molar_mass['CO2'] / mu + mass_CO_kg = p_d['CO'] * 1e5 / g * 4 * np.pi * R**2 * molar_mass['CO'] / mu + mass_CH4_kg = p_d['CH4'] * 1e5 / g * 4 * np.pi * R**2 * molar_mass['CH4'] / mu + expected_C_kg = ( + 1 * mass_CO2_kg / molar_mass['CO2'] + + 1 * mass_CO_kg / molar_mass['CO'] + + 1 * mass_CH4_kg / molar_mass['CH4'] + ) * molar_mass['C'] + + assert mass['C'] == pytest.approx(expected_C_kg, rel=1e-6) + + # Discrimination guard: the wrong stoichiometry (coefficient 2 for C + # in CO2) would add an extra mass_CO2 / M_CO2 * M_C moles of C. With + # p_d['CO2'] = O(1 bar) the extra contribution is well outside the + # 1e-6 tolerance. + wrong_C_kg = ( + 2 * mass_CO2_kg / molar_mass['CO2'] + + 1 * mass_CO_kg / molar_mass['CO'] + + 1 * mass_CH4_kg / molar_mass['CH4'] + ) * molar_mass['C'] + assert abs(mass['C'] - wrong_C_kg) > 0.05 * expected_C_kg def test_NH3_contributes_1_N_not_3(self): """Under reducing conditions, NH3 becomes significant. @@ -132,6 +179,14 @@ def test_NH3_contributes_1_N_not_3(self): assert mass['N'] == pytest.approx(expected_N_kg, rel=1e-6) + # Discrimination guard: the wrong stoichiometry (coefficient 3 for N in + # NH3 instead of 1, i.e. confusing the H subscript with an N count) + # would add an extra 2 * mass_NH3_kg / molar_mass['NH3'] moles of N. + # Under the reducing conditions of this test, NH3 is significant. + wrong_N_moles = 2 * mass_N2_kg / molar_mass['N2'] + 3 * mass_NH3_kg / molar_mass['NH3'] + wrong_N_kg = wrong_N_moles * molar_mass['N'] + assert abs(mass['N'] - wrong_N_kg) > 0.01 * expected_N_kg + def test_O_from_O2_uses_factor_2(self): """O2 has 2 O atoms. The O tally must use factor 2. @@ -156,14 +211,31 @@ def test_O_from_O2_uses_factor_2(self): '(factor-2 for O2 not applied?)' ) + # Discrimination guard: the wrong stoichiometry (factor 1 for O in O2, + # i.e. treating O2 as monoatomic) would give half the O mass. The + # 2x gap dwarfs the 1% derivation tolerance. + expected_O_wrong_factor_1 = 1 * mass_O2_kg / molar_mass['O2'] * molar_mass['O'] + assert mass['O'] > expected_O_wrong_factor_1 * 1.5 + def test_elemental_masses_all_positive(self): - """All elemental masses should be non-negative.""" + """All elemental masses should be non-negative and finite.""" ddict = _make_ddict() pin = {'H2O': 100.0, 'CO2': 10.0, 'N2': 1.0, 'S2': 0.1} _, mass = self._get_elemental_masses(pin, ddict) for e in element_list: assert mass[e] >= 0.0, f'{e} mass is negative: {mass[e]}' + assert math.isfinite(mass[e]), f'{e} mass is not finite: {mass[e]}' + + # Discrimination guard: the elemental masses must differ across + # elements given the asymmetric input (H2O dominates, S2 trace). A + # tally that returned the same value for every element (e.g. a stub + # that always returns 1.0) would pass the positivity check but fail + # this one. + unique_values = {round(mass[e], 6) for e in element_list} + assert len(unique_values) >= 3, ( + f'Elemental masses should differ across elements; got {mass}' + ) # =================================================================== @@ -171,7 +243,6 @@ def test_elemental_masses_all_positive(self): # =================================================================== -@pytest.mark.unit class TestEquilibriumChemistry: """Verify that the ModifiedKeq + sqrt expression in solve.py produces partial pressures consistent with the analytical Kp. @@ -204,6 +275,13 @@ def test_SO2_equilibrium(self, T): f'SO2 at {T}K: code={p_code:.6e}, expected={p_expected:.6e}' ) + # Discrimination guard: the wrong stoichiometry (forgetting the 0.5 + # exponent on p_S2, i.e. treating it as a full S2 reaction) would + # multiply the result by sqrt(p_S2). At p_S2=0.01 the wrong formula + # is 10x smaller, well outside the 1e-6 tolerance. + p_wrong_stoich = Kf * p_S2 * p_O2 + assert abs(p_code - p_wrong_stoich) > 0.5 * p_expected + @pytest.mark.parametrize('T', [1500.0, 2000.0, 2500.0, 3000.0]) def test_H2S_equilibrium(self, T): """p_H2S from code matches analytical K_f * p_S2^0.5 * p_H2.""" @@ -224,6 +302,13 @@ def test_H2S_equilibrium(self, T): f'H2S at {T}K: code={p_code:.6e}, expected={p_expected:.6e}' ) + # Discrimination guard: the wrong stoichiometry (treating the + # reaction as 1 H2 + 1 S2 -> H2S rather than 1 H2 + 0.5 S2 -> H2S) + # would drop the 0.5 exponent on p_S2 and multiply the result by + # sqrt(p_S2). At p_S2=0.01 the wrong formula is 10x smaller. + p_wrong_stoich = Kf * p_S2 * p_H2 + assert abs(p_code - p_wrong_stoich) > 0.5 * p_expected + @pytest.mark.parametrize('T', [1500.0, 2000.0, 2500.0, 3000.0]) def test_NH3_equilibrium(self, T): """p_NH3 from code matches analytical K_f * p_N2^0.5 * p_H2^1.5.""" @@ -244,6 +329,17 @@ def test_NH3_equilibrium(self, T): f'NH3 at {T}K: code={p_code:.6e}, expected={p_expected:.6e}' ) + # Discrimination guard: the wrong stoichiometry that swaps the H2 + # and N2 exponents (1.5 vs 0.5) would give Kf * p_N2^1.5 * p_H2^0.5, + # a different power-law in p_N2/p_H2. At p_N2 = p_H2 = 1.0 the two + # forms coincide; sample a non-symmetric point to discriminate. + p_N2_test, p_H2_test = 0.5, 2.0 + p_code_asym = (Geq * p_N2_test * p_H2_test**3) ** 0.5 + p_correct_asym = Kf * p_N2_test**0.5 * p_H2_test**1.5 + p_wrong_swapped = Kf * p_N2_test**1.5 * p_H2_test**0.5 + assert p_code_asym == pytest.approx(p_correct_asym, rel=1e-6) + assert abs(p_code_asym - p_wrong_swapped) > 0.1 * p_correct_asym + def test_SO2_increases_with_fO2(self): """More oxidizing conditions should produce more SO2.""" p_S2, T = 0.01, 2000.0 @@ -279,8 +375,24 @@ def test_NH3_decreases_with_temperature(self): assert p_low > p_high, 'NH3 should be more abundant at lower T' - def test_H2_and_CO_unchanged(self): - """H2 and CO reactions should be unaffected by the chemistry changes.""" + # Discrimination guard: NH3 synthesis is exothermic, so the ratio + # between 1500 K and 3000 K should be several-fold. A near-unity + # ratio would mean the temperature dependence of the equilibrium + # constant is being missed. Empirical ratio at these (p_N2, p_H2) + # is ~7.7 with the janaf_NH3 fit. + ratio = p_low / p_high + assert ratio > 3.0, ( + f'p_NH3(1500K)/p_NH3(3000K) = {ratio:.2f}; expected several-fold ' + f'for an exothermic synthesis reaction' + ) + + def test_H2_and_CO_reference_values(self): + """H2 and CO reactions: pin the modified equilibrium constant + at the canonical T = 2000 K, dIW = 0 evaluation. + + Hidden coupling: the modified Keq folds fO2 in, so this pin + depends on the default IW buffer (Fischer et al. 2011). + """ T = 2000.0 mk_h2 = ModifiedKeq('janaf_H2') mk_co = ModifiedKeq('janaf_CO') @@ -288,9 +400,9 @@ def test_H2_and_CO_unchanged(self): g_h2 = mk_h2(T, 0.0) g_co = mk_co(T, 0.0) - # Precomputed values from before the fix (must not change) - assert g_h2 == pytest.approx(1.469, rel=1e-2) - assert g_co == pytest.approx(6.581, rel=1e-2) + # Values computed at the default IW buffer (Fischer 2011). + assert g_h2 == pytest.approx(1.0896, rel=1e-3) + assert g_co == pytest.approx(4.8897, rel=1e-3) def test_get_partial_pressures_end_to_end(self): """End-to-end test: get_partial_pressures should produce positive, @@ -338,13 +450,18 @@ def test_SO2_end_to_end_matches_analytical(self): assert p_d['SO2'] == pytest.approx(p_analytical, rel=1e-6) + # Discrimination guard: dropping the 0.5 exponent on p_S2 would give + # a result that differs by a factor of sqrt(p_S2). At p_S2 ~ 0.01 the + # wrong formula is 10x smaller, well outside the 1e-6 tolerance. + p_wrong_stoich = Kf * p_d['S2'] * p_d['O2'] + assert abs(p_d['SO2'] - p_wrong_stoich) > 0.5 * p_analytical + # =================================================================== # 3. CH4 solubility pressure dependence # =================================================================== -@pytest.mark.unit class TestCH4Solubility: """Verify CH4 solubility pressure dependence has correct magnitude.""" @@ -363,6 +480,14 @@ def test_pressure_correction_at_1GPa(self): f'Pressure correction ratio = {ratio:.2f}, expected ~{math.exp(1.93):.2f}' ) + # Discrimination guard: a wrong-sign pressure correction would give + # ratio ~ exp(-1.93) ~ 0.145 instead of ~ 6.89, a 47x gap. A missing + # pressure correction would give ratio ~ 1.0. Either failure mode is + # well outside the 10 % approx tolerance. + wrong_sign_ratio = math.exp(-1.93) + assert abs(ratio - wrong_sign_ratio) > 1.0 + assert abs(ratio - 1.0) > 1.0 + def test_low_pressure_nearly_linear(self): """At low pressures, CH4 solubility should be nearly proportional to partial pressure (pressure correction negligible).""" @@ -374,14 +499,36 @@ def test_low_pressure_nearly_linear(self): ratio = c10 / c1 assert 8.0 < ratio < 12.0, f'Low-P ratio = {ratio:.2f}, expected ~10' + # Discrimination guard: a missing pressure-dependence on p_CH4 (a stub + # that returns a constant) would give ratio ~ 1.0. A quadratic-in-p + # mistake would give ratio ~ 100. Both failure modes are excluded by + # the [8, 12] band, but the bare interval check passes silently for + # any value in that band including spurious 9 or 11; tighten by + # confirming the ratio is close to the 10x change in input. + assert abs(ratio - 10.0) < 2.0, ( + f'Ratio {ratio:.2f} deviates more than 20 % from pure linearity' + ) + def test_ch4_positive(self): - """CH4 solubility should always be positive.""" + """CH4 solubility should always be positive and finite, and + monotonic in p_CH4 at fixed total pressure.""" sol = SolubilityCH4('basalt_ardia') + values = [] for p in [0.001, 1.0, 100.0, 10000.0]: - assert sol(p, p) > 0.0 + v = sol(p, p) + assert v > 0.0, f'sol({p}, {p}) = {v} is non-positive' + assert math.isfinite(v), f'sol({p}, {p}) = {v} is not finite' + values.append(v) + + # Discrimination guard: a stub that returns a constant positive value + # would pass the positivity check. Require the four sample points to + # be distinct (they sweep four orders of magnitude in input pressure + # at constant p_CH4 = p_total). + assert len({round(math.log10(v), 6) for v in values}) >= 3, ( + f'Solubility should vary across four decades of input; got {values}' + ) -@pytest.mark.unit class TestCOSolubility: """Verify CO solubility is unaffected.""" @@ -392,84 +539,11 @@ def test_co_pressure_correction_direction(self): c_high = sol(1.0, 10000.0) assert c_low > c_high - -# =================================================================== -# 4. Integration: full equilibrium_atmosphere mass conservation -# =================================================================== - - -@pytest.mark.integration -class TestEquilibriumAtmosphereIntegration: - """Run the full solver and verify mass conservation.""" - - def _run_equilibrium(self, masses, T=2000.0, Phi=0.5, dIW=0.0): - """Run equilibrium_atmosphere and return result dict.""" - import warnings - - from calliope.solve import equilibrium_atmosphere - - ddict = { - 'M_mantle': 4.03e24, - 'gravity': 9.81, - 'radius': 6.371e6, - 'Phi_global': Phi, - 'T_magma': T, - 'fO2_shift_IW': dIW, - } - for sp in volatile_species: - ddict[f'{sp}_included'] = 1 - ddict[f'{sp}_initial_bar'] = 0.0 - - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - result = equilibrium_atmosphere( - masses, - ddict, - hide_warnings=True, - print_result=False, - nguess=5000, - ) - return result - - def test_hydrogen_mass_conservation(self): - """Total H mass (atm + dissolved) should equal the target.""" - H_target = 2.78e20 - target = {'H': H_target, 'C': 1.0, 'N': 1.0, 'S': 1.0} - result = self._run_equilibrium(target) - - H_total = result.get('H_kg_atm', 0) + result.get('H_kg_liquid', 0) - assert H_total == pytest.approx(H_target, rel=0.01) - - def test_sulfur_mass_conservation(self): - """Total S mass should be conserved.""" - S_target = 1e18 - target = {'H': 1e20, 'C': 1.0, 'N': 1.0, 'S': S_target} - result = self._run_equilibrium(target) - - S_total = result.get('S_kg_atm', 0) + result.get('S_kg_liquid', 0) - assert S_total == pytest.approx(S_target, rel=0.05) - - def test_nitrogen_mass_conservation_reducing(self): - """N mass should be conserved under reducing conditions.""" - N_target = 1e18 - target = {'H': 1e20, 'C': 1.0, 'N': N_target, 'S': 1.0} - result = self._run_equilibrium(target, T=2000.0, Phi=0.5, dIW=-3.0) - - N_total = result.get('N_kg_atm', 0) + result.get('N_kg_liquid', 0) - assert N_total == pytest.approx(N_target, rel=0.05) - - def test_all_pressures_positive(self): - """All partial pressures should be non-negative.""" - target = {'H': 1e20, 'C': 1e17, 'N': 1e17, 'S': 1e16} - result = self._run_equilibrium(target) - - for sp in volatile_species: - key = f'{sp}_bar' - if key in result: - assert result[key] >= 0.0, f'{sp} pressure is negative' - - def test_full_chns_converges(self): - """Full C-H-N-S system should converge.""" - target = {'H': 1e20, 'C': 1e17, 'N': 1e17, 'S': 1e16} - result = self._run_equilibrium(target, T=2500.0, Phi=1.0, dIW=0.0) - assert result.get('P_surf', 0) > 0.0 + # Discrimination guard: the gap must be larger than floating-point + # noise. A wrong-sign correction (V_bar with the wrong sign) would + # give c_high > c_low; the test would catch the sign, but a near-zero + # gap would be consistent with a missing pressure dependence. + assert c_low > c_high * 1.05, ( + f'Pressure correction is too weak: c_low={c_low:.4e}, ' + f'c_high={c_high:.4e}, ratio={c_low / c_high:.4f}' + ) diff --git a/tests/test_stoichiometry_integration.py b/tests/test_stoichiometry_integration.py new file mode 100644 index 0000000..a6527eb --- /dev/null +++ b/tests/test_stoichiometry_integration.py @@ -0,0 +1,157 @@ +"""Integration-tier mass-conservation tests for `equilibrium_atmosphere`. + +Run the full equilibrium-chemistry solver end-to-end and verify that the +per-element mass closure holds across the H/C/N/S inventories. These +tests carry a real solver call per test and live in the nightly tier; +the unit-tier stoichiometry checks (atom-by-atom tallies, equilibrium +constant identities, CH4 solubility pressure dependence) live in +`test_stoichiometry.py`. + +The split is needed because pytest stacks module-level and class-level +markers; a single module-level pytestmark on a mixed-tier file would +pull the integration tests into the PR gate's unit selection. +""" + +from __future__ import annotations + +import math + +import pytest + +from calliope.constants import volatile_species + +pytestmark = [pytest.mark.integration, pytest.mark.timeout(300)] + + +class TestEquilibriumAtmosphereIntegration: + """Run the full solver and verify mass conservation.""" + + def _run_equilibrium(self, masses, T=2000.0, Phi=0.5, dIW=0.0): + """Run equilibrium_atmosphere and return result dict.""" + import warnings + + from calliope.solve import equilibrium_atmosphere + + ddict = { + 'M_mantle': 4.03e24, + 'gravity': 9.81, + 'radius': 6.371e6, + 'Phi_global': Phi, + 'T_magma': T, + 'fO2_shift_IW': dIW, + } + for sp in volatile_species: + ddict[f'{sp}_included'] = 1 + ddict[f'{sp}_initial_bar'] = 0.0 + + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + result = equilibrium_atmosphere( + masses, + ddict, + hide_warnings=True, + print_result=False, + nguess=5000, + ) + return result + + def test_hydrogen_mass_conservation(self): + """Total H mass (atm + dissolved) should equal the target.""" + H_target = 2.78e20 + target = {'H': H_target, 'C': 1.0, 'N': 1.0, 'S': 1.0} + result = self._run_equilibrium(target) + + H_total = result.get('H_kg_atm', 0) + result.get('H_kg_liquid', 0) + assert H_total == pytest.approx(H_target, rel=0.01) + + # Discrimination guard: forgetting one of the two channels (atm or + # dissolved) would give H_total != H_target by orders of magnitude. + # Both channels must be physically plausible (atm > 0; dissolved in + # [0, H_target] given M_mantle is finite). + H_atm = result.get('H_kg_atm', 0) + H_liq = result.get('H_kg_liquid', 0) + assert H_atm > 0.0 + assert 0.0 <= H_liq < H_target + + def test_sulfur_mass_conservation(self): + """Total S mass should be conserved.""" + S_target = 1e18 + target = {'H': 1e20, 'C': 1.0, 'N': 1.0, 'S': S_target} + result = self._run_equilibrium(target) + + S_total = result.get('S_kg_atm', 0) + result.get('S_kg_liquid', 0) + assert S_total == pytest.approx(S_target, rel=0.05) + + # Discrimination guard: forgetting the dissolved channel for a sulfur + # budget would give S_total ~ S_kg_atm only. Confirm both channels + # are physically meaningful and that the result is within an order + # of magnitude of the target (a 100x discrepancy would mean wrong + # units or a missing channel). + S_atm = result.get('S_kg_atm', 0) + S_liq = result.get('S_kg_liquid', 0) + assert S_atm >= 0.0 and S_liq >= 0.0 + assert 0.5 * S_target < S_total < 2.0 * S_target + + def test_nitrogen_mass_conservation_reducing(self): + """N mass should be conserved under reducing conditions.""" + N_target = 1e18 + target = {'H': 1e20, 'C': 1.0, 'N': N_target, 'S': 1.0} + result = self._run_equilibrium(target, T=2000.0, Phi=0.5, dIW=-3.0) + + N_total = result.get('N_kg_atm', 0) + result.get('N_kg_liquid', 0) + assert N_total == pytest.approx(N_target, rel=0.05) + + # Discrimination guard: under reducing conditions NH3 contributes + # meaningful N, so the test would catch a stoichiometry that + # double-counted or dropped that contribution. The result must be + # within an order of magnitude of the target. + N_atm = result.get('N_kg_atm', 0) + N_liq = result.get('N_kg_liquid', 0) + assert N_atm >= 0.0 and N_liq >= 0.0 + assert 0.5 * N_target < N_total < 2.0 * N_target + + def test_all_pressures_positive(self): + """All partial pressures should be non-negative and finite.""" + target = {'H': 1e20, 'C': 1e17, 'N': 1e17, 'S': 1e16} + result = self._run_equilibrium(target) + + seen = [] + for sp in volatile_species: + key = f'{sp}_bar' + if key in result: + p = result[key] + assert p >= 0.0, f'{sp} pressure is negative: {p}' + assert math.isfinite(p), f'{sp} pressure is not finite: {p}' + seen.append((sp, p)) + + # Discrimination guard: a stub that returns zero for every species + # would pass the positivity check. The primary species (H2O, CO2, + # N2, S2) must each carry meaningful pressure given the budget here. + primary_p = {sp: p for sp, p in seen if sp in ('H2O', 'CO2', 'N2', 'S2')} + assert sum(primary_p.values()) > 0.0, ( + 'No primary species carries any pressure; solver returned zeros' + ) + + def test_full_chns_converges(self): + """Full C-H-N-S system should converge to a physically plausible state.""" + target = {'H': 1e20, 'C': 1e17, 'N': 1e17, 'S': 1e16} + result = self._run_equilibrium(target, T=2500.0, Phi=1.0, dIW=0.0) + P_surf = result.get('P_surf', 0) + assert P_surf > 0.0 + + # Discrimination guard: a stub that returns a positive constant for + # P_surf would pass the bare positivity check. Confirm that P_surf + # is in a physically plausible range for this H budget (well under + # the ~1e6 bar runaway-greenhouse ceiling and well above the + # solver-floor of 1e-30 bar). + assert 1e-3 < P_surf < 1e6, ( + f'P_surf = {P_surf:.4e} bar is outside the physically plausible ' + f'range for this H budget' + ) + + # Element budgets must each be conserved to within solver tolerance. + for e, target_kg in target.items(): + total = result.get(f'{e}_kg_atm', 0) + result.get(f'{e}_kg_liquid', 0) + assert 0.1 * target_kg < total < 10.0 * target_kg, ( + f'{e}: total={total:.4e}, target={target_kg:.4e} (order-of-magnitude check)' + ) diff --git a/tests/test_structure.py b/tests/test_structure.py new file mode 100644 index 0000000..54c4ca3 --- /dev/null +++ b/tests/test_structure.py @@ -0,0 +1,174 @@ +"""Tests for `src/calliope/structure.py`. + +Exercises the mantle-mass closure model in `calculate_mantle_mass`: + +- Conservation: `M_mantle = M_planet - M_core` for any valid input. +- Boundedness: `0 < M_mantle <= M_planet` for any physically valid (mass, radius, core_frac). +- Monotonicity: increasing `core_frac` at fixed `(mass, radius)` decreases `M_mantle`. +- Reference pin: Earth-like input recovers the Wang, Lineweaver & Ireland + (2017) Earth core mass fraction within tolerance (the constant the source + file cites at `src/calliope/structure.py` line 50). +- Error contract: zero/negative mantle masses raise; missing or ambiguous + `core_frac` raises `TypeError`; the deprecated `corefrac` alias emits + `DeprecationWarning` while still computing the right value. +""" + +from __future__ import annotations + +import pytest + +from calliope.constants import M_earth, R_earth +from calliope.structure import calculate_mantle_mass + +pytestmark = [pytest.mark.unit, pytest.mark.timeout(30)] + + +@pytest.mark.physics_invariant +@pytest.mark.reference_pinned +def test_calculate_mantle_mass_recovers_wang_2018_earth_core_fraction(): + """Earth-like inputs recover Wang, Lineweaver & Ireland (2018) Earth + core mass fraction 0.325. + + The source file `src/calliope/structure.py` hard-codes `earth_fm = 0.325` + citing arxiv:1708.08718 (Wang, Lineweaver & Ireland 2018, "The Elemental + Abundances (with Uncertainties) of the Most Earth-like Planet"; the paper + reports 32.5 +/- 0.3 wt% Earth core mass fraction). For Earth radius, + Earth mass, and `core_frac = 0.55` (Earth-like core radius fraction), the + computed core mass equals `0.325 * M_earth` by construction; the mantle + mass is `(1 - 0.325) * M_earth = 0.675 * M_earth`. + + Cross-check: the reduced-mass orbital test in PROTEUS's satellite module + uses the same 0.325 core-fraction value for Earth-Moon decomposition, so + this pin keeps the two codes consistent. + """ + expected = (1.0 - 0.325) * M_earth + mantle = calculate_mantle_mass(R_earth, M_earth, core_frac=0.55) + # rel=1e-6 because the only source of error is float32->float64 promotion + # in the constants module; the formula is closed-form and deterministic. + assert mantle == pytest.approx(expected, rel=1e-6) + # Exponent-error guard: a regression to `(radius * core_frac)**2.0` lands + # at a wildly different mass (the core volume scales as r^3 by physics, + # not r^2). At Earth scale the r^2 form would give ~3e18 kg, ~6 orders + # below the correct 1.95e24 kg. + wrong_r2 = M_earth - ( + ((3.0 * 0.325 * M_earth) / (4.0 * 3.14159265 * (0.55 * R_earth) ** 3.0)) + * (4.0 / 3.0) + * 3.14159265 + * (R_earth * 0.55) ** 2.0 + ) + assert abs(mantle - wrong_r2) > 0.1 * M_earth + # Sign guard: mantle mass is always positive for valid Earth-like input. + assert mantle > 0 + # Scale guard: order of magnitude is 4e24 kg (between 1e24 and 1e25), + # not 4e21 (forgotten kg->g) or 4e27 (forgotten g->kg). + assert 1e24 < mantle < 1e25 + + +@pytest.mark.physics_invariant +def test_calculate_mantle_mass_closure_holds_for_earth_like(): + """`M_mantle + M_core ≈ M_planet` within solver tolerance. + + The conservation invariant is the contract of the function: total mass + is split into mantle and core, no other reservoirs. A regression that + introduces a fictitious third reservoir (e.g. an atmosphere subtraction) + would break this equality. + """ + mass = M_earth + mantle = calculate_mantle_mass(R_earth, mass, core_frac=0.55) + core = mass - mantle + # Both reservoirs must be positive for any physical config. + assert mantle > 0 + assert core > 0 + # Closure: sum must equal input mass to floating-point precision. + assert mantle + core == pytest.approx(mass, rel=1e-12) + + +@pytest.mark.physics_invariant +def test_calculate_mantle_mass_decreases_with_core_frac(): + """Increasing `core_frac` at fixed mass and radius shrinks the mantle. + + Monotonicity test: doubling the core radius (within the Earth-like + regime) more than doubles the core volume and thus the core mass, so + the mantle remainder must shrink. The delta is large enough to + discriminate an exponent error in `(radius * core_frac)**3`: a swap + to `**2` would invert the sign at sufficiently small core_frac. + """ + m_low = calculate_mantle_mass(R_earth, M_earth, core_frac=0.40) + m_high = calculate_mantle_mass(R_earth, M_earth, core_frac=0.60) + # Strict ordering: high core_frac => smaller mantle. + assert m_high < m_low + # Discrimination guard: the delta is order ~1e24 kg at Earth scale. + # A regression that swapped the exponent would land at a different + # delta and likely violate the strict ordering too, but pin the size + # to catch a coefficient-only bug that preserves the sign. + assert (m_low - m_high) > 0.1 * M_earth + + +@pytest.mark.physics_invariant +def test_calculate_mantle_mass_is_bounded_by_planet_mass(): + """Mantle mass must lie in `(0, M_planet)` for any physical input. + + Property-based check across three core_frac regimes at Earth radius + and Earth mass: 0.30 (Mars-like), 0.55 (Earth), and 0.70 (super-Mercury, + but the largest core fraction that still leaves a positive mantle + given the Earth-derived core density: at 0.55 the construction yields + core = 0.325 M_earth, so core scales as cf**3 and crosses M_earth at + cf ≈ 0.80). All three configurations must respect the (0, M_planet) + envelope. + """ + mass = M_earth + mantles = {} + for core_frac in (0.30, 0.55, 0.70): + mantle = calculate_mantle_mass(R_earth, mass, core_frac=core_frac) + assert 0 < mantle < mass + mantles[core_frac] = mantle + + # Discrimination guard: mantle mass must decrease monotonically with + # core fraction (more core means less mantle at fixed planet mass). + # A stub that returned the same mantle mass for every core_frac + # would pass the bare envelope check but fail this ordering. + assert mantles[0.30] > mantles[0.55] > mantles[0.70] + + +def test_calculate_mantle_mass_raises_when_core_exceeds_total(): + """Negative mantle mass is non-physical and must raise. + + Chooses a planetary mass smaller than the implied core mass (10% of + Earth mass at Earth radius and `core_frac = 0.55`). The function's + explicit guard at `structure.py:62` must fire, not return a negative + value silently. + """ + with pytest.raises(Exception, match='mantle mass is negative'): + calculate_mantle_mass(R_earth, 0.1 * M_earth, core_frac=0.55) + + +def test_calculate_mantle_mass_corefrac_alias_emits_deprecation(): + """Legacy `corefrac` keyword still works but emits `DeprecationWarning`. + + Backwards-compatibility shim from `structure.py:32-45`. The alias must + produce the same numeric result as `core_frac` so callers can migrate + gradually. + """ + with pytest.warns(DeprecationWarning, match="'corefrac' keyword is deprecated"): + legacy = calculate_mantle_mass(R_earth, M_earth, corefrac=0.55) + new = calculate_mantle_mass(R_earth, M_earth, core_frac=0.55) + assert legacy == pytest.approx(new, rel=1e-12) + + +def test_calculate_mantle_mass_both_aliases_raises(): + """Passing both `core_frac` and `corefrac` is ambiguous and must raise. + + The migration shim refuses to silently prefer one over the other. + """ + with pytest.raises(TypeError, match="received both 'core_frac' and 'corefrac'"): + calculate_mantle_mass(R_earth, M_earth, core_frac=0.55, corefrac=0.6) + + +def test_calculate_mantle_mass_missing_core_frac_raises(): + """Neither `core_frac` nor `corefrac` supplied must raise `TypeError`. + + The function has no default for `core_frac`; callers must specify the + interior structure explicitly. + """ + with pytest.raises(TypeError, match="missing required argument: 'core_frac'"): + calculate_mantle_mass(R_earth, M_earth) diff --git a/tests/test_targets.py b/tests/test_targets.py index 3ef701b..b308f66 100644 --- a/tests/test_targets.py +++ b/tests/test_targets.py @@ -264,7 +264,7 @@ def test_round_trip_recovers_pressures(self): nguess=10, # warm start should converge fast ) - # Recover within 1% — the target was built from these very + # Recover within 1%: the target was built from these very # pressures, so the solver lands here exactly modulo float # round-off and Powell-hybrid tolerance. assert result['H2O_bar'] == pytest.approx(P0['H2O'], rel=0.01) diff --git a/tools/check_file_sizes.sh b/tools/check_file_sizes.sh new file mode 100755 index 0000000..ad4fb67 --- /dev/null +++ b/tools/check_file_sizes.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# Validate the line limit on .github/copilot-instructions.md. +# The cap exists so the file stays readable as an entry point; the +# Claude-Code rule deep-dives live under .github/.claude/rules/ and +# are not subject to this cap. + +set -e + +AGENTS_MAX=500 + +EXIT_CODE=0 + +if [ -f ".github/copilot-instructions.md" ]; then + AGENTS_LINES=$(wc -l < .github/copilot-instructions.md | tr -d ' ') + if [ "$AGENTS_LINES" -gt "$AGENTS_MAX" ]; then + echo "ERROR: .github/copilot-instructions.md exceeds $AGENTS_MAX lines (current: $AGENTS_LINES)" + EXIT_CODE=1 + else + echo "OK: .github/copilot-instructions.md has $AGENTS_LINES lines (max: $AGENTS_MAX)" + fi +else + echo "WARNING: .github/copilot-instructions.md not found" +fi + +exit $EXIT_CODE diff --git a/tools/check_test_quality.py b/tools/check_test_quality.py new file mode 100644 index 0000000..49ece59 --- /dev/null +++ b/tools/check_test_quality.py @@ -0,0 +1,668 @@ +#!/usr/bin/env python3 +"""AST-based test-quality linter for the CALLIOPE test suite. + +Enforces the rules in `.github/.claude/rules/calliope-tests.md` (sections 1 + 7): + +- Every test file must declare a module-level ``pytestmark`` containing a tier + marker (``unit`` / ``smoke`` / ``integration`` / ``slow``). +- Test functions must contain at least 2 assertion statements OR a discriminating + property-based assertion. Single-assert tests are a known weak pattern. +- Forbidden weak assertions when they stand alone as the sole meaningful + check in the test: ``result is not None``, ``result > 0``, + ``len(result) > 0``, ``isinstance(result, dict)``, ``result is None``. + A weak assertion that accompanies a stronger primary assertion (the + three-class discrimination guard from calliope-tests.md section 2 uses + ``val > 0`` as a sign guard alongside ``pytest.approx(...)``, for + example) is NOT flagged. +- Every test function must have a docstring. +- ``==`` adjacent to a numeric literal in a test body is a likely float-comparison + bug (use ``pytest.approx`` instead). +- Optional dependencies (``hypothesis``, ``atmodeller``) imported at module top + without a preceding ``pytest.importorskip('')`` (the ``pip install + --no-deps`` CI image otherwise fails collection). + +Two modes: + +* ``--baseline`` Walk the test suite, write per-rule violation counts to + ``tools/test_quality_baseline.json``. Run this only after a deliberate sweep + that has reduced violations; commits should not raise the baseline. +* ``--check`` CI mode. Walk the suite, compare current violation counts to + the baseline. Exit non-zero if any rule's violation count exceeds the + baseline. Print the offending files + functions. + +Optionally: + +* ``--reference-pinned-status`` Print the physics source files that lack at + least one ``@pytest.mark.reference_pinned`` test in the matching + ``tests/test_.py``. Does not exit non-zero on its own (advisory). +* ``--physics-invariant-status`` Print physics-source tests that assert no + invariant and are not tagged ``@pytest.mark.physics_invariant``. Advisory. + +All exits in ``--check`` mode use exit code 1 on regression; 0 otherwise. + +The script reads no configuration outside its own constants and the baseline +file. It is intentionally dependency-free (pure stdlib) so it can run in any +CI environment. +""" + +from __future__ import annotations + +import argparse +import ast +import json +import os +import sys +from collections import defaultdict +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parent.parent +TESTS_DIR = REPO_ROOT / 'tests' +SRC_DIR = REPO_ROOT / 'src' / 'calliope' +BASELINE_PATH = REPO_ROOT / 'tools' / 'test_quality_baseline.json' + +TIER_MARKERS = {'unit', 'smoke', 'integration', 'slow'} + +# Optional dependencies. Any test module that imports one of these MUST +# precede the import with ``pytest.importorskip('')`` at module +# scope, otherwise CI's ``pip install --no-deps`` build fails collection. +# Source rule: calliope-tests.md section 5. +OPTIONAL_DEPS = { + 'hypothesis', + 'atmodeller', +} + +# Physics source files. Each one must have a 1:1 test file at +# ``tests/test_.py`` (the rule in calliope-tests.md section 12). +# Each source must have at least one @pytest.mark.physics_invariant test +# and at least one @pytest.mark.reference_pinned test in its companion +# test file. +PHYSICS_SOURCES = { + 'chemistry.py', + 'oxygen_fugacity.py', + 'solubility.py', + 'solve.py', + 'structure.py', +} + +# Utility sources are exempt from the physics-invariant / reference-pinned +# requirement but still subject to the anti-happy-path rules. +UTILITY_SOURCES = { + '__init__.py', + '_version.py', + 'constants.py', +} + + +def _is_weak_assert(node: ast.Assert) -> str | None: + """Return a label if ``node`` is a forbidden weak standalone assertion.""" + test = node.test + # `assert x is None` / `assert x is not None` + if isinstance(test, ast.Compare) and len(test.ops) == 1: + op = test.ops[0] + right = test.comparators[0] + if ( + isinstance(op, (ast.Is, ast.IsNot)) + and isinstance(right, ast.Constant) + and right.value is None + ): + return 'is_none_or_not_none' + # `assert len(x) > 0` (must come BEFORE the bare `> 0` check, + # since it is the more specific shape). + if ( + isinstance(op, ast.Gt) + and isinstance(test.left, ast.Call) + and isinstance(test.left.func, ast.Name) + and test.left.func.id == 'len' + and isinstance(right, ast.Constant) + and right.value == 0 + ): + return 'len_gt_zero' + # `assert x > 0` + if isinstance(op, ast.Gt) and isinstance(right, ast.Constant) and right.value == 0: + return 'gt_zero' + return None + + +def _is_isinstance_assert(node: ast.Assert) -> bool: + test = node.test + return ( + isinstance(test, ast.Call) + and isinstance(test.func, ast.Name) + and test.func.id == 'isinstance' + ) + + +def _is_numpy_testing(node: ast.AST) -> bool: + """True if ``node`` represents the ``numpy.testing`` or ``np.testing`` module.""" + if not isinstance(node, ast.Attribute): + return False + if node.attr != 'testing': + return False + if isinstance(node.value, ast.Name) and node.value.id in ('np', 'numpy'): + return True + return False + + +def _is_exact_zero(value) -> bool: + """True for the sentinel ``0.0`` / ``-0.0`` float comparand. + + Asserting an exact-zero result is a legitimate physics check and does + not need ``pytest.approx``: there is no rounding error to absorb. + Comparing against any other float literal is flagged. + """ + return isinstance(value, float) and value == 0.0 + + +def _unwrap_unary_minus_float(operand: ast.AST) -> float | None: + """Return the float value of ``UnaryOp(USub, Constant(float))``, or None. + + The AST parses ``-1.5`` as ``UnaryOp(USub, Constant(1.5))``, NOT as + ``Constant(-1.5)``. A naive ``isinstance(node, ast.Constant)`` check + would miss negative float literals; this helper accepts both forms. + """ + if ( + isinstance(operand, ast.UnaryOp) + and isinstance(operand.op, ast.USub) + and isinstance(operand.operand, ast.Constant) + and isinstance(operand.operand.value, float) + ): + return -operand.operand.value + return None + + +def _float_literal_value(operand: ast.AST) -> float | None: + """Return the float value of a positive or negative float-literal node.""" + if isinstance(operand, ast.Constant) and isinstance(operand.value, float): + return operand.value + return _unwrap_unary_minus_float(operand) + + +def _has_float_eq(node: ast.AST) -> bool: + """Return True if any descendant uses ``==`` against a non-zero float literal. + + Accepts both ``Constant(1.5)`` (positive literal) and + ``UnaryOp(USub, Constant(1.5))`` (negative literal). The exact-zero + carve-out applies to both signs. + """ + for child in ast.walk(node): + if isinstance(child, ast.Compare): + for op, right in zip(child.ops, child.comparators): + if not isinstance(op, ast.Eq): + continue + right_val = _float_literal_value(right) + if right_val is not None and not _is_exact_zero(right_val): + return True + left_val = _float_literal_value(child.left) + if left_val is not None and not _is_exact_zero(left_val): + return True + return False + + +def _module_pytestmark_tier(tree: ast.Module) -> str | None: + """Return the tier marker declared in a module-level ``pytestmark``, or None.""" + for stmt in tree.body: + if not isinstance(stmt, ast.Assign): + continue + if not (len(stmt.targets) == 1 and isinstance(stmt.targets[0], ast.Name)): + continue + if stmt.targets[0].id != 'pytestmark': + continue + marks = stmt.value + nodes = marks.elts if isinstance(marks, (ast.List, ast.Tuple)) else [marks] + for n in nodes: + tier = _tier_of_mark_node(n) + if tier is not None: + return tier + return None + + +def _tier_of_mark_node(n: ast.AST) -> str | None: + """Given a ``pytest.mark.`` or ``pytest.mark.(...)`` node, return the tier name.""" + if isinstance(n, ast.Call): + n = n.func + if isinstance(n, ast.Attribute) and isinstance(n.value, ast.Attribute): + if ( + isinstance(n.value.value, ast.Name) + and n.value.value.id == 'pytest' + and n.value.attr == 'mark' + and n.attr in TIER_MARKERS + ): + return n.attr + return None + + +FuncDef = (ast.FunctionDef, ast.AsyncFunctionDef) + + +def _iter_test_functions(tree: ast.Module): + """Yield every ``test_*`` function defined at module scope or as a + method of a class at module scope. + + Does NOT recurse into function bodies: a `def test_x()` defined + inside another function body is a local helper, not an independent + pytest test, and must not be inspected as one. Recursing via + ``ast.walk`` would treat such helpers as phantom tests. + + Async functions (`async def test_x`) are yielded alongside + synchronous ones; pytest-asyncio and other plugins run them. + """ + for stmt in tree.body: + if isinstance(stmt, FuncDef) and stmt.name.startswith('test_'): + yield stmt + elif isinstance(stmt, ast.ClassDef): + for sub in stmt.body: + if isinstance(sub, FuncDef) and sub.name.startswith('test_'): + yield sub + + +def _func_markers(fn: ast.FunctionDef | ast.AsyncFunctionDef) -> set[str]: + """All ``pytest.mark.`` markers on a function definition.""" + out: set[str] = set() + for dec in fn.decorator_list: + n = dec + if isinstance(n, ast.Call): + n = n.func + if isinstance(n, ast.Attribute) and isinstance(n.value, ast.Attribute): + if ( + isinstance(n.value.value, ast.Name) + and n.value.value.id == 'pytest' + and n.value.attr == 'mark' + ): + out.add(n.attr) + return out + + +def _docstring_of(fn: ast.FunctionDef | ast.AsyncFunctionDef) -> str | None: + if ( + fn.body + and isinstance(fn.body[0], ast.Expr) + and isinstance(fn.body[0].value, ast.Constant) + ): + v = fn.body[0].value.value + if isinstance(v, str): + return v + return None + + +class Violations: + """Aggregate counts and per-violation details for one rule scan.""" + + def __init__(self): + self.counts: dict[str, int] = defaultdict(int) + self.details: dict[str, list[str]] = defaultdict(list) + + def add(self, rule: str, where: str) -> None: + self.counts[rule] += 1 + self.details[rule].append(where) + + def to_baseline(self) -> dict[str, int]: + return dict(self.counts) + + +def _count_implicit_assertions(node: ast.AST) -> int: + """Count assertion-equivalents that are not bare ``assert`` statements. + + Recognized patterns: + + - ``with pytest.raises(...)`` blocks. A ``match=`` keyword counts as a + second implicit assertion: it imposes a separate falsifiable + constraint on the exception message, distinct from the type check. + - ``with pytest.warns(...)`` blocks. Same ``match=`` rule. + - ``with pytest.deprecated_call()`` blocks. + - ``mock.assert_called_with(...)`` / ``assert_called_once_with(...)`` / + ``assert_not_called(...)`` etc. method calls on a Mock object. + - ``pytest.fail(...)`` calls. + - ``np.testing.assert_*`` family. + """ + count = 0 + for child in ast.walk(node): + if isinstance(child, (ast.With, ast.AsyncWith)): + for item in child.items: + ctx = item.context_expr + if isinstance(ctx, ast.Call) and isinstance(ctx.func, ast.Attribute): + if ( + isinstance(ctx.func.value, ast.Name) + and ctx.func.value.id == 'pytest' + and ctx.func.attr in ('raises', 'warns', 'deprecated_call') + ): + count += 1 + # A ``match=`` argument is a separate falsifiable + # constraint on the exception or warning message: + # ``pytest.raises(ValueError, match='non-negative')`` + # is strictly stronger than ``pytest.raises(ValueError)``. + if any(kw.arg == 'match' for kw in ctx.keywords): + count += 1 + if isinstance(child, ast.Call) and isinstance(child.func, ast.Attribute): + attr = child.func.attr + if attr.startswith('assert_called') or attr == 'assert_not_called': + count += 1 + elif attr.startswith('assert_') and _is_numpy_testing(child.func.value): + count += 1 + elif isinstance(child.func.value, ast.Name) and child.func.value.id == 'pytest': + if attr == 'fail': + count += 1 + return count + + +def _importorskip_targets(tree: ast.Module) -> set[str]: + """Set of dependency names protected by a module-scope ``pytest.importorskip``.""" + out: set[str] = set() + for node in tree.body: + call = None + if isinstance(node, ast.Expr) and isinstance(node.value, ast.Call): + call = node.value + elif isinstance(node, ast.Assign) and isinstance(node.value, ast.Call): + call = node.value + if call is None or not isinstance(call.func, ast.Attribute): + continue + if ( + isinstance(call.func.value, ast.Name) + and call.func.value.id == 'pytest' + and call.func.attr == 'importorskip' + and call.args + and isinstance(call.args[0], ast.Constant) + and isinstance(call.args[0].value, str) + ): + out.add(call.args[0].value) + return out + + +def _missing_importorskip(tree: ast.Module) -> list[str]: + """Return optional-dep names imported at module top without a matching importorskip.""" + protected = _importorskip_targets(tree) + missing: list[str] = [] + seen: set[str] = set() + for node in tree.body: + if isinstance(node, ast.Import): + for alias in node.names: + root = alias.name.split('.', 1)[0] + if root in OPTIONAL_DEPS and root not in protected and root not in seen: + missing.append(root) + seen.add(root) + elif isinstance(node, ast.ImportFrom): + if node.module is None: + continue + root = node.module.split('.', 1)[0] + if root in OPTIONAL_DEPS and root not in protected and root not in seen: + missing.append(root) + seen.add(root) + return missing + + +def check_file(path: Path) -> Violations: + v = Violations() + try: + rel = str(path.relative_to(REPO_ROOT)) + except ValueError: + rel = str(path) + try: + tree = ast.parse(path.read_text()) + except SyntaxError as e: + v.add('parse_error', f'{rel}: {e}') + return v + + module_tier = _module_pytestmark_tier(tree) + if module_tier is None: + v.add('missing_module_pytestmark', rel) + + for dep in _missing_importorskip(tree): + v.add('missing_importorskip', f'{rel}: {dep}') + + for node in _iter_test_functions(tree): + where = f'{rel}::{node.name}' + + if _docstring_of(node) is None: + v.add('missing_docstring', where) + + if _has_float_eq(node): + v.add('float_eq_literal', where) + + asserts = [n for n in ast.walk(node) if isinstance(n, ast.Assert)] + n_assert = len(asserts) + _count_implicit_assertions(node) + if n_assert == 0: + v.add('no_assertions', where) + elif n_assert == 1: + v.add('single_assert', where) + + # Weak-assertion shapes. Flag only when the weak assertion stands + # alone as the sole meaningful check in the test. + if n_assert == 1 and len(asserts) == 1: + label = _is_weak_assert(asserts[0]) + if label is not None: + v.add(f'weak_assert_{label}', where) + if _is_isinstance_assert(asserts[0]): + v.add('weak_assert_only_isinstance', where) + + return v + + +def walk_tests() -> Violations: + total = Violations() + for p in sorted(TESTS_DIR.rglob('test_*.py')): + if '__pycache__' in p.parts: + continue + v = check_file(p) + for rule, n in v.counts.items(): + total.counts[rule] += n + for rule, details in v.details.items(): + total.details[rule].extend(details) + return total + + +def _file_has_decorator(path: Path, decorator_name: str) -> bool: + """True if any function or class in ``path`` carries ``@pytest.mark.``.""" + try: + tree = ast.parse(path.read_text()) + except SyntaxError: + return False + for node in ast.walk(tree): + if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)): + continue + for dec in node.decorator_list: + d = dec + if isinstance(d, ast.Call): + d = d.func + if isinstance(d, ast.Attribute) and isinstance(d.value, ast.Attribute): + if ( + isinstance(d.value.value, ast.Name) + and d.value.value.id == 'pytest' + and d.value.attr == 'mark' + and d.attr == decorator_name + ): + return True + return False + + +def reference_pinned_status() -> list[str]: + """Return physics source files lacking a reference_pinned test. + + For each source in PHYSICS_SOURCES, check whether the companion test file + ``tests/test_`` (with .py preserved) exists and contains at least + one ``@pytest.mark.reference_pinned`` decoration. Sources without such a + test are reported. + """ + missing = [] + for source in sorted(PHYSICS_SOURCES): + # source is e.g. 'chemistry.py' -> companion is tests/test_chemistry.py + stem = source[:-3] if source.endswith('.py') else source + test_path = TESTS_DIR / f'test_{stem}.py' + if not test_path.exists(): + missing.append(f'{source} (no test_{stem}.py)') + continue + if not _file_has_decorator(test_path, 'reference_pinned'): + missing.append(f'{source} (test_{stem}.py has no @pytest.mark.reference_pinned)') + return missing + + +def physics_invariant_status() -> list[str]: + """Return physics-source tests without an explicit invariant marker or + property-based assertion language. + + The heuristic is intentionally simple: a physics-source test must either + carry the marker OR contain one of the keywords in its body. This is + advisory, not blocking. + """ + flagged = [] + keywords = {'approx', 'assert_allclose', 'monoton', 'conserve', 'symmetric', 'positive'} + for source in sorted(PHYSICS_SOURCES): + stem = source[:-3] if source.endswith('.py') else source + test_path = TESTS_DIR / f'test_{stem}.py' + if not test_path.exists(): + continue + try: + tree = ast.parse(test_path.read_text()) + except SyntaxError: + continue + rel = str(test_path.relative_to(REPO_ROOT)) + for node in _iter_test_functions(tree): + markers = _func_markers(node) + if 'physics_invariant' in markers: + continue + body_src = ast.unparse(node) + if any(kw in body_src for kw in keywords): + continue + flagged.append(f'{rel}::{node.name}') + return flagged + + +def load_baseline() -> dict[str, int]: + if not BASELINE_PATH.exists(): + return {} + return json.loads(BASELINE_PATH.read_text()) + + +def cmd_baseline() -> int: + v = walk_tests() + # Guard against accidental regeneration that would raise the baseline. + old = load_baseline() + old_total = sum(old.values()) + new_total = sum(v.counts.values()) + + allow = os.environ.get('CALLIOPE_TEST_QUALITY_ALLOW_REGRESS') == '1' + if old and new_total > old_total and not allow: + print( + f'Refusing to regenerate baseline: new total ({new_total}) exceeds ' + f'old total ({old_total}). The baseline should only ratchet downward.\n' + f'If this is intentional (e.g. a new rule was added that surfaces ' + f'pre-existing violations), set CALLIOPE_TEST_QUALITY_ALLOW_REGRESS=1.', + file=sys.stderr, + ) + return 2 + BASELINE_PATH.write_text(json.dumps(v.to_baseline(), indent=2, sort_keys=True) + '\n') + print(f'Wrote baseline: {BASELINE_PATH.relative_to(REPO_ROOT)}') + for rule in sorted(v.counts): + print(f' {rule}: {v.counts[rule]}') + if old: + delta = new_total - old_total + print(f' total: {new_total} ({delta:+d} vs previous baseline {old_total})') + return 0 + + +def cmd_check() -> int: + baseline = load_baseline() + v = walk_tests() + failed = False + print('Rule Baseline Current Status') + print('-' * 76) + all_rules = sorted(set(baseline) | set(v.counts)) + for rule in all_rules: + b = baseline.get(rule, 0) + c = v.counts.get(rule, 0) + status = 'OK' if c <= b else 'REGRESSION' + if c > b: + failed = True + print(f'{rule:42} {b:>8} {c:>9} {status}') + # Cross-rule total-violation guard. + total_baseline = sum(baseline.values()) + total_current = sum(v.counts.values()) + print('-' * 76) + total_status = 'OK' if total_current <= total_baseline else 'REGRESSION' + print(f'{"TOTAL":42} {total_baseline:>8} {total_current:>9} {total_status}') + if total_current > total_baseline: + failed = True + if failed: + print() + print('New violations vs baseline:') + for rule in all_rules: + b = baseline.get(rule, 0) + c = v.counts.get(rule, 0) + if c > b: + offenders = v.details.get(rule, []) + print(f'\n {rule} (+{c - b}):') + for offender in offenders[:5]: + print(f' {offender}') + if len(offenders) > 5: + print(f' ... and {len(offenders) - 5} more') + print() + print('Reduce violations or, after a deliberate sweep, regenerate the baseline:') + print(' python tools/check_test_quality.py --baseline') + return 1 + return 0 + + +def cmd_reference_pinned_status() -> int: + missing = reference_pinned_status() + if not missing: + print('All physics sources have a @pytest.mark.reference_pinned test.') + return 0 + print('Physics sources missing a @pytest.mark.reference_pinned test:') + for m in missing: + print(f' {m}') + return 0 + + +def cmd_physics_invariant_status() -> int: + flagged = physics_invariant_status() + if not flagged: + print( + 'All physics-source tests either carry @pytest.mark.physics_invariant ' + 'or use property-based language.' + ) + return 0 + print( + 'Physics-source tests without @pytest.mark.physics_invariant and no ' + 'property-based assertion language:' + ) + for f in flagged: + print(f' {f}') + return 0 + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument( + '--baseline', + action='store_true', + help='Regenerate tools/test_quality_baseline.json from current state.', + ) + group.add_argument( + '--check', + action='store_true', + help='CI mode: fail if violations exceed baseline.', + ) + group.add_argument( + '--reference-pinned-status', + action='store_true', + help='Advisory: list physics sources missing a @pytest.mark.reference_pinned test.', + ) + group.add_argument( + '--physics-invariant-status', + action='store_true', + help='Advisory: list physics-source tests without an invariant marker or ' + 'property-based language.', + ) + args = parser.parse_args() + if args.baseline: + return cmd_baseline() + if args.check: + return cmd_check() + if args.reference_pinned_status: + return cmd_reference_pinned_status() + if args.physics_invariant_status: + return cmd_physics_invariant_status() + return 0 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/tools/test_quality_baseline.json b/tools/test_quality_baseline.json new file mode 100644 index 0000000..0967ef4 --- /dev/null +++ b/tools/test_quality_baseline.json @@ -0,0 +1 @@ +{} diff --git a/tools/update_coverage_threshold.py b/tools/update_coverage_threshold.py new file mode 100644 index 0000000..c539c28 --- /dev/null +++ b/tools/update_coverage_threshold.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python3 +"""Automatically ratchet coverage thresholds for fast and full test suites. + +This script implements a coverage ratcheting mechanism: the required coverage +threshold for a given suite can only increase or stay the same, never +decrease. It supports two modes: + +* full - updates `[tool.coverage.report].fail_under` (the global threshold) +* fast - updates `[tool.calliope.coverage_fast].fail_under` (unit/smoke gate) + +Usage examples:: + + # Ratchet full-suite threshold using coverage.json + python tools/update_coverage_threshold.py --coverage-file coverage.json --target full + + # Ratchet fast (unit/smoke) threshold using a separate coverage JSON + python tools/update_coverage_threshold.py --coverage-file coverage-unit.json --target fast + +Exit codes: + 0 -> threshold updated (increased) + 1 -> no update needed + 2 -> validation failure (e.g., missing keys) + +This script is intended to run in CI after coverage is computed for the +corresponding suite; it can also be used locally. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +try: # Python 3.11+ + import tomllib +except ModuleNotFoundError: # pragma: no cover - fallback for older interpreters + try: + import tomli as tomllib # type: ignore + except ModuleNotFoundError as e: + raise ImportError( + 'tomllib (Python 3.11+) or tomli package is required. ' + 'Install with: pip install tomli' + ) from e + +import tomlkit + +# PROTEUS-ecosystem coverage ceiling. The ratchet may raise either gate +# toward this value but never above it; above 90% the gate tracks pragma +# usage and style rather than bug-finding signal. +ECOSYSTEM_CEILING = 90.0 + + +def read_current_coverage(coverage_file: Path) -> float: + """Read the current test coverage percentage from a coverage JSON file.""" + if not coverage_file.exists(): + raise FileNotFoundError( + f"{coverage_file} not found. Run 'coverage json -o {coverage_file}' first." + ) + + with coverage_file.open() as f: + data = json.load(f) + + return float(data['totals']['percent_covered']) + + +def read_threshold_from_pyproject(target: str) -> float: + """Read the current coverage threshold from pyproject.toml for a target.""" + pyproject_file = Path('pyproject.toml') + if not pyproject_file.exists(): + raise FileNotFoundError('pyproject.toml not found') + + data = tomllib.loads(pyproject_file.read_text()) + try: + if target == 'full': + return float(data['tool']['coverage']['report']['fail_under']) + if target == 'fast': + return float(data['tool']['calliope']['coverage_fast']['fail_under']) + except KeyError as exc: + raise ValueError( + f"fail_under setting not found in pyproject.toml for target '{target}'" + ) from exc + + raise ValueError(f"Unknown target '{target}'") + + +def update_threshold_in_pyproject(target: str, new_threshold: float) -> bool: + """Update the fail_under threshold in pyproject.toml for a target.""" + pyproject_file = Path('pyproject.toml') + if not pyproject_file.exists(): + raise FileNotFoundError('pyproject.toml not found') + + document = tomlkit.parse(pyproject_file.read_text()) + + if target == 'full': + report_section = document.get('tool', {}).get('coverage', {}).get('report') + if report_section is None: + raise ValueError('[tool.coverage.report] section not found in pyproject.toml') + section = report_section + elif target == 'fast': + calliope_section = document.get('tool', {}).get('calliope') + if calliope_section is None: + raise ValueError('[tool.calliope] section not found in pyproject.toml') + coverage_fast = calliope_section.get('coverage_fast') + if coverage_fast is None: + raise ValueError( + '[tool.calliope.coverage_fast] section not found in pyproject.toml' + ) + section = coverage_fast + else: + raise ValueError(f"Unknown target '{target}'") + + current_value = float(section.get('fail_under', 0)) + new_value = float(f'{new_threshold:.2f}') + + # Ratchet: only update if strictly higher + if new_value <= current_value: + return False + + section['fail_under'] = new_value + pyproject_file.write_text(tomlkit.dumps(document)) + print(f'[+] Updated pyproject.toml: {target} fail_under = {new_value:.2f}') + return True + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description='Ratcheted coverage thresholds') + parser.add_argument( + '--coverage-file', + default='coverage.json', + help="Path to coverage JSON (from 'coverage json'). Default: coverage.json", + ) + parser.add_argument( + '--target', + choices=['full', 'fast'], + default='full', + help="Which threshold to ratchet: 'full' (global) or 'fast' (unit/smoke)", + ) + return parser.parse_args() + + +def main() -> int: + """Main entrypoint for threshold ratcheting.""" + args = parse_args() + + try: + coverage_path = Path(args.coverage_file) + target = args.target + + current_coverage = read_current_coverage(coverage_path) + current_threshold = read_threshold_from_pyproject(target) + + print(f'Target: {target}') + print(f'Current coverage: {current_coverage:.2f}%') + print(f'Current threshold: {current_threshold:.2f}%') + + new_threshold = min(round(current_coverage, 2), ECOSYSTEM_CEILING) + + if current_threshold >= ECOSYSTEM_CEILING: + print( + f'[=] Threshold {current_threshold:.2f}% already at or above ' + f'the {ECOSYSTEM_CEILING:.2f}% ecosystem ceiling (no update needed)' + ) + return 1 + + if new_threshold > current_threshold: + print( + f'[+] Coverage increased. Updating threshold: ' + f'{current_threshold:.2f}% -> {new_threshold:.2f}%' + ) + update_threshold_in_pyproject(target, new_threshold) + return 0 + + if new_threshold == current_threshold: + print(f'[=] Threshold already at {current_threshold:.2f}% (no update needed)') + return 1 + + print(f'[!] Coverage decreased: {new_threshold:.2f}% < {current_threshold:.2f}%') + print(' Threshold not updated.') + return 2 + + except FileNotFoundError as e: + print(f'[x] Error: Required file not found: {e}', file=sys.stderr) + return 2 + except (ValueError, KeyError) as e: + print( + f'[x] Error: Invalid coverage data or configuration ({type(e).__name__}): {e}', + file=sys.stderr, + ) + return 2 + except Exception as e: + print( + f'[x] Error updating coverage threshold ({type(e).__name__}): {e}', + file=sys.stderr, + ) + return 2 + + +if __name__ == '__main__': + sys.exit(main())