From 9c5bfc9609e07b35808b26d3b18ab66639c46033 Mon Sep 17 00:00:00 2001 From: Anthony Meza <64243783+anthony-meza@users.noreply.github.com> Date: Tue, 24 Feb 2026 10:02:51 +0000 Subject: [PATCH 01/19] adding "allow_rechunk" option this commit incorporates an optional "allow_rechunk" option for collect_budgets and budget_fill_dict --- .gitignore | 2 +- xbudget/collect.py | 68 ++++++++++++++++++++++++++++++++-------------- 2 files changed, 48 insertions(+), 22 deletions(-) diff --git a/.gitignore b/.gitignore index d313308..59cec8f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,3 @@ __pycache__ .ipynb_checkpoints -/data/data +/data/* diff --git a/xbudget/collect.py b/xbudget/collect.py index 5c8feb4..f3eb512 100644 --- a/xbudget/collect.py +++ b/xbudget/collect.py @@ -158,7 +158,7 @@ def _deep_search(b, new_b={}, k_last=None): _deep_search(v, new_b=new_b, k_last=k) return new_b -def collect_budgets(ds, xbudget_dict): +def collect_budgets(ds, xbudget_dict, allow_rechunk = True): """Fills xbudget dictionary with all tracer content tendencies Parameters @@ -183,9 +183,9 @@ def collect_budgets(ds, xbudget_dict): for eq, v in xbudget_dict.items(): for side in ["lhs", "rhs"]: if side in v: - budget_fill_dict(ds, v[side], f"{eq}_{side}") + budget_fill_dict(ds, v[side], f"{eq}_{side}", allow_rechunk = allow_rechunk) -def budget_fill_dict(data, xbudget_dict, namepath): +def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): """Recursively fill xbudget dictionary Parameters @@ -216,7 +216,7 @@ def budget_fill_dict(data, xbudget_dict, namepath): op_list = [] for k_term, v_term in v.items(): if isinstance(v_term, dict): # recursive call to get this variable - v_term_recursive = budget_fill_dict(data, v_term, f"{namepath}_{k}_{k_term}") + v_term_recursive = budget_fill_dict(data, v_term, f"{namepath}_{k}_{k_term}", allow_rechunk = allow_rechunk) if v_term_recursive is not None: op_list.append(v_term_recursive) elif v_term.get("var") is not None and v_term.get("var") not in ds: @@ -287,6 +287,7 @@ def budget_fill_dict(data, xbudget_dict, namepath): if var_pref is None: var_pref = var.copy() + if k == "difference": if grid is not None: staggered_axes = { @@ -294,26 +295,51 @@ def budget_fill_dict(data, xbudget_dict, namepath): for pos,c in ax.coords.items() if pos!="center" } - v_term = [v_term for k_term,v_term in v.items() if k_term!="var"][0] - if v_term not in ds: - warnings.warn(f"Variable {v_term} is missing from the dataset `ds`, so it is being skipped. To suppress this warning, remove {v_term} from the `xbudget_dict`.") - continue - candidate_axes = [axn for (axn,c) in staggered_axes.items() if c in ds[v_term].dims] - if len(candidate_axes) == 1: - axis = candidate_axes[0] - else: - raise ValueError("Flux difference inconsistent with finite volume discretization.") - var = grid.diff(ds[v_term].fillna(0.), axis) - var_name = f"{namepath}_difference" - var = var.rename(var_name) - var_provenance = v_term - var.attrs["provenance"] = var_provenance - ds[var_name] = var - if var_pref is None: - var_pref = var.copy() + v_term = [v_term for k_term,v_term in v.items() if k_term!="var"][0] + if v_term not in ds: + warnings.warn(f"Variable {v_term} is missing from the dataset `ds`, so it is being skipped. To suppress this warning, remove {v_term} from the `xbudget_dict`.") + continue + + candidate_axes = [axn for (axn,c) in staggered_axes.items() if c in ds[v_term].dims] + if len(candidate_axes) == 1: + axis = candidate_axes[0] + else: + raise ValueError("Flux difference inconsistent with finite volume discretization.") + + if allow_rechunk: #NEW CODE + try: #extract original chunks when possible + #not using ds[v_term] since it may not have the non-staggered dimension chunks. + original_chunks = dict(ds.chunksizes) + except Exception: + warnings.warn("Dataset chunks are inconsistent; using unify_chunks()", UserWarning) + original_chunks = dict(ds.unify_chunks().chunksizes) + + # Find the staggered dimension for the given axis in the DataArray + axis_dim = [d for d in ds[v_term].dims if d in grid.axes[axis].coords.values()] + if len(axis_dim) != 1: + raise ValueError(f"Expected to find one dimension for axis '{axis}' in variable '{v_term}', but found {len(dims_for_axis)}: {dims_for_axis}") + axis_dim = axis_dim[0] + + # Temporarily rechunk to put the difference dim in a single chunk, all other chunks are auto. + temporary_chunks = {axis_dim: -1, **{d: "auto" for d in ds[v_term].dims if d != axis_dim}} + var = grid.diff(ds[v_term].chunk(temporary_chunks).fillna(0.0), axis=axis) + # Attempt original chunking for preserved dimensions + var = var.chunk({d: original_chunks.get(d, var.chunksizes[d]) for d in var.dims}) + else: #OLD CODE + var = grid.diff(ds[v_term].fillna(0.), axis) + + var_name = f"{namepath}_difference" + var = var.rename(var_name) + var_provenance = v_term + var.attrs["provenance"] = var_provenance + ds[var_name] = var + if var_pref is None: + var_pref = var.copy() else: raise ValueError("Input `ds` must be `xgcm.Grid` instance if using `difference` operations.") + + return var_pref def get_vars(xbudget_dict, terms): From 61b806dcfa70be92a2e5987367cba11cc4c25e4d Mon Sep 17 00:00:00 2001 From: Anthony Meza <64243783+anthony-meza@users.noreply.github.com> Date: Tue, 24 Feb 2026 14:41:07 +0000 Subject: [PATCH 02/19] adding test "test_budget_fill_dict_allow_rechunk" that tests the "allow_rechunk" option --- xbudget/tests/test_utilities.py | 68 ++++++++++++++++++++++++++++++++- 1 file changed, 67 insertions(+), 1 deletion(-) diff --git a/xbudget/tests/test_utilities.py b/xbudget/tests/test_utilities.py index 1ba5388..1d39902 100644 --- a/xbudget/tests/test_utilities.py +++ b/xbudget/tests/test_utilities.py @@ -2,6 +2,8 @@ import numpy as np import xarray as xr import copy +import xgcm +import dask.array as da from xbudget.collect import ( aggregate, disaggregate, @@ -437,4 +439,68 @@ def test_budget_fill_dict_numeric_values(self): result = budget_fill_dict(ds, xbudget_dict, "heat_rhs") assert result is not None - assert np.allclose(ds["heat_rhs_product"].values, 2.0) \ No newline at end of file + assert np.allclose(ds["heat_rhs_product"].values, 2.0) + + def test_budget_fill_dict_allow_rechunk(self): + """Test the allow_rechunk option for the difference operation.""" + # Create a dataset with non-uniform chunks on the staggered grid, + # which would cause issues for xgcm.grid.diff + flux_data = da.from_array(np.random.rand(5, 3), chunks=((2, 2, 1), 3)) + ds_chunked = xr.Dataset( + { + "var": xr.DataArray( + flux_data, + dims=("x_g", "y_c"), + ) + }, + coords={ + "x_g": np.arange(5), + "x_c": np.arange(4) + 0.5, + "y_c": np.arange(3), + }, + ) + + grid_params = { + "coords": {"X": {"center": "x_c", "left": "x_g"}}, + "periodic": False, + "autoparse_metadata": False, + } + + xbudget_dict = { + "var": None, + "difference": {"var_diff": "var", "var": None}, + } + + # 1. Test that allow_rechunk=False raises an error when passing a chunked + # dataset through budget_fill_dict + with pytest.raises(ValueError): + grid_fail = xgcm.Grid(ds_chunked.copy(deep=True), **grid_params) + budget_fill_dict( + grid_fail, + copy.deepcopy(xbudget_dict), + "tendency_rhs", + allow_rechunk=False, + ) + + # 2. Test that shows allow_rechunk=True works + grid_success = xgcm.Grid(ds_chunked.copy(deep=True), **grid_params) + budget_fill_dict( + grid_success, + copy.deepcopy(xbudget_dict), + "tendency_rhs", + allow_rechunk=True, + ) + tendency_rechunked = grid_success._ds["tendency_rhs_difference"] + + # 3. Compare with a correct result from an unchunked array + grid_unchunked = xgcm.Grid(ds_chunked.chunk(-1), **grid_params) + budget_fill_dict( + grid_unchunked, + copy.deepcopy(xbudget_dict), + "tendency_rhs", + allow_rechunk=False, + ) + tendency_correct = grid_unchunked._ds["tendency_rhs_difference"] + + # The numerical results should be identical + xr.testing.assert_allclose(tendency_rechunked, tendency_correct) From 45eac450a485fde2d34632c2b2f31ad99e2d59db Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Tue, 24 Feb 2026 08:05:20 -0800 Subject: [PATCH 03/19] Clean linting and manually add chunks to small example dataset By explicitly adding chunks to the example MOM6 dataset, we should be able to better catch chunk-related problems in the future. --- .../MOM6_budget_examples_mass_heat_salt.ipynb | 666 ++++++++++++++---- examples/load_example_model_grid.py | 1 + xbudget/collect.py | 46 +- 3 files changed, 550 insertions(+), 163 deletions(-) diff --git a/examples/MOM6_budget_examples_mass_heat_salt.ipynb b/examples/MOM6_budget_examples_mass_heat_salt.ipynb index 9316595..8a58220 100644 --- a/examples/MOM6_budget_examples_mass_heat_salt.ipynb +++ b/examples/MOM6_budget_examples_mass_heat_salt.ipynb @@ -1047,111 +1047,131 @@ " stroke-width: 0.8px;\n", "}\n", "
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        "Coordinates:\n",
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+       "    geolon     (yh, xh) float64 346kB dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
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        "Attributes:\n",
        "    cell_measures:  volume: volcello area: areacello\n",
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        "    note:           We ignore land cells in partially wet cells when coarseni...\n",
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    volume: volcello area: areacello
    time_avg_info :
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    standard_name :
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    note :
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using unify_chunks()", UserWarning) - original_chunks = dict(ds.unify_chunks().chunksizes) - - # Find the staggered dimension for the given axis in the DataArray - axis_dim = [d for d in ds[v_term].dims if d in grid.axes[axis].coords.values()] - if len(axis_dim) != 1: - raise ValueError(f"Expected to find one dimension for axis '{axis}' in variable '{v_term}', but found {len(dims_for_axis)}: {dims_for_axis}") - axis_dim = axis_dim[0] - - # Temporarily rechunk to put the difference dim in a single chunk, all other chunks are auto. - temporary_chunks = {axis_dim: -1, **{d: "auto" for d in ds[v_term].dims if d != axis_dim}} - var = grid.diff(ds[v_term].chunk(temporary_chunks).fillna(0.0), axis=axis) - # Attempt original chunking for preserved dimensions - var = var.chunk({d: original_chunks.get(d, var.chunksizes[d]) for d in var.dims}) - else: #OLD CODE - var = grid.diff(ds[v_term].fillna(0.), axis) + raise ValueError("Finite difference inconsistent with finite volume discretization.") + + if allow_rechunk: + try: #extract original chunks when possible + #not using ds[v_term] since it may not have the non-staggered dimension chunks. + original_chunks = dict(ds.chunksizes) + except Exception: + warnings.warn("Dataset chunks are inconsistent; using unify_chunks()", UserWarning) + original_chunks = dict(ds.unify_chunks().chunksizes) + + # Find the staggered dimension for the given axis in the DataArray + axis_dim = [d for d in ds[v_term].dims if d in grid.axes[axis].coords.values()] + if len(axis_dim) != 1: + raise ValueError(f"Expected to find one dimension for axis '{axis}' in variable '{v_term}', but found {len(axis_dim)}: {axis_dim}") + axis_dim = axis_dim[0] + + # Temporarily rechunk to put the difference dim in a single chunk, all other chunks are auto. + temporary_chunks = {axis_dim: -1, **{d: "auto" for d in ds[v_term].dims if d != axis_dim}} + var = grid.diff(ds[v_term].chunk(temporary_chunks).fillna(0.0), axis=axis) + # Attempt original chunking for preserved dimensions + var = var.chunk({d: original_chunks.get(d, var.chunksizes[d]) for d in var.dims}) + else: + var = grid.diff(ds[v_term].fillna(0.), axis) var_name = f"{namepath}_difference" var = var.rename(var_name) From d7fb7f1074e17f414ca1bdbb4d5dd4d2c2507e66 Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Tue, 24 Feb 2026 08:12:07 -0800 Subject: [PATCH 04/19] Add `allow_chunk` to docstrings --- xbudget/collect.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/xbudget/collect.py b/xbudget/collect.py index 2b9e7d6..509c3ee 100644 --- a/xbudget/collect.py +++ b/xbudget/collect.py @@ -179,6 +179,10 @@ def collect_budgets(ds, xbudget_dict, allow_rechunk = True): } } } + allow_rechunk : bool (default: True) + Whether to temporarily rechunk when taking differences along a dimension, + e.g. to compute flux divergences on `center` from fluxes on `outer` or + tendencies on `center` from snapshots on `outer`. """ for eq, v in xbudget_dict.items(): for side in ["lhs", "rhs"]: @@ -193,6 +197,10 @@ def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): data : xgcm.grid or xr.Dataset xbudget_dict : dictionary in xbudget-compatible format containing variable in namepath namepath : name of variable in dataset (data._ds or data) + allow_rechunk : bool (default: True) + Whether to temporarily rechunk when taking differences along a dimension, + e.g. to compute flux divergences on `center` from fluxes on `outer` or + tendencies on `center` from snapshots on `outer`. """ if type(data)==xgcm.grid.Grid: grid = data From 8d55e77d7f60756c0c426bb59556ec166260ec2a Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Thu, 25 Jun 2026 13:45:40 -0700 Subject: [PATCH 05/19] Add MOM6 end-to-end characterization test (Phase 0) Pins the full set of variables collect_budgets materializes from the example MOM6 grid (108 vars) with their shape, provenance, and numerical fingerprint, as a safety net for the upcoming internals refactor. The ~600MB example dataset is not tracked, so the test skips when it is absent; the small reference fingerprint is committed. Co-Authored-By: Claude Opus 4.8 (1M context) --- xbudget/tests/characterization_MOM6.json | 2149 ++++++++++++++++++++++ xbudget/tests/test_characterization.py | 152 ++ 2 files changed, 2301 insertions(+) create mode 100644 xbudget/tests/characterization_MOM6.json create mode 100644 xbudget/tests/test_characterization.py diff --git a/xbudget/tests/characterization_MOM6.json b/xbudget/tests/characterization_MOM6.json new file mode 100644 index 0000000..da212e2 --- /dev/null +++ b/xbudget/tests/characterization_MOM6.json @@ -0,0 +1,2149 @@ +{ + "heat_lhs": { + "dims": [ + "time", + "z_l", + "yh", + "xh" + ], + "nanmax": 3940351370639.1494, + "nanmin": -5551136678721.273, + "nansum": 465392557504166.7, + "provenance": "heat_lhs_sum", + "shape": [ + 1, + 35, + 180, + 240 + ] + }, + "heat_lhs_sum": { + "dims": [ + "time", + "z_l", + "yh", + "xh" + ], + "nanmax": 3940351370639.1494, + "nanmin": -5551136678721.273, + "nansum": 465392557504166.7, + "provenance": [ + 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"nansum": 0.0, + "provenance": "salt_rhs_sum_surface_exchange_flux_sum_advective_product", + "shape": [ + 1, + 35, + 180, + 240 + ] + }, + "salt_rhs_sum_surface_exchange_flux_sum_advective_product": { + "dims": [ + "time", + "z_l", + "yh", + "xh" + ], + "nanmax": 0.0, + "nanmin": -0.0, + "nansum": 0.0, + "provenance": [ + 0.001, + 0.0, + "boundary_forcing_h_tendency", + 1035.0, + "areacello" + ], + "shape": [ + 1, + 35, + 180, + 240 + ] + }, + "salt_rhs_sum_surface_exchange_flux_sum_nonadvective": { + "dims": [ + "time", + "z_l", + "yh", + "xh" + ], + "nanmax": 2276.061950475834, + "nanmin": -4416.354356011937, + "nansum": 153176.1176644049, + "provenance": "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum", + "shape": [ + 1, + 35, + 180, + 240 + ] + }, + "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum": { + "dims": [ + "time", + "z_l", + "yh", + "xh" + ], + "nanmax": 2276.061950475834, + "nanmin": -4416.354356011937, + "nansum": 153176.1176644049, + "provenance": [ + "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum_basal_product" + ], + "shape": [ + 1, + 35, + 180, + 240 + ] + }, + "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum_basal": { + "dims": [ + "time", + "z_l", + "yh", + "xh" + ], + "nanmax": 2276.061950475834, + "nanmin": -4416.354356011937, + "nansum": 153176.1176644049, + "provenance": "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum_basal_product", + "shape": [ + 1, + 35, + 180, + 240 + ] + }, + "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum_basal_product": { + "dims": [ + "time", + "z_l", + "yh", + "xh" + ], + "nanmax": 2276.061950475834, + "nanmin": -4416.354356011937, + "nansum": 153176.1176644049, + "provenance": [ + "sfdsi", + "areacello" + ], + "shape": [ + 1, + 35, + 180, + 240 + ] + }, + "salt_rhs_sum_surface_ocean_flux_advective_negative_rhs": { + "dims": [ + "time", + "yh", + "xh", + "z_l" + ], + "nanmax": 66860.41314122651, + "nanmin": -1196339.2712781483, + "nansum": 21859773.34497478, + "provenance": "salt_rhs_sum_surface_ocean_flux_advective_negative_rhs_product", + "shape": [ + 1, + 180, + 240, + 35 + ] + }, + "salt_rhs_sum_surface_ocean_flux_advective_negative_rhs_product": { + "dims": [ + "time", + "yh", + "xh", + "z_l" + ], + "nanmax": 66860.41314122651, + "nanmin": -1196339.2712781483, + "nansum": 21859773.34497478, + "provenance": [ + -1.0, + 0.001, + "sos", + "boundary_forcing_h_tendency", + 1035.0, + "areacello" + ], + "shape": [ + 1, + 180, + 240, + 35 + ] + } +} \ No newline at end of file diff --git a/xbudget/tests/test_characterization.py b/xbudget/tests/test_characterization.py new file mode 100644 index 0000000..e8f636c --- /dev/null +++ b/xbudget/tests/test_characterization.py @@ -0,0 +1,152 @@ +"""End-to-end characterization ("golden") test for the MOM6 preset. + +This is the safety net for the internals refactor (Phase 0): it pins the exact +set of variables that ``collect_budgets`` materializes from the example MOM6 +grid, together with each variable's shape, provenance, and a numerical +fingerprint. Later phases that restructure the engine can run this to prove they +produce numerically identical results. + +The example dataset (~600 MB) is downloaded on demand from Zenodo and is not +tracked in git, so this test is skipped when the file is absent (e.g. in CI that +does not fetch it). The small reference fingerprint lives next to this file in +``characterization_MOM6.json`` and *is* tracked. + +To regenerate the reference after an intended change:: + + XBUDGET_REGEN_CHARN=1 python -m pytest \ + xbudget/tests/test_characterization.py -s +""" +import os +import json +import warnings + +import numpy as np +import xarray as xr +import xgcm +import pytest + +import xbudget + +DATA_PATH = os.path.normpath( + os.path.join( + os.path.dirname(__file__), + "..", + "..", + "data", + "MOM6_global_example_diagnostics_zlevels_v0_0_6.nc", + ) +) +REFERENCE_PATH = os.path.join( + os.path.dirname(__file__), "characterization_MOM6.json" +) + +# Tolerance for the numerical fingerprints. The recipe is deterministic, so the +# only expected drift is last-bit floating point from library version changes. +RTOL = 1e-9 + + +def _build_example_grid(): + """Reconstruct the example xgcm.Grid from the local data file. + + Mirrors ``examples/load_example_model_grid.py`` but reads the already + downloaded file directly (the example loader uses a relative ``../data`` + path that only resolves from the examples directory). + """ + ds = xr.open_dataset(DATA_PATH).fillna(0.0) + if "z_l" not in ds.dims: + ds = ds.expand_dims(["z_l"]).assign_coords( + { + "z_l": xr.DataArray([3000], dims=("z_l",)), + "z_i": xr.DataArray([0, 6000], dims=("z_i",)), + } + ) + # Chunk so the difference/rechunking code path is exercised, matching the + # example loader. + ds = ds.chunk({"xh": 100, "yh": 100, "xq": 100, "yq": 100, "time": 1}) + coords = { + "X": {"center": "xh", "outer": "xq"}, + "Y": {"center": "yh", "outer": "yq"}, + } + return xgcm.Grid( + ds, + coords=coords, + metrics={("X", "Y"): "areacello"}, + boundary={"X": "periodic", "Y": "extend"}, + autoparse_metadata=False, + ) + + +def _normalize_provenance(prov): + """Make provenance JSON-comparable (numpy arrays/scalars -> lists/floats).""" + if isinstance(prov, np.ndarray): + prov = prov.tolist() + if isinstance(prov, (list, tuple)): + return [_normalize_provenance(p) for p in prov] + if isinstance(prov, np.generic): + return prov.item() + return prov + + +def _fingerprint(ds, created): + """Build a small, comparable fingerprint for each created variable.""" + fp = {} + for name in created: + da = ds[name] + values = np.asarray(da.values, dtype="float64") + fp[name] = { + "dims": list(da.dims), + "shape": list(da.shape), + "nansum": float(np.nansum(values)), + "nanmin": float(np.nanmin(values)) if values.size else 0.0, + "nanmax": float(np.nanmax(values)) if values.size else 0.0, + "provenance": _normalize_provenance(da.attrs.get("provenance")), + } + return fp + + +def _collect_example_budgets(): + """Run the MOM6 preset over the example grid; return (ds, created_names).""" + grid = _build_example_grid() + ds = grid._ds + before = set(ds.data_vars) + budget = xbudget.load_preset_budget("MOM6") + with warnings.catch_warnings(): + # Missing-diagnostic warnings are expected for the example subset. + warnings.simplefilter("ignore") + xbudget.collect_budgets(grid, budget) + created = sorted(set(ds.data_vars) - before) + return ds, created + + +@pytest.mark.skipif( + not os.path.exists(DATA_PATH), + reason="example MOM6 dataset not present (download from Zenodo to run)", +) +def test_mom6_characterization_matches_reference(): + ds, created = _collect_example_budgets() + fingerprint = _fingerprint(ds, created) + + if os.environ.get("XBUDGET_REGEN_CHARN"): + with open(REFERENCE_PATH, "w") as f: + json.dump(fingerprint, f, indent=2, sort_keys=True) + pytest.skip(f"regenerated reference at {REFERENCE_PATH}") + + with open(REFERENCE_PATH) as f: + reference = json.load(f) + + missing = set(reference) - set(fingerprint) + extra = set(fingerprint) - set(reference) + assert not missing, f"variables no longer produced: {sorted(missing)}" + assert not extra, f"unexpected new variables: {sorted(extra)}" + + for name, ref in reference.items(): + got = fingerprint[name] + assert got["dims"] == ref["dims"], f"{name}: dims changed" + assert got["shape"] == ref["shape"], f"{name}: shape changed" + assert got["provenance"] == ref["provenance"], ( + f"{name}: provenance changed" + ) + for stat in ("nansum", "nanmin", "nanmax"): + assert np.isclose(got[stat], ref[stat], rtol=RTOL, atol=0.0), ( + f"{name}: {stat} changed {ref[stat]} -> {got[stat]}" + ) From 44d49d4bde865ac25c06a14c27bef9643e718e90 Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Thu, 25 Jun 2026 13:45:40 -0700 Subject: [PATCH 06/19] Fix difference-op grid guard and dedup warnings (Phase 1) - Fix misplaced grid guard in budget_fill_dict's `difference` branch: the `else: raise(...must be xgcm.Grid...)` was attached to `if var_pref is None` instead of a grid check, so (a) a difference op on a plain Dataset hit an undefined `staggered_axes` (opaque NameError) instead of a clear error, and (b) a difference term reached after another operation in the same node spuriously raised even with a valid grid. Now raises a clear ValueError up front when no grid. - Collapse 4 copy-pasted 'missing variable' warnings into one helper. - Remove mutable default arg (new_b={}) in _deep_search. - Add regression tests covering both difference-guard failure modes. Co-Authored-By: Claude Opus 4.8 (1M context) --- xbudget/collect.py | 45 ++++++++++++++++++----------- xbudget/tests/test_utilities.py | 51 +++++++++++++++++++++++++++++++++ 2 files changed, 79 insertions(+), 17 deletions(-) diff --git a/xbudget/collect.py b/xbudget/collect.py index 509c3ee..d8571c4 100644 --- a/xbudget/collect.py +++ b/xbudget/collect.py @@ -8,6 +8,15 @@ import warnings +def _warn_missing_variable(name): + """Warn that a requested variable is absent from the dataset and skipped.""" + warnings.warn( + f"Variable {name} is missing from the dataset `ds`, so it is being " + f"skipped. To suppress this warning, remove {name} from the " + f"`xbudget_dict`.", + UserWarning, + ) + def aggregate(xbudget_dict, decompose=[]): """Aggregate xbudget dictionary into simpler root-level budgets. @@ -140,15 +149,17 @@ def deep_search(b): -------- aggregate, _deep_search """ - return _deep_search(b, new_b={}, k_last=None) + return _deep_search(b, new_b=None, k_last=None) -def _deep_search(b, new_b={}, k_last=None): +def _deep_search(b, new_b=None, k_last=None): """Recursive function for searching for variables in xbudget dictionary. - + See also -------- aggregate, deep_search """ + if new_b is None: + new_b = {} if type(b) is str: new_b[k_last] = b elif type(b) is dict: @@ -228,14 +239,14 @@ def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): if v_term_recursive is not None: op_list.append(v_term_recursive) elif v_term.get("var") is not None and v_term.get("var") not in ds: - warnings.warn(f"Variable {v_term.get('var')} is missing from the dataset `ds`, so it is being skipped. To suppress this warning, remove {v_term.get('var')} from the `xbudget_dict`.", UserWarning) + _warn_missing_variable(v_term.get("var")) elif isinstance(v_term, numbers.Number): op_list.append(v_term) elif isinstance(v_term, str): if v_term in ds: op_list.append(ds[v_term]) else: - warnings.warn(f"Variable {v_term} is missing from the dataset `ds`, so it is being skipped. To suppress this warning, remove {v_term} from the `xbudget_dict`.", UserWarning) + _warn_missing_variable(v_term) if k=="product": op_list.append(0.) @@ -274,7 +285,7 @@ def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): if k == "reciprocal": v_term = [v_term for k_term, v_term in v.items() if k_term != "var"][0] if v_term['var'] not in ds: - warnings.warn(f"Variable {v_term['var']} is missing from the dataset `ds`, so it is being skipped. To suppress this warning, remove {v_term['var']} from the `xbudget_dict`.") + _warn_missing_variable(v_term['var']) continue #A safe reciprocal that filters zeros out. @@ -297,15 +308,19 @@ def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): if k == "difference": - if grid is not None: - staggered_axes = { - axn:c for axn,ax in grid.axes.items() - for pos,c in ax.coords.items() - if pos!="center" - } + if grid is None: + raise ValueError( + "Input `data` must be an `xgcm.Grid` instance when using " + "`difference` operations." + ) + staggered_axes = { + axn:c for axn,ax in grid.axes.items() + for pos,c in ax.coords.items() + if pos!="center" + } v_term = [v_term for k_term,v_term in v.items() if k_term!="var"][0] if v_term not in ds: - warnings.warn(f"Variable {v_term} is missing from the dataset `ds`, so it is being skipped. To suppress this warning, remove {v_term} from the `xbudget_dict`.") + _warn_missing_variable(v_term) continue candidate_axes = [axn for (axn,c) in staggered_axes.items() if c in ds[v_term].dims] @@ -343,10 +358,6 @@ def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): ds[var_name] = var if var_pref is None: var_pref = var.copy() - else: - raise ValueError("Input `ds` must be `xgcm.Grid` instance if using `difference` operations.") - - return var_pref diff --git a/xbudget/tests/test_utilities.py b/xbudget/tests/test_utilities.py index 1d39902..0b2d8d2 100644 --- a/xbudget/tests/test_utilities.py +++ b/xbudget/tests/test_utilities.py @@ -504,3 +504,54 @@ def test_budget_fill_dict_allow_rechunk(self): # The numerical results should be identical xr.testing.assert_allclose(tendency_rechunked, tendency_correct) + + def test_difference_without_grid_raises_value_error(self): + """A `difference` op without an xgcm.Grid must raise a clear ValueError. + + Regression test: previously the grid guard was misplaced, so passing a + plain Dataset reached an undefined ``staggered_axes`` and raised an + opaque NameError instead. + """ + ds = xr.Dataset( + {"flux": xr.DataArray(np.random.rand(5), dims=("x_g",))}, + coords={"x_g": np.arange(5)}, + ) + xbudget_dict = {"var": None, "difference": {"flux": "flux", "var": None}} + + with pytest.raises(ValueError, match="xgcm.Grid"): + budget_fill_dict(ds, xbudget_dict, "tendency_rhs") + + def test_difference_not_first_term_does_not_raise(self): + """A `difference` term that is not evaluated first must not raise. + + Regression test: the `else: raise(...must be xgcm.Grid...)` was attached + to `if var_pref is None` rather than to a grid check, so any difference + reached after another operation in the same node spuriously errored even + when a valid grid was supplied. + """ + ds = xr.Dataset( + {"flux": xr.DataArray(np.random.rand(5, 3), dims=("x_g", "y_c"))}, + coords={ + "x_g": np.arange(5), + "x_c": np.arange(4) + 0.5, + "y_c": np.arange(3), + }, + ) + grid = xgcm.Grid( + ds, + coords={"X": {"center": "x_c", "left": "x_g"}}, + periodic=False, + autoparse_metadata=False, + ) + # A node with a `product` (evaluated first, sets the running variable) + # followed by a `difference` (previously tripped the misplaced raise). + xbudget_dict = { + "var": None, + "product": {"var": None, "scale": -1.0, "a": "flux"}, + "difference": {"var": None, "d": "flux"}, + } + + budget_fill_dict(grid, xbudget_dict, "tendency_rhs") + + assert "tendency_rhs_product" in grid._ds + assert "tendency_rhs_difference" in grid._ds From 5fde48ac9bea217b977ac6785b08b9474a3fb64f Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Thu, 25 Jun 2026 14:03:26 -0700 Subject: [PATCH 07/19] Add typed AST core: nodes, parser, evaluator (Phase 2a) Introduces an explicit, immutable expression tree for xbudget recipes so the engine can dispatch on node type instead of probing dict keys: - nodes.py: Budget/Term + Sum/Product/Difference/Reciprocal/Constant/ VarRef dataclasses. A Term may carry multiple operations (e.g. a bulk product and an equivalent finer sum). - parse.py: dict -> AST with schema validation (BudgetParseError names the offending path), the single source of schema truth. - evaluate.py: pure evaluator that materializes one variable per operation using simplified path-based names (operator infixes dropped, redundant 'copy' variables collapsed), with xbudget_path/provenance attrs and a legacy<->new alias map. Differential tests prove numerical equivalence to the legacy engine: on the example MOM6 grid the 108 legacy variables collapse to 57, all matching to rtol=1e-9, with every legacy name reachable via the alias map. Covered by a synthetic grid (CI) and the gated MOM6 grid. Public API is unchanged so far; rewiring follows in Phase 2b. Co-Authored-By: Claude Opus 4.8 (1M context) --- xbudget/evaluate.py | 240 +++++++++++++++++++++ xbudget/nodes.py | 102 +++++++++ xbudget/parse.py | 162 ++++++++++++++ xbudget/tests/test_evaluate_equivalence.py | 184 ++++++++++++++++ xbudget/tests/test_parse.py | 117 ++++++++++ 5 files changed, 805 insertions(+) create mode 100644 xbudget/evaluate.py create mode 100644 xbudget/nodes.py create mode 100644 xbudget/parse.py create mode 100644 xbudget/tests/test_evaluate_equivalence.py create mode 100644 xbudget/tests/test_parse.py diff --git a/xbudget/evaluate.py b/xbudget/evaluate.py new file mode 100644 index 0000000..56b2b1f --- /dev/null +++ b/xbudget/evaluate.py @@ -0,0 +1,240 @@ +"""Evaluate a typed budget tree against a dataset/grid. + +The evaluator walks the :mod:`xbudget.nodes` tree and materializes one output +variable per *operation* (a ``sum``/``product``/``difference``/``reciprocal``), +naming it by its structured path with the operator infixes dropped (see +``_new_name``). This collapses the legacy engine's duplicate "copy" variables +while preserving the numerical results. + +It is intentionally side-effecting in one place only: it writes the derived +variables into the dataset. It never mutates the recipe (the typed tree is +immutable). Alongside the new variables it records a legacy->new name map so +callers can offer backward-compatible names and a migration table. +""" +from functools import reduce +from operator import mul + +import numpy as np +import xarray as xr +import xgcm + +from .nodes import ( + Constant, + Difference, + Product, + Reciprocal, + Sum, + Term, + VarRef, +) +from .collect import _warn_missing_variable + + +def _new_name(path): + """Output variable name: the term path joined, without operator infixes.""" + return "_".join(path) + + +class _Evaluator: + def __init__(self, data, allow_rechunk=True): + if isinstance(data, xgcm.grid.Grid): + self.grid = data + self.ds = data._ds + else: + self.grid = None + self.ds = data + self.allow_rechunk = allow_rechunk + # legacy variable name -> new variable name (covers both the legacy + # "actual" operator-suffixed names and the plain "copy" names). + self.alias_map = {} + # new variable name -> {"path", "op", "legacy_actual", "legacy_copy"} + self.records = {} + + def run(self, budgets): + for budget in budgets.values(): + for side, term in budget.sides.items(): + self._eval_term(term, f"{budget.name}_{side}") + return self.alias_map, self.records + + # -- term --------------------------------------------------------------- + + def _eval_term(self, term, legacy_namepath): + """Evaluate a term; emit a variable per operation; return primary value. + + ``legacy_namepath`` is the variable name the previous engine would have + used for this term (operator infixes included via the parents), used to + build the backward-compatibility alias map. + """ + base = _new_name(term.path) + + evaluated = [] # (op, value_or_None, provenance) + for op in term.operations: + value, provenance = self._eval_op(op, term, legacy_namepath) + evaluated.append((op, value, provenance)) + + primary = None + first_emitted = True + for op, value, provenance in evaluated: + if value is None: + continue + new_name = base if first_emitted else f"{base}_{op.kind}" + legacy_actual = f"{legacy_namepath}_{op.kind}" + + out = value.rename(new_name) + out.attrs["provenance"] = provenance + out.attrs["xbudget_path"] = list(term.path) + out.attrs["xbudget_op"] = op.kind + self.ds[new_name] = out + + self.alias_map[legacy_actual] = new_name + self.records[new_name] = { + "path": list(term.path), + "op": op.kind, + "legacy_actual": legacy_actual, + } + if first_emitted: + # The legacy engine also emitted a plain "copy" at the namepath + # for sum/product terms (never for difference/reciprocal). + if op.kind in ("sum", "product"): + self.alias_map[legacy_namepath] = new_name + self.records[new_name]["legacy_copy"] = legacy_namepath + primary = out + first_emitted = False + + return primary + + # -- operations --------------------------------------------------------- + + def _eval_op(self, op, term, legacy_namepath): + if isinstance(op, (Sum, Product)): + return self._eval_nary(op, legacy_namepath) + if isinstance(op, Difference): + return self._eval_difference(op) + if isinstance(op, Reciprocal): + return self._eval_reciprocal(op) + raise TypeError(f"Unknown operation type {type(op).__name__}") + + def _eval_nary(self, op, legacy_namepath): + ds = self.ds + op_list = [] + for name, operand in op.terms: + if isinstance(operand, Term): + child_legacy = f"{legacy_namepath}_{op.kind}_{name}" + child_value = self._eval_term(operand, child_legacy) + if child_value is not None: + op_list.append(child_value) + elif operand.explicit_var is not None and operand.explicit_var not in ds: + _warn_missing_variable(operand.explicit_var) + elif isinstance(operand, Constant): + op_list.append(operand.value) + elif isinstance(operand, VarRef): + if operand.name in ds: + op_list.append(ds[operand.name]) + else: + _warn_missing_variable(operand.name) + if op.kind == "product": + op_list.append(0.0) + + if len(op_list) == 0: + return None, None + var = sum(op_list) if op.kind == "sum" else reduce(mul, op_list, 1) + if not isinstance(var, xr.DataArray): + # Reduced to a pure scalar (e.g. all variable operands missing); + # the legacy engine emitted no variable in this case. + return None, None + provenance = [o.name if isinstance(o, xr.DataArray) else o for o in op_list] + return var, provenance + + def _eval_difference(self, op): + if self.grid is None: + raise ValueError( + "Input `data` must be an `xgcm.Grid` instance when using " + "`difference` operations." + ) + ds = self.ds + if op.source not in ds: + _warn_missing_variable(op.source) + return None, None + + staggered_axes = { + axn: c + for axn, ax in self.grid.axes.items() + for pos, c in ax.coords.items() + if pos != "center" + } + candidate_axes = [ + axn for (axn, c) in staggered_axes.items() if c in ds[op.source].dims + ] + if len(candidate_axes) != 1: + raise ValueError( + "Finite difference inconsistent with finite volume discretization." + ) + axis = candidate_axes[0] + + if self.allow_rechunk: + try: + original_chunks = dict(ds.chunksizes) + except Exception: + import warnings + + warnings.warn( + "Dataset chunks are inconsistent; using unify_chunks()", + UserWarning, + ) + original_chunks = dict(ds.unify_chunks().chunksizes) + + axis_dim = [ + d for d in ds[op.source].dims if d in self.grid.axes[axis].coords.values() + ] + if len(axis_dim) != 1: + raise ValueError( + f"Expected to find one dimension for axis '{axis}' in " + f"variable '{op.source}', but found {len(axis_dim)}: {axis_dim}" + ) + axis_dim = axis_dim[0] + + temporary_chunks = { + axis_dim: -1, + **{d: "auto" for d in ds[op.source].dims if d != axis_dim}, + } + var = self.grid.diff( + ds[op.source].chunk(temporary_chunks).fillna(0.0), axis=axis + ) + var = var.chunk( + {d: original_chunks.get(d, var.chunksizes[d]) for d in var.dims} + ) + else: + var = self.grid.diff(ds[op.source].fillna(0.0), axis) + + return var, op.source + + def _eval_reciprocal(self, op): + ds = self.ds + if op.source not in ds: + _warn_missing_variable(op.source) + return None, None + var = 1.0 / xr.where(ds[op.source] == 0, np.inf, ds[op.source]) + return var, op.source + + +def evaluate_budgets(data, budgets, allow_rechunk=True): + """Evaluate parsed budgets into ``data``; return ``(alias_map, records)``. + + Parameters + ---------- + data : xgcm.Grid or xr.Dataset + The dataset (or grid) to read diagnostics from and write derived + variables into. Modified in place. + budgets : dict of str -> Budget + Parsed budgets, from :func:`xbudget.parse.parse_budgets`. + allow_rechunk : bool, default True + Temporarily rechunk staggered variables for ``difference`` operations. + + Returns + ------- + alias_map : dict + Legacy variable name -> new variable name. + records : dict + New variable name -> metadata ({path, op, legacy_actual, legacy_copy}). + """ + return _Evaluator(data, allow_rechunk=allow_rechunk).run(budgets) diff --git a/xbudget/nodes.py b/xbudget/nodes.py new file mode 100644 index 0000000..928902a --- /dev/null +++ b/xbudget/nodes.py @@ -0,0 +1,102 @@ +"""Typed expression tree for xbudget recipes. + +An xbudget convention (the nested ``xbudget_dict`` loaded from YAML) describes +how to build each budget term from raw model diagnostics. Historically that +recipe was walked directly as untyped nested dicts, with a node's meaning +inferred from which magic keys it happened to carry. These dataclasses give the +recipe an explicit, immutable shape so the parser, evaluator, and query helpers +can dispatch on node *type* instead of probing dict keys. + +The grammar (mirrors the YAML, see ``conventions/*.yaml``):: + + Budget := name, metadata, {side: Term} # side in {lhs, rhs} + Term := name, path, explicit_var?, [Operation] + Operation := Sum | Product | Difference | Reciprocal + Sum/Product := [(operand_name, Operand)] + Difference := source variable name (differenced across a grid axis) + Reciprocal := source variable name (safe 1/x) + Operand := Constant | VarRef | Term + +A ``Term`` may carry more than one ``Operation`` (e.g. a bulk ``Product`` and an +equivalent finer ``Sum`` decomposition of the same quantity); each operation +yields its own output variable when evaluated. +""" +from dataclasses import dataclass + + +@dataclass(frozen=True) +class Constant: + """A scalar factor/addend in a sum or product (e.g. a density, a sign).""" + value: float + + +@dataclass(frozen=True) +class VarRef: + """A reference to a raw diagnostic variable expected in the dataset.""" + name: str + + +@dataclass(frozen=True) +class Sum: + """Add named operands together.""" + terms: tuple # tuple[(name: str, operand: Constant | VarRef | "Term")] + kind = "sum" + + +@dataclass(frozen=True) +class Product: + """Multiply named operands together.""" + terms: tuple # tuple[(name: str, operand: Constant | VarRef | "Term")] + kind = "product" + + +@dataclass(frozen=True) +class Difference: + """Finite-difference a staggered variable across its grid axis.""" + source: str # name of the variable to difference + kind = "difference" + + +@dataclass(frozen=True) +class Reciprocal: + """Safe reciprocal (1/x with zeros mapped to infinity) of a variable.""" + source: str # name of the variable to invert + kind = "reciprocal" + + +@dataclass(frozen=True) +class Term: + """A node in a budget tree: a named quantity defined by its operations. + + Attributes + ---------- + name : str + This term's key under its parent (the budget side name for a root). + path : tuple of str + Full path of term names from the budget root, e.g. + ``("heat", "rhs", "diffusion", "lateral")``. Used as the canonical + identity and to derive output variable names. + operations : tuple of Operation + One or more of Sum/Product/Difference/Reciprocal. The first operation + provides the term's primary value. + explicit_var : str or None + A pre-named output variable, if the convention pinned one (rare). + """ + name: str + path: tuple + operations: tuple + explicit_var: object = None + + +@dataclass(frozen=True) +class Budget: + """A named budget (e.g. ``heat``) with lhs/rhs term trees and metadata.""" + name: str + metadata: dict + sides: dict # {"lhs": Term, "rhs": Term} (either may be absent) + + +# Operations that introduce a single source operand rather than named terms. +UNARY_OPS = {"difference": Difference, "reciprocal": Reciprocal} +NARY_OPS = {"sum": Sum, "product": Product} +OPERATION_KEYS = set(UNARY_OPS) | set(NARY_OPS) diff --git a/xbudget/parse.py b/xbudget/parse.py new file mode 100644 index 0000000..f9b9120 --- /dev/null +++ b/xbudget/parse.py @@ -0,0 +1,162 @@ +"""Parse an ``xbudget_dict`` convention into the typed expression tree. + +This is the single place that knows the YAML/dict schema. It validates as it +builds, so malformed conventions raise a clear ``BudgetParseError`` naming the +offending path instead of surfacing as a deep ``KeyError``/``NameError`` during +evaluation. +""" +import numbers + +from .nodes import ( + Budget, + Constant, + Difference, + Product, + Reciprocal, + Sum, + Term, + VarRef, + NARY_OPS, + UNARY_OPS, + OPERATION_KEYS, +) + +SIDES = ("lhs", "rhs") + + +class BudgetParseError(ValueError): + """Raised when an xbudget convention does not match the expected schema.""" + + +def _fmt(path): + return "/".join(str(p) for p in path) or "" + + +def parse_budgets(xbudget_dict): + """Parse a convention dict into ``{budget_name: Budget}``. + + Parameters + ---------- + xbudget_dict : dict + A convention in xbudget format (e.g. from ``load_preset_budget``). + + Returns + ------- + dict of str -> Budget + """ + if not isinstance(xbudget_dict, dict): + raise BudgetParseError( + f"Top-level xbudget convention must be a dict, got " + f"{type(xbudget_dict).__name__}." + ) + + budgets = {} + for name, body in xbudget_dict.items(): + if not isinstance(body, dict): + raise BudgetParseError( + f"Budget '{name}' must be a dict, got {type(body).__name__}." + ) + metadata = {k: v for k, v in body.items() if k not in SIDES} + sides = {} + for side in SIDES: + if body.get(side) is not None: + sides[side] = _parse_term(body[side], (name, side), side) + budgets[name] = Budget(name=name, metadata=metadata, sides=sides) + return budgets + + +def _parse_term(node, path, name): + """Parse a single term node (a quantity defined by its operations).""" + if not isinstance(node, dict): + raise BudgetParseError( + f"Term at {_fmt(path)} must be a dict, got {type(node).__name__}." + ) + + operations = [] + for key, value in node.items(): + if key == "var": + continue + if key in NARY_OPS: + operations.append(_parse_nary(key, value, path)) + elif key in UNARY_OPS: + operations.append(_parse_unary(key, value, path)) + else: + raise BudgetParseError( + f"Unexpected key '{key}' on term at {_fmt(path)}; expected " + f"'var' or one of {sorted(OPERATION_KEYS)}." + ) + + return Term( + name=name, + path=path, + operations=tuple(operations), + explicit_var=node.get("var"), + ) + + +def _parse_nary(kind, body, path): + """Parse a ``sum`` or ``product`` operation.""" + if not isinstance(body, dict): + raise BudgetParseError( + f"'{kind}' at {_fmt(path)} must be a dict, got " + f"{type(body).__name__}." + ) + terms = [] + for term_name, term_value in body.items(): + if term_name == "var": + continue + operand = _parse_operand(term_value, path + (term_name,), term_name) + if operand is not None: + terms.append((term_name, operand)) + return NARY_OPS[kind](terms=tuple(terms)) + + +def _parse_operand(value, path, name): + """Parse one operand of a sum/product: constant, var reference, or term.""" + if value is None: + # Tolerated: a placeholder operand with no content (legacy behavior + # silently skipped these). + return None + if isinstance(value, dict): + return _parse_term(value, path, name) + if isinstance(value, bool): + raise BudgetParseError( + f"Operand '{name}' at {_fmt(path)} is a bool; expected a number, " + f"variable name, or sub-term." + ) + if isinstance(value, numbers.Number): + return Constant(float(value)) + if isinstance(value, str): + return VarRef(value) + raise BudgetParseError( + f"Operand '{name}' at {_fmt(path)} has unsupported type " + f"{type(value).__name__}." + ) + + +def _parse_unary(kind, body, path): + """Parse a ``difference`` or ``reciprocal`` operation (single source var).""" + if not isinstance(body, dict): + raise BudgetParseError( + f"'{kind}' at {_fmt(path)} must be a dict, got " + f"{type(body).__name__}." + ) + sources = [(k, v) for k, v in body.items() if k != "var"] + if len(sources) != 1: + raise BudgetParseError( + f"'{kind}' at {_fmt(path)} must reference exactly one variable, " + f"found {len(sources)}: {[k for k, _ in sources]}." + ) + _, source_value = sources[0] + # difference references a bare variable name; reciprocal historically wraps + # it in a {{var: ...}} sub-dict. Accept both forms. + if isinstance(source_value, dict): + source = source_value.get("var") + else: + source = source_value + if not isinstance(source, str): + raise BudgetParseError( + f"'{kind}' at {_fmt(path)} must reference a variable name (str), " + f"got {type(source).__name__}." + ) + return UNARY_OPS[kind](source=source) diff --git a/xbudget/tests/test_evaluate_equivalence.py b/xbudget/tests/test_evaluate_equivalence.py new file mode 100644 index 0000000..88a14f5 --- /dev/null +++ b/xbudget/tests/test_evaluate_equivalence.py @@ -0,0 +1,184 @@ +"""Prove the typed evaluator is numerically equivalent to the legacy engine. + +The new engine (parse -> evaluate) emits one variable per operation with the +simplified path-based names, collapsing the legacy engine's duplicate "copy" +variables. These tests run both engines on the same data and assert that every +new variable matches its legacy counterpart (mapped via the alias map) to +floating-point tolerance. + +Two fixtures are used: +- a tiny synthetic grid that always runs (covers sum/product/difference, + multi-operation terms, and missing-variable handling); +- the full example MOM6 grid, skipped when the (~600 MB) file is absent. +""" +import copy +import os +import warnings + +import numpy as np +import xarray as xr +import xgcm +import pytest + +import xbudget +from xbudget.parse import parse_budgets +from xbudget.evaluate import evaluate_budgets + +OPS = {"sum", "product", "difference", "reciprocal"} +RTOL = 1e-9 + +DATA_PATH = os.path.normpath( + os.path.join( + os.path.dirname(__file__), + "..", + "..", + "data", + "MOM6_global_example_diagnostics_zlevels_v0_0_6.nc", + ) +) + + +def _run_legacy(build_grid, preset): + grid = build_grid() + ds = grid._ds + before = set(ds.data_vars) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + # legacy engine mutates the dict, so hand it a deep copy + xbudget.collect_budgets(grid, copy.deepcopy(preset)) + return ds, set(ds.data_vars) - before + + +def _run_new(build_grid, preset): + grid = build_grid() + ds = grid._ds + before = set(ds.data_vars) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + alias_map, records = evaluate_budgets(grid, parse_budgets(preset)) + return ds, set(ds.data_vars) - before, alias_map, records + + +def _assert_equivalent(build_grid, preset): + legacy_ds, legacy_new = _run_legacy(build_grid, preset) + new_ds, new_new, alias_map, records = _run_new(build_grid, preset) + + # Every legacy variable (copies + operator-suffixed actuals) is reachable + # from the alias map. + unmapped = legacy_new - set(alias_map) + assert not unmapped, f"legacy names with no new equivalent: {sorted(unmapped)}" + + # Every new variable matches its legacy "actual" counterpart numerically. + legacy_actuals = {v for v in legacy_new if v.rsplit("_", 1)[-1] in OPS} + assert set(records), "new engine produced no variables" + for new_name, rec in records.items(): + legacy_actual = rec["legacy_actual"] + assert legacy_actual in legacy_ds, ( + f"{new_name}: legacy actual {legacy_actual} missing" + ) + a = np.asarray(new_ds[new_name].values, dtype="float64") + b = np.asarray(legacy_ds[legacy_actual].values, dtype="float64") + assert a.shape == b.shape, f"{new_name}: shape {a.shape} != {b.shape}" + assert np.allclose( + np.nan_to_num(a), np.nan_to_num(b), rtol=RTOL, atol=0.0 + ), f"{new_name}: values differ from legacy {legacy_actual}" + + # The new engine emits exactly one variable per legacy "actual". + assert len(records) == len(legacy_actuals) + return records, alias_map + + +def _build_synthetic_grid(): + """A small chunked grid exercising sum, product, and difference.""" + rng = np.random.RandomState(0) + ds = xr.Dataset( + { + "flux": xr.DataArray(rng.rand(5, 3), dims=("x_g", "y_c")), + "diag_a": xr.DataArray(rng.rand(4, 3), dims=("x_c", "y_c")), + "diag_b": xr.DataArray(rng.rand(4, 3), dims=("x_c", "y_c")), + "area": xr.DataArray(rng.rand(4, 3) + 1.0, dims=("x_c", "y_c")), + }, + coords={ + "x_g": np.arange(5), + "x_c": np.arange(4) + 0.5, + "y_c": np.arange(3), + }, + ).chunk({"x_c": 2, "x_g": 2, "y_c": 3}) + return xgcm.Grid( + ds, + coords={"X": {"center": "x_c", "left": "x_g"}}, + periodic=False, + autoparse_metadata=False, + ) + + +SYNTHETIC_PRESET = { + "tracer": { + "rhs": { + "var": None, + "sum": { + "var": None, + "diffusion": { + "var": None, + "product": {"var": None, "sign": -1.0, "d": "diag_a", "area": "area"}, + }, + # multi-operation term: a bulk product AND a finer sum + "boundary": { + "var": None, + "product": {"var": None, "d": "diag_b", "area": "area"}, + "sum": { + "var": None, + "convergence": { + "var": None, + "difference": {"var": None, "transport": "flux"}, + }, + }, + }, + # references a diagnostic absent from the dataset + "missing": {"var": None, "product": {"var": None, "d": "not_present"}}, + }, + } + } +} + + +def test_equivalent_on_synthetic_grid(): + records, alias_map = _assert_equivalent(_build_synthetic_grid, SYNTHETIC_PRESET) + # The simplified names drop operator infixes and the redundant copies. + assert "tracer_rhs" in records + assert "tracer_rhs_diffusion" in records + assert "tracer_rhs_boundary" in records # primary (product) + assert "tracer_rhs_boundary_sum" in records # second decomposition + assert "tracer_rhs_boundary_convergence" in records # difference leaf + + +def _build_mom6_grid(): + ds = xr.open_dataset(DATA_PATH).fillna(0.0) + if "z_l" not in ds.dims: + ds = ds.expand_dims(["z_l"]).assign_coords( + { + "z_l": xr.DataArray([3000], dims=("z_l",)), + "z_i": xr.DataArray([0, 6000], dims=("z_i",)), + } + ) + ds = ds.chunk({"xh": 100, "yh": 100, "xq": 100, "yq": 100, "time": 1}) + return xgcm.Grid( + ds, + coords={"X": {"center": "xh", "outer": "xq"}, "Y": {"center": "yh", "outer": "yq"}}, + metrics={("X", "Y"): "areacello"}, + boundary={"X": "periodic", "Y": "extend"}, + autoparse_metadata=False, + ) + + +@pytest.mark.skipif( + not os.path.exists(DATA_PATH), + reason="example MOM6 dataset not present (download from Zenodo to run)", +) +def test_equivalent_on_mom6_example(): + records, alias_map = _assert_equivalent( + _build_mom6_grid, xbudget.load_preset_budget("MOM6") + ) + # 108 legacy variables collapse to 57 operation-named variables. + assert len(records) == 57 + assert len(alias_map) == 108 diff --git a/xbudget/tests/test_parse.py b/xbudget/tests/test_parse.py new file mode 100644 index 0000000..a323971 --- /dev/null +++ b/xbudget/tests/test_parse.py @@ -0,0 +1,117 @@ +"""Unit tests for parsing xbudget conventions into the typed tree.""" +import glob +import os + +import pytest + +from xbudget.nodes import ( + Budget, + Constant, + Difference, + Product, + Sum, + Term, + VarRef, +) +from xbudget.parse import BudgetParseError, parse_budgets + +CONVENTIONS = sorted( + glob.glob( + os.path.join(os.path.dirname(__file__), "..", "conventions", "*.yaml") + ) +) + + +def test_parses_simple_sum(): + d = {"heat": {"rhs": {"var": None, "sum": {"var": None, "adv": "advd"}}}} + budgets = parse_budgets(d) + assert set(budgets) == {"heat"} + heat = budgets["heat"] + assert isinstance(heat, Budget) + rhs = heat.sides["rhs"] + assert rhs.path == ("heat", "rhs") + assert len(rhs.operations) == 1 + (op,) = rhs.operations + assert isinstance(op, Sum) + (name, operand) = op.terms[0] + assert name == "adv" + # A bare-string operand resolves to a nested term whose single operation is + # represented by its VarRef value; here "advd" is a leaf variable. + assert operand == VarRef("advd") + + +def test_parses_product_with_constant_and_varref(): + d = { + "mass": { + "lhs": { + "var": None, + "product": {"var": None, "density": 1035.0, "area": "areacello"}, + } + } + } + (op,) = parse_budgets(d)["mass"].sides["lhs"].operations + assert isinstance(op, Product) + operands = dict(op.terms) + assert operands["density"] == Constant(1035.0) + assert operands["area"] == VarRef("areacello") + + +def test_parses_multi_operation_term(): + """A term may carry both a product and a sum (two decompositions).""" + d = { + "mass": { + "rhs": { + "var": None, + "product": {"var": None, "a": "x"}, + "sum": {"var": None, "b": {"var": None, "difference": {"d": "umo"}}}, + } + } + } + term = parse_budgets(d)["mass"].sides["rhs"] + kinds = [op.kind for op in term.operations] + assert kinds == ["product", "sum"] + sum_op = term.operations[1] + (_, sub_term) = sum_op.terms[0] + assert isinstance(sub_term, Term) + assert sub_term.operations[0] == Difference(source="umo") + + +def test_metadata_preserved_and_not_treated_as_side(): + d = {"heat": {"lambda": "thetao", "surface_lambda": "tos", "rhs": {"var": None}}} + heat = parse_budgets(d)["heat"] + assert heat.metadata == {"lambda": "thetao", "surface_lambda": "tos"} + assert set(heat.sides) == {"rhs"} + + +def test_none_operand_is_skipped(): + d = {"heat": {"rhs": {"var": None, "sum": {"var": None, "ghost": None, "a": "x"}}}} + (op,) = parse_budgets(d)["heat"].sides["rhs"].operations + assert [n for n, _ in op.terms] == ["a"] + + +@pytest.mark.parametrize( + "bad", + [ + "not a dict", + {"heat": "not a dict"}, + {"heat": {"rhs": {"var": None, "bogus": {}}}}, + {"heat": {"rhs": {"var": None, "difference": {"a": "x", "b": "y"}}}}, + {"heat": {"rhs": {"var": None, "difference": {"a": 3}}}}, + {"heat": {"rhs": {"var": None, "sum": "not a dict"}}}, + ], +) +def test_invalid_conventions_raise(bad): + with pytest.raises(BudgetParseError): + parse_budgets(bad) + + +@pytest.mark.parametrize("path", CONVENTIONS) +def test_all_shipped_conventions_parse(path): + import yaml + + with open(path) as f: + budgets = parse_budgets(yaml.safe_load(f)) + assert budgets + for budget in budgets.values(): + assert isinstance(budget, Budget) + assert set(budget.sides) <= {"lhs", "rhs"} From 65d55f53654bd355f814bd5ab3732c2a5b35715a Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Thu, 25 Jun 2026 14:09:07 -0700 Subject: [PATCH 08/19] Rewire collect_budgets onto the typed engine (Phase 2b) collect_budgets now parses the convention into the typed tree and runs the pure evaluator instead of the legacy dict-walker: - Emits the simplified path-based variable names by default; no longer mutates the input recipe dict; returns the data object for chaining. - Adds name_scheme='legacy' to additionally write the historical operator-suffixed names as aliases for migration. - Handles leaf terms ({"var": "diag"}) by aliasing the referenced diagnostic, matching legacy behavior. - budget_fill_dict retained as the (now documented-as-legacy) reference engine that the equivalence test pins against. - Exports parse_budgets/evaluate_budgets/BudgetParseError. Tests updated: collect_budgets assertions use the new names with added coverage for legacy mode and no-recipe-mutation; the equivalence oracle drives budget_fill_dict directly; characterization reference regenerated for the typed engine (57 vars). Full suite: 51 passed. Co-Authored-By: Claude Opus 4.8 (1M context) --- xbudget/__init__.py | 2 + xbudget/collect.py | 73 +- xbudget/evaluate.py | 24 +- xbudget/tests/characterization_MOM6.json | 1640 +++++--------------- xbudget/tests/test_characterization.py | 12 +- xbudget/tests/test_evaluate_equivalence.py | 13 +- xbudget/tests/test_utilities.py | 38 +- 7 files changed, 488 insertions(+), 1314 deletions(-) diff --git a/xbudget/__init__.py b/xbudget/__init__.py index 59fc08d..50aaad4 100644 --- a/xbudget/__init__.py +++ b/xbudget/__init__.py @@ -1,4 +1,6 @@ """ xbudget: xarray and xgcm-based functions for evaluating finite-volume budgets""" from .presets import * from .collect import * +from .parse import parse_budgets, BudgetParseError +from .evaluate import evaluate_budgets from .version import __version__ diff --git a/xbudget/collect.py b/xbudget/collect.py index d8571c4..ebd7991 100644 --- a/xbudget/collect.py +++ b/xbudget/collect.py @@ -169,39 +169,66 @@ def _deep_search(b, new_b=None, k_last=None): _deep_search(v, new_b=new_b, k_last=k) return new_b -def collect_budgets(ds, xbudget_dict, allow_rechunk = True): - """Fills xbudget dictionary with all tracer content tendencies +def collect_budgets(data, xbudget_dict, allow_rechunk=True, name_scheme="v1"): + """Materialize every budget term described by ``xbudget_dict`` into ``data``. + + The convention dict is parsed into a typed expression tree + (:mod:`xbudget.nodes`) and evaluated (:func:`xbudget.evaluate.evaluate_budgets`). + Unlike the historical engine, this does **not** mutate ``xbudget_dict``; it + only adds derived variables to the dataset. Parameters ---------- - ds : xr.Dataset containing budget diagnostics - xbudget_dict : dictionary in xbudget-compatible format - Example format: - >>> xbudget_dict = { - "heat": { - "rhs": { - "sum": { - "advection": { - "var":"advective_tendency" - }, - "var": "heat_rhs_sum" - }, - "var": "heat_rhs", - } - } - } + data : xgcm.Grid or xr.Dataset + Budget diagnostics to read from and write derived variables into, + modified in place. A grid is required if the convention uses + ``difference`` operations. + xbudget_dict : dict + A convention in xbudget format (e.g. from ``load_preset_budget``). allow_rechunk : bool (default: True) Whether to temporarily rechunk when taking differences along a dimension, e.g. to compute flux divergences on `center` from fluxes on `outer` or tendencies on `center` from snapshots on `outer`. + name_scheme : {"v1", "legacy"} (default: "v1") + ``"v1"`` names each derived variable by its term path with operator + infixes dropped (e.g. ``heat_rhs_diffusion_lateral``). ``"legacy"`` + additionally writes the historical operator-suffixed names (e.g. + ``heat_rhs_sum_diffusion_sum_lateral_product``) as aliases, for + backward compatibility during migration. + + Returns + ------- + data : xgcm.Grid or xr.Dataset + The same object passed in, for convenience. """ - for eq, v in xbudget_dict.items(): - for side in ["lhs", "rhs"]: - if side in v: - budget_fill_dict(ds, v[side], f"{eq}_{side}", allow_rechunk = allow_rechunk) + from .parse import parse_budgets + from .evaluate import evaluate_budgets + + if name_scheme not in ("v1", "legacy"): + raise ValueError( + f"Unknown name_scheme {name_scheme!r}; expected 'v1' or 'legacy'." + ) + + budgets = parse_budgets(xbudget_dict) + alias_map, _ = evaluate_budgets(data, budgets, allow_rechunk=allow_rechunk) + + if name_scheme == "legacy": + ds = data._ds if isinstance(data, xgcm.grid.Grid) else data + for legacy_name, new_name in alias_map.items(): + if legacy_name not in ds: + ds[legacy_name] = ds[new_name].rename(legacy_name) + + return data def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): - """Recursively fill xbudget dictionary + """Recursively fill xbudget dictionary (legacy engine). + + .. deprecated:: + ``budget_fill_dict`` is the historical dict-walking engine, retained as + a reference implementation. Prefer :func:`collect_budgets`, which is + backed by the typed expression tree (:mod:`xbudget.nodes`) and does not + mutate the recipe dict. This function mutates both ``data`` and + ``xbudget_dict`` in place. Parameters ---------- diff --git a/xbudget/evaluate.py b/xbudget/evaluate.py index 56b2b1f..7c68bb6 100644 --- a/xbudget/evaluate.py +++ b/xbudget/evaluate.py @@ -67,13 +67,31 @@ def _eval_term(self, term, legacy_namepath): """ base = _new_name(term.path) + primary = None + first_emitted = True + + # Leaf term: ``var`` references an existing diagnostic directly (e.g. + # ``{"var": "advective_tendency"}``). The legacy engine aliased it to the + # term's name; we do the same and treat it as the term's primary value. + if isinstance(term.explicit_var, str) and term.explicit_var in self.ds: + out = self.ds[term.explicit_var].rename(base).copy() + out.attrs["provenance"] = term.explicit_var + out.attrs["xbudget_path"] = list(term.path) + out.attrs["xbudget_op"] = "var" + self.ds[base] = out + self.alias_map[legacy_namepath] = base + self.records[base] = { + "path": list(term.path), + "op": "var", + "legacy_actual": legacy_namepath, + } + primary = out + first_emitted = False + evaluated = [] # (op, value_or_None, provenance) for op in term.operations: value, provenance = self._eval_op(op, term, legacy_namepath) evaluated.append((op, value, provenance)) - - primary = None - first_emitted = True for op, value, provenance in evaluated: if value is None: continue diff --git a/xbudget/tests/characterization_MOM6.json b/xbudget/tests/characterization_MOM6.json index da212e2..2c77bfc 100644 --- a/xbudget/tests/characterization_MOM6.json +++ b/xbudget/tests/characterization_MOM6.json @@ -1,23 +1,5 @@ { "heat_lhs": { - "dims": [ - "time", - "z_l", - "yh", - "xh" - ], - "nanmax": 3940351370639.1494, - "nanmin": -5551136678721.273, - "nansum": 465392557504166.7, - "provenance": "heat_lhs_sum", - "shape": [ - 1, - 35, - 180, - 240 - ] - }, - "heat_lhs_sum": { "dims": [ "time", "z_l", @@ -28,9 +10,9 @@ "nanmin": -5551136678721.273, "nansum": 465392557504166.7, "provenance": [ - "heat_lhs_sum_Eulerian_tendency_product", - "heat_lhs_sum_advection_sum", - "heat_lhs_sum_surface_ocean_flux_advective_negative_lhs_product" + "heat_lhs_Eulerian_tendency", + "heat_lhs_advection", + "heat_lhs_surface_ocean_flux_advective_negative_lhs" ], "shape": [ 1, @@ -39,25 +21,7 @@ 240 ] }, - "heat_lhs_sum_Eulerian_tendency": { - "dims": [ - "time", - "z_l", - "yh", - "xh" - ], - "nanmax": 31760549339386.81, - "nanmin": -50587387594801.766, - "nansum": 359498039810372.4, - "provenance": "heat_lhs_sum_Eulerian_tendency_product", - "shape": [ - 1, - 35, - 180, - 240 - ] - }, - "heat_lhs_sum_Eulerian_tendency_product": { + "heat_lhs_Eulerian_tendency": { "dims": [ "time", "z_l", @@ -78,25 +42,7 @@ 240 ] }, - "heat_lhs_sum_advection": { - "dims": [ - "time", - "z_l", - "yh", - "xh" - ], - "nanmax": 50947166350802.836, - "nanmin": -31753242879300.414, - "nansum": -40008497.625, - "provenance": "heat_lhs_sum_advection_sum", - "shape": [ - 1, - 35, - 180, - 240 - ] - }, - "heat_lhs_sum_advection_sum": { + "heat_lhs_advection": { "dims": [ "time", "z_l", @@ -107,8 +53,8 @@ "nanmin": -31753242879300.414, "nansum": -40008497.625, "provenance": [ - "heat_lhs_sum_advection_sum_lateral_product", - "heat_lhs_sum_advection_sum_interfacial_product" + "heat_lhs_advection_lateral", + "heat_lhs_advection_interfacial" ], "shape": [ 1, @@ -117,25 +63,7 @@ 240 ] }, - "heat_lhs_sum_advection_sum_interfacial": { - "dims": [ - "time", - "z_l", - "yh", - "xh" - ], - "nanmax": 52011903632147.234, - "nanmin": -66339650467105.27, - "nansum": -7964552.625, - "provenance": "heat_lhs_sum_advection_sum_interfacial_product", - "shape": [ - 1, - 35, - 180, - 240 - ] - }, - "heat_lhs_sum_advection_sum_interfacial_product": { + "heat_lhs_advection_interfacial": { "dims": [ "time", "z_l", @@ -157,25 +85,7 @@ 240 ] }, - "heat_lhs_sum_advection_sum_lateral": { - "dims": [ - "time", - "z_l", - "yh", - "xh" - ], - "nanmax": 69858875966090.484, - "nanmin": -53668947014140.43, - "nansum": -32043947.75, - "provenance": "heat_lhs_sum_advection_sum_lateral_product", - "shape": [ - 1, - 35, - 180, - 240 - ] - }, - "heat_lhs_sum_advection_sum_lateral_product": { + "heat_lhs_advection_lateral": { "dims": [ "time", "z_l", @@ -197,25 +107,7 @@ 240 ] }, - "heat_lhs_sum_surface_ocean_flux_advective_negative_lhs": { - "dims": [ - "time", - "yh", - "xh", - "z_l" - ], - "nanmax": 204413737780.63818, - "nanmin": -17530828054821.393, - "nansum": 105894557702285.67, - "provenance": "heat_lhs_sum_surface_ocean_flux_advective_negative_lhs_product", - "shape": [ - 1, - 180, - 240, - 35 - ] - }, - "heat_lhs_sum_surface_ocean_flux_advective_negative_lhs_product": { + "heat_lhs_surface_ocean_flux_advective_negative_lhs": { "dims": [ "time", "yh", @@ -250,7 +142,13 @@ "nanmax": 3940351305575.682, "nanmin": -5551136489805.616, "nansum": 465392547223857.06, - "provenance": "heat_rhs_sum", + "provenance": [ + "heat_rhs_diffusion", + "heat_rhs_surface_exchange_flux", + "heat_rhs_surface_ocean_flux_advective_negative_rhs", + "heat_rhs_bottom_flux", + "heat_rhs_frazil_ice" + ], "shape": [ 1, 35, @@ -258,22 +156,19 @@ 240 ] }, - "heat_rhs_sum": { + "heat_rhs_bottom_flux": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 3940351305575.682, - "nanmin": -5551136489805.616, - "nansum": 465392547223857.06, + "nanmax": 15602808942.544117, + "nanmin": 0.0, + "nansum": 34197020834727.336, "provenance": [ - "heat_rhs_sum_diffusion_sum", - "heat_rhs_sum_surface_exchange_flux_product", - "heat_rhs_sum_surface_ocean_flux_advective_negative_rhs_product", - "heat_rhs_sum_bottom_flux_product", - "heat_rhs_sum_frazil_ice_product" + "internal_heat_heat_tendency", + "areacello" ], "shape": [ 1, @@ -282,17 +177,20 @@ 240 ] }, - "heat_rhs_sum_bottom_flux": { + "heat_rhs_diffusion": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 15602808942.544117, - "nanmin": 0.0, - "nansum": 34197020834727.336, - "provenance": "heat_rhs_sum_bottom_flux_product", + "nanmax": 4403310884265.735, + "nanmin": -5668737491808.836, + "nansum": -513722.65625, + "provenance": [ + "heat_rhs_diffusion_lateral", + "heat_rhs_diffusion_interfacial" + ], "shape": [ 1, 35, @@ -300,18 +198,18 @@ 240 ] }, - "heat_rhs_sum_bottom_flux_product": { + "heat_rhs_diffusion_interfacial": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 15602808942.544117, - "nanmin": 0.0, - "nansum": 34197020834727.336, + "nanmax": 4403310884265.735, + "nanmin": -5668737491808.836, + "nansum": -513722.65625, "provenance": [ - "internal_heat_heat_tendency", + "opottempdiff", "areacello" ], "shape": [ @@ -321,17 +219,20 @@ 240 ] }, - "heat_rhs_sum_diffusion": { + "heat_rhs_diffusion_lateral": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 4403310884265.735, - "nanmin": -5668737491808.836, - "nansum": -513722.65625, - "provenance": "heat_rhs_sum_diffusion_sum", + "nanmax": 0.0, + "nanmin": 0.0, + "nansum": 0.0, + "provenance": [ + "opottemppmdiff", + "areacello" + ], "shape": [ 1, 35, @@ -339,19 +240,19 @@ 240 ] }, - "heat_rhs_sum_diffusion_sum": { + "heat_rhs_frazil_ice": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 4403310884265.735, - "nanmin": -5668737491808.836, - "nansum": -513722.65625, + "nanmax": 333573077881.8586, + "nanmin": -86785150.2558585, + "nansum": 10533909859144.72, "provenance": [ - "heat_rhs_sum_diffusion_sum_lateral_product", - "heat_rhs_sum_diffusion_sum_interfacial_product" + "frazil_heat_tendency", + "areacello" ], "shape": [ 1, @@ -360,17 +261,20 @@ 240 ] }, - "heat_rhs_sum_diffusion_sum_interfacial": { + "heat_rhs_surface_exchange_flux": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 4403310884265.735, - "nanmin": -5668737491808.836, - "nansum": -513722.65625, - "provenance": "heat_rhs_sum_diffusion_sum_interfacial_product", + "nanmax": 17392565831274.252, + "nanmin": -5720652791938.084, + "nansum": 314767059341419.3, + "provenance": [ + "boundary_forcing_heat_tendency", + "areacello" + ], "shape": [ 1, 35, @@ -378,18 +282,18 @@ 240 ] }, - "heat_rhs_sum_diffusion_sum_interfacial_product": { + "heat_rhs_surface_exchange_flux_advective": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 4403310884265.735, - "nanmin": -5668737491808.836, - "nansum": -513722.65625, + "nanmax": 17638101725505.902, + "nanmin": -203329990587.71277, + "nansum": -91207548946119.94, "provenance": [ - "opottempdiff", + "heat_content_surfwater", "areacello" ], "shape": [ @@ -399,17 +303,22 @@ 240 ] }, - "heat_rhs_sum_diffusion_sum_lateral": { + "heat_rhs_surface_exchange_flux_nonadvective": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 0.0, - "nanmin": 0.0, - "nansum": 0.0, - "provenance": "heat_rhs_sum_diffusion_sum_lateral_product", + "nanmax": 2595512189140.2495, + "nanmin": -5648622249701.43, + "nansum": 405960520308607.25, + "provenance": [ + "heat_rhs_surface_exchange_flux_nonadvective_latent", + "heat_rhs_surface_exchange_flux_nonadvective_sensible", + "heat_rhs_surface_exchange_flux_nonadvective_longwave", + "heat_rhs_surface_exchange_flux_nonadvective_shortwave" + ], "shape": [ 1, 35, @@ -417,7 +326,7 @@ 240 ] }, - "heat_rhs_sum_diffusion_sum_lateral_product": { + "heat_rhs_surface_exchange_flux_nonadvective_latent": { "dims": [ "time", "z_l", @@ -425,10 +334,10 @@ "xh" ], "nanmax": 0.0, - "nanmin": 0.0, - "nansum": 0.0, + "nanmin": -5751041213211.73, + "nansum": -3.707192070055726e+16, "provenance": [ - "opottemppmdiff", + "hflso", "areacello" ], "shape": [ @@ -438,17 +347,20 @@ 240 ] }, - "heat_rhs_sum_frazil_ice": { + "heat_rhs_surface_exchange_flux_nonadvective_longwave": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 333573077881.8586, - "nanmin": -86785150.2558585, - "nansum": 10533909859144.72, - "provenance": "heat_rhs_sum_frazil_ice_product", + "nanmax": 0.0, + "nanmin": -2250414053810.4854, + "nansum": -1.922110158863712e+16, + "provenance": [ + "rlntds", + "areacello" + ], "shape": [ 1, 35, @@ -456,18 +368,18 @@ 240 ] }, - "heat_rhs_sum_frazil_ice_product": { + "heat_rhs_surface_exchange_flux_nonadvective_sensible": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 333573077881.8586, - "nanmin": -86785150.2558585, - "nansum": 10533909859144.72, + "nanmax": 261182636956.48416, + "nanmin": -1626395528795.3396, + "nansum": -5742803130717152.0, "provenance": [ - "frazil_heat_tendency", + "hfsso", "areacello" ], "shape": [ @@ -477,17 +389,20 @@ 240 ] }, - "heat_rhs_sum_surface_exchange_flux": { + "heat_rhs_surface_exchange_flux_nonadvective_shortwave": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 17392565831274.252, - "nanmin": -5720652791938.084, - "nansum": 314767059341419.3, - "provenance": "heat_rhs_sum_surface_exchange_flux_product", + "nanmax": 6012543456110.167, + "nanmin": 0.0, + "nansum": 6.244178594022014e+16, + "provenance": [ + "rsdoabsorb", + "areacello" + ], "shape": [ 1, 35, @@ -495,19 +410,19 @@ 240 ] }, - "heat_rhs_sum_surface_exchange_flux_product": { + "heat_rhs_surface_exchange_flux_sum": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 17392565831274.252, - "nanmin": -5720652791938.084, - "nansum": 314767059341419.3, + "nanmax": 17380103825523.656, + "nanmin": -5720032356896.66, + "nansum": 314752971362487.25, "provenance": [ - "boundary_forcing_heat_tendency", - "areacello" + "heat_rhs_surface_exchange_flux_nonadvective", + "heat_rhs_surface_exchange_flux_advective" ], "shape": [ 1, @@ -516,38 +431,44 @@ 240 ] }, - "heat_rhs_sum_surface_exchange_flux_sum": { + "heat_rhs_surface_ocean_flux_advective_negative_rhs": { "dims": [ "time", - "z_l", "yh", - "xh" + "xh", + "z_l" ], - "nanmax": 17380103825523.656, - "nanmin": -5720032356896.66, - "nansum": 314752971362487.25, + "nanmax": 204413737780.63818, + "nanmin": -17530828054821.393, + "nansum": 105894557702285.67, "provenance": [ - "heat_rhs_sum_surface_exchange_flux_sum_nonadvective_sum", - "heat_rhs_sum_surface_exchange_flux_sum_advective_product" + -1.0, + 3992.0, + "tos", + "boundary_forcing_h_tendency", + 1035.0, + "areacello" ], "shape": [ 1, - 35, 180, - 240 + 240, + 35 ] }, - "heat_rhs_sum_surface_exchange_flux_sum_advective": { + "mass_lhs": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 17638101725505.902, - "nanmin": -203329990587.71277, - "nansum": -91207548946119.94, - "provenance": "heat_rhs_sum_surface_exchange_flux_sum_advective_product", + "nanmax": 772357898.5436696, + "nanmin": -1098185583.5343657, + "nansum": 115673902.00515747, + "provenance": [ + "mass_lhs_Eulerian_tendency" + ], "shape": [ 1, 35, @@ -555,18 +476,19 @@ 240 ] }, - 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"salt_rhs_sum_surface_exchange_flux_sum_advective": { + "salt_rhs_diffusion_lateral": { "dims": [ "time", "z_l", @@ -1992,9 +1083,12 @@ "xh" ], "nanmax": 0.0, - "nanmin": -0.0, + "nanmin": 0.0, "nansum": 0.0, - "provenance": "salt_rhs_sum_surface_exchange_flux_sum_advective_product", + "provenance": [ + "osaltpmdiff", + "areacello" + ], "shape": [ 1, 35, @@ -2002,21 +1096,18 @@ 240 ] }, - "salt_rhs_sum_surface_exchange_flux_sum_advective_product": { + "salt_rhs_surface_exchange_flux": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 0.0, - "nanmin": -0.0, - "nansum": 0.0, + "nanmax": 2276.0619504758206, + "nanmin": -4416.354356011937, + "nansum": 153176.1176649518, "provenance": [ - 0.001, - 0.0, - "boundary_forcing_h_tendency", - 1035.0, + "boundary_forcing_salt_tendency", "areacello" ], "shape": [ @@ -2026,17 +1117,23 @@ 240 ] }, - "salt_rhs_sum_surface_exchange_flux_sum_nonadvective": { + "salt_rhs_surface_exchange_flux_advective": { "dims": [ "time", "z_l", "yh", "xh" ], - "nanmax": 2276.061950475834, - "nanmin": -4416.354356011937, - "nansum": 153176.1176644049, - "provenance": "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum", + "nanmax": 0.0, + "nanmin": -0.0, + "nansum": 0.0, + "provenance": [ + 0.001, + 0.0, + "boundary_forcing_h_tendency", + 1035.0, + "areacello" + ], "shape": [ 1, 35, @@ -2044,7 +1141,7 @@ 240 ] }, - "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum": { + "salt_rhs_surface_exchange_flux_nonadvective": { "dims": [ "time", "z_l", @@ -2055,7 +1152,7 @@ "nanmin": -4416.354356011937, "nansum": 153176.1176644049, "provenance": [ - "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum_basal_product" + "salt_rhs_surface_exchange_flux_nonadvective_basal" ], "shape": [ 1, @@ -2064,7 +1161,7 @@ 240 ] }, - "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum_basal": { + "salt_rhs_surface_exchange_flux_nonadvective_basal": { "dims": [ "time", "z_l", @@ -2074,7 +1171,10 @@ "nanmax": 2276.061950475834, "nanmin": -4416.354356011937, "nansum": 153176.1176644049, - "provenance": "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum_basal_product", + "provenance": [ + "sfdsi", + "areacello" + ], "shape": [ 1, 35, @@ -2082,7 +1182,7 @@ 240 ] }, - "salt_rhs_sum_surface_exchange_flux_sum_nonadvective_sum_basal_product": { + "salt_rhs_surface_exchange_flux_sum": { "dims": [ "time", "z_l", @@ -2093,8 +1193,8 @@ "nanmin": -4416.354356011937, "nansum": 153176.1176644049, "provenance": [ - "sfdsi", - "areacello" + "salt_rhs_surface_exchange_flux_nonadvective", + "salt_rhs_surface_exchange_flux_advective" ], "shape": [ 1, @@ -2103,25 +1203,7 @@ 240 ] }, - "salt_rhs_sum_surface_ocean_flux_advective_negative_rhs": { - "dims": [ - "time", - "yh", - "xh", - "z_l" - ], - "nanmax": 66860.41314122651, - "nanmin": -1196339.2712781483, - "nansum": 21859773.34497478, - "provenance": "salt_rhs_sum_surface_ocean_flux_advective_negative_rhs_product", - "shape": [ - 1, - 180, - 240, - 35 - ] - }, - "salt_rhs_sum_surface_ocean_flux_advective_negative_rhs_product": { + "salt_rhs_surface_ocean_flux_advective_negative_rhs": { "dims": [ "time", "yh", diff --git a/xbudget/tests/test_characterization.py b/xbudget/tests/test_characterization.py index e8f636c..0a88a52 100644 --- a/xbudget/tests/test_characterization.py +++ b/xbudget/tests/test_characterization.py @@ -1,10 +1,12 @@ """End-to-end characterization ("golden") test for the MOM6 preset. -This is the safety net for the internals refactor (Phase 0): it pins the exact -set of variables that ``collect_budgets`` materializes from the example MOM6 -grid, together with each variable's shape, provenance, and a numerical -fingerprint. Later phases that restructure the engine can run this to prove they -produce numerically identical results. +Pins the exact set of variables that ``collect_budgets`` materializes from the +example MOM6 grid, together with each variable's shape, provenance, and a +numerical fingerprint. This guards the absolute output of the (typed) engine; +``test_evaluate_equivalence`` separately ties it to the legacy engine. + +Originally introduced as the Phase 0 safety net for the internals refactor; the +reference now reflects the typed engine's simplified variable names. The example dataset (~600 MB) is downloaded on demand from Zenodo and is not tracked in git, so this test is skipped when the file is absent (e.g. in CI that diff --git a/xbudget/tests/test_evaluate_equivalence.py b/xbudget/tests/test_evaluate_equivalence.py index 88a14f5..21569fd 100644 --- a/xbudget/tests/test_evaluate_equivalence.py +++ b/xbudget/tests/test_evaluate_equivalence.py @@ -39,13 +39,22 @@ def _run_legacy(build_grid, preset): + """Drive the legacy dict-walking engine (budget_fill_dict) directly. + + ``collect_budgets`` now uses the typed engine, so the equivalence oracle + must call the legacy ``budget_fill_dict`` itself (the same loop the old + ``collect_budgets`` performed). + """ grid = build_grid() ds = grid._ds before = set(ds.data_vars) + preset = copy.deepcopy(preset) # legacy engine mutates the dict with warnings.catch_warnings(): warnings.simplefilter("ignore") - # legacy engine mutates the dict, so hand it a deep copy - xbudget.collect_budgets(grid, copy.deepcopy(preset)) + for eq, sides in preset.items(): + for side in ("lhs", "rhs"): + if side in sides: + xbudget.budget_fill_dict(grid, sides[side], f"{eq}_{side}") return ds, set(ds.data_vars) - before diff --git a/xbudget/tests/test_utilities.py b/xbudget/tests/test_utilities.py index 0b2d8d2..9098a9e 100644 --- a/xbudget/tests/test_utilities.py +++ b/xbudget/tests/test_utilities.py @@ -303,10 +303,44 @@ def test_collect_budgets_basic(self): } collect_budgets(ds, xbudget_dict) + # New scheme: one variable per node, operator infixes dropped. + assert "heat_rhs_forcing" in ds + assert "heat_rhs" in ds + # The redundant operator-suffixed names are gone by default. + assert "heat_rhs_sum" not in ds + assert "heat_rhs_sum_forcing" not in ds + + def test_collect_budgets_legacy_name_scheme(self): + """name_scheme='legacy' also exposes the historical variable names.""" + ds = xr.Dataset({ + "forcing_diag": xr.DataArray(np.random.rand(3, 3), dims=("x", "y")), + }, coords={"x": [0, 1, 2], "y": [0, 1, 2]}) + xbudget_dict = { + "heat": {"rhs": {"sum": {"forcing": {"var": "forcing_diag"}, "var": None}, "var": None}} + } + collect_budgets(ds, xbudget_dict, name_scheme="legacy") + # Both new and legacy names resolve to the same data. + assert "heat_rhs_forcing" in ds assert "heat_rhs_sum_forcing" in ds assert "heat_rhs_sum" in ds - assert "heat_rhs" in ds - + xr.testing.assert_equal( + ds["heat_rhs_sum_forcing"].drop_vars("provenance", errors="ignore"), + ds["heat_rhs_forcing"].drop_vars("provenance", errors="ignore"), + ) + + def test_collect_budgets_does_not_mutate_recipe(self): + """collect_budgets must not mutate the input convention dict.""" + import copy as _copy + ds = xr.Dataset({ + "forcing_diag": xr.DataArray(np.random.rand(3, 3), dims=("x", "y")), + }, coords={"x": [0, 1, 2], "y": [0, 1, 2]}) + xbudget_dict = { + "heat": {"rhs": {"sum": {"forcing": {"var": "forcing_diag"}, "var": None}, "var": None}} + } + original = _copy.deepcopy(xbudget_dict) + collect_budgets(ds, xbudget_dict) + assert xbudget_dict == original + def test_collect_budgets_with_lhs_rhs(self): """Test budget collection with both lhs and rhs""" From 3a3f86ce4b36283ae55aba5042bcf414685f2c72 Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Thu, 25 Jun 2026 14:13:20 -0700 Subject: [PATCH 09/19] Faithful legacy mode, migration notes, notebook update (Phase 2c) - name_scheme='legacy' now reproduces the old engine exactly (historical names + in-place dict filling), so get_vars/aggregate keep working; the default v1 mode stays clean (new names, no mutation). - Add CHANGELOG.md with the migration guide and old->new name table. - Notebook: set name_scheme='legacy' on the single collect_budgets call so the rest of the tutorial (old names, get_vars, aggregate) is unchanged. - Update legacy-mode test to assert faithful old-name output + dict fill. Full suite: 51 passed. Co-Authored-By: Claude Opus 4.8 (1M context) --- CHANGELOG.md | 80 +++++++++++++++++++ .../MOM6_budget_examples_mass_heat_salt.ipynb | 8 +- xbudget/collect.py | 42 ++++++---- xbudget/tests/test_utilities.py | 14 ++-- 4 files changed, 115 insertions(+), 29 deletions(-) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..175b155 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,80 @@ +# Changelog + +## Unreleased — internals refactor (typed engine) + +This release replaces the recursive dict-walking engine with a typed expression +tree (parse → evaluate). The convention/YAML format is **unchanged**; the +in-memory representation, the engine, and the default output variable names are +new. Numerical results are identical to the previous engine (verified by an +end-to-end equivalence test on the example MOM6 grid). + +### Quick migration + +Add `name_scheme="legacy"` to your `collect_budgets` call to keep the previous +behavior exactly — historical variable names *and* the in-place filling of the +recipe dict that `get_vars`/`aggregate` depend on: + +```python +xbudget.collect_budgets(grid, xbudget_dict, name_scheme="legacy") +``` + +Everything downstream (old variable names, `get_vars`, `aggregate`) then works +unchanged. Adopt the new scheme at your own pace. + +### Breaking changes + +1. **Simplified variable names (default `name_scheme="v1"`).** Derived + variables are now named by their term path with the `sum`/`product`/ + `difference` operator infixes dropped, and the redundant "copy" duplicates + the old engine emitted are gone. One variable is produced per operation. + + | Legacy name | New name | + |---|---| + | `heat_rhs` | `heat_rhs` *(unchanged)* | + | `heat_rhs_sum` | `heat_rhs` *(the copy/sum collapse into one)* | + | `heat_rhs_sum_diffusion` | `heat_rhs_diffusion` | + | `heat_rhs_sum_diffusion_sum_lateral_product` | `heat_rhs_diffusion_lateral` | + | `mass_rhs_sum_advection_sum_lateral_sum_zonal_convergence_product_zonal_divergence_difference` | `mass_rhs_advection_lateral_zonal_convergence_zonal_divergence` | + + On the example MOM6 grid this reduces 108 variables to 57. The canonical + identity of each variable is also stored structurally in its + `xbudget_path` attribute (a list of term names), so you never need to parse + the flat name. + +2. **`collect_budgets` no longer mutates the recipe dict** (in `v1` mode). It + previously filled each node's `var` field in place; it now leaves + `xbudget_dict` untouched and returns the data object. Because the legacy + `get_vars`/`aggregate` helpers read those filled `var` fields, they only work + after a `name_scheme="legacy"` run (which still fills the dict). To query the + `v1` output, use the `records`/`alias_map` returned by `evaluate_budgets` + (below) and the `provenance` / `xbudget_path` attributes on each variable. + +3. **`collect_budgets` signature** gained a `name_scheme` keyword and its first + parameter is named `data` (a grid or dataset), matching its long-standing + behavior of accepting either. + +### New + +- `xbudget.parse_budgets(xbudget_dict)` → typed tree (`xbudget.nodes.Budget`), + the single schema-validating entry point; raises `xbudget.BudgetParseError` + with the offending path on malformed conventions. +- `xbudget.evaluate_budgets(data, budgets)` → pure evaluator; returns + `(alias_map, records)` where `alias_map` maps every legacy name to its new + name and `records` maps each new variable to its `{path, op, ...}` metadata. +- Each derived variable carries `xbudget_path` (structured identity) and + `provenance` (immediate inputs) attributes. + +### Fixed + +- The `difference` operation's grid guard was misattached, so a `difference` + on a plain `Dataset` raised an opaque `NameError`, and a `difference` term + evaluated after another operation in the same node raised spuriously even + with a valid grid. It now raises a clear `ValueError` up front when no grid + is supplied. (Also fixes a mutable-default-argument footgun in the internal + search helper.) + +### Deprecated + +- `budget_fill_dict` is retained as the legacy reference engine (still used + internally by `name_scheme="legacy"`) but is superseded by `collect_budgets` + / `evaluate_budgets`. diff --git a/examples/MOM6_budget_examples_mass_heat_salt.ipynb b/examples/MOM6_budget_examples_mass_heat_salt.ipynb index 8a58220..cf4ef60 100644 --- a/examples/MOM6_budget_examples_mass_heat_salt.ipynb +++ b/examples/MOM6_budget_examples_mass_heat_salt.ipynb @@ -286,13 +286,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "527f1b10", "metadata": {}, "outputs": [], - "source": [ - "xbudget.collect_budgets(grid, xbudget_dict)" - ] + "source": "# name_scheme=\"legacy\" reproduces the historical variable names (e.g.\n# \"heat_rhs_sum_diffusion_sum_lateral\") and fills the recipe dict in place,\n# which the get_vars/aggregate helpers used below rely on. The default\n# name_scheme=\"v1\" instead uses simplified names (e.g. \"heat_rhs_diffusion_lateral\")\n# and leaves the recipe dict untouched; see the migration notes in CHANGELOG.md.\nxbudget.collect_budgets(grid, xbudget_dict, name_scheme=\"legacy\")" }, { "cell_type": "markdown", @@ -2086,4 +2084,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/xbudget/collect.py b/xbudget/collect.py index ebd7991..d76c206 100644 --- a/xbudget/collect.py +++ b/xbudget/collect.py @@ -190,34 +190,42 @@ def collect_budgets(data, xbudget_dict, allow_rechunk=True, name_scheme="v1"): e.g. to compute flux divergences on `center` from fluxes on `outer` or tendencies on `center` from snapshots on `outer`. name_scheme : {"v1", "legacy"} (default: "v1") - ``"v1"`` names each derived variable by its term path with operator - infixes dropped (e.g. ``heat_rhs_diffusion_lateral``). ``"legacy"`` - additionally writes the historical operator-suffixed names (e.g. - ``heat_rhs_sum_diffusion_sum_lateral_product``) as aliases, for - backward compatibility during migration. + ``"v1"`` (recommended) uses the typed engine: each derived variable is + named by its term path with operator infixes dropped (e.g. + ``heat_rhs_diffusion_lateral``) and the recipe dict is left untouched. + ``"legacy"`` reproduces the historical behavior exactly: the + operator-suffixed names (e.g. ``heat_rhs_sum_diffusion_sum_lateral_product``) + plus their plain "copy" aliases, **and it mutates ``xbudget_dict`` in + place** to fill in ``var`` fields (which the legacy ``get_vars`` / + ``aggregate`` query helpers rely on). Returns ------- data : xgcm.Grid or xr.Dataset The same object passed in, for convenience. """ - from .parse import parse_budgets - from .evaluate import evaluate_budgets - - if name_scheme not in ("v1", "legacy"): + if name_scheme == "legacy": + # Faithful legacy behavior: reuse the reference engine, which emits the + # historical names and fills the recipe dict in place so that the + # dict-based query helpers (get_vars/aggregate) keep working. + for eq, sides in xbudget_dict.items(): + for side in ("lhs", "rhs"): + if side in sides: + budget_fill_dict( + data, sides[side], f"{eq}_{side}", allow_rechunk=allow_rechunk + ) + return data + + if name_scheme != "v1": raise ValueError( f"Unknown name_scheme {name_scheme!r}; expected 'v1' or 'legacy'." ) - budgets = parse_budgets(xbudget_dict) - alias_map, _ = evaluate_budgets(data, budgets, allow_rechunk=allow_rechunk) - - if name_scheme == "legacy": - ds = data._ds if isinstance(data, xgcm.grid.Grid) else data - for legacy_name, new_name in alias_map.items(): - if legacy_name not in ds: - ds[legacy_name] = ds[new_name].rename(legacy_name) + from .parse import parse_budgets + from .evaluate import evaluate_budgets + budgets = parse_budgets(xbudget_dict) + evaluate_budgets(data, budgets, allow_rechunk=allow_rechunk) return data def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): diff --git a/xbudget/tests/test_utilities.py b/xbudget/tests/test_utilities.py index 9098a9e..2079d33 100644 --- a/xbudget/tests/test_utilities.py +++ b/xbudget/tests/test_utilities.py @@ -311,7 +311,7 @@ def test_collect_budgets_basic(self): assert "heat_rhs_sum_forcing" not in ds def test_collect_budgets_legacy_name_scheme(self): - """name_scheme='legacy' also exposes the historical variable names.""" + """name_scheme='legacy' reproduces the historical names and fills the dict.""" ds = xr.Dataset({ "forcing_diag": xr.DataArray(np.random.rand(3, 3), dims=("x", "y")), }, coords={"x": [0, 1, 2], "y": [0, 1, 2]}) @@ -319,14 +319,14 @@ def test_collect_budgets_legacy_name_scheme(self): "heat": {"rhs": {"sum": {"forcing": {"var": "forcing_diag"}, "var": None}, "var": None}} } collect_budgets(ds, xbudget_dict, name_scheme="legacy") - # Both new and legacy names resolve to the same data. - assert "heat_rhs_forcing" in ds + # Historical variable names are produced... assert "heat_rhs_sum_forcing" in ds assert "heat_rhs_sum" in ds - xr.testing.assert_equal( - ds["heat_rhs_sum_forcing"].drop_vars("provenance", errors="ignore"), - ds["heat_rhs_forcing"].drop_vars("provenance", errors="ignore"), - ) + assert "heat_rhs" in ds + # ...the simplified names are not (legacy mode is faithful to the old engine)... + assert "heat_rhs_forcing" not in ds + # ...and the recipe dict is filled in place (get_vars/aggregate rely on this). + assert xbudget_dict["heat"]["rhs"]["var"] == "heat_rhs" def test_collect_budgets_does_not_mutate_recipe(self): """collect_budgets must not mutate the input convention dict.""" From 1dff116ccbf4573928da31cadfa716226df35679 Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Thu, 25 Jun 2026 14:14:05 -0700 Subject: [PATCH 10/19] Tidy: hoist warnings import in evaluate.py Co-Authored-By: Claude Opus 4.8 (1M context) --- xbudget/evaluate.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/xbudget/evaluate.py b/xbudget/evaluate.py index 7c68bb6..16b8f57 100644 --- a/xbudget/evaluate.py +++ b/xbudget/evaluate.py @@ -11,6 +11,7 @@ immutable). Alongside the new variables it records a legacy->new name map so callers can offer backward-compatible names and a migration table. """ +import warnings from functools import reduce from operator import mul @@ -193,8 +194,6 @@ def _eval_difference(self, op): try: original_chunks = dict(ds.chunksizes) except Exception: - import warnings - warnings.warn( "Dataset chunks are inconsistent; using unify_chunks()", UserWarning, From c1098f30ef2413f03bbb0c653cb17f4468c5b2b4 Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Thu, 25 Jun 2026 14:27:34 -0700 Subject: [PATCH 11/19] Add CLAUDE.md describing the codebase for Claude Code Documents the released (v0.6.2) architecture: the xbudget_dict convention tree, the collect.py dict-walking engine (collect_budgets/budget_fill_dict), the query helpers, and key behaviors (in-place dict filling, missing-diagnostic handling, string-path variable naming). Co-Authored-By: Claude Opus 4.8 (1M context) --- CLAUDE.md | 65 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 CLAUDE.md diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..f103d36 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,65 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## What this is + +`xbudget` wrangles finite-volume budgets (mass, heat, salt) diagnosed from ocean General Circulation Models — primarily MOM6 — into closed budgets using `xarray` and `xgcm`. The library's job is to take a dataset of raw model diagnostics plus a *convention* describing how those diagnostics combine, and materialize every intermediate and aggregate term as a named variable in the dataset. + +## Commands + +Tests use `pytest` (no separate build/lint step): + +```bash +pytest # full suite +pytest xbudget/tests/test_utilities.py # one file +pytest xbudget/tests/test_utilities.py::TestCollectBudgets::test_collect_budgets_basic # one test +``` + +Dev environment (conda + editable install): + +```bash +conda env create -f docs/environment.yml # or ci/environment.yml for the minimal test env +conda activate docs_env_xbudget +pip install -e . +``` + +CI runs `pytest` across the Python versions in `.github/workflows/ci.yml`. `pyproject.toml` declares `requires-python >= 3.11`; the version is sourced from `xbudget/version.py`. + +## Core architecture + +The central abstraction is the **`xbudget_dict`** — a nested provenance tree (loaded from a YAML *convention* file) that describes how to build each budget term from raw diagnostics. Understanding its structure is the key to working in this codebase. + +### The xbudget_dict tree + +Top-level keys are budgets (`mass`, `heat`, `salt`). Each budget has `lhs` and/or `rhs` sub-trees plus metadata keys (`lambda`, `thickness`, `surface_lambda`) that the engine does not interpret. Within a side, terms nest recursively. Every node carries a `var` key (the name of the variable it resolves to, initially `null` in YAML for derived terms) plus optionally one **operation** key: + +- `sum` — add the child terms together +- `product` — multiply child terms (scalar numbers are allowed as factors, e.g. `density: 1035.`, `sign: -1.`) +- `difference` — finite-difference a staggered flux across a grid axis (**requires an `xgcm.Grid`, not a bare Dataset**) +- `reciprocal` — safe `1/x` that maps zeros to infinity to avoid div-by-zero + +A node may carry more than one operation (e.g. a bulk `product` and an equivalent finer `sum` decomposition of the same quantity). Leaf string values (e.g. `"areacello"`, `"umo"`) are raw diagnostic variable names expected in the input dataset. Convention files live in `xbudget/conventions/*.yaml` (`MOM6.yaml` is the canonical/largest; also `MOM6_3Donly`, `MOM6_drift`, `MOM6_surface`). + +### Two modules + +- **`presets.py`** — `load_preset_budget(model="MOM6")` and `load_yaml()` deserialize a convention YAML into the dict. No validation of dict structure. +- **`collect.py`** — all the tree-walking logic: + - `collect_budgets(ds, xbudget_dict)` → top-level entry point. Walks `lhs`/`rhs` of each budget and calls `budget_fill_dict`. + - `budget_fill_dict(data, xbudget_dict, namepath)` → the recursive workhorse. **Mutates the dataset and the recipe dict in place**: it adds a new variable per node named by its `namepath` (e.g. `heat_rhs_sum_diffusion`) and back-fills each node's `var` key with that name. Each created variable gets a `provenance` attr recording its inputs. `data` may be an `xgcm.Grid` (its `._ds` is used; required for `difference`) or a plain `xr.Dataset`. + - `aggregate` / `disaggregate` → collapse a fully-filled tree down to flat root-level budgets; `decompose=[...]` keeps named term types broken out into their parts. + - `get_vars(xbudget_dict, terms)` → query the provenance subtree(s) for given term name(s); reads the `var` fields that `budget_fill_dict` filled in. + - `deep_search`, `flatten_lol` → internal helpers. + +### Key behaviors to know + +- **Input and output are the same dict.** `budget_fill_dict` both reads the recipe and writes results back into it (filling `var` fields), and the query helpers (`get_vars`, `aggregate`) depend on those filled fields. Run `collect_budgets` before querying. Tests `copy.deepcopy` the recipe before filling. +- **Missing diagnostics are skipped with a `UserWarning`, not an error** — a term whose variable is absent from `ds` is dropped, and a `sum`/`product` containing missing inputs collapses accordingly. This lets one convention serve datasets with different available diagnostics. +- **Stringly-typed identity.** Variable names are built by concatenating the key path with underscores (`heat_rhs_sum_diffusion_sum_lateral`), and `get_vars` reverse-engineers structure from those strings. +- `budget_fill_dict` mutates both `ds` and the passed `xbudget_dict`; `deep_search`/`_get_vars` are the recursive helpers behind aggregation and querying. + +## Data & examples + +- `examples/load_example_model_grid.py` — `load_MOM6_coarsened_diagnostics()` downloads (from Zenodo, cached in `data/`) and builds an `xgcm.Grid` with X/Y center/outer coords and `areacello` metrics. This is the standard fixture for realistic end-to-end use. +- `examples/MOM6_budget_examples_mass_heat_salt.ipynb` — worked tutorial notebook. +- The example `.nc` (~600 MB) is gitignored and fetched on demand; only `data/README.md` is tracked. From 52252f33b8b9c56e251832c3ec13e1cf0f3bd95b Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Thu, 25 Jun 2026 14:29:00 -0700 Subject: [PATCH 12/19] Update CLAUDE.md for the typed engine refactor Rewrites the architecture section to describe the parse->evaluate engine (nodes/parse/evaluate), the v1 vs legacy name schemes, collect_budgets no longer mutating the recipe, the retained legacy reference engine, and the new test layout. Points to CHANGELOG.md for migration. Co-Authored-By: Claude Opus 4.8 (1M context) --- CLAUDE.md | 62 +++++++++++++++++++++++++++++++++---------------------- 1 file changed, 37 insertions(+), 25 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index f103d36..5913ca6 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -6,16 +6,20 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co `xbudget` wrangles finite-volume budgets (mass, heat, salt) diagnosed from ocean General Circulation Models — primarily MOM6 — into closed budgets using `xarray` and `xgcm`. The library's job is to take a dataset of raw model diagnostics plus a *convention* describing how those diagnostics combine, and materialize every intermediate and aggregate term as a named variable in the dataset. +> This branch refactors the engine internals. The convention/YAML format is unchanged, but the in-memory representation is now a typed expression tree and the default output variable names are simplified. See `CHANGELOG.md` for the migration guide. + ## Commands -Tests use `pytest` (no separate build/lint step): +Tests use `pytest` (no separate build/lint step). The base conda environment may have a NumPy 1.x/2.x mismatch — run tests in the project env (e.g. `docs_env_xbudget`): ```bash pytest # full suite -pytest xbudget/tests/test_utilities.py # one file +pytest xbudget/tests/test_parse.py # one file pytest xbudget/tests/test_utilities.py::TestCollectBudgets::test_collect_budgets_basic # one test ``` +The end-to-end characterization and engine-equivalence tests need the ~600 MB example MOM6 dataset (gitignored, fetched from Zenodo); they **skip** when it is absent. Regenerate the characterization golden after an intended change with `XBUDGET_REGEN_CHARN=1 pytest xbudget/tests/test_characterization.py -s`. + Dev environment (conda + editable install): ```bash @@ -24,42 +28,50 @@ conda activate docs_env_xbudget pip install -e . ``` -CI runs `pytest` across the Python versions in `.github/workflows/ci.yml`. `pyproject.toml` declares `requires-python >= 3.11`; the version is sourced from `xbudget/version.py`. - ## Core architecture -The central abstraction is the **`xbudget_dict`** — a nested provenance tree (loaded from a YAML *convention* file) that describes how to build each budget term from raw diagnostics. Understanding its structure is the key to working in this codebase. +The central abstraction is the **`xbudget_dict`** — a nested provenance tree (loaded from a YAML *convention* file) describing how to build each budget term from raw diagnostics. It is the public input format. Internally it is parsed into a typed expression tree and evaluated. -### The xbudget_dict tree +### The xbudget_dict tree (input format — unchanged) -Top-level keys are budgets (`mass`, `heat`, `salt`). Each budget has `lhs` and/or `rhs` sub-trees plus metadata keys (`lambda`, `thickness`, `surface_lambda`) that the engine does not interpret. Within a side, terms nest recursively. Every node carries a `var` key (the name of the variable it resolves to, initially `null` in YAML for derived terms) plus optionally one **operation** key: +Top-level keys are budgets (`mass`, `heat`, `salt`). Each budget has `lhs` and/or `rhs` sub-trees plus metadata keys (`lambda`, `thickness`, `surface_lambda`) that the engine does not interpret. Within a side, terms nest recursively. Every node carries a `var` key (a variable name, or `null` for derived terms) plus optionally one or more **operation** keys: - `sum` — add the child terms together -- `product` — multiply child terms (scalar numbers are allowed as factors, e.g. `density: 1035.`, `sign: -1.`) -- `difference` — finite-difference a staggered flux across a grid axis (**requires an `xgcm.Grid`, not a bare Dataset**) -- `reciprocal` — safe `1/x` that maps zeros to infinity to avoid div-by-zero +- `product` — multiply child terms (scalar numbers allowed as factors, e.g. `density: 1035.`, `sign: -1.`) +- `difference` — finite-difference a staggered flux across a grid axis (**requires an `xgcm.Grid`**) +- `reciprocal` — safe `1/x` mapping zeros to infinity + +A node may carry more than one operation (e.g. a bulk `product` and an equivalent finer `sum`). Leaf string values (`"areacello"`, `"umo"`) are raw diagnostic names. Conventions live in `xbudget/conventions/*.yaml` (`MOM6.yaml` is canonical; also `MOM6_3Donly`, `MOM6_drift`, `MOM6_surface`). -A node may carry more than one operation (e.g. a bulk `product` and an equivalent finer `sum` decomposition of the same quantity). Leaf string values (e.g. `"areacello"`, `"umo"`) are raw diagnostic variable names expected in the input dataset. Convention files live in `xbudget/conventions/*.yaml` (`MOM6.yaml` is the canonical/largest; also `MOM6_3Donly`, `MOM6_drift`, `MOM6_surface`). +### The typed engine (parse → evaluate) -### Two modules +``` +xbudget_dict ──parse_budgets──▶ typed tree (nodes.py) ──evaluate_budgets──▶ derived variables + alias map +``` -- **`presets.py`** — `load_preset_budget(model="MOM6")` and `load_yaml()` deserialize a convention YAML into the dict. No validation of dict structure. -- **`collect.py`** — all the tree-walking logic: - - `collect_budgets(ds, xbudget_dict)` → top-level entry point. Walks `lhs`/`rhs` of each budget and calls `budget_fill_dict`. - - `budget_fill_dict(data, xbudget_dict, namepath)` → the recursive workhorse. **Mutates the dataset and the recipe dict in place**: it adds a new variable per node named by its `namepath` (e.g. `heat_rhs_sum_diffusion`) and back-fills each node's `var` key with that name. Each created variable gets a `provenance` attr recording its inputs. `data` may be an `xgcm.Grid` (its `._ds` is used; required for `difference`) or a plain `xr.Dataset`. - - `aggregate` / `disaggregate` → collapse a fully-filled tree down to flat root-level budgets; `decompose=[...]` keeps named term types broken out into their parts. - - `get_vars(xbudget_dict, terms)` → query the provenance subtree(s) for given term name(s); reads the `var` fields that `budget_fill_dict` filled in. - - `deep_search`, `flatten_lol` → internal helpers. +- **`nodes.py`** — immutable dataclasses: `Budget`, `Term`, and the operations `Sum`/`Product`/`Difference`/`Reciprocal` plus `Constant`/`VarRef`. A `Term` carries its structured `path` (its canonical identity) and may hold multiple operations. +- **`parse.py`** — `parse_budgets(dict) -> {name: Budget}`. The single source of schema truth; validates and raises `BudgetParseError` naming the offending path on malformed conventions. +- **`evaluate.py`** — `evaluate_budgets(data, budgets)` walks the tree and materializes **one variable per operation**, named by its term path with operator infixes dropped (e.g. `heat_rhs_diffusion_lateral`). It is pure with respect to the recipe (never mutates it); it only writes derived variables into the dataset. Each variable gets `xbudget_path` (structured identity) and `provenance` (immediate inputs) attrs. Returns `(alias_map, records)` — `alias_map` maps every legacy name to its new name; `records` maps each new variable to its metadata. Dispatch is on node type (`Difference` requires an `xgcm.Grid` in its signature, so a grid-less difference fails fast with a clear error). +- **`collect.py`** — the public surface: + - `collect_budgets(data, xbudget_dict, allow_rechunk=True, name_scheme="v1")` → parses then evaluates. **`v1` (default)** uses the simplified names and does **not** mutate the recipe dict. **`legacy`** reuses `budget_fill_dict` to reproduce the historical operator-suffixed names *and* fill the recipe dict in place. + - `budget_fill_dict(...)` → the legacy dict-walking engine, retained as a reference implementation (pinned by the equivalence test) and used by `name_scheme="legacy"`. It mutates both the dataset and the recipe dict. + - `aggregate` / `disaggregate` / `get_vars` → dict-based query helpers. **They read the `var` fields that the legacy engine fills**, so they only work after a `name_scheme="legacy"` run. For `v1`, query via the `records`/`alias_map` from `evaluate_budgets` and the `provenance`/`xbudget_path` attrs. ### Key behaviors to know -- **Input and output are the same dict.** `budget_fill_dict` both reads the recipe and writes results back into it (filling `var` fields), and the query helpers (`get_vars`, `aggregate`) depend on those filled fields. Run `collect_budgets` before querying. Tests `copy.deepcopy` the recipe before filling. -- **Missing diagnostics are skipped with a `UserWarning`, not an error** — a term whose variable is absent from `ds` is dropped, and a `sum`/`product` containing missing inputs collapses accordingly. This lets one convention serve datasets with different available diagnostics. -- **Stringly-typed identity.** Variable names are built by concatenating the key path with underscores (`heat_rhs_sum_diffusion_sum_lateral`), and `get_vars` reverse-engineers structure from those strings. -- `budget_fill_dict` mutates both `ds` and the passed `xbudget_dict`; `deep_search`/`_get_vars` are the recursive helpers behind aggregation and querying. +- **Naming changed (major-version cleanup).** `v1` emits one variable per node/operation with operator infixes dropped; the legacy engine emitted duplicate "copy" variables (108 → 57 on the MOM6 example). Use `name_scheme="legacy"` or the `alias_map` to bridge. `CHANGELOG.md` has the old→new table. +- **Missing diagnostics are skipped with a `UserWarning`, not an error** — a `sum`/`product` containing missing inputs collapses accordingly, so one convention can serve datasets with different available diagnostics. +- **`difference` rechunking:** `allow_rechunk=True` (default) temporarily rechunks the difference dimension into a single chunk (required by `grid.diff`) then restores chunking. + +### Tests + +- `test_parse.py` — parser units + validation; asserts all shipped conventions parse. +- `test_evaluate_equivalence.py` — proves the typed engine is numerically identical to the legacy `budget_fill_dict` (synthetic grid always; MOM6 grid when the data file is present). +- `test_characterization.py` (+ `characterization_MOM6.json`) — golden snapshot of the typed engine's absolute MOM6 output. +- `test_utilities.py` — the legacy engine, `aggregate`/`get_vars`/`disaggregate`, and `collect_budgets` behavior. ## Data & examples -- `examples/load_example_model_grid.py` — `load_MOM6_coarsened_diagnostics()` downloads (from Zenodo, cached in `data/`) and builds an `xgcm.Grid` with X/Y center/outer coords and `areacello` metrics. This is the standard fixture for realistic end-to-end use. -- `examples/MOM6_budget_examples_mass_heat_salt.ipynb` — worked tutorial notebook. +- `examples/load_example_model_grid.py` — `load_MOM6_coarsened_diagnostics()` downloads (Zenodo, cached in `data/`) and builds an `xgcm.Grid` with X/Y center/outer coords and `areacello` metrics. +- `examples/MOM6_budget_examples_mass_heat_salt.ipynb` — worked tutorial; its `collect_budgets` call uses `name_scheme="legacy"` so the rest of the notebook (old names, `get_vars`, `aggregate`) is unchanged. - The example `.nc` (~600 MB) is gitignored and fetched on demand; only `data/README.md` is tracked. From fae7a531e229202dc0952bb5551c71571c6fbef5 Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Fri, 26 Jun 2026 12:30:08 -0700 Subject: [PATCH 13/19] Address review/audit findings: remove reciprocal, fix naming, docs, metadata Acting on the three-agent audit of the refactor: Engine: - Remove the `reciprocal` operation entirely (unused by any convention, untested): dropped from nodes/parse/evaluate and the legacy budget_fill_dict; removed its test. - Fix latent multi-operation naming bug: the bare path name now goes to the first sum/product op (matching the legacy 'copy'), so a difference/unary op listed first no longer steals it. Only affects never-shipped difference-first multi-op terms; shipped conventions unchanged. Tests: - Add a leaf-term case to the synthetic equivalence fixture; make the equivalence count invariant robust to leaf terms (and assert injectivity). Docs/clarity: - Note that get_vars/aggregate/disaggregate require name_scheme='legacy'. - presets.load_preset_budget lists all four shipped conventions. - Fix disaggregate docstring example; clarify MOM6.yaml shortwave comments (drop the reference to a non-existent finite_difference op). - CLAUDE.md: drop reciprocal, document the xbudget_op attr. Metadata: - Bump version to 0.7.0; retitle CHANGELOG and note reciprocal removal. - Add Python 3.11 to the CI matrix and the test env (matches requires-python). - Add __all__ to collect.py so `import *` stops leaking np/xr/etc. Full suite: 50 passed. Co-Authored-By: Claude Opus 4.8 (1M context) --- .github/workflows/ci.yml | 2 +- CHANGELOG.md | 9 +++- CLAUDE.md | 5 +- ci/environment.yml | 2 +- xbudget/collect.py | 53 +++++++++++----------- xbudget/conventions/MOM6.yaml | 13 ++++-- xbudget/evaluate.py | 52 +++++++++++---------- xbudget/nodes.py | 14 ++---- xbudget/parse.py | 10 ++-- xbudget/presets.py | 4 +- xbudget/tests/test_evaluate_equivalence.py | 26 +++++++++-- xbudget/tests/test_utilities.py | 20 -------- xbudget/version.py | 2 +- 13 files changed, 106 insertions(+), 106 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 0e85e8b..f572876 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -18,7 +18,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ['3.12', '3.13', '3.14'] + python-version: ['3.11', '3.12', '3.13', '3.14'] steps: - name: Cancel previous runs diff --git a/CHANGELOG.md b/CHANGELOG.md index 175b155..ce29411 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # Changelog -## Unreleased — internals refactor (typed engine) +## 0.7.0 — internals refactor (typed engine) This release replaces the recursive dict-walking engine with a typed expression tree (parse → evaluate). The convention/YAML format is **unchanged**; the @@ -78,3 +78,10 @@ unchanged. Adopt the new scheme at your own pace. - `budget_fill_dict` is retained as the legacy reference engine (still used internally by `name_scheme="legacy"`) but is superseded by `collect_budgets` / `evaluate_budgets`. + +### Removed + +- The `reciprocal` operation. It was supported by the engine but used by no + shipped convention and exercised by no test; removed from the typed engine, + the parser, and the legacy `budget_fill_dict`. It can be reintroduced as a + typed node if a future convention needs it. diff --git a/CLAUDE.md b/CLAUDE.md index 5913ca6..c904fb1 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -39,7 +39,6 @@ Top-level keys are budgets (`mass`, `heat`, `salt`). Each budget has `lhs` and/o - `sum` — add the child terms together - `product` — multiply child terms (scalar numbers allowed as factors, e.g. `density: 1035.`, `sign: -1.`) - `difference` — finite-difference a staggered flux across a grid axis (**requires an `xgcm.Grid`**) -- `reciprocal` — safe `1/x` mapping zeros to infinity A node may carry more than one operation (e.g. a bulk `product` and an equivalent finer `sum`). Leaf string values (`"areacello"`, `"umo"`) are raw diagnostic names. Conventions live in `xbudget/conventions/*.yaml` (`MOM6.yaml` is canonical; also `MOM6_3Donly`, `MOM6_drift`, `MOM6_surface`). @@ -49,9 +48,9 @@ A node may carry more than one operation (e.g. a bulk `product` and an equivalen xbudget_dict ──parse_budgets──▶ typed tree (nodes.py) ──evaluate_budgets──▶ derived variables + alias map ``` -- **`nodes.py`** — immutable dataclasses: `Budget`, `Term`, and the operations `Sum`/`Product`/`Difference`/`Reciprocal` plus `Constant`/`VarRef`. A `Term` carries its structured `path` (its canonical identity) and may hold multiple operations. +- **`nodes.py`** — immutable dataclasses: `Budget`, `Term`, and the operations `Sum`/`Product`/`Difference` plus `Constant`/`VarRef`. A `Term` carries its structured `path` (its canonical identity) and may hold multiple operations. - **`parse.py`** — `parse_budgets(dict) -> {name: Budget}`. The single source of schema truth; validates and raises `BudgetParseError` naming the offending path on malformed conventions. -- **`evaluate.py`** — `evaluate_budgets(data, budgets)` walks the tree and materializes **one variable per operation**, named by its term path with operator infixes dropped (e.g. `heat_rhs_diffusion_lateral`). It is pure with respect to the recipe (never mutates it); it only writes derived variables into the dataset. Each variable gets `xbudget_path` (structured identity) and `provenance` (immediate inputs) attrs. Returns `(alias_map, records)` — `alias_map` maps every legacy name to its new name; `records` maps each new variable to its metadata. Dispatch is on node type (`Difference` requires an `xgcm.Grid` in its signature, so a grid-less difference fails fast with a clear error). +- **`evaluate.py`** — `evaluate_budgets(data, budgets)` walks the tree and materializes **one variable per operation**, named by its term path with operator infixes dropped (e.g. `heat_rhs_diffusion_lateral`). It is pure with respect to the recipe (never mutates it); it only writes derived variables into the dataset. Each variable gets `xbudget_path` (structured identity), `xbudget_op` (the operation kind), and `provenance` (immediate inputs) attrs. Returns `(alias_map, records)` — `alias_map` maps every legacy name to its new name; `records` maps each new variable to its metadata. Dispatch is on node type (`Difference` requires an `xgcm.Grid` in its signature, so a grid-less difference fails fast with a clear error). - **`collect.py`** — the public surface: - `collect_budgets(data, xbudget_dict, allow_rechunk=True, name_scheme="v1")` → parses then evaluates. **`v1` (default)** uses the simplified names and does **not** mutate the recipe dict. **`legacy`** reuses `budget_fill_dict` to reproduce the historical operator-suffixed names *and* fill the recipe dict in place. - `budget_fill_dict(...)` → the legacy dict-walking engine, retained as a reference implementation (pinned by the equivalence test) and used by `name_scheme="legacy"`. It mutates both the dataset and the recipe dict. diff --git a/ci/environment.yml b/ci/environment.yml index 144c199..4abea13 100644 --- a/ci/environment.yml +++ b/ci/environment.yml @@ -3,7 +3,7 @@ channels: - conda-forge - nodefaults dependencies: - - python>=3.12 + - python>=3.11 - cftime - netcdf4 - pydap diff --git a/xbudget/collect.py b/xbudget/collect.py index d76c206..bed9d31 100644 --- a/xbudget/collect.py +++ b/xbudget/collect.py @@ -8,6 +8,17 @@ import warnings +__all__ = [ + "aggregate", + "disaggregate", + "deep_search", + "collect_budgets", + "budget_fill_dict", + "get_vars", + "flatten", + "flatten_lol", +] + def _warn_missing_variable(name): """Warn that a requested variable is absent from the dataset and skipped.""" warnings.warn( @@ -20,6 +31,12 @@ def _warn_missing_variable(name): def aggregate(xbudget_dict, decompose=[]): """Aggregate xbudget dictionary into simpler root-level budgets. + .. note:: + This reads the ``var`` fields that the engine fills in, so it only + returns meaningful results after a legacy run, i.e. + ``collect_budgets(data, xbudget_dict, name_scheme="legacy")``. The + default ``name_scheme="v1"`` does not mutate the recipe. + Parameters ---------- xbudget_dict : dictionary in xbudget-compatible format @@ -89,6 +106,8 @@ def aggregate(xbudget_dict, decompose=[]): def disaggregate(b, decompose=[]): """Disaggregate variable's provenance dictionary into summed parts + Operates on a filled recipe (see the note on :func:`aggregate`). + Parameters ---------- b : xbudget sub-dictionary for a variable @@ -115,7 +134,7 @@ def disaggregate(b, decompose=[]): }, "var": "heat_rhs", } - >>> {'advection': 'advective_tendency'} + >>> xbudget.disaggregate(b) {'advection': 'advective_tendency'} >>> xbudget.disaggregate(b, decompose="advection") @@ -316,32 +335,7 @@ def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): # keep record of the first-listed variable if var_pref is None: var_pref = var.copy() - - if k == "reciprocal": - v_term = [v_term for k_term, v_term in v.items() if k_term != "var"][0] - if v_term['var'] not in ds: - _warn_missing_variable(v_term['var']) - continue - - #A safe reciprocal that filters zeros out. - var = 1.0 / xr.where(ds[v_term['var']] == 0, np.inf, ds[v_term['var']]) - - var_name = f"{namepath}_reciprocal" - var = var.rename(var_name) - var.attrs["provenance"] = v_term['var'] - ds[var_name] = var - if v['var'] is None: - v['var'] = var_name - if xbudget_dict["var"] is None: - var_copy = var.copy() - var_copy.attrs["provenance"] = var_name - xbudget_dict["var"] = namepath - if namepath not in ds: - ds[namepath] = var_copy - if var_pref is None: - var_pref = var.copy() - if k == "difference": if grid is None: raise ValueError( @@ -399,6 +393,13 @@ def budget_fill_dict(data, xbudget_dict, namepath, allow_rechunk = True): def get_vars(xbudget_dict, terms): """Get xbudget sub-dictionaries for specified terms. + .. note:: + Reads the ``var`` fields filled in by a legacy run, i.e. + ``collect_budgets(data, xbudget_dict, name_scheme="legacy")``. For the + default ``name_scheme="v1"`` engine, query the ``records``/``alias_map`` + returned by :func:`xbudget.evaluate.evaluate_budgets` and the + ``provenance``/``xbudget_path`` variable attributes instead. + Parameters ---------- xbudget_dict : dictionary in xbudget-compatible format diff --git a/xbudget/conventions/MOM6.yaml b/xbudget/conventions/MOM6.yaml index 1a5af2b..3bb8693 100644 --- a/xbudget/conventions/MOM6.yaml +++ b/xbudget/conventions/MOM6.yaml @@ -182,7 +182,11 @@ heat: # finite-volume heat budget in units of J/s nonadvective: var: null sum: - var: null # This sum is currently broken because the shortwave terms are 3D while the other terms are 2D! + # NOTE: this sum mixes 2D and 3D fields — the shortwave term + # (rsdoabsorb) is 3D while the other terms are 2D — so the summed + # result is only meaningful where they are reconciled (e.g. after + # vertically integrating the shortwave absorption). + var: null latent: var: null product: @@ -207,10 +211,9 @@ heat: # finite-volume heat budget in units of J/s var: null tracer_content_tendency_per_unit_area: "rsdoabsorb" area: "areacello" - # Not yet supported! - #var: "rsdoabsorb" - #finite_difference: - # Z: "rsdo" + # Alternative (not yet supported): derive the per-layer + # shortwave heating from the radiative flux `rsdo` via a vertical + # `difference` along the Z axis, instead of using `rsdoabsorb`. advective: var: null product: diff --git a/xbudget/evaluate.py b/xbudget/evaluate.py index 16b8f57..e46403c 100644 --- a/xbudget/evaluate.py +++ b/xbudget/evaluate.py @@ -1,10 +1,10 @@ """Evaluate a typed budget tree against a dataset/grid. The evaluator walks the :mod:`xbudget.nodes` tree and materializes one output -variable per *operation* (a ``sum``/``product``/``difference``/``reciprocal``), -naming it by its structured path with the operator infixes dropped (see -``_new_name``). This collapses the legacy engine's duplicate "copy" variables -while preserving the numerical results. +variable per *operation* (a ``sum``/``product``/``difference``), naming it by +its structured path with the operator infixes dropped (see ``_new_name``). This +collapses the legacy engine's duplicate "copy" variables while preserving the +numerical results. It is intentionally side-effecting in one place only: it writes the derived variables into the dataset. It never mutates the recipe (the typed tree is @@ -15,7 +15,6 @@ from functools import reduce from operator import mul -import numpy as np import xarray as xr import xgcm @@ -23,7 +22,6 @@ Constant, Difference, Product, - Reciprocal, Sum, Term, VarRef, @@ -89,14 +87,29 @@ def _eval_term(self, term, legacy_namepath): primary = out first_emitted = False - evaluated = [] # (op, value_or_None, provenance) + evaluated = [] # (op, value, provenance) for value-producing operations for op in term.operations: value, provenance = self._eval_op(op, term, legacy_namepath) - evaluated.append((op, value, provenance)) - for op, value, provenance in evaluated: - if value is None: - continue - new_name = base if first_emitted else f"{base}_{op.kind}" + if value is not None: + evaluated.append((op, value, provenance)) + + # Choose which operation gets the bare path name (the term's primary + # value). The legacy engine reserved the plain namepath ("copy") for the + # first sum/product term, so prefer that; a lone difference takes the + # bare name (with no legacy copy). A leaf var, if present, already claimed + # it above (first_emitted is False), so no operation does. + primary_idx = None + if first_emitted: + for i, (op, _v, _p) in enumerate(evaluated): + if op.kind in ("sum", "product"): + primary_idx = i + break + if primary_idx is None and evaluated: + primary_idx = 0 + + for i, (op, value, provenance) in enumerate(evaluated): + is_primary = i == primary_idx + new_name = base if is_primary else f"{base}_{op.kind}" legacy_actual = f"{legacy_namepath}_{op.kind}" out = value.rename(new_name) @@ -111,14 +124,13 @@ def _eval_term(self, term, legacy_namepath): "op": op.kind, "legacy_actual": legacy_actual, } - if first_emitted: + if is_primary: # The legacy engine also emitted a plain "copy" at the namepath - # for sum/product terms (never for difference/reciprocal). + # for sum/product terms (but not for a lone difference). if op.kind in ("sum", "product"): self.alias_map[legacy_namepath] = new_name self.records[new_name]["legacy_copy"] = legacy_namepath primary = out - first_emitted = False return primary @@ -129,8 +141,6 @@ def _eval_op(self, op, term, legacy_namepath): return self._eval_nary(op, legacy_namepath) if isinstance(op, Difference): return self._eval_difference(op) - if isinstance(op, Reciprocal): - return self._eval_reciprocal(op) raise TypeError(f"Unknown operation type {type(op).__name__}") def _eval_nary(self, op, legacy_namepath): @@ -225,14 +235,6 @@ def _eval_difference(self, op): return var, op.source - def _eval_reciprocal(self, op): - ds = self.ds - if op.source not in ds: - _warn_missing_variable(op.source) - return None, None - var = 1.0 / xr.where(ds[op.source] == 0, np.inf, ds[op.source]) - return var, op.source - def evaluate_budgets(data, budgets, allow_rechunk=True): """Evaluate parsed budgets into ``data``; return ``(alias_map, records)``. diff --git a/xbudget/nodes.py b/xbudget/nodes.py index 928902a..404a2a4 100644 --- a/xbudget/nodes.py +++ b/xbudget/nodes.py @@ -11,10 +11,9 @@ Budget := name, metadata, {side: Term} # side in {lhs, rhs} Term := name, path, explicit_var?, [Operation] - Operation := Sum | Product | Difference | Reciprocal + Operation := Sum | Product | Difference Sum/Product := [(operand_name, Operand)] Difference := source variable name (differenced across a grid axis) - Reciprocal := source variable name (safe 1/x) Operand := Constant | VarRef | Term A ``Term`` may carry more than one ``Operation`` (e.g. a bulk ``Product`` and an @@ -57,13 +56,6 @@ class Difference: kind = "difference" -@dataclass(frozen=True) -class Reciprocal: - """Safe reciprocal (1/x with zeros mapped to infinity) of a variable.""" - source: str # name of the variable to invert - kind = "reciprocal" - - @dataclass(frozen=True) class Term: """A node in a budget tree: a named quantity defined by its operations. @@ -77,7 +69,7 @@ class Term: ``("heat", "rhs", "diffusion", "lateral")``. Used as the canonical identity and to derive output variable names. operations : tuple of Operation - One or more of Sum/Product/Difference/Reciprocal. The first operation + One or more of Sum/Product/Difference. The first sum/product operation provides the term's primary value. explicit_var : str or None A pre-named output variable, if the convention pinned one (rare). @@ -97,6 +89,6 @@ class Budget: # Operations that introduce a single source operand rather than named terms. -UNARY_OPS = {"difference": Difference, "reciprocal": Reciprocal} +UNARY_OPS = {"difference": Difference} NARY_OPS = {"sum": Sum, "product": Product} OPERATION_KEYS = set(UNARY_OPS) | set(NARY_OPS) diff --git a/xbudget/parse.py b/xbudget/parse.py index f9b9120..3cf83fd 100644 --- a/xbudget/parse.py +++ b/xbudget/parse.py @@ -10,10 +10,6 @@ from .nodes import ( Budget, Constant, - Difference, - Product, - Reciprocal, - Sum, Term, VarRef, NARY_OPS, @@ -135,7 +131,7 @@ def _parse_operand(value, path, name): def _parse_unary(kind, body, path): - """Parse a ``difference`` or ``reciprocal`` operation (single source var).""" + """Parse a ``difference`` operation (a single source variable).""" if not isinstance(body, dict): raise BudgetParseError( f"'{kind}' at {_fmt(path)} must be a dict, got " @@ -148,8 +144,8 @@ def _parse_unary(kind, body, path): f"found {len(sources)}: {[k for k, _ in sources]}." ) _, source_value = sources[0] - # difference references a bare variable name; reciprocal historically wraps - # it in a {{var: ...}} sub-dict. Accept both forms. + # difference references a bare variable name; tolerate a {{var: ...}} + # sub-dict form as well. if isinstance(source_value, dict): source = source_value.get("var") else: diff --git a/xbudget/presets.py b/xbudget/presets.py index e462744..5745a14 100644 --- a/xbudget/presets.py +++ b/xbudget/presets.py @@ -10,7 +10,9 @@ def load_preset_budget(model="MOM6"): Parameters ---------- model : str (default "MOM6") - Currently supported models: ["MOM6"] + Name of any convention shipped in ``xbudget/conventions/`` (without the + ``.yaml`` suffix). Currently: "MOM6", "MOM6_3Donly", "MOM6_drift", + "MOM6_surface". Please open an Issue if you would like to contribute an xbudget yaml file for a new model–see /conventions/ for examples. diff --git a/xbudget/tests/test_evaluate_equivalence.py b/xbudget/tests/test_evaluate_equivalence.py index 21569fd..e701a54 100644 --- a/xbudget/tests/test_evaluate_equivalence.py +++ b/xbudget/tests/test_evaluate_equivalence.py @@ -77,8 +77,7 @@ def _assert_equivalent(build_grid, preset): unmapped = legacy_new - set(alias_map) assert not unmapped, f"legacy names with no new equivalent: {sorted(unmapped)}" - # Every new variable matches its legacy "actual" counterpart numerically. - legacy_actuals = {v for v in legacy_new if v.rsplit("_", 1)[-1] in OPS} + # Every new variable matches its legacy counterpart numerically. assert set(records), "new engine produced no variables" for new_name, rec in records.items(): legacy_actual = rec["legacy_actual"] @@ -92,8 +91,22 @@ def _assert_equivalent(build_grid, preset): np.nan_to_num(a), np.nan_to_num(b), rtol=RTOL, atol=0.0 ), f"{new_name}: values differ from legacy {legacy_actual}" - # The new engine emits exactly one variable per legacy "actual". - assert len(records) == len(legacy_actuals) + # The new engine emits exactly one variable per legacy "producer": each + # operator-suffixed actual, plus each leaf var (a bare legacy name with no + # operator-suffixed sibling). The redundant legacy "copies" are dropped. + legacy_actuals = {v for v in legacy_new if v.rsplit("_", 1)[-1] in OPS} + bare_legacy = legacy_new - legacy_actuals + legacy_leaves = { + v for v in bare_legacy if not any(f"{v}_{op}" in legacy_new for op in OPS) + } + legacy_producers = legacy_actuals | legacy_leaves + record_targets = {rec["legacy_actual"] for rec in records.values()} + assert record_targets == legacy_producers, ( + f"new->legacy targets {sorted(record_targets)} != " + f"legacy producers {sorted(legacy_producers)}" + ) + # No two new variables collide on a name or a legacy target (injective). + assert len(record_targets) == len(records) return records, alias_map @@ -143,6 +156,8 @@ def _build_synthetic_grid(): }, }, }, + # leaf term: var references an existing diagnostic directly + "direct": {"var": "diag_a"}, # references a diagnostic absent from the dataset "missing": {"var": None, "product": {"var": None, "d": "not_present"}}, }, @@ -159,6 +174,9 @@ def test_equivalent_on_synthetic_grid(): assert "tracer_rhs_boundary" in records # primary (product) assert "tracer_rhs_boundary_sum" in records # second decomposition assert "tracer_rhs_boundary_convergence" in records # difference leaf + # leaf term: var references an existing diagnostic directly + assert "tracer_rhs_direct" in records + assert records["tracer_rhs_direct"]["op"] == "var" def _build_mom6_grid(): diff --git a/xbudget/tests/test_utilities.py b/xbudget/tests/test_utilities.py index 2079d33..f25eda7 100644 --- a/xbudget/tests/test_utilities.py +++ b/xbudget/tests/test_utilities.py @@ -418,26 +418,6 @@ def test_budget_fill_dict_product_operation(self): # Check that product is correct (2.0 * 3.0 = 6.0) assert np.allclose(ds["heat_rhs_product"].values, 6.0) - def test_budget_fill_dict_reciprocal_operation(self): - """Test budget_fill_dict with reciprocal operation, including zero division""" - ds = xr.Dataset({ - "var_to_invert": xr.DataArray([1.0, 0.0, 3.0], dims=("x",)), - }, coords={"x": [0, 1, 2]}) - - xbudget_dict = { - "var": None, - "reciprocal": { - "inverted_var": {"var": "var_to_invert"}, - "var": None, - } - } - - result = budget_fill_dict(ds, xbudget_dict, "heat_rhs") - assert result is not None - assert "heat_rhs_reciprocal" in ds - expected = np.array([1.0, 0.0, 1.0/3.0]) - assert np.allclose(ds["heat_rhs_reciprocal"].values, expected) - def test_budget_fill_dict_missing_variable_warning(self): """Test that missing variables generate warnings""" ds = xr.Dataset({ diff --git a/xbudget/version.py b/xbudget/version.py index 622ca92..48bd9f8 100644 --- a/xbudget/version.py +++ b/xbudget/version.py @@ -1,3 +1,3 @@ """xbudget: version information""" -__version__ = "0.6.2" +__version__ = "0.7.0" From b8d88a52bc516018a7503aa3b7fa8d57130a03bd Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Fri, 26 Jun 2026 12:30:52 -0700 Subject: [PATCH 14/19] CLAUDE.md: document keeping PR description in sync on push MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit When pushing a new commit to an open PR, update the PR body to match the latest commit — including checking off completed task-list items — via gh pr edit. Co-Authored-By: Claude Opus 4.8 (1M context) --- CLAUDE.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/CLAUDE.md b/CLAUDE.md index c904fb1..5e45fe4 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -74,3 +74,13 @@ xbudget_dict ──parse_budgets──▶ typed tree (nodes.py) ──evaluate_b - `examples/load_example_model_grid.py` — `load_MOM6_coarsened_diagnostics()` downloads (Zenodo, cached in `data/`) and builds an `xgcm.Grid` with X/Y center/outer coords and `areacello` metrics. - `examples/MOM6_budget_examples_mass_heat_salt.ipynb` — worked tutorial; its `collect_budgets` call uses `name_scheme="legacy"` so the rest of the notebook (old names, `get_vars`, `aggregate`) is unchanged. - The example `.nc` (~600 MB) is gitignored and fetched on demand; only `data/README.md` is tracked. + +## Pull request workflow + +When you push a new commit to a branch that already has an open pull request, update the PR description (the top comment / body) so it stays consistent with the latest commit — don't leave it describing only the original state: + +- Refresh the summary so it reflects what the branch does now. +- If the description contains a task list / checklist, check off (`- [x]`) the items the new commit completed and add entries for any follow-up work it introduced. +- Reflect scope, naming, or API changes so a reviewer reading only the PR body sees the current truth. + +Update it with the GitHub CLI as part of the same push, e.g. `gh pr edit --body-file ` (or `--body "..."`), so the description never lags behind the commits. From 2938544a221a73ff1012c7246425ba091dca277b Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Tue, 30 Jun 2026 09:31:21 -0700 Subject: [PATCH 15/19] Add ECCO equivalence test, fix + add ECCO example notebooks - test_evaluate_equivalence: add gated ECCO LLC90 typed-vs-legacy test (71 vars, 0 mismatches; exercises reciprocal, difference-of-sub-term, and native lateral_divergence on the 13-tile face-connected grid). - Fix the existing ECCO notebook for the typed engine: drop the deleted xbudget.llc90 import and use name_scheme='legacy' (aggregate/get_vars read the filled dict). - Add eccov4r4_heat_budget_decomposition.ipynb: decomposes the ECCO heat budget via aggregate(decompose=...), mirroring the MOM6 decomposition workflow. (Needs execution against the ECCO data to populate outputs.) Co-Authored-By: Claude Opus 4.8 (1M context) --- ...ov4r4_budget_examples_mass_heat_salt.ipynb | 69 +---- .../eccov4r4_heat_budget_decomposition.ipynb | 266 ++++++++++++++++++ xbudget/tests/test_evaluate_equivalence.py | 81 +++++- 3 files changed, 345 insertions(+), 71 deletions(-) create mode 100644 examples/eccov4r4_heat_budget_decomposition.ipynb diff --git a/examples/eccov4r4_budget_examples_mass_heat_salt.ipynb b/examples/eccov4r4_budget_examples_mass_heat_salt.ipynb index e6105b5..093e3ae 100755 --- a/examples/eccov4r4_budget_examples_mass_heat_salt.ipynb +++ b/examples/eccov4r4_budget_examples_mass_heat_salt.ipynb @@ -13,33 +13,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a5f0eef4-7368-4f12-bc35-e09387046539", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "xarray: 2026.2.0\n", - "xgcm: 0.9.0\n" - ] - } - ], - "source": [ - "import xarray as xr\n", - "import numpy as np\n", - "from scipy.interpolate import griddata\n", - "import xgcm\n", - "import matplotlib.pyplot as plt\n", - "import xbudget\n", - "\n", - "from load_example_ecco_grid import * \n", - "from eccov4r4_budget_diagnostics import * \n", - "from xbudget.llc90 import * \n", - "print(\"xarray:\", xr.__version__)\n", - "print(\"xgcm:\", xgcm.__version__)" - ] + "outputs": [], + "source": "import xarray as xr\nimport numpy as np\nfrom scipy.interpolate import griddata\nimport xgcm\nimport matplotlib.pyplot as plt\nimport xbudget\n\nfrom load_example_ecco_grid import * \nfrom eccov4r4_budget_diagnostics import * \nprint(\"xarray:\", xr.__version__)\nprint(\"xgcm:\", xgcm.__version__)" }, { "cell_type": "markdown", @@ -140,44 +118,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "de0673e9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'mass': {'lambda': 'density',\n", - " 'thickness': 'thkcello',\n", - " 'lhs': {'Eulerian_tendency': 'mass_lhs_sum_Eulerian_tendency'},\n", - " 'rhs': {'advection': 'mass_rhs_sum_advection',\n", - " 'surface_exchange_flux': 'mass_rhs_sum_surface_exchange_flux'}},\n", - " 'heat': {'lambda': 'THETA',\n", - " 'lhs': {'Eulerian_tendency': 'heat_lhs_sum_Eulerian_tendency'},\n", - " 'rhs': {'advection': 'heat_rhs_sum_advection',\n", - " 'diffusion': 'heat_rhs_sum_diffusion',\n", - " 'surface_exchange_flux': 'heat_rhs_sum_surface_exchange_flux',\n", - " 'bottom_flux': 'heat_rhs_sum_bottom_flux'}},\n", - " 'salt': {'lambda': 'SALT',\n", - " 'lhs': {'Eulerian_tendency': 'salt_lhs_sum_Eulerian_tendency'},\n", - " 'rhs': {'advection': 'salt_rhs_sum_advection',\n", - " 'diffusion': 'salt_rhs_sum_diffusion',\n", - " 'surface_exchange_flux': 'salt_rhs_sum_surface_exchange_flux'}}}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Load the preset metadata dictionary and let xbudget attach the derived budget terms.\n", - "xbudget_dict = xbudget.load_preset_budget(model=\"ECCOV4r4_native\").copy()\n", - "xbudget.collect_budgets(grid, xbudget_dict,allow_rechunk= True)\n", - "# Aggregate the full metadata tree into a simpler budget summary for inspection.\n", - "simple_budgets = xbudget.aggregate(xbudget_dict)\n", - "simple_budgets" - ] + "outputs": [], + "source": "# Load the preset metadata dictionary and let xbudget attach the derived budget terms.\n# name_scheme=\"legacy\" reproduces the historical variable names and fills the\n# recipe dict in place, which aggregate()/get_vars() below rely on.\nxbudget_dict = xbudget.load_preset_budget(model=\"ECCOV4r4_native\").copy()\nxbudget.collect_budgets(grid, xbudget_dict, allow_rechunk=True, name_scheme=\"legacy\")\n# Aggregate the full metadata tree into a simpler budget summary for inspection.\nsimple_budgets = xbudget.aggregate(xbudget_dict)\nsimple_budgets" }, { "cell_type": "markdown", @@ -456,4 +401,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/examples/eccov4r4_heat_budget_decomposition.ipynb b/examples/eccov4r4_heat_budget_decomposition.ipynb new file mode 100644 index 0000000..0ffd0ea --- /dev/null +++ b/examples/eccov4r4_heat_budget_decomposition.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "db585993", + "metadata": {}, + "source": [ + "# Tutorial: decomposing the ECCO V4r4 heat budget\n", + "\n", + "This companion to `eccov4r4_budget_examples_mass_heat_salt.ipynb` shows how to **decompose** the native-grid (LLC90) ECCO heat budget into its physical contributions (advection, diffusion, surface forcing, …) with `xbudget`, mirroring the decomposition workflow in `MOM6_budget_examples_mass_heat_salt.ipynb`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15a60801", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "from scipy.interpolate import griddata\n", + "import xgcm\n", + "import matplotlib.pyplot as plt\n", + "import xbudget\n", + "\n", + "from load_example_ecco_grid import * \n", + "from eccov4r4_budget_diagnostics import * \n", + "print(\"xarray:\", xr.__version__)\n", + "print(\"xgcm:\", xgcm.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "3dd29d64", + "metadata": {}, + "source": [ + "### Load the example ECCO V4r4 grid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6135116", + "metadata": {}, + "outputs": [], + "source": [ + "grid = load_ECCOV4r4_coarsened_diagnostics() # this takes about 7 minutes\n", + "\n", + "# Build a time-step coordinate and a cell-volume metric used repeatedly below.\n", + "dt = grid._ds[\"time_bounds\"].diff(\"time_bounds\").rename({\"time_bounds\":\"time\"})\n", + "\n", + "grid._ds = grid._ds.assign_coords(\n", + " {\"dt\":(\"time\", dt.dt.total_seconds().values), \n", + " \"volcello\": (grid._ds[\"drF\"] * grid._ds[\"hFacC\"]) * grid._ds[\"rA\"]\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "02dc29f5", + "metadata": {}, + "source": [ + "### Preprocess the ECCO diagnostics into xbudget's budget components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62ed2b7e", + "metadata": {}, + "outputs": [], + "source": [ + "def zero_top_layer(ds, varname = \"\", zdim = \"k_l\"):\n", + " # Remove the surface layer contribution when a diagnostic should only represent interior transport.\n", + " return xr.where(ds[f\"{zdim}\"] != ds[f\"{zdim}\"].isel({f\"{zdim}\":0}),ds[varname].copy(),0.0,)\n", + "def make_flux_3d(ds, varname = \"\", zdim = \"k\"):\n", + " # Broadcast a surface flux into a 3D array that only occupies the top model layer.\n", + " k = ds[f\"{zdim}\"]\n", + " return xr.where(k == k.isel({f\"{zdim}\":0}), ds[varname].copy().expand_dims({f\"{zdim}\":k}),0.0,)\n", + "\n", + "# Assemble ECCO heat-flux terms into the budget components expected by xbudget.\n", + "grid._ds[\"geothermal_heat_flux_convergence\"] = eccov4r4_geothermal_heat_flux_tendency(grid._ds) \n", + "grid._ds[\"pen_boundary_forcing_heat_tendency\"] = eccov4r4_penetrative_heat_flux_tendency(grid._ds)\n", + "grid._ds[\"nonpen_boundary_forcing_heat_tendency\"] = eccov4r4_nonpenetrative_heat_flux_tendency(grid._ds)\n", + "grid._ds[\"boundary_forcing_heat_tendency\"] = grid._ds[\"pen_boundary_forcing_heat_tendency\"] + grid._ds[\"nonpen_boundary_forcing_heat_tendency\"]\n", + "\n", + "# Put the sea-ice salt exchange and salt-plume tendency onto the same vertical grid.\n", + "SFLUX = grid._ds[\"SFLUX\"].assign_coords(k=0).expand_dims(dim='k',axis=1) # sea-ice salt exchange\n", + "grid._ds[\"boundary_forcing_salt_tendency\"] = xr.concat([SFLUX+grid._ds[\"oceSPtnd\"],grid._ds[\"oceSPtnd\"].isel(k=slice(1,None))], dim='k') # combine surface salt flux and plume tendency\n", + "\n", + "# Separate interior vertical transport from the freshwater boundary forcing term.\n", + "grid._ds[\"WVELMASS_interior\"] = zero_top_layer(grid._ds, varname = \"WVELMASS\", zdim = \"k_l\") #\n", + "grid._ds[\"boundary_forcing_volume_tendency\"] = make_flux_3d(grid._ds, varname = \"oceFWflx\", zdim = \"k\")\n", + "\n", + "# Rechunk once before calling xbudget so later difference and convergence steps stay tractable.\n", + "grid._ds = grid._ds.chunk({\"tile\":2, \"i\":50, \"j\":50, \"i_g\":50, \"j_g\":50, \"k\": 10}).fillna(0.0)\n", + "# to-do: add comparable chunking inside the difference and convergence routines " + ] + }, + { + "cell_type": "markdown", + "id": "8537372d", + "metadata": {}, + "source": [ + "### Collect the budget terms\n", + "\n", + "We use `name_scheme=\"legacy\"` so the historical variable names are produced and the recipe dict is filled in place, which `aggregate`/`get_vars` rely on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c5a3ee9", + "metadata": {}, + "outputs": [], + "source": [ + "xbudget_dict = xbudget.load_preset_budget(model=\"ECCOV4r4_native\").copy()\n", + "xbudget.collect_budgets(grid, xbudget_dict, allow_rechunk=True, name_scheme=\"legacy\")" + ] + }, + { + "cell_type": "markdown", + "id": "e304c401", + "metadata": {}, + "source": [ + "### Decompose the heat budget\n", + "\n", + "`aggregate` collapses the full provenance tree into a flat mapping of high-level terms to variable names. Passing `decompose=[...]` breaks the named terms into their constituent parts — here we split the right-hand-side heat sources/sinks into advection, diffusion, and surface-exchange-flux components." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0fa039e", + "metadata": {}, + "outputs": [], + "source": [ + "decomposed = xbudget.aggregate(\n", + " xbudget_dict, decompose=[\"advection\", \"diffusion\", \"surface_exchange_flux\"]\n", + ")\n", + "heat_rhs_terms = decomposed[\"heat\"][\"rhs\"]\n", + "heat_rhs_terms" + ] + }, + { + "cell_type": "markdown", + "id": "6924d7a0", + "metadata": {}, + "source": [ + "### Plot the decomposed heat-budget terms\n", + "\n", + "Each term is a native volume-integrated heat tendency (J/s); we convert to a column tendency per unit area (W/m²) and remap the LLC tiles to lat-lon for visualization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "997ec4fe", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_interpolated_ecco(fig, ax, ds, vmin=0, vmax=30, cmap=\"coolwarm\"):\n", + " # Build a simple regular lat-lon target grid for quick visual comparison.\n", + " target_lon = np.arange(-180, 180, 1)\n", + " target_lat = np.arange(-90, 90, 1)\n", + " lon_grid, lat_grid = np.meshgrid(target_lon, target_lat)\n", + " \n", + " # Flatten the LLC field into point/value pairs for interpolation.\n", + " points = np.column_stack((ds.XC.values.ravel(), ds.YC.values.ravel()))\n", + " values = ds.values.ravel()\n", + " \n", + " # Use nearest-neighbor remapping to preserve the native diagnostic values.\n", + " interpolated_data = griddata(points, values, (lon_grid, lat_grid), method='nearest')\n", + " \n", + " # Plot the remapped field.\n", + " cb = ax.pcolormesh(lon_grid, lat_grid, interpolated_data, \n", + " vmin=vmin, vmax=vmax, cmap=cmap)\n", + " \n", + " return cb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1ed9bcd", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "area = grid._ds[\"rA\"].where(grid._ds[\"Depth\"] > 0.0)\n", + "\n", + "def column_tendency(varname):\n", + " \"\"\"Column-integrated heat tendency per unit area [W/m^2] at the first time.\"\"\"\n", + " da = (grid._ds[varname] / area).sum(\"k\")\n", + " da = da.where(np.abs(da) > 0.0)\n", + " return da.isel(time=0)\n", + "\n", + "terms = list(heat_rhs_terms.items())\n", + "ncol = 3\n", + "nrow = int(np.ceil(len(terms) / ncol))\n", + "vmax = 1000.0 # W/m^2\n", + "fig, axes = plt.subplots(nrow, ncol, figsize=(5 * ncol, 4 * nrow), squeeze=False)\n", + "for ax in axes.ravel():\n", + " ax.set_visible(False)\n", + "for (name, varname), ax in zip(terms, axes.ravel()):\n", + " ax.set_visible(True)\n", + " cb = plot_interpolated_ecco(fig, ax, column_tendency(varname), vmin=-vmax, vmax=vmax)\n", + " ax.set_title(name, fontsize=9)\n", + "fig.colorbar(cb, ax=axes.ravel().tolist(), orientation=\"vertical\", label=\"W/m^2\", shrink=0.8)\n", + "fig.suptitle(\"ECCO heat budget RHS, decomposed (column tendency)\")" + ] + }, + { + "cell_type": "markdown", + "id": "36ffe8d1", + "metadata": {}, + "source": [ + "### Global-mean time series of each decomposed term\n", + "\n", + "Area-weighted global means summarize the relative magnitude of each contribution over the record." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4aa8b929", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "for name, varname in heat_rhs_terms.items():\n", + " col = (grid._ds[varname] / area).sum(\"k\")\n", + " global_mean = ((col * area) / area.sum()).sum([\"tile\", \"i\", \"j\"])\n", + " global_mean.plot(ax=ax, marker=\"o\", ms=3, label=name)\n", + "ax.set_ylabel(\"W/m^2\")\n", + "ax.set_title(\"Global-mean decomposed heat-budget terms\")\n", + "ax.legend(fontsize=8, ncol=2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "docs_env_xbudget", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/xbudget/tests/test_evaluate_equivalence.py b/xbudget/tests/test_evaluate_equivalence.py index e701a54..43672f2 100644 --- a/xbudget/tests/test_evaluate_equivalence.py +++ b/xbudget/tests/test_evaluate_equivalence.py @@ -24,18 +24,18 @@ from xbudget.parse import parse_budgets from xbudget.evaluate import evaluate_budgets -OPS = {"sum", "product", "difference", "reciprocal"} +OPS = {"sum", "product", "difference", "reciprocal", "lateral_divergence"} RTOL = 1e-9 -DATA_PATH = os.path.normpath( - os.path.join( - os.path.dirname(__file__), - "..", - "..", - "data", - "MOM6_global_example_diagnostics_zlevels_v0_0_6.nc", + +def _data(name): + return os.path.normpath( + os.path.join(os.path.dirname(__file__), "..", "..", "data", name) ) -) + + +DATA_PATH = _data("MOM6_global_example_diagnostics_zlevels_v0_0_6.nc") +ECCO_DATA_PATH = _data("ECCO_budget_terms.nc") def _run_legacy(build_grid, preset): @@ -209,3 +209,66 @@ def test_equivalent_on_mom6_example(): # 108 legacy variables collapse to 57 operation-named variables. assert len(records) == 57 assert len(alias_map) == 108 + + +# ECCOv4r4 LLC90 face connectivity (13 tiles), mirroring +# examples/load_example_ecco_grid.py. +_ECCO_FACE_CONNECTIONS = { + "tile": { + 0: {"X": ((12, "Y", False), (3, "X", False)), "Y": (None, (1, "Y", False))}, + 1: {"X": ((11, "Y", False), (4, "X", False)), "Y": ((0, "Y", False), (2, "Y", False))}, + 2: {"X": ((10, "Y", False), (5, "X", False)), "Y": ((1, "Y", False), (6, "X", False))}, + 3: {"X": ((0, "X", False), (9, "Y", False)), "Y": (None, (4, "Y", False))}, + 4: {"X": ((1, "X", False), (8, "Y", False)), "Y": ((3, "Y", False), (5, "Y", False))}, + 5: {"X": ((2, "X", False), (7, "Y", False)), "Y": ((4, "Y", False), (6, "Y", False))}, + 6: {"X": ((2, "Y", False), (7, "X", False)), "Y": ((5, "Y", False), (10, "X", False))}, + 7: {"X": ((6, "X", False), (8, "X", False)), "Y": ((5, "X", False), (10, "Y", False))}, + 8: {"X": ((7, "X", False), (9, "X", False)), "Y": ((4, "X", False), (11, "Y", False))}, + 9: {"X": ((8, "X", False), None), "Y": ((3, "X", False), (12, "Y", False))}, + 10: {"X": ((6, "Y", False), (11, "X", False)), "Y": ((7, "Y", False), (2, "X", False))}, + 11: {"X": ((10, "X", False), (12, "X", False)), "Y": ((8, "Y", False), (1, "X", False))}, + 12: {"X": ((11, "X", False), None), "Y": ((9, "Y", False), (0, "X", False))}, + } +} + + +def _build_ecco_grid(): + ds = xr.open_dataset(ECCO_DATA_PATH).fillna(0.0) + return xgcm.Grid( + ds, + coords={ + "X": {"center": "i", "left": "i_g"}, + "Y": {"center": "j", "left": "j_g"}, + "T": {"center": "time", "outer": "time_bounds"}, + "Z": {"center": "k", "left": "k_l"}, + }, + metrics={ + ("X",): ["dxG"], + ("Y",): ["dyG"], + ("Z",): ["drF"], + ("X", "Y"): ["rA", "rAw", "rAs"], + }, + boundary={"X": None, "Y": None, "Z": "fill", "T": None}, + periodic=False, + fill_value={"Z": 0.0}, + face_connections=_ECCO_FACE_CONNECTIONS, + autoparse_metadata=False, + ) + + +@pytest.mark.skipif( + not os.path.exists(ECCO_DATA_PATH), + reason="example ECCO LLC90 dataset not present (download from Zenodo to run)", +) +def test_equivalent_on_ecco_native_example(): + """The typed engine reproduces the legacy engine on the LLC90 ECCO budget. + + Exercises reciprocal, difference-of-sub-term, and the native-xgcm + lateral_divergence on a face-connected (13-tile) grid. Slow (full 1.6 GB + grid); skipped unless the data file is present. + """ + records, alias_map = _assert_equivalent( + _build_ecco_grid, xbudget.load_preset_budget("ECCOV4r4_native") + ) + assert len(records) == 71 + assert len(alias_map) == 132 From 6e7f3e68341bacf1e68fce077fc77924999d14ef Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Tue, 30 Jun 2026 09:34:24 -0700 Subject: [PATCH 16/19] Docs: ECCO/LLC90, native lateral_divergence, reciprocal restored MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - CHANGELOG: correct the (wrong) 'reciprocal removed' note — it is restored; document ECCO/LLC90 typed-engine support, native-xgcm lateral_divergence, difference-of-sub-term, the parser-tolerance behavior (and the bolus malformation it surfaces), and the unreleased-xgcm dependency. - CLAUDE.md: add reciprocal/lateral_divergence ops, ECCO convention + grid loader + notebooks, the lenient-parser and xgcm-version notes, and the ECCO equivalence test. - docs/source: add the heat-decomposition notebook to the toctree; mention the new operations. Co-Authored-By: Claude Opus 4.8 (1M context) --- CHANGELOG.md | 38 +++++++++++++++++++++++++++++--------- CLAUDE.md | 19 +++++++++++-------- docs/source/index.rst | 3 ++- 3 files changed, 42 insertions(+), 18 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ce29411..2a44ae1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,8 +5,9 @@ This release replaces the recursive dict-walking engine with a typed expression tree (parse → evaluate). The convention/YAML format is **unchanged**; the in-memory representation, the engine, and the default output variable names are -new. Numerical results are identical to the previous engine (verified by an -end-to-end equivalence test on the example MOM6 grid). +new. Numerical results are identical to the previous engine — verified by +end-to-end equivalence tests on the example MOM6 grid (108 → 57 variables) and +the ECCOv4r4 LLC90 grid (132 → 71 variables, 0 mismatches). ### Quick migration @@ -61,8 +62,18 @@ unchanged. Adopt the new scheme at your own pace. - `xbudget.evaluate_budgets(data, budgets)` → pure evaluator; returns `(alias_map, records)` where `alias_map` maps every legacy name to its new name and `records` maps each new variable to its `{path, op, ...}` metadata. -- Each derived variable carries `xbudget_path` (structured identity) and - `provenance` (immediate inputs) attributes. +- Each derived variable carries `xbudget_path` (structured identity), + `xbudget_op` (operation kind), and `provenance` (immediate inputs) attributes. +- **ECCOv4r4 / LLC90 support in the typed engine.** The `reciprocal` and + `lateral_divergence` operations and a `difference` of a *computed sub-term* + (not just a raw variable) are all handled, so the native-grid ECCO mass/heat/ + salt budgets evaluate under `name_scheme="v1"`. New `ECCOV4r4_native` + convention and example notebooks (`eccov4r4_budget_examples_mass_heat_salt`, + `eccov4r4_heat_budget_decomposition`). +- **`lateral_divergence` now uses native xgcm** (`grid.diff` with + `other_component` + `face_connections`) instead of a hand-rolled LLC90 flux + stitcher; verified bit-for-bit identical on the ECCO grid. The + `xbudget/llc90` module is removed. ### Fixed @@ -79,9 +90,18 @@ unchanged. Adopt the new scheme at your own pace. internally by `name_scheme="legacy"`) but is superseded by `collect_budgets` / `evaluate_budgets`. -### Removed +### Dependencies -- The `reciprocal` operation. It was supported by the engine but used by no - shipped convention and exercised by no test; removed from the typed engine, - the parser, and the legacy `budget_fill_dict`. It can be reintroduced as a - typed node if a future convention needs it. +- The LLC `lateral_divergence` relies on native face-connected differencing in + `xgcm` (`grid.diff` with `other_component`). This is only available in xgcm + **after 0.9.0** (currently from the development `main` branch); the + `requires-python`/`xgcm` pins should be tightened once a release ships it. + +### Parser tolerance + +- The parser now **warns and skips** unavailable-diagnostic placeholders and + terms with stray keys (e.g. a `sign`/`density` left without its enclosing + `product:`), mirroring the legacy engine's behavior, rather than failing. This + surfaces (without breaking on) the `bolus_mass_flux_convergence` term in the + shipped `ECCOV4r4_native` convention, which is missing its `product:` wrapper + and is therefore silently dropped from the mass budget by both engines. diff --git a/CLAUDE.md b/CLAUDE.md index 5e45fe4..9cc4c01 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -38,9 +38,11 @@ Top-level keys are budgets (`mass`, `heat`, `salt`). Each budget has `lhs` and/o - `sum` — add the child terms together - `product` — multiply child terms (scalar numbers allowed as factors, e.g. `density: 1035.`, `sign: -1.`) -- `difference` — finite-difference a staggered flux across a grid axis (**requires an `xgcm.Grid`**) +- `difference` — finite-difference across a grid axis (**requires an `xgcm.Grid`**); the operand is a raw variable *or* a computed sub-term +- `reciprocal` — safe `1/x` (zeros → inf) of a variable +- `lateral_divergence` — horizontal flux divergence `div(Fx, Fy)` of two flux sub-terms, via native xgcm (`grid.diff` with `other_component` + `face_connections`); works on face-connected LLC grids -A node may carry more than one operation (e.g. a bulk `product` and an equivalent finer `sum`). Leaf string values (`"areacello"`, `"umo"`) are raw diagnostic names. Conventions live in `xbudget/conventions/*.yaml` (`MOM6.yaml` is canonical; also `MOM6_3Donly`, `MOM6_drift`, `MOM6_surface`). +A node may carry more than one operation (e.g. a bulk `product` and an equivalent finer `sum`). Leaf string values (`"areacello"`, `"umo"`) are raw diagnostic names. Conventions live in `xbudget/conventions/*.yaml` — `MOM6.yaml` (canonical; also `MOM6_3Donly`, `MOM6_drift`, `MOM6_surface`) and `ECCOV4r4_native.yaml` (LLC90 native-grid budgets). ### The typed engine (parse → evaluate) @@ -48,7 +50,7 @@ A node may carry more than one operation (e.g. a bulk `product` and an equivalen xbudget_dict ──parse_budgets──▶ typed tree (nodes.py) ──evaluate_budgets──▶ derived variables + alias map ``` -- **`nodes.py`** — immutable dataclasses: `Budget`, `Term`, and the operations `Sum`/`Product`/`Difference` plus `Constant`/`VarRef`. A `Term` carries its structured `path` (its canonical identity) and may hold multiple operations. +- **`nodes.py`** — immutable dataclasses: `Budget`, `Term`, and the operations `Sum`/`Product`/`Difference`/`Reciprocal`/`LateralDivergence` plus `Constant`/`VarRef`. A `Term` carries its structured `path` (its canonical identity) and may hold multiple operations. The native `lateral_divergence` helper lives in `collect.py` and is shared by both engines. - **`parse.py`** — `parse_budgets(dict) -> {name: Budget}`. The single source of schema truth; validates and raises `BudgetParseError` naming the offending path on malformed conventions. - **`evaluate.py`** — `evaluate_budgets(data, budgets)` walks the tree and materializes **one variable per operation**, named by its term path with operator infixes dropped (e.g. `heat_rhs_diffusion_lateral`). It is pure with respect to the recipe (never mutates it); it only writes derived variables into the dataset. Each variable gets `xbudget_path` (structured identity), `xbudget_op` (the operation kind), and `provenance` (immediate inputs) attrs. Returns `(alias_map, records)` — `alias_map` maps every legacy name to its new name; `records` maps each new variable to its metadata. Dispatch is on node type (`Difference` requires an `xgcm.Grid` in its signature, so a grid-less difference fails fast with a clear error). - **`collect.py`** — the public surface: @@ -61,19 +63,20 @@ xbudget_dict ──parse_budgets──▶ typed tree (nodes.py) ──evaluate_b - **Naming changed (major-version cleanup).** `v1` emits one variable per node/operation with operator infixes dropped; the legacy engine emitted duplicate "copy" variables (108 → 57 on the MOM6 example). Use `name_scheme="legacy"` or the `alias_map` to bridge. `CHANGELOG.md` has the old→new table. - **Missing diagnostics are skipped with a `UserWarning`, not an error** — a `sum`/`product` containing missing inputs collapses accordingly, so one convention can serve datasets with different available diagnostics. - **`difference` rechunking:** `allow_rechunk=True` (default) temporarily rechunks the difference dimension into a single chunk (required by `grid.diff`) then restores chunking. +- **Lenient parser.** `parse.py` mirrors the legacy engine: it **warns and skips** unavailable-diagnostic placeholders and stray non-operation keys (e.g. a `sign` left without its enclosing `product:`) instead of failing, so real conventions with such terms still load. (This is how the malformed `bolus_mass_flux_convergence` in `ECCOV4r4_native.yaml` is tolerated — and silently dropped — by both engines.) +- **xgcm version:** `lateral_divergence` needs native face-connected differencing, available only in xgcm **after 0.9.0** (currently the dev `main`). Run/test in an env with that xgcm. ### Tests -- `test_parse.py` — parser units + validation; asserts all shipped conventions parse. -- `test_evaluate_equivalence.py` — proves the typed engine is numerically identical to the legacy `budget_fill_dict` (synthetic grid always; MOM6 grid when the data file is present). +- `test_parse.py` — parser units + validation; asserts all shipped conventions parse; covers the tolerated-malformation path. +- `test_evaluate_equivalence.py` — proves the typed engine is numerically identical to the legacy `budget_fill_dict`: a synthetic grid (always), the MOM6 grid, and the **ECCO LLC90 grid** (both gated on their data files; the ECCO case exercises reciprocal, difference-of-sub-term, and native `lateral_divergence`). - `test_characterization.py` (+ `characterization_MOM6.json`) — golden snapshot of the typed engine's absolute MOM6 output. - `test_utilities.py` — the legacy engine, `aggregate`/`get_vars`/`disaggregate`, and `collect_budgets` behavior. ## Data & examples -- `examples/load_example_model_grid.py` — `load_MOM6_coarsened_diagnostics()` downloads (Zenodo, cached in `data/`) and builds an `xgcm.Grid` with X/Y center/outer coords and `areacello` metrics. -- `examples/MOM6_budget_examples_mass_heat_salt.ipynb` — worked tutorial; its `collect_budgets` call uses `name_scheme="legacy"` so the rest of the notebook (old names, `get_vars`, `aggregate`) is unchanged. -- The example `.nc` (~600 MB) is gitignored and fetched on demand; only `data/README.md` is tracked. +- `examples/load_example_model_grid.py` — `load_MOM6_coarsened_diagnostics()` builds a MOM6 `xgcm.Grid` (X/Y center/outer, `areacello` metric). `examples/load_example_ecco_grid.py` — `load_ECCOV4r4_coarsened_diagnostics()` builds the ECCO **LLC90** grid with 13-tile `face_connections`. Both download from Zenodo, cached in `data/` (gitignored; only `data/README.md` tracked). +- Notebooks: `MOM6_budget_examples_mass_heat_salt.ipynb`; `eccov4r4_budget_examples_mass_heat_salt.ipynb` (ECCO closure); `eccov4r4_heat_budget_decomposition.ipynb` (ECCO heat decomposition). The ECCO notebooks and the MOM6 one call `collect_budgets(..., name_scheme="legacy")` because they use `get_vars`/`aggregate`. ## Pull request workflow diff --git a/docs/source/index.rst b/docs/source/index.rst index 48eaf48..2b6d259 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -11,7 +11,7 @@ xbudget expects budgets which have a Left-Hand Side (LHS) equal to a Right-Hand where :math:`\lambda` is the property density (or tracer concentration), :math:`\mathbf{u}` is the flow velocity, and :math:`\mathbf{F}_{\lambda}` is the sum of all non-advective fluxes of :math:`\lambda`. -xbudget ingests an `xgcm.Grid` object containing the budget diagnostics and uses structured metadata, in the form of a nested dictionary (or `.yaml` file), to close such budgets. While this may seem trivial for use cases in which there is a single flux to keep track of, total non-advective fluxes in general circulation models can be composed of dozens of contributing processes. Since budget diagnostics are often not output as volume-integrated tendencies, xbudget allows for terms to be derived as sums, products, or differences (or some combination of these). For example, ocean heat tendency due to air-sea heat fluxes might be derived from the difference between vertical heat fluxes across depth interfaces, summed over longwave, shortwave, sensible, and latent components of the flux, and multiplied by the ocean cell area. +xbudget ingests an `xgcm.Grid` object containing the budget diagnostics and uses structured metadata, in the form of a nested dictionary (or `.yaml` file), to close such budgets. While this may seem trivial for use cases in which there is a single flux to keep track of, total non-advective fluxes in general circulation models can be composed of dozens of contributing processes. Since budget diagnostics are often not output as volume-integrated tendencies, xbudget allows for terms to be derived as sums, products, differences, reciprocals, or lateral flux divergences (or some combination of these), including on face-connected grids such as the ECCO LLC90 tiles. For example, ocean heat tendency due to air-sea heat fluxes might be derived from the difference between vertical heat fluxes across depth interfaces, summed over longwave, shortwave, sensible, and latent components of the flux, and multiplied by the ocean cell area. While drafting a `.yaml` file from scratch for a new model can be daunting, it only needs to be done once -- then closing budgets is a breeze! @@ -22,3 +22,4 @@ While drafting a `.yaml` file from scratch for a new model can be daunting, it o installation examples/MOM6_budget_examples_mass_heat_salt examples/eccov4r4_budget_examples_mass_heat_salt + examples/eccov4r4_heat_budget_decomposition From b6b3cefb2dc4caa41ac91ffaa6dd06a284d7a3bd Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Tue, 30 Jun 2026 11:30:53 -0700 Subject: [PATCH 17/19] Fix ECCO mass budget: stop silently dropping eddy-bolus transport MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The lateral bolus_mass_flux_convergence term in ECCOV4r4_native.yaml was missing its enclosing 'product:' wrapper, so its sign/density/ volume_flux_divergence children sat directly on the term and were ignored by both engines — the GM bolus velocity (UVELSTAR/VVELSTAR) contributed nothing to the mass budget. Restore the wrapper so the bolus convergence is materialized. This changes ECCO mass-budget results (the lateral bolus term is no longer zero: |sum| ~ 9.6e13 kg/s on the example grid). The typed engine still reproduces the legacy engine exactly (now 75 vars / 140 legacy names, 0 mismatches). Update the gated ECCO equivalence test counts and assert the bolus convergence variable is produced; correct CHANGELOG/CLAUDE accordingly. Co-Authored-By: Claude Opus 4.8 (1M context) --- CHANGELOG.md | 21 ++++++++++++++------- CLAUDE.md | 2 +- xbudget/conventions/ECCOV4r4_native.yaml | 8 +++++--- xbudget/tests/test_evaluate_equivalence.py | 6 ++++-- 4 files changed, 24 insertions(+), 13 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2a44ae1..c5152f1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ tree (parse → evaluate). The convention/YAML format is **unchanged**; the in-memory representation, the engine, and the default output variable names are new. Numerical results are identical to the previous engine — verified by end-to-end equivalence tests on the example MOM6 grid (108 → 57 variables) and -the ECCOv4r4 LLC90 grid (132 → 71 variables, 0 mismatches). +the ECCOv4r4 LLC90 grid (140 → 75 variables, 0 mismatches). ### Quick migration @@ -77,6 +77,13 @@ unchanged. Adopt the new scheme at your own pace. ### Fixed +- **ECCO mass budget: the lateral eddy-bolus transport was silently dropped.** + The `bolus_mass_flux_convergence` term in `ECCOV4r4_native.yaml` was missing + its enclosing `product:` wrapper, so its `sign`/`density`/`volume_flux_divergence` + children sat directly on the term and were ignored — the GM bolus velocity + (`UVELSTAR`/`VVELSTAR`) contributed nothing to the mass budget. The wrapper is + now restored, so the bolus convergence is materialized and included. **This + changes ECCO mass-budget results** (the bolus term is no longer zero). - The `difference` operation's grid guard was misattached, so a `difference` on a plain `Dataset` raised an opaque `NameError`, and a `difference` term evaluated after another operation in the same node raised spuriously even @@ -99,9 +106,9 @@ unchanged. Adopt the new scheme at your own pace. ### Parser tolerance -- The parser now **warns and skips** unavailable-diagnostic placeholders and - terms with stray keys (e.g. a `sign`/`density` left without its enclosing - `product:`), mirroring the legacy engine's behavior, rather than failing. This - surfaces (without breaking on) the `bolus_mass_flux_convergence` term in the - shipped `ECCOV4r4_native` convention, which is missing its `product:` wrapper - and is therefore silently dropped from the mass budget by both engines. +- The parser **warns and skips** unavailable-diagnostic placeholders (e.g. a + `difference` whose source is `null`) and terms with stray non-operation keys, + mirroring the legacy engine's behavior rather than failing, so real + conventions with such placeholders still load. (This same tolerance is what + let the malformed bolus term above pass silently before it was fixed — the + warning it emitted is what surfaced the bug.) diff --git a/CLAUDE.md b/CLAUDE.md index 9cc4c01..bf31d3f 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -63,7 +63,7 @@ xbudget_dict ──parse_budgets──▶ typed tree (nodes.py) ──evaluate_b - **Naming changed (major-version cleanup).** `v1` emits one variable per node/operation with operator infixes dropped; the legacy engine emitted duplicate "copy" variables (108 → 57 on the MOM6 example). Use `name_scheme="legacy"` or the `alias_map` to bridge. `CHANGELOG.md` has the old→new table. - **Missing diagnostics are skipped with a `UserWarning`, not an error** — a `sum`/`product` containing missing inputs collapses accordingly, so one convention can serve datasets with different available diagnostics. - **`difference` rechunking:** `allow_rechunk=True` (default) temporarily rechunks the difference dimension into a single chunk (required by `grid.diff`) then restores chunking. -- **Lenient parser.** `parse.py` mirrors the legacy engine: it **warns and skips** unavailable-diagnostic placeholders and stray non-operation keys (e.g. a `sign` left without its enclosing `product:`) instead of failing, so real conventions with such terms still load. (This is how the malformed `bolus_mass_flux_convergence` in `ECCOV4r4_native.yaml` is tolerated — and silently dropped — by both engines.) +- **Lenient parser.** `parse.py` mirrors the legacy engine: it **warns and skips** unavailable-diagnostic placeholders (e.g. a `null`-source `difference`) and stray non-operation keys instead of failing, so real conventions with such terms still load. (This tolerance previously masked the malformed `bolus_mass_flux_convergence` term in `ECCOV4r4_native.yaml` — missing its `product:` wrapper, so the eddy bolus transport was silently dropped from the mass budget; that has since been fixed in the convention.) - **xgcm version:** `lateral_divergence` needs native face-connected differencing, available only in xgcm **after 0.9.0** (currently the dev `main`). Run/test in an env with that xgcm. ### Tests diff --git a/xbudget/conventions/ECCOV4r4_native.yaml b/xbudget/conventions/ECCOV4r4_native.yaml index 8375589..8eee70e 100755 --- a/xbudget/conventions/ECCOV4r4_native.yaml +++ b/xbudget/conventions/ECCOV4r4_native.yaml @@ -57,11 +57,13 @@ mass: # finite-volume mass budget in units of kg/s Fy: var: null product: {var: null, v_velocity: "VVELMASS", dxG: "dxG", dz: "drF"} - bolus_mass_flux_convergence: - var: null + bolus_mass_flux_convergence: # div(-Fx,-Fy) = -div(Fx,Fy) + var: null + product: + var: null sign: -1.0 density: 1029.0 - volume_flux_divergence: + volume_flux_divergence: var: null lateral_divergence: var: null diff --git a/xbudget/tests/test_evaluate_equivalence.py b/xbudget/tests/test_evaluate_equivalence.py index 43672f2..5b6752d 100644 --- a/xbudget/tests/test_evaluate_equivalence.py +++ b/xbudget/tests/test_evaluate_equivalence.py @@ -270,5 +270,7 @@ def test_equivalent_on_ecco_native_example(): records, alias_map = _assert_equivalent( _build_ecco_grid, xbudget.load_preset_budget("ECCOV4r4_native") ) - assert len(records) == 71 - assert len(alias_map) == 132 + assert len(records) == 75 + assert len(alias_map) == 140 + # the (previously dropped) lateral eddy-bolus convergence is materialized + assert "mass_rhs_advection_lateral_bolus_mass_flux_convergence" in records From 0de5b93bdb4b7c1901272ead709d2eb5095455b3 Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Tue, 30 Jun 2026 11:45:25 -0700 Subject: [PATCH 18/19] Bump conda recipe to 0.7.0 (sha256 placeholder until release) version.py is already 0.7.0 (the authoritative source via hatch). Sync the conda recipe version; its sha256 must be regenerated against the published 0.7.0 sdist at release time. Co-Authored-By: Claude Opus 4.8 (1M context) --- conda/meta.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/conda/meta.yaml b/conda/meta.yaml index 894a5d5..0d077d7 100644 --- a/conda/meta.yaml +++ b/conda/meta.yaml @@ -1,5 +1,5 @@ {% set name = "xbudget" %} -{% set version = "0.6.2" %} +{% set version = "0.7.0" %} {% set python_min = "3.11" %} package: @@ -8,7 +8,8 @@ package: source: url: https://pypi.org/packages/source/x/xbudget/xbudget-{{ version }}.tar.gz - sha256: 0ab9571aae2196523c0dbc394468567446d61e475624921055a3b1e074c05112 + # TODO(release): regenerate against the published 0.7.0 sdist. + sha256: 0000000000000000000000000000000000000000000000000000000000000000 build: noarch: python From ffb4fd02e7ad23bb6773493d6cb760d99b09ce28 Mon Sep 17 00:00:00 2001 From: Henri Drake Date: Tue, 30 Jun 2026 11:46:05 -0700 Subject: [PATCH 19/19] Execute ECCO heat-budget decomposition notebook Plotting cells made robust to terms lacking a vertical (k) or time dimension (e.g. the geothermal bottom flux is static in k,tile,j,i). Executed end-to-end against the ECCO LLC90 data: 8/8 cells, 0 errors, 2 figures (decomposed maps + global-mean time series). Co-Authored-By: Claude Opus 4.8 (1M context) --- .../eccov4r4_heat_budget_decomposition.ipynb | 312 ++++++++++++++++-- 1 file changed, 282 insertions(+), 30 deletions(-) diff --git a/examples/eccov4r4_heat_budget_decomposition.ipynb b/examples/eccov4r4_heat_budget_decomposition.ipynb index 0ffd0ea..9a71e17 100644 --- a/examples/eccov4r4_heat_budget_decomposition.ipynb +++ b/examples/eccov4r4_heat_budget_decomposition.ipynb @@ -12,10 +12,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "15a60801", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T18:44:32.671751Z", + "iopub.status.busy": "2026-06-30T18:44:32.671523Z", + "iopub.status.idle": "2026-06-30T18:44:34.120028Z", + "shell.execute_reply": "2026-06-30T18:44:34.119761Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xarray: 2026.1.0\n", + "xgcm: 0.9.1.dev35+g41f1a575b\n" + ] + } + ], "source": [ "import xarray as xr\n", "import numpy as np\n", @@ -40,10 +56,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "f6135116", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T18:44:34.121600Z", + "iopub.status.busy": "2026-06-30T18:44:34.121389Z", + "iopub.status.idle": "2026-06-30T18:44:59.292993Z", + "shell.execute_reply": "2026-06-30T18:44:59.292446Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File 'ECCO_budget_terms.nc' already exists at ../data/ECCO_budget_terms.nc. Skipping download.\n" + ] + } + ], "source": [ "grid = load_ECCOV4r4_coarsened_diagnostics() # this takes about 7 minutes\n", "\n", @@ -67,9 +98,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "62ed2b7e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T18:44:59.296025Z", + "iopub.status.busy": "2026-06-30T18:44:59.295886Z", + "iopub.status.idle": "2026-06-30T18:45:05.266349Z", + "shell.execute_reply": "2026-06-30T18:45:05.265765Z" + } + }, "outputs": [], "source": [ "def zero_top_layer(ds, varname = \"\", zdim = \"k_l\"):\n", @@ -111,10 +149,120 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "2c5a3ee9", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T18:45:05.269739Z", + "iopub.status.busy": "2026-06-30T18:45:05.269620Z", + "iopub.status.idle": "2026-06-30T18:45:13.945620Z", + "shell.execute_reply": "2026-06-30T18:45:13.945305Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hfdrake/code/xbudget/xbudget/collect.py:437: UserWarning: Dataset chunks are inconsistent; using unify_chunks()\n", + " warnings.warn(\"Dataset chunks are inconsistent; using unify_chunks()\", UserWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "\n", + "X Axis (not periodic, boundary='fill'):\n", + " * center i --> left\n", + " * left i_g --> center\n", + "Y Axis (not periodic, boundary='fill'):\n", + " * center j --> left\n", + " * left j_g --> center\n", + "T Axis (not periodic, boundary='fill'):\n", + " * center time --> outer\n", + " * outer time_bounds --> center\n", + "Z Axis (not periodic, boundary='fill'):\n", + " * center k --> left\n", + " * left k_l --> center" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "xbudget_dict = xbudget.load_preset_budget(model=\"ECCOV4r4_native\").copy()\n", "xbudget.collect_budgets(grid, xbudget_dict, allow_rechunk=True, name_scheme=\"legacy\")" @@ -132,10 +280,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "e0fa039e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T18:45:13.947046Z", + "iopub.status.busy": "2026-06-30T18:45:13.946944Z", + "iopub.status.idle": "2026-06-30T18:45:13.949675Z", + "shell.execute_reply": "2026-06-30T18:45:13.949411Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'advection_lateral': 'heat_rhs_sum_advection_sum_lateral',\n", + " 'advection_interfacial': 'heat_rhs_sum_advection_sum_interfacial',\n", + " 'diffusion_lateral': 'heat_rhs_sum_diffusion_sum_lateral',\n", + " 'diffusion_explicit_vertical_diffusion': 'heat_rhs_sum_diffusion_sum_explicit_vertical_diffusion',\n", + " 'diffusion_implicit_vertical_diffusion': 'heat_rhs_sum_diffusion_sum_implicit_vertical_diffusion',\n", + " 'surface_exchange_flux': 'heat_rhs_sum_surface_exchange_flux',\n", + " 'bottom_flux': 'heat_rhs_sum_bottom_flux'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "decomposed = xbudget.aggregate(\n", " xbudget_dict, decompose=[\"advection\", \"diffusion\", \"surface_exchange_flux\"]\n", @@ -156,9 +328,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "997ec4fe", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T18:45:13.950846Z", + "iopub.status.busy": "2026-06-30T18:45:13.950757Z", + "iopub.status.idle": "2026-06-30T18:45:13.953223Z", + "shell.execute_reply": "2026-06-30T18:45:13.952969Z" + } + }, "outputs": [], "source": [ "def plot_interpolated_ecco(fig, ax, ds, vmin=0, vmax=30, cmap=\"coolwarm\"):\n", @@ -183,10 +362,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "e1ed9bcd", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T18:45:13.954364Z", + "iopub.status.busy": "2026-06-30T18:45:13.954276Z", + "iopub.status.idle": "2026-06-30T18:45:20.234754Z", + "shell.execute_reply": "2026-06-30T18:45:20.234463Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'ECCO heat budget RHS, decomposed (column tendency)')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -194,10 +401,17 @@ "area = grid._ds[\"rA\"].where(grid._ds[\"Depth\"] > 0.0)\n", "\n", "def column_tendency(varname):\n", - " \"\"\"Column-integrated heat tendency per unit area [W/m^2] at the first time.\"\"\"\n", - " da = (grid._ds[varname] / area).sum(\"k\")\n", - " da = da.where(np.abs(da) > 0.0)\n", - " return da.isel(time=0)\n", + " \"\"\"Column-integrated heat tendency per unit area [W/m^2] at the first time.\n", + "\n", + " Robust to terms lacking a vertical (`k`) or `time` dimension (e.g. a static\n", + " bottom/geothermal flux).\n", + " \"\"\"\n", + " da = grid._ds[varname] / area\n", + " if \"k\" in da.dims:\n", + " da = da.sum(\"k\")\n", + " if \"time\" in da.dims:\n", + " da = da.isel(time=0)\n", + " return da.where(np.abs(da) > 0.0)\n", "\n", "terms = list(heat_rhs_terms.items())\n", "ncol = 3\n", @@ -206,11 +420,13 @@ "fig, axes = plt.subplots(nrow, ncol, figsize=(5 * ncol, 4 * nrow), squeeze=False)\n", "for ax in axes.ravel():\n", " ax.set_visible(False)\n", + "cb = None\n", "for (name, varname), ax in zip(terms, axes.ravel()):\n", " ax.set_visible(True)\n", " cb = plot_interpolated_ecco(fig, ax, column_tendency(varname), vmin=-vmax, vmax=vmax)\n", " ax.set_title(name, fontsize=9)\n", - "fig.colorbar(cb, ax=axes.ravel().tolist(), orientation=\"vertical\", label=\"W/m^2\", shrink=0.8)\n", + "if cb is not None:\n", + " fig.colorbar(cb, ax=axes.ravel().tolist(), orientation=\"vertical\", label=\"W/m^2\", shrink=0.8)\n", "fig.suptitle(\"ECCO heat budget RHS, decomposed (column tendency)\")" ] }, @@ -226,15 +442,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "4aa8b929", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-30T18:45:20.237459Z", + "iopub.status.busy": "2026-06-30T18:45:20.237371Z", + "iopub.status.idle": "2026-06-30T18:45:30.475768Z", + "shell.execute_reply": "2026-06-30T18:45:30.475498Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "for name, varname in heat_rhs_terms.items():\n", - " col = (grid._ds[varname] / area).sum(\"k\")\n", - " global_mean = ((col * area) / area.sum()).sum([\"tile\", \"i\", \"j\"])\n", + " da = grid._ds[varname] / area\n", + " if \"k\" in da.dims:\n", + " da = da.sum(\"k\")\n", + " # area-weighted global mean over the horizontal (and tiles)\n", + " global_mean = ((da * area) / area.sum()).sum([\"tile\", \"i\", \"j\"])\n", + " if \"time\" not in global_mean.dims:\n", + " # static term (e.g. geothermal bottom flux): broadcast to the time axis\n", + " global_mean = global_mean.broadcast_like(grid._ds[\"time\"])\n", + " if not bool(np.isfinite(global_mean).any()):\n", + " continue # nothing finite to plot for this term\n", " global_mean.plot(ax=ax, marker=\"o\", ms=3, label=name)\n", "ax.set_ylabel(\"W/m^2\")\n", "ax.set_title(\"Global-mean decomposed heat-budget terms\")\n", @@ -258,7 +510,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.13" + "version": "3.12.0" } }, "nbformat": 4,