diff --git a/docs/examples/attrs_xarray_demo.py b/docs/examples/attrs_xarray_demo.py new file mode 100644 index 00000000..32fb8229 --- /dev/null +++ b/docs/examples/attrs_xarray_demo.py @@ -0,0 +1,529 @@ +# # Demo attrs/XArray object model + +# Demonstrate a tentative `attrs`- and `xarray`-based object model, +# where `attrs` is used to define classes and `xarray` is used as +# the underlying data store. + + +from abc import ABC +from datetime import datetime +from pathlib import Path +from typing import Any, Literal, Optional, get_origin + +import numpy as np +from attr import Attribute, Factory, define, field, fields_dict +from numpy.typing import ArrayLike, NDArray +from xarray import Dataset, DataTree + + +def _to_path(value: Any) -> Optional[Path]: + return Path(value) if value else None + + +def _parse_dim_names(shape: str) -> tuple[str, ...]: + return tuple( + [ + dim.strip() + for dim in shape.strip() + .replace("(", "") + .replace(")", "") + .split(",") + if any(dim) + ] + ) + + +def _try_resolve_dim(data: Optional[DataTree], name: str) -> int | str: + name = name.strip() + if data is None: + return name + value = data.get(name, None) + if value is not None: + return value.item() + root = data.root + paths = [ + "tdis", + "dis", + "gwf/dis", + ] + for path in paths: + try: + key = f"{path}/{name}" + return root[key].item() + except: + try: + return root[path].dims[name] + except: + pass + return name + + +def _try_resolve_shape(data: DataTree, attr: Attribute) -> tuple[int | str]: + shape = attr.metadata.get("shape", None) + if shape is None: + raise ValueError(f"Array {attr.name} missing shape metadata") + shape = [_try_resolve_dim(data, dim) for dim in _parse_dim_names(shape)] + return shape + + +def _reshape_array(value: ArrayLike, shape: tuple[int]) -> Optional[NDArray]: + value = np.array(value) + if value.shape == (): + return np.full(shape, value.item()) + elif value.shape != shape: + raise ValueError( + f"Shape mismatch, got {value.shape}, expected {shape}" + ) + return value + + +def _resolve_array( + self, attr: Attribute, value: Optional[ArrayLike] +) -> Optional[NDArray]: + if value is None: + return None + shape = _try_resolve_shape(self.data, attr) + unresolved = [dim for dim in shape if not isinstance(dim, int)] + if any(unresolved): + raise ValueError( + f"Class '{type(self).__name__}' " + f"failed to resolve dims: {', '.join(unresolved)}" + ) + return _reshape_array(value, shape) + + +def _bind_tree(self, parent): + parent.data = parent.data.assign({self.data.name: self.data}) + self.data = parent.data[self.data.name] + grandparent = getattr(parent, "parent", None) + if grandparent is not None: + _bind_tree(parent, grandparent) + + +def _init_tree(self, parent=None, **kwargs): + cls = type(self) + cls_name = cls.__name__.lower() + spec = fields_dict(cls) + data = Dataset() + dims = set() + + # add arrays + for name, attr in spec.items(): + value = kwargs.get(name, attr.default) + shape = attr.metadata.get("shape", None) + if shape is not None: + dim_names = _parse_dim_names(shape) + shape = [ + _try_resolve_dim(parent.data.root if parent else None, dim) + for dim in dim_names + ] + shape = tuple( + [ + (dim if isinstance(dim, int) else kwargs.get(dim, dim)) + for dim in shape + ] + ) + unresolved = [dim for dim in shape if not isinstance(dim, int)] + if any(unresolved): + raise ValueError( + f"Class '{cls_name}' " + f"failed to resolve dims: {', '.join(unresolved)}" + ) + dims.update(dim_names) + value = _reshape_array(value, shape) + if value.shape == (): + raise ValueError( + f"Failed to resolve array '{name}', " + f"make sure these dimensions exist: " + f"{','.join(dims)}" + ) + data[name] = (dim_names, value) + + # add scalars + for name, value in spec.items(): + if name in data or name in dims: + continue + value = kwargs.get(name, attr.default) + data[name] = value + + self.data = DataTree(data, name=cls_name) + if parent is not None: + self.parent = parent + _bind_tree(self, parent) + + +def _setattr(self, attr: Attribute, value: Any): + cls = type(self) + spec = fields_dict(cls) + if attr.name not in spec: + raise AttributeError(f"{cls.__name__} has no attribute {attr.name}") + if value is None: + return + self.data[attr.name] = ( + ( + _parse_dim_names(attr.metadata["shape"]), + _resolve_array(self, attr, value), + ) + if get_origin(attr.type) in [list, np.ndarray] + else value + ) + # TODO run validation? + + +def component(cls): + spec = fields_dict(cls) + + def _get(self, name): + if name in spec: + value = self.data.get(name, None) + if value is not None: + return value + value = self.data.dims.get(name, None) + if value is not None: + return value + return super(cls, self).__getattribute__(name) + + cls.__getattribute__ = _get + return cls + + +class Package(ABC): + data: DataTree = None + + +class Model(ABC): + data: DataTree = None + + +class Sim(ABC): + data: DataTree = None + + +# @component could in theory wrap the @define decorator +@component +@define(init=False, slots=False, on_setattr=_setattr) +class Dis(Package): + length_units: str = field( + # store block as metadata then dynamically + # discover and expose blocks as properties. + default=None, + metadata={"block": "options"}, + # OR, blocks could be attrs classes too... + # that will be necessary if variable names + # at the top level are not unique, but they + # currently are (if we like that constraint + # we should document it and enforce it when + # DFNs are loaded?) + ) + nogrb: bool = field(default=False, metadata={"block": "options"}) + xorigin: float = field(default=None, metadata={"block": "options"}) + yorigin: float = field(default=None, metadata={"block": "options"}) + angrot: float = field(default=None, metadata={"block": "options"}) + export_array_netcdf: bool = field( + default=False, metadata={"block": "options"} + ) + nlay: int = field(default=1, metadata={"block": "dimensions"}) + ncol: int = field(default=2, metadata={"block": "dimensions"}) + nrow: int = field(default=2, metadata={"block": "dimensions"}) + delr: NDArray[np.floating] = field( + # we use a converter both to resolve an array shape + # and check it, handling both conversion/validation + converter=_resolve_array, + default=1.0, + metadata={"block": "griddata", "shape": "(ncol,)"}, + ) + delc: NDArray[np.floating] = field( + converter=_resolve_array, + default=1.0, + metadata={"block": "griddata", "shape": "(nrow,)"}, + ) + top: NDArray[np.floating] = field( + converter=_resolve_array, + default=1.0, + metadata={"block": "griddata", "shape": "(ncol, nrow)"}, + ) + botm: NDArray[np.floating] = field( + converter=_resolve_array, + default=0.0, + metadata={"block": "griddata", "shape": "(ncol, nrow, nlay)"}, + ) + idomain: Optional[NDArray[np.integer]] = field( + converter=_resolve_array, + default=1, + metadata={"block": "griddata", "shape": "(ncol, nrow, nlay)"}, + ) + nodes: Optional[int] = field(default=None) + + def __init__( + self=None, + model=None, + length_units=None, + nogrb=False, + xorigin=None, + yorigin=None, + angrot=None, + export_array_netcdf=False, + nlay=1, + ncol=2, + nrow=2, + delr=1.0, + delc=1.0, + top=1.0, + botm=0.0, + idomain=1, + ): + _init_tree( + self, + parent=model, + length_units=length_units, + nogrb=nogrb, + xorigin=xorigin, + yorigin=yorigin, + angrot=angrot, + export_array_netcdf=export_array_netcdf, + nlay=nlay, + ncol=ncol, + nrow=nrow, + nodes=ncol * nrow * nlay, + delr=delr, + delc=delc, + top=top, + botm=botm, + idomain=idomain, + ) + + +@component +@define(init=False, slots=False, on_setattr=_setattr) +class Ic(Package): + strt: NDArray[np.floating] = field( + converter=_resolve_array, + default=1.0, + metadata={"block": "packagedata", "shape": "(nodes)"}, + ) + export_array_ascii: bool = field( + default=False, metadata={"block": "options"} + ) + export_array_netcdf: bool = field( + default=False, + metadata={"block": "options"}, + ) + + def __init__( + self, + model=None, + strt=1.0, + export_array_ascii=False, + export_array_netcdf=False, + ): + _init_tree( + self, + parent=model, + strt=strt, + export_array_ascii=export_array_ascii, + export_array_netcdf=export_array_netcdf, + ) + + +@component +@define(init=False, slots=False, on_setattr=_setattr) +class Oc(Package): + @define(slots=False) + class Format: + columns: int = field(default=10) + width: int = field(default=11) + digits: int = field(default=4) + format: Literal["exponential", "fixed", "general", "scientific"] = ( + field(default="general") + ) + + @define(slots=False) + class Steps: + first: Optional[Literal["first"]] = field(default="first") + last: Optional[Literal["last"]] = field(default=None) + all: Optional[Literal["all"]] = field(default=None) + frequency: Optional[int] = field(default=None) + steps: Optional[list[int]] = field(default=None) + + budget_file: Optional[Path] = field( + converter=_to_path, + default=None, + metadata={"block": "options"}, + ) + budget_csv_file: Optional[Path] = field( + converter=_to_path, + default=None, + metadata={"block": "options"}, + ) + head_file: Optional[Path] = field( + converter=_to_path, + default=None, + metadata={"block": "options"}, + ) + printhead: Optional[Format] = field( + default=None, init=False, metadata={"block": "options"} + ) + perioddata: list[Steps] = field( + default=Factory(list), + metadata={"block": "perioddata", "shape": "(nper,)"}, + ) + + def __init__( + self, + model=None, + budget_file=None, + budget_csv_file=None, + head_file=None, + printhead=None, + perioddata=None, + ): + _init_tree( + self, + parent=model, + budget_file=budget_file, + budget_csv_file=budget_csv_file, + head_file=head_file, + printhead=printhead, + perioddata=perioddata, + ) + + +@component +@define(init=False, slots=False, on_setattr=_setattr) +class Npf(Package): + # no options, just arrays for now + icelltype: NDArray[np.integer] = field( + converter=_resolve_array, + default=0, + metadata={"block": "griddata", "shape": "(nodes)"}, + ) + k: NDArray[np.floating] = field( + converter=_resolve_array, + default=1.0, + metadata={"block": "griddata", "shape": "(nodes)"}, + ) + k22: Optional[NDArray[np.floating]] = field( + converter=_resolve_array, + default=None, + metadata={"block": "griddata", "shape": "(nodes)"}, + ) + k33: Optional[NDArray[np.floating]] = field( + converter=_resolve_array, + default=None, + metadata={"block": "griddata", "shape": "(nodes)"}, + ) + angle1: Optional[NDArray[np.floating]] = field( + converter=_resolve_array, + default=None, + metadata={"block": "griddata", "shape": "(nodes)"}, + ) + angle2: Optional[NDArray[np.floating]] = field( + converter=_resolve_array, + default=None, + metadata={"block": "griddata", "shape": "(nodes)"}, + ) + angle3: Optional[NDArray[np.floating]] = field( + converter=_resolve_array, + default=None, + metadata={"block": "griddata", "shape": "(nodes)"}, + ) + wetdry: Optional[NDArray[np.floating]] = field( + converter=_resolve_array, + default=None, + metadata={"block": "griddata", "shape": "(nodes)"}, + ) + + def __init__( + self, + model=None, + icelltype=0, + k=1.0, + k22=None, + k33=None, + angle1=None, + angle2=None, + angle3=None, + wetdry=None, + ): + _init_tree( + self, + parent=model, + icelltype=icelltype, + k=k, + k22=k22, + k33=k33, + angle1=angle1, + angle2=angle2, + angle3=angle3, + wetdry=wetdry, + ) + + +@component +@define(init=False, slots=False) +class Gwf(Model): + def __init__( + self, + sim=None, + ): + _init_tree(self, parent=sim) + + +@component +@define(init=False, slots=False, on_setattr=_setattr) +class Tdis(Package): + @define(slots=False) + class PeriodData: + perlen: float = field(default=1.0) + nstp: int = field(default=1) + tsmult: float = field(default=1.0) + + nper: int = field(default=1, metadata={"block": "dimensions"}) + perioddata: list[PeriodData] = field( + default=Factory(list), + metadata={"block": "perioddata", "shape": "(nper)"}, + ) + time_units: Optional[str] = field( + default=None, metadata={"block": "options"} + ) + start_date_time: Optional[datetime] = field( + default=None, metadata={"block": "options"} + ) + + def __init__( + self, + sim=None, + nper=1, + perioddata=None, + time_units=None, + start_date_time=None, + ): + _init_tree( + self, + parent=sim, + nper=nper, + perioddata=perioddata, + time_units=time_units, + start_date_time=start_date_time, + ) + + +@component +@define(slots=False) +class Simulation(Sim): + def __init__(self): + _init_tree(self) + + +# Create a simulation. + +sim = Simulation() +tdis = Tdis(sim=sim, nper=1, perioddata=[Tdis.PeriodData()]) +gwf = Gwf(sim=sim) +dis = Dis(model=gwf) +ic = Ic(model=gwf, strt=1.0) +oc = Oc(model=gwf, perioddata=[Oc.Steps()]) +npf = Npf(model=gwf, icelltype=0, k=1.0) + +# View the data tree. +sim.data diff --git a/flopy4/array.py b/flopy4/array.py index 98152696..0559da97 100644 --- a/flopy4/array.py +++ b/flopy4/array.py @@ -353,31 +353,31 @@ def write(self, f, **kwargs): values = mfa.raw if mfa._how == MFArrayType.internal: if len(values.shape) == 1: - v = f"{PAD*3}" + v = f"{PAD * 3}" v += " ".join([str(x) for x in values]) v += "\n" elif len(values.shape) == 2: - v = f"\n{PAD*3}" + v = f"\n{PAD * 3}" v = v.join(" ".join(str(x) for x in y) for y in values) elif len(values.shape) == 3: - v = f"{PAD*3}" + v = f"{PAD * 3}" for i in range(len(values)): v += " ".join( - f"\n{PAD*3}" + " ".join(str(x) for x in y) + f"\n{PAD * 3}" + " ".join(str(x) for x in y) for y in values[i] ) - lines = f"{PAD*2}" + f"{MFArrayType.to_string(mfa._how)}" + lines = f"{PAD * 2}" + f"{MFArrayType.to_string(mfa._how)}" if mfa._factor: lines += f" FACTOR {mfa._factor}" - lines += f"\n{PAD*3}" + f"{v}\n" + lines += f"\n{PAD * 3}" + f"{v}\n" elif mfa._how == MFArrayType.external: lines = ( - f"{PAD*2}" + f"{MFArrayType.to_string(mfa._how)} " + f"{PAD * 2}" + f"{MFArrayType.to_string(mfa._how)} " f"{mfa._path}\n" ) elif mfa._how == MFArrayType.constant: lines = ( - f"{PAD*2}" + f"{MFArrayType.to_string(mfa._how)} " + f"{PAD * 2}" + f"{MFArrayType.to_string(mfa._how)} " f"{str(mfa._value)}\n" ) f.write(lines) @@ -385,21 +385,21 @@ def write(self, f, **kwargs): values = self.raw if self._how == MFArrayType.internal: if len(values.shape) == 1: - v = f"\n{PAD*3}" + v = f"\n{PAD * 3}" v += " ".join([str(x) for x in values]) elif len(values.shape) == 2: - v = f"\n{PAD*3}" + v = f"\n{PAD * 3}" v = v.join(" ".join(str(x) for x in y) for y in values) elif len(values.shape) == 3: - v = f"{PAD*3}" + v = f"{PAD * 3}" for i in range(len(values)): v += " ".join( - f"\n{PAD*3}" + " ".join(str(x) for x in y) + f"\n{PAD * 3}" + " ".join(str(x) for x in y) for y in values[i] ) lines = ( f"{PAD}" + f"{self.name.upper()}\n" - f"{PAD*2}" + f"{MFArrayType.to_string(self._how)}" + f"{PAD * 2}" + f"{MFArrayType.to_string(self._how)}" ) if self._factor: lines += f" FACTOR {self._factor}" @@ -407,13 +407,13 @@ def write(self, f, **kwargs): elif self._how == MFArrayType.external: lines = ( f"{PAD}" + f"{self.name.upper()}\n" - f"{PAD*2}" + f"{PAD * 2}" + f"{MFArrayType.to_string(self._how)} {self._path}\n" ) elif self._how == MFArrayType.constant: lines = ( f"{PAD}" + f"{self.name.upper()}\n" - f"{PAD*2}" + f"{MFArrayType.to_string(self._how)} " + f"{PAD * 2}" + f"{MFArrayType.to_string(self._how)} " f"{str(self._value)}\n" ) f.write(lines) diff --git a/flopy4/scalar.py b/flopy4/scalar.py index 987ce10f..14dfc78a 100644 --- a/flopy4/scalar.py +++ b/flopy4/scalar.py @@ -134,9 +134,7 @@ def load(cls, f, **kwargs) -> "MFKeyword": def write(self, f, **kwargs): newline = kwargs.pop("newline", True) if self.value: - f.write( - f"{PAD}" f"{self.name.upper()}" + ("\n" if newline else "") - ) + f.write(f"{PAD}{self.name.upper()}" + ("\n" if newline else "")) class MFInteger(MFScalar[int]): diff --git a/notes.md b/notes.md new file mode 100644 index 00000000..faa92a28 --- /dev/null +++ b/notes.md @@ -0,0 +1,10 @@ +# todo +- maybe store scalars as xarray attrs? +- factor out into proper module structure +- demo notebook explaining how it works + - compare/contrast implementations + - attrs vs xarray access pattern +- translate examples to new data model + - emphasize friction points + - inspiration from imod examples +- diff --git a/pixi.lock b/pixi.lock index 3ec3ef65..12f79001 100644 --- a/pixi.lock +++ b/pixi.lock @@ -10,238 +10,244 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda - conda: 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