diff --git a/docs/examples/image/quickstart.png b/docs/examples/image/quickstart.png index e02bb0d6..9e4daba5 100644 Binary files a/docs/examples/image/quickstart.png and b/docs/examples/image/quickstart.png differ diff --git a/docs/examples/quickstart.py b/docs/examples/quickstart.py index 66c7d6b3..b2c707fd 100644 --- a/docs/examples/quickstart.py +++ b/docs/examples/quickstart.py @@ -57,4 +57,4 @@ head.plot.imshow(ax=ax) head.plot.contour(ax=ax, levels=[0.2, 0.4, 0.6, 0.8], linewidths=3.0) budget.plot.quiver(x="x", y="y", u="npf-qx", v="npf-qy", ax=ax, color="white") -fig.savefig(workspace / f"{name}.png") +fig.savefig(workspace / ".." / "image" / "quickstart.png") diff --git a/flopy4/mf6/gwf/__init__.py b/flopy4/mf6/gwf/__init__.py index e39be265..db3f9102 100644 --- a/flopy4/mf6/gwf/__init__.py +++ b/flopy4/mf6/gwf/__init__.py @@ -2,7 +2,6 @@ from typing import Optional import attrs -import imod import xarray as xr from attrs import define from flopy.discretization.grid import Grid @@ -15,7 +14,7 @@ from flopy4.mf6.gwf.npf import Npf from flopy4.mf6.gwf.oc import Oc from flopy4.mf6.model import Model -from flopy4.mf6.utils import open_hds +from flopy4.mf6.utils import open_cbc, open_hds __all__ = ["Gwf", "Chd", "Dis", "Ic", "Npf", "Oc"] @@ -45,10 +44,9 @@ def head(self) -> xr.DataArray: @property def budget(self): # TODO support other extensions than .bud (e.g. .cbc) - return imod.mf6.open_cbc( + return open_cbc( self.parent.parent.path / f"{self.parent.name}.bud", self.parent.parent.path / f"{self.parent.name}.dis.grb", - merge_to_dataset=True, ) dis: Dis = field(converter=convert_grid) diff --git a/flopy4/mf6/utils/__init__.py b/flopy4/mf6/utils/__init__.py index fe804571..be3986c4 100644 --- a/flopy4/mf6/utils/__init__.py +++ b/flopy4/mf6/utils/__init__.py @@ -1,3 +1,4 @@ +from .cbc_reader import open_cbc from .heads_reader import open_hds -__all__ = ["open_hds"] +__all__ = ["open_hds", "open_cbc"] diff --git a/flopy4/mf6/utils/cbc_reader.py b/flopy4/mf6/utils/cbc_reader.py new file mode 100644 index 00000000..43c00ffc --- /dev/null +++ b/flopy4/mf6/utils/cbc_reader.py @@ -0,0 +1,771 @@ +import os +import struct +from collections import defaultdict +from pathlib import Path +from typing import Any, BinaryIO, cast + +import dask +import dask.array +import numpy as np +import pandas as pd +import xarray as xr +from attrs import define +from flopy.discretization import StructuredGrid + +from flopy4.structured_grid import StructuredGridWrapper + +from .grid_utils import get_coords + + +@define +class Imeth1Header: + kstp: int + kper: int + text: str + ndim1: int + ndim2: int + ndim3: int + imeth: int + delt: float + pertim: float + totim: float + pos: int + + +@define +class Imeth6Header: + kstp: int + kper: int + text: str + ndim1: int + ndim2: int + ndim3: int + imeth: int + delt: float + pertim: float + totim: float + pos: int + txt1id1: str + txt2id1: str + txt1id2: str + txt2id2: str + ndat: int + auxtxt: list[str] + nlist: int + + +def open_cbc( + cbc_path: Path, + grb_path: Path, + flowja: bool = False, + simulation_start_time: np.datetime64 | None = None, + time_unit: str | None = "d", +) -> xr.Dataset: + """ + Open modflow6 cell-by-cell (.cbc) file. + + The data is lazily read per timestep and automatically converted into + (dense) xr.DataArrays or xu.UgridDataArrays, for DIS and DISV respectively. + The conversion is done via the information stored in the Binary Grid file + (GRB). + + The ``flowja`` argument controls whether the flow-ja-face array + (if present) is returned in grid form as "as is". + By default ``flowja=False`` and the array is returned in "grid form", + meaning: + + * DIS: in right, front, and lower face flow. All flows are placed in + the cell. + + When ``flowja=True``, the flow-ja-face array is returned as it is found in + the CBC file, with a flow for every cell to cell connection. Additionally, + a ``connectivity`` DataArray is returned describing for every cell (n) its + connected cells (m). + + Parameters + ---------- + cbc_path: str, pathlib.Path + Path to the cell-by-cell flows file + grb_path: str, pathlib.Path + Path to the binary grid file + flowja: bool, default value: False + Whether to return the flow-ja-face values "as is" (``True``) or in a + grid form (``False``). + simulation_start_time : Optional datetime + The time and date correpsonding to the beginning of the simulation. + Use this to convert the time coordinates of the output array to + calendar time/dates. time_unit must also be present if this argument is + present. + time_unit: Optional str + The time unit MF6 is working in, in string representation. + Only used if simulation_start_time was provided. + Admissible values are: + ns -> nanosecond + ms -> microsecond + s -> second + m -> minute + h -> hour + d -> day + w -> week + Units "month" or "year" are not supported, as they do not represent + unambiguous timedelta values durations. + + Returns + ------- + cbc_content: xr.Dataset | Dict[str, xr.DataArray] + DataArray contains float64 data of the budgets, + with dimensions ("time", "layer", "y", "x"). + + Examples + -------- + + Open a cbc file: + + >>> import flopy4.mf6.utils + >>> cbc_content = open_cbc("budgets.cbc", "my-model.grb") + + Check the contents: + + >>> print(cbc_content.keys()) + + Get the drainage budget, compute a time mean for the first layer: + + >>> drn_budget = cbc_content["drn] + >>> mean = drn_budget.sel(layer=1).mean("time") + + """ + grid = StructuredGridWrapper.from_binary_grid_file(grb_path) + cbc = _open_cbc_dis( + cbc_path, grid, flowja, simulation_start_time, time_unit + ) + return xr.merge([cbc]) + + +def _open_cbc_dis( + cbc_path: Path, + grid: StructuredGrid, + flowja: bool = False, + simulation_start_time: np.datetime64 | None = None, + time_unit: str | None = "d", +) -> dict[str, xr.DataArray]: + headers = read_cbc_headers(cbc_path) + indices = None + header_advanced_package = get_first_header_advanced_package(headers) + if header_advanced_package is not None: + # For advanced packages the id2 column of variable gwf contains the MF6 + # ids. Get id's eager from first stress period. + dtype = np.dtype( + [("id1", np.int32), ("id2", np.int32), ("budget", np.float64)] + + [(name, np.float64) for name in header_advanced_package.auxtxt] + ) + table = read_imeth6_budgets( + cbc_path, + header_advanced_package.nlist, + dtype, + header_advanced_package.pos, + ) + indices = table["id2"] - 1 # Convert to 0 based index + cbc_content = {} + for key, header_list in headers.items(): + # TODO: validate homogeneity of header_list, ndat consistent, + # nlist consistent etc. + if key == "flow-ja-face" and isinstance(header_list[0], Imeth1Header): + assert all(isinstance(x, Imeth1Header) for x in header_list) + if flowja: + flowjaface, nm = open_face_budgets_as_flowja( + cbc_path, cast(list[Imeth1Header], header_list), grid + ) + cbc_content["flow-ja-face"] = flowjaface + cbc_content["connectivity"] = nm + else: + right, front, lower = dis_open_face_budgets( + cbc_path, + grid, + cast(list[Imeth1Header], header_list), + ) + cbc_content["flow-right-face"] = right + cbc_content["flow-front-face"] = front + cbc_content["flow-lower-face"] = lower + else: + if isinstance(header_list[0], Imeth1Header): + assert all(isinstance(x, Imeth1Header) for x in header_list) + cbc_content[key] = open_imeth1_budgets( + cbc_path, grid, cast(list[Imeth1Header], header_list) + ) + elif isinstance(header_list[0], Imeth6Header): + assert all(isinstance(x, Imeth6Header) for x in header_list) + + # for non cell flow budget terms, + # use auxiliary variables as return value + if header_list[0].text.startswith("data-"): + for return_variable in header_list[0].auxtxt: + key_aux = f"{header_list[0].txt2id1}-{return_variable}" + cbc_content[key_aux] = open_imeth6_budgets( + cbc_path, + grid, + cast(list[Imeth6Header], header_list), + return_variable, + indices=indices, + ) + else: + cbc_content[key] = open_imeth6_budgets( + cbc_path, + grid, + cast(list[Imeth6Header], header_list), + indices=indices, + ) + if simulation_start_time is not None: + for cbc_name, cbc_array in cbc_content.items(): + cbc_content[cbc_name] = assign_datetime_coords( + cbc_array, simulation_start_time, time_unit + ) + + return cbc_content + + +def get_first_header_advanced_package( + headers: dict[str, list[Any]], +) -> Any: + for key, header_list in headers.items(): + # multimodels have a gwf-gwf budget for flow-ja-face between domains + if "flow-ja-face" not in key and "gwf_" in key: + return header_list[0] + return None + + +def read_cbc_headers( + cbc_path: Path, +) -> dict[str, list[Imeth1Header | Imeth6Header]]: + """ + Read all the header data from a cell-by-cell (.cbc) budget file. + + All budget data for a MODFLOW6 model is stored in a single file. This + function collects all header data, as well as the starting byte position of + the actual budget data. + + This function groups the headers per TEXT record (e.g. "flow-ja-face", + "drn", etc.). The headers are stored as a list of named tuples. + flow-ja-face, storage-ss, and storage-sy are written using IMETH=1, all + others with IMETH=6. + + Parameters + ---------- + cbc_path: str, pathlib.Path + Path to the budget file. + + Returns + ------- + headers: Dict[List[UnionImeth1Header, Imeth6Header]] + Dictionary containing a list of headers per TEXT record in the budget + file. + """ + headers: dict[str, list[Imeth1Header | Imeth6Header]] = defaultdict(list) + with open(cbc_path, "rb") as f: + filesize = os.fstat(f.fileno()).st_size + while f.tell() < filesize: + header = read_common_cbc_header(f) + if header["imeth"] == 1: + # Multiply by -1 because ndim3 is stored as a negative for some + # reason. (ndim3 is the integer size of the third dimension) + datasize = ( + header["ndim1"] * header["ndim2"] * header["ndim3"] * -1 + ) * 8 + header["pos"] = f.tell() + key = header["text"] + headers[key].append(Imeth1Header(**header)) + elif header["imeth"] == 6: + imeth6_header = read_imeth6_header(f) + datasize = imeth6_header["nlist"] * ( + 8 + imeth6_header["ndat"] * 8 + ) + header["pos"] = f.tell() + # key-format: + # "package type"-"optional_package_variable"_"package name" + # for river output: riv_sys1 + # for uzf output: uzf-gwrch_uzf_sys1 + key = header["text"] + "_" + imeth6_header["txt2id2"] + # npf-key can be present multiple times in cases of saved + # saturation + specific discharge + if header["text"].startswith("data-"): + key = ( + imeth6_header["txt2id2"] + + "_" + + header["text"].replace("data-", "") + ) + headers[key].append(Imeth6Header(**header, **imeth6_header)) + else: + raise ValueError( + f"Invalid imeth value in CBC file {cbc_path}. " + f"Should be 1 or 6, received: {header['imeth']}." + ) + # Skip the data + f.seek(datasize, 1) + return headers + + +def read_common_cbc_header(f: BinaryIO) -> dict[str, Any]: + """ + Read the common part (shared by imeth=1 and imeth6) of a CBC header section + """ + content = {} + content["kstp"] = struct.unpack("i", f.read(4))[0] + content["kper"] = struct.unpack("i", f.read(4))[0] + content["text"] = f.read(16).decode("utf-8").strip().lower() + content["ndim1"] = struct.unpack("i", f.read(4))[0] + content["ndim2"] = struct.unpack("i", f.read(4))[0] + content["ndim3"] = struct.unpack("i", f.read(4))[0] + content["imeth"] = struct.unpack("i", f.read(4))[0] + content["delt"] = struct.unpack("d", f.read(8))[0] + content["pertim"] = struct.unpack("d", f.read(8))[0] + content["totim"] = struct.unpack("d", f.read(8))[0] + return content + + +def read_imeth6_header(f: BinaryIO) -> dict[str, Any]: + """ + Read the imeth=6 specific data of a CBC header section. + """ + content: dict[str, str | list[str]] = {} + content["txt1id1"] = f.read(16).decode("utf-8").strip().lower() + content["txt2id1"] = f.read(16).decode("utf-8").strip().lower() + content["txt1id2"] = f.read(16).decode("utf-8").strip().lower() + content["txt2id2"] = f.read(16).decode("utf-8").strip().lower() + ndat = struct.unpack("i", f.read(4))[0] + content["ndat"] = ndat + content["auxtxt"] = [ + f.read(16).decode("utf-8").strip().lower() for _ in range(ndat - 1) + ] + content["nlist"] = struct.unpack("i", f.read(4))[0] + return content + + +def assign_datetime_coords( + da: xr.DataArray, + simulation_start_time: np.datetime64, + time_unit: str | None = "d", +) -> xr.DataArray: + if "time" not in da.coords: + raise ValueError( + "cannot convert time column, " + "because a time column could not be found" + ) + + time = pd.Timestamp(simulation_start_time) + pd.to_timedelta( + da["time"], unit=time_unit + ) + return da.assign_coords(time=time) + + +def open_imeth6_budgets( + cbc_path: Path, + grid: StructuredGrid, + header_list: list[Imeth6Header], + return_variable: str = "budget", + indices: np.ndarray | None = None, +) -> xr.DataArray: + """ + Open the data for an imeth==6 budget section. + + Uses the information of the DIS GRB file to create the properly sized dense + xr.DataArrays (which store the entire domain). + Doing so ignores the boundary condition internal index (id2) and any + present auxiliary columns. + + Parameters + ---------- + cbc_path: str, pathlib.Path + grid: StructuredGrid + header_list: List[Imeth1Header] + return_variable: str + return_id: np.ndarray | None + + Returns + ------- + xr.DataArray with dims ("time", "layer", "y", "x") + """ + # Allocates dense arrays for the entire model domain + dtype = np.dtype( + [("id1", np.int32), ("id2", np.int32), ("budget", np.float64)] + + [(name, np.float64) for name in header_list[0].auxtxt] + ) + shape = (grid.nlay, grid.nrow, grid.ncol) + size = np.prod(shape) + dask_list = [] + time = np.empty(len(header_list), dtype=np.float64) + for i, header in enumerate(header_list): + time[i] = header.totim + a = dask.delayed(read_imeth6_budgets_dense)( + cbc_path, + header.nlist, + dtype, + header.pos, + size, + shape, + return_variable, + indices, + ) + x = dask.array.from_delayed(a, shape=shape, dtype=np.float64) + dask_list.append(x) + + daskarr = dask.array.stack(dask_list, axis=0) + coords = get_coords(grid) + coords["time"] = time + name = header_list[0].text + return xr.DataArray( + daskarr, coords, ("time", "layer", "y", "x"), name=name + ) + + +def read_imeth6_budgets_dense( + cbc_path: Path, + count: int, + dtype: np.dtype, + pos: int, + size: int, + shape: tuple, + return_variable: str, + indices: np.ndarray | None, +) -> np.ndarray: + """ + Read the data for an imeth==6 budget section. + + Utilizes the shape information from the DIS GRB file to create a + dense numpy array. Always allocates for the entire domain + (all layers, rows, columns). + + Parameters + ---------- + cbc_path: str, pathlib.Path + count: int + number of values to read + dtype: numpy dtype + Data type of the structured array. Contains at least "id1", "id2", + and "budget". + Optionally contains auxiliary columns. + pos: int + position in the file where the data for a timestep starts + size: int + size of the entire model domain + shape: tuple[int, int, int] + Shape (nlayer, nrow, ncolumn) of entire model domain. + return_variable: str + variable name to return from budget table + indices: np.ndarray | None + optional array that contains the indices to map return_variable + to model topology + + Returns + ------- + Three-dimensional array of floats + """ + # Allocates a dense array for the entire domain + out = np.full(size, np.nan, dtype=np.float64) + table = read_imeth6_budgets(cbc_path, count, dtype, pos) + if indices is None: + indices = table["id1"] - 1 # Convert to 0 based index + # Zero the relevant values, overwrite the NaN value. + out[indices] = 0 + # Sum all the budget terms. + np.add.at(out, indices, table[return_variable]) + return out.reshape(shape) + + +def read_imeth6_budgets( + cbc_path: Path, count: int, dtype: np.dtype, pos: int +) -> Any: + """ + Read the data for an imeth==6 budget section for a single timestep. + + Returns a numpy structured array containing: + * id1: the model cell number + * id2: the boundary condition index + * budget: the budget terms + * and assorted auxiliary columns, if present + + Parameters + ---------- + cbc_path: str, pathlib.Path + count: int + number of values to read + dtype: numpy dtype + Data type of the structured array. Contains at least "id1", "id2", + and "budget". + Optionally contains auxiliary columns. + pos: + position in the file where the data for a timestep starts + + Returns + ------- + Numpy structured array of type dtype + """ + with open(cbc_path, "rb") as f: + f.seek(pos) + table = np.fromfile(f, dtype, count) + return table + + +def open_imeth1_budgets( + cbc_path: Path, + grid: StructuredGrid, + header_list: list[Imeth1Header], +) -> xr.DataArray: + """ + Open the data for an imeth==1 budget section. Data is read lazily per + timestep. + + Can be used for: + + * STO-SS + * STO-SY + * CSUB-CGELASTIC + * CSUB-WATERCOMP + + Utilizes the shape information from the DIS GRB file to create a dense + array; (lazily) allocates for the entire domain (all layers, rows, columns) + per timestep. + + Parameters + ---------- + cbc_path: str, pathlib.Path + grid: StructuredGrid + header_list: List[Imeth1Header] + + Returns + ------- + xr.DataArray with dims ("time", "layer", "y", "x") + """ + nlayer = grid.nlay + nrow = grid.nrow + ncol = grid.ncol + budgets = cbc_open_imeth1_budgets(cbc_path, header_list) + # Merge dictionaries + coords = get_coords(grid) | {"time": budgets["time"]} + + return xr.DataArray( + data=budgets.data.reshape((budgets["time"].size, nlayer, nrow, ncol)), + coords=coords, + dims=("time", "layer", "y", "x"), + name=budgets.name, + ) + + +def cbc_open_imeth1_budgets( + cbc_path: Path, header_list: list[Imeth1Header] +) -> xr.DataArray: + """ + Open the data for an imeth==1 budget section. Data is read lazily per + timestep. The cell data is not spatially labelled. + + Parameters + ---------- + cbc_path: str, pathlib.Path + header_list: List[Imeth1Header] + + Returns + ------- + xr.DataArray with dims ("time", "linear_index") + """ + # Gather times from the headers + dask_list = [] + time = np.empty(len(header_list), dtype=np.float64) + for i, header in enumerate(header_list): + time[i] = header.totim + count = header.ndim1 * header.ndim2 * header.ndim3 * -1 + a = dask.delayed(read_imeth1_budgets)(cbc_path, count, header.pos) + x = dask.array.from_delayed(a, shape=(count,), dtype=np.float64) + dask_list.append(x) + + return xr.DataArray( + data=dask.array.stack(dask_list, axis=0), + coords={"time": time}, + dims=("time", "linear_index"), + name=header_list[0].text, + ) + + +def read_imeth1_budgets(cbc_path: Path, count: int, pos: int) -> np.ndarray: + """ + Read the data for an imeth=1 budget section. + + Parameters + ---------- + cbc_path: str, pathlib.Path + count: int + number of values to read + pos: + position in the file where the data for a timestep starts + + Returns + ------- + 1-D array of floats + """ + with open(cbc_path, "rb") as f: + f.seek(pos) + timestep_budgets = np.fromfile(f, np.float64, count) + return timestep_budgets + + +def dis_open_face_budgets( + cbc_path: Path, + grid: StructuredGrid, + header_list: list[Imeth1Header], +) -> tuple[xr.DataArray, xr.DataArray, xr.DataArray]: + """ + Open the flow-ja-face, and extract right, front, and lower face flows. + + Parameters + ---------- + cbc_path: str, pathlib.Path + grid: StructuredGrid + header_list: List[Imeth1Header] + + Returns + ------- + right: xr.DataArray of floats with dims ("time", "layer", "y", "x") + front: xr.DataArray of floats with dims ("time", "layer", "y", "x") + lower: xr.DataArray of floats with dims ("time", "layer", "y", "x") + """ + right_index, front_index, lower_index = dis_to_right_front_lower_indices( + grid + ) + budgets = cbc_open_imeth1_budgets(cbc_path, header_list) + right = dis_extract_face_budgets(budgets, right_index) + front = dis_extract_face_budgets(budgets, front_index) + lower = dis_extract_face_budgets(budgets, lower_index) + return right, front, lower + + +def dis_extract_face_budgets( + budgets: xr.DataArray, index: xr.DataArray +) -> xr.DataArray: + """ + Grab right, front, or lower face flows from the flow-ja-face array. + + This could be done by a single .isel() indexing operation, but those + are extremely slow in this case, which seems to be an xarray issue. + + Parameters + ---------- + budgets: xr.DataArray of floats + flow-ja-face array, dims ("time", "linear_index") + The linear index enumerates cell-to-cell connections in this case, not + the individual cells. + index: xr.DataArray of ints + right, front, or lower index array with dims("layer", "y", "x") + + Returns + ------- + xr.DataArray of floats with dims ("time", "layer", "y", "x") + """ + coords = dict(index.coords) + coords["time"] = budgets["time"] + # isel with a 3D array is extremely slow + # this followed by the dask reshape is much faster for some reason. + data = budgets.isel(linear_index=index.values.ravel()).data + da = xr.DataArray( + data=data.reshape((budgets["time"].size, *index.shape)), + coords=coords, + dims=("time", "layer", "y", "x"), + name="flow-ja-face", + ) + return da.where(index >= 0, other=0.0) + + +def dis_to_right_front_lower_indices( + grid: StructuredGrid, +) -> tuple[xr.DataArray, xr.DataArray, xr.DataArray]: + """ + Infer the indices to extract right, front, and lower face flows from the + flow-ja-face array. + + Parameters + ---------- + grid: StructuredGrid + + Returns + ------- + right: xr.DataArray of ints with dims ("layer", "y", "x") + front: xr.DataArray of ints with dims ("layer", "y", "x") + lower: xr.DataArray of ints with dims ("layer", "y", "x") + """ + right, front, lower = dis_indices(grid) + coords = get_coords(grid) + return ( + xr.DataArray(right, coords, ("layer", "y", "x")), + xr.DataArray(front, coords, ("layer", "y", "x")), + xr.DataArray(lower, coords, ("layer", "y", "x")), + ) + + +def dis_indices( + grid: StructuredGrid, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Infer type of connection via cell number comparison. Returns arrays + that can be used for extracting right, front, and lower face flow from the + flow-ja-face array. + + In a structured grid, using a linear index: + * the right neighbor is +(1) + * the front neighbor is +(number of cells in a column) + * the lower neighbor is +(number of cells in a layer) + * lower "vertical passthrough" cells (idomain <0) are multitude of (number + of cells in a layer) + + Parameters + ---------- + grid: StructuredGrid + + Returns + ------- + right: 3D array of ints + front: 3D array of ints + lower: 3D array of ints + """ + shape = (grid.nlay, grid.nrow, grid.ncol) + ncells_per_layer = grid.nrow * grid.ncol + right = np.full(grid.nnodes, -1, np.int64) + front = np.full(grid.nnodes, -1, np.int64) + lower = np.full(grid.nnodes, -1, np.int64) + + for i in range(grid.nnodes): + for nzi in range(grid.ia[i], grid.ia[i + 1]): + nzi -= 1 # python is 0-based, modflow6 is 1-based + j = grid.ja[nzi] - 1 # python is 0-based, modflow6 is 1-based + d = j - i + if d <= 0: # left, back, upper + continue + elif d == 1 and grid.ncol > 1: # right neighbor + right[i] = nzi + elif d == grid.ncol and ncells_per_layer > 1: # front neighbor + front[i] = nzi + elif d == ncells_per_layer: # lower neighbor + lower[i] = nzi + else: # skips one: must be pass through + npassed = int(d / ncells_per_layer) + for ipass in range(0, npassed): + lower[i + ipass * ncells_per_layer] = nzi + + return right.reshape(shape), front.reshape(shape), lower.reshape(shape) + + +def open_face_budgets_as_flowja( + cbc_path: Path, + header_list: list[Imeth1Header], + grid: StructuredGrid, +) -> tuple[xr.DataArray, xr.DataArray]: + flowja = cbc_open_imeth1_budgets(cbc_path, header_list) + flowja = flowja.rename({"linear_index": "connection"}) + n = expand_indptr(grid.ia) + m = grid.ja - 1 + nm = xr.DataArray( + np.column_stack([n, m]), + coords={"cell": ["n", "m"]}, + dims=["connection", "cell"], + ) + return flowja, nm + + +def expand_indptr(ia) -> np.ndarray: + n = np.diff(ia) + return np.repeat(np.arange(ia.size - 1), n) diff --git a/flopy4/mf6/utils/grid_utils.py b/flopy4/mf6/utils/grid_utils.py new file mode 100644 index 00000000..a2c857b0 --- /dev/null +++ b/flopy4/mf6/utils/grid_utils.py @@ -0,0 +1,33 @@ +import collections +from typing import Any + +import numpy as np +from flopy.discretization import StructuredGrid + + +def get_coords(grid: StructuredGrid) -> dict[str, Any]: + # unpack tuples + xmin, xmax, ymin, ymax = grid.extent + dx, dy = (grid.delr, -grid.delc) + coords: collections.OrderedDict[str, Any] = collections.OrderedDict() + # from cell size to x and y coordinates + if isinstance(dx, (int, float, np.int_)): # equidistant + coords["x"] = np.arange(xmin + dx / 2.0, xmax, dx) + coords["y"] = np.arange(ymax + dy / 2.0, ymin, dy) + coords["dx"] = np.array(float(dx)) + coords["dy"] = np.array(float(dy)) + else: # nonequidistant + # even though IDF may store them as float32, + # we always convert them to float64 + dx = dx.astype(np.float64) + dy = dy.astype(np.float64) + coords["x"] = xmin + np.cumsum(dx) - 0.5 * dx + coords["y"] = ymax + np.cumsum(dy) - 0.5 * dy + if np.allclose(dx, dx[0]) and np.allclose(dy, dy[0]): + coords["dx"] = np.array(float(dx[0])) + coords["dy"] = np.array(float(dy[0])) + else: + coords["dx"] = ("x", dx) + coords["dy"] = ("y", dy) + coords["layer"] = np.arange(1, grid.nlay + 1) + return coords diff --git a/flopy4/mf6/utils/heads_reader.py b/flopy4/mf6/utils/heads_reader.py index d5ff7758..c9274056 100644 --- a/flopy4/mf6/utils/heads_reader.py +++ b/flopy4/mf6/utils/heads_reader.py @@ -1,8 +1,6 @@ -import collections import os import struct from pathlib import Path -from typing import Any import dask import numpy as np @@ -10,6 +8,8 @@ import xarray as xr from flopy.discretization import StructuredGrid +from .grid_utils import get_coords + def open_hds( hds_path: Path, @@ -93,34 +93,6 @@ def _open_hds_dis( return data_array -def get_coords(grid: StructuredGrid) -> dict[str, Any]: - # unpack tuples - xmin, xmax, ymin, ymax = grid.extent - dx, dy = (grid.delr, -grid.delc) - coords: collections.OrderedDict[str, Any] = collections.OrderedDict() - # from cell size to x and y coordinates - if isinstance(dx, (int, float, np.int_)): # equidistant - coords["x"] = np.arange(xmin + dx / 2.0, xmax, dx) - coords["y"] = np.arange(ymax + dy / 2.0, ymin, dy) - coords["dx"] = np.array(float(dx)) - coords["dy"] = np.array(float(dy)) - else: # nonequidistant - # even though IDF may store them as float32, - # we always convert them to float64 - dx = dx.astype(np.float64) - dy = dy.astype(np.float64) - coords["x"] = xmin + np.cumsum(dx) - 0.5 * dx - coords["y"] = ymax + np.cumsum(dy) - 0.5 * dy - if np.allclose(dx, dx[0]) and np.allclose(dy, dy[0]): - coords["dx"] = np.array(float(dx[0])) - coords["dy"] = np.array(float(dy[0])) - else: - coords["dx"] = ("x", dx) - coords["dy"] = ("y", dy) - coords["layer"] = np.arange(1, grid.nlay + 1) - return coords - - def read_times(path: Path, ntime: int, nlayer: int, nrow: int, ncol: int) -> np.ndarray: """ Reads all total simulation times. diff --git a/flopy4/structured_grid.py b/flopy4/structured_grid.py new file mode 100644 index 00000000..31cd14ab --- /dev/null +++ b/flopy4/structured_grid.py @@ -0,0 +1,106 @@ +import os + +from flopy.discretization import StructuredGrid + + +class StructuredGridWrapper(StructuredGrid): + def __init__( + self, + delc=None, + delr=None, + top=None, + botm=None, + idomain=None, + lenuni=None, + crs=None, + prjfile=None, + xoff=0.0, + yoff=0.0, + angrot=0.0, + nlay=None, + nrow=None, + ncol=None, + laycbd=None, + ia=None, + ja=None, + **kwargs, + ): + super().__init__( + delc=delc, + delr=delr, + top=top, + botm=botm, + idomain=idomain, + lenuni=lenuni, + crs=crs, + prjfile=prjfile, + xoff=xoff, + yoff=yoff, + angrot=angrot, + nlay=nlay, + nrow=nrow, + ncol=ncol, + laycbd=laycbd, + **kwargs, + ) + + self._ia = ia + self._ja = ja + + @property + def ja(self): + return self._ja + + @property + def ia(self): + return self._ia + + @classmethod + def from_binary_grid_file(cls, file_path, verbose=False): + """ + Instantiate a StructuredGrid model grid from a MODFLOW 6 binary + grid (*.grb) file. + + Parameters + ---------- + file_path : str + file path for the MODFLOW 6 binary grid file + verbose : bool + Write information to standard output. Default is False. + + Returns + ------- + return : StructuredGrid + + """ + from flopy.mf6.utils.binarygrid_util import MfGrdFile + + grb_obj = MfGrdFile(file_path, verbose=verbose) + if grb_obj.grid_type != "DIS": + raise ValueError( + f"Binary grid file ({os.path.basename(file_path)}) " + "is not a structured (DIS) grid." + ) + + idomain = grb_obj.idomain + xorigin = grb_obj.xorigin + yorigin = grb_obj.yorigin + angrot = grb_obj.angrot + + nlay, nrow, ncol = (grb_obj.nlay, grb_obj.nrow, grb_obj.ncol) + delr, delc = grb_obj.delr, grb_obj.delc + top, botm = grb_obj.top, grb_obj.bot + top.shape = (nrow, ncol) + botm.shape = (nlay, nrow, ncol) + return cls( + delc, + delr, + top, + botm, + idomain=idomain, + xoff=xorigin, + yoff=yorigin, + angrot=angrot, + ia=grb_obj.ia, + ja=grb_obj.ja, + ) diff --git a/pixi.lock b/pixi.lock index 5b5a0c85..d7a256f0 100644 --- a/pixi.lock +++ b/pixi.lock @@ -29,10 +29,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda - 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