import collections
import os
import pathlib
import warnings
import cftime
import jinja2
import numpy as np
import pandas as pd
import xarray as xr
from scipy.ndimage import binary_dilation
import imod
from imod.util.time import timestep_duration, to_datetime_internal
from imod.wq.pkggroup import PackageGroups
def _relpath(path, to):
# Wraps os.path.relpath
try:
return pathlib.Path(os.path.relpath(path, to))
except ValueError:
# Fails to switch between drives e.g.
return pathlib.Path(os.path.abspath(path))
# This class allows only imod packages as values
class Model(collections.UserDict):
def __setitem__(self, key, value):
# TODO: raise ValueError on setting certain duplicates
# e.g. two solvers
if self.check == "eager":
value._pkgcheck()
super().__setitem__(key, value)
def update(self, *args, **kwargs):
for k, v in dict(*args, **kwargs).items():
self[k] = v
def visualize(
self,
directory,
cmap="viridis",
overlays=[],
quantile_colorscale=True,
figsize=(8, 8),
):
directory = pathlib.Path(directory)
for key, value in self.items():
if value._pkg_id == "well":
pass
# grouped = value.groupby(["x", "y"])
# x = grouped["x"].first()
# y = grouped["y"].first()
# Select appropriate datasets
if "x" in value.dims and "y" in value.dims:
for varname in value.data_vars:
da = value[varname]
if "x" in da.dims and "y" in da.dims:
if da.isnull().all():
continue
imod.visualize.spatial.imshow_topview(
da=da,
name=varname,
directory=directory / key,
cmap=cmap,
overlays=overlays,
quantile_colorscale=quantile_colorscale,
figsize=figsize,
)
def sel(self, **dimensions):
selmodel = type(self)(self.modelname, self.check)
for pkgname, pkg in self.items():
sel_dims = {k: v for k, v in dimensions.items() if k in pkg.dataset}
if pkg._pkg_id == "bas6":
# da.sel() unsets dimensions for scalars, this messes with bas6
# package, because check_ibound is called upon initialization.
sel_dims = {
k: [v] if np.isscalar(v) else v for k, v in sel_dims.items()
}
if len(sel_dims) == 0:
selmodel[pkgname] = pkg
else:
if pkg._pkg_id != "wel":
selmodel[pkgname] = type(pkg)(**pkg.dataset.loc[sel_dims])
else:
df = pkg.dataset.to_dataframe().drop(columns="save_budget")
for k, v in sel_dims.items():
try:
if isinstance(v, slice):
# slice?
# to account for reversed order of y
low, high = min(v.start, v.stop), max(v.start, v.stop)
df = df.loc[(df[k] >= low) & (df[k] <= high)]
else: # list, labels etc
df = df.loc[df[k].isin(v)]
except Exception:
raise ValueError(
"Invalid indexer for Well package, accepts slice or list-like of values"
)
selmodel[pkgname] = imod.wq.Well(
save_budget=pkg.dataset["save_budget"], **df
)
return selmodel
def isel(self, **dimensions):
selmodel = type(self)(self.modelname, self.check)
for pkgname, pkg in self.items():
sel_dims = {k: v for k, v in dimensions.items() if k in pkg.dataset}
if len(sel_dims) == 0:
selmodel[pkgname] = pkg
else:
selmodel[pkgname].dataset = pkg.dataset[sel_dims]
return selmodel
def to_netcdf(self, directory=".", pattern="{pkgname}.nc", **kwargs):
"""Convenience function to write all model packages
to netcdf files.
Parameters
----------
directory : str, pathlib.Path
Directory into which the different model packages will be written.
pattern : str, optional.
Pattern for filename of each package, in which `pkgname`
signifies the package name. Default is `"{pkgname}.nc"`,
so `model["river"]` would get written to `path / river.nc`.
kwargs :
Additional kwargs to be forwarded to `xarray.Dataset.to_netcdf`.
"""
directory = pathlib.Path(directory)
for pkgname, pkg in self.items():
try:
pkg.dataset.to_netcdf(
directory / pattern.format(pkgname=pkgname), **kwargs
)
except Exception as e:
raise type(e)("{e}\nPackage {pkgname} can not be written to NetCDF")
def _delete_empty_packages(self, verbose=False):
to_del = []
for pkg in self.keys():
dv = list(self[pkg].dataset.data_vars)[0]
if not self[pkg][dv].notnull().any().compute():
if verbose:
warnings.warn(
f"Deleting package {pkg}, found no data in parameter {dv}"
)
to_del.append(pkg)
for pkg in to_del:
del self[pkg]
[docs]
class SeawatModel(Model):
"""
iMOD-WQ SEAWAT model.
Attributes
----------
modelname : str
check : str, optional
When to perform model checks {None, "defer", "eager"}.
Defaults to "defer".
Examples
--------
>>> m = SeawatModel("example")
>>> m["riv"] = River(...)
>>> # ...etc.
>>> m.create_time_discretization(endtime)
>>> m.write()
"""
# These templates end up here since they require global information
# from more than one package
_PACKAGE_GROUPS = PackageGroups
_gen_template = jinja2.Template(
"[gen]\n"
" runtype = SEAWAT\n"
" modelname = {{modelname}}\n"
" writehelp = {{writehelp}}\n"
" result_dir = {{result_dir}}\n"
" packages = {{package_set|join(', ')}}\n"
" coord_xll = {{xmin}}\n"
" coord_yll = {{ymin}}\n"
" start_year = {{start_date[:4]}}\n"
" start_month = {{start_date[4:6]}}\n"
" start_day = {{start_date[6:8]}}\n"
" start_hour = {{start_date[8:10]}}\n"
" start_minute = {{start_date[10:12]}}\n"
)
[docs]
def __init__(self, modelname, check="defer"):
super().__init__()
self.modelname = modelname
self.check = check
def _get_pkgkey(self, pkg_id):
"""
Get package key that belongs to a certain pkg_id, since the keys are
user specified.
"""
key = [pkgname for pkgname, pkg in self.items() if pkg._pkg_id == pkg_id]
nkey = len(key)
if nkey > 1:
raise ValueError(f"Multiple instances of {key} detected")
elif nkey == 1:
return key[0]
else:
return None
def _group(self):
"""
Group multiple systems of a single package E.g. all river or drainage
sub-systems
"""
groups = {}
has_group = set()
groupable = set(self._PACKAGE_GROUPS.__members__.keys())
for key, package in self.items():
pkg_id = package._pkg_id
if pkg_id in groupable:
if pkg_id in has_group: # already exists
groups[pkg_id][key] = package
else:
groups[pkg_id] = {key: package}
has_group.update([pkg_id])
package_groups = []
for pkg_id, group in groups.items():
# Create PackageGroup for every package
# RiverGroup for rivers, DrainageGroup for drainage, etc.
package_groups.append(self._PACKAGE_GROUPS[pkg_id].value(**group))
return package_groups
def _hastime(self, pkg):
return (pkg._pkg_id == "wel" and "time" in pkg.dataset) or (
"time" in pkg.dataset.coords
)
def _use_cftime(self):
"""
Also checks if datetime types are homogeneous across packages.
"""
types = []
for pkg in self.values():
if self._hastime(pkg):
types.append(type(np.atleast_1d(pkg.dataset["time"].values)[0]))
# Types will be empty if there's no time dependent input
set_of_types = set(types)
if len(set_of_types) == 0:
return None
else: # there is time dependent input
if not len(set_of_types) == 1:
raise ValueError(
f"Multiple datetime types detected: {set_of_types}. "
"Use either cftime or numpy.datetime64[ns]."
)
# Since we compare types and not instances, we use issubclass
if issubclass(types[0], cftime.datetime):
return True
elif issubclass(types[0], np.datetime64):
return False
else:
raise ValueError("Use either cftime or numpy.datetime64[ns].")
def time_discretization(self, times):
warnings.warn(
f"{self.__class__.__name__}.time_discretization() is deprecated. "
f"In the future call {self.__class__.__name__}.create_time_discretization().",
DeprecationWarning,
)
self.create_time_discretization(additional_times=times)
[docs]
def create_time_discretization(self, additional_times):
"""
Collect all unique times from model packages and additional given `times`. These
unique times are used as stress periods in the model. All stress packages must
have the same starting time.
The time discretization in imod-python works as follows:
- The datetimes of all packages you send in are always respected
- Subsequently, the input data you use is always included fully as well
- All times are treated as starting times for the stress: a stress is always applied until the next specified date
- For this reason, a final time is required to determine the length of the last stress period
- Additional times can be provided to force shorter stress periods & more detailed output
- Every stress has to be defined on the first stress period (this is a modflow requirement)
Or visually (every letter a date in the time axes):
>>> recharge a - b - c - d - e - f
>>> river g - - - - h - - - - j
>>> times - - - - - - - - - - - i
>>> model a - b - c h d - e - f i
with the stress periods defined between these dates. I.e. the model times are the set of all times you include in the model.
Parameters
----------
times : str, datetime; or iterable of str, datetimes.
Times to add to the time discretization. At least one single time
should be given, which will be used as the ending time of the
simulation.
Note
----
To set the other parameters of the TimeDiscretization object, you have
to set these to the object after calling this function.
Examples
--------
Add a single time:
>>> m.create_time_discretization("2001-01-01")
Add a daterange:
>>> m.create_time_discretization(pd.daterange("2000-01-01", "2001-01-01"))
Add a list of times:
>>> m.create_time_discretization(["2000-01-01", "2001-01-01"])
"""
# Make sure it's an iterable
if not isinstance(
additional_times, (np.ndarray, list, tuple, pd.DatetimeIndex)
):
additional_times = [additional_times]
# Loop through all packages, check if cftime is required.
self.use_cftime = self._use_cftime()
# use_cftime is None if you no datetimes are present in packages
# use_cftime is False if np.datetimes present in packages
# use_cftime is True if cftime.datetime present in packages
for time in additional_times:
if issubclass(type(time), cftime.datetime):
if self.use_cftime is None:
self.use_cftime = True
if self.use_cftime is False:
raise ValueError(
"Use either cftime or numpy.datetime64[ns]. "
f"Received: {type(time)}."
)
if self.use_cftime is None:
self.use_cftime = False
times = [
to_datetime_internal(time, self.use_cftime) for time in additional_times
]
first_times = {} # first time per package
for key, pkg in self.items():
if self._hastime(pkg):
pkgtimes = list(pkg.dataset["time"].values)
first_times[key] = sorted(pkgtimes)[0]
for var in pkg.dataset.data_vars:
if "stress_repeats" in pkg.dataset[var].attrs:
stress_repeats_times = list(
pkg.dataset[var].attrs["stress_repeats"].keys()
)
pkgtimes.extend(stress_repeats_times)
times.extend(pkgtimes)
# np.unique also sorts
times = np.unique(np.hstack(times))
# Check if every transient package commences at the same time.
for key, first_time in first_times.items():
time0 = times[0]
if first_time != time0:
raise ValueError(
f"Package {key} does not have a value specified for the "
f"first time: {time0}. Every input must be present in the "
"first stress period. Values are only filled forward in "
"time."
)
duration = timestep_duration(times, self.use_cftime)
# Generate time discretization, just rely on default arguments
# Probably won't be used that much anyway?
timestep_duration_da = xr.DataArray(
duration, coords={"time": np.array(times)[:-1]}, dims=("time",)
)
self["time_discretization"] = imod.wq.TimeDiscretization(
timestep_duration=timestep_duration_da
)
def _render_gen(self, modelname, globaltimes, writehelp, result_dir):
package_set = {
pkg._pkg_id for pkg in self.values() if pkg._pkg_id not in ("tvc", "mal")
}
package_set.update(("btn", "ssm"))
package_set = sorted(package_set)
baskey = self._get_pkgkey("bas6")
bas = self[baskey]
_, xmin, xmax, _, ymin, ymax = imod.util.spatial.spatial_reference(
bas["ibound"]
)
if not self.use_cftime:
start_time = pd.to_datetime(globaltimes[0])
else:
start_time = globaltimes[0]
start_date = start_time.strftime("%Y%m%d%H%M%S")
d = {}
d["modelname"] = modelname
d["writehelp"] = writehelp
d["result_dir"] = result_dir
d["xmin"] = xmin
d["xmax"] = xmax
d["ymin"] = ymin
d["ymax"] = ymax
d["package_set"] = package_set
d["start_date"] = start_date
return self._gen_template.render(d)
def _render_pkg(self, key, directory, globaltimes, nlayer):
"""
Rendering method for straightforward packages
"""
# Get name of pkg, e.g. lookup "recharge" for rch _pkg_id
pkgkey = self._get_pkgkey(key)
if pkgkey is None:
# Maybe do enum look for full package name?
if (key == "rch") or (key == "evt"): # since recharge is optional
return ""
else:
raise ValueError(f"No {key} package provided.")
return self[pkgkey]._render(
directory=directory / pkgkey, globaltimes=globaltimes, nlayer=nlayer
)
def _render_dis(self, directory, globaltimes, nlayer):
baskey = self._get_pkgkey("bas6")
diskey = self._get_pkgkey("dis")
bas_content = self[baskey]._render_dis(
directory=directory.joinpath(baskey), nlayer=nlayer
)
dis_content = self[diskey]._render(globaltimes=globaltimes)
return bas_content + dis_content
def _render_groups(self, directory, globaltimes):
baskey = self._get_pkgkey("bas6")
nlayer, nrow, ncol = self[baskey]["ibound"].shape
package_groups = self._group()
content = "\n\n".join(
[
group.render(directory, globaltimes, nlayer, nrow, ncol)
for group in package_groups
]
)
ssm_content = "".join(
[
group.render_ssm(directory, globaltimes, nlayer)
for group in package_groups
]
)
# Calculate number of sinks and sources
n_sinkssources = sum([group.max_n_sinkssources() for group in package_groups])
return content, ssm_content, n_sinkssources
def _render_flowsolver(self, directory):
pcgkey = self._get_pkgkey("pcg")
pksfkey = self._get_pkgkey("pksf")
if pcgkey and pksfkey:
raise ValueError("pcg and pksf solver both provided. Provide only one.")
if not pcgkey and not pksfkey:
raise ValueError("No flow solver provided")
if pcgkey:
return self[pcgkey]._render()
else:
baskey = self._get_pkgkey("bas6")
self[pksfkey]._compute_load_balance_weight(self[baskey]["ibound"])
return self[pksfkey]._render(directory=directory.joinpath(pksfkey))
def _render_btn(self, directory, globaltimes, nlayer):
baskey = self._get_pkgkey("bas6")
btnkey = self._get_pkgkey("btn")
diskey = self._get_pkgkey("dis")
self[btnkey].dataset["thickness"] = self[baskey].thickness()
if btnkey is None:
raise ValueError("No BasicTransport package provided.")
btn_content = self[btnkey]._render(
directory=directory.joinpath(btnkey), nlayer=nlayer
)
dis_content = self[diskey]._render_btn(globaltimes=globaltimes)
return btn_content + dis_content
def _render_transportsolver(self, directory):
gcgkey = self._get_pkgkey("gcg")
pkstkey = self._get_pkgkey("pkst")
if gcgkey and pkstkey:
raise ValueError("gcg and pkst solver both provided. Provide only one.")
if not gcgkey and not pkstkey:
raise ValueError("No transport solver provided")
if gcgkey:
return self[gcgkey]._render()
else:
baskey = self._get_pkgkey("bas6")
self[pkstkey]._compute_load_balance_weight(self[baskey]["ibound"])
return self[pkstkey]._render(directory=directory / pkstkey)
def _render_ssm_rch_evt_mal_tvc(self, directory, globaltimes, nlayer):
out = ""
for key, pkg in self.items():
if pkg._pkg_id in ("rch", "evt", "mal", "tvc"):
out += pkg._render_ssm(
directory=directory / key, globaltimes=globaltimes, nlayer=nlayer
)
return out
def _bas_btn_rch_evt_mal_tvc_sinkssources(self):
baskey = self._get_pkgkey("bas6")
btnkey = self._get_pkgkey("btn")
ibound = self[baskey].dataset["ibound"]
icbund = self[btnkey].dataset["icbund"]
n_extra = int(((ibound < 0) | (icbund < 0)).sum())
nlayer, nrow, ncol = ibound.shape
for key in ("rch", "evt", "mal", "tvc"):
pkgkey = self._get_pkgkey(key)
if pkgkey is not None:
pkg = self[pkgkey]
if key in ("rch", "evt"):
pkg._set_ssm_layers(ibound)
n_extra += pkg._ssm_layers.size * nrow * ncol
elif key in ("mal", "tvc"):
_ = pkg._max_active_n("concentration", nlayer, nrow, ncol)
n_extra += pkg._ssm_cellcount
return n_extra
def render(self, directory, result_dir, writehelp):
"""
Render the runfile as a string, package by package.
"""
diskey = self._get_pkgkey("dis")
globaltimes = self[diskey]["time"].values
baskey = self._get_pkgkey("bas6")
nlayer = self[baskey]["layer"].size
content = []
content.append(
self._render_gen(
modelname=self.modelname,
globaltimes=globaltimes,
writehelp=writehelp,
result_dir=result_dir,
)
)
content.append(
self._render_dis(
directory=directory, globaltimes=globaltimes, nlayer=nlayer
)
)
# Modflow
for key in ("bas6", "oc", "lpf", "rch", "evt"):
content.append(
self._render_pkg(
key=key, directory=directory, globaltimes=globaltimes, nlayer=nlayer
)
)
# multi-system package group: chd, drn, ghb, riv, wel
modflowcontent, ssm_content, n_sinkssources = self._render_groups(
directory=directory, globaltimes=globaltimes
)
# Add recharge to sinks and sources
n_sinkssources += self._bas_btn_rch_evt_mal_tvc_sinkssources()
# Add recharge, mass loading, time varying constant concentration to ssm_content
ssm_content += self._render_ssm_rch_evt_mal_tvc(
directory=directory, globaltimes=globaltimes, nlayer=nlayer
)
# Wrap up modflow part
content.append(modflowcontent)
content.append(self._render_flowsolver(directory=directory))
# MT3D and Seawat settings
# Make an estimate of the required number of particles.
advkey = self._get_pkgkey("adv")
if isinstance(self[advkey], (imod.wq.AdvectionMOC, imod.wq.AdvectionHybridMOC)):
ibound = self[baskey]["ibound"]
cell_max_nparticles = self[advkey]["cell_max_nparticles"]
self[advkey]["max_nparticles"] = int(
np.prod(ibound.shape) * 0.5 * cell_max_nparticles
)
content.append(
self._render_btn(
directory=directory, globaltimes=globaltimes, nlayer=nlayer
)
)
for key in ("vdf", "adv", "dsp"):
content.append(
self._render_pkg(
key=key, directory=directory, globaltimes=globaltimes, nlayer=nlayer
)
)
ssm_content = f"[ssm]\n mxss = {n_sinkssources}" + ssm_content
content.append(ssm_content)
content.append(self._render_transportsolver(directory=directory))
return "\n\n".join(content)
def _set_caching_packages(self, reldir):
# TODO:
# Basically every package should rely on bas for checks via ibound
# basic domain checking, etc.
# baskey = self._get_pkgkey("bas6")
# So far, only slv does? Others only depend on themselves.
for pkgname, pkg in self.items():
if hasattr(pkg, "_filehashself"):
# Clear _outputfiles in case of repeat writes
pkg._outputfiles = []
pkg._filehashes[pkgname] = pkg._filehashself
pkg._reldir = reldir
# If more complex dependencies do show up, probably push methods down
# to the individual packages.
def write(
self, directory=pathlib.Path("."), result_dir=None, resultdir_is_workdir=False
):
"""
Writes model input files.
Parameters
----------
directory : str, pathlib.Path
Directory into which the model input will be written. The model
input will be written into a directory called modelname.
result_dir : str, pathlib.Path
Path to directory in which output will be written when running the
model. Is written as the value of the ``result_dir`` key in the
runfile.
See the examples.
resultdir_is_workdir: boolean, optional
Wether the set all input paths in the runfile relative to the output
directory. Because iMOD-wq generates a number of files in its working
directory, it may be advantageous to set the working directory to
a different path than the runfile location.
Returns
-------
None
Examples
--------
Say we wish to write the model input to a file called input, and we
desire that when running the model, the results end up in a directory
called output. We may run:
>>> model.write(directory="input", result_dir="output")
And in the runfile, a value of ``../../output`` will be written for
result_dir.
"""
# Coerce to pathlib.Path
directory = pathlib.Path(directory)
if result_dir is None:
result_dir = pathlib.Path("results")
else:
result_dir = pathlib.Path(result_dir)
# Create directories if necessary
directory.mkdir(exist_ok=True, parents=True)
result_dir.mkdir(exist_ok=True, parents=True)
runfilepath = directory / f"{self.modelname}.run"
results_runfilepath = result_dir / f"{self.modelname}.run"
# Where will the model run?
# Default is inputdir, next to runfile:
# in that case, resultdir is relative to inputdir
# If resultdir_is_workdir, inputdir is relative to resultdir
# render_dir is the inputdir that is printed in the runfile.
# result_dir is the resultdir that is printed in the runfile.
# caching_reldir is from where to check for files. This location
# is the same as the eventual model working dir.
if resultdir_is_workdir:
caching_reldir = result_dir
if not directory.is_absolute():
render_dir = _relpath(directory, result_dir)
else:
render_dir = directory
result_dir = pathlib.Path(".")
else:
caching_reldir = directory
render_dir = pathlib.Path(".")
if not result_dir.is_absolute():
result_dir = _relpath(result_dir, directory)
# Check if any caching packages are present, and set necessary states.
self._set_caching_packages(caching_reldir)
if self.check is not None:
self.package_check()
# Delete packages without data
self._delete_empty_packages(verbose=True)
runfile_content = self.render(
directory=render_dir, result_dir=result_dir, writehelp=False
)
# Start writing
# Write the runfile
with open(runfilepath, "w") as f:
f.write(runfile_content)
# Also write the runfile in the workdir
if resultdir_is_workdir:
with open(results_runfilepath, "w") as f:
f.write(runfile_content)
# Write all IDFs and IPFs
for pkgname, pkg in self.items():
if (
"x" in pkg.dataset.coords
and "y" in pkg.dataset.coords
or pkg._pkg_id == "wel"
):
try:
pkg.save(directory=directory / pkgname)
except Exception as error:
raise RuntimeError(
f"An error occured during saving of package: {pkgname}."
) from error
def package_check(self):
baskey = self._get_pkgkey("bas6")
if baskey is not None:
ibound = self[baskey]["ibound"]
else:
ibound = None
for pkg in self.values():
pkg._pkgcheck(ibound=ibound)
def clip(
self,
extent,
heads_boundary=None,
concentration_boundary=None,
delete_empty_pkg=False,
):
"""
Method to clip the model to a certain `extent`. The spatial resolution of the clipped model is unchanged.
Boundary conditions of clipped model can be derived from parent model calculation results and are applied
along the edge of `extent` (CHD and TVC). Packages from parent that have no data within extent are optionally removed.
Parameters
----------
extent : tuple, geopandas.GeoDataFrame, xarray.DataArray
Extent of the clipped model. Tuple must be in the form of (`xmin`,`xmax`,`ymin`,`ymax`). If a GeoDataFrame, all
polygons are included in the model extent. If a DataArray, non-null/non-zero values are taken as the new extent.
heads_boundary : xarray.DataArray, optional.
Heads to be applied as a Constant Head boundary condition along the edge of the model extent. These heads are assumed
to be derived from calculations with the parent model. Timestamp of boundary condition is shifted to correct for difference
between 'end of period' timestamp of results and 'start of period' timestamp of boundary condition.
If None (default), no constant heads boundary condition is applied.
concentration_boundary : xarray.DataArray, optional.
Concentration to be applied as a Time Varying Concentration boundary condition along the edge of the model extent.
These concentrations can be derived from calculations with the parent model. Timestamp of boundary condition is shifted
to correct for difference between 'end of period' timestamp of results and 'start of period' timestamp of boundary condition.
If None (default), no time varying concentration boundary condition is applied.
*Note that the Time Varying Concentration boundary sets a constant concentration for the entire stress period,
unlike the linearly varying Constant Head. This will inevitably cause a time shift in concentrations along the boundary.
This shift becomes more significant when stress periods are longer. If necessary, consider interpolating concentrations
along the time axis, to reduce the length of stress periods (see examples).*
delete_empty_pkg : bool, optional.
Set to True to delete packages that contain no data in the clipped model. Defaults to False.
Examples
--------
Given a full model, clip a 1 x 1 km rectangular submodel without boundary conditions along its edge:
>>> extent = (1000., 2000., 5000., 6000.) # xmin, xmax, ymin, ymax
>>> clipped = ml.clip(extent)
Load heads and concentrations from full model results:
>>> heads = imod.idf.open("head/head_*.idf")
>>> conc = imod.idf.open("conc/conc_*.idf")
>>> clipped = ml.clip(extent, heads, conc)
Use a shape of a model area:
>>> extent = geopandas.read_file("clipped_model_area.shp")
>>> clipped = ml.clip(extent, heads, conc)
Interpolate concentration results to annual results using xarray.interp(), to improve time resolution of concentration boundary:
>>> conc = imod.idf.open("conc/conc_*.idf")
>>> dates = pd.date_range(conc.time.values[0], conc.time.values[-1], freq="AS")
>>> conc_interpolated = conc.load().interp(time=dates, method="linear")
>>> clipped = ml.clip(extent, heads, conc_interpolated)
"""
baskey = self._get_pkgkey("bas6")
like = self[baskey].dataset["ibound"].isel(layer=0).squeeze(drop=True)
if isinstance(extent, (list, tuple)):
xmin, xmax, ymin, ymax = extent
if not ((xmin < xmax) & (ymin < ymax)):
raise ValueError(
"Either xmin or ymin is equal to or larger than xmax or ymax. "
"Correct order is xmin, xmax, ymin, ymax."
)
extent = xr.ones_like(like)
extent = extent.where(
(extent.x >= xmin)
& (extent.x <= xmax)
& (extent.y >= ymin)
& (extent.y <= ymax),
0,
)
elif isinstance(extent, xr.DataArray):
pass
else:
import geopandas as gpd
if isinstance(extent, gpd.GeoDataFrame):
extent = imod.prepare.rasterize(extent, like=like)
else:
raise ValueError(
"extent must be of type tuple, GeoDataFrame or DataArray"
)
extent = xr.ones_like(like).where(extent > 0)
def get_clip_na_slices(da, dims=None):
"""Clips a DataArray to its maximum extent in different dimensions.
if dims not given, clips to x and y.
"""
if dims is None:
dims = ["x", "y"]
slices = {}
for d in dims:
tmp = da.dropna(dim=d, how="all")[d]
if len(tmp) > 2:
dtmp = 0.5 * (tmp[1] - tmp[0])
else:
dtmp = 0
slices[d] = slice(float(tmp[0] - dtmp), float(tmp[-1] + dtmp))
return slices
# create edge around extent to apply boundary conditions
if not (heads_boundary is None and concentration_boundary is None):
extentwithedge = extent.copy(data=binary_dilation(extent.fillna(0).values))
extentwithedge = extentwithedge.where(extentwithedge)
else:
extentwithedge = extent
# clip to extent with edge
clip_slices = get_clip_na_slices(extentwithedge)
extentwithedge = extentwithedge.sel(**clip_slices)
extent = extent.sel(**clip_slices)
edge = extentwithedge.where(extent.isnull())
# Clip model to extent, set outside extent to nodata or 0
ml = self.sel(**clip_slices)
for pck in ml.keys():
for d in ml[pck].dataset.data_vars:
if d in ["ibound", "icbund"]:
ml[pck][d] = ml[pck][d].where(extentwithedge == 1, 0)
elif "x" in ml[pck][d].dims:
ml[pck][d] = ml[pck][d].where(extentwithedge == 1)
# Create boundary conditions as CHD and/or TVC package
if concentration_boundary is not None:
concentration_boundary = concentration_boundary.sel(**clip_slices)
concentration_boundary = concentration_boundary.where(edge == 1)
# Time shifts: assume heads and conc are calculation results. Then:
# timestamp of result is _end_ of stress period, while timestamp input
# is the _start_ of the stress period.
if "time" in concentration_boundary.dims:
concentration_boundary = concentration_boundary.shift(
time=-1
).combine_first(concentration_boundary)
ml["tvc"] = imod.wq.TimeVaryingConstantConcentration(
concentration=concentration_boundary
)
if heads_boundary is not None:
heads_boundary = heads_boundary.sel(**clip_slices)
head_end = heads_boundary.where(edge == 1)
if "time" in heads_boundary.dims:
head_start = head_end.shift(time=1).combine_first(head_end)
else:
head_start = head_end
ml["chd"] = imod.wq.ConstantHead(
head_start=head_start,
head_end=head_end,
concentration=concentration_boundary, # concentration relevant for fwh calc
)
# delete packages if no data in sliced model
if delete_empty_pkg:
ml._delete_empty_packages(verbose=True)
return ml
