06: FloPy class project: Voronoi grid version

import pathlib as pl
import numpy as np
import matplotlib.pyplot as plt
import geopandas as gpd

import flopy
from flopy.discretization import VertexGrid
from flopy.utils.triangle import Triangle as Triangle
from flopy.utils.voronoi import VoronoiGrid
from flopy.utils.gridintersect import GridIntersect

from project_grid_functions import densify_polyline, circle_function

model_ws = "../temp/voronoi/"
grid_ws = f"{model_ws}grid/"
pl.Path(grid_ws).mkdir(parents=True, exist_ok=True)
pl.Path('figures').mkdir(parents=True, exist_ok=True)

output_folder = pl.Path('../figures')
output_folder.mkdir(parents=True, exist_ok=True)

Load a few raster files

bottom = flopy.utils.Raster.load("../data_project/aquifer_bottom.asc")
top = flopy.utils.Raster.load("../data_project/aquifer_top.asc")
kaq = flopy.utils.Raster.load("../data_project/aquifer_k.asc")

Load a few shapefiles with geopandas

river = gpd.read_file("../data_project/Flowline_river.shp")
inactive = gpd.read_file("../data_project/inactive_area.shp")
active = gpd.read_file("../data_project/active_area.shp")
wells = gpd.read_file("../data_project/pumping_well_locations.shp")

Plot the shapefiles with geopandas

ax = river.plot(color="cyan")
active.plot(ax=ax, color="blue")
inactive.plot(ax=ax, color="white")
wells.plot(ax=ax, color="red", markersize=8);

Make a voronoi model grid

Densify the river vertices.

river_densify = densify_polyline(river.geometry[0], 200)
river_densify.shape, np.unique(river_densify, axis=0).shape

((96, 2), (96, 2))

Modify the end points of river_densify to constrain the river to be within the bounds of the active domain

river_densify[0, 1] = 9999.99
river_densify[-1, 1] = 0.1

Run triangle to generate the nodes

maximum_area = 500.0 * 500.0
tri = Triangle(
    maximum_area=maximum_area, angle=30, nodes=river_densify, model_ws=grid_ws
)
tri.add_polygon(active.geometry[0])
tri.add_polygon(inactive.geometry[0])
tri.add_hole((1500, 6000))
for idx, point in enumerate(wells.centroid):
    center = (point.x, point.y)
    tri.add_polygon(circle_function(center=center, radius=100))
    tri.add_region(center, attribute=idx, maximum_area=maximum_area)
tri.build(verbose=False)

tri.plot()

<matplotlib.collections.LineCollection at 0x161981190>

vor = VoronoiGrid(tri)
gridprops = vor.get_gridprops_vertexgrid()
idomain = np.ones((1, vor.ncpl), dtype=int)
base_grid = VertexGrid(**gridprops, nlay=1, idomain=idomain)

base_grid.plot()
river.plot(color="cyan", ax=plt.gca(), linewidth=0.5)
wells.plot(ax=plt.gca(), color="red", markersize=8, zorder=100);

Define the number of layers and some simple shapes

nlay = 3
shape2d, shape3d = (base_grid.ncpl), (nlay, base_grid.ncpl)
xlen, ylen = 5000.0, 10000.0

Intersect the modelgrid with the shapefiles

Create an intersection object

ix = GridIntersect(base_grid, method="vertex", rtree=True)

Intersect inactive and active shapefiles with the modelgrid

After all of the intersection operations, take a look at the data contained in the returned objects

bedrock = ix.intersect(inactive.geometry[0])
active_cells = ix.intersect(active.geometry[0])

active_cells[:4], active_cells.dtype

(rec.array([(0, <POLYGON ((5000 7875, 4837.765 7875, 4851.886 8125, 5000 8125, 5000 8000, 50...>, 38793.62962818),
            (1, <POLYGON ((5000 7750, 5000 7625, 4927.811 7625, 4805.222 7837.36, 4837.765 7...>, 35065.60370048),
            (2, <POLYGON ((5000 7500, 5000 7375, 4925.376 7375, 4855.031 7498.923, 4927.811 ...>, 27295.68972587),
            (3, <POLYGON ((5000 7250, 5000 7125, 4927.811 7125, 4855.031 7251.077, 4925.376 ...>, 27295.68972587)],
           dtype=[('cellids', 'O'), ('ixshapes', 'O'), ('areas', '<f8')]),
 dtype((numpy.record, [('cellids', 'O'), ('ixshapes', 'O'), ('areas', '<f8')])))

Intersect well shapefile with the modelgrid

well_cells = []
for g in wells.geometry:
    v = ix.intersect(g)
    well_cells += v["cellids"].tolist()

well_cells

[1315, 1421, 1435, 1286, 1420, 1434]

Intersect river shapefile with the modelgrid

river_cells = ix.intersect(river.geometry[0])

river_cells[:4], river_cells.dtype

(rec.array([(367, <LINESTRING (3358.332 9833.087, 3361.701 9782.55)>,  50.6490377 ),
            (368, <LINESTRING (3361.701 9782.55, 3363.599 9754.072, 3382.073 9691.263)>,  94.01139569),
            (369, <LINESTRING (3401.559 9625.008, 3434.568 9512.777)>, 116.98441041),
            (370, <LINESTRING (3434.568 9512.777, 3449.104 9463.355, 3500.407 9403.502)>, 130.34665378)],
           dtype=[('cellids', 'O'), ('ixshapes', 'O'), ('lengths', '<f8')]),
 dtype((numpy.record, [('cellids', 'O'), ('ixshapes', 'O'), ('lengths', '<f8')])))

Intersect constant head line with the modelgrid

Use a line with two points to defined the location of the constant head cells. The line verticase are [(1250, 0.1), (4250, 0.1)].

constant_cells = ix.intersect(
    [(1250, 0.1), (4250, 0.1)], shapetype="linestring"
)

constant_cells[:4], constant_cells.dtype

(rec.array([(38, <LINESTRING (3556.433 0.1, 3613.02 0.1)>,  56.58677601),
            (39, <LINESTRING (3437.5 0.1, 3556.433 0.1)>, 118.93334885),
            (40, <LINESTRING (3234.375 0.1, 3281.25 0.1)>,  46.875     ),
            (41, <LINESTRING (2937.5 0.1, 3031.25 0.1)>,  93.75      )],
           dtype=[('cellids', 'O'), ('ixshapes', 'O'), ('lengths', '<f8')]),
 dtype((numpy.record, [('cellids', 'O'), ('ixshapes', 'O'), ('lengths', '<f8')])))

Resample the raster data to the modelgrid

Use the resample_to_grid() method on each raster object.

rtop = top.resample_to_grid(
    base_grid,
    band=top.bands[0],
    method="linear",
    extrapolate_edges=True,
)
rbot = bottom.resample_to_grid(
    base_grid,
    band=bottom.bands[0],
    method="linear",
    extrapolate_edges=True,
)
rkaq = (
    kaq.resample_to_grid(
        base_grid, band=kaq.bands[0], method="linear", extrapolate_edges=True
    )
    * 86400.0
)

Plot the resampled data

Plot the aquifer top, bottom, and hydraulic conductivity. Also plot the aquifer thickness.

mm = flopy.plot.PlotMapView(modelgrid=base_grid)
cb = mm.plot_array(rtop)
mm.plot_grid(lw=0.5, color="0.5")
plt.colorbar(cb, ax=plt.gca());

mm = flopy.plot.PlotMapView(modelgrid=base_grid)
cb = mm.plot_array(rbot)
mm.plot_grid(lw=0.5, color="0.5")
plt.colorbar(cb, ax=plt.gca());

mm = flopy.plot.PlotMapView(modelgrid=base_grid)
cb = mm.plot_array(rtop - rbot)
mm.plot_grid(lw=0.5, color="0.5")
plt.colorbar(cb);

mm = flopy.plot.PlotMapView(modelgrid=base_grid)
cb = mm.plot_array(rkaq)
mm.plot_grid(lw=0.5, color="0.5")
plt.colorbar(cb);

Build the model data

Create the bottom of each model layer

Assume that the thickness of each layer at a row, column location is equal.

botm = np.zeros(shape3d, dtype=float)
botm[-1, :] = rbot[:]
layer_thickness = (rtop - rbot) / nlay
for k in reversed(range(nlay - 1)):
    botm[k] = botm[k + 1] + layer_thickness

Create the idomain array

Use the intersection data from the active and inactive shapefiles to create the idomain array

idomain = np.zeros(shape3d, dtype=float)
for node in active_cells["cellids"]:
    idomain[:, node] = 1
for node in bedrock["cellids"]:
    idomain[:, node] = 0

Build the well package stress period data

• The pumping rates are in the wells geopandas dataframe

• Pumping rates are in m/sec

• The wells are located in model layer 3

wells

	FID	Q	geometry
0	0	-0.00820	POINT (3875.000 7875.000)
1	1	-0.00410	POINT (3125.000 7375.000)
2	2	-0.00390	POINT (3375.000 5125.000)
3	3	-0.00083	POINT (2375.000 3625.000)
4	4	-0.00072	POINT (1375.000 2875.000)
5	5	-0.00430	POINT (2875.000 1625.000)

well_spd = []
for (cellid, q) in zip(well_cells, wells["Q"]):
    well_spd.append([2, cellid, q * 86400.0])
well_spd

[[2, 1315, -708.48],
 [2, 1421, -354.24],
 [2, 1435, -336.96],
 [2, 1286, -71.712],
 [2, 1420, -62.208000000000006],
 [2, 1434, -371.52]]

Build the river package stress period data

• Calculate the length of the river using the "lengths" key.

• The vertical hydraulic conductivity of the river bed sediments is 3.5 m/d.

• The thickness of river bottom sediments at the upstream (North) and downstream (South) end of the river is 0.5 and 1.5 meters, respectively.

• The river bottom at the upstream and downstream end of the river is 16.5 and 14.5 meters, respectively. The river width at the upstream and downstream end of the river is 5.0 and 10.0 meters, respectively.

• The river stage at the upstream and downstream end of the river is 16.6 and 15.5 meters, respectively.

• Use the boundname upstream for river cells where the upstream end of the river cell is less than 5000 m from the North end of the model. Use the boundname downstream for all other river cells.

Use the upstream and downstream values to interpolate the river sediment thickness, bottom, width, and stage for each river cell.

The river cells will be conected to model layer 1. The river bottom, width, and stage values should be calculated at the center of the river reach.

river_kv = 3.5
river_thickness_up, river_thickness_down = 0.5, 1.5
river_bot_up, river_bot_down = 16.5, 14.5
river_width_up, river_width_down = 5.0, 10.0
stage_up, stage_down = 16.6, 15.5

river_length = river_cells["lengths"].sum()
river_length

12419.290359618699

river_thickness_slope = (
    river_thickness_down - river_thickness_up
) / river_length

river_bot_slope = (river_bot_down - river_bot_up) / river_length

river_width_slope = (river_width_down - river_width_up) / river_length

stage_slope = (stage_down - stage_up) / river_length

boundname = "upstream"
total_length = 0.0
river_spd = []
river_top_delta = []
for idx, (cellid, length) in enumerate(
    zip(river_cells["cellids"], river_cells["lengths"])
):
    if total_length >= 5000.0 and boundname == "upstream":
        boundname = "downstream"
    dx = 0.5 * length
    total_length += dx

    river_thickness = river_thickness_up + river_thickness_slope * total_length
    river_bot = river_bot_up + river_bot_slope * total_length
    river_width = river_width_up + river_width_slope * total_length
    river_stage = stage_up + stage_slope * total_length
    conductance = river_kv * length * river_width / river_thickness
    river_spd.append(
        [0, cellid, river_stage, conductance, river_bot, boundname]
    )
    river_top_delta.append(river_bot - rtop[cellid])

    total_length += dx
river_top_delta = np.array(river_top_delta)
river_spd[:2], river_spd[-2:]

([[0,
   367,
   16.597756959542117,
   1769.1162067371693,
   16.495921744622027,
   'upstream'],
  [0,
   368,
   16.5913505356594,
   3264.9265367307926,
   16.48427370119891,
   'upstream']],
 [[0,
   1882,
   15.501184070837642,
   218.68249850902666,
   14.502152856068442,
   'downstream'],
  [0,
   1944,
   15.500384546364442,
   202.62120587305378,
   14.500699175208076,
   'downstream']])

river_top_delta.min(), river_top_delta.max(), river_top_delta.mean(), river_top_delta[
    -2:
]

(-2.23108535744932,
 -0.09231567157780418,
 -0.8471750038169135,
 array([-1.09793806, -1.66048444]))

Define river observations

riv_obs = {
    "riv_obs.csv": [
        ("UPSTREAM", "RIV", "UPSTREAM"),
        ("DOWNSTREAM", "RIV", "DOWNSTREAM"),
    ],
}

Build SFR datasets

<rno> <cellid(ncelldim)> <rlen> <rwid> <rgrd> <rtp> <rbth> <rhk> <man> <ncon> <ustrf> <ndv> [<aux(naux)>] [<boundname>]

nreaches = river_cells.shape[0]
nreaches

168

boundname = "upstream"
gage1_loc = None
total_length = 0.0
sfr_packagedata = []
sfr_connectivity = []
for idx, (cellid, length) in enumerate(
    zip(river_cells["cellids"], river_cells["lengths"])
):
    if total_length >= 5000.0 and boundname == "upstream":
        boundname = "downstream"
        gage1_loc = idx - 1
    dx = 0.5 * length
    total_length += dx

    river_thickness = river_thickness_up + river_thickness_slope * total_length
    river_bot = river_bot_up + river_bot_slope * total_length
    river_width = river_width_up + river_width_slope * total_length

    if idx == 0:
        nconn = 1
        sfr_connectivity.append((idx, -(idx + 1)))
    elif idx == nreaches - 1:
        nconn = 1
        sfr_connectivity.append((idx, (idx - 1)))
    else:
        nconn = 2
        sfr_connectivity.append((idx, (idx - 1), -(idx + 1)))

    sfr_layer = None
    for k in range(nlay):
        if river_bot - river_thickness > botm[k, cellid]:
            sfr_layer = k

    if sfr_layer is None:
        sfr_cellid = "none"
    else:
        sfr_cellid = (sfr_layer, cellid)

    leakance = river_kv * river_thickness
    sfr_packagedata.append(
        (
            idx,
            sfr_cellid,
            length,
            river_width,
            -river_bot_slope,
            river_bot,
            river_thickness,
            river_kv,
            0.035,
            nconn,
            1.0,
            0,
            boundname,
        )
    )

    total_length += dx

sfr_spd = {0: [(0, "inflow", 864000.0)]}
sfr_spd

{0: [(0, 'inflow', 864000.0)]}

<rno> <cellid(ncelldim)> <rlen> <rwid> <rgrd> <rtp> <rbth> <rhk> <man> <ncon> <ustrf> <ndv> [<aux(naux)>] [<boundname>]

sfr_packagedata[:2], sfr_packagedata[-2:]

([(0,
   (2, 367),
   50.64903769970752,
   5.010195638444929,
   0.0001610397971290692,
   16.495921744622027,
   0.5020391276889858,
   3.5,
   0.035,
   1,
   1.0,
   0,
   'upstream'),
  (1,
   (2, 368),
   94.01139569348081,
   5.039315747002732,
   0.0001610397971290692,
   16.48427370119891,
   0.5078631494005464,
   3.5,
   0.035,
   2,
   1.0,
   0,
   'upstream')],
 [(166,
   (2, 1882),
   9.370424773718105,
   9.994617859828896,
   0.0001610397971290692,
   14.502152856068442,
   1.4989235719657792,
   3.5,
   0.035,
   2,
   1.0,
   0,
   'downstream'),
  (167,
   (2, 1944),
   8.68325992137821,
   9.998252061979809,
   0.0001610397971290692,
   14.500699175208076,
   1.4996504123959618,
   3.5,
   0.035,
   1,
   1.0,
   0,
   'downstream')])

sfr_connectivity[:2], sfr_connectivity[-2:]

([(0, -1), (1, 0, -2)], [(166, 165, -167), (167, 166)])

sfr_obs = {
    "sfr_obs.csv": [
        ("UPSTREAM", "SFR", "UPSTREAM"),
        ("DOWNSTREAM", "SFR", "DOWNSTREAM"),
        ("GAGE1", "downstream-flow", (gage1_loc,)),
        ("GAGE2", "ext-outflow", (nreaches - 1,)),
    ],
}

Define the constant head cells

Assume the constant head cells are located in all three layers and have values equal to the downstream river stage (stage_down). Make sure the constant head stage is greater that the bottom of the layer.

chd_spd = []
for node in constant_cells["cellids"]:
    for k in range(nlay):
        if stage_down > botm[k, node]:
            chd_spd.append((k, node, stage_down))
chd_spd[:2], chd_spd[-2:]

([(0, 38, 15.5), (1, 38, 15.5)], [(1, 1571, 15.5), (2, 1571, 15.5)])

Define recharge rates

• The recharge rate is 0.16000000E-08 m/sec

recharge_rate = 0.16000000e-08 * 86400.0
recharge_rate

0.00013824

Build the model

Build a steady-state model using the data that you have created. Packages to create:

• Simulation

• TDIS (1 stress period, TIME_UNITS='days')

• IMS (default parameters)

• GWF model (SAVE_FLOWS=True)

• DISV (LENGTH_UNITS='meters')

• IC (STRT=40.)

• NPF (Unconfined, same K for all layers, save SAVE_SPECIFIC_DISCHARGE=True)

• RCH (array based)

• RIV (BOUNDNAMES=True,add RIV observations for defined boundnames)

• CHD

• WEL

• OC (Save HEAD ALL and BUDGET ALL)

name = "project"
exe_name = "mf6"
exe_name

'mf6'

sim = flopy.mf6.MFSimulation(
    sim_name=name, sim_ws=model_ws, exe_name=str(exe_name)
)
tdis = flopy.mf6.ModflowTdis(sim, time_units="days")
ims = flopy.mf6.ModflowIms(
    sim,
    linear_acceleration="bicgstab",
    outer_maximum=200,
    inner_maximum=100,
    print_option="all",
)

gwf = flopy.mf6.ModflowGwf(
    sim,
    modelname=name,
    save_flows=True,
    newtonoptions="NEWTON UNDER_RELAXATION",
)

dis = flopy.mf6.ModflowGwfdisv(
    gwf,
    length_units="meters",
    nlay=nlay,
    ncpl=base_grid.ncpl,
    nvert=base_grid.nvert,
    vertices=vor.get_disv_gridprops()["vertices"],
    cell2d=vor.get_disv_gridprops()["cell2d"],
    top=rtop,
    botm=botm,
)
ic = flopy.mf6.ModflowGwfic(gwf, strt=[rtop for k in range(nlay)])
npf = flopy.mf6.ModflowGwfnpf(
    gwf,
    save_specific_discharge=True,
    save_saturation=True,
    k=[rkaq, rkaq, rkaq], icelltype=1
)
rch = flopy.mf6.ModflowGwfrcha(gwf, recharge=recharge_rate)
# riv = flopy.mf6.ModflowGwfriv(gwf, boundnames=True, stress_period_data=river_spd)
# riv.obs.initialize(
#     filename=f"{name}.riv.obs",
#     print_input=True,
#     continuous=riv_obs,
# )
sfr = flopy.mf6.ModflowGwfsfr(
    gwf,
    unit_conversion=86400.0,
    boundnames=True,
    print_flows=True,
    print_stage=True,
    nreaches=nreaches,
    packagedata=sfr_packagedata,
    connectiondata=sfr_connectivity,
    perioddata=sfr_spd,
)
sfr.obs.initialize(
    filename=f"{name}.sfr.obs",
    print_input=True,
    continuous=sfr_obs,
)
wel = flopy.mf6.ModflowGwfwel(gwf, stress_period_data=well_spd)
chd = flopy.mf6.ModflowGwfchd(gwf, stress_period_data=chd_spd)
oc = flopy.mf6.ModflowGwfoc(
    gwf,
    head_filerecord=f"{name}.hds",
    budget_filerecord=f"{name}.cbc",
    saverecord=[("HEAD", "ALL"), ("BUDGET", "ALL")],
)

Write the model files

sim.write_simulation()

writing simulation...
  writing simulation name file...
  writing simulation tdis package...
  writing solution package ims_-1...
  writing model project...
    writing model name file...
    writing package disv...
    writing package ic...
    writing package npf...
    writing package rcha_0...
    writing package sfr_0...
    writing package obs_0...
    writing package wel_0...
INFORMATION: maxbound in ('gwf6', 'wel', 'dimensions') changed to 6 based on size of stress_period_data
    writing package chd_0...
INFORMATION: maxbound in ('gwf6', 'chd', 'dimensions') changed to 140 based on size of stress_period_data
    writing package oc...

Run the model

sim.run_simulation()

FloPy is using the following executable to run the model: ../../../../../../../.local/share/flopy/bin/mf6
                                   MODFLOW 6
                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL
                            VERSION 6.4.2 06/28/2023

   MODFLOW 6 compiled Jul 05 2023 20:29:14 with Intel(R) Fortran Intel(R) 64
   Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0
                             Build 20220726_000000

This software has been approved for release by the U.S. Geological
Survey (USGS). Although the software has been subjected to rigorous
review, the USGS reserves the right to update the software as needed
pursuant to further analysis and review. No warranty, expressed or
implied, is made by the USGS or the U.S. Government as to the
functionality of the software and related material nor shall the
fact of release constitute any such warranty. Furthermore, the
software is released on condition that neither the USGS nor the U.S.
Government shall be held liable for any damages resulting from its
authorized or unauthorized use. Also refer to the USGS Water
Resources Software User Rights Notice for complete use, copyright,
and distribution information.


 Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/02/03 11:13:17

 Writing simulation list file: mfsim.lst
 Using Simulation name file: mfsim.nam

    Solving:  Stress period:     1    Time step:     1

 Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/02/03 11:13:17
 Elapsed run time:  0.292 Seconds


WARNING REPORT:

  1. OPTIONS BLOCK VARIABLE 'UNIT_CONVERSION' IN FILE 'project.sfr' WAS
     DEPRECATED IN VERSION 6.4.2. SETTING UNIT_CONVERSION DIRECTLY.
 Normal termination of simulation.

(True, [])

Post-process the results

Use gwf.output. method to get the observations.

myobs = gwf.sfr.output.obs().get_data()
myobs.dtype

dtype([('totim', '<f8'), ('UPSTREAM', '<f8'), ('DOWNSTREAM', '<f8'), ('GAGE1', '<f8'), ('GAGE2', '<f8')])

myobs["UPSTREAM"], myobs["DOWNSTREAM"]

(array([5516.58121287]), array([-8810.44888484]))

Use gwf.output. method to get the heads and specific discharge. Make a map and cross-sections of the data using flopy.plot methods. Plot specific discharge vectors on the map and cross-sections.

head = gwf.output.head().get_data()

gwf.output.budget().get_unique_record_names()

[b'    FLOW-JA-FACE',
 b'      DATA-SPDIS',
 b'        DATA-SAT',
 b'             WEL',
 b'            RCHA',
 b'             CHD',
 b'             SFR']

spdis = gwf.output.budget().get_data(text="DATA-SPDIS")[0]
qx, qy, qz = flopy.utils.postprocessing.get_specific_discharge(
    spdis, gwf, head=head
)

fig, ax = plt.subplots(
    ncols=1,
    nrows=1,
    figsize=(5, 8),
    constrained_layout=True,
    subplot_kw=dict(aspect="equal"),
)
mm = flopy.plot.PlotMapView(model=gwf, ax=ax, layer=0)
cb = mm.plot_array(head, vmin=10, vmax=30)
river.plot(color="cyan", ax=mm.ax)
mm.plot_grid(lw=0.5, color="0.5")
mm.plot_vector(qx, qy, qz, normalize=True)
cs = mm.contour_array(
    head,
    colors="red",
    levels=np.arange(10, 28, 1),
    linestyles=":",
    linewidths=1.0,
    tri_mask=True,
)
ax.clabel(
    cs,
    inline=True,
    fmt="%1.0f",
    fontsize=10,
    inline_spacing=0.5,
)
plt.colorbar(cb, ax=mm.ax, shrink=0.5)

fig.savefig(output_folder / "freyberg-voronoi.png", dpi=300);

fig, ax = plt.subplots(
    ncols=1,
    nrows=1,
    figsize=(5, 8),
    constrained_layout=True,
    subplot_kw=dict(aspect="equal"),
)

mm = flopy.plot.PlotMapView(model=gwf, ax=ax, layer=0)
cb = mm.plot_array(gwf.dis.top.array, masked_values=[1e30], vmin=15, vmax=30)
river.plot(color="cyan", ax=mm.ax)
mm.plot_grid(lw=0.5, color="0.5", zorder=11)
cs = mm.contour_array(
    gwf.dis.top.array,
    colors="red",
    levels=np.arange(15, 31, 1),
    linestyles=":",
    linewidths=1.0,
    tri_mask=True,
)
ax.clabel(
    cs,
    inline=True,
    fmt="%1.0f",
    fontsize=10,
    inline_spacing=0.5,
)
plt.colorbar(cb, ax=mm.ax, shrink=0.5)

fig.savefig(output_folder / "freyberg-voronoi-grid.png", dpi=300);