Building MfUsg CrossSections with Voronoi Grids

[gonzalophd]

Is there a way to obtain clean cross sections of a MfUsg Model with a Voronoi grid (built in algomesh)?

I'm currently loading the geometry from the .gsf file with the UnstructuredGrid.from_gridspec() function, but that ends up in sections that are not representative (for some reason, additional horizontal lines appear that are not representative of my layers)

Loading the geometry from the flopy.mfusg.MfUsg.load() - the, ml.modelgrid, returns the following error:

File ~\anaconda3\Lib\site-packages\flopy\discretization\unstructuredgrid.py:373 in xyzvertices
self._build_grid_geometry_info()

File ~\anaconda3\Lib\site-packages\flopy\discretization\unstructuredgrid.py:716 in _build_grid_geometry_info
vertexdict = {int(v[0]): [v[1], v[2]] for v in self._vertices}

TypeError: 'NoneType' object is not iterable"

Is there any function or something I could work around to get the clean cross sections I would get from working with the MF6 export of the DISU package?

Thank you!!!

[christianlangevin]

It would be good for us to understand why the UnstructuredGrid.from_gridspec() function is not honoring your data. Would it be possible for you to reproduce this somehow and post the files here? A simple example with a small test grid would be the best, if possible.

[gonzalophd]

Sorry for the delay.

I haven't been able to build another geometry to follow up this example.

The grid is a voronoi grid, built in algomesh and then loaded to GWV to export the Modflow USG packages. The default setup in GWV was used to do the export.

The flopy version i'm using is 3.9.3, and this is the code im using to display the cross section:

import numpy as np
import matplotlib.pyplot as plt
import flopy
from flopy.discretization import UnstructuredGrid
from pathlib import Path

ws = Path(r"C:/Users/Gonzalo/Documents/grid_view/Modflow_data")
gsf = ws / "Model_geometry.gsf"
mfgrid = UnstructuredGrid.from_gridspec(gsf)

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1, aspect="equal")
line = {"line": [(2365000, 6485000), (2370000, 6492500)]}
xsect = flopy.plot.PlotCrossSection(
modelgrid=mfgrid, ax=ax, line=line, geographic_coords=True
)
xsect.plot_grid()
plt.show()

I checked the GSF file and has this structure:
The first section has the 430560 rows, while the second, 200745.

UNSTRUCTURED
200745 15 1 1
430560
2365031.972386 6486817.224728 3812.358689
2364994.038230 6486837.870029 3812.358689
2364966.436704 6486811.431833 3812.358689
2364982.959771 6486768.342471 3812.358689
2365020.024576 6486774.682514 3812.358689
2364975.312818 6486756.794687 3774.667384
2364996.636512 6486709.155032 3774.667384
2365029.033474 6486711.391094 3774.667384

Then:
1 2364999.235337 6486802.008708 3807.358689 1 10 1 2 3 4 5 26911 26912 26913 26914 26915
2 2365009.500000 6486742.000000 3769.667384 1 12 4 6 7 8 9 5 26914 26916 26917 26918 26919 26915
3 2365080.689490 6486894.712718 3872.500086 1 12 10 11 12 13 14 15 26920 26921 26922 26923 26924 26925
4 2365026.974640 6486852.977549 3846.117002 1 14 11 12 16 1 2 17 18 26921 26922 26926 26911 26912 26927 26928
5 2365052.000866 6486787.189722 3801.608427 1 12 19 20 9 5 1 16 26929 26930 26919 26915 26911 26926
6 2365133.616911 6486877.909366 3865.251937 1 12 21 14 13 22 23 24 26931 26924 26923 26932 26933 26934
7 2365087.352615 6486834.148650 3831.042473 1 12 13 22 25 19 16 12 26923 26932 26935 26929 26926 26922
8 2365013.890492 6486678.388793 3733.195206 1 14 7 26 27 28 29 30 8 26917 26936 26937 26938 26939 26940 26918
9 2365063.784092 6486721.919925 3766.261760 1 12 8 30 31 32 20 9 26918 26940 26941 26942 26930 26919
10 2364841.656063 6486480.968355 3662.371783 1 10 33 34 35 36 37 26943 26944 26945 26946 26947
11 2364989.732105 6486611.867076 3700.447764 1 12 28 27 38 39 40 41 26938 26937 26948 26949 26950 26951
12 2364958.433789 6486844.605602 3819.456500 1 12 42 17 2 3 43 44 26952 26927 26912 26913 26953 26954
13 2364948.347508 6486782.495237 3780.726016 1 12 3 4 6 45 46 43 26913 26914 26916 26955 26956 26953
14 2364963.571661 6486721.442298 3748.078346 1 12 45 6 7 26 47 48 26955 26916 26917 26936 26957 26958
15 2364931.553163 6486597.692972 3695.008652 1 12 49 38 39 50 51 52 26959 26948 26949 26960 26961 26962