Import the required libraries

From numpy, matplotlib and flopy as well as Python core libraries.

%matplotlib inline
import sys, os, re
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import flopy
import flopy.utils.binaryfile as bf

Set a background image for the figure

This is not mandatory, but it helps to locate and analyse groundwater flow with respect to geomorphological and geographical features. Backgroundmap can be generated from QGIS by exporting the canvas that actually comes with the world file (.pgw or similar), this world file allows the georeferenciation of the image. There are options to improve the graphics with contour lines and the representation of boundary condition, please check the flopy examples: https://github.com/modflowpy/flopy/tree/develop/examples/Notebooks

wrlFile = open('../Rst/BackgroundImage.pgw').read().split('\n')
cellsize = float(wrlFile[0])
bcgImg = mpimg.imread('../Rst/BackgroundImage.png')
left = float(wrlFile[4])
right = float(wrlFile[4]) + bcgImg.shape[1]*cellsize
bottom = float(wrlFile[5]) - bcgImg.shape[0]*cellsize
top = float(wrlFile[5])

Create our modflow model as an object

Basic setup of a modflow model in flopy

#Model
modPath = '../Model/'
modName = 'SingleLayerModel'
exeName = '../Exe/mf2005.exe'   
ml = flopy.modflow.Modflow.load(modName+'.nam', model_ws=modPath, 
                                exe_name=exeName,check=False)

Import some geospatial parameters from the model

Model Muse inserts some geospatial parameters as comments on the DIS file. We have to import it by opening the DIS file and apply the parameters to the model object. Note: The EPSG code have to be set by hand since it is not written on the DIS file.

#Open the DIS file and extract the spatial parametes
spatialpar = open(modPath + modName + '.dis').readlines()[:6]
ul =  re.findall("\d+\.\d+", spatialpar[1])
lr =  re.findall("\d+\.\d+", spatialpar[4])
xul, yul = float(ul[0]), float(ul[1])
xlr, ylr = float(ul[0]), float(ul[1])
rotation = -float(re.findall("\d+", spatialpar[5])[0])

#Assign parameters to the model object
ml.dis.sr.set_spatialreference(xul=xul, yul=yul, rotation=-rotation)
ml.dis.sr.proj4_str= 'EPSG:32718' #### Defined by the user
ml.dis.sr.attribute_dict

ml.dis.sr

xul:199558.1423; yul:8807344.096; rotation:0; proj4_str:+init=EPSG:32718; units:meters; lenuni:2; length_multiplier:1.0

Import the output heads

h = flopy.utils.formattedfile.FormattedHeadFile(modPath+modName+'.fhd',model=ml)
head = h.get_data(totim=1)

head[0,30:33,30:33] #Sample of the heads values

array([[24.3446, 26.4447, 28.8097],
       [23.7384, 25.8897, 28.1835],
       [23.2935, 25.5388, 27.3164]], dtype=float32)

Create a figure

The figure is generated as a matplotlib figure, where flow direction vectors overlay the background image. There are options to filter the number of arrows with the istep/jstep, this is highly recommended to enhance the representation and user experience.

fig = plt.figure(figsize=(16, 10))
ax = fig.add_subplot(1, 1, 1, aspect='equal')

modelmap = flopy.plot.ModelMap(model=ml)
cbb = bf.CellBudgetFile(modPath+modName+'.cbc')

frf = cbb.get_data(text='FLOW RIGHT FACE', totim=1)[0]
fff = cbb.get_data(text='FLOW FRONT FACE', totim=1)[0]

lc = modelmap.plot_grid(linewidth=1, color='gray',alpha=0.3)
vectors = modelmap.plot_discharge(frf, fff, head=head,istep=2,jstep=2,normalize=False,color='cyan')

image = plt.imshow(bcgImg, extent=(left, right, bottom, top), alpha=0.7)

ax.set_xlabel('Este', size=14)
ax.set_ylabel('Norte', size=14)
fig.tight_layout()

fig.savefig('../Output/FlowDirection.png')