def get_times(self):
if 'mf' not in self._packageContent:
self.read_packages_from_api(['mf'])
head_file = os.path.join(
self._data_folder,
self._model_id,
self._packageContent['mf']['model_ws'],
self._packageContent['mf']['modelname'] + '.hds')
return fu.HeadFile(head_file).get_times()
def get_valid_totim(self, totim=0):
if 'mf' not in self._packageContent:
self.read_packages_from_api(['mf'])
head_file = os.path.join(
self._data_folder,
self._model_id,
self._packageContent['mf']['model_ws'],
self._packageContent['mf']['modelname'] + '.hds')
times = fu.HeadFile(head_file).get_times()
i = 0
while i < len(times):
if times[i] > totim:
totim = times[i]
break
i += 1
return totim
# output control (using the default values). Then we are ready to write all MODFLOW input
# files and run MODFLOW.
pcg = mf.ModflowPcg(ml)
oc = mf.ModflowOc(ml)
ml.write_input()
ml.run_model()
# change back to the starting directory
os.chdir(cwdpth)
# Once the model has terminated normally, we can read the heads file. First, a link to the heads
# file is created with `HeadFile`. The link can then be accessed with the `get_data` function, by
# specifying, in this case, the step number and period number for which we want to retrieve data.
# A three-dimensional array is returned of size `nlay, nrow, ncol`. Matplotlib contouring functions
# are used to make contours of the layers or a cross-section.
hds = fu.HeadFile(os.path.join(workspace, name + '.hds'))
h = hds.get_data(kstpkper=(0, 0))
x = y = np.linspace(0, L, N)
c = plt.contour(x, y, h[0], np.arange(90, 100.1, 0.2))
plt.clabel(c, fmt='%2.1f')
plt.axis('scaled')
plt.show()
x = y = np.linspace(0, L, N)
c = plt.contour(x, y, h[-1], np.arange(90, 100.1, 0.2))
plt.clabel(c, fmt='%1.1f')
plt.axis('scaled')
plt.show()
z = np.linspace(-H / Nlay / 2, -H + H / Nlay / 2, Nlay)
c = plt.contour(x, z, h[:, 50, :], np.arange(90, 100.1, .2))
top=0, botm=[-40.0],
perlen=400, nstp=200)
bas = mf.ModflowBas(ml, ibound=ibound, strt=0.0)
lpf = mf.ModflowLpf(ml, hk=2., vka=2.0, vkcb=0, laytyp=0, layavg=0)
wel = mf.ModflowWel(ml, stress_period_data={0:[(0, 0, 0, 1)]})
swi = mf.ModflowSwi2(ml, npln=1, istrat=1, toeslope=0.2, tipslope=0.2, nu=[0, 0.025],
zeta=z, ssz=0.2, isource=isource, nsolver=1)
oc = mf.ModflowOc(ml, stress_period_data=ocdict)
pcg = mf.ModflowPcg(ml)
# create model files
ml.write_input()
# run the model
m = ml.run_model(silent=False)
# read model heads
headfile = '{}.hds'.format(modelname)
hdobj = fu.HeadFile(headfile)
head = hdobj.get_alldata()
head = np.array(head)
# read model zeta
zetafile = '{}.zta'.format(modelname)
zobj = fu.CellBudgetFile(zetafile)
zkstpkper = zobj.get_kstpkper()
zeta = []
for kk in zkstpkper:
zeta.append(zobj.get_data(kstpkper=kk, text=' ZETASRF 1')[0])
zeta = np.array(zeta)
x = np.arange(0.5 * delr, ncol * delr, delr)
# Wilson and Sa Da Costa
k = 2.0
ibound[:, -1, :] = -1
ibound[:, :, 0] = -1
ibound[:, :, -1] = -1
ibound[0, Nhalf, Nhalf] = -1
start = h1 * np.ones((N, N))
start[Nhalf, Nhalf] = h2
bas = fmf.ModflowBas(ml, ibound=ibound, strt=start)
print "o"
lpf = fmf.ModflowLpf(ml, hk=k)
pcg = fmf.ModflowPcg(ml)
oc = fmf.ModflowOc(ml)
ml.write_input()
ml.run_model()
head_file = '/home/kiruba/PycharmProjects/area_of_curve/hydrology/hydrology/mf_files/lake_example.hds'
hds = fut.HeadFile(head_file)
h = hds.get_data(kstpkper=(1, 1))
print len(h)
x = y = np.linspace(0, L, N)
c = plt.contour(x, y, h[0], np.arange(90, 100.1, 0.2))
plt.clabel(c, fmt='%2.1f')
plt.axis('scaled')
plt.show()
c = plt.contour(x, y, h[-1], np.arange(90, 100.1, 0.2))
plt.clabel(c, fmt='%1.1f')
plt.axis('scaled')
plt.show()
z = np.linspace(-H / Nlay / 2, -H + H / Nlay / 2, Nlay)
c = plt.contour(x, z, h[:, 50, :], np.arange(90, 100.1, .2))
plt.axis('scaled')
plt.show()
def calculate_model(z1_hk, z2_hk, z3_hk):
# Z1_hk = 15 # 3<Z1_hk<15
# Z2_hk = 15 # 3<Z2_hk<15
# Z3_hk = 3 # 3<Z3_hk<15
hobs = [
[0, 20, 10, 69.52],
[0, 40, 10, 71.44],
[0, 60, 10, 72.99],
[0, 80, 10, 73.86],
[0, 20, 45, 58.73],
[0, 40, 45, 50.57],
[0, 60, 45, 54.31],
[0, 80, 45, 58.06],
[0, 20, 80, 56.31],
[0, 40, 80, 52.32],
[0, 60, 80, 46.35],
[0, 80, 80, 29.01],
[0, 20, 100, 57.24],
[0, 40, 100, 54.24],
[0, 60, 100, 39.48],
[0, 80, 100, 48.47],
]
model_path = os.path.join('_model')
if os.path.exists(model_path):
shutil.rmtree(model_path)
modelname = 'parEstMod'
version = 'mf2005'
exe_name = 'mf2005'
if platform.system() == 'Windows':
exe_name = 'mf2005.exe'
ml = mf.Modflow(modelname=modelname,
exe_name=exe_name,
version=version,
model_ws=model_path)
nlay = 1
nrow = 90
ncol = 120
area_width_y = 9000
area_width_x = 12000
delc = area_width_x / ncol
delr = area_width_y / nrow
nper = 1
top = 100
botm = 0
dis = mf.ModflowDis(ml,
nlay=nlay,
nrow=nrow,
ncol=ncol,
delr=delr,
delc=delc,
top=top,
botm=botm,
nper=nper,
steady=True)
ibound = 1
strt = 100
bas = mf.ModflowBas(ml, ibound=ibound, strt=strt)
mask_arr = np.zeros((nlay, nrow, ncol))
mask_arr[:, :, 0] = 80
mask_arr[:, :, -1] = 60
ghb_spd = {0: []}
for layer_id in range(nlay):
for row_id in range(nrow):
for col_id in range(ncol):
if mask_arr[layer_id][row_id][col_id] > 0:
ghb_spd[0].append([
layer_id, row_id, col_id,
mask_arr[layer_id][row_id][col_id], 200
])
ghb = mf.ModflowGhb(ml, stress_period_data=ghb_spd)
rch = 0.0002
rech = {}
rech[0] = rch
rch = mf.ModflowRch(ml, rech=rech, nrchop=3)
welSp = {}
welSp[0] = [
[0, 20, 20, -20000],
[0, 40, 40, -20000],
[0, 60, 60, -20000],
[0, 80, 80, -20000],
[0, 60, 100, -20000],
]
wel = mf.ModflowWel(ml, stress_period_data=welSp)
hk = np.zeros((nlay, nrow, ncol))
hk[:, :, 0:40] = z1_hk
hk[:, :, 40:80] = z2_hk
hk[:, :, 80:120] = z3_hk
lpf = mf.ModflowLpf(ml, hk=hk, layavg=0, layvka=0, sy=0.3, ipakcb=53)
pcg = mf.ModflowPcg(ml, rclose=1e-1)
oc = mf.ModflowOc(ml)
ml.write_input()
ml.run_model(silent=True)
hds = fu.HeadFile(os.path.join(model_path, modelname + '.hds'))
times = hds.get_times()
response = []
for hob in hobs:
observed = hob[3]
calculated = hds.get_data(totim=times[-1])[hob[0]][hob[1]][hob[2]]
response.append(observed - calculated)
return json.dumps(response)
model.write_input()
model.run_model()
#8. After MODFLOW has responded with the positive
#Normal termination of simulation, the calculated
#heads can be read from the binary output file.
#First, a file object is created. As the modelname used
#for all MODFLOW files was specified as gwexample
#in step 1, the file with the heads is called gwexample.
#hds. FloPy includes functions to read data
#from the file object, including heads for specified layers
#or time steps, or head time series at individual cells.
#For this simple mode, all computed heads are read.
hfile = fpu.HeadFile('gwexample.hds')
h = hfile.get_data(totim=1.0)
#The heads are now stored in the Python variable h.
#FloPy includes powerful plotting functions to plot the
#grid, boundary conditions, head, etc. This functionality
#is demonstrated later. For this simple one-dimensional
#example, a plot is created with the matplotlib package,
#resulting in the plot shown in Figure 1.
#SCZ below here; make plot equivalent to Figure 1 in Bakker et al. (2016)
import matplotlib.pyplot as plt
position = np.linspace(0, 2000, np.size(h))
h_plot = h[0, 0, :]
plt.plot(position, h_plot, color="black")
