In [1]:
import os
import sys
import shutil
import numpy as np
import flopy
print(sys.version)
print('numpy version: {}'.format(np.__version__))
print('flopy version: {}'.format(flopy.__version__))
In [2]:
# make a model
nlay,nrow,ncol = 10,20,5
model_ws = os.path.join("data","external_demo")
if os.path.exists(model_ws):
shutil.rmtree(model_ws)
# the place for all of your hand made and costly model inputs
array_dir = os.path.join("data","array_dir")
if os.path.exists(array_dir):
shutil.rmtree(array_dir)
os.mkdir(array_dir)
ml = flopy.modflow.Modflow(model_ws=model_ws)
dis = flopy.modflow.ModflowDis(ml,nlay=nlay,nrow=nrow,ncol=ncol,steady=False,nper=2)
make an hk and vka array. We'll save hk to files - pretent that you spent months making this important model property. Then make an lpf
In [3]:
hk = np.zeros((nlay,nrow,ncol)) + 5.0
vka = np.zeros_like(hk)
fnames = []
for i,h in enumerate(hk):
fname = os.path.join(array_dir,"hk_{0}.ref".format(i+1))
fnames.append(fname)
np.savetxt(fname,h)
vka[i] = i+1
lpf = flopy.modflow.ModflowLpf(ml,hk=fnames,vka=vka)
Let's also have some recharge with mixed args as well. Pretend the recharge in the second stress period is very important and precise
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warmup_recharge = np.ones((nrow,ncol))
important_recharge = np.random.random((nrow,ncol))
fname = os.path.join(array_dir,"important_recharge.ref")
np.savetxt(fname,important_recharge)
rch = flopy.modflow.ModflowRch(ml,rech={0:warmup_recharge,1:fname})
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ml.write_input()
Let's look at the files that were created
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print("model_ws:",ml.model_ws)
print('\n'.join(os.listdir(ml.model_ws)))
We see that a copy of the hk files as well as the important recharge file were made in the model_ws.Let's looks at the lpf file
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open(os.path.join(ml.model_ws,ml.name+".lpf"),'r').readlines()[:20]
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We see that the open/close approach was used - this is because ml.array_free_format is True. Notice that vka is written internally
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ml.array_free_format
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Now change model_ws
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print(ml.model_ws)
ml.model_ws = os.path.join("data","new_external_demo_dir")
Now when we call write_input(), a copy of external files are made in the current model_ws
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ml.write_input()
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# list the files in model_ws that have 'hk' in the name
print('\n'.join([name for name in os.listdir(ml.model_ws) if "hk" in name or "impor" in name]))
In [12]:
# make a model - same code as before except for the model constructor
nlay,nrow,ncol = 10,20,5
model_ws = os.path.join("data","external_demo")
if os.path.exists(model_ws):
shutil.rmtree(model_ws)
# the place for all of your hand made and costly model inputs
array_dir = os.path.join("data","array_dir")
if os.path.exists(array_dir):
shutil.rmtree(array_dir)
os.mkdir(array_dir)
# lets make an external path relative to the model_ws
ml = flopy.modflow.Modflow(model_ws=model_ws, external_path="ref")
dis = flopy.modflow.ModflowDis(ml,nlay=nlay,nrow=nrow,ncol=ncol,steady=False,nper=2)
hk = np.zeros((nlay,nrow,ncol)) + 5.0
vka = np.zeros_like(hk)
fnames = []
for i,h in enumerate(hk):
fname = os.path.join(array_dir,"hk_{0}.ref".format(i+1))
fnames.append(fname)
np.savetxt(fname,h)
vka[i] = i+1
lpf = flopy.modflow.ModflowLpf(ml,hk=fnames,vka=vka)
warmup_recharge = np.ones((nrow,ncol))
important_recharge = np.random.random((nrow,ncol))
fname = os.path.join(array_dir,"important_recharge.ref")
np.savetxt(fname,important_recharge)
rch = flopy.modflow.ModflowRch(ml,rech={0:warmup_recharge,1:fname})
We can see that the model constructor created both model_ws and external_path which is _relative to the modelws
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os.listdir(ml.model_ws)
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Now, when we call write_input(), any array properties that were specified as np.ndarray will be written externally. If a scalar was passed as the argument, the value remains internal to the model input files
In [14]:
ml.write_input()
open(os.path.join(ml.model_ws,ml.name+".lpf"),'r').readlines()[:20]
Out[14]:
Now, vka was also written externally, but not the storage properties.Let's verify the contents of the external path directory. We see our hard-fought hk and important_recharge arrays, as well as the vka arrays.
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ml.lpf.ss.how = "internal"
ml.write_input()
open(os.path.join(ml.model_ws,ml.name+".lpf"),'r').readlines()[:20]
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In [16]:
print('\n'.join(os.listdir(os.path.join(ml.model_ws,ml.external_path))))
In [17]:
# make a model - same code as before except for the model constructor
nlay,nrow,ncol = 10,20,5
model_ws = os.path.join("data","external_demo")
if os.path.exists(model_ws):
shutil.rmtree(model_ws)
# the place for all of your hand made and costly model inputs
array_dir = os.path.join("data","array_dir")
if os.path.exists(array_dir):
shutil.rmtree(array_dir)
os.mkdir(array_dir)
# lets make an external path relative to the model_ws
ml = flopy.modflow.Modflow(model_ws=model_ws, external_path="ref")
# explicitly reset the free_format flag BEFORE ANY PACKAGES ARE MADE!!!
ml.array_free_format = False
dis = flopy.modflow.ModflowDis(ml,nlay=nlay,nrow=nrow,ncol=ncol,steady=False,nper=2)
hk = np.zeros((nlay,nrow,ncol)) + 5.0
vka = np.zeros_like(hk)
fnames = []
for i,h in enumerate(hk):
fname = os.path.join(array_dir,"hk_{0}.ref".format(i+1))
fnames.append(fname)
np.savetxt(fname,h)
vka[i] = i+1
lpf = flopy.modflow.ModflowLpf(ml,hk=fnames,vka=vka)
ml.lpf.ss.how = "internal"
warmup_recharge = np.ones((nrow,ncol))
important_recharge = np.random.random((nrow,ncol))
fname = os.path.join(array_dir,"important_recharge.ref")
np.savetxt(fname,important_recharge)
rch = flopy.modflow.ModflowRch(ml,rech={0:warmup_recharge,1:fname})
ml.write_input()
We see that now the external arrays are being handled through the name file. Let's look at the name file
In [18]:
open(os.path.join(ml.model_ws,ml.name+".nam"),'r').readlines()
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In [19]:
ml.dis.botm[0].format.binary = True
ml.write_input()
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open(os.path.join(ml.model_ws,ml.name+".nam"),'r').readlines()
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In [21]:
open(os.path.join(ml.model_ws,ml.name+".dis"),'r').readlines()
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In [22]:
ml.lpf.hk[0].how
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This will raise an error since our model does not support free format...
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ml.lpf.hk[0].how = "openclose"
ml.lpf.hk[0].how
ml.write_input()
So let's reset hk layer 1 back to external...
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ml.lpf.hk[0].how = "external"
ml.lpf.hk[0].how
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ml.dis.top.how = "external"
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ml.write_input()
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open(os.path.join(ml.model_ws,ml.name+".dis"),'r').readlines()
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In [28]:
open(os.path.join(ml.model_ws,ml.name+".lpf"),'r').readlines()
Out[28]:
In [29]:
open(os.path.join(ml.model_ws,ml.name+".nam"),'r').readlines()
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