def setup_lake_connectiondata(model,
include_horizontal_connections=True):
cfg = model.cfg['lak']
# set up littoral and profundal zones
if model.lake_info is None:
model.lake_info = setup_lake_info(model)
lakzones = make_bdlknc_zones(model.modelgrid, model.lake_info,
include_ids=model.lake_info['feat_id'])
model.setup_external_filepaths('lak', 'lakzones',
cfg['{}_filename_fmt'.format('lakzones')],
nfiles=1)
save_array(model.cfg['intermediate_data']['lakzones'][0], lakzones, fmt='%d')
# make the (2D) areal footprint of lakebed leakance from the zones
bdlknc = make_bdlknc2d(lakzones,
cfg['source_data']['littoral_leakance'],
cfg['source_data']['profundal_leakance'])
# cell tops and bottoms
layer_elevations = np.zeros((model.nlay + 1, model.nrow, model.ncol), dtype=float)
layer_elevations[0] = model.dis.top.array
layer_elevations[1:] = model.dis.botm.array
lakeno = []
cellid = []
bedleak = []
for lake_id in range(1, model.nlakes + 1):
# get the vertical GWF connections for each lake
k, i, j = np.where(model.lakarr == lake_id)
# get just the unique i, j locations for each lake
i, j = zip(*set(zip(i, j)))
# assign vertical connections to the highest active layer
k = np.argmax(model.idomain[:, i, j], axis=0)
cellid += list(zip(k, i, j))
lakeno += [lake_id] * len(k)
bedleak += list(bdlknc[i, j])
df = pd.DataFrame({'lakeno': lakeno,
'cellid': cellid,
'claktype': 'vertical',
'bedleak': bedleak,
'belev': 0.,
'telev': 0.,
'connlen': 0.,
'connwidth': 0.
})
if include_horizontal_connections:
for lake_id in range(1, model.nlakes + 1):
lake_extent = model.lakarr == lake_id
horizontal_connections = get_horizontal_connections(lake_extent,
layer_elevations,
model.dis.delr.array,
model.dis.delc.array,
bdlknc)
# drop horizontal connections to inactive cells
k, i, j = zip(*horizontal_connections['cellid'])
inactive = model.idomain[k, i, j] < 1
horizontal_connections = horizontal_connections.loc[~inactive].copy()
horizontal_connections['lakeno'] = lake_id
df = df.append(horizontal_connections)
# assign iconn (connection number) values for each lake
dfs = []
for lakeno, group in df.groupby('lakeno'):
group = group.copy()
group['iconn'] = list(range(len(group)))
dfs.append(group)
df = pd.concat(dfs)
df['lakeno'] -= 1 # convert to zero-based for mf6
return df
def setup_lak(self):
print('setting up LAKE package...')
t0 = time.time()
# if shapefile of lakes was included,
# lakarr should be automatically built by property method
if self.lakarr.sum() == 0:
print("lakes_shapefile not specified, or no lakes in model area")
return
# source data
source_data = self.cfg['lak']['source_data']
self.lake_info = setup_lake_info(self)
nlakes = len(self.lake_info)
# set up the tab files, if any
tab_files_argument = None
tab_units = None
start_tab_units_at = 150 # default starting number for iunittab
if 'stage_area_volume_file' in source_data:
tab_files = setup_lake_tablefiles(
self, source_data['stage_area_volume_file'])
tab_units = list(
range(start_tab_units_at, start_tab_units_at + len(tab_files)))
# tabfiles aren't rewritten by flopy on package write
self.cfg['lak']['tab_files'] = tab_files
# kludge to deal with ugliness of lake package external file handling
# (need to give path relative to model_ws, not folder that flopy is working in)
tab_files_argument = [os.path.relpath(f) for f in tab_files]
self.setup_external_filepaths(
'lak', 'lakzones',
self.cfg['lak']['{}_filename_fmt'.format('lakzones')])
self.setup_external_filepaths(
'lak',
'bdlknc',
self.cfg['lak']['{}_filename_fmt'.format('bdlknc')],
file_numbers=list(range(self.nlay)))
# make the arrays or load them
lakzones = make_bdlknc_zones(self.modelgrid,
self.lake_info,
include_ids=self.lake_info['feat_id'])
save_array(self.cfg['intermediate_data']['lakzones'][0],
lakzones,
fmt='%d')
bdlknc = np.zeros((self.nlay, self.nrow, self.ncol))
# make the areal footprint of lakebed leakance from the zones (layer 1)
bdlknc[0] = make_bdlknc2d(
lakzones, self.cfg['lak']['source_data']['littoral_leakance'],
self.cfg['lak']['source_data']['profundal_leakance'])
for k in range(self.nlay):
if k > 0:
# for each underlying layer, assign profundal leakance to cells were isbc == 1
bdlknc[k][self.isbc[k] == 1] = self.cfg['lak']['source_data'][
'profundal_leakance']
save_array(self.cfg['intermediate_data']['bdlknc'][0][k],
bdlknc[k],
fmt='%.6e')
# get estimates of stage from model top, for specifying ranges
stages = []
for lakid in self.lake_info['lak_id']:
loc = self.lakarr[0] == lakid
est_stage = self.dis.top.array[loc].min()
stages.append(est_stage)
stages = np.array(stages)
# setup stress period data
tol = 5 # specify lake stage range as +/- this value
ssmn, ssmx = stages - tol, stages + tol
stage_range = list(zip(ssmn, ssmx))
# set up dataset 9
# ssmn and ssmx values only required for steady-state periods > 0
self.lake_fluxes = setup_lake_fluxes(self)
precip = self.lake_fluxes['precipitation'].tolist()
evap = self.lake_fluxes['evaporation'].tolist()
flux_data = {}
for i, steady in enumerate(self.dis.steady.array):
if i > 0 and steady:
flux_data_i = []
for lake_ssmn, lake_ssmx in zip(ssmn, ssmx):
flux_data_i.append(
[precip[i], evap[i], 0, 0, lake_ssmn, lake_ssmx])
else:
flux_data_i = [[precip[i], evap[i], 0, 0]] * nlakes
flux_data[i] = flux_data_i
options = ['tableinput'] if tab_files_argument is not None else None
kwargs = self.cfg['lak']
kwargs['nlakes'] = len(self.lake_info)
kwargs['stages'] = stages
kwargs['stage_range'] = stage_range
kwargs['flux_data'] = flux_data
kwargs[
'tab_files'] = tab_files_argument #This needs to be in the order of the lake IDs!
kwargs['tab_units'] = tab_units
kwargs['options'] = options
kwargs['ipakcb'] = self.ipakcb
kwargs['lwrt'] = 0
kwargs = get_input_arguments(kwargs, fm.mflak.ModflowLak)
lak = fm.ModflowLak(self, **kwargs)
print("finished in {:.2f}s\n".format(time.time() - t0))
return lak
def setup_lake_connectiondata(model,
for_external_file=True,
include_horizontal_connections=True):
cfg = model.cfg['lak']
# set up littoral and profundal zones
if model.lake_info is None:
model.lake_info = setup_lake_info(model)
# zone numbers
# littoral zones are the same as the one-based lake number
# profundal zones are the one-based lake number times 100
# for example, for lake 1, littoral zone is 1; profundal zone is 100.
lakzones = make_bdlknc_zones(model.modelgrid,
model.lake_info,
include_ids=model.lake_info['feat_id'])
littoral_profundal_zones = get_littoral_profundal_zones(lakzones)
model.setup_external_filepaths('lak', 'lakzones',
cfg['{}_filename_fmt'.format('lakzones')])
save_array(model.cfg['intermediate_data']['lakzones'][0],
lakzones,
fmt='%d')
# make the (2D) areal footprint of lakebed leakance from the zones
bdlknc = make_bdlknc2d(lakzones, cfg['source_data']['littoral_leakance'],
cfg['source_data']['profundal_leakance'])
# cell tops and bottoms
layer_elevations = np.zeros((model.nlay + 1, model.nrow, model.ncol),
dtype=float)
layer_elevations[0] = model.dis.top.array
layer_elevations[1:] = model.dis.botm.array
lakeno = []
cellid = []
bedleak = []
zone = []
for lake_id in range(1, model.nlakes + 1):
# get the vertical GWF connections for each lake
k, i, j = np.where(model.lakarr == lake_id)
# get just the unique i, j locations for each lake
i, j = zip(*set(zip(i, j)))
# assign vertical connections to the highest active layer
k = np.argmax(model.idomain[:, i, j], axis=0)
cellid += list(zip(k, i, j))
lakeno += [lake_id] * len(k)
bedleak += list(bdlknc[i, j])
zone += list(littoral_profundal_zones[i, j])
df = pd.DataFrame({
'lakeno': lakeno,
'cellid': cellid,
'claktype': 'vertical',
'bedleak': bedleak,
'belev': 0.,
'telev': 0.,
'connlen': 0.,
'connwidth': 0.,
'zone': zone
})
if include_horizontal_connections:
for lake_id in range(1, model.nlakes + 1):
lake_extent = model.lakarr == lake_id
horizontal_connections = get_horizontal_connections(
lake_extent,
connection_info=True,
layer_elevations=layer_elevations,
delr=model.dis.delr.array,
delc=model.dis.delc.array,
bdlknc=bdlknc)
# drop horizontal connections to inactive cells
k, i, j = horizontal_connections[['k', 'i', 'j']].T.values
inactive = model.idomain[k, i, j] < 1
horizontal_connections = horizontal_connections.loc[
~inactive].copy()
horizontal_connections['lakeno'] = lake_id
k, i, j = horizontal_connections[['k', 'i', 'j']].T.values
horizontal_connections['zone'] = littoral_profundal_zones[i, j]
horizontal_connections['cellid'] = list(zip(k, i, j))
horizontal_connections.drop(['k', 'i', 'j'], axis=1, inplace=True)
df = df.append(horizontal_connections)
# assign iconn (connection number) values for each lake
dfs = []
for lakeno, group in df.groupby('lakeno'):
group = group.copy()
group['iconn'] = list(range(len(group)))
dfs.append(group)
df = pd.concat(dfs)
connections_lookup_file = model.cfg['lak']['output_files'][
'connections_lookup_file'].format(model.name)
connections_lookup_file = os.path.join(
model._tables_path,
os.path.split(connections_lookup_file)[1])
model.cfg['lak']['output_files'][
'connections_lookup_file'] = connections_lookup_file
df.to_csv(connections_lookup_file, index=False)
# convert to one-based and comment out header if df will be written straight to external file
if for_external_file:
df.rename(columns={'lakeno': '#lakeno'}, inplace=True)
df['iconn'] += 1
k, i, j = zip(*df['cellid'])
df.drop('cellid', axis=1, inplace=True)
df['k'] = np.array(k) + 1
df['i'] = np.array(i) + 1
df['j'] = np.array(j) + 1
else:
df['lakeno'] -= 1 # convert to zero-based for mf6
return df