Time series can be set for any package through the package.ts object, and each package.ts object has several attributes that can be set:
Attribute
Type
Description
package.ts.filename
str
Name of time series file to create. The default is packagename + “.ts”.
package.ts.timeseries
recarray
Array containing the time series information
package.ts.time_series_namerecord
str or list
List of names of the time series data columns. Default is to use names from timeseries.dtype.names[1:].
package.ts.interpolation_methodrecord_single
str
Interpolation method. Must be only one time series record. If there are multiple time series records, then the methods attribute must be used.
package.ts.interpolation_methodrecord
float
Scale factor to multiply the time series data column. Can only be used if there is one time series data column.
package.ts.sfacrecord_single
float
Scale factor to multiply the time series data column. Can only be used if there is one time series data column.
package.ts.sfacrecord
list (of floats)
Scale factors to multiply the time series data columns.
The following code sets up a simulation used in the time series examples.
# package import from tempfile import TemporaryDirectory
# set up where simulation workspace will be stored temp_dir = TemporaryDirectory() workspace = temp_dir.name name = "tutorial03_mf6_data"
# create the flopy simulation and tdis objects
sim = flopy.mf6.MFSimulation(
sim_name=name, exe_name="mf6", version="mf6", sim_ws=workspace
)
tdis_rc = [(1.0, 1, 1.0), (10.0, 5, 1.0), (10.0, 5, 1.0), (10.0, 1, 1.0)]
tdis_package = flopy.mf6.modflow.mftdis.ModflowTdis(
sim, time_units="DAYS", nper=4, perioddata=tdis_rc
)
# create the Flopy groundwater flow (gwf) model object
model_nam_file = f"{name}.nam"
gwf = flopy.mf6.ModflowGwf(sim, modelname=name, model_nam_file=model_nam_file)
# create the flopy iterative model solver (ims) package object
ims = flopy.mf6.modflow.mfims.ModflowIms(sim, pname="ims", complexity="SIMPLE")
# create the discretization package
bot = np.linspace(-3.0, -50.0 / 3.0, 3)
delrow = delcol = 4.0
dis = flopy.mf6.modflow.mfgwfdis.ModflowGwfdis(
gwf,
pname="dis",
nogrb=True,
nlay=3,
nrow=101,
ncol=101,
delr=delrow,
delc=delcol,
top=0.0,
botm=bot,
)
# create the initial condition (ic) and node property flow (npf) packages
ic_package = flopy.mf6.modflow.mfgwfic.ModflowGwfic(gwf, strt=50.0)
npf_package = flopy.mf6.modflow.mfgwfnpf.ModflowGwfnpf(
gwf,
save_flows=True,
icelltype=[1, 0, 0],
k=[5.0, 0.1, 4.0],
k33=[0.5, 0.005, 0.1],
)
Time Series Example 1
One way to construct a time series is to pass the time series data to the parent package constructor.
This example uses time series data in a GHB package. First the GHB stress_period_data is built.
# build ghb stress period data
ghb_spd_ts = {}
ghb_period = []
for layer, cond in zip(range(1, 3), [15.0, 1500.0]):
for row in range(0, 15):
ghb_period.append(((layer, row, 9), "tides", cond, "Estuary-L2"))
ghb_spd_ts[0] = ghb_period
Next the time series data is built. The time series data is constructed as a list of tuples, with each tuple containing a time and the value (or values) at that time. The time series data is put in a dictionary along with additional time series information including filename, time_series_namerecord, interpolation_methodrecord, and sfacrecord.
# build ts data
ts_data = []
for n in range(0, 365):
time = float(n / 11.73)
val = float(n / 60.0)
ts_data.append((time, val))
ts_dict = {
"filename": "tides.ts",
"time_series_namerecord": "tide",
"timeseries": ts_data,
"interpolation_methodrecord": "linearend",
"sfacrecord": 1.1,
}
The GHB package is then constructed, passing the time series data into the timeseries parameter.
# build ghb package
ghb = flopy.mf6.modflow.mfgwfghb.ModflowGwfghb(
gwf,
print_input=True,
print_flows=True,
save_flows=True,
boundnames=True,
timeseries=ts_dict,
pname="ghb",
maxbound=30,
stress_period_data=ghb_spd_ts,
)
Time series attributes, like time_series_namerecord, can be modified using the ghb.ts object.
# set required time series attributes ghb.ts.time_series_namerecord = "tides"
# clean up for next example
gwf.remove_package("ghb")
Time Series Example 2
Another way to construct a time series is to initialize the time series through the ghb.ts.initialize method. Additional time series can then be appended using the append_package method.
First the GHB stress period data is built.
# build ghb stress period data
ghb_spd_ts = {}
ghb_period = []
for layer, cond in zip(range(1, 3), [15.0, 1500.0]):
for row in range(0, 15):
if row < 10:
ghb_period.append(((layer, row, 9), "tides", cond, "Estuary-L2"))
else:
ghb_period.append(((layer, row, 9), "wl", cond, "Estuary-L2"))
ghb_spd_ts[0] = ghb_period
Next the time series data is built. The time series data is constructed as a list of tuples, with each tuple containing a time and the value (or values) at that time.
# build ts data
ts_data = []
for n in range(0, 365):
time = float(n / 11.73)
val = float(n / 60.0)
ts_data.append((time, val))
ts_data2 = []
for n in range(0, 365):
time = float(1.0 + (n / 12.01))
val = float(n / 60.0)
ts_data2.append((time, val))
ts_data3 = []
for n in range(0, 365):
time = float(10.0 + (n / 12.01))
val = float(n / 60.0)
ts_data3.append((time, val))
A ghb package is constructed without the time series data
# build ghb package
ghb = flopy.mf6.modflow.mfgwfghb.ModflowGwfghb(
gwf,
print_input=True,
print_flows=True,
save_flows=True,
boundnames=True,
pname="ghb",
maxbound=30,
stress_period_data=ghb_spd_ts,
)
The first time series data are added by calling the initialize method from the ghb.ts object. The times series package’s file name, name record, method record, and sfac record, along with the time series data are set in the initialize method.
# initialize first time series
ghb.ts.initialize(
filename="tides.ts",
timeseries=ts_data,
time_series_namerecord="tides",
interpolation_methodrecord="linearend",
sfacrecord=1.1,
)
The remaining time series data are added using the append_package method. The append_package method takes the same parameters as the initialize method.
# append additional time series
ghb.ts.append_package(
filename="wls.ts",
timeseries=ts_data2,
time_series_namerecord="wl",
interpolation_methodrecord="stepwise",
sfacrecord=1.2,
)
# append additional time series
ghb.ts.append_package(
filename="wls2.ts",
timeseries=ts_data3,
time_series_namerecord="wl2",
interpolation_methodrecord="stepwise",
sfacrecord=1.3,
)
Information can be retrieved from time series packages using the ts attribute of its parent package. Below the interpolation method record for each of the three time series are retrieved.
print(
"{} is using {} interpolation".format(
ghb.ts[0].filename, ghb.ts[0].interpolation_methodrecord.get_data()[0][0]
)
)
print(
"{} is using {} interpolation".format(
ghb.ts[1].filename, ghb.ts[1].interpolation_methodrecord.get_data()[0][0]
)
)
print(
"{} is using {} interpolation".format(
ghb.ts[2].filename, ghb.ts[2].interpolation_methodrecord.get_data()[0][0]
)
)
tides.ts is using linearend interpolation wls.ts is using stepwise interpolation wls2.ts is using stepwise interpolation
try:
temp_dir.cleanup()
except PermissionError:
# can occur on windows: https://docs.python.org/3/library/tempfile.html#tempfile.TemporaryDirectory
pass
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