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DA with Random Initial Conditions on Soil Layers#
This notebook demonstrates how to create an ensemble of models with varying initial conditions for different soil layers. By introducing randomness into the initial conditions, we can better simulate the natural variability in soil properties and improve the robustness of data assimilation (DA) processes.
Estimated time to run the notebook = 2min
import os
import numpy as np
import pyvista as pv
from pyCATHY.DA.perturbate import perturbate_parm
from pyCATHY.DA import perturbate
from pyCATHY.DA.cathy_DA import DA
from pyCATHY.plotters import cathy_plots as CTp
nlay = 6
scenario = {
'per_name':['ic'],
'per_type': [[None]*nlay],
'per_nom':[[-15]*nlay],
'per_mean':[[-15]*nlay],
'per_sigma': [[3.75]*nlay],
'per_bounds': [[None]*nlay],
'sampling_type': [['normal']*nlay],
'transf_type':[[None]*nlay],
'listUpdateParm': ['St. var.']
}
simuWithDA = DA(
dirName='./',
prj_name= 'DA_with_non_uniform_ic',
notebook=True,
)
# linear z depth
# ---------------
zb = np.linspace(0, 2, nlay)
nstr = len(zb)
zr = list(np.ones(len(zb))/nstr)
simuWithDA.update_prepo_inputs(
nstr=nstr,
zratio=zr,
base=max(zb),
)
🏁 Initiate CATHY object
🔄 Update hap.in file
🔄 Update dem_parameters file
🔄 Update dem_parameters file
simuWithDA.update_dem_parameters()
simuWithDA.update_veg_map()
simuWithDA.update_soil()
Traceback (most recent call last):
File "/home/runner/work/pycathy_wrapper/pycathy_wrapper/examples/DA/plot_1_DA_NoUni_ic (conflicted copy 2025-05-21 093615).py", line 57, in <module>
simuWithDA.update_veg_map()
File "/home/runner/work/pycathy_wrapper/pycathy_wrapper/pyCATHY/cathy_tools.py", line 3353, in update_veg_map
indice_veg, str_hd_rootmap = in_CT.read_root_map(os.path.join(
File "/home/runner/work/pycathy_wrapper/pycathy_wrapper/pyCATHY/importers/cathy_inputs.py", line 529, in read_root_map
with open(os.path.join(rootmapfile), "r") as f: # open the file for reading
FileNotFoundError: [Errno 2] No such file or directory: '/home/runner/work/pycathy_wrapper/pycathy_wrapper/examples/DA/./DA_with_non_uniform_ic/input/veg_map'
simuWithDA.NENS = 3
list_pert = perturbate.perturbate(
simuWithDA,
scenario,
simuWithDA.NENS,
)
var_per_dict_stacked = {}
for dp in list_pert:
var_per_dict_stacked = perturbate_parm(
var_per_dict_stacked,
parm=dp,
type_parm = dp['type_parm'], # can also be VAN GENUCHTEN PARAMETERS
mean = dp['mean'],
sd = dp['sd'],
sampling_type = dp['sampling_type'],
ensemble_size = dp['ensemble_size'], # size of the ensemble
per_type= dp['per_type'],
nlayers = nlay,
savefig= os.path.join(simuWithDA.workdir,
simuWithDA.project_name,
simuWithDA.project_name + dp['savefig'])
)
This in normally directly called when using run_DA_sequential()
simuWithDA._create_subfolders_ensemble()
simuWithDA.update_ENS_files(var_per_dict_stacked,
var_per_dict_stacked.keys(),
cycle_nb=0
)
pl = pv.Plotter(shape=(1,2))
for i, ensi in enumerate([1,3]):
DApath = f'DA_Ensemble/cathy_{ensi}/vtk/'
path = os.path.join(simuWithDA.workdir,
simuWithDA.project_name,
DApath,
simuWithDA.project_name + '.vtk'
)
pl.subplot(0,i)
CTp.show_vtk(path,
'ic_nodes',
ax=pl,
clim = [-25,-5],
#show_scalar_bar=True,
)
_ = pl.add_legend('')
pl.add_title(f'Ensemble nb:{ensi}')
pl.show()
Total running time of the script: (0 minutes 0.012 seconds)