Ensemble without assimilating

# general python
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
import numpy as np
import os
from pathlib import Path
import yaml
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import scipy
import xarray as xr
from tqdm import tqdm
import glob
from devtools import pprint
from tqdm import tqdm

# general eWC
import ewatercycle
import ewatercycle.forcing
import ewatercycle.models

set up paths

path = Path.cwd()
forcing_path = path / "Forcing"
observations_path = path / "Observations"
figure_path = path / "Figures"
output_path = path / "Output"

Marrmot

add forcing

from ewatercycle.forcing import sources

marrmot_forcing = ewatercycle.forcing.sources["MarrmotForcing"](
    directory=forcing_path,
    start_time="1989-01-01T00:00:00Z",
    end_time="1992-12-31T00:00:00Z",
    forcing_file="BMI_testcase_m01_BuffaloRiver_TN_USA.mat",
)

import model

ewatercycle.models.sources.MarrmotM14

ewatercycle_marrmot.model.MarrmotM14

from ewatercycle.models import MarrmotM14

import DA function

pip install ewatercycle_DA

from ewatercycle_DA import DA

n_particles = 3

ensemble = DA.Ensemble(N=n_particles)
ensemble.setup()

maximum_soil_moisture_storage_lst = [10.0, 12.0, 14.0]

# values wihch you
setup_kwargs_lst = []
for index in range(n_particles):
    setup_kwargs_lst.append({'maximum_soil_moisture_storage': maximum_soil_moisture_storage_lst[index],
                            'end_time':'1989-02-01T00:00:00Z',
                             })

ensemble.ensemble_list[0].model_name

# this initializes the models for all ensemble members. - ignore warnings for now
ensemble.initialize(model_name=["MarrmotM14"]*n_particles,
                    forcing=[marrmot_forcing]*n_particles,
                    setup_kwargs=setup_kwargs_lst)

Run

ref_model = ensemble.ensemble_list[0].model

n_timesteps = int((ref_model.end_time - ref_model.start_time) /  ref_model.time_step)

time = []
lst_Q = []
for i in tqdm(range(n_timesteps)):
    time.append(pd.Timestamp(ref_model.time_as_datetime.date()))
    ensemble.update()
    lst_Q.append(ensemble.get_value("flux_out_Q").flatten())

ensemble.finalize()

100%|███████████████████████████████████████████████████████████████████████████████████| 31/31 [00:02<00:00, 14.34it/s]

Q_m_arr = np.array(lst_Q).T
df_ensemble = pd.DataFrame(data=Q_m_arr[:,:len(time)].T,index=time,columns=[f'particle {n}' for n in range(n_particles)])

ax = df_ensemble[["particle 0"]].plot(lw=2.5)
df_ensemble[["particle 1"]].plot(ax=ax,ls="--",lw=2.5)
df_ensemble[["particle 2"]].plot(ax=ax,ls="-.",lw=1.5)

<Axes: >