# eWaterCycle-DA
<!-- [](https://github.com/Daafip/ewatercycle-hbv/actions/workflows/test.yml) -->
[](https://ewatercycle-da.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/ewatercycle-DA/)
[](https://github.com/Daafip/ewatercycle-DA)
[](https://fair-software.eu)
Code to run Data Assimilation with hydrological models on the [eWaterCycle](https://github.com/eWaterCycle/ewatercycle) platform.
## Installation
Install this package alongside your [eWaterCycle installation](https://github.com/eWaterCycle/ewatercycle/blob/main/README.md#install)
```console
pip install ewatercycle-DA
```
Then DA becomes available to be used in eWaterCycle
```python
from ewatercycle_DA import DA
```
## docs
Documentation can be found [here](https://ewatercycle-da.readthedocs.io/en/latest/)
## Changelog
Changelog can be found in [CHANGELOG.md](https://github.com/Daafip/ewatercycle-da/blob/main/CHANGELOG.md) on GitHub.
## Quick Usage overiew
_(maybe migrate this to docs?)_
Can be used with or without assimilating, this will run 10 versions of the same model.
By varying the setup_kwargs you can vary the model run itself.
### Without assimilating
```py
HBVForcing = ...
ensemble = DA.Ensemble(N=10)
ensemble.setup()
ensemble.initialize(model_name="HBV",
forcing=HBVForcing,
setup_kwargs={'parameters':'7.6,0.5,460,3.8,0.19,1.3,0.082,0.0061',
'initial_storage':'0,100,0,5'}
)
ref_model = ensemble.ensemble_list[0].model
lst_Q = []
while ref_model.time < ref_model.end_time:
ensemble.update(assimilate=False)
lst_Q.append(ensemble.get_value("Q"))
```
_For running HBV see seperate [docs](https://github.com/Daafip/ewatercycle-hbv)_
### With assimilating
```py
...
ref_model = ...
#... same as above just add two more definitions
def H(Z):
"""returns discharge which is the last value on the state vector for HBV"""
return Z[-1]
ds_obs_dir = ...
ensemble.initialize_da_method(ensemble_method_name = "PF",
hyper_parameters = {
'like_sigma_weights' : 0.05,
'like_sigma_state_vector' : 0.01,
},
state_vector_variables = "all",
# the next three are keyword arguments but are needed:
observation_path = ds_obs_dir,
observed_variable_name = "Q",
measurement_operator = H,
)
lst_Q = []
while ref_model.time < ref_model.end_time:
ensemble.update(assimilate=True)
lst_Q.append(ensemble.get_value("Q"))
```
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"description": "# eWaterCycle-DA\n<!-- [](https://github.com/Daafip/ewatercycle-hbv/actions/workflows/test.yml) -->\n[](https://ewatercycle-da.readthedocs.io/en/latest/?badge=latest)\n[](https://pypi.org/project/ewatercycle-DA/)\n[](https://github.com/Daafip/ewatercycle-DA)\n[](https://fair-software.eu)\n\n\n\n\nCode to run Data Assimilation with hydrological models on the [eWaterCycle](https://github.com/eWaterCycle/ewatercycle) platform. \n\n## Installation\nInstall this package alongside your [eWaterCycle installation](https://github.com/eWaterCycle/ewatercycle/blob/main/README.md#install)\n\n```console\npip install ewatercycle-DA\n```\n\nThen DA becomes available to be used in eWaterCycle\n\n```python\nfrom ewatercycle_DA import DA\n\n```\n\n\n## docs\nDocumentation can be found [here](https://ewatercycle-da.readthedocs.io/en/latest/)\n\n## Changelog\nChangelog can be found in [CHANGELOG.md](https://github.com/Daafip/ewatercycle-da/blob/main/CHANGELOG.md) on GitHub. \n\n## Quick Usage overiew\n_(maybe migrate this to docs?)_\n\nCan be used with or without assimilating, this will run 10 versions of the same model.\nBy varying the setup_kwargs you can vary the model run itself.\n\n### Without assimilating\n```py\n\nHBVForcing = ...\n\nensemble = DA.Ensemble(N=10)\nensemble.setup()\n\nensemble.initialize(model_name=\"HBV\",\n forcing=HBVForcing,\n setup_kwargs={'parameters':'7.6,0.5,460,3.8,0.19,1.3,0.082,0.0061',\n 'initial_storage':'0,100,0,5'}\n )\n\nref_model = ensemble.ensemble_list[0].model\n\nlst_Q = []\nwhile ref_model.time < ref_model.end_time:\n ensemble.update(assimilate=False)\n lst_Q.append(ensemble.get_value(\"Q\"))\n```\n_For running HBV see seperate [docs](https://github.com/Daafip/ewatercycle-hbv)_\n### With assimilating\n\n```py\n\n...\nref_model = ...\n#... same as above just add two more definitions\ndef H(Z):\n \"\"\"returns discharge which is the last value on the state vector for HBV\"\"\"\n return Z[-1] \n\nds_obs_dir = ...\nensemble.initialize_da_method(ensemble_method_name = \"PF\", \n hyper_parameters = {\n 'like_sigma_weights' : 0.05,\n 'like_sigma_state_vector' : 0.01,\n },\n state_vector_variables = \"all\", \n # the next three are keyword arguments but are needed:\n observation_path = ds_obs_dir,\n observed_variable_name = \"Q\",\n measurement_operator = H, \n \n )\nlst_Q = []\nwhile ref_model.time < ref_model.end_time:\n ensemble.update(assimilate=True)\n lst_Q.append(ensemble.get_value(\"Q\"))\n\n```",
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