| Name | hydnam JSON |
| Version |
1.1.2
JSON |
| download |
| home_page | https://github.com/duynguyen02/hydnam |
| Summary | Python implementation of NedborAfstromnings Model (NAM) lumped rainfall–runoff model |
| upload_time | 2025-01-01 17:40:19 |
| maintainer | None |
| docs_url | None |
| author | Duy Nguyen |
| requires_python | <4.0,>=3.10 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# HydNAM
**HydNAM** is a Python implementation of the NedborAfstromnings Model (NAM), a lumped rainfall–runoff model.
This project is based on the [NAM_Model](https://github.com/hckaraman/NAM_Model) created by [hckaraman](https://github.com/hckaraman).
## Installation
```bash
pip install hydnam
```
## Getting Started
### 1. Prepare the Dataset
The dataset must contain the following properties: Date, Temperature, Precipitation, Evapotranspiration, and Discharge.
| Date | Temperature | Discharge | Precipitation | Evapotranspiration |
|------------|-------------|-----------|---------------|--------------------|
| 10/9/2016 | 15.4 | 0.25694 | 0 | 2.79 |
| 10/10/2016 | 14.4 | 0.25812 | 0 | 3.46 |
| 10/11/2016 | 14.9 | 0.30983 | 0 | 3.65 |
| 10/12/2016 | 16.1 | 0.31422 | 0 | 3.46 |
| 10/13/2016 | 20.1 | 0.30866 | 0 | 5.64 |
| 10/14/2016 | 13.9 | 0.30868 | 0 | 3.24 |
| 10/15/2016 | 11.1 | 0.31299 | 0 | 3.41 |
| ... | ... | ... | ... | ... |
Ensure that the time intervals between dates are consistent (e.g., 24 hours) for accurate model performance.
### 2. Initialize the NAM Model
```python
from datetime import datetime
from hydnam.chart import plot_q
from hydnam.dataset import Dataset
from hydnam.hydnam import HydNAM
from hydnam.parameters import Parameters
dataset = Dataset(
timeseries=[
datetime(2016, 10, 9),
datetime(2016, 10, 10),
datetime(2016, 10, 11),
],
temperature=[15.4, 14.4, 14.9],
precipitation=[0.0, 0.0, 0.0],
evapotranspiration=[2.79, 3.46, 3.65],
discharge=[0.25694, 0.25812, 0.30983]
)
params = Parameters(
umax=0.01,
lmax=0.01,
cqof=0.01,
ckif=200.0,
ck12=10.0,
tof=0.0,
tif=0.0,
tg=0.0,
ckbf=500.0,
csnow=0.0,
snowtemp=0.0
)
nam = HydNAM(
dataset=dataset,
parameters=params,
area=58.8,
interval=24.0,
start=None,
end=None,
spin_off=0.0
)
print(f'Parameters: {nam.parameters}')
print(f'Statistics: {nam.statistics}')
df = nam.simulation_result.to_dataframe()
```
### 3. Optimize the Model
```
NAM.optimize()
```
The model will calculate and check which `Parameters` are optimal for the model and use it as the main `Parameters` for
the model.
### 4. Customize Parameters
```
nam.set_parameters(Parameters())
```
### 5. Show Discharge
```
from hydnam.chart import plot_q
...
plot_q(nam.simulation_result, only_obs_and_sim=False).show()
```
## License
This library is released under the MIT License.
## Contact
If you have any questions or issues, please open an issue on [GitHub](https://github.com/duynguyen02/hydnam/issues) or
email us at [duynguyen02.dev@gmail.com](mailto:duynguyen02.dev@gmail.com).
Raw data
{
"_id": null,
"home_page": "https://github.com/duynguyen02/hydnam",
"name": "hydnam",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.10",
"maintainer_email": null,
"keywords": null,
"author": "Duy Nguyen",
"author_email": "duynguyen02.dev@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/d1/72/8c21d1ddf714744cc6f8c52299439310189133fff5b8117d43c2125d3e0a/hydnam-1.1.2.tar.gz",
"platform": null,
"description": "# HydNAM\n\n\n\n**HydNAM** is a Python implementation of the NedborAfstromnings Model (NAM), a lumped rainfall\u2013runoff model.\n\nThis project is based on the [NAM_Model](https://github.com/hckaraman/NAM_Model) created by [hckaraman](https://github.com/hckaraman). \n\n## Installation\n\n```bash\npip install hydnam\n```\n\n## Getting Started\n\n### 1. Prepare the Dataset\n\nThe dataset must contain the following properties: Date, Temperature, Precipitation, Evapotranspiration, and Discharge.\n\n| Date | Temperature | Discharge | Precipitation | Evapotranspiration |\n|------------|-------------|-----------|---------------|--------------------|\n| 10/9/2016 | 15.4 | 0.25694 | 0 | 2.79 |\n| 10/10/2016 | 14.4 | 0.25812 | 0 | 3.46 |\n| 10/11/2016 | 14.9 | 0.30983 | 0 | 3.65 |\n| 10/12/2016 | 16.1 | 0.31422 | 0 | 3.46 |\n| 10/13/2016 | 20.1 | 0.30866 | 0 | 5.64 |\n| 10/14/2016 | 13.9 | 0.30868 | 0 | 3.24 |\n| 10/15/2016 | 11.1 | 0.31299 | 0 | 3.41 |\n| ... | ... | ... | ... | ... |\n\nEnsure that the time intervals between dates are consistent (e.g., 24 hours) for accurate model performance.\n\n### 2. Initialize the NAM Model\n\n```python\nfrom datetime import datetime\nfrom hydnam.chart import plot_q\nfrom hydnam.dataset import Dataset\nfrom hydnam.hydnam import HydNAM\nfrom hydnam.parameters import Parameters\n\ndataset = Dataset(\n timeseries=[\n datetime(2016, 10, 9),\n datetime(2016, 10, 10),\n datetime(2016, 10, 11),\n ],\n temperature=[15.4, 14.4, 14.9],\n precipitation=[0.0, 0.0, 0.0],\n evapotranspiration=[2.79, 3.46, 3.65],\n discharge=[0.25694, 0.25812, 0.30983]\n)\n\nparams = Parameters(\n umax=0.01,\n lmax=0.01,\n cqof=0.01,\n ckif=200.0,\n ck12=10.0,\n tof=0.0,\n tif=0.0,\n tg=0.0,\n ckbf=500.0,\n csnow=0.0,\n snowtemp=0.0\n)\n\nnam = HydNAM(\n dataset=dataset,\n parameters=params,\n area=58.8,\n interval=24.0,\n start=None,\n end=None,\n spin_off=0.0\n)\nprint(f'Parameters: {nam.parameters}')\nprint(f'Statistics: {nam.statistics}')\ndf = nam.simulation_result.to_dataframe()\n```\n\n### 3. Optimize the Model\n\n```\nNAM.optimize()\n```\n\nThe model will calculate and check which `Parameters` are optimal for the model and use it as the main `Parameters` for\nthe model.\n\n### 4. Customize Parameters\n\n```\nnam.set_parameters(Parameters())\n```\n\n### 5. Show Discharge\n\n```\nfrom hydnam.chart import plot_q\n...\nplot_q(nam.simulation_result, only_obs_and_sim=False).show()\n```\n\n## License\n\nThis library is released under the MIT License.\n\n## Contact\n\nIf you have any questions or issues, please open an issue on [GitHub](https://github.com/duynguyen02/hydnam/issues) or\nemail us at [duynguyen02.dev@gmail.com](mailto:duynguyen02.dev@gmail.com).\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Python implementation of NedborAfstromnings Model (NAM) lumped rainfall\u2013runoff model",
"version": "1.1.2",
"project_urls": {
"Homepage": "https://github.com/duynguyen02/hydnam",
"Repository": "https://github.com/duynguyen02/hydnam"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "5ac28d7258557bef0fc110a00d9502721052bab2850f97b3345ceba9da2c4122",
"md5": "9e98d4bb5a7c6dacd667f00f986adceb",
"sha256": "c27debb1d713cad231fb33a82ba77ab28b612a005abfd1ee335f7669f939d5a1"
},
"downloads": -1,
"filename": "hydnam-1.1.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "9e98d4bb5a7c6dacd667f00f986adceb",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4.0,>=3.10",
"size": 8395,
"upload_time": "2025-01-01T17:40:16",
"upload_time_iso_8601": "2025-01-01T17:40:16.470779Z",
"url": "https://files.pythonhosted.org/packages/5a/c2/8d7258557bef0fc110a00d9502721052bab2850f97b3345ceba9da2c4122/hydnam-1.1.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "d1728c21d1ddf714744cc6f8c52299439310189133fff5b8117d43c2125d3e0a",
"md5": "b086a90ff217a2c56250ca20778e1bb8",
"sha256": "d5aa6dc78ae84010ac6aad404f73309e2669216ea697667b04627fcf80dbed9c"
},
"downloads": -1,
"filename": "hydnam-1.1.2.tar.gz",
"has_sig": false,
"md5_digest": "b086a90ff217a2c56250ca20778e1bb8",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4.0,>=3.10",
"size": 7476,
"upload_time": "2025-01-01T17:40:19",
"upload_time_iso_8601": "2025-01-01T17:40:19.266151Z",
"url": "https://files.pythonhosted.org/packages/d1/72/8c21d1ddf714744cc6f8c52299439310189133fff5b8117d43c2125d3e0a/hydnam-1.1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-01 17:40:19",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "duynguyen02",
"github_project": "hydnam",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "hydnam"
}
