# PyLake
This work present methods used to compute meaningful physical properties in aquatic sciences.
The methods are based on Xarray.
Multi-dimensional large time-series array are compatible if an xarray is passed as input.
Algorithms and documentation are sometimes inspired by LakeAnalyzer in R (https://github.com/GLEON/rLakeAnalyzer)
Implemented methods:
* Thermocline
* Mixed layer
* Metalimnion extent (epilimnion and hypolimnion depth)
* Wedderburn Number
* Schmidt stability
* Heat content
* Seiche periode
* Lake Number
* Brunt-Vaisala frequency
* Average layer temperature
* Monin-Obhukov
## Installation
Pylake use Dask which require a python version >=3.8
`pip install pylake`
## Usage
Have a look in the notebooks, an example is provided
```python
import pylake
import numpy as np
Temp = np.array([14.3,14,12.1,10,9.7,9.5,6,5])
depth = np.array([1,2,3,4,5,6,7,8])
epilimnion, hypolimnion = pylake.metalimnion(temp, depth)
```
## Work in progress
Warning messages
Lake metabolizer is being implemented.
Raw data
{
"_id": null,
"home_page": "https://github.com/eawag-surface-waters-research/pylake",
"name": "pylake",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "python, pylake, Lake analyzer, environmental data, Physical properties",
"author": "Hugo Cruz",
"author_email": "<huggcruzz@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/72/f6/ab2f979e839784fc85aa64b9cf4de4cebc6e175f2b03e3be8370a12a6594/pylake-0.1.10.tar.gz",
"platform": null,
"description": "# PyLake\n\nThis work present methods used to compute meaningful physical properties in aquatic sciences.\n\nThe methods are based on Xarray. \nMulti-dimensional large time-series array are compatible if an xarray is passed as input.\n\nAlgorithms and documentation are sometimes inspired by LakeAnalyzer in R (https://github.com/GLEON/rLakeAnalyzer)\n\nImplemented methods:\n* Thermocline\n* Mixed layer\n* Metalimnion extent (epilimnion and hypolimnion depth)\n* Wedderburn Number\n* Schmidt stability\n* Heat content\n* Seiche periode\n* Lake Number\n* Brunt-Vaisala frequency\n* Average layer temperature\n* Monin-Obhukov \n\n## Installation\n\nPylake use Dask which require a python version >=3.8\n\n`pip install pylake`\n\n## Usage\n\n\nHave a look in the notebooks, an example is provided\n\n```python\nimport pylake\nimport numpy as np\n\nTemp = np.array([14.3,14,12.1,10,9.7,9.5,6,5])\ndepth = np.array([1,2,3,4,5,6,7,8])\nepilimnion, hypolimnion = pylake.metalimnion(temp, depth)\n```\n\n ## Work in progress\n Warning messages\n Lake metabolizer is being implemented. \n",
"bugtrack_url": null,
"license": "MIT",
"summary": "pylake",
"version": "0.1.10",
"project_urls": {
"Homepage": "https://github.com/eawag-surface-waters-research/pylake"
},
"split_keywords": [
"python",
" pylake",
" lake analyzer",
" environmental data",
" physical properties"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "72f6ab2f979e839784fc85aa64b9cf4de4cebc6e175f2b03e3be8370a12a6594",
"md5": "d3b1d92a263387d884e1425dab921efa",
"sha256": "89a930dff07feb90cc4f2a33814ecac686411bbbd5fb48709205c32358aec5f9"
},
"downloads": -1,
"filename": "pylake-0.1.10.tar.gz",
"has_sig": false,
"md5_digest": "d3b1d92a263387d884e1425dab921efa",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 21034,
"upload_time": "2024-09-13T09:50:42",
"upload_time_iso_8601": "2024-09-13T09:50:42.554473Z",
"url": "https://files.pythonhosted.org/packages/72/f6/ab2f979e839784fc85aa64b9cf4de4cebc6e175f2b03e3be8370a12a6594/pylake-0.1.10.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-13 09:50:42",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "eawag-surface-waters-research",
"github_project": "pylake",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "pylake"
}
JSON
