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
Pylake use Dask which require a python version >=3.8
pip install pylake
Have a look in the notebooks, an example is provided
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)Warning messages Lake metabolizer is being implemented.
pylake.pylake_metabolizer.o2_at_sat function output data is not exactly the same as the output data of R'LakeMetabolizer' beasuse def o2_at_sat(temp, baro=None, altitude=0, salinity=.2, model='garcia-benson'): salinity = .2 is the default. if is freshwater, must change salinity parameter.