pagos 0.1.1

Python Analysis of Groundwater and Ocean Samples

PAGOS

Python Analysis of Groundwater and Ocean Samples_ (PAGOS) is a Python toolkit for creating and testing hydrological gas exchange models. Datasets from field campaigns containing data for a number of gas tracers can be used to optimise the parameters of gas exchange models, expressed as Python functions. These can be PAGOS' built-in models or user-defined.

Installation

Use the package manager pip to install PAGOS.

pip install pagos

Usage

How quantities are defined in PAGOS

As this package is designed to be used with real-world measurements, quantites in PAGOS encapsulate measurement uncertainty using uncertainties and units with pint. Although many of the functions in PAGOS will work with other types, they are designed for use with Quantity (see Pint) objects with ufloat (see uncertainties) magnitudes. The following code produces such a quantity representing the speed measurement (11.2 ± 0.3) m/s.

from pint import Quantity, UnitRegistry
from uncertainties import ufloat
u = UnitRegistry()
mySpeed = u.Quantity(ufloat(11.2, 0.3), 'm/s')
mySpeed
# -> <Quantity(11.2000+/-0.3000, 'meter / second')>

Writing measurements like this is tedious, so PAGOS implements a shortcut Q():

from pagos import Q
mySpeed = Q(11.2, 'm/s', 0.3)
mySpeed
# -> <Quantity(11.2000+/-0.3000, 'meter / second')>

Gas and water property calculations

The properties of seawater and various gases can be calculated with the water and gas modules. For example, calculating the density of, kinematic viscosity of and vapour pressure over water at a given temperature and salinity:

from pagos import water
# properties of water at (6.0 ± 0.1) °C temperature and (9.12 ± 0.05) ‰ salinity
T1 = Q(6.0, 'degC', 0.1)
S1 = Q(9.12, 'permille', 0.05)
water.calc_dens(T1, S1) # -> <Quantity(1007.16+/-0.04, 'kilogram / meter ** 3')>
water.calc_vappres(T1) # -> <Quantity(9.34698+/-0.06466, 'millibar')>
water.calc_kinvisc(T1, S1) # -> <Quantity((1.48410+/-0.00457)e-06, 'meter ** 2 / second')>

And calculating the Schmidt number and concentration of nitrogen in the water:

from pagos import gas
p1 = Q(1.00, 'atm', 0.01)
gas.calc_Sc('N2', T1, S1) # -> <Quantity(1275.31+/-7.56, 'dimensionless')>
gas.calc_Ceq('N2', T1, S1, p1, 'cc/g') # -> <Quantity(0.0149088+/-0.0001544, 'cubic_centimeter / gram')>

Inverse Modelling

The real power of PAGOS is in its gas exchange modelling capabilities. These can be seen in the tests folder and are briefly explained here. The fitmodel function can be used to fit a number of parameters of a gas exchange model using a least-squares minimisation. Here is an example using the fake concentration data taken from the tests folder:

from pagos.modelling import fitmodel
noblegases = ['He', 'Ne', 'Ar', 'Kr', 'Xe']
argtracerlabels = {g:'sample ' + g for g in noblegases}
argtracerlabels['S'] = 'Sal. qu.'
argtracerlabels['p'] = 'prs. qu.'
fakedata_fit = fitmodel(fitmodel=ua_tim_r_taylor,
                        data=fakedata_concs,
                        to_fit=['T_r', 'A', 'R'],
                        init_guess=[Q(1, 'degC'), Q(1e-4, 'cc/g'), Q(1e-2, 'dimensionless')],
                        tracers_used=noblegases,
                        arg_tracer_labels=argtracerlabels,
                        constraints={'A':(0, np.inf), 'R':(0, 1)},
                        tqdm_bar=True,
                        fitted_only=False)

The arguments are explained in the method docstrings (*and will be explained in future, more thorough documentation!). This produces an extended DataFrame, appending columns with the fitted values of the parameters 'T_r', 'A' and 'R'.

DataFrame: fakedata_fit
|       sample He        |        sample Ne       | ... |    T_r    |      A      |   R  |
|------------------------|------------------------| ... |-----------|-------------|------|
| (1.30 +- 0.04)e-5 cc/g | (3.05 +- 0.04)e-5 cc/g | ... | 0.34 degC | 2.5e-4 cc/g | 0.03 |
| (1.30 +- 0.04)e-5 cc/g | (3.05 +- 0.04)e-5 cc/g | ... | 4.75 degC | 6.0e-4 cc/g | 0.10 |
            :                        :               :        :            :          :

Each row of parameters on the right is fit using the gas tracers on the left.

Forward modelling

The forward_model function can then use previously modelled or simply predefined model parameters to calculate a set of tracer concentrations.

paramlabels = {'T_r':'Rech. temp. qu.', 'A':'Exc. air qu.', 'R':'Ice frac. qu.',
               'S':'Sal. qu.', 'p':'prs. qu.'}
fakedata_concs = forward_model(ua_tim_r_taylor, fakedata_fit, noblegases,
                               paramlabels, fitted_only=False)

In this example, as it is modelling based on parameters which were already fitted from a set of given tracer concentrations, these concentrations should be reproduced.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Feel free to contact the author, Stanley Scott, at [e-mail].

License

BSD-3-Clause, see LICENSE file.
PAGOS was developed for Python 3 by Stanley Scott and Chiara Hubner.