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# MyAMI
The MyAMI Specific Ion Interaction Model for correcting stoichiometric equilibrium constants (*Ks*) for variations in seawater composition, made available available as the `pymyami` python package.
This package is a re-factor of the MyAMI model published by [Hain et al. (2015)](https://doi.org/10.1002/2014GB004986), which is available [here](https://github.com/MathisHain/MyAMI). The key differences between the original model and this package are:
- **Speed**: All calculations have been vectorised using NumPy, making MyAMI 2-3 orders of magnitude faster.
- **Direct Calculation**: `pymyami` directly calculates correction factors using the MyAMI model. This differs from [Hain et al. (2015)](https://doi.org/10.1002/2014GB004986), where the focus was on modifying parameters that could be input into the standard equations for calculating stoichiometric equilibrium products.
- **Correction Factor Focus**: `pymyami` produces *corrections factors* (F<sub>X,MyAMI</sub>) that can be applied to adjust stoichiometric equilibrium constants for variations in seawater composition, following K<sub>X,corr</sub> = K<sub>X,empirical</sub> * F<sub>X,MyAMI</sub>. For the direct calculation of Ks, including the corrections calculated by `pymyami`, please see the [Kgen](https://github.com/PalaeoCarb/Kgen) project.
- **Available Ions**: `pymyami` allows the modification of any ion in the model, rather than just Mg and Ca: Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, Sr<sup>2+</sup>, Cl<sup>-</sup>, B(OH)<sub>4</sub><sup>-</sup>, HCO<sub>3</sub><sup>-</sup>, CO<sub>3</sub><sup>2-</sup> and SO<sub>4</sub><sup>2-</sup>.
- **Parameter Transparrency**: Wherever possible, parameter tables are now constructed on-the-fly from raw tables in the Appendix of [Millero & Pierrot, 1998](https://doi.org/10.1023/A:1009656023546), making the origin of parameters explicit.
- **Pure Python**: There is no longer interface code for interacting with other languages (i.e. MATLAB). This caused a substantial performance bottleneck, and is discouraged. The [Kgen](https://github.com/PalaeoCarb/Kgen) project provides a convenient interface to use `pymyami` in R and MATLAB.
- **Approximation Method**: Where very fast calculations are required (e.g. Monte Carlo methods), `pymyami` uses a high-dimensional polynomial to approximate F<sub>X,MyAMI</sub> as a function of temperature, salinity, Mg and Ca. This is a very fast approximation, but is only accurate to within ~0.25%.
## Kgen
`pymyami` only calculations *correction factors* that can be applied to stoichiometric equilibrium constants (Ks). If you are looking for a convenient way to adjust Ks for variations in seawater composition, please see the [Kgen](https://github.com/PalaeoCarb/Kgen) project.
## Consistency with Hain et al. (2015)
The K correction factors calculated by `pymyami` are similar to those calculated by the code of [Hain et al. (2015)](https://doi.org/10.1002/2014GB004986), although there are some notable deviations of up to 4%. A summary of maximum and average differences compared to Hain et al. (2015) follows:
```
K0: 0.00% max, 0.00% avg
K1: 0.92% max, 0.05% avg
K2: 3.77% max, -0.07% avg
KW: 2.35% max, -0.42% avg
KB: 0.92% max, 0.05% avg
KspA: 1.87% max, 0.04% avg
KspC: 1.87% max, 0.04% avg
KS: 1.83% max, 0.10% avg
```
Note that maximum deviations are seen when the change in Mg and Ca correlates, meaning that these deviations shouldn't be too important for palaeo-seawater calculations because the concentration of Mg and Ca tend to be anti-correlated through geological history.
These differences arise from typo corrections in the original code, and pymyami should be closer to the original MIAMI model of Millerot and Pierrot (1998).
## Installation
The model is available as a PyPI package, which can be installed by:
```python
pip install pymyami
```
## Example Usage
```python
from pymyami impor t calc_Fcorr, approximate_Fcorr
# run the model to calculate correction factors
calc_Fcorr(TempC=35, Sal=36.2, Mg=0.03, Ca=0.012)
>>> {'KspC': 0.7843309390178521,
'KspA': 0.7843309390178521,
'K1': 1.002405617170862,
'K2': 0.7885093392132683,
'KW': 0.7459757009835559,
'KB': 0.9382494946753764,
'K0': 1.0056418412233974,
'KS': 0.9573891319238595}
# use the polynomial approximation to calculate correction factors
approximate_Fcorr(TempC=35, Sal=36.2, Mg=0.03, Ca=0.012)
>>> UserWarning: WARNING: using approximate MyAMI K correction factors instead of calculated ones. These are only accurate to within ~0.25%. Please dont use them for anything critical.
>>> {'K0': array(1.00565919),
'K1': array(1.00238861),
'K2': array(0.78858314),
'KB': array(0.93815884),
'KW': array(0.74594823),
'KspC': array(0.78442705),
'KspA': array(0.78442705),
'KS': array(0.95738293)}
```
Raw data
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"description": "<div align=\"right\">\n<a href=\"https://github.com/PalaeoCarb/MyAMI/actions/workflows/test-myami.yml\"><img src=\"https://github.com/PalaeoCarb/MyAMI/workflows/Check%20MyAMI%20Performance/badge.svg\" height=18></a>\n<a href=\"https://pypi.org/project/pymyami\"><img src=\"https://badge.fury.io/py/pymyami.svg\" height=18></a>\n</div>\n\n# MyAMI\nThe MyAMI Specific Ion Interaction Model for correcting stoichiometric equilibrium constants (*Ks*) for variations in seawater composition, made available available as the `pymyami` python package.\n\nThis package is a re-factor of the MyAMI model published by [Hain et al. (2015)](https://doi.org/10.1002/2014GB004986), which is available [here](https://github.com/MathisHain/MyAMI). The key differences between the original model and this package are:\n- **Speed**: All calculations have been vectorised using NumPy, making MyAMI 2-3 orders of magnitude faster.\n- **Direct Calculation**: `pymyami` directly calculates correction factors using the MyAMI model. This differs from [Hain et al. (2015)](https://doi.org/10.1002/2014GB004986), where the focus was on modifying parameters that could be input into the standard equations for calculating stoichiometric equilibrium products.\n- **Correction Factor Focus**: `pymyami` produces *corrections factors* (F<sub>X,MyAMI</sub>) that can be applied to adjust stoichiometric equilibrium constants for variations in seawater composition, following K<sub>X,corr</sub> = K<sub>X,empirical</sub> * F<sub>X,MyAMI</sub>. For the direct calculation of Ks, including the corrections calculated by `pymyami`, please see the [Kgen](https://github.com/PalaeoCarb/Kgen) project.\n- **Available Ions**: `pymyami` allows the modification of any ion in the model, rather than just Mg and Ca: Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, Sr<sup>2+</sup>, Cl<sup>-</sup>, B(OH)<sub>4</sub><sup>-</sup>, HCO<sub>3</sub><sup>-</sup>, CO<sub>3</sub><sup>2-</sup> and SO<sub>4</sub><sup>2-</sup>.\n- **Parameter Transparrency**: Wherever possible, parameter tables are now constructed on-the-fly from raw tables in the Appendix of [Millero & Pierrot, 1998](https://doi.org/10.1023/A:1009656023546), making the origin of parameters explicit.\n- **Pure Python**: There is no longer interface code for interacting with other languages (i.e. MATLAB). This caused a substantial performance bottleneck, and is discouraged. The [Kgen](https://github.com/PalaeoCarb/Kgen) project provides a convenient interface to use `pymyami` in R and MATLAB.\n- **Approximation Method**: Where very fast calculations are required (e.g. Monte Carlo methods), `pymyami` uses a high-dimensional polynomial to approximate F<sub>X,MyAMI</sub> as a function of temperature, salinity, Mg and Ca. This is a very fast approximation, but is only accurate to within ~0.25%.\n\n## Kgen\n`pymyami` only calculations *correction factors* that can be applied to stoichiometric equilibrium constants (Ks). If you are looking for a convenient way to adjust Ks for variations in seawater composition, please see the [Kgen](https://github.com/PalaeoCarb/Kgen) project.\n\n## Consistency with Hain et al. (2015)\nThe K correction factors calculated by `pymyami` are similar to those calculated by the code of [Hain et al. (2015)](https://doi.org/10.1002/2014GB004986), although there are some notable deviations of up to 4%. A summary of maximum and average differences compared to Hain et al. 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