A package for modelling simultaneous nitrogen aggregation and cooling in diamond. See also Wincott et al. 2026¹.

This package was developed under Python v. 3.12.9. All required packages are specified in requirements.txt

1. Install Python v. 3.12 or higher

2. Download or clone the SNAC package and place into a directory of your choice

3. Create a virtual environment in your working directory, then activate the newly created virtual environment:

   python -m venv \path\to\new\virtual\environment

   or, in VSCode, open the Command Palette (Ctrl+Shift+P), search for the Python: Create Environment command, and select it. Then select a Python interpreter.

   .\venv\Scripts\activate

4. install dependencies

   pip install -r requirements.txt

5. install the snac package

   pip install -e .

Refer to Ipython notebooks in documentation:

automatedSNAC.ipynb

eventfulSNAC.ipynb

manualSNAC.ipynb

This is the main package. It contains the following modules.

This module defines the class Diamond. It is used for storing relevant information about a diamond (core, rim and kimberlite ages as well as nitrogen aggregation data). Diamond objects can be created from json files (Diamond.from_json() method) and stored as json files (Diamond.to_json() method). A Diamond object instance is passed to the SNACmodel (see below).

Example:

from snac.diamond import Diamond diamond = Diamond( age_core=3520, age_rim=1860, age_kimberlite=0, c_NT=625, c_agg=0.863, r_NT=801, r_agg=0.197, ) 

This module defines the class AggregationModel, which is used for the forward modelling of simultaneous nitrogen aggregation and cooling. After instantiating an AggregationModel object, its .run() method (AggregationModel.run()) can be used to optimise the initial temperature and cooling rate so the predicted aggregation state matches the measured data. Upon initialisation, the parameters cooling_rate0 and T_start0 are set, which will be used as first guesses once the run() method is used. The AggregationModel.plot_T_history() and AggregationModel.plot_aggregation_history() methods can be used to produce diagrams from the output. The temperature and aggregation history can be saved as csv via AggregationModel.save_history(filename) (see automatedSNAC.ipynb in documentation).

AggregationModel.run() accesses the function aggregation.aggregate_and_cool(), which is the computational foundation of the SNAC model. Beyond its basic use for predicting nitrogen aggregation during continuous cooling, a number of temperature scenarios can be modelled. Examples are provided in eventfulSNAC.ipynb within documentation.

Basic example:

from SNACmodel import AggregtionModel model = AggregationModel( diamond=diamond, cooling_rate0=0.01, T_start0=1200, rate_bounds=(0.001, 0.12), T_bounds=(1000, 1450), dt=1 ) # fit model to measured N aggregation state of the diamond model.run() # plot results model.plot_T_history() model.plot_aggregation_history() # save results model.save_history('SNACoutput.csv') 

Example with temperature scenario:

from SNACmodel import AggregtionModel model = AggregationModel( diamond=diamond, cooling_rate0=0.01, T_start0=1200, rate_bounds=(0.001, 0.12), T_bounds=(1000, 1450), dt=1, T_scenario='hot_spike', scenario_params=(50, 1000, 25) ) # fit model to measured N aggregation state of the diamond model.run() # plot results model.plot_T_history() model.plot_aggregation_history() # save results model.save_history('SNACoutput.csv') 

Note: using AggregationModel.get_history() or AggregationModel.plot_aggregation_history() without prior fitting (i.e. without using AggregationModel.run() first) will use the initial guesses forward model nitrogen aggregation and cooling. This can be useful to explore the effect of different parameters on the outcome. See also manualSNAC.ipynb in documentation.

This module contains a number of functions, including the main function used to model simultaneous nitrogen aggregation:

• aggregate(): this function is used to calculate the concentration of nitrogen in A-centres after a given duration spent at a given temperature
• Temp_N(): this function can be used to calculate a conventional model temperature based on the measured nitrogen aggregation state and diamond age
• aggregate_and_cool(): the computational heart of the SNAC model. This function models simultaneous nitrogen aggregation and cooling, including temperature "scenarios".

This module provides function describing cooling over time

This directory contains Ipython notebooks that show how the snac package can be used. Each notebook contains a worked example.

• automatedSNAC: Example of using the SNAC model for automated modelling

• eventfulSNAC: Examples of using temperature scenarios for nitrogen aggregation modelling

• manualSNAC: Example of using the SNAC model to manually explore simultaneous cooling and nitrogen aggregation

This script allows calling the SNAC model from another script or, from the command line, e.g.:

python autoSNAC.py --save_dir "C:/User/SNAC/" --model_file "C:/GitHub/SNAC/documentation/example_model.json" 

Note: You may have to activate your virtual Python environment before executing the script, e.g.

.\.venv\Scripts\activate