eos

There are 2 Python .py files to choose from, both serving the same purpose. The key differences between them are outlined below. In both source codes, you can modify the initial guesses for K0 and K0' within the MAIN function. Additionally, you have the option to fix K0 and K0' in the BM and Vinet functions, which can be useful when dealing with low-quality data.

requirements

• pandas
• numpy
• matplotlib.pyplot
• argparse
• scipy.optimize

eos.py

This version is simpler and more straightforward. It reads a .xlsx file, specifically one pressure column and one volume column. The resulting plot is saved.

To run the code, follow these steps:

1. Place the eos.py file in the same directory as your .xlsx data file.
2. Open your terminal.
3. Navigate to the folder containing the files.
4. Execute the following command: 'python eos.py DATA.xlsx PRESSURE_COLUMN VOLUME_COLUMN SAVEFIG_NAME.png'

eos_addition.py

This version is slightly more complex, but still easy to understand. It reads a .xlsx file, allowing you to specify multiple pressure columns and volume columns to process. The data points are plotted using the volume column headers as labels, which are extracted from the .xlsx file. The resulting plot is saved.

To run the code, follow these steps:

1. Place the eos_addition.py file in the same directory as your .xlsx data file.
2. Open your terminal.
3. Navigate to the folder containing the files.
4. Execute the following command: 'python eos.py DESIRED_TITLE_OF_PLOT SAVEFIG_NAME.png DATA.xlsx --pressures PRESSURE_COLUMN1 PRESSURE_COLUMN2 --volumes VOLUME_COLUMN1 VOLUME_COLUMN2'