all_formula_1_table.py

"""
This script generates a statistical analysis for the prediction of specific ion effect property values by fitting all ions to charge transfer model. 

Args:
    file_name (str): path to the CSV file containing the data to be analyzed.
    formula_list (list of str): charge transfer models to be fitted.
    property (str): thermodynamic property to be fitted (e.g. "dG", "dH").
    save_dir_path (str): path to the directory where the output table will be saved.

Output:
    text file (file): A table of the results of the fitting and assess quality of the fit. (parameters and error analysis)
        "all_<property>.txt" is a LaTex formatted table, which you can directly copy to LaTex editor.

Usage:
    python all_formula_1_table.py

Notes:
    The script uses modules from `modules`.
    Good for evaluating the quality of the fit for all ions under one property.
"""

import modules as mod
from tabulate import tabulate
import pandas as pd
from contextlib import suppress
import numpy as np
import time
import os

file_name = f"csv/ions.csv"
formula_list = ["Q1R1", "Q1R2", "Q2R1", "Q2R2", "Q3R1","Q3R2", "Q4R1", "Q4R2", "Q5R1", "Q5R2", "Q6R1","Q6R2", "Q7R1", "Q7R2", "Q8R1", "Q8R2"]
property = "dH"
save_dir_path = "tables"

if __name__ == "__main__":
    start = time.perf_counter()
    if not os.path.exists(save_dir_path):
        os.makedirs(save_dir_path)
    stats_list = []
    aicc_list = []
    for each, formula in enumerate(formula_list):
        formula_func = getattr(mod.formulas, formula)
        df = mod.read_data(file_name, property, formula, by_charge=False)
        with suppress(TypeError, ZeroDivisionError):
            prop, fit_data, parameters, SE = mod.fit_parameters(formula, formula_func, property, df)
            mean_abs_err, rmse, loocv, aicc = mod.get_errors(prop, fit_data, parameters)
            aicc_list.append(aicc)
            stats_df = mod.print_stats(formula, parameters, SE, mean_abs_err, rmse, loocv, aicc)
            stats_list.append(stats_df)
    stats_result = pd.concat(stats_list, axis=1)
    stats_result = mod.replace_aicc(aicc_list, stats_result)
    stats_result = stats_result.fillna("-")
    with open(f"{save_dir_path}/all_{property}.txt", "w", encoding="utf-8") as f:
        f.write(f"All Charges\n")
        f.write(tabulate(stats_result, headers="keys", stralign="center", tablefmt='fancy_grid'))
    end = time.perf_counter()
    print(f'Finished in {round(end-start,2)} seconds')