improved plot exporting

import xml.etree.ElementTree as ET

import csv

import pandas as pd

import os

import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

def write_to_xml(data_dict: dict, path: str):

    root = ET.Element("automaton")

def xml_to_csv(xml_path: str, csv_path: str):

    df = xml_to_df(xml_path)

    df.to_csv(csv_path)

    df.to_csv(csv_path, index=False)

def mode_to_str(mode: str):

        return "pow_f"

    elif mode == "r":

        return "pow_rev"

    elif mode == "p2":

        return "port_2comp"

    return ""

    return base_name + "_" + mode_to_str(mode) + "_" +  "_".join(options)

def merge_dataframes(dfs: list[pd.DataFrame], on_columns = ["name", "orig_size"]) -> pd.DataFrame:

    merged_df = dfs[0]

    for df in dfs[1:]:

        merged_df = pd.merge(merged_df, df, on=on_columns)

    return merged_df

def extension(path: str):

    _, file = os.path.split(path)

    name, ext = os.path.splitext(file)

    return ext

def load_dataframes(dir: str):

    dfs = []

    for filename in os.scandir(dir):

        path = filename.path

        ext = extension(path)

        df = None

        if ext == ".csv":

            df = pd.read_csv(path)

        elif ext == ".xml":

            df = pd.read_xml(path)

        if df is not None:

            dfs.append(df)

    return dfs

def compose_into_one_csv(dir: str, merged_csv_path: str | None = None, drop_columns: list[str] = []):

    dfs = load_dataframes(dir)

    dfs = remove_useess_columns(dfs, drop_columns)

    merged_df = merge_dataframes(dfs)

    if merged_csv_path is None:

        merged_csv_path = os.path.join(dir, "all.csv")

    merged_df.to_csv(merged_csv_path, index=False)

def remove_useess_columns(dfs: list[pd.DataFrame], useless_columns: list[str]) -> list[pd.DataFrame]:

    updated_dfs = []

    for df in dfs:

        for column_name in useless_columns:

            if column_name in df.columns:

                df = df.drop(column_name, axis=1)

        updated_dfs.append(df)

    return updated_dfs

def graph_basic(data: pd.DataFrame, x_col: str, y_col: str, 

                x_col_name: str, y_col_name: str, 

                plot_limit: int | None = None):

    fig, ax = plt.subplots()

    plot = sns.scatterplot(data=data, x=x_col, y=y_col, ax=ax)

    # draw middle line

    max_val = np.max([data[x_col].max(), data[y_col].max()])

    line = np.linspace(0, max_val, 1000)

    ax.plot(line, line, "-r")

    # set axis names

    ax.set_xlabel(x_col_name)

    ax.set_ylabel(y_col_name)

    if plot_limit is not None and plot_limit < max_val:

        plt.xlim(0, plot_limit)

        plt.ylim(0, plot_limit)

def convert_cols_to_bool(df: pd.DataFrame, columns: list[str]) -> pd.DataFrame:

    for col in columns:

        df[col] = df[col].astype(bool)

if __name__ == "__main__":

    directory = "/home/xstepkov/aligater/src/experiments/csv_files/automatark"

    compose_into_one_csv(directory, drop_columns=["success", "Unnamed: 0"])