import glob 
from pathlib import Path 

import boost_histogram as bh
import numpy as np 

def get_first_file(path : Path, file_prefix : str): 
    """
    Get the first file with lowest index in directory that matches the given prefix.
    """
    first_file = min(path.glob(f'{file_prefix}*'), default=None) 
    if first_file is None:
        raise ValueError(f"No files found in {path} with prefix {file_prefix}")
    return first_file


def save_fit_parameters(fit_params : dict, output_file : Path):
    """
    Save the fit parameters to a text file.

    Parameters
    ----------
    fit_params : dict
        The fit parameters to save.
    output_file : Path
        The path to the output file.
    """
    with open(output_file, 'w') as f:
        for key, value in fit_params.items():
            f.write(f"{key}: {value}\n")


def create_histogram_from_data(data : np.ndarray, bin_range : tuple[float, float] = None, bin_width : float = None) -> bh.Histogram:
    """
    Create a histogram from the given data.

    Parameters
    ----------
    data : np.ndarray
        The data to create the histogram from.
    bin_range : tuple[float, float], optional
        The range of the bins. Default is None, which means the range is determined from the data.
    bin_width : float, optional
        The width of each bin. Default is None, which means the number of bins is determined automatically.

    Returns
    -------
    bh.Histogram
        The created boost histogram.
    """

    if bin_range is None:
        min = np.min(data)
        max = np.max(data)
        bin_range = (min - 0.05*(max - min), max + 0.05*(max - min)) # add 5% margin to the range
    
    if bin_width is None: 
        bins = 200 # 0.5 % 
    else: 
        bins = int((bin_range[1] - bin_range[0]) / bin_width)
    
    hist = bh.Histogram(bh.axis.Regular(bins, bin_range[0], bin_range[1]))
    hist.fill(data)
    return hist