import scipy.optimize as sp_opt
import pandas as pd



def construct_mask(x, subinterval_list):

    """ constructs a mask of length len(x) that indicates whether the entries of x lie within the subintervals,
    speficified in the subinterval_list.

    Parameters:
    x (array_like):
    subinterval_list (list of two-element tuples):

    Returns:
    mask (Bool array_like):
    
    Usage:

    x = np.array([0.0 0.25 0.5 0.75 1.5 2.0 2.5 3.0 3.5 4.0])
    subinterval_list = [(0.25,0.75),(2.5,3.5)]
    mask = contruct_mask(x,subinterval_list)     
    
    """

    mask = x < x.min()
    for subinterval in subinterval_list:
        mask = mask | ((x >= subinterval[0]) & (x <= subinterval[1]))

    return mask


def estimate_background(x,y,mask,method: str):

    """fits a background model based on the values of x and y indicated by a mask using a method, among available options.
    
    Parameters:
    x,y (array_like, e.g., np.array, pd.Series): 
    mask (Bool array_like):
    method (str): 

    Returns:
    y_bg (array_like): values of the fitted model at x, or similarly the obtained background estimate
    
    """

    if method == 'linear':
        def linear_model(x,m,b):
            return (m*x) + b
        
        popt, pcov = sp_opt.curve_fit(linear_model,x[mask],y[mask])

        y_bg = linear_model(x,*popt)

    else:
        raise ValueError("Parameter 'method' can only be set as 'linear'. Future code releases may include more options. ")
    
    return y_bg