import numpy as N
class Data:
    def __init__(self, frequency, die_real, die_imag):
        self.frequency = frequency
        self.epsilon  = die_real +1j *die_imag
        self.data_curve = None # mpl object
        self.fitted_curve = None # mpl object
        self.length = len(frequency)
        self.meta = dict()
        self.fit_limits = (frequency.min(),frequency.max(),die_imag.min(),die_imag.max())

    def __del__(self):
        self.remove_curves()

    #def set_fit_limits(self, limits=(None,None,None,None)):
    def get_data(self):
        """
        
        """
        mask = N.ones(len(self.frequency),dtype = 'bool')
        mask = (self.frequency > self.fit_limits[0]) & (self.frequency < self.fit_limits[1])
        mask &= (self.epsilon.imag > self.fit_limits[2]) & (self.epsilon.imag < self.fit_limits[1])
        return self.frequency[mask], self.epsilon[mask]

    def remove_curves(self):
        if self.data_curve is not None: self.data_curve.remove()
        if self.fitted_curve is not None: self.fitted_curve.remove()