29 lines
1.0 KiB
Python
29 lines
1.0 KiB
Python
import numpy as N
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class Data:
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def __init__(self, frequency, die_real, die_imag):
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self.frequency = frequency
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self.epsilon = die_real +1j *die_imag
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self.data_curve = None # mpl object
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self.fitted_curve = None # mpl object
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self.length = len(frequency)
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self.meta = dict()
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self.fit_limits = (frequency.min(),frequency.max(),die_imag.min(),die_imag.max())
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def __del__(self):
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self.remove_curves()
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#def set_fit_limits(self, limits=(None,None,None,None)):
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def get_data(self):
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"""
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"""
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mask = N.ones(len(self.frequency),dtype = 'bool')
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mask = (self.frequency > self.fit_limits[0]) & (self.frequency < self.fit_limits[1])
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mask &= (self.epsilon.imag > self.fit_limits[2]) & (self.epsilon.imag < self.fit_limits[1])
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return self.frequency[mask], self.epsilon[mask]
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def remove_curves(self):
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if self.data_curve is not None: self.data_curve.remove()
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if self.fitted_curve is not None: self.fitted_curve.remove()
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