74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
# -*- encoding: utf8 -*-
|
|
from PyQt4.QtGui import QColor
|
|
import numpy as np
|
|
import pyqtgraph as pg
|
|
|
|
from libmath.BDSlib import FitFunctionCreator
|
|
|
|
|
|
class Data:
|
|
def __init__( self, frequency=np.zeros(1), die_real=np.zeros(1), die_imag=np.zeros(1) ):
|
|
self.frequency = frequency
|
|
self.epsilon = die_real+1j*die_imag
|
|
self.frequency_fit = frequency[:]
|
|
self.epsilon_fit = die_real[:]*0+1j*die_imag[:]*0
|
|
myPen_imag = pg.mkPen(width=2.5, color=(255, 255, 127))
|
|
myPen_real = pg.mkPen(width=2.5, color=(51, 255, 127))
|
|
|
|
self.experimental_curve_imag = pg.PlotDataItem(x=[np.nan], y=[np.nan],
|
|
pen=None,
|
|
symbol='o',
|
|
symbolBrush=(255, 127, 0, 127))
|
|
self.experimental_curve_real = pg.PlotDataItem(x=[np.nan], y=[np.nan],
|
|
pen=QColor(0, 0, 0, 0),
|
|
symbol='o',
|
|
symbolBrush=(119, 202, 92, 127))
|
|
self.model_curve_imag = pg.PlotDataItem(x=[np.nan], y=[np.nan], pen=myPen_imag)
|
|
self.model_curve_real = pg.PlotDataItem(x=[np.nan], y=[np.nan], pen=myPen_real)
|
|
self.length = len(frequency)
|
|
self.meta = dict()
|
|
self.fit_limits = [frequency.min(), frequency.max(), die_imag.min(), die_imag.max()]
|
|
|
|
self.fit_param = None
|
|
self.fit_funcs = None # list of fit functions
|
|
self.hide_funcs = None # remove these func from the data
|
|
|
|
def set_fit( self, param, funcs ):
|
|
self.fit_funcs = funcs
|
|
self.hide_funcs = []
|
|
self.fit_param = param
|
|
fit_real, fit_imag = FitFunctionCreator().fitfcn(param, self.frequency_fit, *funcs)
|
|
self.epsilon_fit = fit_real+1j*fit_imag
|
|
|
|
def set_data( self, f, e_real, e_imag ):
|
|
self.frequency = f
|
|
self.frequency_fit = f[:]
|
|
self.epsilon = e_real+1j*e_imag
|
|
self.epsilon_fit = 0*e_real+1j*e_imag*0
|
|
self.fit_limits = [f.min(), f.max(), e_imag.min(), e_imag.max()]
|
|
self.experimental_curve_imag.setData(f, e_imag)
|
|
self.experimental_curve_real.setData(f, e_real)
|
|
|
|
def set_fit_xlimits( self, xmin, xmax ):
|
|
self.fit_limits[0] = xmin
|
|
self.fit_limits[1] = xmax
|
|
self.frequency_fit = self.frequency[(self.frequency <= xmax) & (self.frequency >= xmin)]
|
|
|
|
def set_fit_ylimits( self, ymin, ymax ):
|
|
self.fit_limits[2] = ymin
|
|
self.fit_limits[3] = ymax
|
|
|
|
def get_data( self ):
|
|
# mask = np.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[3])
|
|
return self.frequency[mask], self.epsilon[mask]
|
|
|
|
def remove_curves( self ):
|
|
print "remove data_curve"
|
|
# if self.data_curve is not None: self.data_curve.remove()
|
|
print "remove fitted_curve"
|
|
#if self.fitted_curve is not None: self.fitted_curve.remove()
|
|
|
|
|