started adding real part, non-lin complex fitting
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374ed7e510
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df48519f5a
69
QDS.py
69
QDS.py
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@ -9,7 +9,7 @@ from PyQt4.QtCore import *
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from PyQt4.QtGui import *
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import matplotlib
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from mathlib import fit_anneal, fit_lbfgsb, fit_odr, hn
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from mathlib import fit_anneal, fit_lbfgsb, fit_odr, hn, FitFunctionCreator, fit_odr_cmplx
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matplotlib.use('agg')
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@ -90,7 +90,8 @@ class AppWindow(QMainWindow):
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self.data = Data()
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self.fit_boundary = pg.LinearRegionItem(brush=QColor(254,254,254,10))
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self.ui.graphicsView.addItem(self.data.data_curve)
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self.ui.graphicsView.addItem(self.data.data_curve_imag)
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self.ui.graphicsView.addItem(self.data.data_curve_real)
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self.ui.graphicsView.addItem(self.data.fitted_curve)
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self.ui.graphicsView.addItem(self.fit_boundary)
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self.ui.graphicsView.setLogMode(x=True, y=True)
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@ -124,58 +125,59 @@ class AppWindow(QMainWindow):
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else:
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f = open("fitresults.log", "a")
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# write header
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f.write('# T ')
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parfmt = "%.2f" # T formatting
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f.write("#%7s"%('T'))
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parfmt = "%8.2f" # T formatting
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# if self.Conductivity != None: pass# always true
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f.write("%8s %8s %8s " % ("e_s", "sig", "pow_sig"))
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parfmt += " %.3g %.3g %.2f " # conductivity formatting
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f.write("%9s%9s%9s " % ("e_s", "sig", "pow_sig"))
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parfmt += "%9.3g%9.3g%9.2f " # conductivity formatting
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for i, pb in enumerate(self.peakBoxes):
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enum_peak = ("e_inf_%i" % i, "tau_%i" % i, "alpha_%i" % i, "beta_%i" % i)
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f.write("%8s %8s %8s %8s " % enum_peak)
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parfmt += " %.3g %.3g %.2f %.2f" # peak formatting
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f.write("high_lim lower_lim") # TODO: store limits
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f.write("%9s%9s%9s%9s " % enum_peak)
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print enum_peak
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parfmt += "%9.3g%9.3g%9.2f%9.2f" # peak formatting
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f.write("fit_xlow fit_xhigh") # TODO: store limits
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parfmt += "%9.3g%9.3g"
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f.write('\n')
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f.flush()
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#f.write("%3.2f "%(self.data.meta["T"]))
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pars = list(self.fitresult)
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pars.insert(0, self.data.meta["T"])
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pars.append(self.data.fit_limits[0])
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pars.append(self.data.fit_limits[1])
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N.savetxt(f, N.array([pars, ]), fmt=parfmt, delimiter=" ")
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f.close()
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def saveFitFigure(self):
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fig = pyplot.Figure(figsize=(3.54, 2.75))
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fig = pyplot.figure(figsize=(3.54, 2.75))
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font = {'family' : 'sans serif',
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'weight' : 'normal',
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'size' : 16}
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'size' : 8}
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matplotlib.rc('font', **font)
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print self.data.epsilon_fit.shape, type(self.data.epsilon_fit)
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pyplot.loglog(self.data.frequency, self.data.epsilon.imag, 'bo', markersize=4, label="Data")
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pyplot.loglog(self.data.frequency, self.data.epsilon_fit, 'r-', lw=2, label="Fit")
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pyplot.loglog(self.data.frequency, self.data.epsilon.imag, 'bo', markersize=3, label="Data")
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pyplot.loglog(self.data.frequency, self.data.epsilon_fit, 'r-', lw=1, label="Fit")
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for i,peak in enumerate(self.peakBoxes):
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f,eps = peak.get_data()
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color = hex2color(str(peak.get_color().name()))
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pyplot.loglog(f,eps, ls="--", color=color , lw=1.5, label="Peak %i"%i)
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pyplot.loglog(f,eps, ls="--", color=color , lw=0.75, label="Peak %i"%i)
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if self.Conductivity != None:
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f,eps = self.Conductivity.get_conductivity()
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color = hex2color(str(self.Conductivity.get_color().name()))
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pyplot.loglog(f,eps, ls="-.", color=color, lw=1.5, label="Cond.")
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pyplot.loglog(f,eps, ls="-.", color=color, lw=0.75, label="Cond.")
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f,eps = self.Conductivity.get_epsilon_static()
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pyplot.loglog(f,eps, ls=":", color=color, lw=1.5, label=r'$\epsilon_0$')
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pyplot.loglog(f,eps, ls=":", color=color, lw=0.75, label=r'$\epsilon_0$')
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pyplot.legend(title="T=%.1f K"%(self.data.meta["T"]) )
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for i in (0,1): pyplot.axvline(x=self.data.fit_limits[i], color='g', ls="--")
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pyplot.legend(title = "T=%.1f K"%(self.data.meta["T"]))
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pyplot.grid()
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pyplot.xlabel('f/Hz')
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pyplot.ylabel('eps"')
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pyplot.savefig("test.png")
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pyplot.savefig(os.path.splitext(self.filepath)[0]+".png")
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fig.clear()
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def set_fit_xlimits(self, xmin, xmax):
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self.data.fit_limits = (xmin, xmax, None, None)
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self.updatePlot()
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def addCond(self, pos):
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if self.Conductivity != None:
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@ -239,18 +241,29 @@ class AppWindow(QMainWindow):
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[start_parameter.append(i) for i in pb.getParameter()]
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[fixed_params.append(i) for i in pb.getFixed()]
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log10fmin, log10fmax = self.fit_boundary.getRegion()
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xmin,xmax,ymin,ymax = self.data.fit_limits
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self.data.fit_limits = [10**log10fmin, 10**log10fmax,ymin,ymax]
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self.data.set_fit_xlimits(10**log10fmin, 10**log10fmax)
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fit_methods = [fit_odr, fit_lbfgsb, fit_anneal]
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print "StartParameter", start_parameter
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print "FixedParameter", fixed_params
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print "Limits (xmin, xmax, ymin, ymax)", self.data.fit_limits
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_freq, _fit = self.data.get_data()
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result = fit_methods[method](_freq, _fit.imag, start_parameter, fixed_params)
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# check new method
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if 1:
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funcs = ["static","conductivity"] if self.Conductivity != None else []
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for pb in self.peakBoxes.keys():
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funcs.append("hn")
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newres = fit_odr_cmplx(_freq, _fit, start_parameter, fixed_params, funcs)
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print newres
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self.fitresult = result
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for i, pb in enumerate(self.peakBoxes.keys()):
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delta_eps, tau, a, b = result[3 + i * 4:3 + (i + 1) * 4]
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delta_eps, tau, a, b = result[3 + i*4 : 3 + (i + 1)*4]
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pb.setParameter(delta_eps, tau, a, b)
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e_static, sigma, sigma_N = result[:3]
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if self.Conductivity != None:
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@ -262,6 +275,7 @@ class AppWindow(QMainWindow):
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def openFile(self):
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path = unicode(QFileDialog.getOpenFileName(self, "Open file"))
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self.filepath=path
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#path = "MCM42PG0_199.96K.dat"
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# TODO anaylize file (LF,MF, HF) and act accordingly
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data = N.loadtxt(path, skiprows=4)
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@ -304,7 +318,8 @@ class AppWindow(QMainWindow):
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fit += self.Conductivity.getParameter()[0] # eps static
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self.data.epsilon_fit = fit[:]
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self.data.data_curve.setData(self.data.frequency, self.data.epsilon.imag)
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self.data.data_curve_imag.setData(self.data.frequency, self.data.epsilon.imag)
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self.data.data_curve_imag.setData(self.data.frequency, self.data.epsilon.real)
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if len(self.peakBoxes) > 0 and self.Conductivity != None:
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self.data.fitted_curve.setData(nu, fit)
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23
data.py
23
data.py
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@ -15,12 +15,14 @@ class Data:
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self.epsilon_fit = die_real*0 + 1j * die_imag*0
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myPen = pg.mkPen(width=3, color=(255,255,127))
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self.data_curve = pg.PlotDataItem(x=[N.nan], y=[N.nan],pen=QColor(0,0,0,0), symbol='o',
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self.data_curve_imag = pg.PlotDataItem(x=[N.nan], y=[N.nan],pen=QColor(0,0,0,0), symbol='o',
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symbolBrush=(255,127,0,127))
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self.data_curve_real = pg.PlotDataItem(x=[N.nan], y=[N.nan],pen=QColor(0,0,0,0), symbol='s',
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symbolBrush=(255,127,0,127))
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self.fitted_curve = pg.PlotDataItem(N.array([N.nan]), N.array([N.nan]), pen=myPen)
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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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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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@ -31,8 +33,18 @@ class Data:
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self.frequency = f
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self.epsilon = e_real + 1j*e_imag
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self.epsilon_fit = 0*e_real + 1j*e_imag*0
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self.fit_limits = (f.min(), f.max(), e_imag.min(), e_imag.max())
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self.data_curve.setData(f,e_imag)
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self.fit_limits = [f.min(), f.max(), e_imag.min(), e_imag.max()]
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self.data_curve_imag.setData(f,e_imag)
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self.data_curve_real.setData(f,e_real)
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def set_fit_xlimits(self, xmin, xmax):
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self.fit_limits[0] = xmin
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self.fit_limits[1] = xmax
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def set_fit_ylimits(self, ymin, ymax):
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self.fit_limits[2] = ymin
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self.fit_limits[3] = ymax
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def get_data(self):
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"""
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@ -40,7 +52,8 @@ class Data:
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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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mask &= (self.epsilon.imag > self.fit_limits[2]) & (self.epsilon.imag < self.fit_limits[3])
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mask &= (self.epsilon.real > self.fit_limits[2]) & (self.epsilon.real < self.fit_limits[3])
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return self.frequency[mask], self.epsilon[mask]
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def remove_curves(self):
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78
mathlib.py
78
mathlib.py
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@ -124,4 +124,80 @@ def multi_hn(p, nu):
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def tau_peak(f, a, b):
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tau = (N.sin(N.pi * a / 2. / (b + 1)) / N.sin(N.pi * a * b / 2. / (b + 1))) ** (1 / a)
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tau /= 2 * N.pi * f
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return tau
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return tau
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### define funcs here
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class Functions:
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def __init__(self):
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self.list = {
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"hn":(self.hn_cmplx,4),
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"conductivity":(self.cond_cmplx,1),
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"power":(self.power_cmplx,2),
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"static":(self.static_cmplx,1),
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}
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def hn_cmplx(self, p, x):
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om = 2*N.pi*x
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hn = om*1j
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eps,t,a,b = p
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hn = eps/(1+(1j*om*t)**a)**b
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cplx = N.array([hn.real, -hn.imag])
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return cplx
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def cond_cmplx(self, p, x):
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om = 2*N.pi*x
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sgma = p[0]
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cond = sgma/(1j*om)
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cplx = N.array([cond.real, -cond.imag])
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return cplx
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def power_cmplx(self, p, x):
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om = 2*N.pi*x
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sgma,n = p
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power = sgma/(om*1j)**n
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cplx = N.array([power.real, -power.imag])
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return cplx
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def static_cmplx(self, p, x):
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eps_inf = p[0]
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static = N.ones((2, len(x)))*eps_inf
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static[:,1] *= 0 # set imag part zero
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#cplx = N.array([static.real, static.imag])
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return static
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def get(self,name):
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return self.list[name]
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class FitFunctionCreator:
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def __init__(self):
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self.data = None
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self.functions = Functions()
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def fitfcn(self, p0, x, *funcs):
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self.data = N.zeros( x.shape )
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ndx = 0
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for fn in funcs: # loop over functions and add the results up
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f,num_p = self.functions.get(fn)
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p = p0[ndx:ndx+num_p]
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self.data += f(p, x[0]) # fit functions take only 1-dim x
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ndx += num_p
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return self.data
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def fit_odr_cmplx(x, y, p0, fixed, fcns):
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f = FitFunctionCreator()
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if x.ndim < 2:
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x = N.resize(x, (2,x.size))
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if N.iscomplexobj(y) and y.ndim == 1:
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y = N.array([y.real, y.imag])
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else:
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raise NotImplementedError,"need complex input for now"
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dat = odr.Data(x, y, 1.0 / y**2)
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mod = odr.Model(f.fitfcn, extra_args=fcns)
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fit = odr.ODR(dat, mod, p0, ifixx=N.zeros(x.ndim), ifixb=fixed, maxit=5000)
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fit.run()
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return fit.output.beta # should return fit.output
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