markusro/qdsfit
1
0
Fork 0
Code Issues Pull Requests Releases Activity

started adding real part, non-lin complex fitting

Browse Source
This commit is contained in:
Markus Rosenstihl
2014-03-05 16:59:35 +01:00
parent 374ed7e510
commit df48519f5a
3 changed files with 137 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files

69
QDS.py
View File

@@ -9,7 +9,7 @@ from PyQt4.QtCore import *
from PyQt4.QtGui import *
import matplotlib
from mathlib import fit_anneal, fit_lbfgsb, fit_odr, hn
from mathlib import fit_anneal, fit_lbfgsb, fit_odr, hn, FitFunctionCreator, fit_odr_cmplx
matplotlib.use('agg')
@@ -90,7 +90,8 @@ class AppWindow(QMainWindow):
self.data = Data()
self.fit_boundary = pg.LinearRegionItem(brush=QColor(254,254,254,10))
self.ui.graphicsView.addItem(self.data.data_curve)
self.ui.graphicsView.addItem(self.data.data_curve_imag)
self.ui.graphicsView.addItem(self.data.data_curve_real)
self.ui.graphicsView.addItem(self.data.fitted_curve)
self.ui.graphicsView.addItem(self.fit_boundary)
self.ui.graphicsView.setLogMode(x=True, y=True)
@@ -124,58 +125,59 @@ class AppWindow(QMainWindow):
else:
f = open("fitresults.log", "a")
# write header
f.write('# T ')
parfmt = "%.2f" # T formatting
f.write("#%7s"%('T'))
parfmt = "%8.2f" # T formatting
# if self.Conductivity != None: pass# always true
f.write("%8s %8s %8s " % ("e_s", "sig", "pow_sig"))
parfmt += " %.3g %.3g %.2f " # conductivity formatting
f.write("%9s%9s%9s " % ("e_s", "sig", "pow_sig"))
parfmt += "%9.3g%9.3g%9.2f " # conductivity formatting
for i, pb in enumerate(self.peakBoxes):
enum_peak = ("e_inf_%i" % i, "tau_%i" % i, "alpha_%i" % i, "beta_%i" % i)
f.write("%8s %8s %8s %8s " % enum_peak)
parfmt += " %.3g %.3g %.2f %.2f" # peak formatting
f.write("high_lim lower_lim") # TODO: store limits
f.write("%9s%9s%9s%9s " % enum_peak)
print enum_peak
parfmt += "%9.3g%9.3g%9.2f%9.2f" # peak formatting
f.write("fit_xlow fit_xhigh") # TODO: store limits
parfmt += "%9.3g%9.3g"
f.write('\n')
f.flush()
#f.write("%3.2f "%(self.data.meta["T"]))
pars = list(self.fitresult)
pars.insert(0, self.data.meta["T"])
pars.append(self.data.fit_limits[0])
pars.append(self.data.fit_limits[1])
N.savetxt(f, N.array([pars, ]), fmt=parfmt, delimiter=" ")
f.close()
def saveFitFigure(self):
fig = pyplot.Figure(figsize=(3.54, 2.75))
fig = pyplot.figure(figsize=(3.54, 2.75))
font = {'family' : 'sans serif',
'weight' : 'normal',
'size' : 16}
'size' : 8}
matplotlib.rc('font', **font)
print self.data.epsilon_fit.shape, type(self.data.epsilon_fit)
pyplot.loglog(self.data.frequency, self.data.epsilon.imag, 'bo', markersize=4, label="Data")
pyplot.loglog(self.data.frequency, self.data.epsilon_fit, 'r-', lw=2, label="Fit")
pyplot.loglog(self.data.frequency, self.data.epsilon.imag, 'bo', markersize=3, label="Data")
pyplot.loglog(self.data.frequency, self.data.epsilon_fit, 'r-', lw=1, label="Fit")
for i,peak in enumerate(self.peakBoxes):
f,eps = peak.get_data()
color = hex2color(str(peak.get_color().name()))
pyplot.loglog(f,eps, ls="--", color=color , lw=1.5, label="Peak %i"%i)
pyplot.loglog(f,eps, ls="--", color=color , lw=0.75, label="Peak %i"%i)
if self.Conductivity != None:
f,eps = self.Conductivity.get_conductivity()
color = hex2color(str(self.Conductivity.get_color().name()))
pyplot.loglog(f,eps, ls="-.", color=color, lw=1.5, label="Cond.")
pyplot.loglog(f,eps, ls="-.", color=color, lw=0.75, label="Cond.")
f,eps = self.Conductivity.get_epsilon_static()
pyplot.loglog(f,eps, ls=":", color=color, lw=1.5, label=r'$\epsilon_0$')
pyplot.loglog(f,eps, ls=":", color=color, lw=0.75, label=r'$\epsilon_0$')
pyplot.legend(title="T=%.1f K"%(self.data.meta["T"]) )
for i in (0,1): pyplot.axvline(x=self.data.fit_limits[i], color='g', ls="--")
pyplot.legend(title = "T=%.1f K"%(self.data.meta["T"]))
pyplot.grid()
pyplot.xlabel('f/Hz')
pyplot.ylabel('eps"')
pyplot.savefig("test.png")
pyplot.savefig(os.path.splitext(self.filepath)[0]+".png")
fig.clear()
def set_fit_xlimits(self, xmin, xmax):
self.data.fit_limits = (xmin, xmax, None, None)
self.updatePlot()
def addCond(self, pos):
if self.Conductivity != None:
@@ -239,18 +241,29 @@ class AppWindow(QMainWindow):
[start_parameter.append(i) for i in pb.getParameter()]
[fixed_params.append(i) for i in pb.getFixed()]
log10fmin, log10fmax = self.fit_boundary.getRegion()
xmin,xmax,ymin,ymax = self.data.fit_limits
self.data.fit_limits = [10**log10fmin, 10**log10fmax,ymin,ymax]
self.data.set_fit_xlimits(10**log10fmin, 10**log10fmax)
fit_methods = [fit_odr, fit_lbfgsb, fit_anneal]
print "StartParameter", start_parameter
print "FixedParameter", fixed_params
print "Limits (xmin, xmax, ymin, ymax)", self.data.fit_limits
_freq, _fit = self.data.get_data()
result = fit_methods[method](_freq, _fit.imag, start_parameter, fixed_params)
# check new method
if 1:
funcs = ["static","conductivity"] if self.Conductivity != None else []
for pb in self.peakBoxes.keys():
funcs.append("hn")
newres = fit_odr_cmplx(_freq, _fit, start_parameter, fixed_params, funcs)
print newres
self.fitresult = result
for i, pb in enumerate(self.peakBoxes.keys()):
delta_eps, tau, a, b = result[3 + i * 4:3 + (i + 1) * 4]
delta_eps, tau, a, b = result[3 + i*4 : 3 + (i + 1)*4]
pb.setParameter(delta_eps, tau, a, b)
e_static, sigma, sigma_N = result[:3]
if self.Conductivity != None:
@@ -262,6 +275,7 @@ class AppWindow(QMainWindow):
def openFile(self):
path = unicode(QFileDialog.getOpenFileName(self, "Open file"))
self.filepath=path
#path = "MCM42PG0_199.96K.dat"
# TODO anaylize file (LF,MF, HF) and act accordingly
data = N.loadtxt(path, skiprows=4)
@@ -304,7 +318,8 @@ class AppWindow(QMainWindow):
fit += self.Conductivity.getParameter()[0] # eps static
self.data.epsilon_fit = fit[:]
self.data.data_curve.setData(self.data.frequency, self.data.epsilon.imag)
self.data.data_curve_imag.setData(self.data.frequency, self.data.epsilon.imag)
self.data.data_curve_imag.setData(self.data.frequency, self.data.epsilon.real)
if len(self.peakBoxes) > 0 and self.Conductivity != None:
self.data.fitted_curve.setData(nu, fit)

23
data.py
View File

@@ -15,12 +15,14 @@ class Data:
self.epsilon_fit = die_real*0 + 1j * die_imag*0
myPen = pg.mkPen(width=3, color=(255,255,127))
self.data_curve = pg.PlotDataItem(x=[N.nan], y=[N.nan],pen=QColor(0,0,0,0), symbol='o',
self.data_curve_imag = pg.PlotDataItem(x=[N.nan], y=[N.nan],pen=QColor(0,0,0,0), symbol='o',
symbolBrush=(255,127,0,127))
self.data_curve_real = pg.PlotDataItem(x=[N.nan], y=[N.nan],pen=QColor(0,0,0,0), symbol='s',
symbolBrush=(255,127,0,127))
self.fitted_curve = pg.PlotDataItem(N.array([N.nan]), N.array([N.nan]), pen=myPen)
self.length = len(frequency)
self.meta = dict()
self.fit_limits = (frequency.min(), frequency.max(), die_imag.min(), die_imag.max())
self.fit_limits = [frequency.min(), frequency.max(), die_imag.min(), die_imag.max()]
def __del__(self):
#self.remove_curves()
@@ -31,8 +33,18 @@ class Data:
self.frequency = 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.data_curve.setData(f,e_imag)
self.fit_limits = [f.min(), f.max(), e_imag.min(), e_imag.max()]
self.data_curve_imag.setData(f,e_imag)
self.data_curve_real.setData(f,e_real)
def set_fit_xlimits(self, xmin, xmax):
self.fit_limits[0] = xmin
self.fit_limits[1] = xmax
def set_fit_ylimits(self, ymin, ymax):
self.fit_limits[2] = ymin
self.fit_limits[3] = ymax
def get_data(self):
"""
@@ -40,7 +52,8 @@ class Data:
"""
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])
mask &= (self.epsilon.imag > self.fit_limits[2]) & (self.epsilon.imag < self.fit_limits[3])
mask &= (self.epsilon.real > self.fit_limits[2]) & (self.epsilon.real < self.fit_limits[3])
return self.frequency[mask], self.epsilon[mask]
def remove_curves(self):

78
mathlib.py
View File

@@ -124,4 +124,80 @@ def multi_hn(p, nu):
def tau_peak(f, a, b):
tau = (N.sin(N.pi * a / 2. / (b + 1)) / N.sin(N.pi * a * b / 2. / (b + 1))) ** (1 / a)
tau /= 2 * N.pi * f
return tau
return tau
### define funcs here
class Functions:
def __init__(self):
self.list = {
"hn":(self.hn_cmplx,4),
"conductivity":(self.cond_cmplx,1),
"power":(self.power_cmplx,2),
"static":(self.static_cmplx,1),
}
def hn_cmplx(self, p, x):
om = 2*N.pi*x
hn = om*1j
eps,t,a,b = p
hn = eps/(1+(1j*om*t)**a)**b
cplx = N.array([hn.real, -hn.imag])
return cplx
def cond_cmplx(self, p, x):
om = 2*N.pi*x
sgma = p[0]
cond = sgma/(1j*om)
cplx = N.array([cond.real, -cond.imag])
return cplx
def power_cmplx(self, p, x):
om = 2*N.pi*x
sgma,n = p
power = sgma/(om*1j)**n
cplx = N.array([power.real, -power.imag])
return cplx
def static_cmplx(self, p, x):
eps_inf = p[0]
static = N.ones((2, len(x)))*eps_inf
static[:,1] *= 0 # set imag part zero
#cplx = N.array([static.real, static.imag])
return static
def get(self,name):
return self.list[name]
class FitFunctionCreator:
def __init__(self):
self.data = None
self.functions = Functions()
def fitfcn(self, p0, x, *funcs):
self.data = N.zeros( x.shape )
ndx = 0
for fn in funcs: # loop over functions and add the results up
f,num_p = self.functions.get(fn)
p = p0[ndx:ndx+num_p]
self.data += f(p, x[0]) # fit functions take only 1-dim x
ndx += num_p
return self.data
def fit_odr_cmplx(x, y, p0, fixed, fcns):
f = FitFunctionCreator()
if x.ndim < 2:
x = N.resize(x, (2,x.size))
if N.iscomplexobj(y) and y.ndim == 1:
y = N.array([y.real, y.imag])
else:
raise NotImplementedError,"need complex input for now"
dat = odr.Data(x, y, 1.0 / y**2)
mod = odr.Model(f.fitfcn, extra_args=fcns)
fit = odr.ODR(dat, mod, p0, ifixx=N.zeros(x.ndim), ifixb=fixed, maxit=5000)
fit.run()
return fit.output.beta # should return fit.output