pure imaginary part fitting implemented
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44
QDS.py
44
QDS.py
@ -10,7 +10,7 @@ from PyQt4.QtGui import *
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import matplotlib
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from Container import Conductivity, PowerComplex, Static, Peak, YAFF
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from mathlib import fit_anneal, fit_lbfgsb, fit_odr_cmplx, FunctionRegister, FitRoutine
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from mathlib import FunctionRegister, FitRoutine
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matplotlib.use('agg')
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@ -26,11 +26,8 @@ import QDSMain
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from data import Data
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import pyqtgraph as pg
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#import yaff
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from ContainerWidgets import ParameterWidget
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from ContainerWidgets import ParameterWidget, YaffWidget
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USE_CROSSH=False
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class AppWindow(QMainWindow):
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@ -73,16 +70,6 @@ class AppWindow(QMainWindow):
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self.ui.pgPlotWidget_imag.addItem(self.data.fitted_curve_imag)
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self.ui.pgPlotWidget_real.addItem(self.data.fitted_curve_real)
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# cross hair
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if USE_CROSSH:
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#self.poslabel = pg.LabelItem(justify='right')
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self.horizontalLine = pg.InfiniteLine(angle=0)
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self.verticalLine = pg.InfiniteLine(angle=90)
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#self.ui.pgPlotWidget_imag.plotItem.addItem(self.poslabel)
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self.ui.pgPlotWidget_imag.addItem(self.horizontalLine)
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self.ui.pgPlotWidget_imag.addItem(self.verticalLine)
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self.ui.pgPlotWidget_imag.addItem(self.fit_boundary_imag)
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self.ui.pgPlotWidget_real.addItem(self.fit_boundary_real)
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@ -144,21 +131,15 @@ class AppWindow(QMainWindow):
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# fitting methods
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fitMenu = self.menuBar().addMenu("Standard Fits")
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# lm
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fit_lmAction = QAction("&Levenberg-Marquardt", self)
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fit_lmAction = QAction("Complex NLS", self)
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fit_lmAction.setShortcut(QKeySequence("Ctrl+F"))
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fitMenu.addAction(fit_lmAction)
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# lbfgsb
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fit_lbfgsbAction = QAction("&L-BFGS-B", self)
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fit_lbfgsbAction = QAction("NLS (Imag.)", self)
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fitMenu.addAction(fit_lbfgsbAction)
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# Simulated Annealing
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fit_annealAction = QAction("&Simulated Annealing", self)
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fitMenu.addAction(fit_annealAction)
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# YAFF
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yaffMenu = self.menuBar().addMenu("YAFF")
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start_yaff = QAction("&Startparam.", self)
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yaffMenu.addAction(start_yaff)
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fit_yaff = QAction("&Fit", self)
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yaffMenu.addAction(fit_yaff)
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self.signalMapper = QSignalMapper(self)
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for i, fit_action in enumerate([fit_lmAction, fit_lbfgsbAction, fit_annealAction
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@ -179,17 +160,11 @@ class AppWindow(QMainWindow):
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else:
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pos = QPointF(0.0, 0.0)
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self.last_pos = pos
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if USE_CROSSH:
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self.horizontalLine.setBounds([self.data.frequency.min(), self.data.frequency.max()])
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self.horizontalLine.setPos(pos.y())
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self.verticalLine.setPos(pos.x())
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def mousePress(self, evt):
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data_pos = self.last_pos
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mouse_in_imag = self.ui.pgPlotWidget_imag.underMouse()
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mouse_in_real = self.ui.pgPlotWidget_real.underMouse()
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msgBox = QMessageBox()
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if self.ui.actionAdd_Peak.isChecked():
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@ -396,7 +371,13 @@ class AppWindow(QMainWindow):
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self.updatePlot()
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def fitData_start(self, method):
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fit_methods = [fit_odr_cmplx, fit_lbfgsb, fit_anneal]
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print method
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#fit_methods = [fit_odr_cmplx, fit_odr_imag, fit_lbfgsb, fit_anneal]
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fit_method = [
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self._fit_method.fit_odr_cmplx,
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self._fit_method.fit_odr_imag,
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][method]
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# build function list
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p0,funcs,fixed_params = [],[],[]
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@ -408,7 +389,8 @@ class AppWindow(QMainWindow):
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_freq, _fit = self.data.get_data()
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if not self._fit_thread.isRunning():
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self._fit_method.fit_odr_cmplx(_freq, _fit, p0, fixed_params, funcs)
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#self._fit_method.fit_odr_cmplx(_freq, _fit, p0, fixed_params, funcs)
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fit_method(_freq, _fit, p0, fixed_params, funcs)
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self._fit_thread.start()
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self.ui.statusbar.showMessage("Fitting ...")
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else:
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409
mathlib.py
409
mathlib.py
@ -11,6 +11,217 @@ from scipy import optimize as opt, odr
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import libyaff
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def id_to_color(id):
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colors = [
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QColor(54,22,115),
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QColor(160,16,36),
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QColor(45,142,15),
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QColor(168,149,17),
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QColor(36,10,85),
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QColor(118,8,23),
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QColor(31,105,7),
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QColor(124,109,8),
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]
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return colors[id % len(colors)]
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class FitFunctionCreator(QObject):
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new_data = pyqtSignal(np.ndarray, np.ndarray)
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def __init__(self):
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super(FitFunctionCreator,self).__init__()
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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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if x.ndim == 2:
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self.data = np.zeros( x.shape )
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else:
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self.data = np.zeros( (2,x.size) )
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ndx = 0
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for fn in funcs: # loop over functions and add the results
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f, num_p = fn.function, fn.param_number
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p = p0[ndx:ndx + num_p]
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if x.ndim == 2:
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x = x[0]
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result = f(p, x)
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self.data += result # fit functions take only 1-dim x
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ndx += num_p
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self.new_data.emit(x, self.data)
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return self.data
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def fitfcn_imag(self, p0, x, *funcs):
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if x.ndim == 2:
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self.data = np.zeros( x.shape )
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else:
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self.data = np.zeros( (2,x.size) )
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ndx = 0
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for fn in funcs: # loop over functions and add the results
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f, num_p = fn.function, fn.param_number
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p = p0[ndx:ndx + num_p]
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if x.ndim == 2:
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x = x[0]
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result = f(p, x)
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self.data += result # fit functions take only 1-dim x
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ndx += num_p
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self.new_data.emit(x, self.data)
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return self.data[1]
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class FitRoutine(QObject):
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finished_fit = pyqtSignal()
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data_ready = pyqtSignal(np.ndarray, np.ndarray)
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def __init__(self):
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super(FitRoutine,self).__init__()
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self.f = FitFunctionCreator()
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self.f.new_data.connect(self.data_ready.emit)
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self._fitter = self.fit_odr_cmplx
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self._odr_fit = None
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@property
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def fitter(self):
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return self._fitter
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@fitter.setter
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def fitter(self, f):
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self._fitter = f
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def fit_odr_cmplx(self, x, y, p0, fixed, fcns):
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#if x.ndim < 2:
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# x = N.resize(x, (2,x.size))
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if np.iscomplexobj(y) and y.ndim == 1:
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weights = 1/np.abs(y)**2
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we = np.resize(weights, (2, weights.size))
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#we = 1/N.array([y.real**2, y.imag**2])
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y = np.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, we=we)
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mod = odr.Model(self.f.fitfcn, extra_args=fcns)
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self._odr_fit = odr.ODR(dat, mod, p0, ifixx=(0,), ifixb=fixed, maxit=800)
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def fit_odr_imag(self, x, y, p0, fixed, fcns):
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if np.iscomplexobj(y) and y.ndim == 1:
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we = 1/np.imag(y)**2
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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.imag, we=we)
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mod = odr.Model(self.f.fitfcn_imag, extra_args=fcns)
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self._odr_fit = odr.ODR(dat, mod, p0, ifixx=(0,), ifixb=fixed, maxit=800)
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@pyqtSlot()
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def fit(self):
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#print "TID in FitRoutine", QThread.thread()
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self._odr_fit.run()
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self.finished_fit.emit()
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def result(self):
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return self._odr_fit.output
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class FunctionRegister:
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def __init__(self):
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self.registry = {}
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def register_function(self, obj):
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#print "FR: Registering:",obj
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id_string = obj.id_label
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if self.registry.has_key(obj):
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raise AssertionError,"The object is already registered! This should NOT happen"
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self.registry[obj]=id_string
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#print "FR: ",self.registry
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def unregister_function(self, obj):
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#print "FR: UnRegistering:",obj
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if self.registry.has_key(obj):
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self.registry.pop(obj)
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else:
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obj.deleteLater()
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raise AssertionError,"The object is not in the registry! This should NOT happen"
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#print "FR: ",self.registry
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def get_registered_functions(self):
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return self.registry
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############## deprecated #####################
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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 np.iscomplexobj(y) and y.ndim == 1:
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weights = 1/np.abs(y)**2
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we = np.resize(weights, (2, weights.size))
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#we = 1/N.array([y.real**2, y.imag**2])
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y = np.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, we=we)
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mod = odr.Model(f.fitfcn, extra_args=fcns)
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fit = odr.ODR(dat, mod, p0, ifixx=(0,), ifixb=fixed, maxit=8000)
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fit.run()
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#print fit.output.pprint()
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return fit.output
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### define funcs here
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class Functions(QObject):
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def __init__(self):
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super(Functions,self).__init__()
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self.list = {
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# provides functions: "id_string":(function, number_of_parameters)
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"hn":(self.hn_cmplx, 4),
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"conductivity":(self.cond_cmplx, 3),
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"power":(self.power_cmplx, 2),
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"static":(self.static_cmplx, 1),
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"yaff":(self.yaff, 8)
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}
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self.YAFF = libyaff.Yaff()
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def hn_cmplx(self, p, x):
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om = 2*np.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 = np.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*np.pi*x
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sgma, isgma, n = p
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cond = sgma/(om**n) + isgma/(1j*om**n) # Jonscher (Universal Dielectric Response: e",e' prop sigma/omega**n
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cplx = np.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*np.pi*x
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sgma,n = p
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power = sgma/(om*1j)**n
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cplx = np.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 = np.ones( (2,x.size) )*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 yaff(self,p,x):
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ya = self.YAFF.yaff(p[:8],x)
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cplx = np.array([ya.imag, ya.real])
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return cplx
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def get(self,name):
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return self.list[name]
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def get_function(self,name):
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return self.list[name][0]
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def fit_anneal(x, y, p0, fixed, funcs):
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raise NotImplementedError
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bounds = [(0, 1e14), (0, 1)]
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@ -85,19 +296,6 @@ def hn(p, nu):
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return e_loss # 2* oder nicht?
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def id_to_color(id):
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colors = [
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QColor(54,22,115),
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QColor(160,16,36),
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QColor(45,142,15),
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QColor(168,149,17),
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QColor(36,10,85),
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QColor(118,8,23),
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QColor(31,105,7),
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QColor(124,109,8),
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]
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return colors[id % len(colors)]
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def mini_func(p, x, y):
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res = y - multi_hn(p, x)
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@ -133,188 +331,3 @@ def tau_peak(f, a, b):
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### define funcs here
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class Functions(QObject):
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def __init__(self):
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super(Functions,self).__init__()
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self.list = {
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# provides functions: "id_string":(function, number_of_parameters)
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"hn":(self.hn_cmplx, 4),
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"conductivity":(self.cond_cmplx, 3),
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"power":(self.power_cmplx, 2),
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"static":(self.static_cmplx, 1),
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"yaff":(self.yaff, 8)
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}
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self.YAFF = libyaff.Yaff()
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def hn_cmplx(self, p, x):
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om = 2*np.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 = np.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*np.pi*x
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sgma, isgma, n = p
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cond = sgma/(om**n) + isgma/(1j*om**n) # Jonscher (Universal Dielectric Response: e",e' prop sigma/omega**n
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cplx = np.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*np.pi*x
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sgma,n = p
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power = sgma/(om*1j)**n
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cplx = np.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 = np.ones( (2,x.size) )*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 yaff(self,p,x):
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ya = self.YAFF.yaff(p[:8],x)
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cplx = np.array([ya.imag, ya.real])
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return cplx
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def get(self,name):
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return self.list[name]
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def get_function(self,name):
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return self.list[name][0]
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class FitFunctionCreator(QObject):
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new_data = pyqtSignal(np.ndarray, np.ndarray)
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def __init__(self):
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super(FitFunctionCreator,self).__init__()
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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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if x.ndim == 2:
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self.data = np.zeros( x.shape )
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else:
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self.data = np.zeros( (2,x.size) )
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ndx = 0
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for fn in funcs: # loop over functions and add the results
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f, num_p = fn.function, fn.param_number
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p = p0[ndx:ndx + num_p]
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if x.ndim == 2:
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x = x[0]
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result = f(p, x)
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self.data += result # fit functions take only 1-dim x
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ndx += num_p
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self.new_data.emit(x, self.data)
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return self.data
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def _fitfcn(self, p0, x, *funcs):
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if x.ndim == 2:
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self.data = np.zeros( x.shape )
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else:
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self.data = np.zeros( (2,x.size) )
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ndx = 0
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for fn in funcs: # loop over functions and add the results
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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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if x.ndim == 2:
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x = x[0]
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result = f(p, x)
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self.data += result # fit functions take only 1-dim x
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ndx += num_p
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self.new_data.emit(x, self.data)
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return self.data
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def fit_odr_cmplx(x, y, p0, fixed, fcns):
|
||||
f = FitFunctionCreator()
|
||||
#if x.ndim < 2:
|
||||
# x = N.resize(x, (2,x.size))
|
||||
if np.iscomplexobj(y) and y.ndim == 1:
|
||||
weights = 1/np.abs(y)**2
|
||||
we = np.resize(weights, (2, weights.size))
|
||||
#we = 1/N.array([y.real**2, y.imag**2])
|
||||
y = np.array([y.real, y.imag])
|
||||
else:
|
||||
raise NotImplementedError, "need complex input for now"
|
||||
dat = odr.Data(x, y, we=we)
|
||||
mod = odr.Model(f.fitfcn, extra_args=fcns)
|
||||
fit = odr.ODR(dat, mod, p0, ifixx=(0,), ifixb=fixed, maxit=8000)
|
||||
fit.run()
|
||||
#print fit.output.pprint()
|
||||
return fit.output
|
||||
|
||||
class FitRoutine(QObject):
|
||||
finished_fit = pyqtSignal()
|
||||
data_ready = pyqtSignal(np.ndarray, np.ndarray)
|
||||
def __init__(self):
|
||||
super(FitRoutine,self).__init__()
|
||||
self.f = FitFunctionCreator()
|
||||
self.f.new_data.connect(self.data_ready.emit)
|
||||
self._fitter = self.fit_odr_cmplx
|
||||
self._odr_fit = None
|
||||
|
||||
@property
|
||||
def fitter(self):
|
||||
return self._fitter
|
||||
|
||||
@fitter.setter
|
||||
def fitter(self,f):
|
||||
self._fitter = f
|
||||
|
||||
def fit_odr_cmplx(self, x, y, p0, fixed, fcns):
|
||||
#if x.ndim < 2:
|
||||
# x = N.resize(x, (2,x.size))
|
||||
if np.iscomplexobj(y) and y.ndim == 1:
|
||||
weights = 1/np.abs(y)**2
|
||||
we = np.resize(weights, (2, weights.size))
|
||||
#we = 1/N.array([y.real**2, y.imag**2])
|
||||
y = np.array([y.real, y.imag])
|
||||
else:
|
||||
raise NotImplementedError, "need complex input for now"
|
||||
|
||||
dat = odr.Data(x, y, we=we)
|
||||
mod = odr.Model(self.f.fitfcn, extra_args=fcns)
|
||||
self._odr_fit = odr.ODR(dat, mod, p0, ifixx=(0,), ifixb=fixed, maxit=10)
|
||||
|
||||
@pyqtSlot()
|
||||
def fit(self):
|
||||
#print "TID in FitRoutine", QThread.thread()
|
||||
self._odr_fit.run()
|
||||
self.finished_fit.emit()
|
||||
|
||||
def result(self):
|
||||
return self._odr_fit.output
|
||||
|
||||
|
||||
|
||||
class FunctionRegister:
|
||||
def __init__(self):
|
||||
self.registry = {}
|
||||
|
||||
def register_function(self, obj):
|
||||
#print "FR: Registering:",obj
|
||||
id_string = obj.id_label
|
||||
if self.registry.has_key(obj):
|
||||
raise AssertionError,"The object is already registered! This should NOT happen"
|
||||
self.registry[obj]=id_string
|
||||
#print "FR: ",self.registry
|
||||
|
||||
def unregister_function(self, obj):
|
||||
#print "FR: UnRegistering:",obj
|
||||
if self.registry.has_key(obj):
|
||||
self.registry.pop(obj)
|
||||
else:
|
||||
obj.deleteLater()
|
||||
raise AssertionError,"The object is not in the registry! This should NOT happen"
|
||||
#print "FR: ",self.registry
|
||||
|
||||
def get_registered_functions(self):
|
||||
return self.registry
|
||||
|
||||
Loading…
Reference in New Issue
Block a user