* moved math functions (fit, hn, etc.) to mathlib.py
* fitresults are stored in a better format
This commit is contained in:
parent
5f49684e01
commit
8fc316f2ff
@ -15,11 +15,12 @@ This software is licensed under the terms of the MIT License
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Derived from 'embedding_in_pyqt4.py':
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Copyright © 2005 Florent Rougon, 2006 Darren Dale
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"""
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from QDSToolbar import CustomToolbar
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__version__ = "1.0.0"
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from PyQt4.QtGui import QSizePolicy
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from PyQt4.QtCore import QSize
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from PyQt4.QtGui import QSizePolicy, QWidget, QVBoxLayout
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from PyQt4.QtCore import QSize, Qt
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from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as Canvas
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from matplotlib.figure import Figure
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@ -123,3 +124,26 @@ if __name__ == '__main__':
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win = ApplicationWindow()
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win.show()
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sys.exit(app.exec_())
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class PlotWidget(QWidget):
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def __init__(self, parent=None):
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QWidget.__init__(self)
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super(PlotWidget, self).__init__(parent)
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self.mplwidget = MatplotlibWidget(hold=True,
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xlim=(1e-2, 1e7),
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xscale='log',
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yscale='log')
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self.canvas = self.mplwidget.figure.canvas # shortcut
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self.canvas.axes.grid(True)
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#self.bbox_size = self.canvas.axes.bbox.size
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self.toolbar = CustomToolbar(self.canvas, self.mplwidget, parent)
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self.toolbar.setToolButtonStyle(Qt.ToolButtonTextUnderIcon)
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layout = QVBoxLayout(parent)
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#self.mplwidget.setLayout(layout)
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layout.addWidget(self.canvas)
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layout.addWidget(self.mplwidget)
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layout.addWidget(self.toolbar)
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self._bg_cache = None
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self._axvlims = []
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self._axvname = []
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258
QDS.py
258
QDS.py
@ -8,19 +8,13 @@ import signal
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from PyQt4.QtCore import *
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from PyQt4.QtGui import *
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import matplotlib
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import matplotlib.colors
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#from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
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from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbar
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#import matplotlib.pyplot as plt
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from mathlib import fit_anneal, fit_lbfgsb, fit_odr, hn, id_to_color
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from matplotlibWidget import PlotWidget
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matplotlib.rc_file("default.mplrc")
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#matplotlib.rc_file("default.mplrc")
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import numpy as N
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import scipy.odr as O
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import scipy.optimize as opt
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import matplotlibWidget
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import QDSMain
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import PeakWidget
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from data import Data, Conductivity, conductivity
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@ -38,13 +32,7 @@ def sigint_handler(*args):
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QApplication.quit()
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def id_to_color(id):
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"""
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"""
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colors = ['b', 'r', 'g', 'c', 'm', 'y', 'k']
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conv = matplotlib.colors.ColorConverter()
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return conv.to_rgb(colors[id % len(colors)])
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def tau_peak(f, a, b):
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@ -53,104 +41,25 @@ def tau_peak(f, a, b):
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return tau
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def hn(p, nu):
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delta_eps, tau, a, b = p
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om = 2 * N.pi * nu
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Phi = N.arctan((om * tau) ** a * N.sin(N.pi * a / 2.) / (1. + (om * tau) ** a * N.cos(N.pi * a / 2.)))
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e_loss = delta_eps * (1 + 2 * (om * tau) ** a * N.cos(N.pi * a / 2.) + (om * tau) ** (2. * a) ) ** (
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-b / 2.) * N.sin(b * Phi)
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e_stor = delta_eps * (1 + 2 * (om * tau) ** a * N.cos(N.pi * a / 2.) + (om * tau) ** (2. * a) ) ** (
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-b / 2.) * N.cos(b * Phi)
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return 2 * e_loss
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def multi_hn(p, nu):
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conductivity = p[1]
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cond_beta = p[2]
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om = 2 * N.pi * nu
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e_loss = conductivity / om ** cond_beta
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e_loss += p[0]
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#for key, igroup in groupby(p[3:], lambda x: x//4):
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for i in xrange(len(p[3:]) / 4):
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delta_eps, tau, a, b = p[3 + i * 4:3 + (i + 1) * 4]
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#delta_eps, tau, a, b = list(igroup)
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#print delta_eps,tau,a,b
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#a = 0.5 *(1 + N.tanh(a))
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#b = 0.5 *(1 + N.tanh(b))
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Phi = N.arctan((om * tau) ** a * N.sin(N.pi * a / 2.) / (1. + (om * tau) ** a * N.cos(N.pi * a / 2.)))
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e_loss += 2 * delta_eps * (1 + 2 * (om * tau) ** a * N.cos(N.pi * a / 2.) + (om * tau) ** (2. * a) ) ** (
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-b / 2.) * N.sin(b * Phi)
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#e_stor = delta_eps * (1+ 2*(om*tau)**a * N.cos(N.pi*a/2.) + (om*tau)**(2.*a) )**(-b/2.)*N.cos(b*Phi)
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return e_loss
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def mini_func(p, x, y):
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res = y - multi_hn(p, x)
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# apply weights
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res /= 1 / y
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return N.sqrt(N.dot(res, res))
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def fit_odr(x, y, p0, fixed):
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dat = O.Data(x, y, 1.0 / y)
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mod = O.Model(multi_hn)
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odr = O.ODR(dat, mod, p0, ifixx=(0,), ifixb=fixed, maxit=2000)
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odr.run()
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return odr.output.beta
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def fit_lbfgsb(x, y, p0, fixed):
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# TODO fixed parameters…
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bounds = [(0, None), (0, 1)]
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for i in xrange(len(p0[3:]) / 4):
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bounds.append((1e-4, 1e12)) # delta_eps
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bounds.append((1e-12, 1e3)) # tau
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bounds.append((0.1, 1)) # a
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bounds.append((0.1, 1)) # b
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x, f_minvalue, info_dict = opt.fmin_l_bfgs_b(mini_func, p0,
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fprime=None,
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args=(x, y),
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approx_grad=True,
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bounds=bounds,
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iprint=0,
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maxfun=4000)
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if info_dict['warnflag'] != 0:
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print info_dict["task"]
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return x
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def fit_anneal(x, y, p0, fixed):
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bounds = [(0, 1e14), (0, 1)]
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for i in xrange(len(p0[2:]) / 4):
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bounds.append((1e-4, 1e12)) # delta_eps
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bounds.append((1e-12, 1e3)) # tau
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bounds.append((0.1, 1)) # a
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bounds.append((0.1, 1)) # b
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ret = opt.anneal(mini_func, p0,
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args=(x, y),
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maxeval=20000,
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maxiter=30000,
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lower=[b[0] for b in bounds],
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upper=[b[1] for b in bounds],
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dwell=100,
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full_output=1)
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#pmin, func_min, final_Temp, cooling_iters,accepted_tests, retval
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#retval : int
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#Flag indicating stopping condition::
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# 0 : Points no longer changing
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# 1 : Cooled to final temperature
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# 2 : Maximum function evaluations
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# 3 : Maximum cooling iterations reached
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# 4 : Maximum accepted query locations reached
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# 5 : Final point not the minimum amongst encountered points
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print "Stop reason", ret
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return ret[0]
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class Peak(QObject):
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@ -250,7 +159,7 @@ class AppWindow(QMainWindow):
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self.signalMapper.mapped.connect(self.fitData)
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# save fitted values
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self.ui.actionSave_FitResult.triggered.connect(self.saveFit)
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self.ui.actionSave_FitResult.triggered.connect(self.saveFitResult)
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# the plot area, a matplotlib widget
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self.mplWidget = PlotWidget(self.ui.mplWidget)
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self.mplWidget.canvas.draw()
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@ -278,29 +187,32 @@ class AppWindow(QMainWindow):
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line.set_animated(True)
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def saveFit(self):
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def saveFitResult(self):
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"""
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Saving fit parameters to fitresults.log
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including temperature
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"""
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if not os.path.exists("fitresults.log"):
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f = open("fitresults.log", "w")
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# write header
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f.write('# T ')
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if self.Conductivity != None:
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f.write("%8s %8s %8s " % ("e_s", "sig", "pow_sig"))
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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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f.write("high_lim lower_lim")
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f.write('\n')
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f.flush()
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else:
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f = open("fitresults.log", "a")
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#f.write("%3.2f "%(self.data.meta["T"]))
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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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# 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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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('\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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N.savetxt(f, N.array([pars, ]), fmt="%8.5g", delimiter=" ")
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N.savetxt(f, N.array([pars, ]), fmt=parfmt, delimiter=" ")
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f.close()
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def set_fit_xlimits(self, xmin, xmax):
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@ -532,120 +444,6 @@ class AppWindow(QMainWindow):
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self.mplWidget.canvas.blit(ax.bbox)
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class PlotWidget(QWidget):
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def __init__(self, parent=None):
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QWidget.__init__(self)
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super(PlotWidget, self).__init__(parent)
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self.mplwidget = matplotlibWidget.MatplotlibWidget(hold=True,
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xlim=(1e-2, 1e7),
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xscale='log',
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yscale='log')
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self.canvas = self.mplwidget.figure.canvas # shortcut
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self.canvas.axes.grid(True)
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#self.bbox_size = self.canvas.axes.bbox.size
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self.toolbar = CustomToolbar(self.canvas, self.mplwidget, parent)
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self.toolbar.setToolButtonStyle(Qt.ToolButtonTextUnderIcon)
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layout = QVBoxLayout(parent)
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#self.mplwidget.setLayout(layout)
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layout.addWidget(self.canvas)
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layout.addWidget(self.mplwidget)
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layout.addWidget(self.toolbar)
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self._bg_cache = None
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self._axvlims = []
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self._axvname = []
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class CustomToolbar(NavigationToolbar):
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# our spanChanged signal
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spanSelectedTrigger = pyqtSignal(float, float, name='spanChanged')
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def __init__(self, plotCanvas, plotWidget, parent=None):
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NavigationToolbar.__init__(self, plotCanvas, plotWidget, coordinates=True)
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self.canvas = plotCanvas
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# Span select Button
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#self.span_button = QAction(QIcon("border-1d-right-icon.png" ), "Span", self)
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self.span_button = QAction(QIcon(QPixmap(":/icons/fit_limits.png")), "Fit Limits", self)
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self.span_button.setCheckable(True)
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self.cids = []
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self.cids.append(self.canvas.mpl_connect('button_press_event', self.press))
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self.cids.append(self.canvas.mpl_connect('motion_notify_event', self.onmove))
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self.cids.append(self.canvas.mpl_connect('button_release_event', self.release))
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self.cids.append(self.canvas.mpl_connect('draw_event', self.update_background))
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# act.setCheckable(True)
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# add actions before the coordinates widget
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self.insertAction(self.actions()[-1], self.span_button)
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self.buttons = {}
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self._pressed = False
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self.background = None
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self.span = None
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self.istart = 0
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self.iend = 0
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self.xstart = 0
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self.xend = 0
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def set_span(self, x1, x2):
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#trans = blended_transform_factory(self.axes.transData, self.axes.transAxes)
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cur = self.span.get_xy()
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cur[0, 0] = x1
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cur[1, 0] = x1
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cur[2, 0] = x2
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cur[3, 0] = x2
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self.span.set_xy(cur)
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def ignore(self, event):
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# 'return ``True`` if *event* should be ignored'
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return event.inaxes != self.canvas.axes or event.button != 1
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def update_background(self, event):
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#if self.canvas.axes is None:
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# raise SyntaxError,"Need an axes reference!"
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self.background = self.canvas.copy_from_bbox(self.canvas.axes.bbox)
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def press(self, event):
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if self.span_button.isChecked():
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if self.background is None:
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self.update_background()
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else:
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self.canvas.restore_region(self.background)
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self.xstart = event.xdata
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self.istart = event.x
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if self.span is None:
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self.span = self.canvas.axes.axvspan(event.xdata, event.xdata, alpha=0.10, color="k", animated=False)
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else:
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self.set_span(event.xdata, event.xdata)
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self._pressed = True
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def onmove(self, event):
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if self.span_button.isChecked() and self._pressed and not self.ignore(event):
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self.set_span(self.xstart, event.xdata)
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self.update()
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def update(self):
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self.canvas.restore_region(self.background)
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self.canvas.axes.draw_artist(self.span)
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for line in self.canvas.axes.get_lines():
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self.canvas.axes.draw_artist(line)
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self.canvas.blit(self.canvas.axes.bbox)
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def release(self, event):
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self.span_button.setChecked(False)
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self.xend = event.xdata
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self.iend = event.x
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if self.iend < self.istart:
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self.iend, self.istart = self.istart, self.iend
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#print "released", self.xstart, self.xend
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if self._pressed:
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if self.ignore(event):
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self.istart = 0
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self.spanSelectedTrigger.emit(self.xstart, self.xend)
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self._pressed = False
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if __name__ == '__main__':
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signal.signal(signal.SIGINT, sigint_handler)
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app = QApplication(sys.argv)
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98
QDSToolbar.py
Normal file
98
QDSToolbar.py
Normal file
@ -0,0 +1,98 @@
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from PyQt4.QtCore import pyqtSignal
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from PyQt4.QtGui import QAction, QIcon, QPixmap
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from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbar, NavigationToolbar2QTAgg
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__author__ = 'markusro'
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class CustomToolbar(NavigationToolbar):
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# our spanChanged signal
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spanSelectedTrigger = pyqtSignal(float, float, name='spanChanged')
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def __init__(self, plotCanvas, plotWidget, parent=None):
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NavigationToolbar.__init__(self, plotCanvas, plotWidget, coordinates=True)
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self.canvas = plotCanvas
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# Span select Button
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#self.span_button = QAction(QIcon("border-1d-right-icon.png" ), "Span", self)
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self.span_button = QAction(QIcon(QPixmap(":/icons/fit_limits.png")), "Fit Limits", self)
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self.span_button.setCheckable(True)
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self.cids = []
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self.cids.append(self.canvas.mpl_connect('button_press_event', self.press))
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self.cids.append(self.canvas.mpl_connect('motion_notify_event', self.onmove))
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self.cids.append(self.canvas.mpl_connect('button_release_event', self.release))
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self.cids.append(self.canvas.mpl_connect('draw_event', self.update_background))
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# act.setCheckable(True)
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# add actions before the coordinates widget
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self.insertAction(self.actions()[-1], self.span_button)
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self.buttons = {}
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self._pressed = False
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self.background = None
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self.span = None
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self.istart = 0
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self.iend = 0
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self.xstart = 0
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self.xend = 0
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def set_span(self, x1, x2):
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#trans = blended_transform_factory(self.axes.transData, self.axes.transAxes)
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cur = self.span.get_xy()
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cur[0, 0] = x1
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cur[1, 0] = x1
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cur[2, 0] = x2
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cur[3, 0] = x2
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self.span.set_xy(cur)
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def ignore(self, event):
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# 'return ``True`` if *event* should be ignored'
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return event.inaxes != self.canvas.axes or event.button != 1
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def update_background(self, event):
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#if self.canvas.axes is None:
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# raise SyntaxError,"Need an axes reference!"
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self.background = self.canvas.copy_from_bbox(self.canvas.axes.bbox)
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def press(self, event):
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if self.span_button.isChecked():
|
||||
if self.background is None:
|
||||
self.update_background()
|
||||
else:
|
||||
self.canvas.restore_region(self.background)
|
||||
self.xstart = event.xdata
|
||||
self.istart = event.x
|
||||
if self.span is None:
|
||||
self.span = self.canvas.axes.axvspan(event.xdata, event.xdata, alpha=0.10, color="k", animated=False)
|
||||
else:
|
||||
self.set_span(event.xdata, event.xdata)
|
||||
self._pressed = True
|
||||
|
||||
def onmove(self, event):
|
||||
if self.span_button.isChecked() and self._pressed and not self.ignore(event):
|
||||
self.set_span(self.xstart, event.xdata)
|
||||
self.update()
|
||||
|
||||
def update(self):
|
||||
"""Overrides method of NavigationToolbar.
|
||||
Allows fast drawing of the span selector with blitting
|
||||
"""
|
||||
self.canvas.restore_region(self.background)
|
||||
self.canvas.axes.draw_artist(self.span)
|
||||
for line in self.canvas.axes.get_lines():
|
||||
self.canvas.axes.draw_artist(line)
|
||||
self.canvas.blit(self.canvas.axes.bbox)
|
||||
|
||||
def release(self, event):
|
||||
self.span_button.setChecked(False)
|
||||
self.xend = event.xdata
|
||||
self.iend = event.x
|
||||
if self.iend < self.istart:
|
||||
self.iend, self.istart = self.istart, self.iend
|
||||
#print "released", self.xstart, self.xend
|
||||
if self._pressed:
|
||||
if self.ignore(event):
|
||||
self.istart = 0
|
||||
self.spanSelectedTrigger.emit(self.xstart, self.xend)
|
||||
self._pressed = False
|
||||
113
mathlib.py
Normal file
113
mathlib.py
Normal file
@ -0,0 +1,113 @@
|
||||
# -*- encoding: utf-8 -*-
|
||||
import matplotlib
|
||||
import numpy as N
|
||||
from scipy import optimize as opt, optimize, odr
|
||||
#from QDS import mini_func, multi_hn
|
||||
|
||||
__author__ = 'markusro'
|
||||
|
||||
|
||||
def fit_anneal(x, y, p0, fixed):
|
||||
bounds = [(0, 1e14), (0, 1)]
|
||||
for i in xrange(len(p0[2:]) / 4):
|
||||
bounds.append((1e-4, 1e12)) # delta_eps
|
||||
bounds.append((1e-12, 1e3)) # tau
|
||||
bounds.append((0.1, 1)) # a
|
||||
bounds.append((0.1, 1)) # b
|
||||
ret = opt.anneal(mini_func, p0,
|
||||
args=(x, y),
|
||||
maxeval=20000,
|
||||
maxiter=30000,
|
||||
lower=[b[0] for b in bounds],
|
||||
upper=[b[1] for b in bounds],
|
||||
dwell=100,
|
||||
full_output=1)
|
||||
#pmin, func_min, final_Temp, cooling_iters,accepted_tests, retval
|
||||
#retval : int
|
||||
#Flag indicating stopping condition::
|
||||
|
||||
# 0 : Points no longer changing
|
||||
# 1 : Cooled to final temperature
|
||||
# 2 : Maximum function evaluations
|
||||
# 3 : Maximum cooling iterations reached
|
||||
# 4 : Maximum accepted query locations reached
|
||||
# 5 : Final point not the minimum amongst encountered points
|
||||
|
||||
print "Stop reason", ret
|
||||
return ret[0]
|
||||
|
||||
|
||||
def fit_lbfgsb(x, y, p0, fixed):
|
||||
# TODO fixed parameters…
|
||||
bounds = [(0, None), (0, 1)]
|
||||
for i in xrange(len(p0[3:]) / 4):
|
||||
bounds.append((1e-4, 1e12)) # delta_eps
|
||||
bounds.append((1e-12, 1e3)) # tau
|
||||
bounds.append((0.1, 1)) # a
|
||||
bounds.append((0.1, 1)) # b
|
||||
|
||||
x, f_minvalue, info_dict = opt.fmin_l_bfgs_b(mini_func, p0,
|
||||
fprime=None,
|
||||
args=(x, y),
|
||||
approx_grad=True,
|
||||
bounds=bounds,
|
||||
iprint=0,
|
||||
maxfun=4000)
|
||||
if info_dict['warnflag'] != 0:
|
||||
print info_dict["task"]
|
||||
return x
|
||||
|
||||
|
||||
def fit_odr(x, y, p0, fixed):
|
||||
dat = odr.Data(x, y, 1.0 / y)
|
||||
mod = odr.Model(multi_hn)
|
||||
fit = odr.ODR(dat, mod, p0, ifixx=(0,), ifixb=fixed, maxit=2000)
|
||||
fit.run()
|
||||
return fit.output.beta
|
||||
|
||||
|
||||
def hn(p, nu):
|
||||
delta_eps, tau, a, b = p
|
||||
om = 2 * N.pi * nu
|
||||
Phi = N.arctan((om * tau) ** a * N.sin(N.pi * a / 2.) / (1. + (om * tau) ** a * N.cos(N.pi * a / 2.)))
|
||||
e_loss = delta_eps * (1 + 2 * (om * tau) ** a * N.cos(N.pi * a / 2.) + (om * tau) ** (2. * a) ) ** (
|
||||
-b / 2.) * N.sin(b * Phi)
|
||||
e_stor = delta_eps * (1 + 2 * (om * tau) ** a * N.cos(N.pi * a / 2.) + (om * tau) ** (2. * a) ) ** (
|
||||
-b / 2.) * N.cos(b * Phi)
|
||||
return 2 * e_loss
|
||||
|
||||
|
||||
def id_to_color(id):
|
||||
"""
|
||||
|
||||
"""
|
||||
colors = ['b', 'r', 'g', 'c', 'm', 'y', 'k']
|
||||
conv = matplotlib.colors.ColorConverter()
|
||||
return conv.to_rgb(colors[id % len(colors)])
|
||||
|
||||
|
||||
def mini_func(p, x, y):
|
||||
res = y - multi_hn(p, x)
|
||||
# apply weights
|
||||
res /= 1 / y
|
||||
return N.sqrt(N.dot(res, res))
|
||||
|
||||
|
||||
def multi_hn(p, nu):
|
||||
conductivity = p[1]
|
||||
cond_beta = p[2]
|
||||
om = 2 * N.pi * nu
|
||||
e_loss = conductivity / om ** cond_beta
|
||||
e_loss += p[0]
|
||||
#for key, igroup in groupby(p[3:], lambda x: x//4):
|
||||
for i in xrange(len(p[3:]) / 4):
|
||||
delta_eps, tau, a, b = p[3 + i * 4:3 + (i + 1) * 4]
|
||||
#delta_eps, tau, a, b = list(igroup)
|
||||
#print delta_eps,tau,a,b
|
||||
#a = 0.5 *(1 + N.tanh(a))
|
||||
#b = 0.5 *(1 + N.tanh(b))
|
||||
Phi = N.arctan((om * tau) ** a * N.sin(N.pi * a / 2.) / (1. + (om * tau) ** a * N.cos(N.pi * a / 2.)))
|
||||
e_loss += 2 * delta_eps * (1 + 2 * (om * tau) ** a * N.cos(N.pi * a / 2.) + (om * tau) ** (2. * a) ) ** (
|
||||
-b / 2.) * N.sin(b * Phi)
|
||||
#e_stor = delta_eps * (1+ 2*(om*tau)**a * N.cos(N.pi*a/2.) + (om*tau)**(2.*a) )**(-b/2.)*N.cos(b*Phi)
|
||||
return e_loss
|
||||
Loading…
Reference in New Issue
Block a user