def test():
from silx.math.fit import fittheories
from silx.math.fit import fitmanager
from silx.math.fit import functions
from silx.gui.plot.PlotWindow import PlotWindow
import numpy
a = qt.QApplication([])
x = numpy.arange(1000)
y1 = functions.sum_gauss(x, 100, 400, 100)
fit = fitmanager.FitManager(x=x, y=y1)
fitfuns = fittheories.FitTheories()
fit.addtheory(name="Gaussian",
function=functions.sum_gauss,
parameters=("height", "peak center", "fwhm"),
estimate=fitfuns.estimate_height_position_fwhm)
fit.settheory('Gaussian')
fit.configure(
PositiveFwhmFlag=True,
PositiveHeightAreaFlag=True,
AutoFwhm=True,
)
# Fit
fit.estimate()
fit.runfit()
w = ParametersTab()
w.show()
w.fillFromFit(fit.fit_results, view='Gaussians')
y2 = functions.sum_splitgauss(x, 100, 400, 100, 40, 10, 600, 50, 500, 80,
850, 10, 50)
fit.setdata(x=x, y=y2)
# Define new theory
fit.addtheory(name="Asymetric gaussian",
function=functions.sum_splitgauss,
parameters=("height", "peak center", "left fwhm",
"right fwhm"),
estimate=fitfuns.estimate_splitgauss)
fit.settheory('Asymetric gaussian')
# Fit
fit.estimate()
fit.runfit()
w.fillFromFit(fit.fit_results, view='Asymetric gaussians')
# Plot
pw = PlotWindow(control=True)
pw.addCurve(x, y1, "Gaussians")
pw.addCurve(x, y2, "Asymetric gaussians")
pw.show()
a.exec_()
def test():
from silx.math.fit import fittheories
from silx.math.fit import fitmanager
from silx.math.fit import functions
from silx.gui.plot.PlotWindow import PlotWindow
import numpy
a = qt.QApplication([])
x = numpy.arange(1000)
y1 = functions.sum_gauss(x, 100, 400, 100)
fit = fitmanager.FitManager(x=x, y=y1)
fitfuns = fittheories.FitTheories()
fit.addtheory(name="Gaussian",
function=functions.sum_gauss,
parameters=("height", "peak center", "fwhm"),
estimate=fitfuns.estimate_height_position_fwhm)
fit.settheory('Gaussian')
fit.configure(PositiveFwhmFlag=True,
PositiveHeightAreaFlag=True,
AutoFwhm=True,)
# Fit
fit.estimate()
fit.runfit()
w = ParametersTab()
w.show()
w.fillFromFit(fit.fit_results, view='Gaussians')
y2 = functions.sum_splitgauss(x,
100, 400, 100, 40,
10, 600, 50, 500,
80, 850, 10, 50)
fit.setdata(x=x, y=y2)
# Define new theory
fit.addtheory(name="Asymetric gaussian",
function=functions.sum_splitgauss,
parameters=("height", "peak center", "left fwhm", "right fwhm"),
estimate=fitfuns.estimate_splitgauss)
fit.settheory('Asymetric gaussian')
# Fit
fit.estimate()
fit.runfit()
w.fillFromFit(fit.fit_results, view='Asymetric gaussians')
# Plot
pw = PlotWindow(control=True)
pw.addCurve(x, y1, "Gaussians")
pw.addCurve(x, y2, "Asymetric gaussians")
pw.show()
a.exec_()
def test():
from .functions import sum_gauss
from . import fittheories
from . import bgtheories
# Create synthetic data with a sum of gaussian functions
x = numpy.arange(1000).astype(numpy.float)
p = [1000, 100., 250,
255, 690., 45,
1500, 800.5, 95]
y = 0.5 * x + 13 + sum_gauss(x, *p)
# Fitting
fit = FitManager()
# more sensitivity necessary to resolve
# overlapping peaks at x=690 and x=800.5
fit.setdata(x=x, y=y)
fit.loadtheories(fittheories)
fit.settheory('Gaussians')
fit.loadbgtheories(bgtheories)
fit.setbackground('Linear')
fit.estimate()
fit.runfit()
print("Searched parameters = ", p)
print("Obtained parameters : ")
dummy_list = []
for param in fit.fit_results:
print(param['name'], ' = ', param['fitresult'])
dummy_list.append(param['fitresult'])
print("chisq = ", fit.chisq)
# Plot
constant, slope = dummy_list[:2]
p1 = dummy_list[2:]
print(p1)
y2 = slope * x + constant + sum_gauss(x, *p1)
try:
from silx.gui import qt
from silx.gui.plot.PlotWindow import PlotWindow
app = qt.QApplication([])
pw = PlotWindow(control=True)
pw.addCurve(x, y, "Original")
pw.addCurve(x, y2, "Fit result")
pw.legendsDockWidget.show()
pw.show()
app.exec_()
except ImportError:
_logger.warning("Could not import qt to display fit result as curve")
def test():
from .functions import sum_gauss
from . import fittheories
from . import bgtheories
# Create synthetic data with a sum of gaussian functions
x = numpy.arange(1000).astype(numpy.float)
p = [1000, 100., 250, 255, 690., 45, 1500, 800.5, 95]
y = 0.5 * x + 13 + sum_gauss(x, *p)
# Fitting
fit = FitManager()
# more sensitivity necessary to resolve
# overlapping peaks at x=690 and x=800.5
fit.setdata(x=x, y=y)
fit.loadtheories(fittheories)
fit.settheory('Gaussians')
fit.loadbgtheories(bgtheories)
fit.setbackground('Linear')
fit.estimate()
fit.runfit()
print("Searched parameters = ", p)
print("Obtained parameters : ")
dummy_list = []
for param in fit.fit_results:
print(param['name'], ' = ', param['fitresult'])
dummy_list.append(param['fitresult'])
print("chisq = ", fit.chisq)
# Plot
constant, slope = dummy_list[:2]
p1 = dummy_list[2:]
print(p1)
y2 = slope * x + constant + sum_gauss(x, *p1)
try:
from silx.gui import qt
from silx.gui.plot.PlotWindow import PlotWindow
app = qt.QApplication([])
pw = PlotWindow(control=True)
pw.addCurve(x, y, "Original")
pw.addCurve(x, y2, "Fit result")
pw.legendsDockWidget.show()
pw.show()
app.exec_()
except ImportError:
_logger.warning("Could not import qt to display fit result as curve")
