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#/*##########################################################################
# Copyright (C) 2004-2022 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF by the Software group.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
#############################################################################*/
"""
This plugin provides methods to replace curves by their median filter average.
3-, 5-, 7- or 9-points filters are provided. The filter can be applied on the
data in its original order, or in a randomized order.
"""
__author__ = "V.A. Sole - ESRF"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
import numpy
from PyMca5 import Plugin1DBase
from PyMca5.PyMcaMath.fitting import SpecfitFuns
from PyMca5.PyMcaMath.PyMcaSciPy.signal.median import medfilt1d
class MedianFilterScanPlugin(Plugin1DBase.Plugin1DBase):
'''Methods to replace curves by their median filter average.'''
def __init__(self, plotWindow, **kw):
Plugin1DBase.Plugin1DBase.__init__(self, plotWindow, **kw)
self.__randomization = True
self.__methodKeys = []
self.methodDict = {}
text = "Use a random order instead\n"
text += "of the plotting order."
info = text
icon = None
function = self.toggleRandomization
method = "Toggle Randomization OFF"
self.methodDict[method] = [function,
info,
icon]
self.__methodKeys.append(method)
method = "Toggle Randomization ON"
text = "Use plotting order instead\n"
text += "of a random order."
self.methodDict[method] = [function,
info,
icon]
self.__methodKeys.append(method)
function = self.applyMedianFilter
for i in [3, 5, 7, 9]:
info = "Replace curves by their %d-point median filter average" % i
method = "Replace by %d-point median filter" % i
self.methodDict[method] = [function,
info,
icon]
self.__methodKeys.append(method)
#Methods to be implemented by the plugin
def getMethods(self, plottype=None):
"""
A list with the NAMES associated to the callable methods
that are applicable to the specified plot.
Plot type can be "SCAN", "MCA", None, ...
"""
if self.__randomization:
return self.__methodKeys[0:1] + self.__methodKeys[2:]
else:
return self.__methodKeys[1:]
def getMethodToolTip(self, name):
"""
Returns the help associated to the particular method name or None.
"""
return self.methodDict[name][1]
def getMethodPixmap(self, name):
"""
Returns the pixmap associated to the particular method name or None.
"""
return None
def applyMethod(self, name):
"""
The plugin is asked to apply the method associated to name.
"""
if name.startswith('Toggle'):
return self.methodDict[name][0]()
n = int(name.split('-')[0].split()[-1])
return self.applyMedianFilter(width=n)
def toggleRandomization(self):
if self.__randomization:
self.__randomization = False
else:
self.__randomization = True
def applyMedianFilter(self, width=3):
curves = self.getAllCurves()
nCurves = len(curves)
if nCurves < width:
raise ValueError("At least %d curves needed" % width)
return
if self.__randomization:
indices = numpy.random.permutation(nCurves)
else:
indices = range(nCurves)
# get active curve
activeCurve = self.getActiveCurve()
if activeCurve is None:
activeCurve = curves[0]
# apply between graph limits
x0 = activeCurve[0][:]
y0 = activeCurve[1][:]
xmin, xmax =self.getGraphXLimits()
idx = numpy.nonzero((x0 >= xmin) & (x0 <= xmax))[0]
x0 = numpy.take(x0, idx)
y0 = numpy.take(y0, idx)
#sort the values
idx = numpy.argsort(x0, kind='mergesort')
x0 = numpy.take(x0, idx)
y0 = numpy.take(y0, idx)
#remove duplicates
x0 = x0.ravel()
idx = numpy.nonzero((x0[1:] > x0[:-1]))[0]
x0 = numpy.take(x0, idx)
y0 = numpy.take(y0, idx)
x0.shape = -1, 1
nChannels = x0.shape[0]
# built a couple of temporary array of spectra for handy access
tmpArray = numpy.zeros((nChannels, nCurves), numpy.float64)
medianSpectra = numpy.zeros((nChannels, nCurves), numpy.float64)
i = 0
for idx in indices:
x, y, legend, info = curves[idx][0:4]
#sort the values
x = x[:]
idx = numpy.argsort(x, kind='mergesort')
x = numpy.take(x, idx)
y = numpy.take(y, idx)
#take the portion of x between limits
idx = numpy.nonzero((x>=xmin) & (x<=xmax))[0]
if not len(idx):
# no overlap
continue
x = numpy.take(x, idx)
y = numpy.take(y, idx)
#remove duplicates
x = x.ravel()
idx = numpy.nonzero((x[1:] > x[:-1]))[0]
x = numpy.take(x, idx)
y = numpy.take(y, idx)
x.shape = -1, 1
if numpy.allclose(x, x0):
# no need for interpolation
pass
else:
# we have to interpolate
x.shape = -1
y.shape = -1
xi = x0[:]
y = SpecfitFuns.interpol([x], y, xi, y0.min())
y.shape = -1
tmpArray[:, i] = y
i += 1
# now perform the median filter
for i in range(nChannels):
medianSpectra[i, :] = medfilt1d(tmpArray[i,:],
kernel_size=width)
tmpArray = None
# now get the final spectrum
y = medianSpectra.sum(axis=1) / nCurves
x0.shape = -1
y.shape = x0.shape
legend = "%d Median from %s to %s" % (width,
curves[0][2],
curves[-1][2])
self.addCurve(x0,
y,
legend=legend,
info=None,
replot=True,
replace=True)
MENU_TEXT = "Median Filter Average"
def getPlugin1DInstance(plotWindow, **kw):
ob = MedianFilterScanPlugin(plotWindow)
return ob
if __name__ == "__main__":
from PyMca5.PyMcaGui import PyMcaQt as qt
app = qt.QApplication([])
from PyMca5.PyMcaGraph import Plot
x = numpy.arange(100.)
y = x * x
plot = Plot.Plot()
plot.addCurve(x, y, "dummy")
plot.addCurve(x+100, -x*x)
plugin = getPlugin1DInstance(plot)
for method in plugin.getMethods():
print(method, ":", plugin.getMethodToolTip(method))
plugin.applyMethod(plugin.getMethods()[0])
curves = plugin.getAllCurves()
for curve in curves:
print(curve[2])
print("LIMITS = ", plugin.getGraphYLimits())
#app = qt.QApplication()
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