/usr/share/pyshared/PyMca/SNIPModule.py is in pymca 4.5.0-4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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# Copyright (C) 2004-2010 European Synchrotron Radiation Facility
#
# This file is part of the PyMCA X-ray Fluorescence Toolkit developed at
# the ESRF by the Beamline Instrumentation Software Support (BLISS) group.
#
# This toolkit is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation; either version 2 of the License, or (at your option)
# any later version.
#
# PyMCA is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# PyMCA; if not, write to the Free Software Foundation, Inc., 59 Temple Place,
# Suite 330, Boston, MA 02111-1307, USA.
#
# PyMCA follows the dual licensing model of Trolltech's Qt and Riverbank's PyQt
# and cannot be used as a free plugin for a non-free program.
#
# Please contact the ESRF industrial unit (industry@esrf.fr) if this license
# is a problem for you.
#############################################################################*/
__author__ = "V.A. Sole - ESRF Data Analysis"
import numpy
try:
import PyMca.SpecfitFuns as SpecfitFuns
except ImportError:
import SpecfitFuns
snip1d = SpecfitFuns.snip1d
snip2d = SpecfitFuns.snip2d
def getSpectrumBackground(spectrum, width, roi_min=None, roi_max=None, smoothing=1):
if roi_min is None:
roi_min = 0
if roi_max is None:
roi_max = len(spectrum)
background = spectrum * 1
background[roi_min:roi_max] = snip1d(spectrum[roi_min:roi_max], width, smoothing)
return background
getSnip1DBackground = getSpectrumBackground
def subtractSnip1DBackgroundFromStack(stack, width, roi_min=None, roi_max=None, smoothing=1):
if roi_min is None:
roi_min = 0
if roi_max is None:
roi_max = len(spectrum)
mcaIndex = -1
if hasattr(stack, "info") and hasattr(stack, "data"):
data = stack.data
mcaIndex = stack.info.get('McaIndex', -1)
else:
data = stack
if not isinstance(data, numpy.ndarray):
raise TypeError("This Plugin only supports numpy arrays")
oldShape = data.shape
if mcaIndex in [-1, len(data.shape)-1]:
data.shape = -1, oldShape[-1]
if roi_min > 0:
data[:, 0:roi_min] = 0
if roi_max < oldShape[-1]:
data[:, roi_max:] = 0
for i in range(data.shape[0]):
data[i,roi_min:roi_max] -= snip1d(data[i,roi_min:roi_max],
width, smoothing)
data.shape = oldShape
elif mcaIndex == 0:
data.shape = oldShape[0], -1
for i in range(data.shape[-1]):
data[roi_min:roi_max, i] -= snip1d(data[roi_min:roi_max, i],
width, smoothing)
data.shape = oldShape
else:
raise ValueError("Invalid 1D index %d" % mcaIndex)
return
def replaceStackWithSnip1DBackground(stack, width, roi_min=None, roi_max=None, smoothing=1):
if roi_min is None:
roi_min = 0
if roi_max is None:
roi_max = len(spectrum)
mcaIndex = -1
if hasattr(stack, "info") and hasattr(stack, "data"):
data = stack.data
mcaIndex = stack.info.get('McaIndex', -1)
else:
data = stack
if not isinstance(data, numpy.ndarray):
raise TypeError("This Plugin only supports numpy arrays")
oldShape = data.shape
if mcaIndex in [-1, len(data.shape)-1]:
data.shape = -1, oldShape[-1]
if roi_min > 0:
data[:, 0:roi_min] = 0
if roi_max < oldShape[-1]:
data[:, roi_max:] = 0
for i in range(data.shape[0]):
data[i,roi_min:roi_max] = snip1d(data[i,roi_min:roi_max],
width, smoothing)
data.shape = oldShape
elif mcaIndex == 0:
data.shape = oldShape[0], -1
for i in range(data.shape[-1]):
data[roi_min:roi_max, i] = snip1d(data[roi_min:roi_max, i],
width, smoothing)
data.shape = oldShape
else:
raise ValueError("Invalid 1D index %d" % mcaIndex)
return
def getImageBackground(image, width, roi_min=None, roi_max=None, smoothing=1):
if roi_min is None:
roi_min = (0, 0)
if roi_max is None:
roi_max = image.shape
background = image * 1
background[roi_min[0]:roi_max[0],roi_min[1]:roi_max[1]]=\
snip2d(image[roi_min[0]:roi_max[0],roi_min[1]:roi_max[1]],
width,
smoothing)
return background
getSnip2DBackground = getImageBackground
def subtractSnip2DBackgroundFromStack(stack, width, roi_min=None, roi_max=None, smoothing=1, index=None):
"""
index is the dimension used to index the images
"""
if roi_min is None:
roi_min = (0, 0)
if roi_max is None:
roi_max = image.shape
if hasattr(stack, "info") and hasattr(stack, "data"):
data = stack.data
if index is None:
index = stack.info.get('McaIndex', 0)
else:
data = stack
if index is None:
index = 2
if not isinstance(data, numpy.ndarray):
raise TypeError("This Plugin only supports numpy arrays")
shape = data.shape
if index == 0:
if (roi_min[0] > 0) or (roi_min[1] > 0):
data[:, 0:roi_min[0], 0:roi_min[1]] = 0
if roi_max[0] < (shape[1]-1):
if roi_max[1] < (shape[2]-1):
data[:, roi_max[0]:, roi_max[1]:] = 0
else:
data[:, roi_max[0]:, :] = 0
else:
if roi_max[1] < (shape[2]-1):
data[:, :, roi_max[1]:] = 0
for i in range(shape[index]):
data[i,roi_min[0]:roi_max[0],roi_min[1]:roi_max[1]] -=\
snip2d(data[i,roi_min[0]:roi_max[0],roi_min[1]:roi_max[1]], width, smoothing)
return
if index == 1:
if (roi_min[0] > 0) or (roi_min[1] > 0):
data[0:roi_min[0], :, 0:roi_min[1]] = 0
if roi_max[0] < (shape[0]-1):
if roi_max[1] < (shape[2]-1):
data[roi_max[0]:, :, roi_max[1]:] = 0
else:
data[roi_max[0]:, :, :] = 0
else:
if roi_max[1] < (shape[2]-1):
data[:, :, roi_max[1]:] = 0
for i in range(shape[index]):
data[roi_min[0]:roi_max[0], i, roi_min[1]:roi_max[1]] -=\
snip2d(data[roi_min[0]:roi_max[0], i, roi_min[1]:roi_max[1]], width, smoothing)
return
if index == 2:
if (roi_min[0] > 0) or (roi_min[1] > 0):
data[0:roi_min[0], 0:roi_min[1],:] = 0
if roi_max[0] < (shape[0]-1):
if roi_max[1] < (shape[1]-1):
data[roi_max[0]:, roi_max[1]:, :] = 0
else:
data[roi_max[0]:, :, :] = 0
else:
if roi_max[1] < (shape[2]-1):
data[:, roi_max[1]:, :] = 0
for i in range(shape[index]):
data[roi_min[0]:roi_max[0],roi_min[1]:roi_max[1], i] -=\
snip2d(data[roi_min[0]:roi_max[0],roi_min[1]:roi_max[1], i], width, smoothing)
return
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