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#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2004-2019 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.
#
#############################################################################*/
__author__ = "V.A. Sole - ESRF Data Analysis"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
import numpy
from .fitting 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):
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")
if roi_min is None:
roi_min = 0
if roi_max is None:
roi_max = data.shape[mcaIndex]
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):
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")
if roi_min is None:
roi_min = 0
if roi_max is None:
roi_max = data.shape[mcaIndex]
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 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")
if roi_min is None:
roi_min = (0, 0)
if roi_max is None:
roi_max = tuple(data.shape[i] for i in range(3) if i != index)
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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