Source code for PyMca5.PyMcaMath.SNIPModule

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# The PyMca X-Ray Fluorescence Toolkit
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__author__ = "V.A. Sole - ESRF Data Analysis"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
import numpy
from PyMca5 import SpecfitFuns

snip1d = SpecfitFuns.snip1d
snip2d = SpecfitFuns.snip2d


[docs]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
[docs]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
[docs]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
[docs]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
[docs]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