/usr/lib/python2.7/dist-packages/pyFAI/massif.py is in pyfai 0.10.2-1.
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# -*- coding: utf-8 -*-
#
# Project: Azimuthal integration
# https://github.com/kif/pyFAI
#
# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# This program 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 3 of the License, or
# (at your option) any later version.
#
# This program 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 this program. If not, see <http://www.gnu.org/licenses/>.
#
__author__ = "Jérôme Kieffer"
__contact__ = "Jerome.Kieffer@ESRF.eu"
__license__ = "GPLv3+"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "27/06/2014"
__status__ = "production"
import sys, os, threading
from math import ceil, sqrt
import logging
logger = logging.getLogger("pyFAI.massif")
import numpy
import fabio
from scipy.ndimage import label
from scipy.ndimage.filters import median_filter
from .bilinear import Bilinear
from .utils import gaussian_filter, binning, unBinning, relabel
if os.name != "nt":
WindowsError = RuntimeError
TARGET_SIZE = 1024
################################################################################
# Massif
################################################################################
class Massif(object):
"""
A massif is defined as an area around a peak, it is used to find neighbouring peaks
"""
def __init__(self, data=None):
"""
"""
if isinstance(data, (str, unicode)) and os.path.isfile(data):
self.data = fabio.open(data).data.astype("float32")
elif isinstance(data, fabio.fabioimage.fabioimage):
self.data = data.data.astype("float32")
else:
try:
self.data = data.astype("float32")
except Exception as error:
logger.error("Unable to understand this type of data %s: %s", data, error)
self._bilin = Bilinear(self.data)
self._blured_data = None
self._median_data = None
self._labeled_massif = None
self._number_massif = None
self._valley_size = None
self._binned_data = None
self.binning = None # Binning is 2-list usually
self._sem = threading.Semaphore()
self._sem_label = threading.Semaphore()
self._sem_binning = threading.Semaphore()
self._sem_median = threading.Semaphore()
def nearest_peak(self, x):
"""
@param x: coordinates of the peak
@returns the coordinates of the nearest peak
"""
out = self._bilin.local_maxi(x)
if isinstance(out, tuple):
res = out
elif isinstance(out, numpy.ndarray):
res = tuple(out)
else:
res = [int(i) for idx, i in enumerate(out) if 0 <= i < self.data.shape[idx] ]
if (len(res) != 2) or not((0 <= out[0] < self.data.shape[0]) and (0 <= res[1] < self.data.shape[1])):
logger.error("in nearest_peak %s -> %s" % (x, out))
return
else:
return res
def calculate_massif(self, x):
"""
defines a map of the massif around x and returns the mask
"""
labeled = self.getLabeledMassif()
if labeled[x[0], x[1]] != labeled.max():
return (labeled == labeled[x[0], x[1]])
def find_peaks(self, x, nmax=200, annotate=None, massif_contour=None, stdout=sys.stdout):
"""
All in one function that finds a maximum from the given seed (x)
then calculates the region extension and extract position of the neighboring peaks.
@param x: seed for the calculation, input coordinates
@param nmax: maximum number of peak per region
@param annotate: call back method taking number of points + coordinate as input.
@param massif_contour: callback to show the contour of a massif with the given index.
@param stdout: this is the file where output is written by default.
@return: list of peaks
"""
listpeaks = []
region = self.calculate_massif(x)
if region is None:
logger.error("You picked a background point at %s", x)
return listpeaks
xinit = self.nearest_peak(x)
if xinit is None:
logger.error("Unable to find peak in the vinicy of %s", x)
return listpeaks
else:
if not region[int(xinit[0] + 0.5), int(xinit[1] + 0.5)]:
logger.error("Nearest peak %s is not in the same region %s", xinit, x)
return listpeaks
if annotate is not None:
try:
annotate(xinit, x)
except Exception as error:
logger.error("Error in annotate %i: %i %i. %s" , len(listpeaks), xinit[0], xinit[1], error)
listpeaks.append(xinit)
mean = self.data[region].mean(dtype=numpy.float64)
region2 = region * (self.data > mean)
idx = numpy.vstack(numpy.where(region2)).T
numpy.random.shuffle(idx)
nmax = min(nmax, int(ceil(sqrt(idx.shape[0]))))
if massif_contour is not None:
try:
massif_contour(region)
except (WindowsError, MemoryError) as error:
logger.error("Error in plotting region: %s", error)
nbFailure = 0
for j in idx:
xopt = self.nearest_peak(j)
if xopt is None:
nbFailure += 1
continue
if (region2[xopt[0], xopt[1]]) and not (xopt in listpeaks):
stdout.write("[ %4i, %4i ] --> [ %5.1f, %5.1f ] after %3i iterations %s" % (tuple(j) + tuple(xopt) + (nbFailure, os.linesep)))
listpeaks.append(xopt)
nbFailure = 0
else:
nbFailure += 1
if (len(listpeaks) > nmax) or (nbFailure > 2 * nmax):
break
return listpeaks
def peaks_from_area(self, mask, Imin=None, keep=1000, **kwarg):
"""
Return the list of peaks within an area
@param mask: 2d array with mask.
@param Imin: minimum of intensity above the background to keep the point
@param keep: maximum number of points to keep
@param kwarg: ignored parameters
@return: list of peaks [y,x], [y,x], ...]
"""
all_points = numpy.vstack(numpy.where(mask)).T
res = []
cnt = 0
numpy.random.shuffle(all_points)
for idx in all_points:
out = self.nearest_peak(idx)
if out is not None:
print("[ %3i, %3i ] -> [ %.1f, %.1f ]" %
(idx[1], idx[0], out[1], out[0]))
p0, p1 = int(out[0]), int(out[1])
if mask[p0, p1]:
if (out not in res) and\
(self.data[p0, p1] > Imin):
res.append(out)
cnt = 0
if len(res) >= keep or cnt > keep:
break
else:
cnt += 1
return res
def initValleySize(self):
if self._valley_size is None:
self.valley_size = max(5., max(self.data.shape) / 50.)
def getValleySize(self):
if self._valley_size is None:
self.initValleySize()
return self._valley_size
def setValleySize(self, size):
new_size = float(size)
if self._valley_size != new_size:
self._valley_size = new_size
# self.getLabeledMassif()
t = threading.Thread(target=self.getLabeledMassif)
t.start()
def delValleySize(self):
self._valley_size = None
self._blured_data = None
valley_size = property(getValleySize, setValleySize, delValleySize, "Defines the minimum distance between two massifs")
def getBinnedData(self):
"""
@return binned data
"""
if self._binned_data is None:
with self._sem_binning:
if self._binned_data is None:
logger.info("Image size is %s", self.data.shape)
self.binning = []
for i in self.data.shape:
if i % TARGET_SIZE == 0:
self.binning.append(max(1, i // TARGET_SIZE))
else:
for j in range(i // TARGET_SIZE - 1, 0, -1):
if i % j == 0:
self.binning.append(max(1, j))
break
else:
self.binning.append(1)
# self.binning = max([max(1, i // TARGET_SIZE) for i in self.data.shape])
logger.info("Binning size is %s", self.binning)
self._binned_data = binning(self.data, self.binning)
return self._binned_data
def getMedianData(self):
"""
@return: a spacial median filtered image
"""
if self._median_data is None:
with self._sem_median:
if self._median_data is None:
self._median_data = median_filter(self.data, 3)
if logger.getEffectiveLevel() == logging.DEBUG:
fabio.edfimage.edfimage(data=self._median_data).write("median_data.edf")
return self._median_data
def getBluredData(self):
"""
@return: a blurred image
"""
if self._blured_data is None:
with self._sem:
if self._blured_data is None:
logger.debug("Blurring image with kernel size: %s" , self.valley_size)
self._blured_data = gaussian_filter(self.getBinnedData(), [self.valley_size / i for i in self.binning], mode="reflect")
if logger.getEffectiveLevel() == logging.DEBUG:
fabio.edfimage.edfimage(data=self._blured_data).write("blured_data.edf")
return self._blured_data
def getLabeledMassif(self, pattern=None):
"""
@return: an image composed of int with a different value for each massif
"""
if self._labeled_massif is None:
with self._sem_label:
if self._labeled_massif is None:
if pattern is None:
pattern = [[1] * 3] * 3 # [[0, 1, 0], [1, 1, 1], [0, 1, 0]]#[[1] * 3] * 3
logger.debug("Labeling all massifs. This takes some time !!!")
labeled_massif, self._number_massif = label((self.getBinnedData() > self.getBluredData()), pattern)
logger.info("Labeling found %s massifs." % self._number_massif)
if logger.getEffectiveLevel() == logging.DEBUG:
fabio.edfimage.edfimage(data=labeled_massif).write("labeled_massif_small.edf")
relabeled = relabel(labeled_massif, self.getBinnedData(), self.getBluredData())
if logger.getEffectiveLevel() == logging.DEBUG:
fabio.edfimage.edfimage(data=relabeled).write("relabeled_massif_small.edf")
self._labeled_massif = unBinning(relabeled, self.binning, False)
if logger.getEffectiveLevel() == logging.DEBUG:
fabio.edfimage.edfimage(data=self._labeled_massif).write("labeled_massif.edf")
logger.info("Labeling found %s massifs." % self._number_massif)
return self._labeled_massif
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