# Copyright 2014 Diamond Light Source Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
.. module:: pyfai_azimuthal_integrator_with_bragg_filter
:platform: Unix
:synopsis: A plugin to integrate azimuthally "symmetric" signals i.e. SAXS,\
WAXS or XRD.Requires a calibration file.
.. moduleauthor:: Aaron D. Parsons <scientificsoftware@diamond.ac.uk>
"""
import logging
import numpy as np
from savu.plugins.azimuthal_integrators.base_azimuthal_integrator \
import BaseAzimuthalIntegrator
from savu.plugins.utils import register_plugin
[docs]@register_plugin
class PyfaiAzimuthalIntegratorWithBraggFilter(BaseAzimuthalIntegrator):
def __init__(self):
logging.debug("Starting 1D azimuthal integration***")
super(PyfaiAzimuthalIntegratorWithBraggFilter,
self).__init__("PyfaiAzimuthalIntegratorWithBraggFilter")
[docs] def process_frames(self, data):
mData = self.params[2]
ai = self.params[3]
lims = self.parameters['thresh']
num_bins_azim = self.parameters['num_bins_azim']
num_bins_rad = self.parameters['num_bins']
remapped, axis, _chi = \
ai.integrate2d(data=data[0], npt_rad=num_bins_rad,
npt_azim=num_bins_azim, unit='q_A^-1')
mask = np.ones_like(remapped)
mask[remapped==0] = 0
out = np.zeros(mask.shape[1])
for i in range(mask.shape[1]):
# print i
idx = mask[:,i] == 1
if np.sum(idx*1)==0:
logging.warning("Found a bin where all the pixels are masked! Bin num: %s" , str(i))
out[i] = 0.0
else:
foo = remapped[:,i][idx]
# print "the shape here is:"+str(foo.shape)
top = np.percentile(foo,lims[1])
bottom = np.percentile(foo,lims[0])
out[i] = np.mean(np.clip(foo,bottom,top))
return out