# 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:: base_azimuthal_integrator
: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 copy
import pyFAI
import logging
import numpy as np
from savu.plugins.plugin import Plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
[docs]class BaseAzimuthalIntegrator(Plugin, CpuPlugin):
"""
a base azimuthal integrator for pyfai
:param use_mask: Should we mask. Default: False.
:param num_bins: number of bins. Default: 1005.
"""
def __init__(self, name='BaseAzimuthalIntegrator'):
logging.debug("Starting 1D azimuthal integrationr")
super(BaseAzimuthalIntegrator, self).__init__(name)
[docs] def pre_process(self):
"""
This method is called after the plugin has been created by the
pipeline framework as a pre-processing step
:param parameters: A dictionary of the parameters for this plugin, or
None if no customisation is required
:type parameters: dict
"""
in_dataset, out_datasets = self.get_datasets()
mData = self.get_in_meta_data()[0]
in_d1 = in_dataset[0]
ai = pyFAI.AzimuthalIntegrator() # get me an integrator object
# prep the goemtry
px_m = mData.get('x_pixel_size')
bc_m = [mData.get("beam_center_x"),
mData.get("beam_center_y")] # in metres
bc = bc_m / px_m # convert to pixels
px = px_m*1e6 # convert to microns
distance = mData.get('distance')*1e3 # convert to mm
wl = mData.get('incident_wavelength')[...] # in m
self.wl = wl
yaw = -mData.get("yaw")
roll = mData.get("roll")
ai.setFit2D(distance, bc[0], bc[1], yaw, roll, px, px, None)
ai.set_wavelength(wl)
logging.debug(ai)
sh = in_d1.get_shape()
if (self.parameters["use_mask"]):
mask = mData.get("mask")
else:
mask = np.zeros((sh[-2], sh[-1]))
# now integrate in radius (1D)print "hello"
self.npts = self.get_parameters('num_bins')
self.params = [mask, self.npts, mData, ai]
# now set the axis values, we shouldn't do this in every slice
axis, __remapped = \
ai.integrate1d(data=mask, npt=self.npts, unit='q_A^-1',
correctSolidAngle=False)
self.add_axes_to_meta_data(axis, mData)
[docs] def setup(self):
in_dataset, out_dataset = self.get_datasets()
# AMEND THE PATTERNS: The output dataset will have one dimension less
# than the in_dataset, so remove the final slice dimension from any
# patterns you want to keep.
rm_dim = str(in_dataset[0].get_data_patterns()
['SINOGRAM']['slice_dims'][-1])
patterns = ['SINOGRAM.' + rm_dim, 'PROJECTION.' + rm_dim]
# AMEND THE AXIS LABELS: Find the dimensions to remove using their
# axis_labels to ensure the plugin is as generic as possible and will
# work for data in all orientations.
axis_labels = copy.copy(in_dataset[0].get_axis_labels())
rm_labels = ['detector_x', 'detector_y']
rm_dims = sorted([in_dataset[0].get_data_dimension_by_axis_label(a)
for a in rm_labels])[::-1]
for d in rm_dims:
del axis_labels[d]
# Add a new axis label to the list
axis_labels.append({'Q': 'Angstrom^-1'})
# AMEND THE SHAPE: Remove the two unrequired dimensions from the
# original shape and add a new dimension shape.
shape = list(in_dataset[0].get_shape())
for d in rm_dims:
del shape[d]
shape += (self.get_parameters('num_bins'),)
# populate the output dataset
out_dataset[0].create_dataset(
patterns={in_dataset[0]: patterns},
axis_labels=axis_labels,
shape=tuple(shape))
spectrum = \
{'core_dims': (-1,), 'slice_dims': tuple(range(len(shape)-1))}
out_dataset[0].add_pattern("SPECTRUM", **spectrum)
# =====================================================================
# ================== populate plugin datasets =========================
in_pData, out_pData = self.get_plugin_datasets()
in_pData[0].plugin_data_setup('DIFFRACTION', 'single')
out_pData[0].plugin_data_setup('SPECTRUM', 'single')
# =====================================================================
[docs] def get_max_frames(self):
return 'single'
[docs] def nOutput_datasets(self):
return 1