Plugin Template 3ΒΆ
Download Plugin Template 3 Tools
# 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:: plugin_template3
:platform: Unix
:synopsis: A template to create a plugin that reduces the data dimensions.
.. moduleauthor:: Developer Name <email@address.ac.uk>
"""
import copy
import numpy as np
from savu.plugins.plugin import Plugin
from savu.plugins.utils import register_plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
@register_plugin
class PluginTemplate3(Plugin, CpuPlugin):
def __init__(self):
super(PluginTemplate3, self).__init__('PluginTemplate3')
def nInput_datasets(self):
return 1
def nOutput_datasets(self):
return 1
def setup(self):
in_dataset, out_dataset = self.get_datasets()
#=================== populate output dataset ==========================
# Due to the reduction in dimensions, the out_dataset will have
# different axis_labels, patterns and shape to the in_dataset and
# these will need to be defined.
# For more information about the syntax used here see:
# http://savu.readthedocs.io/en/latest/api_plugin/savu.data.data_structures.data_create
# 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))
# ASSOCIATE AN EXTRA PATTERN WITH THE DATASET: SINOGRAM and PROJECTION
# patterns are already asssociated with the output dataset, but add
# another one.
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')
#======================================================================
def pre_process(self):
pass
def process_frames(self, data):
# do some processing here
return np.arange(self.parameters['num_bins'])
def post_process(self):
pass
from savu.plugins.plugin_tools import PluginTools
class PluginTemplate3Tools(PluginTools):
"""
A plugin template that reduces the data dimensions, e.g. azimuthal
integration.
"""
def define_parameters(self):
"""
num_bins:
visibility: basic
dtype: int
description: Length of the new dimension
default: 10
"""