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# Copyright 2014 Diamond Light Source Ltd. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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""" |
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.. module:: plugin_template3 |
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:platform: Unix |
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:synopsis: A template to create a plugin that reduces the data dimensions. |
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.. moduleauthor:: Developer Name <[email protected]> |
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""" |
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import copy |
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import numpy as np |
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from savu.plugins.plugin import Plugin |
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from savu.plugins.utils import register_plugin |
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from savu.plugins.driver.cpu_plugin import CpuPlugin |
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@register_plugin |
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class PluginTemplate3(Plugin, CpuPlugin): |
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def __init__(self): |
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super(PluginTemplate3, self).__init__('PluginTemplate3') |
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def nInput_datasets(self): |
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return 1 |
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def nOutput_datasets(self): |
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return 1 |
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View Code Duplication |
def setup(self): |
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in_dataset, out_dataset = self.get_datasets() |
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#=================== populate output dataset ========================== |
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# Due to the reduction in dimensions, the out_dataset will have |
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# different axis_labels, patterns and shape to the in_dataset and |
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# these will need to be defined. |
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# For more information about the syntax used here see: |
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# http://savu.readthedocs.io/en/latest/api_plugin/savu.data.data_structures.data_create |
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# AMEND THE PATTERNS: The output dataset will have one dimension less |
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# than the in_dataset, so remove the final slice dimension from any |
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# patterns you want to keep. |
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rm_dim = str(in_dataset[0].get_data_patterns() |
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['SINOGRAM']['slice_dims'][-1]) |
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patterns = ['SINOGRAM.' + rm_dim, 'PROJECTION.' + rm_dim] |
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# AMEND THE AXIS LABELS: Find the dimensions to remove using their |
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# axis_labels to ensure the plugin is as generic as possible and will |
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# work for data in all orientations. |
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axis_labels = copy.copy(in_dataset[0].get_axis_labels()) |
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rm_labels = ['detector_x', 'detector_y'] |
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rm_dims = sorted([in_dataset[0].get_data_dimension_by_axis_label(a) |
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for a in rm_labels])[::-1] |
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for d in rm_dims: |
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del axis_labels[d] |
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# Add a new axis label to the list |
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axis_labels.append({'Q': 'Angstrom^-1'}) |
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# AMEND THE SHAPE: Remove the two unrequired dimensions from the |
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# original shape and add a new dimension shape. |
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shape = list(in_dataset[0].get_shape()) |
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for d in rm_dims: |
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del shape[d] |
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shape += (self.get_parameters('num_bins'),) |
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# populate the output dataset |
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out_dataset[0].create_dataset( |
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patterns={in_dataset[0]: patterns}, |
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axis_labels=axis_labels, |
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shape=tuple(shape)) |
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# ASSOCIATE AN EXTRA PATTERN WITH THE DATASET: SINOGRAM and PROJECTION |
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# patterns are already asssociated with the output dataset, but add |
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# another one. |
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spectrum = \ |
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{'core_dims': (-1,), 'slice_dims': tuple(range(len(shape)-1))} |
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out_dataset[0].add_pattern("SPECTRUM", **spectrum) |
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#====================================================================== |
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#================== populate plugin datasets ========================== |
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in_pData, out_pData = self.get_plugin_datasets() |
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in_pData[0].plugin_data_setup('DIFFRACTION', 'single') |
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out_pData[0].plugin_data_setup('SPECTRUM', 'single') |
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#====================================================================== |
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def pre_process(self): |
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pass |
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def process_frames(self, data): |
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# do some processing here |
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return np.arange(self.parameters['num_bins']) |
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def post_process(self): |
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pass |
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DiamondLightSource /
Savu
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