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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_template5 |
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:platform: Unix |
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:synopsis: A template to create a plugin with a single input dataset and \ |
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multiple output datasets, that do not resemble the input dataset and are \ |
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not retained by the framework. |
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.. moduleauthor:: Developer Name <[email protected]> |
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""" |
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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.driver.cpu_plugin import CpuPlugin |
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from savu.plugins.utils import register_plugin |
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@register_plugin |
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class PluginTemplate5(Plugin, CpuPlugin): |
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def __init__(self): |
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super(PluginTemplate5, self).__init__('PluginTemplate5') |
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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 2 |
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def setup(self): |
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in_dataset, out_dataset = self.get_datasets() |
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# get the full shape of the data before previewing |
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full_shape = in_dataset[0].get_shape() |
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# reduce the data as per data_subset parameter |
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self.set_preview(in_dataset[0], self.parameters['preview']) |
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# get the reduced shape of the data after previewing |
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reduced_shape = in_dataset[0].get_shape() |
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slice_dirs = np.array(in_dataset[0].get_slice_dimensions()) |
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new_shape = (np.prod(np.array(reduced_shape)[slice_dirs]), 1) |
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full_length = (np.prod(np.array(full_shape)[slice_dirs]), 1) |
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#=================== populate output datasets ========================= |
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# the output datasets are of a different type (i.e different shape, |
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# axis labels and patterns) to the input dataset, so more information |
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# is required. |
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# the first output dataset contains one value for each... |
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out_dataset[0].create_dataset(shape=new_shape, |
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axis_labels=['x.pixels', 'y.pixels'], |
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remove=True, |
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transport='hdf5') |
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# currently there are no patterns assigned to this dataset - add one. |
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out_dataset[0].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,)) |
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# write something here about this being a dummy dataset... |
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out_dataset[1].create_dataset(shape=full_length, |
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axis_labels=['x.pixels', 'y.pixels'], |
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remove=True, |
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transport='hdf5') |
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# currently there are no patterns assigned to this dataset - add one. |
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out_dataset[1].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,)) |
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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('SINOGRAM', self.get_max_frames()) |
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out_pData[0].plugin_data_setup('METADATA', 'single') |
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out_pData[1].plugin_data_setup('METADATA', '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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pass |
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def post_process(self): |
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# Add some information to post process *** how are these datasets |
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# processed |
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pass |
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DiamondLightSource /
Savu
