examples.plugin_examples.plugin_templates.general.plugin_template3.PluginTemplate3.setup() - Code Metrics - Inspection of "Merge pull request #862 from DiamondLightSource/da..." - DiamondLightSource/Savu - Measure and Improve Code Quality continuously with Scrutinizer

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by Daniil

created 2021-10-27 16:13 UTC

PluginTemplate3.setup() A

↳ Parent: examples.plugin_examples.plugin_templates.general.plugin_template3

Complexity

Conditions

Size

Total Lines	54
Code Lines	26

Duplication

Lines	54
Ratio	100 %

Importance

Changes

Metric	Value
cc	3
eloc	26
nop	1
dl	54
loc	54
rs	9.256
c	0
b	0
f	0

How to fix Long Method

# 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 protected]>

"""

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


1		# Copyright 2014 Diamond Light Source Ltd.
2		#
3		# Licensed under the Apache License, Version 2.0 (the "License");
4		# you may not use this file except in compliance with the License.
5		# You may obtain a copy of the License at
6		#
7		# http://www.apache.org/licenses/LICENSE-2.0
8		#
9		# Unless required by applicable law or agreed to in writing, software
10		# distributed under the License is distributed on an "AS IS" BASIS,
11		# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12		# See the License for the specific language governing permissions and
13		# limitations under the License.
14
15		"""
16		.. module:: plugin_template3
17		:platform: Unix
18		:synopsis: A template to create a plugin that reduces the data dimensions.
19
20		.. moduleauthor:: Developer Name <[email protected]>
21
22		"""
23
24		import copy
25		import numpy as np
26
27		from savu.plugins.plugin import Plugin
28		from savu.plugins.utils import register_plugin
29		from savu.plugins.driver.cpu_plugin import CpuPlugin
30
31
32		@register_plugin
33		class PluginTemplate3(Plugin, CpuPlugin):
34
35		def __init__(self):
36		super(PluginTemplate3, self).__init__('PluginTemplate3')
37
38		def nInput_datasets(self):
39		return 1
40
41		def nOutput_datasets(self):
42		return 1
43
44	View Code Duplication	def setup(self):
		0 ignored issues – show Duplication introduced 2021-02-25 17:14 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
45		in_dataset, out_dataset = self.get_datasets()
46
47		#=================== populate output dataset ==========================
48		# Due to the reduction in dimensions, the out_dataset will have
49		# different axis_labels, patterns and shape to the in_dataset and
50		# these will need to be defined.
51		# For more information about the syntax used here see:
52		# http://savu.readthedocs.io/en/latest/api_plugin/savu.data.data_structures.data_create
53
54		# AMEND THE PATTERNS: The output dataset will have one dimension less
55		# than the in_dataset, so remove the final slice dimension from any
56		# patterns you want to keep.
57		rm_dim = str(in_dataset[0].get_data_patterns()
58		['SINOGRAM']['slice_dims'][-1])
59		patterns = ['SINOGRAM.' + rm_dim, 'PROJECTION.' + rm_dim]
60
61		# AMEND THE AXIS LABELS: Find the dimensions to remove using their
62		# axis_labels to ensure the plugin is as generic as possible and will
63		# work for data in all orientations.
64		axis_labels = copy.copy(in_dataset[0].get_axis_labels())
65		rm_labels = ['detector_x', 'detector_y']
66		rm_dims = sorted([in_dataset[0].get_data_dimension_by_axis_label(a)
67		for a in rm_labels])[::-1]
68		for d in rm_dims:
69		del axis_labels[d]
70		# Add a new axis label to the list
71		axis_labels.append({'Q': 'Angstrom^-1'})
72
73		# AMEND THE SHAPE: Remove the two unrequired dimensions from the
74		# original shape and add a new dimension shape.
75		shape = list(in_dataset[0].get_shape())
76		for d in rm_dims:
77		del shape[d]
78		shape += (self.get_parameters('num_bins'),)
79
80		# populate the output dataset
81		out_dataset[0].create_dataset(
82		patterns={in_dataset[0]: patterns},
83		axis_labels=axis_labels,
84		shape=tuple(shape))
85
86		# ASSOCIATE AN EXTRA PATTERN WITH THE DATASET: SINOGRAM and PROJECTION
87		# patterns are already asssociated with the output dataset, but add
88		# another one.
89		spectrum = \
90		{'core_dims': (-1,), 'slice_dims': tuple(range(len(shape)-1))}
91		out_dataset[0].add_pattern("SPECTRUM", **spectrum)
92		#======================================================================
93
94		#================== populate plugin datasets ==========================
95		in_pData, out_pData = self.get_plugin_datasets()
96		in_pData[0].plugin_data_setup('DIFFRACTION', 'single')
97		out_pData[0].plugin_data_setup('SPECTRUM', 'single')
98		#======================================================================
99
100		def pre_process(self):
101		pass
102
103		def process_frames(self, data):
104		# do some processing here
105		return np.arange(self.parameters['num_bins'])
106
107		def post_process(self):
108		pass
109

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PluginTemplate3.setup() A

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