savu.plugins.basic_operations.rotate_90.Rotate90.setup() - Code Metrics - Inspection of "Pre run" - DiamondLightSource/Savu - Measure and Improve Code Quality continuously with Scrutinizer

Test Failed

Pull Request — master (#934)

unknown

created 2022-07-26 15:13 UTC

Rotate90.setup() C

↳ Parent: savu.plugins.basic_operations.rotate_90

Complexity

Conditions

Size

Total Lines	54
Code Lines	37

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	11
eloc	37
nop	1
dl	0
loc	54
rs	5.4
c	0
b	0
f	0

How to fix Long Method Complexity

# 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:: rotate_90
   :platform: Unix
   :synopsis: (Change this) A template to create a simple plugin that takes 
    one dataset as input and returns a similar dataset as output.

.. moduleauthor:: Jacob Williamson <[email protected]>
"""
from copy import deepcopy

from savu.plugins.utils import register_plugin
from savu.plugins.plugin import Plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.core.iterate_plugin_group_utils import enable_iterative_loop, \
    check_if_end_plugin_in_iterate_group, setup_extra_plugin_data_padding
from savu.data.data_structures.data_types.data_plus_darks_and_flats \
    import ImageKey

import numpy as np
import h5py
@register_plugin
class Rotate90(Plugin, CpuPlugin):
# Each class must inherit from the Plugin class and a driver

    def __init__(self):
        super(Rotate90, self).__init__("Rotate90")

    def nInput_datasets(self):
        return 1


    def nOutput_datasets(self):
        return 1


    def setup(self):

        # assumes 3D data and 2D frames

        if self.exp.meta_data.get("pre_run"):
            self.stats_obj.calc_stats = False

        in_dataset, out_dataset = self.get_datasets()
        pattern = self.parameters["pattern"]
        data_info = in_dataset[0].data_info
        core_dims = data_info["data_patterns"][pattern]["core_dims"]
        c0, c1 = core_dims[0], core_dims[1]                        # core dimensions
        s0 = data_info["data_patterns"][pattern]["slice_dims"][0]  # slice dimension

        # swapping round core dimensions in the shape due to rotation
        new_shape = list(data_info["shape"])
        new_shape[c0], new_shape[c1] = data_info["shape"][c1], data_info["shape"][c0]
        if self.exp.meta_data.get("pre_run"):
            new_shape[0] = in_dataset[0].data.image_key.shape[0]
        new_shape = tuple(new_shape)

        # swapping round core dimensions in axis labels
        new_axis_labels = deepcopy(data_info["axis_labels"])
        new_axis_labels[c0], new_axis_labels[c1] = data_info["axis_labels"][c1], data_info["axis_labels"][c0]

        # swapping round core dimensions in data patterns
        new_data_patterns = deepcopy(data_info["data_patterns"])
        for pattern in new_data_patterns:
            for dims in new_data_patterns[pattern]:
                if dims != "main_dir":  # not sure what main_dir is ( = slice dim?)
                    dims_list = list(new_data_patterns[pattern][dims])
                    for i, dim in enumerate(dims_list):
                        if dim == c0:
                            dims_list[i] = c1
                        elif dim == c1:
                            dims_list[i] = c0
                    new_data_patterns[pattern][dims] = tuple(dims_list)

        dtype = in_dataset[0].dtype
        if dtype is None:
            dtype = in_dataset[0].data.data.dtype

        # creating output dataset with new axis, shape and data patterns to reflect rotated image
        if dtype:
            out_dataset[0].create_dataset(shape=new_shape, axis_labels=new_axis_labels, dtype=dtype)
        else:
            out_dataset[0].create_dataset(shape=new_shape, axis_labels=new_axis_labels)
        out_dataset[0].data_info.set("data_patterns", new_data_patterns)


        in_pData, out_pData = self.get_plugin_datasets()

        in_pData[0].plugin_data_setup(self.parameters['pattern'], 'single')
        out_pData[0].plugin_data_setup(self.parameters['pattern'], 'single')


    def pre_process(self):
        if self.exp.meta_data.get("pre_run"):
            in_dataset, out_dataset = self.get_datasets()
            dark = in_dataset[0].data.dark()
            flat = in_dataset[0].data.flat()
            if dark.size:
                in_dataset[0].data.update_dark(self.process_frames_3d(dark))
            if flat.size:
                in_dataset[0].data.update_flat(self.process_frames_3d(flat))
            out_dataset[0].data.image_key = in_dataset[0].data.image_key

    def process_frames(self, data):
        # assumes 2D frame
        if self.parameters["direction"] == "ACW":
            data[0] = np.rot90(data[0], axes=(0, 1))
        elif self.parameters["direction"] == "CW":
            data[0] = np.rot90(data[0], axes=(1, 0))
        return data[0]

    def process_frames_3d(self, data):
        # assumes 3D frame
        if self.parameters["direction"] == "ACW":
            data = np.rot90(data, axes=(1, 2))
        elif self.parameters["direction"] == "CW":
            data = np.rot90(data, axes=(2, 1))
        return data

    def post_process(self):
        if self.exp.meta_data.get("pre_run"):
            in_dataset, out_dataset = self.get_datasets()
            image_key = in_dataset[0].data.image_key

            dark = in_dataset[0].data.dark_updated
            flat = in_dataset[0].data.flat_updated

            new_image_key = np.array([0.] * len(image_key))

            new_image_key[- len(dark):] = [2.] * len(dark)
            new_image_key[- len(dark) - len(flat): - len(dark)] = [1.] * len(flat)

            out_dataset[0].data[- len(dark):] = dark
            out_dataset[0].data[- len(dark) - len(flat): - len(dark)] = flat

            out_dataset[0].data.image_key = new_image_key

            out_dataset[0].data = ImageKey(out_dataset[0], new_image_key, 0)


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:: rotate_90
17			:platform: Unix
18			:synopsis: (Change this) A template to create a simple plugin that takes
19			one dataset as input and returns a similar dataset as output.
20
21			.. moduleauthor:: Jacob Williamson <[email protected]>
22			"""
23			from copy import deepcopy
24
25			from savu.plugins.utils import register_plugin
26			from savu.plugins.plugin import Plugin
27			from savu.plugins.driver.cpu_plugin import CpuPlugin
28			from savu.core.iterate_plugin_group_utils import enable_iterative_loop, \
29			check_if_end_plugin_in_iterate_group, setup_extra_plugin_data_padding
30			from savu.data.data_structures.data_types.data_plus_darks_and_flats \
31			import ImageKey
32
33			import numpy as np
34			import h5py
35			@register_plugin
36			class Rotate90(Plugin, CpuPlugin):
37			# Each class must inherit from the Plugin class and a driver
38
39			def __init__(self):
40			super(Rotate90, self).__init__("Rotate90")
41
42			def nInput_datasets(self):
43			return 1
44
45
46			def nOutput_datasets(self):
47			return 1
48
49
50			def setup(self):
51
52			# assumes 3D data and 2D frames
53
54			if self.exp.meta_data.get("pre_run"):
55			self.stats_obj.calc_stats = False
56
57			in_dataset, out_dataset = self.get_datasets()
58			pattern = self.parameters["pattern"]
59			data_info = in_dataset[0].data_info
60			core_dims = data_info["data_patterns"][pattern]["core_dims"]
61			c0, c1 = core_dims[0], core_dims[1] # core dimensions
62			s0 = data_info["data_patterns"][pattern]["slice_dims"][0] # slice dimension
63
64			# swapping round core dimensions in the shape due to rotation
65			new_shape = list(data_info["shape"])
66			new_shape[c0], new_shape[c1] = data_info["shape"][c1], data_info["shape"][c0]
67			if self.exp.meta_data.get("pre_run"):
68			new_shape[0] = in_dataset[0].data.image_key.shape[0]
69			new_shape = tuple(new_shape)
70
71			# swapping round core dimensions in axis labels
72			new_axis_labels = deepcopy(data_info["axis_labels"])
73			new_axis_labels[c0], new_axis_labels[c1] = data_info["axis_labels"][c1], data_info["axis_labels"][c0]
74
75			# swapping round core dimensions in data patterns
76			new_data_patterns = deepcopy(data_info["data_patterns"])
77			for pattern in new_data_patterns:
78			for dims in new_data_patterns[pattern]:
79			if dims != "main_dir": # not sure what main_dir is ( = slice dim?)
80			dims_list = list(new_data_patterns[pattern][dims])
81			for i, dim in enumerate(dims_list):
82			if dim == c0:
83			dims_list[i] = c1
84			elif dim == c1:
85			dims_list[i] = c0
86			new_data_patterns[pattern][dims] = tuple(dims_list)
87
88			dtype = in_dataset[0].dtype
89			if dtype is None:
90			dtype = in_dataset[0].data.data.dtype
91
92			# creating output dataset with new axis, shape and data patterns to reflect rotated image
93			if dtype:
94			out_dataset[0].create_dataset(shape=new_shape, axis_labels=new_axis_labels, dtype=dtype)
95			else:
96			out_dataset[0].create_dataset(shape=new_shape, axis_labels=new_axis_labels)
97			out_dataset[0].data_info.set("data_patterns", new_data_patterns)
98
99
100			in_pData, out_pData = self.get_plugin_datasets()
101
102			in_pData[0].plugin_data_setup(self.parameters['pattern'], 'single')
103			out_pData[0].plugin_data_setup(self.parameters['pattern'], 'single')
104
105
106			def pre_process(self):
107			if self.exp.meta_data.get("pre_run"):
108			in_dataset, out_dataset = self.get_datasets()
109			dark = in_dataset[0].data.dark()
110			flat = in_dataset[0].data.flat()
111			if dark.size:
112			in_dataset[0].data.update_dark(self.process_frames_3d(dark))
113			if flat.size:
114			in_dataset[0].data.update_flat(self.process_frames_3d(flat))
115			out_dataset[0].data.image_key = in_dataset[0].data.image_key
116
117			def process_frames(self, data):
118			# assumes 2D frame
119			if self.parameters["direction"] == "ACW":
120			data[0] = np.rot90(data[0], axes=(0, 1))
121			elif self.parameters["direction"] == "CW":
122			data[0] = np.rot90(data[0], axes=(1, 0))
123			return data[0]
124
125			def process_frames_3d(self, data):
126			# assumes 3D frame
127			if self.parameters["direction"] == "ACW":
128			data = np.rot90(data, axes=(1, 2))
129			elif self.parameters["direction"] == "CW":
130			data = np.rot90(data, axes=(2, 1))
131			return data
132
133			def post_process(self):
134			if self.exp.meta_data.get("pre_run"):
135			in_dataset, out_dataset = self.get_datasets()
136			image_key = in_dataset[0].data.image_key
137
138			dark = in_dataset[0].data.dark_updated
139			flat = in_dataset[0].data.flat_updated
140
141			new_image_key = np.array([0.] * len(image_key))
			0 ignored issues – show Comprehensibility Best Practice introduced 2022-07-26 15:17 UTC by Report Bug Copy Issue Report The variable `len` does not seem to be defined. Loading history...
142			new_image_key[- len(dark):] = [2.] * len(dark)
143			new_image_key[- len(dark) - len(flat): - len(dark)] = [1.] * len(flat)
144
145			out_dataset[0].data[- len(dark):] = dark
146			out_dataset[0].data[- len(dark) - len(flat): - len(dark)] = flat
147
148			out_dataset[0].data.image_key = new_image_key
149
150			out_dataset[0].data = ImageKey(out_dataset[0], new_image_key, 0)
151

DiamondLightSource / Savu

Pull Request — master (#934)

Rotate90.setup() C

Complexity

Size

Duplication

Importance

How to fix Long Method Complexity

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