torchio.visualization.plot_volume() - Code Metrics - Inspection of "Share some axes to improve visualization" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( 9fff8c...fc33ea )

by Fernando

created 2021-05-20 22:29 UTC

torchio.visualization.plot_volume() D

↳ Parent: torchio.visualization

Complexity

Conditions

Size

Total Lines	67
Code Lines	59

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	59
dl	0
loc	67
rs	4.7018
c	0
b	0
f	0
cc	12
nop	8

How to fix Long Method Complexity Many Parameters

Long Method

Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.

For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.

Commonly applied refactorings include:

If many parameters/temporary variables are present:
- Replace temporary variables with Query
- Introduce parameter object; often combined with preserve whole object
- If the above two are insufficient: Replace method with method object
If you have long conditionals: Decompose Conditional
Otherwise: Extract method

Complexity

Complex classes like torchio.visualization.plot_volume() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.

Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.

Many Parameters

Methods with many parameters are not only hard to understand, but their parameters also often become inconsistent when you need more, or different data.

There are several approaches to avoid long parameter lists:

If you can get the data from another object that the method knows about already: Replace the parameter with method call
If several parameters are part of another object: Preserve Whole Object
If parameters are not yet connected: Introduce Parameter Object

import numpy as np

from .data.image import Image, LabelMap
from .data.subject import Subject
from .transforms.preprocessing.spatial.to_canonical import ToCanonical


def import_mpl_plt():
    try:
        import matplotlib as mpl
        import matplotlib.pyplot as plt
    except ImportError as e:
        raise ImportError('Install matplotlib for plotting support') from e
    return mpl, plt


def rotate(image, radiological=True):
    # Rotate for visualization purposes
    image = np.rot90(image, -1)
    if radiological:
        image = np.fliplr(image)
    return image


def plot_volume(
        image: Image,
        radiological=True,
        channel=-1,  # default to foreground for binary maps
        axes=None,
        cmap=None,
        output_path=None,
        show=True,
        xlabels=True,
        ):
    _, plt = import_mpl_plt()
    fig = None
    if axes is None:
        fig, axes = plt.subplots(1, 3)
    sag_axis, cor_axis, axi_axis = axes

    image = ToCanonical()(image)
    data = image.data[channel]
    indices = np.array(data.shape) // 2
    i, j, k = indices
    slice_x = rotate(data[i, :, :], radiological=radiological)
    slice_y = rotate(data[:, j, :], radiological=radiological)
    slice_z = rotate(data[:, :, k], radiological=radiological)
    kwargs = {}
    is_label = isinstance(image, LabelMap)
    if isinstance(cmap, dict):
        slices = slice_x, slice_y, slice_z
        slice_x, slice_y, slice_z = color_labels(slices, cmap)
    else:
        if cmap is None:
            cmap = 'cubehelix' if is_label else 'gray'
        kwargs['cmap'] = cmap
    if is_label:
        kwargs['interpolation'] = 'none'

    sr, sa, ss = image.spacing
    kwargs['origin'] = 'lower'

    sag_aspect = ss / sa
    sag_axis.imshow(slice_x, aspect=sag_aspect, **kwargs)
    if xlabels:
        sag_axis.set_xlabel('A')
    sag_axis.set_ylabel('S')
    sag_axis.invert_xaxis()
    sag_axis.set_title('Sagittal')

    cor_aspect = ss / sr
    cor_axis.imshow(slice_y, aspect=cor_aspect, **kwargs)
    if xlabels:
        cor_axis.set_xlabel('R')
    cor_axis.set_ylabel('S')
    cor_axis.invert_xaxis()
    cor_axis.set_title('Coronal')

    axi_aspect = sa / sr
    axi_axis.imshow(slice_z, aspect=axi_aspect, **kwargs)
    if xlabels:
        axi_axis.set_xlabel('R')
    axi_axis.set_ylabel('A')
    axi_axis.invert_xaxis()
    axi_axis.set_title('Axial')

    plt.tight_layout()
    if output_path is not None and fig is not None:
        fig.savefig(output_path)
    if show:
        plt.show()


def plot_subject(
        subject: Subject,
        cmap_dict=None,
        show=True,
        output_path=None,
        figsize=None,
        clear_axes=True,
        **kwargs,
        ):
    _, plt = import_mpl_plt()
    subplots_kwargs = {'figsize': figsize}
    try:
        if clear_axes:
            subject.check_consistent_spatial_shape()
            subplots_kwargs['sharex'] = 'col'
            subplots_kwargs['sharey'] = 'col'
    except RuntimeError:  # different shapes in subject
        pass
    fig, axes = plt.subplots(len(subject), 3, **subplots_kwargs)
    # The array of axes must be 2D so that it can be indexed correctly within
    # the plot_volume() function
    axes = axes.reshape(-1, 3)
    iterable = enumerate(subject.get_images_dict(intensity_only=False).items())
    axes_names = 'sagittal', 'coronal', 'axial'
    for row_index, (name, image) in iterable:
        row_axes = axes[row_index]
        cmap = None
        if cmap_dict is not None and name in cmap_dict:
            cmap = cmap_dict[name]
        last_row = row_index == len(axes) - 1
        plot_volume(
            image,
            axes=row_axes,
            show=False,
            cmap=cmap,
            xlabels=last_row,
            **kwargs,
        )
        for axis, axis_name in zip(row_axes, axes_names):
            axis.set_title(f'{name} ({axis_name})')
    plt.tight_layout()
    if output_path is not None:
        fig.savefig(output_path)
    if show:
        plt.show()


def color_labels(arrays, cmap_dict):
    results = []
    for array in arrays:
        si, sj = array.shape
        rgb = np.zeros((si, sj, 3), dtype=np.uint8)
        for label, color in cmap_dict.items():
            if isinstance(color, str):
                mpl, _ = import_mpl_plt()
                color = mpl.colors.to_rgb(color)
                color = [255 * n for n in color]
            rgb[array == label] = color
        results.append(rgb)
    return results


1			import numpy as np
2
3			from .data.image import Image, LabelMap
4			from .data.subject import Subject
5			from .transforms.preprocessing.spatial.to_canonical import ToCanonical
6
7
8			def import_mpl_plt():
9			try:
10			import matplotlib as mpl
11			import matplotlib.pyplot as plt
12			except ImportError as e:
13			raise ImportError('Install matplotlib for plotting support') from e
14			return mpl, plt
15
16
17			def rotate(image, radiological=True):
18			# Rotate for visualization purposes
19			image = np.rot90(image, -1)
20			if radiological:
21			image = np.fliplr(image)
22			return image
23
24
25			def plot_volume(
26			image: Image,
27			radiological=True,
28			channel=-1, # default to foreground for binary maps
29			axes=None,
30			cmap=None,
31			output_path=None,
32			show=True,
33			xlabels=True,
34			):
35			_, plt = import_mpl_plt()
36			fig = None
37			if axes is None:
38			fig, axes = plt.subplots(1, 3)
39			sag_axis, cor_axis, axi_axis = axes
40
41			image = ToCanonical()(image)
42			data = image.data[channel]
43			indices = np.array(data.shape) // 2
44			i, j, k = indices
45			slice_x = rotate(data[i, :, :], radiological=radiological)
46			slice_y = rotate(data[:, j, :], radiological=radiological)
47			slice_z = rotate(data[:, :, k], radiological=radiological)
48			kwargs = {}
49			is_label = isinstance(image, LabelMap)
50			if isinstance(cmap, dict):
51			slices = slice_x, slice_y, slice_z
52			slice_x, slice_y, slice_z = color_labels(slices, cmap)
53			else:
54			if cmap is None:
55			cmap = 'cubehelix' if is_label else 'gray'
56			kwargs['cmap'] = cmap
57			if is_label:
58			kwargs['interpolation'] = 'none'
59
60			sr, sa, ss = image.spacing
61			kwargs['origin'] = 'lower'
62
63			sag_aspect = ss / sa
64			sag_axis.imshow(slice_x, aspect=sag_aspect, **kwargs)
65			if xlabels:
66			sag_axis.set_xlabel('A')
67			sag_axis.set_ylabel('S')
68			sag_axis.invert_xaxis()
69			sag_axis.set_title('Sagittal')
70
71			cor_aspect = ss / sr
72			cor_axis.imshow(slice_y, aspect=cor_aspect, **kwargs)
73			if xlabels:
74			cor_axis.set_xlabel('R')
75			cor_axis.set_ylabel('S')
76			cor_axis.invert_xaxis()
77			cor_axis.set_title('Coronal')
78
79			axi_aspect = sa / sr
80			axi_axis.imshow(slice_z, aspect=axi_aspect, **kwargs)
81			if xlabels:
82			axi_axis.set_xlabel('R')
83			axi_axis.set_ylabel('A')
84			axi_axis.invert_xaxis()
85			axi_axis.set_title('Axial')
86
87			plt.tight_layout()
88			if output_path is not None and fig is not None:
89			fig.savefig(output_path)
90			if show:
91			plt.show()
92
93
94			def plot_subject(
95			subject: Subject,
96			cmap_dict=None,
97			show=True,
98			output_path=None,
99			figsize=None,
100			clear_axes=True,
101			**kwargs,
102			):
103			_, plt = import_mpl_plt()
104			subplots_kwargs = {'figsize': figsize}
105			try:
106			if clear_axes:
107			subject.check_consistent_spatial_shape()
108			subplots_kwargs['sharex'] = 'col'
109			subplots_kwargs['sharey'] = 'col'
110			except RuntimeError: # different shapes in subject
111			pass
112			fig, axes = plt.subplots(len(subject), 3, **subplots_kwargs)
113			# The array of axes must be 2D so that it can be indexed correctly within
114			# the plot_volume() function
115			axes = axes.reshape(-1, 3)
116			iterable = enumerate(subject.get_images_dict(intensity_only=False).items())
117			axes_names = 'sagittal', 'coronal', 'axial'
118			for row_index, (name, image) in iterable:
119			row_axes = axes[row_index]
120			cmap = None
121			if cmap_dict is not None and name in cmap_dict:
122			cmap = cmap_dict[name]
123			last_row = row_index == len(axes) - 1
124			plot_volume(
125			image,
126			axes=row_axes,
127			show=False,
128			cmap=cmap,
129			xlabels=last_row,
130			**kwargs,
131			)
132			for axis, axis_name in zip(row_axes, axes_names):
133			axis.set_title(f'{name} ({axis_name})')
134			plt.tight_layout()
135			if output_path is not None:
136			fig.savefig(output_path)
137			if show:
138			plt.show()
139
140
141			def color_labels(arrays, cmap_dict):
142			results = []
143			for array in arrays:
144			si, sj = array.shape
145			rgb = np.zeros((si, sj, 3), dtype=np.uint8)
146			for label, color in cmap_dict.items():
147			if isinstance(color, str):
148			mpl, _ = import_mpl_plt()
149			color = mpl.colors.to_rgb(color)
150			color = [255 * n for n in color]
151			rgb[array == label] = color
152			results.append(rgb)
153			return results
154

fepegar / torchio

Push — master ( 9fff8c...fc33ea )

torchio.visualization.plot_volume() D

Complexity

Size

Duplication

Importance

How to fix Long Method Complexity Many Parameters

Long Method

Complexity

Many Parameters

Duplication Side-by-Side

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