torchio.visualization.plot_volume() - Code Metrics - Inspection of "Fix visualization for subject with many images (#6..." - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 99e6cf...52d4d3 )

by Fernando

created 2021-09-05 10:13 UTC

torchio.visualization.plot_volume() F

↳ Parent: torchio.visualization

Complexity

Conditions

Size

Total Lines	76
Code Lines	67

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	67
dl	0
loc	76
rs	3.6
c	0
b	0
f	0
cc	14
nop	11

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,
        percentiles=(0.5, 99.5),
        figsize=None,
        reorient=True,
        ):
    _, plt = import_mpl_plt()
    fig = None
    if axes is None:
        fig, axes = plt.subplots(1, 3, figsize=figsize)
    sag_axis, cor_axis, axi_axis = axes

    if reorient:
        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'

    if percentiles is not None:
        p1, p2 = np.percentile(data, percentiles)
        kwargs['vmin'] = p1
        kwargs['vmax'] = p2

    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()
    num_images = len(subject)
    many_images = num_images > 2
    subplots_kwargs = {'figsize': figsize}
    try:
        if clear_axes:
            subject.check_consistent_spatial_shape()
            subplots_kwargs['sharex'] = 'row' if many_images else 'col'
            subplots_kwargs['sharey'] = 'row' if many_images else 'col'
    except RuntimeError:  # different shapes in subject
        pass
    args = (3, num_images) if many_images else (num_images, 3)
    fig, axes = plt.subplots(*args, **subplots_kwargs)
    # The array of axes must be 2D so that it can be indexed correctly within
    # the plot_volume() function
    axes = axes.T if many_images else axes.reshape(-1, 3)
    iterable = enumerate(subject.get_images_dict(intensity_only=False).items())
    axes_names = 'sagittal', 'coronal', 'axial'
    for image_index, (name, image) in iterable:
        image_axes = axes[image_index]
        cmap = None
        if cmap_dict is not None and name in cmap_dict:
            cmap = cmap_dict[name]
        last_row = image_index == len(axes) - 1
        plot_volume(
            image,
            axes=image_axes,
            show=False,
            cmap=cmap,
            xlabels=last_row,
            **kwargs,
        )
        for axis, axis_name in zip(image_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			percentiles=(0.5, 99.5),
35			figsize=None,
36			reorient=True,
37			):
38			_, plt = import_mpl_plt()
39			fig = None
40			if axes is None:
41			fig, axes = plt.subplots(1, 3, figsize=figsize)
42			sag_axis, cor_axis, axi_axis = axes
43
44			if reorient:
45			image = ToCanonical()(image)
46			data = image.data[channel]
47			indices = np.array(data.shape) // 2
48			i, j, k = indices
49			slice_x = rotate(data[i, :, :], radiological=radiological)
50			slice_y = rotate(data[:, j, :], radiological=radiological)
51			slice_z = rotate(data[:, :, k], radiological=radiological)
52			kwargs = {}
53			is_label = isinstance(image, LabelMap)
54			if isinstance(cmap, dict):
55			slices = slice_x, slice_y, slice_z
56			slice_x, slice_y, slice_z = color_labels(slices, cmap)
57			else:
58			if cmap is None:
59			cmap = 'cubehelix' if is_label else 'gray'
60			kwargs['cmap'] = cmap
61			if is_label:
62			kwargs['interpolation'] = 'none'
63
64			sr, sa, ss = image.spacing
65			kwargs['origin'] = 'lower'
66
67			if percentiles is not None:
68			p1, p2 = np.percentile(data, percentiles)
69			kwargs['vmin'] = p1
70			kwargs['vmax'] = p2
71
72			sag_aspect = ss / sa
73			sag_axis.imshow(slice_x, aspect=sag_aspect, **kwargs)
74			if xlabels:
75			sag_axis.set_xlabel('A')
76			sag_axis.set_ylabel('S')
77			sag_axis.invert_xaxis()
78			sag_axis.set_title('Sagittal')
79
80			cor_aspect = ss / sr
81			cor_axis.imshow(slice_y, aspect=cor_aspect, **kwargs)
82			if xlabels:
83			cor_axis.set_xlabel('R')
84			cor_axis.set_ylabel('S')
85			cor_axis.invert_xaxis()
86			cor_axis.set_title('Coronal')
87
88			axi_aspect = sa / sr
89			axi_axis.imshow(slice_z, aspect=axi_aspect, **kwargs)
90			if xlabels:
91			axi_axis.set_xlabel('R')
92			axi_axis.set_ylabel('A')
93			axi_axis.invert_xaxis()
94			axi_axis.set_title('Axial')
95
96			plt.tight_layout()
97			if output_path is not None and fig is not None:
98			fig.savefig(output_path)
99			if show:
100			plt.show()
101
102
103			def plot_subject(
104			subject: Subject,
105			cmap_dict=None,
106			show=True,
107			output_path=None,
108			figsize=None,
109			clear_axes=True,
110			**kwargs,
111			):
112			_, plt = import_mpl_plt()
113			num_images = len(subject)
114			many_images = num_images > 2
115			subplots_kwargs = {'figsize': figsize}
116			try:
117			if clear_axes:
118			subject.check_consistent_spatial_shape()
119			subplots_kwargs['sharex'] = 'row' if many_images else 'col'
120			subplots_kwargs['sharey'] = 'row' if many_images else 'col'
121			except RuntimeError: # different shapes in subject
122			pass
123			args = (3, num_images) if many_images else (num_images, 3)
124			fig, axes = plt.subplots(args, *subplots_kwargs)
125			# The array of axes must be 2D so that it can be indexed correctly within
126			# the plot_volume() function
127			axes = axes.T if many_images else axes.reshape(-1, 3)
128			iterable = enumerate(subject.get_images_dict(intensity_only=False).items())
129			axes_names = 'sagittal', 'coronal', 'axial'
130			for image_index, (name, image) in iterable:
131			image_axes = axes[image_index]
132			cmap = None
133			if cmap_dict is not None and name in cmap_dict:
134			cmap = cmap_dict[name]
135			last_row = image_index == len(axes) - 1
136			plot_volume(
137			image,
138			axes=image_axes,
139			show=False,
140			cmap=cmap,
141			xlabels=last_row,
142			**kwargs,
143			)
144			for axis, axis_name in zip(image_axes, axes_names):
145			axis.set_title(f'{name} ({axis_name})')
146			plt.tight_layout()
147			if output_path is not None:
148			fig.savefig(output_path)
149			if show:
150			plt.show()
151
152
153			def color_labels(arrays, cmap_dict):
154			results = []
155			for array in arrays:
156			si, sj = array.shape
157			rgb = np.zeros((si, sj, 3), dtype=np.uint8)
158			for label, color in cmap_dict.items():
159			if isinstance(color, str):
160			mpl, _ = import_mpl_plt()
161			color = mpl.colors.to_rgb(color)
162			color = [255 * n for n in color]
163			rgb[array == label] = color
164			results.append(rgb)
165			return results
166

fepegar / torchio

Push — master ( 99e6cf...52d4d3 )

torchio.visualization.plot_volume() F

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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