cluster_visualizer - Code Metrics - Inspection of "#356: [doc] Code documentation correction." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 93c3d2...b7ad63 )

by Andrei

created 2017-08-22 14:41 UTC

cluster_visualizer B

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	270
Duplicated Lines	3.33 %

Importance

Changes	1
Bugs	0	Features	0

Metric	Value
dl	9
loc	270
rs	8.3999
c	1
b	0
f	0
wmc	46

7 Methods

Rating	Name	Duplication	Size	Complexity
B	__init__()	0	50	4
F	append_cluster()	0	53	15
A	append_clusters()	0	14	2
A	set_canvas_title()	0	13	2
B	append_cluster_attribute()	0	26	3
D	__draw_canvas_cluster()	9	25	8
F	show()	0	61	12

How to fix Duplicated Code Complexity

"""!

@brief pyclustering module for cluster analysis.

@authors Andrei Novikov ([email protected])
@date 2014-2017
@copyright GNU Public License

@cond GNU_PUBLIC_LICENSE
    PyClustering is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    
    PyClustering is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
@endcond

"""

import matplotlib.pyplot as plt;
import matplotlib.gridspec as gridspec;

import math;

from pyclustering.utils.color import color as color_list;


class canvas_cluster_descr:
    """!
    @brief Description of cluster for representation on canvas.
    
    """

    def __init__(self, cluster, data, marker, markersize, color):
        """!
        @brief Constructor of cluster representation on the canvas.
        
        @param[in] cluster (list): Single cluster that consists of objects or indexes from data.
        @param[in] data (list): Objects that should be displayed, can be None if clusters consist of objects instead of indexes.
        @param[in] marker (string): Type of marker that is used for drawing objects.
        @param[in] markersize (uint): Size of marker that is used for drawing objects.
        @param[in] color (string): Color of the marker that is used for drawing objects.
        
        """
        ## Cluster that may consist of objects or indexes of objects from data.
        self.cluster = cluster;
        
        ## Data where objects are stored. It can be None if clusters consist of objects instead of indexes.
        self.data = data;
        
        ## Marker that is used for drawing objects.
        self.marker = marker;
        
        ## Size of marker that is used for drawing objects.
        self.markersize = markersize;
        
        ## Color that is used for coloring marker.
        self.color = color;
        
        ## Attribures of the clusters - additional collections of data points that are regarded to the cluster.
        self.attributes = [];
    

class cluster_visualizer:
    """!
    @brief Common visualizer of clusters on 2D or 3D surface.
    
    """

    def __init__(self, number_canvases = 1, size_row = 1):
        """!
        @brief Constructor of cluster visualizer.
        
        @param[in] number_canvases (uint): Number of canvases that is used for visualization.
        @param[in] size_row (uint): Amount of canvases that can be placed in one row.
        
        Example:
        @code
            # load 2D data sample
            sample_2d = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
            
            # load 3D data sample
            sample_3d = read_sample(FCPS_SAMPLES.SAMPLE_HEPTA);
            
            # extract clusters from the first sample using DBSCAN algorithm
            dbscan_instance = dbscan(sample_2d, 0.4, 2, False);
            dbscan_instance.process();
            clusters_sample_2d = dbscan_instance.get_clusters();
        
            # extract clusters from the second sample using DBSCAN algorithm
            dbscan_instance = dbscan(sample_3d, 1, 3, True);
            dbscan_instance.process();
            clusters_sample_3d = dbscan_instance.get_clusters();
            
            # create plot with two canvases where each row contains 2 canvases.
            size = 2;
            row_size = 2;
            visualizer = cluster_visualizer(size, row_size);
            
            # place clustering result of sample_2d to the first canvas
            visualizer.append_clusters(clusters_sample_2d, sample_2d, 0, markersize = 5);
            
            # place clustering result of sample_3d to the second canvas
            visualizer.append_clusters(clusters_sample_3d, sample_3d, 1, markersize = 30);
            
            # show plot
            visualizer.show();
        @endcode
        
        """
        
        self.__number_canvases = number_canvases;
        self.__size_row = size_row;
        self.__canvas_clusters = [ [] for _ in range(number_canvases) ];
        self.__canvas_dimensions = [ None for _ in range(number_canvases) ];
        self.__canvas_titles = [ None for _ in range(number_canvases) ];
        
        self.__default_2d_marker_size = 5;
        self.__default_3d_marker_size = 30;
    
    
    def append_cluster(self, cluster, data = None, canvas = 0, marker = '.', markersize = None, color = None):
        """!
        @brief Appends cluster to canvas for drawing.
        
        @param[in] cluster (list): cluster that may consist of indexes of objects from the data or object itself.
        @param[in] data (list): If defines that each element of cluster is considered as a index of object from the data.
        @param[in] canvas (uint): Number of canvas that should be used for displaying cluster.
        @param[in] marker (string): Marker that is used for displaying objects from cluster on the canvas.
        @param[in] markersize (uint): Size of marker.
        @param[in] color (string): Color of marker.
        
        @return Returns index of cluster descriptor on the canvas.
        
        """
        
        if (len(cluster) == 0):
            return;
        
        if (canvas > self.__number_canvases):
            raise NameError('Canvas does ' + canvas + ' not exists.');
        
        if (color is None):
            index_color = len(self.__canvas_clusters[canvas]) % len(color_list.TITLES);
            color = color_list.TITLES[index_color];
        
        added_canvas_descriptor = canvas_cluster_descr(cluster, data, marker, markersize, color);
        self.__canvas_clusters[canvas].append( added_canvas_descriptor );
        
        dimension = 0;
        if (data is None):
            dimension = len(cluster[0]);
            if (self.__canvas_dimensions[canvas] is None):
                self.__canvas_dimensions[canvas] = dimension;
            elif (self.__canvas_dimensions[canvas] != dimension):
                raise NameError('Only clusters with the same dimension of objects can be displayed on canvas.');
                
        else:
            dimension = len(data[0]);
            if (self.__canvas_dimensions[canvas] is None):
                self.__canvas_dimensions[canvas] = dimension;
            elif (self.__canvas_dimensions[canvas] != dimension):
                raise NameError('Only clusters with the same dimension of objects can be displayed on canvas.');

        if ( (dimension < 1) and (dimension > 3) ):
            raise NameError('Only objects with size dimension 1 (1D plot), 2 (2D plot) or 3 (3D plot) can be displayed.');
        
        if (markersize is None):
            if ( (dimension == 1) or (dimension == 2) ):
                added_canvas_descriptor.markersize = self.__default_2d_marker_size;
            elif (dimension == 3):
                added_canvas_descriptor.markersize = self.__default_3d_marker_size;
        
        return len(self.__canvas_clusters[canvas]) - 1;
    
    
    def append_cluster_attribute(self, index_canvas, index_cluster, data, marker = None, markersize = None):
        """!
        @brief Append cluster attribure for cluster on specific canvas.
        @details Attribute it is data that is visualized for specific cluster using its color, marker and markersize if last two is not specified.
        
        @param[in] index_canvas (uint): Index canvas where cluster is located.
        @param[in] index_cluster (uint): Index cluster whose attribute should be added.
        @param[in] data (list): List of points (data) that represents attribute.
        @param[in] marker (string): Marker that is used for displaying objects from cluster on the canvas.
        @param[in] markersize (uint): Size of marker.
        
        """
        
        cluster_descr = self.__canvas_clusters[index_canvas][index_cluster];
        attribute_marker = marker;
        if (attribute_marker is None):
            attribute_marker = cluster_descr.marker;
        
        attribure_markersize = markersize;
        if (attribure_markersize is None):
            attribure_markersize = cluster_descr.markersize;
        
        attribute_color = cluster_descr.color;
        
        added_attribute_cluster_descriptor = canvas_cluster_descr(data, None, attribute_marker, attribure_markersize, attribute_color);
        self.__canvas_clusters[index_canvas][index_cluster].attributes.append(added_attribute_cluster_descriptor);
    
    
    def append_clusters(self, clusters, data = None, canvas = 0, marker = '.', markersize = None):
        """!
        @brief Appends list of cluster to canvas for drawing.
        
        @param[in] clusters (list): List of clusters where each cluster may consist of indexes of objects from the data or object itself.
        @param[in] data (list): If defines that each element of cluster is considered as a index of object from the data.
        @param[in] canvas (uint): Number of canvas that should be used for displaying clusters.
        @param[in] marker (string): Marker that is used for displaying objects from clusters on the canvas.
        @param[in] markersize (uint): Size of marker.
        
        """
        
        for cluster in clusters:
            self.append_cluster(cluster, data, canvas, marker, markersize);
    
    
    def set_canvas_title(self, text, canvas = 0):
        """!
        @brief Set title for specified canvas.
        
        @param[in] text (string): Title for canvas.
        @param[in] canvas (uint): Index of canvas where title should be displayed.
        
        """
        
        if (canvas > self.__number_canvases):
            raise NameError('Canvas does ' + canvas + ' not exists.');
        
        self.__canvas_titles[canvas] = text;
    
    
    def show(self, figure = None, visible_axis = True, visible_grid = True, display = True):
        """!
        @brief Shows clusters (visualize).
        
        @param[in] figure (fig): Defines requirement to use specified figure, if None - new figure is created for drawing clusters.
        @param[in] visible_axis (bool): Defines visibility of axes on each canvas, if True - axes are invisible.
        @param[in] visible_grid (bool): Defines visibility of axes on each canvas, if True - grid is displayed.
        @param[in] display (bool): Defines requirement to display clusters on a stage, if True - clusters are displayed, if False - plt.show() should be called by user."
        
        @return (fig) Figure where clusters are shown.
        
        """
        
        canvas_shift = 0;
        cluster_figure = None;
        if (figure is not None):
            canvas_shift = len(figure.get_axes());
            cluster_figure = figure;
        else:
            cluster_figure = plt.figure();
        
        maximum_cols = self.__size_row;
        maximum_rows = math.ceil( (self.__number_canvases + canvas_shift) / maximum_cols);
        
        grid_spec = gridspec.GridSpec(maximum_rows, maximum_cols);
        
        for index_canvas in range(len(self.__canvas_clusters)):
            canvas_data = self.__canvas_clusters[index_canvas];
            dimension = self.__canvas_dimensions[index_canvas];
            
            #ax = axes[real_index];
            if ( (dimension == 1) or (dimension == 2) ):
                ax = cluster_figure.add_subplot(grid_spec[index_canvas + canvas_shift]);
            else:
                ax = cluster_figure.add_subplot(grid_spec[index_canvas + canvas_shift], projection='3d');
            
            if (len(canvas_data) == 0):
                plt.setp(ax, visible = False);
            
            for cluster_descr in canvas_data:
                self.__draw_canvas_cluster(ax, dimension, cluster_descr);
                
                for attribute_descr in cluster_descr.attributes:
                    self.__draw_canvas_cluster(ax, dimension, attribute_descr);
            
            if (visible_axis is True):
                ax.xaxis.set_ticklabels([]);
                ax.yaxis.set_ticklabels([]);
                
                if (dimension == 3):
                    ax.zaxis.set_ticklabels([]);
            
            if (self.__canvas_titles[index_canvas] is not None):
                ax.set_title(self.__canvas_titles[index_canvas]);
            
            ax.grid(visible_grid);
        
        if (display is True):
            plt.show();
        
        return cluster_figure;
    
    
    """!
    @brief Draw canvas cluster descriptor.
    
    @param[in] ax (Axis): Axis of the canvas where canvas cluster descriptor should be displayed.
    @param[in] dimension (uint): Canvas dimension.
    @param[in] cluster_descr (canvas_cluster_descr): Canvas cluster descriptor that should be displayed.

    @return (fig) Figure where clusters are shown.
    
    """
    def __draw_canvas_cluster(self, ax, dimension, cluster_descr):
        cluster = cluster_descr.cluster;
        data = cluster_descr.data;
        marker = cluster_descr.marker;
        markersize = cluster_descr.markersize;
        color = cluster_descr.color;
        
        for item in cluster:
            if (dimension == 1):

                if (data is None):
                    ax.plot(item[0], 0.0, color = color, marker = marker, markersize = markersize);
                else:
                    ax.plot(data[item][0], 0.0, color = color, marker = marker, markersize = markersize);

            elif (dimension == 2):
                if (data is None):

                    ax.plot(item[0], item[1], color = color, marker = marker, markersize = markersize);
                else:
                    ax.plot(data[item][0], data[item][1], color = color, marker = marker, markersize = markersize);
        
            elif (dimension == 3):
                if (data is None):
                    ax.scatter(item[0], item[1], item[2], c = color, marker = marker, s = markersize);
                else:
                    ax.scatter(data[item][0], data[item][1], data[item][2], c = color, marker = marker, s = markersize);

1		"""!
2
3		@brief pyclustering module for cluster analysis.
4
5		@authors Andrei Novikov ([email protected])
6		@date 2014-2017
7		@copyright GNU Public License
8
9		@cond GNU_PUBLIC_LICENSE
10		PyClustering is free software: you can redistribute it and/or modify
11		it under the terms of the GNU General Public License as published by
12		the Free Software Foundation, either version 3 of the License, or
13		(at your option) any later version.
14
15		PyClustering is distributed in the hope that it will be useful,
16		but WITHOUT ANY WARRANTY; without even the implied warranty of
17		MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18		GNU General Public License for more details.
19
20		You should have received a copy of the GNU General Public License
21		along with this program. If not, see <http://www.gnu.org/licenses/>.
22		@endcond
23
24		"""
25
26		import matplotlib.pyplot as plt;
27		import matplotlib.gridspec as gridspec;
28
29		import math;
30
31		from pyclustering.utils.color import color as color_list;
32
33
34		class canvas_cluster_descr:
35		"""!
36		@brief Description of cluster for representation on canvas.
37
38		"""
39
40		def __init__(self, cluster, data, marker, markersize, color):
41		"""!
42		@brief Constructor of cluster representation on the canvas.
43
44		@param[in] cluster (list): Single cluster that consists of objects or indexes from data.
45		@param[in] data (list): Objects that should be displayed, can be None if clusters consist of objects instead of indexes.
46		@param[in] marker (string): Type of marker that is used for drawing objects.
47		@param[in] markersize (uint): Size of marker that is used for drawing objects.
48		@param[in] color (string): Color of the marker that is used for drawing objects.
49
50		"""
51		## Cluster that may consist of objects or indexes of objects from data.
52		self.cluster = cluster;
53
54		## Data where objects are stored. It can be None if clusters consist of objects instead of indexes.
55		self.data = data;
56
57		## Marker that is used for drawing objects.
58		self.marker = marker;
59
60		## Size of marker that is used for drawing objects.
61		self.markersize = markersize;
62
63		## Color that is used for coloring marker.
64		self.color = color;
65
66		## Attribures of the clusters - additional collections of data points that are regarded to the cluster.
67		self.attributes = [];
68
69
70		class cluster_visualizer:
71		"""!
72		@brief Common visualizer of clusters on 2D or 3D surface.
73
74		"""
75
76		def __init__(self, number_canvases = 1, size_row = 1):
77		"""!
78		@brief Constructor of cluster visualizer.
79
80		@param[in] number_canvases (uint): Number of canvases that is used for visualization.
81		@param[in] size_row (uint): Amount of canvases that can be placed in one row.
82
83		Example:
84		@code
85		# load 2D data sample
86		sample_2d = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
87
88		# load 3D data sample
89		sample_3d = read_sample(FCPS_SAMPLES.SAMPLE_HEPTA);
90
91		# extract clusters from the first sample using DBSCAN algorithm
92		dbscan_instance = dbscan(sample_2d, 0.4, 2, False);
93		dbscan_instance.process();
94		clusters_sample_2d = dbscan_instance.get_clusters();
95
96		# extract clusters from the second sample using DBSCAN algorithm
97		dbscan_instance = dbscan(sample_3d, 1, 3, True);
98		dbscan_instance.process();
99		clusters_sample_3d = dbscan_instance.get_clusters();
100
101		# create plot with two canvases where each row contains 2 canvases.
102		size = 2;
103		row_size = 2;
104		visualizer = cluster_visualizer(size, row_size);
105
106		# place clustering result of sample_2d to the first canvas
107		visualizer.append_clusters(clusters_sample_2d, sample_2d, 0, markersize = 5);
108
109		# place clustering result of sample_3d to the second canvas
110		visualizer.append_clusters(clusters_sample_3d, sample_3d, 1, markersize = 30);
111
112		# show plot
113		visualizer.show();
114		@endcode
115
116		"""
117
118		self.__number_canvases = number_canvases;
119		self.__size_row = size_row;
120		self.__canvas_clusters = [ [] for _ in range(number_canvases) ];
121		self.__canvas_dimensions = [ None for _ in range(number_canvases) ];
122		self.__canvas_titles = [ None for _ in range(number_canvases) ];
123
124		self.__default_2d_marker_size = 5;
125		self.__default_3d_marker_size = 30;
126
127
128		def append_cluster(self, cluster, data = None, canvas = 0, marker = '.', markersize = None, color = None):
129		"""!
130		@brief Appends cluster to canvas for drawing.
131
132		@param[in] cluster (list): cluster that may consist of indexes of objects from the data or object itself.
133		@param[in] data (list): If defines that each element of cluster is considered as a index of object from the data.
134		@param[in] canvas (uint): Number of canvas that should be used for displaying cluster.
135		@param[in] marker (string): Marker that is used for displaying objects from cluster on the canvas.
136		@param[in] markersize (uint): Size of marker.
137		@param[in] color (string): Color of marker.
138
139		@return Returns index of cluster descriptor on the canvas.
140
141		"""
142
143		if (len(cluster) == 0):
144		return;
145
146		if (canvas > self.__number_canvases):
147		raise NameError('Canvas does ' + canvas + ' not exists.');
148
149		if (color is None):
150		index_color = len(self.__canvas_clusters[canvas]) % len(color_list.TITLES);
151		color = color_list.TITLES[index_color];
152
153		added_canvas_descriptor = canvas_cluster_descr(cluster, data, marker, markersize, color);
154		self.__canvas_clusters[canvas].append( added_canvas_descriptor );
155
156		dimension = 0;
157		if (data is None):
158		dimension = len(cluster[0]);
159		if (self.__canvas_dimensions[canvas] is None):
160		self.__canvas_dimensions[canvas] = dimension;
161		elif (self.__canvas_dimensions[canvas] != dimension):
162		raise NameError('Only clusters with the same dimension of objects can be displayed on canvas.');
163
164		else:
165		dimension = len(data[0]);
166		if (self.__canvas_dimensions[canvas] is None):
167		self.__canvas_dimensions[canvas] = dimension;
168		elif (self.__canvas_dimensions[canvas] != dimension):
169		raise NameError('Only clusters with the same dimension of objects can be displayed on canvas.');
170
171		if ( (dimension < 1) and (dimension > 3) ):
172		raise NameError('Only objects with size dimension 1 (1D plot), 2 (2D plot) or 3 (3D plot) can be displayed.');
173
174		if (markersize is None):
175		if ( (dimension == 1) or (dimension == 2) ):
176		added_canvas_descriptor.markersize = self.__default_2d_marker_size;
177		elif (dimension == 3):
178		added_canvas_descriptor.markersize = self.__default_3d_marker_size;
179
180		return len(self.__canvas_clusters[canvas]) - 1;
181
182
183		def append_cluster_attribute(self, index_canvas, index_cluster, data, marker = None, markersize = None):
184		"""!
185		@brief Append cluster attribure for cluster on specific canvas.
186		@details Attribute it is data that is visualized for specific cluster using its color, marker and markersize if last two is not specified.
187
188		@param[in] index_canvas (uint): Index canvas where cluster is located.
189		@param[in] index_cluster (uint): Index cluster whose attribute should be added.
190		@param[in] data (list): List of points (data) that represents attribute.
191		@param[in] marker (string): Marker that is used for displaying objects from cluster on the canvas.
192		@param[in] markersize (uint): Size of marker.
193
194		"""
195
196		cluster_descr = self.__canvas_clusters[index_canvas][index_cluster];
197		attribute_marker = marker;
198		if (attribute_marker is None):
199		attribute_marker = cluster_descr.marker;
200
201		attribure_markersize = markersize;
202		if (attribure_markersize is None):
203		attribure_markersize = cluster_descr.markersize;
204
205		attribute_color = cluster_descr.color;
206
207		added_attribute_cluster_descriptor = canvas_cluster_descr(data, None, attribute_marker, attribure_markersize, attribute_color);
208		self.__canvas_clusters[index_canvas][index_cluster].attributes.append(added_attribute_cluster_descriptor);
209
210
211		def append_clusters(self, clusters, data = None, canvas = 0, marker = '.', markersize = None):
212		"""!
213		@brief Appends list of cluster to canvas for drawing.
214
215		@param[in] clusters (list): List of clusters where each cluster may consist of indexes of objects from the data or object itself.
216		@param[in] data (list): If defines that each element of cluster is considered as a index of object from the data.
217		@param[in] canvas (uint): Number of canvas that should be used for displaying clusters.
218		@param[in] marker (string): Marker that is used for displaying objects from clusters on the canvas.
219		@param[in] markersize (uint): Size of marker.
220
221		"""
222
223		for cluster in clusters:
224		self.append_cluster(cluster, data, canvas, marker, markersize);
225
226
227		def set_canvas_title(self, text, canvas = 0):
228		"""!
229		@brief Set title for specified canvas.
230
231		@param[in] text (string): Title for canvas.
232		@param[in] canvas (uint): Index of canvas where title should be displayed.
233
234		"""
235
236		if (canvas > self.__number_canvases):
237		raise NameError('Canvas does ' + canvas + ' not exists.');
238
239		self.__canvas_titles[canvas] = text;
240
241
242		def show(self, figure = None, visible_axis = True, visible_grid = True, display = True):
243		"""!
244		@brief Shows clusters (visualize).
245
246		@param[in] figure (fig): Defines requirement to use specified figure, if None - new figure is created for drawing clusters.
247		@param[in] visible_axis (bool): Defines visibility of axes on each canvas, if True - axes are invisible.
248		@param[in] visible_grid (bool): Defines visibility of axes on each canvas, if True - grid is displayed.
249		@param[in] display (bool): Defines requirement to display clusters on a stage, if True - clusters are displayed, if False - plt.show() should be called by user."
250
251		@return (fig) Figure where clusters are shown.
252
253		"""
254
255		canvas_shift = 0;
256		cluster_figure = None;
257		if (figure is not None):
258		canvas_shift = len(figure.get_axes());
259		cluster_figure = figure;
260		else:
261		cluster_figure = plt.figure();
262
263		maximum_cols = self.__size_row;
264		maximum_rows = math.ceil( (self.__number_canvases + canvas_shift) / maximum_cols);
265
266		grid_spec = gridspec.GridSpec(maximum_rows, maximum_cols);
267
268		for index_canvas in range(len(self.__canvas_clusters)):
269		canvas_data = self.__canvas_clusters[index_canvas];
270		dimension = self.__canvas_dimensions[index_canvas];
271
272		#ax = axes[real_index];
273		if ( (dimension == 1) or (dimension == 2) ):
274		ax = cluster_figure.add_subplot(grid_spec[index_canvas + canvas_shift]);
275		else:
276		ax = cluster_figure.add_subplot(grid_spec[index_canvas + canvas_shift], projection='3d');
277
278		if (len(canvas_data) == 0):
279		plt.setp(ax, visible = False);
280
281		for cluster_descr in canvas_data:
282		self.__draw_canvas_cluster(ax, dimension, cluster_descr);
283
284		for attribute_descr in cluster_descr.attributes:
285		self.__draw_canvas_cluster(ax, dimension, attribute_descr);
286
287		if (visible_axis is True):
288		ax.xaxis.set_ticklabels([]);
289		ax.yaxis.set_ticklabels([]);
290
291		if (dimension == 3):
292		ax.zaxis.set_ticklabels([]);
293
294		if (self.__canvas_titles[index_canvas] is not None):
295		ax.set_title(self.__canvas_titles[index_canvas]);
296
297		ax.grid(visible_grid);
298
299		if (display is True):
300		plt.show();
301
302		return cluster_figure;
303
304
305		"""!
306		@brief Draw canvas cluster descriptor.
307
308		@param[in] ax (Axis): Axis of the canvas where canvas cluster descriptor should be displayed.
309		@param[in] dimension (uint): Canvas dimension.
310		@param[in] cluster_descr (canvas_cluster_descr): Canvas cluster descriptor that should be displayed.
311
312		@return (fig) Figure where clusters are shown.
313
314		"""
315		def __draw_canvas_cluster(self, ax, dimension, cluster_descr):
316		cluster = cluster_descr.cluster;
317		data = cluster_descr.data;
318		marker = cluster_descr.marker;
319		markersize = cluster_descr.markersize;
320		color = cluster_descr.color;
321
322		for item in cluster:
323	View Code Duplication	if (dimension == 1):
		0 ignored issues – show Duplication introduced 2017-08-22 14:09 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
324		if (data is None):
325		ax.plot(item[0], 0.0, color = color, marker = marker, markersize = markersize);
326		else:
327		ax.plot(data[item][0], 0.0, color = color, marker = marker, markersize = markersize);
328
329		elif (dimension == 2):
330	View Code Duplication	if (data is None):
		0 ignored issues – show Duplication introduced 2017-03-18 21:40 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
331		ax.plot(item[0], item[1], color = color, marker = marker, markersize = markersize);
332		else:
333		ax.plot(data[item][0], data[item][1], color = color, marker = marker, markersize = markersize);
334
335		elif (dimension == 3):
336		if (data is None):
337		ax.scatter(item[0], item[1], item[2], c = color, marker = marker, s = markersize);
338		else:
339		ax.scatter(data[item][0], data[item][1], data[item][2], c = color, marker = marker, s = markersize);

annoviko / pyclustering

Push — master ( 93c3d2...b7ad63 )

cluster_visualizer B

Complexity

Size/Duplication

Importance

7 Methods

How to fix Duplicated Code Complexity

Duplicated Code

Complex Class

Duplication Side-by-Side

Filter issues like