b94012c8-28c1-4e28-bf7f-af294f9dc361

Committed 18 Aug 2025 06:45AM UTC coverage: 78.819%. Remained the same

Build # b94012c8-28c1-4e28-bf7f-af294f9dc361

Build Type

push

circleci

Committed by

web-flow

Commit Message

Merge pull request #76 from seblome/seblome-patch-1

Update plot_cm.py

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1001 of 1270 relevant lines covered (78.82%)

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70.15

/netplotbrain/plotting/plot_cm.py

import numpy as np
from matplotlib import transforms
from matplotlib.collections import LineCollection
import pandas as pd

def _plot_connectivitymatrix(ax, edges, nodes=None, node_color=None, node_colorby=None, **kwargs):
    """Plot connectivity matrix

    Args:
        ax (matplotlib ax)
        edges (pandas datafram): edges.
    """

    # Dataframe to array
    if isinstance(nodes, pd.DataFrame):
        number_of_nodes=len(nodes)
    else:
        number_of_nodes=edges[['i', 'j']].max().max()+1

    cm_order = kwargs.get('cm_order')
    cm_boundary = kwargs.get('cm_boundary')
    cm_boundarywidth = kwargs.get('cm_boundarywidth')
    cm_boundaryalpha = kwargs.get('cm_boundaryalpha')
    cm_border = kwargs.get('cm_border')
    cm_borderwidth = kwargs.get('cm_borderwidth')
    cm_bordercolor = kwargs.get('cm_bordercolor')


    vmin = kwargs.get('cm_vmin')
    vmax = kwargs.get('cm_vmax')
    #if cm_boundary == 'Auto' and (cm)
    #if cm_boundarycolor == 'auto' and cm_boundary is not None:
    #    cm_boundarycolor = 'black'
    #else:
    #    cm_boundarycolor = None
    # Set cm_order to node_colorby if it is set to auto and node_color is set
    if cm_order == 'auto' and node_colorby is not None:
        cm_order = node_colorby
        if cm_boundary == 'auto':
            cm_boundary = True
    elif cm_order == 'auto':
        cm_order = None
    if cm_order is not None:
        # Check if cm_order consists of unqiue indicies or are communities/groups
        if len(np.unique(cm_order))==len(cm_order):
            nodeorder = cm_order
        else:
            communities = nodes[cm_order].astype('category').cat.codes.values
            nodeorder = np.argsort(communities)
    else:
        nodeorder = np.arange(number_of_nodes)
    # If it is still set to auto, set ot None
    if cm_boundary == 'auto':
        cm_boundary = None

    adj = np.zeros((number_of_nodes, number_of_nodes))
    if 'weight' in edges:
        adj[edges['i'], edges['j']] = edges['weight']
        # TODO Assume the matrix is symmetric, fix later in kwargs
        adj[edges['j'], edges['i']] = edges['weight']
    else:
        adj[edges['i'], edges['j']] = 1
        # TODO Assume the matrix is symmetric, fix later in kwargs
        adj[edges['j'], edges['i']] = 1

    # rotate the plot
    rotate = kwargs.get('cm_rotate')
    if rotate:
        tr = transforms.Affine2D().rotate_deg(45) + ax.transData
    else:
        tr = ax.transData
    ax.pcolormesh(adj[nodeorder][:, nodeorder], cmap='RdBu_r', vmin=vmin, vmax=vmax, transform=tr, rasterized=True, zorder=1)

    # Plot
    # Add squares for different communities to the connectivity plot
    if cm_border:
        if cm_bordercolor == 'networks':
            line_blank = np.zeros(number_of_nodes+1)
            line_fill = np.zeros(number_of_nodes+1)
            for i in range(number_of_nodes+1):
                line_fill[i] = i-0.5
            points_x = np.array([line_fill, line_blank]).T.reshape((-1, 1, 2))
            points_y = np.array([line_blank, line_fill]).T.reshape((-1, 1, 2))
            segments_x  = np.concatenate([points_x[:-1], points_x[1:]], axis=1)
            segments_y  = np.concatenate([points_y[:-1], points_y[1:]], axis=1)
            lc_x = LineCollection   (segments_x,
                                    colors      = node_color[nodeorder],
                                    linewidth   = cm_borderwidth*2,
                                    transform   = tr,
                                    clip_on     = False,
                                    zorder      = -1)
            lc_y = LineCollection   (segments_y,
                                    colors      = node_color[nodeorder],
                                    linewidth   = cm_borderwidth*2,
                                    transform   = tr,
                                    clip_on     = False,
                                    zorder      = -1)
            ax.add_collection(lc_x)
            ax.add_collection(lc_y)

        else:
            ax.plot([0, 0], [0, number_of_nodes], color=cm_bordercolor, linewidth=cm_borderwidth, transform=tr,zorder=5)
            ax.plot([number_of_nodes, 0], [number_of_nodes, number_of_nodes], color=cm_bordercolor, linewidth=cm_borderwidth, transform=tr,zorder=5)
            ax.plot([0, number_of_nodes], [0, 0], color=cm_bordercolor, linewidth=cm_borderwidth, transform=tr,zorder=5)
            ax.plot([number_of_nodes, number_of_nodes], [0, number_of_nodes], color=cm_bordercolor, linewidth=cm_borderwidth, transform=tr,zorder=5)

    if cm_boundary:
        for coms in np.unique(communities):
            no = np.where(communities[nodeorder] == coms)[0]
            ax.plot([no[0], no[0]], [no[0], no[-1]+1], color=node_color[nodeorder[no[0]]][:3], linewidth=cm_boundarywidth, transform=tr,zorder=10, alpha = cm_boundaryalpha)
            ax.plot([no[-1]+1, no[0]], [no[-1]+1, no[-1]+1], color=node_color[nodeorder[no[0]]][:3], linewidth=cm_boundarywidth, transform=tr,zorder=10, alpha = cm_boundaryalpha)
            ax.plot([no[0], no[-1]+1], [no[0], no[0]], color=node_color[nodeorder[no[0]]][:3], linewidth=cm_boundarywidth, transform=tr,zorder=10, alpha = cm_boundaryalpha)
            ax.plot([no[-1]+1, no[-1]+1], [no[0], no[-1]+1], color=node_color[nodeorder[no[0]]][:3], linewidth=cm_boundarywidth, transform=tr,zorder=10, alpha = cm_boundaryalpha)

1	import numpy as np	1✔
2	from matplotlib import transforms	1✔
3	from matplotlib.collections import LineCollection	1✔
4	import pandas as pd	1✔
5
6	def _plot_connectivitymatrix(ax, edges, nodes=None, node_color=None, node_colorby=None, **kwargs):	1✔
7	"""Plot connectivity matrix
8
9	Args:
10	ax (matplotlib ax)
11	edges (pandas datafram): edges.
12	"""
13
14	# Dataframe to array
15	if isinstance(nodes, pd.DataFrame):	1✔
16	number_of_nodes=len(nodes)	1✔
17	else:
18	number_of_nodes=edges[['i', 'j']].max().max()+1	×
19
20	cm_order = kwargs.get('cm_order')	1✔
21	cm_boundary = kwargs.get('cm_boundary')	1✔
22	cm_boundarywidth = kwargs.get('cm_boundarywidth')	1✔
23	cm_boundaryalpha = kwargs.get('cm_boundaryalpha')	1✔
24	cm_border = kwargs.get('cm_border')	1✔
25	cm_borderwidth = kwargs.get('cm_borderwidth')	1✔
26	cm_bordercolor = kwargs.get('cm_bordercolor')	1✔
27
28
29	vmin = kwargs.get('cm_vmin')	1✔
30	vmax = kwargs.get('cm_vmax')	1✔
31	#if cm_boundary == 'Auto' and (cm)
32	#if cm_boundarycolor == 'auto' and cm_boundary is not None:
33	# cm_boundarycolor = 'black'
34	#else:
35	# cm_boundarycolor = None
36	# Set cm_order to node_colorby if it is set to auto and node_color is set
37	if cm_order == 'auto' and node_colorby is not None:	1✔
38	cm_order = node_colorby	1✔
39	if cm_boundary == 'auto':	1✔
40	cm_boundary = True	1✔
41	elif cm_order == 'auto':	×
42	cm_order = None	×
43	if cm_order is not None:	1✔
44	# Check if cm_order consists of unqiue indicies or are communities/groups
45	if len(np.unique(cm_order))==len(cm_order):	1✔
46	nodeorder = cm_order	×
47	else:
48	communities = nodes[cm_order].astype('category').cat.codes.values	1✔
49	nodeorder = np.argsort(communities)	1✔
50	else:
51	nodeorder = np.arange(number_of_nodes)	×
52	# If it is still set to auto, set ot None
53	if cm_boundary == 'auto':	1✔
54	cm_boundary = None	×
55
56	adj = np.zeros((number_of_nodes, number_of_nodes))	1✔
57	if 'weight' in edges:	1✔
58	adj[edges['i'], edges['j']] = edges['weight']	1✔
59	# TODO Assume the matrix is symmetric, fix later in kwargs
60	adj[edges['j'], edges['i']] = edges['weight']	1✔
61	else:
62	adj[edges['i'], edges['j']] = 1	×
63	# TODO Assume the matrix is symmetric, fix later in kwargs
64	adj[edges['j'], edges['i']] = 1	×
65
66	# rotate the plot
67	rotate = kwargs.get('cm_rotate')	1✔
68	if rotate:	1✔
69	tr = transforms.Affine2D().rotate_deg(45) + ax.transData	1✔
70	else:
71	tr = ax.transData	1✔
72	ax.pcolormesh(adj[nodeorder][:, nodeorder], cmap='RdBu_r', vmin=vmin, vmax=vmax, transform=tr, rasterized=True, zorder=1)	1✔
73
74	# Plot
75	# Add squares for different communities to the connectivity plot
76	if cm_border:	1✔
77	if cm_bordercolor == 'networks':	1✔
78	line_blank = np.zeros(number_of_nodes+1)	×
79	line_fill = np.zeros(number_of_nodes+1)	×
80	for i in range(number_of_nodes+1):	×
81	line_fill[i] = i-0.5	×
82	points_x = np.array([line_fill, line_blank]).T.reshape((-1, 1, 2))	×
83	points_y = np.array([line_blank, line_fill]).T.reshape((-1, 1, 2))	×
84	segments_x = np.concatenate([points_x[:-1], points_x[1:]], axis=1)	×
85	segments_y = np.concatenate([points_y[:-1], points_y[1:]], axis=1)	×
86	lc_x = LineCollection (segments_x,	×
87	colors = node_color[nodeorder],
88	linewidth = cm_borderwidth*2,
89	transform = tr,
90	clip_on = False,
91	zorder = -1)
92	lc_y = LineCollection (segments_y,	×
93	colors = node_color[nodeorder],
94	linewidth = cm_borderwidth*2,
95	transform = tr,
96	clip_on = False,
97	zorder = -1)
98	ax.add_collection(lc_x)	×
99	ax.add_collection(lc_y)	×
100
101	else:
102	ax.plot([0, 0], [0, number_of_nodes], color=cm_bordercolor, linewidth=cm_borderwidth, transform=tr,zorder=5)	1✔
103	ax.plot([number_of_nodes, 0], [number_of_nodes, number_of_nodes], color=cm_bordercolor, linewidth=cm_borderwidth, transform=tr,zorder=5)	1✔
104	ax.plot([0, number_of_nodes], [0, 0], color=cm_bordercolor, linewidth=cm_borderwidth, transform=tr,zorder=5)	1✔
105	ax.plot([number_of_nodes, number_of_nodes], [0, number_of_nodes], color=cm_bordercolor, linewidth=cm_borderwidth, transform=tr,zorder=5)	1✔
106
107	if cm_boundary:	1✔
108	for coms in np.unique(communities):	1✔
109	no = np.where(communities[nodeorder] == coms)[0]	1✔
110	ax.plot([no[0], no[0]], [no[0], no[-1]+1], color=node_color[nodeorder[no[0]]][:3], linewidth=cm_boundarywidth, transform=tr,zorder=10, alpha = cm_boundaryalpha)	1✔
111	ax.plot([no[-1]+1, no[0]], [no[-1]+1, no[-1]+1], color=node_color[nodeorder[no[0]]][:3], linewidth=cm_boundarywidth, transform=tr,zorder=10, alpha = cm_boundaryalpha)	1✔
112	ax.plot([no[0], no[-1]+1], [no[0], no[0]], color=node_color[nodeorder[no[0]]][:3], linewidth=cm_boundarywidth, transform=tr,zorder=10, alpha = cm_boundaryalpha)	1✔
113	ax.plot([no[-1]+1, no[-1]+1], [no[0], no[-1]+1], color=node_color[nodeorder[no[0]]][:3], linewidth=cm_boundarywidth, transform=tr,zorder=10, alpha = cm_boundaryalpha)	1✔

wiheto / netplotbrain / b94012c8-28c1-4e28-bf7f-af294f9dc361

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