Specifying node colormaps¶

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See example about colors for different way the color columns in the dataframe can be specified

There are two different ways to specify colormaps. This holds for both nodes (node_cmap) edges (edge_cmap)

The first way entails that you specify a matplotlib colormap (e.g. Set1, Inferno, (see options here)

The second way is to provide a list of matplotlib colors to create your own colormap (see named color options here)

This notebook will shows an example of each of these. We will use 100 ROIs from the Schaefer atlas and color the 7 Yeo networks

In [1]:

# Import necessary packages 
import netplotbrain
import pandas as pd
import templateflow.api as tf

# Import necessary packages import netplotbrain import pandas as pd import templateflow.api as tf

In [2]:

# Load templateflow information about the Schaefer atlas
atlasinfo = tf.get(template='MNI152NLin2009cAsym',
                   atlas='Schaefer2018',
                   desc='100Parcels7Networks',
                   extension='.tsv')
atlas_df = pd.read_csv(str(atlasinfo), sep='\t')
atlas_df.head()

# Load templateflow information about the Schaefer atlas atlasinfo = tf.get(template='MNI152NLin2009cAsym', atlas='Schaefer2018', desc='100Parcels7Networks', extension='.tsv') atlas_df = pd.read_csv(str(atlasinfo), sep='\t') atlas_df.head()

Out[2]:

	index	name	color
0	1	7Networks_LH_Vis_1	#781283
1	2	7Networks_LH_Vis_2	#781284
2	3	7Networks_LH_Vis_3	#781285
3	4	7Networks_LH_Vis_4	#781287
4	5	7Networks_LH_Vis_5	#781288

In [3]:

# Create a column called "network" by extracting the relevant information from the column "name"
atlas_df['network'] = list(map(lambda x: x.split('_')[2], atlas_df.name.values))
atlas_df.head()

# Create a column called "network" by extracting the relevant information from the column "name" atlas_df['network'] = list(map(lambda x: x.split('_')[2], atlas_df.name.values)) atlas_df.head()

Out[3]:

	index	name	color	network
0	1	7Networks_LH_Vis_1	#781283	Vis
1	2	7Networks_LH_Vis_2	#781284	Vis
2	3	7Networks_LH_Vis_3	#781285	Vis
3	4	7Networks_LH_Vis_4	#781287	Vis
4	5	7Networks_LH_Vis_5	#781288	Vis

In [4]:

# Define the TemplateFlow parcels that we want to plot 
nodes = {'template': 'MNI152NLin2009cAsym',
         'atlas': 'Schaefer2018',
         'desc': '100Parcels7Networks',
         'resolution': 1}

# Define the TemplateFlow parcels that we want to plot nodes = {'template': 'MNI152NLin2009cAsym', 'atlas': 'Schaefer2018', 'desc': '100Parcels7Networks', 'resolution': 1}

In [5]:

# Plot with a the Set2 colormap
netplotbrain.plot(nodes=nodes, 
                  node_type='parcels', 
                  nodes_df=atlas_df, 
                  node_color='network', 
                  node_cmap='Set2')

# Plot with a the Set2 colormap netplotbrain.plot(nodes=nodes, node_type='parcels', nodes_df=atlas_df, node_color='network', node_cmap='Set2')

Out[5]:

(<Figure size 216x252 with 2 Axes>,
 [<Axes3D: title={'center': 'Left'}>, <Axes: >])

In [6]:

# Create custom colormap
node_cmap = ['blue', 'red', 'green', 'black', 'purple', 'yellow', 'orange']

# Create custom colormap node_cmap = ['blue', 'red', 'green', 'black', 'purple', 'yellow', 'orange']

In [7]:

# Plot with a the custom colormap
netplotbrain.plot(nodes=nodes, 
                  node_type='parcels', 
                  nodes_df=atlas_df, 
                  node_color='network', 
                  node_cmap=node_cmap)

# Plot with a the custom colormap netplotbrain.plot(nodes=nodes, node_type='parcels', nodes_df=atlas_df, node_color='network', node_cmap=node_cmap)

Out[7]:

(<Figure size 216x252 with 2 Axes>,
 [<Axes3D: title={'center': 'Left'}>, <Axes: >])