calculate_nodal_measures doesn't work on graph bundles

[KirstieJane]

I though that graph bundles had all the same attributes as graphs....but I get the following error when I try to run calculate_nodal_measures() on bundleGraphs (setup as described in the collapsed section below).

bundleGraphs.calculate_nodal_measures()

---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-3-5972c4b80e32> in <module>
----> 1 bundleGraphs.calculate_nodal_measures()

AttributeError: 'GraphBundle' object has no attribute 'calculate_nodal_measures'

I also get the error for calculate_global_measures():

---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-18-8a0f9f100859> in <module>
 1 # Calculate the global measures
----> 2 bundleGraphs.calculate_global_measures()
 3 #bundleGraphs_measures = bundleGraphs.report_global_measures()

AttributeError: 'GraphBundle' object has no attribute 'calculate_global_measures'

In contrast bundleGraphs.report_global_measures() gives the expected output 😄, but bundleGraphs.report_nodal_measures() doesn't 😢 (same error as above).

And to be clear bundleGraphs['real_graph'].calculate_nodal_measures() works as expected too 😸

So is this a feature or a bug? Which attributes are supposed to be passed from graphs to the bundles?

---

Click the arrow below to see the MWE I ran to get the errors above.

Click here to expand

import scona as scn
import scona.datasets as datasets
import numpy as np
import networkx as nx
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# Read in sample data from the NSPN WhitakerVertes PNAS 2016 paper.
df, names, covars, centroids = datasets.NSPN_WhitakerVertes_PNAS2016.import_data()

# calculate residuals of the matrix df for the columns of names
df_res = scn.create_residuals_df(df, names, covars)

# create a correlation matrix over the columns of df_res
M = scn.create_corrmat(df_res, method='pearson')

# Initialise a weighted graph G from the correlation matrix M
G = scn.BrainNetwork(network=M, parcellation=names, centroids=centroids)

# Threshold G at cost 10 to create a binary graph with 10% as many edges as the complete graph G.
G10 = G.threshold(10)

# Create a GraphBundle object that contains the G10 graph called "real_graph"
bundleGraphs = scn.GraphBundle([G10], ["real_graph"])

[wingedRuslan]

@KirstieJane, this is the GraphBundle's behavior we have right now and I hope @Islast will confirm my below-mentioned explanations.

So, in general, it is a feature 😃
GraphBundle is a dictionary, where value - is the BrainNetwork Graph and key - is the name of the graph. Right now methods like calculate_nodal_measures && report_nodal_measures for this class GraphBundle are not implemented. That's why there are errors saying that these operations (methods) on GraphBundle do not exist.

And to be clear bundleGraphs['real_graph'].calculate_nodal_measures() works as expected too

This works as expected because we take one BrainNetwork Graph (keyed by the name - real_graph]) from a bunch of Graphs (GraphBundle) and perform nodal measures calculation only on this one Graph.
bundleGraphs['real_graph'] returns the BrainNetwork Graph, as this is a single BrainNetwork object we can calculate_nodal_measures().
In your case these 2 lines calculate nodal measures on the same object G10 :

bundleGraphs['real_graph'].calculate_nodal_measures() 
G10.calculate_nodal_measures()

Speaking about calculate_global_measures(), we have report_global_measures that will calculate global measures (if not already calculated) and report the results as a pandas dataframe.

PS. adding the support of calculate_nodal_measures for GraphBundle is pretty straightforward (pseudocode):

for each graph in GraphBundle:
 measures = graph.calculate_nodal_measures()
 store the reported nodal measures of each graph in a dataframe

return dataframe

We can discuss this in details during the next week's meeting if you want to have this functionality ;)