cerebellum | powerful set of tools that help you structure | Frontend Framework library

use enumerate():

Here's a way to do what you want:

1. First after loading your adjacency matrices to pandas you can convert them to two different graphs with nx.convert_matrix.from_pandas_adjacency
2. You can then join the two graph into a single graph by using nx.disjoint_union. The nodes of both graphs are basically concatenated onto a single graph (see more here).
3. Once you have the full graph, you can randomly draw nodes from the cortex part of the full graph with a 0.01 probability.
4. Similarly you can draw the same number of nodes on the cerebellum part of the graph to act as recipients of the connection.
5. Finally you can create the edges between the chosen nodes on both sides.
6. And you can get your adjacency matrix from the final graph by using adj_matrix_full=nx.linalg.graphmatrix.adjacency_matrix(full_g,weight=None)

See full code below for more details:

for the "k" metric:

This is not really a programming question, but I will try to answer it. The webpage with the data sources states that the adjacent matrix files for brain samples give distances between connected nodes expressed in pixels of the images used to reconstruct the networks. The paper then explains that to get the real adjacency matrix Mij (with 0 and 1 values only) the authors consider as connected nodes where the distance is at most 16 micrometers. I don't see the information on how many pixels in the image corresponds to one micrometer. This would be needed to compute the same matrix Mij that the authors used in their calculations.

Furthermore, the value〈k〉is not the degree centrality or the clustering coefficient (that depend on a node), but rather the average number of connections per node in the network, computed using the matrix Mij. The paper then compares the observed distributions of degree centralities and clustering coefficients in the brain and cosmic networks to the distribution one would see in a random network with the same number of nodes and the same value of〈k〉. The conclusion is that brain and cosmic networks are highly non-random.

Edits:

1. The conversion of 0.32 micrometers per pixel seems to be right. In the files with data on brain samples (both for cortex and cerebellum) the largest value is 50 pixels, which with this conversion corresponds to 16 micrometers. This suggests that the authors of the paper already thresholded the matrices, listing in them only distances not exceeding 16 micrometers. In view of this, to obtain the matrix Mij with 0 and 1 values only, one simply needs to replace all non-zero values with 1. An issue is that using the matrices obtained in this way one gets 〈k〉 = 9.22 for cerebellum and 〈k〉 = 7.13 for cortex, which is somewhat outside the ranges given in the paper. I don't know how to account for this discrepancy.

2. Negative centrality values are due to a mistake (missing parentheses) in the code. It should be:

One option is to use data Standardization. Meaning that you recalculate the values into values between -1 and 1, but keep the relative diferences.

• There are two issues, and they only occur when trying to send a single tuple to the function.
  1. xy should be a tuple of tuples
     • It should be similar to (('CerebrumVolume', 'CerebellumVolume '), ('CerebrumVolume', 'CerebellumSurfaceArea'), ('CerebellumVolume ', 'CerebellumSurfaceArea'))
     • You are giving it ('Cerebellum Surface Area', 'Cerebrum Volume') which is not correctly unpacked.
     • xy=(('CerebrumVolume', 'CerebellumVolume '),) works for sending a single tuple to the function.
  2. When there is only one set of tuples to unpack, plt.subplots creates a single axes instead of an array of axes
     • Use squeeze=False so there will always be an array, even if there is only one axes, and then flatten the array with axes.flatten().

Up front: in general, don't use for in a %>%-pipe, it almost never is what you intend.

Since you have the %>%, I'll infer you're using dplyr and/or other related packages. Here's one way:

The error reads

You probably need scale_colour_manual.