cosmic | Computer Operating System Main Interface Components

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:

It looks like, in calculate_community_modularity, you use greedy_modularity_communities to create a dict, modularity_dict, which maps a node in your graph to a community. If I understand correctly, you can take each subgraph community in modularity_dict and pass it into shannon_entropy to calculate the entropy for that community.

this is pseudo code, so there may be some errors. This should convey the principle, though.

after running calculate_community_modularity, you have a dict like this, where the key is each node, and the value is that which the community belongs to

You are printing the same expression that you use in the if condition, so if the if expression is true, it will obviously print 1..

I managed to find a solution. I added 'absolute' as a prop to v-navigation-drawer. The menu open above the content (I wanted that, so it's okay) and doesn't shift the content anymore. But if anyone would like to shift content when it opens only when needed, I guess you have to use the @media rule and margin with a negative value to shift back when the screen width is under a specific value.

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:

Looking at the line

Computer languages do not worry about this unlikely problem (bits in the CPU randomly being changed by some outside force). Other forms of error are also not the applications area of responsibility, for example network data which is much more likely to be corrupted often has internal checks in the protocols to detect errors (checksums for example). The same is true for some storage.

In the rare environment where this really matters (space vehicles being the main one I know of) They have redundant applications running and compare the results of both to see if they match.

So in answer to you question these kind of issues are not the concern of the language, They are either handled at a lower level (checksums on network packets etc...), or a higher level (redundancy). Generally these kind of issues are only worried about in very rare circumstances such as space vehicles, nuclear power, etc...

You have two variables named 'main', one is the getElementById and the other is from the forEach loop. Change one and it should work

Your startDownloading function returns after it registers callbacks to listen to the Stream and does not wait for the Stream to complete.

To wait for the Stream to complete, you can save the StreamSubscription returned by .listen and then await the Future from StreamSubscription.asFuture: