micron | microInteraction library built with CSS Animations | Animation library

@stefan has solved the problem in the comment.

update_values called in the event "Append" of thruput_window.

In function update_values, you do something wrong

• bind return key to element 'USER-INPUT', binding should be done right after your thruput_window finalized, then while loop to read event.

Naming thing: those aren't methods, they're functions; now: if you modify a DataFrame within a function those changes still happen to the DataFrame. If you want a new DataFrame, declare it as a copy of the one being passed in.

At the very least at the top of binarize_matrix() do: new_df = df.copy(). More detail about why that's necessary in this SO answer and comments: https://stackoverflow.com/a/39628860/42346

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:

Try:

Clean Testing: You should try the package on a clean virtual to see if you have a "dirty state" on your main box - from prior experiments. It is not uncommon to find several versions of your package in Add / Remove - potentially with different names. This can happen in several ways, but usually it is because of misconfigured major upgrade settings for one or more test versions. Files are not uninstalled before all "clones" are uninstalled. There can also be other reasons for files remaining after uninstall.

List Installed Packages: You can run this script to get a list of all MSI packages installed on your machine: https://github.com/glytzhkof/all/blob/master/MsiHtmlReport-Mini-V4.vbs

Service Installation: You should not use custom actions to install services when using Windows Installer. Here is an old answer to explain why and a sample of how services are supposed to be installed:

• How to install services with WiX / MSI
• https://github.com/Robs79/How-to-create-a-Windows-Service-MSI-Installer-Using-WiX

Essentially you use the ServiceInstall and ServiceControl constructs inside the MSI.

I'm not sure I completely understand what you're trying to do. Nevertheless, here's a suggestion:

In the following I'm using as an example an Excel-file test.xlsx with the content

Get rid of the aspect=1 flag for axes so that line reads

See DataGrid API. The props is called columns, not cols