Band | Band enables UIViewController to present placeholder views

I want to calculate the % according to the total of the entire table, not just row or column in a pandas table with python. I can do by row and by column separately, but i rather to do the entire table. Any suggestions are welcome. Thanks so much

data

I am trying to recode my education variable from a factor with 18 levels to a factor with 7 levels,ranging from no qualification - GCSE D-G, GCSE A*-C- A Level -Undergraduate -Postgraduate - other.

I am working on setting up git repositories on a windows server (2019), attempting to follow https://git-scm.com/book/en/v2/Git-on-the-Server-Setting-Up-the-Server. However, no matter what I do, git push comes up with fatal: ''/Users/testing/testrepo.git'' does not appear to be a git repository.

I ran:
git init
git add test.txt (test file, just contained test)
git commit -m "test"
git remote add origin testing@(removed):Users/testing/testrepo.git
git push origin master

After struggling with this for a while, I enabled logging for ssh and found this line on the server side log file:
7744 2021-06-09 03:58:37.691 debug1: Executing command: "c:\windows\system32\cmd.exe" /c "git-receive-pack '/Users/testing/testrepo.git'" with no pty

I tried manually running git-recieve-pack '/Users/testing/testrepo.git', and it came up with the same error. However, I then tried running git-receive-pack "/Users/testing/testrepo.git", and it gave
00bb0000000000000000000000000000000000000000 capabilities^{} report-status report-status-v2 delete-refs side-band-64k quiet atomic ofs-delta object-format=sha1 agent=git/2.32.0.windows.1 0000

The only difference was " instead of '. Is this something weird with windows server, or am I doing something wrong?

I am using the following code to read geotiff images (~300). Each image file contains 15 bands.

code:

I want to design and train a neural network for the automatic recognition of the edges, in some microscopic images. I am using Keras for a start, I may consider PyTorch later.

The structure of the images is rather simple, with some dark areas, and some clear areas, relatively easy to distinguish, and the task is to select the pixels of the contour between dark and clear areas. The transition between dark and clear is gradual, so my result is not a single line of edge pixels, but rather a 10 or 15 pixels wide "ribbon" at the edge.

I have manually annotated 200-something images, so for each image I have another image, of the same size, where the pixels of the contours are black, and all the other pixels are white.

I have seen many tutorials on how to design, compile and fit a model (a neural network), and then how to test it, using the manually annotated data.

However, most of the tutorials work on problems of classification, where the number of neurons in the output layer is the number of categories.

My problem is not a problem of classification, and ideally my output should be an image of the same size of the input.

So, here is my question:

What is the best way to design the output layer? Is a layer with a number of neurons equal to the number of pixels the best idea? Or this is a waste, and there is a more efficient way?

Addendum

1. The images are "easy", but it is still difficult to find the contour pixels, so I believe that it is worth using the machine learning approach.
2. The transition between dark and clear is a little gradual, so my result is not a single line of pixels on the edge, but rather a band, a 10 or 15 wide ribbon of edge pixels. Since I am after a ribbon of pixels, my categories should be "edge" and "not-edge". If I use the categories "dark pixels" and "clear pixels", and then numerically find the pixels between the two areas I do not get the "ribbon" result, which I need.

I'm developing an app that has live video, I managed to implement LibVLCSharp, but I have a problem, I need to remove everything from the screen, this is just missing:

Here is my code:

XAML

I am using a code written by Victor Velasquez to extract data from raster files which contain dayly precipitation data since 1981. When I run the code, I get this error that some index is out of bounds. I did a little research and found that this is common and there are a lot of similar questions here, but I haven´t been able to find the specific solution for this case.

The error:

I am working with numpy.ndarray including 286 images with the shape of (286, 16, 16, 3). Each image contains 3 bands with varying pixel values with float32 data types. The maximum value of pixel value in each band can be more than 255. Is it possible to normalize this numpy.ndarray between [0-1]?

code for reading the images: