reconstruction | 3d reconstruction of objects from data in the DB | 3D Printing library

I'm following the answer to this question and this scikit-learn tutorial to remove artifacts from an EEG signal. They seem simple enough, and I'm surely missing something obvious here.

The components extracted don't have the same length as my signal. I have 88 channels of several hours of recordings, so the shape of my signal matrix is (88, 8088516). Yet the output of ICA is (88, 88). In addition to being so short, each component seems to capture very large, noisy-looking deflections (so out of 88 components only a couple actually look like signal, the rest look like noise). I also would have expected only a few components to look noisy. I suspect I'm doing something wrong here?

The matrix of (channels x samples) has shape (88, 8088516).

Sample code (just using a random matrix for minimum working purposes):

I am training a VQVAE with this dataset (64x64x3). I have downloaded it locally and loaded it with keras in Jupyter notebook. The problem is that when I ran fit() to train the model I get this error: ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors. Inputs received: [, ] . I have taken most of the code from here and adapted it myself. But for some reason I can't make it work for other datasets. You can ignore most of the code here and check it in the page, help is much appreciated.

The code I have so far:

In this program I am trying to send key shares though UDP broadcast to the client(s). The issue I'm facing is with encoding of strings and bytes into one message, there are number of errors are produced.

I would like to send from the server to the client a random ID and a key share, which include index (integer) and share (16-bytes string). I have added ":" between id, index and share to be able to manage it at the client later (by splitting the message into parts).

I have tried converting everything into string, such as: message = (str(id) + ":" + str(index) + ":").encode('utf-16') + str(share, 'utf-16'). But this causing issues in key reconstruction at the client, where the key share should be a byte string and it looks like b"b'\xff\xfej\xb9\xdb\x8c&\x7f\x06\x87\x98Ig\xfc\x1eJ\xf6\xb5'".

Then I have tried encoding id and index to utf-16 and sending a message to the client, and then decode it, but this does not let me reconstruct the key and I'm getting an error: ValueError: The encoded value must be an integer or a 16 byte string.

When I decode at the client, the data looks like this: f91f7e52-865d-49bc-bb45-ad80255e9ef9:5:륪賛缦蜆䦘ﱧ䨞뗶. However, some shares after decoding do not contain dilimiter and thus not being able to split.

Is there a way the server can send all the following data in one message (id, index, share), so that it can be separated correctly at the client?

The desired output at server would be (id = string, index = int, share = 16-byte string):

I am learning to program in fortran and was making a basic program. In it I am trying to return an array from a function in my program, which is resulting in this error: Interface mismatch in global procedure 'test1' at (1): Rank mismatch in function result (0/1).

Below is a reconstruction of the error:

I'm using the CIFAR-10 pre-trained VAE from lightning-bolts. It should be able to regenerate images with the quality shown on this picture taken from the docs (LHS are the real images, RHS are the generated)

However, when I write a simple script that loads the model, the weights, and tests it over the training set, I get a much worse reconstruction (top row are real images, bottom row are the generated ones):

Here is a link to a self-contained colab notebook that reproduces the steps I've followed to produce the pictures.

Am I doing something wrong on my inference process? Could it be that the weights are not as "good" as the docs claim?

Thanks!

I am new to Keras, and I am trying to use autoencoder in Keras for denoising purposes, but I do not know why my model loss increases rapidly! I applied autoencoder on this data set:

https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+Classification#

So, we have 756 instances with 753 features. (eg. x.shape=(756,753))

This is what I have done so far:

I am trying to plot the fall of an object (an optical fork to be precise) as a function of time in order to verify that the law of gravity is indeed 9.81. The different data are supposed to represent the passage at each slot. The different slits are spaced 1 centimeter apart and there are 11 slits in all. I measured these data with an Arduino setup and I plot the graph and fit with Python. I have the data in a CSV file but when I run my code, I get an error "Unable to coerce to Series, length must be 1: given 11". However, when I enter the values manually one by one instead of reading the file, the code works and I get this graph, which is what I expect.

Here is the instruction I use (I added the values at each iteration by entering them manually and I thought that by doing the same thing in my CSV file the code would work but unfortunately it doesn't work either)

I'm attempting to show the decomposition of an affine matrix with sympy as shown in the following stackexchange post:

https://math.stackexchange.com/questions/612006/decomposing-an-affine-transformation

I've setup two matrices A_params and A_matrix, where the former represents the raw matrix values and the latter is the matrix constructed from its underlying parameters.

I tried to use shutil to delete a directory and all contained files, as follows: