density | Superfast compression library | Compression library

I am trying to make a next-word prediction model with LSTM + Mixture Density Network Based on this implementation(https://www.katnoria.com/mdn/).

Input: 300-dimensional word vectors*window size(5) and 21-dimensional array(c) representing topic distribution of the document, used to train hidden initial states.

Output: mixing coefficient*num_gaussians, variance*num_gaussians, mean*num_gaussians*300(vector size)

x.shape, y.shape, c.shape with an experimental 161 obserbations gives me such:

(TensorShape([161, 5, 300]), TensorShape([161, 300]), TensorShape([161, 21]))

I have been following the leaflets tutorial where it is guiding on how to build an Interactive Choropleth map. Not all things are entirely included and once in a while I have to go online in an attempt to tweak things to get the to work. I got this point where I was labeling the population density of the states and my legend does not show a range in the labels like in this map and instead looks like this (in photo below).

Below is my code that gives the legend above

I have the following dataset:

For the past days I've been trying to plot circular data with python, by constructing a circular histogram ranging from 0 to 2pi and fitting a Von Mises Distribution. What I really want to achieve is this:

1. Directional data with fitted Von-Mises Distribution. This plot was constructed with Matplotlib, Scipy and Numpy and can be found at: http://jpktd.blogspot.com/2012/11/polar-histogram.html

1. This plot was produced using R, but gives the idea of what I want to plot. It can be found here: https://www.zeileis.org/news/circtree/

WHAT I HAVE DONE SO FAR:

I have traffic density (number of cars) data of various segments of a road. I want to plot a contour map like in the attached figure in R.

I have two overlaid plots like the following:

This is my first post here and I am not that experienced, so please excuse my ignorance.

I am building a Monte Carlo simulation in C++ for my PhD and I need help in optimizing its computational time and performance. I have a 3d cube repeated in each coordinate as a simulation volume and inside every cube magnetic particles are generated in clusters. Then, in the central cube a loop of protons are created and move and at each step calculate the total magnetic field from all the particles (among other things) that they feel.

At this moment I define everything inside the main function and because I need the position of the particles for my calculations (I calculate the distance between the particles during their placement and also during the proton movement), I store them in dynamic arrays. I haven't used any class or function,yet. This makes my simulations really slow because I have to use eventually millions of particles and thousands of protons. Even with hundreds it needs days. Also I use a lot of for and while loops and reading/writing to .dat files.

I really need your help. I have spent weeks trying to optimize my code and my project is behind schedule. Do you have any suggestion? I need the arrays to store the position of the particles .Do you think classes or functions would be more efficient? Any advice in general is helpful. Sorry if that was too long but I am desperate...

Ok, I edited my original post and I share my full script. I hope this will give you some insight regarding my simulation. Thank you.

Additionally I add the two input files

parametersDiffusion_spher_shel.txt

parametersIONP_spher_shel.txt

I have a large array of small structs that I would like to serialize to file.

The struct:

I have a somewhat mobile responsive header, but there are some dropdown menus that I would like to keep flush to the edge of the header element regardless of changes in viewport size as the header adjusts.

I tried putting those dropdowns in their own element such as a div or section and adding all the same css from the individual selectors, but I did not make progress there.