SciPy-CookBook | Scipy cookbook converted to Sphinx | Theme library

I played a bit with Mayavi and particularly with tvtk but I struggle finding examples in which glyphs are placed on the scene at different orientation than default.

Based on this example I prepared the following scene with two cyllinders, one red and one blue,

I have been using scipy.integrate.solve_ivp to solve a system of 2nd order ODEs.

Assuming the code available here (note this uses odeint, but similar approach with solve_ivp): https://scipy-cookbook.readthedocs.io/items/CoupledSpringMassSystem.html

Now, making some parameters (b1 and b2) time & solution-dependent, say: e.g.

I read this tutorial https://scipy-cookbook.readthedocs.io/items/CorrelatedRandomSamples.html on how to get a matrix C so that C*C^T = R, with R being a given covariance matrix. The code example implements two differents methods, Cholesky decomposition or using the eigenvalues. To my suprise printing the resulting C of the two different methods gives me two different matrices:

Eigenvalue method result:

Following this post I'm trying to implement a one-dimensional smoothing algorithm. When creating a flat window for a simple moving average the call is as follows:

I have a bunch of similar curves, for example 1000 sine waves with slightly varying amplitude, frequency and phases, they look like as in this plot:

In the above plot the color of each sine wave is from the standard pandas colormap; I would like to get a plot where the color is related to the "density" of the curves.

My first idea is to imitate an old oscilloscope screen (search for "persistence mode" or look at https://en.wikipedia.org/wiki/Eye_pattern for some background):

and so I set one color for all the curves:

but the plot is "flat" and the "density" information is not so good.

I would really like a plot like this one:

In the above plot the yellow colour means that a number of curves between 25 and 30 "pass" through the same point (or the same pixel). I hand-made the above plot and I am asking whether it can be done better and more directly with pandas or matplotlib.

Above figures are made with this program, it takes a while (a dozen or seconds) because the Bresenham's line algorithm is not optimized.

I am learning how to code on python and I am trying to figure out how I could find the sum of solutions from an ODE system at a specific time.

For example, this is from the SciPy Cookbook the example is called "Modelling a Zombie Apocalypse" https://scipy-cookbook.readthedocs.io/items/Zombie_Apocalypse_ODEINT.html

Here is part of the code from the website:

I'm trying to write a simple python c-extension which includes some opencv code. Here is my c++ code:

I was working on implementing a solver for sparse undetermined systems in Python (discussed here) and I was trying to rebuild the nullspace function that uses the standard numpy svd function (numpy.linalg.svd) in the SciPy cookbook using the scipy.sparse version of svd (scipy.sparse.linalg.svds) but it outputs different left and right singular vectors for the examples I ran - including the matrices:

My specific issue is that I cannot seem to get my data to converted to floating points. I have data and simply want to fit a robust curve using my model equation:

y = a * e^(-b*z)

This cookbook is my reference: click

Below is my attempt. I am getting this:

TypeError: 'data type not understood'

which I believe is because my columns are strings, so I tried pd.Series.astype().