ipython-books.github.io | IPython books website , by Cyrille Rossant | Data Visualization library

I'm trying to plot the time evolution graph for Ornstein-Uhlenbeck Process, which is a stochastic process, and then find the probability distribution at each time steps. I'm able to plot the graph for 1000 realizations of the process. Each realization has a 1000 time step, with width of the time step as .001. I used a 1000 x 1000 array to store the data. Each rows hold value of each realizations. And column wise i-th columns correspond value at i-th time step for 1000 realizations.

Now I want bin results at each time steps together and then plot the probability distribution corresponding to each time step. I'm quite confused with doing it (I tried modifying code from IPython Cookbook, where they don't store each realizations in the memory).

The code that I made from the IPython Cookbook:

I've got a persistent error:

I am currently putting together a band-pass filter using the following code: https://ipython-books.github.io/116-applying-digital-filters-to-speech-sounds/

I made few edits to the above code, namely the file is no longer pulled from an url but instead from a local WAV file. Here are the associated edits

Trying to display UI, mainy realtime data graphics and statistics on a screen directly connected to rpi3.

Using some example code from Getting started with Vispy

been trying to display anything, but when running the example code i only get a warning message:

"WARNING: QXcbConnection: Could not connect to display"

As far as i understand i have installed all the dependencies numpy, matlibplot, pyqt5 and others. Supposedly enabled opwnGL on rpi following the video How to Enable OpenGL in Raspberry Pi Cant understand the main problem. Firstly is it possible to use vispy on raspberry and are there any suggestions as to how do i connect to the display from my code?

example code:

numpy.take can be applied in 2 dimensions with

What is np.compress doing internally that makes it faster than boolean indexing?

In this example, compress is ~20% faster, but the time savings varies on the size of a and the number of True values in the boolean array b, but on my machine compress is always faster.