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I want to create and save a number of sequential plots so I can then make an mp4 movie out of those plots. I want the color of the plot to depend on z (the value of the third axis):

The code I am using:

I have a list that contains outputs from multiple correlation tests

I am trying to learn a custom environment using the TFAgents package. I am following the Hands-on-ML book (Code in colab see cell 129). My aim is to use DQN agent on a custom-written grid world environment.

Grid-World environment:

I am trying to implement a simple iterative guessing scheme within the equation section in a model, however, I am getting the following error: No viable alternative near token: while in OpenModelica. Is there a simple explanation to why this while-loop does not work?

I'm using the robinhood.options.get_option_market_data API from the robin_stocks library to read information about stock options into my Python 3 program.

The documentation for this API indicates it returns a dictionary of key/value pairs

robin_stocks.robinhood.options.get_option_market_data_by_id(id,info=None)

Returns the option market data for a stock, including the greeks, open interest, change of profit, and adjusted mark price.Parameters•id(str) – The id of the stock.•info(Optional[str]) – Will filter the results to get a specific value. Returns a dictionary of key/value pairs for the stock. If info parameter is provided, the value of the key that matches info is extracted. Complete Docs - https://readthedocs.org/projects/robin-stocks/downloads/pdf/latest/

I use the following code to retrieve information about this particular option.

I am trying to solve a 1D PDE coupled with an ODE (solved as explicit Euler). I am getting a stack-overflow error if I use small dt. I tried looking at the length of the stack but cannot figure out anything useful from there. I am not very experienced with python (old fortran numerics coder).
The code:

I'm currently exploring the Lorenz system with Python and R and have noticed subtle differences in the ode packages. odeint from Python and ode both say they use lsoda to calculate their derivatives. However, using the lsoda command for both seems to give far different results. I have tried ode45 for the ode function in R to get something more similar to Python but am wondering why I can't get exactly the same results:

I have a python code (example from Cantera.org) that uses scipy.integrate.ode to solve a system of ODE. The code works fine and the results are reasnoable. However, I noticed something about the ode solver that does not make sense to me.

I have a put a print function inside (print("t inside ODE function", t)) the function the calculates the derivative vector (__call__(self, t, y)), and outside that function in the while loop (print("t outside ODE function", solver.t);). I expect that inside print has to be called when the solver does the time integration, and then the outside print is called. In other words, two "t outside ODE function" cannot appear right after another without "t inside ODE function" in between. However, this occurs in some of iterations in the while loop, which mean the solver does the integration without calculating the derivatives.

I am wondering how this is possible

This may be more of a math question than numpy, so I'm not sure, this may need to be posted on Cross Validated instead.

I am using numpy's function np.random.multivariate_normal() to produce randomized, correlated data. I want to have 5 correlated variables, each distributed standard normal. I believe my covariance matrix is correctly specified, with diagonals equal to 1.0 and the inner values being my desired correlations. The generated data consistently have the correct mean of 0, but the standard deviation is unpredictable.

Here is a reproducible code example: