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StackOverflow has a similar question to yours. Although, it is not exactly the same. The following is a pipeline for what you want: StreamTracer1 |— Transform1 |— Transform2 |— Transform3 Where each transformation correspond to a projection and a translation, namely: $$P_x = \begin{pmatrix} 0 &0 &0 &\Delta x\\ 0 &1 &0 &0\\ 0 &...


2

This is a typical use case for a paired t-test. The idea is to consider only the runtime difference $\Delta t$ for each problem and test for the null hypothesis $E(\Delta t)=0$. For a step-by-step explanation, see e.g. (the article refers to segmentation evaluation, but on an abstract level the problem is identiclal to yours): Mao, Kanungo: "Empirical ...


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It is generally better to encapsulate such stateful calls to library functions within a new function. Then monitoring_callback can become a local nested function, and callback_dict will be a regular variable with function scope, visible only to the nested function. def compute_fit_params(my_fit_params): callback_dict = {'counter': 0} def ...


2

You might be interested in trying other visualization tools such as ParaView, Mayavi or vtki. But, since the question is about Matplotlib, I would suggest that you use tricontour, tricontourf or tripcolor. They already accept the data in your format. Keep in mind that the enumeration of nodes starts from 0 in Python. The following snippet show your data ...


1

I'd recommend that you use the XYZ file format. Once you have a XYZ trajectory file, go into VMD, load the molecule by clicking File > New molecule, and display the particles by clicking Graphics > Representations > Drawing methods, then choose Points or VDW, for example. Here's a quick script to produce the desired output: def write_xyz(xyz: np.array, ...


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