Backupy: Backup files from local machine to NAS and cloud using backupy and python

The concept is:

Backup is important. There are many backup tools in the world. There are also a backup manager tool written in python. This page presents an example of runnning backupy.

TarManager: combine many pickle files into some tar files using Python

The concept is:

This page shows how to combine many pickle files into some tar, tar.gz, or tar.xz using Python. In some simulations, we generate thousands and millions of output files for varying some parameters and some values for each parameters. The problem of such huge number of small files is the difficulty to handle on making backup to NAS, external HDD, and cloud storage services.

Extract data from tar.gz and expand on RAM using tarfile library, then plot using matplotlib

The concept of this article is:

This page shows an example of how to extract data from tar.gz file and expand not on HDD/SSD but on RAM, then plot some data using matplotlib.

Speed up generating figures by running external python script parallelly using Python and matplotlib.pyplot

The result is:

This page shows my suggestion to process data and generate figure parallelly by running some external python script.

Compare plt.plot and plt.scatter in speed in Python and Matplotlib.pyplot

This page compares the speed of "plt.plot" and "plt.scatter" to generate same following figure:

It seems that "plt.plot" is faster than "plt.scatter" to plot simple and large point scatter plot.

Speed up plotting magnified waveforms using Python & Matplotlib.pyplot

We can reduce the calculation time as the orange line in the following figure:

This page shows how to speed up plotting magnified waveforms in matplotlib.
By limiting the data before plotting, we can improve the performance of the matplotlib.
Especially, this method is suitable when the data range for plot is very short compared with the whole data range.