Following my thoughts on #5, I'm more and more convinced that using pandas DataFrame could bring many benefits. This would make the integration of mpf as a true notebook more straightforward, giving to the user the data collected by experiments as DataFrames for them to filter, select or aggregate results in any desired way.
Also, I've been briefly looking at libraries that could be used to produce graphes based on DataFrames and found these two. The amount of configuration seems reasonable. If we were to design a smart guessing algo to produce plots, I would make it drive one of these, instead of reinventing everything atop matplotlib.
Following my thoughts on #5, I'm more and more convinced that using pandas DataFrame could bring many benefits. This would make the integration of mpf as a true notebook more straightforward, giving to the user the data collected by experiments as DataFrames for them to filter, select or aggregate results in any desired way.
Also, I've been briefly looking at libraries that could be used to produce graphes based on DataFrames and found these two. The amount of configuration seems reasonable. If we were to design a smart guessing algo to produce plots, I would make it drive one of these, instead of reinventing everything atop matplotlib.
https://nbviewer.org/gist/santosjorge/f3b07b2be8094deea8c6 https://plotly.com/python/