A simple matplotlib wrapper which enables single-call plot generation, without needing to include all the matplotlib setup calls every time. Several "rules" for generating visually-appealing scientific plots are also included (e.g. requiring axis labels).

The basic syntax accepts arguments

X_data - a 2D list of X values... e.g.[X_data0,X_data1]

Y_data - a 2D list of corresponding Y values... e.g.[Y_data0,Y_data1]

For the simple case:

X_data = np.linspace(0,10,20) (creating the x range )

Y_data = X_data**2 (the x range squared)

#! plot([X_data], [Y_data])

You can see we have left out some major component of a "good plot" this being "Title, xlabel, lable and the dataname" lets add these in with some simple keyword arguments

xlabel - a string of the title of the x axis

ylabel - a string of the title of the y axis

title - a string of the title

labels - a list of strings that correspond to title of each dataset

Lets try it out... dont forget your Units!😤😤

plot([X_data], [Y_data], title="Test Plot", xlabel="x values (arb. u.)", xlabel="x^2 values (arb. u.)", labels=["Best Data"])

🤯 Much Better!

markers can be single-character color and form codes ("k-" = "black line", "b." = "blue dots" and so on), or comma-separated ("blue,:" = "blue dotted", "#9a1ee0" = "purple, default markers")