zdir | A directory listing program developed by Golang + Vue3

If you want to normalise Z, you need to divide it not by the sum, but by the maximum over all its values. Thus you ensure that the new maximum is 1:

You can use numpy.where.

In your example, remove the for loops and just use:

I looked through matlotlib's documentation, but I don't think there is a convenient way to add thickness to a closed polygon, so I came up with a method that's probably not the most elegant but seems to work.

Basically what you need to do is loop through the pairs of coordinates on each polygon, then draw the face that extends down in the z-direction by whatever thickness you want. I used Poly3DCollection instead of PolyCollection so that we can work with 3D coordinates, and wrote convenience functions to convert your 2D coordinates to 3D coordinates, as well as calculate the 3D coordinates of each face based on the coordinates of the polygons.

By specifying thickness of 1 for each of the polygons you used, you can get a "tiered wedding cake" as you asked.

This is a well-known problem with matplotlib 3D plotting: objects are drawn in a particular order, and those plotted last appear on "top" of the others, regardless of which should be in front in a "true" 3D plot.

See the FAQ here: https://matplotlib.org/mpl_toolkits/mplot3d/faq.html#my-3d-plot-doesn-t-look-right-at-certain-viewing-angles

My 3D plot doesn’t look right at certain viewing angles

This is probably the most commonly reported issue with mplot3d. The problem is that – from some viewing angles – a 3D object would appear in front of another object, even though it is physically behind it. This can result in plots that do not look “physically correct.”

Unfortunately, while some work is being done to reduce the occurrence of this artifact, it is currently an intractable problem, and can not be fully solved until matplotlib supports 3D graphics rendering at its core.

The problem occurs due to the reduction of 3D data down to 2D + z-order scalar. A single value represents the 3rd dimension for all parts of 3D objects in a collection. Therefore, when the bounding boxes of two collections intersect, it becomes possible for this artifact to occur. Furthermore, the intersection of two 3D objects (such as polygons or patches) can not be rendered properly in matplotlib’s 2D rendering engine.

This problem will likely not be solved until OpenGL support is added to all of the backends (patches are greatly welcomed). Until then, if you need complex 3D scenes, we recommend using MayaVi.

Have a look at the code below:

You created a polygon by connecting the first and last vertex of your curves. As these vertices have y = 2 everything gets connected with the horizontal line at that y-value. To close the polygon at zero, repeat the first and the last x-value (np.pad(eps, 1, mode='edge')) and pad the y-values with a zero at both ends (np.pad(N, 1)).

If desired, ax.set_yticklabels(...) can show the y-ticks as a formula with pi.

Further, matplotlib seems to have a serious problem about deciding the relative depth of each polygon, showing them all mixed up. A workaround could be to rotate everything 180 degrees, e.g. by setting ax.view_init(elev=22, azim=130).

Here is an example to create something similar to the desired plot. Some toy data are used to create 4 curves.

To fill the area below the curves, the approach from this tutorial is used. For the y tick labels, it seems ax.set_yticklabels(..., ha='left') together with ax.tick_params(axis='y', pad=0) get quite close to the desired result.

To make the polygons fully opaque, set the opaqueness alpha in PolyCollection(...) to a value closer to 1. Usually a small bit of transparency gives a better feeling of being a 3D plot. You can leave out the call to ax.plot(...) if the thicker "border" isn't needed.

I don't think it makes a difference that the data are stored in a pandas dataframe. In 2D, you could transform your data and use pandas plotting wrapper that tries to guess a lot of matlotlib parameters (including the label of a data series). However, this is a 3D plot, which is imho not supported by pandas plotting. So, back to the old zip approach:

Could you please elaborate more or add more details in the curve ?