Unexpected behaviour in `stack_plot()`

Description of the issue

I’ve been trying to use the stack_plot() function for some follow-up SR15 assessment, and noticed two major issues.

1. Timeseries data starting with zero are ignored completely
2. Multiple crossings of the zero baseline end up shown wrong

Illustration

import pandas as pd
import pyam
df = pyam.IamDataFrame(pd.DataFrame([
    ['a', 1, 2, 3, 4],
    ['b', 0, 1, 2, 3],
    ['c', -1, 1, -1, 1],
    ['d', 1, 1, 1, -1]
    ],
    columns=['variable', 2010, 2020, 2030, 2040],
), model='model_a', scenario='scen_a', region='World', unit='some_unit')
df.stack_plot()

More insights

In the source of stack_plot(), there is this docstring…

    # Sort lines so that negative timeseries are on the right, positive
    # timeseries are on the left and timeseries which go from positive to
    # negative are ordered such that the timeseries which goes negative first
    # is on the right (case of timeseries which go from negative to positive
    # is an edge case we haven't thought about as it's unlikely to apply to
    # us).

[znicholls]

I was thinking that splitting all timeseries data into a positive and negative component might do the trick… But making it pretty afterwards will be a pain.

Yep I think that’s probably best. Keeping track of colours and joining dots as they cross back and forth will be hard. If you do it with loops initially I think it should be easier to implement and then it can be refactored to vector operations afterwards.

[danielhuppmann]

I was thinking that splitting all timeseries data into a positive and negative component might do the trick… But making it pretty afterwards will be a pain.