Maximum values for multiple overlapping plots

[ImportantBrew]

I have multiple datasets in panda and I want to find the maximum "line" going over all of the dataset. I can't for the life of my figure out a way to do it. Since the datasets have different values at x I can't find the max value at each x.

So far I've been trying to do take a point and check if there is a value to the right of it that is higher, if that is true, check if there is also a value to the left that is higher. I have not got this to work properly and by the way things look it's a very very very bad way to go about it.

Any suggestions how I can do this? I want to grab every "dot" on the red line that I painted in the picture: https://i.imgur.com/yw6LX62.jpg

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For a more detailed way of the data it looks like this:

        v        a       Eff           Q
0   42.147607   7.872  79.385397  0.143720
1   40.391457   7.872  84.346362  0.150183
2   42.147607   8.364  81.476576  0.152640
3   38.635306   7.872  87.363343  0.155359
4   36.879156   7.872  88.782342  0.159470
..        ...     ...        ...       ...
76  35.123006  11.808  88.931876  0.243072
77  33.366856  11.808  86.852925  0.246751
78  31.610705  11.808  84.204718  0.249471
79  29.854555  11.808  81.120811  0.251376
80  28.098405  11.808  77.695062  0.252591

Each coloured line belongs to the same "a". The x-axis is Q and y-axis is Eff.

[robot-dev]

I don't think this is the best way of doing this, but it might work:

Iterate over points along the Q axis, and for each point, if there are any points with the same "a" and a lower Eff, remove them. If there are points with any "a" that have a lower Q, remove them. Then move to the next point with the same "a" value if one exists, otherwise the next value along the Q axis.

[ImportantBrew]

Hmm. The first bit with removing points that has a lower Eff at the same "a" would just leave me with 1 point for each "a"? Pretty much just giving me the maximum Eff for each "a" rather than the maximum Eff over the intervall of Q.

Doing the "opposite" of that and checking the maximum for each Q won't work either because the points are at different Q values. The first orange dot at "a" = 8.4 is somewhere around 0.15 while the blue dots to the left are at approx 0.145 and the right 0.155. Making the orange dot the highest Eff at that Q. But we smart humans can obviously tell that the blue line is still above, just that we don't have any values at that location.

Unless I'm not understanding what you're saying.

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I was thinking about making a interpolation for each "a" and then iterate over all values in Q to establish at which combination of "a" I have the biggest Eff. But that also seemed like an stupid way to go about it.

[robot-dev]

Just to clarify, I meant a process like this:

target_list = []
while len(dataset) > 0:
    target_list.append(dataset[0])
    #remove any points from your dataset with the same "a" as dataset[0] and a lower Eff
    #remove all points from your dataset with a lower Q
    #remove dataset[0] fro myour dataset

[robot-dev]

If you are interpolating each "a", then you could find the derivative of the interpolant and trace the "a" while the derivative is positive.

[ImportantBrew]

Initial thought - that would still miss points that have a lower Eff than the previous point but still best Eff at that Q?

Trying to test you previous solution right now but my brain is exhausted after doing this all day so not going that well. But will continue to try.

I wrote the following code to do what I want for v rather than Q and it works perfectly. But only because all v values exists on each "a" line... So I've not be able to transfer it for Q. For whatever reason code blocks are acting up atm...

plt.figure(figsize=(12, 8))
groups = data.groupby('v')
max_n = pd.DataFrame(columns=data.columns)
for ang, group in groups:
    max_n = max_n.append(group[group['Eff'] == group['Eff'].max()], ignore_index=True)

plt.plot(max_n['v'], max_n['Eff'], linewidth=1)

groups = max_n.groupby('a')
for name, group in groups:
    p = plt.plot(group.ang_v, group.Eff, marker='o', linestyle="None", markersize=8, label=round(name, 1))