I’m trying to recreate this plot:
He says it’s a contour plot, but I can’t understand how it works. I’ve recreated with a Contour Plot as well as a Carpet Contour plot and it still isn’t quite right.
My thoughts are either I have some of the settings wrong for the Carpet Contour or Contour plots, or it’s not the correct plot to be using (I’m using Plotly + Python).
Notice there aren’t “regions” (as a Contour/Carpet) plot will normally create, there are just these stacked lines that can cross etc. I’ve scoured the settings for both the Contour Plot and the Carpet Contour but nothing sticks out to me.
The data is from a “rolling” histogram esque data set: https://twitter.com/woonomic/status/1373552028945846272?s=20&t=K_SWDT7JZN0Ry3L_zQesKg
Would love any help or pointers, and I can provide more clarification as well if needed. Thank you!
Update: here’s what I’ve got / where I’ve got to basically. I know the coloring is off, but I’m more concerned with some of the bigger picture questions: “Is this even the right plot type?”, “If it is the right plot type, which settings am I missing / not utilizing correctly?”, etc.
Welcome @mattrowsboats !
What do the individual lines represent? What’s the underlying dataset look like? That’ll help determine how to map the data to the chart type.
It could be a contour plot without the shaded region (see “Contour Lines”): Contour plots in Python if the data was in the format of a matrix.
Or it could just be a set of lines if the data is a set of time series. Each line styled very thin and grey except for the main one.
Great thoughts, thank you @chriddyp ! I’m not sure exactly what the individual lines represent - I was assuming they were contour lines of the underlying data (I did link to it above), but now I’m wondering if they are some sort of weighting as well because the underlying data doesn’t match up.
Also, I think if it was a Contour Plot it would probably be a Carpet Contour given the “grid” sliding up over time.
Will do more investigating soon and hope to follow-up after. Thank you!