[GOAL] Scale bar - Githubissues

Summary and Links

scale-bar (lead: @mattpap for Bokeh, @Hoxbro for HoloViews)

Resulting Issues and PRs:

Relevant Workflows

Waveform
Stacked-timeseries
Video Viewer

Resources

Requirements

General

[x] Compatibility: Ensure compatibility with different data types like lines, images, image stacks
[x] Responsiveness: Must adapt to various screen sizes and resolutions.
[x] Performance: Efficient rendering

Scale Bar Styling

[x] Style Control: color, transparency, line width, line style, orientation, font styling, text alignment, padding, etc. Ideally the options should be similar in scope to matplotlib-scalebar.

Scale Reference

[x] Size Control: Ability to specify a reference line size, e.g., "1 um" or "10 uV". And if we need separate scale bars for the x and y axes, they should be independently settable.
[ ] 2D Scale Bars: Support for separate scale bars for the x and y axes, with the ability to independently configure each axis, including size, unit, precision, and notation.
[ ] Interaction and Intersection of Scale Bars: Options to define how the x and y scale bars intersect and interact with each other. For example, users might want them to intersect at a right angle at a specific corner of the plot, or have them without intersection.
[x] Precision and Notation: The decimal precision and notation should be configurable. This allows users to control how many decimal places are shown and whether to use standard or scientific (e.g. 1 × 10^10) notation, depending on the specific data and audience. ~- Unit Conversion: Support for various units and conversion between them, such as converting between millimeters and inches.~

Interaction with Data

[ ] Zoom Integration: Scale bar should adjust dynamically based on the zoom level for both x and y axes, maintaining an accurate representation of scale. For example, zooming in on the x-axis might require the x-axis scale bar to adjust, while the y-axis scale bar remains the same.
[ ] (Low priority) Tooltip Information: Hovering over the scale bar could provide additional information

Position

[ ] Configurable Position: Scale bar position can be set and changed dynamically. For example, a user could move the scale bar to the top-right corner if it's obstructing important data.
[ ] Draggable: Option to allow users to drag and reposition the scale bar within the plot, offering flexibility in positioning.

Multiple Subplots and Coordinates

[ ] Multiple Scale Bars: Ability to place multiple scale bars on the same plot, with individual control over each.
[ ] Linked Control: Control over which data a particular scale bar is linked to when multiple subplots/subcoordinates are on a canvas. For example, in a plot with multiple types of timeseries, one scale bar may correspond only to the EEG data and another to the MEG data. See image:

Image:

[ ] Independent Styling: Each scale bar can have independent styling properties, allowing for different colors or fonts within the same plot.
[ ] Dynamic Visibility: Visibility can be made dynamic in response to interaction. For example, the scale bar might for one group of subplots may disappear when panning beyond their visible range. See video:

Video:

https://github.com/holoviz-topics/neuro/assets/6613202/d470ad93-8d81-4e69-8bfe-5f303f51000a

the options should be similar in scope to matplotlib-scalebar.

Agreed. I don't see any support in matplotlib-scalebar for the 2D scale bars needed here (separate scales for x and y axes), so we'd also need those options to be controllable per axis and some options about how the two scale bars interact/intersect.

Control over which data a particular scale bar is linked to when multiple subplots/subcoordinates are on a canvas.

In HoloViews, I think the user experience will be simplest and most powerful overall if the MEG and EEG traces are each an NdOverlay, laid out one above the other as a Layout. That way we could have e.g. an annotation that easily applies only to the MEG traces or only to the EEG traces, and similarly for any of the other convenient HoloViews features. I think items in a HoloViews Layout correspond to separate Bokeh figures (may be misusing terminology here across libraries), so my default preference is for these two groups of curves to be in separate plots, not somehow subsets of the same plot. But see below!

Visibility can be made dynamic in response to interaction. For example, the scale bar might for one group of subplots may disappear when panning beyond their visible range. See video:

Hmm. I'm not sure if this requirement is compatible with my conception above of each group being a separate plot, at least not if we want the behavior in that video. Specifically, panning normally happens either per plot, or (for linked axes), in a concerted manner linking plots to cover the same space. In the video, panning reveals or hides MEG and EEG traces indiscriminately, treating them as the same apart from their different colors and scales. If this mode of interaction is important, then that's an argument for having some sort of hierarchical NdLayout in a single plot, with multiple groups with different options but all on a single pair of axes. On the other hand, this behavior might not even be desirable; it may just be the way it was easy to implement in the original viewer. So we should explicitly decide if we want panning behavior like this.

Below I'm trying to be careful about the word 'subplot', so I distinguish subplots being either on same or separate canvas.

I don't see any support in matplotlib-scalebar for the 2D scale bars needed here (separate scales for x and y axes), so we'd also need those options to be controllable per axis and some options about how the two scale bars interact/intersect.

Good point @jbednar ! I've added relevant parts to the following:

Updates to requirements:

### Scale Reference - [ ] **Size Control**: Ability to specify a reference line size, e.g., "1 um" or "10 uV". And if we need separate scale bars for the x and y axes, they should be independently settable. - [ ] **2D Scale Bars**: Support for separate scale bars for the x and y axes, with the ability to independently configure each axis, including size, unit, precision, and notation. - [ ] **Interaction and Intersection of Scale Bars**: Options to define how the x and y scale bars intersect and interact with each other. For example, users might want them to intersect at a right angle at a specific corner of the plot, or have them without intersection. ### Interaction with Data - [ ] **Zoom Integration**: Scale bar should adjust dynamically based on the zoom level for both x and y axes, maintaining an accurate representation of scale. For example, zooming in on the x-axis might require the x-axis scale bar to adjust, while the y-axis scale bar remains the same.

In HoloViews, I think the user experience will be simplest and most powerful overall if the MEG and EEG traces are each an NdOverlay, laid out one above the other as a Layout. That way we could have e.g. an annotation that easily applies only to the MEG traces or only to the EEG traces, and similarly for any of the other convenient HoloViews features. I think items in a HoloViews Layout correspond to separate Bokeh figures (may be misusing terminology here across libraries), so my default preference is for these two groups of curves to be in separate plots, not somehow subsets of the same plot. But see below!

Yea this is a tricky one. Yesterday I was leaning toward imposing separate plots per data type, but I was just thinking that this will complicate the minimap/rangetool creation and control. We probably don't want one minimap per subplot (separate canvases), right? We'd have to bidirectionally link the x_range of each range tool to each other (maybe not crazy? idk). @Hoxbro do you have any intuition of how feasible it would be for a bidirectional link across two rangetool x_ranges that each have a different target plot?

If, as MNE does (see video below), you have a single minimap for all data types, but you want to have separate subplots (separate canvases) for the different data types.. then I don't see how you could control the y_range of each plot. (A crazy/awkward idea is to allow multiple rangetool overlays on the same minimap with linked x_range but independent y_range while not letting them float into each others vertical minimap area). This is all why I am now leaning towards having all the data types on the same plot but hopefully using some reasonable API approach allowing for differently scaled groups. To be fair, it's not clear to me when something like that will be completed. I'll try to chat with @philippjfr early next week about it.

Video of minimap-panning across data types

https://github.com/holoviz-topics/neuro/assets/6613202/3f73a143-4eee-47df-b45e-899472920f65

Hmm. I'm not sure if this requirement is compatible with my conception above of each group being a separate plot, at least not if we want the behavior in that video. Specifically, panning normally happens either per plot, or (for linked axes), in a concerted manner linking plots to cover the same space. In the video, panning reveals or hides MEG and EEG traces indiscriminately, treating them as the same apart from their different colors and scales. If this mode of interaction is important, then that's an argument for having some sort of hierarchical NdLayout in a single plot, with multiple groups with different options but all on a single pair of axes. On the other hand, this behavior might not even be desirable; it may just be the way it was easy to implement in the original viewer. So we should explicitly decide if we want panning behavior like this.

I guess we would not need this dynamic visibility behavior if we had separate subplots (separate canvases) because some of each data type would always be present; the reason to have it disappear is to not have data mismatched scale bar on display. Although having subplots (separate canvases) would solve this, I don't know if it's the right solution given my concerns above.

Sidenote, I think the use/benefits of an NdOverlay container go out the window as soon as we replace our current suboptimal approach (of copying the data and adding an offset value to accommodate the vertical stacking), and instead adopt a better API approach utilizing some form or Bokeh's subplots on the same canvas. Hopefully, the solution will be performant AND and a good API that doesn't involve making a copy of the data in order to plot it.

We probably don't want one minimap per subplot (separate canvases), right?

Good point; definitely want one minimap. But I'm not sure I follow your concerns about the x_range above; seems like a minimap's selected range can be respected by multiple plots or by a single plot about equally easily. In the other direction, updating the xrange on one of the plots should then update the xrange of the minimap and of the other plot, which again seems doable. But I could easily be missing something.

Also note that the minimap won't always be a heatmap as in these examples; we also want minimaps even when there are just one or a few traces, in which case ideally the minimap will show the traces like in a sparkline, not as a heatmap.

Sidenote, I think the use/benefits of an NdOverlay container go out the window as soon as we replace our current suboptimal approach (of copying the data and adding an offset value to accommodate the vertical stacking), and instead adopt a better API approach utilizing some form or Bokeh's subplots on the same canvas.

I guess I'm getting confused between an NdLayout and an NdOverlay here. It's definitely Nd, but whether it's an overlay or layout is hard to say, because it's both; the traces do overlay each other, but they are offset as in a layout. So I actually don't know what the right abstraction is here.

Continuing the Minimap related converstion here

holoviz-topics / neuro

[GOAL] Scale bar #34