add symmetrical log scale

[liuyigh]

Dear fellow developers and plot.ly users,

I would like to request a new feature: "symmetrical log" scale. Matplotlib has it:

http://matplotlib.org/examples/pylab_examples/symlog_demo.html

Also see: What is the difference between 'log' and 'symlog'? link

It would be great if plot.ly can adopt this scale. Thanks!

-- Y

[mdtusz]

This isn't a priority right now, but shouldn't be too difficult to implement down the road. We're always open to pull requests as well!

[veged]

maybe any workarounds here without changes in the library itself? for example as in Highcharts http://jsfiddle.net/gh/get/library/pure/highcharts/highcharts/tree/samples/highcharts/yaxis/type-log-negative/

[veged]

@etpinard is there any reason to keep current log scale in state of unsupported negative values? maybe it will be enough to change https://github.com/plotly/plotly.js/blob/master/src/lib/to_log_range.js to properly support negative values?

I can make PR if you show me all places to change exept those I found by myself:

src/lib/to_log_range.js
17:module.exports = function toLogRange(val, range) {

src/lib/index.js
25:lib.toLogRange = require('./to_log_range');

src/components/annotations/convert_coords.js
13:var toLogRange = require('../../lib/to_log_range');
45: if(toLog) newVal = toLogRange(currentVal, ax.range);

src/components/images/convert_coords.js
13:var toLogRange = require('../../lib/to_log_range');
57: newPos = toLogRange(currentPos, ax.range);

[alexcjohnson]

symlog is a little funny, as it's not a smooth mapping. A variant I've seen before though that is smooth is arcsinh (inverse hyperbolic sine) - this one is particularly nice as you can define both the zero and the linear-ish range, using y' = arcsinh( (y - y0) / yL ). This is good for situations like a noisy signal with a nonzero base level, where you want to see both small and large peaks popping out with either sign.

[immotus]

@alexcjohnson, this is exactly the approach we are taking in our signal processing app where log scale would be ideal but where negative values are commonly present.

[pewinski]

I'm confused now after reading the thread, I'm happy it worked for someone but arcsinh is a little complicated concept. Could somebody explain, can it be used as replacement of symlog somehow? 😳

[alexcjohnson]

Just for a little more context here - @melmoth-the-wanderer apologies for missing your question last fall - symlog is a piecewise combination of three mappings: a regular log mapping at large positive values, a negated log mapping at large negative values, and a linear mapping connecting the two at small positive/negative values. This allows us to show very large positive and negative values while maintaining visibility into small changes in the mid-range. Symlog scales generally have a configurable linearity threshold, and often the ability to offset the zero to account for a baseline value in the data.

The problem with this though is it's not a smooth mapping. Matplotlib in fact doesn't seem to worry about anything more than matching values at the breakpoints, leading to kinks like this:

It should be possible to do better than that, and match the first derivative at the breakpoints by stretching the log mappings appropriately, but it would still have higher-order discontinuities.

That's where y=arcsinh(x) comes in. (ref https://reference.wolfram.com/language/ref/ArcSinh.html)

It's a single smooth function that approximates a log y=ln(x) (up to a scaling constant) at large positive x, a negated log y = -ln(-x) at large negative x, and a linear y=x around zero. We can incorporate a linearity threshold and offset by generalizing to y=arcsinh((x-x0)/xL)

@PavelGolodoniuc applied this transformation to his data and simply plotted that as y. That gives the desired curve shapes - detail at small values while keeping both positive and negative large values on scale. The challenge with a plot like this is connecting back to the pre-transformed data. Assuming the mapping he used was a simple y=arcsinh(x), the data span from about -200 to +11000 but the ticks at +/-2 are ~+/-3.6. It's hard to develop an intuition about what these values mean. It's possible to put the original data into tick labels (using tickvals and ticktext) but it's difficult to choose good values even for one specific plot (here you might do something like 0->0, +/-1.44->2, +/-3.00->10, +/-5.29->100, 7.60->1K, 9.90->10K), more difficult to automatically find good values in the general case. If we were to add this feature to plotly.js that's where the vast majority of the work would be.

[kwenlyou]

is there any plan to deliver this feature recently?