Jody,
This has come up before and the consensus seemed to be that for the anomaly
data sets knowing where the zero is is very important and the default color
limits will probably get that wrong. So long as the user has to set the
limits, they can also select one of the diverging color maps.
I also advocate for users/domains which typically plot anomaly/diverging
data sets to write helper functions like
def im_diverging(ax, data, cmap='RbBu', *args, **kwargs):
limits = some_limit_function(data)
return ax.imshow(data, cmap=cmap, vmin=limits[0], vmax=limits[1],
*args, **kwargs)
Tom
On Fri, Jun 5, 2015 at 12:18 PM Jody Klymak <jkly...@uvic.ca> wrote:
> Hi,
>
> This is a great initiative, I love colormaps and am always disatisfied.
>
> However, I am concerned about these proposed defaults. As Ben says, there
> are two types of data sets: “intensity” or “density” data, and data sets
> with a natural zero (i.e. positive or negative anomaly or velocity). I’d
> be fine with any of the proposed colormaps for “intensity” data sets, but I
> would *never* use them for anomaly data sets; I couldn’t tell where the
> middle (zero) of any of those colormaps are intuitively.
>
> Jet and parula, for all their sins, are decent compromises for the naive
> user (or the user in a rush) because they do a good job of representing
> both types of data. Even in black and white jet does something reasonable,
> which is go to dark at extreme values and white-ish in the middle. Jet
> also has a nice central green hue between blue and yellow that signals zero
> (or at least it does to me after years of looking at it). I don’t see that
> jet really loses that under colorblindness; in fact I almost prefer the
> “Moderate Deuter” version of jet to the actual jet.
>
> Anyways, I guess I am advocating trying to find a colormap with a very
> obvious central hue to represent zero. Anomaly data sets are *very*
> common, so having a default colormap that doesn’t do something reasonable
> with them may be a turn off to new users.
>
> Cheers, Jody
>
>
>
> On 5 Jun 2015, at 8:36 AM, Benjamin Root <ben.r...@ou.edu> wrote:
>
> It is funny that you mention that you prefer the warmer colors over the
> cooler colors. There has been some back-n-forth about which is better. I
> personally have found myself adverse to using just cool or just warm
> colors, preferring a mix of cool and warm colors. Perhaps it is my
> background in meteorology and viewing temperature maps?
>
> Another place where a mix of cool and warm colors are useful is for
> severity indications such as radar maps. It is no accident that radar maps
> are colored greens and blues for weak precipitation, then yellow for
> heavier, and then reds for heaviest (possibly severe) precipitation -- it
> came from the old FAA color guides. While we all know that that colormap is
> fundamentally flawed, there was a rationale behind it.
>
> Hopefully I will have some time today to play around with the D option. I
> want to see if I can shift the curve a bit to include more yellows and
> orange so that it can have a mix of cool and warm colors.
>
> Ben Root
>
>
> On Fri, Jun 5, 2015 at 11:21 AM, Philipp A. <flying-sh...@web.de> wrote:
>
>> I vote for A and B. Only B if i get just one vote.
>>
>> C is too washed out and i like the warm colors more than the cold ones in
>> D.
>>
>> It’s funny that this comes up while I’m handling colormaps in my own work
>> at the moment.
>>
>> Neal Becker <ndbeck...@gmail.com> schrieb am Fr., 5. Juni 2015 um
>> 12:58 Uhr:
>>
>>> I vote for D, although I like matlab's new default even better
>>>
>>>
>>>
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> --
> Jody Klymak
> http://web.uvic.ca/~jklymak/
>
>
>
>
>
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