matplotlib-users Mailing List for matplotlib

Dang it, Joe,
How do you do everything l try to do like 1000x better?
Guess I'll be closing this:
https://github.com/matplotlib/matplotlib/pull/3858
-paul
On Fri, Jun 5, 2015 at 2:57 PM, Joe Kington <jof...@gm...> wrote:
> Not to plug one of my own answers to much, but here's a basic example.
> http://stackoverflow.com/questions/20144529/shifted-colorbar-matplotlib
>
> I've been meeting to submit a PR with a more full featured version for a
> few years now, but haven't.
> On Jun 5, 2015 4:45 PM, "Sourish Basu" <sou...@gm...> wrote:
>
>> On 06/05/2015 01:20 PM, Eric Firing wrote:
>>
>> Reminder: in matplotlib, color mapping is done with the combination of a
>> colormap and a norm. This allows one to design a norm to handle the
>> mapping, including any nonlinearity or difference between the handling
>> of positive and negative values. This is more general than customizing
>> a colormap; once you have a norm to suit your purpose, you can use it
>> with any colormap.
>>
>> Maybe this is actually what you are already doing, but I wanted to point
>> it out here in case some readers are not familiar with this
>> colormap+norm strategy.
>>
>>
>> Actually, I didn't use norms because I never quite figured out how to use
>> them or how to make my own. If there's a way to create a norm with a custom
>> mid-point, I'd love to know/use that.
>>
>> -Sourish
>>
>>
>> Eric
>>
>> ------------------------------------------------------------------------------
>> _______________________________________________
>> Matplotlib-users mailing lis...@li...https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>>
>> --
>> *Q:* What if you strapped C4 to a boomerang? Could this be an effective
>> weapon, or would it be as stupid as it sounds?
>> *A:* Aerodynamics aside, I’m curious what tactical advantage you’re
>> expecting to gain by having the high explosive fly back at you if it misses
>> the target.
>>
>>
>> ------------------------------------------------------------------------------
>>
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
> ------------------------------------------------------------------------------
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

Not to plug one of my own answers to much, but here's a basic example.
http://stackoverflow.com/questions/20144529/shifted-colorbar-matplotlib
I've been meeting to submit a PR with a more full featured version for a
few years now, but haven't.
On Jun 5, 2015 4:45 PM, "Sourish Basu" <sou...@gm...> wrote:
> On 06/05/2015 01:20 PM, Eric Firing wrote:
>
>
> Reminder: in matplotlib, color mapping is done with the combination of a
> colormap and a norm. This allows one to design a norm to handle the
> mapping, including any nonlinearity or difference between the handling
> of positive and negative values. This is more general than customizing
> a colormap; once you have a norm to suit your purpose, you can use it
> with any colormap.
>
> Maybe this is actually what you are already doing, but I wanted to point
> it out here in case some readers are not familiar with this
> colormap+norm strategy.
>
>
> Actually, I didn't use norms because I never quite figured out how to use
> them or how to make my own. If there's a way to create a norm with a custom
> mid-point, I'd love to know/use that.
>
> -Sourish
>
>
>
> Eric
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing lis...@li...https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
>
> --
> *Q:* What if you strapped C4 to a boomerang? Could this be an effective
> weapon, or would it be as stupid as it sounds?
> *A:* Aerodynamics aside, I’m curious what tactical advantage you’re
> expecting to gain by having the high explosive fly back at you if it misses
> the target.
>
>
> ------------------------------------------------------------------------------
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

<html>
 <head>
 <meta content="text/html; charset=utf-8" http-equiv="Content-Type">
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 <div class="moz-cite-prefix">On 06/05/2015 01:20 PM, Eric Firing
 wrote:
 </div>
 <blockquote cite="mid:557...@ha..." type="cite">
 <pre wrap="">
Reminder: in matplotlib, color mapping is done with the combination of a 
colormap and a norm. This allows one to design a norm to handle the 
mapping, including any nonlinearity or difference between the handling 
of positive and negative values. This is more general than customizing 
a colormap; once you have a norm to suit your purpose, you can use it 
with any colormap.
Maybe this is actually what you are already doing, but I wanted to point 
it out here in case some readers are not familiar with this 
colormap+norm strategy.</pre>
 </blockquote>
 <br>
 Actually, I didn't use norms because I never quite figured out how
 to use them or how to make my own. If there's a way to create a norm
 with a custom mid-point, I'd love to know/use that.<br>
 <br>
 -Sourish<br>
 <br>
 <blockquote cite="mid:557...@ha..." type="cite">
 <pre wrap="">
Eric
------------------------------------------------------------------------------
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Matplotlib-users mailing list
<a class="moz-txt-link-abbreviated" href="mailto:Mat...@li...">Mat...@li...</a>
<a class="moz-txt-link-freetext" href="https://lists.sourceforge.net/lists/listinfo/matplotlib-users">https://lists.sourceforge.net/lists/listinfo/matplotlib-users</a>
</pre>
 </blockquote>
 <br>
 <br>
 <div class="moz-signature">-- <br>
 <b>Q:</b> What if you strapped C4 to a boomerang? Could this be an
 effective weapon, or would it be as stupid as it sounds?<br>
 <b>A:</b> Aerodynamics aside, I’m curious what tactical advantage
 you’re expecting to gain by having the high explosive fly back at
 you if it misses the target.<br>
 </div>
 </body>
</html>

<html>
 <head>
 <meta content="text/html; charset=utf-8" http-equiv="Content-Type">
 </head>
 <body bgcolor="#FFFFFF" text="#000000">
 <div class="moz-cite-prefix">On 06/05/2015 12:44 PM, Jody Klymak
 wrote:<br>
 </div>
 <blockquote cite="mid:851...@uv..."
 type="cite">
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
 <br class="">
 <div>
 <blockquote type="cite" class="">
 <div class="">On 5 Jun 2015, at 11:39 AM, Sourish Basu &lt;<a
 moz-do-not-send="true"
 href="mailto:sou...@gm..." class="">sou...@gm...</a>&gt;
 wrote:</div>
 <br class="Apple-interchange-newline">
 <div class=""><span style="font-family: LucidaSans-Typewriter;
 font-size: 12px; font-style: normal; font-variant: normal;
 font-weight: normal; letter-spacing: normal; line-height:
 normal; orphans: auto; text-align: start; text-indent:
 0px; text-transform: none; white-space: normal; widows:
 auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;
 background-color: rgb(255, 255, 255); float: none;
 display: inline !important;" class="">This problem is
 reasonably common for me, BTW. I can have a carbon
 monoxide field with an average/background of 60 ppb, but
 variations from 30 to 550 ppb. So I need a color scale
 which (a) is white at 60, and (b) shows small variations
 below 60 and large variations above 60 with equal
 "clarity".</span></div>
 </blockquote>
 <br class="">
 </div>
 <div>If you need to see small changes at low values and they are
 equally important to large changes at high values, then taking
 the logarithm is often useful (or scaling your colorbar
 logarithmically). <br>
 </div>
 </blockquote>
 <br>
 Which would still have the problem that similar color
 saturations/values at the two ends of the colorbar would represent
 different (linear) distances away from the median/"zero" value.<br>
 <br>
 But I see your point, in my specific example the confusion is made
 worse because the two ends have the same sat/val, just different
 hues. Lately I've started 'sandwiching' different types of colorbars
 (see attached) to get around that issue.<br>
 <br>
 Cheers,<br>
 Sourish<br>
 <br>
 <blockquote cite="mid:851...@uv..."
 type="cite">
 <div><br class="">
 </div>
 <div>Cheers,  Jody</div>
 <div><br class="">
 </div>
 <br class="">
 <div apple-content-edited="true" class="">
 <span class="Apple-style-span" style="border-collapse: separate;
 border-spacing: 0px; color: rgb(0, 0, 0); font-family: 'Lucida
 Sans Typewriter'; font-size: 12px; font-style: normal;
 font-variant: normal; font-weight: normal; letter-spacing:
 normal; line-height: normal; text-indent: 0px; text-transform:
 none; orphans: 2; white-space: normal; widows: 2;
 word-spacing: 0px;">
 <div class="">--</div>
 <div class="">Jody Klymak  </div>
 <div class=""><a moz-do-not-send="true"
 href="http://web.uvic.ca/%7Ejklymak/" class="">http://web.uvic.ca/~jklymak/</a></div>
 <div class=""><br class="khtml-block-placeholder">
 </div>
 <div class=""><br class="khtml-block-placeholder">
 </div>
 <br class="Apple-interchange-newline">
 </span><br class="Apple-interchange-newline">
 </div>
 <br class="">
 </blockquote>
 <br>
 <br>
 <div class="moz-signature">-- <br>
 <b>Q:</b> What if you strapped C4 to a boomerang? Could this be an
 effective weapon, or would it be as stupid as it sounds?<br>
 <b>A:</b> Aerodynamics aside, I’m curious what tactical advantage
 you’re expecting to gain by having the high explosive fly back at
 you if it misses the target.<br>
 </div>
 </body>
</html>

On 2015年06月05日 11:13 AM, Jody Klymak wrote:
> Though I was hazily aware of norms, I’d not really seen that before.
> I particularly like the example
> athttp://matplotlib.org/examples/pylab_examples/pcolor_log.html
>
> This seems useful enough that a section under "User Guide:Advanced
> Guide" would be really helpful. An example that displays all the
> canned norms, and maybe how to make a custom norm. I only found the
> pcolor_log example by searching for colors.lognorm, which I only knew
> about from your comment above. There a few hits on stackexchange,
> but those are for specific instances and hard to find by random.
>
> I could help do this, but it’d take a while to actually learn how to
> use the norms.
Jody,
Contributions to the documentation would be very welcome.
Eric

Hi Eric,
> On 5 Jun 2015, at 12:20 PM, Eric Firing <ef...@ha...> wrote:
> 
> Reminder: in matplotlib, color mapping is done with the combination of a 
> colormap and a norm. This allows one to design a norm to handle the 
> mapping, including any nonlinearity or difference between the handling 
> of positive and negative values. This is more general than customizing 
> a colormap; once you have a norm to suit your purpose, you can use it 
> with any colormap.
Though I was hazily aware of norms, I’d not really seen that before. I particularly like the example at http://matplotlib.org/examples/pylab_examples/pcolor_log.html
This seems useful enough that a section under "User Guide:Advanced Guide" would be really helpful. An example that displays all the canned norms, and maybe how to make a custom norm. I only found the pcolor_log example by searching for colors.lognorm, which I only knew about from your comment above. There a few hits on stackexchange, but those are for specific instances and hard to find by random.
I could help do this, but it’d take a while to actually learn how to use the norms.
Thanks, Jody
> 
> Maybe this is actually what you are already doing, but I wanted to point 
> it out here in case some readers are not familiar with this 
> colormap+norm strategy.
> 
> Eric
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
--
Jody Klymak 
http://web.uvic.ca/~jklymak/

Furthermore, I think there is some work being done to add functionality to
the Norm to allow specifying a middle value along with a vmin and a vmax.
Ben Root
On Fri, Jun 5, 2015 at 3:20 PM, Eric Firing <ef...@ha...> wrote:
> On 2015年06月05日 8:17 AM, Sourish Basu wrote:
> > Very often the "zero" of an anomaly is not at the center of the extrema,
> > and requires creating a custom diverging colormap anyway (see attached
> > example).
>
> Reminder: in matplotlib, color mapping is done with the combination of a
> colormap and a norm. This allows one to design a norm to handle the
> mapping, including any nonlinearity or difference between the handling
> of positive and negative values. This is more general than customizing
> a colormap; once you have a norm to suit your purpose, you can use it
> with any colormap.
>
> Maybe this is actually what you are already doing, but I wanted to point
> it out here in case some readers are not familiar with this
> colormap+norm strategy.
>
> Eric
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

On 2015年06月05日 8:17 AM, Sourish Basu wrote:
> Very often the "zero" of an anomaly is not at the center of the extrema,
> and requires creating a custom diverging colormap anyway (see attached
> example).
Reminder: in matplotlib, color mapping is done with the combination of a 
colormap and a norm. This allows one to design a norm to handle the 
mapping, including any nonlinearity or difference between the handling 
of positive and negative values. This is more general than customizing 
a colormap; once you have a norm to suit your purpose, you can use it 
with any colormap.
Maybe this is actually what you are already doing, but I wanted to point 
it out here in case some readers are not familiar with this 
colormap+norm strategy.
Eric

> On 5 Jun 2015, at 11:39 AM, Sourish Basu <sou...@gm...> wrote:
> 
> This problem is reasonably common for me, BTW. I can have a carbon monoxide field with an average/background of 60 ppb, but variations from 30 to 550 ppb. So I need a color scale which (a) is white at 60, and (b) shows small variations below 60 and large variations above 60 with equal "clarity".
If you need to see small changes at low values and they are equally important to large changes at high values, then taking the logarithm is often useful (or scaling your colorbar logarithmically). 
Cheers, Jody
--
Jody Klymak 
http://web.uvic.ca/~jklymak/

<html>
 <head>
 <meta content="text/html; charset=utf-8" http-equiv="Content-Type">
 </head>
 <body bgcolor="#FFFFFF" text="#000000">
 <div class="moz-cite-prefix">On 06/05/2015 12:22 PM, Jody Klymak
 wrote:<br>
 </div>
 <blockquote cite="mid:C4E...@uv..."
 type="cite">
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
 Hi,
 <div class=""><br class="">
 <div>
 <blockquote type="cite" class="">
 <div class="">On 5 Jun 2015, at 11:17 AM, Sourish Basu &lt;<a
 moz-do-not-send="true"
 href="mailto:sou...@gm..." class="">sou...@gm...</a>&gt;
 wrote:</div>
 <br class="Apple-interchange-newline">
 <div class="">
 <meta content="text/html; charset=utf-8"
 http-equiv="Content-Type" class="">
 <div bgcolor="#FFFFFF" text="#000000" class="">
 <div class="moz-cite-prefix">On 06/05/2015 10:17 AM,
 Jody Klymak wrote:<br class="">
 </div>
 <blockquote
 cite="mid:EA9...@uv..."
 type="cite" class="">
 <meta http-equiv="Content-Type" content="text/html;
 charset=utf-8" class="">
 <div class="">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. <br class="">
 </div>
 </blockquote>
 <br class="">
 I agree that jet does a bad job with anomaly data, but I
 disagree that having a diverging colormap as default (or
 even a "diverging" argument to anything that takes a
 cmap value) would solve that. Very often the "zero" of
 an anomaly is not at the center of the extrema, and
 requires creating a custom diverging colormap anyway
 (see attached example).<br class="">
 </div>
 </div>
 </blockquote>
 <div><br class="">
 </div>
 <div>Well, I *strongly* disagree with that attached example!
 It makes it look like -0.5 is equivalent to +1.5! Unless
 there is a really strong reason to do that, I think that is
 poor practice as it makes your negative anomalies look far
 stronger than your positive, and that is not the case in the
 underlying numbers.</div>
 </div>
 </div>
 </blockquote>
 <br>
 Yes, that is indeed a problem. However, if I want to plot a field
 which is mostly zeros, then I prefer to use a colormap which is
 white at zero. I could just extend the smaller absolute value (-0.5)
 to the same absolute value as the larger one, and plot -1.5 to 1.5.
 But in that case, I'd only be using a third of the possible
 dynamical range of the negative (blue) part, which IMO is a waste.
 If I have a field which has a zero median (which I want mapped to
 white), goes from -0.5 to +1.5, and I actually want to show the
 difference between (say) -0.3 and -0.4, what other option do I have?<br>
 <br>
 This problem is reasonably common for me, BTW. I can have a carbon
 monoxide field with an average/background of 60 ppb, but variations
 from 30 to 550 ppb. So I need a color scale which (a) is white at
 60, and (b) shows small variations below 60 and large variations
 above 60 with equal "clarity".<br>
 <br>
 Cheers,<br>
 Sourish<br>
 <br>
 <blockquote cite="mid:C4E...@uv..."
 type="cite">
 <div class="">
 <div>
 <div><br class="">
 </div>
 <div>Cheers,  Jody</div>
 <div><br class="">
 </div>
 <div><br class="">
 </div>
 <br class="">
 <blockquote type="cite" class="">
 <div class="">
 <div bgcolor="#FFFFFF" text="#000000" class=""> OT, I
 recently found a nice alternative to jet here: <a
 moz-do-not-send="true" class="moz-txt-link-freetext"
href="https://mycarta.wordpress.com/2014/11/13/new-rainbow-colormap-sawthoot-shaped-lightness-profile/">https://mycarta.wordpress.com/2014/11/13/new-rainbow-colormap-sawthoot-shaped-lightness-profile/</a><br
 class="">
 It takes care of my biggest crib with jet, which is that
 there is not enough perceptual variation in the middle
 of the range.<br class="">
 <br class="">
 Cheers,<br class="">
 Sourish Basu<br class="">
 </div>
 <span
 id="cid:D73...@uv...">&lt;ff_adjustment_winter.png&gt;</span>------------------------------------------------------------------------------<br
 class="">
 _______________________________________________<br
 class="">
 Matplotlib-users mailing list<br class="">
 <a moz-do-not-send="true"
 href="mailto:Mat...@li..."
 class="">Mat...@li...</a><br
 class="">
<a class="moz-txt-link-freetext" href="https://lists.sourceforge.net/lists/listinfo/matplotlib-users">https://lists.sourceforge.net/lists/listinfo/matplotlib-users</a><br
 class="">
 </div>
 </blockquote>
 </div>
 <br class="">
 <div apple-content-edited="true" class="">
 <span class="Apple-style-span" style="border-collapse:
 separate; border-spacing: 0px; color: rgb(0, 0, 0);
 font-family: 'Lucida Sans Typewriter'; font-size: 12px;
 font-style: normal; font-variant: normal; font-weight:
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 text-indent: 0px; text-transform: none; orphans: 2;
 white-space: normal; widows: 2; word-spacing: 0px;">
 <div class="">--</div>
 <div class="">Jody Klymak  </div>
 <div class=""><a moz-do-not-send="true"
 href="http://web.uvic.ca/%7Ejklymak/" class="">http://web.uvic.ca/~jklymak/</a></div>
 <div class=""><br class="khtml-block-placeholder">
 </div>
 <div class=""><br class="khtml-block-placeholder">
 </div>
 <br class="Apple-interchange-newline">
 </span><br class="Apple-interchange-newline">
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 <br class="">
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 <fieldset class="mimeAttachmentHeader"></fieldset>
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 <pre wrap="">_______________________________________________
Matplotlib-users mailing list
<a class="moz-txt-link-abbreviated" href="mailto:Mat...@li...">Mat...@li...</a>
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</pre>
 </blockquote>
 <br>
 <br>
 <div class="moz-signature">-- <br>
 <b>Q:</b> What if you strapped C4 to a boomerang? Could this be an
 effective weapon, or would it be as stupid as it sounds?<br>
 <b>A:</b> Aerodynamics aside, I’m curious what tactical advantage
 you’re expecting to gain by having the high explosive fly back at
 you if it misses the target.<br>
 </div>
 </body>
</html>

Hi,
> On 5 Jun 2015, at 11:17 AM, Sourish Basu <sou...@gm...> wrote:
> 
> On 06/05/2015 10:17 AM, Jody Klymak wrote:
>> 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. 
> 
> I agree that jet does a bad job with anomaly data, but I disagree that having a diverging colormap as default (or even a "diverging" argument to anything that takes a cmap value) would solve that. Very often the "zero" of an anomaly is not at the center of the extrema, and requires creating a custom diverging colormap anyway (see attached example).
Well, I *strongly* disagree with that attached example! It makes it look like -0.5 is equivalent to +1.5! Unless there is a really strong reason to do that, I think that is poor practice as it makes your negative anomalies look far stronger than your positive, and that is not the case in the underlying numbers.
Cheers, Jody
> OT, I recently found a nice alternative to jet here:https://mycarta.wordpress.com/2014/11/13/new-rainbow-colormap-sawthoot-shaped-lightness-profile/ <https://mycarta.wordpress.com/2014/11/13/new-rainbow-colormap-sawthoot-shaped-lightness-profile/>
> It takes care of my biggest crib with jet, which is that there is not enough perceptual variation in the middle of the range.
> 
> Cheers,
> Sourish Basu
> <ff_adjustment_winter.png>------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
--
Jody Klymak 
http://web.uvic.ca/~jklymak/

On 2015年06月05日 6:15 AM, Joe Kington wrote:
> 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.
>
>
>
> I was thinking the same thing earlier. Here's my attempt:
Joe,
Thank you--that's an interesting option. It reminds me of the middle 
half of cubehelix in the Miscellaneous set:
http://matplotlib.org/examples/color/colormaps_reference.html
Cubehelix is also generated by an algorithm.
Your blu_grn_pink2 looks worth adding to the mpl collection.
Eric

<html>
 <head>
 <meta content="text/html; charset=utf-8" http-equiv="Content-Type">
 </head>
 <body bgcolor="#FFFFFF" text="#000000">
 <div class="moz-cite-prefix">On 06/05/2015 10:17 AM, Jody Klymak
 wrote:<br>
 </div>
 <blockquote cite="mid:EA9...@uv..."
 type="cite">
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
 <div class="">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. <br>
 </div>
 </blockquote>
 <br>
 I agree that jet does a bad job with anomaly data, but I disagree
 that having a diverging colormap as default (or even a "diverging"
 argument to anything that takes a cmap value) would solve that. Very
 often the "zero" of an anomaly is not at the center of the extrema,
 and requires creating a custom diverging colormap anyway (see
 attached example).<br>
 <br>
 OT, I recently found a nice alternative to jet here:
 <a class="moz-txt-link-freetext"
href="https://mycarta.wordpress.com/2014/11/13/new-rainbow-colormap-sawthoot-shaped-lightness-profile/">https://mycarta.wordpress.com/2014/11/13/new-rainbow-colormap-sawthoot-shaped-lightness-profile/</a><br>
 It takes care of my biggest crib with jet, which is that there is
 not enough perceptual variation in the middle of the range.<br>
 <br>
 Cheers,<br>
 Sourish Basu<br>
 </body>
</html>

> On 5 Jun 2015, at 9:27 AM, Thomas Caswell <tca...@gm...> wrote:
> 
> 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.
OK, fair enough - if the consensus is that people who want diverging colormaps need to know what they are doing, and the default is only for sequential data, then that argument has merit. I do not look forward to seeing the first student talks that try to contour velocity data using one of these colormaps, but maybe the results will be so ghastly the naive user will realize they need to do something more appropriate. 
However, if sequential is what you have decided, then it is useful to say how the underlying data is distributed: For uniform distributions like those used in the plotted examples, I *prefer* C and D. However, for data like that in the movies, which look to be more Gaussian, I would actually prefer B, or a version of D that went to black and white to better represent the extreme values. Put another way, I’d use A and B, but most of the time I’d set my data limits so that they didn’t saturate as much as they do in the plotted examples. Hopefully that makes sense.
Cheers, Jody
> 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 <jk...@uv... <mailto:jk...@uv...>> 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...@ou... <mailto:ben...@ou...>> 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. <fly...@we... <mailto:fly...@we...>> 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 <ndb...@gm... <mailto:ndb...@gm...>> 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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>> Mat...@li... <mailto:Mat...@li...>
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users <https://lists.sourceforge.net/lists/listinfo/matplotlib-users>
>> 
>> ------------------------------------------------------------------------------
>> 
>> _______________________________________________
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>> 
>> 
>> ------------------------------------------------------------------------------
>> _______________________________________________
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>> Mat...@li... <mailto:Mat...@li...>
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users <https://lists.sourceforge.net/lists/listinfo/matplotlib-users>
> 
> --
> Jody Klymak 
> http://web.uvic.ca/~jklymak/ <http://web.uvic.ca/~jklymak/>
> 
> 
> 
> 
> 
> ------------------------------------------------------------------------------
> _______________________________________________
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> Mat...@li... <mailto:Mat...@li...>
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users <https://lists.sourceforge.net/lists/listinfo/matplotlib-users>
--
Jody Klymak 
http://web.uvic.ca/~jklymak/

On Fri, Jun 5, 2015 at 11:15 AM, Joe Kington <jof...@gm...> wrote:
> 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.
>>
>>
>>
> I was thinking the same thing earlier. Here's my attempt:
>
Not to jump back on topics too much, but I forgot to attach the colormap to
my earlier e-mail. Here it is.

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 <jk...@uv...> 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...@ou...> 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. <fly...@we...> 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 <ndb...@gm...> schrieb am Fr., 5. Juni 2015 um
>> 12:58 Uhr:
>>
>>> I vote for D, although I like matlab's new default even better
>>>
>>>
>>>
>>> ------------------------------------------------------------------------------
>>> _______________________________________________
>>> Matplotlib-users mailing list
>>> Mat...@li...
>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>>
>>
>>
>> ------------------------------------------------------------------------------
>>
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
> --
> Jody Klymak
> http://web.uvic.ca/~jklymak/
>
>
>
>
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

On Fri, Jun 5, 2015 at 9:17 AM, Jody Klymak <jk...@uv...> wrote:
>
>
> 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.
>
>
Personally, I disagree. I think that sequential colormaps make better
defaults b/c then the software isn't making an assumptions about the
central tendency of your data.
You raise a good point though. Perhaps a compromise is to make "sequential"
and "diverging" valid arguments to any function that takes "cmap" and falls
back to the default colormap and e.g. "coolwarm", respectively.

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...@ou...> 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. <fly...@we... <mailto:fly...@we...>> 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 <ndb...@gm... <mailto:ndb...@gm...>> schrieb am Fr., 5. Juni 2015 um 12:58 Uhr:
> I vote for D, although I like matlab's new default even better
> 
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li... <mailto:Mat...@li...>
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users <https://lists.sourceforge.net/lists/listinfo/matplotlib-users>
> 
> ------------------------------------------------------------------------------
> 
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li... <mailto:Mat...@li...>
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users <https://lists.sourceforge.net/lists/listinfo/matplotlib-users>
> 
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
--
Jody Klymak 
http://web.uvic.ca/~jklymak/

>
> 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.
>
>
>
I was thinking the same thing earlier. Here's my attempt:
​
 Note to Ben, et al, you got an e-mail from me earlier that bounced to the
list (too big). This is a less muted version of that colormap.

Hi, Experts,
My colleagues and I have a question, how we can make a plot via python like
below. According to a guy's original paper, "Each panel shows the
normalized histograms of the observed data (bar plots) and the model
prediction (black lines) ".
I believe that people can make it with Matplotlib. Any code suggestion
(with simple example data) would be much appreciated.
(I am more comfortable with Matlab, but now the python code is preferred).
J
[image: Inline image 3]

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. <fly...@we...> 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 <ndb...@gm...> schrieb am Fr., 5. Juni 2015 um
> 12:58 Uhr:
>
>> I vote for D, although I like matlab's new default even better
>>
>>
>>
>> ------------------------------------------------------------------------------
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>
>
> ------------------------------------------------------------------------------
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

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 <ndb...@gm...> schrieb am Fr., 5. Juni 2015 um 12:58 Uhr:
> I vote for D, although I like matlab's new default even better
>
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

I opt for B,
and adding the matlab-default as secondary. 
cheers
THomas
Thomas Sprinzing
Dipl.-Ing. (FH)
Labor Tiefdruck 
Studiengang Druck- und Medientechnologie
Hochschule der Medien
University of Applied Sciences
Nobelstr. 10
70569 Stuttgart
Telefon: +49 711 8923 2196
www.hdm-stuttgart.de/dt
Am 05.06.2015 um 13:20 schrieb Jan Heczko <jan...@gm...>:
> I'd choose D.
> A and B are too dark. Also, A-C seem to hide some detail in the simulation of color blindness.
> 
> On 4 June 2015 at 22:42, Eric Firing <ef...@ha...> wrote:
> I am forwarding a message from Nathaniel Smith which is the start of a
> long thread on matplotlib-devel
> http://news.gmane.org/gmane.comp.python.matplotlib.devel
> related to changes that are in the works for matplotlib, and that are
> therefore of interest to matplotlib users. Specifically, we will be
> updating the default color cycle for line plots, and the default
> colormap for image-type plots, including contourf and pcolormesh. The
> most important part of Nathaniel's message is the link:
> 
> https://bids.github.io/colormap/
> 
> which has been updated since his first message below.
> 
> Note that we are looking for a new *default* colormap--the one that will
> be used if you have not specified an alternative in your matplotlibrc
> file, your function keyword arguments, or anywhere else. It does not in
> any way limit your ability to specify a colormap that you prefer for a
> particular application, or as your own default. Rather, it should be a
> good all-around choice, that works reasonably well in a variety of
> applications, and that most people will find *comfortable* as well as
> functional. It will become part of matplotlib's "look"; it should
> attract rather than repel prospective and new users. We have some
> consensus about some of the other criteria, and these are coded into the
> tool that Nathaniel and Stéfan have developed for generating colormaps.
> So far, 4 alternatives generated with this tool have been proposed at
> the link above; more might be added.
> 
> Eric
> 
> -------- Forwarded Message --------
> Subject: [matplotlib-devel] RFC: candidates for a new default colormap
> Date: Tue, 2 Jun 2015 18:46:21 -0700
> From: Nathaniel Smith <nj...@po...>
> To: mat...@li...
> <mat...@li...>
> 
> Hi all,
> 
> As was hinted at in a previous thread, Stéfan van der Walt and I have
> been using some Fancy Color Technology to attempt to design a new
> colormap intended to become matplotlib's new default. (Down with jet!)
> 
> Unfortunately, while our Fancy Color Technology includes a
> computational model of perceptual distance, it does not include a
> computational model of aesthetics. So this is where you come in.
> 
> We've put up three reasonable candidates at:
> https://bids.github.io/colormap/
> (along with some well-known colormaps for comparison), and we'd like
> your feedback.
> 
> They are all optimal on all of the objective criteria we know how to
> measure. What we need judgements on is which one you like best, both
> aesthetically and as a way of visualizing data. (There are some sample
> plots to look at there, plus you can download them and play with them
> on your own data if you want.)
> 
> We especially value input from anyone with anomalous color vision.
> There are some simulations there, but computational models are
> inherently limited here. (It's difficult to ask someone with
> colorblindness "does this look to you, the same way this other picture
> looks to me?")
> 
> -n
> 
> --
> Nathaniel J. Smith -- http://vorpus.org
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
> 
> 
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

I'd choose D.
A and B are too dark. Also, A-C seem to hide some detail in the simulation
of color blindness.
On 4 June 2015 at 22:42, Eric Firing <ef...@ha...> wrote:
> I am forwarding a message from Nathaniel Smith which is the start of a
> long thread on matplotlib-devel
> http://news.gmane.org/gmane.comp.python.matplotlib.devel
> related to changes that are in the works for matplotlib, and that are
> therefore of interest to matplotlib users. Specifically, we will be
> updating the default color cycle for line plots, and the default
> colormap for image-type plots, including contourf and pcolormesh. The
> most important part of Nathaniel's message is the link:
>
> https://bids.github.io/colormap/
>
> which has been updated since his first message below.
>
> Note that we are looking for a new *default* colormap--the one that will
> be used if you have not specified an alternative in your matplotlibrc
> file, your function keyword arguments, or anywhere else. It does not in
> any way limit your ability to specify a colormap that you prefer for a
> particular application, or as your own default. Rather, it should be a
> good all-around choice, that works reasonably well in a variety of
> applications, and that most people will find *comfortable* as well as
> functional. It will become part of matplotlib's "look"; it should
> attract rather than repel prospective and new users. We have some
> consensus about some of the other criteria, and these are coded into the
> tool that Nathaniel and Stéfan have developed for generating colormaps.
> So far, 4 alternatives generated with this tool have been proposed at
> the link above; more might be added.
>
> Eric
>
> -------- Forwarded Message --------
> Subject: [matplotlib-devel] RFC: candidates for a new default colormap
> Date: Tue, 2 Jun 2015 18:46:21 -0700
> From: Nathaniel Smith <nj...@po...>
> To: mat...@li...
> <mat...@li...>
>
> Hi all,
>
> As was hinted at in a previous thread, Stéfan van der Walt and I have
> been using some Fancy Color Technology to attempt to design a new
> colormap intended to become matplotlib's new default. (Down with jet!)
>
> Unfortunately, while our Fancy Color Technology includes a
> computational model of perceptual distance, it does not include a
> computational model of aesthetics. So this is where you come in.
>
> We've put up three reasonable candidates at:
> https://bids.github.io/colormap/
> (along with some well-known colormaps for comparison), and we'd like
> your feedback.
>
> They are all optimal on all of the objective criteria we know how to
> measure. What we need judgements on is which one you like best, both
> aesthetically and as a way of visualizing data. (There are some sample
> plots to look at there, plus you can download them and play with them
> on your own data if you want.)
>
> We especially value input from anyone with anomalous color vision.
> There are some simulations there, but computational models are
> inherently limited here. (It's difficult to ask someone with
> colorblindness "does this look to you, the same way this other picture
> looks to me?")
>
> -n
>
> --
> Nathaniel J. Smith -- http://vorpus.org
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-devel mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>
>
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

I vote for D, although I like matlab's new default even better