Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
On 06/05/2015 03:57 PM, Joe Kington 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. This is great, but it has a slightly bothersome side effect on the colorbar ticks. In your original example, I changed the line 'data = "" * (data - 0.8)' to 'data = "" * (data - 0.85)', so that the numbers are now in between -8.5 and +1.5. As a result, when the colorbar is drawn, you get a tick at -8, as well as one at -9 (similarly at +1 and +2). Example attached. As in, the colorbar method seems intent on adding those tick marks at -9 and +2. The result is not aesthetically pleasing. In one of my real-data example, the minimum value of the data happened to be -4.003, and as a result there was a tick label at -4 and an overlapping tick label at -5. Why does this happen only when I specify 'norm' in imshow? How do I get matplotlib to not do that? Thanks, Sourish On Jun 5, 2015 4:45 PM, "Sourish Basu" sourish.b...@gmail.com 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 list Matplotlib-users@lists.sourceforge.net 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 Matplotlib-users@lists.sourceforge.net 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. import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import Normalize class MidpointNormalize(Normalize): def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False): self.midpoint = midpoint Normalize.__init__(self, vmin, vmax, clip) def __call__(self, value, clip=None): # I'm ignoring masked values and all kinds of edge cases to make a # simple example... x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1] return np.ma.masked_array(np.interp(value, x, y)) data = np.random.random((10,10)) # generate data between -8.5 and +1.5 data = 10 * (data - 0.85) fig, ax = plt.subplots() norm = MidpointNormalize(midpoint=0.0) im = ax.imshow(data, norm=norm, cmap=plt.cm.seismic, interpolation='none') fig.colorbar(im) plt.show() -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
On 2015/06/05 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
On 5 Jun 2015, at 9:27 AM, Thomas Caswell tcasw...@gmail.com 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 jkly...@uvic.ca mailto: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 mailto: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 mailto: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 mailto: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 -- ___ Matplotlib-users mailing list
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
Hi, On 5 Jun 2015, at 11:17 AM, Sourish Basu sourish.b...@gmail.com 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- Jody Klymak http://web.uvic.ca/~jklymak/ -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
On 06/05/2015 12:22 PM, Jody Klymak wrote: Hi, On 5 Jun 2015, at 11:17 AM, Sourish Basu sourish.b...@gmail.com 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. 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? 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". Cheers, Sourish 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/ 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- Jody Klymak http://web.uvic.ca/~jklymak/ -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
On 5 Jun 2015, at 11:39 AM, Sourish Basu sourish.b...@gmail.com 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/ -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
On Fri, Jun 5, 2015 at 9:17 AM, Jody Klymak jkly...@uvic.ca 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. -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
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 -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- Jody Klymak http://web.uvic.ca/~jklymak/ -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
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 mailto: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 mailto: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 -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net mailto:Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net mailto:Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- Jody Klymak http://web.uvic.ca/~jklymak/ -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
Hi Eric, On 5 Jun 2015, at 12:20 PM, Eric Firing efir...@hawaii.edu 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- Jody Klymak http://web.uvic.ca/~jklymak/ -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
On 2015/06/05 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 -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
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 list Matplotlib-users@lists.sourceforge.net 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
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 sourish.b...@gmail.com 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 listMatplotlib-users@lists.sourceforge.nethttps://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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap
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 efir...@hawaii.edu wrote: On 2015/06/05 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
