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2D Normalization & Colormapping for ScalerMappables#8738
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lib/matplotlib/colors.py Outdated
| @@ -1346,6 +1346,60 @@ def __call__(self, value, clip=None): | |||
| def inverse(self, value): | |||
| return value | |||
| class Norm2d: | |||
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"BivariateNorm" just so that it can follow the mpl convention of "Xnorm"?
lib/matplotlib/colors.py Outdated
| """ | ||
| Normalize a list of two values corresponding to two 1D normalizers | ||
| """ | ||
| def __init__(self, norm_instances=None): |
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is there are a reason norm_instances = none and is not:
def__init__(self,norm1=None,norm2=None):
patniharshitJun 9, 2017 • edited
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No reason. I think what you are proposing is more convenient from user's perspective.
lib/matplotlib/colors.py Outdated
| norm_instances : | ||
| A list of length two having instances of 1D normalizers | ||
| """ | ||
| if norm_instances is None: |
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How do you handle the use case[BoundaryNorm(), None] or[None, BoundaryNorm]?
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Yes this should be handled.
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This is being handled now.
How would this relate to actual plotting functions? |
@anntzer Currently, functions like imshow, pcolormesh, scalar mappable etc expects 1D normalizer. To extend their support for bivariate colormaps there is a need for bivariate norm also. |
I understand, but right now all I see this PR implements is some trivial way of combining to normalizers. If there's no bigger example usage, it's hard to judge whether that's sufficient, or too simple, or too complex. |
story645 commentedJun 11, 2017 • edited
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This should stay in its own PR for reviewing ease, but what about postponing the decision on whether to merge this until the whole set of 2d coloring PRs come in? The issue with an N-color normalizer (I like it in theory/kinda agree on creating it and then sub classing to this) is that past 3 variables it's not really implementable. |
I agree with keeping the PR open for now. Not sure why you cannot have more three dimensions, at the end we're just writing a mapping from R^n -> {colors}. |
story645 commentedJun 11, 2017 • edited
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Yes, you can technically have N dimensions, but: |
Well a color triangle really is only two dimensional (the third variable is determined by the first two). |
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Further PRs on nonscalar normalizers need to come in before we can judge whether this design makes sense.
1a3d16b to8e93d20Compare13627d3 to2f8b5bfComparelib/matplotlib/axes/_axes.py Outdated
| if len(args) == 1: | ||
| C = np.asanyarray(args[0]) | ||
| isBivari = (isinstance(norm, mcolors.BivariateNorm) or |
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try to stick to pep8 (is_bivar)
also this can br written asisinstance(norm, (mcolors.BivariateNorm, mcolors.BivariateColormap))
(similarly below)
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Updated.
patniharshitJul 29, 2017 • edited
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The previous change was reverted because two different variablesnorm andcmap are being checked for their respective instances.
| xlabel='', | ||
| ylabel='', | ||
| ): | ||
| #: The axes that this colorbar lives in. |
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Unless there is a strong reason to expose them, attributes should be private (start with an underscore) by default. Otherwise, they all become part of the public API, which is very difficult to change while maintaining back-compatibility.
(Same comment throughout the PR.)
patniharshitJul 27, 2017 • edited
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Any specific attribute you are pointing to? I tried to keep it similar to ColorbarBase class.
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ax is probably fine to stay public (it is public on everyArtist), but the xvalues, xboundaries, etc can probably be made private.
lib/matplotlib/axes/_axes.py Outdated
| isNorm = isinstance(norm, (mcolors.Normalize, mcolors.BivariateNorm)) | ||
| if norm is not None and not isNorm: | ||
| msg = "'norm' must be an instance of 'mcolors.Normalize' " \ | ||
| "or 'mcolors.BivariateNorm'" |
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shouldn'tBivariateNorm subclass Normalize? 'specially since now it's normalizing down to a 1d space?
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BivariateNorm does not inherit anything from Normalize so I thought it should not subclass it.
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With the new design, BivariateNorm should havel all the same classes as Normalize...and I think subclassing it so it's registered generically as a Norm is preferable to having to treat it as a special case when it doesn't need to be.
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This can be done with anABChttps://docs.python.org/3/library/abc.html#abc.ABCMeta.register which both Normalize and BivariateNorm register with.
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Sure, but I don't see a reason why BivariateNorm shouldn't be subclassing Norm as it has the same architecture as any other norm...take data (scaler or vector)->map to 1D lut value ->get color, and color ->lut->scaler/vector.
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If the code paths are completely different, why force the sub-class? In general, sub-classing is only worth it when you can share significant amounts of implementation details, otherwise duck-typing is better.
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My point is mostly that the code paths shouldn't end up being all that different and more to the point in mpl land there's probably a ton of code that checks if things are norms and if this code works in those instances it doesn't make sense to change all that code to cover both cases when this is still fundamentally a norm. But I think I'm on the same page as you on the solution maybe being a "Norm" metaclass.
lib/matplotlib/colors.py Outdated
| temp[0] = temp[0] * (256) | ||
| temp[1] = temp[1] * (256) | ||
| temp = temp.astype(int) | ||
| return temp[0] + temp[1] * 256 |
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can you guarantee that this scheme always yields uniqueness?
(I'm confused about what's going on here and sort of have been thinking about a more general mapping between nd arrays and numbers wherein you get a sorted unique list of the nd tuples in the dataset so that the lookup/map is just the index of the value in the sorted list.
patniharshitJul 27, 2017 • edited
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Since after normalization every variable ends up between 0 to 1. So here I am changing it to index flattened 1d lut of BivariateColormap by mapping 0-1 to 1 to 256*256. Here I am currently hardcoding 256 as number of rows and columns in lut of 2d colormap. But this should be changed somehow to useN attribute of BivariateColormap.
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This is column major indexing (row * num_cols + col is c / numpy style,col * num_rows + row).
I am more concerned that the descritization is happening here rather than in the color map (which is donehttps://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/colors.py#L483) which prevents the size of the colormap from having to be known by the norm.
Why can't this return a Nd array? I think the most important places the norm and color map are called ishttps://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/cm.py#L209 and
https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/image.py#L365 . In the first case it can be blindly passed through and in the second that resampling needs to happen over each plane.
In the case of the color bar, that already needs to be heavily special cased.
20:11 $ git grep '\.norm[ (]' | grep -v linalglib/matplotlib/cm.py: self.norm = normlib/matplotlib/cm.py: x = self.norm(x)lib/matplotlib/cm.py: self.norm = normlib/matplotlib/collections.py: self.norm = other.normlib/matplotlib/colorbar.py: y = self.norm(self._boundaries.copy())lib/matplotlib/colorbar.py: b = self.norm(self._boundaries, clip=False).filled()lib/matplotlib/colorbar.py: xn = self.norm(x, clip=False).filled()lib/matplotlib/colorbar.py: self.norm = mappable.normlib/matplotlib/colorbar.py: facecolor=self.cmap(self.norm(val)),lib/matplotlib/image.py: A = self.norm(A)There was a problem hiding this comment.
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I am still a bit concerned that the descritization is being done here rather than in the color map. Won't this break for any colormap which is not 256 by 256?
lib/matplotlib/axes/_axes.py Outdated
| isNorm = isinstance(norm, (mcolors.Normalize, mcolors.BivariateNorm)) | ||
| if norm is not None and not isNorm: | ||
| msg = "'norm' must be an instance of 'mcolors.Normalize' " \ | ||
| "or 'mcolors.BivariateNorm'" |
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This can be done with anABChttps://docs.python.org/3/library/abc.html#abc.ABCMeta.register which both Normalize and BivariateNorm register with.
lib/matplotlib/axes/_axes.py Outdated
| temp = np.asarray(X) | ||
| if temp.ndim == 3 and isinstance(norm, mcolors.BivariateNorm): | ||
| temp = norm(temp) | ||
| X = cmap(temp, alpha=self.get_alpha(), bytes=True) |
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Doing this here means you can not change anything about the color map or norm later.
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Removed this.
lib/matplotlib/axes/_axes.py Outdated
| if len(args) == 1: | ||
| C = np.asanyarray(args[0]) | ||
| isBivari = isinstance(norm, (mcolors.BivariateNorm, |
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Why would the norm ever be a colormap?
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Thanks for pointing this out. Fixed now.
lib/matplotlib/axes/_axes.py Outdated
| cmap = mcolors.BivariateColormap() | ||
| if norm is None: | ||
| norm = mcolors.BivariateNorm() | ||
| C = norm(C) |
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Why do this up-front? defering all of this to_make_image means the colormap and norm and be updated.
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Check now.
lib/matplotlib/colorbar.py Outdated
| linthresh=norm.linthresh, | ||
| base=10) | ||
| else: | ||
| if mpl.rcParams['_internal.classic_mode']: |
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Don't need any of the classic shims as there is no back-compatibility to maintain.
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Removed.
lib/matplotlib/colorbar.py Outdated
| or len(self._x) >= self.n_rasterize): | ||
| self.solids.set_rasterized(True) | ||
| def _ticker(self, norm): |
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Probably better to factor this asdef _make_ticker(norm, values, boundaries, locator, formatter): as a local function inupdate_ticks.
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You mean local/private function of class or nested function?
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nested function
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Done.
lib/matplotlib/colors.py Outdated
| temp[0] = temp[0] * (256) | ||
| temp[1] = temp[1] * (256) | ||
| temp = temp.astype(int) | ||
| return temp[0] + temp[1] * 256 |
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This is column major indexing (row * num_cols + col is c / numpy style,col * num_rows + row).
I am more concerned that the descritization is happening here rather than in the color map (which is donehttps://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/colors.py#L483) which prevents the size of the colormap from having to be known by the norm.
Why can't this return a Nd array? I think the most important places the norm and color map are called ishttps://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/cm.py#L209 and
https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/image.py#L365 . In the first case it can be blindly passed through and in the second that resampling needs to happen over each plane.
In the case of the color bar, that already needs to be heavily special cased.
20:11 $ git grep '\.norm[ (]' | grep -v linalglib/matplotlib/cm.py: self.norm = normlib/matplotlib/cm.py: x = self.norm(x)lib/matplotlib/cm.py: self.norm = normlib/matplotlib/collections.py: self.norm = other.normlib/matplotlib/colorbar.py: y = self.norm(self._boundaries.copy())lib/matplotlib/colorbar.py: b = self.norm(self._boundaries, clip=False).filled()lib/matplotlib/colorbar.py: xn = self.norm(x, clip=False).filled()lib/matplotlib/colorbar.py: self.norm = mappable.normlib/matplotlib/colorbar.py: facecolor=self.cmap(self.norm(val)),lib/matplotlib/image.py: A = self.norm(A)| self.ax.set_xlabel(self._xlabel, **self._labelkw) | ||
| self.stale = True | ||
| def set_label(self, xlabel, ylabel, **kw): |
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The different signature from theColorBar base class is a bit problematic, but not sure what to do about it.
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perhaps set_labels(self, xylabel) (where xylabel=(xlabel, ylabel)); set_xlabel; set_ylabel?
let set_label raise an error
this likely would play better with traitlets
@anntzer can you have a look at this PR again? Some things have changed since your last review. |
lib/matplotlib/axes/_axes.py Outdated
| C = C.ravel() | ||
| # convert to one dimensional arrays if univariate | ||
| if isinstance(norm, mcolors.BivariateNorm): | ||
| C = np.asarray([C[0].ravel(), C[1].ravel()]) |
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This may be better done asnp.asarray([c.ravel() for c in C]) to be a bit future proof.
lib/matplotlib/axes/_axes.py Outdated
| msg = "'norm' must be an instance of 'mcolors.Normalize'" | ||
| if norm is not None and not isinstance(norm, mcolors.Norms): | ||
| msg = "'norm' must be an instance of 'mcolors.Normalize' " \ |
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We have a style preference for using() for continuation rather than\ so
msg= ('...'+'...')
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The + is not needed.
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Done.
lib/matplotlib/axes/_axes.py Outdated
| if len(args) == 1: | ||
| C = np.asanyarray(args[0]) | ||
| numRows, numCols = C.shape | ||
| isBivari = (isinstance(norm, mcolors.BivariateNorm) |
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use_underscores_notCapsForVariables
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ChangedisBivari tois_bivari.
| @@ -730,7 +730,8 @@ def update_scalarmappable(self): | |||
| """ | |||
| if self._A is None: | |||
| return | |||
| if self._A.ndim > 1: | |||
| if (self._A.ndim > 1 and | |||
| not isinstance(self.norm, mcolors.BivariateNorm)): | |||
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should check ndim == 2 in this case?
| """ | ||
| Label the axes of the colorbar | ||
| """ | ||
| self._xlabel = '%s' % (xlabel, ) |
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just self._xlabel = xlabel should be enough (unless we explicitly want to support any object via conversion to string, and even then str(xlabel) is clearer.
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Just imitating how it is done inColorbarBase.set_label.
lib/matplotlib/colors.py Outdated
| """ | ||
| Abstract Base Class to group `Normalize` and `BivariateNorm` | ||
| """ | ||
| __metaclass__ = ABCMeta |
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definitely wrong on Py3
use six
should have been caught by pycodestyle perhaps?
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Fixed now.
Appveyor seems to be having issues with connectivity the last day or so. It is failing on almost all PRs. The circle / docs failures do need to be fixed though. |
| # Bivariate plotting demo | ||
| # ----------------------- | ||
| air_temp = np.load(get_sample_data('air_temperature.npy')) |
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How big are these data files? I have a slight preference for 'generated' data for examples (to keep the repository size small).
patniharshitAug 27, 2017 • edited
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84.2 kB each.
Couldn't come up with generated data so used these.
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That isn't too bad, suspect that is smaller than the test images.
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One argument in favour of this can be that the user seeing the example might relate to use case of bivariate plotting better with a real world example than with some mathematical function.
lib/matplotlib/axes/_axes.py Outdated
| to colors based on the `norm` (mapping scalar to scalar) | ||
| and the `cmap` (mapping the normed scalar to a color). | ||
| cmap : `~matplotlib.colors.Colormap`, optional, default: None | ||
| cmap : `~matplotlib.colors.Colormap`, |
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This is the one exception to the() over\ rule so that numpydoc correctly grabs this as a single lin.
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These might be the source of the docs failures?
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Added\ but still failing.
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It is probably fastest to build the docs locally. That also lets you get access to the full traceback.
pip install -r doc-requirements.txtcd docspython make.py htmlshould work.
This is looking good 👍 |
Can you build the docs locally? |
@tacaswell doc build is passing now. |
@patniharshit I broke your branch via#8966 this needs to be rebased (sorry). I think it is ok to either just update the shim around the resampling code or to move it all into a loop (I think clearer now) to resample each plane. |
I don't have access to my laptop for two days. Will resolve the conflicts by Wednesday. |
| - MxNx3 -- RGB (float or uint8) | ||
| - MxNx4 -- RGBA (float or uint8) | ||
| - 2xMxN -- bivariate values to be mapped (float or int) |
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What happens when the input is shape(2, 3, 4)?
This sort of ambiguity should be avoided (and indicates that this API is probably not the right design choice).
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This PR came up in#14168.
I'm not sure the status of this, but in case someone comes back to it...
This looks technically well done. However, I'mnot a fan of this API (see@shoyer comment as well). Its very complex from a documentation point of view. There is no way from the dosctrings that someone who didn't know what this PR did would understand what is meant by "2xMxN -- bivariate values to be mapped (float or int)". Further propagating support for two norms across all the 2-D plotting functions is going to be a maintenance nightmare. I'd far prefer this just got its own methods, i.e.:ax.bivariate_pcolormesh(x, y, Z1, Z1, norm1, norm2) etc.
I'm also very concerned about adding a whole new colorbar implementation. Maybe its unavoidable, but given that its so different, I wonder if it should also get its own method...
shoyer commentedMay 8, 2019
I agree with putting this into a separate API. In shaping this API, I think it would be helping to identify clear use-cases / critical user journeys and make sure they are well supported and documented. For example, one good use case is visualizing complex-valued functions with luminosity and (perceptually uniform?) hue. My concern is that in general this isn't a terribly effective visualization technique. That's not to say matplotlib shouldn't have this functionality, but let's try to limit its impact on the rest of the API. |
jklymak commentedMay 8, 2019 • edited
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My use case is shading a quantity based on gradient strength... If you'll forgive the sacrilege of linking to mathworks. The API for this was just: shadedpcolor(x,z,U,(dU),[-11]/4,[-11]/300,0.7,cmp,0);axisij; shadedcolorbar('v',[-11]/4,0.7,cmp); https://www.mathworks.com/matlabcentral/fileexchange/14157-shaded-pseudo-color |
Since this Pull Request has not been updated in 60 days, it has been marked "inactive." This does not mean that it will be closed, though it may be moved to a "Draft" state. This helps maintainers prioritize their reviewing efforts. You can pick the PR back up anytime - please ping us if you need a review or guidance to move the PR forward! If you do not plan on continuing the work, please let us know so that we can either find someone to take the PR over, or close it. |
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PR Summary
This PR is in referance to GSoC project '2D colormaps'.
(Project proposal ishere)
The
BivariateColormapclass subclassesColormapclass. After generation it is flattened to 1d look up table.The implementation of
BivariateColormapis not perfect yet and just something has been generated to show proof of concept.The bivariate data is identified by ndim == 3 and if an instance of either Bivariate Colormap or Norm has been passed in. This data is normalized and mapped to univariate integer index so as to directly index flattened BivariateColormap.
Plotting some air temperature and surface pressure data:
Feel free to review.
@story645@tacaswell
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