Movatterモバイル変換

[0]ホーム
URL:

Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

 Sign up
Appearance settings

2D Normalization & Colormapping for ScalerMappables#8738

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to ourterms of service andprivacy statement. We’ll occasionally send you account related emails.

Already on GitHub?Sign in to your account

Closed
patniharshit wants to merge35 commits intomatplotlib:mainfrompatniharshit:norm2d
Closed
Show file tree
Hide file tree
Changes fromall commits
Commits
Show all changes
35 commits
Select commitHold shift + click to select a range
201dc2a
Initailize class 2D norm
patniharshitJun 9, 2017
7217536
Change name of class to BivariateNorm, take normalizer instances sepa…
patniharshitJun 9, 2017
0178730
Bivariate works with imshow, pcolor, pcolormesh, pcolorfast
patniharshitJul 3, 2017
575a244
add blank line between bivariate classes
patniharshitJul 9, 2017
8425637
Add support for bivariate in scatter
patniharshitJul 9, 2017
4a5831d
Fix missing norm2 in BivaraiteNorm
patniharshitJul 11, 2017
85df736
Fixed bug in if condition bivariate mapping
patniharshitJul 12, 2017
8e1bc63
Add autoscale, autoscale_None, scaled methods for BivariateNorm
patniharshitJul 13, 2017
a9aace3
trying to show ticks and labels on both x and y axis
patniharshitJul 16, 2017
55a7a68
commented outline code, changed gridspace to show sqaure colorbar
patniharshitJul 17, 2017
c2eb617
2d colorbar printing now
patniharshitJul 17, 2017
daef751
Add code for Colorsquare
patniharshitJul 22, 2017
96af3d3
Modify axis aspect and fraction for colorsquare
patniharshitJul 22, 2017
9285372
fix failing commits
patniharshitJul 22, 2017
01b5932
Change PEP8 violations
patniharshitJul 27, 2017
28288e2
Revert isBivari, remove classic locator
patniharshitJul 29, 2017
ca2b111
Subclass normalizers from Abstract Base Class
patniharshitAug 2, 2017
75b4eba
move _ticker function inside update_ticks
patniharshitAug 2, 2017
bf65847
move _ticker function inside update_ticks
patniharshitAug 2, 2017
f9dc4b1
remove cmap in imshow and pcolorfast
patniharshitAug 5, 2017
8a985f0
Defer call to norm and cmap in imshow
patniharshitAug 7, 2017
8f6928d
Fix bivariate handling with pcolormesh
patniharshitAug 15, 2017
d54cb44
Make pcolor work with bivariate
patniharshitAug 16, 2017
4d03552
Do 2D->1D mapping in call method of colormap
patniharshitAug 20, 2017
2573610
Use list comprehensions to ravel
patniharshitAug 20, 2017
9228480
Reflect on reviews
patniharshitAug 21, 2017
5c8dc65
Update docstrings
patniharshitAug 25, 2017
21ea65f
Add bivariate example
patniharshitAug 25, 2017
b8d43a9
Treat one as slightly less than one
patniharshitAug 25, 2017
7681d14
Change conditions for bivar
patniharshitAug 25, 2017
59f56af
Add image comparison test for bivariate
patniharshitAug 25, 2017
f98978f
Update is_bivar condition to expect either BivariateNorm or Bivariate…
patniharshitAug 26, 2017
75a289f
Add line continuation in docstrings
patniharshitAug 27, 2017
7803974
Debug doc build fail
patniharshitAug 28, 2017
e40c87b
Add missing symbols in docs
patniharshitAug 28, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
44 changes: 44 additions & 0 deletionsexamples/images_contours_and_fields/bivariate_demo.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,44 @@
"""
==============
Bivariate Demo
==============

Plotting bivariate data.

imshow, pcolor, pcolormesh, pcolorfast allows you to plot bivariate data
using a bivaraite colormap.

In this example we use imshow to plot air temperature with surface pressure
alongwith a color square.
"""
import matplotlib.colors as colors
from matplotlib.cbook import get_sample_data
import matplotlib.pyplot as plt
import numpy as np


###############################################################################
# Bivariate plotting demo
# -----------------------

air_temp = np.load(get_sample_data('air_temperature.npy'))
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others.Learn more.

How big are these data files? I have a slight preference for 'generated' data for examples (to keep the repository size small).

Copy link
ContributorAuthor

@patniharshitpatniharshitAug 27, 2017•
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others.Learn more.

84.2 kB each.
Couldn't come up with generated data so used these.

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others.Learn more.

That isn't too bad, suspect that is smaller than the test images.

Copy link
ContributorAuthor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others.Learn more.

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.

surf_pres = np.load(get_sample_data('surface_pressure.npy'))

fig, ax = plt.subplots()

bivariate = [air_temp, surf_pres]

###############################################################################
# To distinguish bivariate data either BivariateNorm or BivariateColormap must
# be passed in as argument

cax = ax.imshow(bivariate, norm=colors.BivariateNorm(),
cmap=colors.BivariateColormap())

###############################################################################
# If input data is bivariate then colorbar automatically draws colorsquare
# instead of colorbar

cbar = fig.colorbar(cax, xlabel='air_temp', ylabel='surf_pres')

plt.show()
166 changes: 128 additions & 38 deletionslib/matplotlib/axes/_axes.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -4044,7 +4044,8 @@ def scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None,

if colors is None:
if norm is not None and not isinstance(norm, mcolors.Normalize):
msg = "'norm' must be an instance of 'mcolors.Normalize'"
msg = ("'norm' must be an instance of 'mcolors.Normalize' or "
"'mcolors.BivariateNorm'")
raise ValueError(msg)
collection.set_array(np.asarray(c))
collection.set_cmap(cmap)
Expand DownExpand Up@@ -4403,7 +4404,8 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
accum = bins.searchsorted(accum)

if norm is not None and not isinstance(norm, mcolors.Normalize):
msg = "'norm' must be an instance of 'mcolors.Normalize'"
msg = ("'norm' must be an instance of 'mcolors.Normalize' or "
"'mcolors.BivariateNorm'")
raise ValueError(msg)
collection.set_array(accum)
collection.set_cmap(cmap)
Expand DownExpand Up@@ -5037,23 +5039,25 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,

Parameters
----------
X : array_like, shape (n, m) or (n, m, 3) or (n, m, 4)
X : array_like, shape (n, m) or (n, m, 3) or (n, m, 4) or (2, n, m)
Display the image in `X` to current axes. `X` may be an
array or a PIL image. If `X` is an array, it
can have the following shapes and types:

- MxN -- values to be mapped (float or int)
- MxN --univariatevalues to be mapped (float or int)
- MxNx3 -- RGB (float or uint8)
- MxNx4 -- RGBA (float or uint8)
- 2xMxN -- bivariate values to be mapped (float or int)

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others.Learn more.

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).


The value for each component of MxNx3 and MxNx4 float arrays
should be in the range 0.0 to 1.0. MxN arrays are mapped
should be in the range 0.0 to 1.0. MxNand 2xMxNarrays are mapped
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`, \
`~matplotlib.colors.BivariateColormap`, optional, default: None
If None, default to rc `image.cmap` value. `cmap` is ignored
if `X` is 3-D, directly specifying RGB(A) values.
if `X` is 3-D but not bivariate, directly specifying RGB(A) values.

aspect : ['auto' | 'equal' | scalar], optional, default: None
If 'auto', changes the image aspect ratio to match that of the
Expand All@@ -5077,7 +5081,8 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
on the Agg, ps and pdf backends. Other backends will fall back to
'nearest'.

norm : `~matplotlib.colors.Normalize`, optional, default: None
norm : `~matplotlib.colors.Normalize`, \
`matplotlib.colors.BivariateNorm`, optional, default: None
A `~matplotlib.colors.Normalize` instance is used to scale
a 2-D float `X` input to the (0, 1) range for input to the
`cmap`. If `norm` is None, use the default func:`normalize`.
Expand DownExpand Up@@ -5137,16 +5142,29 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
of pixel (0, 0).

"""

if not self._hold:
self.cla()

if norm is not None and not isinstance(norm, mcolors.Normalize):
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' or "
"'mcolors.BivariateNorm'")
raise ValueError(msg)

temp = np.asarray(X)
is_bivari = (isinstance(norm, mcolors.BivariateNorm) or
isinstance(cmap, mcolors.BivariateColormap))
if is_bivari:
if temp.ndim != 3 and temp.shape[0] != 2:
raise TypeError("Expected shape like (2, n, m)")
if cmap is None:
cmap = mcolors.BivariateColormap()
if norm is None:
norm = mcolors.BivariateNorm()

if aspect is None:
aspect = rcParams['image.aspect']
self.set_aspect(aspect)

im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent,
filternorm=filternorm, filterrad=filterrad,
resample=resample, **kwargs)
Expand All@@ -5173,7 +5191,6 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,

@staticmethod
def _pcolorargs(funcname, *args, **kw):
# This takes one kwarg, allmatch.
# If allmatch is True, then the incoming X, Y, C must
# have matching dimensions, taking into account that
# X and Y can be 1-D rather than 2-D. This perfect
Expand All@@ -5186,10 +5203,17 @@ def _pcolorargs(funcname, *args, **kw):
# is False.

allmatch = kw.pop("allmatch", False)
norm = kw.pop("norm", None)
cmap = kw.pop("cmap", None)

if len(args) == 1:
C = np.asanyarray(args[0])
numRows, numCols = C.shape
is_bivari = (isinstance(norm, mcolors.BivariateNorm) or
isinstance(cmap, mcolors.BivariateColormap))
if is_bivari:
numRows, numCols = C.shape[1:]
else:
numRows, numCols = C.shape
if allmatch:
X, Y = np.meshgrid(np.arange(numCols), np.arange(numRows))
else:
Expand All@@ -5200,7 +5224,12 @@ def _pcolorargs(funcname, *args, **kw):

if len(args) == 3:
X, Y, C = [np.asanyarray(a) for a in args]
numRows, numCols = C.shape
is_bivari = (isinstance(norm, mcolors.BivariateNorm) or
isinstance(cmap, mcolors.BivariateColormap))
if is_bivari:
numRows, numCols = C.shape[1:]
else:
numRows, numCols = C.shape
else:
raise TypeError(
'Illegal arguments to %s; see help(%s)' % (funcname, funcname))
Expand DownExpand Up@@ -5235,7 +5264,7 @@ def _pcolorargs(funcname, *args, **kw):
@docstring.dedent_interpd
def pcolor(self, *args, **kwargs):
"""
Create a pseudocolor plot of a 2-D array.
Create a pseudocolor plot of a 2-Dunivariate or 3-D bivariatearray.

Call signatures::

Expand DownExpand Up@@ -5273,10 +5302,12 @@ def pcolor(self, *args, **kwargs):
vectors, they will be expanded as needed into the appropriate 2-D
arrays, making a rectangular grid.

cmap : `~matplotlib.colors.Colormap`, optional, default: None
cmap : `~matplotlib.colors.Colormap` or \
`matplotlib.colors.BivariateColormap`, optional, default: None
If `None`, default to rc settings.

norm : `matplotlib.colors.Normalize`, optional, default: None
norm : `matplotlib.colors.Normalize` or \
`matplotlib.colors.BivariateNorm`, optional, default: None
An instance is used to scale luminance data to (0, 1).
If `None`, defaults to :func:`normalize`.

Expand DownExpand Up@@ -5382,9 +5413,20 @@ def pcolor(self, *args, **kwargs):
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)

X, Y, C = self._pcolorargs('pcolor', *args, allmatch=False)
kw = {'norm': norm, 'cmap': cmap, 'allmatch': False}
X, Y, C = self._pcolorargs('pcolor', *args, **kw)
Ny, Nx = X.shape

is_bivari = (isinstance(norm, mcolors.BivariateNorm) or
isinstance(cmap, mcolors.BivariateColormap))
if is_bivari:
if C.ndim != 3 and C.shape[0] != 2:
raise TypeError("Expected shape like (2, n, m)")
if cmap is None:
cmap = mcolors.BivariateColormap()
if norm is None:
norm = mcolors.BivariateNorm()

# unit conversion allows e.g. datetime objects as axis values
self._process_unit_info(xdata=X, ydata=Y, kwargs=kwargs)
X = self.convert_xunits(X)
Expand All@@ -5399,7 +5441,10 @@ def pcolor(self, *args, **kwargs):
xymask = (mask[0:-1, 0:-1] + mask[1:, 1:] +
mask[0:-1, 1:] + mask[1:, 0:-1])
# don't plot if C or any of the surrounding vertices are masked.
mask = ma.getmaskarray(C) + xymask
if isinstance(norm, mcolors.BivariateNorm):
mask = ma.getmaskarray(C[0]) + ma.getmaskarray(C[1]) + xymask
else:
mask = ma.getmaskarray(C) + xymask

newaxis = np.newaxis
compress = np.compress
Expand All@@ -5423,7 +5468,15 @@ def pcolor(self, *args, **kwargs):
axis=1)
verts = xy.reshape((npoly, 5, 2))

C = compress(ravelmask, ma.filled(C[0:Ny - 1, 0:Nx - 1]).ravel())
if isinstance(norm, mcolors.BivariateNorm):
C = np.array([
compress(
ravelmask,
ma.filled(c[0:Ny - 1, 0:Nx - 1]).ravel()
) for c in C
])
else:
C = compress(ravelmask, ma.filled(C[0:Ny - 1, 0:Nx - 1]).ravel())

linewidths = (0.25,)
if 'linewidth' in kwargs:
Expand All@@ -5450,9 +5503,12 @@ def pcolor(self, *args, **kwargs):

collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None and not isinstance(norm, mcolors.Normalize):
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' or "
"'mcolors.BivariateNorm'")
raise ValueError(msg)

collection.set_cmap(cmap)
collection.set_norm(norm)
collection.set_clim(vmin, vmax)
Expand DownExpand Up@@ -5518,11 +5574,13 @@ def pcolormesh(self, *args, **kwargs):
Keyword arguments:

*cmap*: [ *None* | Colormap ]
A :class:`matplotlib.colors.Colormap` instance. If *None*, use
rc settings.
A :class:`matplotlib.colors.Colormap` or
:class:`matplotlib.colors.BivariateColormap` instance. If *None*,
use rc settings.

*norm*: [ *None* | Normalize ]
A :class:`matplotlib.colors.Normalize` instance is used to
A :class:`matplotlib.colors.Normalize` or
:class:`matplotlib.colors.BivariateNorm` instance is used to
scale luminance data to 0,1. If *None*, defaults to
:func:`normalize`.

Expand DownExpand Up@@ -5582,25 +5640,41 @@ def pcolormesh(self, *args, **kwargs):

allmatch = (shading == 'gouraud')

X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch)
kw = {'norm': norm, 'cmap': cmap, 'allmatch': allmatch}
X, Y, C = self._pcolorargs('pcolormesh', *args, **kw)
Ny, Nx = X.shape

is_bivari = (isinstance(norm, mcolors.BivariateNorm) or
isinstance(cmap, mcolors.BivariateColormap))
if is_bivari:
if C.ndim != 3 and C.shape[0] != 2:
raise TypeError("Expected shape like (2, n, m)")
if cmap is None:
cmap = mcolors.BivariateColormap()
if norm is None:
norm = mcolors.BivariateNorm()

# unit conversion allows e.g. datetime objects as axis values
self._process_unit_info(xdata=X, ydata=Y, kwargs=kwargs)
X = self.convert_xunits(X)
Y = self.convert_yunits(Y)

# convert to one dimensional arrays
C = C.ravel()
# convert to one dimensional arrays if univariate
if isinstance(norm, mcolors.BivariateNorm):
C = np.asarray([c.ravel() for c in C])
else:
C = C.ravel()

coords = np.column_stack((X.flat, Y.flat)).astype(float, copy=False)

collection = mcoll.QuadMesh(Nx - 1, Ny - 1, coords,
antialiased=antialiased, shading=shading,
**kwargs)
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None and not isinstance(norm, mcolors.Normalize):
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' or "
"'mcolors.BivariateNorm'")
raise ValueError(msg)
collection.set_cmap(cmap)
collection.set_norm(norm)
Expand DownExpand Up@@ -5634,7 +5708,7 @@ def pcolormesh(self, *args, **kwargs):
@docstring.dedent_interpd
def pcolorfast(self, *args, **kwargs):
"""
pseudocolor plot of a 2-D array
pseudocolor plot of a 2-Dunivariate or 3-D bivariatearray

Experimental; this is a pcolor-type method that
provides the fastest possible rendering with the Agg
Expand DownExpand Up@@ -5693,11 +5767,13 @@ def pcolorfast(self, *args, **kwargs):
Optional keyword arguments:

*cmap*: [ *None* | Colormap ]
A :class:`matplotlib.colors.Colormap` instance from cm. If *None*,
use rc settings.
A :class:`matplotlib.colors.Colormap` or
:class:`matplotlib.colors.BivariateColormap` instance from cm.
If *None*, use rc settings.

*norm*: [ *None* | Normalize ]
A :class:`matplotlib.colors.Normalize` instance is used to scale
A :class:`matplotlib.colors.Normalize` or
:class:`matplotlib.colors.BivariateNorm` instance is used to scale
luminance data to 0,1. If *None*, defaults to normalize()

*vmin*/*vmax*: [ *None* | scalar ]
Expand All@@ -5723,12 +5799,26 @@ def pcolorfast(self, *args, **kwargs):
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
if norm is not None and not isinstance(norm, mcolors.Normalize):
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' or "
"'mcolors.BivariateNorm'")
raise ValueError(msg)

C = args[-1]
nr, nc = C.shape
C = np.asarray(args[-1])

is_bivari = (isinstance(norm, mcolors.BivariateNorm) or
isinstance(cmap, mcolors.BivariateColormap))
if is_bivari:
if C.ndim != 3 and C.shape[0] != 2:
raise TypeError("Expected shape like (2, n, m)")
if cmap is None:
cmap = mcolors.BivariateColormap()
if norm is None:
norm = mcolors.BivariateNorm()
nr, nc = C.shape[1:]
else:
nr, nc = C.shape
if len(args) == 1:
style = "image"
x = [0, nc]
Expand Down
1 change: 0 additions & 1 deletionlib/matplotlib/axes/_subplots.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -177,7 +177,6 @@ def subplot_class_factory(axes_class=None):
(SubplotBase, axes_class),
{'_axes_class': axes_class})
_subplot_classes[axes_class] = new_class

return new_class

# This is provided for backward compatibility
Expand Down
Loading
[8]ページ先頭
©2009-2025 Movatter.jp