Using AxesGrid does not mean that color scales are shared. It only
takes care of axes placement. And it is your responsibility to sync
colorbars of multiple images.
One option is to use *norm* attribute of images.
norm = matplotlib.colors.Normalize()
for i in range(3):
im = grid[i].imshow(Z, extent=extent, interpolation="nearest",
norm=norm)
im = grid[3].imshow(1000 * Z, extent=extent, interpolation="nearest",
norm=norm)
Regards,
-JJ
On Wed, May 25, 2011 at 11:05 AM, Paul Anton Letnes
<paul.anton.let...@gmail.com> wrote:
> Hi!
>
> I am wondering if there is a bug in:
> mpl_toolkits.axes_grid1.AxesGrid
>
> I am trying to run this example (I am of course working on something else,
> but I am trying to build a minimal example of my problem):
> http://matplotlib.sourceforge.net/examples/axes_grid/demo_axes_grid.html
> This would be a very compact and nice way of showing my results, and I am
> beginning to love it. There is one thing, though. Given the code attached at
> the end of this message, the two versions produce figures that look
> identical. To my mind, however, it doesn't exactly seem that the colorbars
> are "shared" in any real sense.
>
> Try commenting out the 2-3 lines marked # Version 1 and # Version 2. I attach
> two plots of what I get: same-looking plots with wildly varying colorbar
> numbers. I'd like to get the numbers to be the same.
>
> TL;DR: In version 1 I am scaling the data for one of the contour plots by a
> factor 1000, and would expect that plot to look quite different from the
> others.
>
> Cheers
> Paul.
>
>
> # CODE:
> import matplotlib
> matplotlib.use('agg')
> import matplotlib.pyplot as plt
> from mpl_toolkits.axes_grid1 import AxesGrid
>
>
> def get_demo_image():
> import numpy as np
> from matplotlib.cbook import get_sample_data
> f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
> z = np.load(f)
> # z is a numpy array of 15x15
> return z, (-3,4,-4,3)
>
> def demo_grid_with_single_cbar(fig):
> """
> A grid of 2x2 images with a single colobar
> """
> grid = AxesGrid(fig, 132, # similar to subplot(132)
> nrows_ncols = (2, 2),
> axes_pad = 0.0,
> share_all=True,
> label_mode = "L",
> cbar_location = "top",
> cbar_mode="single")
>
> Z, extent = get_demo_image()
>
> # Version 1
> #for i in range(3):
> #im = grid[i].imshow(Z, extent=extent, interpolation="nearest")
> #im = grid[3].imshow(1000 * Z, extent=extent, interpolation="nearest")
> # Version 2
> for i in range(4):
> im = grid[i].imshow(Z, extent=extent, interpolation="nearest")
>
> # The rest is the same
> grid.cbar_axes[0].colorbar(im)
>
> # This affects all axes as share_all = True.
> grid.axes_llc.set_xticks([-2, 0, 2])
> grid.axes_llc.set_yticks([-2, 0, 2])
>
>
> if 1:
> F = plt.figure(1, (5.5, 2.5))
>
> F.subplots_adjust(left=0.05, right=0.98)
>
> demo_grid_with_single_cbar(F)
>
> plt.savefig('cbars')
> #plt.draw()
> #plt.show()
>
>
> ------------------------------------------------------------------------------
> vRanger cuts backup time in half-while increasing security.
> With the market-leading solution for virtual backup and recovery,
> you get blazing-fast, flexible, and affordable data protection.
> Download your free trial now.
> http://p.sf.net/sfu/quest-d2dcopy1
> _______________________________________________
> Matplotlib-users mailing list
> Matplotlib-users@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
------------------------------------------------------------------------------
Simplify data backup and recovery for your virtual environment with vRanger.
Installation's a snap, and flexible recovery options mean your data is safe,
secure and there when you need it. Data protection magic?
Nope - It's vRanger. Get your free trial download today.
http://p.sf.net/sfu/quest-sfdev2dev
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
