问题
I have an patch collection that I'd like to display a color map for. Because of some manipulations I do on top of the colormap, it's not possible for me to define it using a matplotlib.colorbar
instance. At least not as far as I can tell; doing so strips some manipulations I do with my colors that blank out patches lacking data:
cmap = matplotlib.cm.YlOrRd
colors = [cmap(n) if pd.notnull(n) else [1,1,1,1]
for n in plt.Normalize(0, 1)([nullity for _, nullity in squares])]
# Now we draw.
for i, ((min_x, max_x, min_y, max_y), _) in enumerate(squares):
square = shapely.geometry.Polygon([[min_x, min_y], [max_x, min_y],
[max_x, max_y], [min_x, max_y]])
ax0.add_patch(descartes.PolygonPatch(square, fc=colors[i],
ec='white', alpha=1, zorder=4))
So I define a matplotlib.colorbar.ColorbarBase
instance instead, which works:
matplotlib.colorbar.ColorbarBase(ax1, cmap=cmap, orientation='vertical',
norm=matplotlib.colors.Normalize(vmin=0, vmax=1))
Which results in e.g.:
The problem I have is that I want to reduce the size of this colorbar (specifically, the shrink it down to a specific vertical size, say, 500 pixels), but I don't see any obvious way of doing this. If I had a colorbar
instance, I could adjust this easily using its axis property arguments, but ColorbarBase
lacks these.
For further reference:
- The example my implementation is based on.
- The source code in question (warning: lengthy).
回答1:
The size and shape is defined with the axis. This is a snippet from code I have where I group 2 plots together and add a colorbar at the top independently. I played with the values in that add_axes instance until I got a size that worked for me:
cax = fig.add_axes([0.125, 0.925, 0.775, 0.0725]) #has to be as a list - starts with x, y coordinates for start and then width and height in % of figure width
norm = mpl.colors.Normalize(vmin = low_val, vmax = high_val)
mpl.colorbar.ColorbarBase(cax, cmap = self.cmap, norm = norm, orientation = 'horizontal')
回答2:
The question may be a bit old, but I found another solution that can be of help for anyone who is not willing to manually create a colorbar axes for the ColorbarBase class.
The solution below uses the matplotlib.colorbar.make_axes class to create a dependent sub_axes from the given axes. That sub_axes can then be supplied for the ColorbarBase class for the colorbar creation.
The code is derived from the matplotlib code example describe in here
Here is a snippet code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.colorbar as mcbar
from matplotlib import ticker
import matplotlib.colors as mcolors
# Make some illustrative fake data:
x = np.arange(0, np.pi, 0.1)
y = np.arange(0, 2 * np.pi, 0.1)
X, Y = np.meshgrid(x, y)
Z = np.cos(X) * np.sin(Y) * 10
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # R -> G -> B
n_bins = [3, 6, 10, 100] # Discretizes the interpolation into bins
cmap_name = 'my_list'
fig, axs = plt.subplots(2, 2, figsize=(9, 7))
fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)
for n_bin, ax in zip(n_bins, axs.ravel()):
# Create the colormap
cm = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bin)
# Fewer bins will result in "coarser" colomap interpolation
im = ax.imshow(Z, interpolation='nearest', origin='lower', cmap=cm)
ax.set_title("N bins: %s" % n_bin)
cax, cbar_kwds = mcbar.make_axes(ax, location = 'right',
fraction=0.15, shrink=0.5, aspect=20)
cbar = mcbar.ColorbarBase(cax, cmap=cm,
norm=mcolors.Normalize(clip=False),
alpha=None,
values=None,
boundaries=None,
orientation='vertical', ticklocation='auto', extend='both',
ticks=n_bins,
format=ticker.FormatStrFormatter('%.2f'),
drawedges=False,
filled=True,
extendfrac=None,
extendrect=False, label='my label')
if n_bin <= 10:
cbar.locator = ticker.MaxNLocator(n_bin)
cbar.update_ticks()
else:
cbar.locator = ticker.MaxNLocator(5)
cbar.update_ticks()
fig.show()
来源:https://stackoverflow.com/questions/40227283/setting-the-size-of-a-matplotlib-colorbarbase-object