Related
I need to create the legend as a separate figure, and more importantly separate instance that can be saved in a new file. My plot consists of lines and a filled in segment.
The problem is the fill_between element, I can not add it to the external figure/legend.
I realise, this is a different type of object, it is a PolyCollection, while to line-plots are Line2D elements.
How do I handle the PolyCollection so that I can use it in the external legend?
INFO: matplotlib version 3.3.2
import matplotlib.pyplot as plt
import numpy as np
# Dummy data
x = np.linspace(1, 100, 1000)
y = np.log(x)
y1 = np.sin(x)
# Create regular plot and plot everything
fig = plt.figure('Line plot')
ax = fig.add_subplot(111)
line1, = ax.plot(x, y)
line2, = ax.plot(x, y1)
fill = ax.fill_between(x, y, y1)
ax.legend([line1, line2, fill],['Log','Sin','Area'])
ax.plot()
# create new plot only for legend
legendFig = plt.figure('Legend plot')
legendFig.legend([line1, line2],['Log','Sin']) <----- This works
# legendFig.legend([line1, line2, fill],['Log','Sin', 'Area']) <----- This does not work
You forgot to mention what does not work means here.
Apparently, you get an error message: RuntimeError: Can not put single artist in more than one figure.
Matplotlib doesn't allow elements placed in one figure to be reused in another. It is just a lucky coincidence that the line don't give an error.
To use an element in another figure, you can create a new element, and that copy the style from the original element:
from matplotlib.lines import Line2D
from matplotlib.collections import PolyCollection
legendFig = plt.figure('Legend plot')
handle_line1 = Line2D([], [])
handle_line1.update_from(line1)
handle_line2 = Line2D([], [])
handle_line2.update_from(line2)
handle_fill = PolyCollection([])
handle_fill.update_from(fill)
legendFig.legend([handle_line1, handle_line2, handle_fill], ['Log', 'Sin', 'Area'])
I've spent entirely too long researching how to get two subplots to share the same y-axis with a single colorbar shared between the two in Matplotlib.
What was happening was that when I called the colorbar() function in either subplot1 or subplot2, it would autoscale the plot such that the colorbar plus the plot would fit inside the 'subplot' bounding box, causing the two side-by-side plots to be two very different sizes.
To get around this, I tried to create a third subplot which I then hacked to render no plot with just a colorbar present.
The only problem is, now the heights and widths of the two plots are uneven, and I can't figure out how to make it look okay.
Here is my code:
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches
from matplotlib.ticker import NullFormatter
# SIS Functions
TE = 1 # Einstein radius
g1 = lambda x,y: (TE/2) * (y**2-x**2)/((x**2+y**2)**(3/2))
g2 = lambda x,y: -1*TE*x*y / ((x**2+y**2)**(3/2))
kappa = lambda x,y: TE / (2*np.sqrt(x**2+y**2))
coords = np.linspace(-2,2,400)
X,Y = np.meshgrid(coords,coords)
g1out = g1(X,Y)
g2out = g2(X,Y)
kappaout = kappa(X,Y)
for i in range(len(coords)):
for j in range(len(coords)):
if np.sqrt(coords[i]**2+coords[j]**2) <= TE:
g1out[i][j]=0
g2out[i][j]=0
fig = plt.figure()
fig.subplots_adjust(wspace=0,hspace=0)
# subplot number 1
ax1 = fig.add_subplot(1,2,1,aspect='equal',xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{1}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
plt.ylabel(r"y ($\theta_{E}$)",rotation='horizontal',fontsize="15")
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.imshow(g1out,extent=(-2,2,-2,2))
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
e1 = patches.Ellipse((0,0),2,2,color='white')
ax1.add_patch(e1)
# subplot number 2
ax2 = fig.add_subplot(1,2,2,sharey=ax1,xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{2}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
ax2.yaxis.set_major_formatter( NullFormatter() )
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
plt.imshow(g2out,extent=(-2,2,-2,2))
e2 = patches.Ellipse((0,0),2,2,color='white')
ax2.add_patch(e2)
# subplot for colorbar
ax3 = fig.add_subplot(1,1,1)
ax3.axis('off')
cbar = plt.colorbar(ax=ax2)
plt.show()
Just place the colorbar in its own axis and use subplots_adjust to make room for it.
As a quick example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
plt.show()
Note that the color range will be set by the last image plotted (that gave rise to im) even if the range of values is set by vmin and vmax. If another plot has, for example, a higher max value, points with higher values than the max of im will show in uniform color.
You can simplify Joe Kington's code using the axparameter of figure.colorbar() with a list of axes.
From the documentation:
ax
None | parent axes object(s) from which space for a new colorbar axes will be stolen. If a list of axes is given they will all be resized to make room for the colorbar axes.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
This solution does not require manual tweaking of axes locations or colorbar size, works with multi-row and single-row layouts, and works with tight_layout(). It is adapted from a gallery example, using ImageGrid from matplotlib's AxesGrid Toolbox.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
# Set up figure and image grid
fig = plt.figure(figsize=(9.75, 3))
grid = ImageGrid(fig, 111, # as in plt.subplot(111)
nrows_ncols=(1,3),
axes_pad=0.15,
share_all=True,
cbar_location="right",
cbar_mode="single",
cbar_size="7%",
cbar_pad=0.15,
)
# Add data to image grid
for ax in grid:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
# Colorbar
ax.cax.colorbar(im)
ax.cax.toggle_label(True)
#plt.tight_layout() # Works, but may still require rect paramater to keep colorbar labels visible
plt.show()
Using make_axes is even easier and gives a better result. It also provides possibilities to customise the positioning of the colorbar.
Also note the option of subplots to share x and y axes.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
cax,kw = mpl.colorbar.make_axes([ax for ax in axes.flat])
plt.colorbar(im, cax=cax, **kw)
plt.show()
As a beginner who stumbled across this thread, I'd like to add a python-for-dummies adaptation of abevieiramota's very neat answer (because I'm at the level that I had to look up 'ravel' to work out what their code was doing):
import numpy as np
import matplotlib.pyplot as plt
fig, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3)
axlist = [ax1,ax2,ax3,ax4,ax5,ax6]
first = ax1.imshow(np.random.random((10,10)), vmin=0, vmax=1)
third = ax3.imshow(np.random.random((12,12)), vmin=0, vmax=1)
fig.colorbar(first, ax=axlist)
plt.show()
Much less pythonic, much easier for noobs like me to see what's actually happening here.
Shared colormap and colorbar
This is for the more complex case where the values are not just between 0 and 1; the cmap needs to be shared instead of just using the last one.
import numpy as np
from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig, axes = plt.subplots(nrows=2, ncols=2)
cmap=cm.get_cmap('viridis')
normalizer=Normalize(0,4)
im=cm.ScalarMappable(norm=normalizer)
for i,ax in enumerate(axes.flat):
ax.imshow(i+np.random.random((10,10)),cmap=cmap,norm=normalizer)
ax.set_title(str(i))
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
As pointed out in other answers, the idea is usually to define an axes for the colorbar to reside in. There are various ways of doing so; one that hasn't been mentionned yet would be to directly specify the colorbar axes at subplot creation with plt.subplots(). The advantage is that the axes position does not need to be manually set and in all cases with automatic aspect the colorbar will be exactly the same height as the subplots. Even in many cases where images are used the result will be satisfying as shown below.
When using plt.subplots(), the use of gridspec_kw argument allows to make the colorbar axes much smaller than the other axes.
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
Example:
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,8), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,8), vmin=0, vmax=1)
ax.set_ylabel("y label")
fig.colorbar(im, cax=cax)
plt.show()
This works well, if the plots' aspect is autoscaled or the images are shrunk due to their aspect in the width direction (as in the above). If, however, the images are wider then high, the result would look as follows, which might be undesired.
A solution to fix the colorbar height to the subplot height would be to use mpl_toolkits.axes_grid1.inset_locator.InsetPosition to set the colorbar axes relative to the image subplot axes.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(7,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,16), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,16), vmin=0, vmax=1)
ax.set_ylabel("y label")
ip = InsetPosition(ax2, [1.05,0,0.05,1])
cax.set_axes_locator(ip)
fig.colorbar(im, cax=cax, ax=[ax,ax2])
plt.show()
New in matplotlib 3.4.0
Shared colorbars can now be implemented using subfigures:
New Figure.subfigures and Figure.add_subfigure allow ... localized figure artists (e.g., colorbars and suptitles) that only pertain to each subfigure.
The matplotlib gallery includes demos on how to plot subfigures.
Here is a minimal example with 2 subfigures, each with a shared colorbar:
fig = plt.figure(constrained_layout=True)
(subfig_l, subfig_r) = fig.subfigures(nrows=1, ncols=2)
axes_l = subfig_l.subplots(nrows=1, ncols=2, sharey=True)
for ax in axes_l:
im = ax.imshow(np.random.random((10, 10)), vmin=0, vmax=1)
# shared colorbar for left subfigure
subfig_l.colorbar(im, ax=axes_l, location='bottom')
axes_r = subfig_r.subplots(nrows=3, ncols=1, sharex=True)
for ax in axes_r:
mesh = ax.pcolormesh(np.random.randn(30, 30), vmin=-2.5, vmax=2.5)
# shared colorbar for right subfigure
subfig_r.colorbar(mesh, ax=axes_r)
The solution of using a list of axes by abevieiramota works very well until you use only one row of images, as pointed out in the comments. Using a reasonable aspect ratio for figsize helps, but is still far from perfect. For example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(9.75, 3))
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
The colorbar function provides the shrink parameter which is a scaling factor for the size of the colorbar axes. It does require some manual trial and error. For example:
fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.75)
To add to #abevieiramota's excellent answer, you can get the euqivalent of tight_layout with constrained_layout. You will still get large horizontal gaps if you use imshow instead of pcolormesh because of the 1:1 aspect ratio imposed by imshow.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
for ax in axes.flat:
im = ax.pcolormesh(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.flat)
plt.show()
I noticed that almost every solution posted involved ax.imshow(im, ...) and did not normalize the colors displayed to the colorbar for the multiple subfigures. The im mappable is taken from the last instance, but what if the values of the multiple im-s are different? (I'm assuming these mappables are treated in the same way that the contour-sets and surface-sets are treated.) I have an example using a 3d surface plot below that creates two colorbars for a 2x2 subplot (one colorbar per one row). Although the question asks explicitly for a different arrangement, I think the example helps clarify some things. I haven't found a way to do this using plt.subplots(...) yet because of the 3D axes unfortunately.
If only I could position the colorbars in a better way... (There is probably a much better way to do this, but at least it should be not too difficult to follow.)
import matplotlib
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
cmap = 'plasma'
ncontours = 5
def get_data(row, col):
""" get X, Y, Z, and plot number of subplot
Z > 0 for top row, Z < 0 for bottom row """
if row == 0:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 1
else:
pnum = 2
elif row == 1:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = -np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 3
else:
pnum = 4
print("\nPNUM: {}, Zmin = {}, Zmax = {}\n".format(pnum, np.min(Z), np.max(Z)))
return X, Y, Z, pnum
fig = plt.figure()
nrows, ncols = 2, 2
zz = []
axes = []
for row in range(nrows):
for col in range(ncols):
X, Y, Z, pnum = get_data(row, col)
ax = fig.add_subplot(nrows, ncols, pnum, projection='3d')
ax.set_title('row = {}, col = {}'.format(row, col))
fhandle = ax.plot_surface(X, Y, Z, cmap=cmap)
zz.append(Z)
axes.append(ax)
## get full range of Z data as flat list for top and bottom rows
zz_top = zz[0].reshape(-1).tolist() + zz[1].reshape(-1).tolist()
zz_btm = zz[2].reshape(-1).tolist() + zz[3].reshape(-1).tolist()
## get top and bottom axes
ax_top = [axes[0], axes[1]]
ax_btm = [axes[2], axes[3]]
## normalize colors to minimum and maximum values of dataset
norm_top = matplotlib.colors.Normalize(vmin=min(zz_top), vmax=max(zz_top))
norm_btm = matplotlib.colors.Normalize(vmin=min(zz_btm), vmax=max(zz_btm))
cmap = cm.get_cmap(cmap, ncontours) # number of colors on colorbar
mtop = cm.ScalarMappable(cmap=cmap, norm=norm_top)
mbtm = cm.ScalarMappable(cmap=cmap, norm=norm_btm)
for m in (mtop, mbtm):
m.set_array([])
# ## create cax to draw colorbar in
# cax_top = fig.add_axes([0.9, 0.55, 0.05, 0.4])
# cax_btm = fig.add_axes([0.9, 0.05, 0.05, 0.4])
cbar_top = fig.colorbar(mtop, ax=ax_top, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_top)
cbar_top.set_ticks(np.linspace(min(zz_top), max(zz_top), ncontours))
cbar_btm = fig.colorbar(mbtm, ax=ax_btm, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_btm)
cbar_btm.set_ticks(np.linspace(min(zz_btm), max(zz_btm), ncontours))
plt.show()
plt.close(fig)
## orientation of colorbar = 'horizontal' if done by column
This topic is well covered but I still would like to propose another approach in a slightly different philosophy.
It is a bit more complex to set-up but it allow (in my opinion) a bit more flexibility. For example, one can play with the respective ratios of each subplots / colorbar:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec
# Define number of rows and columns you want in your figure
nrow = 2
ncol = 3
# Make a new figure
fig = plt.figure(constrained_layout=True)
# Design your figure properties
widths = [3,4,5,1]
gs = GridSpec(nrow, ncol + 1, figure=fig, width_ratios=widths)
# Fill your figure with desired plots
axes = []
for i in range(nrow):
for j in range(ncol):
axes.append(fig.add_subplot(gs[i, j]))
im = axes[-1].pcolormesh(np.random.random((10,10)))
# Shared colorbar
axes.append(fig.add_subplot(gs[:, ncol]))
fig.colorbar(im, cax=axes[-1])
plt.show()
The answers above are great, but most of them use the fig.colobar() method applied to a fig object. This example shows how to use the plt.colobar() function, applied directly to pyplot:
def shared_colorbar_example():
fig, axs = plt.subplots(nrows=3, ncols=3)
for ax in axs.flat:
plt.sca(ax)
color = np.random.random((10))
plt.scatter(range(10), range(10), c=color, cmap='viridis', vmin=0, vmax=1)
plt.colorbar(ax=axs.ravel().tolist(), shrink=0.6)
plt.show()
shared_colorbar_example()
Since most answers above demonstrated usage on 2D matrices, I went with a simple scatter plot. The shrink keyword is optional and resizes the colorbar.
If vmin and vmax are not specified this approach will automatically analyze all of the subplots for the minimum and maximum value to be used on the colorbar. The above approaches when using fig.colorbar(im) scan only the image passed as argument for min and max values of the colorbar.
Result:
I've spent entirely too long researching how to get two subplots to share the same y-axis with a single colorbar shared between the two in Matplotlib.
What was happening was that when I called the colorbar() function in either subplot1 or subplot2, it would autoscale the plot such that the colorbar plus the plot would fit inside the 'subplot' bounding box, causing the two side-by-side plots to be two very different sizes.
To get around this, I tried to create a third subplot which I then hacked to render no plot with just a colorbar present.
The only problem is, now the heights and widths of the two plots are uneven, and I can't figure out how to make it look okay.
Here is my code:
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches
from matplotlib.ticker import NullFormatter
# SIS Functions
TE = 1 # Einstein radius
g1 = lambda x,y: (TE/2) * (y**2-x**2)/((x**2+y**2)**(3/2))
g2 = lambda x,y: -1*TE*x*y / ((x**2+y**2)**(3/2))
kappa = lambda x,y: TE / (2*np.sqrt(x**2+y**2))
coords = np.linspace(-2,2,400)
X,Y = np.meshgrid(coords,coords)
g1out = g1(X,Y)
g2out = g2(X,Y)
kappaout = kappa(X,Y)
for i in range(len(coords)):
for j in range(len(coords)):
if np.sqrt(coords[i]**2+coords[j]**2) <= TE:
g1out[i][j]=0
g2out[i][j]=0
fig = plt.figure()
fig.subplots_adjust(wspace=0,hspace=0)
# subplot number 1
ax1 = fig.add_subplot(1,2,1,aspect='equal',xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{1}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
plt.ylabel(r"y ($\theta_{E}$)",rotation='horizontal',fontsize="15")
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.imshow(g1out,extent=(-2,2,-2,2))
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
e1 = patches.Ellipse((0,0),2,2,color='white')
ax1.add_patch(e1)
# subplot number 2
ax2 = fig.add_subplot(1,2,2,sharey=ax1,xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{2}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
ax2.yaxis.set_major_formatter( NullFormatter() )
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
plt.imshow(g2out,extent=(-2,2,-2,2))
e2 = patches.Ellipse((0,0),2,2,color='white')
ax2.add_patch(e2)
# subplot for colorbar
ax3 = fig.add_subplot(1,1,1)
ax3.axis('off')
cbar = plt.colorbar(ax=ax2)
plt.show()
Just place the colorbar in its own axis and use subplots_adjust to make room for it.
As a quick example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
plt.show()
Note that the color range will be set by the last image plotted (that gave rise to im) even if the range of values is set by vmin and vmax. If another plot has, for example, a higher max value, points with higher values than the max of im will show in uniform color.
You can simplify Joe Kington's code using the axparameter of figure.colorbar() with a list of axes.
From the documentation:
ax
None | parent axes object(s) from which space for a new colorbar axes will be stolen. If a list of axes is given they will all be resized to make room for the colorbar axes.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
This solution does not require manual tweaking of axes locations or colorbar size, works with multi-row and single-row layouts, and works with tight_layout(). It is adapted from a gallery example, using ImageGrid from matplotlib's AxesGrid Toolbox.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
# Set up figure and image grid
fig = plt.figure(figsize=(9.75, 3))
grid = ImageGrid(fig, 111, # as in plt.subplot(111)
nrows_ncols=(1,3),
axes_pad=0.15,
share_all=True,
cbar_location="right",
cbar_mode="single",
cbar_size="7%",
cbar_pad=0.15,
)
# Add data to image grid
for ax in grid:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
# Colorbar
ax.cax.colorbar(im)
ax.cax.toggle_label(True)
#plt.tight_layout() # Works, but may still require rect paramater to keep colorbar labels visible
plt.show()
Using make_axes is even easier and gives a better result. It also provides possibilities to customise the positioning of the colorbar.
Also note the option of subplots to share x and y axes.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
cax,kw = mpl.colorbar.make_axes([ax for ax in axes.flat])
plt.colorbar(im, cax=cax, **kw)
plt.show()
As a beginner who stumbled across this thread, I'd like to add a python-for-dummies adaptation of abevieiramota's very neat answer (because I'm at the level that I had to look up 'ravel' to work out what their code was doing):
import numpy as np
import matplotlib.pyplot as plt
fig, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3)
axlist = [ax1,ax2,ax3,ax4,ax5,ax6]
first = ax1.imshow(np.random.random((10,10)), vmin=0, vmax=1)
third = ax3.imshow(np.random.random((12,12)), vmin=0, vmax=1)
fig.colorbar(first, ax=axlist)
plt.show()
Much less pythonic, much easier for noobs like me to see what's actually happening here.
Shared colormap and colorbar
This is for the more complex case where the values are not just between 0 and 1; the cmap needs to be shared instead of just using the last one.
import numpy as np
from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig, axes = plt.subplots(nrows=2, ncols=2)
cmap=cm.get_cmap('viridis')
normalizer=Normalize(0,4)
im=cm.ScalarMappable(norm=normalizer)
for i,ax in enumerate(axes.flat):
ax.imshow(i+np.random.random((10,10)),cmap=cmap,norm=normalizer)
ax.set_title(str(i))
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
As pointed out in other answers, the idea is usually to define an axes for the colorbar to reside in. There are various ways of doing so; one that hasn't been mentionned yet would be to directly specify the colorbar axes at subplot creation with plt.subplots(). The advantage is that the axes position does not need to be manually set and in all cases with automatic aspect the colorbar will be exactly the same height as the subplots. Even in many cases where images are used the result will be satisfying as shown below.
When using plt.subplots(), the use of gridspec_kw argument allows to make the colorbar axes much smaller than the other axes.
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
Example:
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,8), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,8), vmin=0, vmax=1)
ax.set_ylabel("y label")
fig.colorbar(im, cax=cax)
plt.show()
This works well, if the plots' aspect is autoscaled or the images are shrunk due to their aspect in the width direction (as in the above). If, however, the images are wider then high, the result would look as follows, which might be undesired.
A solution to fix the colorbar height to the subplot height would be to use mpl_toolkits.axes_grid1.inset_locator.InsetPosition to set the colorbar axes relative to the image subplot axes.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(7,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,16), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,16), vmin=0, vmax=1)
ax.set_ylabel("y label")
ip = InsetPosition(ax2, [1.05,0,0.05,1])
cax.set_axes_locator(ip)
fig.colorbar(im, cax=cax, ax=[ax,ax2])
plt.show()
New in matplotlib 3.4.0
Shared colorbars can now be implemented using subfigures:
New Figure.subfigures and Figure.add_subfigure allow ... localized figure artists (e.g., colorbars and suptitles) that only pertain to each subfigure.
The matplotlib gallery includes demos on how to plot subfigures.
Here is a minimal example with 2 subfigures, each with a shared colorbar:
fig = plt.figure(constrained_layout=True)
(subfig_l, subfig_r) = fig.subfigures(nrows=1, ncols=2)
axes_l = subfig_l.subplots(nrows=1, ncols=2, sharey=True)
for ax in axes_l:
im = ax.imshow(np.random.random((10, 10)), vmin=0, vmax=1)
# shared colorbar for left subfigure
subfig_l.colorbar(im, ax=axes_l, location='bottom')
axes_r = subfig_r.subplots(nrows=3, ncols=1, sharex=True)
for ax in axes_r:
mesh = ax.pcolormesh(np.random.randn(30, 30), vmin=-2.5, vmax=2.5)
# shared colorbar for right subfigure
subfig_r.colorbar(mesh, ax=axes_r)
The solution of using a list of axes by abevieiramota works very well until you use only one row of images, as pointed out in the comments. Using a reasonable aspect ratio for figsize helps, but is still far from perfect. For example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(9.75, 3))
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
The colorbar function provides the shrink parameter which is a scaling factor for the size of the colorbar axes. It does require some manual trial and error. For example:
fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.75)
To add to #abevieiramota's excellent answer, you can get the euqivalent of tight_layout with constrained_layout. You will still get large horizontal gaps if you use imshow instead of pcolormesh because of the 1:1 aspect ratio imposed by imshow.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
for ax in axes.flat:
im = ax.pcolormesh(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.flat)
plt.show()
I noticed that almost every solution posted involved ax.imshow(im, ...) and did not normalize the colors displayed to the colorbar for the multiple subfigures. The im mappable is taken from the last instance, but what if the values of the multiple im-s are different? (I'm assuming these mappables are treated in the same way that the contour-sets and surface-sets are treated.) I have an example using a 3d surface plot below that creates two colorbars for a 2x2 subplot (one colorbar per one row). Although the question asks explicitly for a different arrangement, I think the example helps clarify some things. I haven't found a way to do this using plt.subplots(...) yet because of the 3D axes unfortunately.
If only I could position the colorbars in a better way... (There is probably a much better way to do this, but at least it should be not too difficult to follow.)
import matplotlib
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
cmap = 'plasma'
ncontours = 5
def get_data(row, col):
""" get X, Y, Z, and plot number of subplot
Z > 0 for top row, Z < 0 for bottom row """
if row == 0:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 1
else:
pnum = 2
elif row == 1:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = -np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 3
else:
pnum = 4
print("\nPNUM: {}, Zmin = {}, Zmax = {}\n".format(pnum, np.min(Z), np.max(Z)))
return X, Y, Z, pnum
fig = plt.figure()
nrows, ncols = 2, 2
zz = []
axes = []
for row in range(nrows):
for col in range(ncols):
X, Y, Z, pnum = get_data(row, col)
ax = fig.add_subplot(nrows, ncols, pnum, projection='3d')
ax.set_title('row = {}, col = {}'.format(row, col))
fhandle = ax.plot_surface(X, Y, Z, cmap=cmap)
zz.append(Z)
axes.append(ax)
## get full range of Z data as flat list for top and bottom rows
zz_top = zz[0].reshape(-1).tolist() + zz[1].reshape(-1).tolist()
zz_btm = zz[2].reshape(-1).tolist() + zz[3].reshape(-1).tolist()
## get top and bottom axes
ax_top = [axes[0], axes[1]]
ax_btm = [axes[2], axes[3]]
## normalize colors to minimum and maximum values of dataset
norm_top = matplotlib.colors.Normalize(vmin=min(zz_top), vmax=max(zz_top))
norm_btm = matplotlib.colors.Normalize(vmin=min(zz_btm), vmax=max(zz_btm))
cmap = cm.get_cmap(cmap, ncontours) # number of colors on colorbar
mtop = cm.ScalarMappable(cmap=cmap, norm=norm_top)
mbtm = cm.ScalarMappable(cmap=cmap, norm=norm_btm)
for m in (mtop, mbtm):
m.set_array([])
# ## create cax to draw colorbar in
# cax_top = fig.add_axes([0.9, 0.55, 0.05, 0.4])
# cax_btm = fig.add_axes([0.9, 0.05, 0.05, 0.4])
cbar_top = fig.colorbar(mtop, ax=ax_top, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_top)
cbar_top.set_ticks(np.linspace(min(zz_top), max(zz_top), ncontours))
cbar_btm = fig.colorbar(mbtm, ax=ax_btm, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_btm)
cbar_btm.set_ticks(np.linspace(min(zz_btm), max(zz_btm), ncontours))
plt.show()
plt.close(fig)
## orientation of colorbar = 'horizontal' if done by column
This topic is well covered but I still would like to propose another approach in a slightly different philosophy.
It is a bit more complex to set-up but it allow (in my opinion) a bit more flexibility. For example, one can play with the respective ratios of each subplots / colorbar:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec
# Define number of rows and columns you want in your figure
nrow = 2
ncol = 3
# Make a new figure
fig = plt.figure(constrained_layout=True)
# Design your figure properties
widths = [3,4,5,1]
gs = GridSpec(nrow, ncol + 1, figure=fig, width_ratios=widths)
# Fill your figure with desired plots
axes = []
for i in range(nrow):
for j in range(ncol):
axes.append(fig.add_subplot(gs[i, j]))
im = axes[-1].pcolormesh(np.random.random((10,10)))
# Shared colorbar
axes.append(fig.add_subplot(gs[:, ncol]))
fig.colorbar(im, cax=axes[-1])
plt.show()
The answers above are great, but most of them use the fig.colobar() method applied to a fig object. This example shows how to use the plt.colobar() function, applied directly to pyplot:
def shared_colorbar_example():
fig, axs = plt.subplots(nrows=3, ncols=3)
for ax in axs.flat:
plt.sca(ax)
color = np.random.random((10))
plt.scatter(range(10), range(10), c=color, cmap='viridis', vmin=0, vmax=1)
plt.colorbar(ax=axs.ravel().tolist(), shrink=0.6)
plt.show()
shared_colorbar_example()
Since most answers above demonstrated usage on 2D matrices, I went with a simple scatter plot. The shrink keyword is optional and resizes the colorbar.
If vmin and vmax are not specified this approach will automatically analyze all of the subplots for the minimum and maximum value to be used on the colorbar. The above approaches when using fig.colorbar(im) scan only the image passed as argument for min and max values of the colorbar.
Result:
I would like to draw animated subplots with ArtistAnimation. Unfortunately, I cannot figure out how to have an animated legend. I tried out different methods I found on StackOverflow. If I manage to get a legend it is not animated, but just the legends of all animation steps together.
My code looks like this:
import numpy as np
import pylab as pl
import matplotlib.animation as anim
fig, (ax1, ax2, ax3) = pl.subplots(1,3,figsize=(11,4))
ims = []
im1 = ['im11','im12','im13']
im2 = ['im21','im22','im23']
x = np.arange(0,2*np.pi,0.1)
n=50
for i in range(n):
for sp in (1,2,3):
pl.subplot(1,3,sp)
y1 = np.sin(sp*x + i*np.pi/n)
y2 = np.cos(sp*x + i*np.pi/n)
im1[sp-1], = pl.plot(x,y1)
im2[sp-1], = pl.plot(x,y2)
pl.xlim([0,2*np.pi])
pl.ylim([-1,1])
lab = 'i='+str(i)+', sp='+str(sp)
im1[sp-1].set_label([lab])
pl.legend(loc=2, prop={'size': 6}).draw_frame(False)
ims.append([ im1[0],im1[1],im1[2], im2[0],im2[1],im2[2] ])
ani = anim.ArtistAnimation(fig,ims,blit=True)
pl.show()
I thought this code would be equivalent to the method used here How to add legend/label in python animation but obviously I am missing something.
I also tried to set the labels as suggested in Add a legend for an animation (of Artists) in matplotlib but I do not really understand how to use it for my case. Like this
im2[sp-1].legend(handles='-', labels=[lab])
I get an AttributeError: 'Line2D' object has no attribute 'legend'.
[EDIT]: I did not state it clearly: I would like to have a legend for both lines in the plots.
I do not know what exactly the legend should look like, but I'd imaginge you simply want to let it display the current value of the one line from the current frame. You'd therefore better update the data of the line, instead of plotting 150 new plots.
import numpy as np
import pylab as plt
import matplotlib.animation as anim
fig, axes = plt.subplots(1,3,figsize=(8,3))
ims = []
im1 = [ax.plot([],[], label="label")[0] for ax in axes]
im2 = [ax.plot([],[], label="label")[0] for ax in axes]
x = np.arange(0,2*np.pi,0.1)
legs = [ax.legend(loc=2, prop={'size': 6}) for ax in axes]
for ax in axes:
ax.set_xlim([0,2*np.pi])
ax.set_ylim([-1,1])
plt.tight_layout()
n=50
def update(i):
for sp in range(3):
y1 = np.sin((sp+1)*x + (i)*np.pi/n)
y2 = np.cos((sp+1)*x + (i)*np.pi/n)
im1[sp].set_data(x,y1)
im2[sp].set_data(x,y2)
lab = 'i='+str(i)+', sp='+str(sp+1)
legs[sp].texts[0].set_text(lab)
legs[sp].texts[1].set_text(lab)
return im1 + im2 +legs
ani = anim.FuncAnimation(fig,update, frames=n,blit=True)
plt.show()
This should be easy but I'm having a hard time with it. Basically, I have a subplot in matplotlib that I'm drawing a hexbin plot in every time a function is called, but every time I call the function I get a new colorbar, so what I'd really like to do is update the colorbar. Unfortunately, this doesn't seem to work since the object the colorbar is attached to is being recreated by subplot.hexbin.
def foo(self):
self.subplot.clear()
hb = self.subplot.hexbin(...)
if self.cb:
self.cb.update_bruteforce() # Doesn't work (hb is new)
else:
self.cb = self.figure.colorbar(hb)
I'm now in this annoying place where I'm trying to delete the colorbar axes altogether and simply recreate it. Unfortunately, when I delete the colorbar axes, the subplot axes don't reclaim the space, and calling self.subplot.reset_position() isn't doing what I thought it would.
def foo(self):
self.subplot.clear()
hb = self.subplot.hexbin(...)
if self.cb:
self.figure.delaxes(self.figure.axes[1])
del self.cb
# TODO: resize self.subplot so it fills the
# whole figure before adding the new colorbar
self.cb = self.figure.colorbar(hb)
I think the problem is that with del you cancel the variable, but not the referenced object colorbar.
If you want the colorbar to be removed from plot and disappear, you have to use the method remove of the colorbar instance and to do this you need to have the colorbar in a variable, for which you have two options:
holding the colorbar in a value at the moment of creation, as shown in other answers e.g. cb=plt.colorbar()
retrieve an existing colorbar, that you can do following (and upvoting :)) what I wrote here: How to retrieve colorbar instance from figure in matplotlib
then:
cb.remove() plt.draw() #update plot
Full code and result:
from matplotlib import pyplot as plt
import numpy as np
plt.ion()
plt.imshow(np.random.random(15).reshape((5,3)))
cb = plt.colorbar()
plt.savefig('test01.png')
cb.remove()
plt.savefig('test02.png')
Alright, here's my solution. Not terribly elegant, but not a terrible hack either.
def foo(self):
self.subplot.clear()
hb = self.subplot.hexbin(...)
if self.cb:
self.figure.delaxes(self.figure.axes[1])
self.figure.subplots_adjust(right=0.90) #default right padding
self.cb = self.figure.colorbar(hb)
This works for my needs since I only ever have a single subplot. People who run into the same problem when using multiple subplots or when drawing the colorbar in a different position will need to tweak.
I managed to solve the same issue using fig.clear() and display.clear_output()
import matplotlib.pyplot as plt
import IPython.display as display
import matplotlib.tri as tri
from pylab import *
%matplotlib inline
def plot_res(fig):
ax=fig.add_axes([0,0,1,1])
ax.set_xlabel("x")
ax.set_ylabel('y')
plotted=ax.imshow(rand(250, 250))
ax.set_title("title")
cbar=fig.colorbar(mappable=plotted)
display.clear_output(wait=True)
display.display(plt.gcf())
fig.clear()
fig=plt.figure()
N=20
for j in range(N):
plot_res(fig)
If you have a matplotlib figure object you just need to do fig.delaxes(fig.axes[1])
For example:
Plot with colorbar
import matplotlib.pyplot as plt
# setup some generic data
N = 37
x, y = np.mgrid[:N, :N]
Z = (np.cos(x*0.2) + np.sin(y*0.3))
# mask out the negative and positive values, respectively
Zpos = np.ma.masked_less(Z, 0)
Zneg = np.ma.masked_greater(Z, 0)
fig, ax1 = plt.subplots(figsize=(13, 3), ncols=1)
# plot just the positive data and save the
# color "mappable" object returned by ax1.imshow
pos = ax1.imshow(Zpos, cmap='Blues', interpolation='none')
# add the colorbar using the figure's method,
# telling which mappable we're talking about and
# which axes object it should be near
fig.colorbar(pos, ax=ax1)
Remove colorbar
import matplotlib.pyplot as plt
# setup some generic data
N = 37
x, y = np.mgrid[:N, :N]
Z = (np.cos(x*0.2) + np.sin(y*0.3))
# mask out the negative and positive values, respectively
Zpos = np.ma.masked_less(Z, 0)
Zneg = np.ma.masked_greater(Z, 0)
fig, ax1 = plt.subplots(figsize=(13, 3), ncols=1)
# plot just the positive data and save the
# color "mappable" object returned by ax1.imshow
pos = ax1.imshow(Zpos, cmap='Blues', interpolation='none')
# add the colorbar using the figure's method,
# telling which mappable we're talking about and
# which axes object it should be near
fig.colorbar(pos, ax=ax1)
fig.delaxes(fig.axes[1])
I had a similar problem and played around a little bit. I came up with two solutions which might be slightly more elegant:
Clear the whole figure and add the subplot (+colorbar if wanted) again.
If there's always a colorbar, you can simply update the axes with autoscale which also updates the colorbar.
I've tried this with imshow, but I guess it works similar for other plotting methods.
from pylab import *
close('all') #close all figures in memory
#1. Figures for fig.clf method
fig1 = figure()
fig2 = figure()
cbar1=None
cbar2=None
data = rand(250, 250)
def makefig(fig,cbar):
fig.clf()
ax = fig.add_subplot(111)
im = ax.imshow(data)
if cbar:
cbar=None
else:
cbar = fig.colorbar(im)
return cbar
#2. Update method
fig_update = figure()
cbar3=None
data_update = rand(250, 250)
img=None
def makefig_update(fig,im,cbar,data):
if im:
data*=2 #change data, so there is change in output (look at colorbar)
#im.set_data(data) #use this if you use new array
im.autoscale()
#cbar.update_normal(im) #cbar is updated automatically
else:
ax = fig.add_subplot(111)
im = ax.imshow(data)
cbar=fig.colorbar(im)
return im,cbar,data
#Execute functions a few times
for i in range(3):
print i
cbar1=makefig(fig1,cbar1)
cbar2=makefig(fig2,cbar2)
img,cbar3,data_update=makefig_update(fig_update,img,cbar3,data_update)
cbar2=makefig(fig2,cbar2)
fig1.show()
fig2.show()
fig_update.show()
I needed to remove colorbars because I was plotting a pcolormesh and adding colorbar to a figure in a loop. Each loop would create a new colorbar and after ten loops I would have ten colorbars. That was bad.
To remove colorbars, I name the pcolormesh and colorbar a variable, then at the end of my loop I remove each. It is important to remove the colorbar before removing the pcolormesh.
Psudo Code:
for i in range(0,10):
p = plt.pcolormesh(datastuff[i])
cb = plt.colorbar(p)
plt.savefig('name_'+i)
cb.remove()
p.remove()
Again, it was necessary to remove the colorbar before the pcolormesh
I am using matplotlib 1.4.0. This is how I solve this problem:
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
# A contour plot example:
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = 10.0 * (Z2 - Z1)
#
# first drawing
fig = plt.figure()
ax = fig.add_subplot(111) # drawing axes
c = ax.contourf(Z) # contour fill c
cb = fig.colorbar(c) # colorbar for contour c
# clear first drawimg
ax.clear() # clear drawing axes
cb.ax.clear() # clear colorbar axes
# replace with new drawing
# 1. drawing new contour at drawing axes
c_new = ax.contour(Z)
# 2. create new colorbar for new contour at colorbar axes
cb_new = ax.get_figure().colorbar(c_new, cax=cb.ax)
plt.show()
Above code draws a contour fill plot with colorbar, clear it and draw a new contour plot with new colorbar at the same figure.
By using
cb.ax
i am able to identify the colorbar axes and clear the old colorbar.
And specifying cax=cb.ax simply draws the new colorbar in the old colorbar axes.
Don't want to take anything away from the author of this blog post (Joseph Long) but this is clearly the best solution I've found so far. It includes pieces of code, great explanations and many examples.
To summarize, from any output of an axis ax of the command: plot, image, scatter, collection, etc. such as:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(5,5), dpi=300)
ax = fig.add_subplot(1, 1, 1)
data = ax.plot(x,y)
# or
data = ax.scatter(x, y, z)
# or
data = ax.imshow(z)
# or
data = matplotlib.collection(patches)
ax.add_collection(data)
You create a color bar axis using the make_axes_locatable and the original axis of the plot.
from mpl_toolkits.axes_grid1 import make_axes_locatable
# the magical part
divider = make_axes_locatable(ax)
caxis = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(data, cax=caxis)
plt.show()
The created colorbar will have the same size as the figure or subplot and you can modify it's width, location, padding when using the divider.append_axes command.
My solution consists in having an Axes whose only purpose is to hold the colorbar, and clear it entirely when needed.
For example, define those once:
figure, ax = plt.subplots() # All the plotting is done on `ax`.
cax = ax.inset_axes([1.03, 0, 0.1, 1], transform=ax.transAxes) # Colorbar is held by `cax`.
Then do this as many times as needed:
cax.clear()
colorbar = figure.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
ax=ax,
cax=cax,
**kwargs)
"on_mappable_changed" worked in my case. However, according to docs, the method "Typically ... should not be called manually."
if self.cb:
self.cb.on_mappable_changed(hb)
else:
self.cb = self.fig.colorbar(hb)