I want to draw a figure in matplotib where the axis are displayed within the plot itself not on the side
I have tried the following code from here:
import math
import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
a = []
for item in x:
a.append(1/(1+math.exp(-item)))
return a
x = np.arange(-10., 10., 0.2)
sig = sigmoid(x)
plt.plot(x,sig)
plt.show()
The above code displays the figure like this:
What I would like to draw is something as follows (image from Wikipedia)
This question describes a similar problem, but it draws a reference line in the middle but no axis.
One way to do it is using spines:
import math
import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
a = []
for item in x:
a.append(1/(1+math.exp(-item)))
return a
x = np.arange(-10., 10., 0.2)
sig = sigmoid(x)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# Move left y-axis and bottom x-axis to centre, passing through (0,0)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
# Eliminate upper and right axes
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
# Show ticks in the left and lower axes only
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.plot(x,sig)
plt.show()
shows:
Basically, I want to comment on the accepted answer (but my rep doesn't allow that).
The use of
ax.spines['bottom'].set_position('center')
draws the x-axes such that it intersect the y-axes in its center. In case of asymmetric ylim this means that x-axis passes NOT through y=0. Jblasco's answer has this drawback, the intersect is at y=0.5 (the center between ymin=0.0 and ymax=1.0)
However, the reference plot of the original question has axes that intersect each other at 0.0 (which is somehow conventional or at least common).
To achieve this behaviour,
ax.spines['bottom'].set_position('zero')
has to be used.
See the following example, where 'zero' makes the axes intersect at 0.0 despite asymmetrically ranges in both x and y.
import numpy as np
import matplotlib.pyplot as plt
#data generation
x = np.arange(-10,20,0.2)
y = 1.0/(1.0+np.exp(-x)) # nunpy does the calculation elementwise for you
fig, [ax0, ax1] = plt.subplots(ncols=2, figsize=(8,4))
# Eliminate upper and right axes
ax0.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
# Show ticks on the left and lower axes only
ax0.xaxis.set_tick_params(bottom='on', top='off')
ax0.yaxis.set_tick_params(left='on', right='off')
# Move remaining spines to the center
ax0.set_title('center')
ax0.spines['bottom'].set_position('center') # spine for xaxis
# - will pass through the center of the y-values (which is 0)
ax0.spines['left'].set_position('center') # spine for yaxis
# - will pass through the center of the x-values (which is 5)
ax0.plot(x,y)
# Eliminate upper and right axes
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
# Show ticks on the left and lower axes only (and let them protrude in both directions)
ax1.xaxis.set_tick_params(bottom='on', top='off', direction='inout')
ax1.yaxis.set_tick_params(left='on', right='off', direction='inout')
# Make spines pass through zero of the other axis
ax1.set_title('zero')
ax1.spines['bottom'].set_position('zero')
ax1.spines['left'].set_position('zero')
ax1.set_ylim(-0.4,1.0)
# No ticklabels at zero
ax1.set_xticks([-10,-5,5,10,15,20])
ax1.set_yticks([-0.4,-0.2,0.2,0.4,0.6,0.8,1.0])
ax1.plot(x,y)
plt.show()
Final remark: If ax.spines['bottom'].set_position('zero') is used but zerois not within the plotted y-range, then the axes is shown at the boundary of the plot closer to zero.
The title of this question is how to draw the spine in the middle and the accepted answer does exactly that but what you guys draw is the sigmoid function and that one passes through y=0.5. So I think what you want is the spine centered according to your data. Matplotlib offers the spine position data for that (see documentation)
import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
return 1 / (1 + np.exp(-x))
sigmoid = np.vectorize(sigmoid) #vectorize function
values=np.linspace(-10, 10) #generate values between -10 and 10
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
#spine placement data centered
ax.spines['left'].set_position(('data', 0.0))
ax.spines['bottom'].set_position(('data', 0.0))
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.plot(values, sigmoid(values))
plt.show()
Looks like this (Github):
You can simply add:
plt.axhline()
plt.axvline()
It's not fixed to the center, but it does the job very easily.
Working example:
import matplotlib.pyplot as plt
import numpy as np
def f(x):
return np.sin(x) / (x/100)
delte = 100
Xs = np.arange(-delte, +delte +1, step=0.01)
Ys = np.array([f(x) for x in Xs])
plt.axhline(color='black', lw=0.5)
plt.axvline(color='black', lw=0.5)
plt.plot(Xs, Ys)
plt.show()
If you use matplotlib >= 3.4.2, you can use Pandas syntax and do it in only one line:
plt.gca().spines[:].set_position('center')
You might find it cleaner to do it in 3 lines:
ax = plt.gca()
ax.spines[['top', 'right']].set_visible(False)
ax.spines[['left', 'bottom']].set_position('center')
See documentation here.
Check your matplotlib version with pip freeze and update it with pip install -U matplotlib.
According to latest MPL Documentation:
ax = plt.axes()
ax.spines.left.set_position('zero')
ax.spines.bottom.set_position('zero')
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'm trying to create a plot using pyplot that has a discontinuous x-axis. The usual way this is drawn is that the axis will have something like this:
(values)----//----(later values)
where the // indicates that you're skipping everything between (values) and (later values).
I haven't been able to find any examples of this, so I'm wondering if it's even possible. I know you can join data over a discontinuity for, eg, financial data, but I'd like to make the jump in the axis more explicit. At the moment I'm just using subplots but I'd really like to have everything end up on the same graph in the end.
Paul's answer is a perfectly fine method of doing this.
However, if you don't want to make a custom transform, you can just use two subplots to create the same effect.
Rather than put together an example from scratch, there's an excellent example of this written by Paul Ivanov in the matplotlib examples (It's only in the current git tip, as it was only committed a few months ago. It's not on the webpage yet.).
This is just a simple modification of this example to have a discontinuous x-axis instead of the y-axis. (Which is why I'm making this post a CW)
Basically, you just do something like this:
import matplotlib.pylab as plt
import numpy as np
# If you're not familiar with np.r_, don't worry too much about this. It's just
# a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)
# plot the same data on both axes
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
# zoom-in / limit the view to different portions of the data
ax.set_xlim(0,1) # most of the data
ax2.set_xlim(9,10) # outliers only
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
# Make the spacing between the two axes a bit smaller
plt.subplots_adjust(wspace=0.15)
plt.show()
To add the broken axis lines // effect, we can do this (again, modified from Paul Ivanov's example):
import matplotlib.pylab as plt
import numpy as np
# If you're not familiar with np.r_, don't worry too much about this. It's just
# a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)
# plot the same data on both axes
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
# zoom-in / limit the view to different portions of the data
ax.set_xlim(0,1) # most of the data
ax2.set_xlim(9,10) # outliers only
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
# Make the spacing between the two axes a bit smaller
plt.subplots_adjust(wspace=0.15)
# This looks pretty good, and was fairly painless, but you can get that
# cut-out diagonal lines look with just a bit more work. The important
# thing to know here is that in axes coordinates, which are always
# between 0-1, spine endpoints are at these locations (0,0), (0,1),
# (1,0), and (1,1). Thus, we just need to put the diagonals in the
# appropriate corners of each of our axes, and so long as we use the
# right transform and disable clipping.
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
ax.plot((1-d,1+d),(-d,+d), **kwargs) # top-left diagonal
ax.plot((1-d,1+d),(1-d,1+d), **kwargs) # bottom-left diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d,d),(-d,+d), **kwargs) # top-right diagonal
ax2.plot((-d,d),(1-d,1+d), **kwargs) # bottom-right diagonal
# What's cool about this is that now if we vary the distance between
# ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
# the diagonal lines will move accordingly, and stay right at the tips
# of the spines they are 'breaking'
plt.show()
I see many suggestions for this feature but no indication that it's been implemented. Here is a workable solution for the time-being. It applies a step-function transform to the x-axis. It's a lot of code, but it's fairly simple since most of it is boilerplate custom scale stuff. I have not added any graphics to indicate the location of the break, since that is a matter of style. Good luck finishing the job.
from matplotlib import pyplot as plt
from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
import numpy as np
def CustomScaleFactory(l, u):
class CustomScale(mscale.ScaleBase):
name = 'custom'
def __init__(self, axis, **kwargs):
mscale.ScaleBase.__init__(self)
self.thresh = None #thresh
def get_transform(self):
return self.CustomTransform(self.thresh)
def set_default_locators_and_formatters(self, axis):
pass
class CustomTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
lower = l
upper = u
def __init__(self, thresh):
mtransforms.Transform.__init__(self)
self.thresh = thresh
def transform(self, a):
aa = a.copy()
aa[a>self.lower] = a[a>self.lower]-(self.upper-self.lower)
aa[(a>self.lower)&(a<self.upper)] = self.lower
return aa
def inverted(self):
return CustomScale.InvertedCustomTransform(self.thresh)
class InvertedCustomTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
lower = l
upper = u
def __init__(self, thresh):
mtransforms.Transform.__init__(self)
self.thresh = thresh
def transform(self, a):
aa = a.copy()
aa[a>self.lower] = a[a>self.lower]+(self.upper-self.lower)
return aa
def inverted(self):
return CustomScale.CustomTransform(self.thresh)
return CustomScale
mscale.register_scale(CustomScaleFactory(1.12, 8.88))
x = np.concatenate((np.linspace(0,1,10), np.linspace(9,10,10)))
xticks = np.concatenate((np.linspace(0,1,6), np.linspace(9,10,6)))
y = np.sin(x)
plt.plot(x, y, '.')
ax = plt.gca()
ax.set_xscale('custom')
ax.set_xticks(xticks)
plt.show()
Check the brokenaxes package:
import matplotlib.pyplot as plt
from brokenaxes import brokenaxes
import numpy as np
fig = plt.figure(figsize=(5,2))
bax = brokenaxes(
xlims=((0, .1), (.4, .7)),
ylims=((-1, .7), (.79, 1)),
hspace=.05
)
x = np.linspace(0, 1, 100)
bax.plot(x, np.sin(10 * x), label='sin')
bax.plot(x, np.cos(10 * x), label='cos')
bax.legend(loc=3)
bax.set_xlabel('time')
bax.set_ylabel('value')
A very simple hack is to
scatter plot rectangles over the axes' spines and
draw the "//" as text at that position.
Worked like a charm for me:
# FAKE BROKEN AXES
# plot a white rectangle on the x-axis-spine to "break" it
xpos = 10 # x position of the "break"
ypos = plt.gca().get_ylim()[0] # y position of the "break"
plt.scatter(xpos, ypos, color='white', marker='s', s=80, clip_on=False, zorder=100)
# draw "//" on the same place as text
plt.text(xpos, ymin-0.125, r'//', fontsize=label_size, zorder=101, horizontalalignment='center', verticalalignment='center')
Example Plot:
For those interested, I've expanded upon #Paul's answer and added it to the matplotlib wrapper proplot. It can do axis "jumps", "speedups", and "slowdowns".
There is no way currently to add "crosses" that indicate the discrete jump like in Joe's answer, but I plan to add this in the future. I also plan to add a default "tick locator" that sets sensible default tick locations depending on the CutoffScale arguments.
Adressing Frederick Nord's question how to enable parallel orientation of the diagonal "breaking" lines when using a gridspec with ratios unequal 1:1, the following changes based on the proposals of Paul Ivanov and Joe Kingtons may be helpful. Width ratio can be varied using variables n and m.
import matplotlib.pylab as plt
import numpy as np
import matplotlib.gridspec as gridspec
x = np.r_[0:1:0.1, 9:10:0.1]
y = np.sin(x)
n = 5; m = 1;
gs = gridspec.GridSpec(1,2, width_ratios = [n,m])
plt.figure(figsize=(10,8))
ax = plt.subplot(gs[0,0])
ax2 = plt.subplot(gs[0,1], sharey = ax)
plt.setp(ax2.get_yticklabels(), visible=False)
plt.subplots_adjust(wspace = 0.1)
ax.plot(x, y, 'bo')
ax2.plot(x, y, 'bo')
ax.set_xlim(0,1)
ax2.set_xlim(10,8)
# hide the spines between ax and ax2
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax.yaxis.tick_left()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.yaxis.tick_right()
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
on = (n+m)/n; om = (n+m)/m;
ax.plot((1-d*on,1+d*on),(-d,d), **kwargs) # bottom-left diagonal
ax.plot((1-d*on,1+d*on),(1-d,1+d), **kwargs) # top-left diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d*om,d*om),(-d,d), **kwargs) # bottom-right diagonal
ax2.plot((-d*om,d*om),(1-d,1+d), **kwargs) # top-right diagonal
plt.show()
This is a hacky but pretty solution for x-axis breaks.
The solution is based on https://matplotlib.org/stable/gallery/subplots_axes_and_figures/broken_axis.html, which gets rid of the problem with positioning the break above the spine, solved by How can I plot points so they appear over top of the spines with matplotlib?
from matplotlib.patches import Rectangle
import matplotlib.pyplot as plt
def axis_break(axis, xpos=[0.1, 0.125], slant=1.5):
d = slant # proportion of vertical to horizontal extent of the slanted line
anchor = (xpos[0], -1)
w = xpos[1] - xpos[0]
h = 1
kwargs = dict(marker=[(-1, -d), (1, d)], markersize=12, zorder=3,
linestyle="none", color='k', mec='k', mew=1, clip_on=False)
axis.add_patch(Rectangle(
anchor, w, h, fill=True, color="white",
transform=axis.transAxes, clip_on=False, zorder=3)
)
axis.plot(xpos, [0, 0], transform=axis.transAxes, **kwargs)
fig, ax = plt.subplots(1,1)
plt.plot(np.arange(10))
axis_break(ax, xpos=[0.1, 0.12], slant=1.5)
axis_break(ax, xpos=[0.3, 0.31], slant=-10)
if you want to replace an axis label, this would do the trick:
from matplotlib import ticker
def replace_pos_with_label(fig, pos, label, axis):
fig.canvas.draw() # this is needed to set up the x-ticks
labs = axis.get_xticklabels()
labels = []
locs = []
for text in labs:
x = text._x
lab = text._text
if x == pos:
lab = label
labels.append(lab)
locs.append(x)
axis.xaxis.set_major_locator(ticker.FixedLocator(locs))
axis.set_xticklabels(labels)
fig, ax = plt.subplots(1,1)
plt.plot(np.arange(10))
replace_pos_with_label(fig, 0, "-10", axis=ax)
replace_pos_with_label(fig, 6, "$10^{4}$", axis=ax)
axis_break(ax, xpos=[0.1, 0.12], slant=2)