I have two subplots that share the same x-axis. I removed the xticklabels of the upper subplot, but the offset "1e7" remains visible. How can I hide it?
Here is the code I used :
import matplotlib.pyplot as plt
import numpy as np
plt.figure()
s1 = plt.subplot(2,1,1)
s1.plot(np.arange(0,1e8,1e7),np.arange(10))
s1.tick_params(axis="x", labelbottom=False)
s2 = plt.subplot(2,1,2, sharex=s1)
s2.plot(np.arange(0,1e8,1e7),np.arange(10))
I also tried s1.get_xaxis().get_major_formatter().set_useOffset(False), but it did nothing and I also tried s1.get_xaxis().get_major_formatter().set_powerlimits((-9,9)) but it impacted also the lower suplot.
An alternative is to use plt.subplots to create the subplots, and use sharex=True as an option there. This automatically turns off all ticklabels and the offset_text from the top subplot. From the docs:
sharex : string or bool
If True, the X axis will be shared amongst all subplots. If True and
you have multiple rows, the x tick labels on all but the last row of
plots will have visible set to False
import matplotlib.pyplot as plt
import numpy as np
fig, (s1, s2) = plt.subplots(2, 1, sharex=True)
s1.plot(np.arange(0,1e8,1e7),np.arange(10))
s2.plot(np.arange(0,1e8,1e7),np.arange(10))
plt.show()
I finally find the answer on https://github.com/matplotlib/matplotlib/issues/4445. I need to add the following line to my code :
plt.setp(s1.get_xaxis().get_offset_text(), visible=False)
Related
I do have a question with matplotlib in python. I create different figures, where every figure should have the same height to print them in a publication/poster next to each other.
If the y-axis has a label on the very top, this shrinks the height of the box with the plot. So I use MaxNLocator to remove the upper and lower y-tick. In some plots, I want to have the 1.0 as a number on the y-axis, because I have normalized data. So I need a solution, which expands in these cases the y-axis and ensures 1.0 is a y-Tick, but does not corrupt the size of the figure using tight_layout().
Here is a minimal example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
x = np.linspace(0,1,num=11)
y = np.linspace(1,.42,num=11)
fig,axs = plt.subplots(1,1)
axs.plot(x,y)
locator=MaxNLocator(prune='both',nbins=5)
axs.yaxis.set_major_locator(locator)
plt.tight_layout()
fig.show()
Here is a link to a example-pdf, which shows the problems with height of upper boxline.
I tried to work with adjust_subplots() but this is of no use for me, because I vary the size of the figures and want to have same the font size all the time, which changes the margins.
Question is:
How can I use MaxNLocator and specify a number which has to be in the y-axis?
Hopefully someone of you has some advice.
Greetings,
Laenan
Assuming that you know in advance how many plots there will be in 1 row on a page one way to solve this would be to put all those plots into one figure - matplotlib will make sure they are alinged on axes:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
x = np.linspace(0, 1, num=11)
y = np.linspace(1, .42, num=11)
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,3), gridspec_kw={'wspace':.2})
ax1.plot(x,y)
ax2.plot(x,y)
locator=MaxNLocator(prune='both', nbins=5)
ax1.yaxis.set_major_locator(locator)
# You don't need to use tight_layout and using it might give an error
# plt.tight_layout()
fig.show()
When making a semi-log plot (y is log), the minor tick marks (8 in a decade) on the y axis appear automatically, but it seems that when the axis range exceeds 10**10, they disappear. I tried many ways to force them back in, but to no avail. It might be that they go away for large ranges to avoid overcrowding, but one should have a choice?
solution for matplotlib >= 2.0.2
Let's consider the following example
which is produced by this code:
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
y = np.arange(12)
x = 10.0**y
fig, ax=plt.subplots()
ax.plot(x,y)
ax.set_xscale("log")
plt.show()
The minor ticklabels are indeed gone and usual ways to show them (like plt.tick_params(axis='x', which='minor')) fail.
The first step would then be to show all powers of 10 on the axis,
locmaj = matplotlib.ticker.LogLocator(base=10,numticks=12)
ax.xaxis.set_major_locator(locmaj)
where the trick is to set numticks to a number equal or larger the number of ticks (i.e. 12 or higher in this case).
Then, we can add minor ticklabels as
locmin = matplotlib.ticker.LogLocator(base=10.0,subs=(0.2,0.4,0.6,0.8),numticks=12)
ax.xaxis.set_minor_locator(locmin)
ax.xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())
Note that I restricted this to include 4 minor ticks per decade (using 8 is equally possible but in this example would overcrowd the axes). Also note that numticks is again (quite unintuitively) 12 or larger.
Finally we need to use a NullFormatter() for the minor ticks, in order not to have any ticklabels appear for them.
solution for matplotlib 2.0.0
The following works in matplotlib 2.0.0 or below, but it does not work in matplotlib 2.0.2.
Let's consider the following example
which is produced by this code:
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
y = np.arange(12)
x = 10.0**y
fig, ax=plt.subplots()
ax.plot(x,y)
ax.set_xscale("log")
plt.show()
The minor ticklabels are indeed gone and usual ways to show them (like plt.tick_params(axis='x', which='minor')) fail.
The first step would then be to show all powers of 10 on the axis,
locmaj = matplotlib.ticker.LogLocator(base=10.0, subs=(0.1,1.0, ))
ax.xaxis.set_major_locator(locmaj)
Then, we can add minor ticklabels as
locmin = matplotlib.ticker.LogLocator(base=10.0, subs=(0.1,0.2,0.4,0.6,0.8,1,2,4,6,8,10 ))
ax.xaxis.set_minor_locator(locmin)
ax.xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())
Note that I restricted this to include 4 minor ticks per decade (using 8 is equally possible but in this example would overcrowd the axes). Also note - and that may be the key here - that the subs argument, which gives the multiples of integer powers of the base at which to place ticks (see documentation), is given a list ranging over two decades instead of one.
Finally we need to use a NullFormatter() for the minor ticks, in order not to have any ticklabels appear for them.
From what I can tell, as of Matplotlib 3.5.2:
With 8 or fewer major tick marks, the minor ticks show
with 9 to 11 major tick marks, subs="auto" will show the minor tick marks
with 12 or more, you need to set subs manually.
Using subs="auto"
from matplotlib import pyplot as plt, ticker as mticker
fig, ax = plt.subplots()
y = np.arange(11)
x = 10.0**y
ax.semilogx(x, y)
ax.xaxis.set_major_locator(mticker.LogLocator(numticks=999))
ax.xaxis.set_minor_locator(mticker.LogLocator(numticks=999, subs="auto"))
Setting subs manually
from matplotlib import pyplot as plt, ticker as mticker
fig, ax = plt.subplots()
y = np.arange(12)
x = 10.0**y
ax.semilogx(x, y)
ax.xaxis.set_major_locator(mticker.LogLocator(numticks=999))
ax.xaxis.set_minor_locator(mticker.LogLocator(numticks=999, subs=(.2, .4, .6, .8)))
Major ticks with empty labels will generate ticks but no labels.
ax.set_yticks([1.E-6,1.E-5,1.E-4,1.E-3,1.E-2,1.E-1,1.E0,1.E1,1.E2,1.E3,1.E4,1.E5,])
ax.set_yticklabels(['$10^{-6}$','','','$10^{-3}$','','','$1$','','','$10^{3}$','',''])
Wrapping the excellent answer from importanceofbeingernest for matplotlib >= 2.0.2 into a function:
import matplotlib.pyplot as plt
from typing import Optional
def restore_minor_ticks_log_plot(
ax: Optional[plt.Axes] = None, n_subticks=9
) -> None:
"""For axes with a logrithmic scale where the span (max-min) exceeds
10 orders of magnitude, matplotlib will not set logarithmic minor ticks.
If you don't like this, call this function to restore minor ticks.
Args:
ax:
n_subticks: Number of Should be either 4 or 9.
Returns:
None
"""
if ax is None:
ax = plt.gca()
# Method from SO user importanceofbeingernest at
# https://stackoverflow.com/a/44079725/5972175
locmaj = mpl.ticker.LogLocator(base=10, numticks=1000)
ax.xaxis.set_major_locator(locmaj)
locmin = mpl.ticker.LogLocator(
base=10.0, subs=np.linspace(0, 1.0, n_subticks + 2)[1:-1], numticks=1000
)
ax.xaxis.set_minor_locator(locmin)
ax.xaxis.set_minor_formatter(mpl.ticker.NullFormatter())
This function can then be called as
plt.plot(x,y)
plt.xscale("log")
restore_minor_ticks_log_plot()
or more explicitly
_, ax = plt.subplots()
ax.plot(x, y)
ax.set_xscale("log")
restore_minor_ticks_log_plot(ax)
The answers here ignore the convenient fact that the log-scaled axis already has the requisite locators. At least as of Matplotlib 3.6, it is enough to use set_params() with values that force minor ticks:
import matplotlib.pyplot as plt
import numpy as np
y = np.arange(12)
x = 10.0**y
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xscale('log')
ax.xaxis.get_major_locator().set_params(numticks=99)
ax.xaxis.get_minor_locator().set_params(numticks=99, subs=[.2, .4, .6, .8])
plt.show()
I would like to minimize white space in my figure. I have a row of sub plots where four plots share their y-axis and the last plot has a separate axis.
There are no ylabels or ticklabels for the shared axis middle panels.
tight_layout creates a lot of white space between the the middle plots as if leaving space for tick labels and ylabels but I would rather stretch the sub plots. Is this possible?
import matplotlib.gridspec as gridspec
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
fig = plt.figure()
gs = gridspec.GridSpec(1, 5, width_ratios=[4,1,4,1,2])
ax = fig.add_subplot(gs[0])
axes = [ax] + [fig.add_subplot(gs[i], sharey=ax) for i in range(1, 4)]
axes[0].plot(np.random.randint(0,100,100))
barlist=axes[1].bar([1,2],[1,20])
axes[2].plot(np.random.randint(0,100,100))
barlist=axes[3].bar([1,2],[1,20])
axes[0].set_ylabel('data')
axes.append(fig.add_subplot(gs[4]))
axes[4].plot(np.random.randint(0,5,100))
axes[4].set_ylabel('other data')
for ax in axes[1:4]:
plt.setp(ax.get_yticklabels(), visible=False)
sns.despine();
plt.tight_layout(pad=0, w_pad=0, h_pad=0);
Setting w_pad = 0 is not changing the default settings of tight_layout. You need to set something like w_pad = -2. Which produces the following figure:
You could go further, to say -3 but then you would start to get some overlap with your last plot.
Another way could be to remove plt.tight_layout() and set the boundaries yourself using
plt.subplots_adjust(left=0.065, right=0.97, top=0.96, bottom=0.065, wspace=0.14)
Though this can be a bit of a trial and error process.
Edit
A nice looking graph can be achieved by moving the ticks and the labels of the last plot to the right hand side. This answer shows you can do this by using:
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
So for your example:
axes[4].yaxis.tick_right()
axes[4].yaxis.set_label_position("right")
In addition, you need to remove sns.despine(). Finally, there is now no need to set w_pad = -2, just use plt.tight_layout(pad=0, w_pad=0, h_pad=0)
Using this creates the following figure:
I do have a question with matplotlib in python. I create different figures, where every figure should have the same height to print them in a publication/poster next to each other.
If the y-axis has a label on the very top, this shrinks the height of the box with the plot. So I use MaxNLocator to remove the upper and lower y-tick. In some plots, I want to have the 1.0 as a number on the y-axis, because I have normalized data. So I need a solution, which expands in these cases the y-axis and ensures 1.0 is a y-Tick, but does not corrupt the size of the figure using tight_layout().
Here is a minimal example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
x = np.linspace(0,1,num=11)
y = np.linspace(1,.42,num=11)
fig,axs = plt.subplots(1,1)
axs.plot(x,y)
locator=MaxNLocator(prune='both',nbins=5)
axs.yaxis.set_major_locator(locator)
plt.tight_layout()
fig.show()
Here is a link to a example-pdf, which shows the problems with height of upper boxline.
I tried to work with adjust_subplots() but this is of no use for me, because I vary the size of the figures and want to have same the font size all the time, which changes the margins.
Question is:
How can I use MaxNLocator and specify a number which has to be in the y-axis?
Hopefully someone of you has some advice.
Greetings,
Laenan
Assuming that you know in advance how many plots there will be in 1 row on a page one way to solve this would be to put all those plots into one figure - matplotlib will make sure they are alinged on axes:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
x = np.linspace(0, 1, num=11)
y = np.linspace(1, .42, num=11)
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,3), gridspec_kw={'wspace':.2})
ax1.plot(x,y)
ax2.plot(x,y)
locator=MaxNLocator(prune='both', nbins=5)
ax1.yaxis.set_major_locator(locator)
# You don't need to use tight_layout and using it might give an error
# plt.tight_layout()
fig.show()
If I create a plot with matplotlib using the following code:
import numpy as np
from matplotlib import pyplot as plt
xx = np.arange(0,5, .5)
yy = np.random.random( len(xx) )
plt.plot(xx,yy)
plt.imshow()
I get a result that looks like the attached image. The problem is the
bottom-most y-tick label overlaps the left-most x-tick label. This
looks unprofessional. I was wondering if there was an automatic
way to delete the bottom-most y-tick label, so I don't have
the overlap problem. The fewer lines of code, the better.
In the ticker module there is a class called MaxNLocator that can take a prune kwarg.
Using that you can remove the first tick:
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import numpy as np
xx = np.arange(0,5, .5)
yy = np.random.random( len(xx) )
plt.plot(xx,yy)
plt.gca().xaxis.set_major_locator(MaxNLocator(prune='lower'))
plt.show()
Result:
You can pad the ticks on the x-axis:
ax.tick_params(axis='x', pad=15)
Replace ax with plt.gca() if you haven't stored the variable ax for the current figure.
You can also pad both the axes removing the axis parameter.
A very elegant way to fix the overlapping problem is increasing the padding of the x- and y-tick labels (i.e. the distance to the axis). Leaving out the corner most label might not always be wanted. In my opinion, in general it looks nice if the labels are a little bit farther from the axis than given by the default configuration.
The padding can be changed via the matplotlibrc file or in your plot script by using the commands
import matplotlib as mpl
mpl.rcParams['xtick.major.pad'] = 8
mpl.rcParams['ytick.major.pad'] = 8
Most times, a padding of 6 is also sufficient.
This is answered in detail here. Basically, you use something like this:
plt.xticks([list of tick locations], [list of tick lables])