I need all of my figures to have xlabel, xticks and xticklabels on the top.
Since of that, I wrote a function to adjust plt.rcParams which serves for initializing purpose.
However, it seems there is no such parameter to setup xlabel to the top in advance. Here is a simplified showcase:
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['xtick.bottom'] = False
plt.rcParams['xtick.labelbottom'] = False
plt.rcParams['xtick.top'] = True
plt.rcParams['xtick.labeltop'] = True
data = np.arange(9).reshape((3,3))
f,ax = plt.subplots()
ax.imshow(data)
ax.set_xlabel('x label')
ax.set_ylabel('y label')
Output:
Currently the way I found to adjust it is putting ax.xaxis.set_label_position('top') after calling ax.set_xlabel('x label').
I'm looking for a solution with two goals:
It change the default x-label position so that every time ax.set_xlabel() is called, it shows up at the top.
This step could be executed before calling ax.set_xlabel()
So I don't have to use ax.xaxis.set_label_position() individually every time.
Extra:
As #r-beginners mentioned, the official reference did provide a example. But in the script they called is ax.set_title('xlabel top'), which is different from ax.set_xlabel('x label'). Note that a title is always on the top by default, regardless setting up plt.rcParams or not. I assume they missed this issue by mistake.
As far as I can tell, the position of the label of the x axis is hard-coded.
Let's look at the definition of the XAxis class, the relevant file is .../matplotlib/axis.py
class XAxis(Axis):
...
def _get_label(self):
# x in axes coords, y in display coords (to be updated at draw
# time by _update_label_positions)
label = mtext.Text(x=0.5, y=0,
fontproperties=font_manager.FontProperties(
size=rcParams['axes.labelsize'],
weight=rcParams['axes.labelweight']),
color=rcParams['axes.labelcolor'],
verticalalignment='top',
horizontalalignment='center')
label.set_transform(mtransforms.blended_transform_factory(
self.axes.transAxes, mtransforms.IdentityTransform()))
self._set_artist_props(label)
self.label_position = 'bottom'
return label
...
As you can see, the vertical position of the label is hard-coded in the call to Text, y=0 in display coordinates, to be updated at display time by _update_label_positions and the label_position is hard-coded to 'bottom'.
There is an explanation in the official reference. This will help you deal with it.
import matplotlib.pyplot as plt
import numpy as np
# plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = False
# plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
x = np.arange(10)
fig, ax = plt.subplots()
ax.plot(x)
ax.set_xlabel('xlabel top') # Note title moves to make room for ticks
secax = ax.secondary_xaxis('top')
secax.set_xlabel('new label top')
plt.show()
Related
I have intention in making multiple subplot to present my results. I used subplots from matplotlib. I have a problem with text sizes. As you can see in the trivial code here. In the plt.title documentation it says title(label, fontdict=None, loc='center', pad=None, **kwargs)
import random
from matplotlib.pyplot import figure, plot, xlabel, ylabel, legend, close, subplots, title, savefig, get_current_fig_manager, show, pause, clf
x = []
for i in range(10):
x.append(random.random()*i)
y_1 = []
for i in range(10):
y_1.append(random.random()*i)
y_2 = []
for i in range(10):
y_2.append(random.random()*i)
fig, ax = subplots(1, 2, squeeze = False, figsize = (10,10))
ax[0,1].title.set_text('y_1', fontdict = {'font.size':22})
ax[0,1].plot(x,y_1)
ax[0,1].set_xlabel('x')
ax[0,1].set_ylabel('y_1')
ax[0,0].title.set_text('y_2', fontdict = {'font.size':22})
ax[0,0].plot(x,y_2)
ax[0,0].set_xlabel('x')
ax[0,0].set_ylabel('y_2')
but if I run this code I get an error TypeError: set_text() got an unexpected keyword argument 'fontdict'
am I using the wrong command.
This is really just a minor issue:
To set the title of a specific axes you should use the set_title method of the axes. Using plt.title sets the title of the current axes instance.
Basically replace your ax[0,0].title.set_text with ax[0,0].set_title and you are good to go!
You can also simply use fontsize=22 directly , as in
ax[0,1].set_title('y_1', fontsize=22)
I am trying to animate a one-dimensional function where the function inputs are same but function parameters are changing with time. The function I am trying to animate is
f(x)=sin(a* pi * x)/(b*x)+ (x-1)^4
Here the data to be plotted is same, but a, b are changing with every update.I am using python and matplotlib library. My initial attempt is as follows:
fig,ax = plt.subplots()
line, = ax.plot([],[])
def animate(i,func_params):
x = np.linspace(-0.5,2.5,num = 200)
a=func_params[i][0]
b=func_params[i][1]
y=np.sin(a*math.pi*x)/b*x + (x-1)**4
line.set_xdata(x)
line.set_ydata(y)
return line,
ani = animation.FuncAnimation(fig,animate,frames=len(visualize_pop),fargs=(visualize_func,),interval = 100,blit=True)
plt.show()
The above code is not plotting anything.
EDIT: Updated code based on comment.
Your problem is that with plot([],[]) you give matplotlib no data and therefore no way do determine the limits of the axes. Therefore it uses some default values which are way out of the range of the data you actually want to plot. Therefore you have two choices:
1) Set the limits to some values that will contain all your plotted data for all cases,
e.g.
ax.set_xlim([-0.5,2.5])
ax.set_ylim([-2,6])
2) Let ax compute the limits automatically each frame and re-scale the plot see here using these two commands within your animate function (note that this option only works correctly if you turn blitting off):
ax.relim()
ax.autoscale_view()
Here still a completely working version of your code (the commands for solution (1) are commented out and I changed some of the notations):
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
fig,ax = plt.subplots()
x = np.linspace(-0.5,2.5,num = 200)
line, = ax.plot([],[])
#ax.set_xlim([-0.5,2.5])
#ax.set_ylim([-2,6])
##assuming some parameters, because none were given by the OP:
N = 20
func_args = np.array([np.linspace(1,2,N), np.linspace(2,1,N)])
def animate(i,func_params):
a=func_params[0,i]
b=func_params[1,i]
y=np.sin(a*np.pi*x)/b*x + (x-1)**4
line.set_xdata(x)
line.set_ydata(y)
ax.relim()
ax.autoscale_view()
return line, ax
##blit=True will not update the axes labels correctly
ani = FuncAnimation(
fig,animate,frames=N, fargs=(func_args,),interval = 100 #, blit=True
)
plt.show()
My code is something (roughly) like this:
UPDATE: I've redone this with some actual mock-up code that reflects my general problem. Also, realized that the colorbar creation is in the actual loop as otherwise there's nothing to map it to. Sorry for the code before, typed it up in frantic desperation at the very end of the workday :).
import numpy
import matplotlib as mplot
import matplotlib.pyplot as plt
import os
#make some mock data
x = np.linspace(1,2, 100)
X, Y = np.meshgrid(x, x)
Z = plt.mlab.bivariate_normal(X,Y,1,1,0,0)
fig = plt.figure()
ax = plt.axes()
'''
Do some figure-related stuff that take up a lot of time,
I want to avoid having to do them in the loop over and over again.
They hinge on the presence of fig so I can't make
new figure to save each time or something, I'd have to do
them all over again.
'''
for i in range(1,1000):
plotted = plt.plot(X,Y,Z)
cbar = plt.colorbar(ax=ax, orientation = 'horizontal')
plt.savefig(os.path.expanduser(os.path.join('~/', str(i))))
plt.draw()
mplot.figure.Figure.delaxes(fig, fig.axes[1]) #deletes but whitespace remains
'''
Here I need something to remove the colorbar otherwise
I end up with +1 colorbar on my plot at every iteration.
I've tried various things to remove it BUT it keeps adding whitespace instead
so doesn't actually fix anything.
'''
Has anyone come across this problem before and managed to fix it? Hopefully this is enough
for an idea of the problem, I can post more code if needed but thought it'd be less of a clutter if I just give an overview example.
Thanks.
colorbar() allows you explicitly set which axis to render into - you can use this to ensure that they always appear in the same place, and not steal any space from another axis. Furthermore, you could reset the .mappable attribute of an existing colorbar, rather than redefine it each time.
Example with explicit axes:
x = np.linspace(1,2, 100)
X, Y = np.meshgrid(x, x)
Z = plt.mlab.bivariate_normal(X,Y,1,1,0,0)
fig = plt.figure()
ax1 = fig.add_axes([0.1,0.1,0.8,0.7])
ax2 = fig.add_axes([0.1,0.85,0.8,0.05])
...
for i in range(1,5):
plotted = ax1.pcolor(X,Y,Z)
cbar = plt.colorbar(mappable=plotted, cax=ax2, orientation = 'horizontal')
#note "cax" instead of "ax"
plt.savefig(os.path.expanduser(os.path.join('~/', str(i))))
plt.draw()
I had a very similar problem, which I finally managed to solve by defining a colorbar axes in a similar fashion to:
Multiple imshow-subplots, each with colorbar
The advantage compared to mdurant's answer is that it saves defining the axes location manually.
import matplotlib.pyplot as plt
import IPython.display as display
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pylab import *
%matplotlib inline
def plot_res(ax,cax):
plotted=ax.imshow(rand(10, 10))
cbar=plt.colorbar(mappable=plotted,cax=cax)
fig, axarr = plt.subplots(2, 2)
cax1 = make_axes_locatable(axarr[0,0]).append_axes("right", size="10%", pad=0.05)
cax2 = make_axes_locatable(axarr[0,1]).append_axes("right", size="10%", pad=0.05)
cax3 = make_axes_locatable(axarr[1,0]).append_axes("right", size="10%", pad=0.05)
cax4 = make_axes_locatable(axarr[1,1]).append_axes("right", size="10%", pad=0.05)
# plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0.3, hspace=0.3)
N=10
for j in range(N):
plot_res(axarr[0,0],cax1)
plot_res(axarr[0,1],cax2)
plot_res(axarr[1,0],cax3)
plot_res(axarr[1,1],cax4)
display.clear_output(wait=True)
display.display(plt.gcf())
display.clear_output(wait=True)
Is there a way of telling pyplot.text() a location like you can with pyplot.legend()?
Something like the legend argument would be excellent:
plt.legend(loc="upper left")
I am trying to label subplots with different axes using letters (e.g. "A","B"). I figure there's got to be a better way than manually estimating the position.
Thanks
Just use annotate and specify axis coordinates. For example, "upper left" would be:
plt.annotate('Something', xy=(0.05, 0.95), xycoords='axes fraction')
You could also get fancier and specify a constant offset in points:
plt.annotate('Something', xy=(0, 1), xytext=(12, -12), va='top'
xycoords='axes fraction', textcoords='offset points')
For more explanation see the examples here and the more detailed examples here.
I'm not sure if this was available when I originally posted the question but using the loc parameter can now actually be used. Below is an example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnchoredText
# make some data
x = np.arange(10)
y = x
# set up figure and axes
f, ax = plt.subplots(1,1)
# loc works the same as it does with figures (though best doesn't work)
# pad=5 will increase the size of padding between the border and text
# borderpad=5 will increase the distance between the border and the axes
# frameon=False will remove the box around the text
anchored_text = AnchoredText("Test", loc=2)
ax.plot(x,y)
ax.add_artist(anchored_text)
plt.show()
The question is quite old but as there is no general solution to the problem till now (2019) according to Add loc=best kwarg to pyplot.text(), I'm using legend() and the following workaround to obtain auto-placement for simple text boxes:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpl_patches
x = np.linspace(-1,1)
fig, ax = plt.subplots()
ax.plot(x, x*x)
# create a list with two empty handles (or more if needed)
handles = [mpl_patches.Rectangle((0, 0), 1, 1, fc="white", ec="white",
lw=0, alpha=0)] * 2
# create the corresponding number of labels (= the text you want to display)
labels = []
labels.append("pi = {0:.4g}".format(np.pi))
labels.append("root(2) = {0:.4g}".format(np.sqrt(2)))
# create the legend, supressing the blank space of the empty line symbol and the
# padding between symbol and label by setting handlelenght and handletextpad
ax.legend(handles, labels, loc='best', fontsize='small',
fancybox=True, framealpha=0.7,
handlelength=0, handletextpad=0)
plt.show()
The general idea is to create a legend with a blank line symbol and to remove the resulting empty space afterwards. How to adjust the size of matplotlib legend box? helped me with the legend formatting.
In a matplotlib figure, how can I make the font size for the tick labels using ax1.set_xticklabels() smaller?
Further, how can one rotate it from horizontal to vertical?
There is a simpler way actually. I just found:
import matplotlib.pyplot as plt
# We prepare the plot
fig, ax = plt.subplots()
# We change the fontsize of minor ticks label
ax.tick_params(axis='both', which='major', labelsize=10)
ax.tick_params(axis='both', which='minor', labelsize=8)
This only answers to the size of label part of your question though.
To specify both font size and rotation at the same time, try this:
plt.xticks(fontsize=14, rotation=90)
Please note that newer versions of MPL have a shortcut for this task. An example is shown in the other answer to this question: https://stackoverflow.com/a/11386056/42346
The code below is for illustrative purposes and may not necessarily be optimized.
import matplotlib.pyplot as plt
import numpy as np
def xticklabels_example():
fig = plt.figure()
x = np.arange(20)
y1 = np.cos(x)
y2 = (x**2)
y3 = (x**3)
yn = (y1,y2,y3)
COLORS = ('b','g','k')
for i,y in enumerate(yn):
ax = fig.add_subplot(len(yn),1,i+1)
ax.plot(x, y, ls='solid', color=COLORS[i])
if i != len(yn) - 1:
# all but last
ax.set_xticklabels( () )
else:
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(14)
# specify integer or one of preset strings, e.g.
#tick.label.set_fontsize('x-small')
tick.label.set_rotation('vertical')
fig.suptitle('Matplotlib xticklabels Example')
plt.show()
if __name__ == '__main__':
xticklabels_example()
Alternatively, you can just do:
import matplotlib as mpl
label_size = 8
mpl.rcParams['xtick.labelsize'] = label_size
Another alternative
I have two plots side by side and would like to adjust tick labels separately.
The above solutions were close however they were not working out for me. I found my solution from this matplotlib page.
ax.xaxis.set_tick_params(labelsize=20)
This did the trick and was straight to the point. For my use case, it was the plot on the right that needed to be adjusted. For the plot on the left since I was creating new tick labels I was able to adjust the font in the same process as seting the labels.
ie
ax1.set_xticklabels(ax1_x, fontsize=15)
ax1.set_yticklabels(ax1_y, fontsize=15)
thus I used for the right plot,
ax2.xaxis.set_tick_params(labelsize=24)
ax2.yaxis.set_tick_params(labelsize=24)
A minor subtlety... I know... but I hope this helps someone :)
Bonus points if anyone knows how to adjust the font size of the order of magnitude label.
plt.tick_params(axis='both', which='minor', labelsize=12)
In current versions of Matplotlib, you can do axis.set_xticklabels(labels, fontsize='small').
The following worked for me:
ax2.xaxis.set_tick_params(labelsize=7)
ax2.yaxis.set_tick_params(labelsize=7)
The advantage of the above is you do not need to provide the array of labels and works with any data on the axes.
For smaller font, I use
ax1.set_xticklabels(xticklabels, fontsize=7)
and it works!
You can also change label display parameters like fontsize with a line like this:
zed = [tick.label.set_fontsize(14) for tick in ax.yaxis.get_major_ticks()]