Develop Reference

Incomple text while saving seaborn figures [duplicate]

Updated MRE with subplots
I'm not sure of the usefulness of the original question and MRE. The margin padding seems to be properly adjusted for large x and y labels.
The issue is reproducible with subplots.
Using matplotlib 3.4.2
fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(8, 6))
axes = axes.flatten()
for ax in axes:
ax.set_ylabel(r'$\ln\left(\frac{x_a-x_b}{x_a-x_c}\right)$')
ax.set_xlabel(r'$\ln\left(\frac{x_a-x_d}{x_a-x_e}\right)$')
plt.show()
Original
I am plotting a dataset using matplotlib where I have an xlabel that is quite "tall" (it's a formula rendered in TeX that contains a fraction and is therefore has the height equivalent of a couple of lines of text).
In any case, the bottom of the formula is always cut off when I draw the figures. Changing figure size doesn't seem to help this, and I haven't been able to figure out how to shift the x-axis "up" to make room for the xlabel. Something like that would be a reasonable temporary solution, but what would be nice would be to have a way to make matplotlib recognize automatically that the label is cut off and resize accordingly.
Here's an example of what I mean:
import matplotlib.pyplot as plt
plt.figure()
plt.ylabel(r'$\ln\left(\frac{x_a-x_b}{x_a-x_c}\right)$')
plt.xlabel(r'$\ln\left(\frac{x_a-x_d}{x_a-x_e}\right)$', fontsize=50)
plt.title('Example with matplotlib 3.4.2\nMRE no longer an issue')
plt.show()
The entire ylabel is visible, however, the xlabel is cut off at the bottom.
In the case this is a machine-specific problem, I am running this on OSX 10.6.8 with matplotlib 1.0.0

Use:
import matplotlib.pyplot as plt
plt.gcf().subplots_adjust(bottom=0.15)
# alternate option without .gcf
plt.subplots_adjust(bottom=0.15)
to make room for the label, where plt.gcf() means get the current figure. plt.gca(), which gets the current Axes, can also be used.
Edit:
Since I gave the answer, matplotlib has added the plt.tight_layout() function.
See matplotlib Tutorials: Tight Layout Guide
So I suggest using it:
fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(8, 6))
axes = axes.flatten()
for ax in axes:
ax.set_ylabel(r'$\ln\left(\frac{x_a-x_b}{x_a-x_c}\right)$')
ax.set_xlabel(r'$\ln\left(\frac{x_a-x_d}{x_a-x_e}\right)$')
plt.tight_layout()
plt.show()

In case you want to store it to a file, you solve it using bbox_inches="tight" argument:
plt.savefig('myfile.png', bbox_inches="tight")

An easy option is to configure matplotlib to automatically adjust the plot size. It works perfectly for me and I'm not sure why it's not activated by default.
Method 1
Set this in your matplotlibrc file
figure.autolayout : True
See here for more information on customizing the matplotlibrc file: http://matplotlib.org/users/customizing.html
Method 2
Update the rcParams during runtime like this
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
The advantage of using this approach is that your code will produce the same graphs on differently-configured machines.

plt.autoscale() worked for me.

You can also set custom padding as defaults in your $HOME/.matplotlib/matplotlib_rc as follows. In the example below I have modified both the bottom and left out-of-the-box padding:
# The figure subplot parameters. All dimensions are a fraction of the
# figure width or height
figure.subplot.left : 0.1 #left side of the subplots of the figure
#figure.subplot.right : 0.9
figure.subplot.bottom : 0.15
...

There is also a way to do this using the OOP interface, applying tight_layout directly to a figure:
fig, ax = plt.subplots()
fig.set_tight_layout(True)
https://matplotlib.org/stable/api/figure_api.html

for some reason sharex was set to True so I turned it back to False and it worked fine.
df.plot(........,sharex=False)

You need to use sizzors to modify the axis-range:
import sizzors as sizzors_module
sizzors_module.reshape_the_axis(plt).save("literlymylief.tiff")

Python: equal axes plots with inline graphics?

I have just installed anaconda on a machine running Windows 7 Enterprise, 64 bit, and I'm using the ipython console within Spyder, which supports inline graphics. I am trying to plot a sequence of points with equal axes. According to the documentation, something like this should work:
import pylab
pylab.axes().set_aspect('equal')
pylab.plot(b[:,0],b[:,1],'.')
pylab.show()
However, any call to pylab immediately results in the creation of a figure, so with the above commands the first pylab call creates an empty figure (but with equal axes), and the second pylab call creates a new figure of the plot - but with unequal axes. How can I get both pylab calls to refer to the same figure so that I end up with one figure containing the plot and with equal axes?

In general pylab is not really recommended. It is better to import pyplot directly. Does this work for you?
from matplotlib import pyplot as plt
import numpy as np
b = np.arange(20.)
b.shape = (10, 2)
b[:, 1] *= 0.2
ax = plt.subplot(1,1,1)
ax.plot(b[:,0], b[:,1], '.')
ax.set_aspect('equal')
Setting the aspect ratio is generally one of the last operations I perform on an axes because other plotting operations often messes it up. Also note here that I have followed the 'use pyplot to create the figures and then the OO interface for plotting' guidance on this page.

Update/Refresh matplotlib plots on second monitor

At the moment I am working with Spyder and doing my plotting with matplotlib. I have two monitors, one for development and another for (data) browsing and other stuff. Since I am doing some calculations and my code often changes, I often (re)execute the code and have a look at the plots to check if the results are valid.
Is there any way to place my matplotlib plots on a second monitor and refresh them from the main monitor?
I have already searched for a solution but could not find anything. It would be really helpful for me!
Here's some additional information:
OS: Ubuntu 14.04 (64 Bit)
Spyder-Version: 2.3.2
Matplotlib-Version: 1.3.1.-1.4.2.

I know it's an old question but I came across a similar problem and found this question. I managed to move my plots to a second display using the QT4Agg backend.
import matplotlib.pyplot as plt
plt.switch_backend('QT4Agg')
# a little hack to get screen size; from here [1]
mgr = plt.get_current_fig_manager()
mgr.full_screen_toggle()
py = mgr.canvas.height()
px = mgr.canvas.width()
mgr.window.close()
# hack end
x = [i for i in range(0,10)]
plt.figure()
plt.plot(x)
figManager = plt.get_current_fig_manager()
# if px=0, plot will display on 1st screen
figManager.window.move(px, 0)
figManager.window.showMaximized()
figManager.window.setFocus()
plt.show()
[1] answer from #divenex: How do you set the absolute position of figure windows with matplotlib?

This has to do with matplotlib, not Spyder. Placing the location of a figure explicitly appears to be one of those things for which there's really just workarounds ... see the answers to the question here. That's an old question, but I'm not sure there's been change since then (any matplotlib devs, feel free to correct me!).
The second monitor shouldn't make any difference, it sounds like the issue is just that the figure is being replaced with a new one.
Fortunately you can update figures you've moved to where you want them pretty easily, by using the object interface specifically, and updating the Axes object without creating a new figure. An example is below:
import matplotlib.pyplot as plt
import numpy as np
# Create the figure and axes, keeping the object references
fig = plt.figure()
ax = fig.add_subplot(111)
p, = ax.plot(np.linspace(0,1))
# First display
plt.show()
# Some time to let you look at the result and move/resize the figure
plt.pause(3)
# Replace the contents of the Axes without making a new window
ax.cla()
p, = ax.plot(2*np.linspace(0,1)**2)
# Since the figure is shown already, use draw() to update the display
plt.draw()
plt.pause(3)
# Or you can get really fancy and simply replace the data in the plot
p.set_data(np.linspace(-1,1), 10*np.linspace(-1,1)**3)
ax.set_xlim(-1,1)
ax.set_ylim(-1,1)
plt.draw()

How to make savefig() save image for 'maximized' window instead of default size

I am using pylab in matplotlib to create a plot and save the plot to an image file. However, when I save the image using pylab.savefig( image_name ), I find that the SIZE image saved is the same as the image that is shown when I use pylab.show().
As it happens, I have a lot of data in the plot and when I am using pylab.show(), I have to maximize the window before I can see all of the plot correctly, and the xlabel tickers don't superimpose on each other.
Is there anyway that I can programmatically 'maximize' the window before saving the image to file? - at the moment, I am only getting the 'default' window size image, which results in the x axis labels being superimposed on one another.

There are two major options in matplotlib (pylab) to control the image size:
You can set the size of the resulting image in inches
You can define the DPI (dots per inch) for output file (basically, it is a resolution)
Normally, you would like to do both, because this way you will have full control over the resulting image size in pixels. For example, if you want to render exactly 800x600 image, you can use DPI=100, and set the size as 8 x 6 in inches:
import matplotlib.pyplot as plt
# plot whatever you need...
# now, before saving to file:
figure = plt.gcf() # get current figure
figure.set_size_inches(8, 6)
# when saving, specify the DPI
plt.savefig("myplot.png", dpi = 100)
One can use any DPI. In fact, you might want to play with various DPI and size values to get the result you like the most. Beware, however, that using very small DPI is not a good idea, because matplotlib may not find a good font to render legend and other text. For example, you cannot set the DPI=1, because there are no fonts with characters rendered with 1 pixel :)
From other comments I understood that other issue you have is proper text rendering. For this, you can also change the font size. For example, you may use 6 pixels per character, instead of 12 pixels per character used by default (effectively, making all text twice smaller).
import matplotlib
#...
matplotlib.rc('font', size=6)
Finally, some references to the original documentation:
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.savefig, http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.gcf, http://matplotlib.sourceforge.net/api/figure_api.html#matplotlib.figure.Figure.set_size_inches, http://matplotlib.sourceforge.net/users/customizing.html#dynamic-rc-settings
P.S. Sorry, I didn't use pylab, but as far as I'm aware, all the code above will work same way in pylab - just replace plt in my code with the pylab (or whatever name you assigned when importing pylab). Same for matplotlib - use pylab instead.

You set the size on initialization:
fig2 = matplotlib.pyplot.figure(figsize=(8.0, 5.0)) # in inches!
Edit:
If the problem is with x-axis ticks - You can set them "manually":
fig2.add_subplot(111).set_xticks(arange(1,3,0.5)) # You can actually compute the interval You need - and substitute here
And so on with other aspects of Your plot. You can configure it all. Here's an example:
from numpy import arange
import matplotlib
# import matplotlib as mpl
import matplotlib.pyplot
# import matplotlib.pyplot as plt
x1 = [1,2,3]
y1 = [4,5,6]
x2 = [1,2,3]
y2 = [5,5,5]
# initialization
fig2 = matplotlib.pyplot.figure(figsize=(8.0, 5.0)) # The size of the figure is specified as (width, height) in inches
# lines:
l1 = fig2.add_subplot(111).plot(x1,y1, label=r"Text $formula$", "r-", lw=2)
l2 = fig2.add_subplot(111).plot(x2,y2, label=r"$legend2$" ,"g--", lw=3)
fig2.add_subplot(111).legend((l1,l2), loc=0)
# axes:
fig2.add_subplot(111).grid(True)
fig2.add_subplot(111).set_xticks(arange(1,3,0.5))
fig2.add_subplot(111).axis(xmin=3, xmax=6) # there're also ymin, ymax
fig2.add_subplot(111).axis([0,4,3,6]) # all!
fig2.add_subplot(111).set_xlim([0,4])
fig2.add_subplot(111).set_ylim([3,6])
# labels:
fig2.add_subplot(111).set_xlabel(r"x $2^2$", fontsize=15, color = "r")
fig2.add_subplot(111).set_ylabel(r"y $2^2$")
fig2.add_subplot(111).set_title(r"title $6^4$")
fig2.add_subplot(111).text(2, 5.5, r"an equation: $E=mc^2$", fontsize=15, color = "y")
fig2.add_subplot(111).text(3, 2, unicode('f\374r', 'latin-1'))
# saving:
fig2.savefig("fig2.png")
So - what exactly do You want to be configured?

I think you need to specify a different resolution when saving the figure to a file:
fig = matplotlib.pyplot.figure()
# generate your plot
fig.savefig("myfig.png",dpi=600)
Specifying a large dpi value should have a similar effect as maximizing the GUI window.

Check this:
How to maximize a plt.show() window using Python
The command is different depending on which backend you use. I find that this is the best way to make sure the saved pictures have the same scaling as what I view on my screen.
Since I use Canopy with the QT backend:
pylab.get_current_fig_manager().window.showMaximized()
I then call savefig() as required with an increased DPI per silvado's answer.

You can look in a saved figure it's size, like 1920x983 px (size when i saved a maximized window), then I set the dpi as 100 and the size as 19.20x9.83 and it worked fine. Saved exactly equal to the maximized figure.
import numpy as np
import matplotlib.pyplot as plt
x, y = np.genfromtxt('fname.dat', usecols=(0,1), unpack=True)
a = plt.figure(figsize=(19.20,9.83))
a = plt.plot(x, y, '-')
plt.savefig('file.png',format='png',dpi=100)

I had this exact problem and this worked:
plt.savefig(output_dir + '/xyz.png', bbox_inches='tight')
Here is the documentation:
[https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.savefig.html][1]

I did the same search time ago, it seems that he exact solution depends on the backend.
I have read a bunch of sources and probably the most useful was the answer by Pythonio here How to maximize a plt.show() window using Python
I adjusted the code and ended up with the function below.
It works decently for me on windows, I mostly use Qt, where I use it quite often, while it is minimally tested with other backends.
Basically it consists in identifying the backend and calling the appropriate function. Note that I added a pause afterwards because I was having issues with some windows getting maximized and others not, it seems this solved for me.
def maximize(backend=None,fullscreen=False):
"""Maximize window independently on backend.
Fullscreen sets fullscreen mode, that is same as maximized, but it doesn't have title bar (press key F to toggle full screen mode)."""
if backend is None:
backend=matplotlib.get_backend()
mng = plt.get_current_fig_manager()
if fullscreen:
mng.full_screen_toggle()
else:
if backend == 'wxAgg':
mng.frame.Maximize(True)
elif backend == 'Qt4Agg' or backend == 'Qt5Agg':
mng.window.showMaximized()
elif backend == 'TkAgg':
mng.window.state('zoomed') #works fine on Windows!
else:
print ("Unrecognized backend: ",backend) #not tested on different backends (only Qt)
plt.show()
plt.pause(0.1) #this is needed to make sure following processing gets applied (e.g. tight_layout)

Old question but to anyone in need, Here's what had worked for me a while ago:
You have to have a general idea of the aspect ratio that would maximise your plot fitting. This will take some trial and error to get right, but generally 1920x1080 would be a good aspect ratio for most modern monitors. I would still suggest playing around with the aspect ratios to best suit your plot.
Steps:
Before initiating the plot, set the size for the plot, use:
plt.figure(19.20, 10.80)
**notice how I have multiplied my aspect ratio by '0.01'.
At the end of the plot, when using plt.savefig, save it as follows:
plt.savefig('name.jpg', bbox_inches='tight', dpi=1000)

If I understand correctly what you want to do, you can create your figure and set the size of the window. Afterwards, you can save your graph with the matplotlib toolbox button. Here an example:
from pylab import get_current_fig_manager,show,plt,imshow
plt.Figure()
thismanager = get_current_fig_manager()
thismanager.window.wm_geometry("500x500+0+0")
#in this case 500 is the size (in pixel) of the figure window. In your case you want to maximise to the size of your screen or whatever
imshow(your_data)
show()

When to use cla(), clf() or close() for clearing a plot in matplotlib?

Matplotlib offers these functions:
cla() # Clear axis
clf() # Clear figure
close() # Close a figure window
When should I use each function and what exactly does it do?

They all do different things, since matplotlib uses a hierarchical order in which a figure window contains a figure which may consist of many axes. Additionally, there are functions from the pyplot interface and there are methods on the Figure class. I will discuss both cases below.
pyplot interface
pyplot is a module that collects a couple of functions that allow matplotlib to be used in a functional manner. I here assume that pyplot has been imported as import matplotlib.pyplot as plt.
In this case, there are three different commands that remove stuff:
See matplotlib.pyplot Functions:
plt.cla() clears an axis, i.e. the currently active axis in the current figure. It leaves the other axes untouched.
plt.clf() clears the entire current figure with all its axes, but leaves the window opened, such that it may be reused for other plots.
plt.close() closes a window, which will be the current window, if not specified otherwise.
Which functions suits you best depends thus on your use-case.
The close() function furthermore allows one to specify which window should be closed. The argument can either be a number or name given to a window when it was created using figure(number_or_name) or it can be a figure instance fig obtained, i.e., usingfig = figure(). If no argument is given to close(), the currently active window will be closed. Furthermore, there is the syntax close('all'), which closes all figures.
methods of the Figure class
Additionally, the Figure class provides methods for clearing figures.
I'll assume in the following that fig is an instance of a Figure:
fig.clf() clears the entire figure. This call is equivalent to plt.clf() only if fig is the current figure.
fig.clear() is a synonym for fig.clf()
Note that even del fig will not close the associated figure window. As far as I know the only way to close a figure window is using plt.close(fig) as described above.

There is just a caveat that I discovered today.
If you have a function that is calling a plot a lot of times you better use plt.close(fig) instead of fig.clf() somehow the first does not accumulate in memory. In short if memory is a concern use plt.close(fig) (Although it seems that there are better ways, go to the end of this comment for relevant links).
So the the following script will produce an empty list:
for i in range(5):
fig = plot_figure()
plt.close(fig)
# This returns a list with all figure numbers available
print(plt.get_fignums())
Whereas this one will produce a list with five figures on it.
for i in range(5):
fig = plot_figure()
fig.clf()
# This returns a list with all figure numbers available
print(plt.get_fignums())
From the documentation above is not clear to me what is the difference between closing a figure and closing a window. Maybe that will clarify.
If you want to try a complete script there you have:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1000)
y = np.sin(x)
for i in range(5):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, y)
plt.close(fig)
print(plt.get_fignums())
for i in range(5):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, y)
fig.clf()
print(plt.get_fignums())
If memory is a concern somebody already posted a work-around in SO see:
Create a figure that is reference counted

plt.cla() means clear current axis
plt.clf() means clear current figure
also, there's plt.gca() (get current axis) and plt.gcf() (get current figure)
Read more here: Matplotlib, Pyplot, Pylab etc: What's the difference between these and when to use each?