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()
I'm struggling to deal with my plot margins in matplotlib. I've used the code below to produce my chart:
plt.imshow(g)
c = plt.colorbar()
c.set_label("Number of Slabs")
plt.savefig("OutputToUse.png")
However, I get an output figure with lots of white space on either side of the plot. I've searched google and read the matplotlib documentation, but I can't seem to find how to reduce this.
One way to automatically do this is the bbox_inches='tight' kwarg to plt.savefig.
E.g.
import matplotlib.pyplot as plt
import numpy as np
data = np.arange(3000).reshape((100,30))
plt.imshow(data)
plt.savefig('test.png', bbox_inches='tight')
Another way is to use fig.tight_layout()
import matplotlib.pyplot as plt
import numpy as np
xs = np.linspace(0, 1, 20); ys = np.sin(xs)
fig = plt.figure()
axes = fig.add_subplot(1,1,1)
axes.plot(xs, ys)
# This should be called after all axes have been added
fig.tight_layout()
fig.savefig('test.png')
You can adjust the spacing around matplotlib figures using the subplots_adjust() function:
import matplotlib.pyplot as plt
plt.plot(whatever)
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
This will work for both the figure on screen and saved to a file, and it is the right function to call even if you don't have multiple plots on the one figure.
The numbers are fractions of the figure dimensions, and will need to be adjusted to allow for the figure labels.
All you need is
plt.tight_layout()
before your output.
In addition to cutting down the margins, this also tightly groups the space between any subplots:
x = [1,2,3]
y = [1,4,9]
import matplotlib.pyplot as plt
fig = plt.figure()
subplot1 = fig.add_subplot(121)
subplot1.plot(x,y)
subplot2 = fig.add_subplot(122)
subplot2.plot(y,x)
fig.tight_layout()
plt.show()
Sometimes, the plt.tight_layout() doesn't give me the best view or the view I want. Then why don't plot with arbitrary margin first and do fixing the margin after plot?
Since we got nice WYSIWYG from there.
import matplotlib.pyplot as plt
fig,ax = plt.subplots(figsize=(8,8))
plt.plot([2,5,7,8,5,3,5,7,])
plt.show()
Then paste settings into margin function to make it permanent:
fig,ax = plt.subplots(figsize=(8,8))
plt.plot([2,5,7,8,5,3,5,7,])
fig.subplots_adjust(
top=0.981,
bottom=0.049,
left=0.042,
right=0.981,
hspace=0.2,
wspace=0.2
)
plt.show()
In case anybody wonders how how to get rid of the rest of the white margin after applying plt.tight_layout() or fig.tight_layout(): With the parameter pad (which is 1.08 by default), you're able to make it even tighter:
"Padding between the figure edge and the edges of subplots, as a fraction of the font size."
So for example
plt.tight_layout(pad=0.05)
will reduce it to a very small margin. Putting 0 doesn't work for me, as it makes the box of the subplot be cut off a little, too.
Just use ax = fig.add_axes([left, bottom, width, height])
if you want exact control of the figure layout. eg.
left = 0.05
bottom = 0.05
width = 0.9
height = 0.9
ax = fig.add_axes([left, bottom, width, height])
plt.savefig("circle.png", bbox_inches='tight',pad_inches=-1)
inspired by Sammys answer above:
margins = { # vvv margin in inches
"left" : 1.5 / figsize[0],
"bottom" : 0.8 / figsize[1],
"right" : 1 - 0.3 / figsize[0],
"top" : 1 - 1 / figsize[1]
}
fig.subplots_adjust(**margins)
Where figsize is the tuple that you used in fig = pyplot.figure(figsize=...)
With recent matplotlib versions you might want to try Constrained Layout:
constrained_layout automatically adjusts subplots and decorations like
legends and colorbars so that they fit in the figure window while
still preserving, as best they can, the logical layout requested by
the user.
constrained_layout is similar to tight_layout, but uses a constraint
solver to determine the size of axes that allows them to fit.
constrained_layout needs to be activated before any axes are added to
a figure.
Too bad pandas does not handle it well...
The problem with matplotlibs subplots_adjust is that the values you enter are relative to the x and y figsize of the figure. This example is for correct figuresizing for printing of a pdf:
For that, I recalculate the relative spacing to absolute values like this:
pyplot.subplots_adjust(left = (5/25.4)/figure.xsize, bottom = (4/25.4)/figure.ysize, right = 1 - (1/25.4)/figure.xsize, top = 1 - (3/25.4)/figure.ysize)
for a figure of 'figure.xsize' inches in x-dimension and 'figure.ysize' inches in y-dimension. So the whole figure has a left margin of 5 mm, bottom margin of 4 mm, right of 1 mm and top of 3 mm within the labels are placed. The conversion of (x/25.4) is done because I needed to convert mm to inches.
Note that the pure chart size of x will be "figure.xsize - left margin - right margin" and the pure chart size of y will be "figure.ysize - bottom margin - top margin" in inches
Other sniplets (not sure about these ones, I just wanted to provide the other parameters)
pyplot.figure(figsize = figureSize, dpi = None)
and
pyplot.savefig("outputname.eps", dpi = 100)
For me, the answers above did not work with matplotlib.__version__ = 1.4.3 on Win7. So, if we are only interested in the image itself (i.e., if we don't need annotations, axis, ticks, title, ylabel etc), then it's better to simply save the numpy array as image instead of savefig.
from pylab import *
ax = subplot(111)
ax.imshow(some_image_numpyarray)
imsave('test.tif', some_image_numpyarray)
# or, if the image came from tiff or png etc
RGBbuffer = ax.get_images()[0].get_array()
imsave('test.tif', RGBbuffer)
Also, using opencv drawing functions (cv2.line, cv2.polylines), we can do some drawings directly on the numpy array. http://docs.opencv.org/2.4/modules/core/doc/drawing_functions.html
# import pyplot
import matplotlib.pyplot as plt
# your code to plot the figure
# set tight margins
plt.margins(0.015, tight=True)
I have the following issue displayed in the image below:
For an improved clarity I want do delete the stripes on the x axis or put them below the x axis. (Also it would be nice If you know a solution to the problem of overlapping numbers)
Assuming you have defined your plot and axes as below:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
If you want to remove the x axis tick marks you can do:
ax.tick_params(axis='x', top='off', bottom='off')
If you want to change the direction of the tick marks you can do:
ax.tick_params(axis='x', direction='out')
If you want to change the x axis labels then use:
set_xticklabels()
You have to pass a list of labels to use, although I'm not sure why your labels aren't evenly spaced. The documentation at the link below should help:
matplotlib.axes documentation
I would like to rotate the colorbar tick labels so that they read vertically rather than horizontally. I have tried as many variations as I can think of with cbar.ax.set_xticklabels and cbar.ax.ticklabel_format and so on with rotation='vertical' but haven't quite landed it yet.
I've provided a MWE below:
import numpy as np
import matplotlib.pyplot as plt
#example function
x,y = np.meshgrid(np.linspace(-10,10,200),np.linspace(-10,10,200))
z = x*y*np.exp(-(x+y)**2)
#array for contourf levels
clevs = np.linspace(z.min(),z.max(),50)
#array for colorbar tick labels
clevs1 =np.arange(-200,100,10)
cs1 = plt.contourf(x,y,z,clevs)
cbar = plt.colorbar(cs1, orientation="horizontal")
cbar.set_ticks(clevs1[::1])
plt.show()
Any pointers would be greatly appreciated - I'm sure this must be pretty simple...
If you're happy with tick locations and labels and only want to rotate them:
cbar.ax.set_xticklabels(cbar.ax.get_xticklabels(), rotation='vertical')
This is the idiomatic way to rotate tick labels as of Matplotlib 3.4 (and very likely earlier versions too)
cbar.ax.tick_params(rotation=45)
You can use cbar.ax.set_xticklabels to change the rotation (or set_yicklabels if you had a vertical colorbar).
cbar.ax.set_xticklabels(clevs1[::1],rotation=90)
EDIT:
To set the ticks correctly, you can search for where in your clevs1 array the first tick should be using np.argmin, and use that to index clevs1 when you set_xticklabels:
tick_start = np.argmin(abs(clevs1-clevs[0]))
cbar.ax.set_xticklabels(clevs1[tick_start:],rotation=90)
In the following code snippet:
import numpy as np
import pandas as pd
import pandas.rpy.common as com
import matplotlib.pyplot as plt
mtcars = com.load_data("mtcars")
df = mtcars.groupby(["cyl"]).apply(lambda x: pd.Series([x["cyl"].count(), np.mean(x["wt"])], index=["n", "wt"])).reset_index()
plt.plot(df["n"], range(len(df["cyl"])), "o")
plt.yticks(range(len(df["cyl"])), df["cyl"])
plt.show()
This code outputs the dot plot graph, but the result looks quite awful, since both the xticks and yticks don't have enough space, that it's quite difficult to notice both 4 and 8 of the cyl variable output its values in the graph.
So how can I plot it with enough space in advance, much like you can do it without any hassles in R/ggplot2?
For your information, both of this code and this doesn't work in my case. Anyone knows the reason? And do I have to bother to creating such subplots in the first place? Is it impossible to automatically adjust the ticks with response to the input values?
I can't quite tell what you're asking...
Are you asking why the ticks aren't automatically positioned or are you asking how to add "padding" around the inside edges of the plot?
If it's the former, it's because you've manually set the tick locations with yticks. This overrides the automatic tick locator.
If it's the latter, use ax.margins(some_percentage) (where some_percentage is between 0 and 1, e.g. 0.05 is 5%) to add "padding" to the data limits before they're autoscaled.
As an example of the latter, by default, the data limits can be autoscaled such that a point can lie on the boundaries of the plot. E.g.:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(range(10), 'ro')
plt.show()
If you want to avoid this, use ax.margins (or equivalently, plt.margins) to specify a percentage of padding to be added to the data limits before autoscaling takes place.
E.g.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(range(10), 'ro')
ax.margins(0.04) # 4% padding, similar to R.
plt.show()