I would like to plot some data with a fractional logscale, such that the y axis has the ticks at 10^(-0.1), 10^(-0.2), 10^(-0.3), etc.
The problem is that when I plot my data, there are only ticks at 10^0 and 10^-1, which leaves the slope of the line too slight to see.
Is is possible to set a fractional logscale this way?
Thanks
It sounds like you want tick labels, not the tick marks in particular. In most figures, the minor tick marks are already there where you want them.
The following may then work, though I would think there's an easier way. Note that I'm applying labels to the minor tick marks only: the (two) major tick marks already have a label. Unfortunately, the fonts of the two types of tick marks are not the same; I think that's a result of the LaTeX equation usage.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
X = np.logspace(0, 3)
Y = X**-0.2
plt.plot(X,Y)
plt.yscale('log')
yticks = np.linspace(-0.1, -0.9, 9)
ax.set_yticks(10**yticks, minor=True)
ax.set_ylim(0.1, 1)
ax.set_yticklabels(['$10^{{{:.1f}}}$'.format(ytick) for ytick in yticks], minor=True)
plt.show()
which results in:
For the issue of the different label fonts, you can manually change the major tick labels:
ax.set_yticks([1, 0.1])
ax.set_yticklabels(['$10^0$', '$10^{-1}$'])
(and probably the same for the x-axis).
Related
I am trying to add an arbitrarily big white margin (or padding) to a figure with subplots because I would like the subtitle of the figure not to overlap with any of the subplots or titles of these subplots. I am using Matplotlib 3.1.2.
Currently, I have the following source code.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(2, 1, figsize=(15, 10))
n = 10
x = np.arange(0, n)
y = np.random.rand(n)
ax[0].plot(x, y)
ax[0].set_xlabel('x')
ax[0].set_ylabel('y')
y = np.random.rand(n)
ax[1].plot(x, y)
ax[1].set_xlabel('x')
ax[1].set_ylabel('y')
fig.suptitle("I want to have white space around me!!!")
# fig.tight_layout(rect=[0, 0.03, 1, 0.80])
plt.subplots_adjust(top=0.85)
plt.show()
If I try to use either tight_layout or subplots_adjust (as suggested in several answers to this question Matplotlib tight_layout() doesn't take into account figure suptitle), it doesn't seem to have any effect on the margins. Here's the result of the execution of the previous example.
Is there a way to add an arbitrarily big white margin to the left, right, bottom and (or) top of a figure (with subplots)? I would like to specify the figure size and arbitrarily increase or decrease the white space around an image. I also would like the solution to work in case I decide to add a title for each of the subplots. How can this be done?
fig, axs = plt.subplots(2,1, figsize=(5,5))
fig.patch.set_facecolor('grey')
fig.suptitle("Look at all that grey space around me!!!")
fig.subplots_adjust(top=0.6, bottom=0.4, left=0.4, right=0.6)
I can't figure out how to rotate the text on the X Axis. Its a time stamp, so as the number of samples increase, they get closer and closer until they overlap. I'd like to rotate the text 90 degrees so as the samples get closer together, they aren't overlapping.
Below is what I have, it works fine with the exception that I can't figure out how to rotate the X axis text.
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 8}
matplotlib.rc('font', **font)
values = open('stats.csv', 'r').readlines()
time = [datetime.datetime.fromtimestamp(float(i.split(',')[0].strip())) for i in values[1:]]
delay = [float(i.split(',')[1].strip()) for i in values[1:]]
plt.plot(time, delay)
plt.grid(b='on')
plt.savefig('test.png')
This works for me:
plt.xticks(rotation=90)
Many "correct" answers here but I'll add one more since I think some details are left out of several. The OP asked for 90 degree rotation but I'll change to 45 degrees because when you use an angle that isn't zero or 90, you should change the horizontal alignment as well; otherwise your labels will be off-center and a bit misleading (and I'm guessing many people who come here want to rotate axes to something other than 90).
Easiest / Least Code
Option 1
plt.xticks(rotation=45, ha='right')
As mentioned previously, that may not be desirable if you'd rather take the Object Oriented approach.
Option 2
Another fast way (it's intended for date objects but seems to work on any label; doubt this is recommended though):
fig.autofmt_xdate(rotation=45)
fig you would usually get from:
fig = plt.gcf()
fig = plt.figure()
fig, ax = plt.subplots()
fig = ax.figure
Object-Oriented / Dealing directly with ax
Option 3a
If you have the list of labels:
labels = ['One', 'Two', 'Three']
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(labels, rotation=45, ha='right')
In later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks([1, 2, 3], labels, rotation=45, ha='right')
Option 3b
If you want to get the list of labels from the current plot:
# Unfortunately you need to draw your figure first to assign the labels,
# otherwise get_xticklabels() will return empty strings.
plt.draw()
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
As above, in later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks(ax.get_xticks(), ax.get_xticklabels(), rotation=45, ha='right')
Option 4
Similar to above, but loop through manually instead.
for label in ax.get_xticklabels():
label.set_rotation(45)
label.set_ha('right')
Option 5
We still use pyplot (as plt) here but it's object-oriented because we're changing the property of a specific ax object.
plt.setp(ax.get_xticklabels(), rotation=45, ha='right')
Option 6
This option is simple, but AFAIK you can't set label horizontal align this way so another option might be better if your angle is not 90.
ax.tick_params(axis='x', labelrotation=45)
Edit:
There's discussion of this exact "bug" but a fix hasn't been released (as of 3.4.0):
https://github.com/matplotlib/matplotlib/issues/13774
Easy way
As described here, there is an existing method in the matplotlib.pyplot figure class that automatically rotates dates appropriately for you figure.
You can call it after you plot your data (i.e.ax.plot(dates,ydata) :
fig.autofmt_xdate()
If you need to format the labels further, checkout the above link.
Non-datetime objects
As per languitar's comment, the method I suggested for non-datetime xticks would not update correctly when zooming, etc. If it's not a datetime object used as your x-axis data, you should follow Tommy's answer:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
Try pyplot.setp. I think you could do something like this:
x = range(len(time))
plt.xticks(x, time)
locs, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.plot(x, delay)
Appart from
plt.xticks(rotation=90)
this is also possible:
plt.xticks(rotation='vertical')
I came up with a similar example. Again, the rotation keyword is.. well, it's key.
from pylab import *
fig = figure()
ax = fig.add_subplot(111)
ax.bar( [0,1,2], [1,3,5] )
ax.set_xticks( [ 0.5, 1.5, 2.5 ] )
ax.set_xticklabels( ['tom','dick','harry'], rotation=45 ) ;
If you want to apply rotation on the axes object, the easiest way is using tick_params. For example.
ax.tick_params(axis='x', labelrotation=90)
Matplotlib documentation reference here.
This is useful when you have an array of axes as returned by plt.subplots, and it is more convenient than using set_xticks because in that case you need to also set the tick labels, and also more convenient that those that iterate over the ticks (for obvious reasons)
If using plt:
plt.xticks(rotation=90)
In case of using pandas or seaborn to plot, assuming ax as axes for the plot:
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
Another way of doing the above:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
My answer is inspired by cjohnson318's answer, but I didn't want to supply a hardcoded list of labels; I wanted to rotate the existing labels:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
The simplest solution is to use:
plt.xticks(rotation=XX)
but also
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom=X.XX)
e.g for dates I used rotation=45 and bottom=0.20 but you can do some test for your data
import pylab as pl
pl.xticks(rotation = 90)
To rotate the x-axis label to 90 degrees
for tick in ax.get_xticklabels():
tick.set_rotation(45)
It will depend on what are you plotting.
import matplotlib.pyplot as plt
x=['long_text_for_a_label_a',
'long_text_for_a_label_b',
'long_text_for_a_label_c']
y=[1,2,3]
myplot = plt.plot(x,y)
for item in myplot.axes.get_xticklabels():
item.set_rotation(90)
For pandas and seaborn that give you an Axes object:
df = pd.DataFrame(x,y)
#pandas
myplot = df.plot.bar()
#seaborn
myplotsns =sns.barplot(y='0', x=df.index, data=df)
# you can get xticklabels without .axes cause the object are already a
# isntance of it
for item in myplot.get_xticklabels():
item.set_rotation(90)
If you need to rotate labels you may need change the font size too, you can use font_scale=1.0 to do that.
I am trying to draw a polar plot with matplotlib and would like to do the following:
a) show tick labels with scientific notation
b) show radius circles at specified intervals.
Can anyone give me advice on how to do a) and b) with the code below
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
theta =[np.pi/3, np.pi/3]
theta2 =[np.pi/6, np.pi/6]
r = [0.0, 8.0e-04]
r2 = [0.0, 7.0e-04]
ax.plot(theta, r, 'r-', label ='Observed')
ax.plot(theta2, r2, 'b-', label ='Simulated')
ax.grid(True)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.25),
ncol=3, fancybox=True, shadow=True)
plt.show()
For specifying location of radial ticks, it is very simple - you can just set rticks:
ax.set_rticks([0.0002, 0.0004, 0.0006, 0.0008])
There are a couple of options for the formatting, it depends on how exactly you want the ticks to be displayed. The default formatter will automatically switch to scientific for large and small numbers. If you want to change its threshold for what it considers "small", you can do that by modifying the yaxis formatter (the y axis is the radial axis):
ax.yaxis.get_major_formatter().set_powerlimits((-3,4)) # Things smaller than 1e-3
# will be in scientific
# notation
However, that looks a bit funny to me, it puts the little "1e-4" in the upper left of the plot.
So, if you want to force the current radial ticks to be scientific notation where they are, one way is to use your own format for them. The following uses FormatStrFormatter:
import matplotlib.ticker as ticker
# plotting code here
frmtr = ticker.FormatStrFormatter('%4.1e')
ax.yaxis.set_major_formatter(frmtr)
There are plenty of options available via matplotlib.ticker if this doesn't do exactly what you wanted. The second formatting option give me this:
Is there a way to increase the padding/offset of the polar plot tick labels (theta)?
import matplotlib
import numpy as np
from matplotlib.pyplot import figure, show, grid
# make a square figure
fig = figure(figsize=(2, 2))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True, axisbg='#d5de9c')
ax.set_yticklabels([])
r = np.arange(0, 3.0, 0.01)
theta = 2*np.pi*r
ax.plot(theta, r, color='#ee8d18', lw=3)
ax.set_rmax(2.0)
show()
I'd like to have theta tick labels further away from the polar plot so they don't overlap.
First of all; seeing as how you have specified the figsize to be (2,2) and having the ax occupy 80 % of both the width and height, you have very little space left over to pad the ticklabels. This could cause the ticklabels to be "cut off" at the figure's egdes. This can easily be "fixed" by either
Specifying bigger figsize
Make the ax occupy less space on the (2,2) sized figure
Use smaller fontsize for the ticklabels
or any combination of these. Another, in my opinion better, solution to this "problem" is to use a subplot rather than specifying the Axes's bounds;
ax = fig.add_subplot(111, polar=True, axisbg='#d5de9c')
as this makes it possible to use the method tight_layout() which automatically configures the figure layout to nicely include all elements.
Then over to the real problem at hand; the padding. On a PolarAxes you can set, among other things, the radial placement of the theta-ticks. This is done by specifying the fraction of the polar axes radius where you want the ticklabels to be placed as an argument to the frac parameter of the PolarAxes's set_thetagrids() method. The argument should be a fraction of the axes' radius where you want the ticklabels placed. I.e. for frac < 1 the ticklabels will be placed inside the axes, while for frac > 1 they will be placed outside the axes.
Your code could then be something like this:
import numpy as np
from matplotlib.pyplot import figure, show, grid, tight_layout
# make a square figure
fig = figure(figsize=(2, 2))
ax = fig.add_subplot(111, polar=True, axisbg='#d5de9c')
ax.set_yticklabels([])
r = np.arange(0, 3.0, 0.01)
theta = 2*np.pi*r
ax.plot(theta, r, color='#ee8d18', lw=3)
ax.set_rmax(2.0)
# tick locations
thetaticks = np.arange(0,360,45)
# set ticklabels location at 1.3 times the axes' radius
ax.set_thetagrids(thetaticks, frac=1.3)
tight_layout()
show()
You should try different values for frac to find a value that is best suited for your needs.
If you don't specify a value to the parameter frac as above, i.e. frac has default value None, the code outputs a plot as below. Notice how the radius of the plot is bigger, as the ticklabels don't "occupy as much space" as in the example above.
As of matplotlib 2.1.0, the functionality of the original answer is now deprecated - polar axes now obey to the parameters of ax.tick_params:
ax.tick_params(pad=123)
should do the trick.
I'm drawing the bloxplot shown below using python and matplotlib. Is there any way I can reduce the distance between the two boxplots on the X axis?
This is the code that I'm using to get the figure above:
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['ytick.direction'] = 'out'
rcParams['xtick.direction'] = 'out'
fig = plt.figure()
xlabels = ["CG", "EG"]
ax = fig.add_subplot(111)
ax.boxplot([values_cg, values_eg])
ax.set_xticks(np.arange(len(xlabels))+1)
ax.set_xticklabels(xlabels, rotation=45, ha='right')
fig.subplots_adjust(bottom=0.3)
ylabels = yticks = np.linspace(0, 20, 5)
ax.set_yticks(yticks)
ax.set_yticklabels(ylabels)
ax.tick_params(axis='x', pad=10)
ax.tick_params(axis='y', pad=10)
plt.savefig(os.path.join(output_dir, "output.pdf"))
And this is an example closer to what I'd like to get visually (although I wouldn't mind if the boxplots were even a bit closer to each other):
You can either change the aspect ratio of plot or use the widths kwarg (doc) as such:
ax.boxplot([values_cg, values_eg], widths=1)
to make the boxes wider.
Try changing the aspect ratio using
ax.set_aspect(1.5) # or some other float
The larger then number, the narrower (and taller) the plot should be:
a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect=’equal’.
http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_aspect
When your code writes:
ax.set_xticks(np.arange(len(xlabels))+1)
You're putting the first box plot on 0 and the second one on 1 (event though you change the tick labels afterwards), just like in the second, "wanted" example you gave they are set on 1,2,3.
So i think an alternative solution would be to play with the xticks position and the xlim of the plot.
for example using
ax.set_xlim(-1.5,2.5)
would place them closer.
positions : array-like, optional
Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to range(1, N+1) where N is the number of boxes to be drawn.
https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.boxplot.html
This should do the job!
As #Stevie mentioned, you can use the positions kwarg (doc) to manually set the x-coordinates of the boxes:
ax.boxplot([values_cg, values_eg], positions=[1, 1.3])