I'm using matplotlib to draw a bar chart with 3 bars. I want to add some extra space along the x-axis (so that the x-axis line is drawn longer).
Below is what I have:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
dt = [1,3,2]
plt.figure()
xvals = range(len(dt))
plt.bar(xvals, dt, width=0.5)
plt.tick_params(bottom=False)
plt.xticks(xvals, ['a','b','c'])
plt.yticks(range(0,4), [0,1,2,3])
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.show()
This code produces:
I simply want (note the elongated x-axis):
Change the limits of the x-axis using xlim()
for ex:
plt.xlim(-0.5,3.5) # adjust as necessary
Just add the following limits. Yo can use None as the left hand limit to let the plot choose the limit as the default value. Since the x-values are 0, 1, 2 and now you add the right hand side limit as 3, you will have an extended axis. Replace 3 by whatever value you want.
plt.xlim(None, 3)
Related
I'm trying to adjust a suptitle above a multi-panel figure and am having trouble figuring out how to adjust the figsize and subsequently position the suptitle.
The problem is that calling plt.suptitle("my title", y=...) to adjust the position of the suptitle also adjusts the figure dimensions. A few questions:
where does suptitle(..., y=1.1) actually put the title? As far as I can tell, the documentation for the y parameter of suptitle points to matplotlib.text.Text, but I don't know what figure coordinates mean when you have multiple subplots.
what is the effect on figure size when specifying y to suptitle?
how do I manually adjust figure size and spacing (subplots_adjust?) to add a figure title per panel and a suptitle for the entire figure, maintaining the size of each ax in the figure?
An example:
data = np.random.random(size=100)
f, a = plt.subplots(2, 2, figsize=(10, 5))
a[0,0].plot(data)
a[0,0].set_title("this is a really long title\n"*2)
a[0,1].plot(data)
a[1,1].plot(data)
plt.suptitle("a big long suptitle that runs into the title\n"*2, y=1.05);
Obviously I can tweak y each time I make a figure, but I need a solution that generally works without manual intervention. I've tried both constrained layout and tight layout; neither works reliably with figures of any complexity.
1. What do figure coordinates mean?
Figure coordinates go 0 to 1, where (0,0) is the lower left corner and (1,1) is the upper right corner. A coordinate of y=1.05 is hence slightly outside the figure.
2. what is the effect on figure size when specifying y to suptitle?
Specifying y to suptitle has no effect whatsoever on the figure size.
3a. How do I manually adjust figure size and spacing to add a figure title per panel and a suptitle for the entire figure?
First, one would not add an additional linebreak. I.e. if you want to have 2 lines, don't use 3 linebreaks (\n). Then one can adjust the subplot parameters as desired to leave space for the titles. E.g. fig.subplots_adjust(top=0.8) and use a y <= 1 for the title to be inside the figure.
import matplotlib.pyplot as plt
import numpy as np
data = np.random.random(size=100)
fig, axes = plt.subplots(2, 2, figsize=(10, 5))
fig.subplots_adjust(top=0.8)
axes[0,0].plot(data)
axes[0,0].set_title("\n".join(["this is a really long title"]*2))
axes[0,1].plot(data)
axes[1,1].plot(data)
fig.suptitle("\n".join(["a big long suptitle that runs into the title"]*2), y=0.98)
plt.show()
3b. ... while maintaining the size of each ax in the figure?
Maintaining the size of the axes and still have enough space for the titles is only possible by changing the overall figure size.
This could look as follows, where we define a function make_space_above which takes the array of axes as input, as well as the newly desired top margin in units of inches. So for example, you come to the conclusion that you need 1 inch of margin on top to host your titles:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.random(size=100)
fig, axes = plt.subplots(2, 2, figsize=(10, 5), squeeze = False)
axes[0,0].plot(data)
axes[0,0].set_title("\n".join(["this is a really long title"]*2))
axes[0,1].plot(data)
axes[1,1].plot(data)
fig.suptitle("\n".join(["a big long suptitle that runs into the title"]*2), y=0.98)
def make_space_above(axes, topmargin=1):
""" increase figure size to make topmargin (in inches) space for
titles, without changing the axes sizes"""
fig = axes.flatten()[0].figure
s = fig.subplotpars
w, h = fig.get_size_inches()
figh = h - (1-s.top)*h + topmargin
fig.subplots_adjust(bottom=s.bottom*h/figh, top=1-topmargin/figh)
fig.set_figheight(figh)
make_space_above(axes, topmargin=1)
plt.show()
(left: without calling make_space_above; right: with call to make_space_above(axes, topmargin=1))
Short Answer
For those coming from Google for adjusting the title position on a scatter matrix, you can simply set the y parameter to a value slightly lower than 1:
plt.suptitle('My Title', y=0.92)
... or use constrained_layout:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.random(size=100)
f, a = plt.subplots(2, 2, figsize=(10, 5), constrained_layout=True)
a[0,0].plot(data)
a[0,0].set_title("this is a really long title\n"*2)
a[0,1].plot(data)
a[1,1].plot(data)
plt.suptitle("a big long suptitle that runs into the title\n"*2);
A bit of a hacky solution, but if your plots only have 1 column, perhaps consider just add the main title to the title of the first plot, like so:
ax[0].set_title("Main Title\nFirst Plot")
I made in Python a horizontal bar plot, with a bar coming from left to right:
I would like to add an additional horizontal bar, on the same horizontal plane of the previous bar, this time coming from right to left. Both horizontal bars should be present at the same time.
Does anybody have any idea on how to do that? If I use the reverse function everything is reversed, but I need to reverse only the new specific bar without changing anything else.
Ideally, on the new picture the new bar graph would come from the right and stop at 25, with error bars from 23 to 27 (-/+ 2).
Here is my script:
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(9.5, 2.7))
# Create horizontal bars
plt.barh(0, 18,height=0.2,facecolor='orange',edgecolor='black',linewidth=2)
plt.errorbar(x=[18], y=[0], xerr=[2],color='black',fmt='none',linewidth=5,zorder=4)
plt.xticks(np.arange(10, 30+1, 1.0),fontsize=14)
plt.yticks([])
plt.xlim(10, 30)
plt.ylim(-.13, .13)
plt.show()
The trick is to use left to specify where the bar should start, then pass a negative width for your bar to make it extend right to left. Since the right side of the window will also change with your data, you might also want to make that some sort of parameter i.e. x_max:
import numpy as np
import matplotlib.pyplot as plt
x_max = 30
plt.figure(figsize=(9.5, 2.7))
# Create horizontal bars
plt.barh(0, 18,height=0.2,facecolor='orange',edgecolor='black',linewidth=2)
plt.errorbar(x=[18], y=[0], xerr=[2],color='black',fmt='none',linewidth=5,zorder=4)
# new code: use left to specify the start position, then make its width negative
# to extend right to left
plt.barh(0, -5, height=0.2, left=x_max, facecolor='red',edgecolor='black',linewidth=2)
# place error bars the same as you did for the above.
plt.errorbar(x=[x_max - 5], y=[0], xerr=[2],color='black',fmt='none',linewidth=5,zorder=4)
plt.xticks(np.arange(10, 30+1, 1.0),fontsize=14)
plt.yticks([])
plt.xlim(10, x_max)
plt.ylim(-.13, .13)
plt.show()
I tried to make a plot showing many lines, but it is hard to tell them apart. They have different colors, but I would like to make it easy to show which line is which. A normal legend does not really work so well, since I have more than 10 lines.
The lines follow a logical sequence. I would like to (1) have their color automatically chosen from a colormaps (preferably one that has a smooth ordering, such as viridis or a rainbow). Then I would like (2) to have the tick marks next to the color bar to correspond to the index i for each line (or better a text label from an array of strings textlabels[i]).
Here's a minimal piece of code (with some gaps where I am not sure what to use). I hope this illustrates what I am trying.
import numpy as np
import matplotlib.pyplot as plt
# Genereate some values to plot on the x-axis
x = np.linspace(0,1,1000)
# Some code to select a (discrete version of) a rainbow/viridis color map
...
# Loop over lines that should appear in the plot
for i in range(0,9):
# Plot something (using straight lines with different slope as example)
plt.plot(i*x)
# Some code to plot a discrete color bar next
# to the plot with ticks showing the value of i
...
I currently have this. I would like the color bar to have the ticks with values of i, i.e. 0, 1, 2, ... next to it as tick marks.
Example figure of what I have now. It is hard to tell the lines apart now.
One gets a colormap via plt.get_cmap("name of cmap", number_of_colors).
This colormap can be used to compute the colors for the plots. It can also be used to generate a colorbar.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
n = 10 # how many lines to draw or number of discrete color levels
x = np.linspace(0,1,17)
cmap = plt.get_cmap("viridis", n)
for i in range(0,n):
plt.plot(i*x, color=cmap(i))
norm= matplotlib.colors.BoundaryNorm(np.arange(0,n+1)-0.5, n)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
plt.colorbar(sm, ticks=np.arange(0,n))
plt.show()
I have a simple plot executed using matplotlib. My x and y axes start at 0,0 respectively. A matplotlib plot shows 2 zeros corresponding to the 2 axes. I want only one zero (somewhere in the middle of the start point, if possible).
How can this be done?
Here's what I used:
import matplotlib.pyplot as plt
plt.plot([1,2,3], [4,5,6])
plt.xlim([0,5])
plt.ylim([0,10])
plt.show()
UPDATE:
I used #nostradamus' solution and it got rid of one of the zeros. I want the zero a little centred if possible.
I used:
plt.gca().xaxis.set_major_locator(MaxNLocator(prune='lower'))
plt.gca().yaxis.get_majorticklabels()[0].set_x(-0.05)
I want the reverse of this. I want the zero on the y axis to move down or the one from x axis left. So tried:
plt.gca().yaxis.set_major_locator(MaxNLocator(prune='lower'))
plt.gca().xaxis.get_majorticklabels()[0].set_x(-0.05)
It doesn't work. I think the bottom and left boundaries for the zeros are set to ensure they don't go beyond the area.
The keyword is prune, which allows you to kill the upper, lower or both tick labels. Here is a working examples that gets rid of the 0 of the x-axis:
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
plt.plot([1,2,3], [4,5,6])
plt.xlim([0,5])
plt.ylim([0,10])
plt.gca().xaxis.set_major_locator(MaxNLocator(prune='lower'))
plt.show()
The second part (to move the remaining zero to the corner) seems to be much more difficult. It seems that you can move single tick labels using
plt.gca().yaxis.get_majorticklabels()[0].set_x(-0.05)
However, I was unable to figure out how to move it below the lower limit of the corresponding axis (i.e. plt.gca().yaxis.get_majorticklabels()[0].set_y(-0.05) is doing nothing).
Changing the vertical distance between two subplot using tight_layout(h_pad=-1) changes the total figuresize. How can I define the figuresize using tight_layout?
Here is the code:
#define figure
pl.figure(figsize=(10, 6.25))
ax1=subplot(211)
img=pl.imshow(np.random.random((10,50)), interpolation='none')
ax1.set_xticklabels(()) #hides the tickslabels of the first plot
subplot(212)
x=linspace(0,50)
pl.plot(x,x,'k-')
xlim( ax1.get_xlim() ) #same x-axis for both plots
And here is the results:
If I write
pl.tight_layout(h_pad=-2)
in the last line, then I get this:
As you can see, the figure is bigger...
You can use a GridSpec object to control precisely width and height ratios, as answered on this thread and documented here.
Experimenting with your code, I could produce something like what you want, by using a height_ratio that assigns twice the space to the upper subplot, and increasing the h_pad parameter to the tight_layout call. This does not sound completely right, but maybe you can adjust this further ...
import numpy as np
from matplotlib.pyplot import *
import matplotlib.pyplot as pl
import matplotlib.gridspec as gridspec
#define figure
fig = pl.figure(figsize=(10, 6.25))
gs = gridspec.GridSpec(2, 1, height_ratios=[2,1])
ax1=subplot(gs[0])
img=pl.imshow(np.random.random((10,50)), interpolation='none')
ax1.set_xticklabels(()) #hides the tickslabels of the first plot
ax2=subplot(gs[1])
x=np.linspace(0,50)
ax2.plot(x,x,'k-')
xlim( ax1.get_xlim() ) #same x-axis for both plots
fig.tight_layout(h_pad=-5)
show()
There were other issues, like correcting the imports, adding numpy, and plotting to ax2 instead of directly with pl. The output I see is this:
This case is peculiar because of the fact that the default aspect ratios of images and plots are not the same. So it is worth noting for people looking to remove the spaces in a grid of subplots consisting of images only or of plots only that you may find an appropriate solution among the answers to this question (and those linked to it): How to remove the space between subplots in matplotlib.pyplot?.
The aspect ratios of the subplots in this particular example are as follows:
# Default aspect ratio of images:
ax1.get_aspect()
# 1.0
# Which is as it is expected based on the default settings in rcParams file:
matplotlib.rcParams['image.aspect']
# 'equal'
# Default aspect ratio of plots:
ax2.get_aspect()
# 'auto'
The size of ax1 and the space beneath it are adjusted automatically based on the number of pixels along the x-axis (i.e. width) so as to preserve the 'equal' aspect ratio while fitting both subplots within the figure. As you mentioned, using fig.tight_layout(h_pad=xxx) or the similar fig.set_constrained_layout_pads(hspace=xxx) is not a good option as this makes the figure larger.
To remove the gap while preserving the original figure size, you can use fig.subplots_adjust(hspace=xxx) or the equivalent plt.subplots(gridspec_kw=dict(hspace=xxx)), as shown in the following example:
import numpy as np # v 1.19.2
import matplotlib.pyplot as plt # v 3.3.2
np.random.seed(1)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 6.25),
gridspec_kw=dict(hspace=-0.206))
# For those not using plt.subplots, you can use this instead:
# fig.subplots_adjust(hspace=-0.206)
size = 50
ax1.imshow(np.random.random((10, size)))
ax1.xaxis.set_visible(False)
# Create plot of a line that is aligned with the image above
x = np.arange(0, size)
ax2.plot(x, x, 'k-')
ax2.set_xlim(ax1.get_xlim())
plt.show()
I am not aware of any way to define the appropriate hspace automatically so that the gap can be removed for any image width. As stated in the docstring for fig.subplots_adjust(), it corresponds to the height of the padding between subplots, as a fraction of the average axes height. So I attempted to compute hspace by dividing the gap between the subplots by the average height of both subplots like this:
# Extract axes positions in figure coordinates
ax1_x0, ax1_y0, ax1_x1, ax1_y1 = np.ravel(ax1.get_position())
ax2_x0, ax2_y0, ax2_x1, ax2_y1 = np.ravel(ax2.get_position())
# Compute negative hspace to close the vertical gap between subplots
ax1_h = ax1_y1-ax1_y0
ax2_h = ax2_y1-ax2_y0
avg_h = (ax1_h+ax2_h)/2
gap = ax1_y0-ax2_y1
hspace=-(gap/avg_h) # this divided by 2 also does not work
fig.subplots_adjust(hspace=hspace)
Unfortunately, this does not work. Maybe someone else has a solution for this.
It is also worth mentioning that I tried removing the gap between subplots by editing the y positions like in this example:
# Extract axes positions in figure coordinates
ax1_x0, ax1_y0, ax1_x1, ax1_y1 = np.ravel(ax1.get_position())
ax2_x0, ax2_y0, ax2_x1, ax2_y1 = np.ravel(ax2.get_position())
# Set new y positions: shift ax1 down over gap
gap = ax1_y0-ax2_y1
ax1.set_position([ax1_x0, ax1_y0-gap, ax1_x1, ax1_y1-gap])
ax2.set_position([ax2_x0, ax2_y0, ax2_x1, ax2_y1])
Unfortunately, this (and variations of this) produces seemingly unpredictable results, including a figure resizing similar to when using fig.tight_layout(). Maybe someone else has an explanation for what is happening here behind the scenes.