This question already has an answer here:
Inconsistent figsize resizing in matplotlib
(1 answer)
Closed 3 years ago.
I am trying to generate a bar chart with lots of bars. If I keep the figsize at defaults, the data is squeezed together and the plot is unusable.
I have the following code snippet to reproduce my problem:
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(1)
ax = fig.add_subplot(111)
N=100
# Example data
labels = [chr(x) for x in range(N)]
y_pos = np.arange(len(labels))
performance = 3 + 10 * np.random.rand(len(labels))
error = np.random.rand(len(labels))
ax.barh(y_pos, performance, xerr=error, align='center')
ax.set_yticks(y_pos)
ax.set_yticklabels(labels)
ax.set_xlabel('Performance')
ax.set_title('How fast do you want to go today?')
plt.savefig('a.png', bbox_inches='tight')
plt.show()
If I manually set the height of the figure (for example figsize=(8,N*0.2)), the plotted data looks nice, but there is an annoying vertical whitespace before the firs bar and after the last one.
Is there any way to automatically size the plot properly?
One thing I've used is
plt.tight_layout()
it generates less whitespace for subplots, but may work with just 1 plot.
Here's more info:
https://matplotlib.org/users/tight_layout_guide.html
Another thing that may work is aspect auto when showing the plot.
plt.imshow(X, aspect='auto')
Yet another solution is 'scaled' axis
plt.axis('scaled') #this line fits your images to screen
Also if you mean the overall plot size,I generally just pick a generic size that fits, say 15 x 10 on a laptop screen, or 30x20 on a monitor. Guess and test.
Related
This question already has answers here:
How to highlight specific x-value ranges
(2 answers)
Closed 1 year ago.
I went through the examples in the matplotlib documentation, but it wasn't clear to me how I can make a plot that fills the area between two specific vertical lines.
For example, say I want to create a plot between x=0.2 and x=4 (for the full y range of the plot). Should I use fill_between, fill or fill_betweenx?
Can I use the where condition for this?
It sounds like you want axvspan, rather than one of the fill between functions. The differences is that axvspan (and axhspan) will fill up the entire y (or x) extent of the plot regardless of how you zoom.
For example, let's use axvspan to highlight the x-region between 8 and 14:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(range(20))
ax.axvspan(8, 14, alpha=0.5, color='red')
plt.show()
You could use fill_betweenx to do this, but the extents (both x and y) of the rectangle would be in data coordinates. With axvspan, the y-extents of the rectangle default to 0 and 1 and are in axes coordinates (in other words, percentages of the height of the plot).
To illustrate this, let's make the rectangle extend from 10% to 90% of the height (instead of taking up the full extent). Try zooming or panning, and notice that the y-extents say fixed in display space, while the x-extents move with the zoom/pan:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(range(20))
ax.axvspan(8, 14, ymin=0.1, ymax=0.9, alpha=0.5, color='red')
plt.show()
This question already has answers here:
Removing axes margins in 3D plot
(2 answers)
Closed 4 years ago.
I'm having an issue with setting limits for my 3d plots in matplotlib; I'm finding that no matter how I set my limits for the x,y, and z axes, the plotting routine for 3dplots adds an extra buffer.
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111,projection='3d')
ax.axes.set_xlim3d(left=0, right=10)
ax.axes.set_ylim3d(bottom=0, top=10)
ax.axes.set_zlim3d(bottom=0, top=10)
plt.show()
This produces the following plot:
As you can see, the limits are supposed to be at x, y, z = {0, 10} however the 3D plotting always adds a little bit of a buffer to each edge. Does anyone know a way to turn this effect off?
I've also used plt.xlims(); and ax.axes.set_xlims() but they produce the same effect.
I think this is deliberate (see e.g. this), if you try plotting ax.axes.set_xlim3d(left=0.000001, right=9.9999999) then you get no 0 or 10 displayed on your figure.
Even making the numbers as arbitrarily close as possible doesn't work, e.g.
eps = 1e-16
ax.axes.set_xlim3d(left=0.-eps, right=10+eps)
ax.axes.set_ylim3d(bottom=0.-eps, top=10+eps)
ax.axes.set_zlim3d(bottom=0.-eps, top=10+eps)
The best solution I've found is to set the ticks manually and then slightly scale so the overlap is hidden.
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111,projection='3d')
ax.set_xticks([0,2,4,6,8,10])
ax.set_yticks([0,2,4,6,8,10])
ax.set_zticks([0,2,4,6,8,10])
ax.axes.set_xlim3d(left=0.2, right=9.8)
ax.axes.set_ylim3d(bottom=0.2, top=9.8)
ax.axes.set_zlim3d(bottom=0.2, top=9.8)
plt.show()
This gives,
This is pretty hacky but could be made more general (and I always end up setting ticks manually for publication quality figures). Alternatively, it may be better to turn off the lowest grid line or hide the grid...
I am generating plots like this one:
When using less ticks, the plot fits nicely and the bars are wide enough to see them correctly. Nevertheless, when there are lots of ticks, instead of making the plot larger, it just compress the y axe, resulting in thin bars and overlapping tick text.
This is happening both for plt.show() and plt.save_fig().
Is there any solution so it plots the figure in a scale which guarantees that bars have the specified width, not more (if too few ticks) and not less (too many, overlapping)?
EDIT:
Yes, I'm using barh, and yes, I'm setting height to a fixed value (8):
height = 8
ax.barh(yvalues-width/2, xvalues, height=height, color='blue', align='center')
ax.barh(yvalues+width/2, xvalues, height=height, color='red', align='center')
I don't quite understand your code, it seems you do two plots with the same (only shifted) yvalues, but the image doesn't look so. And are you sure you want to shift by width/2 if you have align=center? Anyways, to changing the image size:
No, I am not sure there is no other way, but I don't see anything in the manual at a glance. To set image size by hand:
fig = plt.figure(figsize=(5, 80))
ax = fig.add_subplot(111)
...your_code
the size is in cm. You can compute it beforehand, try for example
import numpy as np
fig_height = (max(yvalues) - min(yvalues)) / np.diff(yvalue)
this would (approximately) set the minimum distance between ticks to a centimeter, which is too much, but try to adjust it.
I think of two solutions for your case:
If you are trying to plot a histogram, use hist function [1]. This will automatically bin your data. You can even plot multiple overlapping histograms as long as you set alpha value lower than 1. See this post
import matplotlib.pyplot as plt
import numpy as np
x = mu + sigma*np.random.randn(10000)
plt.hist(x, 50, normed=1, facecolor='green',
alpha=0.75, orientation='horizontal')
You can also identify interval of your axis ticks. This will place a tick every 10 items. But I doubt this will solve your problem.
import matplotlib.ticker as ticker
...
ax.yaxis.set_major_locator(ticker.MultipleLocator(10))
What I would like to achive are plots with equal scale aspect ratio, and fixed width, but a dynamically chosen height.
To make this more concrete, consider the following plotting example:
import matplotlib as mpl
import matplotlib.pyplot as plt
def example_figure(slope):
# Create a new figure
fig = plt.figure()
ax = fig.add_subplot(111)
# Set axes to equal aspect ratio
ax.set_aspect('equal')
# Plot a line with a given slope,
# starting from the origin
ax.plot([x * slope for x in range(5)])
# Output the result
return fig
This example code will result in figures of different widths, depending on the data:
example_figure(1).show()
example_figure(2).show()
Matplotlib seems to fit the plots into a certain height, and then chooses the width to accomodate the aspect ratio. The ideal outcome for me would be the opposite -- the two plots above would have the same width, but the second plot would be twice as tall as the first.
Bonus — Difficulty level: Gridspec
In the long run, I would like to create a grid in which one of the plots has a fixed aspect ratio, and I would again like to align the graphs exactly.
# Create a 2x1 grid
import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(2, 1)
# Create the overall graphic, containing
# the top and bottom figures
fig = plt.figure()
ax1 = fig.add_subplot(gs[0, :], aspect='equal')
ax2 = fig.add_subplot(gs[1, :])
# Plot the lines as before
ax1.plot(range(5))
ax2.plot(range(5))
# Show the figure
fig.show()
The result is this:
So again, my question is: How does one create graphs that vary flexibly in height depending on the data, while having a fixed width?
Two points to avoid potential misunderstandings:
In the above example, both graphs have the same x-axis. This cannot be
taken for granted.
I am aware of the height_ratios option in the gridspec. I can compute
the dimensions of the data, and set the ratios, but this unfortunately
does not control the graphs directly, but rather their bounding boxes,
so (depending on the axis labels), graphs of different widths still occur.
Ideally, the plots' canvas would be aligned exactly.
Another unsolved question is similar, but slightly more convoluted.
Any ideas and suggestions are very welcome, and I'm happy to specify the question further, if required. Thank you very much for considering this!
Have you tried to fix the width with fig.set_figwidth()?
This question already has answers here:
How to show minor tick labels on log-scale with Matplotlib
(2 answers)
Closed 7 years ago.
Upon adding the line plt.yscale('log') to my simple plotting script
import numpy as np
residuals = np.loadtxt('res_jacobi.txt', skiprows=1)
import matplotlib.pyplot as plt
fig = plt.figure()
steps = np.arange(0, len(residuals), 1)
plt.plot(steps, residuals, label='$S$')
plt.xlabel("Step",fontsize=20)
plt.ylabel("$S$",fontsize=20)
plt.ylim(0.95 * min(residuals), 1.05 * max(residuals))
plt.yscale('log')
plt.savefig('jacobi-res.pdf', bbox_inches='tight', transparent=True)
the y labels disappear.
I'm sure there is simple fix for this but searching did not turn one up. Any help would be much appreciated.
The normal behavior for matplotlib is to only label major tick marks in log-scaling --- which are even orders of magnitude, e.g. {0.1, 1.0}. Your values are all between those. You can:
rescale your axes to larger bounds,
plt.gca().set_ylim(0.1, 1.0)
label the tick-marks manually,
plt.gca().yaxis.set_minor_formatter(FormatStrFormatter("%.2f"))
semilogy works for me.
Change:
plt.plot(steps, residuals, label='$S$')
Into:
plt.semilogy(steps, residuals, label='$S$')
Remove plt.yscale('log')