How can I flip the origin of a matplotlib plot to be in the upper-left corner - as opposed to the default lower-left? I'm using matplotlib.pylab.plot to produce the plot (though if there is another plotting routine that is more flexible, please let me know).
I'm looking for the equivalent of the matlab command: axis ij;
Also, I've spent a couple hours surfing matplotlib help and google but haven't come up with an answer. Some info on where I could have looked up the answer would be helpful as well.
The easiest way is to use:
plt.gca().invert_yaxis()
After you plotted the image. Origin works only for imshow.
axis ij just makes the y-axis increase downward instead of upward, right? If so, then matplotlib.axes.invert_yaxis() might be all you need -- but I can't test that right now.
If that doesn't work, I found a mailing post suggesting that
setp(gca(), 'ylim', reversed(getp(gca(), 'ylim')))
might do what you want to resemble axis ij.
For an image or contour plot, you can use the keyword origin = None | 'lower' | 'upper' and for a line plot, you can set the ylimits high to low.
from pylab import *
A = arange(25)/25.
A = A.reshape((5,5))
figure()
imshow(A, interpolation='nearest', origin='lower')
figure()
imshow(A, interpolation='nearest')
d = arange(5)
figure()
plot(d)
ylim(5, 0)
show()
The following is a basic way to achieve this
ax=pylab.gca()
ax.set_ylim(ax.get_ylim()[::-1])
This
plt.ylim(max(plt.ylim()), min(plt.ylim()))
has an advantage over this
plt.gca().invert_yaxis()
and is that if you are in interactive mode and you repeatedly plot the same plot (maybe with updated data and having a breakpoint after the plot) the y axis won't keep inverting every time.
Related
I did the following plot with the figure size (which should not be changed):
plt.figure(figsize=(14, 5))
Unfortunately, the x-label is not fully visible. My question: Is it possible, to move the whole grafic to the top (because there would be enough space)?
The code:
plt.figure(figsize=(14, 5))
plt.plot(time_load,load, linewidth=1.5)
plt.grid(True)
plt.tick_params(labelsize=16)
plt.xlabel('Time [hours]',fontsize=16)
plt.xlim([0,24])
plt.xticks([0,4,8,12,16,20,24])
plt.legend(loc="upper left",fontsize = 'large')
Thank you very much for your help!
You can also try
plt.subplots_adjust(bottom=0.19)
If 0.19 adds too much or too little space to see the x-label better, then try adjusting little by little up or down.
A very simple approach is to use
plt.tight_layout()
See: http://matplotlib.org/users/tight_layout_guide.html
Thank you very much for your help!
You can use the axes() command that allows you to specify the location as axes. You can also use the xlim() command and ylim() command. In your case, you can use the latter. So just as an example, the xlim method can be used like this:
plt.xlim(X.min()*1.1, X.max()*1.1)
plt.ylim(C.min()*1.1, C.max()*1.1)
This will make some space for the data points to be seen clearly. Hope that helps.
Just seen the code so you're using xlim method. You want to create the subplot somewhere else, for that you can use the axis() method.
plt.axes([0.3, 0.3, .5, .5])
You can adjust plot accordingly. This one will create a subplot in the upper right corner of the figure.
I am looking for a way to set a black border on the errorbars in my plot,
The following code:
ax.errorbar(x, y, yerr, fmt='o', label='label',color="#8da0cb",capthick=2, elinewidth=2,zorder=10)
produces:
I find it more aesthetically pleasing if there was a black border around the errorbar like there is on the marker.
Thanks for any help you can provide
Not a great solution, but you could get close by plotting the errorbars again behind your original ones, with a wider line and cap thinkness, and setting the colour of those ones to black. We can make use of the zorder kwarg to put them behind the others.
Heres a MWE:
import matplotlib.pyplot as plt
import numpy as np
# Fake data
x=np.arange(0,5,1)
y=np.ones(x.shape)
yerr = np.ones(x.shape)/4.
# Create figure
fig,ax = plt.subplots(1)
# Set some limits
ax.set_xlim(-1,5)
ax.set_ylim(-2,4)
# Plot errorbars with the line color you want
ax.errorbar(x,y,yerr, fmt='o',color='r',capthick=2,elinewidth=2,capsize=3,zorder=10)
# Plot black errorbars behind (lower zorder) with a wider line and cap thinkness
ax.errorbar(x,y,yerr, fmt='o',color='k',capthick=4,elinewidth=4,capsize=4,zorder=5)
plt.show()
Again, not a perfect solution, but at least it allows you to include it in the legend. This time, rather than plot the errorbars twice, we will use the matplotlib.patheffects module to add a Stroke to the errorbars.
errorbar returns several Line2D and LineCollection objects, so we need to apply the stroke to each of the relevant ones.
import matplotlib.patheffects as path_effects
e = ax.errorbar(x,y,yerr, fmt='o',color='r',capthick=2,elinewidth=2, label='path effects')
e[1][0].set_path_effects([path_effects.Stroke(linewidth=4, foreground='black'),
path_effects.Normal()])
e[1][1].set_path_effects([path_effects.Stroke(linewidth=4, foreground='black'),
path_effects.Normal()])
e[2][0].set_path_effects([path_effects.Stroke(linewidth=4, foreground='black'),
path_effects.Normal()])
ax.legend(loc=0)
As far as I can see from the information provided in the webpage of pyplot I do not see a valid kwargs that exists for what you are asking.
There exists mfc, mec, ms and mew which are markerfacecolor, markeredgecolor, markersize and markeredgewith. It can probably be asked in GitHub so that people take this into consideration and add it in the next version of matplotlib.
Also taking a look at the answer for this question asked in Stackoverflow, I don't believe it can be done.
How can I flip the origin of a matplotlib plot to be in the upper-left corner - as opposed to the default lower-left? I'm using matplotlib.pylab.plot to produce the plot (though if there is another plotting routine that is more flexible, please let me know).
I'm looking for the equivalent of the matlab command: axis ij;
Also, I've spent a couple hours surfing matplotlib help and google but haven't come up with an answer. Some info on where I could have looked up the answer would be helpful as well.
The easiest way is to use:
plt.gca().invert_yaxis()
After you plotted the image. Origin works only for imshow.
axis ij just makes the y-axis increase downward instead of upward, right? If so, then matplotlib.axes.invert_yaxis() might be all you need -- but I can't test that right now.
If that doesn't work, I found a mailing post suggesting that
setp(gca(), 'ylim', reversed(getp(gca(), 'ylim')))
might do what you want to resemble axis ij.
For an image or contour plot, you can use the keyword origin = None | 'lower' | 'upper' and for a line plot, you can set the ylimits high to low.
from pylab import *
A = arange(25)/25.
A = A.reshape((5,5))
figure()
imshow(A, interpolation='nearest', origin='lower')
figure()
imshow(A, interpolation='nearest')
d = arange(5)
figure()
plot(d)
ylim(5, 0)
show()
The following is a basic way to achieve this
ax=pylab.gca()
ax.set_ylim(ax.get_ylim()[::-1])
This
plt.ylim(max(plt.ylim()), min(plt.ylim()))
has an advantage over this
plt.gca().invert_yaxis()
and is that if you are in interactive mode and you repeatedly plot the same plot (maybe with updated data and having a breakpoint after the plot) the y axis won't keep inverting every time.
In Matplotlib, I make dashed grid lines as follows:
fig = pylab.figure()
ax = fig.add_subplot(1,1,1)
ax.yaxis.grid(color='gray', linestyle='dashed')
however, I can't find out how (or even if it is possible) to make the grid lines be drawn behind other graph elements, such as bars. Changing the order of adding the grid versus adding other elements makes no difference.
Is it possible to make it so that the grid lines appear behind everything else?
According to this - http://matplotlib.1069221.n5.nabble.com/axis-elements-and-zorder-td5346.html - you can use Axis.set_axisbelow(True)
(I am currently installing matplotlib for the first time, so have no idea if that's correct - I just found it by googling "matplotlib z order grid" - "z order" is typically used to describe this kind of thing (z being the axis "out of the page"))
To me, it was unclear how to apply andrew cooke's answer, so this is a complete solution based on that:
ax.set_axisbelow(True)
ax.yaxis.grid(color='gray', linestyle='dashed')
If you want to validate the setting for all figures, you may set
plt.rc('axes', axisbelow=True)
or
plt.rcParams['axes.axisbelow'] = True
It works for Matplotlib>=2.0.
I had the same problem and the following worked:
[line.set_zorder(3) for line in ax.lines]
fig.show() # to update
Increase 3to a higher value if it does not work.
You can also set the zorder kwarg in matplotlib.pyplot.grid
plt.grid(which='major', axis='y', zorder=-1.0)
You can try to use one of Seaborn's styles. For instance:
import seaborn as sns
sns.set_style("whitegrid")
Not only the gridlines will get behind but the looks are nicer.
For some (like me) it might be interesting to draw the grid behind only "some" of the other elements. For granular control of the draw order, you can use matplotlib.artist.Artist.set_zorder on the axes directly:
ax.yaxis.grid(color='gray', linestyle='dashed')
ax.set_zorder(3)
This is mentioned in the notes on matplotlib.axes.Axes.grid.
I'm plotting some data with matplotlib. I want the plot to focus on a specific range of x-values, so I'm using set_xlim().
Roughly, my code looks like this:
fig=plt.figure()
ax=fig.add_subplot(111)
for ydata in ydatalist:
ax.plot(x_data,y_data[0],label=ydata[1])
ax.set_xlim(left=0.0,right=1000)
plt.savefig(filename)
When I look at the plot, the x range ends up being from 0 to 12000. This occurs whether set_xlim() occurs before or after plot(). Why is set_xlim() not working in this situation?
Out of curiosity, what about switching in the old xmin and xmax?
fig=plt.figure()
ax=fig.add_subplot(111)
ax.plot(x_data,y_data)
ax.set_xlim(xmin=0.0, xmax=1000)
plt.savefig(filename)
The text of this answer was taken from an answer that was deleted almost immediately after it was posted.
set_xlim() limits the data that is displayed on the plot.
In order to change the bounds of the axis, use set_xbound().
fig=plt.figure()
ax=fig.add_subplot(111)
ax.plot(x_data,y_data)
ax.set_xbound(lower=0.0, upper=1000)
plt.savefig(filename)
In my case the following solutions alone did not work:
ax.set_xlim([0, 5.00])
ax.set_xbound(lower=0.0, upper=5.00)
However, setting the aspect using set_aspect worked, i.e:
ax.set_aspect('auto')
ax.set_xlim([0, 5.00])
ax.set_xbound(lower=0.0, upper=5.00)
I have struggled a lot with the ax.set_xlim() and couldn't get it to work properly and I found out why exactly. After setting the xlim I was setting the xticks and xticklabels (those are the vertical lines on the x-axis and their labels) and this somehow elongated the axis to the needed extent. So if the last tick was at 300 and my xlim was set at 100, it again widened the axis to the 300 just to place the tick there.
So the solution was to put it just after the troublesome code:
ax.set_xlabel(label)
ax.set_xticks(xticks)
ax.set_xticklabels(xticks, rotation=60)
ax.set_xlim(xmin=0.0, xmax=100.0)
The same thing occurred to me today. My issue was that the data was not in the right format, i.e. not floats. The limits I set (itself floats) became meaningless compared to e.g. strings. After putting float() around the data, everything worked as expected.