When plotting multiple plots using plt.subplots, most of the time the spacing between subplots is not ideal so the the xtick labels of the top plot would overlap with the title of the bottom plots. There is a way to fix this manually by calling say plt.subplots_adjust(hspace=0.5) and changing the parameters interactively to obtain a decent looking plot. Is there a way to calculate the subplot_adjust parameter automatically? Meaning finding the minimum hspace and wspace so that there is not overlap between texts of the plots.
You can use tight_layout https://matplotlib.org/stable/tutorials/intermediate/tight_layout_guide.html or constrained_layout https://matplotlib.org/stable/tutorials/intermediate/constrainedlayout_guide.html
I'm pretty certain that the closest your going to find to an inbuilt calculation method is:
plt.tight_layout()
or
figure.Figure.tight_layout() #if you are using the object version of the code
Related
I've made the following scatterplot. I have an outlier on the positive side of the graph and also the negative end of the graph.
I think normally if I were plotting this using matplotlib.axes.Axes.plot I could use clip_on
But since this was graphed using matplotlib.pyplot.scatter there is no clipping parameter. Do I basically have to adjust the data somehow? Or is there a matplotlib way of doing this?
Is there a way to let matplotlib know to recompute the optimal bounds of a plot?
My problem is that, I am manually computing a bunch of boxplots, putting them at various locations in a plot. By the end, some boxplots extend beyond the plot frame. I could hard-code some xlim and ylim's for now, but I want a more general solution.
What I was thinking was a feature where you say "ok plt I am done plotting, now please adjust the bounds so that all my data is nicely within the bounds".
Is this possible?
EDIT:
The answer is yes.
Follow-up question: Can this be done for the ticks as well?
You want to use matplotlib's automatic axis scaling. You can do this with either axes.axis with the "auto" input or axes.set_autoscale_on
ax.axis('auto')
ax.set_autoscale_on()
If you want to auto-scale only the x or y axis, you can use set_autoscaley_on or set_autoscalex_on.
Some code gives me the following matplotlib figure:
Unfortunately, the figure size is fixed and hence on the top right, the legend and the lines overlap. Is there any way to have the legend not stack on top of the lines?
I am aware that legend allows ax2.legend(loc=0), where 0 will put it into the "best" location. However, with two y axis as here, this will stack both legends on top of each other - not really the best allocation.
My next best try would be to "scale up" the figure, as manually done with an interactive graph, where I have only scaled up both axis:
Doing this with the "real" figure scale requires iterated "trying numbers and checking how far it goes" procedure - which may need to be redone if the graph changes. Is there any way of having matplotlib compute the scale "intelligently"?
If the best location plt.legend(loc='best') fails, try putting the legend outside of the plot:
plt.legend(loc='upper left', bbox_to_anchor=(1.02, 1), borderaxespad=0)
You can scale only legend, not the whole plot. Link here
More on legends here and also here.
I need to return both a histogram and a scatterplot in one function using matplotlib, but when I try to create the histogram after creating the scatterplot, it appears that the scatterplot gets overridden. Just wondering if anyone has any advice on this issue? Is there a way to return two plots at once if they share an x-axis?
For instance, there is paragraph included in this link http://matplotlib.org/users/pyplot_tutorial.html about how to have 2 subplots. But I'm not sure how to do that with plt.hist() and plt.plot().
Since the histogram fills the bars it is probably better to do it first then the scatter plot.
I am trying to minimize margins around a 1X2 figure, a figure which are two stacked subplots. I searched a lot and came up with commands like:
self.figure.subplots_adjust(left=0.01, bottom=0.01, top=0.99, right=0.99)
Which leaves a large gap on top and between the subplots. Playing with these parameters, much less understanding them was tough (things like ValueError: bottom cannot be >= top)
My questions :
What is the command to completely minimize the margins?
What do these numbers mean, and what coordinate system does this follow (the non-standard percent thing and origin point of this coordinate system)? What are the special rules on top of this coordinate system?
Where is the exact point this command needs to be called? From experiment, I figured out it works after you create subplots. What if you need to call it repeatedly after you resize a window and need to resize the figure to fit inside?
What are the other methods of adjusting layouts, especially for a single subplot?
They're in figure coordinates: http://matplotlib.sourceforge.net/users/transforms_tutorial.html
To remove gaps between subplots, use the wspace and hspace keywords to subplots_adjust.
If you want to have things adjusted automatically, have a look at tight_layout
Gridspec: http://matplotlib.sourceforge.net/users/gridspec.html