I was trying to create a bivariate scatterplot of each variable against every other that are in a dataframe, and I found sns.pairplot() was exactly what I needed.
However, no matter what I do (and I have tried all of the advice found in this question), the plots keep coming out too spread out, as well as in general too big. In the picture below, only the first two rows and four columns out of 12 variables display on my entire screen.
I have found out that my use of
%config InlineBackend.figure_format = 'svg'
to create non-blurry graphs on my high-ppi screen is partially at blame, as without it, I instead get this graph, which fits perfectly on my screen, but is now too small and I would prefer to scroll slightly around while having a bigger pic.
(note: the additional options below have no effect)
How can I make the grid of plots customizable in its overall size as well as spacing? As it stands, no options work, and one graphics backend (the default one) produces too small graphs, while the 'svg' backend produces too large ones.
EDIT: Editing sns.set(rc={'figure.figsize':(x,y)}) or the height/ aspect options improve nothing: the former produces no change, while the latter two change how big the individual plots are (with height=1 making them indecipherable), but the overall "grid" is still as bulky and overly large as before.
Essentially you are asking how to display the figure in its original size in a jupyter notebook.
That translates into how to add scrollbars if it exceeds the room it's given by the layout of the output cell.
I think for the horizontal direction this can be done as follows. However for the vertical direction this does not seem to work.
%matplotlib inline
# Cell2
from IPython.display import display, HTML
CSS = """div.output_area img {max-width:None !important;max-height: None !important";}"""
display(HTML('<style>{}</style>'.format(CSS)))
# Cell3
import matplotlib.pyplot as plt
fig, ax = plt.subplots(ncols=8, figsize=(20,10))
Related
I have been using Cartopy to plot data using 'equal' aspect, resulting in all manner of non-square Axes sizes. These usually look OK in Jupyter notebooks, but when saving the images (or when doing more complicated operations like adding colorbars), the resulting Figures are often huge, with a lot of blank space around the Axes plotting area. They also look bad when using %matplotlib widget. An example is provided below.
It seems that the figure in this case is too big in at least one dimension. I would like to remove that extra space in the final output figure, without shrinking the size of the plotting area itself.
I know that I can adjust the figure size itself with .set_figwidth and .set_figheight, as well as setting figsize= upon creation. But I don't know how to figure out the correct dimensions to shrink the figure without shrinking the axes, and I haven't seen any way to do this automatically. What's the correct solution? I would like to avoid manually editing my images after creating them!
It seems that the figsize option only changes the ratio of the height to width. Atleast this is the case when using jupyter notebooks. Here is an example:
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
plt.figure(figsize=(16,8))
plt.plot(np.arange(1,10),np.arange(1,10))
plt.show()
plt.figure(figsize=(24,6))
plt.plot(np.arange(1,10),np.arange(1,10))
plt.show()
I was hoping that figsize intended inches, not a relative ratio. How would you go about enforcing that in python/ jupyter notebooks.
If you use a large figsize, say figsize=(50, 5) you will notice that the lines, the labels, everything is incredibly thin and small with respect to a plot with normal size.
This happens because you are using widths that are not compatible with the width of the output cell
and the notebook just scales down the figure to make it fit in the output cell.
To have the behavior you asked for, you need a horizontal scrolling capability in the output cell. I don't know of a `nbextension` that can enable horizontal scrolling in output cells.
After a bit of experimenting, it looks like using the nbagg backend
%matplotlib nbagg
gives you a scrollable output cell, and an interactive one as well, inside the notebook and possibly it is what you want.
Addendum
I've found this issue on IPython's github, with a request for horizontal scrolling in output cell — as you can see it's dated 2012 and there is no followup of sort.
plt.gcf().set_size_inches(16, 8)
After change figsize the figure size do changed when the parameter in a certain range.In my condition,size not growing after size above (24,8).When it's still below the range the size do increase.It's base on your displayer dpi, you can set the dpi in figure but eventually it's rely on your hardware.
The figaspect is set by matplotlib.figure.figaspect
If you save figures to files use savefig,you will see the image size increase also.
I'm preparing some plots for a scientific paper, which need to be wide and short in order to fit into the page limit. However, when I save them as pdf, the x axis labels are missing, because (I think) they're outside the bounding box.
Putting the following into an iPython notebook reproduces the problem.
%pylab inline
pylab.rcParams['figure.figsize'] = (8.0, 2.0)
plot([1,5,2,4,6,2,1])
xlabel("$x$")
ylabel("$y$")
savefig("test.pdf")
The resulting pdf file looks like this:
How can I change the bounding box of the pdf file? Ideally I'd like a solution that "does it properly", i.e. automatically adjusts the size so that everything fits neatly, including getting rid of that unnecessary space to the left and right - but I'm in a hurry, so I'll settle for any way to change the bounding box, and I'll guess numbers until it looks right if I have to.
After a spot of Googling, I found an answer: you can give bbox_inches='tight' to the savefig command and it will automatically adjust the bounding box to the size of the contents:
%pylab inline
pylab.rcParams['figure.figsize'] = (8.0, 2.0)
plot([1,5,2,4,6,2,1])
xlabel("$x$")
ylabel("$y$")
savefig("test.pdf",bbox_inches='tight')
Those are some tight inches, I guess.
Note that this is slightly different from Ffisegydd's answer, since it adjusts the bounding box to the plot, rather than changing the plot to fit the bounding box. (But both are fine for my purposes.)
You can use plt.tight_layout() to have matplotlib adjust the layout of your plot. tight_layout() will automatically adjust the dimensions, and can also be used when you have (for example) overlapping labels/ticks/etc.
%pylab inline
pylab.rcParams['figure.figsize'] = (8.0, 2.0)
plot([1,5,2,4,6,2,1])
xlabel("$x$")
ylabel("$y$")
tight_layout()
savefig("test.pdf")
Here is a .png of the output (can't upload pdfs to SO but I've checked it and it works the same way for a pdf).
If you are preparing the plot for a scientific paper, I suggest to do the 'clipping' by yourself,
using
plt.subplots_adjust(left,right,bottom,top,..)
after the creation of the figure and before saving it. If you are running from an ipython console you can also call subplots_adjust after the generation of the figure, and tune the margins by trial and error. Some backends (I think at least the Qt backend) also expose a GUI for this feature.
Doing this by hand takes time, but most times provides a more precise result, especially with Latex rendering in my experience.
This is the only option whenever you have to stack vertically or horizontally two figures (with a package like subfigure for example), as tight_layout will not guarantee the same margins in the two figures, and the axis will result misaligned in the paper.
This is a nice link on using matplotlib for publications, covering for example how to set the figure width to match the journal column width.
I want to position the legend outside the drawing box. I do not find a clean way to do this. The main problem is having everything fit on the file saved. The only thing I have been able to figure out is this code:
#! /usr/bin/python
import matplotlib
# matplotlib.use('pdf')
from matplotlib.pyplot import *
subplot(111)
plot([1,2,3], label="test1")
l=legend(bbox_to_anchor=(1.05, 1), loc=2,borderaxespad=0)
tight_layout(rect=(0,0,0.8,1))
savefig('test.pdf')
There are a couple of caveats:
The tight_layout seems to be incompatible with matplotlib.use('pdf')
The 0.8 in the tight_layout has been found by trials and errors. If I replace label="test" by label="this is a very very long test", the legend will once again get out of the border of the file. I would like that to be adjusted automatically. I have not been able to retrieve the size of the legend. l.get_frame().get_width() seems to always return 1.0.
The tight_layout changes the size of the plot. What I would like to achieve is: specify the size of the plot in cm (or in inches). Placing the legend outside the plot. Having a file with the correct size in order that everything fits in, including the legend; without having to resort to trial and errors.
You can look at the answer to this question by Joe Kington.
The answer describes most of the options you can play around with regarding placement of legends.
How can I save Python plots at very high quality?
That is, when I keep zooming in on the object saved in a PDF file, why isn't there any blurring?
Also, what would be the best mode to save it in?
png, eps? Or some other? I can't do pdf, because there is a hidden number that happens that mess with Latexmk compilation.
If you are using Matplotlib and are trying to get good figures in a LaTeX document, save as an EPS. Specifically, try something like this after running the commands to plot the image:
plt.savefig('destination_path.eps', format='eps')
I have found that EPS files work best and the dpi parameter is what really makes them look good in a document.
To specify the orientation of the figure before saving, simply call the following before the plt.savefig call, but after creating the plot (assuming you have plotted using an axes with the name ax):
ax.view_init(elev=elevation_angle, azim=azimuthal_angle)
Where elevation_angle is a number (in degrees) specifying the polar angle (down from vertical z axis) and the azimuthal_angle specifies the azimuthal angle (around the z axis).
I find that it is easiest to determine these values by first plotting the image and then rotating it and watching the current values of the angles appear towards the bottom of the window just below the actual plot. Keep in mind that the x, y, z, positions appear by default, but they are replaced with the two angles when you start to click+drag+rotate the image.
Just to add my results, also using Matplotlib.
.eps made all my text bold and removed transparency. .svg gave me high-resolution pictures that actually looked like my graph.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
# Do the plot code
fig.savefig('myimage.svg', format='svg', dpi=1200)
I used 1200 dpi because a lot of scientific journals require images in 1200 / 600 / 300 dpi, depending on what the image is of. Convert to desired dpi and format in GIMP or Inkscape.
Obviously the dpi doesn't matter since .svg are vector graphics and have "infinite resolution".
You can save to a figure that is 1920x1080 (or 1080p) using:
fig = plt.figure(figsize=(19.20,10.80))
You can also go much higher or lower. The above solutions work well for printing, but these days you want the created image to go into a PNG/JPG or appear in a wide screen format.
Okay, I found spencerlyon2's answer working. However, in case anybody would find himself/herself not knowing what to do with that one line, I had to do it this way:
beingsaved = plt.figure()
# Some scatter plots
plt.scatter(X_1_x, X_1_y)
plt.scatter(X_2_x, X_2_y)
beingsaved.savefig('destination_path.eps', format='eps', dpi=1000)
In case you are working with seaborn plots, instead of Matplotlib, you can save a .png image like this:
Let's suppose you have a matrix object (either Pandas or NumPy), and you want to take a heatmap:
import seaborn as sb
image = sb.heatmap(matrix) # This gets you the heatmap
image.figure.savefig("C:/Your/Path/ ... /your_image.png") # This saves it
This code is compatible with the latest version of Seaborn. Other code around Stack Overflow worked only for previous versions.
Another way I like is this. I set the size of the next image as follows:
plt.subplots(figsize=(15,15))
And then later I plot the output in the console, from which I can copy-paste it where I want. (Since Seaborn is built on top of Matplotlib, there will not be any problem.)