A radical with an arbitrary base seems supported by latex without external libraries, but matplotlib's mathtext can't parse it. Is there anyway I can get matplotlib to display it?
Thanks.
Related
Is it possible to use Matplotlib markers in the axis ticks? For example, I want to achieve something, as shown below. I have three designs, and to identify them, I want to use the markers in the x-axis ticks.
Is there a particular reason to want to use matplotlib markers? Or is only the shape important?
You can use most unicode symbols:
pd.DataFrame({'value': [35,45,30]}, index=['★', '□', '◯']).plot.bar()
It also works for many (not all) LaTeX symbols:
pd.DataFrame({'value': [35,45,30]}, index=[r'$\star$', r'$\bigtriangleup$', r'$\bigcirc$']).plot.bar()
The advantage with the latter is that you can directly use LaTeX math symbols (actually any arbitrary text, including unicode symbols) as marker:
plt.plot([1,4,2,3,0], marker=r'$\bigtriangleup$', markersize=20)
plt.plot([0,1,4,2,3], marker='$\u26A1$', markersize=20)
Let's say that I have two Raster objects (or any other Holoviews object really). I can easily visualize one with appropriate color scaling, and I can do a layout to get both figures with the same scaling and coloring. What if I want to do two figures (e.g. because I need them on different pages), but with the same coloring and scaling so that the figures are comparable.
If there's no way to do this automatically, is there any way to access the relevant settings and then feed them manually to the second figure?
If you're using a notebook: The %opts line magic : IPython specific syntax applied globally [string format]http://holoviews.org/user_guide/Customizing_Plots.html and I think hv.opts works globally in script.
For both backends, you can do hv.renderer('bokeh').get_plot(your_element_variable).state (or replace bokeh with matplotlib) and get the original bokeh/matplotlib items.
Then you can use matplotlib's plt.getp() or bokeh's attribute calling (as I've done here https://github.com/ahuang11/holoext/blob/master/holoext/xbokeh.py#L501-L508) to get the base item's color/font/labels/etc.
This is a follow up to my post three weeks ago here How do I use m_map in octave, without really being a nuissance to kind and busy people. My problem is simply how does one overlay a basemap on an octave contour plot. After interpolating my irregularly spaced data (works for both contour lines and filled contours) I plot with the code:
contour(xi, yi, obsi, cstart:cstep:cend)
colorbar;
xlabel('Longitude'),ylabel('Latitude')
title('Mean Rain Onset')
saveas(gcf,'rainzam.pdf')
And I get
I have downloaded several map formats: ne_50m_admin_0_countries.zip, the apparently obsolete m_map (with associated tbase.Z, gshhg-bin-2.3.2.zip), soa.7z, world-bounds.7z, gshhg-gmt-2.3.2.tar.gz, dcw-gmt-1.1.1.tar.gz.
My question is has anyone used any of these maps in octave or gnuplot, and how to? I would appreciate any assistance.
Basically you have to load those maps in octave, they represent borders or coastlines with two variables (x,y) which you can then add to your plot with
hold on
plot(x,y)
That's the easy part, the hard part is to load the maps. All of them have different formats, which means it is a completely different story how to load them. For instance, the ne_50m_admin_0_countries.zip has a dbf format. Either you convert it first to ascii text and load it easily with the load function of octave or you need the OI package (http://wiki.octave.org/IO_package), which in turn demands java (http://wiki.octave.org/Java_package). I don't think this is the easy way for a newbie, so I suggest to convert the maps individually to text: google for "convert dbf to csv", "convert dbf to text", "convert dbf to ascii", etc... Perhaps some of those maps can be even loaded with excel and then saved as text (csv), the important issue is to convert them to text!
If you want to draw physical coastlines, you may download them from this link
https://www.naturalearthdata.com/downloads/
Then, after the drawing of a contour map of your own datas, you may add the coastlines using the following commands:
pkg load mapping
hold on
h = shapedraw ('FileName.shp','r','linewidth',1)
I'm trying to build PDF-documents on the server-side in a Django-Installation using reportlab. These documents should contain several graphs which are to be created with matplotlib.
I already figured out how to make reportlab use matplotlib's images without dumping them to the filesystem temporarily by passing PIL-Image objects directly to the Image()-flowable. This works surprisingly well for rasterized images formats like PNG.
Now, the icing on the cake would be able to embed vector based graphics (like SVG).
I used svglib to convert SVGs generated by matplotlib to reportlab graphic objects but unfortunately svglib does omit the tickmarks and axis labels. On some graphs it fails in general.
Do you have any ideas?
This page has a solution that I haven't had a chance to test myself yet: https://web.archive.org/web/20120725125858/http://lateral.netmanagers.com.ar/weblog/posts/BB753.html
You can generate matplotlib graphics as pdf and use pdfrw to embed it in reportlab canvas as described in this answer
This may be a naive question, but I was wondering if there's a better way than using text() to adding text to a plot. Note, I'm also using layout() as well. Specifically, I have a section of a plot where I would like to add some text with headings followed by regular text.
text() is fine it seems for simple annotations, but to get the spacing right for several lines of text seems to require a lot of manual manipulation of the x and y and cex parameters. Any suggestions?
Here are some alternative options to consider:
- the gplots package has a textplot function to add some text output in a base graphics plot.
- plotrix has a function addtable2plot
- for grid graphics grid.text() is available and in gridExtra there is a function grid.table() (see, e.g., R-Wiki)
If you're using base graphics, then text() is probably your best bet, and fiddling with coordinates etc is part of the game. If you want to learn a new framework, the lattice package is a reworking of the basic approach to plotting in R. It be installed by default so help(package='lattice') will get you started.
Here's a pretty good guide (pdf) to graphics in general in R, with a substantial section on lattice:
download