I am using Bokeh to plot my research study data. I use the log scale a lot. But by default, the axis label of the log axis is shown like 10^2, instead of a superscript 2. The example plot from the Reference doc is exactly so: https://docs.bokeh.org/en/latest/docs/gallery/logaxis.html
I have checked answers to similar questions, and it seems one can use Latex to format the label (https://github.com/bokeh/bokeh/issues/6031). But the solution seems too complicated and it is hard to find out exactly how.
I wonder if there is a simple solution to this issue.
Thanks for any help.
Note from maintainers: Initial built in LaTeX support was added in version 2.4, see this new answer https://stackoverflow.com/a/69198542/3406693
LaTeX can be used to add a label on top of the existing plot. Right now, it cannot be used for axes' titles.
However, the comment from the issue that you've linked attempts to solve it in a different way - by just using special superscript symbols.
Here's my attempt to make that solution shorter and easier to read:
p.yaxis[0].formatter = FuncTickFormatter(code="""
return 10 + (Math.log10(tick).toString()
.split('')
.map(function (d) { return d === '-' ? '⁻' : '⁰¹²³⁴⁵⁶⁷⁸⁹'[+d]; })
.join(''));
""")
As of Bokeh 2.0, passing y_axis_type="log" to figure automatically displays exponents on log axes in a nice way:
For more complicated scenarios, Bokeh 2.4 adds support for LaTeX (and MathML) to some elements in Bokeh, including axis labels. You can now use plot.xaxis.axis_label = r"$$10^2$$", for example (using MathJax delimiters).
Currently, you can use LaTeX on axis labels, tick labels, div widgets, and paragraph widgets. LaTeX support for more elements should be added soon. For more information about the new math text feature and how to use them, see the Bokeh 2.4 release blogpost, the new blackbody radiation example, and the Bokeh user guide!
Related
all
Is there any chance I can use the math mode (latex code) in Bokeh? I checked all the Bokeh git issues (and possible options to solve) but nothing seems to work in my case :frowning:
Let’s say I have $\alpha_\beta$ (so alpha_beta) in my dataset (.csv) and I want to include it in a plot or hover - how would I do that? Sure I can use alpha symbol but how would I make beta be a subscript of alpha?
Thank you in advance!
Bokeh 2.4 adds support for LaTeX (and MathML) to some elements in Bokeh. Currently, you can use LaTeX on axis labels, tick labels, div widgets, and paragraph widgets. Unfortunately, LaTeX on hover labels is not yet supported, but LaTeX support for more elements should be added soon. For more information about the new math text feature and how to use them, see the Bokeh 2.4 release blogpost, the new blackbody radiation example, and the Bokeh user guide!
I learnt how to add text by using Label in Bokeh in this question.
However, I found that the text doesn't rescale as I zoom in and out.
The ideal behavior is something like Patches, which becomes larger as you zoom in.
How can I configure for this feature?
Related Questions
Selectively show text in Bokeh plot based on zoom level
As of Bokeh 2.3 scalable text is still an open issue:
https://github.com/bokeh/bokeh/issues/9407
There are some potential partial workarounds discussed there, but nothing that concretely works all the time. Depending on your use case, you could potentially use CustomJS callback on the plot ranges to update the text size that you care about in some way.
First time here and newbie so please bear with me.
I'm following along a data viz tutorial with matplotlib. I'm getting the same results (go me) but the font used (in the plot legend in particular) and the default co lours are different and no parameter has been set to change them. And of course the tutorial's stuff looks much nicer. I don't know why this happens but I'm thinking that maybe I did change some of the stuff in unrelated notebooks and those choices stuck. If so, how do I "reset" please?
I am not allowed to attach screenshots yet. Basically, my plot lines are light blue and orange while his are the traditional discrete dark blue and green. My legend font looks like bad excel while his looks like LateX.
Thank you for your suggestions.
There are a number of ways you can do this and they vary depending on the specific use case but the one I use most often is
from matplotlib import rcParams, rcParamsDefault
rcParams.update(rcParamsDefault)
Although it is likely the case that the tutorial is using a style other than the default so I wouldn't necessarily expect this to make your output look exactly like the tutorial. When using LaTex with matplotlib I use
pgf_with_latex = {
"text.usetex": True,
"pgf.rcfonts": False,
"pgf.preamble": [
r'\usepackage{color}'
]
}
rcParams.update(pgf_with_latex)
You can find more information here regarding customization and here regarding using LaTex with matplotlib.
To find all the built in matplotlib themes, use
plt.style.available
I think the normal one is 'default'. So add this before you make your plots:
plt.style.use(['default'])
But you can make your plots look even better than the tutorial by using some of the other ones.
It's a simple thing but I've searched for quite a while without success: I want to customise a figure legend by reversing the horizontal order of the symbols and labels.
In Gnuplot, this is simply achieved by set key reverse. Example: change x data1 to data1 x. In matplotlib, there seems to be no user-friendly solution. Thus, I thought about changing a kind of handle anchor or just shifting the handle's position, but couldn't find any point to start with.
The requested feature is already there, as the keyword markerfirst of the legend command.
plt.plot([1,2],[3,4], label='labeltext')
plt.legend(markerfirst=False)
plt.show()
If you want to make it your default behaviour, you can change the value of legend.markerfirst in rcParams, see customizing matplotlib.
I have been looking for a way to be able to select which series are visible on a plot, after a plot is created.
I need this as i often have plots with many series. they are too many to plot at the same time, and i need to quickly and interactively select which series are visible. Ideally there will be a window with a list of series in the plot and checkboxes, where the series with the checked checkbox is visible.
Does anyone know if this has been already implemented somewhere?, if not then can someone guide me of how can i do it myself?
Thanks!
Omar
It all depends on how much effort you are willing to do and what the exact requirements are, but you can bet it has already been implemented somewhere :-)
If the aim is mainly to not clutter the image, it may be sufficient to use the built-in capabilities; you can find relevant code in the matplotlib examples library:
http://matplotlib.org/examples/event_handling/legend_picking.html
http://matplotlib.org/examples/widgets/check_buttons.html
If you really want to have a UI, so you can guard the performance by limiting the amount of plots / data, you would typically use a GUI toolbox such as GTK, QT or WX. Look here for some articles and example code:
http://eli.thegreenplace.net/2009/05/23/more-pyqt-plotting-demos/
A list with checkboxes will be fine if you have a few plots or less, but for more plots a popup menu would probably be better. I am not sure whether either of these is possible with matplotlib though.
The way I implemented this once was to use a slider to select the plot from a list - basically you use the slider to set the index of the series that should be shown. I had a few hundred series per dataset, so it was a good way to quickly glance through them.
My code for setting this up was roughly like this:
fig = pyplot.figure()
slax = self.fig.add_axes((0.1,0.05,0.35,0.05))
sl = matplotlib.widgets.Slider(slax, "Trace #", 0, len(plotlist), valinit=0.0)
def update_trace():
ax.clear()
tracenum = int(np.floor(sl.val))
ax.plot(plotlist[tracenum])
fig.canvas.draw()
sl.on_changed(update_trace)
ax = self.fig.add_axes((0.6, 0.2, 0.35, 0.7))
fig.add_subplot(axes=self.traceax)
update_trace()
Here's an example:
Now that plot.ly has opened sourced their libraries, it is a really good choice for interactive plots in python. See for example: https://plot.ly/python/legend/#legend-names. You can click on the legend traces and select/deselect traces.
If you want to embed in an Ipython/Jupyter Notebook, that is also straightforward: https://plot.ly/ipython-notebooks/gallery/