I am drawing a pie chart out of provided data, and this can potentially get out of hand as the length of the labels can be pretty long, and there can be a lot of them overlapping each other. Because of this, it is crucial to find a good startangle parameter to my pie chart drawing.
Conceptually, I want to use a mouse scroll event to rotate the whole pie chart by 5 degrees every time the user uses the scroll wheel. Rotating the wedges isn't too much trouble with their theta1 and theta2 properties, but repositioning the labels and autotexts is serious trouble because of alignment properties and the lining up with wedges. I also want to retain an interactive frame rate, so clearing the figure and redrawing is not an option.
Here is one such situation where this is useful. The labels are too big and rotating the whole pie chart would help reposition them in sight. Of course in this case it would be enough to resize the chart instead of rotating but you get my point.
Is there a way to achieve this that does not imply rewriting the entire label and autotext positioning code for my own use?
In particular, I'm wondering if it wouldn't be possible to do something that is conceptually equivalent to making the same pyplot.pie call as before, only with a different startangle. Alternatively, maybe Text objects have methods I can use for positioning them around the newly rotated wedges that spare me working with just positions and sizes.
Related
I have a scatter plot of data and would like to highlight certain ranges of the x-axis. When the number ranges to highlight are relatively small, using BoxAnnotation works well. However, I'm trying to make many adjacent highlightings (with different opacity). With many adjacent BoxAnnotations, zoomed out, the boxes slightly overlap, creating lines. Additionally, thousands of BoxAnnotations takes a long time to generate and does not run smoothly when interacting with the plot.
To be more specific about my case, I have some temporal data and a predictive model detecting the probability of some event occurring in the data. I want each segment to be highlighted with an opacity given by the probability that an event is occurring at that point in time. However, my current BoxAnnotation approach results in artificial lines from overlap of boxes when zoomed out (they disappear when zooming in on a region), and slow responsiveness of the interactive plot.
Is there a way to accomplish something similar to this without the artifacts and with a smoother experience?
Current method:
source = ColumnDataSource(data=data_frame)
figure_ = figure(x_axis_label='Time', y_axis_label='Intensity')
for index in range(data_frame.shape[0] - 1):
figure_.add_layout(
BoxAnnotation(left=data_frame['time'].values[index], right=data_frame['time'].values[index + 1],
fill_alpha=data_frame['prediction'].values[index], fill_color='red', line_alpha=0)
)
figure_.circle(x='time', y='intensity', source=source)
show(figure_)
Example of artificial lines when there are too many small adjacent BoxAnnotations:
When zooming on the x-axis, the lines disappear:
There's probably not any way to salvage this exact approach. The artifacts are due to the functioning of the underlying raster HTML canvas, and here's not anything that can be one about that. And any slowness is due to the fact that this kind of use of BoxAnnotation (with so very many individual instances) is not at all what was envisioned, and it is simply not optimized to show hundreds of instances the way e.g. scatter glyphs are. You are trying to use box annotations to construct a sort of translucent heat map, and that is not a good fit for it, for the reasons above.
You could potentially overcome slowness by using a single rect or vbar glyph that draws all the boxes at once in a vectorized way. But that won't alleviate the compositing issues.
Your best bet is to create a semi-transparent "heatmap" image overlay yourself with a tool or code that can afford better control over the details of rasterization and compositing. I can't really advise you on how to do that in any detail. The Datashader library might be useful for this.
I have drawn a trajectory plot in python using matplotlib of a boat like so:
Now I want to add some arrows, like wind direction, true heading etc. However I want the arrows to have the same size no matter which zoom-level the plot is at. I tried matplotlib.pyplot.arrow, however there I have to define the length of the arrows. I could make matplotlib.pyplot.arrow work, but then I'd have to get the height and width of the plot, and scale my arrows accordingly, so I wondered if there was a better way to obtain scale-independent arrows for these points?
You want to use matplotlib.axes.Axes.annotate instead. See the docs for more info!
Basically, set the xycoords parameter to "axes fraction" to instruct it to plot using relative fraction of the axes itself rather than data coordinates.
I have built a GUI with matplotlib and it contains several plots of values versus time. Now I need a special plot which just shows if a value is on or off (binary state).
Kinda like a control lamp on an analog control panel. I have 5 of those on/off values and I dont know how to do it the best way.
The "lamps" must be updateable because I stream the data from serial and analyze it in real time in my GUI.
I attached a picture where you see my current GUI. In the bottom right corner is now a bar chart, I tried to visualize the ON/OFF state with a bar, but it didn't work well and I wasn't able to animate it.
So yeah, how could I display 5 values with each an ON/OFF state in that area?
Instead of passing via bar charts I would directly plot a number of rectangles and then dynamically change their color.
You can find the documentation for rectangular patches here: http://matplotlib.org/api/patches_api.html#matplotlib.patches.Rectangle
If you need some pointers on how to animate such a patch have a look here:
https://nickcharlton.net/posts/drawing-animating-shapes-matplotlib.html
I have a matplotlib contour plot of wind speed (m/s) with contour labels using clabel. Unfortunately, the default clabel locations are poorly placed - see the top right corner:
I would like to change this to make the plot easier to read.
I understand how to manually set the contour labels from the second response to this post.
However, I have so many contours and multiple figures that it seems like a very impractical solution to do this manually. Is there a non-manual way to clean up the contour label locations? Also, could I have more than one contour label per contour without doing it manually?
I'm trying to refresh some plots that I have within a gui everytime I go once through a fitting procedure. Also, these plots are within a framw which can be resized, so the axes and labels etc need to be redrawn after the resizing. So was wondering if anyone knew how to update the sides of a figure using something like plot.figure.canvas.copy_from_bbox and blit. This appears to only copy and blit the background of the graphing area (where the lines are being drawn) and not to the sides of the graph or figure (where the labels and ticks are). I have been trying to get my graphs to update by trial and error and reading mpl documentation, but so far my code has jst become horrendously complex with things like self.this_plot.canvas_of_plot..etc.etc.. .plot.figure.canvas.copy_from_bbox... which is probably far too convoluted.
I know that my language might be a little off but I've been trying to read through the matplotlb documentation and the differences between Figure, canvas, graph, plot, figure.Figure, etc. are starting to evade me. So my first and foremost question would be:
1 - How do you update the ticks and labels around a matplotlib plot.
and secondly, since I would like to have a better grasp on what the answer to this question,
2 - What is the difference between a plot, figure, canvas, etc. in regards to the area which they cover in the GUI.
Thank you very much for the help.
All this can certainly be rather confusing at first!
To begin with, if you're chaining the ticks, etc, there isn't much point in using blitting. Blitting is just a way to avoid re-drawing everything if only some things are changing. If everything is changing, there's no point in using blitting. Just re-draw the plot.
Basically, you just want fig.canvas.draw() or plt.draw()
At any rate, to answer your first question, in most cases you won't need to update them manually. If you change the axis limits, they'll update themselves. You're running into problems because you're blitting just the inside of the axes instead of redrawing the plot.
As for your second question, a good, detailed overview is the Artist Tutorial of the Matplotlib User's Guide.
In a nutshell, there are two separate layers. One deals with grouping things into the parts that you'll worry about when plotting (e.g. the figure, axes, axis, lines, etc) and another that deals with rendering and drawing in general (the canvas and renderer).
Anything you can see in a matplotlib plot is an Artist. (E.g. text, a line, the axes, and even the figure itself.) An artist a) knows how to draw itself, and b) can contain other artists.
For an artist to draw itself, it uses the renderer (a backend-specific module that you'll almost never touch directly) to draw on a FigureCanvas a.k.a. "canvas" (an abstraction around either a vector-based page or a pixel buffer). To draw everything in a figure, you call canvas.draw().
Because artists can be groups of other artists, there's a hierarchy to things. Basically, something like this (obviously, this varies):
Figure
Axes (0-many) (An axes is basically a plot)
Axis (usually two) (x-axis and y-axis)
ticks
ticklabels
axis label
background patch
title, if present
anything you've plotted, e.g. Line2D's
Hopefully that makes things a touch clearer, anyway.
If you really do want to use blitting to update the tick labels, etc, you'll need to grab and restore the full region including them. This region is a bit tricky to get, because it isn't exactly known until after draw-time (rendering text in matplotlib is more complicated than rendering other things due to latex support, etc). You can do it, and I'll be glad to give an example if it's really what you want, but it's typically not going to yield a speed advantage over just drawing everything. (The exception is if you're only updating one subplot in a figure with lots of subplots.)