I have a script which I'm adapting to include a GUI. In it, I create a plot with subplots (the arrangement of which depends on the number of plots - e.g. 4 plots go into a square rather than 4-across). That plot (with a subplot for each of the "targets" analyzed) gets saved to a .png.
In building the GUI, I'm writing up the 'results' frame and would like to show these individual subplots on their own tabs. I've written the code to lay out the frame how I want it, but in order to separate the subplots into their own plots, I need to draw the completed Axes object (e.g. the entire subplot for that target) onto a new figure in the frame.
Since the number of subplots isn't known before runtime, I already have my Axes objects/subplots in an array (/list?) axs, whose members are the individual Axes objects (each containing data points created with ax.scatter() and several lines and annotations created with ax.plot() and ax.annotate).
When I initially create the axes, I do so with
fig, axs = plt.subplots(num='Title', nrows=numrow, ncols=numcol,
figsize=[numcol*5, numrow*5],
subplot_kw={'adjustable':'box', 'aspect':1})
Is there a way to now take these axes and draw them onto a new figure (the one that will be contained in the 'results' frame of the GUI)? In my searches, I only came up with ways to plot multiple axes onto a single figure (i.e. how to use subplots()) but nothing came up on how I'd throw a pre-existing Axes object into a new figure that it wasn't originally associated with. I'd rather not re-draw the axes from scratch -- there's quite a bit of decoration and multiple datasets / lines plotted onto them already.
Any ideas? Happy to post code as requested, but since this more of a "How do I do this" than a "why doesn't my code work", I didn't post much of it.
Thank you!
I believe that's not possible and you will have to recreate the Axes objects inside the other figure. Which is just a matter of code reorganization. Note that your approach would not noticeably improve rendering performance. Matplotlib would have to re-render the Axes objects anyway, and that's the computationally expensive part. Creating the objects is relatively cheap.
What you're trying to do is pretty much this:
from matplotlib import pyplot
pyplot.ion()
figure1 = pyplot.figure()
axes = figure1.add_subplot()
axes.plot([0, 1], [0, 1])
figure2 = pyplot.figure()
figure2.add_axes(axes)
Which raises:
ValueError: The Axes must have been created in the present figure
And the documentation of add_axes() notes:
In rare circumstances, add_axes may be called with a single argument, an Axes instance already created in the present figure but not in the figure's list of Axes.
So that's a pretty clear indication that this is not a supported use case.
Related
In matplotlib.pyplot, what is the difference between plt.clf() and plt.close()? Will they function the same way?
I am running a loop where at the end of each iteration I am producing a figure and saving the plot. On first couple tries the plot was retaining the old figures in every subsequent plot. I'm looking for, individual plots for each iteration without the old figures, does it matter which one I use? The calculation I'm running takes a very long time and it would be very time consuming to test it out.
plt.close() will close the figure window entirely, where plt.clf() will just clear the figure - you can still paint another plot onto it.
It sounds like, for your needs, you should be preferring plt.clf(), or better yet keep a handle on the line objects themselves (they are returned in lists by plot calls) and use .set_data on those in subsequent iterations.
I think it is worth mentioning that plt.close() releases the memory, thus is preferred when generating and saving many figures in one run.
Using plt.clf() in such case will produce a warning after 20 plots (even if they are not going to be shown by plt.show()):
More than 20 figures have been opened. Figures created through the
pyplot interface (matplotlib.pyplot.figure) are retained until
explicitly closed and may consume too much memory.
plt.clf() clears the entire current figure with all its axes, but leaves the window opened, such that it may be reused for other plots.
plt.close() closes a window, which will be the current window, if not specified otherwise.
There is a slight difference between the two functions.
plt.close() - It altogether plots the graph in seperate windows,releasing
memory,retaining each window for view.
plt.clf() - We can say,it displays the graph in the same window one after other
For illustration, I have plotted two graphs with paramters year and views on X axis and Y axis each. Initially I have used closed function.it displayed the graphs in two seperate windows…
Afterwords, when I run the program with clf() it clears the graph and displays next one in same window i.e figure 1.
Here is the code snippet -
import matplotlib.pyplot as plt
year = [2001,2002,2003,2004]
Views= [12000,14000,16000,18000]
Views2 = [15000,1800,24000,84000]
plt.plot(year,Views)
plt.show()
plt.clf()
plt.plot(year,Views2)
plt.show()
plt.clf()
I am familiar with the idea of putting several plots in an overlay by calling the plot function several times
plt.plot(f1)
plt.plot(f2)
plt.plot(f2)
plt.show()
But what if I want to plot data that I have already put on a canvas?
i was thinking to use the matplotlib.lines.Line2D object returned by each plt.plot. If I store them in suitable variables then I can reuse them at a later time. But how do I get a plot out of these matplotlib.lines.Line2D? Maybe I am trying a doomed strategy
p3000=plt.plot([1,2,3])
and then
fig, ax = plt.subplots()
ax.add_line(p3000[0])
but I get RuntimeError: Can not put single artist in more than one figure
I have also tried get_xydata member but ti does not give the points in a form I am able to read.
So I have been using matplot for a while now, and one thing that confuses me how it handles subplots.
It can be done as such:
fig = plt.figure()
ax = fig.add_subplot(1,2,1)
Or. It could be done as such:
plt.subplots(1,2)
Or, one could do this instead:
plt.subplot(211)
Or of course, if we only need one plot we can immediately run
plt.plot(x,y) # or .scatter or whatever.
Why? Is there any actual reason why you should use one over the others?
Matplotlib is a Python library, that was significantly influenced by MatLab and aimed in part at former and current MatLab users, therefore has 2 types of syntax:
MatLab syntax, plt.sublot(211); plt.plot(); plt.colorbar();, that implies that every time you create a figure or subplot it is stored inside as last active object and all plotting and changing of parameters is applied to it. It is to make it comfortable to use for those, who transitioned from MatLab. The idea is you create an element and instantly apply all actions to it, then create next and never return to previous until you call plt.show .
Classic programming syntax with explicit object declaration and operations on said objects. It is comfortable for everyone else and allows one to go back to previously created objects (figures and axes) and make additional changes.
The matlab way makes it hard to work with multiple figures. (figure is an independent picture, axes is region of that picture that you plot data in) plt.show always shows the last figure you created, example:
plt.figure() #new figure created and stored as current active
Plt.subplot() #new axes created in current figure and stored as current axes
Plt.plot() #data plotted in current axes
Plt.subplot() #second axes added to figure and made current active
Plt.plot() #plot in current active that is second now
Plt.figure() #the new figure created, old can still be found with difficulty i believe,
#but current active is now different and plt.show will not show anything you plotted before.
But some people find it better for quick and dirty plotting.
You can transition between the 2 by using fig_1 = plt.gcf();, ax_1 = plt.gca(); which are get current figure and get current axes redpectively. There are also multiple ways to change appearance, one for matlab
plt.make_current_axes_without_ticks( param)
and one for oop languages (
fig=plt.figure;
ax=fig.add_axes();
axis=ax.y_axis();
yticks=axis.ticks();
yticks.set_visible(false);
or smth like that.
Multiple interfaces sure make learning it harder, but it makes easier to transition from other similar tools and make it less rigid, meaning sometimes there're very simple ways to make frequently used but convoluted changes. (See ticks example)
I'm not really new to matplotlib and I'm deeply ashamed to admit I have always used it as a tool for getting a solution as quick and easy as possible. So I know how to get basic plots, subplots and stuff and have quite a few code which gets reused from time to time...but I have no "deep(er) knowledge" of matplotlib.
Recently I thought I should change this and work myself through some tutorials. However, I am still confused about matplotlibs plt, fig(ure) and ax(arr). What is really the difference?
In most cases, for some "quick'n'dirty' plotting I see people using just pyplot as plt and directly plot with plt.plot. Since I am having multiple stuff to plot quite often, I frequently use f, axarr = plt.subplots()...but most times you see only code putting data into the axarr and ignoring the figure f.
So, my question is: what is a clean way to work with matplotlib? When to use plt only, what is or what should a figure be used for? Should subplots just containing data? Or is it valid and good practice to everything like styling, clearing a plot, ..., inside of subplots?
I hope this is not to wide-ranging. Basically I am asking for some advice for the true purposes of plt <-> fig <-> ax(arr) (and when/how to use them properly).
Tutorials would also be welcome. The matplotlib documentation is rather confusing to me. When one searches something really specific, like rescaling a legend, different plot markers and colors and so on the official documentation is really precise but rather general information is not that good in my opinion. Too much different examples, no real explanations of the purposes...looks more or less like a big listing of all possible API methods and arguments.
pyplot is the 'scripting' level API in matplotlib (its highest level API to do a lot with matplotlib). It allows you to use matplotlib using a procedural interface in a similar way as you can do it with Matlab. pyplot has a notion of 'current figure' and 'current axes' that all the functions delegate to (#tacaswell dixit). So, when you use the functions available on the module pyplot you are plotting to the 'current figure' and 'current axes'.
If you want 'fine-grain' control of where/what your are plotting then you should use an object oriented API using instances of Figure and Axes.
Functions available in pyplot have an equivalent method in the Axes.
From the repo anatomy of matplotlib:
The Figure is the top-level container in this hierarchy. It is the overall window/page that everything is drawn on. You can have multiple independent figures and Figures can contain multiple Axes.
But...
Most plotting occurs on an Axes. The axes is effectively the area that we plot data on and any ticks/labels/etc associated with it. Usually we'll set up an Axes with a call to subplot (which places Axes on a regular grid), so in most cases, Axes and Subplot are synonymous.
Each Axes has an XAxis and a YAxis. These contain the ticks, tick locations, labels, etc.
If you want to know the anatomy of a plot you can visit this link.
I think that this tutorial explains well the basic notions of the object hierarchy of matplotlib like Figure and Axes, as well as the notion of current figure and current Axes.
If you want a quick answer: There is the Figure object which is the container that wraps multiple Axes(which is different from axis) which also contains smaller objects like legends, line, tick marks ... as shown in this image taken from matplotlib documentation
So when we do
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> type(fig)
<class 'matplotlib.figure.Figure'>
>>> type(ax)
<class 'matplotlib.axes._subplots.AxesSubplot'>
We have created a Figure object and an Axes object that is contained in that figure.
pyplot is matlab like API for those who are familiar with matlab and want to make quick and dirty plots
figure is object-oriented API for those who doesn't care about matlab style plotting
So you can use either one but perhaps not both together.
I'd like to create two independent matplotlib plots within a python script, and potentially jump back and forth between them as I add lines, annotations, etc. to the various plots (for example, perhaps I call a function which adds lines to both plots, and then another function which adds the annotations).
I expect that by working off matplotlib examples I'd be able to figure out some solution that works, but I'd like to know what the preferred and cleanest way of doing this is. I tend to get confused about when I should be doing things like
fig,ax=plt.subplots()
and when I should be doing things like:
fig=plt.figure()
Furthermore, how should I be switching back and forth between plots. If I did something like
fig1,ax1=plt.subplots()
fig2,ax2=plt.subplots()
can I then just refer to these plots by doing something like:
ax1.plt.plot([some stuff])
ax2.plt.plot([otherstuff]
? I ask this because often in the matplotlib examples they don't refer to the plot like this after calling plt.subplot() but instead call commands like
plt.plot([stuff])
where presumably it doesn't matter that they didn't specify ax1 or ax2 because there's only one plot in the example. At the end I'd like to save both plots to file using something like
plt.savefig(....)
although I need, again, to be able to refer to both plots independently. So what's the proper way of implementing this?
If you want to be able to write code that clearly applies commands to distinct axes, you want to use the object oriented interface.
Actually, both of your first two examples are using this interface. The differences is that plt.subplots() will create both a figure object and a grid of axes, while plt.figure() just creates the figure.
The figure object has methods to create axes within it. So, these two blocks of code are equivalent:
fig, ax = plt.subplots()
and
fig = plt.figure()
ax = fig.add_subplot(111)
Generally, the latter approach is only going to be more useful when you want multiple axes within the figure that don't follow a regular grid. So, you could do:
fig = plt.figure()
ax = fig.add_axes([.1, .1, .2, .8])
which will add a tall axes object on the left side of the figure.
Next, how do you get multiple axes to plot on?
The subplots function takes two positional arguments specifying the number of rows and columns in the grid (these default to (1, 1). So if you want two axes side by side, you would do
fig, axes = plt.subplots(1, 2)
Now axes is going to be a (1, 2) object array that is filled with Axes objects. It's often more convenient, for a small grid, to use Python's tuple unpacking and get direct references to the objects:
fig, (ax1, ax2) = plt.subplots(1, 2)
Now, what do you do with these objects, and what's the relationship between them and the MATLAB-style procedure interface?
Most functions in the pyplot namespace also exist as methods on either Figure or Axes objects. Matplotlib (and MATLAB) has a concept of the "current" figure and axes. When you call a function like plt.plot, it draws on the current axis (usually the most recently created one). When you call a function like plt.savefig, it saves the current figure.
For simple task, this is a bit more direct and usually easier than using the object-oriented interface. However, when you start making more complex plots, e.g. a grid of axes where each grid has multiple layers (maybe a scatterplot and a regression line), being able to structure the code around what you are doing rather than where you are doing has substantial advantages. Generally, plotting code that is written in an object-oriented fashion will scale much better than code written in a procedural fashion.