Matplotib tutorial(2)

Figures, Subplots, Axes and Ticks

So far we have used implicit figure and axes creation. This is handy for fast plots. We can have more control over the display using figure, subplot, and axes explicitly. A figure in matplotlib means the whole window in the user interface. Within this figure there can be subplots. While subplot positions the plots in a regular grid, axes allows free placement within the figure. Both can be useful depending on your intention. We've already worked with figures and subplots without explicitly calling them. When we call plot, matplotlib calls gca() to get the current axes and gca in turn calls gcf() to get the current figure. If there is none it calls figure() to make one, strictly speaking, to make a subplot(111). Let's look at the details.

Figures

A figure is the windows in the GUI that has "Figure #" as title. Figures are numbered starting from 1 as opposed to the normal Python way starting from 0. This is clearly MATLAB-style. There are several parameters that determine what the figure looks like:

Argument	Default	Description
num	1	number of figure
figsize	figure.figsize	figure size in in inches (width, height)
dpi	figure.dpi	resolution in dots per inch
facecolor	figure.facecolor	color of the drawing background
edgecolor	figure.edgecolor	color of edge around the drawing background
frameon	True	draw figure frame or not

The defaults can be specified in the resource file and will be used most of the time. Only the number of the figure is frequently changed.

When you work with the GUI you can close a figure by clicking on the x in the upper right corner. But you can close a figure programmatically by calling close. Depending on the argument it closes (1) the current figure (no argument), (2) a specific figure (figure number or figure instance as argument), or (3) all figures (all as argument).

As with other objects, you can set figure properties with the set_something methods.

Subplot

With subplot you can arrange plots in a regular grid. You need to specify the number of rows and columns and the number of the plot. Note that the  gridspec  command is a more powerful alternative.

Axis

Axes are very similar to subplots but allow placement of plots at any location in the figure. So if we want to put a smaller plot inside a bigger one we do so with axes.

Ticks

Well formatted ticks are an important part of publishing-ready figures. Matplotlib provides a totally configurable system for ticks. There are tick locators to specify where ticks should appear and tick formatters to give ticks the appearance you want. Major and minor ticks can be located and formatted independently from each other. Per default minor ticks are not shown, i.e. there is only an empty list for them because it is as NullLocator (see below).

Tick Locators

There are several locators for different kind of requirements:

Class	Description
NullLocator	No ticks.
IndexLocator	Place a tick on every multiple of some base number of points plotted.
FixedLocator	Tick locations are fixed.
LinearLocator	Determine the tick locations.
MultipleLocator	Set a tick on every integer that is multiple of some base.
AutoLocator	Select no more than n intervals at nice locations.
LogLocator	Determine the tick locations for log axes.

All of these locators derive from the base class matplotlib.ticker.Locator. You can make your own locator deriving from it. Handling dates as ticks can be especially tricky. Therefore, matplotlib provides special locators in matplotlib.dates.

Animation

For quite a long time, animation in matplotlib was not an easy task and was done mainly through clever hacks. However, things have started to change since version 1.1 and the introduction of tools for creating animation very intuitively, with the possibility to save them in all kind of formats (but don't expect to be able to run very complex animation at 60 fps though).

Documentation

• See Animation

The most easy way to make an animation in matplotlib is to declare a FuncAnimation object that specifies to matplotlib what is the figure to update, what is the update function and what is the delay between frames.

Drip drop

A very simple rain effect can be obtained by having small growing rings randomly positioned over a figure. Of course, they won't grow forever since the wave is supposed to damp with time. To simulate that, we can use a more and more transparent color as the ring is growing, up to the point where it is no more visible. At this point, we remove the ring and create a new one.

First step is to create a blank figure:

fig = plt.figure(figsize=(6,6),facecolor='white')
ax = fig.add_axes([0,0,1,1], frameon=False, aspect=1)
plt.show()

Next, we need to create several rings. For this, we can use the scatter plot object that is generally used to visualize points cloud, but we can also use it to draw rings by specifying we don't have a facecolor. We have also to take care of initial size and color for each ring such that we have all size between a minimum and a maximum size and also to make sure the largest ring is almost transparent.

#rain position
P = np.random.uniform(0,1,(n,2))
#ring color
C = np.ones((n,4)) * (0,0,0,1)
C[:,3] = np.linspace(0,1,n)
S = np.linspace(size_min, size_max, n)
scat = ax.scatter(P[:,0],P[:,1],s=S, lw=0.5, edgecolors=C, facecolors='None')#x坐标 y坐标 大小 粗细 边缘线颜色 内部颜色 
ax.set_xlim(0,1), ax.set_xticks([])
ax.set_ylim(0,1), ax.set_yticks([])

Now, we need to write the update function for our animation. We know that at each time step each ring should grow be more transparent while largest ring should be totally transparent and thus removed. Of course, we won't actually remove the largest ring but re-use it to set a new ring at a new random position, with nominal size and color. Hence, we keep the number of ring constant.

def update(frame):
    global P, C, S

    # Every ring is made more transparent 颜色更新
    C[:,3] = np.maximum(0, C[:,3] - 1.0/n)

    # Each ring is made larger 大小更新
    S += (size_max - size_min) / n

    # Reset ring specific ring (relative to frame number) 消失后重新复位
    i = frame % 50
    P[i] = np.random.uniform(0,1,2)
    S[i] = size_min
    C[i,3] = 1

    # Update scatter object 更新点的属性
    scat.set_edgecolors(C)
    scat.set_sizes(S)
    scat.set_offsets(P)
    return scat,

animation = ani.FuncAnimation(fig, update, interval=10)#interval代表更新间隔