plt.close() will close current instance.
plt.close(2) will close figure 2
plt.close(plot1) will close figure with instance plot1
plt.close('all') will close all fiures
Found here.
Remember that plt.show() is a blocking function, so in the example code you used above, plt.close() isn't being executed until the window is closed, which makes it redundant.
You can use plt.ion() at the beginning of your code to make it non-blocking, although this has other implications.
EXAMPLE
After our discussion in the comments, I've put together a bit of an example just to demonstrate how the plot functionality can be used.
Below I create a plot:
fig = plt.figure(figsize=plt.figaspect(0.75))
ax = fig.add_subplot(1, 1, 1)
....
par_plot, = plot(x_data,y_data, lw=2, color='red')
In this case, ax above is a handle to a pair of axes. Whenever I want to do something to these axes, I can change my current set of axes to this particular set by calling axes(ax).
par_plot is a handle to the line2D instance. This is called an artist. If I want to change a property of the line, like change the ydata, I can do so by referring to this handle.
I can also create a slider widget by doing the following:
axsliderA = axes([0.12, 0.85, 0.16, 0.075])
sA = Slider(axsliderA, 'A', -1, 1.0, valinit=0.5)
sA.on_changed(update)
The first line creates a new axes for the slider (called axsliderA), the second line creates a slider instance sA which is placed in the axes, and the third line specifies a function to call when the slider value changes (update).
My update function could look something like this:
def update(val):
A = sA.val
B = sB.val
C = sC.val
y_data = A*x_data*x_data + B*x_data + C
par_plot.set_ydata(y_data)
draw()
The par_plot.set_ydata(y_data) changes the ydata property of the Line2D object with the handle par_plot.
The draw() function updates the current set of axes.
Putting it all together:
from pylab import *
import matplotlib.pyplot as plt
import numpy
def update(val):
A = sA.val
B = sB.val
C = sC.val
y_data = A*x_data*x_data + B*x_data + C
par_plot.set_ydata(y_data)
draw()
x_data = numpy.arange(-100,100,0.1);
fig = plt.figure(figsize=plt.figaspect(0.75))
ax = fig.add_subplot(1, 1, 1)
subplots_adjust(top=0.8)
ax.set_xlim(-100, 100);
ax.set_ylim(-100, 100);
ax.set_xlabel('X')
ax.set_ylabel('Y')
axsliderA = axes([0.12, 0.85, 0.16, 0.075])
sA = Slider(axsliderA, 'A', -1, 1.0, valinit=0.5)
sA.on_changed(update)
axsliderB = axes([0.43, 0.85, 0.16, 0.075])
sB = Slider(axsliderB, 'B', -30, 30.0, valinit=2)
sB.on_changed(update)
axsliderC = axes([0.74, 0.85, 0.16, 0.075])
sC = Slider(axsliderC, 'C', -30, 30.0, valinit=1)
sC.on_changed(update)
axes(ax)
A = 1;
B = 2;
C = 1;
y_data = A*x_data*x_data + B*x_data + C;
par_plot, = plot(x_data,y_data, lw=2, color='red')
show()
A note about the above: When I run the application, the code runs sequentially right through (it stores the update function in memory, I think), until it hits show(), which is blocking. When you make a change to one of the sliders, it runs the update function from memory (I think?).
This is the reason why show() is implemented in the way it is, so that you can change values in the background by using functions to process the data.
