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I posted this answer in sage-support, but for completeness, I'll post it here as well. The following works for me:

sage: from animate_decay import *
sage: ani.save('blah.mp4')

I'm trying to run thisexample from matplotlib.org. I downloaded the source code and ran:

sage: load('animate_decay.py')

sage: plt.savefig('foo.png')

The file, foo.png, contained an empty Cartesian plane. I also typed in the code verbatim into the Sage interpreter and had the same results. I am running Sage Version 5.5, Release Date: 2012-12-22 on Ubuntu 12.04.1 LTS. Any suggestions would be greatly appreciated!

Could you give a bit more detail in this answer, please?

How can I restrict the max/min values in the solutions? Is there a uniform way to do this or restrict the axes' range (e.g., force the plot onto [-2,2]x[-2,2] )

sage: maxima('plotdf([-y,-x],[x,y],[x,-2,2],[y,-2,2])')

Every click in the obtained field gives you a new trajectory

Precise coordinates of an initial point can be provided in plot setup

New x y coordinates + Enter adds a new trajectory

@calc314: could you describe what happens when you enter the above code?

I'm trying to plot solutions to two dimensional ordinary differential equations. I found that Sage makes it easy to plot a vector field and, using ode_solver(), I can plot solutions on top of the vector field by specifying an initial condition y_0 and some range of time to run (t_span).

However, this method I'm using seems to be quite ad hoc, as I have to choose the right initial conditions and time span / know a lot about my system in order to plot a nice picture. Let's make this more concrete:

Say I want to draw a nice phase portrait for

$\dot{x} = -y$

$\dot{y} = -x$

First I generate the vector field:

var('x y')
VF=plot_vector_field([-y,-x],[x,-2,2],[y,-2,2])

Then I use ode_solver() to plot solutions with initial conditions going around the unit circle:

T = ode_solver()
T.function=lambda t,y:[-y[1],-y[0]]
solutions = []
c = circle((0,0), 1, rgbcolor=(1,0,1))
for k in range(0,8):
    T.ode_solve(y_0=[cos(k*pi/4),sin(k*pi,t_span=[0,1],num_points=100)
    solutions.append(line([p[1] for p in T.solution]))

This generates the following picture:

But if I change run the system for one more unit of time (set t_span=[0,2]), the picture gets skewed:

This makes sense, of course, because the magnitude of the vectors along $y=-x$ get big as you get further away from the origin. Similarly, the trajectory along $y=x$ has trouble getting to the origin because the magnitude of those vectors get very small. Which all makes me think there's a better way to do this. Thoughts?