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# If you want the figures to appear in the notebook,
# and you want to interact with them, use
# %matplotlib notebook
# If you want the figures to appear in the notebook,
# and you don't want to interact with them, use
# %matplotlib inline
# If you want the figures to appear in separate windows, use
# %matplotlib qt5
# to switch from one to another, you have to select Kernel->Restart
%matplotlib notebook
from modsim import *
This notebook solves the Spider-Man problem from spiderman.ipynb, demonstrating a different development process for physical simulations.
In pendulum_sympy, we derive the equations of motion for a springy pendulum without drag, yielding:
$ \ddot{x} = \frac{k length_{0} x}{m \sqrt{x^{2} + y^{2}}} - \frac{k x}{m} $
$ \ddot{y} = - g + \frac{k length_{0} y}{m \sqrt{x^{2} + y^{2}}} - \frac{k y}{m} $
We'll use the same conditions we saw in spiderman.ipynb
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condition = Condition(g = 9.8,
m = 75,
area = 1,
rho = 1.2,
v_term = 60,
duration = 30,
length0 = 100,
angle = (270 - 45),
k = 20)
Now here's a version of make_system that takes a Condition object as a parameter.
make_system uses the given value of v_term to compute the drag coefficient C_d.
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def make_system(condition):
"""Makes a System object for the given conditions.
condition: Condition with height, g, m, diameter,
rho, v_term, and duration
returns: System with init, g, m, rho, C_d, area, and ts
"""
unpack(condition)
theta = np.deg2rad(angle)
x, y = pol2cart(theta, length0)
P = Vector(x, y)
V = Vector(0, 0)
init = State(x=P.x, y=P.y, vx=V.x, vy=V.y)
C_d = 2 * m * g / (rho * area * v_term**2)
ts = linspace(0, duration, 501)
return System(init=init, g=g, m=m, rho=rho,
C_d=C_d, area=area, length0=length0,
k=k, ts=ts)
Let's make a System
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system = make_system(condition)
system
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system.init
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To write the slope function, we can get the expressions for ax and ay directly from SymPy and plug them in.
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def slope_func(state, t, system):
"""Computes derivatives of the state variables.
state: State (x, y, x velocity, y velocity)
t: time
system: System object with length0, m, k
returns: sequence (vx, vy, ax, ay)
"""
x, y, vx, vy = state
unpack(system)
ax = k*length0*x/(m*sqrt(x**2 + y**2)) - k*x/m
ay = -g + k*length0*y/(m*sqrt(x**2 + y**2)) - k*y/m
return vx, vy, ax, ay
As always, let's test the slope function with the initial conditions.
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slope_func(system.init, 0, system)
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And then run the simulation.
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%time run_odeint(system, slope_func)
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xs = system.results.x
ys = system.results.y
The simplest way to visualize the results is to plot x and y as functions of time.
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newfig()
plot(xs, label='x')
plot(ys, label='y')
decorate(xlabel='Time (s)',
ylabel='Position (m)')
We can plot the velocities the same way.
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vxs = system.results.vx
vys = system.results.vy
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newfig()
plot(vxs, label='vx')
plot(vys, label='vy')
decorate(xlabel='Time (s)',
ylabel='Velocity (m/s)')
Another way to visualize the results is to plot y versus x. The result is the trajectory through the plane of motion.
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newfig()
plot(xs, ys, label='trajectory')
decorate(xlabel='x position (m)',
ylabel='y position (m)')
We can also animate the trajectory. If there's an error in the simulation, we can sometimes spot it by looking at animations.
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newfig()
decorate(xlabel='x position (m)',
ylabel='y position (m)',
xlim=[-100, 100],
ylim=[-200, -50],
legend=False)
for x, y in zip(xs, ys):
plot(x, y, 'bo', update=True)
sleep(0.01)
Here's a function that encapsulates that code and runs the animation in (approximately) real time.
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def animate2d(xs, ys, speedup=1):
"""Animate the results of a projectile simulation.
xs: x position as a function of time
ys: y position as a function of time
speedup: how much to divide `dt` by
"""
# get the time intervals between elements
ts = xs.index
dts = np.diff(ts)
dts = np.append(dts, 0)
# decorate the plot
newfig()
decorate(xlabel='x position (m)',
ylabel='y position (m)',
xlim=[xs.min(), xs.max()],
ylim=[ys.min(), ys.max()],
legend=False)
# loop through the values
for x, y, dt in zip(xs, ys, dts):
plot(x, y, 'bo', update=True)
sleep(dt / speedup)
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animate2d(system.results.x, system.results.y)
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