This distribution provides some code generation facilities for PyDy. For now, it generates functions that can evaluate the right hand side of the ordinary differential equations generated with sympy.physics.mechanics with three different backends: SymPy's lambdify, Theano, and Cython.
- NumPy: 1.7.1
- Cython: 0.19.2
- SymPy: master
- Theano: master
For now, download the source and install manually. Make sure your environment has all the dependencies met. Here is one option:
$ git clone git@github.com:PythonDynamics/pydy-code-gen.git
$ cd pydy-code-gen
$ python setup.py install- NumPy: 1.7.1
- SciPy: 0.13.0
- Cython: 0.19.2
- nose: 1.3.0
- matplotlib: 1.3.1
- SymPy: master (>0.7.3)
- Theano: master (>0.6.0rc3)
The following installation assumes you have virtualenv wrapper and all the dependencies needed to build the packages:
$ mkvirtualenv pydy-dev
(pydy-dev)$ pip install numpy scipy cython nose
(pydy-dev)$ pip install matplotlib # make sure to do this after numpy
(pydy-dev)$ git clone git@github.com:Theano/Theano.git
(pydy-dev)$ cd Theano
(pydy-dev)$ python setup.py install
(pydy-dev)$ cd ..
(pydy-dev)$ git clone git@github.com:sympy/sympy.git
(pydy-dev)$ cd sympy
(pydy-dev)$ python setup.py install
(pydy-dev)$ cd ..
(pydy-dev)$ git clone git@github.com:PythonDynamics/pydy-code-gen.git
(pydy-dev)$ cd pydy-code-gen
(pydy-dev)$ python setup.py installRun the tests:
(pydy-dev)$ nosetestsRun the benchmark to test the n-link pendulum problem.:
(pydy-dev)$ cd misc
(pydy-dev)$ python benchmark.py <max # of links> <# of time steps>This is an example of a simple 1 degree of freedom system: a mass, spring, damper system under the influence of gravity and a force:
/ / / / / / / / /
-----------------
| | | | g
\ | | | V
k / --- c |
| | | x, v
-------- V
| m | -----
--------
| F
VDerive the system:
from sympy import symbols
import sympy.physics.mechanics as me
mass, stiffness, damping, gravity = symbols('m, k, c, g')
position, speed = me.dynamicsymbols('x v')
positiond = me.dynamicsymbols('x', 1)
force = me.dynamicsymbols('F')
ceiling = me.ReferenceFrame('N')
origin = me.Point('origin')
origin.set_vel(ceiling, 0)
center = origin.locatenew('center', position * ceiling.x)
center.set_vel(ceiling, speed * ceiling.x)
block = me.Particle('block', center, mass)
kinematic_equations = [speed - positiond]
force_magnitude = mass * gravity - stiffness * position - damping * speed + force
forces = [(center, force_magnitude * ceiling.x)]
particles = [block]
kane = me.KanesMethod(ceiling, q_ind=[position], u_ind=[speed],
kd_eqs=kinematic_equations)
kane.kanes_equations(forces, particles)Store the expressions and symbols in sequences for the code generation:
mass_matrix = kane.mass_matrix_full
forcing_vector = kane.forcing_full
constants = (mass, stiffness, damping, gravity)
coordinates = (position,)
speeds = (speed,)
specified = (force,)Now generate the function needed for numerical evaluation of the ODEs. The generator can use various back ends: lambdify, theano, or cython:
from pydy_code_gen.code import numeric_right_hand_side
right_hand_side = numeric_right_hand_side(mass_matrix, forcing_vector,
constants, coordinates, speeds, specified, generator='cython')Integrate the equations of motion:
from numpy import array, linspace
from scipy.integrate import odeint
x0 = array([0.1, -1.0])
args = {'constants': array([1.0, 1.0, 0.2, 9.8]),
'specified': array([1.0]),
'num_coordinates': 1}
t = linspace(0.0, 10.0, 1000)
y = odeint(right_hand_side, x0, t, args=(args,))Plot the results:
import matplotlib.pyplot as plt
plt.plot(t, y)
plt.legend((str(position), str(speed)))
plt.show()