Tooling to generate toy MDOF dynamics data for arbitrary linear and nonlinear systems in python
Project description
Toybox
Tooling to generate toy MDOF dynamics data for arbitrary physical linear and nonlinear systems in python.
Feel free to raise any issues of make suggestions.
Quickstart guide
import toybox as tb
# Initialise a linear symetric sytem
system = tb.symetric(dofs=2, m=1, c=20, k=1e5)
# Define a nonlinearity
def quadratic_cubic_stiffness_2dof_single(_, t, y, ydot):
return np.dot(y**2, np.array([5e7, 0])) + np.dot(y**3, np.array([1e9, 0]))
# Attach the nonlinearity
system.N = quadratic_cubic_stiffness_2dof_single
#Define some excitations for the system
system.excitation = [tb.forcings.white_gaussian(0, 1), None]
# Simulate
n_points = 1e3
fs = 1/500
normalised_data = system.simulate((n_points, fs), normalise=True)
# Denormalise later if required
data = sytem.denormalise()
data
is a python dict
with time series as follows:
Variable | Description | Dictionary key |
---|---|---|
t | Time points | 'ts' |
Xd(t) | Forcing at location d | 'x{d}' |
Yd(t) | Displacement at location d | 'y{d}' |
Y'd(t) | Velocity at location d | 'ydot{d}' |
Customisation
Arbitrary systems
toybox.system
Allows the specification of arbitrary M, C and K matrices.
Arbitrary forcing
Set your_system.excitation
to a per degree-of-freedom iterable. Entries can include either:
- Premade excitations (such as white_gaussian or sinusoidal)
- Timeseries (with shape
(n_points, ndofs)
) None
for unforced degrees of freedom.
Project details
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