Dynlab is a python package to make dynamical systems analysis faster and easier, by providing practitioners prebuilt modules for calculating common diagnostics. Dynlab currently offers several easy to use diagnostics for analyzing your dynamical systems including an FTLE calculator FLTE, an attraction and repulsion rate calculator AttractionRate, and a trajectory repulsion rate calculator TrajectoryRepulsionRate. Dynlab also offers users a variety of prewritten flows to experiment and play around with, including the double gyre double_gyre, the Duffing oscillator duffing_oscillator, the pendulum with damping and forcing pendulum, the Van Der Pol oscillator van_der_pol_oscillator, the Lotka-Voltera flow lotka_volterra, the Lorenz system lorenz, and many more. Note that all the flows are written in such a way that they can easily be passed to an ode integrator and as such even the autonomous flows will still require a time parameter to be passed to them.
Currently supported Lagrangian diagnostics are:
Finite-Time Lyapunov Exponent: FTLE
Lagrangian Coherent Structures: LCS
Currently supported Eulerian diagnostics are:
Attraction Rate: AttractionRate
Repulsion Rate: RepulsionRate
infinitesimal Lyapunov ExponentS: iLES
Trajectory Repulsion Rate: TrajectoryRepulsionRate
Trajectory Repulsion Ratio: TrajectoryRepulsionRatio
This package is readily available on pypi and can be easily installed with the command pip install dynlab.
Note that dynlab is written with python 3.11 and you may need to update your python installation to take advantage of it.
import matplotlib.pyplot as plt
from dynlab.diagnostics import FTLE
from dynlab.flows import double_gyre
x = np.linspace(0, 2, 101)
y = np.linspace(0, 1, 101)
ftle = FTLE(num_threads=1).compute(x, y, double_gyre, (10, 0), edge_order=2, rtol=1e-8, atol=1e-8)
plt.pcolormesh(x, y, ftle, shading='gouraud')
import matplotlib.pyplot as plt
from dynlab.diagnostics import LCS
from dynlab.flows import double_gyre
x = np.linspace(0, 2, 201)
y = np.linspace(0, 1, 101)
lcs = LCS()
attracting_lcs = lcs.compute(x, y, f=double_gyre, t=(10, 0), percentile=80)
for line in attracting_lcs:
plt.plot(line[:, 0],line[:, 1], 'b')
import matplotlib.pyplot as plt
from dynlab.diagnostics import iLES
from dynlab.flows import double_gyre
x = np.linspace(0, 2, 201)
y = np.linspace(0, 1, 101)
attracting_iles = iLES().compute(x, y, f=double_gyre, t=0, kind='attracting', force_eigenvectors=True)
repelling_iles = iLES().compute(x, y, f=double_gyre, t=0, kind='repelling', force_eigenvectors=True)
xx, yy = np.meshgrid(x[::10],y[::10])
u, v = double_gyre(0, (xx, yy))
plt.quiver(xx,yy,u,v)
for line in attracting_iles:
plt.plot(line[:, 0],line[:, 1], 'b', linewidth=3)
for line in repelling_iles:
plt.plot(line[:, 0],line[:, 1], 'r', linewidth=3)
import matplotlib.pyplot as plt
from dynlab.diagnostics import AttractionRate
from dynlab.flows import double_gyre
x = np.linspace(0, 20000, 101)
y = np.linspace(-4000, 4000, 101)
u, v = bickley_jet(0, np.meshgrid(x, y))
attraction_rate = AttractionRate().compute(x, y, u=u, v=v, edge_order=2)
# note that lower values of the attraction rate field equate to higher levels of attraction
# so we'll plot the negative of the attraction rate field to highlight areas of greatest attraction.
plt.pcolormesh(x, y, -attraction_rate, shading='gouraud')
import matplotlib.pyplot as plt
from dynlab.diagnostics import Rhodot
from dynlab.flows import bead_on_a_rotating_hoop
x = np.linspace(-1, 1, 401)*3
y = np.linspace(-1, 1, 401)*2.5
rhodot = TrajectoryRepulsionRate().compute(x, y, f=bead_on_a_rotating_hoop, t=0, edge_order=2)
plt.pcolormesh(x, y, rhodot, shading='gouraud')
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