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Stochastic Bifurcation Code and Documentation
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# Stochastic P-Bifurcation Detection | ||
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Stochastic P-bifurcations are points of topological changes in the joint probability density function (PDF) of a stochastic system. Please cite the papers [“A topological framework for identifying phenomenological bifurcations in stochastic dynamical systems”](https://doi.org/10.1007/s11071-024-09289-1) and [“Topological Detection of Phenomenological Bifurcations with Unreliable Kernel Densities”](https://doi.org/10.48550/arXiv.2401.16563) when using these functions. These modules can be used to detect P-bifurcation given | ||
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- [Analytical Densities](#analytical-density) | ||
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## Analytical Density | ||
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Given the analytical PDFs, the homological bifurcation plot can be generated with the module below | ||
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```{eval-rst} | ||
.. automodule:: teaspoon.SP.StochasticP | ||
:members: analytical_homological_bifurcation_plot | ||
``` | ||
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### Example | ||
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The following example plots an analytical bifurcation plot for a set of PDFs where the system shifts from a monostability to a limit cycle: | ||
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```python | ||
import numpy as np | ||
from teaspoon.SP.StochasticP import analytical_homological_bifurcation_plot | ||
X, Y = np.meshgrid(np.linspace(-3,3,100), np.linspace(-3,3,100)) | ||
factors = np.linspace(-1,1,20) | ||
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PDFs = [] | ||
for h in factors: | ||
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p = np.exp(-0.5*((X**2+Y**2)**2 + h*(X**2 + Y**2))) | ||
PDFs.append(p) | ||
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M = analytical_homological_bifurcation_plot(PDFs, bifurcation_parameters=factors, dimensions=[1], filter=0.02, maxEps=1, numStops=100, plotting=True) | ||
``` | ||
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The output for this example is | ||
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```{image} figures/analytical_homological_plot.png | ||
:alt: Analytical Homological Plot | ||
:width: 300px | ||
:align: left | ||
``` |
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