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Brownian Meander#
Simulate and visualise paths
# Author: Dialid Santiago <d.santiago@outlook.com>
# License: MIT
# Description: Simulate and visualise a Brownian Meander
from aleatory.processes import BrownianMeander
from aleatory.styles import qp_style
qp_style() # Use quant-pastel-style
p = BrownianMeander()
fig = p.draw(n=100, N=100, figsize=(12, 7), colormap="spring")
fig.show()
![Brownian Meander on [0, 1.0], Monte Carlo Simulated Paths $\{{X_t, t \in [t_0, T]\}}$, $X_T$ Marginal](../_images/sphx_glr_plot_brownian_meander_001.png)
p = BrownianMeander(fixed_end=True, end=3.0)
fig = p.draw(n=100, N=100, figsize=(12, 7), colormap="RdPu")
fig.show()
![Tied Brownian Meander ending at 3.00 on [0, 1.0], Simulated Paths $X_t, t \in [t_0, T]$](../_images/sphx_glr_plot_brownian_meander_002.png)
/home/docs/checkouts/readthedocs.org/user_builds/aleatory/checkouts/latest/aleatory/utils/plotters.py:476: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
ax1.legend()
fig = p.plot(n=100, N=10, figsize=(12, 7))
fig.show()
![Tied Brownian Meander ending at 3.00 on [0, 1.0]](../_images/sphx_glr_plot_brownian_meander_003.png)
Total running time of the script: (0 minutes 1.773 seconds)