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extremeDiffusion1D

Description

High performance simulations of random walks in various environments. The costly functions are written in C++ and then ported to Python using PyBind11.

Installation

Installation is automated in bash with the setup.sh script.

Dependences:

Python Iterfaces

Data Structures

Numerical data is handled using the npquad numpy extension. Floating point data is returned as numpy arrays with dtype=np.quad. Note that quad precision support is limited so downcasting to np.float64 after all calculations are done is recommended. Some helper functions are located in /pysrc/fileIO.py and /pysrc/quadMath.py.

Classes

  • pydiffusionCDF.DiffusionTimeCDF
  • pydiffusionCDF.DiffusionPositionCDF
  • pydiffusionPDF.DiffusionPDF
  • pyfirstPassagePDF.FirstPassagePDF

Examples

import sys

sys.path.append("pysrc")
from pydiffusionPDF as DiffusionPDF
import matplotlib

matplotlib.use("Agg")
from matplotlib import pyplot as plt

# Specify some constants like the number of particles, beta, and number of
# timesteps to evolve the system
nParticles = 1e50
beta = 1
num_of_timesteps = 10_000

# Initialize the system with parameters and other key word arguments
d = DiffusionPDF(
    nParticles,
    beta=beta,
    occupancySize=num_of_timesteps,
    probDistFlag=False,
)

# Evolve the system to the specified number of timesteps
d.evolveToTime(num_of_timesteps)

# Get the rightmost edge and the time
maxEdge = d.maxDistance
time = d.time

# Plot the rightmost edge over time and save
fig, ax = plt.subplots()
ax.set_xlabel("Time")
ax.set_ylabel("Distance to Center")
ax.set_xscale("log")
ax.set_yscale("log")
ax.plot(time, maxEdge)
plt.show()

plot