Add error modeling to CADET-Process

[schmoelder]

This PR adds basic functionality for error modeling in CADET-Process.
This includes providing different error distributions to Process objects, e.g. to model fluctuations in pump flow rate, valve switching delays, variations in concentrations etc.
Moreover, a Variator class should handle the registration / connection between the individual distributions and the actual parameters of the model (i.e. Process).

To do

• Add RNGs to distributions to improve reproducibility
• Handle indices for parameters with multiple entries (e.g. only vary concentration of certain component) (see also in Events or OptimizationVariable)
• Open question: How to include parameters of the DistributionBase in parameter estimation routines (i.e. the parameters of the distribution need to be exposed somehow)
• Open question: How to specify errors that don't directly affect the Process but simulation output (e.g. sensor noise?)
• ...
• MCMC?

---

Demo

Here is a little snippet how different distributions could be used

from CADETProcess.errorModel import (
    UniformDistribution,
    NormalDistribution,
    ExponentialDistribution,
    BinomialDistribution,
    PoissonDistribution,

)
# Create instances of different distributions
normal = NormalDistribution(mu=0, sigma=1)
uniform = UniformDistribution(lb=0, ub=10)
exponential = ExponentialDistribution(lambda_=2)
binomial = BinomialDistribution(n=10, p=0.5)
poisson = PoissonDistribution(lambda_=3)

# Example usage
print("Normal Samples:", normal.sample(size=5))
print("Normal Mean:", normal.mean())
print("Normal Variance:", normal.var())

print("Uniform Samples:", uniform.sample(size=5))
print("Exponential Samples:", exponential.sample(size=5))
print("Binomial Samples:", binomial.sample(size=5))
print("Poisson Samples:", poisson.sample(size=5))