Is this section worth it? There is an example which the user will already have read which talks about adding noise to a model. I do not think there is much more to say, unless we want to through some domain-specific examples here (e.g. fake a grouped X-ray spectrum, simulate an image with different noise/response components).
Simulating a data set normally involves:
- evaluate the model
- add in noise
This may need to be repeated several times for complex models, such as when different components have different noise models or the noise needs to be added before evaluation by a component.
The model evaluation would be performed using the techniques described in this section, and then the noise term can be handled with :pysherpa.utils.poisson_noise or routines from NumPy or SciPy to evaluate noise, such as numpy.random.standard_normal.
>>> import numpy as np
>>> from sherpa.models.basic import Polynom1D
>>> np.random.seed(235)
>>> x = np.arange(10, 100, 12)
>>> mdl = Polynom1D('mdl')
>>> mdl.offset = 35
>>> mdl.c1 = 0.5
>>> mdl.c2 = 0.12
>>> ymdl = mdl(x)
>>> from sherpa.utils import poisson_noise
>>> ypoisson = poisson_noise(ymdl)
>>> from numpy.random import standard_normal, normal
>>> yconst = ymdl + standard_normal(ymdl.shape) * 10
>>> ydata = ymdl + normal(scale=np.sqrt(ymdl))