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License: Apache License 2.0
Simulation library for very simple simulations to benchmark machine learning algorithms
License: Apache License 2.0
This will replace the individual images that were in the overleaf. They should be much smaller.
Should be able to copy and paste much of what Becky has done from pendulum test
Currently, we're adding Gaussian noise only to acceleration_due_to_gravity, but I'd like the option of introducing noise to big G and phi separately.
All actions are failing due to a versioning (???) error in the action itself. Fix that so actions work lmao.
Project is currently missing a command line interface for quick generation, along with documentation for generation.
move these out of notebook.
Originally posted by omaripaul57 April 27, 2023
What are the differences between them? Which version should we be using?
Some of the issues on DeepBench do not show up in our roadmap because they're somehow not linked to the project, please help Maggie!
Match the naming convention for AstroObject
This is for the alpha version, it may change down the road, but it would be nice to have it pip-installable.
Currently create_noise draws one parameter value for all moments in time. I'm wondering if we'd also like it to draw a new parameter value for every moment in time. Alternately, we could introduce a new type of noise (i.e. Gaussian noise on the measurement itself instead of the parameter values).
I think this warrants a discussion.
Current function looks like this:
def create_noise(imshape, noise_level):
"""
Create noise image
https://kmdouglass.github.io/posts/modeling-noise-for-image-simulations.html
"""
seed = 42
rs = np.random.RandomState(seed)
return rs.poisson(noise_level, (np.shape(imshape)[0],np.shape(imshape)[1]))
Expected behavior
Poisson function should look like rs.poisson(noise level, input_shape) but since (np.shape(imshape)[0],np.shape(imshape)[1]) is forwarding shape of the shape this returns error message. The correct version should be:
def create_noise(imshape, noise_level):
"""
Create noise image
https://kmdouglass.github.io/posts/modeling-noise-for-image-simulations.html
"""
seed = 42
rs = np.random.RandomState(seed)
return rs.poisson(noise_level, size=imshape)
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