Welcome to PySilentSubstitution!

PySilSub is a Python toolbox for performing the method of silent substitution in vision and nonvisual photoreception research.

Note: See also, PyPlr, a sister project offering a Python framework for researching the pupillary light reflex with the Pupil Core eye tracking platform.

With PySilSub, observer- and device-specific solutions to silent substitution problems are found with linear algebra or numerical optimisation via a configurable, intuitive interface.

# Example 1 - Target melanopsin with 100% contrast (no background 
# specified), whilst ignoring rods and minimizing cone contrast, 
# for a 42-year-old observer and field size of 10 degrees. Solved
# with numerical optimization.

from pysilsub import observers, problems

ssp = problems.SilentSubstitutionProblem.from_package_data('STLAB_1_York')  # Load example data
ssp.observer = observers.ColorimetricObserver(age=42, field_size=10)  # Assign custom observer model
ssp.ignore = ['rh']  # Ignore rod photoreceptors
ssp.silence = ['sc', 'mc', 'lc']  # Minimise cone contrast
ssp.target = ['mel']  # Target melanopsin
ssp.target_contrast = 1.0  # With 100% contrast 
solution = ssp.optim_solve()  # Solve with optimisation
fig = ssp.plot_solution(solution.x)  # Plot the solution

Another example:

# Example 2 - Target S-cones with 45% contrast against a specified 
# background spectrum (all primaries, half max) whilst ignoring rods 
# and minimizing contrast on L/M cones and melanopsin, assuming 
# 32-year-old observer and 10-degree field size. Solved with linear 
# algebra.

from pysilsub import problems

ssp = problems.SilentSubstitutionProblem.from_package_data('STLAB_1_York')  # Load example data
ssp.background = [.5] * ssp.nprimaries  # Specify background spectrum
ssp.ignore = ['rh']  # Ignore rod photoreceptors
ssp.silence = ['mc', 'lc', 'mel']  # Silence L/M cones and melanopsin
ssp.target = ['sc']  # Target S cones
ssp.target_contrast = .45  # With 45% contrast 
solution = ssp.linalg_solve()  # Solve with linear algebra
fig = ssp.plot_solution(solution)  # Plot the solution

Some features may serve a broader purpose in vision and circadian research. For example, computing and saving a full set of CIEPO06- and CIES026-compliant action spectra for a given observer age and field size.

from pysilsub.observers import ColorimetricObserver

ColorimetricObserver(age=32, field_size=10).save_action_spectra()

For more information, check out the code, read the docs, and run pip install pysilsub to try out the examples above.