Welcome to PySilSub’s documentation!¶
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
# 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.
Contents:
Tables and indices
