Quickstart

Evolutionary optimization

Run a CMAES optimization of reduced Wong Wang model with G and wEE as free parameters:

from cubnm import datasets, optimize

problem = optimize.BNMProblem(
    model = 'rWW',
    params = {
        'G': (0.5, 2.5),
        'wEE': (0.05, 0.75),
        'wEI': 0.15,
    },
    emp_fc_tril = datasets.load_functional('FC', 'schaefer-100'),
    emp_fcd_tril = datasets.load_functional('FCD', 'schaefer-100'),
    duration = 60,
    TR = 1,
    sc_path = datasets.load_sc('strength', 'schaefer-100'),
    states_ts = True,
    out_dir = './cmaes_homo',
)
cmaes = optimize.CMAESOptimizer(popsize=20, n_iter=10, seed=1)
cmaes.setup_problem(problem)
cmaes.optimize()
cmaes.save()

Using command line interface:

cubnm optimize \
    --model rWW --sc example --emp_fc_tril example --emp_fcd_tril example \
    --out_dir ./cmaes_homo_cli \
    --TR 1 --duration 60 --states_ts \
    --params G=0.5:2.5,wEE=0.05:0.75,wEI=0.15 \
    --optimizer CMAES --optimizer_seed 0 --n_iter 10 --popsize 20

Run a CMAES optimization of reduced Wong Wang model with G as a global free parameter and wEE and wEI as regional free parameters that are regionally heterogeneous based on a weighted combination of two fixed maps (HCP T1w/T2w, HCP FC G1):

from cubnm import datasets, optimize

problem = optimize.BNMProblem(
    model = 'rWW',
    params = {
        'G': (0.5, 2.5),
        'wEE': (0.05, 0.75),
        'wEI': (0.05, 0.75),
    },
    het_params = ['wEE', 'wEI'],
    maps_path = datasets.load_maps(['myelinmap', 'fcgradient01'], 'schaefer-100', norm='zscore'),
    emp_fc_tril = datasets.load_functional('FC', 'schaefer-100'),
    emp_fcd_tril = datasets.load_functional('FCD', 'schaefer-100'),
    duration = 60,
    TR = 1,
    sc_path = datasets.load_sc('strength', 'schaefer-100'),
    states_ts = True,
    out_dir = './cmaes_hetero',
)
cmaes = optimize.CMAESOptimizer(popsize=30, n_iter=10, seed=1)
cmaes.setup_problem(problem)
cmaes.optimize()
cmaes.save()

Using command line interface:

cubnm optimize \
    --model rWW --sc example --emp_fc_tril example --emp_fcd_tril example \
    --out_dir ./cmaes_hetero_cli \
    --TR 1 --duration 60 --states_ts \
    --params G=0.5:2.5,wEE=0.05:0.75,wEI=0.15 \
    --optimizer CMAES --optimizer_seed 0 --n_iter 10 --popsize 30 \
    --het_params wEE wEI --maps example

Grid search

Run a 10x10 grid search of reduced Wong Wang model with G and wEE as free parameters:

from cubnm import datasets, optimize

gs = optimize.GridSearch(
    model = 'rWW',
    params = {
        'G': (0.5, 2.5, 10),
        'wEE': (0.05, 0.75, 10),
        'wEI': 0.21
    },
    duration = 60,
    TR = 1,
    sc_path = datasets.load_sc('strength', 'schaefer-100'),
    states_ts = True,
    out_dir = './grid',
    sim_verbose = True
)
emp_fc_tril = datasets.load_functional('FC', 'schaefer-100')
emp_fcd_tril = datasets.load_functional('FCD', 'schaefer-100')
scores = gs.evaluate(emp_fc_tril, emp_fcd_tril)

Using command line interface:

cubnm grid \
    --model rWW --sc example --emp_fc_tril example --emp_fcd_tril example \
    --out_dir ./grid_cli \
    --TR 1 --duration 60 --states_ts \
    --params G=0.5:2.5:10,wEE=0.05:0.75:10,wEI=0.21 --sim_verbose