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05_sample_generate_stats.py
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05_sample_generate_stats.py
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from pathlib import Path
from netneurotools.stats import get_dominance_stats
from scipy.io import loadmat
from .utils import *
data_dir = Path("data")
deriv_dir = data_dir / "derivatives"
pid_term_names = [
"rtr",
"rtx",
"rty",
"rts",
"xtr",
"xtx",
"xty",
"xts",
"ytr",
"ytx",
"yty",
"yts",
"str",
"stx",
"sty",
"sts",
]
pid_type_index = 0
full_pidres = loadmat(deriv_dir / "HCP_S1200_schaefer100x7_PhiIDFull_MMI.mat")[
"full_res"
]
termwise_mean_fc_schaefer100x7 = np.load(
deriv_dir / "pyspi_hcp_schaefer100x7_term_mean.npy"
)
pidres_grouavg_dom_mat = np.zeros((pyspi_clean_dim, len(pid_term_names)))
# termwise_mean_fc
full_pidres_groupavg = np.mean(full_pidres, axis=(0, 1))
full_pidres_groupavg_iu = np.array(
[
full_pidres_groupavg[:, :, pid_idx][schaefer100x7_iu]
for pid_idx in range(len(pid_term_names))
]
).T
for term_it in range(pyspi_clean_dim):
curr_term_name = pyspi_clean_terms[term_it]
curr_term_prefix = pyspi_clean_terms_prefix[term_it]
curr_pyspi_term_iu = termwise_mean_fc_schaefer100x7[term_it, :, :][schaefer100x7_iu]
model_metrics, _ = get_dominance_stats(
full_pidres_groupavg_iu, curr_pyspi_term_iu, verbose=True
)
pidres_grouavg_dom_mat[term_it, :] = model_metrics["total_dominance"]