coeff_constant.append(const)
coeff_constant_sepseq.append(const_sepseq)
plot_GAT(np.asarray(coeff_complexity),times,save_folder='/SVM/GAT/',suffix = 'regression_complexity_SW_train_different_blocks',compute_significance=[0,0.6],chance = 0,tail=-1,clim=[-0.005,0.005])
plot_GAT(np.asarray(coeff_constant),times,save_folder='/SVM/GAT/',suffix = 'regression_const_SW_train_different_blocks',compute_significance=[0,0.6],chance = 0,tail=-1)
plot_GAT(np.asarray(coeff_complexity_sepseq),times,save_folder='/SVM/GAT/',suffix = 'regression_complexity_SW_train_different_blocks_and_sequences',compute_significance=[0,0.6],chance = 0,tail=-1,clim=[-0.005,0.005])
plot_GAT(np.asarray(coeff_constant_sepseq),times,save_folder='/SVM/GAT/',suffix = 'regression_const_SW_train_different_blocks_and_sequences',compute_significance=[0,0.6],chance=0,tail=-1)
# ___________________________________________________________________________
# ============== GAT for the different features ===========================
# ___________________________________________________________________________
anal_name = 'feature_decoding/' + "full_data_" + "ordinal_code_quads_tested_others"
SVM_funcs.plot_gat_simple(anal_name, config.subjects_list, "full_data_" + "ordinal_code_quads_tested_others.npy", chance=0.25, score_field='score',
vmin=None, vmax=None, compute_significance=[0., 0.6])
vmin = [0.45,0.45,0.45,0.20]
vmax = [0.55,0.55,0.55,0.3]
for residual_analysis in [True]:
if residual_analysis:
suffix = 'resid_cv_'
else:
suffix = 'full_data_'
chance = [0.5,0.5,0.5,0.25]
for ii,name in enumerate(['RepeatAlter_score_dict','ChunkEnd_score_dict','ChunkBeginning_score_dict','WithinChunkPosition_score_dict']):
anal_name = 'feature_decoding/'+suffix+name
SVM_funcs.plot_gat_simple(anal_name,config.subjects_list,suffix+name,chance = 0.,score_field='distance',vmin=None,vmax=None,compute_significance=[0.,0.6])
# ___________________________________________________________________________
# ============== GAT decoding structure ===========================
# ___________________________________________________________________________
for name in [
'full_data_clean_OpenedChunks_score_dict',
'full_data_clean_ClosedChunks_score_dict',
'full_data_clean_ChunkDepth_score_dict'
]:
anal_name = 'feature_decoding/' + name
coucou = SVM_funcs.plot_gat_simple(
anal_name,
config.subjects_list,
'/feature_decoding/' +
name.replace('full_data_clean_', '').replace('_score_dict', '') +
'/r_',
chance=0,
score_field='regression',
compute_significance=[0, 0.6],
plot_per_subjects=True,
vmin=-0.1,
vmax=0.1)
chances = [0.5, 0.5, 0.5, 0.25]
for ii, name in enumerate([
'full_data_clean_ChunkBeginning_score_dict',
'full_data_clean_ChunkEnd_score_dict',
'full_data_clean_RepeatAlter_score_dict',
'full_data_clean_WithinChunkPosition_score_dict'
]):
anal_name = 'feature_decoding/' + name