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Adopt glm into config
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,76 +1,46 @@ | ||
| import osl | ||
| import os | ||
| import numpy as np | ||
| import glmtools as glm | ||
| import glmtools | ||
| import matplotlib.pyplot as plt | ||
| from dask.distributed import Client | ||
| from osl_ephys import preprocessing, glm | ||
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||
| def first_level(dataset, userargs): | ||
| DC = glm.design.DesignConfig() | ||
| DC.add_regressor(name="famous", rtype="Categorical", codes=[5,6,7]) | ||
| DC.add_regressor(name="unfamiliar", rtype="Categorical", codes=[13,14,15]) | ||
| DC.add_regressor(name="scrambled", rtype="Categorical", codes=[17,18,19]) | ||
| DC.add_contrast(name="Mean", values={"famous": 1/3, "unfamiliar": 1/3, "scrambled": 1/3}) | ||
| DC.add_contrast(name="Faces - Scrambled", values={"famous": 1, "unfamiliar": 1, "scrambled": -2}) | ||
| dataset['glm'] = osl.glm.glm_epochs(DC, dataset['epochs']) | ||
| dataset['glm'].design.plot_summary(savepath=os.path.join( | ||
| os.path.dirname(dataset['raw'].filenames[0]), 'subject_design.png')) | ||
| return dataset | ||
|
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| def second_level(dataset, userargs): | ||
| firstlevel_contrast = userargs.get('firstlevel_contrast', 'Faces - Scrambled') | ||
| group_contrast = userargs.get('group_contrast', 'Mean') | ||
| tmin = userargs.get('tmin', -np.Inf) | ||
| tmax = userargs.get('tmax', np.Inf) | ||
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| groupDC = glm.design.DesignConfig() | ||
| info = {"Subject": np.repeat(np.arange(1, 20), 6)} | ||
| for i in range(19): | ||
| # Add subject mean regressors | ||
| groupDC.add_regressor(name=f"Subj{i+1}", rtype="Categorical", datainfo="Subject", codes=[i+1]) | ||
|
|
||
| # add group contrast | ||
| groupDC.add_contrast(name='Mean', values={f"Subj{i+1}": 1/19 for i in range(19)}) | ||
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| # group level model | ||
| dataset['group_glm'] = osl.glm.group_glm_epochs(dataset['glm'], groupDC) | ||
| dataset['group_glm'].design.plot_summary(savepath=os.path.join(userargs['figdir'], 'group_design.png')) | ||
|
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||
| # max stat permutation test | ||
| dataset['group_glm_perm'] = osl.glm.glm_base.SensorMaxStatPerm(dataset['group_glm'], dataset['group_glm'].contrast_names.index(group_contrast), | ||
| dataset['group_glm'].fl_contrast_names.index(firstlevel_contrast), tmin=tmin, tmax=tmax) | ||
| dataset['group_glm_perm'].plot_sig_clusters(99) | ||
| plt.savefig(os.path.join(userargs['figdir'], f'group_contrast-{firstlevel_contrast}-significance.png')) | ||
| return dataset | ||
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||
|
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||
| if __name__ == "__main__": | ||
| client = Client(n_workers=16, threads_per_worker=1) | ||
|
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| config = """ | ||
| preproc: | ||
| - read_dataset: {ftype: sflip_parc-raw} | ||
| - epochs: {picks: misc, tmin: -0.2, tmax: 0.5} | ||
| - first_level: {} | ||
| - epochs: {picks: misc, tmin: -0.2, tmax: 0.3} | ||
| - glm_add_regressor: {name: famous, rtype: Categorical, codes: [5 6 7]} | ||
| - glm_add_regressor: {name: unfamiliar, rtype: Categorical, codes: [13 14 15]} | ||
| - glm_add_regressor: {name: scrambled, rtype: Categorical, codes: [17 18 19]} | ||
| - glm_add_contrast: {name: Mean, values: {famous: 1/3, unfamiliar: 1/3, scrambled: 1/3}} | ||
| - glm_add_contrast: {name: Faces-Scrambled, values: {famous: 1, unfamiliar: 1, scrambled: -2}} | ||
| - glm_fit: {target: epochs, method: glm_epochs} | ||
| group: | ||
| - second_level: {tmin: 0.05, tmax: 0.3, figdir: ds117/figures} | ||
| """ | ||
|
|
||
| - glm_add_regressor: {name: Subject, rtype: Categorical, key: Subject, codes: unique} | ||
| - glm_add_contrast: {name: Mean, values: unique, key: Subject} | ||
| - glm_fit: {method: epochs, tmin: 0.05, tmax: 0.3} | ||
| - glm_permutations: {method: epochs, target: group_glm, contrast: Mean, type: max, nperms: 1000, threshold: 0.99} | ||
| """ | ||
| proc_dir = "ds117/processed" | ||
| src_files = sorted(osl.utils.Study(os.path.join(proc_dir, "sub{sub_id}-run{run_id}", "sub{sub_id}-run{run_id}_sflip_parc-raw.fif")).get()) | ||
| src_files = sorted(utils.Study(os.path.join(proc_dir, | ||
| "sub{sub_id}-run{run_id}", "sub{sub_id}-run{run_id}_sflip_parc-raw.fif")).get()) | ||
| subjects = [f"sub{i+1:03d}-run{j+1:02d}" for i in range(19) for j in range(6)] | ||
|
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||
| osl.preprocessing.run_proc_batch( | ||
| covs = [f"Subject": [sub.split("-")[0]for sub in subjects] | ||
|
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||
| preprocessing.run_proc_batch( | ||
| config, | ||
| src_files, | ||
| subjects, | ||
| outdir=proc_dir, | ||
| ftype='raw', | ||
| extra_funcs=[first_level, second_level], | ||
| covs=covs, | ||
| dask_client=True, | ||
| overwrite=True, | ||
| gen_report=False, | ||
| skip_save=['events', 'raw', 'ica', 'event_id', 'sflip_parc-raw'], | ||
| random_seed=3557485304, | ||
| ) | ||
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