def generate_df(metaroi_vals, outdir):
"""Generates a dataframe with the metaroi values for each subject
Parameters
----------
metaroi_vals : dict
Dictionary where keys are the filename and values are the mean
FDG value
outdir : string
Full path where FDG output file should be saved
Returns
-------
metaroi_df : pandas DataFrame
DataFrame where each row is an FDG scan
"""
metaroi_df = pd.DataFrame.from_dict(metaroi_vals, orient="index")
metaroi_df.reset_index(level=0, inplace=True)
metaroi_df.rename(columns={0: "roi_vals", "index": "path"}, inplace=True)
metaroi_df["roi_vals"] = [float(x) for x in metaroi_df["roi_vals"]]
metaroi_df["codea"] = [cf.get_id(x) for x in metaroi_df["path"]]
metaroi_df = metaroi_df.rename(columns={"roi_vals": "FDG_val"})
metaroi_df = metaroi_df.drop("path", axis=1)
cf.save_xls_and_pkl(metaroi_df, "fdg_metaroi", outdir)
return metaroi_df
def mri_run(datadir, outdir, rois):
"""Main function to collect MRI volume data
Parameters
----------
datadir : string
Full path to root directory of freesurfer processed data. Expect file tree
to be datadir/subcode/stats/aseg.stats
outdir : string
Full path to directory where data will be saved
rois : list of strings
List of freesurfer rois of interest. These volumes of these rois will be
inserted in aseg_change along with their rates of change
Returns
-------
aseg_stats : pandas DataFrame
DataFrame where each row is a scan, and columns are volumes of all
freesurfer processed regions
aseg_change : pandas DataFrame
DataFrame where each row is a scan, and columns are the volumes of interest,
their rates of change, and icv correction
"""
#get aseg_stats data from freesurfer processed data
outfile = '%sFS_aseg_stats.txt' %outdir
subs, asegout, output = extractFSasegstats(datadir, outfile)
aseg_stats = pd.read_csv(outfile, header=0, delim_whitespace=True)
#add columns for SubjID and MRI_TP
aseg_stats['codea'] = [cf.get_id(sub) for sub in subs]
aseg_stats['MRI_Tp'] = [cf.get_tp(sub) for sub in subs]
aseg_stats.drop('Measure:volume', axis=1, inplace=True)
#get dates of MRI scans that were processed with freesurfer
mridates = bacs_pet_mri_date_batch(datadir)
aseg_change = pd.merge(aseg_change, mridates, on=['codea','MRI_Tp'])
rois_icvcorr = dict([(roi, '%s_icvcorr' %roi) for roi in rois])
aseg_change = icvcorr(aseg_change, rois_icvcorr, 'IntraCranialVol')
#calculate rate of change in years
for roi in rois:
aseg_change = cf.rate_of_change(aseg_change, 'codea', 'MRI_Tp',
'MRI_Scandate', roi, '%s_sl' %roi)
cf.save_xls_and_pkl(aseg_stats, 'aseg_stats', outdir)
cf.save_xls_and_pkl(aseg_change, 'aseg_change', outdir)
return aseg_stats, aseg_change
