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Fix spelling errors in code #488

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merged 1 commit into from
Feb 3, 2025
+9 −9
Merged

Fix spelling errors in code #488

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2 changes: 1 addition & 1 deletion aslprep/utils/asl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ def determine_multi_pld(metadata):
Returns
-------
:obj:`bool`
True if the data are multi-delay/TI. Fale if not.
True if the data are multi-delay/TI. False if not.
"""
plds = np.array(metadata['PostLabelingDelay'])
return np.unique(plds).size > 1
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16 changes: 8 additions & 8 deletions aslprep/utils/cbf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -302,8 +302,8 @@ def _getcbfscore(cbfts, wm, gm, csf, mask, thresh=0.7):
# robust mean and median
median_gm_cbf = np.median(gm_cbf_ts)
mad_gm_cbf = median_abs_deviation(gm_cbf_ts) / 0.675
indx = 1 * (np.abs(gm_cbf_ts - median_gm_cbf) > (2.5 * mad_gm_cbf))
R = np.mean(cbfts[:, :, :, indx == 0], axis=3)
index = 1 * (np.abs(gm_cbf_ts - median_gm_cbf) > (2.5 * mad_gm_cbf))
R = np.mean(cbfts[:, :, :, index == 0], axis=3)
V = (
n_gm_voxels * np.var(R[gm == 1])
+ n_wm_voxels * np.var(R[wm == 1])
Expand All @@ -314,29 +314,29 @@ def _getcbfscore(cbfts, wm, gm, csf, mask, thresh=0.7):
V1 = V
CC = np.zeros(cbfts.shape[3]) * (-2)
for s in range(cbfts.shape[3]):
if indx[s] != 0:
if index[s] != 0:
break
else:
tmp1 = cbfts[:, :, :, s]
CC[s] = np.corrcoef(R[mask1 > 0], tmp1[mask1 > 0])[0][1]

inx = np.argmax(CC)
indx[inx] = 2
R = np.mean(cbfts[:, :, :, indx == 0], axis=3)
index[inx] = 2
R = np.mean(cbfts[:, :, :, index == 0], axis=3)
V = (
(n_gm_voxels * np.var(R[gm == 1]))
+ (n_wm_voxels * np.var(R[wm == 1]))
+ (n_csf_voxels * np.var(R[csf == 1]))
)

config.loggers.utils.warning(f'SCORE retains {np.sum(indx == 0)}/{indx.size} volumes')
cbfts_recon = cbfts[:, :, :, indx == 0]
config.loggers.utils.warning(f'SCORE retains {np.sum(index == 0)}/{index.size} volumes')
cbfts_recon = cbfts[:, :, :, index == 0]
cbfts_recon1 = np.zeros_like(cbfts_recon)
for i in range(cbfts_recon.shape[3]):
cbfts_recon1[:, :, :, i] = cbfts_recon[:, :, :, i] * mask

cbfts_recon1 = np.nan_to_num(cbfts_recon1)
return cbfts_recon1, indx
return cbfts_recon1, index


def _robust_fit(
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