ENH: Leverage new spun-off apply interface

mriqc/data/testdata/group_bold.tsv

@@ -1,10 +1,10 @@
 bids_name	aor	aqi	dummy_trs	dvars_nstd	dvars_std	dvars_vstd	efc	fber	fd_mean	fd_num	fd_perc	fwhm_avg	fwhm_x	fwhm_y	fwhm_z	gcor	gsr_x	gsr_y	size_t	size_x	size_y	size_z	snr	spacing_tr	spacing_x	spacing_y	spacing_z	summary_bg_k	summary_bg_mad	summary_bg_mean	summary_bg_median	summary_bg_n	summary_bg_p05	summary_bg_p95	summary_bg_stdv	summary_fg_k	summary_fg_mad	summary_fg_mean	summary_fg_median	summary_fg_n	summary_fg_p05	summary_fg_p95	summary_fg_stdv	tsnr
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-sub-ds205s07_task-view_run-02_bold	0.0027991304347826083	0.008415782065217392	0	27.396022419560428	1.345632817142857	1.2136871409890106	0.5333849667366366	1016.1077880859375	0.15451170465747024	18	19.565217391304348	2.271710833333333	2.1726275	2.433375	2.20913	0.0505144	-0.005830460228025913	0.03532366082072258	92	53	53	27	4.598664236595992	2.200000047683716	4.0	4.0	4.0	36.078683930448996	6.575017673595487	43.39332640278381	22.565217971801758	56589.0	1.22826087474823	155.56521606445312	75.49560044499137	1.6540993322199018	136.17376514793506	717.751513360383	719.2989501953125	19254.0	443.8260803222656	952.5978393554688	156.41069531029106	64.89047080953605
-sub-ds205s09_task-view_acq-LR_run-01_bold	0.003159041095890411	0.010028379999999998	0	20.396152721388898	1.090989740416667	1.0059027894444443	0.5048353890125871	1243.320556640625	0.3258302280827085	45	61.64383561643836	2.0852608333333333	2.0588275	2.2995125	1.8974425	0.0461463	-0.0019858605228364468	0.021903924643993378	73	53	53	27	5.292682271102816	2.200000047683716	4.0	4.0	4.0	33.98009379704732	5.949264906571304	39.07313696894552	22.956466674804688	58369.0	1.0936830043792725	132.37034606933594	62.99641049623399	2.7398312528413937	115.58686871143783	791.6591133225957	809.4622802734375	17474.0	483.6707458496094	994.6715698242188	152.93552051211557	66.51667785644531
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-sub-ds205s09_task-view_acq-RL_run-01_bold	0.015073150684931508	0.015836369041095893	0	34.205267455972226	1.4370176472222222	1.2820233763888884	0.5044353080190683	1274.420654296875	0.5699059645824405	53	72.6027397260274	2.08056	2.068795	2.288455	1.88443	0.0560156	-0.0013055995805189013	0.018262900412082672	73	53	53	27	5.229556365898414	2.200000047683716	4.0	4.0	4.0	31.57766555712935	5.840893620522815	38.89056185642749	22.814455032348633	58350.0	3.888171911239624	132.03933715820312	61.89358205002823	2.599912401497959	117.24294190485038	794.526159305144	814.4539184570312	17493.0	471.96441650390625	999.9562377929688	155.73608576273267	52.76657485961914
+sub-ds205s03_task-functionallocalizer_run-01_bold	0.006812560975609757	0.0115670556097561	0	25.1070652945679	1.097686276419753	1.0416085834567903	0.549	939.7262	0.26118389619646404	32	39.02439024390244	2.4148566666666667	2.3126025	2.6542	2.2777675	0.0530274	-0.0060755726881325245	0.05317099019885063	82	53	53	27	4.302882006355157	2.200000047683716	4.0	4.0	4.0	34.9329	6.8706	47.6781	25.0	55237.0	0.0	166.0	74.859	1.3937	141.0732	761.3085	766.0	20606.0	433.0	1043.0	178.0159	54.188138624303974
+sub-ds205s03_task-view_run-01_bold	0.005475217391304348	0.013897893369565218	0	21.735744036263725	1.010356766043956	0.9669979157142857	0.5465	989.9058	0.16520736608810047	18	19.565217391304348	2.3125358333333335	2.223185	2.54445	2.1699725	0.0183059	-0.002810143167153001	0.046066656708717346	92	53	53	27	4.270553474790369	2.200000047683716	4.0	4.0	4.0	32.4062	6.9134	44.6849	25.0	54949.0	0.0	151.0	65.9059	1.6759	136.8668	773.1685	781.0	20894.0	421.0	1055.0	182.8759	53.13771811965853
+sub-ds205s03_task-view_run-02_bold	0.0006215217391304349	0.010315522826086956	0	24.14205940978022	0.9597649761538458	0.8938210656043953	0.5485	943.6607	0.22854716046970905	44	47.82608695652174	2.3873583333333332	2.2945375	2.63535	2.2321875	0.328611	-0.00470705283805728	0.05208124592900276	92	53	53	27	4.292060341610993	2.200000047683716	4.0	4.0	4.0	33.8004	6.9296	46.8711	25.0	55158.0	0.0	163.0	72.0558	1.4929	140.0737	764.2329	769.0	20685.0	434.0	1045.0	179.1637	42.4139485983178
+sub-ds205s07_task-functionallocalizer_run-01_bold	0.0097325	0.008122680138888888	0	22.073046106619717	1.1242277719718308	1.0418761863380284	0.5321	1003.9077	0.1550487344317312	15	20.833333333333332	2.2189575	2.116755	2.370535	2.1695825	0.0295023	-0.003530273912474513	0.0362667478621006	72	53	53	27	4.409949445356891	2.200000047683716	4.0	4.0	4.0	40.4268	6.6305	40.5373	23.0	56224.0	0.0	143.0	67.4229	1.8008	135.9876	712.2398	716.0	19619.0	415.0	952.0	162.356	65.09344540000893
+sub-ds205s07_task-view_run-01_bold	0.005717500000000001	0.015141807826086954	0	21.867432625934065	0.9809244036263738	0.9361462984615384	0.5298	1015.919	0.1520993052375479	15	16.304347826086957	2.1996341666666663	2.110345	2.336455	2.1521025	0.028804	-0.0024572680704295635	0.03488033637404442	92	53	53	27	4.467323221209426	2.200000047683716	4.0	4.0	4.0	39.4612	5.8821	39.4347	22.0	56329.0	0.0	137.0	65.029	1.8043	135.4567	710.9964	715.0	19514.0	423.0	948.0	160.047	50.81706294429023
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mriqc/workflows/functional/base.py

@@ -535,12 +535,16 @@ def hmc(name='fMRI_HMC', omp_nthreads=None):
 
     # Apply transforms to other echos
     apply_hmc = pe.MapNode(
-        niu.Function(function=_apply_transforms, input_names=['in_file', 'in_xfm']),
+        niu.Function(
+            function=_apply_transforms,
+            input_names=['in_file', 'in_xfm', 'max_concurrent'],
+        ),
         name='apply_hmc',
         iterfield=['in_file'],
         # NiTransforms is a memory hog, so ensure only one process is running at a time
         n_procs=config.environment.cpu_count,
     )
+    apply_hmc.inputs.max_concurrent = 4
 
     # fmt: off
     workflow.connect([
@@ -764,14 +768,21 @@ def _parse_tout(in_file):
     return data.mean()
 
 
-def _apply_transforms(in_file, in_xfm):
+def _apply_transforms(in_file, in_xfm, max_concurrent):
     from pathlib import Path
 
     from nitransforms.linear import load
+    from nitransforms.resampling import apply
 
     from mriqc.utils.bids import derive_bids_fname
 
-    realigned = load(in_xfm, fmt='afni', reference=in_file, moving=in_file).apply(in_file)
+    realigned = apply(
+        load(in_xfm, fmt='afni', reference=in_file, moving=in_file),
+        in_file,
+        dtype_width=4,
+        serialize_nvols=2,
+        max_concurrent=max_concurrent,
+    )
     out_file = derive_bids_fname(
         in_file,
         entity='desc-realigned',

pyproject.toml

@@ -27,7 +27,7 @@ dependencies = [
   "nilearn >= 0.5.1",
   "nipype ~= 1.4",
   "nireports ~= 23.1",
-  "nitransforms ~= 23.0",
+  "nitransforms ~= 24.0",
   "niworkflows ~=1.10.1",
   "numpy ~=1.20",
   "pandas",