-
Notifications
You must be signed in to change notification settings - Fork 12
/
reference.html
1370 lines (1332 loc) · 144 KB
/
reference.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta property="og:title" content="8. Reference documentation: all nilearn functions" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://nilearn.github.io/modules/reference.html" />
<meta property="og:site_name" content="Nilearn" />
<meta property="og:description" content="This is the class and function reference of nilearn. Please refer to the user guide for more information and usage examples. List of modules: nilearn.connectome: Functional Connectivity, nilearn.da..." />
<meta property="og:image" content="https://nilearn.github.io/_static/nilearn-logo.png" />
<meta property="og:image:alt" content="Nilearn" />
<title>Nilearn: Statistical Analysis for NeuroImaging in Python — Machine learning for NeuroImaging</title>
<link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
<link rel="stylesheet" type="text/css" href="../_static/nature.css" />
<link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
<script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
<script src="../_static/jquery.js"></script>
<script src="../_static/underscore.js"></script>
<script src="../_static/doctools.js"></script>
<script src="../_static/clipboard.min.js"></script>
<script src="../_static/copybutton.js"></script>
<link rel="shortcut icon" href="../_static/favicon.ico"/>
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="8.1.1. nilearn.connectome.ConnectivityMeasure" href="generated/nilearn.connectome.ConnectivityMeasure.html" />
<link rel="prev" title="7.2. Downloading statistical maps from the Neurovault repository" href="../building_blocks/neurovault.html" />
<meta content="True" name="HandheldFriendly">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=0">
<meta name="keywords" content="nilearn, neuroimaging, python, neuroscience, machinelearning">
<script type="text/javascript">
function updateTopMenuPosition(height, width) {
if($(window).scrollTop() > height && $(window).outerWidth() > 1024) {
//begin to scroll
$('.related-wrapper').css("z-index", 1000);
$('.related-wrapper').css("position", "sticky");
$('.related-wrapper').css("top", 0);
$('.related-wrapper').css("width", width)
} else {
//lock it back into place
$('.related-wrapper').css("position", "relative");
$('.related-wrapper').css("top", 0)
}
}
$(function() {
var banner_height = $('#logo-banner').outerHeight();
var banner_width = $('#logo-banner').outerWidth();
var width = $('.related-wrapper').css("height", $('.related').outerHeight());
updateTopMenuPosition(banner_height, width);
$(window).scroll(function(event) {
updateTopMenuPosition(banner_height, width)
});
$(window).resize(function(event) {
var banner_width = $('#logo-banner').outerWidth();
var menu_height = $('.related').outerHeight();
$('.related').css("width", banner_width);
$('.related-wrapper').css("height", menu_height);
updateTopMenuPosition(banner_height, width)
})
});
</script>
<script type="text/javascript">
function updateSideBarPosition(top, offset, sections) {
var pos = $(window).scrollTop();
// Lock the table of content to a fixed position once we scroll enough
var topShift = 2 * offset;
if(pos > top + topShift + 1) {
// begin to scroll with sticky menu bar
var topShift = -topShift + 1;
if ($(window).outerWidth() < 1024) {
// compensate top menu that disappears
topShift -= offset + 1
}
$('.sphinxsidebarwrapper').css("position", "fixed");
$('.sphinxsidebarwrapper').css("top", topShift)
}
else {
//lock it back into place
$('.sphinxsidebarwrapper').css("position", "relative");
$('.sphinxsidebarwrapper').css("top",0)
}
// Highlight the current section
i = 0;
current_section = 0;
$('a.internal').removeClass('active');
for(i in sections) {
if(sections[i] > pos) {
break
}
if($('a.internal[href$="' + i + '"]').is(':visible')){
current_section = i
}
}
$('a.internal[href$="' + current_section + '"]').addClass('active');
$('a.internal[href$="' + current_section + '"]').parent().addClass('active')
}
$(function () {
// Lock the table of content to a fixed position once we scroll enough
var tocOffset = $('.related-wrapper').outerHeight();
var marginTop = parseFloat($('.sphinxsidebarwrapper').css('margin-top').replace(/auto/, 0));
var top = $('.sphinxsidebarwrapper').offset().top - marginTop;
sections = {};
url = document.URL.replace(/#.*$/, "");
// Grab positions of our sections
$('.headerlink').each(function(){
sections[this.href.replace(url, '')] = $(this).offset().top - 50
});
updateSideBarPosition(top, tocOffset, sections);
$(window).scroll(function(event) {
updateSideBarPosition(top, tocOffset, sections)
});
$(window).resize(function(event) {
tocOffset = $('.related-wrapper').outerHeight();
updateSideBarPosition(top, tocOffset, sections)
});
});
</script>
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-41920728-1']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
</head><body>
<div id="logo-banner">
<div class="logo">
<a href="../index.html">
<img src="../_static/nilearn-logo.png" alt="Nilearn logo" border="0" />
</a>
</div>
<!-- A tag cloud to make it easy for people to find what they are
looking for -->
<div class="tags">
<ul>
<li>
<big><a href="../auto_examples/decoding/plot_haxby_anova_svm.html">SVM</a></big>
</li>
<li>
<small><a href="../connectivity/parcellating.html">Ward
clustering</a></small>
</li>
<li>
<a href="../decoding/searchlight.html">Searchlight</a>
</li>
<li>
<big><a href="../connectivity/resting_state_networks.html">ICA</a></big>
</li>
<li>
<a href="../manipulating_images/data_preparation.html">Nifti IO</a>
</li>
<li>
<a href="##module-nilearn.datasets">Datasets</a>
</li>
</ul>
</div>
<div class="banner">
<h1>Nilearn:</h1>
<h2>Statistics for NeuroImaging in Python</h2>
</div>
<div class="search_form">
<div class="gcse-search" id="cse" style="width: 100%;"></div>
<script>
(function() {
var cx = '017289614950330089114:elrt9qoutrq';
var gcse = document.createElement('script');
gcse.type = 'text/javascript';
gcse.async = true;
gcse.src = 'https://cse.google.com/cse.js?cx=' + cx;
var s = document.getElementsByTagName('script')[0];
s.parentNode.insertBefore(gcse, s);
})();
</script>
</div>
</div>
<div class=related-wrapper>
<div class="related" role="navigation" aria-label="related navigation">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="../py-modindex.html" title="Python Module Index"
>modules</a></li>
<li class="right" >
<a href="generated/nilearn.connectome.ConnectivityMeasure.html" title="8.1.1. nilearn.connectome.ConnectivityMeasure"
accesskey="N">next</a> |</li>
<li class="right" >
<a href="../building_blocks/neurovault.html" title="7.2. Downloading statistical maps from the Neurovault repository"
accesskey="P">previous</a> |</li>
<li><a href="../index.html">Nilearn Home</a> | </li>
<li><a href="../user_guide.html">User Guide</a> | </li>
<li><a href="../auto_examples/index.html">Examples</a> | </li>
<li><a href="#">Reference</a> | </li>
<li id="navbar-about"><a href="../authors.html">About</a>| </li>
<li><a href="../glossary.html">Glossary</a>| </li>
<li><a href="../bibliography.html">Bibliography</a>| </li>
<li id="navbar-ecosystem"><a href="http://www.nipy.org/">Nipy ecosystem</a></li>
<li class="nav-item nav-item-1"><a href="../user_guide.html" accesskey="U">User guide: table of contents</a> »</li>
<li class="nav-item nav-item-this"><a href="">Nilearn: Statistical Analysis for NeuroImaging in Python</a></li>
</ul>
</div>
</div>
<div class="stable-banner">
This is the <em>stable</em> documentation for the latest release of Nilearn,
the current development version is available <a href="https://nilearn.github.io/dev/index.html">here</a>.
</div>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="section" id="reference-documentation-all-nilearn-functions">
<h1><span class="section-number">8. </span>Reference documentation: all nilearn functions<a class="headerlink" href="#reference-documentation-all-nilearn-functions" title="Permalink to this headline">¶</a></h1>
<p>This is the class and function reference of nilearn. Please refer to
the <a class="reference internal" href="../user_guide.html#user-guide"><span class="std std-ref">user guide</span></a> for more information and usage examples.</p>
<div class="contents local topic" id="list-of-modules">
<p class="topic-title"><strong>List of modules</strong></p>
<ul class="simple">
<li><p><a class="reference internal" href="#module-nilearn.connectome" id="id4"><a class="reference internal" href="#module-nilearn.connectome" title="nilearn.connectome"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.connectome</span></code></a>: Functional Connectivity</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.datasets" id="id5"><a class="reference internal" href="#module-nilearn.datasets" title="nilearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.datasets</span></code></a>: Automatic Dataset Fetching</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.decoding" id="id6"><a class="reference internal" href="#module-nilearn.decoding" title="nilearn.decoding"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.decoding</span></code></a>: Decoding</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.decomposition" id="id7"><a class="reference internal" href="#module-nilearn.decomposition" title="nilearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.decomposition</span></code></a>: Multivariate Decompositions</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.image" id="id8"><a class="reference internal" href="#module-nilearn.image" title="nilearn.image"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.image</span></code></a>: Image Processing and Resampling Utilities</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.interfaces" id="id9"><a class="reference internal" href="#module-nilearn.interfaces" title="nilearn.interfaces"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces</span></code></a>: Loading components from interfaces</a></p>
<ul>
<li><p><a class="reference internal" href="#module-nilearn.interfaces.bids" id="id10"><a class="reference internal" href="#module-nilearn.interfaces.bids" title="nilearn.interfaces.bids"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces.bids</span></code></a></a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.interfaces.fmriprep" id="id11"><a class="reference internal" href="#module-nilearn.interfaces.fmriprep" title="nilearn.interfaces.fmriprep"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces.fmriprep</span></code></a></a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.interfaces.fsl" id="id12"><a class="reference internal" href="#module-nilearn.interfaces.fsl" title="nilearn.interfaces.fsl"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces.fsl</span></code></a></a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#module-nilearn.maskers" id="id13"><a class="reference internal" href="#module-nilearn.maskers" title="nilearn.maskers"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.maskers</span></code></a>: Extracting Signals from Brain Images</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.masking" id="id14"><a class="reference internal" href="#module-nilearn.masking" title="nilearn.masking"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.masking</span></code></a>: Data Masking Utilities</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.regions" id="id15"><a class="reference internal" href="#module-nilearn.regions" title="nilearn.regions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.regions</span></code></a>: Operating on Regions</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.mass_univariate" id="id16"><a class="reference internal" href="#module-nilearn.mass_univariate" title="nilearn.mass_univariate"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.mass_univariate</span></code></a>: Mass-Univariate Analysis</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.plotting" id="id17"><a class="reference internal" href="#module-nilearn.plotting" title="nilearn.plotting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.plotting</span></code></a>: Plotting Brain Data</a></p>
<ul>
<li><p><a class="reference internal" href="#module-nilearn.plotting.displays" id="id18"><a class="reference internal" href="#module-nilearn.plotting.displays" title="nilearn.plotting.displays"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.plotting.displays</span></code></a>: Interacting with figures</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#module-nilearn.signal" id="id19"><a class="reference internal" href="#module-nilearn.signal" title="nilearn.signal"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.signal</span></code></a>: Preprocessing Time Series</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.glm" id="id20"><a class="reference internal" href="#module-nilearn.glm" title="nilearn.glm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.glm</span></code></a>: Generalized Linear Models</a></p>
<ul>
<li><p><a class="reference internal" href="#module-nilearn.glm.first_level" id="id21"><a class="reference internal" href="#module-nilearn.glm.first_level" title="nilearn.glm.first_level"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.glm.first_level</span></code></a></a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.glm.second_level" id="id22"><a class="reference internal" href="#module-nilearn.glm.second_level" title="nilearn.glm.second_level"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.glm.second_level</span></code></a></a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#module-nilearn.reporting" id="id23"><a class="reference internal" href="#module-nilearn.reporting" title="nilearn.reporting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.reporting</span></code></a>: Reporting Functions</a></p></li>
<li><p><a class="reference internal" href="#module-nilearn.surface" id="id24"><a class="reference internal" href="#module-nilearn.surface" title="nilearn.surface"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.surface</span></code></a>: Manipulating Surface Data</a></p></li>
</ul>
</div>
<div class="section" id="module-nilearn.connectome">
<span id="nilearn-connectome-functional-connectivity"></span><span id="connectome-ref"></span><h2><a class="toc-backref" href="#id4"><span class="section-number">8.1. </span><a class="reference internal" href="#module-nilearn.connectome" title="nilearn.connectome"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.connectome</span></code></a>: Functional Connectivity</a><a class="headerlink" href="#module-nilearn.connectome" title="Permalink to this headline">¶</a></h2>
<p>Tools for computing functional connectivity matrices and also
implementation of algorithm for sparse multi subjects learning
of Gaussian graphical models.</p>
<p><strong>Classes</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.connectome.ConnectivityMeasure.html#nilearn.connectome.ConnectivityMeasure" title="nilearn.connectome.ConnectivityMeasure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConnectivityMeasure</span></code></a>([cov_estimator, kind, …])</p></td>
<td><p>A class that computes different kinds of functional connectivity matrices.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.connectome.GroupSparseCovariance.html#nilearn.connectome.GroupSparseCovariance" title="nilearn.connectome.GroupSparseCovariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GroupSparseCovariance</span></code></a>([alpha, tol, …])</p></td>
<td><p>Covariance and precision matrix estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.connectome.GroupSparseCovarianceCV.html#nilearn.connectome.GroupSparseCovarianceCV" title="nilearn.connectome.GroupSparseCovarianceCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GroupSparseCovarianceCV</span></code></a>([alphas, …])</p></td>
<td><p>Sparse inverse covariance w/ cross-validated choice of the parameter.</p></td>
</tr>
</tbody>
</table>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.connectome.sym_matrix_to_vec.html#nilearn.connectome.sym_matrix_to_vec" title="nilearn.connectome.sym_matrix_to_vec"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sym_matrix_to_vec</span></code></a>(symmetric[, discard_diagonal])</p></td>
<td><p>Return the flattened lower triangular part of an array.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.connectome.vec_to_sym_matrix.html#nilearn.connectome.vec_to_sym_matrix" title="nilearn.connectome.vec_to_sym_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vec_to_sym_matrix</span></code></a>(vec[, diagonal])</p></td>
<td><p>Return the symmetric matrix given its flattened lower triangular part.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.connectome.group_sparse_covariance.html#nilearn.connectome.group_sparse_covariance" title="nilearn.connectome.group_sparse_covariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">group_sparse_covariance</span></code></a>(subjects, alpha[, …])</p></td>
<td><p>Compute sparse precision matrices and covariance matrices.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.connectome.cov_to_corr.html#nilearn.connectome.cov_to_corr" title="nilearn.connectome.cov_to_corr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cov_to_corr</span></code></a>(covariance)</p></td>
<td><p>Return correlation matrix for a given covariance matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.connectome.prec_to_partial.html#nilearn.connectome.prec_to_partial" title="nilearn.connectome.prec_to_partial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prec_to_partial</span></code></a>(precision)</p></td>
<td><p>Return partial correlation matrix for a given precision matrix.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.datasets">
<span id="nilearn-datasets-automatic-dataset-fetching"></span><span id="datasets-ref"></span><h2><a class="toc-backref" href="#id5"><span class="section-number">8.2. </span><a class="reference internal" href="#module-nilearn.datasets" title="nilearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.datasets</span></code></a>: Automatic Dataset Fetching</a><a class="headerlink" href="#module-nilearn.datasets" title="Permalink to this headline">¶</a></h2>
<p>Helper functions to download NeuroImaging datasets</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="../manipulating_images/input_output.html#datasets"><span class="std std-ref">Fetching open datasets from Internet</span></a> section for further details.</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_craddock_2012.html#nilearn.datasets.fetch_atlas_craddock_2012" title="nilearn.datasets.fetch_atlas_craddock_2012"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_craddock_2012</span></code></a>([data_dir, url, …])</p></td>
<td><p>Download and return file names for the Craddock 2012 parcellation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_destrieux_2009.html#nilearn.datasets.fetch_atlas_destrieux_2009" title="nilearn.datasets.fetch_atlas_destrieux_2009"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_destrieux_2009</span></code></a>([lateralized, …])</p></td>
<td><p>Download and load the Destrieux cortical atlas (dated 2009).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_harvard_oxford.html#nilearn.datasets.fetch_atlas_harvard_oxford" title="nilearn.datasets.fetch_atlas_harvard_oxford"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_harvard_oxford</span></code></a>(atlas_name[, …])</p></td>
<td><p>Load Harvard-Oxford parcellations from FSL.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_juelich.html#nilearn.datasets.fetch_atlas_juelich" title="nilearn.datasets.fetch_atlas_juelich"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_juelich</span></code></a>(atlas_name[, data_dir, …])</p></td>
<td><p>Load Juelich parcellations from FSL.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_msdl.html#nilearn.datasets.fetch_atlas_msdl" title="nilearn.datasets.fetch_atlas_msdl"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_msdl</span></code></a>([data_dir, url, resume, …])</p></td>
<td><p>Download and load the MSDL brain atlas.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_difumo.html#nilearn.datasets.fetch_atlas_difumo" title="nilearn.datasets.fetch_atlas_difumo"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_difumo</span></code></a>([dimension, …])</p></td>
<td><p>Fetch DiFuMo brain atlas</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_coords_power_2011.html#nilearn.datasets.fetch_coords_power_2011" title="nilearn.datasets.fetch_coords_power_2011"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_coords_power_2011</span></code></a>([legacy_format])</p></td>
<td><p>Download and load the Power et al. brain atlas composed of 264 ROIs.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_coords_seitzman_2018.html#nilearn.datasets.fetch_coords_seitzman_2018" title="nilearn.datasets.fetch_coords_seitzman_2018"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_coords_seitzman_2018</span></code></a>([…])</p></td>
<td><p>Load the Seitzman et al. 300 ROIs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_smith_2009.html#nilearn.datasets.fetch_atlas_smith_2009" title="nilearn.datasets.fetch_atlas_smith_2009"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_smith_2009</span></code></a>([data_dir, mirror, …])</p></td>
<td><p>Download and load the Smith <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> and BrainMap atlas (2009).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_yeo_2011.html#nilearn.datasets.fetch_atlas_yeo_2011" title="nilearn.datasets.fetch_atlas_yeo_2011"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_yeo_2011</span></code></a>([data_dir, url, …])</p></td>
<td><p>Download and return file names for the Yeo 2011 parcellation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_aal.html#nilearn.datasets.fetch_atlas_aal" title="nilearn.datasets.fetch_atlas_aal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_aal</span></code></a>([version, data_dir, url, …])</p></td>
<td><p>Downloads and returns the AAL template for SPM 12.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_basc_multiscale_2015.html#nilearn.datasets.fetch_atlas_basc_multiscale_2015" title="nilearn.datasets.fetch_atlas_basc_multiscale_2015"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_basc_multiscale_2015</span></code></a>([version, …])</p></td>
<td><p>Downloads and loads multiscale functional brain parcellations.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_allen_2011.html#nilearn.datasets.fetch_atlas_allen_2011" title="nilearn.datasets.fetch_atlas_allen_2011"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_allen_2011</span></code></a>([data_dir, url, …])</p></td>
<td><p>Download and return file names for the Allen and MIALAB <a class="reference internal" href="../glossary.html#term-ICA"><span class="xref std std-term">ICA</span></a> atlas (dated 2011).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_pauli_2017.html#nilearn.datasets.fetch_atlas_pauli_2017" title="nilearn.datasets.fetch_atlas_pauli_2017"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_pauli_2017</span></code></a>([version, data_dir, …])</p></td>
<td><p>Download the Pauli et al. (2017) atlas.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_coords_dosenbach_2010.html#nilearn.datasets.fetch_coords_dosenbach_2010" title="nilearn.datasets.fetch_coords_dosenbach_2010"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_coords_dosenbach_2010</span></code></a>([…])</p></td>
<td><p>Load the Dosenbach et al. 160 ROIs.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_abide_pcp.html#nilearn.datasets.fetch_abide_pcp" title="nilearn.datasets.fetch_abide_pcp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_abide_pcp</span></code></a>([data_dir, n_subjects, …])</p></td>
<td><p>Fetch ABIDE dataset.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_adhd.html#nilearn.datasets.fetch_adhd" title="nilearn.datasets.fetch_adhd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_adhd</span></code></a>([n_subjects, data_dir, url, …])</p></td>
<td><p>Download and load the ADHD resting-state dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_development_fmri.html#nilearn.datasets.fetch_development_fmri" title="nilearn.datasets.fetch_development_fmri"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_development_fmri</span></code></a>([n_subjects, …])</p></td>
<td><p>Fetch movie watching based brain development dataset (fMRI)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_haxby.html#nilearn.datasets.fetch_haxby" title="nilearn.datasets.fetch_haxby"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_haxby</span></code></a>([data_dir, subjects, …])</p></td>
<td><p>Download and loads complete haxby dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_icbm152_2009.html#nilearn.datasets.fetch_icbm152_2009" title="nilearn.datasets.fetch_icbm152_2009"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_icbm152_2009</span></code></a>([data_dir, url, resume, …])</p></td>
<td><p>Download and load the ICBM152 template (dated 2009).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_icbm152_brain_gm_mask.html#nilearn.datasets.fetch_icbm152_brain_gm_mask" title="nilearn.datasets.fetch_icbm152_brain_gm_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_icbm152_brain_gm_mask</span></code></a>([data_dir, …])</p></td>
<td><p>Downloads ICBM152 template first, then loads the ‘gm’ mask.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_localizer_button_task.html#nilearn.datasets.fetch_localizer_button_task" title="nilearn.datasets.fetch_localizer_button_task"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_localizer_button_task</span></code></a>([data_dir, url, …])</p></td>
<td><p>Fetch left vs right button press contrast maps from the localizer.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_localizer_contrasts.html#nilearn.datasets.fetch_localizer_contrasts" title="nilearn.datasets.fetch_localizer_contrasts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_localizer_contrasts</span></code></a>(contrasts[, …])</p></td>
<td><p>Download and load Brainomics/Localizer dataset (94 subjects).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_localizer_calculation_task.html#nilearn.datasets.fetch_localizer_calculation_task" title="nilearn.datasets.fetch_localizer_calculation_task"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_localizer_calculation_task</span></code></a>([…])</p></td>
<td><p>Fetch calculation task contrast maps from the localizer.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_miyawaki2008.html#nilearn.datasets.fetch_miyawaki2008" title="nilearn.datasets.fetch_miyawaki2008"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_miyawaki2008</span></code></a>([data_dir, url, resume, …])</p></td>
<td><p>Download and loads Miyawaki et al. 2008 dataset (153MB).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_surf_nki_enhanced.html#nilearn.datasets.fetch_surf_nki_enhanced" title="nilearn.datasets.fetch_surf_nki_enhanced"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_surf_nki_enhanced</span></code></a>([n_subjects, …])</p></td>
<td><p>Download and load the NKI enhanced resting-state dataset, preprocessed and projected to the fsaverage5 space surface.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_surf_fsaverage.html#nilearn.datasets.fetch_surf_fsaverage" title="nilearn.datasets.fetch_surf_fsaverage"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_surf_fsaverage</span></code></a>([mesh, data_dir])</p></td>
<td><p>Download a Freesurfer fsaverage surface.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_surf_destrieux.html#nilearn.datasets.fetch_atlas_surf_destrieux" title="nilearn.datasets.fetch_atlas_surf_destrieux"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_surf_destrieux</span></code></a>([data_dir, url, …])</p></td>
<td><p>Download and load Destrieux et al, 2010 cortical atlas.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_talairach.html#nilearn.datasets.fetch_atlas_talairach" title="nilearn.datasets.fetch_atlas_talairach"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_talairach</span></code></a>(level_name[, …])</p></td>
<td><p>Download the Talairach atlas.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_atlas_schaefer_2018.html#nilearn.datasets.fetch_atlas_schaefer_2018" title="nilearn.datasets.fetch_atlas_schaefer_2018"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_atlas_schaefer_2018</span></code></a>([n_rois, …])</p></td>
<td><p>Download and return file names for the Schaefer 2018 parcellation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_oasis_vbm.html#nilearn.datasets.fetch_oasis_vbm" title="nilearn.datasets.fetch_oasis_vbm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_oasis_vbm</span></code></a>([n_subjects, …])</p></td>
<td><p>Download and load Oasis “cross-sectional MRI” dataset (416 subjects).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_megatrawls_netmats.html#nilearn.datasets.fetch_megatrawls_netmats" title="nilearn.datasets.fetch_megatrawls_netmats"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_megatrawls_netmats</span></code></a>([dimensionality, …])</p></td>
<td><p>Downloads and returns Network Matrices data from MegaTrawls release in HCP.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_neurovault.html#nilearn.datasets.fetch_neurovault" title="nilearn.datasets.fetch_neurovault"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_neurovault</span></code></a>([max_images, …])</p></td>
<td><p>Download data from neurovault.org that match certain criteria.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_neurovault_ids.html#nilearn.datasets.fetch_neurovault_ids" title="nilearn.datasets.fetch_neurovault_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_neurovault_ids</span></code></a>([collection_ids, …])</p></td>
<td><p>Download specific images and collections from neurovault.org.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_neurovault_auditory_computation_task.html#nilearn.datasets.fetch_neurovault_auditory_computation_task" title="nilearn.datasets.fetch_neurovault_auditory_computation_task"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_neurovault_auditory_computation_task</span></code></a>([…])</p></td>
<td><p>Fetch a contrast map from NeuroVault showing the effect of mental subtraction upon auditory instructions</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_neurovault_motor_task.html#nilearn.datasets.fetch_neurovault_motor_task" title="nilearn.datasets.fetch_neurovault_motor_task"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_neurovault_motor_task</span></code></a>([data_dir, verbose])</p></td>
<td><p>Fetch left vs right button press group contrast map from NeuroVault.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.get_data_dirs.html#nilearn.datasets.get_data_dirs" title="nilearn.datasets.get_data_dirs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_data_dirs</span></code></a>([data_dir])</p></td>
<td><p>Returns the directories in which nilearn looks for data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.load_mni152_template.html#nilearn.datasets.load_mni152_template" title="nilearn.datasets.load_mni152_template"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_mni152_template</span></code></a>([resolution])</p></td>
<td><p>Load the MNI152 skullstripped T1 template.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.load_mni152_gm_template.html#nilearn.datasets.load_mni152_gm_template" title="nilearn.datasets.load_mni152_gm_template"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_mni152_gm_template</span></code></a>([resolution])</p></td>
<td><p>Load the MNI152 grey-matter template.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.load_mni152_wm_template.html#nilearn.datasets.load_mni152_wm_template" title="nilearn.datasets.load_mni152_wm_template"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_mni152_wm_template</span></code></a>([resolution])</p></td>
<td><p>Load the MNI152 white-matter template.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.load_mni152_brain_mask.html#nilearn.datasets.load_mni152_brain_mask" title="nilearn.datasets.load_mni152_brain_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_mni152_brain_mask</span></code></a>([resolution, threshold])</p></td>
<td><p>Load the MNI152 whole-brain mask.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.load_mni152_gm_mask.html#nilearn.datasets.load_mni152_gm_mask" title="nilearn.datasets.load_mni152_gm_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_mni152_gm_mask</span></code></a>([resolution, threshold, …])</p></td>
<td><p>Load the MNI152 grey-matter mask.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.load_mni152_wm_mask.html#nilearn.datasets.load_mni152_wm_mask" title="nilearn.datasets.load_mni152_wm_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_mni152_wm_mask</span></code></a>([resolution, threshold, …])</p></td>
<td><p>Load the MNI152 white-matter mask.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_language_localizer_demo_dataset.html#nilearn.datasets.fetch_language_localizer_demo_dataset" title="nilearn.datasets.fetch_language_localizer_demo_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_language_localizer_demo_dataset</span></code></a>([…])</p></td>
<td><p>Download language localizer demo dataset.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_bids_langloc_dataset.html#nilearn.datasets.fetch_bids_langloc_dataset" title="nilearn.datasets.fetch_bids_langloc_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_bids_langloc_dataset</span></code></a>([data_dir, verbose])</p></td>
<td><p>Download language localizer example <a class="reference internal" href="../glossary.html#term-BIDS"><span class="xref std std-term">bids</span></a> dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_openneuro_dataset_index.html#nilearn.datasets.fetch_openneuro_dataset_index" title="nilearn.datasets.fetch_openneuro_dataset_index"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_openneuro_dataset_index</span></code></a>([data_dir, …])</p></td>
<td><p>Download a file with OpenNeuro <a class="reference internal" href="../glossary.html#term-BIDS"><span class="xref std std-term">BIDS</span></a> dataset index.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.select_from_index.html#nilearn.datasets.select_from_index" title="nilearn.datasets.select_from_index"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_from_index</span></code></a>(urls[, inclusion_filters, …])</p></td>
<td><p>Select subset of urls with given filters.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.patch_openneuro_dataset.html#nilearn.datasets.patch_openneuro_dataset" title="nilearn.datasets.patch_openneuro_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">patch_openneuro_dataset</span></code></a>(file_list)</p></td>
<td><p>Add symlinks for files not named according to latest <a class="reference internal" href="../glossary.html#term-BIDS"><span class="xref std std-term">BIDS</span></a> conventions.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_openneuro_dataset.html#nilearn.datasets.fetch_openneuro_dataset" title="nilearn.datasets.fetch_openneuro_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_openneuro_dataset</span></code></a>([urls, data_dir, …])</p></td>
<td><p>Download OpenNeuro <a class="reference internal" href="../glossary.html#term-BIDS"><span class="xref std std-term">BIDS</span></a> dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_localizer_first_level.html#nilearn.datasets.fetch_localizer_first_level" title="nilearn.datasets.fetch_localizer_first_level"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_localizer_first_level</span></code></a>([data_dir, verbose])</p></td>
<td><p>Download a first-level localizer fMRI dataset</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_spm_auditory.html#nilearn.datasets.fetch_spm_auditory" title="nilearn.datasets.fetch_spm_auditory"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_spm_auditory</span></code></a>([data_dir, data_name, …])</p></td>
<td><p>Function to fetch SPM auditory single-subject data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_spm_multimodal_fmri.html#nilearn.datasets.fetch_spm_multimodal_fmri" title="nilearn.datasets.fetch_spm_multimodal_fmri"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_spm_multimodal_fmri</span></code></a>([data_dir, …])</p></td>
<td><p>Fetcher for Multi-modal Face Dataset.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.datasets.fetch_fiac_first_level.html#nilearn.datasets.fetch_fiac_first_level" title="nilearn.datasets.fetch_fiac_first_level"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_fiac_first_level</span></code></a>([data_dir, verbose])</p></td>
<td><p>Download a first-level fiac fMRI dataset (2 sessions)</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.decoding">
<span id="nilearn-decoding-decoding"></span><span id="decoding-ref"></span><h2><a class="toc-backref" href="#id6"><span class="section-number">8.3. </span><a class="reference internal" href="#module-nilearn.decoding" title="nilearn.decoding"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.decoding</span></code></a>: Decoding</a><a class="headerlink" href="#module-nilearn.decoding" title="Permalink to this headline">¶</a></h2>
<p>Decoding tools and algorithms.</p>
<p><strong>Classes</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.decoding.Decoder.html#nilearn.decoding.Decoder" title="nilearn.decoding.Decoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Decoder</span></code></a>([estimator, mask, cv, param_grid, …])</p></td>
<td><p>A wrapper for popular classification strategies in neuroimaging.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.decoding.DecoderRegressor.html#nilearn.decoding.DecoderRegressor" title="nilearn.decoding.DecoderRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DecoderRegressor</span></code></a>([estimator, mask, cv, …])</p></td>
<td><p>A wrapper for popular regression strategies in neuroimaging.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.decoding.FREMClassifier.html#nilearn.decoding.FREMClassifier" title="nilearn.decoding.FREMClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FREMClassifier</span></code></a>([estimator, mask, cv, …])</p></td>
<td><p>State of the art decoding scheme applied to usual classifiers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.decoding.FREMRegressor.html#nilearn.decoding.FREMRegressor" title="nilearn.decoding.FREMRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FREMRegressor</span></code></a>([estimator, mask, cv, …])</p></td>
<td><p>State of the art decoding scheme applied to usual regression estimators.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.decoding.SpaceNetClassifier.html#nilearn.decoding.SpaceNetClassifier" title="nilearn.decoding.SpaceNetClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SpaceNetClassifier</span></code></a>([penalty, loss, …])</p></td>
<td><p>Classification learners with sparsity and spatial priors.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.decoding.SpaceNetRegressor.html#nilearn.decoding.SpaceNetRegressor" title="nilearn.decoding.SpaceNetRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SpaceNetRegressor</span></code></a>([penalty, l1_ratios, …])</p></td>
<td><p>Regression learners with sparsity and spatial priors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.decoding.SearchLight.html#nilearn.decoding.SearchLight" title="nilearn.decoding.SearchLight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SearchLight</span></code></a>(mask_img[, process_mask_img, …])</p></td>
<td><p>Implement search_light analysis using an arbitrary type of classifier.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.decomposition">
<span id="nilearn-decomposition-multivariate-decompositions"></span><span id="decomposition-ref"></span><h2><a class="toc-backref" href="#id7"><span class="section-number">8.4. </span><a class="reference internal" href="#module-nilearn.decomposition" title="nilearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.decomposition</span></code></a>: Multivariate Decompositions</a><a class="headerlink" href="#module-nilearn.decomposition" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-nilearn.decomposition" title="nilearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.decomposition</span></code></a> module includes a subject level
variant of the ICA called Canonical ICA.</p>
<p><strong>Classes</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.decomposition.CanICA.html#nilearn.decomposition.CanICA" title="nilearn.decomposition.CanICA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CanICA</span></code></a>([mask, n_components, smoothing_fwhm, …])</p></td>
<td><p>Perform Canonical Independent Component Analysis <a class="reference internal" href="generated/nilearn.decomposition.CanICA.html#r637c2563345c-1" id="id1"><span>[R637c2563345c-1]</span></a> <a class="reference internal" href="generated/nilearn.decomposition.CanICA.html#r637c2563345c-2" id="id2"><span>[R637c2563345c-2]</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.decomposition.DictLearning.html#nilearn.decomposition.DictLearning" title="nilearn.decomposition.DictLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DictLearning</span></code></a>([n_components, n_epochs, …])</p></td>
<td><p>Perform a map learning algorithm based on spatial component sparsity, over a <a class="reference internal" href="../glossary.html#term-CanICA"><span class="xref std std-term">CanICA</span></a> initialization <a class="reference internal" href="generated/nilearn.decomposition.DictLearning.html#rd0eec3116114-1" id="id3"><span>[Rd0eec3116114-1]</span></a>.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.image">
<span id="nilearn-image-image-processing-and-resampling-utilities"></span><span id="image-ref"></span><h2><a class="toc-backref" href="#id8"><span class="section-number">8.5. </span><a class="reference internal" href="#module-nilearn.image" title="nilearn.image"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.image</span></code></a>: Image Processing and Resampling Utilities</a><a class="headerlink" href="#module-nilearn.image" title="Permalink to this headline">¶</a></h2>
<p>Mathematical operations working on Niimg-like objects like a (3+)D block of
data, and an affine.</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.binarize_img.html#nilearn.image.binarize_img" title="nilearn.image.binarize_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binarize_img</span></code></a>(img[, threshold, mask_img])</p></td>
<td><p>Binarize an image such that its values are either 0 or 1.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.clean_img.html#nilearn.image.clean_img" title="nilearn.image.clean_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clean_img</span></code></a>(imgs[, runs, detrend, …])</p></td>
<td><p>Improve SNR on masked fMRI signals.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.concat_imgs.html#nilearn.image.concat_imgs" title="nilearn.image.concat_imgs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">concat_imgs</span></code></a>(niimgs[, dtype, ensure_ndim, …])</p></td>
<td><p>Concatenate a list of 3D/4D niimgs of varying lengths.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.coord_transform.html#nilearn.image.coord_transform" title="nilearn.image.coord_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coord_transform</span></code></a>(x, y, z, affine)</p></td>
<td><p>Convert the x, y, z coordinates from one image space to another</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.copy_img.html#nilearn.image.copy_img" title="nilearn.image.copy_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy_img</span></code></a>(img)</p></td>
<td><p>Copy an image to a nibabel.Nifti1Image.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.crop_img.html#nilearn.image.crop_img" title="nilearn.image.crop_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">crop_img</span></code></a>(img[, rtol, copy, pad, return_offset])</p></td>
<td><p>Crops an image as much as possible.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.get_data.html#nilearn.image.get_data" title="nilearn.image.get_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_data</span></code></a>(img)</p></td>
<td><p>Get the image data as a <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.22)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.high_variance_confounds.html#nilearn.image.high_variance_confounds" title="nilearn.image.high_variance_confounds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">high_variance_confounds</span></code></a>(imgs[, n_confounds, …])</p></td>
<td><p>Return confounds signals extracted from input signals with highest variance.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.index_img.html#nilearn.image.index_img" title="nilearn.image.index_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">index_img</span></code></a>(imgs, index)</p></td>
<td><p>Indexes into a 4D Niimg-like object in the fourth dimension.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.iter_img.html#nilearn.image.iter_img" title="nilearn.image.iter_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">iter_img</span></code></a>(imgs)</p></td>
<td><p>Iterates over a 4D Niimg-like object in the fourth dimension.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.largest_connected_component_img.html#nilearn.image.largest_connected_component_img" title="nilearn.image.largest_connected_component_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">largest_connected_component_img</span></code></a>(imgs)</p></td>
<td><p>Return the largest connected component of an image or list of images.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.load_img.html#nilearn.image.load_img" title="nilearn.image.load_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_img</span></code></a>(img[, wildcards, dtype])</p></td>
<td><p>Load a Niimg-like object from filenames or list of filenames.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.math_img.html#nilearn.image.math_img" title="nilearn.image.math_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">math_img</span></code></a>(formula, **imgs)</p></td>
<td><p>Interpret a numpy based string formula using niimg in named parameters.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.mean_img.html#nilearn.image.mean_img" title="nilearn.image.mean_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_img</span></code></a>(imgs[, target_affine, …])</p></td>
<td><p>Compute the mean of the images over time or the 4th dimension.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.new_img_like.html#nilearn.image.new_img_like" title="nilearn.image.new_img_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">new_img_like</span></code></a>(ref_niimg, data[, affine, …])</p></td>
<td><p>Create a new image of the same class as the reference image</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.resample_img.html#nilearn.image.resample_img" title="nilearn.image.resample_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resample_img</span></code></a>(img[, target_affine, …])</p></td>
<td><p>Resample a Niimg-like object</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.resample_to_img.html#nilearn.image.resample_to_img" title="nilearn.image.resample_to_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resample_to_img</span></code></a>(source_img, target_img[, …])</p></td>
<td><p>Resample a Niimg-like source image on a target Niimg-like image (no registration is performed: the image should already be aligned).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.reorder_img.html#nilearn.image.reorder_img" title="nilearn.image.reorder_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reorder_img</span></code></a>(img[, resample])</p></td>
<td><p>Returns an image with the affine diagonal (by permuting axes).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.smooth_img.html#nilearn.image.smooth_img" title="nilearn.image.smooth_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">smooth_img</span></code></a>(imgs, fwhm)</p></td>
<td><p>Smooth images by applying a Gaussian filter.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.image.swap_img_hemispheres.html#nilearn.image.swap_img_hemispheres" title="nilearn.image.swap_img_hemispheres"><code class="xref py py-obj docutils literal notranslate"><span class="pre">swap_img_hemispheres</span></code></a>(img)</p></td>
<td><p>Performs swapping of hemispheres in the indicated NIfTI image.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.image.threshold_img.html#nilearn.image.threshold_img" title="nilearn.image.threshold_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">threshold_img</span></code></a>(img, threshold[, …])</p></td>
<td><p>Threshold the given input image, mostly statistical or atlas images.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.interfaces">
<span id="nilearn-interfaces-loading-components-from-interfaces"></span><span id="interfaces-ref"></span><h2><a class="toc-backref" href="#id9"><span class="section-number">8.6. </span><a class="reference internal" href="#module-nilearn.interfaces" title="nilearn.interfaces"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces</span></code></a>: Loading components from interfaces</a><a class="headerlink" href="#module-nilearn.interfaces" title="Permalink to this headline">¶</a></h2>
<p>Interfaces for Nilearn.</p>
<div class="section" id="module-nilearn.interfaces.bids">
<span id="nilearn-interfaces-bids"></span><h3><a class="toc-backref" href="#id10"><span class="section-number">8.6.1. </span><a class="reference internal" href="#module-nilearn.interfaces.bids" title="nilearn.interfaces.bids"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces.bids</span></code></a></a><a class="headerlink" href="#module-nilearn.interfaces.bids" title="Permalink to this headline">¶</a></h3>
<p>Functions for working with BIDS datasets.</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.interfaces.bids.get_bids_files.html#nilearn.interfaces.bids.get_bids_files" title="nilearn.interfaces.bids.get_bids_files"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_bids_files</span></code></a>(main_path[, file_tag, …])</p></td>
<td><p>Search for files in a <a class="reference internal" href="../glossary.html#term-BIDS"><span class="xref std std-term">BIDS</span></a> dataset following given constraints.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.interfaces.bids.parse_bids_filename.html#nilearn.interfaces.bids.parse_bids_filename" title="nilearn.interfaces.bids.parse_bids_filename"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parse_bids_filename</span></code></a>(img_path)</p></td>
<td><p>Return dictionary with parsed information from file path.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.interfaces.fmriprep">
<span id="nilearn-interfaces-fmriprep"></span><h3><a class="toc-backref" href="#id11"><span class="section-number">8.6.2. </span><a class="reference internal" href="#module-nilearn.interfaces.fmriprep" title="nilearn.interfaces.fmriprep"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces.fmriprep</span></code></a></a><a class="headerlink" href="#module-nilearn.interfaces.fmriprep" title="Permalink to this headline">¶</a></h3>
<p>The <a class="reference internal" href="#module-nilearn.interfaces.fmriprep" title="nilearn.interfaces.fmriprep"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces.fmriprep</span></code></a> module includes tools to preprocess
neuroimaging data and access <a class="reference internal" href="../glossary.html#term-fMRIPrep"><span class="xref std std-term">fMRIPrep</span></a> generated confounds.</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.interfaces.fmriprep.load_confounds.html#nilearn.interfaces.fmriprep.load_confounds" title="nilearn.interfaces.fmriprep.load_confounds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_confounds</span></code></a>(img_files[, strategy, …])</p></td>
<td><p>Use confounds from <a class="reference internal" href="../glossary.html#term-fMRIPrep"><span class="xref std std-term">fMRIPrep</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.interfaces.fmriprep.load_confounds_strategy.html#nilearn.interfaces.fmriprep.load_confounds_strategy" title="nilearn.interfaces.fmriprep.load_confounds_strategy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_confounds_strategy</span></code></a>(img_files[, …])</p></td>
<td><p>Use preset strategy to load confounds from <a class="reference internal" href="../glossary.html#term-fMRIPrep"><span class="xref std std-term">fMRIPrep</span></a>.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.interfaces.fsl">
<span id="nilearn-interfaces-fsl"></span><h3><a class="toc-backref" href="#id12"><span class="section-number">8.6.3. </span><a class="reference internal" href="#module-nilearn.interfaces.fsl" title="nilearn.interfaces.fsl"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.interfaces.fsl</span></code></a></a><a class="headerlink" href="#module-nilearn.interfaces.fsl" title="Permalink to this headline">¶</a></h3>
<p>Functions for working with the FSL library.</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.interfaces.fsl.get_design_from_fslmat.html#nilearn.interfaces.fsl.get_design_from_fslmat" title="nilearn.interfaces.fsl.get_design_from_fslmat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_design_from_fslmat</span></code></a>(fsl_design_matrix_path)</p></td>
<td><p>Extract design matrix dataframe from FSL mat file.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-nilearn.maskers">
<span id="nilearn-maskers-extracting-signals-from-brain-images"></span><span id="maskers-ref"></span><h2><a class="toc-backref" href="#id13"><span class="section-number">8.7. </span><a class="reference internal" href="#module-nilearn.maskers" title="nilearn.maskers"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.maskers</span></code></a>: Extracting Signals from Brain Images</a><a class="headerlink" href="#module-nilearn.maskers" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-nilearn.maskers" title="nilearn.maskers"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.maskers</span></code></a> contains masker objects.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="../manipulating_images/masker_objects.html#nifti-masker"><span class="std std-ref">NiftiMasker: applying a mask to load time-series</span></a> section for further details.</p>
<p><strong>Classes</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.maskers.BaseMasker.html#nilearn.maskers.BaseMasker" title="nilearn.maskers.BaseMasker"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseMasker</span></code></a>()</p></td>
<td><p>Base class for NiftiMaskers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.maskers.NiftiMasker.html#nilearn.maskers.NiftiMasker" title="nilearn.maskers.NiftiMasker"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NiftiMasker</span></code></a>([mask_img, runs, …])</p></td>
<td><p>Applying a mask to extract time-series from Niimg-like objects.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.maskers.MultiNiftiMasker.html#nilearn.maskers.MultiNiftiMasker" title="nilearn.maskers.MultiNiftiMasker"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiNiftiMasker</span></code></a>([mask_img, smoothing_fwhm, …])</p></td>
<td><p>Class for masking of Niimg-like objects.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.maskers.NiftiLabelsMasker.html#nilearn.maskers.NiftiLabelsMasker" title="nilearn.maskers.NiftiLabelsMasker"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NiftiLabelsMasker</span></code></a>(labels_img[, labels, …])</p></td>
<td><p>Class for masking of Niimg-like objects.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.maskers.NiftiMapsMasker.html#nilearn.maskers.NiftiMapsMasker" title="nilearn.maskers.NiftiMapsMasker"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NiftiMapsMasker</span></code></a>(maps_img[, mask_img, …])</p></td>
<td><p>Class for masking of Niimg-like objects.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.maskers.NiftiSpheresMasker.html#nilearn.maskers.NiftiSpheresMasker" title="nilearn.maskers.NiftiSpheresMasker"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NiftiSpheresMasker</span></code></a>(seeds[, radius, …])</p></td>
<td><p>Class for masking of Niimg-like objects using seeds.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.masking">
<span id="nilearn-masking-data-masking-utilities"></span><span id="masking-ref"></span><h2><a class="toc-backref" href="#id14"><span class="section-number">8.8. </span><a class="reference internal" href="#module-nilearn.masking" title="nilearn.masking"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.masking</span></code></a>: Data Masking Utilities</a><a class="headerlink" href="#module-nilearn.masking" title="Permalink to this headline">¶</a></h2>
<p>Utilities to compute and operate on brain masks</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="../building_blocks/manual_pipeline.html#masking"><span class="std std-ref">Masking the data: from 4D image to 2D array</span></a> section for further details.</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.masking.compute_epi_mask.html#nilearn.masking.compute_epi_mask" title="nilearn.masking.compute_epi_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_epi_mask</span></code></a>(epi_img[, lower_cutoff, …])</p></td>
<td><p>Compute a brain mask from <a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a> data in 3D or 4D <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.22)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.masking.compute_multi_epi_mask.html#nilearn.masking.compute_multi_epi_mask" title="nilearn.masking.compute_multi_epi_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_multi_epi_mask</span></code></a>(epi_imgs[, …])</p></td>
<td><p>Compute a common mask for several sessions or subjects of <a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a> data.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.masking.compute_brain_mask.html#nilearn.masking.compute_brain_mask" title="nilearn.masking.compute_brain_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_brain_mask</span></code></a>(target_img[, threshold, …])</p></td>
<td><p>Compute the whole-brain, grey-matter or white-matter mask.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.masking.compute_multi_brain_mask.html#nilearn.masking.compute_multi_brain_mask" title="nilearn.masking.compute_multi_brain_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_multi_brain_mask</span></code></a>(target_imgs[, …])</p></td>
<td><p>Compute the whole-brain, grey-matter or white-matter mask for a list of images.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.masking.compute_background_mask.html#nilearn.masking.compute_background_mask" title="nilearn.masking.compute_background_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_background_mask</span></code></a>(data_imgs[, …])</p></td>
<td><p>Compute a brain mask for the images by guessing the value of the background from the border of the image.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.masking.compute_multi_background_mask.html#nilearn.masking.compute_multi_background_mask" title="nilearn.masking.compute_multi_background_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_multi_background_mask</span></code></a>(data_imgs[, …])</p></td>
<td><p>Compute a common mask for several sessions or subjects of data.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.masking.intersect_masks.html#nilearn.masking.intersect_masks" title="nilearn.masking.intersect_masks"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intersect_masks</span></code></a>(mask_imgs[, threshold, …])</p></td>
<td><p>Compute intersection of several masks.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.masking.apply_mask.html#nilearn.masking.apply_mask" title="nilearn.masking.apply_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply_mask</span></code></a>(imgs, mask_img[, dtype, …])</p></td>
<td><p>Extract signals from images using specified mask.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.masking.unmask.html#nilearn.masking.unmask" title="nilearn.masking.unmask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unmask</span></code></a>(X, mask_img[, order])</p></td>
<td><p>Take masked data and bring them back into 3D/4D.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.regions">
<span id="nilearn-regions-operating-on-regions"></span><h2><a class="toc-backref" href="#id15"><span class="section-number">8.9. </span><a class="reference internal" href="#module-nilearn.regions" title="nilearn.regions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.regions</span></code></a>: Operating on Regions</a><a class="headerlink" href="#module-nilearn.regions" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="#module-nilearn.regions" title="nilearn.regions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.regions</span></code></a> class module includes region extraction
procedure on a 4D statistical/atlas maps and its function.</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.regions.connected_regions.html#nilearn.regions.connected_regions" title="nilearn.regions.connected_regions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">connected_regions</span></code></a>(maps_img[, …])</p></td>
<td><p>Extraction of brain connected regions into separate regions.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.regions.connected_label_regions.html#nilearn.regions.connected_label_regions" title="nilearn.regions.connected_label_regions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">connected_label_regions</span></code></a>(labels_img[, …])</p></td>
<td><p>Extract connected regions from a brain atlas image defined by labels (integers).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.regions.img_to_signals_labels.html#nilearn.regions.img_to_signals_labels" title="nilearn.regions.img_to_signals_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">img_to_signals_labels</span></code></a>(imgs, labels_img[, …])</p></td>
<td><p>Extract region signals from image.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.regions.signals_to_img_labels.html#nilearn.regions.signals_to_img_labels" title="nilearn.regions.signals_to_img_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">signals_to_img_labels</span></code></a>(signals, labels_img[, …])</p></td>
<td><p>Create image from region signals defined as labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.regions.img_to_signals_maps.html#nilearn.regions.img_to_signals_maps" title="nilearn.regions.img_to_signals_maps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">img_to_signals_maps</span></code></a>(imgs, maps_img[, mask_img])</p></td>
<td><p>Extract region signals from image.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.regions.signals_to_img_maps.html#nilearn.regions.signals_to_img_maps" title="nilearn.regions.signals_to_img_maps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">signals_to_img_maps</span></code></a>(region_signals, maps_img)</p></td>
<td><p>Create image from region signals defined as maps.</p></td>
</tr>
</tbody>
</table>
<p><strong>Classes</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.regions.RegionExtractor.html#nilearn.regions.RegionExtractor" title="nilearn.regions.RegionExtractor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RegionExtractor</span></code></a>(maps_img[, mask_img, …])</p></td>
<td><p>Class for brain region extraction.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.regions.Parcellations.html#nilearn.regions.Parcellations" title="nilearn.regions.Parcellations"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Parcellations</span></code></a>(method[, n_parcels, …])</p></td>
<td><p>Learn <a class="reference internal" href="../glossary.html#term-parcellation"><span class="xref std std-term">parcellations</span></a> on <a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a> images.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.regions.ReNA.html#nilearn.regions.ReNA" title="nilearn.regions.ReNA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReNA</span></code></a>(mask_img[, n_clusters, scaling, …])</p></td>
<td><p>Recursive Neighbor Agglomeration (<a class="reference internal" href="../glossary.html#term-ReNA"><span class="xref std std-term">ReNA</span></a>): Recursively merges the pair of clusters according to 1-nearest neighbors criterion.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.regions.HierarchicalKMeans.html#nilearn.regions.HierarchicalKMeans" title="nilearn.regions.HierarchicalKMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HierarchicalKMeans</span></code></a>(n_clusters[, init, …])</p></td>
<td><p>Hierarchical KMeans: First clusterize the samples into big clusters.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.mass_univariate">
<span id="nilearn-mass-univariate-mass-univariate-analysis"></span><h2><a class="toc-backref" href="#id16"><span class="section-number">8.10. </span><a class="reference internal" href="#module-nilearn.mass_univariate" title="nilearn.mass_univariate"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.mass_univariate</span></code></a>: Mass-Univariate Analysis</a><a class="headerlink" href="#module-nilearn.mass_univariate" title="Permalink to this headline">¶</a></h2>
<p>Defines a Massively Univariate Linear Model estimated with OLS and permutation test</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.mass_univariate.permuted_ols.html#nilearn.mass_univariate.permuted_ols" title="nilearn.mass_univariate.permuted_ols"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permuted_ols</span></code></a>(tested_vars, target_vars[, …])</p></td>
<td><p>Massively univariate group analysis with permuted OLS.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-nilearn.plotting">
<span id="nilearn-plotting-plotting-brain-data"></span><span id="plotting-ref"></span><h2><a class="toc-backref" href="#id17"><span class="section-number">8.11. </span><a class="reference internal" href="#module-nilearn.plotting" title="nilearn.plotting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.plotting</span></code></a>: Plotting Brain Data</a><a class="headerlink" href="#module-nilearn.plotting" title="Permalink to this headline">¶</a></h2>
<p>Plotting code for nilearn</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.find_cut_slices.html#nilearn.plotting.find_cut_slices" title="nilearn.plotting.find_cut_slices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_cut_slices</span></code></a>(img[, direction, n_cuts, …])</p></td>
<td><p>Find ‘good’ cross-section slicing positions along a given axis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.find_xyz_cut_coords.html#nilearn.plotting.find_xyz_cut_coords" title="nilearn.plotting.find_xyz_cut_coords"><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_xyz_cut_coords</span></code></a>(img[, mask_img, …])</p></td>
<td><p>Find the center of the largest activation connected component.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.find_parcellation_cut_coords.html#nilearn.plotting.find_parcellation_cut_coords" title="nilearn.plotting.find_parcellation_cut_coords"><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_parcellation_cut_coords</span></code></a>(labels_img[, …])</p></td>
<td><p>Return coordinates of center of mass of 3D parcellation atlas.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.find_probabilistic_atlas_cut_coords.html#nilearn.plotting.find_probabilistic_atlas_cut_coords" title="nilearn.plotting.find_probabilistic_atlas_cut_coords"><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_probabilistic_atlas_cut_coords</span></code></a>(maps_img)</p></td>
<td><p>Return coordinates of center probabilistic atlas 4D image.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_anat.html#nilearn.plotting.plot_anat" title="nilearn.plotting.plot_anat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_anat</span></code></a>([anat_img, cut_coords, …])</p></td>
<td><p>Plot cuts of an anatomical image (by default 3 cuts: Frontal, Axial, and Lateral)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_img.html#nilearn.plotting.plot_img" title="nilearn.plotting.plot_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_img</span></code></a>(img[, cut_coords, output_file, …])</p></td>
<td><p>Plot cuts of a given image (by default Frontal, Axial, and Lateral)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_epi.html#nilearn.plotting.plot_epi" title="nilearn.plotting.plot_epi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_epi</span></code></a>([epi_img, cut_coords, output_file, …])</p></td>
<td><p>Plot cuts of an EPI image (by default 3 cuts: Frontal, Axial, and Lateral)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_matrix.html#nilearn.plotting.plot_matrix" title="nilearn.plotting.plot_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_matrix</span></code></a>(mat[, title, labels, figure, …])</p></td>
<td><p>Plot the given matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_roi.html#nilearn.plotting.plot_roi" title="nilearn.plotting.plot_roi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_roi</span></code></a>(roi_img[, bg_img, cut_coords, …])</p></td>
<td><p>Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_stat_map.html#nilearn.plotting.plot_stat_map" title="nilearn.plotting.plot_stat_map"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_stat_map</span></code></a>(stat_map_img[, bg_img, …])</p></td>
<td><p>Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>(stat_map_img[, …])</p></td>
<td><p>Plot 2d projections of an ROI/mask image (by default 3 projections: Frontal, Axial, and Lateral).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_connectome.html#nilearn.plotting.plot_connectome" title="nilearn.plotting.plot_connectome"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_connectome</span></code></a>(adjacency_matrix, node_coords)</p></td>
<td><p>Plot connectome on top of the brain glass schematics.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_markers.html#nilearn.plotting.plot_markers" title="nilearn.plotting.plot_markers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_markers</span></code></a>(node_values, node_coords[, …])</p></td>
<td><p>Plot network nodes (markers) on top of the brain glass schematics.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_prob_atlas.html#nilearn.plotting.plot_prob_atlas" title="nilearn.plotting.plot_prob_atlas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_prob_atlas</span></code></a>(maps_img[, bg_img, …])</p></td>
<td><p>Plot the probabilistic atlases onto the anatomical image by default MNI template</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_carpet.html#nilearn.plotting.plot_carpet" title="nilearn.plotting.plot_carpet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_carpet</span></code></a>(img[, mask_img, mask_labels, …])</p></td>
<td><p>Plot an image representation of voxel intensities across time.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_surf.html#nilearn.plotting.plot_surf" title="nilearn.plotting.plot_surf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_surf</span></code></a>(surf_mesh[, surf_map, bg_map, …])</p></td>
<td><p>Plotting of surfaces with optional background and data</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_surf_roi.html#nilearn.plotting.plot_surf_roi" title="nilearn.plotting.plot_surf_roi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_surf_roi</span></code></a>(surf_mesh, roi_map[, bg_map, …])</p></td>
<td><p>Plotting ROI on a surface mesh with optional background</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_surf_contours.html#nilearn.plotting.plot_surf_contours" title="nilearn.plotting.plot_surf_contours"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_surf_contours</span></code></a>(surf_mesh, roi_map[, …])</p></td>
<td><p>Plotting contours of ROIs on a surface, optionally over a statistical map.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_surf_stat_map.html#nilearn.plotting.plot_surf_stat_map" title="nilearn.plotting.plot_surf_stat_map"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_surf_stat_map</span></code></a>(surf_mesh, stat_map[, …])</p></td>
<td><p>Plotting a stats map on a surface mesh with optional background</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_img_on_surf.html#nilearn.plotting.plot_img_on_surf" title="nilearn.plotting.plot_img_on_surf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_img_on_surf</span></code></a>(stat_map[, surf_mesh, …])</p></td>
<td><p>Convenience function to plot multiple views of plot_surf_stat_map in a single figure.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_img_comparison.html#nilearn.plotting.plot_img_comparison" title="nilearn.plotting.plot_img_comparison"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_img_comparison</span></code></a>(ref_imgs, src_imgs, masker)</p></td>
<td><p>Creates plots to compare two lists of images and measure correlation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_design_matrix.html#nilearn.plotting.plot_design_matrix" title="nilearn.plotting.plot_design_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_design_matrix</span></code></a>(design_matrix[, rescale, …])</p></td>
<td><p>Plot a design matrix provided as a <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.4.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_event.html#nilearn.plotting.plot_event" title="nilearn.plotting.plot_event"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_event</span></code></a>(model_event[, cmap, output_file])</p></td>
<td><p>Creates plot for event visualization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.plot_contrast_matrix.html#nilearn.plotting.plot_contrast_matrix" title="nilearn.plotting.plot_contrast_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_contrast_matrix</span></code></a>(contrast_def, design_matrix)</p></td>
<td><p>Creates plot for contrast definition.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.view_surf.html#nilearn.plotting.view_surf" title="nilearn.plotting.view_surf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">view_surf</span></code></a>(surf_mesh[, surf_map, bg_map, …])</p></td>
<td><p>Insert a surface plot of a surface map into an HTML page.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.view_img_on_surf.html#nilearn.plotting.view_img_on_surf" title="nilearn.plotting.view_img_on_surf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">view_img_on_surf</span></code></a>(stat_map_img[, surf_mesh, …])</p></td>
<td><p>Insert a surface plot of a statistical map into an HTML page.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-obj docutils literal notranslate"><span class="pre">view_connectome</span></code></a>(adjacency_matrix, node_coords)</p></td>
<td><p>Insert a 3d plot of a connectome into an HTML page.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.view_markers.html#nilearn.plotting.view_markers" title="nilearn.plotting.view_markers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">view_markers</span></code></a>(marker_coords[, marker_color, …])</p></td>
<td><p>Insert a 3d plot of markers in a brain into an HTML page.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.view_img.html#nilearn.plotting.view_img" title="nilearn.plotting.view_img"><code class="xref py py-obj docutils literal notranslate"><span class="pre">view_img</span></code></a>(stat_map_img[, bg_img, cut_coords, …])</p></td>
<td><p>Interactive html viewer of a statistical map, with optional background.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.show.html#nilearn.plotting.show" title="nilearn.plotting.show"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show</span></code></a>()</p></td>
<td><p>Show all the figures generated by nilearn and/or matplotlib.</p></td>
</tr>
</tbody>
</table>
<div class="section" id="module-nilearn.plotting.displays">
<span id="nilearn-plotting-displays-interacting-with-figures"></span><h3><a class="toc-backref" href="#id18"><span class="section-number">8.11.31. </span><a class="reference internal" href="#module-nilearn.plotting.displays" title="nilearn.plotting.displays"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.plotting.displays</span></code></a>: Interacting with figures</a><a class="headerlink" href="#module-nilearn.plotting.displays" title="Permalink to this headline">¶</a></h3>
<p>Display objects and utilities.</p>
<p>These objects are returned by plotting functions
from the <a class="reference internal" href="#module-nilearn.plotting" title="nilearn.plotting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">plotting</span></code></a> module.</p>
<p><strong>Functions</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.get_projector.html#nilearn.plotting.displays.get_projector" title="nilearn.plotting.displays.get_projector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_projector</span></code></a>(display_mode)</p></td>
<td><p>Retrieve a projector from a given display mode.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.get_slicer.html#nilearn.plotting.displays.get_slicer" title="nilearn.plotting.displays.get_slicer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_slicer</span></code></a>(display_mode)</p></td>
<td><p>Retrieve a slicer from a given display mode.</p></td>
</tr>
</tbody>
</table>
<p><strong>Classes</strong>:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.OrthoProjector.html#nilearn.plotting.displays.OrthoProjector" title="nilearn.plotting.displays.OrthoProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OrthoProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>A class to create linked axes for plotting orthogonal projections of 3D maps.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.XZProjector.html#nilearn.plotting.displays.XZProjector" title="nilearn.plotting.displays.XZProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">XZProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">XZProjector</span></code> class enables to combine sagittal and axial views on the same figure through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.YZProjector.html#nilearn.plotting.displays.YZProjector" title="nilearn.plotting.displays.YZProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">YZProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">YZProjector</span></code> class enables to combine coronal and axial views on the same figure through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.YXProjector.html#nilearn.plotting.displays.YXProjector" title="nilearn.plotting.displays.YXProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">YXProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">YXProjector</span></code> class enables to combine coronal and sagittal views on the same figure through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.XProjector.html#nilearn.plotting.displays.XProjector" title="nilearn.plotting.displays.XProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">XProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">XProjector</span></code> class enables sagittal visualization through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.YProjector.html#nilearn.plotting.displays.YProjector" title="nilearn.plotting.displays.YProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">YProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">YProjector</span></code> class enables coronal visualization through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.ZProjector.html#nilearn.plotting.displays.ZProjector" title="nilearn.plotting.displays.ZProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ZProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">ZProjector</span></code> class enables axial visualization through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.LZRYProjector.html#nilearn.plotting.displays.LZRYProjector" title="nilearn.plotting.displays.LZRYProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LZRYProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">LZRYProjector</span></code> class enables ? visualization on the same figure through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.LYRZProjector.html#nilearn.plotting.displays.LYRZProjector" title="nilearn.plotting.displays.LYRZProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LYRZProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">LYRZProjector</span></code> class enables ? visualization on the same figure through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.LYRProjector.html#nilearn.plotting.displays.LYRProjector" title="nilearn.plotting.displays.LYRProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LYRProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">LYRProjector</span></code> class enables ? visualization on the same figure through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/nilearn.plotting.displays.LZRProjector.html#nilearn.plotting.displays.LZRProjector" title="nilearn.plotting.displays.LZRProjector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LZRProjector</span></code></a>(cut_coords[, axes, black_bg, …])</p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">LZRProjector</span></code> class enables hemispheric sagittal visualization on the same figure through 2D projections with <a class="reference internal" href="generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>.</p></td>
</tr>